AA3528SURCK中文资料

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KAA-3528SURKCGKC中文资料

Absolute Maximum Ratings at TA=25°C °
P ar am et er Power dissipation DC Forward Current Peak Forward Current [1] Reverse Voltage Operating/Storage Temperature
Note: 1. 1/10 Duty Cycle, 0.1ms Pulse Width.
H y p er R ed 170 30 185 5 -40°C To +85°C
Gr een 105 30 150
Un its mW mA mA V
SPEC NO: DSAA6955 APPROVED : J. Lu
REV NO: V.2 CHECKED: Allen Liu
DATE:DEC/12/2002 DRAWN:Y.H.LI
PAGE: 3 OF 4ห้องสมุดไป่ตู้
元器件交易网
KAA-3528SURKCGKC SMT Reflow Soldering Instruction
Number of reflow process shall be less than 2 times and cooling process to normal temperature is required between first and second soldering process.
元器件交易网
3.5x2.8mm SURFACE MOUNT LED LAMP
KAA-3528SURKCGKC
HYPER RED / GREEN
Features
!BOTH CHIPS CAN BE CONTROLLED SEPARATELY. !SUITABLE FOR ALL SMT ASSEMBLY AND SOLDER PROCESS. !AVAILABLE ON TAPE AND REEL. !PACKAGE: 1500PCS / REEL.

S3528WH-EA中文资料

3528 Size Flash White Color SMD Type LED Part Number : S3528WH-EA2004. 12. 7NiNEX Co., Ltd.Head Quarter/ Factory:#449-12, Mogok-Dong, PyongTaek-Si, Kyongki-Do, KoreaTel: 82-31-660-0800, Fax: 82-31-611-9048Marketing Office#358-1, Korea Exchange Bank Building 5FYatap-Dong, Bundang-Ku, Sungnam-Si, Kyongki-Do, KoreaTel: 82-31-703-2606~7, Fax: 82-31-704-26071. General DescriptionThe document describes the specification of 3528 size, flash white color SMD type LED.Chip LEDs, or SMD type LEDs, are designed for automatic surface mounting process of ordinary electronic equipments. Some major applications include office electronic equipments, telecommunication equipments and household appliances.A generally acceptable driving current for a chip LED is relatively low compared to ordinary lighting devicesand high operation current or voltage may cause a catastrophic failure. All customers are expected to keep the guidance and the cautions described in the document and strongly recommended to consult NiNEX prior to applying the devices for sensitive applications, particularly when exceptional quality and reliability arerequired.1-1.Featuresz Size : 3.5 x 2.8 x 1.8 mm (L×W×H) – SMD(Surface-Mount Device) typez Encapsulating Epoxy Resin : Phosphor dispersed water clear typez Viewing Angle Φ1/2 : 120˚z Color Coordinates: x=0.29~0.35, y=0.26~0.39 according to CIE 1931, at If= 60mAz Color Mixing: AlInGaN based Blue LED Chip and special phosphor were used to convert blue emission to white color.z Electrodes for soldering: Ag plating on Copper1-2.Dimension(unit: mm, tolerance: ± 0.1mm)2. Specification2-1.Absolute Maximum Rating(T a = 25°C)Parameter SymbolMaximumRating Units Power Dissipation P D 240 mW Forward Current I F 60 mA Peak Forward Current*1I F_PEAK 250 mAReverse V oltage V R 5 V Operation Temperature T OP -30 ~ +85 °CStorage Temperature T ST-40 ~ +100 °CSoldering Temperature T SOL Reflow soldering (lead free): 260°C for 5sec. Reflow soldering (lead ): 240°C for 5sec*1: Duty ratio = 1/10, Pulse width = 10ms2-2.Electrical and Optical Characteristics(T a = 25°C) Parameter SymbolTestConditionMinMaxUnits Forward V oltage*1V F I F = 60mA 3.0 3.8 VLuminous Intensity*2I V I F = 60mA 2.5 5.5 cdx I F = 60mA 0.29 0.35Color Coordinate*3y I F = 60mA 0.26 0.39Reverse Current I R V R = 5V 100 µA*1: 0.05V tolerance for the forward voltage may be caused by measurement inaccuracy.*2: 10% tolerance for luminous intensity may be caused by measurement inaccuracy.*3: The measurement tolerance of color coordinate is 0.013. Part Number DescriptionPart Number: S 3528 WH – E A3-1. Device Type:z A: Application Productsz C: LED Chip (Dice)z D: Dot Matrixz I: Illumination Productsz L: Lamp type LEDz P: High Power Package LEDz S: SMD type LED3-2. Package Size: , Package Thickness:(unit: mm)Package Dimension(W × L × T)Conventional Name1608 4 1.6 × 0.8 × 0.4 Chip LED 0.4t1608 6 1.6 × 0.8 × 0.6 Chip LED 0.6t1608 8 1.6 × 0.8 × 0.8 Chip LED 0.8t1612 4 1.6 × 1.25 × 0.4 Bi-Color(2 in 1) 0.4t1612 6 1.6 × 1.25 × 0.6 Bi-Color(2 in 1) 0.6t1612 8 1.6 × 1.25 × 0.8 Bi-Color(2 in 1) 0.8t1615 4 1.6 × 1.5 × 0.4 Full Color(3 in 1) 0.4t1615 6 1.6 × 1.5 × 0.6 Full Color(3 in 1) 0.6t1615 8 1.6 × 1.5 × 0.8 Full Color(3 in 1) 0.8t3528 E 3.5 × 2.8 × 1.8 3528 Package3530 A 3.5 × 3.0 × 1.07 Flash LED4014 6 4.0 × 1.4 × 0.6 Sideview 0.6t4014 8 4.0 × 1.4 × 0.8 Sideview 0.8t4014 1 4.0 × 1.4 × 1.0 Sideview 1.0t5450 E 5.4 × 5.0 × 1.8 5450 Package3-3. Emission Color:z Emission color from 1 chip in 1 packagez Converted color emission from 1 chip in 1 packageDescription WH BW VW YW WW Emission Color White Blue-White Violet-WhiteYellow-WhiteWarm Whitez Multi color emission from multi chip in 1 packageDescription GR BR BG FC 7CColor Green+Red Blue+RedBlue+Green Red+Green+BlueRed+Yellow-Green+Blue3-4. Package Type Information:Description InformationRemarks A Common AnodeMulti Chip Package B Anode & Cathode for each diceMulti Chip Package CCommon CathodeMulti Chip PackageD Diffused Epoxy T Transparent EpoxyY Yellow Phosphor White color application ZInclude in Zener Diode1615, 4014, 3528 PackageDescription RE OR AM YL YG GN BL VL Emission ColorRed Orange Amber Yellow Yellow-GreenGreenBlueViolet4. Sorting Ranks4-1. Chromaticity Coordinates *1(T a =25°C)*21 2 Cx Cy Cx Cy0.29 0.26 0.32 0.305 0.29 0.30 0.32 0.345 0.32 0.345 0.35 0.39 Rank0.32 0.305 0.35 0.35*1: The CIE (1931) standard colorimetric System*2: Measurement Condition: 20ms pulse @ I F=60mA, 0.01sr (CIE.LED_B)Intensity*14-2. Luminouscd,T a=25°C)*2(unit:Rank Min. Typ. Max.A 2.5 3.0 3.5B 3.5 4.0 4.5C 4.5 5.0 5.5*1: 10% tolerance for luminous intensity may be caused by measurement inaccuracy.*2: Measurement Condition: 20ms pulse @ I F=60mAVoltage*14-3. Forward(unit: V, T a=25°C)*2Rank Min. Max.a 3.0 3.2b 3.2 3.4c 3.4 3.6d 3.6 3.8*1: 0.05V tolerance for the forward voltage may be caused by measurement inaccuracy.*2: Measurement Condition: 20ms pulse @ I F=60mAz Each product belongs to a rank for each sorting parameter.z Combination of the ranks composes sorting bins(ex. 1Bc, 2Cc, etc)z Products which belong to the same sorting bin are taped together.z Bin mixing is not allowed within a reel.5. Taping5-1.Carrier Tape*1 Dimensionmm)(unit:Ω Ω5-2.Reel Dimension*1(unit: mm)*1: Material: PS, Conductivity: 104Ω ~ 105Ω6. Packing*1(unit: mm) Packing unit Size (W × L × D)Quantity (ea) Antistatic shielding bag (1 Reel) 220 × 2502,000 Inner carton box (10 Reels) 220 × 220 × 145 20,000Outer carton box (60 Reels) 450 × 300 × 230 120,000 *1: Each reel sealed in an antistatic shielding bag with silica-gelAntistatic shielding bag Inner carton boxOuter carton box Label7. Lot Number DescriptionLot Number: NS3528WHEA-B1BP 4 B 2 - 000z NiNEX Product Number:z Production Year (3 for 2003, 4 for 2004, 5 for 2005):z Production Month and DateMonth Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecA B C D E F G H I J K LDate 1 2 3 4 5 6 7 8 9 1011 12 13 14151 2 3 4 5 6 7 8 9 10 A B C D EDate 16 17 18 19 20 212223 242526 27 28 2930F G H I J K L M N O P Q R S Tz Serial No.:8. Soldering8-1.Recommended Soldering Condition*1, *2Reflow Soldering Manual SolderingLead-free*2Lead solder Lead-free *2LeadSolder Pre-heating 140 ~ 160 °C 180~200°C Temperature max. 300°C max.350°C.Pre-heat time 60 ~ 120 sec. 120 sec. Time max. 3 sec. max. 3 sec.Peak temperature max. 240°C max.260°CNo multiple soldering allowed Soldering time max. 5 sec. max. 5 sec.*1: After reflow soldering, rapid cooling should be avoided.*2: N2 reflow is recommended8-2.Recommended Reflow Soldering Profile● Lead Solder ● Lead-free Solder8-3.Recommended Soldering Pattern(Unit : mm)z During the soldering process, keep the minimum clearance between the resin and the soldering point. z Resin should not contact molten solder.z No mechanical distortion or stress allowed after soldering.z During soldering, do not apply any stress to the lead frame, particularly when heated. z A soldering iron with a grounded tip is recommended.An isolator should also be installed where risk of static generation is high.Time (sec.)T e m p e r a t u r e (Time (sec.)T e m p e r a t u r e ( )9. Cautions9-1.Safetyz Customers should comply with the laws and public regulations concerning safety.z Operation temperature or driving current may affect emission color.Please check sorting condition and characteristic diagram to estimate color shift.z Moisture and dust may affect reliability issues.Do not open the shielding bag under humid or dirty environment.z When installing the product in PCB, the device should not contact with other components.z Do not apply force to the LED under high-temperature condition.z Do not apply friction to the LED using hard material.z Avoid exposure to chemicals which may dissolve the LED package and the epoxy.z Use IPA(Isopropyl Alcohol) as a solvent when washing is required.9-2.Static Electricityz These products are sensitive to static electricity.Anti–electrostatic glove or wristband is recommended when handling the LEDs.z A protection device should be installed in the LED driving circuit to eliminate orminimize the surge current effect.z Proper grounding of Products, use of conductive mat, semi-conductive working uniform and shoes, and semi-conductive containers are considered to be effective as countermeasures againststatic electricity and surge.9-3.Storage Conditionz Before opening the anti-static shielding package:LEDs should be kept at 30°C or less and RH 80% or less.Maximum acceptable storage period is 6 months.z After opening the anti-static shielding package:LEDs should be kept at 30°C or less and RH 70% or less.LEDs should be soldered within 7 days after opening the package.10. Characteristic Diagram●Ambient Temperature vs.Forward Voltage●Ambient Temperature vs.Relative Luminosity●Ambient Temperature vs.Allowable Forward Current ● Forward Voltage vs.Forward Current1020304050607080AllowableForwardCurrent(mA)● Forward Current vs.Relative Luminosity●Radiation Diagram●Spectrum●Forward Current vs.Chromaticity CoordinateTa=25IF=60mA0.280.290.300.310.320.330.340.280.290.300.310.320.330.340.350.36xy11. Reliability Test11-1. Test items and resultsTest Item Reference Standard Test ConditionsNumber of Damaged PartsNormal Temperature Lifetime Test MIL-STD-883:1005JIS C 7035T a=25 I f=60mATest Time=1,000hrs0/22High Temperature Operating Life (HTOL) MIL STD 883E-1005EIAJ ED 4701-100T a =85 I f =10mATest Time=1,000hrs0/22Temperature Humidity Bias (THB) EIAJ ED 4701-100T a=60 / RH=90%I f =30mATest Time=500hrs0/22Temperature Cycle MIL-STD-883 :1010EIAJ ED 4701-100-30 ~ 25 ~ 100 ~ 2530min 5min 30min 5minTest Time=50cycles0/22High Temperature Storage MIL-STD-883 : 1008EIAJ ED 4701-200T a=100Test Time=1,000hrs0/22Low Temperature Storage EIAJ ED 4701-200T a=-40Test Time=1,000hrs0/22ESD(Electro-static Discharge) Rating MIL STD-883E : 3015EIAJ ED 4701-300HBM(Human Body Model)C=100pF R =1.5KΩDischarge times : 3 times0/2211-2. Criteria of failure for the reliabilityTest Item Symbol Test Condition Judgment Criteria Forward Voltage V f I f = 60mA V f > 1.1×U.S.L*1 Luminous Intensity I v I f = 60mA I v < 0.7×Initial valueESD Rating*2ESD HBM Class 2 or more*1: U.S.L : Upper Standard Level*2: ESD Rating forward directed by HBM (Human Body Model)Classification*1Class 1 Class 2 Class 3A Class 3B Withstand V oltage 250 - 1,999V 2,000 - 3,999V 4,000 -7,999V 8,000V or more *1: EIAJ ED 4701-300Part Number. S3528WH-EADocument ID NS-120Events S/N Date Summary of Revision Remarks1 2004. 08. 9 Newly Establishment -2 2004. 12. 07 Revise IV Rank & Lot No. -。

FL-3528S-FS中文资料

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Flange 1.25 Gbps 2.5 Gbps Pin Assignment Type A Type B Type C Code Blank S Isolator No Single-Stage
Pout (mw) 0.2 0.5 1 2
Code S SB F FB T
Connector Type SC/PC SC/PC (Board Mount) FC/PC FC/PC (Board Mount) ST/PC
λc Δλ t r, t f
CW, Ith+20mA CW, Ith+20mA, RMS IF=Ith, Ith+20mA,10~90% APC, -40~+85 ºC CW, Ith+20mA ,VRD=1V VRD=5V
nm nm ns dB μA μA
ΔPf / P f Tracking Error Im ID PD Monitor Current PD Dark Current

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元器件交易网
Optoway
1310 nm LASER DIODE MODULES
UNCOOLED MQW-FP LD WITH RECEPTACLE FEATURES ² 1310 nm typical emission wavelength ² High reliability, long operation life ² High temperature operation without active cooling ² Build-in InGaAs monitor APPLICATION Trunk Line, FitL DESCRIPTION

3528红光规格书

东莞市荧月电子科技有限公司3528红光规格书 3528 RED SPECIFICATION产品型号 Model NO.:HT-K3528RC 版次 REV NO.:V01版文件编号Document NO.:HT-C030006 日期 DATE :2015.3.1 1.外形尺寸D imensions单位(Units):毫米(mm)1(-)2(+)2. 光电特性Electrical / Optical characteristics1/10周期, 0.1 msec 脉宽IFP Conditions : 1/10 Duty Cycle, 0.1 msec Pulse Width.½ Rja = Heat resistance from Dice to Ambient temperature (Ta) Rjs = Heat resistance from Dice to Solder temperature of Cathode Side (Ts)(3) 原始光电参数Initial Electrical/Optical Characteristics (TA=25ºC) Array(4)发光强度范围Luminous Intensity Ranking (TA=25ºC)允许误差± 10%Luminous Intensity Measurement allowance is ± 10%.(5)颜色范围Color Ranking (TA=25ºC)Color Coordinates Measurement allowance is ± 0.01.3.包装PACKAGING(1)LEDS在装带之后纸箱包装. The LEDs are packed in cardboard boxes after taping.(2)装带规格Taping Specifications (单位:毫米Units:mm)(3)卷轴尺寸Reel Dimension装带数量2000个/卷2000Pcs/Reel(3) 最小包装标签注明以下:产品名称.批号.光电范围.数量.The label on the minimum packing unit shows ; Part Number, Lot Number, Ranking, Quantity.(4)请注意防水防潮Keep away from wate r, moisture in order to protect the LEDs.(5) 须采取适当防护措施,以防包装箱跌落或受到强力撞击造成对产品的损伤.The LEDS may be damaged if the boxes are dropped or receive a strong impact against them. so precautions must be taken to prevent any damage.4.可靠性RELIABILITY5.注意事项Cautions(1) 焊接条件Soldering Conditions本产品最多只可回焊两次,且在首次回焊后须冷却至室温之后方可进行第二次回焊.Number of reflow process shall be less than 2 times and cooling process to normal temperature is required between first and Second soldering process. 推荐焊接条件(Recommended soldering conditions)有铅回焊（Lead Solder ）无铅回焊(Lead-Free Solder)推荐焊盘式样(Recommended Soldering Pattern ) 单位:毫米( Units:mm )(2)静电 Static Electricity触摸LED 时,推荐使用防静电手腕带或防静电手套.It is recommended that a wrist band or an anti-electrostatic glove be used when handling the LEDs. 所有装置、设备、机器均应接地.All devices, equipment and machinery must be properly grounded.静电损坏的LED会显示出异常特征:正向电压变低或在低电流时死灯.标准: I F=0.5mA时, V F > 2.0VDamaged LEDs will show some unusual characteristics such as the forward voltage becomes lower, or the LEDs do not light at the low current. Criteria : (V F > 2.0V at I F=0.5mA)(3)防潮包装Moisture Proof Package使用防潮包装It is recommended that moisture proof package be used .(4)储藏Storage打开包装袋之前,LED在温度为30°C或更低湿度70%RH以下,可保存一年.Before opening the package ,The LEDs should be kept at 30°C or less and 70%RH or less. The LEDs should be used within a year.（5）打开包装之后,应在24hrs 内焊接完毕.After opening the package, The LEDs should be soldered within 24 hours (1days) after opening the package. If unused LEDs remain, they should be stored in moisture proof packages, such as sealed containers with packages of moisture absorbent material (silica gel).下列情况发生时，须要在焊接前重新烘烤60 ± 5°C,24小时以上。

爱默生CKS3528钟控收音机操作说明书

爱默生CKS3528钟控收音机操作说明书1．设定机器主时间：A：设日历机器插电后，会处于初始状态，由于有时钟数据保存功能，插电后机器会自动显示日历和时间。

如果你需要改变日历，可同时按下机器顶部的“MONTH/DATE”键与“SET —”与“SET+”这二个键当中的一个，机器的日历显示窗口即会飞速“向前”或者“向后”计数，等到了你需要的日历，你松开按键，日历数据就能正常显示了。

日历数据在调整的开始一刻计数较慢，过了10个计数单位后就会飞速计数！B：设时间同时按下机器顶部的“TIME”键与“SET —”与“SET+”这二个键当中的一个，机器的时刻显示窗口即会飞速“向前”或者“向后”计数，等到了你需要的时刻，你松开按键，时钟数据就能正常显示了。

时钟数据在调整的开始一刻计数较慢，过了10个计数单位后就会飞速计数！2．设定闹钟一共可以设定二组闹钟，分别是“ALARM1”与“ALARM2”。

你可以选择RADIO（收音机）开机，BUZZER（闹铃）闹钟功能。

ALARM1（闹钟1）与ALMAR2（闹钟2）的设定方法相同，我们以ALM1（闹钟1）为标准来介绍闹钟。

按下顶部的“ALM1”（闹钟1）键，机器就会显示一个新的时间，并且这时，显示屏右上角的表示星期几的灯也会亮。

显示的时间是前一次设定的闹钟时间，如果你需要调整闹钟时间，就用同时按下机器顶部的“ALM1”（闹钟1）键与“SET —”与“SET+”这二个键当中的一个，机器的时刻显示窗口即会飞速“向前”或者“向后”计数，等到了你需要的时刻，你松开“SET —”与“SET+”这二个键当中的一个，这时机器就会显示新的闹钟时间，如果你再松开“ALM1”（闹钟1）键，机器会显示系统时间。

设定好闹钟时间后，如果需要使用闹钟，还要打开机器右背的相应的闹钟开关ALARM1ALARM1是一个三位开关，分别是RADIO/BUZZER/OFF，RADIO，将开关拔到这一位，表示到了闹钟时间，收音机将自动打开当闹钟。

铭州中性3528W(1)

Specification No.: QWE-03TA3528WSPECIFICATIONS FOR SURFACE MOUNT LED表面贴装式 LED 产品规格书ATTENTIONO B SER V E PR EC A U TIO N SELECTROSTATIC SENSITIVE DEVICESCustomer 客户名称 Product Discription 产品描述 Model 型号 Issue Date 发出日期: : : : WHITE TOP LED 3528W 2014-03-21Prepared By 制定连晓铃Checked By 审核李海超Approved By 批准吴常青APPROVED SIGNATURES 客户确认 Purchase Dept. Quality Dept. Engineering Dept. 采购部品质部技术部Specification No.: QWE-03TA3528W QWE※ Features (产品特征)1. Outline Package: 3.5×2.7×1.9mm ×1.9mm (Top View White LED) 外型尺寸：3.5×2.7×1.9mm 2. Emitted Color: White 发光颜色：白色 3. Lens Appearance: Yellow Disffuse 胶体颜色：黄色不透明 4. Sutiable for all SMT assembly methods 适用于所有表面贴装技术组装生产 5. Comply with RoHS 完全符合 ROHS 指令※ Appearance (外观图片)※ Applications （产品应用）1. Indoor and outdoor displays 室内室外显示屏 3. Backlighting 背光 5. Substitution ubstitution of micro incandescent lamps 替代微型白炽灯 2. Optical indicators 光学指示 4. Interior nterior automotive lighting 汽车内部照明 6. Signal ignal and symbol luminaire 信号照明※ Package Outline Dimensions 外型尺寸图PAD Lay Out PCB 焊盘设计NOTES:1. All dimensions are in millimeters (inches); 单位：毫米（英寸） 2. Tolerances are ±0.2mm (0.008inch) unless otherwise noted. 未标公差处公差为 0.2mm(0.008 寸)Specification No.: QWE-03TA3528W※Absolute maximum ratings at Ta=25℃ 最大绝对额定值 Parameter Symbol 参数符号Power dissipation 功率耗损 Forward current 正向电流 Reverse voltage 反向电压 Operating temperature range 工作温度范围 Storage temperature range 贮存温度范围 Peak pulsing current 最大脉冲电流 Electrostatic Discharge 抗静电能力 Pd If Vr Top Tstg Ifp ESDValue 值60 20 5 -20 ~+80 -30~+100 30 2000(HBM)Unit 单位mW mA V ℃ ℃ mA VNOTE: IFP Conditions: Pulse Width≦10msec. and Duty cycle≦1/10. IFP 条件：脉冲持续时间≦10msec，占空因素≦1/10※ Electrical-optical characteristics at Ta=25℃ 电性光电特性 Parameter 参数Forward voltage 正向电压 Luminous Flux 光通量 Reverse current 反向电流 White(正白) Color Temperature CRI 显色指数 Viewing angle at 50% Iv 半功率角Test Condition 测试条件If=20mA If=20mA Vr=5VSymbol 符号Vf φ IrValue 值 Min.-6 --Typ.3.3 ---Max.3.6 7 10Unit 单位V lm mAIf=20mATc6000--6500KIf=20mA If=20mARa 2 θ 1/2--70 120----degNOTE: 1. Tolerance of luminous intensity is ±15% 发光强度公差为±10% 2. Tolerance of forward voltage is ±0.1V 正向电压公差±0.1VSpecification No.: QWE-03TA3528W※ Typical optical characteristics curves 典型光电特性曲线Spectral DistributionRelative Intensity VS. Wavelength (Ta=25℃) Forward Voltage VS. Forward Current (Ta=25℃) Relative Intensity VS. Forward Current (Ta=25℃)Forward Current (mA)Relative IntensityRelative Intensitywavelength (nm)Forward Voltage VF (V)Forward Current (mA)DeratingRelative Intensity VS. Amblient Temperature Amblient Temperature VS. Maxinum Forward Current Dominate Wavelength (nm) Forward Current VS. Chromaticity (Ta=25℃)Amblient Temperature (℃)Forward Current (mA)Relative IntensityAmblient Temperature (℃)Forward Current (mA)Diagram characteristics of radiation40 30 20 10 0 1.0 500.8600.6700.4800.290 0 20 40 60 80 100Specification No.: QWE-03TA3528W※ Reliability Test Items and Conditions 可靠性测试项目及测试条件 No. Test Item 测试项目Resistance to Soldering Heat(Reflow Soldering) 回流焊可承受条件测试 Temperature Cycle 温度循环测试 Thermal Shock 冷热冲击测试 High Temperature Storage 高温贮藏测试 Temperature Humidity Storage 恒温恒湿贮藏测试 Low Temperature Storage 低温贮藏测试 Power On/off Cycle Test IF=20mA 亮暗测试 Life Test 常温寿命测试 High Humidity Heat Life Test 恒温恒湿寿命测试 Low Temperature Life Test 低温寿命测试 Drop 跌落测试Test Conditions 测试条件Note 频次Number of Damaged允许破坏数0/2201Tsld=260℃,10sec -30℃ 30min ↑↓5min 85℃ 30min -30℃ 15min ↑↓ 85℃ 15min Ta=80℃ Ta=85℃ RH=85% Ta=-30℃ On 2 hours ↑↓ Off 10min Ta=25℃ IF=20mA 60℃ RH=85% IF=20mA Ta=-30℃ IF=20mA 75cm2 times02100 cycle0/10003 04 05 06 07 08 09 10 11100 cycle0/1001000 hrs0/1001000 hrs0/1001000 hrs0/100100 cycle0/1001000 hrs 500 hrs 1000 hrs 3 times0/100 0/100 0/100 0/10※ Criteria for Judging the Damage 破坏判定标准 ItemForward Voltage 正向电压 Reverse Current 反向电流 Luminous Intensity 发光强度SymbolVF IR IVTest ConditionsVR=5V IF=20mA IF=20mA.Criteria for Judgement Min._ _ L.S.L**)×0.7Max.U.S.L*)×1.1 U.S.L*)×2.0 _Specification No.: QWE-03TA3528W※ Packaging Specifications 包装规格● Feeding Direction 卷带输送方向 ● Dimensions of Reel (Unit: mm) 卷盘尺寸Feeding Direction● Dimensions of Tape (Unit: mm) 卷带尺寸FEED DIRECTIONPolarity MarkTop Tape● Arrangement of Tape 卷带排列规格NOTES 1. Empty component pockets are sealed with top cover tape; 无材料部分同样用上封带密封 2. The maximum number of missing lamps is two; 一卷材料最大差异数为 2 3. The cathode is oriented towards the tape sprocket hole 材料负极方向与卷带齿孔方向一致 4. 2,000 pcs/ Reel. 2000pcs/卷Specification No.: QWE-03TA3528W※ Precautions for use 使用规范 Reflow Profile 回流焊规范 Pb-free Solder temperature Profile 无铅产品回流焊温度条件曲线规范Note: a)Reflow soldering should not be done more than two times. 材料焊接次数不超过 2 次。

3528灯珠，3528灯珠参数，3528贴片led灯珠参数有哪些？

3528灯珠，3528灯珠参数，3528贴片led灯珠参数有哪
些？
3528灯珠，3528灯珠参数，3528贴片led灯珠参数有哪些？
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在LED灯珠的应用中，3528灯珠，5050灯珠，2835灯珠多少伏，都是经常问到的参数问题。

了解3528灯珠参数，正负极，对于LED的应用及选型，都是有很大好处的。

但是，因为3528灯珠有单色，双色，三色等多个系列，所以，LED灯珠的参数就会有很多种。

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3528灯珠参数
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3528灯珠的功率是0.06w。

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3528贴片led灯珠参数
3528灯珠参数图
3528灯珠正负极，3528灯珠规格书
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AAAAA——Suvpervised——Link Prediction using Supervised Learning

Link Prediction using Supervised Learning∗Mohammad Al Hasan,Vineet Chaoji,Saeed Salem,and Mohammed Zaki Rensselaer Polytechnic Institute,Troy,New York12180{alhasan,chaojv,salems,zaki}@AbstractSocial network analysis has attracted much attention in re-cent years.Link prediction is a key research direction within this area.In this paper,we study link prediction as a su-pervised learning task.Along the way,we identify a set of features that are key to the performance under the super-vised learning setup.The identiﬁed features are very easy to compute,and at the same time surprisingly eﬀective in solv-ing the link prediction problem.We also explain the eﬀec-tiveness of the features from their class density distribution. Then we compare diﬀerent classes of supervised learning al-gorithms in terms of their prediction performance using var-ious performance metrics,such as accuracy,precision-recall, F-values,squared error etc.with a5-fold cross validation. Our results on two practical social network datasets shows that most of the well-known classiﬁcation algorithms(deci-sion tree,k-NN,multilayer perceptron,SVM,RBF network) can predict links with comparable performances,but SVM outperforms all of them with narrow margin in all perfor-mance measures.Again,ranking of features with popular feature ranking algorithms shows that a small subset of fea-tures always plays a signiﬁcant role in link prediction.1Introduction and BackgroundSocial networks are a popular way to model the interac-tion among the people in a group or community.They can be visualized as graphs,where a vertex corresponds to a person in some group and an edge represents some form of association between the corresponding persons. The associations are usually driven by mutual interests that are intrinsic to a group.However,social networks are very dynamic objects,since new edges and vertices are added to the graph over the time.Understanding the dynamics that drives the evolution of social network is a complex problem due to a large number of variable ∗This material is based upon work funded in whole or in part by the US Government and any opinions,ﬁndings,conclusions, or recommendations expressed in this material are those of the author(s)and do not necessarily reﬂect the views of the US Government.This work was supported in part by NSF CAREER Award IIS-0092978,DOE Career Award DE-FG02-02ER25538, and NSF grants EIA-0103708and EMT-0432098.parameters.But,a comparatively easier problem is to understand the association between two speciﬁc nodes. Several variations of the above problems make interest-ing research topics.For instance,some of the interesting questions that can be posed are–how does the associa-tion pattern change over time,what are the factors that drive the associations,how is the association between two nodes aﬀected by other nodes.The speciﬁc prob-lem instance that we address in this research is to pre-dict the likelihood of a future association between two nodes,knowing that there is no association between the nodes in the current state of the graph.This problem is commonly known as the Link Prediction problem.We use the coauthorship graph from scientiﬁc pub-lication data for our experiments.We prepare datasets from the coauthorship graphs,where each data point corresponds to a pair of authors,who never coauthored in training years.Depending on the fact whether they coauthored in the testing year or not,the data point has either a positive label or a negative label.We ap-ply diﬀerent types of supervised learning algorithms to build binary classiﬁer models that distinguish the set of authors who will coauthor in the testing year from the rest who will not coauthor.Predicting prospective links in coauthorship graph is an important research direction,since it is identical, both conceptually and structurally to many practical social network problems.The primary reason is that a coauthorship network is a true example of social net-work,where the scientists in the community collaborate to achieve a mutual goal.Researchers[20]have shown that this graph also obeys the power-law distribution, an important property of a typical social network.To name some practical problems that very closely match with the one we study in this research,we consider the task of analyzing and monitoring terrorist networks. The objective in analyzing terrorist networks is to con-jecture that particular individuals are working together even though their interactions cannot be identiﬁed from the current information base.Intuitively,we are pre-dicting hidden links in a social network formed by the group of terrorists.In general,link prediction provides a measure of social proximity between two vertices in asocial group,which,if known,can be used to optimize an objective function over the entire group,especially in the domain of collaborativeﬁltering[22],Knowledge Management Systems[8],etc.It can also help in mod-eling the way a disease,a rumor,a fashion or a joke,or an Internet virus propagates via a social network[13].Our research has the following contributions:1.We explain the procedural aspect of constructinga machine learning dataset to perform link predic-tion.2.We identify a short list of features for link pre-diction in a particular domain,speciﬁcally,in the coauthorship domain.These features are powerful enough to provide remarkable accuracy and general enough to be applicable in other social network do-mains.They are also very inexpensive to obtain.3.We experiment with a set of learning algorithms toevaluate their performance in link prediction prob-lem and perform a comparative analysis among them.4.We evaluate each feature;ﬁrst visually,by compar-ing their class density distribution and then algo-rithmically through some well known feature rank-ing algorithms.2Related WorkAlthough most of the early research in social network has been done by social scientists and psychologists[19], numerous eﬀorts have been made by computer scien-tists recently.Most of the work has concentrated on analyzing the social network graphs[2,9].Few eﬀorts have been made to solve the link prediction problem, specially for social network domain.The closest match with our work is that of D.Liben,et al.[17],where the authors extracted several features from the network topology of a coauthorship network.Their experiments evaluated the eﬀectiveness of these features for the link prediction problem.The eﬀectiveness was judged by the factor by which the prediction accuracy was im-proved over a random predictor.This work provides an excellent starting point for link prediction as the fea-tures they extracted can be used in a supervised learning framework to perform link prediction in a more system-atic manner.But,they used features based on network topology only.We,on the other hand,added several non-topological features and found that they improve the accuracy of link prediction substantially.In prac-tice,such non-topological data are available(for exam-ple,overlap of interest between two persons)and they should be exploited to achieve a substantial improve-ment in the results.Moreover,we compare diﬀerent machine learning algorithms for the link prediction task.Another recent work by Faloutsos et al.[10],al-though does not directly perform link prediction,is worth mentioning in this context.They introduced an object,called connection subgraph,which is deﬁned as a small subgraph that best captures the relationship between two nodes in a social network.They also pro-posed eﬃcient algorithm based on electrical circuit laws toﬁnd the connection subgraph from large social net-work eﬃciently.Connection subgraph can be used to ef-fectively compute several topological feature values for supervised link prediction problem,especially when the network is very large.There are many other interesting recent eﬀorts[11, 3,5]related to social network,but none of these were targeted explicitly to solve the link prediction problem. Nevertheless,experiences and ideas from these papers were helpful in many aspects of this work.Goldenberg et al.[11]used Bayesian Networks to analyze the social network graphs.Baumes et al.[3]used graph clustering approach to identify sub-communities in a social network.Cai et al.[5]used the concept of relation network,to project a social network graph into several relation graphs and mine those graphs to eﬀectively answer user’s queries.In their model,they extensively used optimization algorithms toﬁnd the most optimal combination of existing relations that best match the user’s query.3Data and Experimental SetupConsider a social network G= V,E in which each edge e= u,v ∈E represents an interaction between u and v at a particular time t.In our experimental domain the interaction is deﬁned as coauthoring a research article.Each article bears,at least,author information and publication year.To predict a link,we partition the range of publication years into two non-overlapping sub-ranges.Theﬁrst sub-range is selected as training years and the later one as the testing years.Then, we prepare the classiﬁcation dataset,by choosing those author pairs,that appeared in the training years,but did not publish any papers together in those years. Each such pair either represents a positive example or a negative example,depending on whether those author pairs published at least one paper in the testing years or not.Coauthoring a paper in testing years by a pair of authors,establishes a link between them,which was not there in the training years.Classiﬁcation model of link prediction problem needs to predict this link by successfully distinguishing the positive classes from the dataset.Thus,link prediction problem can be posed as a binary classiﬁcation problem,that can be solvedby employing eﬀective features in a supervised learning framework.In this research,we use two bibliographic datasets: Elsevier BIOBASE()and DBLP(http://dblp.uni-trier.de/xml/),that have information about diﬀerent research publications in the ﬁeld of biology and computer science,respectively.For BIOBASE,we used5years of data from1998to 2002,where theﬁrst4years are used as training and the last as testing.For DBLP,we used15years of data,from1990to2004.First11years were used as training and the last4years as testing.Pairs of authors that represent positive class or negative class were chosen randomly from the list of pairs that qualify. Then we constructed the feature vector for each pair of authors.A detailed description of the features is given in the following sub-section.The datasets have been summarized in table1.Dataset Number of papers Number of authors BIOBASE831478156561DBLP5404591564617Table1:Statistics of Datasets3.1Feature Set Choosing an appropriate feature set is the most critical part of any machine learning algo-rithm.For link prediction,we should choose features that represent some form of proximity between the pair of vertices that represent a data point.However,the deﬁnition of such features may vary from domain to domain for link prediction.In this research,we name these as proximity features.For example,for the case of coauthorship network,two authors are close(in the sense of a social network)to each other,if their research work evolves around a larger set of identical keywords.A similar analogy can be given for a terrorist network, wherein,two suspects can be close,if they are experts in an identical set of dangerous skills.In this research, although we restrict our discussion to the feature set for coauthorship link analysis,the above generic deﬁni-tion of proximity measure provides a clear direction to choose conceptually identical features in other network domains.One favorable property of these features is that they are very cheap to compute.Beside the proximity measure,there exist individual attributes that can also provide helpful clues for link prediction.Since,these attributes only pertain to one node in the social network,some aggregation functions need to be used to combine the attribute values of the corresponding nodes in a node-pair.We name these as aggregated features.To illustrate further, let’s consider the following example.We choose two arbitrary scientists x and y from the social network. The probability that x and y coauthor is,say p1.Then, we choose one scientist z,from the same network,who works mostly on multi-disciplinary research,thus has established a rich set of connections in the community. Now,if p2is the probability that x will coauthor with z,the value of p2is always higher than p1,with the available information that z is a proliﬁc researcher.We summarize the idea with this statement:if either(or both)of the scientists are proliﬁc,it is more likely that they will collaborate.Before aggregation,the individual measure is how proliﬁc a particular scientist is and the corresponding individual feature is the number of diﬀerent areas(s)he has worked on.Summing the value to combine these,yields an aggregated feature that is meaningful for the pair of authors for link prediction.In this example,the higher the attribute value,the more likely that they will collaborate.A similar individual feature,in a terrorist network,can be the number of languages a suspect can speak.Again,aggregating the value produces an aggregated feature for link prediction in a terrorist network.Finally,we like to discuss about the most impor-tant set of features that arise from the network topology. Most importantly,they are applicable equally to all do-mains since their values depends only on the structure of the network.Here,we name these as topological fea-tures.Several recent initiatives[17,14,15]have studied network topological features for diﬀerent application ar-eas,like link analysis,collaborativeﬁltering,etc.How-ever,for link prediction the most obvious among these feature is the shortest distance among the pair of nodes being considered.The shorter the distance,the bet-ter the chance that they will collaborate.There are other similar measures,like number of common neigh-bors,Jaccard’s coeﬃcient,edge disjoint k shortest dis-tances,etc.For a more detailed list,see[17].There are some features,that could be a part of more than one category.For example,we can aggregate a topological feature that corresponds to a single network node.However,in our discussion,we place them under the category that we consider to be most appropriate.Next we provide a short description of all the fea-tures that we used for link prediction in a coauthorship network.We also describe our intuitive argument on choosing them as a feature for link prediction problem. Note that,not all the features were applied to both the datasets,due to the unavailability of information.3.1.1Proximity Features In the BIOBASE database,we only had one such feature.Since keyword data was not available in DBLP dataset,we could notuse this feature there.•Keyword Match Count This feature directly measures the proximity of a pair of nodes(authors). Here we list all the keywords that the individual authors had introduced in his papers and take a intersection of both the sets.The larger the size of the intersection, the more likely they are to work in related areas and hence a better candidate to be a future coauthor pair.3.1.2Aggregated Features As we described ear-lier,these features are usually related to a single node. We used the simplest aggregation function,namely,sum to convert the feature to a meaningful candidate for link prediction.A more complex aggregation function can be introduced if it seems appropriate.•Sum of Papers The value of this feature is calculated by adding the number of papers that the pair of authors published in the training years.Since, all authors did not appear in all the training years,we normalized the paper count of each author by the years they appeared in.The choice of this feature comes from the fact that authors having higher paper count are more proliﬁc.If either(or both)of the authors is (are)proliﬁc,the probability is higher that this pair will coauthor compared to the probability for the case of any random pair of authors.•Sum of Neighbors This feature represents the social connectivity of the pair of authors,by adding the number of neighbors they have.Here,neighborhood is obtained from the coauthorship information.Several variants of this feature exist.A more accurate measure would consider the weighted sum of neighbors,where the weights represent the number of publication that a node has with that speciﬁc neighbor.We considered all the weights to be1.This feature is intended to embed the fact that a more connected person is more likely to establish new coauthor links.Note that,this feature can also be placed under topological features,where the number of neighbors can be found by the degree of a node.•Sum of Keyword Counts In scientiﬁc pub-lication,keywords play a vital role in representing the speciﬁc domain of work of researchers.Researchers that have a wide range of interests or those who work on in-terdisciplinary research usually use more keywords.In this sense they have better chance to collaborate with new researchers.Here,also we used the sum function to aggregate this attribute for both the author pair.•Sum of Classiﬁcation Code Usually,research publication are categorized in code strings to organize related areas.Similar to keyword count,a publication that has multiple codes is more likely to be an inter-disciplinary work,and researchers in these area usually have more collaborators.•Sum of log(Secondary Neighbors Count) While number of primary neighbors is signiﬁcant,the number of secondary neighbors sometimes play an im-portant role,especially in a scientiﬁc research collab-oration.If an author is directly connected to another author who is highly connected(consider a new grad-uate student with a very well-known adviser),the for-mer person has a better chance to coauthor with a dis-tant node through the later person.Since,the number of secondary neighbors in social network usually grow exponentially,we take the logarithm of the secondary neighbor count of the pair of authors before we sum the individual node values.This attribute can also be placed under topological feature as it can be computed only from the network topology.Calculation of this fea-ture is somewhat costly.3.1.3Topological Features We used the following three features in our research,but there are other features that can be useful as well.•Shortest Distance This feature is one of the most signiﬁcant in link prediction as we found in our research.Kleinberg[16,20]discovered that in social network most of the nodes are connected with a very short distance.This remarkable characteristic makes it a very good feature for link prediction.We used smallest hop count as the shortest distance between two nodes. There are several variants of this feature.Instead of computing one shortest distance,we can compute k edge-disjoint shortest distance.Each of these can be one feature.Importance of the feature gradually decreases as k increases.Moreover,a shortest distance can be weighted,where each edge has an actual weight instead of a value1as it is for unweighted shortest distance. For any pair of nodes,the weight on the edge can be chosen to be the reciprocal of the number of papers the corresponding author pair has coauthored.However, each of these variants are more costly to compute.•Clustering Index Many initiatives within so-cial network research[17,20]have indicated cluster-ing index as an important features of a node in a social network.It is reported that a node that in dense locally is more likely to grow more edges com-pared to one that is located in a more sparse neigh-borhood.The clustering index measures the localized density.Newman[20]deﬁnes clustering index as the fraction of pairs of a person’s collaborators who have also collaborated with one another.Mathematically,If u is a node of a graph,The clustering index of u is: 3×number of triangles with u as one node•Shortest Distance in Author-KW graph We considered this as a topological attribute,although it requires an extended social network to compute it.To compute this attribute we extended the social network by adding Keyword(KW)nodes.Each KW node is connected to an author node by an edge if that keyword is used by the author in any of his papers.Moreover, two keywords that appear together in any paper are also connected by an edge.A shortest distance between two nodes in this extended graph is computed to get this attribute value.In addition to these features,we also tried several other features,like Jaccard’s coeﬃcient, Adamic/Adar[1],etc.,mostly related to network topol-ogy.Unfortunately,they did not provide any signiﬁcant improvement on the classiﬁer performance.We normalize the feature values to have zero mean and one standard deviation before using them in the classiﬁcation model.3.2Classiﬁcation Algorithms There exist a plethora of classiﬁcation algorithms for supervised learning.Although their performances are comparable, some usually work better than others for a speciﬁc dataset or domain.In this research,we experimented with seven diﬀerent classiﬁcation algorithms.For some of these,we tried more than one variation and reported the result that showed the best performance. The algorithms that we used are SVM(two diﬀer-ent kernels),Decision Tree,Multilayer Perceptron, K-Nearest Neighbors(diﬀerent variations of distance measure),Naive Bayes,RBF Network and Bagging. For SVM,we used the SVM-Light implementation ().For K-Nearest neighbors,we programmed the algorithm using Matlab. For the rest of the algorithms,a well known machine learning library,WEKA[24]was used.Then we compared the performance of the above classiﬁers using diﬀerent performance metrics like ac-curacy,precision-recall,F-value,squared-error etc.For all the algorithms,we used5-fold cross validation for the results reported.For algorithms that have tunable parameters,like SVM,K-Nearest Neighbors,etc.,we used a separate validation set toﬁnd the optimum pa-rameter values.In SVM the trade-oﬀbetween training error and margin of8was found to be optimum.For k-nearest neighbor,a value of12for k yielded the best performance for BIOBASE dataset and a value of32for the DBLP dataset.For others,default parameter values of WEKA worked quite well.However,for most of the models the classiﬁer performance was found not to be very sensitive with respect to model parameter values unless they were quite oﬀfrom the optimal setting.4Results and DiscussionsTable2and3show the performance comparison for dif-ferent classiﬁers on the BIOBASE and DBLP datasets respectively.In both the datasets,counts of positive class and the negative class were almost the same.So, a baseline classiﬁer would have an accuracy around50% by classifying all the testing data points to be equal to 1or0,whereas all the models that we tried reached an accuracy above80%.This indicates that the features that we had selected have good discriminating ability. For BIOBASE dataset we used9features and for the DBLP dataset we used only4features.There was not enough information available with the DBLP dataset. Name of the feature used,for each of the dataset are available from table4and5.On accuracy metrics,SVM with RBF kernel per-formed the best for both the datasets with an accu-racy of90.56%and83.18%,respectively.Naturally, the performance on DBLP dataset is worse compared to BIOBASE as fewer features were used in the for-mer dataset.Moreover,DBLP dataset was obtained using15years of published articles and the accuracy of link prediction deteriorates over the longer range of time span since the institution aﬃliations,coauthors and re-search areas of researchers may vary over time.So,pre-dicting links in this dataset is comparably more diﬃ-cult than the BIOBASE dataset,where we used only5 years of data.In both the datasets,other popular clas-siﬁers,like decision tree,k-nearest neighbors and multi-layer perceptron also have similar performances,usually 0.5%to1%less accurate than SVM.Such a small dif-ference is not statistically signiﬁcant,so no conclusion can be drawn from the accuracy metric about the most suited algorithm for the link prediction.To further analyze the performance,we also applied the most popular ensemble classiﬁcation techniques, bagging for link prediction.Bagging groups the deci-sions from a number of classiﬁers,hence the resulting model is no more susceptible to variance errors.Perfor-mance improvement of bagging,over the independent classiﬁers are high when the overlap of the misclassiﬁca-tion sets of the independent classiﬁers is small[7].The bagging accuracy for the datasets is90.87and82.13, which indicates almost no improvements.This implies that majority of misclassiﬁcations are from the bias er-ror introduced by inconsistent feature values in those samples.Hence,most of the classiﬁers failed on these samples.To understand the inconsistency in feature values, we investigate the distribution of positively and neg-atively labeled samples for four important features in each dataset as shown inﬁgure1and 2.The distri-bution of feature values are plotted along the y-axis for(a)KeywordMatch(b)Sum of Neighborscount(c)Sum of Paperscount (d)Shortest distanceFigure 1:Evaluation of features using class density distribution in BIOBASEdataset(a)ShortestDistance(b)Sum of paper count(c)Sum of neighborscount (d)Second shortest distanceFigure 2:Evaluation of features using class density distribution in DBLP datasetClassiﬁcation model Accuracy Precision Recall F-value Squared Error Decision Tree90.0191.6089.1090.400.1306 SVM(Linear Kernel)87.7892.8083.1886.820.1221 SVM(RBF Kernel)90.5692.4388.6690.510.0945K Nearest Neighbors88.1792.2683.6387.730.1826 Multilayer Perceptron89.7893.0087.1090.000.1387 RBF Network83.3194.9072.1081.900.2542Naive Bayes83.3295.1071.9081.900.1665Bagging90.8792.590.0091.230.1288 Table2:Performance of diﬀerent classiﬁcation algorithms for BIOBASE database Classiﬁcation model Accuracy Precision Recall F-value Squared Error Decision Tree82.5687.7079.583.400.3569 SVM(Linear Kernel)83.0485.8882.9284.370.1818 SVM(RBF Kernel)83.1887.6680.9384.160.1760K Nearest Neighbors82.4285.1082.5283.790.2354 Multilayer Perceptron82.7387.7080.2083.700.3481 RBF Network78.4978.9083.4081.100.4041Naive Bayes81.2487.6076.9081.900.4073Bagging82.1386.7080.0083.220.3509 Table3:Performance of diﬀerent classiﬁcation algorithms for DBLP datasetvarious feature values.For comparison sake,we nor-malize the distribution so that the area under both the curves is the same.For most of the features,the distri-bution of positive and negative class exhibit signiﬁcant diﬀerence,thus facilitating the classiﬁcation algorithm to pick patterns from the feature value to correctly clas-sify the samples.However,there is a small overlap re-gion between the distributions for some features.The fraction of population that lies in the critical overlap re-gion for most of the features are most likely the candi-dates for misclassiﬁcation.We shall discuss more about the distribution later.Among all the classiﬁers,RBF network model per-forms the worst in both the datasets and may not be the one that is suitable for the link prediction problem. RBF networks are usually aﬀected severely by irrelevant or inconsistent features and link prediction datasets are heavily noisy,hence,the performance value for RBF is poor.On the other hand,we have naive Bayes algo-rithm,which also performed bad.Naive Bayes is prob-ably not powerful enough to catch the patterns in the data set which are helpful for classiﬁcation.In the same tables,we also list Precision,Recall and F-value for the positive class.F-value is the har-monic mean of precision-recall that is sometimes consid-ered a better performance measure for a classiﬁcation model in comparison to accuracy,especially if the pop-ulation of the classes are biased in the training dataset. Considering the F-value metric,the rank of the classi-ﬁers do not really change,indicating that all the models have similar precision-recall behavior.Now,comparing the precision and recall columns,weﬁnd that most of the classiﬁers have precision value signiﬁcantly higher than the recall value for the positive class.This indi-cates that our models have more false negatives than false positives.Intuitively,the models are missing ac-tual links more than they are predicting false links.For coauthorship network,it makes sense because there ex-ist some coauthor pairs that seem to coauthor merely by coincidence.Moreover,it can happen that the link is actually not there in real life also,but the dataset had it because of name aggregation.Note that in the dataset that we used,all the names that had the same spelling were considered to be the same person,which is not always correct.This problem has been addressed in many concurrent researches and several entity disam-biguation methodologies have been proposed[18,4]to cope with it.So,a better performance will be observed, if such methodologies are applied to the dataset as a preprocessing step before feeding it into the learning al-gorithms.Finally,we use the average squared error as our last performance comparison metric.Recent research[6] shows that this metric is remarkably robust and has。

AA3528SESJ3-AMT;中文规格书,Datasheet资料

3.5x2.8mm SURFACE MOUNT LED LAMP 3. The specifications, characteristics and technical data described in the datasheet are subject to change without prior notice.Selection GuideNotes:1. θ1/2 is the angle from optical centerline where the luminous intensity is 1/2 of the optical peak value.2. Luminous intensity/ luminous Flux: +/-15%.Part No.DiceLens TypeIv (mcd) [2] @ 20mA Viewing Angle [1] Code. Min. Max. 2θ1/2AA3528SES/J3-AMT Hyper Red (AlGaInP) Water Clear120°V 1300 1600W 1600 1900 X 1900 2300 Y 2300 2700ParameterSymbol Value Unit Power dissipation P D 140 mW Reverse Voltage V R 5 V Junction temperature T J 120 °C Operating Temperature Top -40 To +100 °C Storage Temperature Tstg -40 To +120°C DC Forward Current[1] I F 50 mA Peak Forward Current [2]I FM150 mA Electrostatic Discharge Threshold (HBM) 3000 V Thermal Resistance (Junction/ambient) [1]R th j-a 350°C/WNotes:1.The dominant Wavelength (λ d) above is the setup value of the sorting machine. (Tolerance λ d : ±1nm. )2. Forward Voltage: +/-0.1V.Electrical / Optical Characteristics at T a =25°CNotes:1. Rth(j-a) Results from mounting on PC board FR4 (pad size ≥16 mm 2 per pad),2. 1/10 Duty Cycle, 0.1ms Pulse Width.Absolute Maximum Ratings at T a =25°CParameterSymbol Value Unit Wavelength at peak emission I F =20mA [Typ.]λpeak 640 nm Dominant Wavelength I F =20mA [Min.] λ dom [1] 615 nm Dominant Wavelength I F =20mA [Max.] λ dom [1] 635 nm Spectral bandwidth at 50%ΦREL MAXI F =20mA [Typ.]Δλ25 nmForward Voltage I F =20mA [Min.] V F [2]-VForward Voltage I F =20mA [Typ.] 2.2 Forward Voltage I F =20mA [Max.] 2.8Reverse Current (V R = 5V) [Max.] I R 10 uA Temperature coefficient of λpeak I F =20mA, -10°C ≤ T ≤100°C [Typ.] TC λpeak 0.14 nm/°C Temperature coefficient of λdom I F =20mA, -10°C ≤ T ≤100°C [Typ.] TC λdom 0.04 nm/°C Temperature coefficient of V FI F =20mA, -10°C ≤ T ≤100°C [Typ.]TC V-2.0mV/°CReliability Test Items And ConditionsThe reliability of products shall be satisfied with items listed below Lot Tolerance Percent Defective (LTPD) : 10%No. Test Item Standards Test Condition Test Times /CyclesNumber ofDamaged1 Continuous operating test - Ta =25°C ,IF = maximum rated current* 1,000 h 0 / 222 High Temp. operating test EIAJ ED-4701/100(101)Ta = 100°C IF = maximum rated current* 1,000 h 0 / 223 Low Temp. operating test - Ta = -40°C, IF = maximum rated current* 1,000 h 0 / 224 High temp. storage test EIAJ ED-4701/100(201)Ta = maximum rated storage temperature 1,000 h 0 / 225 Low temp. storage test EIAJ ED-4701/100(202)Ta = -40°C 1,000 h 0 / 226 High temp. & humidity storage test EIAJ ED-4701/100(103)Ta = 60°C, RH = 90% 1,000 h 0 / 227 High temp. & humidity operating test EIAJ ED-4701/100(102)Ta = 60°C, RH = 90%IF = maximum rated current*1,000 h 0 / 228 Soldering reliability test EIAJ ED-4701/100(301)Moisture soak : 30°C,70% RH, 72hPreheat : 150~180°C(120s max.)Soldering temp : 260°C(10s)3 times 0 / 189 Thermal shock operating test - Ta = -40°C(15min) ~ 100°C(15min)IF = derated current at 100°C1,000 cycles 0 / 2210 Thermal shock test - Ta = -40°C(15min) ~ maximum ratedstorage temperature(15min)1,000 cycles 0 / 2211 Electric Static Discharge (ESD) EIAJ ED-4701/100(304)C = 100pF , R2 = 1.5KΩ V = 3000VOnce eachPolarity0 / 2212 Vibration test - a = 196m/s² , f = 100~2KHz ,t = 48min for all xyz axes4 times 0 / 22* : Refer to forward current vs. derating curve diagramItems Symbols Conditions Failure Criterialuminous Intensity lv I F = 20mA Testing Min. Value <Spec.Min.Value x 0.5 Forward Voltage V F I F = 20mA Testing Max. Value ≥Spec.Max.Value x 1.2 Reverse Current I R V R = Maximum Rated Reverse Voltage Testing Max. Value ≥Spec.Max.Value x 2.5High temp. storage test - - Occurrence of notable decoloration, deformation and crackingFailure Criteria分销商库存信息: KINGBRIGHTAA3528SES/J3-AMT。

3528贴片规格书

中之光电科技有限公司ZT3528W0S1-****(0.06W)规格书3.5 mm SMD Hyper White Top LED 3.5 mm 贴片超亮白色发光二极管Features 特征• Package Size ：3.5 (L) ×2.8(W) × 1.2 (T) mm封装尺寸： 3.5 (长) ×2.8 (宽) ×1.2 (厚) mm •Silicone Packed 采用硅胶封装•Super long lifetime超长寿命 •Anti UV 防紫外线•White colors are available in(1800K-15000K) 可供白光(1800K-15000K) •High CRI products 高显色性产品 •Wide viewing angle (2θ1/2=120°) 宽角度 (2θ1/2=120°)Applications •产品应用•Indoor lighting : Fluorescent lamp, tube •室内照明：日光灯管、灯条•Commercial illumination and displays : •商业照明显示：广告字、广告灯箱 Advertising words, light box•LCD Backlighting •LCD 背光源•Decorative lighting : light strip •装饰照明：柔性灯条•Automotive interior auxiliary lighting •Other illumination and displays •其它照明和显示类OOTop View 顶视Side View 侧视OOBin Range of Forward Voltage电压等级代码(at I F = 20 mA, T a = 25 O C )Part No. Description产品型号说明Color Bin Limit色度代码;(at I F = 20 mA, T a = 25 O C )Color Bin色坐标常用BIN区(Common Bin)可选BIN区（Optional Bin）Typical Electro-Optical Characteristics Curves 典型光-电特性曲线图Solder Pad一、PRECAUTONS IN USE LED/使用LED注意事项；LED Soldering condition/ LED焊接条件；1：烙铁焊接：烙铁最高30W尖端温度不超过300℃；焊接时不超过3秒；Manual soldering：iron Maximum 30W，iron bit temperature can not over 300 degree；soldering time should not be more than 3 seconds；Cleanout/清洗；当用化学品清洗LED胶体时须特别小心，因为有些化学品对胶体表面有损伤并引起褪色如三氯乙烯、丙酮，可用乙醇擦拭浸渍，时间在常温下不超过2分钟。

SMD 3528系列LED应用指南

日中案
提高灯珠寿命
的解决方

手动焊接时注意事项
图片位预留
正确操作方法
错误操作方法
日中案
提高灯珠寿命
的解决方

二、回流焊焊结
回流焊温度曲线（平头SMD LED产品）如下：
日中案
提高灯珠寿命
的解决方

日中案
提高灯珠寿命
的解决方

2.防潮包装 2.防潮包装
日中案
提高灯珠寿命
的解决方

为了防止在运输和贮存过程中湿气侵入到 SMD LED，SMD LED必须要用防潮袋密 LED， LED必须要用防潮袋密封包装。包装时要在里面放入干燥剂和湿度色卡（如下图）。变成紫红色部分为产品已受潮，蓝色的为正常。
的解决方

C：产品使用说明 1.清洗 1.清洗
为了防止损害 SMD LED，请不要使 LED，请不要使用无详细说明的化学液体清洗SMD 用无详细说明的化学液体清洗SMD LED。当有必要清洗时，请在室温下 LED。当有必要清洗时，请在室温下把SMD LED浸在酒精里，且时间不 LED浸在酒精里，且时间不超过1 超过1分钟，然后在室温下自然干燥 15分钟就可以正常使用了。（尽量不 15分钟就可以正常使用了。（尽量不清洗）清洗）
日中案
提高灯珠寿命
的解决方

5.焊接 5.焊接
一、用烙铁手动焊接 1、焊接时推荐使用的电铬铁小于25W，、焊接时推荐使用的电铬铁小于25W，当在焊接产品时烙铁的温度应保持在300℃ 当在焊接产品时烙铁的温度应保持在300℃ 以下且须在3 以下且须在3秒内完成焊接。 2、焊接时烙铁头不要触到SMD LED 、焊接时烙铁头不要触到SMD 硅胶部分。 3、焊接时不要有任何机械压力施加在产品硅胶部分。 3、焊接完产品后，只有当产品温度降到40℃以下时才可以进行后续的处理，这是 40℃以下时才可以进行后续的处理，这是为了防止产品由于后续工作的机械的热压力而失效。

AA3021SURSK, 规格书,Datasheet 资料

Recommended Soldering Pattern (Units : mm; Tolerance: ± 0.1)
Reel Dimension
Tape Dimensions (Units : mm)
SPEC NO: DSAL0858 APPROVED: WYNEC
REV NO: V.2 CHECKED: Allen Liu
SPEC NO: DSAL0858 APPROVED: WYNEC
REV NO: V.2 CHECKED: Allen Liu
DATE: APR/13/2011 DRAWN: J.Yu
PAGE: 1 OF 6 ERP: 1201002772
芯天下--/
Handling Precautions
Note: 1. 1/10 Duty Cycle, 0.1ms Pulse Width.
Hyper Red 75 30 185 5 -40°C To +85°C -40°C To +85°C
Units mW mA mA V
SPEC NO: DSAL0858 APPROVED: WYNEC
REV NO: V.2 CHECKED: Allen Liu
Notes: 1. θ1/2 is the angle from optical centerline where the luminous intensity is 1/2 of the optical peak value. 2. Luminous intensity/ luminous Flux: +/-15%.
Compare to epoxy encapsulant that is hard and brittle, silicone is softer and flexible. Although its characteristic significantly reduces thermal stress, it is more susceptible to damage by external mechanical force. As a result, special handling precautions need to be observed during assembly using silicone encapsulated LED products. Failure to comply might lead to damage and premature failure of the LED. 1. Handle the component along the side surfaces by using forceps or appropriate tools.

3528证书

Shenzhen Huate Optoelectronics Co.,LTD承认书ACCEPT SHEET文件编号: EN-QS-1-A-001NO.客户名称:Customer:客户料号:Part.No.:品名:Description SMD 3528规格：Model：HT35-21WYS/J送样日期:Day:HUA TE OPTO-ELECTRONICS CO.,LTD.Address: 7F,B3 Building Xinjianxing Industrial Park,Fengxin RD,Guangming New Zone,Shenzhen City,GuangDong Province,ChinaTel:+86-755-33266698; Fax: +86-755-33687390HT35-21WYS/J ◆Features* High brightness surface mount technology.* Emitting view angle 120o* Suitable for all SMT assembly method.* IR reflow soldering and vapor phase reflow soldering.* For outdoor and indoor display, backlight application.◆Package DimensionsNotes:1. All dimensions are in mm.2. Tolerance is ±0.25mm unless otherwise noted.3. Lead spacing is measured where the leads emerge from the package.◆Description◆Absolute Maximum Ratings (T=25℃)A﹡Pulse w idth≤0.1msec Duty Ratio ≤1/10◆Electrical and Optical Characteristics (T=25℃)ANotes:1.The dominant Wavelength,λdom is derived from the CIE chromaticity diagram andrepresents the single wavelength which define the color of the device.2.2θ1/2 is the off-axis angle where the luminous intensity is one half the on-axis intensity.3.Luninous intensity is measured by HUA TE TECH.’S equipment on TopLEDin the same lot.◆Typical Electrical/Optical Characteristic Curves (If=20mA;T A =25℃)Spectrum Distribution Ta=25o0255075100400450500550600650700Wavelength λPR e l a t i n e l u m i n o u s l n t e n s i t y (%)F o r w a r d C u r r e n t I F (m A )1020403050Forward Current vs.Forward VoltageForward Voltage(V)F o r w a r d C u r r e n tR e l a t i v e L u m i n o u s I n t e n s i t yRelative Luminous Intensity vs.Forward CurrentAmbient Temperature Ta (。

贴片发光二极管 3528翠绿色 SMD TOP LED 灯珠中英文参数资料

符号 Symbol
IF IFP VR PD Topr Tstg
焊接温度Soldering Temperature
Tsld
1/10 周期, 0.1 msec 脉宽 IFP Conditions : 1/10 Duty Cycle, 0.1 msec Pulse Width.
最大额定值 Absolute Maximum Rating
第7页共8页
1. 包装 PACKAGING
(1) LEDS 在装带之后纸箱包装. The LEDs are packed in cardboard boxes after taping. (2) 装带规格 TapingSpecifications (单位:毫米 Units:mm)
编制 Prepared by
洸子其确认
CONFIRMATION
审核
核准
Checked by
Approved by
市场部 Market Dept.
确认 Confirmed by
客户确认
CUSTOMER CONFIRMATION
审核
核准
Checked by
Approved by
确认 Confirmed by
30 150
9 100
-40ºC To +85ºC -40ºC To +85ºC
ReflowSoldering:260ºC Hand Soldering : 350ºC
单位 Unit mA mA
V mW °C °C for 10sec. for 3sec.
(2) 样品光电参数 Initial Electrical/Optical Characteristics (TA=25±5ºC)

华宏光电（深圳）有限公司单晶贴片产品规格书说明书

产品规格书Specification3528单晶贴片3528 Single Chip Top LED产品型号/ Part No : WW-WNA30TS-U1批准审核制定牛焕东陶源孔利媚华宏光电子（深圳）有限公司WAH WANG OPTOELECTRONIC (SHENZHEN)COMPANY LIMITED地址:深圳市龙华新区大浪街道华荣路联建科技工业园第五栋Address：Floor Block 5, Lianjian Science ＆Technology lndustrial Park,Crossing of HuaRong Road, Dalang street - New District of Longhua, ShenZhen City.电话：*************Tel**************传真：*************Fax************** S.D.N. / D.N. No. 送货单编号Customer Name客户名称Sample Approval Signature客戶确认签署Date日期■ 产品特性 ■ Features ：PLCC 封装的产品尺寸为:3.5(L) x 2.8(W) x 1.9 (H) mmPLCC LED dimensions: 3.5(L) x 2.8(W) x 1.9(H) mm 发散视角120°Wide view angle 120ﾟ环保防静电胶带包装Available on tape and reel with Anti-electrostatic bag 产品性能稳定可靠Compatible for all SMT Assembly and Lead-Free Soldering RoHS Compliant■ 应用■ Applications ：背光液晶开关和显示Backlight for LCD Switch and Display 装饰照明Decorative Lighting 一般照明General Lighting 汽车内部照明Automotive Interior Lighting 常规使用General Use■ 产品尺寸图■ Package Dimensions ：建议焊盘尺寸■注释■Notes：1、上图所有尺寸的单位为毫米All dimension units are in millimeters.2、所有外形尺寸公差为 ± 0.25毫米，除非另有说明All dimension tolerances are ± 0.25mm unless otherwise noted. ■最大绝对额定值■Absolute Maximum Ratings：参数Parameter符号Symbol数值Value单位Unit功耗Power dissipationP d60 mW 连续正向电流Continuous Forward CurrentI F20 mA峰值正向电流Peak Forward Current I FP50 mA 反向电压Reverse VoltageV R 5 V 静电放电(HBM)Electrostatic Discharge (HBM)ESD 1000 V 工作温度范围Operating Temperature RangeT opr-25 to +85 ℃存储温度范围Storage Temperature RangeT stg-40 to +100 ℃无铅焊接温度Lead Soldering Temperature T sol 260( for 5 sec) ℃Recommended Soldering Pads Dimensions Recommended Soldering Pads Dimensions光电参数规格：Electrical Optical Characteristics: WW-WNA30TS-U1参数 Parameter 符号 Symbol最小值 Min. 平均值 Typ. 最大值 Max. 单位 Unit 测试条件发光强度I v 2100 --- 3000 mcd I F =20mA参考光通量 Φv 7.5 8.5 --- lm 正向电压 V F 2.7 --- 3.3 V 颜色等级T C4700---5300K 色区 Bin CA4/5-CB4/5 NIL视角Viewing Angle 2θ1/2---120---Deg显色指数Color Rendering IndexRa80----------反向电流 I R --- --- 5 µA V R = 5VWW-WNA30TS-U1参数 Parameter 符号 Symbol最小值 Min. 平均值 Typ. 最大值 Max. 单位 Unit 测试条件发光强度I v 2100 --- 3000 mcd I F =20mA参考光通量 Φv 7.5 8.5 --- lm 正向电压 V F 2.7 --- 3.3 V 颜色等级T C5300---6000K 色区 Bin CC4/5-CD4/5 NIL视角Viewing Angle 2θ1/2---120---Deg显色指数Color Rendering IndexRa80----------反向电流 I R --- --- 5 µA V R = 5VWW-WNA30TS-U1参数 Parameter 符号 Symbol最小值 Min. 平均值 Typ. 最大值 Max. 单位 Unit 测试条件发光强度I v 2100 --- 3000 mcd I F =20mA参考光通量 Φv 7.5 8.5 --- lm 正向电压 V F 2.7 --- 3.3 V 颜色等级T C5700---6500K 色区 Bin CD4/5-CE4/5 NIL视角Viewing Angle 2θ1/2---120---Deg显色指数Color Rendering IndexRa80----------反向电流 I R --- --- 5 µA V R = 5V注释Notes:1、发光强度、功耗和光通量的误差为 ± 10％WW maintains a tolerance of ±10% on flux and power measurements. 2、波长的误差为 ±1nm ；电压的误差为 ± 0.1Vλd ±1nm ； A tolerance of ±0.1V on forward voltage measurements 3、显色指数测试误差为±2The above Color Rendering Index measurement allowance tolerance is ±2 4、所有测试都是基于华宏现有的标准测试平台All measurements were made undr the satandardized environment of wahwang■光电特性Typical Optical Characteristics CurvesAmbient Temperature TA(°C)环境温度(℃)R e l a t i v e L u m i n o u s I n t e n s i t y (%)发光强度(%)Forward Current Derating Curve 环境温度与电流的关系曲线图Radiation Diagram发光角度图Spectral Distribution波长曲线图Luminous Intensity vs AmbientTemperature发光强度与温度的关系曲线图F o r w a r d C u r r e n t (m A )电流(m A) Ambient Temperature TA(°C)环境温度(℃)R e l a t i v e L u m i n o u s I n t e n s i t y (%)光通量（%）Forward Current (mA)电流(mA)Forward Voltage (V)电压(V)F o r w a r d C u r r e n t (m A )电流(m A ) Forward Current vs Forward Voltage伏安特性曲线Forward Current vs Relative Luminous Intensity电流和光通量的关系■ 色区坐标■ Chromaticity Coordinate:色区 Ranks暖白 Warm White白光WhiteTWa X 0.5259 0.5045 0.4799 0.4993 TWf X 0.3898 0.3680 0.3741 0.3997 Y 0.4342 0.4344 0.3967 0.3967 Y 0.3716 0.3581 0.3856 0.4005 TWb X 0.4876 0.4648 0.4758 0.5002 TWg X 0.3680 0.3512 0.3548 0.3741 Y 0.4072 0.4038 0.4225 0.4265 Y 0.3581 0.34650.3736 0.3856 TWc X 0.4648 0.4420 0.4515 0.4758 TWh X 0.3512 0.3360 0.3376 0.3548 Y 0.4038 0.3985 0.4168 0.4225 Y 0.3465 0.3369 0.3616 0.3736 TWd X 0.442 0.4185 0.4261 0.4515 TWi X 0.3366 0.3222 0.3205 0.3376 Y 0.3985 0.3902 0.4077 0.4168 Y 0.3369 0.3243 0.3470 0.3616 TWeX 0.4185 0.3917 0.3970 0.4261 TWjX 0.3294 0.3145 0.3117 0.3292 Y0.3902 0.3773 0.3936 0.4077Y0.33060.31870.3393 0.3548Notes:1.客户可以选择以上任何一种颜色的色区Customer can choose any group2.X/Y是参照CIE1931，色坐标X/Y的误差为 ± 0.01;色温误差：9000K-5000K ±500K /低于 5000K ±50K *X and Y are CIE1931; Color Coordinates Measurement allowance is ±0.01Tolerance of Color Temperature: 9000K-5000K ±500K / below 5000K ±50K■产品可靠性检测项目：序號No.实验项目Test Item标准测试方法Standard TestMethod测试条件Test Conditions检测时间和周期Duration不良数/测试数Failure Rate1正常工作寿命Steady StateOperating LifeJEITA ED-4701100 103If=20mA1000hrs 0/22Ta=25℃2低温贮藏Low TemperatureStorageJEITA ED-4701Ta=-40℃1000hrs 0/22200 2023高温贮存High TemperatureStorageJEITA ED-4701Ta=100℃1000hrs 0/22200 2014高温高湿贮藏TemperatureHumidity StorageJEITA ED-4701 Ta=60℃1000hrs 0/22100 103 RH=90%5冷热冲击Thermal ShockJEITA ED-4701 0℃ ~ +100℃10 cycles 0/22300 3075min~ 15sec ~5min6JEITA ED-4701100 105H：+100℃ 30min.100cycles0/22 高低温循环Temperature Cycle∫ : +25℃ 5min.L：－40℃ 30min7烫锡Solder HeatJEITA ED-4701 Tsld=260℃, 10sec(Max.)2 times 0/22300 301■失效产品判定标准：项目Item符号Symbol测试条件Test Condition最小值Min.最大值Max.正向电压Forward VoltageV F I F = 20mA -- *U.S.L×1.1 反向电流Reverse CurrentI R V R = 5V -- *U.S.L×2.0发光强度Luminous IntensityI V I F = 20mA **L.S.L×0.7 --*U.S.L.:超出标准最大值*U.S.L.: Upper Standard Level** L.S.L.:低于标准最低值** L.S.L.: Lower Standard Level■.■ ■ ■ Dimensions of the reel ：■ .包装 ■ Packing ：防潮、防静电真空密封包装■ Moisture, anti-static vacuum sealed packages■ 注释 ■ Note ：上图所有尺寸的单位为毫米，公差为 ± 2.0毫米，除非另有说明All dimensions are in mm, tolerance is ± 2.0mm unless otherwise noted.■注意事项：PRECAUTION IN USE1.存储要求:1.1推荐储存环境温度：5o C ~ 30o C的 (41o F ~ 86o F)湿度：相对湿度60％以下1.2防潮袋密封包装储存时间3个月，起始时间以包装标签日期为准，需在包装袋封口良好并无漏气现象，且湿度卡未变为粉红色前提下使用，如超过3个月的LED需按2. 2要求除潮烘烤后才能正常使用。

华宏光电子（深圳）有限公司 WAH WANG OPTOELECTRONIC (SHENZHEN) C

产品规格书Specification3528单晶贴片3528 Single Chip Top LED产品型号/ Part No : WW-WNA30TS-Q1批准审核制定牛焕东陶源孔利媚华宏光电子（深圳）有限公司WAH WANG OPTOELECTRONIC (SHENZHEN)COMPANY LIMITED地址:深圳市龙华新区大浪街道华荣路联建科技工业园第五栋Address：Floor Block 5, Lianjian Science ＆Technology lndustrial Park,Crossing of HuaRong Road, Dalang street - New District of Longhua, ShenZhen City.电话：*************Tel**************传真：*************Fax************** S.D.N. / D.N. No. 送货单编号Customer Name客户名称Sample Approval Signature客戶确认签署Date日期■ 产品特性 ■ Features ：PLCC 封装的产品尺寸为:3.5(L) x 2.8(W) x 1.9 (H) mmPLCC LED dimensions: 3.5(L) x 2.8(W) x 1.9(H) mm 发散视角120°Wide view angle 120ﾟ环保防静电胶带包装Available on tape and reel with Anti-electrostatic bag 产品性能稳定可靠Compatible for all SMT Assembly and Lead-Free Soldering RoHS Compliant■ 应用■ Applications ：背光液晶开关和显示Backlight for LCD Switch and Display 装饰照明Decorative Lighting 一般照明General Lighting 汽车内部照明Automotive Interior Lighting 常规使用General Use■ 产品尺寸图■ Package Dimensions ：建议焊盘尺寸■注释■Notes：1、上图所有尺寸的单位为毫米All dimension units are in millimeters.2、所有外形尺寸公差为 ± 0.25毫米，除非另有说明All dimension tolerances are ± 0.25mm unless otherwise noted. ■最大绝对额定值■Absolute Maximum Ratings：参数Parameter符号Symbol数值Value单位Unit功耗Power dissipationP d60 mW 连续正向电流Continuous Forward CurrentI F20 mA峰值正向电流Peak Forward Current I FP50 mA 反向电压Reverse VoltageV R 5 V 静电放电(HBM)Electrostatic Discharge (HBM)ESD 1000 V 工作温度范围Operating Temperature RangeT opr-25 to +85 ℃存储温度范围Storage Temperature RangeT stg-40 to +100 ℃无铅焊接温度Lead Soldering Temperature T sol 260( for 5 sec) ℃Recommended Soldering Pads Dimensions Recommended Soldering Pads Dimensions光电参数规格：Electrical Optical Characteristics: WW-WNA30TS-Q1参数 Parameter 符号 Symbol最小值 Min. 平均值 Typ. 最大值 Max. 单位 Unit 测试条件发光强度I v 1700 --- 2500 mcd I F =20mA参考光通量 Φv 7 8 --- lm 正向电压 V F 2.7 --- 3.3 V 颜色等级T C5700---6500K 色区 Bin CD4/5-CE4/5 NIL视角Viewing Angle 2θ1/2---120---Deg显色指数Color Rendering IndexRa80----------反向电流 I R --- --- 5 µA V R = 5VWW-WNA30TS-Q1参数 Parameter 符号 Symbol最小值 Min. 平均值 Typ. 最大值 Max. 单位 Unit 测试条件发光强度I v 1700 --- 2500 mcd I F =20mA参考光通量 Φv 7 8 --- lm 正向电压 V F 2.7 --- 3.3 V 颜色等级T C5300---6000K 色区 Bin CC4/5-CD4/5视角Viewing Angle 2θ1/2---120---Deg显色指数Color Rendering IndexRa80----------反向电流 I R --- --- 5 µA V R = 5VWW-WNA30TS-Q1参数 Parameter 符号 Symbol最小值 Min. 平均值 Typ. 最大值 Max. 单位 Unit 测试条件发光强度I v 1700 --- 2500 mcd I F =20mA参考光通量 Φv 7 8 --- lm 正向电压 V F 2.7 --- 3.3 V 颜色等级T C4750---5300K 色区 Bin CA4/5-CB4/5视角Viewing Angle 2θ1/2---120---Deg显色指数Color Rendering IndexRa80----------反向电流 I R --- --- 5 µA V R = 5V注释Notes:1、发光强度、功耗和光通量的误差为 ± 10％WW maintains a tolerance of ±10% on flux and power measurements. 2、波长的误差为 ±1nm ；电压的误差为 ± 0.1Vλd ±1nm ； A tolerance of ±0.1V on forward voltage measurements 3、显色指数测试误差为±2The above Color Rendering Index measurement allowance tolerance is ±2 4、所有测试都是基于华宏现有的标准测试平台All measurements were made undr the satandardized environment of wahwang■光电特性Typical Optical Characteristics CurvesAmbient Temperature TA(°C)环境温度(℃)R e l a t i v e L u m i n o u s I n t e n s i t y (%)发光强度(%)Forward Current Derating Curve 环境温度与电流的关系曲线图Radiation Diagram发光角度图Spectral Distribution波长曲线图Luminous Intensity vs AmbientTemperature发光强度与温度的关系曲线图F o r w a r d C u r r e n t (m A )电流(m A) Ambient Temperature TA(°C)环境温度(℃)R e l a t i v e L u m i n o u s I n t e n s i t y (%)光通量（%）Forward Current (mA)电流(mA)Forward Voltage (V)电压(V)F o r w a r d C u r r e n t (m A )电流(m A ) Forward Current vs Forward Voltage伏安特性曲线Forward Current vs Relative Luminous Intensity电流和光通量的关系颜色序号色区等级 BIN明细相关色温(K)Color Ref.No. Ranks BinsCorrelated ColorTemperature暖白 1 TWa Wa4/Wa5/Wb4/Wb5 2000-23002 TWb Wc4/Wc5/Wd4/Wd5 2300-26003 TWc We4/We5/Wf4/Wf5 2600-29004 TWd Wg4/Wg5/Wh4/Wh5 2900-32005 TWe Wi4/Wi5/Wj4/Wj5 3200-3700自然白 6 TWf Na4/Na5/Nb4/Nb5 3700-42507 TWg Nc4/Nc5/Nd4/Nd5 4250-4750白光 8 TWh Ca4/Ca5/Cb4/Cb5 4750-53009 TWi Cc4/Cc5/Cd4/Cd5 5300-600010 TWj Cd4/Cd5/Ce4/Ce5 5700-6500Notes:1.客户可以选择以上任何一种颜色的色区Customer can choose any group2.X/Y是参照CIE1931，色坐标X/Y的误差为 ± 0.01;色温误差：9000K-5000K ±500K /低于 5000K ±50K *X and Y are CIE1931; Color Coordinates Measurement allowance is ±0.01Tolerance of Color Temperature: 9000K-5000K ±500K / below 5000K ±50K■产品可靠性检测项目：序號No.实验项目Test Item标准测试方法Standard TestMethod测试条件Test Conditions检测时间和周期Duration不良数/测试数Failure Rate1正常工作寿命Steady StateOperating LifeJEITA ED-4701100 103If=20mA1000hrs 0/22Ta=25℃2低温贮藏Low TemperatureStorageJEITA ED-4701Ta=-40℃1000hrs 0/22200 2023高温贮存High TemperatureStorageJEITA ED-4701Ta=100℃1000hrs 0/22200 2014高温高湿贮藏TemperatureHumidity StorageJEITA ED-4701 Ta=60℃1000hrs 0/22100 103 RH=90%5冷热冲击Thermal ShockJEITA ED-4701 0℃ ~ +100℃10 cycles 0/22300 3075min~ 15sec ~5min6JEITA ED-4701100 105H：+100℃ 30min.100cycles0/22 高低温循环Temperature Cycle∫ : +25℃ 5min.L：－40℃ 30min7烫锡Solder HeatJEITA ED-4701 Tsld=260℃, 10sec(Max.)2 times 0/22300 301■失效产品判定标准：项目Item符号Symbol测试条件Test Condition最小值Min.最大值Max.正向电压Forward VoltageV F I F = 20mA -- *U.S.L×1.1 反向电流Reverse CurrentI R V R = 5V -- *U.S.L×2.0发光强度Luminous IntensityI V I F = 20mA **L.S.L×0.7 --*U.S.L.:超出标准最大值*U.S.L.: Upper Standard Level** L.S.L.:低于标准最低值** L.S.L.: Lower Standard Level■.■ ■ ■ Dimensions of the reel ：■ .包装 ■ Packing ：防潮、防静电真空密封包装■ Moisture, anti-static vacuum sealed packages■ 注释 ■ Note ：上图所有尺寸的单位为毫米，公差为 ± 2.0毫米，除非另有说明All dimensions are in mm, tolerance is ± 2.0mm unless otherwise noted.■注意事项：PRECAUTION IN USE1.存储要求:1.1推荐储存环境温度：5o C ~ 30o C的 (41o F ~ 86o F)湿度：相对湿度60％以下1.2防潮袋密封包装储存时间3个月，起始时间以包装标签日期为准，需在包装袋封口良好并无漏气现象，且湿度卡未变为粉红色前提下使用，如超过3个月的LED需按2. 2要求除潮烘烤后才能正常使用。

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Remarks: If special sorting is required (e.g. binning based on forward voltage,luminous intensity, or wavelength), the typical accuracy of the sorting process is as follows: 1. Wavelength: +/-1nm 2. Luminous Intensity: +/-15% 3. Forward Voltage: +/-0.1V Note: Accuracy may depend on the sorting parameters.
Description
The Hyper Red source color devices are made with DH InGaAlP on GaAs substrate Light Emitting Diode.
Package Dimensions
N ot es : 1. A l l di m ens i ons are i n m i l l i m et ers (i nc hes ). 2. T ol eranc e i s ±0. 25 (0. 0 1" ) unl es s ot her wi s e not e d. 3. S pec i f i c at i ons a re s ubj e c t t o c hange wi t hout n ot i c e.
SPEC NO: DSAA6808 APPROVED: J. Lu
REV NO: V.6 CHECKED: Allen Liu
DATE: MAR/21/2005 DRAWN: W.J.ZHU
PAGE: 1 OF 4 ERP:1201000394
元器件交易网
Selection Guide
元器件交易网
3.5x2.8 mm SMD CHIP LED LAMP
AA3528SURCK HYPER RED
Features
SINGLE COLOR. SUITABLE FOR ALL SMT ASSEMBLY AND SOLDER PROCESS. AVAILABLE ON TAPE AND REEL. IDEAL FOR BACKLIGHTING. PACKAGE : 1500PCS / REEL. RoHS COMPLIANT.
Electrical / Optical Characteristics at TA=25°C
Symbol λpeak λD ∆λ1/2 C VF IR Parameter Peak Wavelength Dominant Wavelength Spectral Line Half-width Capacitance Forward Voltage Reverse Current Device Hyper Red Hyper Red Hyper Red Hyper Red Hyper Red Hyper Red Typ. 650 635 28 35 1.95 2.5 10 Max. Units nm nm nm pF V uA Test Conditions IF=20mA IF=20mA IF=20mA VF=0V;f=1MHz IF=20mA VR = 5V
Part No. Dice Lens Type Iv (mcd) @ 20mA Min. AA3528SURCK HYPER RED (InGaAlP) WATER CLEAR 70 Typ. 200 Viewing Angle 2θ1/2 θ 120°
Note: 1. θ1/2 is the angle from optical centerline where the luminous intensity is 1/2 the optical centerline value.
DATE: MAR/21/2005 DRAWN: W.J.ZHU
PAGE: 2 OF 4 ERP:1201000394
元器件交易Hale Waihona Puke
Hyper Red
AA3528SURCK
SPEC NO: DSAA6808 APPROVED: J. Lu
REV NO: V.6 CHECKED: Allen Liu
Note: 1. 1/10 Duty Cycle, 0.1ms Pulse Width.
Hyper Red 75 30 185 5 -40°C To +85°C
Units mW mA mA V
SPEC NO: DSAA6808 APPROVED: J. Lu
REV NO: V.6 CHECKED: Allen Liu
SPEC NO: DSAA6808 APPROVED: J. Lu
REV NO: V.6 CHECKED: Allen Liu
DATE: MAR/21/2005 DRAWN: W.J.ZHU
PAGE: 4 OF 4 ERP:1201000394
DATE: MAR/21/2005 DRAWN: W.J.ZHU
PAGE: 3 OF 4 ERP:1201000394
元器件交易网
AA3528SURCK
Recommended Soldering Pattern (Units : mm)
Tape Specifications (Units : mm)
Absolute Maximum Ratings at TA=25°C
Parameter Power dissipation DC Forward Current Peak Forward Current [1] Reverse Voltage Operating / storage Temperature