For decades,manufacturers have boasted about how small they can make microchip components.Transistors have shrunk by about 1000-fold over the last 50 years,for example[1].But Cerebras Systems,Inc.of Sunnyvale,CA,USA t...For decades,manufacturers have boasted about how small they can make microchip components.Transistors have shrunk by about 1000-fold over the last 50 years,for example[1].But Cerebras Systems,Inc.of Sunnyvale,CA,USA takes pride in how big its chips are.Produced from a single silicon wafer,its Wafer-Scale Engine(WSE)-2 chips measure 46225 mm^(2),56 times the size of a standard Nvidia microprocessor(Fig.1)[2].展开更多
A model following adaptive control system for CSIM is presented in this paper. A dynamic mathematical model of slip control based system is obtained. With the help of model reducing technique, full order model is ...A model following adaptive control system for CSIM is presented in this paper. A dynamic mathematical model of slip control based system is obtained. With the help of model reducing technique, full order model is reduced to simplify the design without degrading much of the performance. Model following adaptive control laws in discrete form are derived. These laws satisfy the hyperstability condition for taking care of the load and machine parameter changes of the drive. A microprocessor 8098 is used to develop the speed controller. The implementation of the control system uses only available variables of the reference model and the controlled plant. Experimental results are given to demonstrate the good performance of the system.展开更多
One of the major causes of road accidents is sleepy drivers.Such accidents typically result in fatalities and financial losses and disadvantage other road users.Numerous studies have been conducted to identify the dri...One of the major causes of road accidents is sleepy drivers.Such accidents typically result in fatalities and financial losses and disadvantage other road users.Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system.Most studies have examined how the mouth and eyelids move.However,this limits the system’s ability to identify drowsiness traits.Therefore,this study designed an Accident Detection Framework(RPK)that could be used to reduce road accidents due to sleepiness and detect the location of accidents.The drowsiness detectionmodel used three facial parameters:Yawning,closed eyes(blinking),and an upright head position.This model used a Convolutional Neural Network(CNN)consisting of two phases.The initial phase involves video processing and facial landmark coordinate detection.The second phase involves developing the extraction of frame-based features using normalization methods.All these phases used OpenCV and TensorFlow.The dataset contained 5017 images with 874 open eyes images,850 closed eyes images,723 open-mouth images,725 closed-mouth images,761 sleepy-head images,and 1084 non-sleepy head images.The dataset of 5017 images was divided into the training set with 4505 images and the testing set with 512 images,with a ratio of 90:10.The results showed that the RPK design could detect sleepiness by using deep learning techniques with high accuracy on all three parameters;namely 98%for eye blinking,96%for mouth yawning,and 97%for head movement.Overall,the test results have provided an overview of how the developed RPK prototype can accurately identify drowsy drivers.These findings will have a significant impact on the improvement of road users’safety and mobility.展开更多
针对电子设备间歇性故障诊断的难题,提出一种基于声音传感器的电子设备间歇性故障诊断方法(Intermittent Fault Diagnosis Method for Electronic Devices Based on Acoustic Sensors,IFDM-AS)。IFDM-AS利用微机电系统(Micro-Electro-Me...针对电子设备间歇性故障诊断的难题,提出一种基于声音传感器的电子设备间歇性故障诊断方法(Intermittent Fault Diagnosis Method for Electronic Devices Based on Acoustic Sensors,IFDM-AS)。IFDM-AS利用微机电系统(Micro-Electro-Mechanical System,MEMS)麦克风采集设备运行声音,通过梅尔频率倒谱系数(Mel-Frequency Cepstral Coefficients,MFCC)特征提取、支持向量机(Support Vector Machine,SVM)模式识别、贝叶斯网络故障诊断决策技术,实现设备健康状态的实时诊断。实验结果表明,IFDM-AS在5种电子设备状态下的平均识别准确率为96.7%,有效性和可靠性较强。展开更多
文摘For decades,manufacturers have boasted about how small they can make microchip components.Transistors have shrunk by about 1000-fold over the last 50 years,for example[1].But Cerebras Systems,Inc.of Sunnyvale,CA,USA takes pride in how big its chips are.Produced from a single silicon wafer,its Wafer-Scale Engine(WSE)-2 chips measure 46225 mm^(2),56 times the size of a standard Nvidia microprocessor(Fig.1)[2].
文摘A model following adaptive control system for CSIM is presented in this paper. A dynamic mathematical model of slip control based system is obtained. With the help of model reducing technique, full order model is reduced to simplify the design without degrading much of the performance. Model following adaptive control laws in discrete form are derived. These laws satisfy the hyperstability condition for taking care of the load and machine parameter changes of the drive. A microprocessor 8098 is used to develop the speed controller. The implementation of the control system uses only available variables of the reference model and the controlled plant. Experimental results are given to demonstrate the good performance of the system.
基金The Faculty of Information Science and Technology,Universiti Kebangsaan Malaysia,provided funding for this research through the Research Grant“An Intelligent 4IR Mobile Technology for Express Bus Safety System Scheme DCP-2017-020/2”.
文摘One of the major causes of road accidents is sleepy drivers.Such accidents typically result in fatalities and financial losses and disadvantage other road users.Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system.Most studies have examined how the mouth and eyelids move.However,this limits the system’s ability to identify drowsiness traits.Therefore,this study designed an Accident Detection Framework(RPK)that could be used to reduce road accidents due to sleepiness and detect the location of accidents.The drowsiness detectionmodel used three facial parameters:Yawning,closed eyes(blinking),and an upright head position.This model used a Convolutional Neural Network(CNN)consisting of two phases.The initial phase involves video processing and facial landmark coordinate detection.The second phase involves developing the extraction of frame-based features using normalization methods.All these phases used OpenCV and TensorFlow.The dataset contained 5017 images with 874 open eyes images,850 closed eyes images,723 open-mouth images,725 closed-mouth images,761 sleepy-head images,and 1084 non-sleepy head images.The dataset of 5017 images was divided into the training set with 4505 images and the testing set with 512 images,with a ratio of 90:10.The results showed that the RPK design could detect sleepiness by using deep learning techniques with high accuracy on all three parameters;namely 98%for eye blinking,96%for mouth yawning,and 97%for head movement.Overall,the test results have provided an overview of how the developed RPK prototype can accurately identify drowsy drivers.These findings will have a significant impact on the improvement of road users’safety and mobility.
