Soft-switching PWM boost full-bridge converter

This paper proposes a family of PWM modulation strategies for boostfull-bridge (FB) converters. The modulation strategies can be classified into two kinds according tothe turn-on sequence of the diagonal switches. The concept of leading switches and lagging switchesis introduced to realize soft-switching. According to the soft-switching realized by the leadingswitches and the lagging switches, two kinds of soft-switching techniques for PWM boost FBconverters yield: zero-current-switching (ZCS) and zero-current and zero-voltage-switching (ZCZVS).Simulation results verify the analysis.

Design considerations for the improved current-doubler-rectifier ZVS PWM full-bridge converter

The improved current-doubler-rectifier zero-voltage-switching PWM full-bridge converter (CDR ZVS PWM FB converter) achieves ZVS for the switches in a wide load range with the use of the energy stored in the output filter inductances, and the rectifier diodes commute naturally, therefore no oscillation and voltage spike occurs. The transformer needs no special manufacture method to limit the leakage inductance. The ZVS achievement and the design considerations for the output filter inductances and the blocking capacitor are discussed for the improved CDR ZVS PWM FB converter. A 540 W prototype converter is built in the lab to verify the operational principle and design considerations for the improved converter, the experimental results are also included.

Application research on ZVZCS full-bridge converter technique

Based on ZVZCS （zero voltage zero current switching） full bridge converter technique, a novel inverter welding power supply is designed, in which the secondary side of the transformer adopts passive clamping circuit to reduce voltage stress of rectifying components. This supply can realize power switches ZVS （zero voltage switching ） or ZCS （zero current switching） within a very wide range of load; Only through setting up blocking capacitor in the primary side of transformer, the power transformer＇s bias in the full-bridge converter is suppressed and the primary current can be reset easily. In addition, how to calculate the blocking capacitor and its influence to power supply performance are also subjects discussed in this paper.

Research on the Intelligent Control Strategy of the Fuel Cell Phase-Shifting Full-Bridge Power Electronics DC-DC Converter

focus of all countries.As an effective new energy,the fuel cell has attracted the attention of scholars.However,due to the particularity of proton exchange membrane fuel cell(PEMFC),the performance of traditional PI controlled phase-shifted full-bridge power electronics DC-DC converter cannot meet the needs of practical application.In order to further improve the dynamic performance of the converter,this paper first introduces several main topologies of the current mainstream front-end DC-DC converter,and analyzes their performance in the fuel cell system.Then,the operation process of the phase-shifted fullbridge power electronics DC-DC converter is introduced,and the shortcomings of the traditional PI control are analyzed.Finally,a double closed-loop adaptive fuzzy PI controller is proposed,which is characterized by dynamically adjusting PI parameters according to different working states to complete the intelligent control of phase-shifted full-bridge DC-DC converter.The simulation results in MATLAB/Simulink show that the proposed algorithm has good a control effect.Compared with the traditional algorithm,the overshoot and stabilization time of the system are shorter.The algorithm can effectively suppress the fluctuation of the output current of the fuel cell converter,and is a very practical control method.

Research on Phase-Shifted Full-Bridge Circuit Based on Frequency and Phase-Shift Synthesis Modulation Strategy

The full-bridge converters usually use transformer leakage inductance and parallel resonant capacitors to achieve smooth current commutation and soft switching functions,which can easily cause problems such as energy leakage and significant duty cycle loss.This paper designs a novel full-bridge zero-current(FB-ZCS)converter with series resonant capacitors and proposes a frequency and phase-shift synthesis modulation(FPSSM)control strategy based on this topology.Compared with the traditional parallel resonant capacitor circuit,the passive components used are significantly reduced,the structure is simple,and there is only a slight energy loss.By controlling the charging time of the capacitor,it can be achieved without additional switches or auxiliary circuits.The automatic control of capacitor energy based on input current addresses the low efficiency of the traditional control strategies.This paper introduces its principle in detail and verifies it through simulation.Finally,an experimental prototype was built further to demonstrate the feasibility of the theory through experiments.The module can be applied to a photovoltaic DC collection system using input parallel output series(IPOS)cascade to provide a new topology for large-scale,long-distance DC transmission.

The Implementation of a High Efficiency Full-Bridge Converter

A high efficiency full-bridge converter is investigated and implemented in this paper. The measured data result from the other converter implemented by IC UCC3895 is to compare with that of the previous converter. This full-bridge converter proposed and implemented converter can obtain about 96% power efficiency in conversion procedure when compared with that of 90%, which were ever published by the conventional techniques. Apart from, the L-C resonance circuits were developed and embedded into the popular PWM (pulse width modulation) power converter, which is referred as the soft-switching, so as to down sizing the volume of the IC which can totally reduces the power losses caused in the duration of a semi-con- ductor switching.

Knowledge, Attitudes and Practices Relating to HIV among Motorbike Taxi Drivers in Parakou, Benin, in 2021

Background and Objective: HIV infection is a major global Public Health threat worldwide, particularly in Sub-Saharan Africa of which Benin. The level of knowledge determines the attitudes and behaviors of the populations towards this infection. The study objective was to assess knowledge, attitudes and practices related to HIV infection among motorbike taxi drivers (MTD) in Parakou in 2021. Methods: This was a descriptive cross-sectional study targeting MTD in Parakou in 2021. Participants were selected by cluster sampling. Pretested Digitized questionnaire using KoboCollect@ applicationserved as a data collection tool. Knowledge, attitudes and practices variable were treated on a score scale. A knowledge score was considered to reflect a good knowledge of HIV if at least two-thirds of the knowledge statements had been correctly answered provided the subject recognized the sexual route as one of modes of HIV transmission, identified at least one preventive measure and meant the incurability of the disease. Quantitative and qualitative variables were appropriately described using the EPI Info 7.1.3.3 software. The participant was classified at positive attitude/practice for HIV prevention, when it has a score of at least 80% and suggests a good preventive measure face a risk of exposure to HIV. Results: A total of 374 subjects were recruited into the study. The mean age was 31.51 ± 7.76 years. Most participants (86.06%) had good knowledge of condom use as an HIV prevention method. The sources of information mentioned were mainly the media (77.07%), relatives or friends (63.38%), and field-workers from non-governmental organizations (37.26%). Routine HIV testing was 50.53%. Among participants, 76.10% reported at least two different sexual partners. Condom use was 59.18 % during the casual sexual intercourse. Within the client-provider relationship with female sex workers, 33.17% had had sexual intercourse with them. The sexual route was the most cited (92.99%), and 90.23% stated that HIV infection can be stabilized by medication in a health structure. Conclusion: The level of knowledge of motorbike taxi drivers in Parakou does not match their behavior with regard to HIV prevention. Appropriate strategies are needed to develop prevention skills in this population. To effectively comb at HIV, it will be necessary to strengthen the targeted HIV preventive interventions at key and bridge populations including motorbike taxi drivers in Benin.

Leading Pattern of Spring Drought Variability over East Asia and Associated Drivers

Drought events have become more frequent and intense over East Asia in recent decades,leading to huge socioeconomic impacts.Although the droughts have been studied extensively by cases or for individual regions,their leading variability and associated causes remain unclear.Based on the Standardized Precipitation Evapotranspiration Index(SPEI)and ERA5 reanalysis product from 1979 to 2020,this study evealuates the severity of spring droughts in East Asia and investigates their variations and associated drivers.The results indicate that North China and Mongolia have experienced remarkable trends toward dryness during spring in recent decades,while southwestern China has witnessed an opposite trend toward wetness.The first Empirical Orthogonal Function mode of SPEI variability reveals a similar seesawing pattern,with more severe dryness in northwestern China,Mongolia,North China,South Korea,and Japan but increased wetness in Southwestern China and southeast Asia.Further investigation reveals that the anomalously dry(wet)surface in North(Southwestern)China is significantly associated with anomalously high(low)temperature,less(more)precipitation,and reduced(increased)soil moisture during the previous winter and early spring,regulated by an anomalous anticyclone(cyclone)and thus reduced(increased)water vapor convergence.The spring dry-wet pattern in East Asia is also linked to cold sea surface temperature anomalies in the central-eastern Pacific.The findings of this study have important implications for improving the prediction of spring drought events in East Asia.

Seroprevalence Survey of HIV and Hepatitis B Virus and Behavioral Characteristics among Heavy Truck Drivers along Port Sudan-Khartoum Highways

The prevalence of human immunodeficiency virus (AIDS) and hepatitis B virus among heavy truck drivers and their assistants has been well documented globally in correlation with their behavioral characteristics. The present study aimed to screen for human immunodeficiency virus (HIV), hepatitis B virus (HBV), and behavioral characteristics among heavy truck drivers in Port Sudan. A cross-sectional study was conducted on 274 heavy truck drivers and their assistants who used the highway Port Sudan-Khartoum in Port Sudan city during 2019-2021. Data on behavioral characteristics and substance use habits were collected using a structured questionnaire, and an ELISA test was used to screen for HIV and HBV infections in the study participants. The chi-square test, odds ratio, and confidence intervals were used to find the association between behavioral characteristics and seropositive HIV/HBV. Of the 274 enrolled participants, the seroprevalence rates of HIV were 2.7% and HBV was 23.7%. Ninety-four (34.3%) of them had a history of high-risk sexual behavior outside of marriage;only two (0.7%) used condoms;14.2% of participants reported alcohol use;and 1.1% reported drug use. Univariate analysis revealed that having a sex history outside of marriage with ≥1 sex partner and never using a condom with a spouse or casual partner were significant risk factors for HIV and HBV among drivers. Fortunately, we found that most of the drivers reported low alcohol and drug use. Concerning this study, the seroprevalence of HIV and HBV is highly associated with a history of having sex outside of marriage and sexual behavior among truck drivers and assistances. Additional studies are needed to further investigate other STIs and behavioral characteristics associated with factors in truck drivers/assistance in different truck stop regions in Sudan.

OSMR is a potential driver of inflammation in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a neurodegenerative disease,and the molecular mechanism underlying its pathology remains poorly understood.However,inflammation is known to play an important role in the development of this condition.To identify driver genes that affect the inflammatory response in amyotrophic lateral sclerosis,as well as potential treatment targets,it is crucial to analyze brain tissue samples from patients with both sporadic amyotrophic lateral sclerosis and C9orf72-related amyotrophic lateral sclerosis.Therefore,in this study we used a network-driven gene analysis tool,NetBID2.0,which is based on SJARACNe,a scalable algorithm for the reconstruction of accurate cellular networks,to experimentally analyze sequencing data from patients with sporadic amyotrophic lateral sclerosis.The results showed that the OSMR gene is pathogenic in amyotrophic lateral sclerosis and participates in the progression of amyotrophic lateral sclerosis by mediating the neuroinflammatory response.Furthermore,there were differences in OSMR activity and expression between patients with sporadic amyotrophic lateral sclerosis and those with C9orf72-related amyotrophic lateral sclerosis.These findings suggest that OSMR may be a diagnostic and prognostic marker for amyotrophic lateral sclerosis.

A Framework for Driver DrowsinessMonitoring Using a Convolutional Neural Network and the Internet of Things

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.

Statistical Analysis of Accident Proneness of Drivers

A mathematical model describing the risks of road accidents has been built on the basis of statistical data of drivers’ accident rate. It has been revealed that drivers can be divided by the degree of their accident proneness into four categories with sharply differing probabilities of road accidents. It has been shown that there is a possibility of classification of drivers in accordance with specified categories.

Sonam Wangta:A Driver on the Worlds Highest Train Line

In midsummer,the banks of the Yarlung Zangbo River were covered in lush green.Bathed in the bright plateau sunshine,the Fuxing bullet train ran through the mountains and valleys like a long green dragon.In the cab,Sonam Wangdrak was holding the brake handle,his eyes fixed on the horizon ahead and his handsome face full of confidence.

Research on the driver fatigue early warning model of electric vehicles based on the fusion of EMG and ECG signals

Electric vehicles have been rapidly developing worldwide due to the use of new energy.However,at the same time,serious traffic accidents caused by driver fatigue in emergency situations have also drawn widespread attention.The lack of datasets in real vehicle test environments has always been a bottleneck in the research of driver fatigue in electric vehicles.Therefore,this study establishes a dataset from real vehicle test,applies the Bayesian optimization support vector machine(BOA-SVM)algorithm to take features of electromyography(EMG)and electrocardiography(ECG)signals as input and develop an early warning model for driving fatigue detection.Firstly,the driver’s EMG and ECG signals are collected through real vehicle testing experiments and then combined with the driver’s subjective fatigue evaluation scores to establish the dataset.Secondly,the study establishes a driver fatigue early warning model for emergency situations.Time-domain and frequency-domain features are extracted from the EMG signals.Principal component analysis(PCA)is applied for dimensionality reduction of these features.The experimental results show that based on the input of dimensionality reduced EMG features and ECG features,the BOA-SVM algorithm achieved an accuracy of 94.4%in classification.

A road hypnosis identification method for drivers based on fusion of biological characteristics

Risky driving behaviors,such as driving fatigue and distraction have recently received more attention.There is also much research about driving styles,driving emotions,older drivers,drugged driving,DUI(driving under the influence),and DWI(driving while intoxicated).Road hypnosis is a special behavior significantly impacting traffic safety.However,there is little research on this phenomenon.Road hypnosis,as an unconscious state,is can frequently occur while driving,particularly in highly predictable,monotonous,and familiar environments.In this paper,vehicle and virtual driving experiments are designed to collect the biological characteristics including eye movement and bioelectric parameters.Typical scenes in tunnels and highways are used as experimental scenes.LSTM(Long Short-Term Memory)and KNN(K-Nearest Neighbor)are employed as the base learners,while SVM(Support Vector Machine)serves as the meta-learner.A road hypnosis identification model is proposed based on ensemble learning,which integrates bioelectric and eye movement characteristics.The proposed model has good identification performance,as seen from the experimental results.In this study,alternative methods and technical support are provided for real-time and accurate identification of road hypnosis.

手机用TFT-LCD Source Driver电路模块研究与设计

SourceDriver电路模块是彩色TFT-LCD驱动控制芯片的关键电路之一,其功能是将数字图像显示数据转换成模拟驱动电压,从而驱动TFT-LCD显示各种彩色图像。本文从TFT-LCD的驱动原理出发,提出了一种适合于手机用TFT-LCD的低功耗、小面积的SourceDriver电路体系结构,用0.25μmCMOS工艺设计并实现了显示26万(26×26×26)色,支持132RGB×176分辨率的手机用TFT-LCD驱动控制芯片中的SourceDriver电路,电路面积约15mm×0.6mm。Hspice仿真结果表明,SourceDriver电路的响应时间为1.49μs,静态功耗小于1mW。

基于IVI Driver的仪器互换性的研究

分析IVI Driver的分类及IVI的体系结构,说明IVI驱动器的三个主要技术特征,即硬件无关性、状态缓存机制和仪器仿真功能。讨论了IVI Driver技术中类驱动器的作用,特别对动态链接库调用方式的选择和类驱动器影射方法的实现进行了研究,提出不修改用户测试程序,对类仪器前缀和特定仪器前缀进行替换,最终实现仪器互换。同时给出实现类驱动器的一般方法,并进行了实验验证。

基于Mirror Driver的自适应屏幕录制算法

提出一种基于Mirror Driver的自适应屏幕录制算法MDAC,该算法运用Mirror Driver技术检测屏幕显示内容的变化,并通过检测该区域的更新频率,自适应地选择图像编码算法。实验结果表明,该算法能够在保证屏幕录像质量的情况下,大幅度降低CPU占用率和录制文件的大小。

Intersection capacity based on driver's visual characteristics

In order to reflect the influence of the drivers＇ characteristic differences on intersection capacity under a mixed traffic flow, a driver correction coefficient for the intersection capacity calculation according to the driver＇s visual characteristics is proposed. First, the parameters of the driver＇s visual characteristics at some real roads, including gaze fixation distribution, mean fixation duration, visual angle distribution and some other parameters at intersections, are collected. Then, the relationship between the traffic flow rate at intersections and the parameters of driver eye movements are established. The analytical results indicate that when the traffic flow is unsaturated, the parameters of driver eye movements change relatively little; however, when the traffic flow is saturated, the parameters of driver eye movements change drastically. Finally, the saturation-flow-rate model is modified according to the parameters of driver eye movements; thus, a capacity model of intersections considering the driver＇s visual characteristics is obtained.

Robot driver for vehicle durability emission test on chassis dynamometer

A method for a vehicle durability emission test using a robot driver insteadof human drivers on the chassis dynamometer is presented. The system architecture of vehicledurability emission test cell, the road load simulation strategy and the tele-monitoring systembased on Browser/Client structure are described. Furthermore, the construction of the robot driver,vehicle performance self-learning algorithm, multi-mode vehicle control model and vehicle speedtracking strategy based on fuzzy logic arealso discussed. Besides, the capability of controlparameters self-compensation on-line makes it possible to compensate the wear of vehicle componentsand the variety of clutch true bite point during the long term test. Experimental results show thattherobot driver can be applicable to a wide variety of vehicles and the obtained results stay withina tolerance band of ± 2 km/h. Moreover the robot driver is able to control tested vehicles withgood repeatability and consistency; therefore, this methodpresents a solution to eliminate theuncertainty of emission test results by human drivers and to ensure the accuracy and reliability ofemission test results.