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作者： Zheng, Xiang Hang, Lijun Zhou, Yimin Tang, Sai Chen, Yandong Xiao, Yongli He, Yuanbin He, Zhen Zeng, Qingwei Yan, Dong Wang, Zhiqiang Zeng, Pingliang Regional Energy Internet Technology Zhejiang Engineering Laboratory School of Automation Hangzhou Dianzi University 310018 China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China College of Electrical and Information Engineering Hunan University Changsha410082 China Company Ltd. Hangzhou310063 China Monolithic Power Systems Inc. Hangzhou310030 China Department of Automation North China University of Water Resources and Electric Power Zhenzhou450046 China

SiC MOSFETs are increasingly used in various power electronic converters due to their low switching losses, high operating frequency and high power applicability. The crosstalk phenomenon which will lead shoot-through in bridge-leg configuration and overvoltage breakdown of the device is more serious due to the high switching speed characteristics. In this paper, a smart self-driving crosstalk suppression gate driver is proposed based on the resistor–capacitor–diode (RCD) level shifter circuit for SiC MOSFETs, in which a low impedance branch of Miller clamping circuit is provided for the crosstalk current. In addition, no extra control signal is required which does not increase the complexity of the driver circuit. Then, by modeling the equivalent circuit of the proposed gate driver, the quantization relationship between the clamping capacitor and the crosstalk voltage is derived. Finally, a double-pulse test platform is built to verify the effectiveness of the proposed gate driver under different clamping capacitors, input voltage and load current conditions. The experimental results show that the proposed gate driver can suppress the crosstalk voltage effectively. Meanwhile, the switching loss is reduced by a maximum of 16.6% by using the designed clamping capacitor under 600 V/20 A, while ensuring the switching speed. © 2023 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Silicon carbide

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Explore the Future Earth with Wander 2.0: AI Chatbot Driven By Knowledge-base Story Generation and Text-to-image Model

Explore the Future Earth with Wander 2.0: AI Chatbot Driven ...

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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing systems, CHI EA 2023

作者： Sun, Yuqian Xu, Ying Cheng, Chenhang Li, Yihua Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art London United Kingdom Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Shanghai China Department of Product Design College of Fashion and Design Donghua University Shanghai China Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450394222

People always envision the future of earth through science fiction (Sci-fi), so can we create a unique experience of "visiting the future earth"through the lens of artificial intelligence (AI)? We introduce Wander 2.0, an AI chatbot that co-creates sci-fi stories through knowledge-based story generation on daily communication platforms like WeChat and Discord. Using location information from Google Maps, Wander generates narrative travelogues about specific locations (e.g. Paris) through a large-scale language model (LLM). Additionally, using the large-scale text-to-image model (LTGM) Stable Diffusion, Wander transfers future scenes that match both the text description and location photo, facilitating future imagination. The project also includes a real-time visualization of the human-AI collaborations on a future map. Through journeys with visitors from all over the world, Wander demonstrates how AI can serve as a subjective interface linking fiction and reality. Our research shows that multi-modal AI systems have the potential to extend the artistic experience and creative world-building through adaptive and unique content generation for different people. Wander 2.0 is available at http://***/ © 2023 Owner/Author.

关键词： Knowledge based systems

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Minimization of Age of information for Satellite-Terrestrial Covert Communication with a Full-Duplex Receiver

Minimization of Age of Information for Satellite-Terrestrial...

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International Conference on Networking and Network Applications (NaNA)

作者： Yizhi Cao Zewei Guo Qifeng Miao Ranran Sun Ji He Xiaochen Li Hangzhou Institute of Technology Xidian University Hangzhou China Shaanxi Key Laboratory of Network and System Security Xidian University Xi’an China School of Systems Information Science Future University Hakodate Hakodate Japan School of Computer Science and Technology Xidian University Xi’an China PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

This paper investigates the freshness of information for covert communication in a satellite-terrestrial system consisting of a satellite transmitter Alice, a terrestrial full-duplex receiver Bob, and a terrestrial warden Willie. Particularly, Alice tries to transmit covert information to Bob under the detection of Willie, while Bob emits jamming signals to confuse Willie simultaneously. We first provide theoretical modeling for the average age of information (AOI) of Bob and the covert constraint of Willie. Based on the theoretical model, we then drive a optimal transmit power of Alice to minimize the average AoI with the constraint of covertness requirement. Finally, numerical results are presented to verify the theoretical results and to illustrate the impacts of transmit and jamming power on the covertness and average AoI.

关键词： Satellites Transmitters Rician channels Receivers Full-duplex system information age Space-air-ground integrated networks

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Dataflow Management in the Internet of Things: Sensing,Control, and Security

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Tsinghua science and technology 2021年第6期26卷 918-930页

作者： Dawei Wei Huansheng Ning Feifei Shi Yueliang Wan Jiabo Xu Shunkun Yang Li Zhu School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China Run Technologies Co. Ltd.Beijing 100192China School of Information Engineering Xinjiang Institute of EngineeringUrumqi 830023China School of Reliability and Systems Engineering Beihang UniversityBeijing 100191China Northern Cloud Research Team Engineering University of PAPXi'an 710086China

The pervasiveness of the smart Internet of Things(IoTs) enables many electric sensors and devices to be connected and generates a large amount of dataflow. Compared with traditional big data, the streaming dataflow is faced with representative challenges, such as high speed, strong variability, rough continuity, and demanding timeliness, which pose severe tests of its efficient management. In this paper, we provide an overall review of IoT dataflow management. We first analyze the key challenges faced with IoT dataflow and initially overview the related techniques in dataflow management, spanning dataflow sensing, mining, control, security, privacy protection,etc. Then, we illustrate and compare representative tools or platforms for IoT dataflow management. In addition,promising application scenarios, such as smart cities, smart transportation, and smart manufacturing, are elaborated,which will provide significant guidance for further research. The management of IoT dataflow is also an important area, which merits in-depth discussions and further study.

关键词： Internet of Things(IoTs) dataflow security privacy management

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NuScenes-QA: A Multi-Modal Visual Question Answering Benchmark for Autonomous Driving Scenario

arXiv

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arXiv 2023年

作者： Qian, Tianwen Chen, Jingjing Zhuo, Linhai Jiao, Yang Jiang, Yu-Gang Academy for Engineering and Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China

We introduce a novel visual question answering (VQA) task in the context of autonomous driving, aiming to answer natural language questions based on street-view clues. Compared to traditional VQA tasks, VQA in autonomous driving scenario presents more challenges. Firstly, the raw visual data are multi-modal, including images and point clouds captured by camera and LiDAR, respectively. Secondly, the data are multi-frame due to the continuous, real-time acquisition. Thirdly, the outdoor scenes exhibit both moving foreground and static background. Existing VQA benchmarks fail to adequately address these complexities. To bridge this gap, we propose NuScenes-QA, the first benchmark for VQA in the autonomous driving scenario, encompassing 34K visual scenes and 460K question-answer pairs. Specifically, we leverage existing 3D detection annotations to generate scene graphs and design question templates manually. Subsequently, the question-answer pairs are generated program-matically based on these templates. Comprehensive statistics prove that our NuScenes-QA is a balanced large-scale benchmark with diverse question formats. Built upon it, we develop a series of baselines that employ advanced 3D detection and VQA techniques. Our extensive experiments highlight the challenges posed by this new task. Codes and dataset are available at https://***/qiantianwen/NuScenes-QA. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

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Computers, Materials & Continua 2021年第4期67卷 51-71页

作者： Mohamed Elhoseny Mazin Abed Mohammed Salama A.Mostafa Karrar Hameed Abdulkareem Mashael S.Maashi Begonya Garcia-Zapirain Ammar Awad Mutlag Marwah Suliman Maashi Department of Computer Science College of Computer Information TechnologyAmerican University in the Emirates503000United Arab Emirates College of Computer Science and Information Technology University of AnbarRamadi31001Iraq Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaJohor86400Malaysia College of Agriculture Al-Muthanna UniversitySamawah66001Iraq Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia eVIDA Lab University of DeustoBilbao48007Spain Department of Pure Science Ministry of EducationGeneral Directorate of CurriculaBaghdadIraq Department of Medical Laboratory Science Faculty of Applied Medical ScienceKing Abdulaziz UniversityJeddah21589Saudi Arabia

Heart disease(HD)is a serious widespread life-threatening *** heart of patients with HD fails to pump sufcient amounts of blood to the entire *** the occurrence of HD early and efciently may prevent the manifestation of the debilitating effects of this disease and aid in its effective *** methods for diagnosing HD are sometimes unreliable and insufcient in analyzing the related *** an alternative,noninvasive medical procedures based on machine learning(ML)methods provide reliable HD diagnosis and efcient prediction of HD ***,the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classication features from patients with *** this study,we propose an automated heart disease diagnosis(AHDD)system that integrates a binary convolutional neural network(CNN)with a new multi-agent feature wrapper(MAFW)*** MAFW model consists of four software agents that operate a genetic algorithm(GA),a support vector machine(SVM),and Naïve Bayes(NB).The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classication.A nal tuning to CNN is then performed to ensure that the best set of features are included in HD *** CNN consists of ve layers that categorize patients as healthy or with HD according to the analysis of optimized HD *** evaluate the classication performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using across-validation technique and by assessing six evaluation *** AHDD system achieves the highest accuracy of 90.1%,whereas the other ML and conventional CNN models attain only 72.3%–83.8%accuracy on ***,the AHDD system proposed herein has the highest capability to identify patients with *** system can be used by medical

关键词： Heart disease machine learning multi-agent feature wrapper model heart disease diagnosis HD cleveland datasets convolutional neural network

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A Linear Feedback Coding Scheme for Computation Over Gaussian Multiple-Access Channels

A Linear Feedback Coding Scheme for Computation Over Gaussia...

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IEEE International Symposium on information Theory

作者： Bin Dai Fan Cheng Yuhan Yang Dengfeng Xia School of Information Science and Technology Southwest Jiaotong University Chengdu China Chongqing Key Laboratory of Mobile Communications Technology Chongqing China Provincial Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The problem of reliably reconstructing a function of sources over a multiple-access channel (MAC) is revisited by considering channel feedback. First, a linear feedback scheme is proposed to compute the average of the Gaussian sources transmitted over the Gaussian MAC. Next, numerical results show that in some cases, our proposed scheme performs better than the existing ones without using channel feedback. Finally, we show that with a slight modification, our scheme can be directly extended to compute any linear function of the Gaussian sources transmitted over the Gaussian MAC.

关键词： Encoding Reliability

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From Rule-Based Models to Deep Learning Transformers Architectures for Natural Language Processing and Sign Language Translation systems: Survey, Taxonomy and Performance Evaluation

arXiv

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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

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A CNN Based Approach to Classify the Folk Dances of Odisha

A CNN Based Approach to Classify the Folk Dances of Odisha

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Samantaray, Swati Patra, Sudhansu Shekhar Mohanty, Amlan Barik, Lalbihari Barik, Akash Brahma, Aditya Narayan Kiit Deemed to Be University School of Humanities Bhubaneswar India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Iiit Department of Computer Science Bhubaneswar India King Abdulaziz University Faculty of Computing & Information Technology in Rabigh Department of Information Systems Saudi Arabia New Horizon College of Engineering Dept. of Computer Science Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Folk dances are a primordial expression of ecstasy and bliss and serve as a mode of communication. They also facilitate in keeping the people connected to their traditions as well as ancestry. Furthermore, these dances help in preserving cultural unity and their tales can often reveal a lot about the periods these dances have developed through. Mahari, Gotipua and Odissi are such folk dance forms of the eastern state of Odisha. These folk dances are on the verge of extinction owing to the reducing number of practitioners, audience, absence of documentation, and digitalization. The absence of documentation has presented a barrier to the legitimacy of folk dance as an academic field of study. This research paper intends to focus on the classification of the folk dances of Odisha such as Mahari, Gotipua and Odissi which will help them to digitize and preserve. We propose deep learning-based methods for the classification and then it can be preserved. To validate the poses and hand motions, we offer a convolutional neural network model (CNN). © 2022 IEEE.

关键词： Neural network models

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Iot Based Smart Framework to Predict Air Quality in Congested Traffic Areas Using Sv-Cnn Ensemble and Knn Imputation Model

SSRN

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SSRN 2024年

作者： Cascone, Lucia Alnowaiser, Khaled Alarfaj, Aisha Ahmed Alabdulqader, Ebtisam Abdullah Umer, Muhammad Alankar, Bhavya Department of Computer Engineering College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University Saudi Arabia Department of Computer Science & Information Technology The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Department of Computer Science University of Salerno Fisciano Italy Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi110062 India

Addressing air pollution presents a significant environmental challenge in the context of smart city environments. Real-time monitoring of pollution data empowers local authorities to assess current traffic conditions and make informed decisions accordingly. The integration of Internet of Things (IoT) sensors has revolutionized air quality prediction, with human activities being the primary contributors to air pollution, posing threats to all forms of life. Gases such as SO2, PM10, O3, CO, NO2, among others, are key pollutants. Exposure to air pollution can lead to severe health issues and fatalities, underscoring the criticality of air quality monitoring. Distinguishing between breathable and non-breathable air quality further enhances the value of air quality monitoring. However, existing techniques face challenges in achieving high accuracy, exacerbated by missing values in datasets, which can significantly impact machine learning model performance. In response to these challenges, this research introduces an IoT-based automated system designed to classify air quality. This system is adept at managing missing data and attaining high accuracy levels. Central to this approach is a stacked ensemble voting classifier model, which combines two machine learning models. Additionally, the system incorporates the KNN Imputer to address missing values effectively. The work assesses the system's performance against seven alternative machine learning algorithms across two scenarios: one with missing values removed and another with KNN imputation applied. Notably, the proposed strategy achieves remarkable metrics, including 99.17% accuracy, 97.75% precision, 95.24% recall, and a 96.52% F1 score, when leveraging the KNN imputer. These results underscore the effectiveness of the proposed model compared to current state-of-the-art methodologies. © 2024, The Authors. All rights reserved.

关键词： Air quality

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