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2nd EAI International Conference on Edge Computing and IoT: Systems, Management and Security, ICECI 2021

作者： Li, Yihong Luo, Chengzhe College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031042300

In professional vocal training, a way to evaluate the quality of vocalization is often needed. In order to solve various problems caused by the lack of professional instructors, a vocal training system for detecting the closed state of the vocal cords is proposed. We have proposed a robust vocal training system for the detection of the closed state of the vocal cords on the mobile terminal, which can analyze the closure of the vocal cords of the human body during vocalization, so as to evaluate the vocal cord ability without a professional teacher or professional equipment. In this system, we can use two wearable sensors, vibrating plate and headset, to collect the signals of the human vocal cords, or directly use the microphone of the mobile device to collect. And we use the convolutional neural network to analyze the signals and classify the closed state of the vocal cords. In order to build this system, we constructed a vowel data set classified by degree vocal cords closure, including the wearable sensors and mobile phone microphones we used. We compared the performance of the traditional vocalization pattern classification method and the convolutional neural network method we used to classify the vocal cord closure types on the data set we constructed and the public data set, and finally tested under two noise environments, and the preliminary results proved the usability of our system. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Professional aspects

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IVRSandplay: An Immersive Virtual Reality Sandplay System Coupled with Hand Motion Capture and Eye Tracking 26

IVRSandplay: An Immersive Virtual Reality Sandplay System Co...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Wang, Haibo Ding, Ding Liu, Yuhao Wang, Chi Southeast University School of Computer Science and Engineering Nanjing211189 China Southeast University College of Software Engineering Nanjing211189 China

ISBN: (纸本)9798350331684

Sandplay therapy is a novel psychological consultation and treatment method for people suffering from mental stress and psychological issues. It is popular among the masses owing to its interest and curative effect. However, traditional sandplay games face several problems, such as the requirement of large space, low accessibility, difficulty in keeping treatment records, and lack of engagement. To solve these problems, we put forward a sandplay system based on virtual reality technology. This system provides a virtual scenario of the sandplay room, allowing users to place various sandplay miniatures naturally and smoothly into the sandbox with multiple themes by using motion capture gloves. What is more, users can switch to the first-person perspective to experience the world they have built. Additionally, the system could collect users' eye-tracking data and analyze it by a machine learning algorithm to estimate individuals' emotions which could assist in further treatment. Finally, twelve participants were invited to experience the system. They were asked to evaluate this system as well as their emotional status before and after using it. The experimental results indicate that the participants are satisfied with this system, while their emotions can be improved to a certain extent. © 2023 IEEE.

关键词： Virtual reality

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A Novel Stackelberg-Game-Based Energy Storage Sharing Scheme Under Demand Charge

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 462-473页

作者： Bingyun Li Qinmin Yang Innocent Kamwa IEEE the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China Huzhou Institute of Zhejiang University China the Department of Electrical and Computer Engineering Laval UniversityQuebec CityQCG1V 0A6Canada

Demand response(DR)using shared energy storage systems(ESSs)is an appealing method to save electricity bills for users under demand charge and time-of-use(TOU)price.A novel Stackelberg-game-based ESS sharing scheme is proposed and analyzed in this *** this scheme,the interactions between selfish users and an operator are characterized as a Stackelberg *** holds a large-scale ESS that is shared among users in the form of energy *** sells energy to users and sets the selling price *** maximizes its profit through optimal pricing and ESS *** purchase some energy from operator for the reduction of their demand charges after operator's selling price is *** game-theoretic ESS sharing scheme is characterized and analyzed by formulating and solving a bi-level optimization *** upper-level optimization maximizes operator's profit and the lower-level optimization minimizes users'*** bi-level model is transformed and linearized into a mixed-integer linear programming(MILP)model using the mathematical programming with equilibrium constraints(MPEC)method and model linearizing *** studies with actual data are carried out to explore the economic performances of the proposed ESS sharing scheme.

关键词： Bi-level optimization demand charge energy storage system(ESS)sharing energy transaction mathematical program with equilibrium constraints(MPEC) stackelberg game

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Clustering-based Autoencoding for Dynamic Multiobjective Evolutionary Optimization 4

Clustering-based Autoencoding for Dynamic Multiobjective Evo...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Ye, Yulong Zhu, Qingling Li, Lingjie Chen, Jianyong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Dynamic multiobjective optimization problems (DMOPs) usually contain multiple conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environment changes. In recent years, transfer learning (TL)-based methods have been considered promising in solving DMOPs. However, most existing TL-based methods are computationally extensive and therefore time-consuming. In this paper, a clustering based autoencoding for DMOEA, called CAE-DMOEA, is proposed, which aims to generate a high-quality initial population to accelerate the evolutionary process and improve the optimization performance. In particular, by learning the mapping relationship between the regional centroids of the approximate Pareto-optimal solutions (POS) from the previous two environments, the CAE-DMOEA can effectively predict the regional centroids of the POS in the new environment, which helps to tracking the moving POS and enhance the optimization efficiency. To study the performance of the proposed method, extensive experiments have been carried out by comparing three state-of-the-art DMOEAs. The experimental results show that the overall performance of the CAE-DMOEA is superior to that of the compared algorithms. © 2022 IEEE.

关键词： Evolutionary algorithms

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An artificial systems,computational experiments and parallel execution-based surface electromyogram-driven anti-disturbance zeroing neurodynamic strategy for upper limb human-robot interaction control

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 511-525页

作者： Yongbai Liu Keping Liu Gang Wang Jiliang Zhang Yao Chou Zhongbo Sun Department of Mechanical Engineering Changchun University of TechnologyChangchunChina Department of Control Engineering Changchun University of TechnologyChangchunChina School of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchunChina College of Information Science and Engineering Northeastern UniversityShenyangChina Department of Electrical and Computer Engineering Brigham Young UniversityProvoUtahUSA

In recent years,intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation,wherein the human-robot interaction(HRI)control strategy is a momentous part that needs to be ***,the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be *** deal with these urgent issues,in this study,artificial systems,computational experiments and a parallel execution intelligent control framework are constructed for the HRI *** upper limb-robotic exoskeleton system is re-modelled as an artificial *** on surface electromyogram-based subject's active motion intention in the practical system,a non-convex function activated anti-disturbance zeroing neurodynamic(NC-ADZND)controller is devised in the artificial system for parallel interaction and HRI control with the practical ***,the linear activation function-based zeroing neurodynamic(LAF-ZND)controller and proportionalderivative(posterior deltoid(PD))controller are presented and *** results substantiate the global convergence and robustness of the proposed controller in the presence of different external *** addition,the simulation results verify that the NC-ADZND controller is better than the LAF-ZND and the PD controllers in respect of convergence order and anti-disturbance characteristics.

关键词： neural network pattern recognition robotics signal processing

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Poster: Towards Accurate and Fast Federated Learning in End-Edge-Cloud Orchestrated Networks 43

Poster: Towards Accurate and Fast Federated Learning in End-...

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43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023

作者： Li, Mingze Sun, Peng Zhou, Huan Zhao, Liang Liu, Xuxun Leung, Victor C. M. College of Computer and Information Technology China Three Gorges University Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering Yichang China College of Computer Science and Electronic Engineering Hunan University Changsha China College of Electronic and Information Engineering South China University of Technology Guangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350339864

This work proposes a novel three-layer federated learning (FL) framework with parameter selection and pre-synchronization (PSPFL) to achieve fast and accurate model training. The basic idea of PSPFL is that clients select partial model parameters for transmission and then base stations aggregate them cooperatively (i.e., pre-synchronization) and send the aggregated results to the server for global model update periodically. However, there is an intrinsic trade-off between parameter transmission overhead and model training loss. To strike a desirable balance between them, we investigate the optimal parameter pre-synchronization round and local training round under PSPFL. Specifically, we propose a Deep Q-Network (DQN)-based method to obtain the local training round and parameter pre-synchronization round. Finally, extensive experiments are conducted to evaluate the performance of the proposed method on commonly used datasets. The results show that the proposed method can reduce the sum of FL completion time and training loss by an average of 8.17%-18.82% compared to benchmarks. © 2023 IEEE.

关键词： Synchronization

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Fair Machine Learning-An Analytical Study Based on CiteSpace 3

Fair Machine Learning-An Analytical Study Based on CiteSpace

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3rd International Conference on Consumer Electronics and computer engineering, ICCECE 2023

作者： Luo, Xiangxi Cui, Jianfeng Ma, Shuai College of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Software Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9798350331578

With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular research topic in this field. At present, the research on fair machine learning is still in its initial stage. In this paper, we analyzed the research and articles related to fair machine learning (January 2011 to December 2022) using CiteSpace visualization software, explored research collaboration networks (authors, institutions, and countries), keyword co-occurrence and clustering networks, and literature co-citation and clustering networks, and analyzed and constructed knowledge graphs. To understand the research foundation, related research progress, the latest research directions, and the research methods receiving attention in the field of fair machine learning through the analysis of the knowledge graph. Relevant key articles are discussed, and future research directions are envisioned. © 2023 IEEE.

关键词： Machine learning

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DNBP-CCA:A Novel Approach to Enhancing Heterogeneous Data Traffic and Reliable Data Transmission for Body Area Network

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computers, Materials & Continua 2024年第5期79卷 2851-2878页

作者： Abdulwadood Alawadhi Mohd.Hasbullah Omar Abdullah Almogahed Noradila Nordin Salman A.Alqahtani Atif M.Alamri InterNetWorks Research Laboratory School of ComputingUniversiti Utara MalaysiaSintok06010Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn MalaysiaParit RajaJohor86400Malaysia School of Games&Creative Technology University for the Creative ArtsFarnhamGU97DSUK Computer Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use ofBody Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, includingcontention during finite backoff periods, association delays, and traffic channel access through clear channelassessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions,and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet deliveryratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear ChannelAssessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination ofthe Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA)scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model’s inference system toquantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. Onthe other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission tothe coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result,simulations demonstrate that our proposed schemes are better than some existing representative approaches andenhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, anddecrease average packet drop rate and packet delay.

关键词： Internet of Medical Things body area networks backoff period tsk fuzzy model clear channel assessment media access control

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Internet of Things (IoT) applications security trends and challenges

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Discover Internet of Things 2024年第1期4卷 1-22页

作者： Laghari, Asif Ali Li, Hang Khan, Abdullah Ayub Shoulin, Yin Karim, Shahid Khani, Muhammad Adnan Kaim Software College Shenyang Normal University Shenyang China Department of Computer Science Bahria University Karachi Campus Karachi75000 Pakistan School of Information Engineering Xi’an Eurasia University Shaanxi Xian710065 China Department of Computer Science Ilma University Karachi Pakistan

The idea behind layered design is the foundation of the Internet of Things. Each tier uses a variety of technologies for capacity, preparation, and information transmission. With regard to the risks and vulnerabilities related to IoT-enabled devices, as well as potential assurance mechanisms in light of equipment constraints and novel information transfer techniques, this study attempts to analyze the current architecture of the Internet of Things. After that, we talk about IoT architecture and applications. The following is a list of effective real-time IoT applications that are now in use: Self-driving cars, traffic management systems, smart grids, logistic management hierarchies, environment monitoring, building safety applications, and many more are examples of emerging technologies. Specifically, we highlight the latest improvements and challenges associated with IoT security. Finally, we discuss the state-of-the-art research and its conclusions, as well as the outstanding problems for future IoT security research. © The Author(s) 2024.

关键词： Cloud security

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Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

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computers, Materials & Continua 2023年第7期76卷 993-1007页

作者： A.Manju R.Kaladevi Shanmugasundaram Hariharan Shih-Yu Chen Vinay Kukreja Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang Department of Computer Science and Engineering SRM Institute of Science and TechnologyRamapuramChennaiIndia Department of Computer Science and Engineering Saveetha Engineering CollegeChennaiIndia Department of Computer Science and Engineering Vardhaman College of EngineeringHyderabadIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin 64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin 64002Taiwan Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjabIndia Department of Computing Science University of AberdeenAberdeenUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh 12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia School of Computer&Communication Engineering Changsha University of Science&TechnologyChangshaChina

The medical community has more concern on lung cancer *** experts’physical segmentation of lung cancers is time-consuming and needs to be *** research study’s objective is to diagnose lung tumors at an early stage to extend the life of humans using deep learning ***-Aided Diagnostic(CAD)system aids in the diagnosis and shortens the time necessary to detect the tumor *** application of Deep Neural Networks(DNN)has also been exhibited as an excellent and effective method in classification and segmentation *** research aims to separate lung cancers from images of Magnetic Resonance Imaging(MRI)with threshold *** Honey hook process categorizes lung cancer based on characteristics retrieved using several *** this principle,the work presents a solution for image compression utilizing a Deep Wave Auto-Encoder(DWAE).The combination of the two approaches significantly reduces the overall size of the feature set required for any future classification process performed using *** proposed DWAE-DNN image classifier is applied to a lung imaging dataset with Radial Basis Function(RBF)*** study reported promising results with an accuracy of 97.34%,whereas using the Decision Tree(DT)classifier has an accuracy of 94.24%.The proposed approach(DWAE-DNN)is found to classify the images with an accuracy of 98.67%,either as malignant or normal *** contrast to the accuracy requirements,the work also uses the benchmark standards like specificity,sensitivity,and precision to evaluate the efficiency of the *** is found from an investigation that the DT classifier provides the maximum performance in the DWAE-DNN depending on the network’s performance on image testing,as shown by the data acquired by the categorizers themselves.

关键词： Lung tumor deep wave auto encoder decision tree classifier deep neural networks extraction techniques

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