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Multi-Objective Task Assignment for Maximizing Social Welfare in Spatio-Temporal Crowdsourcing

China Communications 2021年第11期18卷 11-25页

作者： Shengnan Wu Yingjie Wang Xiangrong Tong School of Computer and Control Engineering Yantai UniversityYantai 264005China

With the development of the Internet of Things(IoT),spatio-temporal crowdsourcing(mobile crowdsourcing)has become an emerging paradigm for addressing location-based sensing ***,the delay caused by network transmission has led to low data processing ***,edge computing can solve this problem,effectively reduce the delay of data transmission,and improve data processing capacity,so that the crowdsourcing platform can make better decisions ***,this paper combines spatio-temporal crowdsourcing and edge computing to study the Multi-Objective Optimization Task Assignment(MOO-TA)problem in the edge computing *** proposed online incentive mechanism considers the task difficulty attribute to motivate crowd workers to perform sensing tasks in the unpopular *** this paper,the Weighted and Multi-Objective Particle Swarm Combination(WAMOPSC)algorithm is proposed to maximize both platform’s and crowd workers’utility,so as to maximize social *** algorithm combines the traditional Linear Weighted Summation(LWS)algorithm and Multi-Objective Particle Swarm Optimization(MOPSO)algorithm to find pareto optimal solutions of multi-objective optimization task assignment problem as much as possible for crowdsourcing platform to *** comparison experiments on real data sets,the effectiveness and feasibility of the proposed method are evaluated.

关键词： spatio-temporal crowdsourcing edge computing task assignment multi-objective optimization particle swarm optimization Pareto optimal solution

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Toward Safe Human-Robot Interaction: More Efficient Safety Monitoring Technology For Robotic Arms 5

Toward Safe Human-Robot Interaction: More Efficient Safety M...

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5th World Robot Conference Symposium on Advanced Robotics and Automation, WRC SARA 2023

作者： Gong, Zihang Liu, Zhaowei Su, Hang Yantai University School of Computer and Control Engineering Yantai264005 China Guandong Univerisity of Technology School of Automation Guangzhou China

ISBN: (纸本)9798350307320

Presently, advanced humanoid robotic arms have the ability to work independently, and although most current research can make the robotic arm react appropriately and autonomously to the changing environment, it cannot completely prevent the robotic arm from reacting incorrectly due to a variety of uncontrollable factors;therefore, it is necessary to detect the overall posture of the operating robotic arm. In this paper, we propose a more effective vision fusion-based robot safety monitoring method that can detect the robot arm's posture change in real time, thereby significantly enhancing the safety of the robot arm at work. The visual fusion method utilized in this paper enables faster and more stable skeleton visualization tracking of the robot arm's motion, and at the same time, more accurate skeleton information extraction of the robot arm in three-dimensional space, which increases the accuracy of detecting the bending angle of each joint under each posture of the robot arm. The research experiment demonstrates that the method proposed in this paper has a positive effect on monitoring the robotic arm in real time. © 2023 IEEE.

关键词： Robotic arms

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Vibration-induced bias error reduction using loop gain compensation for high-precision fiber optic gyroscopes

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Journal of Systems engineering and Electronics 2025年第1期36卷 224-232页

作者： CHEN Heyu QIN Xuexin XIE Huan KONG Linghai ZHENG Yue School of Instrumentation Science and Opto-Electronics Engineering Beihang UniversityBeijing 100191China Beijing Institute of Computer Technology and Application Beijing 100854China Hunan Shuaiwei Control Technology Co. LTDChangsha 421000China Beijing Institute of Control and Electronic Technology Beijing 100038China

Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision *** conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under *** this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put ***,the loop gain is extracted out by adding a gain-monitoring *** demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation *** experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 *** technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.

关键词： vibration-induced error fiber optic gyroscope(FOG) loop gain

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Comprehensive multimodal approach for Parkinson’s disease classification using artificial intelligence: insights and model explainability

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Soft Computing 2025年第3期29卷 1845-1877页

作者： Balaha, Hossam Magdy Hassan, Asmaa El-Sayed Ahmed, Rawan Ayman Balaha, Magdy Hassan Bioengineering Department J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Computer Science and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt Mathematics and Engineering Physics Department Faculty of Engineering Mansoura University Mansoura Egypt Department of Obstetrics and Gynecology Faculty of Medicine Tanta University Tanta Egypt

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder affecting millions worldwide. Early detection is vital for effective management, yet remains challenging. In this study, we investigated four distinct datasets for PD detection. Through comprehensive experimentation employing ensemble methods and feature selection, we achieved high classification accuracies across the datasets. For the Oxford Parkinson’s Disease Detection Dataset, an accuracy of 95.67%, precision of 97.59%, recall of 84.5%, specificity of 99.32%, and F1-score of 90.57% were achieved. For the Alzheimer Parkinson Diseases 3 Class Dataset, the "Stacking" approach surpasses individual models, reaching an accuracy of 99.85%, precision of 99.81%, recall of 99.81%, specificity of 99.86%, and F1 of 99.81%. For the NewHandPD dataset, Regarding the Spiral category, The "Base-P32-384" model surpasses others with an accuracy of 97.35%, precision of 96.50%, recall of 98.57%, and F1-score of 97.53%. The collective "Stacking" approach proves highly effective regarding the Circle category, achieving 100% across all performance metrics. Regarding the Meander category, the "Base-P16-224" model achieves an accuracy of 97.35%, precision of 99.26%, recall of 95.71%, specificity of 99.19%, and F1 of 97.45%. The Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset contains two datasets. Regarding the "SpontaneousDialogue" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 94.03%, 92.83%, 90.78%, 100.0%, and 85.67%, respectively. Regarding the "ReadText" dataset, accuracy, BAC, precision, recall, specificity, and F1-score were computed, resulting in values of 91.89%, 90.62%, 87.5%, 100.0%, and 81.25%, respectively. Our findings highlight the efficacy of leveraging diverse data sources and advanced machine learning techniques to enhance PD detection accuracy. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germa

关键词： Neurodegenerative diseases

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2022 Global Conference on Robotics, Artificial Intelligence and Information Technology, GCRAIT 2022

作者： Li, Yu Tong, Xiangrong School of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9781665481922

ISBN: (纸本)9781665481922

In view of the insufficient researches on the dynamic change of user trust in existing trust-based recommendation algorithms, a DDPG-TRA algorithm based on deep reinforcement learning and attention mechanism is proposed, which can catch the user interests and dynamically update user trust. The algorithm firstly uses the state module combined with the attention mechanism to model the user's state, and then recommends item. When the user adopts the item, it integrates social trust relationships and similarity relationships to predict item score, and computes the error between the predicted score and the actual score. Finally, the algorithm gets the recommendation reward and updates the trust with the score difference. We conduct experiments on three datasets and compared with other algorithms, and the proposed algorithm can get more accurate results. © 2022 IEEE.

关键词： Reinforcement learning

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A Recommendation Model with Attention-Aware Graph Matching 5

A Recommendation Model with Attention-Aware Graph Matching

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Ma, Xinxin Cui, Zhendong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9781665499132

Under the background of information overload, recommendation system can provide decision-making tools for information producers to promote information and information consumers to obtain personalized preference information. Use user-item attribute information for interaction can effectively improve the performance of the recommendation model. Existing work models and aggregates the attribute interaction of users and items in the same way, without effectively distinguishing them. However, this can't express the feature interaction between attributes well. In this work, we propose Attention-aware Graph Matching Recommendation model (AGMRec), which convert the recommendation process into a graph matching problem. In particular, we design internal-node message passing and external-node matching to model different orders feature interactions between attributes. Then the proposed model uses an attention approach to fuse different aspects of the nodes. Experimental results on two real-world datasets show its superiority over the state-of-the-arts. © 2022 IEEE.

关键词： Recommender systems

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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An Evolutionary Model for Earthquake Prediction Considering Time-Series Evolution and Feature Extraction and Its Application

An Evolutionary Model for Earthquake Prediction Considering ...

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2022 International Conference on computer Modelling and Big Data Analytics, CMBDA 2022

作者： Du, Depeng Cui, Zhendong School of Computer and Control Engineering Yantai University Yantai China

Seismic activity presents characteristics such as relatively concentrated distribution, high destructiveness, huge secondary hazards, complex change patterns, and unpredictable future activity conditions. It is of great theoretical significance and application and promotion value to research magnitude prediction for earthquake-prone areas. This study attempts to develop an earthquake prediction model based on seismic data from 1970 to 2021 and to predict earthquakes of magnitude (4.5-6). Firstly, the data are statistically analyzed using data statistics to analyze trends, identify patterns and characteristics of change, and make decisions conducive to development. Secondly, feature extraction and selection are carried out by convolutional neural networks to select the optimal features. The CNN_BiLSTM model was constructed and compared with other prediction models (long and short-term memory neural network, back propagation neural network, and support vector regression) to obtain prediction results for sensitivity, specificity, true prediction values, false prediction values, and accuracy values were compared and analyzed. The earthquake magnitude predictions for Japan and surrounding areas using the above techniques show essential and encouraging results, thus taking an essential step towards an eventual robust prediction mechanism. © Published under licence by IOP Publishing Ltd.

关键词： Feature extraction

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Ring-Overlap: A Storage Scaling Mechanism For Consortium Blockchain

Ring-Overlap: A Storage Scaling Mechanism For Consortium Blo...

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2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Liu, Wenxuan Zhang, Donghong Zhao, Jindong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

With the rapid development of blockchain applications, the data that need to be stored increases dramatically, and the blockchain is about to face the problem of storage limitations. To deal with this problem, this paper proposes a storage scaling mechanism for Hyperledger Fabric, which relieves the storage pressure by dividing the peer nodes into clusters, and each node only stores partial data instead of the whole ledger. First, all accounting nodes are divided into clusters that include several nodes respectively, and part of the whole block data is stored in a single cluster;then, the block data are stored overlappingly on some nodes in the cluster, and each block is guaranteed to have some copies in a cluster. By arranging the copies on selected nodes according to our proposed mechanism, all the blocks are overlapped in a cluster. Theoretical analysis and simulation show that the storage volume occupied by nodes is decreased significantly in blockchain applications with frequent transactions, and in the case that the number of node failures in a single cluster does not exceed the threshold, the mechanism can still guarantee data integrity. Moreover, for applications with frequent transactions, storage space consumption can be significantly reduced without increasing excessive query time overhead. © 2022 IEEE.

关键词： Blockchain

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Explicit and Implicit Feature Interaction Based on Attention Networks for Click-Through Rate Prediction 5

Explicit and Implicit Feature Interaction Based on Attention...

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Li, Shiqi Cui, Zhendong Yantai University School of Computer and Control Engineering Yantai China

ISBN: (纸本)9781665499132

Click-through rate (CTR) prediction, which aims to estimate the probability of a user to click on a given item, plays a critical role in both recommender systems and online advertising. Recently, a number of deep neural network-based approaches have been developed in an implicit and bit-wise interaction, which the order of interaction is inconclusive. In this work, a novel model called eXtreme Deep Attention Interaction Network (xAtInt) is proposed to capture meaningful combination features explicitly. On the one hand, xAtInt learn meaningful feature interaction via attention mechanism;on the other hand, it is capable of finding arbitrary degree interaction at a vector-wise. Furthermore, to improve performance, we integrate DNN with xAtInt learning explicit and implicit feature interactions. Extensive experiments are conducted on real-world dataset. The results show that our model present good performance compared existing state-of-the-art approaches. © 2022 IEEE.

关键词： Deep neural networks

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