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A Generalized ChatGPT-Based Collaborative Multi-Objective Decision-Making Framework for Robust Vehicle Platoon Collision Avoidance

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7212-7225页

作者： Zhang, Hanwen Hou, Chenfei Chen, Jicheng Zhang, Hui Wang, Fei-Yue Beihang University Hangzhou International Innovation Institute Hangzhou311115 China Beihang University School of Transportation Science and Engineering Beijing100191 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong999077 Hong Kong University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100190 China Obuda University Physiological Controls Research Center University Research and Innovation Center Budapest1034 Hungary Macau University of Science and Technology Macau Institute of Systems Engineering China

In this work, we propose a ChatGPT-based framework that collaborates with human expert instructions for safe and efficient vehicle platoon control. The collaborative framework integrates multi-objective decision-making to address challenges such as robust collision avoidance in complex traffic environments. To incorporate human-like reasoning, ChatGPT is firstly used to interpret human expert driving instructions, which often involve linguistic variables. It generates behavioral decisions and computes feasible trajectories for each vehicle to ensure driving safety. A distributed model predictive controller is then utilized for accurate, real-time path tracking. To efficiently handle ChatGPT response failures, a receding instruction utilization strategy is proposed. For critical response failures, hindsight experience replay is used to supplement ChatGPT's knowledge database and improve response accuracy in complex scenarios. Simulation tests and experimental validations demonstrate the framework's effectiveness and generalizability across various traffic conditions, highlighting its potential for broader applications in platoon control. © 1967-2012 IEEE.

关键词： Reinforcement learning

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Intelligent Hardware Solutions for COVID -19 and Alike Diagnosis - A survey 1

Intelligent Hardware Solutions for COVID -19 and Alike Diagn...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Aeri, Manisha Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin Nicosia10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Globally, 210 nations have been affected by the 2019 Novel Coronavirus (COVID-19), which has been classified as a pandemic. The modern health system, as well as the economic, educational, and social sides of society, have all been severely impacted. While the rate of transmission keeps increasing, several cooperative strategies between stakeholders to create cutting-edge methods of screening and detecting COVID-19 instances among people at a comparable rate have been noticed. Also, the importance of computational models connected to the technologies of the fourth industrial revolution in accomplishing the desired feat has been emphasized. Unfortunately, there is a gap in the precision of COVID-19 case detection, prediction, and contact tracking. For patients with COVID-19 in isolation units, teleultrasound (TUS), particularly with the support of fifth generation (5G) wireless transmission technology, can offer rapid monitoring, quick clinical progress assessment, and assistance with guiding interventional procedures. Also, it helps conserve medical resources like equipment and supplies while lowering the risk of infection among medical personnel. The review of computer models presented in this work can be used to improve the effectiveness of COVID-19 pandemic case detection and prediction. We concentrate on adoptable big data, AI, and nature-inspired computing solutions for the current pandemic. According to the review, models inspired by nature have shown strong performance in feature selection for medical problems. In pandemic-related cases like COVID-19, contact tracing using big data analytics should also be investigated. © 2023 IEEE.

关键词： COVID-19

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Hypergraph-based Self-supervised Multi-channel Network for Drug-target Interaction Prediction

Hypergraph-based Self-supervised Multi-channel Network for D...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zeng, Xiaoting Liang, Yu Yang, Peng Chen, Weilin Lei, Baiying Shenzhen University School of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Medical School Shenzhen University Institute of Biological Therapy Shenzhen China Shenzhen University School of Biomedical Engineering Health Science Center Shenzhen China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is vital for drug discovery and repurposing. Hypergraph is utilized in DTI prediction for modeling higher-order relationships in biomedical networks. Although the strategies of modeling hypergraph-based drug-related interactions with multi-channel and utilizing self-supervised learning task to improve DTI prediction performance have been proven promising, current researches fail to effectively model feature interaction across different channels and fully exploit cross-channel information for self-supervised task. In this study, we propose a hypergraph-based self-supervised multi-channel interaction framework HSMI-DTI for DTI prediction. HSMI-DTI aims to extract hypergraph features effectively and model cross-channel correlations, leveraging hierarchical self-supervised learning to uncover the discover correlations between different channels. We compare HSMI-DTI with advanced baselines, and experiment results show our model outperforms existing methods, thereby optimizing DTI prediction performance. © 2024 IEEE.

关键词： Self-supervised learning

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Credit scoring model based on SyMProD-ENN and logistic regression 24

Credit scoring model based on SyMProD-ENN and logistic regre...

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8th International Conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Feng, Jiangang Jiang, Yuwen Bo, Chen Jiang, Chengxuan School of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China School of Computer Science and Engineering Guangzhou Institute of Science and Technology GuangDong Guangzhou China School of Mathematics and Statistics Shenzhen University Guangdong Shenzhen China International Course Center of GuangZhou No.6 Middle School Guangdong Guangzhou China

ISBN: (纸本)9798400710094

Unbalanced data distribution is a common problem in the development process of credit scoring cards. This paper proposes a new method of mixed sampling, SyMProD-ENN, to improve the problem of developing effective credit scoring cards on imbalanced datasets. The experimental results show that the SyMProD-ENN method can effectively improve the classification performance of the credit scoring model constructed based on the logistic regression methods, especially suitable for credit data with imbalance category, high-dimensional characteristics, non-normal distribution and outliers. © 2024 Copyright held by the owner/author(s).

关键词： Logistic regression

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A Low-Rank Matching Attention Based Cross-Modal Feature Fusion Method for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 1177-1189页

作者： Shou, Yuntao Liu, Huan Cao, Xiangyong Meng, Deyu Dong, Bo Xi'An Jiaotong School of Computer Science and Technology Ministry of Education Key Laboratory for Intelligent Networks and Network Security Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Shaanxi Xi'an710049 China Xi'An Jiaotong School of Computer Science and Technology Shaanxi Xi'an710049 China BigKEJoint Innovation Center SHMEEA Shanghai China Xi'An Jiaotong University School of Mathematics and Statistics Xi'an China

Conversational emotion recognition (CER) is an important research topic in human-computer interactions. Although recent advancements in transformer-based cross-modal fusion methods have shown promise in CER tasks, they tend to overlook the crucial intra-modal and inter-modal emotional interaction or suffer from high computational complexity. To address this, we introduce a novel and lightweight cross-modal feature fusion method called Low-Rank Matching Attention Method (LMAM). LMAM effectively captures contextual emotional semantic information in conversations while mitigating the quadratic complexity issue caused by the self-attention mechanism. Specifically, by setting a matching weight and calculating inter-modal features attention scores row by row, LMAM requires only one-third of the parameters of self-attention methods. We also employ the low-rank decomposition method on the weights to further reduce the number of parameters in LMAM. As a result, LMAM offers a lightweight model while avoiding overfitting problems caused by a large number of parameters. Moreover, LMAM is able to fully exploit the intra-modal emotional contextual information within each modality and integrates complementary emotional semantic information across modalities by computing and fusing similarities of intra-modal and inter-modal features simultaneously. Experimental results verify the superiority of LMAM compared with other popular cross-modal fusion methods on the premise of being more lightweight. Also, LMAM can be embedded into any existing state-of-the-art CER methods in a plug-and-play manner, and can be applied to other multi-modal recognition tasks, e.g., session recommendation and humour detection, demonstrating its remarkable generalization ability. © 2024 IEEE.

关键词： Semantics

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Efficient Deep Molecular Dynamic Model Training on Heterogeneous System 29

Efficient Deep Molecular Dynamic Model Training on Heterogen...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Du, Shaokang You, Xin Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

Molecular dynamics is a widely adopted simulation method for analyzing the movement of atoms and molecules. Traditional molecular dynamics simulation methods are computationally intensive and difficult to simulate a large number of atoms. In contrast, molecular dynamics based on deep potential models such as DeePMD can leverage deep learning techniques to improve simulation efficiency. Although DeePMD has incorporated mainstream deep learning frameworks, it still suffers from low performance and efficiency during its model training on heterogeneous systems such as CPU and GPU. Particularly, a large number of operators cannot be accelerated by GPU, resulting in low utilization of GPU computational resources. In this paper, we comprehensively analyze the computational bottlenecks and the corresponding root causes of DeePMD. We correspondingly propose several novel optimization strategies. Specifically, for preprocessing, we identify the computation redundancies and the GPU parallelization opportunities for performance optimization. For training, we propose optimization strategies such as operator fusion, redundancy elimination, and concurrent execution of multiple streams and threads in the computation process. Moreover, we apply systematical optimization of computational graphs and operators. The evaluation results show that DeePMD can achieve significant speedups in several cases after applying our proposed optimizations, resulting in a maximum overall speedup of 6.36× with acceptable accuracy. © 2023 IEEE.

关键词： DeePMD GPU Performance Optimization

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EPIMR:Prediction of Enhancer-Promoter Interactions by Multi-Scale ResNet on Image Representation

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Big Data Mining and Analytics 2024年第3期7卷 668-681页

作者： Qiaozhen Meng Yinuo Lyu Xiaoqing Peng Junhai Xu Jijun Tang Fei Guo School of Computer Science and Technology College of Intelligence and ComputingTianjin UniversityTianjin 300350China Aeronautical Information Service Center of the Civil Aviation Administration of China(AISC.ATMB.CAAC) Beijing 100015China Center for Medical Genetics&Hunan Key Laboratory of Medical Genetics School of Life SciencesCentral South UniversityChangsha 410038China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518000China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Prediction of enhancer-promoter interactions(EPIs)is key to regulating gene expression and diagnosing genetic *** to limited resolution,biological experiments perform not as well as expected while precisely identifying specific interactions,giving rise to computational biology *** EPI predictors have been developed,but their prediction accuracy still needs to be ***,we design a new model named EPIMR to identify enhancer-promoter ***,Hilbert Curve is utilized to represent sequences to images to preserve the position and spatial ***,a multi-scale residual neural network(ResNet)is used to learn the distinguishing features of different abstraction ***,matching heuristics are adopted to concatenate the learned features of enhancers and promoters,which pays attention to their potential interaction *** results on six cell lines indicate that EPIMR performs better than existing methods,with higher area under the precision-recall curve(AUPR)and area under the receiver operating characteristic(AUROC)results on benchmark and under-sampling ***,our model is pre-trained on all cell lines,which improves not only the transferability of cross-cell line prediction,but also cell line-specific prediction *** conclusion,our method serves as a valuable technical tool for predicting enhancer-promoter interactions,contributing to the understanding of gene transcription *** code and results are available at https://***/guofei-tju/EPIMR.

关键词： enhancer-promoter interactions Hilbert Curve multi-scale residual neural network(ResNet)

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A Novel Cable-Driven Soft Robot for Surgery

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Journal of Shanghai Jiaotong university(science) 2024年第1期29卷 60-72页

作者：李茹陈方俞文伟 IGARASH Tatsuo 舒雄鹏谢叻 Institute of Forming Technology and Equipment School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200030China The Sixth People's Hospital Affiliated to Shanghai Jiao Tong University Shanghai 200233China Center for Frontier Medical Engineering Chiba UniversityChiba 263-8522Japan Seirei Sakura Citizen Hospital Chiba 285-8765Japan Institute of Medical Robotics Shanghai Jiao Tong UniversityShanghai 200030China School of Mathematics and Computer Science Quanzhou Normal UniversityQuanzhou 362000FujianChina

Robot-assisted laparoscopic radical prostatectomy(RARP)is widely used to treat prostate *** rigid instruments primarily used in RARP cannot overcome the problem of blind areas in surgery and lead to more trauma such as more incision for the passage of the instrument and additional tissue damage caused by rigid *** robots are relatively fexible and theoretically have infinite degrees of freedom which can overcome the problem of the rigid instrument.A soft robot system for single-port transvesical robot-assisted radical prostatectomy(STvRARP)is developed in this *** soft manipulator with 10 mm in diameter and a maximum bending angle of 270°has good fexibility and *** design and mechanical structure of the soft robot are *** kinematics of the soft manipulator is established and the inverse kinematics is compensated based on the characteristics of the designed soft *** master-slave control system of soft robot for surgery is built and the feasibility of the designed soft robot is verified.

关键词： soft robot kinematics model robot-assisted surgery

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Wireless Sensor Networks and Green Networking for 6G communication- An Overview 1

Wireless Sensor Networks and Green Networking for 6G communi...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bordoloi, Dibyahash Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Dehradun Rs Cse Graphic Era University Dept. of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

Wireless sensor networks and green networking have been major research areas in the field of communication technology for the past few decades. In particular, the development of 6G communication systems has brought renewed focus on these areas, as the demands for higher data rates, more diverse applications, and lower energy consumption continue to increase. The use of wireless sensor networks in 6G communication systems has been widely explored in the literature, and has shown great potential for a wide range of applications, including environmental monitoring, industrial process control, and healthcare. However, a key challenge in the deployment of these networks is ensuring their energy efficiency, as the demands on the network will be much higher than in previous generations. To address this challenge, researchers have proposed various green networking techniques, such as energy harvesting and low-power communication protocols, which have been shown to significantly reduce the energy consumption of the sensors. In addition to improving the energy efficiency of wireless sensor networks, researchers have also studied their reliability, scalability, and adaptability. These properties are important in 6G communication systems, as the demands on the network are expected to be much higher than in previous generations. Researchers have proposed various methods to improve the reliability, scalability, and adaptability of wireless sensor networks, including the use of error-correction codes, redundant communication channels, and machine learning algorithms. © 2023 IEEE.

关键词： Wireless sensor networks

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Identity-preserving 3D Facial Completion under Skull Constraints 18

Identity-preserving 3D Facial Completion under Skull Constra...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Yu, Longtao Zhao, Junli Duan, Fuqing Lv, Chenlei Li, Dantong Pan, Zhenkuan Zhou, Mingquan Qingdao University College of Computer Science and Technology China Beijing Normal University School of Artificial Intelligence China Shenzhen University College of Computer Science and Software Engineering China Cardiff University School of Computer Science and Informations United Kingdom Northwest University School of Information Science and Technology United States Northwest University National and Local Joint Engineering Research Center for Cultural Heritage Digitization United States

ISBN: (纸本)9798350364132

3D face shape completion is a necessary pre-process for various facial applications as they are often mutilated due to the acquiring environment or occlusion. However, it is challenging to ensure identity consistency when completing faces with large missing regions. Therefore, we introduce craniofacial information to supervise the completion of face shapes. Firstly, a novel dual encoder-decoder structure for face depth image inpainting is constructed by combining dilated convolution and the coherent semantic attention mechanism, guaranteed to generate smooth results with complete semantic information even in the presence of large missing regions in the face model. Then, we innovatively design a craniofacial superimposition module to determine the probability that the inpainted face and corresponding skull come from the same person, constraining the inpainting network to learn identity consistency information. Finally, extensive experimental results show that our method can effectively complete 3D face shapes containing large arbitrary missing regions while guaranteeing identity consistency. © 2024 IEEE.

关键词： 3D modeling

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