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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Xu, Leiyang Wang, Qiang Yang, Chenguang Harbin Institute Of Technology Department Of Control Science And Engineering Harbin China University Of Liverpool Department Of Computer Science LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9798350340266

Motion infilling is a fundamental and challenging research field in human motion modeling and analysis, which aims to generate natural and visually coherent transitions to fill in missing motion frames based on the start and end motion sequences. However, most current methods ignore the spatial structure formed by joints, which may lose some spatial information. This work proposes a novel spatiotemporal graph U-Net that supports flexible inputs for skeleton-based motion infilling. We apply spatiotemporal graph convolutional layers, skeleton pooling layers, and skeleton unpooling layers to extract spatial and temporal features in the motion sequence. At the same time, we use the U-Net structure to integrate the information in the start and end motion sequences. In addition, the generative adversarial mechanism is introduced to ensure the generated skeleton poses are smooth and natural. We conduct experiments on two motion datasets, including one large-scale public dataset and one self-built dataset. The model inputs are joint quaternions or joint coordinates. Experimental results show that our method can improve the performance of skeleton-based motion infilling and achieve state-of-the-art results when using joint coordinates as model input. © 2024 IEEE.

关键词： Generative adversarial networks

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Sustainable Generation of Sulfate Radicals and Decontamination of Micropollutants via Sequential Electrochemistry

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engineering 2023年第11期30卷 144-152页

作者： Wentian Zheng Shijie You Yuan Yao Nanqi Ren Bin Ding Fang Li Yanbiao Liu College of Environmental Science and Engineering Donghua UniversityShanghai 201620China State Key Laboratory of Urban Water Resource and Environment School of EnvironmentHarbin Institute of TechnologyHarbin 150090China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150080China Innovation Center for Textile Science and Technology College of TextilesDonghua UniversityShanghai 200051China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

The removal of emerging micropollutants in the aquatic environment remains a global *** routes are often chemically,energetically,and operationally intensive,which decreases their sustainability during ***,we develop an advanced chemical-free strategy for micropollutants decontamination that is solely based on sequential electrochemistry involving ubiquitous sulfate anions in natural and engineered *** can be achieved via a chain reaction initiated by electrocatalytic anodic sulfate(SO_(4)^(2-))oxidation to produce persulfate(S_(2)O_(8)^(2-))and followed by a cathodic persulfate reduction to produce sulfate radicals(SO_(4)^(·-)).These SO_(4)^(·-)are powerful reactive species that enable the unselective degradation of micropollutants and yield SO_(4)^(2-)again in the treated *** proposed flow-through electrochemical system achieves the efficient degradation(100.0%)and total organic carbon removal(65.0%)of aniline under optimized conditions with a single-pass *** also reveal the effectiveness of the proposed system for the degradation of a wide array of emerging micropollutants over a broad pH range and in complex *** work provides the first proof-ofconcept demonstration using ubiquitous sulfate for micropollutants decontamination,making water purification more sustainable and more economical.

关键词： Advanced oxidation Chain reaction Sulfate radical Micropollutants Sequential electrochemistry

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Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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Synergizing 3D-printed structure and sodiophilic interface enables highly efficient sodium metal anodes

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Chinese Chemical Letters 2024年第11期35卷 528-534页

作者： Changyuan Bao Yunpeng Jiang Haoyin Zhong Huaizheng Ren Junhui Wang Binbin Liu Qi Zhao Fan Jin Yan Meng Chong Jianguo Sun Fei Wang Bo Wang Ximeng Liu Dianlong Wang John Wang School of Chemical and Environmental Engineering Yancheng Teachers UniversityYancheng 224007China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage State Key Laboratory of Urban Water Resource and EnvironmentSchool of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Materials Science and Engineering National University of SingaporeSingapore 117575Singapore Institute of Materials Research and Engineering Agency for ScienceTechnology and Research(ASTAR)Singapore 138634Singapore

Sodium(Na)metal batteries have gained increasing attention more recently,owing to their high energy densities and cost efficiencies,but are severely handicapped by the unsatisfactory Coulombic efficiency(CE)and cycling stability stemming from dendrite growth on Na *** this study,we developed a strategy of direct ink writing(DIW)3D printing combined with electroless deposition to construct a hierarchical Cu grid coated with a dense nanoscale Ag interfacial layer as the host material for Na *** sodiophilic Ag interface contributes to a fall in the Na nucleation energy,hence enabling uniform Na deposition on each 3D-printed *** constructed 3D-printed structure can effectively moderate the electric-field distribution and lower the local current density for relieving Na inhomogeneous growth,as confirmed by finite element simulation and Na plating/stripping morphology evolution *** particular,the unique 3D structure also promotes the lateral growth of Na,thus the volume change of Na metal was accommodated to stabilize the solid electrolyte interphase(SEI).As a result,the CE of the half-cell can reach 99.9%at the current density of 1 m A/cm^(2)after 300 cycles and the full-cell exhibits outstanding electrochemical performance(capacity retention of 91.0%after 500 cycles at 2 C).

关键词： 3D-printed host Sodiophilic interface Dendrite-free High reversibility Sodium metal batteries

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Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline 18th

Profinder: Towards Professionals Recognition on Mobile Devi...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Comparative analysis of twelve transfer learning models for the prediction and crack detection in concrete dams,based on borehole images

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Frontiers of Structural and Civil engineering 2024年第10期18卷 1507-1523页

作者： Umer Sadiq KHAN Muhammad ISHFAQUE Saif Ur Rehman KHAN Fang Xu Lerui CHEN Yi LEI School of Computer and Information Science Hubei Engineering UniversityXiaogan 432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan 432000China College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing 210098China School of Computer Science and Engineering Central South UniversityChangsha 410083China College of Aviation Zhongyuan University of TechnologyZhengzhou 451191China School of Civil Engineering Central South UniversityChangsha 410083China

Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.

关键词： concrete dam borehole closed-circuit television deep learning models crack detection water resources management management

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Track correlation algorithm based on CNN-LSTM for swarm targets

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Journal of Systems engineering and Electronics 2024年第2期35卷 417-429页

作者： CHEN Jinyang WANG Xuhua CHEN Xian Research Institute for National Defense Engineering Academy of Military SciencesLuoyang 471023China Information Engineering University Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi’an 710071China

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for *** this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm ***, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

关键词： track correlation correlation accuracy rate swarm target convolutional neural network(CNN) long short-term memory(LSTM)neural network

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Design and Research on Visual Recognition System for Fruit Sorting Collaborative Robot 5

Design and Research on Visual Recognition System for Fruit S...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Ma, Yingchen Zhu, Hanan Jia, Hongli Guo, Chunlai Li, Jiaqi Li, Zhe Kalaf, Omer College of Intelligent Engineering Harbin Institute of Petroleum Harbin China IE Technology Research Center Harbin Cambridge University Harbin China Department of Mechanical Engineering Higher institute of science and Technology-Algarabulli Libya

ISBN: (纸本)9781643685243

This study aimed to enhance the efficiency and accuracy of fruit sorting through the development of a collaborative robotic vision recognition system based on neural networks. Leveraging convolutional neural networks (CNNs), a substantial dataset of fruit images was collected, preprocessed, and utilized for training. The core algorithm employed was the neural network, particularly effective in pattern recognition and feature extraction. Integration with the control system of collaborative robots allowed real-time fruit identification and analysis using visual sensors during sorting. The system's design considered optimization and acceleration of neural networks, incorporating advanced deep learning techniques like CNNs and recurrent neural networks. This fusion of collaborative robots, neural networks, and visual recognition technologies significantly improved fruit sorting efficiency and accuracy. The automated sorting process achieved through thoughtful system design and optimization reduces labor costs and enhances productivity. Despite notable progress in the design and research of collaborative robotic and neural network-based vision recognition systems, challenges and areas for improvement persist. Future work may involve further optimizing neural network models for increased precision and robustness and refining collaborative robot control strategies for more complex task collaboration. The collaborative robotic and neural network-based vision recognition system opens new possibilities in the field of fruit sorting, propelling the industry towards intelligent and efficient practices, thereby creating greater economic and societal value. © 2024 The Authors.

关键词： Recurrent neural networks

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Effect of competitive crystal growth on microstructural characteristics of directionally solidified nickel-based single crystal superalloy

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China Foundry 2022年第2期19卷 109-116页

作者： Mang Xu Xiao-qi Geng Xiang-long Zhang Guo-huai Liu Ye Wang Zhao-dong Wang Jing-jie Guo State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819China School of Materials Science and Engineering Harbin University of Science and TechnologyHarbin 150040China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China

Directionally solidified single crystal superalloy test bars were prepared by the spiral grain selection *** microstructural evolution and orientation characteristics of the starter block and spiral part were studied,and the influence of the competitive growth of crystals on the microstructural characteristics was *** results show that the divergent grain groups,with small size and randomly oriented grains,appear at the bottom of the start block due to the chilling effect,which is an important area for competitive *** the height of the starter block increases,the primary dendrite spacing increases,and the grain density decreases;furthermore,the proportion of grains with an orientation deflection angle less than 10°gradually ***<001>texture gradually becomes stronger as the height of the starter block increases,which indicates that the competitive growth of crystals gradually *** the initial stage of the crystal selection in the spiral part,the obstacle of adjacent grains and spiral passage is the main working *** grains located at the inner side of the front edge of the spiral passage have the growth *** single crystal screening process is achieved at about two-thirds of the spiral height,and the single crystal with the orientation deviation angle of 6.7°from the casting axis is prepared.

关键词： directional solidification Ni-based superalloy microstructural evolution spiral grain selector single crystal

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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

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Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West university of Timisoara). All rights reserved.

关键词： COVID-19

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