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SSRN 2023年

作者： Chen, Yadang Ji, Chuanjun Yang, Zhi-Xin Wu, Enhua Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China Beijing100190 China

Semi-supervised video object segmentation is the process of tracking and segmenting objects in a video sequence based on annotated masks for one or more frames. Recently, memory-based methods have attracted a significant amount of attention due to their strong performance. Having too much redundant information stored in memory, however, makes such methods inefficient and inaccurate. Moreover, a global matching strategy is usually used for memory reading, so these methods are susceptible to interference from semantically similar objects and are prone to incorrect segmentation. We propose a spatial constraint network to overcome these problems. In particular, we introduce a time-varying sensor and a dynamic feature memory to adaptively store pixel information to facilitate the modeling of the target object, which greatly reduces information redundancy in the memory without missing critical information. Furthermore, we propose an efficient memory reader that is less computationally intensive and has a smaller footprint. More importantly, we introduce a spatial constraint module to learn spatial consistency to obtain more precise segmentation;the target and distractors can be identified by the learned spatial response. The experimental results indicate that our method is competitive with state-of-the-art methods on several benchmark datasets. Our method also achieves an approximately 30 FPS inference speed, which is close to the requirement for real-time systems. © 2023, The Authors. All rights reserved.

关键词： Real time systems

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Improving Performance Insensitivity of Large-scale Multiobjective Optimization via Monte Carlo Tree Search

arXiv

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arXiv 2023年

作者： Hong, Haokai Jiang, Min Yen, Gary G. The Department of Artificial Intelligence Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism School of Informatics Xiamen University Fujian361005 China The School of Electrical and Computer Engineering Oklahoma State University United States

The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering fields can be modeled as LSMOPs;simultaneously, engineering applications require insensitivity in performance. This requirement usually means that the results from the algorithm runs should not only be good for every run in terms of performance but also that the performance of multiple runs should not fluctuate too much, i.e., the algorithm shows good insensitivity. Considering that substantial computational resources are requested for each run, it is essential to improve upon the performance of the large-scale multiobjective optimization algorithm, as well as the insensitivity of the algorithm. However, existing large-scale multiobjective optimization algorithms solely focus on improving the performance of the algorithms, leaving the insensitivity characteristics unattended. In this work, we propose an evolutionary algorithm for solving LSMOPs based on Monte Carlo tree search, the so-called LMMOCTS, which aims to improve the performance and insensitivity for large-scale multiobjective optimization problems. The proposed method samples the decision variables to construct new nodes on the Monte Carlo tree for optimization and evaluation. It selects nodes with good evaluation for further search to reduce the performance sensitivity caused by large-scale decision variables. We compare the proposed algorithm with several state-of-the-art designs on different benchmark functions. We also propose two metrics to measure the sensitivity of the algorithm. The experimental results confirm the effectiveness and performance insensitivity of the proposed design for solving large-scale multiobjective optimization problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations

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BMC Biology 2025年第1期23卷 1-14页

作者： Wei, Hang Hou, Jialu Liu, Yumeng Shaytan, Alexey K. Liu, Bin Wu, Hao School of Computer Science and Technology Xidian University Shaanxi Xi’an 710126 China SMBU-MSU-BIT Joint Laboratory On Bioinformatics and Engineering Biology Shenzhen MSU-BIT University Guangdong Shenzhen 518172 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China College of Big Data and Internet Shenzhen Technology University Guangdong Shenzhen 518118 China Department of Biology Lomonosov Moscow State University Moscow 119234 Russian Federation International Laboratory of Bioinformatics AI and Digital Sciences Institute Faculty of Computer Science HSE University Moscow 109028 Russian Federation Zhongguancun Academy Beijing 100094 China

Background: Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. Several computational methods have been presented for identifying associations between piRNAs and diseases. However, the existing methods encounter challenges such as over-smoothing in feature learning and overlooking specific local proximity relationships, resulting in limited representation of piRNA-disease pairs and insufficient detection of association patterns. Results: In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. Additionally, it integrates their refined and macroscopic inferences to derive the final prediction result. Conclusions: The experimental results show that iPiDA-LGE effectively leverages the advantages of both local and global graph learning, thereby achieving more discriminative pair representation and superior predictive performance. © The Author(s) 2025.

关键词： Graph ensemble learning Local context graph piRNA-disease association identification

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Novel Generation Injection Solution for Security Constrained Optimal Power Flow of Utility Practical Transmission Utility Network

Novel Generation Injection Solution for Security Constrained...

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Global Conference for Advancement in Technology (GCAT)

作者： Bhoori Singh Avdhesh Sharma Akhil Ranjan Garg Om Prakash Mahela Baseem Khan Department of Electrical Engineering MBM University Jodhpur India Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Ltd. Jaipur India Innovader Research Labs Innovader IITian Padhaiwala LearnlAb Pvt. Ltd. Jaipur India Department of Electrical and Computer Engineering Hawassa University Ethiopia Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji China Department of Technical Sciences Western Caspian University Baku Azerbaijan

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The security constrained optimal power flow (SCOPF) optimally configures and dispatches the power system in normal conditions to ensure that steady-state operating conditions do not exceed the emergency ratings when contingency is observed. Suitable methods of SCOPF helps to operate the grid optimally. Hence, this paper discussed a SCOPF considering the transmission loss minimization and minimization of bus voltage deviation (BVD). Study is performed for base case of a practical transmission utility network (TUN) and proposed case TUN by addition of a thermal power plant (TPP). TPP addition improves the voltage profile of the TUN buses, minimizes BVD and transmission losses of TUN. Study is performed using the MiPower software. Proposed study is effective compared to many studies reported in literature for a practical transmission grid.

关键词： Power transmission lines Voltage Propagation losses Minimization Software Steady-state Power systems Security Power generation Load flow

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A Parameter-Centric Approach for Enhancing Misalignment Tolerance in Wireless Power Transfer systems

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IEEE Transactions on Power Electronics 2025年

作者： Yuan, Zhaoyang Yang, Qingxin Cai, Changsong Zhang, Pengcheng Wang, Ran Ma, Xianjie Lin, Hongjian Saeedifard, Maryam Wuhan University Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy School of Electrical Engineering and Automation Wuhan430072 China Tianjin University of Technology Tianjin300384 China Tsinghua University Department of Electrical Engineering Beijing100084 China Heze Power Supply Company State Grid Shandong Electric Power Company Heze274000 China Laixi Power Supply Company State Grid Shandong Electric Power Company Qingdao266600 China University of Alberta Department of Electrical and Computer Engineering ABT6G 2R3 Canada Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States

This paper introduces a misalignment-tolerant design method for wireless power transfer (WPT) systems by optimization of compensation network parameters. Leveraging the derivation of two-port impedance parameters and adjoint sensitivity analysis, the coupling misalignment characteristics of the system are assessed. Subsequently, a parameter-centric misalignment tolerant approach, independent of the coil structure, is established through a linear fitting method. The proposed approach ensures zero-voltage switching (ZVS) and facilitates efficient stable constant current (CC) output. Experimental validation is performed on a scaled-down 500-W rated SS-compensated WPT prototype equipped with non-specialized square-shaped coils, demonstrating the effectiveness of the proposed approach. The proposed method enhances the SS-compensated system's ability to tolerate both horizontal and vertical misalignments, surpassing the performance of conventional SS topologies. Specifically, the system achieves over 85% efficiency within the misalignment tolerance range, with a peak efficiency of 93.2%. Additionally, the load current variation remains within 5.8% for 100 mm x-misalignment and -15 to +50 mm z-misalignment. © 1986-2012 IEEE.

关键词： Inductive power transmission

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Data-driven approximation of control invariant set for linear system based on convex piecewise linear fitting

arXiv

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arXiv 2022年

作者： Xu, Jun Chen, Fanglin School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Engineering Harbin Institute of Technology Shenzhen518055 China

Control invariant set is critical for guaranteeing safe control and the problem of computing control invariant set for linear discrete-time system is revisited in this paper by using a data-driven approach. Specifically, sample points on convergent trajectories of linear MPC are recorded, of which the convex hull formulates a control invariant set for the linear system. To approximate the convex hull of multiple sample points, a convex piecewise linear (PWL) fitting framework has been proposed, which yields a polyhedral approximation with predefined complexity. A descent algorithm for the convex PWL fitting problem is also developed, which is guaranteed to converge to a local optimum. The proposed strategy is flexible in computing the control invariant set in high dimension with a predefined complexity. Simulation results show that the proposed data-driven approximation can compute the approximated control invariant set with high accuracy and relatively low computational cost. © 2022, CC0.

关键词： Linear systems

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Ac Magnetic Field-Driven Wireless Charging Dual-Oriented Fibrous Magnetoelectric Scaffold Cfo/Pvdf Promotes Peripheral Nerve Repair

SSRN

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SSRN 2024年

作者： Li, Nan Wang, Lu Dang, Pengrui Zheng, Huimin Wei, Ling Jiang, Shengjie Wang, Jiechen Cai, Yuting Wang, Wenliang Zhang, Chenguang Xia, Juan Hospital of Stomatology Guanghua School of Stomatology Sun Yat-sen University Guangdong Guangzhou510275 China Guangdong Provincial Key Laboratory of Stomatology Guangdong Guangzhou510275 China The VIP Department School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Liaoning Shenyang110002 China Department of Geriatric Dentistry NMPA Key Laboratory for Dental Materials National Engineering Laboratory for Digital and Material Technology of Stomatology Peking University School Hospital of Stomatology Beijing100081 China Third Clinical Division Peking University School Hospital of Stomatology National Center of Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Devices Beijing Key Laboratory of Digital Stomatology Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health NMPA Key Laboratory for Dental Materials Beijing100081 China Department of Stomatology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430022 China Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States School of Materials Science and Engineering South China University of Technology Guangzhou510640 China Guangdong Shenzhen518020 China

Efficient repair of peripheral nerve injuries (PNI) poses a significant challenge in neurosurgery. Wireless charging systems developed from piezoelectric materials, which can convert various forms of exogenous or endogenous mechanical signal into electrical stimulation, are highly regarded in the field of nerve regeneration. However, the potential mobility restrictions of patients with PNI and the attenuation of mechanical signal in deep tissues make it difficult to generate optimal electrical stimulation for inducing nerve regeneration, thereby limiting their clinical applications. Here, we developed a dual-oriented fibrous scaffold with excellent magnetoelectric conversion performance to provide stable and controllable wireless electrical stimulation for PNI repair. The oriented arrangement of cobalt ferrite (CoFe2O4, CFO) fibrous fillers combined with the aligned orientation of polyvinylidene fluoride (PVDF) fibers improves the magnetoelectric conversion performance of the scaffolds, resulting in the generation of approximately 150 mV under a low-frequency alternating magnetic field (150 Oe, 1000 Hz). Gait analysis demonstrated that stable electrical stimulation resulted in significant motor function recovery in rats when using the scaffold wrapped around Gelatin Methacryloyl (GelMA) hydrogel with neural stem cells (NSCs) under an AC magnetic field. Further, single-cell transcriptomics and histological studies confirmed that the scaffold modulates immune activity, promotes nerve growth factor expression, accelerates stem cell differentiation into neurons, and aids tissue regeneration. This study presents a novel wireless charging magnetoelectric (ME) scaffold, contributing to address critical gap in therapeutic strategies for PNI. © 2024, The Authors. All rights reserved.

关键词： Iron compounds

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Performance Assessment for Leader-following UAV Formation via Belief Rule Base

Performance Assessment for Leader-following UAV Formation vi...

引用

Fully Actuated system Theory and Applications (CFASTA), Conference on

作者： Haoran Zhang Mingrui Hao Ruohan Yang Keyuan Yue Zhichao Feng School of Computer Science and Information Engineering Harbin Normal University Harbin P. R. China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory School of Electronics and Information Northwestern Polytechnical University Xi’an P. R. China Department of Missile Engineering Rocket Force University of Engineering Xi’an P. R. China

The article puts focus on the the problem of performance assessment for UAV formation with leader-following structure and switching communication topology in the presence of unknown exogenous perturbations. First, two time-varying indicators are devised on the basis of the regulated outputs of the followers. Then, a trainable performance assessment model is constructed by means of belief rule base expert system (BRB) with the two time-varying indicators as the inputs and the assessment utility as the final output. At last, relevant simulation experiments are conducted to validate the practicability of the established performance assessment model for leader-following UAV formation subject to switching communication topology and unknown external perturbations.

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Effectiveness Evaluation for Clinical Depression Detection Using Deep Learning Based Synthetic House-Tree-Person Test

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chen, Zhuolong Yin, Xiaoqing Yang, Fan Li, Xiaofan Zhao, Zixuan Li, Xueying Liu, Jianghu Zhao, Yubin Xu, Cheng-Zhong Zheng, Fangfang Lin, Yong Sun Yat-Sen University School of Microelectronics Science and Technology 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Department of Psychiatry 0000 China The Fifth Affiliated Hospital of Sun Yat-Sen University Pediatrics Department 0000 China Jinan University School of Intelligent System Science and Engineering 0000 China University of Macau State Key Laboratory of IoTSC Department of Computer and Information Science Macau0000 China

Depression is one of the most common mood disorders and the number of patients increases significantly in recent years. Due to the lack of biomarkers, conversation between patients and psychiatrists is still the main clinical diagnostic method which is easily influenced by subjectivity of both patients and psychiatrists. Synthetic House-tree-person test (S-HTP), a convenient and efficient mental assessment tool, minimizes subjective influences from patients, while its effectiveness is limited by the professional ability of analyst. Here we introduce a deep learning model DeHTP, a flexible and convenient depression detection method based on S-HTP without interaction between people. Experimental results demonstrate that DeHTP achieves 0.963 AUC and 0.9 accuracy, and outperforms the conventional manual analysis of S-HTP, which is conducted on the guideline of 50 conclusions from previous study related to depression. In addition, it reveals 22 depression-correlated drawing features aligned with conclusions above from the perspective of our proposed model. Leveraging the advantages of deep learning and S-HTP, this approach has the potential for widespread promotion and adoption as the available tool for daily self-mental monitoring, as well as the promising auxiliary diagnostic method in clinical. © 2013 IEEE.

关键词： Contrastive Learning

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Quantum reversible circuits for multiplicative inverse

引用

EPJ Quantum Technology 2022年第1期9卷 1-17页

作者： Luo, Qing-bin Yang, Guo-wu Li, Xiao-yu Li, Qiang Department of Computer Science and Technology School of Information Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China

The synthesis of quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are discussed in this paper. We first convert the multiplicative inverse operation in $\operatorname{GF}(2^{8})$ to arithmetic operations in the composite field $\operatorname{GF}((2^{4})^{2})$ , and then discuss the expressions of the square calculation, the inversion calculation and the multiplication calculation separately in the finite field $\operatorname{GF}(2^{4})$ , where the expressions of multiplication calculation in $\operatorname{GF}(2^{4})$ are given directly in $\operatorname{GF}(2^{4})$ and given through being transformed into the composite field $\operatorname{GF}((2^{2})^{2})$ . Then the quantum circuits of these calculations are realized one by one. Finally, two quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are synthesized. They both use 21 qubits, the first quantum circuit uses 55 Toffoli gates and 107 CNOT gates and the second one uses 37 Toffoli gates and 209 CNOT gates. As an example of the application of multiplication inverse, we apply these quantum circuits to the implementations of the S-box quantum circuit of the AES cryptographic algorithm. Two quantum circuits for implementing the S-box of the AES cryptographic algorithm are presented. The first quantum circuit uses 21 qubits, 55 Toffoli gates, 131 CNOT gates and 4 NOT gates and the second one uses 21 qubits, 37 Toffoli gates, 233 CNOT gates and 4 NOT gates. Through the evaluation of quantum cost, the two quantum circuits of the S-box of AES cryptographic algorithm use less quantum resources than the existing schemes.

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