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A novel clinical artificial intelligence model for disease detection via retinal imaging

The Innovation 2024年第2期5卷 11-12页

作者： Yidian Fu Liang Ma Sheng Wan Shengfang Ge Zhi Yang Department of Ophthalmology Ninth People’s HospitalShanghai JiaoTong University School of MedicineShanghai 200011China Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai 200011China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing 210000China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210000China

Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of *** AI algorithms have been developed for disease diagnosis and prediction and treatment recommendation across various clinical data types,e.g.,chest X-rays,electrocardiograms,and other radiological images.1 In ophthalmology,particularly,great progress has been made in AI systems over the past *** fundus photography(CFP)and optical coherence tomography(OCT),which are readily available in routine clinical practice,are both mainstream and useful retinal imaging modalities in *** September 2023,the 2023 Lasker-Debakey Clinical Medical Research Award was awarded to three scientists for their work on OCT for accurate retinal disease detection.

关键词： clinical routine diagnosis

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A parallel tabu search algorithm based on uniform sampling for generalized job-shop scheduling problems 50

A parallel tabu search algorithm based on uniform sampling f...

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50th International Conference on computers and Industrial Engineering: Sustainable Digital Transformation, CIE 2023

作者： Gui, Lin Li, Xinyu Zhang, Qingfu Gao, Liang State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong 999077 Hong Kong

ISBN: (纸本)9781713886952

Shop scheduling system plays a very important role in production. However, there are different shop scheduling problems in different enterprises, or even in different workshops of the same enterprise. Therefore, to improve the development efficiency and reduce the development cost of a shop scheduling system, it is necessary to design an algorithm that can solve different scheduling problems effectively. This paper proposes a parallel tabu search algorithm based on uniform sampling for the generalized job-shop scheduling problem, that is, the proposed algorithm can solve a set of job-shop scheduling problems with different production constraints or production objectives effectively. Firstly, by analyzing the characteristics of the job-shop scheduling problem and the existing algorithms, the shortcomings of the existing research were pointed out. Then a uniform sampling method is proposed for the problem. After that, the proposed algorithmic framework is introduced. The algorithm is compared with the other two state-of-the-art algorithms on three different job-shop scheduling problems. The experimental results show that the proposed algorithm has an excellent performance, including optimality and stability, on these three problems. The limitations of the algorithm and future research directions are also given in the paper. © 2023 computers and Industrial Engineering. All rights reserved.

关键词： Tabu search

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection 30

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Li, Juan Li, Rui Lu, Youshui Cai, Bowen Cao, Yulin Lin, Hang Li, Xingxing Li, Chan Qinghai Normal University School of Computer Science Xining China Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University School of Computer Science and Technology Xian China School of the Instrument Science and Technology School of Electrical Engineering Xi'an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis United States

ISBN: (纸本)9798331515966

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model's feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness. © 2024 IEEE.

关键词： Deep learning

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Spectral period doubling and encoding of dissipative optical solitons via gain control

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PhotoniX 2024年第1期5卷 290-305页

作者： Kangwen Yang Yi Zhou Yuqing Ling Kevin K.Tsia Heping Zeng Kenneth K.Y.Wong Shanghai Key Laboratory of Modern Optical System and Engineering Research Center of Optical Instrument and SystemMinistry of EducationSchool of Optical Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Electronic Engineering The University of Hong KongHong KongChina Advanced Biomedical Instrumentation Centre Hong Kong Science ParkHong KongChina State Key Laboratory of Precision Spectroscopy East China Normal UniversityShanghai 200062China

Period-doubling bifurcation,as an intermediate state between order and chaos,is ubiquitous in all disciplines of nonlinear ***,previous experimental observations of period doubling in ultrafast fiber lasers are mainly restricted to self-sustained steady state,controllable manipulation and dynamic switching between period doubling and other intriguing dynamical states are still largely ***,we propose to expand the vision of dissipative soliton periodic doubling,which we illustrate experimentally by reporting original spontaneous,collisional,and controllable spectral period doubling in a polarization-maintaining ultrafast fiber ***,the spontaneous period doubling can be observed in both single-and *** mechanism of the switchable state and periodic doubling was revealed by numerical ***,state transformation of individual solitons can be resolved during the collision of triple solitons involving stationary,oscillating,and period ***,controllable deterministic switching between period doubling and other dynamical states,as well as exemplifying the application of period-doubling-based digital encoding,is achieved under programmable pump *** results open a new window for unveiling complex Hopf bifurcation in dissipative systems and bring useful insights into nonlinear science and applications.

关键词： Period doubling Mode locking Ultrafast fiber lasers

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Online crowdsourced truck delivery using historical information

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期301卷 486-501页

作者： Zhang, Huili Luo, Kelin Xu, Yao Xu, Yinfeng Tong, Weitian [a]School of Management Xi’an Jiaotong University and State Key Lab for Manufacturing Systems Engineering Xi’an 710049 China [b]Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven the Netherlands [c]Department of Computer Science Georgia Southern University Statesboro GA 30458 USA

Various crowdsourced logistics platforms are forming rapidly along with the booming mobile Internet. Motivated by modern crowdsourced truck logistics platforms, we introduce the online crowdsourced truck delivery (OCTD) problem and reformulate it as the online bipartite hyper-matching problem. We then explore the possibility of accommodating historical information to design efficient online algorithms to serve online orders. To the best of our knowledge, it is the first work on incorporating historical information to solve the online bipartite hyper-matching problem. Depending on whether orders can be partially served, we investigate two practical situations, i.e. separable and inseparable cases. For the inseparable case, we propose a randomized online algorithm, named HYPER-MATCHING , whose competitive ratio is a non-decreasing function of the amount of historical information. For the separable case, we modify HYPER-MATCHING to present another randomized online algorithm, named SEPARABLE-HYPER-MATCHING . It is worth noting that the competitive ratios of HYPER-MATCHING and SEPARABLE-HYPER-MATCHING either beat or match the current best online algorithms when no historical information is considered. We then present four computationally efficient heuristic algorithms, including a greedy variant and a batch processing variant for each of the inseparable and separable cases. We perform a sequence of experiments using synthetic and real-world datasets, with an emphasis on the influence that historical information has on algorithm performance. The experiment results demonstrate the effectiveness of our algorithms and particularly the positive influence of historical information on our algorithms.(c) 2021 Elsevier B.V. All rights reserved.

关键词： Transportation Online crowdsourced truck delivery Online bipartite hyper-matching Random order model Historical information

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Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management

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Journal of Modern Power systems and Clean Energy 2022年第3期10卷 743-754页

作者： Chao Huang Hongcai Zhang Long Wang Xiong Luo Yonghua Song IEEE State Key Laboratory of Internet of Things for Smart City University of MacaoMacao S.A.R.China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate School University of Science and Technology BeijingFoshan 528399China State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.China

This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household ***-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV ***,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous ***,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)*** DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous *** MDRL algorithm learns optimal strategy by trialand-error interactions with the ***,unsafe actions,which violate system constraints,can give rise to great *** handle such problem,a safe-MDRL algorithm is further *** studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy *** proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test ***,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

关键词： Demand response deep reinforcement learning discrete-continuous action space home energy management safe reinforcement learning

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Quantification of valve stiction in control loops using adaptive differential evolution algorithm

Quantification of valve stiction in control loops using adap...

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2024 China Automation Congress, CAC 2024

作者： Yang, Dong Lang, Xun Wu, Jiande Yu, Wanke Liu, Songhua Xie, Lei Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China School of Automation China University of Geosciences Wuhan China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368604

Accurately and promptly quantifying stiction is crucial for valve maintenance in industrial settings and ensuring the stability of control loop performance. However, current Hammerstein-based stiction quantification methods overlook valve dynamics, potentially leading to inaccurate quantification. Therefore, this paper proposes a novel stiction quantification method based on an adaptive differential evolution algorithm. In addition to considering the impact of valve dynamics on stiction, the proposed method demonstrates a certain level of robustness against the interference of unknown noise. Finally, the effectiveness of the proposed method is demonstrated through a series of simulations and industrial case studies. © 2024 IEEE.

关键词： Adaptive algorithms

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Experimental study on gas production and solution composition during the interaction of femtosecond laser pulse and liquid

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Chinese Physics B 2022年第7期31卷 17-22页

作者： Yichun Wang Han Wu Wenkang Lu Meng Li Ling Tao Xiuquan Ma The State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and EngineeringHuazhong University of Science and Technology(HUST)Wuhan 430074China Guangdong Intelligent Robotics Institute Dongguan 523808China Department of Cardiology Xijing HospitalThe Fourth Military Medical UniversityXi’an 710032China

The process of ionizing normal saline induced by femtosecond laser is studied from the perspective of gas production rate and *** the repetition rate is less than 1000 Hz,each laser pulse independently generates ionization *** the same time,we discovered the inhibitory effect of meglumini diatrizoici on the ionization process and explained the reasons for this ***,the gas composition proved that the primary gas production mechanism of the femtosecond laser is the decomposition of water molecular,and the composition of the solution after the reaction proved the decomposition effect of the laser on meglumine.

关键词： femtosecond laser ionization gas production rate meglumini diatrizoici

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A Double-Side Self-Tuning IPT Converter Immune to Parameters Variation Under Misalignment Condition 50

A Double-Side Self-Tuning IPT Converter Immune to Parameters...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zou, Bowei Huang, Zhicong Iam, Io-Wa Chi-Seng, Lam Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China Institute of Microelectronics University of Macau China Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781665464543

For inductive power transfer (IPT) systems, the loosely coupled transformer (LCT) is a crucial component. Variations in the air gap can lead to fluctuations in the parameters of the LCT, such as self-inductance and mutual inductance, caused by positional deviations of the ferrite cores on both sides. However, such variable LCT parameters can damage the resonant tank, thus impacting transmission efficiency and output stability. To address this issue, this paper proposes a self-tuning S/S IPT system based on switch-controlled capacitors (SCCs). The system utilizes gradient descent methods independently on double-side to achieve decoupled tuning control. This approach effectively mitigates the impact of LCT parameter fluctuations without requiring wireless communication. Furthermore, all switches in the system operate at fixed frequencies and implement soft switching. Finally, a prototype is constructed to validate the effectiveness of the proposed system. © 2024 IEEE.

关键词： Energy transfer

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Unveiling Context-Related Anomalies: Knowledge Graph Empowered Decoupling of Scene and Action for Human-Related Video Anomaly Detection

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Chen, Chenglizhao Liu, Xinyu Song, Mengke Li, Luming Yuan, Shaojiang Yu, Xu Pang, Shanchen Qingdao Institute of Software College of Computer Science and Technology China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software China State Key Laboratory of Chemical Safety China

Video anomaly detection methods are mainly classified into two categories based on their primary feature types: appearance-based and action-based. Appearance-based methods rely on low-level visual features like color, texture, and shape, learning patterns specific to training scenes. While effective in familiar settings, they struggle with unknown or altered scenes due to poor generalization and limited understanding of action-scene relationships. In contrast, action-based methods focus on detecting action anomalies but often overlook contextual scene associations, leading to misjudgments (e.g., running on a street being deemed normal without considering scene context). To overcome these limitations, we propose a novel decoupling-based anomaly detection architecture (DecoAD). Its core lies in the decoupling and interweaving of scenes and actions, enabling explicit modeling of their complex relationships. By reconstructing these interactions using knowledge graphs, DecoAD achieves a deeper understanding of behaviors and contexts. This design ensures strong performance in both known and unknown scenarios, significantly enhancing generalization. To evaluate its effectiveness in dynamic scenes and its ability to handle scene-related anomalies, we introduce UFSR, the first video anomaly detection dataset featuring dynamic scenes and scene-related anomalies. DecoAD supports fully-supervised, weakly-supervised, and unsupervised settings, improving AUC on UBnormal by 1.1%, 3.1%, and 2.1% in fully-supervised, weakly-supervised, and unsupervised settings, and on UFSR by 1.2% and 8.2% in weakly-supervised and unsupervised settings. The source code and datasets are available at: https://***/liuxy3366/DecoAD. © 1991-2012 IEEE.

关键词： Video analysis

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