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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

Computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized Support Vector Machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

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Asymmetric Dual-Stream Networks for Lightweight RGB-D Salient Object Detection

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Journal of Shanghai Jiaotong University (Science) 2025年 1-11页

作者： Wang, Yan Zheng, Wanlu Xia, Yaozheng Wang, Shaorong School of Information Science and Technology Beijing Forest University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China Beijing Virtual Simulation and Visualization Engineering Center Beijing100871 China

Integrating image and depth information for RGB-D salient object detection has become a research hotspot in the field of saliency detection. Balancing the efficiency and performance of salient object detection models under resource constraints is a key challenge. To address this, this paper proposes an asymmetric lightweight network suitable for real-time RGB-D salient object detection tasks. The network reduces the number of network parameters by designing different lightweight feature extraction networks for different input modalities. Additionally, a multi-modal feature enhancement fusion module is designed to effectively fuse multi-modal features while compensating for the information loss caused by the lightweight backbone network. Moreover, this paper utilizes a global context module for dense decoding, aggregating local and global information of multi-scale features without significantly increasing computational complexity. The experimental results on five benchmarks show that the proposed lightweight RGB-D salient object detection network not only outperforms most mainstream models quantitatively and qualitatively, but also significantly outperforms other models in terms of efficiency, only with a parameter count of 5.1 million and a computational load of 0.77 gigaflops. This achievement validates the proposed method’s ability to achieve lightweight salient object detection while maintaining high efficiency. © Shanghai Jiao Tong University 2024.

关键词： Object recognition

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D-Shaped Tellurite Photonic Crystal Fiber Hydrogen and Methane Sensor Based on Four-Wave Mixing With SPR Effect

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Photonic Sensors 2023年第1期13卷 25-39页

作者： Hai LIU Bo WU Cong CHEN Benlei ZHAO Xu ZHANG Hancheng ZHANG School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China Engineering Research Center of Intelligent Control for Underground Space(Ministry of Education) China University of Mining and TechnologyXuzhou 221116China

A new D-shaped tellurite photonic crystal fiber sensor based on the four-wave mixing(FWM)effect with the surface plasmon resonance(SPR)effect is designed and *** substrate of the D-shaped photonic crystal fiber(D-PCF)is tellurite glass,and the polished surface is plated with the gold film and hydrogen gas-sensitive *** air hole of the inner cladding,which is plated with the gold film and methane gas-sensitive film,is selected as the second sensing channel to simultaneously measure the concentration of hydrogen and *** on the four-wave mixing,the wavelength shifts of the Stokes and anti-Stokes spectra resulting from the variation of the gas concentration can be used to accurately detect the concentrations of methane and ***,it is found that the SPR effect can increase the wavelength shifts,which means the sensitivity of methane and hydrogen *** parameter optimization,the maximum sensitivities of methane and hydrogen are 4.03 nm/%and-14.19 nm/%,*** the linearities are up to 99.9%.The resolution of methane is 1.25×10^(-2)%and hydrogen is 7.14×10^(-3)%.Moreover,the fiber length of this sensor is only 20mm,which is conducive to the construction of a compact or ultra-compact embedded FWM fiber sensor.

关键词： Four-wave mixing surface plasmon resonance D-shaped photonic crystal fiber gas sensing

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Data-Driven control of Distributed Event-Triggered Network Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 351-364页

作者： Xin Wang Jian Sun Gang Wang Frank Allgower Jie Chen the National Key Laboratory of Autonomous Intelligent Unmanned Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the Institute for Systems Theory and Automatic Control University of StuttgartStuttgart 70550Germany the Department of Control Science and Engineering Tongji UniversityShanghai 201804 the National Key Laboratory of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China

The present paper deals with data-driven event-triggered control of a class of unknown discrete-time interconnected systems(*** systems).To this end,we start by putting forth a novel distributed event-triggering transmission strategy based on periodic sampling,under which a model-based stability criterion for the closed-loop network system is derived,by leveraging a discrete-time looped-functional *** the model-based criterion with a data-driven system representation recently developed in the literature,a purely data-driven stability criterion expressed in the form of linear matrix inequalities(LMIs)is ***,the data-driven stability criterion suggests a means for co-designing the event-triggering coefficient matrix and the feedback control gain matrix using only some offline collected state-input ***,numerical results corroborate the efficacy of the proposed distributed data-driven event-triggered network system(ETS)in cutting off data transmissions and the co-design procedure.

关键词： Data-driven control distributed event-triggered network system(ETS) linear matrix inequalitie(LMI) looped-functional stability

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Robust Multi-agent Federated Reinforcement Learning for Task Offloading 7th

Robust Multi-agent Federated Reinforcement Learning for Task...

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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Yan, Dibao Wang, Yongfeng Hou, Wenjing Song, Huanhuan Wen, Hong Ma, Wendi Sun, Fan School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

With the proliferation of various mobile smart devices, edge computing and computational offloading technologies have emerged as pivotal support mechanisms, enhancing the service quality of these devices. To facilitate the learning of task offloading strategies across diverse and complex scenarios, this study introduces a federated learning strategy algorithm based on multi-agent deep reinforcement learning. This algorithm aims to aggregate the training strategies of multiple edge computing devices, thereby enabling the synthesis of superior task offloading strategies tailored to a wide array of environments while also providing safeguards against malicious nodes. This paper specifically examines the reward inversion attack, illustrating the algorithm’s capability to identify and counteract such malicious threats effectively. Experimental results validate that the proposed algorithm not only robustly defends against these attacks but also adeptly learns the task offloading strategies pertinent to each node. © The Author(s) 2025.

关键词： Federated learning

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Pressure Ulcer Stage Evaluation Algorithm Based on Multi-Scale Attention Residual Network 8

Pressure Ulcer Stage Evaluation Algorithm Based on Multi-Sca...

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8th International Symposium on Computer Science and intelligent control, ISCSIC 2024

作者： Geng, Yanli Sun, Hao Li, Zelei Yang, Shucai Hebei University of Technology Enginneering Research Center of Intelligent Rehabilitation Device Detection Technology Ministry of Education School of Artificial Intelligence Control Engineering Technology Innovation Center of Hebei Province Tianjin China Tianjin Magic Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798350380286

Pressure ulcer, alternatively referred to as a pressure injury, mostly occurs in long-term bedridden patients. This issue has emerged as a significant global health concern. Accurate diagnosis is the key to the treatment of pressure sores in clinical application scenarios. Conventional contact-based artificial pressure ulcer detection methods can cause discomfort to patients and potentially elevate the risk of infection. In response to these problems, a pressure ulcer stage evaluation algorithm based on multi-scale attention residual network was proposed. The residual network was used as the basic framework, the stage 0 was improved to enhance the network's nonlinear expressive capacity, and the residual structure was improved to enhanced the model's generalization capability, the FPN feature pyramid was added to fuse the high-level features with the underlying features to achieve feature pyramid feature extraction. The incorporation of the attention mechanism enhanced the model's overall performance and recognition accuracy. The pressure damage stage dataset was used in an experiment to validate the model's performance. The experimental outcomes demonstrate that the proposed method could achieve the precision, recall, F1-score and accuracy of the algorithm of 82.64 %, 83.26 %, 0.83 and 82.37% respectively. Compared with other deep learning models, this proposed algorithm can improve the accuracy and identify the pressure ulcer effectively. © 2024 IEEE.

关键词： Deep learning

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Memory enhancement of the new tryptamine-like components in the walnut kernel

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Food Bioscience 2023年第2期52卷 396-403页

作者： Yida Xu Yongli Ye Xiulan Sun State Key Laboratory of Food Science and Technology School of Food Science and TechnologyNational Engineering Research Center for Functional FoodSynergetic Innovation Center of Food Safety and Quality ControlJiangnan

Walnut kernel has been applied in daily diets for thousands of years with its memory enhancement *** further observing the effect of memory enhancement,the walnut kernel was extracted and separated into active parts,with the evaluation in mice.30%ethanol elution from AB-8 macroporous resin showed a decreased escape latency time and increased cumulative time and crossing frequency through the Morris *** also increased the latency time and decreased jumps in the step-down *** results indicated the memory enhancement effect of the *** further separation and purification,four compounds were acquired with tryptamine-like structures first found in the walnut kernel,and compounds III and IV were new *** III showed decreased escape latency time,increased cumulative time,increased crossing frequency through the Morris test,and increased latency time,decreased jumps in the step-down *** III also alleviated acetylcholinesterase activity and promoted acetylcholine transferase *** silico docking and molecular dynamic studies displayed the most polar interactions of compound III on 5-hydroxytryptamine receptor 4(5-HTR4)with the most polar interactions and the lowest binding energies,and anti-degeneration effect to monoamine oxidase A(MAO-A)with the high binding energy and sizeable steric *** results illustrated the memory enhancement of compound III in the walnut kernel,indicating more potential activities of tryptamine-like components and more applications for walnut kernel in clinical use or daily diets.

关键词： Walnut kernel Mice Evaluation Tryptamine

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Univariate imputation method for recovering missing data in wastewater treatment process

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Chinese Journal of Chemical engineering 2023年第1期53卷 201-210页

作者： Honggui Han Meiting Sun Huayun Han Xiaolong Wu Junfei Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing 100124China Engineering Research Center of Digital Community Ministry of EducationBeijing 100124China

High-quality data play a paramount role in monitoring,control,and prediction of wastewater treatment process(WWTP)and can effectively ensure the efficient and stable operation of *** values seriously degrade the accuracy,reliability and completeness of the data quality due to network collapses,connection errors and data transformation *** these cases,it is infeasible to recover missing data depending on the correlation with other *** tackle this issue,a univariate imputation method(UIM)is proposed for WWTP integrating decomposition method and imputation ***,the seasonal-trend decomposition based on loess method is utilized to decompose the original time series into the seasonal,trend and remainder components to deal with the nonstationary characteristics of WWTP ***,the support vector regression is used to approximate the nonlinearity of the trend and remainder components respectively to provide estimates of its missing values.A self-similarity decomposition is conducted to fill the seasonal component based on its periodic ***,all the imputed results are merged to obtain the imputation ***,six time series of WWTP are used to evaluate the imputation performance of the proposed UIM by comparing with existing seven methods based on two *** experimental results illustrate that the proposed UIM is effective for WWTP time series under different missing ***,the proposed UIM is a promising method to impute WWTP time series.

关键词： Univariate Self-similarity Waste water Algorithm Integration

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An Offline Learning-Based Anti-interference Communication Scheme for UAV Networks 7th

An Offline Learning-Based Anti-interference Communication Sc...

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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei Shi, Weihong Peng, Yulin School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent IoT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Unmanned Aerial Vehicles (UAV) face challenges from advanced interference technologies, making them susceptible to malicious node attacks, data interception, and tampering. Traditional anti-interference decisions have limitations, as they cannot adaptively adjust to changes in interference signals. Moreover, anti-interference communication models based on Deep Reinforcement Learning (DRL) require prolonged interactions with the environment, demanding high requirements for anti-interference environments. This paper investigates an offline anti-interference decision based on Decision-Transformers, which can quickly and stably acquire practical anti-interference decision models. Simulation experiments have verified the effectiveness of this algorithm in making anti-interference decisions under AWGN and fading channel conditions. Furthermore, this offline approach can achieve the expected reward targets with fewer training iterations. © The Author(s) 2025.

关键词： Deep reinforcement learning

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Adaptive Communication Spectrum Sensing Algorithm Based on Energy Detection 7th

Adaptive Communication Spectrum Sensing Algorithm Based on E...

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7th International Conference on Wireless Communications, Networking and Applications, WCNA 2023

作者： Shi, Weihong Tang, Tao Zhao, Runhui Wen, Hong Feng, Xuewei School of Aeronautics and Astronautics University of Electronic Science and Technology of China Chengdu611731 China Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province Chengdu611731 China Sichuan Intelligent loT Communication Technology Engineering Research Center UESTC Chengdu China

ISBN: (纸本)9789819624089

Based on the traditional energy detection algorithm, an adaptive communication spectrum sensing algorithm based on energy detection is proposed for signals whose noise power is difficult to be estimated accurately. The algorithm takes into account the problem of large error in calculating the energy detection judgment threshold by noise power when the estimated value of noise power is inaccurate, and is able to perform adaptive communication spectrum sensing for signals with less a priori information and noise power that cannot be estimated accurately. Simulation results show that the adaptive communication spectrum sensing algorithm based on energy detection can effectively improve the performance of spectrum sensing for signals, and the algorithm can effectively detect and sense signals under the condition of reasonable setting of judgment coefficients. © The Author(s) 2025.

关键词： Adaptive Cognitive radio Energy detection Spectrum sensing

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