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Judgment of blast furnace iron-tapping status based on data differential processing and dynamic window analysis algorithm

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Progress in Natural Science:Materials International 2023年第4期33卷 450-457页

作者： Youcheng Zong Zixian Wang Xiang Liu Yi Nian Jianian Pan Chaojie Zhang Yingxue Wang Jianhua Chu Liqiang Zhang The Intelligent Metallurgy Research Institute School of Metallurgical Engineering Anhui University of Technology Department of Technical Quality Manufacturing Management Division(Technical Center) Anhui Changjiang Steel Co. Ltd. National Engineering Research Center for Public Safety Risk Perception and Control by Big Data(RPP) China Academic of Electronics and Information Technology

Ironmaking plants hold a core position in steel production. Efficient level detection and iron-tapping status judgment can accurately measure the liquid level and predict the amount of molten iron in the ladle, thereby ensuring production safety and improving production efficiency. At present, the judgment of the start and end statuses of blast furnace iron-tapping mainly relies on manual visual inspection. However, due to the high-temperature environment on-site and interference from iron powder, gunning mud, and other dust, operators find it challenging to make accurate judgments. To address these issues, this study proposes a robust and accurate method for judging the iron-tapping status of blast furnaces. This study collected four million on-site liquid level data, underwent interpolation, noise reduction, normalization, and other preprocessing steps, and then used differential processing to calculate the iron-tapping rate. Combined with dynamic window feature analysis, it effectively captured the change characteristics of the liquid level in the ladle under different iron-tapping *** simulated on-site experiments, the method achieved a 96.6 % accuracy rate for iron-tapping status *** real-time iron-tapping status judgment method based on differential processing of liquid level data and dynamic window feature analysis proposed in this study demonstrates high precision and robustness in the complex environment of ironmaking plants. This technology not only improves the production efficiency of the iron and steel link but also frees on-site operators from the harsh working environment, eliminating human errors in judgments and omissions, further promoting the intelligent process of ironmaking plants.

关键词： Blast furnace iron-tapping Molten iron level Radar level gauge Iron-tapping status judgment Data differential processing Sliding window

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OPS-YOLO: A Lightweight Foreign Objectors Detection Model for Overhead Power System 43

OPS-YOLO: A Lightweight Foreign Objectors Detection Model fo...

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43rd Chinese control Conference, CCC 2024

作者： Liu, Boning Shi, Rui Deng, Heng Zhang, Liguo Beijing University of Technology Faculty of Information Technology Beijing100124 China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing100124 China

ISBN: (纸本)9789887581581

The performance of modern railway transit systems heavily relies on the overhead power system (OPS). However, OPSs built on the ground are vulnerable to foreign objects that can pose a threat to railway safety. In this paper, an improved lightweight detection method called OPS-YOLO, which is based on YOLO-v5, is proposed to detect foreign objects in OPSs. First, the Ghostnet-v2 is improved to compress the lightweight backbone model to slim down the original YOLO-v5 and reduce the complexity and computation. Then, a parameter-free attention mechanism SimAM is introduced to enhance the detection precision of OPS-YOLO. Furthermore, the Focal-EIoU is chosen as the loss function of the OPS-YOLO to improve object border localization precision while accelerating bounding box regression speed. To evaluate the performance of OPS-YOLO, we generate a customized dataset of OPS scenarios by using augmentation methods to expand the existing samples and improve training results. Finally, we extensive experimental tests on the dataset are conducted to verify the proposed method. Results show that the proposed OPS-YOLO improves the mAP by 0.5% compared to the classical YOLO-v5s network. Additionally, OPS-YOLO compresses the model parameters by 37.9% and reduces the model size by 37.5%. It is demonstrated that OPSYOLO can achieve a balance between computational efficiency and detection accuracy, making it suitable for automating OPS supervision and reducing reliance on human inspection. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

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Frontiers of Environmental Science & engineering 2024年第3期18卷 69-82页

作者： Junchen Li Sijie Lin Liang Zhang Yuheng Liu Yongzhen Peng Qing Hu School of Environment Harbin Institute of TechnologyHarbin 150090China School of Environmental Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Engineering Innovation Center of SUSTech(Beijing) Southern University of Science and TechnologyBeijing 100083China Faculty of Environment and Life Beijing University of TechnologyBeijing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of EducationBeijing 100124China

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment *** this study,we aimed to develop an integrated method for predicting effluent COD and NH3 *** employed a 200 L pilot-scale sequencing batch reactor(SBR)to gather multimodal data from urban sewage over 40 *** we collected data on critical parameters like COD,DO,pH,NH_(3),EC,ORP,SS,and water temperature,alongside wastewater surface images,resulting in a data set of approximately 40246 *** we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network(BITF-CL)using this *** innovative model synergized sewage imagery with water quality data,enhancing prediction *** a result,the BITF-CL model reduced prediction error by over 23%compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor ***,this research presents a cost-effective and precise prediction system for sewage treatment,demonstrating the potential of brain-inspired models.

关键词： Wastewater treatment system Water quality prediction Data driven analysis Brain-inspired model Multimodal data Attention mechanism

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research on Fault Diagnosis of Spacecraft control System Based on Graph Neural Network 8

Research on Fault Diagnosis of Spacecraft Control System Bas...

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8th International Conference on intelligent Computing and Signal Processing, ICSP 2023

作者： Ning, Zhicheng Liu, Xiaoxiang Li, Xinyuan Liu, Jun Beijing Insitute of Control Engineering Science and Technology on Space Intelligent Control Laboratory Beijing China Beijing University of Posts and Telecommunications Intelligent Perception and Computing Research Center Beijing China

ISBN: (纸本)9798350302455

The fault diagnosis technology of spacecraft control systems is a crucial component of spacecraft health management. Traditional fault diagnosis technologies, such as model-based diagnosis and signal processing, are relatively mature but are limited in their ability to fully utilize information in big data environments. Neural network-based fault diagnosis methods often overlook the sequential nature of spacecraft data as time series data. Given that the state of a spacecraft at any given time is closely related to preceding data, it is possible to conduct fault diagnosis by extracting relevant features from fault sample data. In this paper, we propose a graph neural network-based fault diagnosis method for spacecraft control systems. To address the issue of limited fault samples, we construct a spacecraft control system model and generate various types of gyro and flywheel fault data through fault injection. Utilizing the Graphstar model - a powerful graph neural network with strong information mining capabilities - we transform one-dimensional fault data into graph data with spatial structure and extract relevant characteristics through deep learning to diagnose faults. Simulation results demonstrate that our method can effectively diagnose faults with high accuracy. © 2023 IEEE.

关键词： Failure analysis

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Event-triggered-based self-organizing fuzzy neural network control for the municipal solid waste incineration process

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Science China(Technological Sciences) 2023年第4期66卷 1096-1109页

作者： HE HaiJun MENG Xi TANG Jian QIAO JunFei Faculty of Information Technology Beijing University of TechnologyBejing 100124China Beijing Laboratory of Smart Environmental Protection Beijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Bejing 100124China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Bejing 100124China Xi'an Institute of Electromechanical Information Technology Xi'an 710065China

Due to the large uncertainty in the municipal solid waste incineration(MSWI)process,the furnace temperature of the MSWI process is difficult to control and the controller is updated *** improve the accuracy and reduce the number of controller updates,a novel event-triggered control method based correntropy self-organizing TS fuzzy neural network(ETCSTSFNN)is ***,the neurons of the rule layer are grown or pruned adaptively based on activation intensity and control error to meet the dynamic change of the actual operating ***,the performance index is designed based on the correntropy of tracking errors,and the parameters of the controller are adjusted by gradient descent ***,a fixed threshold event-triggered condition is designed to determine whether the current controller is updated or *** stability of the control system is proved based on the Lyapunov stability ***,the furnace temperature control experiments are conducted based on the actual data of a municipal solid waste incineration plant in *** experimental results show that the proposed ET-CSTSFNN controller shows a better control performance,which can reduce the number of the controller update significantly while achieving accurate furnace temperature control compared with other traditional control methods.

关键词： municipal solid waste incineration furnace temperature fuzzy control event-triggered

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Distributed robust matrix-scaled consensus control of perturbed multi-agent systems

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control Theory and Technology 2024年第4期22卷 623-637页

作者： Bin Cheng Gang Li Kun An Chunhui Yu Department of Control Science and Engineering Tongji UniversityShanghai201804China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghai201804China Shanghai Sunshine Rehabilitation Center Shanghai201613China

Distributed matrix-scaled consensus is a kind of generalized cooperative control problem and has broad applications in the field of social network and *** paper addresses the robust distributed matrix-scaled consensus of perturbed multi-agent systems suffering from unknown *** discontinuous protocols are first proposed to drive agents to achieve cluster consensus and suppress the effect of *** protocols with time-varying gains obeying differential equations are also designed,which are completely distributed and rely on no global *** the boundary layer technique,smooth protocols are proposed to avoid the unexpected chattering effect due to discontinuous *** a cost,under the designed smooth protocols,the defined matrix-scaled consensus error tends to a residual set rather than zero,in which the residual bound is arbitrary small by choosing proper ***,distributed dynamic event-based matrix-scalar consensus controllers are also proposed to avoid continuous *** examples are provided to further verify the designed algorithms.

关键词： Matrix-scaled consensus Perturbed multi-agent systems Adaptive control Distributed control Event-triggered control

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Deep Learning Algorithm for Person Re-Identification Based on Dual Network Architecture

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Computers, Materials & Continua 2025年第5期83卷 2889-2905页

作者： Meng Zhu Xingyue Wang Honge Ren Abeer Hakeem Linda Mohaisen College of Information Engineering Harbin UniversityHarbin150086China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin UniversityHarbin150086China College of Computer and Control Engineering Northeast Forestry UniversityHarbin150040China Heilongjiang Forestry Intelligent Equipment Engineering Research Center Northeast Forestry UniversityHarbin150040China Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method shows superior outcomes on widely recognized benchmarks,which are the Market-1501,MSMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.

关键词： Person reidentification ReID computer vision self-attention spatial-temporal information

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Seek-and-Take Games of Heterogeneous Agent Teams With Large Language Model

Seek-and-Take Games of Heterogeneous Agent Teams With Large ...

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2024 China Automation Congress, CAC 2024

作者： Li, Haolin Yi, Peng Wei, Dixiao Bai, Wenyan The Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201203 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China Beijing Aerospace Automatic Control Institute Beijing100854 China

ISBN: (纸本)9798350368604

The problem of confrontation games between multi-agent teams has attracted considerable interest, and the question of how to ensure effective coordination of heterogeneous agents in dynamic and adversarial environments remains a significant challenge. In this paper, we present a decision-making methodology for a confrontation game between two teams, which is based on large language models (LLMs). The game is designed as a competitive Seek-and-Take task comprising three distinct robot types: reconnaissance, grabbing, and blocking robots. The LLMs enhance the robot team’s decision-making efficiency, delivering high-level commands that significantly improve coordination and adversarial performance. The game is initially formalized as a partially observable Markov decision process. Subsequently, a systematic construction of the LLM agents is provided, enabling real-time decisions based on the state information of a heterogeneous robot team (HRT). Detailed prompt design and the "Zero-shot-CoT" procedure are provided, and a cue word iteration method with reflections is proposed as a means of enhancing the decision-making efficiency of the LLMs and generating optimal collaboration strategies. Finally, the efficacy of the methodology is validated through experimentation with multiple LLMs in a multi-robot simulation environment. The results demonstrate that LLMs improve the efficiency and adaptability of decision-making in dynamic environments by serving as a core decision module, generating optimal strategies through real-time analysis. © 2024 IEEE.

关键词： Efficiency

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Enhanced Nature Inspired-Support Vector Machine for Glaucoma Detection

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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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Chimera-inspired dynamics: When higher-order interactions are expressed differently

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Physical Review E 2024年第4期110卷 044204页

作者： Xinrui Ji Xiang Li Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems The Frontiers Science Center for Intelligent Autonomous Systems and The State key laboratory of Intelligent Autonomous Systems Department of Control Science and Engineering College of Electronics and Information Engineering

The exploration of chimera-inspired dynamics in nonlocally coupled networks of Kuramoto oscillators with higher-order interactions is still in its nascent stages. Concurrently, the investigation of collective phenomena in higher-order interaction networks is gaining attraction. Here, we observe that hypergraph networks tend to synchronize through lower-order interactions, whereas simplicial complex networks exhibit a preference for higher-order interactions. This observation suggests that higher-order representations manifest substantial differences in chimera-inspired synchronization regions. Moreover, we introduce an explicit expression for identifying the chimera state. With a comprehensive basin stability analysis and the interplay of pairwise and higher-order interaction strengths, the emergence of the chimera state is inherent in high-order interaction networks. Our findings contribute to the understanding of chimera-inspired dynamics in higher-order interaction networks.

关键词： Chimera states Continuous phase transition Coupled oscillators Synchronization Synchronization transition

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