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BioSensors Conference (BioSensors), IEEE

作者： Noppasin Ajchareeyasoontorn Jade Udomkittivorakul Tatinee Changlor Chanyanut Rungchaiwattanakul Jenyuk Lohwacharin Charusluk Viphavakit International School of Engineering (ISE) Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Environmental Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Intelligent Control Automation of Process Systems Research Unit Faculty of Engineering Chulalongkorn University Bangkok Thailand

An optical fiber-based sensor in the configuration of single-multi-single (SMS) modes structure is proposed to detect polyethylene microplastic particles inside aquatic environments via the interaction of light at the interface of optical fiber and the microplastic solutions. Microplastics negatively affect biological systems since living creatures may consume the pollutant. The data on the relationship between the transmittance spectrum and its corresponding wavelength value can be used to correlate and identify the concentration of the microplastic solution. Deposition of gold on the sensing region through sputtering is investigated to improve the sensitivity and reliability of the SMS sensor. This suggests the potential for a gold-coated SMS sensor for in-situ, low-cost and real-time microplastic sensing applications.

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Classification of skin lesions using morpho-granulometry features derived from color clusters

Classification of skin lesions using morpho-granulometry fea...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics "Dunarea de Jos" University of Galati Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics The Modelling & Simulation Laboratory "Dunarea de Jos" University of Galati Galati Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

The skin lesion can be thought of as a biological system, so the morpho-granulometry of significant color clusters found in skin lesions is one of the elements that reproduce in a natural way the structure of the lesion, this novelty is highlighted in this study. Important features of skin lesions can be modulated by fusing neural networks (NN) and machine learning (ML). By choosing the nevus and melanoma classes, the primary goal was accomplished, and three databases were used to test the methodology. The characteristics based on morpho-granulometry allowed for the identification of microstructure within the images, which can be very helpful in characterizing the biological system. Based on random forest (RF) and extreme gradient boosting (XGboost) classifiers, this work aimed to improve the classification performance of important feature selection. The selected features from three free image databases with three NNs were classified. In a binary classification of nevus vs. melanoma, the results showed that the pattern recognition neural network (PRNN), according to the PH2 database, provided an accuracy of 0.923 and an F1-score of 0.876. The classification is interpretable if it is not validated. In our study, the best results were verified with a logistic regression (LR) classifier.

关键词： Accuracy Recurrent neural networks Image color analysis Melanoma Biological systems Artificial neural networks Feature extraction Skin Lesions Classification tree analysis

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Linear parameter-varying model predictive control for nonlinear systems using general polytopic tubes

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Automatica 2024年 160卷

作者： Abbas, Hossam Seddik Institute for Electrical Engineering in Medicine University of Lübeck Moislinger Allee 53-55 Lübeck23558 Germany Automation and Control Group Department of Electrical and Information Engineering Faculty of Engineering Kiel University Kaiserstraße 2 Kiel24143 Germany

This paper deals with model predictive control (MPC) for nonlinear systems using linear-parameter varying (LPV) embedding of the nonlinear dynamics (LPVMPC). The proposed LPVMPC can incorporate information of the future evolution of the scheduling parameter over the MPC prediction horizon with uncertainty bounds, which are used to construct anticipated scheduling tubes for robustification. Therefore, this approach is less conservative than the methods that only consider knowledge of the bounds on the scheduling parameter's rate of variation. The scheduling tubes are employed to synthesize online general polytopic invariant state tubes. The optimization problem of the proposed LPVMPC is a single quadratic program. Recursive feasibility is proven. A numerical example is presented for demonstrating the effectiveness of the proposed LPVMPC algorithm compared to nonlinear MPC and other standard approaches. © 2023 Elsevier Ltd

关键词： Model predictive control

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A Dynamic Event-Triggered Transmission Strategy for Distributed Convex Optimization 42

A Dynamic Event-Triggered Transmission Strategy for Distribu...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Pengfei Liu, Kun-Zhi Zhang, Dianfeng Du, Shengli Dalian University of Technology Academy of Control Science and Engineering Dalian116024 China School of Mathematics and Informational Science Yantai University Yantai100124 China College of Automation Faculty of Information Technology Beijing University of Technology Yantai100124 China

ISBN: (纸本)9789887581543

A dynamic event-triggered transmission strategy is considered for the continuous-time distributed convex optimization algorithm over a strongly connected and weight-balanced directed graph. An auxiliary variable is introduced in the dynamic event-triggered control (DETC) that is free of Zeno phenomenon. We model the DETC system into a hybrid system. Using Lyapunov stability theory, we give a detailed proof for convergence of the continuous-time distributed convex optimization algorithm. Finally, a numerical simulation shows that the DETC can effectively save network communication resources compared with a static event-triggered control (SETC). © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Convex optimization

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AirVista: Empowering UAVs with 3D Spatial Reasoning Abilities Through a Multimodal Large Language Model Agent 27

AirVista: Empowering UAVs with 3D Spatial Reasoning Abilitie...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Lin, Fei Tian, Yonglin Wang, Yunzhe Zhang, Tengchao Zhang, Xinyuan Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Capital University of Economics and Business Beijing100070 China University of Chinese Academy of Sciences Department of Artificial Intelligence Beijing100049 China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Department of Engineering Science 999078 China

ISBN: (纸本)9798331505929

In urban environments, complex transportation demands, including land, air, and maritime transport, are increasingly growing. While significant advancements have been made in land transportation and autonomous driving, research on urban air mobility (UAM) systems is still in its early stages. This paper presents an innovative urban air unmanned aerial vehicle (UAV) framework: AirVista, which is designed and built based on the Artificial Systems, Computational experiments, and Parallel execution (ACP) approach, integrated with a multimodal large language model (MLLM) agent. Considering that UAM tasks often require UAVs to possess fine-grained spatial perception and reasoning capabilities, and given that existing MLLMs are somewhat lacking in exploring 3D spaces, this paper further proposes an instruction fine-tuning strategy integrated with 3D spatial knowledge, which has been validated experimentally for its effectiveness. Additionally, to enhance the understanding of the efficiency of MLLM for UAV tasks, this paper delves into prompt fine-tuning templates tailored for UAV task decomposition. Through experimental demonstrations, we showcase that the prompt-tuned MLLM exhibits efficient task decomposition and execution capabilities in handling complex UAV tasks. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Robust controller Design with Two-Degree-of-Freedom Feedback Loop Factorization for Oscillating Plant with Uncertain Time Delay*

Robust Controller Design with Two-Degree-of-Freedom Feedback...

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International Conference on control, automation and Diagnosis (ICCAD)

作者： Marek Dlapa Department of Automation and Control Engineering Faculty of Applied Informatics Tomas Bata University in Zlin Zlin Czech Republic

Application of Robust control Toolbox for Time Delay Systems implemented in the Matlab system to the oscillating plant with uncertain time delay using the D-K iteration and algebraic approach. The algebraic approach combines the structured singular value, algebraic theory and algorithm of global optimization solving remaining issues in structured singular value framework. The algorithm of global optimization can be alternated with direct search methods such as Nelder-Mead simplex method giving solutions for problems with one local extreme. As a global optimization method, Differential Migration is used proved to be reliable in solving this type of problems. The D-K iteration represents a standard method in the structured singular value theory. The results obtained from the D-K iteration are compared with the algebraic approach.

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Interpretable prediction method of Matte grade based on GA-BiLSTM and SHAP

Interpretable prediction method of Matte grade based on GA-B...

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第43届中国控制会议

作者： Huaibo Ma Jun Ma Xiang Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Aiming at the problem that the ice-copper grade prediction process is difficult to effectively capture the long-term dependency relationship between data,which leads to low prediction accuracy,it is proposed to construct a model using the powerful long-term memory capability and bidirectional information prediction ability of bidirectional long and short-term memory network(BiLSTM) model,and further optimize the model by using the genetic algorithm(GA) to achieve accurate prediction of ice-copper ***,the genetic algorithm was used to find the optimal hyperparameter combination of the ***,the predicted values of the BiLSTM model optimized by the genetic algorithm are compared with the predicted values of the existing methods and the unoptimized Bi LSTM model using different evaluation ***,the SHAP method is used to give the overall and local interpretation of the prediction results of the BiLSTM *** experimental results show that the RMSE of the BiLSTM model optimized by the genetic algorithm is reduced by at least 3.05%,the MAE is reduced by at least 2.75%,the MAPE is reduced by at least 3.29%,and the R2 is increased by at least 1.51%,which verifies the effectiveness of the proposed method in solving the problem of ice-copper grade prediction,and the SHAP method is able to explain the output of the Bi LSTM model efficiently and increase the transparency of the *** SHAP method can effectively explain the output of the BiLSTM model and increase the transparency of the model.

关键词： Matte grade prediction Genetic algorithm BiLSTM model SHAP model interpretation

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Anomaly Detection of Copper Melting Process Data Based on Anomaly Transformer

Anomaly Detection of Copper Melting Process Data Based on An...

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第43届中国控制会议

作者： Junjia Zhang Jun Ma Xiang Li Faculty of Information Engineering And Automation Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this paper,aiming at the copper melting process,the relevant process variable parameters are coupled with each other,complicated and change with the production cycle,which leads to the anomaly detection of copper melting *** accuracy of copper melting data,we propose an Anomaly Transformer model based on the Transformer model,which replaces the original Self-Attention mechanism with Anomaly-Attention mechanism,to realize accurate and reliable copper melting process data anomaly *** order to realize accurate and reliable anomaly detection of copper melting process ***,the degree of anomaly of copper melting process data at each time point is measured by the correlation dispersion,and the KL dispersion distance value is utilized to quantify the degree of *** value of KL dispersion to quantify the change of the value of each neighboring time point,and secondly,the above quantized distance value is transform the its attention mechanism into the anomaly attention mechanism form.,in order to present the anomaly deviation and weight of each time point,and distinguish normal points from anomaly points,and finally,the trained anomaly Transformer model is analyzed on the test set for anomaly ***,the trained anomaly Transformer model is analyzed on the test set for anomaly detection,and the results are analyzed and interpreted using F1 evaluation *** experimental results show that in the production process dataset of copper *** application of correlation-based discretization brings a certain 3.7%improvement in the F1 *** addition of the anomalous attention module achieved 43.4% performance improvement,and the final combination of the combination of the combined KL dispersion and the anomaly attention module yielded an F1 score of 71.31,achieving a better anomaly detection result.

关键词： Anomaly Detection of Copper Melting Process Data KL dispersion distance Anomaly-Attention Transformer

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A Bayes CNN-LSTM approach for aero-engine remaining life predictions

A Bayes CNN-LSTM approach for aero-engine remaining life pre...

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第43届中国控制会议

作者： Jiangyan Zhu Jun Ma Jiande Wu Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Remaining life prediction(RUL) plays an important role in prognostic health *** recent years,deep learning-based methods have been widely used for RUL ***,deep learning-based methods all focus on point prediction of RUL,ignoring the uncertainty associated with inaccuracies in the data and *** the prominence of Bayesian methods in uncertainty modelling,this paper uses a Monte Carlo dropout(MC-Dropout)-based Bayesian approximation to capture the epistemic and contingent uncertainty in the prediction process.A Bayes CNN-LSTM-based spatial-temporal feature extraction model is proposed to implement RUL prediction and quantify *** proposed method is also validated on the CMAPSS dataset and compared with CNN,LSTM and CNN-LSTM to verify the effectiveness of Bayesian neural networks.

关键词： Remaining life prediction Bayesian neural networks Epistemic uncertainty Spatial-temporal feature

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