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International Joint Conference on Neural Networks (IJCNN)

作者： Zhongzheng Qiao Xuan Huy Pham Savitha Ramasamy Xudong Jiang Erdal Kayacan Andriy Sarabakha ERI@N Interdisciplinary Graduate Programme Nanyang Technological University Singapore Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Institute of Infocomm Research (I2R) Agency for Science Technology and Research (A*STAR) Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In autonomous and mobile robotics, a principal challenge is resilient real-time environmental perception, particularly in situations characterized by unknown and dynamic elements, as exemplified in the context of autonomous drone racing. This study introduces a perception technique for detecting drone racing gates under illumination variations, which is common during high-speed drone flights. The proposed technique relies upon a lightweight neural network backbone augmented with capabilities for continual learning. The envisaged approach amalgamates predictions of the gates' positional coordinates, distance, and orientation, encapsulating them into a cohesive pose tuple. A comprehensive number of tests serve to underscore the efficacy of this approach in confronting diverse and challenging scenarios, specifically those involving variable lighting conditions. The proposed methodology exhibits notable robustness in the face of illumination variations, thereby substantiating its effectiveness.

关键词： Continuing education Robot kinematics Neural networks Lighting Logic gates Robustness Real-time systems

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Coordinated control of distributed multi-agent systems using event-triggered predictive control strategy

Coordinated control of distributed multi-agent systems using...

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

作者： Lei Cao Chuanguo Chi Erhan Yumuk Clara-Mihaela Ionescu Guo-Ping Liu Department of Control Science and Engineering Harbin Institute of Technology Harbin P. R. China Department of Electromechanical Systems and Metal Engineering Dynamical Systems and Control Research Group Ghent University Ghent Belgium Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey Center for Control Science and Technology Southern University of Science and Technology Shenzhen P. R. China

In this paper, the coordinated control problem for a class of distributed networked multi-agent systems (NMASs) is studied. An event-triggered predictive coordinated control (ETPCC) scheme is proposed to compensate for the communication delay between controllers as well as to reduce the communication frequency. With the aim of reducing the frequency of data transfer among controllers and saving resources, a distributed event-triggered condition is designed to decide when to transfer information. Further, a cost function using predicted values is proposed to measure the cooperative performance among the agents, and the required control increment for each agent is obtained by minimizing the cost function. Simulation and experimental examples show that the proposed ETPCC strategy works effectively.

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Manufacturing Service Network of Digital Twin Systems Under Cloud Computing Environment 32

Manufacturing Service Network of Digital Twin Systems Under ...

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32nd CIRP Design Conference, CIRP Design 2022

作者： Xiang, Feng Zhou, Ping Zuo, Ying Tao, Fei Zhang, Dashun Key Laboratory Of Metallurgical Equipment And Control Technology Ministry Of Education Wuhan University Of Science And Technology Wuhan430081 China Hubei Key Laboratory Of Mechanical Transmission And Manufacturing Engineering Wuhan University Of Science And Technology Wuhan430081 China Precision Manufacturing Institute Wuhan University Of Science And Technology Wuhan430081 China Research Institute For Frontier Science Beihang University Beijing100191 China School Of Automation Science And Electrical Engineering Beihang University Beijing100191 China No.55 Research Institute China North Industries Group Corporation Changchun130012 China

With the promotion and application of the digital twin system in the industrial Internet platform, and around the "intelligent, integrated, and service-oriented"manufacturing characteristics, a digital twin system manufacturing service network with full-process, full-object, and full-factor coverage of virtual and real resources has gradually formed. In a typical cloud computing environment, this paper defines the manufacturing service network of the digital twin system based on the virtual and real mapping, simulation calculation, feedback control and other characteristic requirements of the digital twin system, and constructs the network model from the network nodes and network connections. Based on this, the optimization steps for resource allocation of manufacturing service network based on the digital twin system, and the key technologies for the construction of the manufacturing service network of the digital twin system are analyzed. © 2022 The Authors.

关键词： Resource allocation

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Study on Robust Estimation of Combined Heat and Power Networks based on Huber Estimation

Study on Robust Estimation of Combined Heat and Power Networ...

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2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021

作者： Hu, Yunlong Wang, Qi Ni, Ming Yang, Yi Zhu, Yongkang Yao, Xiecheng Nanjing Normal University College of Electrical and Automation Engineering Nanjing China Nari Group Corporation State Key Lab of Smart Grid Protection and Control Nanjing China State Grid Jiangsu Electric Power Research Institute Nanjing China

ISBN: (纸本)9781665414395

Combined heat and power state estimation is the basis for safety assessment, optimal dispatching and other functions in multi-flow energy management centers, and has important theoretical value and practical significance. The measurement system inevitably has gross errors or bad data. The traditional weighted least squares method can't restrain its influence, so the robust estimation method of combined heat and power network based on Huber estimation is proposed. The results of calculation examples show that the proposed method has good robustness and can restrain the influence of gross errors on the estimation accuracy. © 2021 IEEE.

关键词： Electric power transmission networks

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A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-Based Feature Fusion

A Wavelet Guided Attention Module for Skin Cancer Classifica...

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IEEE International Symposium on Biomedical Imaging

作者： Ayush Roy Sujan Sarkar Sohom Ghosal Dmitrii Kaplun Asya Lyanova Ram Sarkar Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering International Institute of Information Technology Naya Raipur Chhattisgarh India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be very helpful. In this paper, we propose a novel model, which uses a novel attention mechanism to pinpoint the differences in features across the spatial dimensions and symmetry of the lesion, thereby focusing on the dissimilarities of various classes based on symmetry, uniformity in texture and color, etc. Additionally, to take into account the variations in the boundaries of the lesions for different classes, we employ a gradient-based fusion of wavelet and soft attention-aided features to extract boundary information of skin lesions. We have tested our model on the multi-class and highly class-imbalanced dataset, called HAM10000, and achieved promising results, with a 91.17% F1-score and 90.75% accuracy. The code is made available at: https://***/AyushRoy2001/WAGF-Fusion.

关键词： Solid modeling Image color analysis Biological system modeling Melanoma Medical services Feature extraction Skin

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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An Experimental research on Temperature Accuracy Algorithm in Fan Coil Thermostat

An Experimental Research on Temperature Accuracy Algorithm i...

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China International Electrical and Energy Conference (CIEEC)

作者： Yunus Arı Ergün Cücük Seda Karateke Electric and Electronic Engineering Institute of Graduate Studies Istanbul University - Cerrahpaşa Istanbul Turkey Department of Research and Development Ges Teknik Air Conditioning Control and Automation Systems Industry and Trade Inc. Istanbul Turkey Department of Computer Engineering Faculty of Engineering Istanbul Topkapı University Istanbul Turkey

The efficient operation of HVAC&R systems are based on keeping indoor temperature and air quality at an optimum level without disturbing comfort. Starting from this point, in this experimental research, the factors affecting the measurement accuracy during the room temperature detection of a wall mount fan coil thermostat were examined. Among the external effects, the effect of the device components on the measurement was focused and the solution of the measurement deviations occurred in this extent was investigated. Based on the experiments, the algorithm that must be applied to reach the optimum temperature measurement of a room thermostat developed for Fan Coil systems was proposed.

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A WAVELET GUIDED ATTENTION MODULE FOR SKIN CANCER CLASSIFICATION WITH GRADIENT-BASED FEATURE FUSION

arXiv

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arXiv 2024年

作者： Roy, Ayush Sarkar, Sujan Ghosal, Sohom Kaplun, Dmitrii Lyanova, Asya Sarkar, Ram Department of Electrical Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Electronics and Communication Engineering International Institute of Information Technology Chhattisgarh Naya Raipur India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Processes Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

关键词： Wavelet transforms

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Short - Term Prediction of Coke Pushing Current Peak Based on Improved ARIMA Model

Short - Term Prediction of Coke Pushing Current Peak Based o...

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Industrial Cyber-Physical Systems (ICPS)

作者： Haiyang Wei Luefeng Chen Jie Hu Yi Ren Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan

Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.

关键词： Resistance Ovens Time series analysis Noise reduction Production Predictive models Prediction algorithms

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Context-aware Emotion Recognition Based on Vision-Language Pre-trained Model

Context-aware Emotion Recognition Based on Vision-Language P...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： XingLin Li Xinde Li Chuanfei Hu Huaping Liu School of Automation Southeast University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China Nanjing Center for Applied Mathematics Nanjing China Southeast University Shenzhen Research Institute Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Given the difficulty of recognizing ambiguous emotions in facial expression recognition tasks, we propose a visual-language model named CAER-CLIP to address this challenge. The proposed CAER-CLIP standed for Context-Aware Emotion Recognition (CAER), and were incorporated structure of the Contrastive Language–Image Pre-training (CLIP) model as promising alternative to classifier. There are two parts in CAER-CLIP model. In the visual part, facial expressions and contextual information of the image are simultaneously extracted to obtain the final feature embeddings, which are then used as a learnable “class” token for text-image pairing with desired module. In the textual part, we use text labels for emotion recognition classes as input. The outputs were merged to participate the comparative study to generated parameters of the model. The experiments demonstrate the effectiveness of the proposed method and show that our CAER-CLIP outperforms the state-of-the-art results on the CAER benchmark. The ablation experiment verified the effectiveness of both the classifier-based and text-based (ours without classifier) models, demonstrating that our method with the CAER-CLIP structure performed better, and the incorporation of a text encoder in the deep network model architecture effectively enhancing recognition accuracy.

关键词： Training Emotion recognition Visualization Accuracy Text recognition Face recognition Benchmark testing Feature extraction Data mining Context modeling

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