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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是191-200 订阅

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data driven Multi-Agent Deep Reinforcement learning Based Operational Optimization of Industrial Aerodynamic System 13

Data Driven Multi-Agent Deep Reinforcement Learning Based Op...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Huang, Yunxiang Zhong, Lulu Liu, Yang Zhao, Jun Wang, Wei Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

Industrial aerodynamic system (IAS) involves a series of compressors to provide the compressed air for consumer of steel industrial park, which consumes a substantial amount of electrical energy and attracts a considerable attention for energy conservation and efficiency enhancement. the usage pattern of these compressors, influenced by the cyclic and intermittent nature of production schedules, leads to varying operational profiles. Traditional manual adjustments are often inadequate due to the differ in expert knowledge, resulting in increased energy consumption. To tackle this challenge, we propose an innovative approach termed as data-driven multi-agent deep reinforcement learning (DMA-DRL) to achieve the operational optimization of IAS. Initially, we construct a multi-operational model for the air compressor group by leveraging historical data from industrial sites and understanding equipment operation principles;Considering the constraints in terms of production process conditions and equipment safety, we establish a data-driven optimization scheduling method for air compressor units based on multi-agent deep reinforcement learning, which ensures the secure and stable operation of the system under complex conditions. Finally, the proposed method is applied to the performance testing of an industrial park air compressor simulation system, which outperforms the various optimization strategies in aspect of operational cost and energy efficiency. © 2024 ieee.

关键词： Energy efficiency

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Improving the ALNS Algorithm for PDPTW Problem using a DDQN Agent 13

Improving the ALNS Algorithm for PDPTW Problem using a DDQN ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wang, Qianlong Wu, Peng Guo, Lili Chen, Ping Ling, Yu School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing100094 China Business School Nankai University Tianjin300071 China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan430063 China

ISBN: (纸本)9798350361674

In response to practical challenges in food delivery logistics, we introduce a novel algorithm to address the Pickup and Delivery Problem with Time Windows (PDPTW). Our method employs a Double Deep Q-Network (DDQN)-based Large Neighborhood Search (LNS) strategy, which builds on the Adaptive Large Neighborhood Search (ALNS) framework, tailored specifically for the PDPTW. To mitigate the issues of slow convergence and instability observed in the ALNS algorithm, we have refined the state representation of the DDQN agent by integrating information on the current solution, historical data, and the time usage patterns of operators. We evaluated our algorithm using publicly available benchmark instances. the empirical results indicate that our approach not only surpasses the ALNS algorithm in terms of macro-level solution quality and stability but also demonstrates superior performance at the micro-level by achieving faster convergence and enhanced efficiency in operator usage strategy. © 2024 ieee.

关键词： Deep reinforcement learning

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A Speech Emotion Recognition System Based on Energy Valley Optimizer Improved Multi-Kernel Extreme learning Machine 13

A Speech Emotion Recognition System Based on Energy Valley O...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Aihua Zhao, Xuanjing College of Physical Science and Technology Bohai University Jinzhou121013 China

ISBN: (纸本)9798350361674

In response to the issue of low accuracy in speech emotion recognition, this paper proposes an improved classification model that utilizes the Energy Valley Optimizer (EVO) to enhance the Multi-kernel Extreme learning Machine (MKELM). the Multi-kernel Extreme learning Machine (MKELM) has been widely applied in classification problems, enhancing learning and generalization capabilities compared to traditional Extreme learning Machines (ELM). However, its performance is highly dependent on parameters, making parameter optimization crucial. the EVO algorithm offers fast convergence, parameter-free operation, and the lowest possible objective function evaluations, providing significant advantages in parameter optimization. the paper conducted two sets of comparative experiments. Firstly, it compared MKELM with basic networks such as BP, SVM, LSTM, CNN in terms of runtime and accuracy, demonstrating the superiority of the MKELM model. Secondly, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and EVO were utilized to optimize the MKELM model separately. the classification results were compared in terms of accuracy, precision, and recall, demonstrating the feasibility of the proposed EVO-MKELM model in speech emotion recognition. © 2024 ieee.

关键词： Adversarial machine learning

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WF-Velo: A New Method for RNA Velocity Analysis Based on Single-Cell data 13

WF-Velo: A New Method for RNA Velocity Analysis Based on Sin...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Li, Cheng Cao, Wenjie Zhang, Zihao Tang, Wenbo Shi, Yazhou Zhang, Ben-Gong School of Mathematical & Physical Science Wuhan Textile University Wuhan430200 China Sun Yat-Sen University Guangzhou510275 China

ISBN: (纸本)9798350361674

the direction of cell differentiation is very important to study the process of cell differentiation. there was a strong hypothesis that the direction was fixed in previous studies until the RNA velocity method appearance. It is a powerful tool to analyze cell differentiation. However, there is still some problem of it due to the low expression of unspliced mRNA readings and high randomness. this paper intends to propose a new method called WF-velo which means the whole feature (WF) are used during the process of RNA velocity estimations, while the part feature was used by the traditional method, to overcome the problem of the existed methods. By using the characteristics of the gene in the whole transcription process, the RNA velocity of cells can be estimated more realistically, and the cell differentiation process is more in line with biological phenomena compared with the cellDancer, scvelo, Deepvelo and other RNA velocity estimation algorithms. the pancreatic endocrine development dataset and the mouse hippocampus development dataset are tested by WF-velo. the cell differentiation trajectory shows that it is a good method to estimate the RNA velocity. © 2024 ieee.

关键词： Transcription

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data-driven Soft Sensing of Polyoxymethylene Melt Index using Takagi-Sugeno Fuzzy Neural Network and Quantum Genetic Algorithm 13

Data-Driven Soft Sensing of Polyoxymethylene Melt Index usin...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Yu, Guangji Zhao, Haiquan Liang, Yangyang Gao, Yong Zhang, Weiyong Sader, Malika Methanol Branch Ningxia Coal Co. Ltd. Yinchuan China Technologies Co. Ltd Beijing China Technologies Co. Ltd Beijing China Tsinghua University Department of Automation Beijing China

ISBN: (纸本)9798350361674

As a frequently used polymer material, polyoxymethylene (POM) has played an indispensable role in a variety of industrial fields. For production efficiency and safety, online monitoring of POM melt index is of importance in industrial practice. However, current data-driven soft sensors for POM melt index cannot address the pure delays within process variables. In this work, we propose time-delayed Takagi-Sugeno (T-S) fuzzy neural network as a novel data-driven soft sensor model, and improved quantum genetic algorithm (IQGA) for parameter estimation. By considering time-delayed process variables, the predictive performance and interpretability of soft sensor models can be improved. However, it is computationally cumbersome to find an optimal choice of time delays, and thus IQGA is proposed to resolve this problem. Case studies on industrial data from a real-world POM plant illustrate the usefulness of the proposed soft sensing method for POM melt index prediction. © 2024 ieee.

关键词： Fuzzy inference

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Identification of the Hammerstein Nonlinear systems Utilizing Correlation Analysis and Maximum Likelihood Methods 13

Identification of the Hammerstein Nonlinear Systems Utilizin...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Dai, Guangqiang Li, Feng Zhu, Yanzong Zhou, Zhou School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China

ISBN: (纸本)9798350361674

In this paper, a parameter identification method for the Hammerstein nonlinear systems is proposed, which is derived by exploiting the correlation analysis and maximum likelihood methods. the Hammerstein system proposed in this study consists of two modules, one of which is approximated by a neural fuzzy model, and a dynamical linear block that is represented by an autoregressive moving average model with additional input. the separation of parameters identification in the Hammerstein system is accomplished by employing a combination of signals encompassing both binary signals and random signals. First of all, based on the correlation function relationship of separable signals in nonlinear systems, the parameters of the dynamic linear block are established through a correlation analysis method, which the issue with colored noise interference is solved. To improve recognition accuracy, a maximum likelihood recursive least squares method was obtained by using the maximum likelihood principle to identify the nonlinear block and colored noise model based on random signals. Simulation results confirm the effectiveness of the proposed approach in accurately identifying Hammerstein nonlinear systems. © 2024 ieee.

关键词： White noise

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Event-Triggered Neural Tracking control of Cable-driven Lower-Limb Exoskeleton Robots 13

Event-Triggered Neural Tracking Control of Cable-Driven Lowe...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Hongqiang Zhang, Faxiang Na, Jing Gao, Guanbin Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China

ISBN: (纸本)9798350361674

this paper gives an event-triggered neural tracking control strategy for cable-driven lower-limb exoskeleton robots. To reduce the number of triggers and prevent the jitter of the control signal, a switching threshold is used to drive the event trigger, which is different from the traditional trigger cases of fixed and relative thresholds. Firstly, a neural network (NN) is adopted to approximate the unknown nonlinear function, with the extracted estimation error being new leakage terms developed to update the weight of NN. then an adaptive controller is designed to achieve the uniformly ultimately bounded(UUB) performance of the closed-loop system, and at the same time both transient and steady-state performance of the tracking error is guaranteed via a predefined performance function. Finally, the effectiveness of the proposed method is verified by simulation. © 2024 ieee.

关键词： Exoskeleton (Robotics)

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Adaptive Multi-Agent Coordination among Different Team Attribute Tasks via Contextual Meta-Reinforcement learning 13

Adaptive Multi-Agent Coordination among Different Team Attri...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Huang, Shangjing Zhao, Zijie Zhu, Yuanheng Zhao, Dongbin Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350361674

In the realm of Multi-Agent Reinforcement learning (MARL), the challenge of ensuring effective coordination in different multi -agent teams remains a significant hurdle. Existing methods often fall short in generalizing learned policies to novel team compositions, sizes, and capabilities. Addressing this gap, our study focuses on systems with variable and obscure attribute compositions, harnessing a context-based meta-reinforcement learning framework. the approach is twofold: context inference and context-based decision-making. Agents interpret historical data to identify the system's attribute composition, guiding their collective efforts. the accuracy of these inferences is crucial, prompting us to integrate contrastive learning to refine the context inference network via unsupervised training. In the process of decision-making, agents integrate the inferred context with the observation features to select the optimal strategy. Our empirical results underscore the method's efficacy in bolstering decision-making efficiency and precision amidst attribute diversity, marking a significant stride in adaptive teaming for robust multi-robot deployments in dynamic real-world scenarios. © 2024 ieee.

关键词： Contrastive learning

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VDKD: A ViT-Based Student-Teacher Knowledge Distillation for Multi-Texture Class Anomaly Detection 13

VDKD: A ViT-Based Student-Teacher Knowledge Distillation for...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Chen, Yibo Peng, Haolong Zhu, Ke Zhang, Jianming College of Control Science and Engineering Zhejiang University Hangzhou310027 China Zhejiang Polytechnic Institute Polytechnic Institute Zhejiang University Hangzhou310027 China Zhejiang University Robotics Institute Yuyao314500 China

ISBN: (纸本)9798350361674

'With the progression of network perception and detection capabilities, multi-class anomaly detection and localization have become promising research areas. the primary challenge lies in mitigating minor textural defects while maintaining adaptability to a variable external environment. In response to this, we introduce a state-of-the-art pipeline, the ViT-based Discriminative Knowledge Distillation (VDKD). Contrary to existing lightweight models, VDKD uniquely leverages a robust teacher network to train a compact student model using innovative modules. Furthermore, the integrated Semantic Alignment Module (SAM) and Abnormal Assessment Module (AAM) assist in distilling essential information. We also propose the Discriminative Feature Judgment Module (DFJM), designed to emulate the teacher's output within the student model with limited sample intervention. Extensive self-supervised experiments on texture datasets, including KSDD2, MT, and MVTec-AD, underscore the effectiveness of our approach with VDKD. © 2024 ieee.

关键词： Semantics

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Short-Term Load Prediction Using CNN-LSTM Network and Sparrow Search Algorithm for Combined Cooling Heating and Power systems 13

Short-Term Load Prediction Using CNN-LSTM Network and Sparro...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhou, Yaxin Li, Feng Chen, Yonghui School of Electrical and Information Engineering Jiangsu University of Technology Changzhou213001 China

ISBN: (纸本)9798350361674

In combined cooling heating and power (CCHP) systems, the interaction and impact between cooling, heating and electrical energy play a vital role, and accurate load forecasting is the key to economic dispatch and efficient operation of CCHP systems. In this paper, based on convolutional neural networks-long short-term memory network (CNN-LSTM) model and sparrow search algorithm (SSA), the short-term multiple load prediction of CCHP system is studied. First, the spatio-temporal features of the load data are extracted using CNN and LSTM respectively, and the CNN-LSTM network model is constructed. Subsequently, the SSA is used to optimize the learning parameters of the CNN-LSTM network model, namely, learning rate, training times and number of neurons of the LSTM. Simulation results show that compared with other existing prediction techniques, the proposed method can achieve better prediction accuracy in cooling, heating and electrical energy, and mean absolute error and mean absolute percentage error are reduced, which testify the effectiveness and feasibility of the proposed method. © 2024 ieee.

关键词： Cooling systems

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