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A Quality Prediction Hybrid Model of Manufacturing Process Based on Genetic Programming 11

A Quality Prediction Hybrid Model of Manufacturing Process B...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Peng, Chong Cheng, Zhijian Ren, Hongru Lu, Renquan Guangdong University of Technology School of Computer Science and Technology and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China Guangdong University of Technology School of Automation and Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangzhou510006 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the design of manufacturing parameters in the initial stage is backed by quality prediction to realise intelligent manufacturing. Accurate prediction translates to better quality, lower costs and more flexibility. However, the real production is a complicated and variable process, most of which involved multiple parameters simultaneously. the data on the basis of feature construction can filter the impurities of data, accuracy of predictive model can be satisfied. Existing approaches to provide results are useless when the insufficient mining of the relationship between the data or the some case without adequate manufacturing data and expertise. In this paper, a two-stage hybrid approach with genetic programming is proposed for quality prediction. the feature construction is realized by genetic programming in the first stage, and the new features are utilized as additives to subsequent stage of the extreme gradient boosting. the comparison experiments indicate that the two-stage hybrid model outperforms the existing methods in overall performance. © 2022 ieee.

关键词： Genetic programming

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Federated learning Privacy-preserving Method Based on Bregman Optimization 10

Federated Learning Privacy-preserving Method Based on Bregma...

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10th ieee International conference on Cyber Security and Cloud Computing and 9th ieee International conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Gengming Zhang, Jiyong Zhang, Shaobo Yin, Yijie Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350312461

Federated learning has received a lot of attention for its ability to solve the data silo problem, but it is also limited by the problem of data heterogeneity and privacy. Non-Independent Identical Distribution (Non-I.I.D) data leads to performance degradation of federation models, and privacy problem have been studied as a hot topic in the field of federated learning. However, current research rarely considers non-I.I.D data and privacy simultaneously. In this paper, we propose a federated learning scheme based on Bregman and differential privacy (FLBDP). Our approach adopts Bregman distance for personalized model training, which aims to control the difference between local model and global model in a limited range, the FLBDP can reduce the model difference to improve the model performance by Bregman optimization. In addition, we use a Gaussian mechanism to perturb the personalized model and update the local model by the perturbed personalized model, which enables the model parameters to satisfy differential privacy in the uplink channel to enhance user privacy protection. © 2023 ieee.

关键词： learning systems

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Deep Reinforcement learning-Based AoI Minimization in UAV-Assisted Multi-Platoon Vehicular Networks 17

Deep Reinforcement Learning-Based AoI Minimization in UAV-As...

引用

ieee Congress on Cybermatics: 17th ieee International conference on Internet of things, ithings 2024, 20th ieee International conference on Green Computing and Communications, GreenCom 2024, 17th ieee International conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th ieee International conference on Smart data, Smartdata 2024

作者： Du, Bochun Qu, Long Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China

ISBN: (纸本)9798350351637

In the current landscape of intelligent transportation, vehicle platooning has become a key strategy for improving traffic efficiency and safety. However, as multiple platoons move at high speeds, the platoon encounters an unpredictable communication environment, which poses a major challenge for coordinated control of multi-platoon vehicles. Meanwhile, the freshness of information plays a pivotal role in ensuring road traffic safety, quantified by the Age of Information (AoI). this study focuses on a UAV-assisted platooning system in Manhattan scenarios, aiming to minimize vehicle AoI while improving the communication success rate between vehicles and UAV nodes. To tackle this challenge, we model this problem as a Partially Observable Markov Decision Process (POMDP). We propose a trajectory and scheduling strategy based on the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach. Additionally, to better adapt to the dynamic nature of multi-platoon vehicular networks, we integrate the Prioritized Experience Replay (PER) technique to prioritize learning and processing of critical experience samples. through simulation experiments and comparisons with baseline methods, the superiority of the proposed algorithm is verified. © 2024 ieee.

关键词： Unmanned aerial vehicles (UAV)

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A data-driven Coordinated Active and Reactive Dispatching Strategy for Photovoltaics 10th

A Data-Driven Coordinated Active and Reactive Dispatching St...

引用

10th International conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Xu, Ye Du, Haizhou Department of Computer Science and Technology Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9789819787487

the influx of photovoltaic systems brings about fluctuations on the grid and risks of overvoltage, issues which may be remedied by fully exploiting the reactive power support capabilities of inverters. However, reliable inverter operation is closely tied to the condition of its Insulated Gate Bipolar Transistor (IGBT) junction temperatures. In this paper, we propose an active-reactive coordinated control strategy called AGVC that considers the dependability of inverter components to simultaneously enhance the response of AGC and AVC. Firstly, we introduce a data-driven method for assessing component dependability which employs a temporal convolution network (TCN) model to calculate IGBT junction temperature data. then, we establish an active-reactive coordinated optimization model for the distribution network with attention to component dependability, introducing the value of IGBT junction temperatures into the optimization objective. Finally, we complete agent training using a gradient reinforcement learning algorithm of multi-agent deep deterministic policy. the ieee 33-node system demonstrates the advantages of the proposed strategy in improving active dispatch and voltage balance compared to recent research methods, with average active regulation error and voltage stability enhanced. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Insulated gate bipolar transistors (IGBT)

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the Forecast of Health Human Resources During the Period of “Healthy China 2030” in Shandong Province

The Forecast of Health Human Resources During the Period of ...

引用

data driven control and learning systems (ddcls)

作者： Luyu Zang Hongyan Li Peng Gao School of Management Shanghai University of Engineering Science Shanghai

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Objective: To analyze the development status of health human resources in Shandong Province from 2009 to 2021, and predict the development trend of health human resources in Shandong Province during the “Healthy China 2030” period. Method: Based on the relevant data on health human resources from 2009 to 2021, a grey GM (1,1) prediction model was constructed to predict and analyze the allocation of health human resources in Shandong Province from 2022 to 2030. Result: by 2030, the number of health personnel, health technicians, licensed (assistant) physicians, and registered nurses reached 1.56 million, 1.41 million, 585000, and 751000, respectively; the number of licensed (assistant) physicians and registered nurses per thousand population will reach 5.44 and 6.98 respectively; the healthcare ratio will reach 1:1.28. Conclusion: By 2030, the number of health human resources in Shandong Province will increase, but the number of nursing staff still cannot alleviate the shortage of health human resources and the unreasonable proportion of medical staff in Shandong Province.

关键词： learning systems Sociology Medical services Predictive models Market research control systems data models

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Smart Last-Mile Delivery with IoT-Connected Robot Vehicles 10

Smart Last-Mile Delivery with IoT-Connected Robot Vehicles

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10th International conference on Smart Computing and Communication, ICSCC 2024

作者： Sujatha, M. Kovarasan, Rajesh Kambattan Sudha, K. Mishra, Nilamadhab thiyagarajan, G. Srinivasan, C. Easwari Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Business Administration Tamil Nadu Mannargudi India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350363104

this research discusses using IoT technologies with robot vehicles to improve last-mile deliveries. this novel method optimizes the last stage of the distribution chain, which sometimes presents logistical issues. the system examines robot vehicles' last-mile delivery monitoring, data collecting, and communication capabilities via an Internet of things connection. these trucks collect and communicate traffic, packaging, and environmental data using sensors and communication equipment. Analyzing and processing this data improves route planning, delivery timing, and performance. through the IoT, autonomous delivery trucks, a centralized control center, and other components of the delivery ecosystem could operate in combination. this collaborative network helps respond to changing situations such as highway congestion and package urgency by rerouting or raising delivery priority. the potential for delivery trucks to improve productivity, intelligence, and focus on the customer in the last mile of a package's journey. the IoT-driven solutions may be highlighted as major results, advantages, and implications for the logistics sector, addressing urbanization, consumer demands, and environmental concerns in modern delivery systems. © 2024 ieee.

关键词： Trucks

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Policy learning with Deep Koopman Operator Networks 8

Policy Learning with Deep Koopman Operator Networks

引用

8th ieee International conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhao, Dongdong Li, Yichang Yang, Xiaodi Yan, Shi School of Information Science and Engineering Lanzhou University Gansu730000 China

ISBN: (纸本)9798350300178

In this paper, a deep policy learning method based on Koopman operator networks is proposed for reinforcement learning tasks. Specifically, a deep neural network based on the Koopman operator is designed for nonlinear systems in reinforcement learning, and the environment dynamics are embedded in a high-dimensional space to obtain a highly approximate linear model. An iterative linear quadratic regulator method based on a value function is also proposed to complete the learning and solution of the optimal policy. the proposed model-based policy learning algorithm makes the policy in the reinforcement learning task easy to solve and improves the data efficiency and algorithm performance. the results of robot control experiments conducted at OpenAI Gym show that the proposed control algorithm has better control performance than the representative reinforcement learning and data-driven methods. © 2023 ieee.

关键词： Reinforcement learning

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data-driven Secure Authentication for Smart Grid IoT Networks 9

Data-driven Secure Authentication for Smart Grid IoT Network...

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9th ieee International conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Singhal, Saumyashree Tripathi, Sharda Birla Institute of Technology and Science Pilani Pilani India

ISBN: (纸本)9798350334395

Internet of things (IoT) has found wide-spread usage in cyber physical systems. We consider the security aspects related to cyber physical systems, particularly the smart grid, wherein IoT devices are deployed for monitoring and control, often in the open without any active surveillance, thereby increasing their vulnerability to security attacks. To this end, device authentication is an essential security feature for smart grid IoT networks. A desirable authentication protocol designed for IoT devices should not only be robust when the device is compromised by an adversary, it also needs to be computationally efficient in the wake of limited storage and computational capabilities of IoT devices. In this work, we address the critical issue of device security in smart grid IoT networks, and propose a data-driven, yet lightweight and privacy preserving authentication scheme for IoT devices. the performance analysis based on our experiments show that the proposed scheme is robust against heterogeneous network attacks, and significantly reduces computational load. © 2023 ieee.

关键词： Machine learning

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Image Recognition of Chinese Herbal Medicine Using Adaptive Gamma Correction Based on Convolutional Neural Network

Image Recognition of Chinese Herbal Medicine Using Adaptive ...

引用

data driven control and learning systems (ddcls)

作者： Honghai Ji Xinjing Liu Li Wang Lingling Fan Shida Liu School of Electronics & Control Engineering North China University of Technology Beijing China School of Automation Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Chinese herbal medicine (CHM) has gained significant attention due to its potential in treating various diseases. CHM image classification is crucial in fields such as smart medical care and medical teaching. However, accurately classifying CHM images has been a challenging task due to their fine-grained variability. To achieve automatic identification and classification of Chinese herbal medicines, we designed a method that combines image processing and deep learning. We established a dataset of CHM images, which includes 10 kinds of common Chinese herbal medicines, with 15,719 images in total. the dataset covers medicinal materials in various natural backgrounds and provides ample samples for training deep learning models. Regarding image processing, we proposed a method based on adaptive gamma correction(AGC) and compared it with CLAHE(Contrast Limited Adaptive Histogram Equalization)and traditional gamma correction(TGC) methods. Subsequently, we employed three pre-training models of convolutional neural networks based on transfer learning, namely GoogLeNet, ResNet-18, and MobileNetV2, to identify CHM images. the results of the verification indicate that the AGC-CNN model outperforms the CLAHE-CNN and TGC-CNN models in terms of classification accuracy and image quality improvement.

关键词： Training Image quality Deep learning Adaptation models Accuracy Image recognition data models

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A Multi-Scale Cross Transformer Network-Based Fault Diagnosis Method for Industrial Process

A Multi-Scale Cross Transformer Network-Based Fault Diagnosi...

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data driven control and learning systems (ddcls)

作者： Yuan Xu Rui-Ze Fan Wei Ke Yan-Lin He Qun-Xiong Zhu Ming-Qing Zhang Yang Zhang College of Information Science & Technology Beijing University of Chemical Technology Beijing China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China Faculty of Applied Sciences Macao Polytechnic University Macao SAR P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the process of industrial production is becoming increasingly complex in modern times, posing higher demands for safety. It's worth noting that data from modern industrial processes often exhibit multivariate, temporal and multiscale characteristics. To address these problems, this paper presents a novel fault diagnosis method based on the Multi-Scale Cross Transformer model. We integrate encoder, convolution and patching mechanisms, making it capable of extracting temporal features from different time scales, effectively capturing short-term dynamics and long-term trends. Additionally, a two-stage attention mechanism is introduced to automatically learn correlations across both time and space dimensions. Experiments conducted on the Tennessee-Eastman process demonstrate the superiority of our proposed method. It shows over 10% accuracy enhancement in diagnostic performance compared to state-of-the-art methods. Furthermore, the method can gradually map data to a discriminative feature space with better intra-class compactness and inter-class separability, reflecting its reliability and validity.

关键词： Fault diagnosis Correlation Attention mechanisms Accuracy Process control Production Transformers

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