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Scalable Approaches for Environmental Monitoring Solutions 23

Scalable Approaches for Environmental Monitoring Solutions

引用

23rd international conference on control Systems and computer Science Technologies, CSCS 2021

作者： Tovarnitchi, Vasile M. University Politehnica of Bucharest Faculty of Automatic Control and Computers Bucharest Romania

ISBN: (纸本)9781665439398

Digital technologies nowadays play a crucial role in many aspects of our lives, such as health, work, security, wellbeing, etc. A lot of such technologies are used in conjunction with Cloud Technologies. Cloud Technologies are seen as a pool of (virtually) unlimited amount of computing resources, which can be used on demand as a technical foundation for various 'as-a-Service' solutions. But in order to be able to use efficiently this 'infinite' amount of resources, special architectural approaches are needed. This paper presents a particular way of approaching and implementing scalability in software architectures, with a focus on environmental monitoring systems. Also, is presented the author's vision of implementing digital twins using advantages of actors from the actor model. © 2021 IEEE.

关键词： Scalability

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Research and system architecture design of dispatching data chain technology for load regulation and control

Research and system architecture design of dispatching data ...

引用

Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Hejian Wang Xuan Li Jiaxing Xuan Qinglei Guo Lihua Zhao Ke Yang State Grid Blockchain Technology (Beijing) Co. Ltd. State Grid Digital Technology Holding Co. Ltd. Beijing China State Grid Jibei Electric Power Company Beijing China

The construction of the new power system makes the traditional “source and load” grid regulation mode change to “source and load interaction”, and the large-scale access of load-side distributed power sources, the unified management of adjustable loads, and the participation of multiple entities in energy operation have put forward new requirements for the security, trustworthiness, openness and transparency of the load dispatching environment. In this context, this paper carries out research on the consensus mechanism, encryption technology and identity authentication of data chain for load regulation and control, and designs the system architecture of dispatching data chain to effectively solve the problems of mutual trust among multiple parties, data security and business authentication in the process of business interaction involving a large number of external entities in the field of regulation and control, so as to build a dispatching environment with information transparency, mutual supervision and mutual trust for power grid enterprises.

关键词： Data security Systems architecture Process control Authentication Reliability theory Reliability engineering Regulation

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Analysis of Performance Enhancement of Composite Specimen of Wood and PLA prepared through the computer controlled FDM Technique 3

Analysis of Performance Enhancement of Composite Specimen of...

引用

3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Bhayana, Mohit Singh, Jaswinder Singh, Jaspreet Goyal, Rakesh Gupta, Rupesh Chitkara University Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9781665438117

Biocomposites as the feedstock material for additively manufactured parts have been gaining a considerable attention since long back due to the rising concerns about depleting resources and worsening pollution. Among all the bio composite materials available in the market, the bio composite made up by mixing wood and PLA is having much popularity in computer controlled fused deposition modelling (FDM), an additive manufacturing process. Many researchers have examined the natural fiber as wood reinforced PLA composites for the substitution of non-renewable based materials. The properties of wood fibers such as wood content by weight, aspect ratio, moisture present in wood, etc., play a vital role in bio composites because these properties affect the mechanical properties to a great extent. In this review paper, the effects of printing process parameters and the properties of wood as a reinforcement affecting the mechanical properties of bio composite are studied. Also, the selection of parameters based upon optimization techniques to optimize the mechanical properties will be reviewed. © 2021 IEEE.

关键词： Additives

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Design of General Automated Test System for Integrated Guidance and control Device

Design of General Automated Test System for Integrated Guida...

引用

international conference on Electronics and Information Technology (EIT)

作者： Mingzhe Liu Wei Zhou Gaitang Wang Yue Jiang Xi'an Institute of Modern Control Technology Xi’an China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

A universal automated testing system for integrated control devices has been developed to facilitate the automated testing and verification of these devices. This system encompasses the architecture of the testing framework as well as the design and implementation of the associated testing software. By simulating various on-board components that interact with the integrated guidance and control device, the system enables comprehensive functional testing and verification in accordance with the specified testing requirements. The results of the tests indicate that the system operates reliably and effectively meets the functional, performance, and reliability testing needs of diverse products. Consequently, this advancement enhances testing efficiency and fulfills the automation integration testing requirements for integrated control devices.

关键词： Performance evaluation Automation Process control computer architecture Software Software reliability Information technology Testing Centralized control

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Tabular Monte Carlo Reinforcement Learning for Valve Position control

Tabular Monte Carlo Reinforcement Learning for Valve Positio...

引用

Electrical, computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Piotr Felisiak Department of Cryogenics and Aerospace Engineering Wrocław University of Science and Technology Wrocław Poland

The work solves the problem of valve stem position control using a tabular, off-policy Monte Carlo reinforcement learning. The work proposes a model of control valve, involving the stem friction. The control problem is seen as a finite Markov decision process. The reinforcement learning algorithm is a slight modification of the Sutton–Barto Monte Carlo policy estimation method. Such method learns value functions and optimal policies from experience in the form of learning episodes. The modification involves shortening of learning episodes, for their more economical exploitation. As the original algorithm, the method can be used to learn optimal behavior directly from interaction with the environment, with no model of the environment’s dynamics. It is easy and efficient to focus such a method on a small subset of states. Such method does not bootstrap and therefore it is less harmed by violations of the Markov property. The proposed control method has been verified by a series of computer simulations, where it is compared with a three-state bang-bang controller. The simulations show that the proposed controller performs stable and as desired. The proposed method requires very few tuning hyperparameters, which are easy to tune. The target policy learns in few minutes, when interacting with the valve model. The work provides insights on possibilities and limitations of the Monte Carlo learning method, which is still unsettled.

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Construction of LC Filter Integrated Circuit and Analysis of control System Function

Construction of LC Filter Integrated Circuit and Analysis of...

引用

Power Electronics, computer Applications (ICPECA), IEEE international conference on

作者： Mingzhou Jiang Rui Bi Zimo Xue Beijing Jiaotong University Beijing China Hainan University Haikou China Nanjing University of Posts and Telecommunications Nanjing China

As one of the important components of integrated circuits, inductors play an important role in filtering, energy storage and impedance matching in many fields, such as communication and manufacturing. However, the existing traditional filters are not highly integrated, and have the defects of large volume and high energy consumption. Therefore, in this paper, constructs an integrated square spiral inductor model based on MEMS technology by using a finite element simulation method. First, through the simulation analysis of performing the model, the inductance coefficient is calculated and we analyze the spatial electromagnetic field distribution characteristics, and then the functional rationality of the model is verified. It is used to build the LC filter and analyze the principle of different filtering functions. Second, based on Proteus simulation software, an integrated circuit single-chip microcomputer control system with an LC filtering function composed of square spiral inductors is built. Finally, through the analysis of the simulation results, the results show the LC filter integrated circuit constructed in this paper has the characteristics of low-pass, high-pass, band-pass and band-cease filtering functions, and can be associated with the external control system to realize the intelligent operation of the integrated circuit.

关键词： Band-pass filters Micromechanical devices Analytical models Spirals Filtering Simulation control systems

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A Detection and Classification Method of Asphalt Pavement Crack based on vision Transformer 23

A Detection and Classification Method of Asphalt Pavement Cr...

引用

proceedings of the 2023 3rd international conference on Big Data, Artificial Intelligence and Risk Management

作者： Yulong Yang Chen Guo Chen Zuo Bingxin Yang School of Information Engineering Chang? an University China College of Transportation Engineering Chang? an University China

ISBN: (纸本)9798400716478

Road surface distress detection is an important component of smart maintenance of transportation infrastructure, and crack damage is a common and far-reaching category of distress. Accurately and quickly detecting road cracks is of great significance for road prevention work. In recent years, the deep learning technology represented by convolutional neural networks has been widely used in the field of road damage detection. However, the convolutional neural network relies on a stack of convolutional layers to extract visual features from training images. Constrained by its ability to characterize long-distance correlations, it is challenging to apply convolutional neural network to tackle transverse cracks and longitudinal cracks in the real-world scenario. As an emerging deep learning architecture, Transformer model has received considerable attention in the fields of natural language processing and computer vision. In this paper, we employ the vision Transformer (ViT) to realize road damage classification. First, the histogram equalization technique is adopted to fulfill image preprocessing. The program enhances image contrast and eliminates the influence of illumination variation. Second, ViT, ResNet, DenseNet, and EfficientNet are separately implemented. We expand the training dataset with the data augmentation technique. Third, the computer evaluates the classification quality by means of accuracy, F1-score, and recall. A group of datasets of varying size are used to examine deep neural networks. The experiment result indicates that ViT outperforms CNN models in terms of classification quality. The long-range crack structures are reasonably identified by a fine-tune ViT model.

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3rd international Workshop on Human Brain and Artificial Intelligence, HBAI 2022, held in conjunction with 31st international Joint conference on Artificial Intelligence and 23rd European conference on Artificial Intelligence, IJCAI-ECAI 2022

3rd International Workshop on Human Brain and Artificial Int...

引用

3rd international Workshop on Human Brain and Artificial Intelligence, HBAI 2022, held in conjunction with 31st international Joint conference on Artificial Intelligence and 23rd European conference on Artificial Intelligence, IJCAI-ECAI 2022

ISBN: (纸本)9789811982217

The proceedings contain 19 papers. The special focus in this conference is on Human Brain and Artificial Intelligence. The topics include: Brain Network Analysis of Hand Motor Execution and Imagery Based on Conditional Granger Causality;a Hybrid Brain-computer Interface for Smart Car control;a Spiking Neural Network for Brain-computer Interface of Four Classes Motor Imagery;virtual Drone control Using Brain-computer Interface Based on Motor Imagery Brain Magnetic Fields;brain controlled Manipulator System Based on Improved Target Detection and Augmented Reality Technology;Optimization of Stimulus Color for SSVEP-Based Brain-computer Interfaces in Mixed Reality;white Matter Maturation and Hemispheric Asymmetry During Childhood Based on Chinese Population;A Digital Gaming Intervention Combing Multitasking and Alternating Attention for ADHD: A Preliminary Study;A BCI Speller with 120 Commands Encoded by Hybrid P300 and SSVEP Features;Delving into Temporal-Spectral Connections in Spike-LFP Decoding by Transformer Networks;a Mask Image Recognition Attention Network Supervised by Eye Movement;DFC-SNN: A New Approach for the Recognition of Brain States by Fusing Brain Dynamics and Spiking Neural Network;DSNet: EEG-Based Spatial Convolutional Neural Network for Detecting Major Depressive Disorder;SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder;Emotion Recognition from EEG Using All-Convolution Residual Neural Network;Salient Object Detection with Fusion of RGB Image and Eye Tracking Data;multi-source Domain Adaptation Based on Data Selector with Soft Actor-Critic.

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BCViMer 1.0: A Deep Network Pipeline for the Identification of Breast Cancer Using MRI Data

BCViMer 1.0: A Deep Network Pipeline for the Identification ...

引用

Industrial Electronics: Developments & Applications (ICIDeA), IEEE international conference on-

作者： Tejaswini Das Ashis Kumar Ratha Sushree Sasmita Dash Rakesh Pandey Sabyasachi Mohanty Debasish Swapnesh Kumar Nayak Department of Statistics Utkal University Bhubaneswar INDIA School of Computer science and Engineering IILM University Greater Noida Uttar Pradesh INDIA Department of AI & DS IIMT College of Engineering Greater Noida INDIA Department of Computer Science and Engineering Centurion University of Technology and Management Bhubaneswar INDIA

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Breast cancer ranks among the foremost causes of cancer-related deaths in women globally, with early diagnosis via advanced imaging techniques significantly enhancing patient outcomes. Convolutional neural networks (CNNs) demonstrate considerable potential in the automation of breast cancer detection. Nonetheless, their constrained capacity to capture global image features may hinder their performance. This study presents BCViMer 1.0, a deep learning pipeline utilizing the vision Transformer (ViT) to overcome existing limitations and enhance breast cancer classification accuracy from MRI images. This research aimed to assess the efficacy of BCViMer 1.0 relative to established CNN architectures, specifically ResNet-50, ResNet-101, VGG-16, and VGG-19. Experimental results indicate that BCViMer 1.0 excels these CNN models, attaining an average classification accuracy that is 4.15% higher. The enhanced performance of BCViMer 1.0 is due to its capacity to capture long-range dependencies and global context in MRI images, facilitated by the self-attention mechanism of the ViT. In conclusion, BCViMer 1.0 exhibits superior diagnostic accuracy compared to conventional CNN-based models, representing a significant advancement in breast cancer detection. This study emphasizes the capabilities of vision Transformers in medical imaging and establishes a basis for future investigation in clinical applications.

关键词： Deep learning computer vision Accuracy Magnetic resonance imaging Pipelines Transformers Breast cancer Data models Robustness Convolutional neural networks

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Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices 1

引用

3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

作者： Wen, Wenkan Liu, Yiwen Gao, Yanxia Zhu, Zhirong Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Machine learning

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