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Comparative analysis of permissioned blockchain frameworks for industrial applications

Blockchain(Research and Applications) 2023年第1期4卷 58-70页

作者： Vittorio Capocasale Danilo Gotta Guido Perboli Department of Control and Computer Engineering Politecnico di Torino10129 TurinItaly Service Innovation TIMTurinItaly Department of Management and Production Engineering Politecnico di Torino10129 TurinItaly

Blockchain is a technology that creates trust among non-trusting parties without relying on any ***,it has attracted the interest of companies operating in a multitude of ***,due to the number of different blockchain solutions that have emerged in the last few years and their rapid changes,it is challenging for such companies to orient their technological *** paper presents a comparative analysis of the key dimensions—namely,governance,maturity,support,latency,privacy,interoperability,flexibility,efficiency,resiliency,and scalability—of some of the most-used permissioned blockchain ***,we present the results of a performance evaluation considering the following frameworks:Hyperledger Fabric 2.2,Hyperledger Sawtooth 1.2,and ConsenSys Quorum 21.1(with both the GoQuorum client and the Hyperledger Besu client).The platforms were tested under similar conditions,and official releases were used,such that our findings provide a reference for companies establishing their technological orientation.

关键词： Blockchain performance evaluation Hyperledger Besu Hyperledger Fabric Hyperledger Sawtooth Quorum

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The Task Collaborative Migration Method for Marine IoT Based on Edge Computing-RESTful Architecture 6

The Task Collaborative Migration Method for Marine IoT Based...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Hu, Yifan Wang, Fuqiang School of Information and Control Engineering Qingdao University of Technology Qingdao China Shandong Computer Science Center and Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350344349

Marine Internet of Things (Marine IoT) has garnered increasing interest in monitoring oceanic environments. But establishing a Marine IoT system for observing and processing marine environmental data presents various challenges. These include limited underwater communication bandwidth, and uneven distribution of tasks among data-sensing devices. This paper proposes an Edge Computing-RESTful (ECR) architecture to efficiently integrate and process the substantial volume of data gathered through marine observations. 1) The ECR method is introduced for task collaborative migration, aiming to schedule and manage the tasks within the Marine IoT system. 2) Through unified scheduling and management of tasks, the edge gateway can facilitate migration, scheduling, and interaction of task loads among various edge sensing devices. The proposed dynamic and adaptive approach matches computing tasks with edge sensing devices, minimizing energy consumption and migration delay within the Marine IoT system. Simulation results validate the superiority of the proposed method for Marine IoT, showcasing improved performance metrics such as reduced time delays and lower energy consumption during task collaborative migration. © 2024 IEEE.

关键词： Edge computing

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Multi Modal EEG-Based Classification of Alzheimer's Disease and Mild Cognitive Impairment Using Hilbert Huang Transform

Multi Modal EEG-Based Classification of Alzheimer's Disease ...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Gopu, G. Sathish, S. Shanmathan, T.S. Suthakar, G. Sivakumaran, N. Geetha Devasena, M.S. Sri Ramakrishna Engineering College Electronics And Communication Engineering Coimbatore India National Institute Of Technology Instrumentation And Control Engineering Tiruchirappalli India Sri Ramakrishna Engineering College Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

This study investigates whether high-resolution brain-functional connectivity, which is measured by using Electroencephalography (EEG) combined with Hilbert Huang Transform (HHT) can be useful in Alzheimer Disease Diagnosis. The major issue with Alzheimer's disease (AD) is that it leads to cognitive decline and affects millions of people worldwide. Early prediction of the disease is mandatory and crucial for timely intervention and effective care. EEG signals, a non-invasive and cost-effective method to assess brain activity, have shown potential in predicting this disease. In this study, we propose an approach that utilizes EEG data combined with Hilbert-Huang Transform (HHT) and machine learning algorithms for classification which is a novelty of the study that enables early prediction of Alzheimer's disease. Our methodology was tested on a dataset comprising EEG signals recorded using 19 electrodes from both healthy individuals and patients with Alzheimer's and the Mild Cognitive Impairment (MCI). The results were promising, demonstrating high accuracy, sensitivity, and specificity. Machine learning techniques can be employed for categorizing EEG signals, but it essential to validate their effectiveness using large and diverse datasets. This research highlights the potential of HHT and EEG data in the prediction of this disease. Principal Component Analysis (PCA) is used for dimensional reduction of the dataset. Notably EEG signals combined with Hilbert Huang Transform and Principal Component Analysis gives better accuracy of 98.6% for KNN. This research highlights the potential of EEG analysis combined with advanced techniques for accurate classification of dementia-related disorders. © 2024 IEEE.

关键词： Empirical mode decomposition

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A General Method for Power Equipment State Classification Using Time Series Data Based on Attention Mechanism

A General Method for Power Equipment State Classification Us...

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2023 IEEE International Conference on Power Science and Technology, ICPST 2023

作者： Zhao, Wenqing Wang, Jifa Chen, Rui Liu, Liang Xu, Lijiao North China Electric Power University School of Control and Computer Engineering Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education Baoding China North China Electric Power University School of Control and Computer Engineering Baoding China

ISBN: (纸本)9798350311358

With refinement of power equipment, the identification and classification of its operation states is quite important for the stable operation of the system. As the power equipment states are rather complicated, it is difficult to realize general classification due to the limitation of data types and model characteristics when using deep learning. In the light of this, based on the time series data of various power equipment, an equipment state classification method is proposed by combining Recurrent Neural Network (RNN), Long short-term memory (LSTM) and attention mechanism. Firstly, RNN is used as the bottom frame network, and the LSTM core network integrating attention mechanism is added to construct the complex Feature Extraction Module (FEM). Secondly, main feature extraction is implemented in the LSTM network integrated with attention mechanism. Then, the extracted features are input into the complex FEM model and trained to match the evaluation state and obtain the evaluation results. In this paper, confusion matrix, F1micro and accuracy are used as evaluation indexes. Three cases were selected to verify the method and five groups of comparison models were set for each case. Results show that the proposed method has good accuracy in power equipment state classification and also has strong generalization ability in other data classification. © 2023 IEEE.

关键词： Long short-term memory

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Short-term Load Prediction of Integrated Energy System Based on MCNN-LSTM Neural Network 6

Short-term Load Prediction of Integrated Energy System Based...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Lv, You Shi, Yijun Tian, Helu School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9798350346671

The high coupling characteristics between various energy of integrated energy system (IES) and the uncertainty of each link of power-grid-load make the dynamic process of IES more complex than that of the traditional single-load system, which brings great difficulties to the effective operation of IES. To solve the above problems, this paper proposes a method for short term multivariate load prediction based on deep neural network. Firstly, the correlation between each single load is used to divide the categories, and the load data at the same time is reorganized into an image mode to maximize the use of correlation;then, the multi-channel convolutional neural networks (MCNN) are used to extract and fuse the features of the images, and the fused feature data is input into the long short-term memory (LSTM) neural network to realize the prediction of multi-load data from the time level;finally, the feasibility of the proposed method is verified by an actual example analysis. © 2023 IEEE.

关键词： Long short-term memory

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Research on Fine-grained Access control Technology for Energy Data Based on Blockchain

Research on Fine-grained Access Control Technology for Energ...

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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Liang, Yin Li, Jianbin Li, Shike North China Electric Power University School of Control and Computer Engineering Beijing102206 China

ISBN: (纸本)9798350357677

In data-sharing scenarios in the energy sector, it is essential to establish a sound and reliable access control mechanism to protect energy resources and make informed decisions. However, traditional access control has flaws such as centralization, low transparency, and low flexibility, making it challenging to meet the current security needs of energy data sharing. In addition, due to the distributed, dynamic, and sensitive nature of energy data, it is necessary to ensure its security and auditability. To solve the above problems, this paper proposes an auditable access control model for energy data based on blockchain. The model adopts the attribute-based access control (ABAC) authorization model, which uses attributes as the adjudication element to build access control policies while adding data and user hierarchy attributes to improve adjudication efficiency and meet the need for fine-grained authorization. Based on this authorization model, blockchain technology ensures the data-sharing process is decentralized and transparent. The access control process is automated through four types of smart contracts, and the traceability of blockchain is used to store the access control operation records to monitor the illegal behavior of data requesters. The experimental results show that the model can control the access rights of data requesters with different attributes and ensure the security and controllability of data. © 2023 IEEE.

关键词： Authorization

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Model-Based Optimization of Vaccination Strategies in Different Phases of Pandemic Virus Spread 18th

Model-Based Optimization of Vaccination Strategies in Diff...

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18th International Conference on Informatics in control, Automation and Robotics, ICINCO 2021

作者： Liu, Zonglin Omayrat, Muhammed Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany

ISBN: (纸本)9783031264733

This paper aims at applying optimal control principles to investigate optimal vaccination strategies in different phases of a pandemic. Background of the study is that many countries have started their vaccination procedures against the COVID-19 disease in the beginning of 2021, but supply shortages for the vaccines prevented that everyone could be vaccinated immediately. At the beginning of 2022, in contrast, the vaccine supply was ample, but the effectiveness of different existing vaccines to avoid infection by new virus variants was in doubt, as well as the acceptance of booster doses decreased over time. To account for these effects, two formulations of optimization tasks based on different epidemic models are proposed in this paper. The solution of these tasks determines optimal distribution strategies for available vaccines, and optimized vaccination schemes to reduce the need of booster doses for later phase. Effectiveness of these strategies compared with other popular strategies (as applied in practice) is demonstrated through a series of simulations © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vaccines

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Massive Natural Language Processing in Distributed Environment 20th

Massive Natural Language Processing in Distributed Environm...

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20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology Kielce Poland

ISBN: (纸本)9783031383175

Natural Language Processing (NLP) models are one of the most promising topics nowadays. Applications like ChatGPT uncover the power of such models and their broad applications. However, developing such models requires an extensive amount of training data sets to operate on. As such data sets grow, it becomes impossible to process them on a single machine, even using parallel computations. In this paper, the problem of distributing the NLP computations is taken into consideration. The Scalable Distributed Two-Layer Data Structures were used to organize the distributed environment. Stanford Core NLP library was used to perform accurate NLP computations. The experiments focused on the efficiency of the computation in the distributed environment and the accuracy of the predictions in relation to the scale of the distributions. The results clearly show that an increase in performance can be obtained without a significant decrease in accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Temperature-triggered liquid metal actuators for fluid manipulation by leveraging phase transition control

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International Journal of Smart and Nano Materials 2024年第4期15卷 730-742页

作者： Hongda Lu Jiayi Yang Mengqing Zhao Qingtian Zhang Jialu Wang Xiangbo Zhou Yipu Guo Liping Gong Zexin Chen Shi-Yang Tang Weihua Li School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongWollongongAustralia School of Mechanical Electronic and Control EngineeringBeijing Jiaotong UniversityBeijingP.R.China School of Mechanical and Manufacturing Engineering University of New South WalesSydneyAustralia School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Small-scale pumps for controlling microfluidics have promising applications in drug delivery and chemical *** metal(LM)demonstrates excellent flow pumping performance due to its simple structure and the electrocapillary effect under an electric ***,LM droplets risk escaping from constrained structures,which can lead to pump *** regulation is also a critical parameter in optimizing chemical reactions in fluidic systems,however,integrating it into a compact system remains ***,we develop a temperature-triggered gallium-based actuator(TTGA)by introducing a gallium(Ga)droplet wetted on a copper(Cu)plate as the core element for flow *** Cu plate prevents the Ga droplet from escaping the chamber and significantly increases the flow *** leveraging the electrochemical method to inhibit the supercooling effect of Ga,the TTGA enables activation/deactivation for flow actuation at different *** investigate the impact of electrode position,solution concentration,and applied voltage on TTGA’s pumping *** dynamically tuning the Ga droplet’s temperature to control phase transition,TTGA allows for accurate flow actuation ***,placing Ga and eutectic Ga-indium(EGaIn)droplets in different channels enables the expected flow divergence for fluids with different *** development of TTGA presents new opportunities in microfluidics and biomedical treatment.

关键词： Liquid metal actuators temperature sensation phase transition

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An Improved NSGAII Algorithm for Flexible Job-shop Scheduling Problem Based on A New Decoding Mechanism 13

An Improved NSGAII Algorithm for Flexible Job-shop Schedulin...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Li, Man Ma, Kai Guo, Shiliang Li, Shuai Yang, Bin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350315196

The flexible job-shop scheduling problem (FJSP) is a famous combinatorial optimization problem. FJSP is widely used in process manufacturing industry, so it is of great significance to study FJSP to improve production efficiency. In this paper, a non-dominated sorting genetic algorithm II based on a new decoding mechanism (NSGAII-NDM) is proposed. Firstly, the opposite-based learning strategy is introduced on the basis of NSGAII, and the sufficiency of global search is improved by generating the opposite value. Secondly, an improved adaptive crossover and mutation probability strategy is added, which can dynamically adjust the inherent crossover and mutation probability with the number of iterations and improve the convergence speed of the algorithm. Furthermore, based on fully active scheduling strategy, a new decoding mechanism is proposed, which can get a better scheduling scheme by arranging the machines and starting time of each process more reasonably. Finally, the improved algorithm is tested in a standard test case, which verifies the superiority of NSGAII-NDM in solving the flexible job-shop scheduling problem. © 2023 IEEE.

关键词： Production efficiency

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