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Features of Ensuring IoE (Internet of Energy) in E-judgment: Technical Formation and Storage of Metadata

Features of Ensuring IoE (Internet of Energy) in E-judgment:...

引用

International conference on control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Jakhongir Jurayev Muxtabar Xusanova Zhanagul Ismailovna Balkibayeva Shukhrat Shulliev Ruslan Sadikov Dilmurod Karimov Tashkent State University of Law Tashkent Uzbekistan Judge of the Constitutional Court Republic of Uzbekistan Tashkent Uzbekistan

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

This paper examines the technical aspects of implementing Internet of Energy (IoE) principles in E-Judgment systems, focusing on the formation and storage of energy-related metadata. Through comparative and inductive analysis of existing literature and technical reports, we explore how IoE concepts can address inefficiencies in energy consumption and data management within digital justice platforms. Key findings include the potential for unified energy metadata structures, edge computing applications for data preprocessing, and blockchain technologies for ensuring data integrity. We propose strategies for developing energy-efficient metadata management systems that optimize E-Judgment processes. While IoE approaches show promise for enhancing the sustainability and efficiency of digital court systems, challenges in process adaptation and data confidentiality remain. This research highlights IoE as a critical tool for optimizing energy use and improving the effectiveness of E-Judgment systems through advanced metadata management techniques.

关键词： Solid modeling Sharding data preprocessing Energy Internet Metadata Writing Energy efficiency Blockchains Sustainable development Edge computing

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Backscattering Characteristics of Millimeter Wave Landing Radar on Mars Surface 3

Backscattering Characteristics of Millimeter Wave Landing Ra...

引用

2022 3rd International conference on Computer Information and Big data Applications, CIBDA 2022

作者： Zhang, Aijun Zheng, Shi He, Yapeng Tan, Xiaomin Dang, Hongxing Yuan, Qigang Xi'an Institute of Space Radio Technology Xi'an710100 China

ISBN: (纸本)9783800758760

During the EDL (entry, descent and landing) prcess of the TianWen-1 Mars probe, the millimeter wave landing radar provides the GNC (Guidance, Navigation and control) sub-system with high-precision ranging and velocity measurement data between the landing rover and the Martian surface to ensure the landing rover landing safely. The radar backscattering coefficient of Mars surface is the key characteristic parameter of the target detection, which directly affects the radar detection range and measurement accuracy, and is the most critical and uncertain parameter in the design process of millimeter wave landing radar. This paper proposes a method to calculate the radar scattering coefficient of Martian surface. This method, through setting up the mathematical model for radar scattering coefficient with incident angle, relief, and the components of the Martian surface. This radar scattering coefficient, is very important input for millimeter wave landing radar for range and precision analysis. At the same time, the backscattering coefficients obtained by electromagnetic calculation and in-orbit telemetry data inversion are compared and analyzed, which effectively verifies the correctness of the electromagnetic modeling of the radar backscattering coefficients of Mars surface, and provides a theoretical basis for the design and improvement for the deep-space landing radar. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Uncertainty analysis

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Random Search Optimization Approach for Human-Robot Systems modeling

Random Search Optimization Approach for Human-Robot Systems ...

引用

2022 International Russian Automation conference, RusAutoCon 2022

作者： Zaitceva, Iuliia Kuznetsov, Nikolay V. Andrievsky, Boris St. Petersburg State University Control of Comlex Systems Lab. Institute of Problems in Mechanical Engineering of Ras Applied Cybernetics Department St.Petersburg Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Human operator behavior in manual control of any electro-mechanical system can be complex and varied. It is possible to single out human operator behavior well described by linear differential equations. It allows for studying many tasks of monotonous manual control, such as tracking a reference signal. The analysis of such a system forms the basis for understanding its stability and controllability features. It is necessary to know the parameters of the human operator model for studying and designing such a system, which may depend on the parameters of the plant. This paper proposes an approach to human operator parameters calculation in manual control systems based on derivative-free optimization method. For three coefficients of the PID controller, the parameters of the human operator model were calculated using the proposed algorithm in MATLAB/Simulink. The evolution of the system parameters in the process of searching for the extremum of the selected function is illustrated graphically. The proposed method can be applied as part of the robot's intelligent control in real-time. © 2022 IEEE.

关键词： Human robot interaction

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A Novel Adaptive Just-in-Time Learning Soft Sensor modeling Method With a Density-Based data Update Strategy

A Novel Adaptive Just-in-Time Learning Soft Sensor Modeling ...

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Automation, Robotics and Computer Engineering (ICARCE), International conference on

作者： Yueli Song Yili Chu Yanan Du School of Health Management Anhui Medical University Hefei China School of Humanistic Medicine Anhui Medical University Hefei China

With the rapid development of information technology, data-driven soft sensors are often used in industrial process quality control. In recent years, soft sensor modeling method based on just-in-time learning (JITL) has been widely studied and applied due to its strong adaptability to time-varying characteristics of processes, simple modeling principles, and easy implementation. However, when the working conditions of the production process migrate due to various reasons such as sensor drift, catalyst deterioration, and raw material changes, the historical data used for modeling may not cover the current working condition, and the performance of the JITL model will also sharply decline even fail. Therefore, this article proposes a density based modeling data adaptive update management mechanism under the JITL framework to effectively enhance the adaptive tracking ability of soft sensors for process condition migration, thereby maintaining the longterm effectiveness of the model in practical use. Finally, the advantages of the proposed method were verified through numerical simulation and comparative analysis.

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Improved NSGA-ii based process dynamic multi-objective algorithm

Improved NSGA-II based process dynamic multi-objective algor...

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control, Electronics and Computer Technology (ICCECT), IEEE International conference on

作者： Yaze Zhang Beijing Key Laboratory on Integration and Analysis of Large-Cale Stream Data North China University of Technology Beijing China

The NSGA-ii-T algorithm is proposed to solve the scheduling problem with dynamic changes in process data processing in the service Internet, modelling the problem as a dynamic multi-objective optimisation problem. In the event of regular changes in load, the solution in the next environment is predicted by the transformer model and added to the original population; in the event of changes caused by physical factors such as node downtime, the population is added randomly. The algorithm uses different means of adding populations to address the lack of diversity that can occur in static multi-objective algorithms when the environment changes. Experiments conducted on the WorkflowSim platform show that the NSGA-ii-T algorithm outperforms other classical algorithms in all performance metrics when the environment changes significantly, demonstrating the effectiveness of the algorithm.

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Automated Corrosion Log Quality control and Interpretation Using Machine-Learning

Automated Corrosion Log Quality Control and Interpretation U...

引用

2022 Offshore Technology conference Asia, OTCA 2022

作者： Zeghlache, Mohamed Larbi Rourke, Marvin Liu, Xiaotian Saudi Aramco Saudi Arabia GOWell

ISBN: (纸本)9781613998359

With the increasing focus on data mining and machine learning (ML) applications in the oil and gas industry, the substantial number of well integrity logs and variety of data types represent a suitable candidate for the implementation of automated corrosion log processing. Convolutional neural networks (CNNs) are used in many fields, especially for image processing and features recognition. On the other hand, genetic algorithms (GA) add a valuable benefit to data processing in terms of global search and optimization. This paper demonstrates the integration of ML techniques with legacy well integrity log data, improving the results and leading to a tangible time and cost savings. A downhole well integrity evaluation triangle comprises three important services for comprehensive diagnosis: 1) cement evaluation, 2) corrosion inspection, and 3) leak detection. These services produce multiple datasets from a variety of logging tools. The types and sources of these datasets include synthetic data from simulation and modeling, tool calibration and lab testing, as well as raw, processed, and interpreted data. This paper describes the use of advanced ML techniques to scrutinize and improve well integrity evaluation. The new process resolves the recurrent challenges of well integrity evaluation in complex completion and downhole environments. It also maximizes value from existing well and field data. Image features recognition enables major improvements in the data analysis, such as the identification of concentric casings and tubing as well as their respective collar depths and types. In addition, input parameters and well schematics promote quality control of recorded data versus the model data. The new process helps to identify casing and completion accessories and provides a reliable benchmark. Another major element is the qualitative and quantitative evaluation of corrosion using deep learning algorithms combined with the GA. This evaluation is achieved using feature extr

关键词： Genetic algorithms

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Automation of data center airflow and heat transfer analysis for transient scenario 24

Automation of data center airflow and heat transfer analysis...

引用

Proceedings of the 15th ACM International conference on Future and Sustainable Energy Systems

作者： Wojciech Szeliga Ariel Oleksiak Robert Roszak Poznan Supercomputing and Networking Center Poland and Poznan University of Technology Poland Poznan Supercomputing and Networking Center Poland Poznan University of Technology Poland

ISBN: (纸本)9798400704802

The global share of energy consumption by data centers has reached the level of several percent and continues to rise. For this reason, ensuring optimal energy efficiency of the cooling process in data centers is an essential matter. As most data centers are air-cooled, it is required to investigate an airflow pattern inside as well as the accompanying heat transfer phenomena to assess the efficiency of the cooling process of the object. What is more, the thermal state of a server room as well as its power consumption often has a transient nature not only due to turbulent airflow inside but also because of dynamically changing servers’ load and time-varying output conditions of the coolers as a consequence of their control algorithms. That is why it is necessary to use transient modeling to get a deep and complete insight into the nature of the airflow and heat transfer phenomena in a server room and the resulting energy usage. A technique which allows modeling of the airflow and heat transfer in a detailed, volumetric way is computational fluid dynamics (CFD). Unfortunately, employing CFD for complex models especially in a transient manner can become a tedious process for an engineer and a challenging task for computational resources. In this paper we present a framework which is designed for automatic numerical heat transfer analysis of data center objects, using in the backend OpenFOAM, a well-known open-source CFD toolkit. Besides the automation process, the presented framework includes the implementation of transient models of active server room equipment and modeling of their energy usage as well as support for using high performance computing (HPC) systems equipped with SLURM queuing system. The software description is followed by a presentation of its application for a study of a hypothetical server room. A transient scenario of rapid application of servers’ load is simulated, including an energy consumption analysis of cooling devices.

关键词： CFD data Center OpenFOAM automation cooling efficiency heat transfer

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Interplay of Attacker Behaviors and Dependability Attributes in Industrial control System Impact analysis

Interplay of Attacker Behaviors and Dependability Attributes...

引用

International conference on Dependable Systems and Their Applications (DSA)

作者： Alvi Jawad Jason Jaskolka Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331532390

ISBN: (纸本)9798331532406

Industrial control systems (ICS) are increasingly vulnerable to cyberattacks that can propagate to impact physical industrial processes. Existing research on ICS impact analysis views ICS dependability attributes as an afterthought and focuses primarily on attacks but not the attackers and their different behaviors. In this work, we include explicit considerations for ICS dependability attributes and attacker behaviors in ICS impact analysis. By adopting the Structured Cyberattack Impact analysis (SCIA) approach, our model-based impact analysis is demonstrated on a manufacturing ICS modeled in UPPAAL-SMC. More specifically, we visualize and quantify, respectively, using simulations and statistical model checking, the potential impact of data tampering attacks when performed by attackers with different behaviors (random, relentless, and informed). Furthermore, the impact analysis results highlight the interplay of ICS dependability attributes in terms of (1) how attacks on ICS security can impact system reliability and availability, (2) how improving security can improve system availability and reliability, and (3) how accepting some sacrifices on one attribute (availability) can end up improving other attributes (security and reliability).

关键词： Analytical models process control data visualization Model checking data models Manufacturing Security Reliability Computer crime Integrated circuit modeling

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Deep Learning Based Bow Wave modeling for UAV Aerial Refueling Docking

Deep Learning Based Bow Wave Modeling for UAV Aerial Refueli...

引用

International conference on Guidance, Navigation and control, ICGNC 2020

作者： Lun, Yuebin Wang, Honglun Liu, Yiheng School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China

ISBN: (纸本)9789811581540

The autonomous aerial refueling (AAR) has become an effective way to prolong air combat time in modern warfare. However, the control of the docking process should be precise enough because of the strong instability of the drogue. The bow wave is an important factor in disturbing the motion of drogue which is few mentioned in relevant research. A deep learning method is proposed to estimate the effect of the bow wave, which is hard to be predicted by accurate modeling. Considering the authenticity of model, the computational fluid dynamics (CFD) analysis is used to obtain the experiment data in the docking. Through the simulations and comparison, it is proved that the model fitted by deep learning is more accurate and the calculation speed is faster, which provides a feasible way for online optimization of AAR. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational fluid dynamics

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Temperature control System for Biological Liquefaction of Dead Livestock and Poultry Based on Fuzzy Neural Network PID

Temperature Control System for Biological Liquefaction of De...

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Information Technology,Big data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Hualong Li Xiang Huang Liqin Wu Xuanjiang Yang Miao Li Institute of Intelligent Machinery Hefei Institute of Materials Science Chinese Academy of Sciences Anhui China Anhui Agricultural University Anhui China Anhui University of Science and Technology Anhui China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

At present, the efficient bio-liquefaction technology of dead livestock and poultry has become an important technical means for the harmless treatment of dead livestock and poultry because of its advantages of high value-added liquid amino acid fertilizer. How to achieve the precise control of temperature in the process of efficient bio-liquefaction of dead livestock and poultry is a difficult problem that needs to be solved urgently. In this study, a temperature control system for the bio-liquefaction of dead livestock and poultry based on fuzzy neural network PID was proposed. The fuzzy neural network algorithm blurs inputs, models rules, and fuzzy reasoning through fuzzy set theory to simulate human fuzzy reasoning ability to achieve the modeling and control of complex systems. analysis showed that the temperature control was effective. The temperature change amplitude was ± 2. The experimental results showed that the system had the characteristics of simple structure, small static error, and fast dynamic response speed. An effective technical means for the precise control of temperature in the process of efficient bio-liquefaction of dead livestock and poultry is provided in this study, which has a wide range of application prospects.

关键词： Fuzzy control Temperature distribution PI control process control Fuzzy neural networks Livestock Robustness Temperature control PD control Oscillators

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