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Blockchain-based data control for complex product assembly collaboration process 3

Blockchain-based data control for complex product assembly c...

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3rd World Symposium on Software Engineering, WSSE 2021

作者： Cai, Hongxia Tan, Qiqi Sch. of Mechatronic Eng. and Automat. Shanghai Univ. Shanghai Key Lab. of Intelligent Mfg. and Robotics Shanghai University Shanghai200444 China

ISBN: (纸本)9781450384094

The collaborative development model of complex products brings the challenge to the data interaction management. There are many manufacturers and suppliers involved in the whole life cycle of the assembly process, which makes it difficult to ensure the data security and traceability. The Hyperledger-Fabric architecture in blockchain technology has modular design, pluggable architecture, complete authority control and security, which can well solve the data security and traceability management in the collaborative development of complex products. Therefore, this paper proposes a framework based on the Hyperledger-Fabric architecture of blockchain for the whole life cycle data management of complex products. We also demonstrate the effectiveness of our proposed new framework integrating blockchain technology through the case of quality data control during the aircraft final assembly collaborative process. © 2021 ACM.

关键词： Blockchain

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An iterative feature selection procedure for a classification problem based on the method of logical analysis of data 3

An iterative feature selection procedure for a classificatio...

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3rd international Scientific conference on Applied Physics, Information Technologies and Engineering, APITECH-III 2021

作者： Kuzmich, R.I. Stupina, A.A. Zhirnova, I.S. Slinitsyna, O.V. Boubriak, I.I. Department of Business Informatics and Business Process Modeling Siberian Federal University Krasnoyarsk Russia Department of Digital Control Technologies Siberian Federal University Krasnoyarsk Russia Department of System Analysis and Operation Research Reshetnev Siberian State University of Science and Technology Krasnoyarsk Russia Department of International Management Krasnoyarsk State Agrarian University Krasnoyarsk Russia Department of Economics and Business Process Management Siberian Federal University Krasnoyarsk Russia Department of Biochemistry Oxford University Oxford United Kingdom

An iterative procedure for selecting features for classifying observations is proposed. The main principles of the proposed iterative procedure are ranking and selection of features according to the frequency of their use when constructing logical patterns based on the method of logical analysis of data. The empirical confirmation of the expediency of this procedure is given. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Classification (of information)

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A3BAC: Attribute-Based Access control Model with Anonymous Access 3rd

A3BAC: Attribute-Based Access Control Model with Anonymous A...

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3rd international conference on Security and Privacy in New Computing Environments, SPNCE 2020

作者： Zhang, Runnan Liu, Gang Li, Shancang Wei, Yongheng Wang, Quan School of Computer Science and Technology Xidian University Xi’an710071 China Department of Computer Science and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom

ISBN: (纸本)9783030669218

Researchers believe that anonymous access can protect private information even if it does not store in authorization organization. The current solution supports anonymous access by using a certificate instead of a subject identity or Attribute-Based Encryption. In a solution using a certificate, access may be linked to the certificate, which poses a risk of re-identification. The encryption of objects based on attributes limits the types of objects. The ABAC with anonymous access proposed in this paper called A3BAC inherits the features of the ABAC model, such as fine-grained authorization, policy flexibility, and unlimited object types. By combining HABS, it strengthens the identity-less of ABAC, so that the access does not involve a unique identification, reducing the risk of subject identity re-identification. It is a secure anonymous access framework. © 2021, ICST Institute for Computer Sciences, Social informatics and Telecommunications Engineering.

关键词： Cryptography

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Design and development of an Autonomous Self-Driving RC Car for the Emirates robotics Competition in 2023: With emphasis on Hardware and Software

Design and development of an Autonomous Self-Driving RC Car ...

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3rd international conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Jose, Jerin Shaikh, Saud Reddy, Vahin Prasad Abhilash, Adithya Singh, Joravar Antony, Levin Malik, Ashish Mondal, Amit Kumar Choudhary, Sachi Dept. of Mechatronics Engineering Manipal Academy of Higher Education Dubai United Arab Emirates Manipal University Jaipur India School of Computer Science UPES Dehradun India

An ongoing challenge encountered by contemporary engineering educators pertains to the effective cultivation of students' enthusiasm towards the fields of science and engineering. One of the most effective strategies to address this challenge involves the implementation of a technological competition that encompasses elements of publicity, technology, and student engagement. This study investigates the construction of an autonomous self-driving 1/10th scale remote-controlled car. The objective of the project was to actively engage in and emerge victorious in the Emirates robotics Competition of 2023, while enabling the research into suitable technologies for its uses in urban autonomous vehicles. The RC car platform implemented in this study utilizes the Waveshare JetRacer Pro AI Kit B, which is built upon the NVIDIA Jetson Nano platform. The RC car platform underwent customization in accordance with the specific requirements of the competition, resulting in its ability to execute rapid and secure maneuvers. Autonomous driving systems encompass a range of essential functions, including obstacle detection, traffic sign detection, road following, and vehicle control. These functions are achieved through the integration of various pre-existing algorithms that have been appropriately modified to meet the specific requirements of autonomous driving. This paper presents a comprehensive analysis of the hardware and software components of the RC car, as well as an in-depth examination of the deep learning architecture and effective modifications made to the existing algorithms, which will serve grounds for better navigation in live environments that modern vehicles are subjected daily. © 2025 Elsevier B.V.. All rights reserved.

关键词： Robots

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Development of Inverse Kinematics Learning Media Using Hexapod Robot for robotics Course 3

Development of Inverse Kinematics Learning Media Using Hexap...

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3rd international conference on Electrical, Electronics, informatics, and Vocational Education, ICE-ELINVO 2020

作者： Suprihono, H. Mashoedah Department of Electronics Engineering Education Yogyakarta State University Indonesia

This study aims to (1) get an inverse kinematic learning media design, (2) find out inverse kinematic learning media performance, (3) find out feasibility level of inverse kinematic learning media using hexapod robot. This type of research is research and development using ADDIE model, with stages: (1) Analysis, (2) Design, (3) Develop, (4) Implement, and (5) Evaluate. The object of research is hexapod robot. The subjects of research are students of Electronic Engineering Education Study Program who took the concentration of Industrial Electronics. Methods of data collection using classroom observation and questionnaires. The data analysis technique is descriptive qualitative analysis. The result of this research and development shows: (1) inverse kinematic learning media design using hexapod robot, (2) the performance through black box testing get result 100%. Each robot's feet can move with an average axis error percentage x of 7,4%, y of 11,67% and z of 7,87% and produces 14 types of movement, (3) feasibility level of learning media based on the assessment of material expert obtained 94,46% (very feasible), media expert obtained 96,2% (very feasible), and users obtained 80,99% (feasible). © 2021 Published under licence by IOP Publishing Ltd.

关键词： Inverse kinematics

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Improving the Efficacy of Binary Communication Security in Communication Networks of Smart Cities

Improving the Efficacy of Binary Communication Security in C...

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3rd international conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2024

作者： Salama, Ramiz Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Mersin 10 Nicosia Turkey Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey

ISBN: (纸本)9789819627660

Recently, smart cities have become more and more popular as a way to enhance society and the standard of living for its people. These are the results of the increased social and economic prospects that the advancement of information and communication technology (ICT) has brought about. But there are certain issues with our presumptions about privacy and security. The usage of cellphones and other technologies has already enhanced people’s engagement and sense of connection. Gadgets, smart energy meters, and security appliances are commonplace these days. The Internet of Things will soon be fully connected to homes, public spaces, healthcare systems, and other social institutions. In this work, we improve the security performance of communication networks in smart cities by implementing a novel framework and scheme to guarantee strong data secrecy and authentication. The smart city system can set up shared session key procedures and mutual authentication to secure a two-way communication channel between the control center and smart meters. Our comprehensive simulation was used to test and evaluate the security performance of the smart city communication network both with and without our suggested technique. Throughput, latency, load, and traffic received packets per second were all measured. The resources available in our suggested solution were further limited by the fact that we used a network intrusion detection system (NIDS) and a man-in-the-middle (MITM) attack to evaluate and validate the security criteria. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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An Excited Binary Grey Wolf Optimizer for Feature Selection in Highly Dimensional Datasets 17

An Excited Binary Grey Wolf Optimizer for Feature Selection ...

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17th international conference on informatics in control, automation and robotics (ICINCO)

作者： Segera, Davies Mbuthia, Mwangi Nyete, Abraham Univ Nairobi Dept Elect & Informat Engn Harry Thuku Rd Nairobi Kenya

ISBN: (纸本)9789897584428

Currently, feature selection is an important but challenging task in both data mining and machine learning, especially when handling highly dimensioned datasets with noisy, redundant and irrelevant attributes. These datasets are characterized by many attributes with limited sample-sizes, making classification models overfit. Thus, there is a dire need to develop efficient feature selection techniques to aid in deriving an optimal informative subset of features from these datasets prior to classification. Although grey wolf optimizer (GWO) has been widely utilized in feature selection with promising results, it is normally trapped in the local optimum resulting into semi-optimal solutions. This is because its position-updated equation is good at exploitation but poor at exploration. In this paper, we propose an improved algorithm called excited binary grey wolf optimizer (EBGWO). In order to improve on exploration, a new position-updating criterion is adopted by utilizing the fitness values of vectors (X) over right arrow (1), (X) over right arrow (2) and (X) over right arrow (3) to determine new candidate individuals. Moreover, in order to make full use of and balance the exploration and exploitation of the existing BGWO, a novel nonlinear control parameter strategy is introduced, i.e. the control parameter of (a) over right arrow is innovatively decreased via the concept of the complete current response of a direct current (DC) excited resistor-capacitor (RC) circuit. The experimental results on seven standard gene expression datasets demonstrate the appropriateness and efficiency of the fitness value based position-updating criterion and the novel nonlinear control strategy in feature selection. Moreover, EBGWO achieved a more compact set of features along with the highest accuracy among all the contenders considered in this paper.

关键词： Meta-Heuristic Feature Selection Evolutionary Algorithm Binary Grey Wolf Optimizer

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Computational methods based on fuzzy control algorithms for operational control and identification of control systems in smart production 3

Computational methods based on fuzzy control algorithms for ...

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3rd international Scientific conference on Applied Physics, Information Technologies and Engineering, APITECH-III 2021

作者： Zolkin, A.L. Losev, A.N. Sychanina, S.N. Melnik, T.E. Buryakova, O.S. Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara443010 Russia Department of Applied Informatics Russian State Agrarian University - Moscow Timiryazev Agricultural Academy Moscow127550 Russia Department of Management Federal State Budgetary Educational Institution of Higher Education "Kuban State Agrarian University named after I.T. Trubilin" Krasnodar350044 Russia Faculty of algebra and mathematical methods in economy Orel State University named after I.S. Turgenev Orel302026 Russia of DSTU in Shakhty Rostov region Shakhty346500 Russia

The problem of the functioning of complex technical systems is studied in this article. The authors consider the problem of structural and functional redundancy (the complexity of formalizing computational methods for dynamic and nonlinear systems used in modern industrial production). On the example of considering the structure of the climate control subsystem for a closed loop of an industrial unit, a fuzzy configurator model for controlling the operation of a refrigerator is formalized. It is reduced to: linearization of the reading of instantaneous temperatures from various loops of the considered control system, followed by fuzzification and antecedent analysis. Eventually this allows to implement a complex method of fuzzy control and calculation of structural redundancy in any configuration with a different probability ratio. The authors also raise the question of the comparability of the obtained data and application of the developed method as a tool for predictive analytics in technological processes of production management. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Computational methods

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Neuromorphic system development based on adaptive neuronal network to modulate synaptic transmission in hippocampus 3

Neuromorphic system development based on adaptive neuronal n...

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3rd international conference "Neurotechnologies and Neurointerfaces", CNN 2021

作者： Lebedeva, Albina Gerasimova, Svetlana Fedulina, Anastasiya Mishchenko, Mikhail Beltyukova, Anna Matveeva, Mariya Mikhaylov, Alexey Pisarchik, Alexander Kazantsev, Victor Neurotechnologies Department N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Laboratory of Physics and Technology of Thin Films Department of Solid State Electronics and Optoelectronics Research Institute of Physics and Technology N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Department of Theory of Oscillations and Automatic Control Faculty of Radiophysics N.I. Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Biomedical Technology Technical University of Madrid Pozuelo de Alarcón Madrid Spain Center for Technologies in Robotics and Mechatronics Component Innopolis University Innopolis Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665424745

The project presents fundamental research on the development of concept and basic components of physical circuit based on optoelectronic elements (neuromorphic generators, memristors, optical fibers). This network can replace some functions of brain’s neural networks (such as memory) through adaptive stimulation of neural interfaces at the cellular and cellular networks level. Developing learning system will perform the electrical stimulation of brain areas based on the neural activity recorded and will include electronic devices for recording and processing signals. In addition, this system will include electronic devices for generating electrical stimuli based on hardware models of neurons and memristive devices. The basic idea is to integrate an artificial network with living microcircuits of the brain. The artificial network is capable of adaptively generating electrical output signals and can stimulate a given area of the brain according to the recorded pattern of input signals. We suppose that such a network will be able to reproduce the functions of brain systems, based on the use of learning algorithms. It can be used to expand the functionality of these brain systems and to replace lost functions in pathology or trauma. © 2021 IEEE.

关键词： Memristors

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Automatic Feature Extraction for Bearings' Degradation Assessment using Minimally Pre-processed Time series and Multi-modal Feature Learning 3

Automatic Feature Extraction for Bearings' Degradation Asses...

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3rd international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2022

作者： Alfeo, Antonio L. Cimino, Mario G.C.A. Gagliardi, Guido Department of Information Engineering University of Pisa Pisa Italy Bioengineering and Robotics Research Center E. Piaggio University of Pisa Pisa Italy University of Florence Florence Italy Department of Electrical Engineering KU Leuven Leuven Belgium

Maintenance activities can be better planned by employing machine learning technologies to monitor an asset's health conditions. However, the variety of observable measures (e.g. temperature, vibration) and behaviours characterizing the health degradation process results in time-consuming manual feature extraction to ensure accurate degradation stage recognitions. Indeed, approaches able to provide automatic feature extraction from multiple and heterogeneous sources are more and more required in the field of predictive maintenance. This issue can be addressed in a data-driven fashion by using feature learning technology, enabling the transformation of minimally processed time series into informative features. Given its capability of discovering meaningful patterns in data while enabling data fusion, many feature learning approaches are based on deep learning technology (e.g. autoencoders). In this work, an architecture based on autoencoders is used to automatically extract degradation-representative features from minimally preprocessed time series of vibration and temperature data. Different autoencoder architectures are implemented to compare different data fusion strategies. The proposed approach is tested considering both the recognition performances and the quality of the learned features with a publicly available real-world dataset about bearings' progressive degradation. The proposed approach is also compared against manual feature extraction and the state-of-the-art technology in feature learning. Copyright © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Degradation

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