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Towards the Application of Blockchain technology for Smart Grids in Kazakhstan 21

Towards the Application of Blockchain technology for Smart G...

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21st international conference on Advanced Communication technology (ICACT)

作者： Orazgaliyev, Dinar Lukpanov, Yerbolat Ukaegbu, Ikechi A. Nunna, H. S. V. Sivanand Kumar Nazarbayev Univ Sch Engn Elect & Comp Engn Dept Astana Kazakhstan

ISBN: (纸本)9791188428021

The concept of smart grid infrastructure is already being implemented and progressing rapidly. The number of countries are transforming the traditional power grid so that it will be able to maintain the smart grid requirements. Smart grid is known as an evolutionary power grid which is proposed to make intelligent decisions on its own based on a current state of electrical power system. Microgrid (MG) is a "building block" of smart grid which supposed to provide wider control capability into the system, thus allowing to operate without connection to the grid. However, the remote and autonomous control of the smart grid may result in problems related to security. The main purpose of this paper is to consider various application scenarios of the Blockchain technology in smart grid. The utmost example is smart contract feature. It allows to reduce cost and increase security of trading operations without including third parties. In other words, direct communication between seller and consumer is implemented. Particularly, paper explain how the security and resilience of the system can be improved by the application of Blockchain technology. Furthermore, decentralized machine-to-machine interaction scheme is proposed. First of all, auction based electricity trading platform is established in Jade platform, and further on electricity trading history is stored in blockchain. The proposed case study shows implementation possibility of smart contracts in smart grid environment.

关键词： Blockchain technology Smart grid Electric vehicle distributed energy Internet of Things

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Inverse Model Optimization by Differential Evolution to improve Neural Predictive Control

Inverse Model Optimization by Differential Evolution to impr...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Edgar Ademir Morales-Perez Hitoshi Iba Information Science & Technology The University of Tokyo Tokyo Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

In this paper a heuristic-optimizer based on Differential Evolution has been enhanced with Inverse-Model control signals, common in feedforward control methods, to improve a Neural Network-based Predictive control scheme (NPC). By using experimental data, we train LstM Neural Networks as model predictors, for the NPC and for the Inverse approximation, that supply our optimizer with a good starting point for the search of optimal control sequences. We achieve a search space reduction where convergence is reached faster by initializing our differential evolution-based optimizer with an initial population created from the Inverse Model. Moreover, our method surpasses conventional techniques, tested using nonlinear control benchmarks, proving an increase in results accuracy and the feasibility of combining Predictive Control with Evolutionary techniques.

关键词： Artificial neural networks Predictive models statistics Sociology Computational modeling Prediction algorithms Optimal control

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Robust and self-evolving IDS for in-vehicle network by enabling spatiotemporal information 21

Robust and self-evolving IDS for in-vehicle network by enabl...

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21st IEEE international conference on High Performance computing and Communications, 17th IEEE international conference on Smart City and 5th IEEE international conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Xiao, Junchao Wu, Hao Li, Xiangxue Jinggangshan University East China Normal University China CNCERT/CC China Department of Computer Science and Technology East China Normal University China

ISBN: (纸本)9781728120584

The Controller Area Network (CAN bus) is the most popular vehicle bus standard for communication between Electronics Control Units (ECUs) and related electronic devices. Modern vehicles are generally equipped with many (dozens of, or even hundreds of) electronic and intelligent devices so that drivers can gain a more comfortable driving experience. Protecting this increasingly complex vehicle ecosystem can be an arduous task, especially as the proliferation of data across different connected devices makes them more vulnerable than ever before. Intrusion detection systems (IDSs) have been found extremely helpful in monitoring CAN traffic and detecting potential intrusions. The paper proposes a novel and robust intrusion detection mechanism for in-vehicle CAN network by using spatiotemporal information enabled time series prediction. The proposed IDS analyzes the CAN traffic generated by the in-vehicle network in real time and identifies the abnormal state of the vehicle practically. Our model utilizes substantially both the time and space information of the instructions information sent by the ECUs in detecting the intrusions and thus outperforms the classical Long Short Term Memory model. The intrusion detection mechanism provides strong robustness in the sense that it can perform fast online learning of packet features (while the vehicle is running) to implement new protocols for intrusion detection (enabling to capture the fresh state of the vehicle) if the underlying communication protocol of the vehicle has changed. In particular, the IDS can evolve by itself as exotic environment factors (such as road conditions, atmospheric conditions, etc.) change and manage its model by itself in a self-organized way. Experiment results demonstrate the performance of the model and the effectiveness against both known and unknown attacks. © 2019 IEEE.

关键词： Vehicles

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A study on Coordination of Exercise Difficulty in Cognitive Training System for Older Adults

A Study on Coordination of Exercise Difficulty in Cognitive ...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Daisuke Kitakoshi Kentarou Suzuki Masato Suzuki National Institute of Technology Tokyo College Tokyo Faculty of Health Sciences Kyorin University Tokyo Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

We have been engaging in development of a cognitive training system based on a kind of match-up game with software agent on tablet devices. Several experiments have been conducted to evaluate characteristics of the system. The results have demonstrated that the software agent in the system which employs a reinforcement learning (RL) method could promote older adults' motivation to use it for a long term and habitual engagement in the cognitive training as well, although learning settings of the system should be refined to further improve its learning/adaptive performance. This paper reviews the state settings in the agent based on the previous experimental results and the users' comments, and revise the settings so that the system can provide with more suitable difficulty of the game depending on respective users' condition such as their degree of concentration or fatigue.

关键词： Training Games Software agents Fatigue Finance Aging statistics

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Experimental Verification of Control Rules for A Lane-keeping Controller Using Similarity Fuzzy Reasoning

Experimental Verification of Control Rules for A Lane-keepin...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Yuka Nishiyama Yuki Shinomiya Toshimi Yamamoto Yukinobu Hoshino Kochi University of Technology Kochi Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

In general, the design of membership functions in fuzzy reasoning requires converting human experience and knowledge into rules and the function. Because not all experience and knowledge can be expressed by a convex fuzzy set. Therefore, it is difficult to convert easily and accurately. Fuzzy control in lane-keeping controllers can improve the performance by adjusting rules and membership functions based on human knowledge. On the other hand, it is necessary to deal with the increase in the amount of operations and nonconvex fuzzy sets. This study applied similarity calculation to the fuzzy inference of a lane-keeping controller to reduce the computational complexity with fewer membership functions and rules, and we verified the optimal number of inference rules for the controller using the similarity fuzzy inference method.

关键词： Fuzzy reasoning Sensors Cognition Fuzzy sets Automobiles standards Motion pictures

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Methods of Conceptual Modeling of intelligent Decision Support Systems for Managing Complex Objects at All stages of Its Life Cycle 3rd

Methods of Conceptual Modeling of Intelligent Decision Suppo...

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3rd international Scientific conference on intelligent Information Technologies for Industry (IITI)

作者： Bakhmut, Aleksey D. Koromyslichenko, Vladislav N. Krylov, Aleksey V. Okhtilev, Michael Yu. Okhtilev, Pavel A. Sokolov, Boris V. Ustinov, Anton V. Zyanchurin, Alexander E. St Petersburg State Univ Aerosp Instrumentat St Petersburg Russia Sci Ctr Petrokometa St Petersburg Russia Russian Acad Sci SPIIRAS St Petersburg Inst Informat & Automat St Petersburg Russia

ISBN: (纸本)9783030018214

The article suggests a set of methods for conceptual modeling of the main components of specialized intelligent decision support systems: models and methods for integration and analysis of data, applications, a complex of distributed data and knowledge bases, the design of user interfaces and the organization of distributed computations as part of an intelligent computer-aided engineering system of specialized software. The detailed substantiation of application of a new intelligent information technology and its methods for information modeling of functioning of complex objects in various subject areas of state and industrial purpose is given.

关键词： Decision support systems Artificial intelligence Computer-Aided software engineering

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Unsupervised Feature Learning for Output Control of Generative Models

Unsupervised Feature Learning for Output Control of Generati...

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international Symposium on Soft computing and intelligent Systems (SCIS)

作者： Kazuki Toda Kyohei Atarashi Satoshi Oyama Masahito Kurihara Graduate School of Information Science and Technology Hokkaido University Sapporo Hokkaido Japan Faculty of Information Science and Technology Hokkaido University Sapporo Hokkaido Japan

ISBN: (数字)9781728197326

ISBN: (纸本)9781728197333

Deep generative models are being actively studied, particularly variational autoencoders (VAEs) because they can generate high-quality images. The M2 model supports semi-supervised learning from both labeled and unlabeled data, which enables the generated images to be easily controlled by changing the class label values. However, generative models must be learned from only unlabeled data when class labels are not available. A model is presented that incorporates a deep clustering method into the M2 model, which enables clusters to be identified among unlabeled data so that each data point can be assigned to one of the clusters. The generated images in unlabeled datasets can easily be controlled by changing the cluster assignment of each data point.

关键词： Data models Linear programming Training Unsupervised learning Perturbation methods Semisupervised learning Entropy

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Efficient differentiated storage architecture for large-scale flow tables in openflow networks 21

Efficient differentiated storage architecture for large-scal...

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作者： Xiong, Bing Wu, Rengeng Liao, Zhuofan Wang, Jin School of Computer AND Communication Engineering Changsha University of Science AND Technology Changsha410114 China Department of Computer and Information Sciences Temple University Philadelphia19122 United States Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science AND Technology Changsha China

ISBN: (纸本)9781728120584

As a novel network paradigm, Software Defined Networking (SDN) decouples control logic functions from data forwarding devices, and introduces a separate control plane to manipulate underlying switches via southbound interfaces like OpenFlow. However, it leads to large-scale flow tables and poses serious challenges on their storage resources and lookup performance in OpenFlow switches. This paper is thus motivated to propose an efficient differentiated storage architecture for large-scale flow tables in OpenFlow networks. Firstly, we investigate into the impact of wildcards in match fields on packet-in-batch property within a flow based on network traffic locality. Then, packet flows are dynamically distinguished into active ones and idle ones in terms of their short-term states. Subsequently, we store the match fields of active flows and idle flows respectively in TCAM and SRAM, and the content fields of both types of flows in DRAM, to effectively relieve the insufficiency of TCAM capacity. Finally, we evaluate the performance of our proposed flow table storage architecture with real network traffic traces by experiments. The experimental results indicate that our proposed storage architecture obviously outperforms the traditional one applying the elephant/mice flow differentiation method in terms of TCAM hit rates and average flow table access time. © 2019 IEEE.

关键词： Software defined networking

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Digital support in logistics of home-care nurses for disabled and elderly people 1st

Digital support in logistics of home-care nurses for disable...

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1st international conference on Human Interaction and Emerging Technologies, IHIET 2019

作者： Reinhold, Karin Tint, Piia Traumann, Ada Tamme, Piret Tuulik, Viiu Voolma, Silja-Riin Faculty of Economics Department of Business Administration Tallinn University of Technology Ehitajate 5 Tallinn19086 Estonia TTK University of Applied Sciences Pärnu 62 Tallinn10135 Estonia Lifelong-Learning Centre Tallinn Health Care College Kännu 67 Tallinn13418 Estonia Cognitive Psychology Department National University of Singapore 21 Lower Kent Ridge Rd Singapore119077 Singapore

ISBN: (纸本)9783030256289

The paper gives results of possibilities to use information technology organizing the work of health care nurses while working at patients’ homes. Information technology helps to improve care-quality of patients and has positive an economic impact. This paper analyses the results of three studies investigating Estonian healthcare nurses in the workplace. This study was conducted in 2007–2019. Significant changes have taken place in this timeframe. New improvements are possible if the latest information technology is utilized: tablets, mobile phones, wearables, etc. In the countryside, services are more patient-centric, but nurses in rural areas must commute further than care professionals who live in towns. Some Estonian retired people do not have enough resources to live in nursing homes. Instead, the elderly and their families make the economic decision to live at home. © 2020, Springer Nature Switzerland AG.

关键词： Nursing

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1st international conference on Artificial Intelligence and Computer Visions, AICV 2020

1st International Conference on Artificial Intelligence and ...

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1st international conference on Artificial Intelligence and Computer Visions, AICV 2020

ISBN: (纸本)9783030442880

The proceedings contain 81 papers. The special focus in this conference is on Artificial Intelligence and Computer Visions. The topics include: Role of Financial technology FinTech: A Survey;applications of Integrated Management and Distribution Information in Power Supply System;vortex Swarm Optimization: New Metaheuristic Algorithm;an Artificial Intelligence System for Apple Fruit Disease Classification Based on Support Vector Machine and Cockroach Swarm Optimization;controlling Directed Particle Swarm Optimization for Delivering Nano-robots to Cancer Cells;feature Selection Method Based on Chaotic Maps and Butterfly Optimization Algorithm;dynamic Cost Ant Colony Algorithm for Optimize distributed Database Query;clustering Analysis Based on Coyote Search Technique;Brain Tumor Segmentation in 3D-MRI Based on Artificial Bee Colony and Level Set;machine Learning for Apple Fruit Diseases Classification System;self-adaptive Parameters Optimization for Incremental Classification in Big Data Using Swarm Intelligence;a Modified Sunflower Optimization Algorithm for Wireless Sensor Networks;hybrid Approach for Improving Intrusion Detection Based on Deep Learning and Machine Learning Techniques;prediction of the Electrical Load for Egyptian Energy Management Systems: Deep Learning Approach;Comparative Performance of Machine Learning and Deep Learning Algorithms for Arabic Hate Speech Detection in OSNs;abnormal Events and Behavior Detection in Crowd Scenes Based on Deep Learning and Neighborhood Component Analysis Feature Selection;classifying Upper Limb Activities Using Deep Neural Networks;Experimental Kinematic Modeling of 6-DOF Serial Manipulator Using Hybrid Deep Learning;a Hybrid Deep Learning Based Autonomous Vehicle Navigation and Obstacles Avoidance;Deep Learning Based Kinematic Modeling of 3-RRR Parallel Manipulator;metadata Extraction for Low-Quality Semi-structured Spreadsheets.

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