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2020 IEEE 15th international Scientific and Technical conference on computer Sciences and Information Technologies, CSIT 2020 - Proceedings

2020 IEEE 15th International Scientific and Technical Confer...

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15th IEEE international Scientific and Technical conference on computer Sciences and Information Technologies, CSIT 2020

ISBN: (纸本)9781728174433

The proceedings contain 174 papers. The topics discussed include: simulation of environmental monitoring using wireless sensor networks;modeling of a surface plasmons frequency spectrum in dielectric/metal/dielectric structures: the influence of the coulomb correlations;web data displaying approach based on user`s semantic profile templates;the investigation of artificial intelligence methods for identifying states and analyzing system transitions between states;analysis of the interrelations between elements of geoinformation system structure;searchless intelligent system of modern production control;linearization of problem on placing a maximum-radius hypersphere in polyhedral region;GMDH-based models for mid-term forecast of cryptocurrencies (on example of waves);and algorithmic support for auto-modes of adaptive short-term forecasting in predictive analytics systems.

关键词： Forecasting

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2020 IEEE 15th international Scientific and Technical conference on computer Sciences and Information Technologies, CSIT 2020 - Proceedings

2020 IEEE 15th International Scientific and Technical Confer...

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15th IEEE international Scientific and Technical conference on computer Sciences and Information Technologies, CSIT 2020

ISBN: (纸本)9781728174433

关键词： Forecasting

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international conference on Mechatronics, 2019

International conference on Mechatronics, 2019

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international conference on Mechatronics, 2019

ISBN: (纸本)9783030299927

The proceedings contain 62 papers. The special focus in this conference is on Mechatronics. The topics include: distributed collection of environmental data using IoT technology;vibration detection and diagnosis for civil aircraft improved maintenance;Automatic mechatronic test stand development for embedded electronics using NI LabVIEW;Wavefront sensor in measurements of MEMS vibrations;methods of joint stiffness measurement using a manually actuated dynamometer;electronic filters measurement device;mechatronics solutions in process of transport infrastructure monitoring and diagnostics;laboratory stand for fluxgate level measurement;the method of semantic structuring of virtual community content;test stand for studying flowmeter performance in presence of pulsatile flow;Pedestrian indoor localization using IoT sensors RSSI signal strength measurement;calibration of bell prover test stands with critical flow venturi nozzle;magnetic moment measurement stand;identification of cyclic changes in the operation mode of the production facility based on the monitoring data;the application of hexagonal grids in mobile robot navigation;development of logical control system for the purification department at molasses production;features of control for multi-assortment technological process;simple and low-cost analog tactile sensor for robot;evaluation of visual markers detection used for autonomous mobile robot docking navigation;using linear matrix inequalities for synthesis of modal control of multidimensional linear systems;design and realization of a performance and reliability evaluation module for commercialized anti-lock braking systems;Model reference control for SISO 2-D system with input delay;mechatronic system for weeding;monitoring tilt of elevated loads using MEMS accelerometers;seebeck Coefficient Measurement in Amorphous Alloys;foreword.

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intelligent DDoS Detection System in Software-Defined Networking (SDN) 6

Intelligent DDoS Detection System in Software-Defined Networ...

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6th IEEE international conference on Electronics, Computing and Communication Technologies, CONECCT 2020

作者： Kumar, Chaitanya Kumar, Bathini Pranay Chaudhary, Aditya Gupta, Ayush Dev, Kapil Sharma, Ayushi Srivastava, B. Rajitha, Shashank Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9781728168289

Software defined network (SDN) is a network paradigm that decouples the control and data plane of network layer by introducing a logically centralized control known as controller. SDN promises network monitoring, easy and instant network implementation and effective central control but at the same time this central control introduces a single point of failure thereby increasing the risk of distributed Denial of Service (DDoS) attacks. Thus, this paper analyses the various types of DDoS attacks that can be performed in SDN environment with the help of various attack models. Further, an attempt is made to analyze such attacks and bring out the features that could uniquely classify legitimate traffic and attack traffic using machine learning techniques. Finally, an Open Network Operating System (ONOS) controller module is developed that could live detect any ongoing DDoS attack. © 2020 IEEE.

关键词： Denial-of-service attack

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A Device-Free Wi-Fi Sensing Method for Pedestrian monitoring Using Channel State Information

A Device-Free Wi-Fi Sensing Method for Pedestrian Monitoring...

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ASCE international conference on Transportation and Development (ASCE ICTD)

作者： Pu, Ziyuan Zhang, Qiannan Zhuang, Yifan Lv, Yongqiang Wang, Yinhai Univ Washington Dept Civil & Environm Engn Seattle WA 98195 USA Shanghai Maritime Univ Merchant Marine Coll Shanghai Peoples R China Tsinghua Univ Res Inst Informat Technol Beijing Peoples R China

ISBN: (数字)9780784483138

ISBN: (纸本)9780784483138

Pedestrian detection accuracy strongly impacts the effectiveness and reliability of intelligent pedestrian-related control systems. Traditional sensing technologies usually sense pedestrians based on the reflected signal of the transmitted infrared ray, sound wave, and electromagnetic wave which only can count the number of times that pedestrians passing a line of sight (LoS) but the moving feature monitoring, e.g., moving direction, speed, etc. For pedestrian monitoring based on computer vision-based sensing technology, the level of errors is relatively large and highly sensitive to environmental factors, such as illumination, weather conditions, and occlusion. Wi-Fi channel state information (CSI) represents the amplitudes and phases information for Orthogonal frequency-division multiplexing (OFDM) subcarriers, which is mainly impacted by the static environment and moving object in surrounding areas. Previously, scholars utilized Wi-Fi CSI to analyzed multiple microscopic human movements, e.g., gesture, gait, and fall action in the indoor environment, but no application in the outdoor environment for pedestrian monitoring. The main objective of this research is to demonstrate the feasibility and reliability of the Wi-Fi CSI-based sensing method for pedestrian existence and moving direction recognition. The impacts of the CSI signal sampling ratio on the detection accuracy was investigated as well. The experiments were conducted in both indoor and outdoor environments. According to the results, the accuracy of pedestrian existence detection based on the data of the 100 Hz sampling ratio achieved 99.23% accuracy and 0.26% false positive rate. For the moving direction recognition, the detection accuracy in the indoor environment achieved 100% and 96.92% for two directions, and got 92.21% and 93.51% in the outdoor environment. The findings of this research demonstrate the proposed Wi-Fi CSI signal is highly effective for pedestrian existence detection and moving direc

关键词： Wireless local area networks (WLAN)

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Research on Diagnosis Method of Abnormal Heating of Transformer High Voltage Composite Pipe Jacket

Research on Diagnosis Method of Abnormal Heating of Transfor...

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Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), international conference on

作者： Yuning GAO Computer and Information Engineering Huali College Guangdong University of Technology Guangzhou China

ISBN: (数字)9781665415408

ISBN: (纸本)9781665447065

In order to realize automatic diagnosis of abnormal heating of transformer high-voltage composite pipe sleeve, an intelligent diagnosis method of abnormal heating of transformer high-voltage composite pipe sleeve based on impedance characteristic parameter fusion detection is proposed. The impedance response component and voltage component of transformer high-voltage composite pipe sleeve are analyzed, and the parameter characteristics of transformer high-voltage composite pipe sleeve are analyzed by frequency coupling control method. The inversion parameter fusion and adaptive decoupling control model is established. The fault characteristics of abnormal heating are discriminated according to the input and output rotation vectors of transformer high-voltage composite pipe sleeve, and the intelligent diagnosis distribution model under various frequency coupling States is established. The abnormal heating diagnosis of transformer high-voltage composite pipe sleeve is realized by adopting the impedance abnormal characteristic analysis method. The test results show that this method is intelligent and accurate in diagnosing abnormal heating of transformer high-voltage composite pipe jacket.

关键词： Heating systems Wireless sensor networks Matched filters Feature extraction Sensors environmental monitoring Impedance

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Artificial Intelligence at the Edge in the Blockchain of Things 8th

Artificial Intelligence at the Edge in the Blockchain of Thi...

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8th EAI international conference on Mobile Communication and Healthcare, MobiHealth 2019

作者： Nguyen Gia, Tuan Nawaz, Anum Peña Querata, Jorge Tenhunen, Hannu Westerlund, Tomi Group Department of Future Technologies University of Turku Turku Finland Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

ISBN: (纸本)9783030492885

Traditional cloud-centric architectures for Internet-of-Things applications are being replaced by distributed approaches. The Edge and Fog computing paradigms crystallize the concept of moving computation towards the edge of the network, closer to where the data originates. This has important benefits in terms of energy efficiency, network load optimization and latency control. The combination of these paradigms with embedded artificial intelligence in edge devices, or Edge AI, enables further improvements. In turn, the development of blockchain technology and distributed architectures for peer-to-peer communication and trade allows for higher levels of security. This can have a significant impact on data-sensitive and mission-critical applications in the IoT. In this paper, we discuss the potential of an Edge AI capable system architecture for the Blockchain of Things. We show how this architecture can be utilized in health monitoring applications. Furthermore, by analyzing raw data directly at the edge layer, we inherently avoid the possibility of breaches of sensitive information, as raw data is never stored nor transferred outside of the local network. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2020.

关键词： Blockchain

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Evaluation of Homogeneous and Heterogeneous distributed Ensemble Feature Selection Approaches for Classification of Rice Plant Diseases

Evaluation of Homogeneous and Heterogeneous Distributed Ense...

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international conference on ent Computing and control Systems (ICICCS)

作者： Rajendra Prasad Bellapu Ramashri Tirumala Rama Naidu Kurukundu JNTUA CEA: Jawaharlal Nehru Technological University Anantapur College of Engineering Ananthapuramu Andhra Pradesh INDIA SVUCE: Sri Venkateswara University College of Engineering Tirupati Andhra Pradesh INDIA

Nowadays, agricultural products have very few nutritional values due to various factors, such as environmental issues, genetic dilution effect, and excessive use of chemical pesticides to cure plant diseases. Currently, India, as well as the whole world, is encountering nutritional starvation. In developing countries, pesticide poisoning deaths are a significant problem nowadays. To reduce the spread of diseases and to facilitate an effective management strategy, developing a computer-aided diagnosis system for early recognition and classification of rice plant diseases is essential. Technological innovations in the machine learning models for predicting plant diseases using classifiers and discoveries in digital image processing techniques play a significant role in the agriculture industry. However, the accuracy of these classifiers purely depends on training the network with relevant features. Training these models with relevant features avoids overfitting and underfitting problems. Hence Feature Selection (FS) task has turned out to be an essential pre-processing task prior to classification, which not only reduces the computational cost of training and memory requirement; but also reduces the generalization capability of the network due to the “curse of dimensionality” issue. Therefore, to address this issue, a proper FS technique is required for Dimensionality Reduction (DR). This paper aims to evaluate the Ensemble Feature Selection (EFS) using homogeneous and heterogeneous approaches to classify rice plant diseases. ReliefF, Correlation-Based Feature Selection (CFS), Pearson Correlation Coefficient (PCC), Mean-Variance (MV) are the FS algorithms used for Heterogeneous distributed Ensemble Approach (HeDEA), and an aggregator is used to select the final subset of features. ReliefF FS algorithm is used in Homogeneous distributed Ensemble Approach (HoDEA). Deep Convolution Neural Network (DCNN) is used as a classifier for both approaches. HeDEA offers better per

关键词： Training Technological innovation Plants (biology) Computational modeling Neural networks Training data Feature extraction

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Forest cover type classification based on environment characteristics and machine learning technology 3rd

Forest cover type classification based on environment charac...

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3rd international conference on Data Stream and Mining and Processing, DSMP 2020

作者： Kiyko, Vasyl Lytvyn, Vasyl Chyrun, Lubomyr Vyshemyrska, Svitlana Lurie, Iryna Hrubel, Mykhailo Lviv Polytechnic National University Lviv Ukraine Ivan Franko National University of Lviv Lviv Ukraine Kherson National Technical University Kherson Ukraine Hetman Petro Sahaidachnyi National Army Academy Lviv Ukraine

ISBN: (纸本)9783030616557

Today, one of lot global problems is forest deforestation and monitoring. The article describes the system creation for forests control and monitoring. This work aims is to develop a model for the forest cover type determination based on environmental characteristics and machine learning as the currently developing project part "monitoring the trees condition using drones". The project aims is to simplify and partially automate the control and monitoring of trees using drones and machine learning to improve the forest situation. The task is to create a model for predict what trees types grow in the area based on environmental characteristics. Therefore, the main system will able to compare the existing values/characteristics with the predicted ones (which tree should normally there) to find discrepancies. Eventually, the main system will able to use this information to report and inform relevant staff and authorities. This work is based on a data set for learning system that includes observations of trees from four areas of Roosevelt National Forest in Colorado. All observations are cartographic variables (without remote sensing) from 30 × 30 -m forest areas. In total, there are more than half a million measurements. The work aim is the development of a forest cover types classification model depending on the environment and its characteristics. © 2020, Springer Nature Switzerland AG.

关键词： intelligent systems

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Design of temperature humidity and illumination control system for indoor flowers 3

Design of temperature humidity and illumination control syst...

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2020 3rd international conference on Mechatronics and computer Technology Engineering, MCTE 2020

作者： Liu, Haidong Wang, Xiaosong Institute of Intelligent Manufacturing Panzhihua University Panzhihua Sichuan617000 China

In order to improve the automation of indoor flower environment monitoring, enable the flowers grow in the best environment, this paper designs an automatic control system of temperature, humidity and illumination based on STC89C52 single chip microcomputer. Temperature sensor DS18B20 is applied to sense indoor air temperature, meantime, YL-69 soil moisture sensor and photosensitive resistance are used to sense soil relative humidity and light intensity respectively. Temperature, humidity and light intensity are transmitted to single chip microcomputer for analysis and processing, and the results are displayed by OLED display module. The upper and lower limits of environmental parameters are manually preset through the keyboard. When the temperature and light intensity exceed the upper limit, the system can send out alarm signals and output control signals to drive the corresponding actuator to work;when the soil moisture is lower than the lower limit, the system will automatically alarm and water automatically;when the humidity reaches the upper limit, watering will be stopped to ensure the indoor environmental parameters in the best value suitable for flower growth. The system has been tested and debugged. The results show that the control system can effectively process and display the flower environment information, achieving the purpose of automatically adjusting and controlling the temperature, humidity and illumination of flowers, has strong promotion value. © Published under licence by IOP Publishing Ltd.

关键词： Microcomputers

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