Network Communication Layer Research Articles

IoT-based Deep Learning Neural Network (DLNN) algorithm for voltage stability control and monitoring of solar power generation

Today, Solar Photovoltaic (SPV) energy, an advancing and attractive clean technology with zero carbon emissions, is widely used. It is crucial to pay serious attention to the maintenance and application of Solar Power Generation (SPG) to harness it effectively. The design was more costly, and the automatic monitoring is not precise. The main objective of the work related to designed and built up the Internet of Things (IoT) platform to monitor the SPV Power Plants (SPVPP) to solve the issue. IoT platform designing and Data Analytics (DA) are the two phases of the proposed methodology. For building the IoT device in the IoT platform designing phase, diverse lower-cost sensors with higher end-to-end delivery ratio, higher network lifetime, throughput, residual energy, and better energy consumption are considered. Then, Sigfox communication technology is employed at the Low-Power Wireless Area Network (LPWAN) communication layer for lower-cost communication. Therefore, in the DA phase, the sensor monitored values are evaluated. In the analysis phase, which is the most significant part of the work, the input data are first pre-processed to avoid errors. Next, to monitor the Energy Loss (EL), the fault, and Potential Energy (PE), the solar features are extracted as of the pre-processed data. The significance of utilizing the Transformation Search centered Seagull Optimization (TSSO) algorithm, the significant features are chosen as of the extracted features. Therefore, the computational time of the solar monitoring has been decreased by the Feature Selection (FS). Next, the features are input into the Gaussian Kernelized Deep Learning Neural Network (GKDLNN) algorithm, which predicts the faults, PE, and EL. In the experimental evaluation, solar generation is assessed based on Wind Speed (WS), temperature, time, and Global Solar Radiation (GSR). The systems are satisfactory and produce more power during the time interval from 12:00 PM to 1:00 PM. The performance of the proposed method is evaluated based on performance metrics and compared with existing research techniques. When compared to these techniques, the proposed framework achieves superior results with improved precision, accuracy, F-measure, and recall.

EXAM SURVEILLANCE USING MACHINE LEARNING

Recent publications on video surveillance systems in conferences and journals show that researchers are paying attention. This review's objectives are to look at the most recent works that have been published in journals, suggest a new classification scheme for video surveillance systems, and look into each component of that scheme. Using the article's title and keyword, this paper presents a thorough and organized analysis of the prior literature on video surveillance systems from 2010 to 2011. The review was taken from six online digital libraries. The architecture of video surveillance systems, which consists of six layers: the Concept and Foundation Layer, Network Infrastructure Layer, Processor Layer, Communication Layer, Application Layer, and User Interaction Layer, serves as the foundation for the suggested classification framework. This analysis demonstrates that while real-time aspects of the problem are the subject of many publications and studies, the use of extracted and retrieved information for forensic video surveillance has received less attention. Key Words: CCTV, video surveillance system, exam surveillance, student activity recognition, frameworks, datasets, network infrastructure layer, processing layer, communication layer, application layer, and user interaction layer.

Application of Intelligent Sensing in Power Test System

Smart grid is the development trend of the power industry under the new situation. As an important part of smart grid, smart power consumption directly faces the society and power users, and plays a very important role in the construction of smart grid. With the development of intelligent power construction, the traditional power service can not meet the needs of users to participate in demand side management and interact with the power grid in time. Therefore, the construction of a new intelligent sensing power measurement system to improve the quality and level of intelligent power service has become the focus of current power industry research. Firstly, according to the requirements analysis and design principles of the intelligent sensing power detection system, this paper proposes the function analysis and three-tier architecture design of the intelligent sensing power detection system. The intelligent sensing power detection system is logically divided into business application layer, network communication layer and sensing device layer. Externally, it realizes data integration and interaction with power consumption information collection system, marketing business management system and other intelligent power consumption information systems. Internally, it realizes interconnection with user side perception interactive terminal and perception unit, and realizes data acquisition, information transmission and control functions of power consumption terminal equipment. This paper presents a method of applying intelligent sensing technology to data editing environment of power simulation software, and realizes online error detection of simulation original data, automatic completion of keywords, automatic prompt of data card and intelligent help of data domain. The power simulation data editing software with intelligent sensing function developed in this paper is suitable for primary and advanced users, which can improve work efficiency and reduce error rate. The configurable rule management method applied in the implementation of intelligent sensing technology extends the application of data editor to the developers of models and algorithms, and provides a simple way for the timely upgrade and release of new models and algorithms.

Application Design and Implementation for Smart Kindergarten

The advent of the “Internet +” era has made the construction of smart kindergartens necessary. However, the reality is the information construction of kindergartens is relatively lagging behind. There are some problems such as low information literacy of kindergarten educators, lack of initiative to accept information technology, lack of interconnection and interoperability of existing application systems, which lead to difficulties in using information technology. It is suitable to adopt a hierarchical architecture. From bottom to top, the layers are: Smart perception layer, Network communication layer, Data storage and access layer, Smart application layer and User interaction Layer. All layers can be expanded in the future and provide service interfaces to the upper layer. Applications for smart kindergartens can be divided into Teaching, Internal management, Parents-kindergarten cooperation, and Social promotion. The Smart kindergarten application deployment architecture should use a hybrid architecture of public and private clouds, and interoperates with third-party platforms. The construction of smart kindergartens is a long-term process and should be implemented in phases.

Multi-GPU, Multi-Node Algorithms for Acceleration of Image Reconstruction in 3D Electrical Capacitance Tomography in Heterogeneous Distributed System.

Electrical capacitance tomography (ECT) is one of non-invasive visualization techniques which can be used for industrial process monitoring. However, acquiring images trough 3D ECT often requires performing time consuming complex computations on large size matrices. Therefore, a new parallel approach for 3D ECT image reconstruction is proposed, which is based on application of multi-GPU, multi-node algorithms in heterogeneous distributed system. This solution allows to speed up the required data processing. Distributed measurement system with a new framework for parallel computing and a special plugin dedicated to ECT are presented in the paper. Computing system architecture and its main features are described. Both data distribution as well as transmission between the computing nodes are discussed. System performance was measured using LBP and the Landweber’s reconstruction algorithms which were implemented as a part of the ECT plugin. Application of the framework with a new network communication layer reduced data transfer times significantly and improved the overall system efficiency.

Research on environmental monitoring and control technology based on intelligent Internet of Things perception

This study studies the technology of environmental monitoring and control in an intelligent Internet of Things (IoT) perception. To improve the issue of small coverage and low stability in the traditional environmental parameter testing, a new environment monitoring technique based on intelligent perception is proposed. The technical framework consists of three layers, intelligent perception layer, network communication layer, and application layer. The intelligent perception collects and transmits data to the application-level server via the network communication layer for data analysis and diversified display. Based on this technique, we constructed a smart home environment monitoring and control system. The experimental test result shows that the system can accurately collect and process the data through the server and diversified displayed, which verifies the accuracy and reliability of this technique which is potential for further application.

One Kind of Routing Algorithm Modified in Wireless Sensor Network

The wireless sensor networks are the emerging next generation sensor networks, Routing technology is the wireless sensor network communication layer of the core technology. To build reliable paths in wireless sensor networks, we can consider two ways: providing multiple paths utilizing the redundancy to assure the communication reliability or constructing transmission reliability mechanism to assure the reliability of every hop. Braid multipath algorithm and ReInforM routing algorithm are the realizations of these two mechanisms. After the analysis of these two algorithms, this paper proposes a ReInforM routing algorithm based braid multipath routing algorithm.

The Study of Prognostic and Health Management System for Smart Grid Based on Wireless Sensor Networks

This paper studies prognostic and health management (PHM) system for smart grid based on wireless sensor networks. The system function is analyzed firstly. Then, problems like networking hardware selection and data processing method have been discussed. The application of data fusion is discussed specially. In the end, the system platform based on a data-centric hierarchical technology architecture is designed with four layers: network configuration layer, network communication layer, platform basis layer and application layer. The research work of this paper is theoretical support for the realization of PHM for smart grid and the application of WSNs in smart grid.

One Kind of Routing Algorithm Modified in Wireless Sensor Network

The wireless sensor networks are the emerging next generation sensor networks, Routing technology is the wireless sensor network communication layer of the core technology. To build reliable paths in wireless sensor networks, we can consider two ways: providing multiple paths utilizing the redundancy to assure the communication reliability or constructing transmission reliability mechanism to assure the reliability of every hop. Braid multipath algorithm and ReInforM routing algorithm are the realizations of these two mechanisms. After the analysis of these two algorithms, this paper proposes a ReInforM routing algorithm based braid multipath routing algorithm.

Systematic Review and Classification on Video Surveillance Systems

Recently, various conferences and journals have published articles related to Video Surveillance Systems, indicating researchers' attention. The goal of this review is to examine the latest works were published in journals, propose a new classification framework of video surveillance systems and investigate each aspect of this classification framework. This paper provides a comprehensive and systematic literature review of video surveillance systems from 2010-2011, extracted from six online digital libraries using article's title and keyword. The proposed classification framework is expanded on the basis of architecture of video surveillance systems, which is composed of six layers: Concept and Foundation Layer, Network Infrastructure Layer, Processing Layer, Communication Layer, Application Layer, and User Interaction Layer. This review shows, although many publication and research focus on real-time aspect of the challenge, only few researches have investigated the deployment of extracted and retrieved information for forensic video surveillance.

Computer network traffic analysis with the use of statistical self-similarity factor

The optimal computer network performance models require accurate traffic models, which can capture the statistical characteristic of actual traffic. If the traffic models do not represent traffic accurately, one may overestimate or underestimate the network performance. The paper presents confirmation of the self-similar nature of the selected protocols in the computer network communication layer. It shows that the good measure of self-similarity is a Hurst factor.

Computer network traffic analysis with the use of statistical self-similarity factor

The optimal computer network performance models require accurate traffic models, which can capture the statistical characteristic of actual traffic. If the traffic models do not represent traffic accurately, one may overestimate or underestimate the network performance. The paper presents confirmation of the self-similar nature of the selected protocols in the computer network communication layer. It shows that the good measure of self-similarity is a Hurst factor.

The Monitoring and Control System of the LHCb Event Filter Farm

The LHCb experiment at CERN will have an on-line trigger farm composed of up to 2000 PCs. In order to monitor and control each PC and to supervise the overall status of the farm, a farm monitoring and control system (FMC) was developed. The FMC is based on distributed information management (DIM) system as network communication layer, it is accessible both through a command line interface and through the Prozeszligvisualisierungs und Steuerungssystem (PVSS) graphical interface, and it is interfaced to the finite state machine (FSM) of the LHCb experiment control system (ECS) in order to manage anomalous farm conditions. The FMC is an integral part of the ECS, which is in charge of monitoring and controlling all on-line components; it uses the same tools (DIM, PVSS, FSM, etc.) to guarantee its complete integration and a coherent look and feel throughout the whole control system.

Parallel Languages and Compilers: Perspective From the Titanium Experience

We describe the rationale behind the design of key features of Titanium—an explicitly parallel dialect of Java for high-performance scientific programming—and our experiences in building applications with the language. Specifically, we address Titanium's partitioned global address space model, single program multiple data parallelism support, multi-dimensional arrays and array-index calculus, memory management, immutable classes (class-like types that are value types rather than reference types), operator overloading, and generic programming. We provide an overview of the Titanium compiler implementation, covering various parallel analyses and optimizations, Titanium runtime technology and the GASNet network communication layer. We summarize results and lessons learned from implementing the NAS parallel benchmarks, elliptic and hyperbolic solvers using adaptive mesh refinement, and several applications of the immersed boundary method.