Smart Moves Journal IJOSTHE (International Journal Online of Sports Technology & Human Engineering)
Not a member yet
128 research outputs found
Sort by
Review on Power System Performance in High/Low Voltage Distribution System
No full textThe HVDS system must reconfigure the existing low voltage (LT) network into a high voltage distribution system. The advent of high-power converters makes the modern power grid more active than before. In the existing LT system, large capacity transformers are provided at one point and connections to each load are extended across the LT lines. This document explains the distribution networks of the low voltage distribution system (LVDS) and the distribution system currently in use, the high voltage distribution system (HVDS). This paper presents the advantages and disadvantages of high voltage distribution systems
A Review of Intrusion Detection using Deep Learning
No full textAs network applications grow rapidly, network security mechanisms require more attention to improve speed and accuracy. The development of new types of intruders poses a serious threat to network security: although many tools for network security have been developed, the rapid growth of intrusion activity remains a serious problem. Intrusion Detection Systems (IDS) are used to detect intrusive network activity. Preventing and detecting unauthorized access to a computer is an IT security concern. Therefore, network security provides a measure of the level of prevention and detection that can be used to avoid suspicious users. Deep learning has been used extensively in recent years to improve network intruder detection. These techniques allow for automatic detection of network traffic anomalies. This paper presents literature review on intrusion detection techniques
Study on Control Strategies of Cascaded Solar Module System
No full textSolar photovoltaic (PV) systems have mainly been used in the past decade. Inverter-powered photovoltaic grid topologies are prominently used to meet electricity demand and to integrate renewable forms of energy into power grids. Coping with the growing demand for electricity is currently a major challenge. This article presents the basic architecture of a photovoltaic system and the characteristic performance curve of the photovoltaic generator. The description of DC voltage regulation in this paper
Review of Control Methodologies for Offshore Wind Farm Based HVDC Transmission System
No full textWind power is growing rapidly around the world and the offshore wind farm is currently seen as a promising solution to meet the growing demand for renewable energy sources. In addition to increasing the capacity of offshore wind farms and the distance between offshore wind farms and land, high voltage direct current (HVDC) is attractive. In addition, the DC grid may also be interested in connecting wind turbines at the collection level. It is possible to establish a DC grid for the offshore wind farm where the wind energy collection system and the power transmission system use DC technology. Existing grid codes for wind turbines mainly focus on air conditioning systems. Therefore, fault analysis in the DC network and the corresponding fault protection is required for the DC network
Depression Detection Using Stacked Autoencoder from Facial Features and NLP
No full textDepression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods
Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function
No full textDue to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods
Design of Efficient Power Filter with Reduced Distortion Using Control Algorithm
No full textThe electrical distribution system is facing undesirable power quality disturbances due to different types of linear/nonlinear loads on the supply system. The objective of the project is to reduce the distortion level in voltage or current input to the load and at the output of the filter. To design a simple but highly viable hybrid active power buffer that is capable of feeding less distorted voltage to the nonlinear load model. To present an optimal controlling of these buffers so as to minimize the voltage distortion by designing a different algorithm for the same. Comparing the THD levels of the output voltage waveform with the standard controlling method with the proposed control design to further enhance the proposed design such that it is practically feasible to be implemented in grid system having renewable energy resources. In this work, a power filter has been designed using different algorithms with an objective to reduce the Total Harmonic Distortion in the voltage output waveforms. The total harmonic distortion in the voltage output waveform being fed to the load using only the PQ_RLS algorithm is found to be 2.18 %. In the case of the output voltage from the power buffer using PQ_RLS algorithm, the THD level is 0.17 %. The distortion level in the output voltage waveforms in both the cases being fed to the load when compared, it is found that RLS algorithm in combination with PQ algorithm is more effective in reducing the distortion as compared to standard RLS method or PQ method
Performance Characteristics of Multilevel Converter in Grid Connected System with Renewable Energy Resources
No full textThe multi-stage cascade converter structure can be fascinating for high-performance solar photovoltaic (PV) systems due to its interchangeability, expansion, and MPPT (Maximum Power Point Tracking) exception. However, power discrepancies in cascaded uniform PV converter modules can cause unstable voltages and system operation. This article highlights the problem, examines the effects of reactive power compensation and optimization on the safety and performance characteristics of the system and proposes a synchronized distribution of active and reactive power in the network in order to reduce this instability. Furthermore, a wind turbine is connected in parallel to the photovoltaic system to increase the reliability of the system. This document presents the standards and specifications of grid-connected photovoltaic inverters and the different topologies of grid-connected photovoltaic inverters. And he also discussed monitoring maximum credit points
IMPROVEMENT IN OUTPUT POWER BY DESIGNING ADAPTIVE REFERENCE CONTROL FOR BOOST CONVERTER IN SOLAR SYSTEM
No full textMaximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point (MPP) which depends on panels temperature and on irradiance conditions. In this work we have made a comparison between P & O algorithm with proposed adaptive reference algorithm. It has been concluded that The power output with adaptive reference algorithm at the load terminal is coming to be 6.5 kilo Watts approximately where as with P & O it is calculated to be 1.5 kilo watts approximately. Hence it is a better proposed algorithm as compared to traditional P & O techniqu
A Review on Malware Analysis by using an Approach of Machine Learning Techniques
No full textIn the Internet age, malware (such as viruses, trojans, ransomware, and bots) has posed serious andevolving security threats to Internet users. To protect legitimate users from these threats, anti-malware softwareproducts from different companies, including Comodo, Kaspersky, Kingsoft, and Symantec, provide the majordefense against malware. Unfortunately, driven by the economic benefits, the number of new malware sampleshas explosively increased: anti-malware vendors are now confronted with millions of potential malware samplesper year. In order to keep on combating the increase in malware samples, there is an urgent need to developintelligent methods for effective and efficient malware detection from the real and large daily sample collection.One of the most common approaches in literature is using machine learning techniques, to automatically learnmodels and patterns behind such complexity, and to develop technologies to keep pace with malware evolution.This survey aims at providing an overview on the way machine learning has been used so far in the context ofmalware analysis in Windows environments. This paper gives an survey on the features related to malware filesor documents and what machine learning techniques they employ (i.e., what algorithm is used to process the inputand produce the output). Different issues and challenges are also discussed