In our work describes a method for accurately tracking persons in indoor surveillance video stream obtained from a static camera with difficult scene properties including illumination changes and solves the major occlusion problem by using Kalman filtering. First, moving objects are precisely extracted by determining its motion, for further processing. The scene illumination changes are averaged to obtain the accurate moving object during background subtraction process. In case of objects occlusion, we use the color feature information to accurately distinguish between objects. The method is able to identify moving persons, track them and provide unique tag for the tracked persons. The effectiveness of the proposed method is demonstrated with experiments in an indoor environment.
It's important to realize, optimized dispatching of generating units would be useless without a method of control. Frequency regulation in interconnected network is one of the main challenges posed by wind turbine in modern power system. Obviously the method of load frequency control (LFC) plays a vital role in large interconnected power system. However the modern LFC should be able to handle complex multi objective regulation optimization problems and to ensure that the LFC systems are capable to maintain generation load balance, following serious disturbances. The additional injection of mechanical power due to rapid increases of wind power fluctuation imposes a power imbalance between generation and load it tends to causes the frequency deviation from nominal value. Based on the obtained wind turbine model, a power control structure was developed that takes into consideration the dynamical aspects of the wind turbine as well as constraints. This thesis addresses a new eminent of LFC with fuzzy controller for simultaneous minimization of frequency deviation and tie line power changes in the presence of high penetration of wind turbine. It can be done by proposed model of fuzzy controller with the optimized data and an explicit parametric controller, a novel control method, was designed using MATLAB for two area wind power system.
Modern supply chain management relies heavily on crop processing technologies and blockchain technology. However, the majority of crop processing technology fails due to a lack of available supply chain management technologies. A supply chain management system based on crop processing is developed in this research. Digital ledger technology (blockchain) takes care of supply chains and deep learning image processing (crop processing). To process the harvested crops, the study makes use of machine learning techniques. Blockchain supply chain management technology is then used to deliver the processed crops to the shops. Consequently, the research permits the accurate and transparent distribution of crops to users in an optimal and secure manner. The full hybrid model is tested in a simulation to see if it can improve agricultural production and supply chain management. This new strategy improves agricultural processing rates, and when combined with the blockchain distributed ledger technology, this results in optimal crop management for the required users.
Vehicular Ad Hoc Network (VANET) is a cutting-edge technology that enables communication between vehicles and the surrounding road infrastructure, paving the way for intelligent transportation systems. Ensuring stable connections in VANETs requires a robust and reliable routing protocol, as these networks lack central coordination, exhibit high node mobility, and possess highly dynamic topologies, making routing a significant challenge. Many existing mobility-based routing protocols fall short in addressing Quality of Service (QoS) requirements, as their performance is heavily influenced by vehicle speed and driving conditions. On the other hand, QoS-based approaches often fail to account for the challenges posed by high-speed mobility, leading to frequent connection failures due to the increasing mobility of nodes within a given area. To overcome these challenges, this paper introduces a novel Cluster-Based Congestion Control Routing (CBCCR) protocol designed to enhance overall network efficiency by improving route throughput, optimizing bandwidth utilization, and minimizing end-to-end latency. The CBCCR protocol addresses the limitations of repetitive route detection and frequent Cluster Head (CH) reelection processes, thereby enhancing route stability and network performance.The proposed approach involves several key steps. First, the network is divided into stationary clusters. Next, a new distributed CH selection method is introduced, leveraging specific parameters to optimize the selection process. To further improve efficiency, an Enhanced BAT Algorithm (EBA) is employed for CH selection, and a novel routing method is developed to identify the most suitable CH based on the destination's position and the locations of neighboring CHs.Simulation results highlight the effectiveness of the CBCCR protocol, showcasing significant improvements in bandwidth utilization, increased throughput, and reduced transmission delays. These findings demonstrate that the CBCCR protocol offers a robust and efficient solution to the routing challenges in VANETs, making it a promising approach for advancing intelligent transportation systems.
The frequency control is basically a matter of speed control of the machines in the generating stations. The load frequency control (LFC) helps to keep the net interchange of power between pool members at predetermined values. The objective of LFC is to minimize the transient deviations and to provide zero steady state errors of these variables in a very short time. This paper deals with various controllers like without Controller, proportional integral (PI)Controller and fuzzy logic Controller for three area load frequency control in co-ordination with auxiliary frequency controllers is proposed by using MATLAB/SIMULINK. It is designed for a power system comprising areas interconnected by normal tie-lines and frequency controllable High Voltage Direct Current transmission links were auxiliary frequency controllers are used. The qualitative and quantitative comparisons have been carried out for without Controller, proportional integral (PI) and fuzzy logic Controllers. The superiority of the performance of fuzzy over without Control and PI is highlighted.
Modern data centre energy consumption accounts for a large amount of operational cost. The idea of data centre energy optimisation deals with the job distribution between computing servers depending upon the workload. This paper projects the job role of communication material in the data centre energy consumption and proposes a scheduling approach that has both network awareness and efficient optimisation of energy consumption. This scheduling approach stabilises the energy consumption of the data centre, traffic demands and individual job performance. The proposed method enhances the server consolidation (to efficiently turn on/off the servers based on the workload) and also the distribution of traffic patterns.
Energy Efficiency is important role of the Wireless Sensor Networks Researchers. The Energy Efficiency is one of the roles where the data is transmitted to the base station. Energy Techniques is used to improve the reliability of a link. When the data is transmitted communicates to the nodes at exact power to the clustering technique algorithm using graph theory approaches. Secure data aggregation is a challenging task in the wireless sensor networks. These issues are needed to overcome using the clustering efficient techniques. We propose a graph theory based secure data aggregation which has a three phases. We assume the transmitted power and sensing power of the nodes. First phase performs the clustering and cluster head election process. Second phase performs the each clusters are calculated the distance, Energy and also dependence. Third phase performs the shortest path calculation was transmitted the data to secured or not. Finally the aggregated data was transmitted from the cluster heads to the base station. Our proposed models are analysis the acknowledgement through the base stations.
The control issues in the network caused by the increasing proportion of dispersed production & sources of clean energy need coordinated handling of these assets. Virtual power plants (VPPs) may be thought of as systems that comprise resources like electric vehicles, energy storage, controlled loads, and other distributed generators. Operation, resource unpredictability, energy management, involvement in electrical markets, etc. are just a few of the many obstacles VPPs must overcome. The challenge of the study is to optimize the performance of VPPs in managing dispersed clean energy sources, and the proposed model employs a distributed control approach and particle swarm optimization (PSO)-based learning reinforcement technique to address this challenge by maximizing VPP output and operational efficiency in various energy market scenarios. In this research, we explore the topic of ongoing electrical power in massively distributed systems based on VPPs. To maximize the VPP output & reach an ideal operational point, the authors of this work used a distributed control approach. The purpose of this article is to examine VPP performance under a variety of markets for energy configurations. Further, a PSO-based learning reinforcement technique is used to examine the benefits and drawbacks of VPPs' power management.
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