Snail Trails on Pv Modules in Brazil's Tropical Climate:Detection, Chemical Properties, Bubble Formation, and Performance Effects

In recent years cloud computing is one of the emerging research area that encompasses virtualization, networking, storage, software and on-demand web services. Clients, distributed servers and datacenters are the fundamental components of cloud infrastructure. The number of users in cloud computing is growing in an exponential rate at every day. Large number of user requests tries to allocate the resources for many applications which lead to high load on cloud server. To reducing the heavy load on server, the virtual machines are allocated for resources based on priority. In this paper the distributed dynamic priority based algorithm is used for balancing the load on instances effectively and to improve the system consistency, minimum response time and increase the throughput. Allocating the resources on virtual machines based on priority achieves the better response time and processing time. Load balancing ensures all instances in a node in the networks do the equal amount of work at any instant of time. Priority based resource provision to improve the utilization of resources and reducing response time of cloud services.

An Efficient Distributed Dynamic Load Balancing Algorithm for Private Cloud Environment

Snail trails on PV modules in Brazil’s tropical climate: Detection, chemical Properties, bubble formation, and performance effects

Resource allocation management in a cloud computing environment is the vital problem. Resource allocation models, QoS, time, conditions of the request for access to services are factors to be focused. Therefore this paper proposes the dynamic resource allocation system which allocates to cloud users. The multi factor technique is applied to optimize the allocation of the virtual machine in cloud computing. In Virtual Machine, while job is allocated and executed in the job scheduler, the first process gets to server and allocates resources. The other remains in waiting state. This refer analyses different virtual machines multi factor such as CPU, Time and Memory usage, Space and Speed. It is used to reduce the job makespan of data processing time and job waiting time, turn out time, to allocate resource efficiently. This system that effectively reducesthe system overload and with multi factors load prediction, the number of physical servers. It achieves better performance in terms of server resource utilization with the response time and minimizes the energy consumption.

http://ijecs.in/issue/v5-i8/35%20ijecs.pdf

Performance Enhancement System for the Cloud with Multi Factor Resource Allocation Technique

In recent years cloud computing is one of the emerging research area that encompasses virtualization, networking, storage, software and on-demand web services. Clients, distributed servers and datacenters are the fundamental components of cloud infrastructure. The number of users in cloud computing is growing in an exponential rate at every day. Large number of user requests tries to allocate the resources for many applications which lead to high load on cloud server. To reducing the heavy load on server, the virtual machines are allocated for resources based on priority. In this paper the distributed dynamic priority based algorithm is used for balancing the load on instances effectively and to improve the system consistency, minimum response time and increase the throughput. Allocating the resources on virtual machines based on priority achieves the better response time and processing time. Load balancing ensures all instances in a node in the networks do the equal amount of work at any instant of time. Priority based resource provision to improve the utilization of resources and reducing response time of cloud services

/pdf/an-efficient-distributed-dynamic-loadbalancing-algorithm-for-40bylzro6v.pdf

An Efficient Distributed Dynamic LoadBalancing Algorithm for Private CloudEnvironment

Priority Based Load Balancing in Cloud for Data Intensive Applications

ABSTRACT Fault occurrences in photovoltaic (PV) modules can hinder the performance of the system, resulting in reduced lifetime and performance of the modules. PV module (PVM) faults if unmonitored can affect the power transmission through the system, thereby creating short circuits that can be hazardous. Unmanned aerial vehicle (UAV)-based monitoring is one of the most common and widely adopted techniques to detect faults in PVM. Visual images of PVM contain the necessary information about the faults, but sometimes, it becomes difficult even for expert professional to work on large amount of image data. Automatic classification of PVM faults using deep learning techniques can help in providing improved analysis and instantaneous results. The present study adopts renowned deep convolution neural network (CNN) models such as MobileNet V2, Inception V3, and Xception for the classification of PVM. The aforementioned models were trained individually, and the classification performances of the models were observed to be 97.03%, 95.55%, and 92.27%, respectively. A hybrid deep ensemble model is proposed in the study that merges all the aforementioned models. The proposed model produced classification accuracy higher than each of the individual model with a value of 99.04%. Automatic classification using deep ensemble model can help in the accurate identification of faults in PVM from images acquired through UAV. Consequently, this computer-aided and quick diagnosis can eliminate the downtime and fire hazards.

Deep learning-based ensemble model for classification of photovoltaic module visual faults

Monitoring tire condition plays a deterministic role in the overall safety and economy of an automobile. The tire condition monitoring system (TCMS) alerts the driver of the vehicle if the inflation pressure of a particular tire decreases below a specific value. Owing to the high costs involved in realizing this system, most vehicles do not feature this technology as a standard. With highly robust and accurate sensors making their way into an increasing number of applications, obtaining signals of varied types (especially vibration signals) is becoming easier and more modularized. In addition, feature-based machine learning techniques that enable accurate responses to varied input conditions have sought greater scientific attention. However, deep learning is gradually finding greater applications pertaining to condition monitoring. One approach of deep learning is presented in this paper, which instantaneously monitors the vehicle tire condition. For this purpose, vibration signals were obtained through the rotation of the tire under different inflation pressure conditions using a low-cost microelectromechanical system (MEMS) accelerometer.

/pdf/tire-condition-monitoring-using-transfer-learning-based-deep-jh93qrsg.pdf

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Tyre pressure monitoring systems (TPMS) are electronic devices that monitor tyre pressure in vehicles. Existing systems rely on wheel speed sensors or pressure sensors. They rely on batteries and radio transmitters, which add to the expense and complexity. There are two types of basic tyres: non-pneumatic and pneumatic tyres. Non-pneumatic tyres lack air and combine the tyre and wheel into a single unit. When it comes to noise reduction, durability, and shock absorption, pneumatic tyres are more valuable than non-pneumatic tyres. In this study, nitrogen-filled pneumatic tyres were considered due to the uniform pressure management property. Additionally, nitrogen has less of an effect on thermal expansion than regular air-filled tyres. This work aimed to offer a deep learning approach for TPMS. An accelerometer captured vertical vibrations from a moving vehicle’s wheel hub, which were then converted in the form of vibration plots and categorized using pretrained networks. The most popular pretrained networks such as AlexNet, GoogLeNet, ResNet-50 and VGG-16 were employed in this study. From these pretrained networks, the best-performing pretrained network was determined and suggested for TPMS by varying the hyperparameters such as learning rate (LR), batch size (BS), train-test split ratio (TR), and solver (SR). Findings: A higher classification accuracy of 97.20% was obtained while using ResNet-50.

/pdf/enhanced-tyre-pressure-monitoring-system-for-nitrogen-filled-1jclahmf.pdf

Enhanced Tyre Pressure Monitoring System for Nitrogen Filled Tyres Using Deep Learning

The increasing adoption of photovoltaic (PV) systems worldwide highlights the need for reliable fault detection techniques to ensure optimal system performance. This paper presents a comprehensive analysis of various types of faults in PV systems, addressing all major types of physical, electrical and environmental faults of PV systems and their effects. The review also covers a wide range of fault detection methods proposed over the past decade for PV systems. The role and importance of a fault diagnosis technique in maintaining the smooth functioning and real-time energy production of PV systems is also highlighted. The work aims to provide researchers and professionals in the PV field with valuable benchmarks and updated information to improve fault detection and diagnosis techniques.

N. Sridharan

Papers

Priority Based Load Balancing in Cloud for Data Intensive Applications

Deep learning-based ensemble model for classification of photovoltaic module visual faults

Tire Condition Monitoring Using Transfer Learning-Based Deep Neural Network Approach

Enhanced Tyre Pressure Monitoring System for Nitrogen Filled Tyres Using Deep Learning

A Holistic Study on Failures and Diagnosis Techniques in Photovoltaic Modules, Components and Systems