An Energy Management (EM) System for Photovoltaic (PV)-powered Electric Vehicle (EV) charging with grid integration and Battery Energy Storage System (BESS) optimizes energy consumption by integrating renewable energy...
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ISBN:
(数字)9798331501488
ISBN:
(纸本)9798331501495
An Energy Management (EM) System for Photovoltaic (PV)-powered Electric Vehicle (EV) charging with grid integration and Battery Energy Storage System (BESS) optimizes energy consumption by integrating renewable energy, storage, and grid capabilities. However, drawbacks include the high initial costs associated with installation and infrastructure, as well as on-going operational costs for managing and maintaining both the storage and charging systems. Furthermore, the intermittency of solar energy can lead to inefficiencies in utilizing Renewable Energy Sources (RESs), requiring additional storage capacity to ensure a reliable power supply, while emissions from battery production, operation, and disposal may counteract some of the system’s environmental benefits. To overcome these drawbacks, this manuscript proposes an approach for EM for PV powered EV charging with BESS. The suggested method is Dung Beetle Optimizer (DBO). The major objective of this work is to reduce operational costs, improve energy efficiency, and lower carbon emissions by improving the use of renewable energy, minimizing grid reliance, and optimizing battery storage operations. The DBO optimizes the scheduling of EV charging, the operation of battery storage (charging and discharging cycles), and the interaction with the grid. Artificial Neural Network (ANN), Genetic Algorithm (GA), Adaptive Interaction Artificial Neural Network (AI-ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) are some of the existing methods that are compared with the suggested method once it is implemented in MATLAB. By achieving the lowest operational cost of 0.121M$/year and reducing carbon emissions to 505.73ktons, the suggested DBO method outperforms traditional approaches while maintaining high energy efficiency and environmental sustainability in PV-powered EV charging with BESS.
The performance analysis of hard-switching based mixed free-space optical (FSO)/ intelligent reflecting surface (IRS)-aided radio frequency (RF) communication system is carried out in terms of outage probability (OP)....
The performance analysis of hard-switching based mixed free-space optical (FSO)/ intelligent reflecting surface (IRS)-aided radio frequency (RF) communication system is carried out in terms of outage probability (OP). The FSO link with larger bandwidth is considered as primary link, it is active for transmission when the FSO channel condition is good enough. The RF link referred to as the back-up link is activated for transmission whenever the FSO link is poor. Further, the transmission via RF link is assisted by an IRS consisting of reflecting elements that are programmable and passive in nature. For this setup, the OP is evaluated for different numbers of reflecting elements at the IRS. In order to obtain 0:001 OP with 28 dB transmit signal-to-noise ratio (SNR) of the RF link, the required transmit SNR of the FSO link with 90 elements at the IRS in the RF link is 31 dB whereas, with 100 elements at the IRS, the required SNR is 24 dB. Thus, it has been observed that just by adding 10 extra passive IRS elements, required SNR of the FSO link can be reduced by 7 dB while keeping the OP fixed. The impact of the IRS on the OP is significant specifically when the FSO link is weak. Further, the OP performance of hardswitching scheme based mixed IRS-aided RF/FSO system has been analyzed considering both strong and moderate atmospheric turbulences, and different link distances.
—LiDAR (Light Detection And Ranging) is an indispensable sensor for precise long- and wide-range 3D sensing, which directly benefited the recent rapid deployment of autonomous driving (AD). Meanwhile, such a safety-c...
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Today's wireless communication networks have many requirements such as high data rate, high reliability, low latency, low error data transmission, and high energy efficiency. High-performance index modulation (IM)...
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ISBN:
(数字)9798350303582
ISBN:
(纸本)9798350303599
Today's wireless communication networks have many requirements such as high data rate, high reliability, low latency, low error data transmission, and high energy efficiency. High-performance index modulation (IM) techniques and reconfigurable intelligent surface (RIS) technology, which has recently attracted the attention of researchers, are strong candidates to meet these requirements. This paper introduces a novel RIS-supported code IM-based receive spatial modulation (RIS-CIM-RSM) system. The proposed RIS-CIM-RSM system uses quadrature amplitude modulation (QAM) symbols, receive antenna indices, and spreading code indices for wireless data transmission. In the proposed system, an RIS applies a phase rotation that maximizes signal-to-noise ratio (SNR) to the signals coming to the reflecting elements and directs them to the selected receive antenna. Performance analyses of the proposed RIS-CIM-RSM system such as data rate, throughput, and energy saving are obtained. The results obtained show that the proposed RIS-CIM-RSM system is superior to the counterpart RIS-based IM systems in the literature in terms of data rate, throughput, energy saving, and error performance.
Modern classification problems tackled by using Decision Tree (DT) models often require demanding constraints in terms of accuracy and scalability. This is often hard to achieve due to the ever-increasing volume of da...
Modern classification problems tackled by using Decision Tree (DT) models often require demanding constraints in terms of accuracy and scalability. This is often hard to achieve due to the ever-increasing volume of data used for training and testing. Bayesian approaches to DTs using Markov Chain Monte Carlo (MCMC) methods have demonstrated great accuracy in a wide range of applications. However, the inherently sequential nature of MCMC makes it unsuitable to meet both accuracy and scaling constraints. One could run multiple MCMC chains in an embarrassingly parallel fashion. Despite the improved run-time, this approach sacrifices accuracy in exchange for strong scaling. Sequential Monte Carlo (SMC) samplers are another class of Bayesian inference methods that also have the appealing property of being parallelizable without trading off accuracy. Nevertheless, finding an effective parallelization for the SMC sampler is difficult, due to the challenges in parallelizing its bottleneck, redistribution, in such a way that the workload is equally divided across the processing elements, especially when dealing with variable-size models such as DTs. This study presents a parallel SMC sampler for DTs on Shared Memory (SM) architectures, with an $O(log_{2} N)$ parallel redistribution for variable-size samples. On an SM machine mounting 32 cores, the experimental results show that our proposed method scales up to a factor of 16 compared to its serial implementation, and provides comparable accuracy to MCMC, but 51 times faster.
This article presents a 17.7-20.2 GHz eight-element four-beam RF-beamforming transmitter in 65-nm CMOS for satellite communication (SATCOM). The transmitter utilizes an analog scheme in the variable-gain amplifier (VG...
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The rapid development of the smart grid has intensified research efforts towards automating power transmission system inspections using unmanned intelligent vehicles (UIVs). A major challenge hindering their large-sca...
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Cell Characteristics are useful in drug development. In particular, single cell analysis can be applied to study drug efficacy. In this study, we used the electrorotation (EROT) technique to determine the electrical p...
Cell Characteristics are useful in drug development. In particular, single cell analysis can be applied to study drug efficacy. In this study, we used the electrorotation (EROT) technique to determine the electrical parameters of cells such as the membrane capacitance and the cytoplasm conductivity. We developed a fast and accurate method for analyzing rotation rate of cells under rotating electric field. We generated a rotating electric field by quadrupole microelectrodes. An image processing technique was implemented to calculate the cell rotation rate from the recorded ROT video. In conclusion, we have demonstrated differences in the response to rotating electric field between malaria-infected red blood cells (iRBCs) and normal red blood cells (nRBCs).
The rise of wearable devices being used into our daily life have been observed the disputes, when it is utilized by the clients for durable is quiet an issue with growth of Internet of Things (IOT). In this paper we r...
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We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to...
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ISBN:
(数字)9798331542559
ISBN:
(纸本)9798331542566
We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies.
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