The increase in the number of sensitive loads in power systems has made power quality,particularly voltage sag,a prominent problem due to its effects on consumers from both the utility and customer ***,to evaluate the...
详细信息
The increase in the number of sensitive loads in power systems has made power quality,particularly voltage sag,a prominent problem due to its effects on consumers from both the utility and customer ***,to evaluate the effects of voltage sag caused by short circuits,it is necessary to determine the areas of vulnerability(AOVs).In this paper,a new method is proposed for the AOV determination that is applicable to large-scale *** false position method(FPM)is proposed for the precise calculation of the critical points of the system ***,a new method is proposed for the voltage sag monitor(VSM)placement to detect the fault locations.A systematic placement scheme is used to provide the highest fault location detection(FLD)index at buses and lines for various short-circuit fault *** assess the efficiency of the proposed methods for AOV determination and VSM placement,simulations are conducted in IEEE standard *** results demonstrate the accuracy of the proposed method for AOV *** addition,through VSM placement,the fault locations at buses and lines are detected.
Distance and size estimation of objects of interests is an inevitable task for many navigation and obstacle avoidance algorithms mainly used in autonomus and robotic systems. Stereo vision systems, inspired by human v...
详细信息
In analyzing phenomena around us, clustering is among the most commonly used techniques in machine learning for comparing, and categorizing them into different groups based on intrinsic features. One of the main chall...
详细信息
In this paper the authors consider the operational problem of optimal signalling and control,called control-coding capacity(with feedback),C_(FB) in bits/second,of discrete-time nonlinear partially observable stochast...
详细信息
In this paper the authors consider the operational problem of optimal signalling and control,called control-coding capacity(with feedback),C_(FB) in bits/second,of discrete-time nonlinear partially observable stochastic systems in state space form,subject to an average cost constraint.C_(FB) is the maximum rate of encoding signals or messages into randomized controller-encoder strategies with feedback,which control the state of the system,and reproducing the messages at the output of the system using a decoder or estimator with arbitrary small asymptotic error *** the first part of the paper,the authors characterize C_(FB) by an information theoretic optimization problem of maximizing directed information from the inputs to the outputs of the system,over randomized strategies(controller-encoders).The authors derive equivalent characterizations of C_(FB),using randomized strategies generated by either uniform or arbitrary distributed random variables(RVs),sufficient statistics,and a posteriori distributions of nonlinear filtering *** the second part of the paper,the authors analyze C_(FB) for linear-quadratic Gaussian partially observable stochastic systems(LQG-POSSs).The authors show that randomized strategies consist of control,estimation and signalling parts,and the sufficient statistics are,two Kalman-filters and an orthogonal innovations *** authors prove a semi-separation principle which states,the optimal control strategy is determined explicitly from the solution of a control matrix difference Riccati equation(DRE),independently of the estimation and signalling ***,the authors express the optimization problem of C_(FB) in terms of two filtering matrix DREs,a control matrix DRE,and the covariance of the innovations *** the paper,the authors illustrate that the expression of C_(FB) includes as degenerate cases,problems of stochastic optimal control and channel capacity of information transmission.
All wireless communication systems are moving towards higher and higher frequencies day by day which are severely attenuated by rains in outdoor environment. To design a reliable RF system, an accurate prediction meth...
Rank aggregation is the combination of several ranked lists from a set of candidates to achieve a better ranking by combining information from different sources. In feature selection problem, due to the heterogeneity ...
详细信息
Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of v...
详细信息
Wide field of view and light weight optics are critical for advanced eyewear,with applications in augmented/virtual reality and night *** refractive lenses are often stacked to correct aberrations at a wide field of view,leading to limited performance and increased size and *** particular,simultaneously achieving a wide field of view and large aperture for light collection is desirable but challenging to realize in a compact ***,we demonstrate a wide field of view(greater than 60°)meta-optic doublet eyepiece with an entrance aperture of 2.1 *** the design wavelength of 633 nm,the meta-optic doublet achieves comparable performance to a refractive lens-based eyepiece *** meta-doublet eyepiece illustrates the potential for meta-optics to play an important role in the development of high-quality monochrome near-eye displays and night vision systems.
This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease ...
详细信息
This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen
Identifying influential nodes has attracted the attention of many researchers in recent years. Because of the weak tradeoff between accuracy and running time, and ignoring the community structure by the proposed algor...
详细信息
暂无评论