Distribution system state estimation with measurement data using different compression methods

Radhoush, Sepideh; Shabaninia, Faridoon; Lin, Jeremy

doi:10.1109/tpec.2018.8312080

Cited by 13 publications

(5 citation statements)

References 20 publications

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“…Detection in a crowd scene may be like occlusion and impacted by factors fluctuating lighting. [41] Exhibit a tiered strategy encompassing low, medium, and high Crowds make it hard to identify and follow individual people. When faced with unexpected situations, learning, are adaptive, and incremental approaches not effective.…”

Section: Deep Metric Learning To Take Use Of Advantage Of Label Corre...mentioning

confidence: 99%

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

Ya-meng¹,

Wang²,

Shen-shen³

2022

IJACSA

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The analysis of human movement has attracted the attention of many scholars of various disciplines today. The purpose of such systems is to perceive human behavior from a sequence of video images. They monitor the population to find common properties among pedestrians on the scene. In video surveillance, the main purpose of detecting specific or malicious events is to help security personnel. Different methods have been used to detect human behavior from images. This paper has used an efficient computational algorithm for detecting anomalies in video images based on the combined approach of the differential evolution algorithm and cellular neural network. In this method, the input image's gray-level image is first generated. Because it may be possible to identify several large areas in the image after the threshold, the largest white area is selected as the target area. The images are then used to remove noise, smooth the image, and fade the morphology. The results showed that the proposed method has higher speed and accuracy than other methods. The advantage of the algorithm is that it has a runtime of three seconds on a home computer, and the average sensitivity criterion is 98.6% (97.2%).

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Section: Deep Metric Learning To Take Use Of Advantage Of Label Corre...mentioning

confidence: 99%

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

Ya-meng¹,

Wang²,

Shen-shen³

2022

IJACSA

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“…State estimation Principal Component Analysis (PCA) algorithm [23] State estimation t-distributed stochastic neighbor embedding method [24] Feature extraction Pearson Correlation Coeflcient based Extra tree classifier [24] Feature selection to remove irrelevant data Support Vector Machine (SVM) classifier [24] Electricity price forecasting Sparse auto encoder [25] Over voltage identification based on feature extraction Multi level Discrete Wavelet Transform (DWT) [26] Dimensionality reduction of daily load curve Fuzzy Based Feature Selection (FBFS) [27] Feature selection Swinging Door Trending (SDT) [28] PMU data compression Principal Component Analysis (PCA) [29][30][31] Dimensionality reduction of PMU data Event oriented auto-adjustable sliding window method [32] Event detection and AMI), historical data and forecasting data (weather, consumer and generation power patterns). The sizes of these heterogeneous data are measured in terabytes and petabytes, which causes congestion and requires an increased bandwidth for the communication paths.…”

Section: Dimensionality Reduction Technique Applicationmentioning

confidence: 99%

The Paradigm Revolution in the Distribution Grid: The Cutting-Edge and Enabling Technologies

Thasnimol

Rajathy

2020

Open Computer Science

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Bi-directional information and energy flow, renewable energy sources, battery energy storage, electric vehicle, self-healing capability, and demand response programs, etc., revolutionized the traditional distribution network into the smart distribution network. Adoption of modern technologies like intelligent meters such as advanced metering infrastructure & Micro-phasor measurement units, data storage, and analysis techniques and incentive-based electricity trading mechanisms can bring this paradigm shift. This study presents an overview of popular technologies that facilitate this transformation, giving focus on some prime technologies such as real-time monitoring based on Micro-phasor measurement units, data storage and analytics, blockchain technology, multi-agent systems, and incentive-based energy trading mechanisms

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“…In the above results, we achieved mining of criminal terms with 100% accuracy, and documents containing maximum criminal terms with 80% accuracy. Although there are several studies [38,[40][41][42] that implemented Singular Value Decomposition to prove its importance in related fields, we executed SVD with annotation and annotators, Lemmatization, StopWord Remover, and TF-IDF to retrieve criminal information in a distributed computing environment and proved its importance. Furthermore, in [10], the researchers only considered evaluation as a performance measure, while we also considered the efficiency and accuracy of the work in demonstrating its significance.…”

Section: For K = 6 and Numterms = 5000mentioning

confidence: 99%

“…Therefore, SVD is one an important tool for generating different data sets by maintaining the originality of the information. In [40], Radhoush et al utilized SVD and the Principle Component Analysis (PCA) algorithm to feed compressed big data into the state estimation step, and showing that compressed data save time and speed up processing in distributed systems. To measure the difference between the original dataset and the distorted dataset for the degree of privacy protection, Xu et al [38] proposed a sparsified SVD for data distortion techniques.…”

Section: Introductionmentioning

confidence: 99%

Detection of Sensitive Data to Counter Global Terrorism

et al. 2019

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Global terrorism has created challenges to the criminal justice system due to its abnormal activities, which lead to financial loss, cyberwar, and cyber-crime. Therefore, it is a global challenge to monitor terrorist group activities by mining criminal information accurately from big data for the estimation of potential risk at national and international levels. Many conventional methods of computation have successfully been implemented, but there is little or no literature to be found that solves these issues through the use of big data analytical tools and techniques. To fill this literature gap, this research is aimed at the determination of accurate criminal data from the huge mass of varieties of data using Hadoop clusters to support Social Justice Organizations in combating terrorist activities on a global scale. To achieve this goal, several algorithmic approaches, including parallelization, annotators and annotations, lemmatization, stop word Remover, term frequency and inverse document frequency, and singular value decomposition, were successfully implemented. The success of this work is empirically compared using the same hardware, software, and system configuration. Moreover, the efficacy of the experiment was tested with criminal data with respect to concepts and matching scores. Eventually, the experimental results showed that the proposed approach was able to expose criminal data with 100% accuracy, while matching of multiple criminal terms with documents had 80% accuracy; the performance of this method was also proved in multiple node clusters. Finally, the reported research creates new ways of thinking for security agencies in combating terrorism at global scale.

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Distribution system state estimation with measurement data using different compression methods

Cited by 13 publications

References 20 publications

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

Detection of Abnormal Human Behavior in Video Images based on a Hybrid Approach

The Paradigm Revolution in the Distribution Grid: The Cutting-Edge and Enabling Technologies

Detection of Sensitive Data to Counter Global Terrorism

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