Miracle-dz/UniFAR-FTUs huggingface dataset:
Discussion on lightning protection of distribution network and its differentiation

The distribution network serves as a connection hub with users in the power system, supplying electric energy to users or factories, and has a close relationship with users. As individuals have higher requirements for the quality, reliability and economy of electric energy, the safety and reliability of the distribution network is getting increasing number of attention. Due to the low insulation level of distribution lines, harsh operating environment in some areas, complex network structure, and susceptibility to lightning strikes, the majority of the current researches give priority to transmission lines, thus the lightning protection of the distribution network needs to be focused. Although there are quite a few kinds of lightning protection measures, it is difficult to find a universal lightning protection measure in the distribution network. For the lightning protection measures of the distribution network, specific problems need to be analyzed in detail, and they are suitable for local conditions. Here we discuss the scientific and effective design of lightning protection schemes through “active lightning protection”, “passive lightning protection” and “differential lightning protection” to improve the lightning protection efficiency of the distribution network and ensure the safe and reliable operation of the system.
A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.
Statistical Studies Using Goodness-of-Fit Techniques With Dynamic Underwater Visible Light Communication Channel Modeling

In this paper, we studied the probability distribution function (PDF) of the channel gain of dynamic underwater visible light communication (UVLC) model for different types of water using goodness-of-fit (GoF). We used different water channels at different system parameters with dynamic scenarios. First, the Zemax Optics Studio simulator is used to simulate dynamic UVLC channels. UVLC links are examined using Monte Carlo Ray-Tracing (MCRT) simulation for three different water channels; namely, pure sea water channel, clear ocean water channel and coastal ocean water channel at different configuration types. With the presence of blocking divers, we added a dynamic movement in a single input multiple users (SIMU) scenario. Our simulation is based on Zemax Programming Language (ZPL) in sequence with the Zemax Optics Studio. The GoF tests are used to get the degree of fitness between the simulation data and the set of well-known candidate distributions to determine the best fit. We used the R statistical programming language and applied predefined algorithms to determine the optimum degree of fitting for each statistical result. The Kolmogorov-Smirnov (KS), Chi-Square (CS), Cramer-Von-Mises (CVM) and Anderson-Darling (AD) tests are used to represent the four GoF statistical computation techniques for each channel scenario. The received power is enhanced by 35% when the detector movement area increases from 25 m2 to 100 m2 in clear ocean water channel. The obtained results reveal that the UVLC is best represented by Weibull, Gamma or Lognormal distributions.
Lightning Impulse Long Delay Breakdown of Centimeter Vacuum Gaps

This paper presents an experimental investigation into the breakdown voltage fall time tf of lightning voltage breakdown of 2–4 centimeter vacuum gaps having a long breakdown delay time td(>30 µs). The results show that the average 50% breakdown voltage fall time t50 decreases with increasing vacuum gap. It is considered that the lightning impulse breakdown of the centimeter vacuum gaps with a long breakdown delay time may be initiated by the interaction between the cathode thermal processes and the micro-particles phenomena. The increase of the electrode diameter and the decrease of the electrode radius of curvature may contribute to the decrease of the breakdown voltage fall time.
K-Submodlar Function Based Incentive Mechanisms for Crowd Multi-Labeling

Crowd labeling, as a new paradigm of labeling classification problems, has enabled it to create a tremendous amount of high-quality labeling datasets by harnessing extensive ordinary human comprehension at a low cost. However, existing works mainly focus on a single label scene(one instance is only associated with a single label or a category). They do not fit some real applications well where one instance can associate with multiple labels and different categories can have different budget limits. In this paper, we find that the issue can be addressed by introducing <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-submodlar function, which has received extensive attention recently. Moreover, we further propose a <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-submodlar function based incentive mechanism for crowd multi-labeling scene, satisfying the truthfulness, individual rationality, computational efficiency. Extensive simulations validate the theoretical properties of our mechanism.
Effective Collaboration to Maximize Throughput Based on Multiuser Cooperative Mobility in Social-Physical Ad Hoc Networks

Social networks are playing an increasingly important role in information dissemination in wireless ad hoc networks with the wide adoption of the mobile Internet. In particular, in social application scenarios such as Facebook and Twitter, the questions of how to fully consider the social relationships between users to ensure the quality of service (QoS) urgently needs to be explored. This paper investigates an optimal social relay selection scheme with high intimacy requirements to maximize the system throughput for social-physical ad hoc networks with device-to-device (D2D) communication. Different from previous studies, we jointly consider feasible relays and multiuser cooperative mobility with satisfactory link reliability for throughput maximization. On the one hand, we formulate a nonlinear and nonconvex problem, which is typically NP-hard, for throughput optimization. Specifically, this optimization problem is divided into two subproblems: i) selecting the optimal relays and ii) determining the best mobility strategy in terms of throughput. On the other hand, we first convert the relay selection subproblem into an optimal stopping phase problem, and then propose a definition of a graph representing the degrees of interference between physical links, transforming the original optimization problem into a convex problem that is solvable using the Lagrange multiplier method. Based on the above, we propose the Relay selection and Link Interference Degree Graph (RS-LIDG) algorithm to solve the two subproblems. Numerical simulations verify that the proposed RS-LIDG method improves the throughput gain by 26.29%, 123.43%, and 236.47% compared to the intuitive method (IM), the social-trust-based random mobility selection method (STS-RM), and the physical-based random mobility selection method (PHS-RM), respectively.
Estimating Soil Moisture Over Winter Wheat Fields During Growing Season Using Machine-Learning Methods

Soil moisture is vital for the crop growth and directly affects the crop yield. The conventional synthetic aperture radar (SAR) based soil moisture monitoring is often influenced by vegetation cover and surface roughness. The machine-learning methods are not constrained by physical parameters and have high nonlinear fitting capabilities. In this study, machine-learning methods were applied to estimate soil moisture over winter wheat fields during its growing season. RADARSAT-2 data with quad polarizations and 240 sample plots in the study area were acquired and collected, respectively. In addition to the four linear polarization channels, polarimetric decomposition parameters were extracted to expand the SAR feature space. Three advanced machine-learning models were selected and compared, which were support vector regression, random forests (RF), and gradient boosting regression tree. To improve the performances of the models, three feature-selection methods were compared, which were based on Pearson correlation, support vector machine recursive feature elimination, and RF, respectively. The coefficient of determination (R2) and root-mean-square error (RMSE) were used to compare and assess the performances of those models. The results revealed that polarimetric decomposition parameters were effective in estimating soil moisture, and RF model obtained the highest prediction accuracy (training set: RMSE = 2.44 vol.% and R2 = 0.94; and validation set: RMSE = 4.03 vol.%, and R2 = 0.79). This study finally concluded that using polarimetric decomposition parameters combined with machine-learning and feature-selection methods could effectively estimate soil moisture at a high accuracy, which helps monitor soil moisture across the agricultural field during its growing season.
A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring

The health-care gets huge stress in a pandemic or epidemic situation. Some diseases such as COVID-19 that causes a pandemic is highly spreadable from an infected person to others. Therefore, providing health services at home for noncritical infected patients with isolation shall assist to mitigate this kind of stress. In addition, this practice is also very useful for monitoring the health-related activities of elders who live at home. The home health monitoring, a continuous monitoring of a patient or elder at home using visual sensors is one such nonintrusive sub-area of health services at home. In this article, we propose a transfer learning-based edge computing method for home health monitoring. Specifically, a pre-trained convolutional neural network-based model can leverage edge devices with a small amount of ground-labeled data and fine-tuning method to train the model. Therefore, on-site computing of visual data captured by RGB, depth, or thermal sensor could be possible in an affordable way. As a result, raw data captured by these types of sensors is not required to be sent outside from home. Therefore, privacy, security, and bandwidth scarcity shall not be issues. Moreover, real-time computing for the above-mentioned purposes shall be possible in an economical way.
Design and Implementation of Automatic Robot for Floating Solar Panel Cleaning System using AI Technique

Now a day's energy is generated from both renewable and non-renewable resources. But the generation of electricity from a renewable resource is more authentic and less harmful than the non-renewable resource. Solar energy is the amplest source of renewable energy. Generation of electricity using sun energy that is possible by the solar panels. But the efficiency of solar panels is ostentatious by environmental effects like dust accumulation, temperature and humidity. Therefore, it is essential to have regular and proper cleaning of panel. In general cleaning is done by manually. This type of cleaning is not uniform and has the following drawbacks like damage of panels, injuries to people and movement difficulties. These disadvantages can be overcome by using an automated robot for cleaning the solar panel. This will also increase the efficiency. The vital role of the automatic robotic cleaner is cleaning the floating solar panels where manual cleaning is highly impossible. The robot is driven by the gear motors, motor driver and the robot consists of another motor such that a cleaning membrane is attached to that motor for cleaning them with water. The camera monitors the solar panels and uploads the data to the cloud storage for damage and cleaning references.
Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark

Machine learning algorithms have been intensively applied to perform load forecasting to obtain better accuracies as compared to traditional statistical methods. However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. Optimal and effective use of the available computational resources can be attained by maximizing the effective utilization of the cluster nodes. Parallel computing is demanded to allow for optimal resource utilization in dealing with smart grid big data. In this paper, a master-slave parallel computing paradigm is utilized and experimented with for load forecasting in a multi-AMI environment. The paper proposes a concurrent job scheduling algorithm in a multi-energy data source environment using Apache Spark. An efficient resource utilization strategy is proposed for submitting multiple Spark jobs to reduce job completion time. The optimal value of clustering is used in this paper to cluster the data into groups to be able to reduce the computational time additionally. Multiple tree-based machine learning algorithms are tested with parallel computation to evaluate the performance with tunable parameters on a real-world dataset. One thousand distribution transformers’ real data from Spain for three years are used to demonstrate the performance of the proposed methodology with a trade-off between accuracy and processing time.
Face Recognition Algorithm Based on Weighted Intensity PCNN

Face recognition is an important identification technology in daily life and one of the research hotspots in the field of computer vision and intelligent image processing. However, many algorithms based on Pulse Coupled Neural Network (PCNN) do not adequately describe features, same parameters are used to process the entire face image, ignoring the difference between the local recognition rate and gray distribution of each part of the face image. This paper proposes a block face recognition algorithm based on the weighted intensity PCNN model, we have refined the description of the features through parameter adaptation, partitioned the face according to the degree of contribution in the recognition process, and set the corresponding weight parameters through the improved grid search algorithm, which can effectively improve the PCNN model’s ability to recognize faces. Experiments show that in different face databases, the performance of this model is greatly improved compared with common methods such as PCA and ICA.
An Empirical study of the IoT arrhythmia detection methods: Review and research gaps

Cardiac arrhythmia is a condition of irregular heartbeat, which causes sudden loss of life, which insists the necessity for the continuous monitoring of the arrhythmia patients so that an appropriate medical procedure is provided, for which the smart healthcare plays a major role. Cardiac arrhythmia is one among a type of cardiovascular diseases (CVDs), which reports 12% of total deaths all-over the world. Even though there is a lot of growth in IoT-health monitoring, the manual method suffers from a lot of drawbacks. Hence, there is a need for an automatic method in health-care specifically, for classification of arrhythmia. In this research, we analyze various automatic methods used for classifying arrhythmia in IoT network. This research presents the detailed review of 25 research papers suggesting the methodologies based on various Machine Learning Methods, Deep learning method, nature-inspired methodologies, and so on, for the automatic prediction of Arrhythmia. The collected papers are classified based on different prediction and clustering classification techniques. The research gaps and the challenges faced by the existing techniques are listed and elaborated, which helps the researchers to upgrade the future works. The works utilize various datasets, software tools, performance evaluation measures, prediction techniques, which are analyzed and the performance attained by different techniques are discussed and the research gaps for further contributions are identified in such a way that it enables the researcher to contribute much for the benefit of the society.
Life Expectancy Estimation of Thermally Aged Cu Contaminant-Diffused Oil Impregnated Pressboard

Life expectancy of a thermally aged oil impregnated pressboard (OIP) in the presence of corrosive dibenzyl disulphide (DBDS) is studied in this work using a fast estimation method based on level of diffusion of Cu contaminant. Data from laser induced breakdown spectroscopy (LIBS) is used as the degradation parameter. Thermogravimetric analysis (TGA) is used for rapid evaluation of life expectancy at various temperatures. Aged OIP material is tested for its surface flashover breakdown strength and its thermal stability is studied by TGA and differential scanning calorimetry. Thermal activation energy is estimated using Flynn-Wall-Ozawa method and is agreement with isothermally calculated values. Aging law is derived and found to be nearly first order based on which temperature dependent life expectancy law is formulated. LIBS data of OIP collected from a failed transformer is considered to be end-of-life condition. Estimated life expectancy values using proposed fast method are compared with traditional long-aging evaluation and are in good agreement. Increase in corrosive DBDS concentration and change in aging medium using proposed method and are found to have a significant impact on life expectancy of the OIP insulation.
An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.
Optimal Coordination of Phasor Data Concentrators in Hierarchical Synchrophasor Networks

Phasor data concentrators (PDCs) are essential functions in synchrophasor networks that collect and aggregate phasor measurement unit (PMU) data from across the electric grid. PDC buffers synchrophasor datasets during the “wait time” period, then communicates them to target applications or higher-level PDCs in hierarchy. The time-varying and inherent uncertain nature of communication latencies limits the performance of PDCs especially for applications with hard time constraints. Moreover, the accumulation of latencies in the hierarchy significantly complicates the process of PDC optimal wait time assessment and PDCs coordination. In this article, a method for the optimal coordination of PDCs in synchrophasor networks is proposed. A mathematical model of the hierarchy synchrophasor scheme that incorporates both the “absolute” and “relative” wait time policies required by the IEEE Standard C37.247-2019 is presented. The wait times are optimally designed in order to achieve maximum application throughput and grid observability within the time constraints. The performance of the proposed method is verified using realistic PMU data.
Joint OSNR and Frequency Offset Estimation Using Signal Spectrum Correlations

We propose an efficient and modulation-format-transparent method to jointly estimate optical signal-to-noise ratio (OSNR) and frequency offset (FO) by using signal spectrum correlations for coherent optical fiber communication systems. Based on the signal spectrum correlation analysis, a coarse FO estimation (FOE) and the corresponding compensation can be conducted after chromatic dispersion compensation (CDC). Then, OSNR monitoring can be accurately operated without deterioration caused by FO. Meanwhile, to realize fine FOE, down-sampling process of signal can be used to reduce the complexity of fast Fourier transform-based FOE (FFT-FOE) without losing FOE resolution. Simulation results show that when the OSNR is in the range of 10 to 30 dB, the proposed scheme presents an absolute OSNR estimation error lower than 0.18 dB. Finally, we experimentally demonstrate our scheme in an optical back-to-back (B2B) transmission link using 28 Gbaud dual-polarization (DP)-4/16/32-quadrature amplitude modulation (QAM) formats, and an absolute OSNR estimation error lower than 0.66 dB is achieved for OSNR ranging from 15 to 30 dB.