Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic State and Parameter Estimation of Distributed Generators using Constraint Recursive Least Square Algorithm and μ-PMU Data

In an active distribution network with increasing penetration of Distributed generators (DGs), it is critical to model DGs and keep the model updated for analysis. However, the generator parameter provided by the manufacturer changes because of aging and errors. Modeling parameters are typically provided by manufacturers and routinely adjusted with offline field testing. With availability of the high resolution real time measurement data such as $\mu$-PMUs, parameter can be estimated without taking generator offline. Model validation and calibration allows non-intrusive estimation of the parameters in distributed generators. This work aims at estimating the state and transient reactances of a distributed synchronous generator based combined heat and power generation unit connected to a medium voltage distribution network. The state and parameter estimation is performed on-line using phasor data collected by micro-phasor measurement unit ($\mu$-PMU) connected at the terminals of the machine. A constrained least-squares algorithm is used in recursion to track the changes in the state and parameter of the synchronous generator. The parameters are estimated considering different switching and dynamic events like faults, islanding, and load/capacitor switching. Results show the superiority of the proposed approach.
Performance Evaluation of Spatial Data Management Systems Using GeoSpark

In this paper, we evaluate the performance of spatial data management systems in distributed computing environments. Given that GeoSpark outperforms other spatial systems in many scenarios as reported in several studies, we choose spatial data management systems using GeoSpark for this evaluation. Even though GeoSpark supports various storage engines as its underlying data store, the effects of the storage engines for spatial data processing have not been well studied. To address this limitation, we evaluate the performance of GeoSpark using two underlying data stores: 1) HDFS and 2) MongoDB. We first design and build distributed experimental environments based on Amazon EC2 and EMR using up to 10 nodes. Through the extensive experiments on three synthetic and real data sets, we show that the overall performance of both HDFS-and MongoDB-based GeoSpark improves as we increase the number of nodes. We also show that HDFS-based GeoSpark generally outperforms MongoDB-based GeoSpark, especially for large-scale data sets. In addition, we demonstrate that the proper use of caching on HDFS-based GeoSpark can improve the overall query processing performance by up to three orders of magnitude.
Poisoning Attack on Show and Tell Model and Defense Using Autoencoder in Electric Factory

Recently, deep neural network technology has been developed and used in various fields. The image recognition model can be used for automatic safety checks at the electric factory. However, as the deep neural network develops, the importance of security increases. A poisoning attack is one of security problems. It is an attack that breaks down by entering malicious data into the training data set of the model. This paper generates adversarial data that modulates feature values to different targets by manipulating less RGB values. Then, poisoning attacks in one of the image recognition models, the show and tell model. Then use autoencoder to defend adversarial data
Emotion Recognition with the Feature extracted from brain Networks

Emotion plays a crucial role in humans' daily life, which affects the decision and communication of human. Moreover, the effective recognition of emotion is essential to establish the affective Human-Computer Interaction (aHCI) systems. In this work, we mainly focus on feature extraction from the brain networks constructed with EEG to perform the emotion recognition. The analysis based on the public emotion dataset MAHNOB-HCI reveals that the proposed approach could achieved 100.00%, 99.95% and 99.99% for Negative-Neutral, Negative-Positive, and Positive-Neutral paired emotion states, respectively. Compared with the previous work for MAHNOB-HCI dataset, the proposed approach achieved the better classification results, and the experiment results have indicated that the feature extracted from brain networks is promising for the emotion classification.
Evaluation of Stabilized Electro-Optical System

Stabilized Electro-Optical System (SEOS) controls the Line-of-Sight (LOS) of Electro-Optical (EO) sensors in inertial frame. These systems are extensively used in dynamic surveillance and warfare system. SEOS is integrated with land, naval and airborne platforms to facilitate the mission requirements like detection, recognition, acquisition, tracking and engagement of targets. Also, these systems provide larger field of regard for small field of view EO sensors. It is pertinent to evaluate such systems for their servo performance before fielding the equipment for integration and exploitation on the actual platform. This paper elaborates on the scheme, test procedures, methodology, recording of data and analysis for evaluation of the SEOS.
Artificial Neural Network Inspired by Neuroimaging Connectivity: Application in Autism Spectrum Disorder

Distinguishing the autism spectrum disorder (ASD) from typical control (TC) using resting-state functional magnetic resonance imaging (rs-fMRI) is very difficult because ASD has heterogenetic properties and induce small changes in the brain structure. Moreover, distinguishing ASD from TC using the data obtained from many sites is even more difficult because many factors might negatively affect the classification model leading to unstable results. This difficulty is especially true for existing rs-fMRI analysis methods such as functional connectivity analysis. Recent studies have shown better ASD classification performance using models constructed using recurrent neural network (RNN). However, a blinded application of RNN not considering the multi-site factors is sub-optimal. In this paper, we present an artificial neural network model inspired by the existing functional connectivity analysis modeling. Our model includes layers that play the role of spatial reduction, temporal feature extraction, and combining phenotypic data (inclusive of multisite data) for classifying ASD. We applied the cross validation framework to the multi-site rs-fMRI dataset to test the proposed model. Our best model showed an accuracy of 74.54%, which is superior to the existing functional connectivity analysis with an accuracy of 54.05%.
An interlaboratory study of the reproducibility of on-wafer S-parameter measurements from 140 GHz to 220 GHz

The development, modelling and characterization of millimeter-wave semiconductor devices calls for accurate and reproducible on-wafer measurements. We report on an interlaboratory study involving on-wafer S-parameter measurements in the 140 GHz to 220 GHz band, conducted by three well-established measurement laboratories. The measurements can be used to form typical reproducibility limits for these measurements when conducted in different laboratories using different equipment and calibration methods.
Time to look beyond ageing as a factor? Alternative explanations for the continuing rise in drug related deaths in Scotland

Purpose The purpose of this paper is to examine the reasons and risk factors that explain the threefold increase in drug-related deaths from 267 in 1996 to 934 in 2017 in Scotland. The authors explore the known links between deprivation and problem drug use (PDU) and discuss the impact of drug policy and service provision on PDU and drug-related deaths. Design/methodology/approach Using quantitative data sets from the National Records of Scotland (NRS) for drug-related deaths registered in 2017 and data sets from the Scottish Index of Multiple Deprivation (SIMD), we produce statistical data on mortality rates relating to areas of deprivation, gender and age. Findings The data highlight the disproportionate number of deaths in the most deprived areas in comparison to the least deprived areas and the national average. Findings indicate that one quarter of male and female DRD in 2017 were under 35. When examining the least deprived vingtile, drug-related deaths account for 2.84 per 100,000 population. Based on this mortality rate calculation, the amount of drug-related deaths are 23 times higher in the most deprived area than the least deprived area. Research limitations/implications The research design uses data obtained from the NRS and data from Scottish Multiple Index of Deprivation. Due to the limitations of available data, the research design focused on SIMD population vingtiles. Practical implications This research contributes to making unarguable links between entrenched structural inequality and increased drug-related death. Social implications This paper contributes to knowledge on the need for drug policy advisors to recognise the importance of deprivation that plays a major part in risks of problematic drug use and harms. Originality/value While several national data sets have published information by SIMD vingtile, no published research has sought to investigate the disproportionate number of deaths by population in the most deprived areas.
A Cooperative Game Theory-Based Algorithm for Overlapping Community Detection

Community detection is of great significance and has more guiding for network analysis which has attract a lot of attention and is widely used in many areas. The process of community formation is modeled as a game in this paper, nodes in networks are taken as players, and communities are regarded as coalitions. An overlapping community detection algorithm is presented based on cooperative game theory. Furthermore, an edge weight computation is proposed for calculating Shapley value of node and coalition, and a strategy of optimal income for the individual is designed. It is for each player to choose its best coalition to stay or leave during the game process. Through choosing coalitions which can improve the value of their characteristic function, coalitions can be merged to avoid small size coalitions. It does not need any prior information about the number and the size of communities and the overlapping community structure is stable at the end of the algorithm. The experimental results both on synthetic and real networks demonstrate the good performance of the proposed method. It can detect overlapping communities more accurately.
Multimodal Web Application to Infer Emotional Intelligence of Adolescent Counsellor

There are only 0.3 psychiatrists and 0.047 psychologists per 100,000 people in India, compared to a country like the US, which has 29 psychologists per 100,000 people (according to WHO), thereby leading to lack of counselling services and mental health-care. Fortunately, researchers in India have found mental health interventions delivered by lay counsellors rather than specialists to be effective in treating and preventing mental health problems. However, choosing a lay counsellor from a pool of candidates becomes a very important but time-consuming and tedious task because of our deficits in evaluating emotional capabilities, implicit biases and facilitation skills in a resume and standard interview. In this paper, we present a highly scalable web application that can help in hiring emotionally intelligent lay-counselors. The backend framework measures several vital emotional intelligence features that are crucial in a prospective lay counsellor. The framework uses multi-modal data and provides a ranking of potential counsellors. The results and inferencing help establish the importance of each modality and gives insights on features that are key to identify the emotional skills. We compare the predicted rankings to those given by the interviewers (a clinical psychologist and a psychiatrist) and recognize the benefits of automation of the process as well as a need for a deeper analysis of interview questions, discriminative features and importance of multi-modality assessments.
Design and Implementation of Decentralized Control for Distributed generation based Off-grid System

In this paper decentralized control for distributed generation based off-grid system to realize a save and optimal operation under perturbation problems, is presented. However, perturbation and observation (P&O) technique is reinforced by iterative interpolation method to realize accurate and fast convergence toward the maximum global operating point without speed information. Furthermore, enhanced indirect control is developed to operate the solar photovoltaic system (SPVS) without any MPPT technique. To achieve high performance during the transition, as well as to mitigate the 5th and 7th order-harmonics, a simple robust control, is employed, where the AC source current is estimated using the generated and consumed power in the system. Optimal gain design of the AWPI and AWPRC controllers are given. The behavior of the developed concept for the distributed generation based off-grid system is tested under presence of several conditions.
Dynamic Energy Management in DC Microgrid using Composite Energy Storage System

This paper describes the power management in DC microgrid system which consists of solar energy system, Wind Energy Conversion System and Composite Energy Storage System. Both the sources are operated in Maximum Power Point Tracking (MPPT) mode to extract maximum energy from the respective sources. The intermittent nature of solar/wind power makes the output power fluctuating. To make it reliably available, a composite energy storage system is used. In the proposed scheme, off-MPPT control and dump load scheme are compared. A simulation study is carried out in MATLAB/Simulink to validate the proposed Power Management Algorithm (PMA).
Adaptive sliding mode control design for the 2-DOF robot arm manipulators

In this paper, an adaptive sliding mode (ASMC) controller designed for a multi-inputs and multi-outputs (MIMO) robotic arm nonlinear manipulator system. The controller is designed based on Lyapunov theorem to achieve a robust trajectory tracking taking into consideration the system’s uncertainties in addition to reducing the effect of chattering problem associated with the traditional sliding mode control. It’s found that, even in the presence of the parameters’ uncertainties, the performance of the proposed controller for the trajectory tracking is robust. Furthermore, chattering is significantly reduced.
Application of optimization Algorithms for the Improvement of the Transient Stability of SMIB Integrated with SSSC Controller

The property of using Static Synchronous Series Compensator (SSSC) as a transient stability solution has been carried out in this paper. Designing a controller of SSSC has been performed using a Particle Swarm optimization algorithm (PSO) code for a machine connected to the infinite busbar. The purpose of using an optimization algorithm is to find the optimum parameters of the controller by minimizing the time domain of the objective function. The deviation in rotor speed has been formulated as an objective function. The paper presents many scenarios to check the robustness of using PSO optimization algorithm as a parameter searching tool. In other words, stabilized responses with the help of PSO were compared with conventional control approaches typically using phase compensation technique. The responses have been checked for different fault locations and different fault types. Using the PSO algorithm proves its capability of improving the transient stability of the system as it surpasses the performance of conventional phase compensation methodology.
A Consensus-based Adaptive Virtual Output Impedance Control Scheme for Reactive Power Sharing in Meshed Microgrids

This paper presents a distributed secondary control scheme for accurate reactive power sharing in an islanded meshed microgrid. The scheme employs consensus control to adaptively tune the virtual output impedance (VOI) into achieving power correction. The adaptive VOI-based control structure is essentially non-linear. However, this work shows that the approximate range of stable coupling gain can be established by linearizing the problem about every probable operating point of the distributed energy resources (DERs). On the basis of islanding mode, it is shown also that only the dynamic virtual output reactance component is needed while the static component, which has been used extensively to date, can be nullified. The proposed control scheme can realize an accurate power sharing among the DERs regardless of the microgrid topology, load condition, and communication delay (within the allowable limits defined by the consensus theorem). The study is carried out in conjunction with an islanded microgrid model modified from the IEEE 34 Node Test Feeder.
A Robust Method for Nuclei Segmentation of H&E Stained Histopathology Images

Segmentation of histopathology images is an initial and vital step for image understanding. To increase the throughput and to maintain high accuracy, we have to go for an automatic image segmentation method. Here, a robust method for segmentation of cell nuclei in Hematoxylin and Eosin (H&E) stained histopathology images is proposed. The proposed segmentation step consists of an initial pre-processing step containing adaptive colour de-convolution and a succession of morphological operations, followed by multilevel thresholding and post-processing steps. Minimum region size is the one parameter which is necessary for this method and set according to the resolution of histopathology image. The proposed nuclei segmentation method does not require any assumptions or prior information about cell morphology. Hence, proposed method applies to the analysis of a wide range of tissues such as liver, kidney, breast, gastric mucosa, and bone marrow and H&E stained liver histopathology images from the Hospital. Results yield that proposed nuclei segmentation provides better results in terms of quantitatively and qualitatively on two datasets.
A Survey on Automatic Fake News Identification Techniques for Online and Socially Produced Data

Fake news are spread on online sites and social media at an alarming speed and in large quantities. Fake news aim to mislead and deceive readers with verifiable false information and they are published on untrusted websites and social media accounts. As they have a very big impact on readers, it is critical to develop efficient models for detecting fake news. This paper reviews the literature on fake news detection and categorizes detection approaches into Knowledge Based approaches and Machine Learning based approaches. Machine Learning based approaches that have been covered in this paper are divided into Conventional approaches and Neural Network approaches. It provides Support Vector Machines (SVMs) and Naïve Bayes for Conventional approaches. In addition to Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for Neural Network approaches. Also, the paper discusses the provided approaches.
A Variational Approach to Image Despeckling under Varied Blur

Restoring blurred and speckle corrupted images have been a traditional problem in the area of image processing. In this paper, we introduce a new variational Bayesian approach for deblurring and despeckling of images corrupted by multilevel noise and different types of blur. We introduce a L1 norm based data-fidelity term and coupled with Total Variational (TV) regularization filters in our optimization problem. Such an approach, as is evident through experimental result, is capable of retaining finer details in the image while producing high quality despeckled and deblurred image. Comparison with existing stateof-the-art shows the high-quality visual and quantitative results, justifying the superiority of the proposed approach.
Predicting Potential Applicants for any Private College using LightGBM

Predicting potential applicants who are probable to take admission is an essential aspect of a college/university. Our research will help the college/universities to increase their efficiency in terms of admissions and will attempt to reduce the effort and cost of the admission process. Machine Learning algorithms can change the face of modern marketing methods to be used by the college/universities and help them in identification of students based on various parameters such as 10th percentage, 12th percentage, JEE All India Rank, opted for coaching, availability of backup options, distinct extracurricular activities, reasons to apply in this college, yearly family income etc. In this paper, we have performed a comparative analysis by using Logistic Regression, Neural Networks, and Light Gradient Boosting Machine (LightGBM). The machine learning algorithms used are used for the prediction of the potential applicants who would take admission. Finally, the algorithm achieved an accuracy rate of 98.5%.
Performance Estimation on Heterogeneous Systems: Making the most of Static Analysis

Heterogeneous Computing System (HCS) comprising of accelerators such as GPU, FPGA and DSP are extensively used in the parallel computing domain. The diversity in their micro-architectures makes them suitable for the various parallel scientific applications. Most of the existing systems that address data distribution in HCS heavily depend on the target architecture that limits the design space exploration to a known device micro-architecture. In contrast, this work uses static code analysis to develop a target-independent performance model to suggest the suitability of a data-parallel regular application to CPU or GPU in an heterogeneous node. This model uses information available at compile time to estimate the performance and the objective is to statically obtain relative performance. With the performance estimates for both the CPU and GPU code for varied problem sizes, an application is classified as CPU-GPU or GPU-only. Furthermore, the approach also gives an optimal data distribution ratio for the application. This approach is evaluated using data-parallel applications that have varied speedups on GPU w.r. t. multi-core CPU. Using the proposed technique, an average performance improvement of 38.44% is seen across CPU-GPU benchmarks, with the co-execution of CPU+GPU as compared to CPU alone.
SEPIC Converter Based Solar Energy Driven Sensorless Induction Motor Drive

Most generic design objectives of research are good conversion efficiency, reliability and cost optimization. This paper proposes a solar energy fed single ended primary input converter (SEPIC) as a DC-DC conversion circuit. The SEPIC is controlled for extracting the maximum power from photovoltaic (PV) array and feeding the power to the DC bus of three-leg voltage source inverter (VSI). The air-conditioning system is extensively used in many energy intensive applications and contributes to major loads on grid supply system. This work presents an optimal solution using solar energy to drive the compressor unit of an air conditioning plant. The use of speed sensor less induction motor (IM) for compressor system results in cost effective and energy efficient solution. The compressor operates under change in speed with change in solar irradiation. A direct torque control (DTC) based scheme is employed for the IM side control. Proposed concept is validated using MATLAB based simulink model.