Miracle-dz/UniFAR-FTUs huggingface dataset:
The Study of Knowledge Graph Framework for Faults Disposal of Power Transformer

Currently, the maintenance strategy decision making mainly relied on the preventive tests and artificial participation, and this cannot meet the increasing scale of power grid and the number of equipment. So, it is necessary to study the fault maintenance strategy of power transformer under the background of new power system characterized by big data. In this paper, a knowledge graph is designed for transformer fault disposal in the field of power grid regulation, composed of knowledge extraction, knowledge fusion and knowledge update. For the text data in the OMS and the PMS system, using the natural language processing (NLP) technology, knowledge extraction includes establishing the power proper noun dictionary and specifying the regularization template, such as “station +voltage level +transformer”. When a transformer fault occurs, the type identification of the transformer fault information is carried out according to the knowledge graph: on the basis of the grid dispatching operation and fault disposal rules stored as knowledge elements, the similarity between this fault event and historical fault case events is calculated to give disposal plans for fault events with a similarity greater than 70% and sorted by similarity priority. The result is imported into the Neo4j graph database to generate multiple discrete data nodes, so as to connect the discrete nodes through relationships. The knowledge graph for transformer fault disposal can provide traversal and high performance retrieval of the data network, the similarity of fault description reaches more than 90%, and the traversal speed almost not affected by the amount of data, suitable for providing the corresponding intelligent decision making plan in the field of regulation.
An Interpretation on Brain Gate System Network and Technology- A Study

The process of thoughts-to-movement is a scientific discovery that makes a severely disabled human to manipulate a device using thoughts. These were achieved, in a great portion through the brain gate network technology. The network system uses a different kind of Brain neural signal which regulates the language of the neuron's sensing, transmission, perception, and implementation. The working idea of the brain gate system is that brainwave patterns are produced with brain function and not transmitted to the body. It is a brain implant device developed at Brown University in partnership with the biotech firm‚ Cyber-Kinetics Neuroscience. Even though effective BCI research has been arrived in the past 25 years, this paper aims to study the working, core principles and research aspects of brain gate technology in depth.
Generating Synthetic Time Series for Machine-Learning-Empowered Monitoring of Electric Motor Test Benches

The development of new electric traction machines is a time-consuming process as it involves intensive testing on motor test benches. Machine-Learning-empowered monitoring offers the opportunity to anticipate costly failures early and hence reduce development time. However, machine learning (ML) for process monitoring requires large amounts of training data, especially as the targeted fault states are scarce and yet diverse in their appearances.Therefore, we propose to use synthetic time series data to leverage the high cost of acquiring training data from experiments in real test benches. In this article, we present a novel scheme to generate synthetic data based on a sub-dimensional time series representation. We introduce a highly flexible model by mapping the data to a latent representation and approximating the latent data distribution by a Gaussian Mixture Model. In addition, we propose the Fréchet InceptionTime Distance (FITD) as a new distance measure to evaluate the generated data. It allows extracting characteristics at different scales by using multiple kernel sizes. In this way, we ensure that the synthesized data contains characteristics similar to those present in the real data. In our experiment, we train two types of fault detectors, one based on real data of a motor test bench and the other based on synthetic data. We also consider employing fault-aware conditional architectures to generate training data for different fault types explicitly. Our final results show that using synthesized data in the training process increases the performance in terms of classification accuracy score (CAS) up to 29%.
Emotion Recognition of Power Workers Based on Weighted Fusion

The emotion of power workers will directly affect their mental state and physical coordination. Emotional recognition of power workers can effectively reduce misjudgment and misoperation, thus reducing the occurrence of safety accidents. Therefore, this paper proposes an emotion recognition of power workers based on weighted fusion. The method first uses the voice activation detection (VAD) algorithm to extract speech key frames. Then, the information entropy is used to model that the generation of emotion is a continuous process, and the expression key frames are extracted. In addition, to fully use the complementarity between speech and expression modalities, the speech and expression key frame alignment techniques are utilized to calculate speech and expression weights. These weights are input into the feature fusion layer for weighted fusion, which effectively improves the recognition rate. Finally, the experimental results on the RML, eNTERFACE05 and BAUM-1s datasets show that the recognition rate of this method is higher than other benchmark methods. The experimental results also show that the method has high accuracy for emotion recognition of power workers.
Comparative Stratification of Steganalysis Techniques to Interpret & Target Anomalies

The art of detecting hidden messages in media is known as steganalysis. Statistically and visually data is hidden in the media. Using deep learning techniques is evidently favorable as they are efficient in learning hierarchical data. Through this research, we tend to investigate and compare various steganalysis techniques and implement classifiers to compare the results achieved. Neural networks, Clustering Algorithms, and other Tools are used to design a model for the investigation of Experimental Findings. Classifiers like the SVM classifier, K-NN classifier, Random Forests classifier, MLP NN classifier, and Naive Bayes classifier are trained and tested to detect images undergoing steganography. The results acquired are compared and analyzed.
Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM

The maximum likelihood estimation (MLE) is known to provide benchmark performance for direction-of-arrival (DOA) estimation. Due to high nonconvexity of the MLE problem, however, effective implementations of the MLE are rare in practice. In this paper, we consider DOA estimation with a uniform linear array and formulate by using re-parameterization and majorization minimization the stochastic MLE as a series of rank-constrained semidefinite programs that are solved using the alternating direction method of multipliers (ADMM). Numerical results are provided to illustrate the superior statistical performance of the proposed method as compared to existing approaches in the absence/presence of coherent sources.
Faster Convergence for Iterative Solutions to Systems via Three-Part Splitting

For the linear system $Ax = y_0 $, we explore linear stationary second degree methods, or so-called three-part splittings (which include the first-degree methods of Jacobi, Gauss–Seidel and SOR) for defining the sequence $\{ x_n \} $ where $x_n \to x$. By measuring the asymptotic rates of convergence of the sequence, we are able to determine when the second-degree method is superior to a corresponding first-degree method. In fact, if B is the iteration matrix of the first degree splitting, then this improvement is analyzed if the spectrum of B is real (§ 5) and when the spectrum of B is purely imaginary (§ 6).
A Novel Ring Oscillator PUF for FPGA Based on Feedforward Ring Oscillators

A Physical Unclonable Function (PUF) exploits uncontrollable variations in manufacturing to characterize an integrated circuit. There have been many PUF designs proposed which apply different strategies to extract process variation on Field Programmable Gate Arrays (FPGAs). Ring Oscillator PUF (RO PUF) is one of the FPGA-friendly designs taking advantage of the difference in hardware delay to generate an unpredictable output of a silicon device. This paper proposes a novel variation of RO PUF on FPGA based on feedforward ring oscillators (FRO). The experiment results of FRO PUF are conducted on Xilinx Artix-7 FPGA and illustrate satisfactory results in uniqueness, uniformity, and reliability with 50.23%, 52.64%, and 95.92%, respectively.
Identifying Patterns in Common Vulnerabilities and Exposures Databases with Exploratory Data Analysis

Cybersecurity threats are targeted by unauthorized users for data stealing, manipulation, harming the reputation of another organization, and damaging the information system. A standardized record of Vulnerability is maintained in National Vulnerability Database that is accessible to the community. Each reported security flaw’s detail can be fetched through Common Vulnerability and Exposures ID. This database is recorded for more than a decade and the detailed analysis of the reported security flaws helps network administrators and security professionals to enhance the cybersecurity practices in an organization. The manual study of the vulnerability database is a challenging task and the threat alert tools provide an automatic method of fetching the vulnerability records from the central database. Threat Intelligence is an evolving field where comprehensive analysis can be generated using artificial intelligence methodologies with previous data records. Before applying any machine learning approach to a vulnerability database for threat intelligence, it is necessary to perform exploratory data analysis of the recorded security flaws. In this paper, the authors extracted the recent vulnerability data from the national vulnerability database and performed exploratory data analysis to understand the trends in reported Common Vulnerability and Exposures to frame the future directions for threat intelligence solutions. This experimental study helps in identifying the main attributes of artificial intelligence-based threat alert generation solutions.
An efficient scheme of source localization using TDOA

This paper focuses on the problem of source tracking in the 3-D space using a time-difference-of-arrival (TDOA)-based localization system. Different from existing studies, the new scheme considers a more realistic data model that includes synchronization offsets, sensor location errors and distance-dependent noises, which dramatically complicates source tracking with a TDOA-based localization system. First, we derive the Fisher information matrix (FIM) and then address the optimal sensor placement under the restriction of geographical conditions. Finally, combining with the maximum likelihood estimation (MLE), Kalman filtering and sensor motion strategy, we propose a source tracking algorithm guided by the optimal sensor placement configuration. Mathematical analyses and numerical simulations validate the optimal sensor placement and efficiencies of the source tracking algorithm.
A Unique Model for Detecting the Diseases using Decision Making Approach (DMA)

Today medical data analysis is complicated to analyze the patient status because of unpredictable health conditions. It is challenging to find diseases in the early stages. Medical data analysis can be used to analyze several clinical data, such as computed tomography (CT) scan images, X-ray images, and MRI images, to diagnose diseases. Clinical data consists of patient health details with every patient’s health condition in a CSV file. CT scan images are used to visualize the parts of the human body and analyzed to diagnose disease or injuries and treat radiation. Another medical data analysis is MRI scan images used to investigate the conditions of tissues such as tumors that may consist of cancer or noncancerous cells. These MRI scan images show the injuries in soft tissues if there are any damaged ligaments, joint diseases, or injuries. MRI is an essential scan that uses strong magnetic fields and radio waves to generate clear images within the body. In this paper, A Unique Model for Detecting Diseases using Decision Making Approach (DMA) is introduced for medical data analysis, such as tumors in the human body, analyzing cancer and noncancerous tumors from the various datasets.
Broadband Orbital Angular Momentum Beam Generation Based on the Discrete Dielectric Lens antenna

The electromagnetic waves carrying orbital angular momentum (OAM) have great potential for applications. Broadband OAM beams are the preference in many sectors. Existed studies show that the beams have been widely explored. Considering the narrow bandwidth of the existing lens antenna carrying OAM we engineer a new discrete dielectric lens (DDL) antenna carrying broadband OAM. The antenna adopts a dielectric unit with an anti-reflection structure and is a cross-shaped air column. By increasing the phase compensation range, the bandwidth of the vortex beam is effectively increased. A broadband OAM generator is designed based on the DDL from 22 to 30 GHz. A proof-of-concept example was proved and validated by simulation. Above 31% vortex beam bandwidth and 64.6% OAM average energy weight are measured throughout the entire design band. This kind of structure offers an alternative mechanism to realize broadband high-efficiency microwave, millimeter-wave, terahertz, or optical OAM generation and can hereafter get position in high-capacity communications and imaging.
Design of Mobile Application for SME Business Sustainability During Post Covid-19

In the 21st century, mobile and portable devices have become an integral part of people's lives, assisting them in managing daily tasks, whether for work or personal reasons. Furthermore, this technology will benefit people, particularly those who run small to medium-sized businesses (SMEs), by allowing them to manage their firms’ using smartphones and other portable devices from anywhere and at any time. However, many SMEs in Malaysia do not own a mobile app. During this Covid-19 pandemic, the country's economic growth rate has slowed. It has impacted negatively on all types of businesses on a massive scale. Several major retailers have temporarily closed their doors. Aside from that, poor footfalls are causing problems for medium and small-sized enterprises. They have entirely lost their businesses due to the absence of a mobile app. They might still earn profits if they had their smartphone app because the delivery sectors were still operating as usual. The primary purpose of this study is to create importance of using electronic commerce mobile applications among SME business owners. This ecommerce mobile application was developed using Android studio with Kotlin programming language. UI of this application was designed with simple widget and layout. Google Firebase platform was connected to Android studio application which is used to store user details and verify whenever user try to login.
Optimal Power Allocation for the Joint Radar and Communications With OFDM Waveform Transmission

In this paper, we propose an optimal power allocation strategy for the joint radar and communications (JRC) with orthogonal frequency division multiplexing (OFDM) waveform transmission to improve the performance of radar detection, where the performance of radar detection is measured by kullback-Leibler divergence (KLD). The strategy maximizes the KLD and controls the total transmit power of radar under the constraint of the achievable communication rate to implement the match between radar transmitted powers in subcarriers and the corresponding subchannel conditions of radar and communications. The match can mitigate the noise and interference from the radar point of view, and meanwhile it can keep the achievable communication rate within an acceptable range. Simulation results show that the design based on KLD is effective, and it is also preferable to mutual information (MI).
GAN vs. LSTM: Network State Prediction

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic.
Siamese Neural Networks for Content-based Visual Art Recommendation

The global art has experienced a steady growth to tens of billion dollars in annual sales. The huge profits behind art trades unfortunately have been largely overlooked and rarely been studied in most of the machine learning and recommendation system (RS) research. As a popular Deep Metric Learning (DML) model, the Siamese Neural Network (SNN) has been widely used in music and other e-commerce RS, but not been used in art recommendation tasks. In this paper we propose an art similarity metric with SNN, and based on which built a content-based art RS, followed by clustering for reducing comparison numbers. Performance evaluation of the proposed SNN-based art RS has been conducted, in comparison with our original, simpler model basing on cosine similarity. Results shows that the SNN-based visual art RS performs significantly better in every experiment subgroup, is more robust with strong resistance to overfitting and confusion. Additional experiments show that it is nontrivial to further improve these recommendation results. To the best of our knowledge, this is the first visually-aware RS that took advantage of both SNN and content-based recommendation framework in visual art recommendation. We believe that this work opens wide opportunities for applying machine-learning and deep-learning techniques in the exciting area of visual art recommendation.
Automated Machine Learning for Steel Production: A Case Study of TPOT for Material Mechanical Property Prediction

Steel is a highly important product for which there has been an ever increasing demand. A critical challenge for steel production is the development of accurate methods for material mechanical property prediction (MMPP). The increasing availability of data has enabled the use of machine learning (ML) to address this challenge. Recently various ML- based methods have been developed, amongst which, however, no single one can achieve competitive results in all product prediction problems. In this context, automated machine learning (AutoML) has been proposed to automatically solve the model selection problem in a way that is more efficient than traditional machine learning. This method is particularly useful for situations in which a considerable amount of human expert knowledge is required to construct specialized ML models. Although AutoML methods are useful and effective, their applications are still limited and difficult due to the lack of analysis of their strengths and weaknesses. Thus, this paper aims to analyze the application of AutoML methods to MMPP processes with datasets collected in the real world by comparing two state-of-the-art AutoML methods (i.e. Auto-Sklearn and TPOT). Results show that AutoML can be considered as a powerful approach to address the model selection problem in MMPP. In particular, TPOT demonstrates the advantages of computational consumption and competitive accuracy.
Impact of excessive use of social media on students learning performance: Gratifications theory perspective

Social media is a growing field, mostly everyone is availing the benefits of it. It seems that overuse of this technology may cripple academic standards of our next generations. Nevertheless research studies in this direction is limited, and recent information systems scholars has warranted more research in this direction. The motive of this study is to explore how excessive social media use affects students' academic performance and come up with a suggestion to reduce social media usage and increase educational learning of an individual. Using primary data collected from Pakistani university students, 265 entries were analyzed using SPSS tool. Findings suggest that usage of social networking sites has a significant result on undue use of social media. Furthermore, results also indicated that unwarranted use of social media has a detrimental effect on learning performance. Results also supported the idea that student technology self-efficacy diminishes the undesirable association between excessive social media use and academic performance.
Low-carbon economic optimal dispatch strategy of integrated energy system considering electric-heat flexible load and carbon trading

In order to improve the consumption of renewable energy, stabilize load fluctuations and control total carbon emissions, this paper proposes a low-carbon economic optimal dispatch strategy of integrated energy system (IES) considering electric-heat flexible load and carbon trading. Combined with the curtailable, shiftable and transferable characteristics of flexible load on the demand side, the strategy establishes an optimal dispatch model under the ladder-type carbon trading mechanism, which comprehensively considers the operation cost, flexible load dispatch cost and carbon trading cost. The analysis of the case study proves that the participation and interaction of flexible electric and thermal loads can significantly improve the consumption rate of renewable energy, realize load peak shaving and valley filling, and relieve peak power supply pressure.
Incremental constrained clustering with application to remote sensing images time series

Automatically extracting knowledge from various datasets is a valuable task to help experts explore new types of data and save time on annotations. This is especially required for new topics such as emergency management or environmental monitoring. Traditional unsupervised methods often tend to not fulfill experts' intuitions or non-formalized knowledge. On the other hand, supervised methods tend to require a lot of knowledge to be efficient. Constrained clustering, a form of semi-supervised methods, mitigates these two effects, as it allows experts to inject their knowledge into the clustering process. However, constraints often have a poor effect on the result because it is hard for experts to give both informative and coherent constraints. Based on the idea that it is easier to criticize than to construct, this article presents a new method, I-SAMARAH, an incremental constrained clustering method. Through an iterative process, it alternates between a clustering phase where constraints are incorporated, and a criticize phase where the expert can give feedback on the clustering. We demonstrate experimentally the efficiency of our method on remote sensing image time series. We compare it to other constrained clustering methods in terms of result quality and to supervised methods in terms of number of annotations.