Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Artificial Intelligence of Things for Anomaly Detection in Advanced Metering Infrastructures

The integration of more sensory and actuation components to the Smart Grid produces high volume of data. Consequently, this big data stretches the transmission, processing, and storage capabilities of the Smart Grid infrastructures. The vulnerability of advanced metering infrastructures (AMIs) is on the rise, as more devices are connected to the Internet these days. The aforementioned realities have continued to necessitate a debate on the future of cloud-centered artificial intelligence (AI) services for latency-sensitive user-centric IoT applications. It is rapidly becoming necessary to leverage on the applicability of EdgeAI directly on IoT sensory nodes involved in energy metering. This paper proposes the applicability of Artificial Intelligence situated on smart meter, to perform micro analytics at the edge of AMI networks: Artificial Intelligence of Things. Therefore, a functional AMI model based on IoT and EdgeAI is presented herein. Additionally, an integration architecture for the anticipated Smart Grid based on IoT and EdgeAI is presented. On implementation, the proposed model would provide high performance analytics and Edge computing capabilities to enable AMIs initiate instant data check at the source and relay relevant real-time data to the Utility through the Internet.
Predicting Agri-Food Prices with Time-Series and Data-Mining based Methods

Onion, potato and tomato (OPT) are some of the most essential commodities of every Indian household due to their affordability and need. But most of the times it has been seen that the prices fluctuate throughout the year and sometimes affording some of these items become difficult for a typical middle-class family in India. Especially, the case of onion, being a food item in most of the households, the market behaves very erratically and is a matter of concern. There is hardly any concrete evidence available why the price of onion fluctuates drastically leading to consumers' grief. In this paper, the price of onion has been modeled using Winters' and (S)ARIMA(X) (auto-regressive integrated moving average method with seasonality (S) and exogenous variable (X)) algorithms using Python. The paper has tried to predict the prices and cites some of the causes for such fluctuations. It is observed when supply/arrival has no major role in the price fluctuations; there the volatile wholesale price is statistically significant causing the higher price fluctuations. The question behind high wholesale price fluctuations, given constant/relatively constant supply, is a matter of further supply chain research.
WASTE ACTIVATED SLUDGE REMEDIATION OF METAL– PLATING EFFLUENTS

Waste activated sludge is capable of binding and removing metal ions from solution. Sludge was exposed to an industrial metal-plating effluent, containing a range of heavy metals, in order to assess its bioadsorptive capacity. Experiments were conducted in fully mixed reactors on a batch basis. Greatest affinity was shown for the Zn cation which consistently proved to be present in untreated waste water samples in the highest concentration. Although Zn dominated the forward adsorption reaction, ionic competition amongst the other metals analysed showed Cr 3+ and Cu to be the most weakly bound, often displaced to the soluble phase by other metal contaminants. Three agents viz., HCl, H 2 SO 4 and NaCl, were assessed for their ability to desorb immobilised metals from the surface of the sludge biomass. Based upon performance and cost, H 2 SO 4 showed greatest potential for further experimentation and process optimisation. This study shows that waste activated sludge, with further process optimisation, is suitable as a bioadsorbent material for the remediation of metal-plating effluents.
A Modular Multi-Phase Actively Controlled Resistive Load Bank with Zero-Current Switching Capability and Integrated Snubbers

Increased demand for electric vehicles has created a growing market for high-performance inverters. These new drives are capable of supplying high power at effective switching frequencies approaching MHz and modulation frequencies at several kHz for high-speed machines. To emulate realistic operating conditions for testing of these inverters, a test load must handle a demanding combination of high frequency, high power, and high voltage not frequently found in commercially available equipment. Moreover, resistive load banks are preferred, as the inverter's fast digital control loops may interact in undesirable ways with an active electronic load bank controller. This work presents an easy-to-use computer-controlled modular resistive load bank designed for testing of advanced, high-frequency inverters. After an overview of existing technologies, key system-level design choices are discussed. Next, specifics of the switch implementation are presented, followed by a description of the control system, switch signaling, and zero-current sensing. In addition, the Python-based user interface is outlined. Finally, experimental results demonstrating high-power switching, both at peak current and zero current, are shown.
Transmission equipment image recognition based on Ensemble Learning

Unmanned aerial vehicle (UAV) inspection is the most popular inspection method in power transmission line inspection. UAV inspection mainly obtains the inspection image of power transmission equipment through machine vision and detects it. Image classification technology is mainly used for the identification of power transmission equipment, which can be used for fault diagnosis after sorting out the equipment. In order to accurately classify transmission equipment images, a classification method based on ensemble learning is proposed. This paper classifies and identifies four kinds of equipment in transmission line, including insulator, damper, interval rods, and corona ring. The experimental results show that the efficiency of the proposed ensemble learning method is better than that of single machine learning classifier.
Multi-Mission Planning of Service Robot Based on L-HMM

In recent years, the service robot has been developed rapidly, which has brought a lot of convenience for industries and human life. It is of great significance to study service robot. Mission planning is the key component of robot, which is used to solve the sequential decision problem of robot. Because of the highly dynamic and uncertain working environment of service robot, how to model and deal with these complex environments is very important for agents to complete mission planning correctly and efficiently. In this paper, a multimission planning method based on L-HMM and utility function description considering path cost for service robot are proposed, high-dimensional partially observable states of agents are abstractly classified into three-dimensional states, combined with HMM model based on Bayesian network. L-HMM not only reasonably models the stochastic process of agent, but also avoids the dimension explosion problem in the process of model solving. In addition, in the iterative process of utility function, considering the impact of path cost on planning effect prevents the agent from planning irrationally and inefficiently due to the encapsulation of navigation nodes. Finally, humanoid robot NAO is used to verify the effect of the theory proposed, multi-mission experiments show the planning results are excellent and highefficient.
Insole-based Real-time Gait Analysis: Feature Extraction and Classification

Gait assessment relies on clinical tools based on observation by trained staffs who give a subjective opinion. Objective gait analysis via motion capture systems (e.g. Qualisys) have limited availability as they are laboratory based and require complex equipment. A low-cost user-friendly Inertial Measurement Units (IMUs) embedded insole and an Android App based personalized gait analysis system is developed for uses in home or clinics. Accelerometer and gyroscope synchronous data are collected from both right and left legs for 10 young and 10 older adults a period of 100 consecutive days. We propose an automatic gait features extraction method, real-time visualization and age-groups classification. Accuracy of stride detection method is 100% for young. Accuracy for older adults is 91% for right and 88% for left leg. Convolutional neural networks (CNNs) are used to extract features from gait data and are combined with long short-term memory (LSTM) to exploit the time information between features. This is evaluated empirically using traditional classification and deep learning techniques (CNN+LSTM RNN) regardless of feature engineering. Accuracy to classify young and older adults with CNN-LSTM, NB, SVM and J48 is 100%. Our insole-based gait analysis automatically interprets the gait features and users can monitor their gait at home using our simple visualization tool that allows widespread home-based diagnosis and management of gait abnormalities and rehabilitation.
Economic Adaptive Cruise Control for Electric Vehicles Based on ADHDP in a Car-Following Scenario

In order to ensure vehicle safety, enhance riding comfort, extend the battery life of electric vehicles (EVs), and improve the energy economy, an ADHDP-based economic adaptive cruise control (Eco-ACC) strategy for EVs in car-following scenarios is proposed in this paper. First, the longitudinal dynamics of EVs is modeled, and the control objectives are presented; then, the actor-critic structure of ADHDP is introduced, and the policy iteration formulas of the critic and actor networks in the ADHDP framework are given; finally, after the state variables, control variables, unity function and value function are determined, the ADHDP-based Eco-ACC strategy for EVs is designed. Extensive simulation results under different driving cycles show that the proposed Eco-ACC strategy can not only ensure vehicle safety, improve riding comfort and reduce energy consumption, but also significantly reduce the battery capacity loss and extend the battery life compared with the benchmark algorithm. In addition, the proposed Eco-ACC strategy is model-free and real-time, and can be robust in different car-following scenarios.
Similarity Based Collaborative Filtering Model for Movie Recommendation Systems

Recommendation systems are information filtering tools and techniques which provide valuable suggestions to users about items they may like by considering various attributes. In Recommendation algorithms, similarity metrics are the main components and performances of these will have direct influence on the recommendations generated. Selecting suitable Similarity measures will be a major factor in improving performance of data mining techniques. These are mainly used for finding the similarity between the users or items as per the requirement for different purposes. In this paper, performance of various similarity metrics applied in recommendation tasks are compared by considering MovieLens dataset for developing a recommendation system for movies using user-based and item-based
Optimization of Human Resources Allocation for Airport Security Check Business Based on Genetic Algorithm

Regarding the problem of the combinatorial explosion of allocation methods in the quantitative development of human resources optimization for enterprises, a method of human resources allocation optimization based on a genetic algorithm is proposed by introducing a genetic algorithm to the field of program optimization. This paper first introduces the basic contents and execution process of the genetic algorithm, then takes the airport security check in the civil aviation business as the application object to further introduce the construction process of genetic algorithm models such as individual gene sequence, population and fitness function in detail, as well as the execution details of the genetic algorithm model. Not only can this method provide guidance for optimizing human resources allocation in civil aviation security, it can also be popularized and applied to other business areas through the proper modification of the adaptability evaluation function.
A Bibliometric Analysis of Research on Auditing for Sustainable Corporate Governance

Auditing has been playing an increasingly critical role in corporate governance, which can contribute to the organizations by ensuring a legal, reasonable, and sustainable development. Thus, auditing has become a research hotspot, which leads to the rapid development of auditing methods and technology. In this study, we conducted co-word analysis and co-citation analysis to explore the research trends and hotspots in auditing, based on 3993 papers published in the Web of Science from 2002 to 2019. Results showed a rapid increase in the number of publications, a broader scope of themes, and tighter interconnections among keywords. Since the global economic crisis, corporate governance has gained wide attention to prevent the next financial crisis. Audit quality is the most dominant theme across the study periods, as credible audit quality is a precondition for corporate sustainability. More specific and prominent themes of audit quality have been the attractive hotspot in recent years, including the professional skepticism and the integrated report. Furthermore, emerging technologies, such as big data, have been introduced to improve audit efficiency and quality.
Detection of Phenolic Compounds in Green and Black Teas using a Simple Carbon Electrode

In recent years, the consumption of green tea and black tea worldwide has gained importance in nutrition and preventive medicine since it is associated with different health improvements related to the polyphenols’ antioxidant action [1 – 3]. This fact has motivated the use of different characterization techniques to detect and quantify tea’s polyphenol content. In this work, green and black tea’s electrochemical characterization was carried out using a cyclic voltammetry technique using a glassy carbon as a working electrode in a standard three-electrode cell. From the voltammetric records obtained, it was possible to perform the carbon electrode calibration model with $R^{2}\gt$ 0.99 and a preliminary Principal Component Analysis that reveals that the electrochemical signals of green and black tea could be used to classify both teas by selecting some potentials of oxidation and reduction mainly associated with polyphenolic compounds they contain.
Metal Corrosion Assessment Method of Power Equipment Based on Multi-Feature Fusion and PCA Dimensionality Reduction Analysis

The "exposure method" was used to conduct field test on Q235b steel, a metal material for power transmission and transformation equipment in the substations, and the corrosion images with different corrosion degree were obtained. On this basis, firstly, the relevant characteristic parameter values of images with different corrosion levels were calculated by color moment, information entropy, singular value, wavelet and fractal analysis methods. Then, the principal component analysis(PCA) method was used to reduce the dimension of the multi-dimensional image features. Finally, the particle swarm optimization least squares support vector machine (PSO-LSSVM) algorithm was used to establish the metal corrosion evaluation model of power equipment. The results show that the colour moment feature, information entropy, singular value, wavelet coefficient and fractal feature corresponding to the metal corrosion image can reflect the evolution law of the metal corrosion morphology well. The corrosion evaluation model established by PCA and PSO-LSSVM can accurately evaluate the corrosion degree of metals, and the evaluation accuracy rate is 94.6%.
Can Managerial Ability Enhance Corporate Social Responsibility? An Empirical Evidence Based on Linear Regression Analysis

Our study expands the literature of corporate social responsibility (CSR) in transition economies by investigating the influence of managerial ability (MA) on CSR and the moderating effects of state ownership using multivariable linear regression analysis. Using a sample of 4706 firm-year observations from China during 2007-2015, we find MA is positively associated with CSR, indicating that MA plays an important role in strengthening CSR engagement. Furthermore, we construct a moderating variable to examine the role of state ownership. We find that state ownership enriches the positive relationship between MA and CSR, indicating the supplementary effects between MA and state ownership on CSR-enhancing. Finally, we estimate a two-stage least squares (2SLS) regression to reduce possible reverse causality.
A Study on the Optimal Scheduling and Evaluation of Urban Hydropower Stations Based on Power Generation, Ecology and Shipping

With the development of hydropower stations towards multi-objective development, such as water supply, flood control, power generation and navigation, rational allocation of water resources has become a major problem. Based on liuzhou Honghua Hydropower station, an optimal scheduling model with multi-constraint, multi-objective, and multi-variable hydropower stations is proposed supported by the objective function based on the maximum generating capacity, navigation capacity and the least breakdown power. The Pareto optimal solution is employed to solve the multi-objective problem of optimal scheduling of hydropower stations. Consequently, a comprehensive evaluation model for optimal scheduling of hydropower stations was established according to the TOPISIS and gray correlation degree analysis. By selecting the scheduling solutions of hydropower stations, the result could be more accurate and closer to real conditions, which could provide guidance for project decision-makers.
Propagation at mmWaves frequencies: some considerations

The existing frequency spectrum used at mobile communications is starting to be insufficient to serve the continuous capacity demand. In order to solve this requirement, the millimeter waves (mmWaves) band spanning between 3 to 300 GHz are seen as possible solution due to the availability of unlicensed bandwidth. However, these frequencies face several propagation challenges, like severe path loss, atmospheric and rain attenuation, reasons to why this spectrum was not fully considered to be used until now. Through the use of higher frequencies is possible to achieve faster and higher data rates, which is a very attractive reason to use them on new mobile applications with gigabit-per-second throughput requirements. This paper presents an overview on the main issues that this band faces by analyzing a network link budget and provide some system guidelines by discussing how the link distance, bandwidth and frequency used can determine the data rate provided. The larger blocks of available bandwidth combined with the use of antenna arrays can be the solution to make this technology feasible in a near future.
Design of Frequency Control System for Excitation Synchronous Motor

In order to realize the vector control strategy of excitation synchronous motor, an efficient and stable hardware system platform is necessary for the hardware design of frequency conversion speed regulation platform of excitation synchronous motor. The performance of hardware platform will directly affect the actual control effect of control algorithm on motor. With the development and wide application of high performance digital signal processor, the accurate control of excitation synchronous motor is realized. For this reason, this paper designs and develops the variable frequency start control hardware platform of excitation synchronous motor with digital signal processor DSP as the control core, which provides the hardware foundation for vector control of excitation synchronous motor. The test results shows the feasibility of the frequency control system.
SOI technologies for RF and millimeter-wave integrated circuits

Performances of RF and millimeter-wave integrated circuits are directly linked to the analog and high frequency characteristics of the transistors, the quality of the back-end of line process as well as the electromagnetic properties of the substrate. Today, Partially Depleted Silicon-on-Insulator (SOI) MOSFET is the mainstream technology for RF SOI systems. Fully Depleted SOI MOSFET is foreseen as one of the most promising candidates for the development of future lower power wireless communication systems operating in the millimeter-wave range.
Analysis on the Application of Deep Learning Technology in the Field of English Education Big Data Mining

With the convening of the big data Summit on artificial Intelligence and Education in the world, scholars from many countries have discussed the new trend of educational reform, confirming that the deep integration of technology and English education will bring more opportunities and challenges. Among them, as a hot issue in the field of AI, in-depth learning will become the key to the development of education. Through the screening and statistical research of related research, this paper analyzes the concept of deep learning in different fields. Finally, it clarifies the significance of introducing deep learning into the field of English educational big data mining. At the same time, in view of the objects and problems to be solved in the field of educational big data mining, this paper puts forward some suggestions on the further development of deep learning technology in the field of English educational big data mining.
A DC Line Protection Scheme Based on Current Traveling Wave for MMC-MTDC Grids

The multi-terminal high voltage direct current (MTDC) grids make it possible to connect large scale clean energy to the grid. However, the lack of effective methods minimizing the DC line faults impact limits the development of MTDC grids in some extent. This paper first analyses the propagation characteristics of the fault traveling wave in DC line. Then, the expressions of the initial line-mode fault current traveling wave (ILCTW) under typical internal and external fault are obtained. Moreover, a fast nonunit protection scheme is proposed based on the filtered fault transient current traveling wave. A modular multilevel converter (MMC)-based four-terminal high voltage direct current (HVDC) grid is established in PSCAD/MTDC, and extensive simulation results are conducted to validate that the protection scheme can not only identify the internal fault accurately and rapidly, but is also effective in detecting different fault locations and large fault resistance.
Adaptive Cascade Control of DC Motor under Condition of Parametric Uncertainty Using Lyapunov Stability Criterion

A problem of a DC motor control under the conditions of parametric uncertainty is the scope of this research. It is solved for a cascade control system, in which the armature current and motor speed controllers are adjusted using the model reference adaptive control scheme. The stability of this system is proved using the Lyapunov criterion. Robustness of the adaptive armature current and speed controllers are provided by means of a back-EMF compensation and the e-modification respectively. A mathematical model of a real DC motor is used as a plant. Its inertia moment and an armature winding resistance values are changed from their nominal ones by two and 1.5 times respectively. The results of the mathematical modeling show that the obtained adaptive control system adjusts the parameters of both the above-mentioned controllers effectively and provide the required control quality when the speed setpoint is: 1) implemented as a signal, which meets the condition of the "persistent excitation" (PE), and 2) a conventional step-like signal, which does not meet the PE condition.