Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Hybrid Systems with Transzsi-DVR for Mitigation of Power Quality Issues

The quality of power supplied by electrical utilities has become a significant concern due to its potential impact on both power companies and their customers. The identification and analysis of power disturbances, such as voltage sag, swell, and interruptions, have resulted in an increased awareness of the importance of power quality. These power quality issues can cause deviations in current, voltage, and frequency, leading to damage or failure of equipment used by end-users. To address these issues, the use of specialized power converters, such as the TransZSI based DVR, is crucial, injecting a desired voltage at the PCC. Adopting this method has several advantages compared to the conventional Voltage Source Inverter (VSI), such as the ability to buck or boost, minimized passive segments, amplification of voltage, and diminished voltage stress. Through MATLAB simulation, the efficacy of the TransZSI-DVR was compared to conventional ZSI and VSI-DVR and found to be superior. The usefulness of it was further validated through the implementation of the (UVT-MCBC) hybrid technique, which controls injected voltage harmonics.
The Superiority of Coronavirus Optimization Algorithm for Optimal Designing of Photovoltaic/Wind/Fuel Cell Hybrid System Considering Cost Minimization Approach to Improve Reliability

This article proposes the best design for a hybrid system that incorporates wind turbines, solar panels, and fuel cells (FC) to satisfy the load requirement. The design's goal is to reduce the system's energy production costs considering the load supply's reliability. System costs include initial investment costs, operation and maintenance, replacement and replacement costs, and load loss costs. The optimal capacity of the hybrid system's equipment has been calculated with the help of the Coronavirus Optimization Algorithm (COVIDOA). The results obtained from the optimization have been compared and analyzed with those obtained from the Differential Evolution (DE) algorithm. The results have shown that the COVIDOA optimization method, like the DE optimization method, has obtained favourable results. In the COVIDOA method, the system's production costs have increased slightly, but the reliability of the load supply has been improved. Therefore, in the suggested approach, in addition to considering the economic aspect of the design, much attention has been paid to the technical aspect of the design, in other words, the reliability level of the system.
Development of Algorithm to Extract Features from Capnograph Signal

Cardiopulmonary diseases are generally detected using spirometry and peak expiratory flow meter which is tedious for a patient for the fastest blow required. Hence doctors recommend using Capnography which detects the CO2 saturation level from exhaled breath. This paper gives an algorithm to detect vital features from the Capnographic datasets (available in capnobase) which might be suitable for classifying cardiopulmonary diseases like Asthma, COPD, hypoventilation, hyperventilation, etc. Some of the signals are composed of artifacts while some are of spontaneous breath which makes it difficult to identify and extract the features properly. We propose a method to extract a total of 25 features from the Capnograph automatically using digital signal processing. We also identified the significant differences for apnea and cardiogenic oscillation. Among the features, some of them are slopes that properly fit with the original signal with 99.95% sensitivity for regular Capnogram and about 97-99% for irregular Capnogram.
An Analysis on Semidefinite Relaxation of Classical Optimal Power Flow Problem

A significant number of optimal power flow problems with positive semidefinite convex matrix and quadratic cost function can be resolved in polynomial time algorithm. In this paper we have considered the traditional optimal power flow problem considering line loss with bus injection model and reviewed the mathematical interpretations that allow to convert the problem in semidefinite programming. Any semidefinite programming can be solved efficiently with interior point method. We have summarized on previous results that the requirement for exactness to recover original solution depends on network structure. Finally we have verified our result with a IEEE benchmark system.
A TEMPORAL MODEL FOR PITCH MULTIPLICITY AND TONAL CONSONANCE

One aspect of tonal consonance relevant to the pitch stability of chords and harmonic tension concerns the degree to which a collection of notes produces a unified, strong low pitch. We used a neurophysiologically-grounded temporal model for pitch to test Stumpf's general hypothesis that consonance is based on degree of tonal fusion. Patterns of consonance estimates generated from the model were compared with corresponding human judgments of consonance ("clearness") obtained by Kameoka & Kurogawa (1969). The model used an auditory nerve simulation (72 fibers, CFs, 8016000 Hz) to generate neural responses to pairs of tones separated by different musical intervals. The simulation used stimuli identical to those used in psychophysical experiments (pairs of pure tones and harmonic complexes, F0root = 440 Hz, n=1-6). Population-wide interspike interval distributions were then compiled from the responses of individual simulated auditory nerve fibers. The relative strengths of pitch-related interval patterns in these distributions were then measured using subharmonic sieves to yield predictions of which pitche(s) should be heard as well as their relative strengths (saliences). The estimated salience of the strongest pitch (maximum salience) is used as an estimate of pitch fusion and perceived consonance. Consonance values estimated from the model were then compared with those obtained by K&K. The high degree of correspondences observed (r = 0.88 for "consonances" of pure tones and r = -0.97 for "absolute dissonances" of complex tones) support the viability of pitch-based models for consonance that depend on the strength of the fundamental bass. 1. BACKGROUND
Improvement of the IoT Computing Platform for water meter network

The Internet of Things (IoT) is now extensively used. Sensors, processors, and communication hardware are among the smart devices that make up this ecosystem. IoT hubs are used in traditional IoT systems to act as a bridge between tiny underlying sensors and the cloud, allowing applications to accumulate, send, and analyse collected data in real time. The IoT hub and sensors are two separate isolated layers in the traditional system, which makes the system complex and increases network dependency. The purpose of this presented paper is to analyse and modify the IoT architecture in order to design an autonomous and distributed IoT module for a water meter system that supports the internet and the LoRa network. The module incorporates M2M (Machine to Machine) communication to address the aforementioned issue while also increasing the scalability of IoT water meter devices. The designed meter reading module can be used as an IoT device, connecting to the network via Ethernet, Wi-Fi, or LoRa WAN and providing an interface for users (cloud servers, people, and devices) to communicate with one another. Furthermore, the proposed modules are self-contained and can easily interface with any programming language. Resource management and security of IoT systems have also been taken into account. As a result, the water meter system's performance could be improved.
Clustering Multiple Views of Data through Many-objective Evolutionary Approach

Multiple data sources must be taken into account in several application areas. Each of those data views often offers a unique viewpoint on a certain group of things. Several data perspectives with varying degrees of dependability may also be produced in practice as a result of some complicated real-world challenges. The clustering algorithms in general can incorporate the information of given data points. Existing algorithms like Co-clustering and Multi-view K Means failed in considering the varying degrees of dependability of different data points in a single dataset and are limited to considering only two data views at a time. Here, to address these types of challenges, we propose the Multi-view Multiple Clustering (MVMC) approach. MVMC is designed to allow the inclusion of multiple data sources in the clustering process. Furthermore, it is capable of automatically adjusting the weights assigned to the different data views to obtain the best clustering results.
A Review: Heart Disease Prediction in Machine Learning & Deep Learning

The whole world has come to know the fact that heart disease is not a trivial issue. Although the years have changed, throngs of patients are diagnosed with this lethal disease and not only is it not decreasing, but increasing and this is evident from the analysis of death rates across the country. Heart disease is caused by several main factors such as negligence in taking care of diet and daily life. Causes such as age and genetics cannot be fully controlled by humans. So it is the responsibility of each individual to take care of critical risk factors that can be controlled by humans. Innovation and data mining technology should also help to reduce the death rate caused by heart disease. Existing data should be used to find hidden relationships, which are very useful for early diagnosis, which can be a new advanced technology and potentially improve the level of the national health system. This paper underlines previous studies that leverage machine learning and deep learning expertise to predict heart disease. Each algorithm produces variations in accuracy and this study will examine the variables that enable these results. At the end of the study, the results achieved by deep learning proved to be ahead of machine learning by showing consistency in a satisfactory range between 84% to 99%.
A TCM Disease Retrieval Method Based on Weighted Word Vector

TCM intelligent diagnosis algorithm is of great significance to promote the standardization, intelligence and popularization of TCM consultation. Disease diagnosis task is the key link of TCM intelligent diagnosis algorithm. In recent years, word vector models have been applied to disease diagnosis tasks. However, it is difficult to use the word vector model for disease diagnosis alone to meet the requirements of accuracy. In order to improve this situation, this paper transforms the problem of disease diagnosis into a disease retrieval problem, and designs and implements a TCM disease retrieval method based on weighted word vectors. The method retrieves the patient’s symptoms collected by the TCM intelligent diagnosis algorithm in the TCM disease database, and finally retrieves the disease that best matches the patient’s symptoms as the disease diagnosis result. The experimental results show that the method has a good effect on the task of disease diagnosis, and can provide a new idea for the realization of the task of disease diagnosis.
Socio-expressive Robot Navigation: How Motion Profiles can convey Frailty and Confidence

Human-aware navigation and Social Navigation are growing fields of robotics, attempting to tackle challenging navigation problems in human environments. The physical tasks themselves are difficult to handle algorithmically due to their dynamic nature and the difficulty to predict human behaviour. The challenge of this work is to show that in addition to the practical aspects of navigation, the manner in which a robot navigates - analogous to speech and gesture prosody - conveys fine cues which are strongly perceived as expressive behaviours. In a prior work, primitive types of navigation profiles were defined, and combined with minimal cues of appearance to create an audio-visual corpus of robot motion. In this work, we show through online and in-person perception experiments that some of these profiles are significantly associated to impressionistic values related to robot intentions and personality. These socio-affective values have an influence on care according to prior works involving people considered as frail, in particular exploring the idea of frail robots for tender care. Initial results show in particular that using saccadic or smooth motion can impact people's perceptions of whether the robot inspires confidence or not, seems frail or sturdy, authoritative or polite, aggressive or gentle, confident or doubtful, and nice or disagreeable.
Visualization analysis of comprehensive evaluation of academic papers based on knowledge graph

Academic paper evaluation research has been the focus of scholars in recent years. This paper uses VOSviewer visualization software to show the world situation, overall development, hot research and development direction of the research field from the perspective of publication time, country distribution, institution distribution, important keywords distribution, journal distribution and so on. In this paper, a number of exquisite knowledge graphs are drawn to make the results more intuitive and clearer. The results can provide guidance and help for scholars in this field. This paper also provides suggestions for scholars to conduct follow-up research in this field.
A New Non-Isolated High Step-Up DC-DC Converter Suitable for Renewable Energy Applications

In this paper, a new non-isolated high step-up DC-DC converter is proposed. The proposed converter is based on voltage lift switched inductor cells (VL-SL). The switched-inductor cell has been modified so that the proposed converter improves the voltage gain compared to similar converters with the same number of elements only by one switch. The proposed converter can operate in a single-input single-output (SISO) mode. The proposed structure contains one switch, three inductors, three capacitors, and five diodes. In order to realize the main benefit of the proposed converter, the steady state analysis is accomplished and the mathematical equations are evaluated in a comparison study with related converters. In addition, continuous conduction modes (CCM) are explained to design converter’s inductors. In the end, MATLAB/SIMULINK software is employed to simulate the proposed converter.
Affective Interaction in Domestic Service System: Emotion Evaluation and Regulation for the Older Adults

Driven by the shortage of qualified nurses and the high percentage of aging populations, the ambient assisted living style using intelligent service robots and smart home systems has become an excellent choice to alleviate the heavy burden on families and society. The efforts to help achieve healthy aging in place should not only be paid to the support for activities of daily living, but also the care of mental health. However, older adults' unique living environment and the differences in ability to express themselves make the affective interaction very difficult. This paper develops a domestic service system and proposes a mechanism for evaluating and regulating their emotions. Facial recognition, as well as a behavior model established according to the usage of the service system, was used to evaluate their physical and mental state. A set of affective interaction scenarios tailored to their preferences was developed to improve their emotions. The experiment results show that the system can meet their affective interaction needs and can be accepted by older adults.
A novel space-time LDPC code with time varying precoding

In this paper, a novel space-time low-density parity-check (LDPC) code with full diversity is studied. Preceded by time varying linear transformations (TVLT), space-time LDPC codes can achieve full transmit diversity gain without the intricate requirements of exhaustive tests according to the conventional criterions. A proposed lemma about the effects of TVLT on space-time codes is revised and reproved. With a view to the necessary condition of the application of TVLT to space-time codes, a tight upper bound on the minimum Hamming distances of LDPC codes is discussed. Combined with the favourable distance property of LDPC codes, the time varying precoding provides space-time codes with the full transmit diversity as well as the reasonable coding gain compared with that of the conventional counterparts.
Novel Symmetric Hybrid Multilevel Inverter with Reduced Switch Count and Low Blocked Voltage by Power Transistors

In this paper, a new symmetric single-phase topology for multilevel inverters with a reduced number of switches and low blocking voltage by the switches is presented. The proposed topology is composed of three parts and can be expanded by increasing the second part modules to make higher output voltages with more levels. The third part of the proposed topology employs capacitors to increase the number of levels. To examine the proposed model, a 37-level inverter using the proposed topology is simulated by MATLAB/SIMULINK, and the results are presented. Finally, a comparison between the proposed topology and other topologies in terms of total blocking voltage and the number of switches is made to show the advantages of the proposed multilevel inverter over a number of the other topologies.
Design and Analysis of Compliant Continuum Robots for Suturing

Compliant continuum robots are a class of manipulators that show a deviation in design and operation from the general category of rigid body manipulators. They possess high degrees of freedom, capable of changing shape along their length. Due to these characteristics of continuum robots, it is an ideal choice for minimally invasive surgical manipulators. In this paper, various fully compliant continuum robot designs have been proposed, optimized, analyzed, and compared for this sole purpose. The classical single backbone structure was considered the benchmark for comparison. The cross-axis flexure dual backbone continuum tooltip has performed well, showing 63.8% better displacement than the benchmark.
Modeling Second Order Anisotropy of Monodomain Magnetic Body

The switching of a ferromagnet’s magnetization direction is a fundamental aspect in modeling spintronic devices. The switching can be achieved by applying an external magnetic field or spin current, and one of the key determinants of the switching process is the magnetocrystalline anisotropy. To model the magnetization dynamics, considering only the first order magnetocrystalline perpendicular anisotropy contributions had been sufficient thus far as it’s contributions are predominant. However, as the dimensions of the magnetic body are scaled down, or subject to voltage control of anisotropy (VCMA) effect, the magnetocrystalline perpendicular anisotropy and the out-of-plane demagnetizing field both end up having equal order, and nowadays low barrier magnets are purposefully designed to have magnetic anisotropy close to zero for special applications. In these cases, the contribution of the second order anisotropy of the ferromagnet need to be considered while studying the magnetization dynamics. In this paper, we developed a physics-based SPICE-compatible compact model of a monodomain magnetic body considering the second-order magnetic anisotropy contributions. The magnetization dynamics are described by the stochastic Landau-Lifshitz-Gilbert (sLLG) equation and can be used to study both perpendicular magnetic anisotropy (PMA) magnets and in-plane magnetic anisotropy (IMA) magnets. Here, we studied the effect of second order anisotropy in the critical switching field and current for PMA magnet and found that the second-order contributions to the switching field and current become significant when it is in the same order as the first order anisotropy.
Analysing the Voltage Sensitivity in the Distribution Grid in Terms of Charging and Discharging of Electric Vehicle

Electric vehicle (EV) grid integration at a large scale is highly reliant on power costs, energy storage systems and renewable energy sources. This study employs an effective charge flow analysis method from a system for energy management to charge and discharge electric vehicles (EVs) in line with user needs. It consists of a regulated operating technique that utilizes the EV’s state of charge (SOC). The operation is primarily centered on two modalities, each of which completes the mission through EV SOC early detection. When EV charging and discharge may happen whenever during the day. The suggested method is put into practice and created in OpenDSS and Matlab to test the outcomes in various situations. This paper is proposed that the state of charge (SOC) of an electric vehicle (EV) when it is incorporated into a grid is determined using a control approach proposed in this research as an efficient way to charge and discharge EVs in a distribution system. In the first case, the EV’s SOC is determined by the controlling method system, and if it is within a certain range, the operation is started normally. In the second scenario, the user is free to choose a charging or discharging mode after the system reaches the predetermined limit. In addition, if VPP is unable to meet EV demand while charging during additional peak hours, the other mode will be engaged to do so and ensure appropriate functioning.
Smart Grid Security by Embedding Cryptography Hardware Chip

Modern electric grids are innovative enough to incorporate high-speed sensing, distributed resources, and control, smart grids, and other cutting-edge metering technology. The smart grid faces a number of issues, including cybersecurity. The fact that communications are so crucial to the process of the smart grid makes cyber security a top priority. The research work focuses on the solution of grid security by integrating the embedded hardware chip into the communication infrastructure. The real-time chip integration with the newest cryptographic algorithm in the system addresses the hardware security challenges in smart grids. A distribution system for the smart grid that integrates technology can use cryptographic-based encryption and decryption technique. The objective of the research work is to design the cryptographic advanced encryption standard (AES) algorithm hardware chip for a smart grid in Xilinx 14.7 software and simulate the functionality of the embedded chip design with cryptographic encryption and decryption for ensuring secured data communication in a smart grid. The novelty of the work is that the design and simulation of the embedded chip of the secured data communication support 128 -bit data size with the key size alternating from 8-bit to 256-bit. The functional test cases have verified the grid data with a maximum frequency of 395.00 MHz.
AN INTERACTIVE ALGORITHM FOR DECOMPOSING: THE PARAMETRIC SPACE PROGRAMMING PROBLEMS

The aim of this chapter is to study the stability of multi-objective dynamic programming (MODP) problems with fuzzy parameters in the objective functions and in the constraints. These fuzzy parameters are characterized by fuzzy numbers. For such problems, the concept and notion of the stability set of the first kind in parametric nonlinear programming problems are redefined and analyzed qualitatively under the concept of �. -Pareto optimality. An interactive fuzzy decision-making algorithm for the determination of any subset of the parametric space that has the same corresponding�. -Pareto optimal solution is proposed. A numerical example is given to illustrate the method developed in the chapter.