bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploratory data analysis for electric vehicle driving range prediction: insights and evaluation
Authors: Mishra Debani Prasad , Kumar Prince , Rai Priyanka , Kumar Ayush , Salkuti Surender Reddy
Published: April 2024

One of the biggest challenges of electric vehicle (EV) users has been predicting the amount of driving time their vehicles will have on one battery charge. Planning a trip and reducing range anxiety depends on an accurate range estimate. This study aims to anticipate the EV driving range using machine learning methods. In this research, several regression models for predicting EV driving range will be developed and compared. A real-world dataset comprising various factors affecting EV range, such as power, trip distance, energy consumption, driving style, and environmental factors, is used for analysis. The dataset is preprocessed using exploratory data analysis methods to manage missing values, outliers, and categorical variables. The findings of this study contribute to the expanding area of EV range prediction and provide EV buyers, producers, and regulators with insightful information. The user experience can be improved, EV adoption can be boosted, and effective charging infrastructure design is made possible with accurate range prediction. The study also highlights the importance of model selection and data pretreatment in making accurate predictions.
Recent research and developments of degradation assessment and its diagnosis methods for solar PV plant: a review
Authors: Verma Sumit , Yadav Dinesh Kumar
Published: April 2024

The world is moving forward to a transition in the form of increasing the contribution of renewable energy sources in the energy sector, and among these, solar photovoltaic-based power generation is catching pace. Several factors are responsible for the lowering of outputs due to different degradation causes such as hotspots, corrosion, humidity, ultraviolet (UV) irradiation, temperature effects, dust, aging, weathering, yellowing, snail trails, discoloration, junction box failure, delamination, cracks, and faults from the solar photovoltaic (PV) plants. This paper presents a comprehensive review of the various form of degradation and their implications on solar PV power plant performance. The review has been carried out considering the different degradation causes and their identification methods in solar PV plant. Further, the analysis has been done on the basis of the earlier studies to understand the rates of degradation for various solar PV power plants in various climatic conditions. The PV technologies used in solar power plants are also responsible for the change in the performance of power plants over time; therefore, degradation based on different solar PV cell technologies is also analyzed. The visual inspection tools like thermal imaging with IR cameras help identify areas with abnormal heat patterns, indicating potential issues like cell or interconnect failures, loose electrical connections, or bypass diode malfunctions while EL cameras are used to identify low-level electrical excitation and defects such as cracks, hotspots, and cell-level degradation.
Bibliometric analysis highlighting the role of women in addressing climate change challenges and achieving sustainable development goals for greener future
Authors: Rezk Hegazy , Sayed Enas Taha
Published: April 2024

Fossil fuel consumption increased quickly, contributing to climate change that is evident in unusual flooding and draughts, and global warming. Over the past ten years, women's involvement in society has grown dramatically, and they succeeded in playing a noticeable role in reducing climate change. A bibliometric analysis of data from the last ten years has been carried out to examine the role of women in addressing the climate change. The analysis's findings discussed the relevant to the sustainable development goals (SDGs), particularly SDG 7 and SDG 13. The results considered contributions made by women in the various sectors while taking geographic dispersion into account. The bibliometric analysis delves into topics including women's leadership in environmental groups, their involvement in policymaking, their contributions to sustainable development projects, and the influence of gender diversity on attempts to mitigate climate change. This study's results highlight how women have influenced policies and actions related to climate change, point out areas of research deficiency and recommendations on how to increase role of the women in addressing the climate change and achieving sustainability. To achieve more successful results, this initiative aims to highlight the significance of gender equality and encourage inclusivity in climate change decision-making processes.
An online battery–state of charge estimation method using the varying forgetting factor recursive least square - unscented Kalman filter algorithm on electric vehicles
Authors: Thi Diep Nguyen , Trung Nguyen Kien
Published: April 2024

Accurate and fast estimation of the state of charge is important for the battery management system of electric vehicles. This paper proposes a method to estimate the state of charge of Lithium-ion batteries by the variable forgetting factor recursive least square (VFFRLS) – unscented Kalman filter (UKF) algorithm in real-time without the off-line battery testing data. Since the state observation requires an accurate model, an equivalent circuit model was constructed. Then, the VFFRLS algorithm is used to identify online the battery model parameters based on voltage and current measurements. An advantage of this algorithm is that it requires less initial information and shorter identification time than offline parameter identification. After the model parameters are well identified, the unscented Kalman filter estimates the state of charge and minimizes noise characteristics and uncertainty in the parameter identification process. The VFFRLS algorithm applied in this paper has shown a good result with the model output error of less than 1%, and the identification achieves real-time response. The state of charge obtained by the UKF algorithm has shown satisfactory estimation results with fast convergence speed and small errors. The UKF filter provides the results with a 1.5% error from the reference and converges after 10 cycles.
Advanced control scheme of doubly fed induction generator for wind turbine using second sliding mode control
Authors: Bekouche Hafida , Chaker Abdelkader
Published: April 2024

This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator(DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC).Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Local Fourier features for handwriting digit images classification
Authors: Alain Bernard Djimeli-Tsajio , Thierry Noulamo , Jean-Pierre Lienou T. , Daniel Tchiotsop , Nagabhushan Panduranga
Published: April 2024

Multiple choice questions (MCQ) are effective in normative assessment and offline testing is still relevant due to the lack of efficient mass infrastructures and maintenance. For the automatic correction of MCQ paper form and reporting of the grade, it is generally necessary to read and recognize a handwriting digit in a box. This paper focuses on local feature extraction in the frequency domain using Fourier transform. The pre-process begins with the extraction of the fields from the entity map, followed by the application of 2D fast Fourier transform (2DFFT) and the reduction of computed coefficients to obtain the corresponding final local characteristic in the representation. The experimental results of the Modified National Institute of Standards and Technology (MNIST) handwriting digits dataset show that the local characteristics extracted in the frequency domain used as input to a support vector machine (SVM) classifier are efficient in terms of 99.51% accuracy. The proposed system successfully helped in the reporting of all the marks for seven subjects in a class of 98 students during the automatic correction of the MCQ exam papers.
Graph neural network based human detection in videos during occlusion environments
Authors: Sriram Kusuma , Purushotham Kiran
Published: April 2024

One of the most difficult perceptual problems for many applications is accurately recognizing the human object in a variety of circumstances. This can be difficult due to obstructions, weather, complex backdrops, cast shadows, and occlusions. Occlusion is a challenging open problem where a detector can only perceive a portion of the target human because of obstacles in the surrounding. In this research, an experimental investigation was conducted using the multi object tracking (MOT17) datasets to construct a graph neural network-based solution for the detection of humans in videos while considering the possibility of occlusion. Graph neural network (GNN) is used for the construction of neural solver model for detecting human object in occlusion scenario. The results obtained shows that this proposed method offers a considerable improvement in efficiency in comparison to the ways that have been used in the past. The values obtained for the standard performance metrics are higher than the state-of-the-art methods.
Prediction of paroxysmal atrial fibrillation using a convolutional neural network and electrocardiogram signals
Authors: Castro Henry , Garcia-Racines Juan David , Bernal-Norena Alvaro
Published: April 2024

Atrial fibrillation (AF) is the most clinically diagnosed arrhythmia in cardiac pathology. The incidence of AF begins at a very early age and its initial state is paroxysmal atrial fibrillation (PAF). This type of heart disease can be detected and predicted by analyzing the spectrogram of a surface electrocardiogram (ECG) signal. In many studies, different ECG signal formats and convolutional neural network (CNN) architectures have been used. However, the lack of good signal preprocessing or signal adequacy may have affected the accuracy, especially on short-term ECG signals. In this study, we analyzed a preprocessed ECG signal, determined the optimal set to predict PAF, and evaluated the accuracy using ECG signals of different durations. The PAF Prediction Challenge–PhysioNet database was used to extract spectrograms in 30-sec and 5-sec windows for two classes (Normal, PAF) and 3 classes (Normal, Close-AF, Distant-AF). Then, the AlexNet architecture was used. The proposed method achieved a two-class accuracy of 99.92% with a 30-sec window and 99.42% with a 5-sec window, improving the PAF prediction performance compared with similar works. In addition, the three-class accuracies were 96.92% and 97.43% with windows of 30-sec, and 5-sec, respectively. These results prove the efficacy of the method for the early diagnosis of PAF, even based on short-term ECG signals.
Design and performance evaluation of a 350 m free space optical communications link for pico-macrocell backhauling
Authors: Kassim Abduljalal Yusha'u , Oduol Vitalice Kalecha , Usman Aliyu Danjuma
Published: April 2024

Fibreless optics or free space optical communications (FSOC) has been at the forefront of many academic research in telecommunications due to its numerous benefits of large spectrum, high-speed data transmission, security, low transmit power, unlicensed spectrum and non-interfering links. Among the technical challenges of dense deployment of small cells in heterogeneous networks (HetNet) is a flexible and cost-effective backhaul link. This paper proposes, designs, simulates and evaluates the performance of a 350 m FSOC link under different atmospheric impairments for picocell to macrocell backhauling applications. The performance of the FSOC link is assessed by evaluating bit error rate (BER), eye diagram and quality factor (Q-factor). Results obtained recommend the FSOC link deployment for pico-macrocell backhauling under the weather conditions of clear sky with/without turbulence, heavy rain, heavy haze, heavy fog and wet snow.
60 GHz millimeter-wave indoor propagation path loss models for modified indoor environments
Authors: Qasem Nidal , Alkhawatrah Mohammad
Published: April 2024

The 60 GHz band has been selected for short-range communication systems to meet consumers’ needs for high data rates. However, this frequency is attenuated by obstacles. This study addresses the limitations of the 60 GHz band by modifying indoor environments with square loop (SL) frequency selective surfaces (FSSs) wallpaper, thereby increasing its utilization. The SL FSS wallpaper response at a 61.5 GHz frequency has been analyzed using both MATLAB and CST Studio Suite software. ‘Wireless InSite’ is also used to demonstrate enhanced wave propagation in a building modified with SL FSSs wallpaper. The demonstration is applied to multiple input multiple output system to verify the effectiveness of FSSs on such systems’ capacity, as well as the effect of the human body on capacity. Simulation results presented here show that modifying a building using SL FSS wallpaper is an attractive scheme for significantly improving the indoor 60 GHz wireless communications band. This paper also presents and compares two large-scale indoor propagation path loss models, the close-in (CI) free space reference distance model and the floating intercept (FI) model. Data obtained from ‘Wireless InSite’ over distances ranging from 4 to 14.31 m is analyzed. Results show that the CI model provides good estimation and exhibits stable behavior over frequencies and distances, with a solid physical basis and less computational complexity when compared to the FI model.
Drone direction estimation: phase method with two-channel direction finder
Authors: Kozhabayeva Indira , Yerzhan Assel , Boykachev Pavel , Manbetova Zhanat , Imankul Manat , Yauheni Builou , Solonar Andrey , Dunayev Pavel
Published: April 2024

This scientific article presents a block diagram of a two-channel radio direction finder that effectively uses the phase method to determine the direction of the signal source. The main attention is paid to the mathematical model of the formation of the cardioid radiation pattern of biconical antennas, which have unique directivity characteristics. These features significantly affect the accuracy and reliability of the bearing determination process. The developed algorithm aims to accurately determine the direction of motion of an unmanned aerial vehicle, especially in the context of a two-channel radio receiver and a five-element antenna system. This antenna system provides unique capabilities for increased resolution and directional accuracy. The article also touches on the issue of software implementation of the developed algorithm, which is aimed at increasing the number of generated bearing estimates in conditions of limited time for observing an unmanned aerial vehicle. Thus, the proposed method is of interest in the field of precision direction finding in the context of small unmanned vehicles.
Detecting vulnerabilities in website using multiscale approaches: based on case study
Authors: Chowdhury Mudassor Ahmed , Rahman Mushfiqur , Rahman Sifatnur
Published: April 2024

In the realm of modern web applications, security stands as an utmost priority. To address this critical concern, we've developed a versatile Python script with the primary goal of proactively identifying vulnerabilities and thwarting transient attacks. Leveraging various libraries, this tool comprehensively covers a broad spectrum of threats, including SQL injection (SQLi), cross-site scripting (XSS), cross-site request forgery (CSRF), sensitive data leakage, security misconfiguration, distributed denial-of-service (DDoS) vulnerabilities, and secure socket layer (SSL) or transport layer security (TLS). This Python-based solution prioritizes adaptability, ensuring seamless integration of future updates to effectively combat evolving threats. Utilizing innovative methods such as SQLi and XSS payload injection, the script assesses the susceptibility of input fields. And addressing CSRF vulnerabilities, the script generates and validates tokens, fortifying defenses against unauthorized actions. Employing pattern analysis, it combats sensitive data exposure and security misconfigurations, adeptly identifying elements like credit card numbers, passwords, and headers. Furthermore, the script enhances overall security by scrutinizing SSL/TLS protocols and monitoring port accessibility. It reinforces DDoS detection by actively monitoring traffic patterns, identifying anomalies, and proactively averting disruptions.
Overbounding Ifree errors based on bayesian Gaussian mixture model for ground-based augmentation system
Authors: Banu Sheher , Shanavas Hameem
Published: April 2024

A dual-frequency measurement is employed in conjunction with an innovative Ifree filtering technique for mitigating the primary sources of Ifree influence on ground-based augmentation systems (GBAS) to safeguard the reliability of GBAS. The protective level achieved through the conventional Gaussian overbounding approach that are considered as much conventional technique. This adherence to tradition results in decreased reliability and a higher likelihood of false alarms. In contrast, the utilization of the Ifree algorithm contributes to reducing errors associated with dual-frequency measurements. This paper proposes the overbounding process according to Bayesian Gaussian mixture model (GMM) for maintaining Ifree-based GBAS range error. The Bayesian GMM is utilized for single-frequency model errors to examine the ambiguity estimations. The Monte Carlo (MC) simulation is established for defining estimated GMM assurance level accuracy which is attained through the general estimation method. Then, the last Bayesian GMM which is utilized for overbounding Ifree error distribution is investigated. According to the property of convolution invariance, the vertical protection in position field is determined without presenting difficult numerical calculations.
Comparative study on satellite based image encryption methods: a survey
Authors: Vasudevaiah Chethana , Shivaswamy Rashmi
Published: April 2024

The availability of high-resolution satellite images increases with advancements in remote sensing technology. These satellite images are used in various earth observation applications such as disaster management, military applications, weather forecasting, land use and cover, and many more. Satellite images have large volumes stored in memory devices. These satellite images are transmitted to the ground station for processing and analysis. In these cases, images are vulnerable to privacy issues. As technology advances, onboard processing of satellite images using intelligent systems processes the images faster. A model such as field programmable gate arrays (FPGA) is used in onboard processing to process satellite images. However, images are susceptible to faults induced by harsh radiation environments in space. Encryption is one of the most assured methods to provide privacy to satellite images. Hence, encryption of satellite images during processing, storage, and transmission is the present rising demand. There are various encryption methods implemented using algorithms such as advanced encryption standard (AES), homomorphic, advanced encryption standard-counter (AES-CTR), and chaotic maps. Concurrent processing and encryption of images using MapReduce with Hadoop Framework perform the task faster. The focus of this paper is a comparative study of the various encryption methods used in recent years.
Integrating green computing into rational unified process for sustainable development goals: a comprehensive approach
Authors: Firmansyah Filan , Sudirman M Yoga Distra , Putra Rakhmadi Irfansyah
Published: April 2024

This research explores the incorporation of green computing variables into the rational unified process (RUP) methodology, specifically focusing on sustainable development goal (SDGs) 12-responsible consumption and production. Supported by three additional papers using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method. Our study aims to promote eco-friendly software development practices and tools (artifacts) aligned with green computing principles to support SDGs throughout RUP development phases. We conducted a matrix thorough analysis of existing green computing adaptability within RUP, yielding key findings: a system charter for inception, system requirement specification for elaboration, software development result for construction, and software test report/user acceptance test for transition. As a result, we've compiled comprehensive phase-specific documents, emphasizing the need for educational initiatives to foster green computing adoption among developers. This study advocates for cross-disciplinary collaboration to ensure successful implementation of eco-friendly software development processes.
Ad hoc wireless network implementing BEE-LEACH
Authors: Kumar Arun , Chakravarthy Sumit , Gaur Nishant , Nanthaamornphong Aziz
Published: April 2024

Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The protocol will then assign sensors to cluster heads based on distance relative to the head. This algorithm achieves the goal of extending battery life and offers itself as a promising alternative to standard LEACH algorithms. The algorithm is tested by comparing sensor battery life, total sensors communicating at a given time, and sensors with residual energy. This paper addresses the strengths and vulnerabilities of the algorithm, as well as proposed work for further implementation for the following groups looking to create their own LEACH protocol.
Simaksaja: visual novel game with TyranoBuilder software for Islamic moderation in elementary schools
Authors: Ibda Hamidulloh , Aniqoh Aniqoh , Muntakhib Ahmad , Mar’atussolichah Mar’atussolichah , Fadhilah Trifka Dila , Rakhmawati Nurma Febri
Published: April 2024

The study aims to determine the development, feasibility, and effectiveness of the Simaksaja game. The research was conducted due to the lack of development of digital games based on religious moderation in elementary schools. This research and development refer to the analysis, design, development, implementation, and evaluation (ADDIE) type of Dick, Carey, and Carey model. Data were collected through student and teacher needs analysis, media expert and material expert validation tests, effectiveness tests, structured observation, in-depth interviews, and document study. The research subjects were 46 teachers and 46 students for the needs analysis and effectiveness test in 2 schools in the District and City of Magelang. The research findings stated that the game features character-driven material, the introduction of the characters Ning and Gus, and quizzes. Based on the feasibility test, the game expert scored 72%, the moderation material expert 88%, and the Aswaja Annahdliyah material expert 72%, and it was feasible to use. This game is effective based on the effectiveness test in two elementary schools, scoring 80% from the educators’ response and 87% from the students’ response. The novelty of the research is that the TyranoBuilder game contains religious moderation characters, Aswaja Annahdliyah, Pancasila student profile, and Rahmatan Lil Alamin student profile.
Predictive models in Alzheimer's disease: an evaluation based on data mining techniques
Authors: Andrade-Arenas Laberiano , Rubio-Paucar Inoc , Yactayo-Arias Cesar
Published: April 2024

The increasing prevalence of Alzheimer's disease in older adults has raised significant concern in recent years. Aware of this challenge, this research set out to develop predictive models that allow early identification of people at risk for Alzheimer's disease, considering several variables associated with the disease. To achieve this objective, data mining techniques were employed, specifically the decision tree algorithm, using the RapidMiner Studio tool. The sample explore modify model and assess (SEMMA) methodology was implemented systematically at each stage of model development, ensuring an orderly and structured approach. The results obtained revealed that 45.00% of people with dementia present characteristics that identify them as candidates for confirmation of a diagnosis of Alzheimer's disease. In contrast, 52.78% of those who do not have dementia show no danger of contracting the disease. In the conclusion of the research, it was noted that most patients diagnosed with Alzheimer's are older than 65 years, indicating that this stage of life tends to trigger brain changes associated with the disease. This finding underscores the importance of considering age as a key factor in the early identification of the disease.
Pairwise test case generation with harmony search, one-parameter-at-at-time, seeding, and constraint mechanism integration
Authors: Aminu Muazu Aminu , Hashim Ahmad Sobri , Maiwada Umar Danjuma , Isma'ila Umar Audi , Yakubu Muhammad Muntasir , Abubakar Ibrahim Muhammad
Published: April 2024

Pairwise testing is a method for identifying defects through combinatorial analysis. It involves testing all possible combinations of input parameters in pairs within a system, ensuring that each pair is tested at least once. The field of test case generation is highly active in the realm of combinatorial interaction testing. Research in this area is particularly encouraged, as it falls under the category of non-deterministic polynomial-time hardness. A big challenge in this field is the combinatorial explosion problem. It is about finding the best test suite that covers all possible combinations of interaction strength. In this paper, we present the task of discovering a pairwise test set as a search problem and introduce an innovative testing tool referred to as pairwise test case generation in harmony search algorithm with seeding and constraint mechanism (PHOSC). Experimental results show that PHOSC performs better compared to some existing pairwise strategies in terms of test suite size. Additionally, PHOSC provides a comprehensive framework and serves as a research platform for the generation of pairwise test sets employing the harmony search algorithm. It adopts an approach that focuses on one parameter at a time (OPAT) and incorporates seeding and constraint mechanisms at the same time, thereby enhancing the efficiency and effectiveness of the testing process.
Enhancing currency prediction in international e-commerce: Bayesian-optimized random forest approach using the Klarna dataset
Authors: Rhouas Sara , El Attaoui Anas , El Hami Norelislam
Published: April 2024

In the ever-evolving landscape of global commerce, marked by the convergence of digital transformation and borderless markets, this research addresses the intricate challenges of currency exchange and risk management. Leveraging Bayesian optimization, the study fine-tunes the random forest algorithm using the extensive Klarna E-commerce dataset. Through systematic analysis, the research uncovers insights into managing currency prediction amid dynamic global markets. Emphasizing the role of Bayesian optimization parameters, the study reveals nuanced trade-offs in model performance. Notably, the optimal simulation, conducted with 14 iterations, 1 job, and a random state set to 684, exhibits a standout performance, showcasing a negative mean squared error (MSE) of approximately -0.9891 and an accuracy rate of 74.63%. The primary objective is to assess the impact of Bayesian optimization in enhancing the random forest algorithm's predictive capabilities, particularly in currency prediction within international e-commerce. These findings offer refined strategies for businesses navigating the intricate landscape of global finance, empowering decision-making through a comprehensive understanding of data, algorithms, and challenges in international commerce.
Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs
Authors: Mayasari Dita Ayu , Hawari Ihtifazhuddin , Dwiyanti Sheba Atma , Noviyadi Nathasya Reinelda , Andryani Dinda Syaqila , Utomo Muhammad Satrio , Hikmah Nada Fitrieyatul , Asmaria Talitha
Published: April 2024

The clavicle is a long bone that tends to be frequently fractured in the midshaft region. The plate and screw fixing method is mainly applied to address this issue. This study aims to construct a clavicle bone implant design with a consideration to achieve a high accuracy and high-quality surface between the plate and the clavicle surface. The computational tomography scanning (CT-scan) image series data were processed using a convolutional neural network (CNN) to classify the clavicle image. The CNN outcomes were gathered as three-dimensional (3D) volume data of clavicle bone. This 3D model was then proposed for the plate design. The CNN testing results of 97.4% for the image clavicle bones classification, whereas the prints of the 3D model from clavicle bone and its plate and screw design reveal compatibility between the bone surface and the plate surface. Overall, the CNN application to the series of CT images could ease the classification of clavicle bone images that would precisely construct the 3D model of clavicle bone and its suitable clavicle bone plate design. This study could contribute as a guideline for other bone plate areas that need to fit the patient’s bone geometry.
Crack detection based on mel-frequency cepstral coefficients features using multiple classifiers
Authors: Altayeb Muneera , Arabiat Areen
Published: April 2024

Crack detection plays an essential role in evaluating the strength of structures. In recent years, the use of machine learning and deep learning techniques combined with computer vision has emerged to assess the strength of structures and detect cracks. This research aims to use machine learning (ML) to create a crack detection model based on a dataset consisting of 2432 images of different surfaces that were divided into two groups: 70% of the training dataset and 30% of the testing dataset. The Orange3 data mining tool was used to build a crack detection model, where the support vector machine (SVM), gradient boosting (GB), naive Bayes (NB), and artificial neural network (ANN) were trained and verified based on 3 sets of features, mel-frequency cepstral coefficients (MFCC), delta MFCC (DMFCC), and delta-delta MFCC (DDMFCC) were extracted using MATLAB. The experimental results showed the superiority of SVM with a classification accuracy of (100%), while for NB the accuracy reached (93.9%-99.9%), and (99.9%) for ANN, and finally in GB the accuracy reached (99.8%).
Negation handling for sentiment analysis task: approaches and performance analysis
Authors: Ilmawan Lutfi Budi , Muladi Muladi , Prasetya Didik Dwi
Published: April 2024

Negation plays an essential role in sentiment analysis within natural language processing (NLP). Its integration involves two key aspects: identifying the scope of negation and incorporating this information into the sentiment model. Before delving into scope detection, the specific negation cue must be identified, with explicit and implicit negation cues being the two main types. Various methodologies, such as rule-based, machine learning, and hybrid approaches, address the negation scope detection challenge. Strategies for leveraging negation information in sentiment models encompass heuristic polarity modification, feature space augmentation, end-to-end approach, and hierarchical multi-task learning. Notably, there is a need for more studies addressing implicit negation cue detection, even within the state-of-the-art bidirectional encoder representation for transformers (BERT) approach. Some studies have employed reinforcement learning and hybrid techniques to address the implicit negation problem. Further exploration, particularly through a hybrid and multi-task learning approach, is warranted to make potential contributions to the nuanced challenges of handling negation in sentiment analysis, especially in complex sentence structures.
Heat stroke prediction: a perspective from the internet of things and machine learning approach
Authors: Ke Yin Lim , Yogarayan Sumendra , Abdul Razak Siti Fatimah , Ali Bukar Umar , Sayeed Md. Shohel
Published: April 2024

With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threatening condition occurs when the body’s temperature exceeds 104 degrees Fahrenheit (40 degrees Celsius). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently, monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heat stroke at an early stage.
Analysis of the effect of mutual inductance on reducing output current ripple of the modified DC-DC Cuk converter
Authors: Driantama Nabil , Dahono Andriazis , Rizqiawan Arwindra , Furqani Jihad
Published: April 2024

DC-DC converters exhibiting minimal input and output ripple are extensively employed across a diverse range of applications. This paper presents a proposed modification to the DC-DC Cuk converter. The converter is developed by modifying a conventional Cuk DC-DC converter to exhibit boost characteristics. The DC-DC converters possess the capability to function as versatile units that can be employed in a multitude of applications, ranging from DC-DC conversion to DC-AC conversion (inversion). This paper presents a novel DC-DC converter design that aims to achieve reduced ripple in both input and output currents. By employing the principle of mutual inductance, it is possible to further mitigate the presence of ripple current. The analysis of the impact of mutual inductance on the output voltage and ripple current of the proposed converter has been conducted. The validity of the derived analytical method has been confirmed through both simulated and experimental investigations. The utilization of mutual inductance in the proposed modified Cuk converter has been demonstrated to result in reduced current ripple.
Experimental investigation of bidirectional series resonant DC-DC converter in different operating modes
Authors: Lichev Angel , Madankov Yasen , Mihov Vasil
Published: April 2024

This paper presents a study of bidirectional series resonant DC/DC converter. The main operating modes of the converter with phase-shift control technique are observed. An experimental survey with a laboratory model of such type of converter working over the resonant frequency is made. The functionality of the studied device operating in a wide area of loading is demonstrated with waveforms of the main parameters. The conducted measurements are used to build the experimental output characteristics of the converter. A similarity of the experimental results with the theoretical are denoted, and the ability to achieve current source characteristics for the whole range of loading is proven. The possibility of the studied converter for operation both in forward and reverse modes of energy flow is proved, which positions the device as an appropriate variant for implementation in the storage system control area.
Design and verification of q-axis perturbation based active islanding detection schemes for DG systems
Authors: Sarojam Praveen Raj Rajaswamy , Savier Joseph Sarojini
Published: April 2024

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.
The wind turbine's direct power control of the doubly-fed induction generator
Authors: Hammoudi Ben Ali , Serhoud Hicham
Published: April 2024

The study suggests a comprehensive approach to modeling and controlling variable-speed wind turbine systems that utilize doubly fed induction generators (DFIGs). To make sure that energy is transferred efficiently between the DFIG rotor and the grid, a two-level inverter with perfect bidirectional switches is used. Using the tip speed ratio algorithm and taking into consideration the randomness in wind speed, the maximum power at the wind turbine is optimized. Then, the control strategy utilizes direct power control (DPC) due to its various advantages. The advantages of employing this control technique are manifold. Firstly, it eliminates the necessity for rotor current control loops. Secondly, it obviates the need for controllers such as PI controllers to manage torque and flux. Furthermore, it has yielded exceptional simulation results when implementing direct power control (DPC) within the MATLAB/Simulink environment, specifically in the context of a doubly fed induction generator (DFIG) wind power system.
Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction
Authors: Ponkumar Ganesapandiyan , Jayaprakash Subramanian , Ramasamy Dharmaprakash , Priyasivakumar Amudha
Published: April 2024

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.
Optimal coordination of directional over current relays for distribution systems using hybrid GWO-CSA
Authors: Bhemasenarao Arathi Pothakanahalli , Byalihal Shankaralingappa Channappa
Published: April 2024

Coordination of protective relays is a critical aspect of electrical distribution systems, ensuring effective and reliable protection against faults. In modern power systems, the integration of distributed generation (DG) sources adds complexity to the coordination task. The dynamic nature of DG systems requires adaptive relay settings that can swiftly detect and isolate faults while minimizing potential damage and downtime. The purpose of this research is to improve the coordination of directional over current relays in electrical distribution systems, particularly in DG systems. An optimization technique combining the grey wolf optimization (GWO) and cuckoo search algorithm (CSA) is developed to identify the best relay settings that reduce overall operation time while ensuring excellent fault identification and isolation. To address relay faults caused by DG integration, a suitable primary and backup relay design is chosen, and the influence of time multiplier settings (TMS) on system performance and reliability is investigated. The proposed GWO-CSA technique is evaluated and implemented on IEEE 3, 8 and 15-bus systems using MATLAB. Simulation results show that the GWO-CSA strategy outperforms well compared to previous algorithms, enabling optimal coordination and increased protection in DG systems while drastically lowering relay operating time.
Investigation of 1.5 kW secondary side power controlled method in a inductive wireless power transfer system
Authors: Bhavsingh Bhukya , Gotluru Suresh Babu , Bhukya Mangu , Bhukya Ravikumar
Published: April 2024

The contemporary and utilitarianism of the existing consumer world is advancing towards the better world technical benefits in the electrical world such as wired phase to wireless phase utilizing its volatile features. This paper addresses the battery performance in constant current (CC), constant voltage (CV) through inductive wireless power transfer (IWPT) systems. To analyze this workable mode, the researcher has proposed the series-series (S-S) compensation topology which is load independent current output. While charging the battery through wireless, the coil resistance is found to be affected by the battery's current and power. To figure out a practical solution, the researcher has introduced novel closed loop bi-directional switches with duty cycle control. The existing theoretical and simulated results have been analyzed with 1.5 kW, 120 mm air-gap and 85 kHz frequency. In this connection, the researcher has self-developed a prototype to better understand the theoretical perceptions of the proposed WPT system.
A Taguchi method-based optimization algorithm for the analysis of the wind driven-self-excited induction generator
Authors: Boukenoui Rachid , Bradai Rafik , Kheldoun Aissa
Published: April 2024

This paper investigates the use of a new global optimization algorithm that is based on Taguchi method to determine the performance parameters of self-excited induction generator being driven by variable speed wind. This analysis is based on solving equations obtained from the per-phase equivalent circuit of the induction generator. The equations have two unknowns namely the frequency and the magnetizing reactance. Both unknown are strongly dependent on the wind turbine speed, the capacity of the excitation, the load being connected at the terminals of the stator and eventually the per-phase equivalent circuit parameters. The resulting equations are nonlinear and subsequently to solve them one can employ either gradient-based algorithms or heuristic algorithms. This paper uses a new heuristic algorithm based on the Taguchi method which, in addition to its global research capability, offers superior characteristics in terms of accuracy and ease of implementation. A comparison with recently published optimization methods is carried out to show its performances in terms of accuracy and ease of implementation. The MATLAB software will be used to perform this analysis on a machine of 0.75 kW while some will be validated experimentally to confirm the aforementioned benefits.
The implementation of an optimized neural network in a hybrid system for energy management
Authors: Jarmouni Ezzitouni , Mouhsen Ahmed , Lamhamdi Mohamed , Ennajih Elmehdi , Ennaoui Ilias , Krari Ayoub
Published: April 2024

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.
Distributed voltage unbalance mitigation in islanded microgrid using moth flame optimization and firebug swarm optimization
Authors: Gandikoti Chakrapani , Jha Shashank Kumar , Jha Bishnu Mohan , Mishra Pankaj
Published: April 2024

In recent trends, hybrid renewable energy sources (HRES) provide a better solution to meet energy demand, maximizing the productivity of electricity network. Due to the above-mentioned features, several researchers have given more attention to PV-wind-based HRES systems. A stable energy supply must be added, including batteries, diesel generator (DG), to meet demand in the event of a grid failure. To meet the voltage unbalance in islanded mode, the sizes of DG have need to be selected sensibly and HRES requires additional energy storage. To examine the voltage unbalance problems of an islanded microgrid, a hybrid optimization approach known as moth flame optimization (MFO) and firebug swarm optimization (FSO) is introduced. Due to various loads, harmonic distortion causes the voltage to unbalance, which can result in voltage collapse. To deliver a quick response in an island mode, a comprehensive algorithm called MFO-FSO control is proposed. MATLAB software is used to validate the results which demonstrate that the proposed MFO-FSO outperforms the conventional decoupling double synchronous reference frame (DDSRF) methods by reducing total harmonic distortion (THD) up to 1.21% and voltage unbalance factor (VUF) up to 1.427%.