Journal of Electrical Systems最新文献

{"title":"Using Affine Arithmetic to Design a Controller for Large Uncertain System","authors":"Dr. T. Narasimhulu, Prof. P Mallikarjuna Rao","doi":"10.52783/jes.3541","DOIUrl":"https://doi.org/10.52783/jes.3541","url":null,"abstract":"This study presents a approach for creating the supervisor of expansive uncertain systems. The Controller for a particular high order system is designed using a reduced order model. The numerator and denominator polynomial in the suggested reduction approach is derived using modified polynomial differential method. A lower order model with least ISE optimization is obtained. Assuming that the initial high-order system is stable, the proposed approach guarantees the stability of the streamlined model. Through With reference to the original high-order systems, a PID controller is created for the suggested low-order model. In order to explain the method's efficiency, a few numerical examples were taken into consideration. It has been demonstrated that applying Control from the lower order model to the higher order system is improved and the controlled system's performance. Common numerical illustrations seen in the literature have been used to test the method, and the results show that it works satisfactorily.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ZnCl2 activated Tomato waste derived porous carbon material for high power density for flexible supercapacitor applications","authors":"Yakub Banothu","doi":"10.52783/jes.3545","DOIUrl":"https://doi.org/10.52783/jes.3545","url":null,"abstract":"Developing economic and efficient energy storage devices is crucial in the shift towards sustainable energy solutions. Our present work explores the potential of utilizing decayed tomato as a sustainable precursor to produce activated porous carbon electrodes in the construction of supercapacitors. By utilizing the carbonization-activation process and carefully adjusting the preparation parameters, we have achieved an excellent specific surface area (SSA) of 880.33 m2 g-1. This tomato waste char (TWC) predominantly consists of nanopores with a total pore volume of 0.5713 cm3 g-1 and an average pore diameter of about 2.5958 nm. These parameters play a vital role in determining the supercapacitor behavior of the sample. A specific capacitance of 110 F g-1 at 1 mA has been recorded. Furthermore, an energy density of 14.97 Wh kg-1 at 1 mA is observed. This work has reported a high-value power density of about 4163.4 W kg-1 using a symmetric type supercapacitor.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Assembly Sequence Planning of Hybrid Power Transmission Device Based on Improved Genetic Algorithm","authors":"Liyong Zhang","doi":"10.52783/jes.3521","DOIUrl":"https://doi.org/10.52783/jes.3521","url":null,"abstract":"Aiming at the complex optimisation problem involved in the assembly process of DM-i hybrid system, an improved genetic algorithm based on the assembly sequence planning problem is proposed to be investigated. Two matrices, assembly priority and assembly space interference, are used to constrain the assembly relationship of parts, and the feasibility, optimality and flexibility of assembly are considered; the initial population of the genetic algorithm is optimised in terms of algorithmic improvement, and inverse learning is used to generate the initial population and an elite inverse learning mechanism is introduced to avoid the algorithm from falling into a local optimal solution; the search strategy of the algorithm is improved, which consists of four main steps, i.e., binary tournament selection, partial matching crossover, exchange mutation and elite individual retention strategy. The feasibility and superiority of the proposed improved genetic algorithm in solving the assembly sequence planning problem of DM-i hybrid system are verified by example algorithms and on-site assembly verification.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of English Teaching Capability Evaluation Model Under Big Data Analysis","authors":"Liqin He, Chaojie Hu, Ling Nie, Chunxia Li, Honglian Liu","doi":"10.52783/jes.3527","DOIUrl":"https://doi.org/10.52783/jes.3527","url":null,"abstract":"Traditional methods of evaluating English teaching capability involve a considerable degree of subjective human judgment, leading to classification errors in big data information. To improve the comprehensiveness and accuracy of English teaching capability evaluation, it is necessary to construct a corresponding evaluation model based on big data. This paper employs the k-means clustering analysis algorithm to devise a system structure design for English teaching capability, applies constrained parameter big data structure analysis, and proposes utilizing a quantitative recursive approach to evaluate the teaching capabilities of big data information models based on cluster analysis. The simulation results demonstrate that the method designed in this paper can enhance the comprehensiveness and precision of English teaching capability evaluation, thereby contributing to the advancement of information fusion analysis capabilities.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Guidance for Predicting Early Readmission in Diabetic Patients","authors":"Duddukunta Rajasekhara Reddy, P. L. Prasanna","doi":"10.52783/jes.3555","DOIUrl":"https://doi.org/10.52783/jes.3555","url":null,"abstract":"This study builds on the first paper's look at using machine learning to predict which diabetic patients will need to go back to the hospital. The information used comes from Kaggle and is made up of details of diabetic patients. The first steps include importing packages, exploring datasets, and cleaning, which includes changing binary classes and getting rid of columns that aren't needed. Using Seaborn and Matplotlib to make a full picture of the information helps people understand it better. LabelEncoder is used for label encoding, and feature selection methods are used. After that, the data is split into sets that are used to train and test both deep learning and machine learning models. As part of the study, different models for binary and multi-class classification were built. These models include S V M, Random Forest, Guided Artificial Neural Networks (ANN) with different optimization methods, and Voting Classifier and Stacking Classifier ensemble methods. It was also very accurate when a combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) was used. The study also includes a Flask framework that is connected to SQLite for user registration and lets users enter feature values for forecast. The data that has already been handled is then used by the learned models, and the end results are shown on the front end. The study was expanded by using ensemble methods, which showed that CNN + LSTM is more accurate than past methods. Adding user identification and interface development makes the models more useful in real life, giving us a more complete way to predict diabetic return.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach for Dynamic Analysis of Wind-Integrated Multi-Machine Power Systems","authors":"Ankhi Gulati","doi":"10.52783/jes.3536","DOIUrl":"https://doi.org/10.52783/jes.3536","url":null,"abstract":"Indeed, the rapid expansion of Renewable Energy Sources (RES) in recent years has brought about numerous benefits, including reduced carbon emissions, increased energy independence, and the creation of new economic opportunities. However, integrating these variable and intermittent sources into existing power systems poses several challenges for power system management. Faults in electrical networks are among the key factors and sources of network disturbances. Control and automation strategies are among the key fault clearing techniques responsible for the safe operation of the system. In recent years, the increasing penetration of wind energy in multi-machine power systems has posed unique challenges to power grid stability and reliability. Accurate assessment methodologies are required to ensure the effective integration of wind energy sources while maintaining grid stability. Several researchers have revealed various constraints of control and automation strategies such as a slow dynamic response, the inability to switch the network on and off remotely, a high fault clearing time and loss minimization. It's important to note that the impact of wind energy on system inertia is a complex and dynamic aspect of power system operation. Ongoing research and technological advancements aim to improve the integration of wind and other renewable energy sources while ensuring grid stability and reliability. As the energy transition continues, addressing these technical challenges is crucial for building a sustainable and resilient power system. In this paper, the influence of doubly-fed induction generator (DFIG) penetration is analyzed to examine the transient stability of power system networks. The concept of a Coupling Strength Index (CSI) derived from Network Structural Characteristics Theory sounds intriguing, especially in the context of identifying critical elements susceptible to the impact of a three-phase fault in a network. The investigation involves studying the transient stability of a power system under different conditions, specifically with and without doubly-fed induction generators (DFIGs) connected to a weak bus. Additionally, a three-phase fault is applied at the middle of the identified weakest line for both the IEEE 9 and 39 bus systems. The investigation of generator speed, rotor angle, and electric power during transient stability analysis provides a holistic view of how a power system responds to disturbances. This information is crucial for ensuring the reliability and stability of the system, especially when studying the integration of renewable energy sources and addressing potential challenges associated with faults and weak buses. This paper presents a pioneering non-iterative framework for dynamically assessing wind energy dominated multi-machine power systems. The proposed framework aims to address the shortcomings of traditional iterative methods, providing a more efficient and reliable approach to power system analysis.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141000240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Joint Inversion Method Algorithm for T2-Pc two Dimensional NMR based on Logistic Functions","authors":"Jing Su, Caiguang Liu, Rong Zhang, Zhenlin Wang, Yingyao Qin, Gong Zhang","doi":"10.52783/jes.3522","DOIUrl":"https://doi.org/10.52783/jes.3522","url":null,"abstract":"Data processing is a key step in the analysis of nuclear magnetic resonance (NMR) experimental data, and efficient and accurate computer inversion algorithms are the core of the T2-Pc two-dimensional NMR experiment. T2-Pc two-dimensional NMR experiment is an advanced experimental method that characterizes reservoir connectivity through two dimensions: relaxation time and capillary pressure, and can obtain irreducible water saturation under different pressure differences. However, the algorithm currently used for T2-Pc two-dimensional spectrum inversion uses a mutation kernel function, resulting in low accuracy of calculation results. This paper uses the Logistic function as the two-dimensional inversion kernel function, rewrites the inversion algorithm, and obtains more accurate inversion results. Numerical simulations have proven that the T2-Pc two-dimensional map obtained by this method not only has higher resolution, but also has greater applicability in the case of low signal-to-noise ratio and a small number of centrifugal echo groups. Practice has found that the proposed method can reduce the number of centrifugations during the experiment and significantly improve the efficiency of T2-Pc two-dimensional nuclear magnetic resonance experiments.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on the Characteristics of Industrial Talent Demand Depending on Big Data Technology","authors":"Li Li","doi":"10.52783/jes.3532","DOIUrl":"https://doi.org/10.52783/jes.3532","url":null,"abstract":"For a long time, there is a mismatch between supply and demand for industrial talents, and it is difficult to achieve a dynamic balance between talent supply and job demand. This paper aims to mine and analyze the characteristics of industrial talent demand through big data technology, so as to provide skills training reference for talent supply and provide talents with high matching degree for job demand. In this paper, TF-IDF is used as a feature selection method to extract highly representative feature words to help distinguish different talent information data to a greater extent. In addition, combined with the construction method of statistical analysis, quantitative indexing analysis of talent information data is carried out, which enhances the expansion of label system and makes talent portraits more precise and accurate. Through the experimental research, it is proved that the research system of industrial talent demand characteristics proposed in this paper can effectively analyze and match the characteristics of industrial talents. Therefore, in the social employment and higher education, we can combine the methods proposed in this paper to assist decision-making, and promote the dynamic balance between the demand and supply of industrial talents.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big Data Analysis: The Impact of Agricultural Non-point Source Pollution on Household Decision-making","authors":"Ying Wang, Chao Xue","doi":"10.52783/jes.3528","DOIUrl":"https://doi.org/10.52783/jes.3528","url":null,"abstract":"Based on intergenerational support theory, a simultaneous equation model is used in this article to analyze the impact of fertilizer loss on household medical and care decisions in rural China. Results of the big data empirical analysis indicate that one kg/ha increase in fertilizer loss alters the medical costs by CNY 35 (USD 5) per year and the opportunity cost of household caring time by CNY 5 (USD 0.7) per year. This is equivalent CNY 760 million (USD 109 million) at nation economic loss. Furthermore, fertilizer loss has a significant lag and cumulative impact on medical expenses.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NDVI prediction using Machine learning after Geofencing on satellite data of Sugarcane crop","authors":"Mansi Kambli","doi":"10.52783/jes.3550","DOIUrl":"https://doi.org/10.52783/jes.3550","url":null,"abstract":"The manual fencing of the crop is too tedious and time-consuming for the farmers. The farmers have to physically see that they are not crossing the boundaries and usually, farmers fight related to the plots. In this paper focus is on the satellite data imagery of Satara district and if geofencing is done for sugarcane crops then monitoring the crop is comparatively easy and analysis can be further done related to sugarcane plots. Geofencing helps then to have the precise data of the farmer and boundaries can be detected to avoid hassles. The Cane plots that have been geofenced can then be labeled according to the plots of the farmers, thus streamlining th classification process. The digitization of the farmer plots is used in this paper and converted into shape files by using GIS which helps to do the further analysis of the crop. Further after doing the pre-processing by using GIS, the Normalized Difference Vegetation Index (NDVI) is predicted prior using Machine learning technique in python. The NDVI actual and predicted for one life cycle of sugarcane is shown in the paper. The NDVI predicted values can be helpful for sustainable agriculture of sugarcane crop in terms of disease detection, cane classification and prediction also. The Machine learning   algorithms can be applied further and the geofencing can be done district wise in future scope along with the vegetation   indices.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141129223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}