Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101273
Jihua He
Design and implement the simulation integration platform. The functional requirements of the system are divided into modules, including desktop management, simulation design, job management, user data management, cluster monitoring and audit management modules, and the technical solutions and overall architecture of the system required for specific implementation are studied according to the actual needs. Because it is a Web system with B/S architecture, it uses the Spring Boot framework to quickly build the system, and the database uses ostgreSQL. According to the needs of colleges and universities for simulation design software, the system has built several default simulation software. It also supports the administrator to dynamically conFigure through the platform, and provides users with the relevant data archiving, management and sharing functions for each simulation job.
{"title":"Construction of Computer Front-End Resource Sharing Platform Based on Web","authors":"Jihua He","doi":"10.1109/INOCON57975.2023.10101273","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101273","url":null,"abstract":"Design and implement the simulation integration platform. The functional requirements of the system are divided into modules, including desktop management, simulation design, job management, user data management, cluster monitoring and audit management modules, and the technical solutions and overall architecture of the system required for specific implementation are studied according to the actual needs. Because it is a Web system with B/S architecture, it uses the Spring Boot framework to quickly build the system, and the database uses ostgreSQL. According to the needs of colleges and universities for simulation design software, the system has built several default simulation software. It also supports the administrator to dynamically conFigure through the platform, and provides users with the relevant data archiving, management and sharing functions for each simulation job.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404193","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101294
Harsha Sai Kalyanapu, Nandan Vemuri, Venkata Pavan Sai Hemanth Rayapati, A. Yadav, Gnana Prasanna Yella
A unique combination of its extraordinary qualities has made graphene one of the most promising nanomaterials, it is not only the thinnest material, but also one of the strongest materials. It is a superb electrical conductor and does so better than any other material. Instead of using polymers, polymer composites are employed in numerous applications. Due to its uses, stable graphene dispersions with high graphene concentrations have received a lot of attention recently. To enhance the dispersion of graphene and create a stable graphene solution with a high concentration, 1-vinyl 2pyrrolidone was used. To create ZnO/graphene composites, this stable graphene solution was combined with ZnO. A sol-gel method was used to deposit a thin coating of Graphene doped ZnO composite. With the addition of graphene, ZnO’s electrical conductivity was significantly increased.
{"title":"Metal-Semiconductor – Metal structure on Graphene Doped ZnO Thin Film","authors":"Harsha Sai Kalyanapu, Nandan Vemuri, Venkata Pavan Sai Hemanth Rayapati, A. Yadav, Gnana Prasanna Yella","doi":"10.1109/INOCON57975.2023.10101294","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101294","url":null,"abstract":"A unique combination of its extraordinary qualities has made graphene one of the most promising nanomaterials, it is not only the thinnest material, but also one of the strongest materials. It is a superb electrical conductor and does so better than any other material. Instead of using polymers, polymer composites are employed in numerous applications. Due to its uses, stable graphene dispersions with high graphene concentrations have received a lot of attention recently. To enhance the dispersion of graphene and create a stable graphene solution with a high concentration, 1-vinyl 2pyrrolidone was used. To create ZnO/graphene composites, this stable graphene solution was combined with ZnO. A sol-gel method was used to deposit a thin coating of Graphene doped ZnO composite. With the addition of graphene, ZnO’s electrical conductivity was significantly increased.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"564 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117151431","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101148
U. Malviya, S. Chauhan
Real-time data on natural disasters are collected, explained, analysed, predicted, and shown in the disaster management system. The development of GIS-based informational understanding has been documented (GIS). Using GIS and geographic data mining, the disaster management approach can pinpoint the epicentre of an occurrence and direct relief workers along the safest possible paths to the scene. The precise geological state and geographical placement of many areas makes them vulnerable to a wide range of natural disasters, including earthquakes, floods, land debris, landslides, cloud bursts, and human casualties. An efficient real-time system for predicting natural occurrences and locations is necessary to minimise damages and suffering. This research presents a unique methodology for predicting the location of disasters using density-based spatiotemporal clustering and global positioning system data. Before implementing clustering and feature selection, the process of data cleansing removes redundant, irrelevant, and inconsistent information from the news databases based on natural events. Areas prone to natural disasters like earthquakes, floods, landslides, and so on will be culled using a spatiotemporal clustering technique. The clustered data is then sorted by terms associated with natural catastrophes, and features are selected accordingly. In order to aid event detectors and location estimators, extracted features are supplied to a decision tree, which then categorises the data into both positive and negative classes.
{"title":"Multiple Agents based Disaster Prediction for Public Environments using Data Mining Techniques","authors":"U. Malviya, S. Chauhan","doi":"10.1109/INOCON57975.2023.10101148","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101148","url":null,"abstract":"Real-time data on natural disasters are collected, explained, analysed, predicted, and shown in the disaster management system. The development of GIS-based informational understanding has been documented (GIS). Using GIS and geographic data mining, the disaster management approach can pinpoint the epicentre of an occurrence and direct relief workers along the safest possible paths to the scene. The precise geological state and geographical placement of many areas makes them vulnerable to a wide range of natural disasters, including earthquakes, floods, land debris, landslides, cloud bursts, and human casualties. An efficient real-time system for predicting natural occurrences and locations is necessary to minimise damages and suffering. This research presents a unique methodology for predicting the location of disasters using density-based spatiotemporal clustering and global positioning system data. Before implementing clustering and feature selection, the process of data cleansing removes redundant, irrelevant, and inconsistent information from the news databases based on natural events. Areas prone to natural disasters like earthquakes, floods, landslides, and so on will be culled using a spatiotemporal clustering technique. The clustered data is then sorted by terms associated with natural catastrophes, and features are selected accordingly. In order to aid event detectors and location estimators, extracted features are supplied to a decision tree, which then categorises the data into both positive and negative classes.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194861","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101269
Midhun P. Mathew, Sudheep Elayidom, Vp Jagathyraj
In modern days, artificial intelligence plays an important role in every scenario of life. Our economy mainly relies on agriculture, so this backwardness of technology affects the economy. When we are concerned about agriculture, the main issue that the agriculture sector facing now is, disease identification. Identification of diseases in the correct time can avoid loss of crops and finance of cultivator. Most farmers depend on a traditional method of detection, this method requires enormous amounts of work and time, but correctness of prediction is low. This Paper mainly focuses on disease identification in bell peppers in large farms based on deep learning networks such as Vgg 16, Vgg 19, and AlexNet. Generally, farmers won’t able to find out whether their plant is affected by diseases or not. The spread of diseases affects crop production. Only method to avoid the loss of crop production is by identifying the diseases at its early stage. We do testing based on the image from the different parts of the farm. We also intend to study pre-trained CNN architecture of VGG and AlexNet known as transfer learning, to detect disease detection in bell pepper. Based on our study we found out that Vgg 19 has better performance for disease detection in bell pepper.
{"title":"Disease Classification in Bell Pepper Plants Based on Deep Learning Network Architecture","authors":"Midhun P. Mathew, Sudheep Elayidom, Vp Jagathyraj","doi":"10.1109/INOCON57975.2023.10101269","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101269","url":null,"abstract":"In modern days, artificial intelligence plays an important role in every scenario of life. Our economy mainly relies on agriculture, so this backwardness of technology affects the economy. When we are concerned about agriculture, the main issue that the agriculture sector facing now is, disease identification. Identification of diseases in the correct time can avoid loss of crops and finance of cultivator. Most farmers depend on a traditional method of detection, this method requires enormous amounts of work and time, but correctness of prediction is low. This Paper mainly focuses on disease identification in bell peppers in large farms based on deep learning networks such as Vgg 16, Vgg 19, and AlexNet. Generally, farmers won’t able to find out whether their plant is affected by diseases or not. The spread of diseases affects crop production. Only method to avoid the loss of crop production is by identifying the diseases at its early stage. We do testing based on the image from the different parts of the farm. We also intend to study pre-trained CNN architecture of VGG and AlexNet known as transfer learning, to detect disease detection in bell pepper. Based on our study we found out that Vgg 19 has better performance for disease detection in bell pepper.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124739789","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101337
Shashikala S, D. K.
Image forensics detects manipulation of digital images by tampering and counterfeiting process. While most works on Image forensics detect splicing, retouching and copy-move, very few have addressed colorization forgeries. Colorization or Fake colorization is a rapidly emerging area where colors of certain regions in image are manipulated with realistic colors. This is done maliciously to confound object recognition algorithms. Though some works are proposed to detect fake colorization, they can be deceived easily by introducing the pixel differences using statistical techniques. This work proposes a deep learning technique for detection of colorization forgeries which is resilient against deceiving attacks. Best set of discriminating features are extracted from Deep learning layers to recognize the differences in multiple channels of hue, saturation, value with aim to increase the accuracy of colorization forgery detection. Compared to most recent histogram based features, deep learning model is able to learn more intricate features about the distribution of intensity in hue, saturation, dark and value channels. Through experimental analysis, the proposed solution is found to provide at least 2% higher fake colorization detection accuracy compared to existing works
{"title":"Ensemble deep learning fusion for detection of colorization based image forgeries","authors":"Shashikala S, D. K.","doi":"10.1109/INOCON57975.2023.10101337","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101337","url":null,"abstract":"Image forensics detects manipulation of digital images by tampering and counterfeiting process. While most works on Image forensics detect splicing, retouching and copy-move, very few have addressed colorization forgeries. Colorization or Fake colorization is a rapidly emerging area where colors of certain regions in image are manipulated with realistic colors. This is done maliciously to confound object recognition algorithms. Though some works are proposed to detect fake colorization, they can be deceived easily by introducing the pixel differences using statistical techniques. This work proposes a deep learning technique for detection of colorization forgeries which is resilient against deceiving attacks. Best set of discriminating features are extracted from Deep learning layers to recognize the differences in multiple channels of hue, saturation, value with aim to increase the accuracy of colorization forgery detection. Compared to most recent histogram based features, deep learning model is able to learn more intricate features about the distribution of intensity in hue, saturation, dark and value channels. Through experimental analysis, the proposed solution is found to provide at least 2% higher fake colorization detection accuracy compared to existing works","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126119839","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101281
Sagarika M Chavan, M. S. Prerana, Ramit Bathula, Sreenath Saikumar, Geetha Dayalan
Correcting handwritten answer booklets manually can be a challenging task for professors, involving significant time and effort. To address this issue, the paper proposes an automated evaluation system that uses DL and NLP techniques. The suggested approach begins by extracting raw text from image files using a proven GCP OCR text extract model, which is well-known for its better accuracy and efficiency. Furthermore, Natural Language Processing methods like BERT and GPT-3 are used to extract keywords and summarize extensive answers. The suggested technique gives marks that are usually comparable to those issued by manual evaluation. Furthermore, the article suggests a web tool that simplifies the evaluation procedure. The application outputs the raw text of student answers and the answer key, a synopsis of the student’s response, and the marks gained based on the extracted keywords.
{"title":"Automated Script Evaluation using Machine Learning and Natural Language Processing","authors":"Sagarika M Chavan, M. S. Prerana, Ramit Bathula, Sreenath Saikumar, Geetha Dayalan","doi":"10.1109/INOCON57975.2023.10101281","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101281","url":null,"abstract":"Correcting handwritten answer booklets manually can be a challenging task for professors, involving significant time and effort. To address this issue, the paper proposes an automated evaluation system that uses DL and NLP techniques. The suggested approach begins by extracting raw text from image files using a proven GCP OCR text extract model, which is well-known for its better accuracy and efficiency. Furthermore, Natural Language Processing methods like BERT and GPT-3 are used to extract keywords and summarize extensive answers. The suggested technique gives marks that are usually comparable to those issued by manual evaluation. Furthermore, the article suggests a web tool that simplifies the evaluation procedure. The application outputs the raw text of student answers and the answer key, a synopsis of the student’s response, and the marks gained based on the extracted keywords.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123771124","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101335
V. Saravanakrishnan., N. Dhanush, S. Israk Hussian., G. Thamizhselvan., A. Janagiraman
This paper presents a transformer-less electrical converter is a power converter that synthesizes voltage from several levels of DC voltages. In Transformer-less electrical converter, selective harmonic elimination is major one for each medium-and high-voltage applications. The performance of output voltage and power is increased with the reduction of total harmonic distortion. Harmonic elimination in Transformer-less inverters established huge thought for past few decades with the smallest total harmonic distortion. But, the elimination of selective harmonics in Transformer-less electrical converter still experiences certain drawbacks. Recently, several analysis works are introduced for enhancing the performance of output voltage and power in transformerless electrical converter. In an existing hybrid formula was projected in seven-level Transformer-less electrical converter for selective harmonic elimination with condensed switches. HERIC optimization based mostly Selective harmonic elimination methodology is proposed for selective harmonic elimination with known optimum switching angle. Then, switching angle is measured and initialized for activity of the memetic optimization. The output voltage improvement is earned by proposing a shuffled frog leaping switching angle optimization based mostly selective harmonic elimination methodology in transformer-less inverters. After initializing switching angles, shuffled frog leaping switching angle optimization algorithm is utilized for selective harmonic elimination. A 120W model was also designed and tested in the laboratory, and the simulation and experimental results are finally conferred to show the wonderful performance of the proposed PV electrical converter.
{"title":"Control Strategy for Renewable Energy System Using Transformerless HERIC Bridge Inverter","authors":"V. Saravanakrishnan., N. Dhanush, S. Israk Hussian., G. Thamizhselvan., A. Janagiraman","doi":"10.1109/INOCON57975.2023.10101335","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101335","url":null,"abstract":"This paper presents a transformer-less electrical converter is a power converter that synthesizes voltage from several levels of DC voltages. In Transformer-less electrical converter, selective harmonic elimination is major one for each medium-and high-voltage applications. The performance of output voltage and power is increased with the reduction of total harmonic distortion. Harmonic elimination in Transformer-less inverters established huge thought for past few decades with the smallest total harmonic distortion. But, the elimination of selective harmonics in Transformer-less electrical converter still experiences certain drawbacks. Recently, several analysis works are introduced for enhancing the performance of output voltage and power in transformerless electrical converter. In an existing hybrid formula was projected in seven-level Transformer-less electrical converter for selective harmonic elimination with condensed switches. HERIC optimization based mostly Selective harmonic elimination methodology is proposed for selective harmonic elimination with known optimum switching angle. Then, switching angle is measured and initialized for activity of the memetic optimization. The output voltage improvement is earned by proposing a shuffled frog leaping switching angle optimization based mostly selective harmonic elimination methodology in transformer-less inverters. After initializing switching angles, shuffled frog leaping switching angle optimization algorithm is utilized for selective harmonic elimination. A 120W model was also designed and tested in the laboratory, and the simulation and experimental results are finally conferred to show the wonderful performance of the proposed PV electrical converter.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125378485","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101116
Tahsinur Rahman, Nusaiba Ahmed, Shama Monjur, Fasbeer Mohammad Haque, Muhammad Iqbal Hossain
As the world progresses towards a digital era, the transfer of data in Portable Document Format (PDF) has become ubiquitous. Regrettably, this format is susceptible to malware attacks and the conventional anti-malware and anti-virus software may not be able to detect PDF malware effectively. In response to this problem, the implementation of machine learning algorithms and neural networks has been proposed in the past. However, the lack of transparency in these models raises concerns regarding their ethical and responsible decision-making. To address this concern, the utilization of Explainable AI (XAI) with the SHAP framework is proposed to classify PDF files as either malicious or clean, providing both a global and local understanding of the models’ decisions. The algorithms employed in this endeavor include Stochastic Gradient Descent (SGD), XGBoost Classifier, Single Layer Perceptron, and Artificial Neural Network (ANN).
{"title":"Interpreting Machine and Deep Learning Models for PDF Malware Detection using XAI and SHAP Framework","authors":"Tahsinur Rahman, Nusaiba Ahmed, Shama Monjur, Fasbeer Mohammad Haque, Muhammad Iqbal Hossain","doi":"10.1109/INOCON57975.2023.10101116","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101116","url":null,"abstract":"As the world progresses towards a digital era, the transfer of data in Portable Document Format (PDF) has become ubiquitous. Regrettably, this format is susceptible to malware attacks and the conventional anti-malware and anti-virus software may not be able to detect PDF malware effectively. In response to this problem, the implementation of machine learning algorithms and neural networks has been proposed in the past. However, the lack of transparency in these models raises concerns regarding their ethical and responsible decision-making. To address this concern, the utilization of Explainable AI (XAI) with the SHAP framework is proposed to classify PDF files as either malicious or clean, providing both a global and local understanding of the models’ decisions. The algorithms employed in this endeavor include Stochastic Gradient Descent (SGD), XGBoost Classifier, Single Layer Perceptron, and Artificial Neural Network (ANN).","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115183668","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101286
Suryanarayan Sharma, D. Yadav
Communities using Sustainable Energy Systems (RES) seek to meet their electrical needs while reducing their reliance on public utilities by integrating renewable energy sources. Additionally, an intelligent micro grid makes it simple to access services for controlling energy use, which might lower utility costs for locals. These infrastructures are influenced by ML technologies, big data, AI, the IoT, and sensor technologies. New advancements in ML technology are required to produce precise learning approaches that can be used in the electricity analytical process, such as such monitoring, forecasting, prediction, scheduling, and decision-making. This will improve power control assistance and the spread of renewable energy sources. However, as the complexity of issues with the smart grid system, such as non-linearity and unpredictability, rises, so does the complexity of the resulting energy data format. The learning process cannot be completed by the fundamental ML approach since it can only evaluate fundamental raw data. Therefore, despite the data’s intricate and extensive structure, the Deep Learning (DL) approach may be used. A Convolutional Neural Network (CNN) will be developed in this study as a learning model to provide precise forecasts of future power usage and renewable energy installations. The echo state network is used to learn temporal features once interesting patterns have been retrieved from the past using the convolution process. The resultant spatiotemporal feature representation is ultimately given to fully connected layers for prediction. The proposed method was developed after thorough testing of both deep learning and machine learning models. When compared to state-of-the-art models, the results show that the recommended model performs as a model for energy equilibrium among production resources and consumers, with significant decreases in forecasting errors using MAE, MSE, RMSE, and NRMSE metrics.
{"title":"Renewable Energy Systems Energy Modeling using Deep Learning Techniques","authors":"Suryanarayan Sharma, D. Yadav","doi":"10.1109/INOCON57975.2023.10101286","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101286","url":null,"abstract":"Communities using Sustainable Energy Systems (RES) seek to meet their electrical needs while reducing their reliance on public utilities by integrating renewable energy sources. Additionally, an intelligent micro grid makes it simple to access services for controlling energy use, which might lower utility costs for locals. These infrastructures are influenced by ML technologies, big data, AI, the IoT, and sensor technologies. New advancements in ML technology are required to produce precise learning approaches that can be used in the electricity analytical process, such as such monitoring, forecasting, prediction, scheduling, and decision-making. This will improve power control assistance and the spread of renewable energy sources. However, as the complexity of issues with the smart grid system, such as non-linearity and unpredictability, rises, so does the complexity of the resulting energy data format. The learning process cannot be completed by the fundamental ML approach since it can only evaluate fundamental raw data. Therefore, despite the data’s intricate and extensive structure, the Deep Learning (DL) approach may be used. A Convolutional Neural Network (CNN) will be developed in this study as a learning model to provide precise forecasts of future power usage and renewable energy installations. The echo state network is used to learn temporal features once interesting patterns have been retrieved from the past using the convolution process. The resultant spatiotemporal feature representation is ultimately given to fully connected layers for prediction. The proposed method was developed after thorough testing of both deep learning and machine learning models. When compared to state-of-the-art models, the results show that the recommended model performs as a model for energy equilibrium among production resources and consumers, with significant decreases in forecasting errors using MAE, MSE, RMSE, and NRMSE metrics.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122981688","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}
Pub Date : 2023-03-03DOI: 10.1109/INOCON57975.2023.10101234
R. Balakrishnan, M. Munirajulu
Computational Fluid Dynamics (CFD) is a technology used innovatively in the design of cooling and backup power for business continuity in data centers. Well-designed cooling and backup power infrastructure is critical to data center performance, even as the data center facilities are poised for rapid growth around the world. In this paper, performance-based design using CFD analysis to ensure the designed system is meeting all the requirements of proper cooling and power backup is presented.
{"title":"CFD Simulation of Tier 4 Data Center for Cooling and Backup Power","authors":"R. Balakrishnan, M. Munirajulu","doi":"10.1109/INOCON57975.2023.10101234","DOIUrl":"https://doi.org/10.1109/INOCON57975.2023.10101234","url":null,"abstract":"Computational Fluid Dynamics (CFD) is a technology used innovatively in the design of cooling and backup power for business continuity in data centers. Well-designed cooling and backup power infrastructure is critical to data center performance, even as the data center facilities are poised for rapid growth around the world. In this paper, performance-based design using CFD analysis to ensure the designed system is meeting all the requirements of proper cooling and power backup is presented.","PeriodicalId":113637,"journal":{"name":"2023 2nd International Conference for Innovation in Technology (INOCON)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128316780","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}
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