Pub Date : 2021-12-01DOI: 10.1109/ComPE53109.2021.9751934
V. Duhoon, R. Bhardwaj
The paper deals with the objective to study the different artificial intelligence methods and compare their efficiency of forecasting the temperature, rainfall, wind speed in order to contribute in policy making and forecast upcoming disaster if any. Daily data of weather parameters such as Minimum Temperature, Maximum Temperature, Relative Humidity, Evaporation, Bright sunshine, Rainfall, Wind Speed for Delhi region from January 1, 2017 to April 15, 2018 is considered. The behaviour of the considered data set is studied for weather parameters Temperature, Rainfall and Wind Speed daily basis and prediction are made and compared for the period April 16-30, 2018 using Multilayer perceptron (MLP), Radial Basis Function(RBF) and Sequential Minimal Optimization(SMO) artificial intelligence techniques. On comparing these methods, it is observed that MLP Regression shows the least error and maximum Correlation coefficient and is concluded to be the more efficient artificial intelligence technique for forecasting weather parameters. The study will help the concerned authorities for future planning and take preventive steps for the future coming calamities if any. It will also help the government to make effective policies.
{"title":"Artificial Intelligence Technique for Weather Parameter Forecasting","authors":"V. Duhoon, R. Bhardwaj","doi":"10.1109/ComPE53109.2021.9751934","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9751934","url":null,"abstract":"The paper deals with the objective to study the different artificial intelligence methods and compare their efficiency of forecasting the temperature, rainfall, wind speed in order to contribute in policy making and forecast upcoming disaster if any. Daily data of weather parameters such as Minimum Temperature, Maximum Temperature, Relative Humidity, Evaporation, Bright sunshine, Rainfall, Wind Speed for Delhi region from January 1, 2017 to April 15, 2018 is considered. The behaviour of the considered data set is studied for weather parameters Temperature, Rainfall and Wind Speed daily basis and prediction are made and compared for the period April 16-30, 2018 using Multilayer perceptron (MLP), Radial Basis Function(RBF) and Sequential Minimal Optimization(SMO) artificial intelligence techniques. On comparing these methods, it is observed that MLP Regression shows the least error and maximum Correlation coefficient and is concluded to be the more efficient artificial intelligence technique for forecasting weather parameters. The study will help the concerned authorities for future planning and take preventive steps for the future coming calamities if any. It will also help the government to make effective policies.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133922878","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752340
V. Kiruthika, G. G. R. Krishna, G. Karthik, X. B. Xavier, K. Sankaran, B. Kavitha
Movement for blind and elderly people is a challenging problem as they face many difficulties in their daily life. To overcome this problem most commonly, hand stick is used as a support system. Walking stick helps the user to know the presence of obstacle which is in close proximity but does not facilitate detection of obstacles, pits or water that is in the pathway. It does not give information about the location of the user too. Moreover, blind and elderly people are also in a need to monitor their health conditions such as blood pressure and pulse rate. An intelligent system incorporating multiple features will serve as an optimized device for the blind and elderly people. So, a new concept of smart wearable device with multiple features is proposed in this study which will help both blind and elderly people in their daily life. This device enables the movement of both blind and elder people in any environment and monitor their health conditions as well. In this device different sensors such as ultrasonic sensor, infrared sensor, water sensor, blood pressure sensor, pulse sensor, ADXL335 accelerometer sensor, and GPS/GSM technology are embedded to assist the blind and elderly at various instances. During emergencies the information can be communicated to the registered mobile number. This novel system will make the blind and elder people to move confidently and feel their environment.
{"title":"Smart Wearable Device for Blind and Elderly People","authors":"V. Kiruthika, G. G. R. Krishna, G. Karthik, X. B. Xavier, K. Sankaran, B. Kavitha","doi":"10.1109/ComPE53109.2021.9752340","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752340","url":null,"abstract":"Movement for blind and elderly people is a challenging problem as they face many difficulties in their daily life. To overcome this problem most commonly, hand stick is used as a support system. Walking stick helps the user to know the presence of obstacle which is in close proximity but does not facilitate detection of obstacles, pits or water that is in the pathway. It does not give information about the location of the user too. Moreover, blind and elderly people are also in a need to monitor their health conditions such as blood pressure and pulse rate. An intelligent system incorporating multiple features will serve as an optimized device for the blind and elderly people. So, a new concept of smart wearable device with multiple features is proposed in this study which will help both blind and elderly people in their daily life. This device enables the movement of both blind and elder people in any environment and monitor their health conditions as well. In this device different sensors such as ultrasonic sensor, infrared sensor, water sensor, blood pressure sensor, pulse sensor, ADXL335 accelerometer sensor, and GPS/GSM technology are embedded to assist the blind and elderly at various instances. During emergencies the information can be communicated to the registered mobile number. This novel system will make the blind and elder people to move confidently and feel their environment.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134576486","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752229
Chandrika Acharjee, Sumanta Deb
The prevalent COVID 19 pandemic is incessantly taking toll on the lives of people throughout the world. Moreover, the dearth of effectual remedies has caused an expeditious rise in the total COVID 19 cases. Though vaccines have been developed, the enormous task of vaccinating a large population is still challenging. Also, as new variants emanate, the resilience from infections conceivably decreases. Hence, it’s most unlikely that we’ll achieve herd immunity globally so soon. Thus, since the transmission of COVID causing coronavirus roots mainly to social proximity between people, it is necessary to stringently comply to the non pharmaceutical preventive measures of wearing masks and maintaining physical distancing. Howbeit, it has evidently been found that people are being lethargically ignorant to the social distancing norms with passing time. Hence, an autonomous mechanism intended at social distancing violation detection through monitoring of people is needed to be introduced at an authority level. In this paper, the implementation of YOLO Object detection transfer learning process has been used for accomplishing this aim of real time detection of social distancing violation. Our social distance prediction approach uses a pre-trained YOLOv3 object tracking algorithm for identifying people in an input video stream. A Distance estimation algorithm is further used, that works by computing euclidean distance between the centroids of each pair of detected people. This approach highlights the people violating the social distancing criteria as well as calculates the number of times social distancing gets violated as any two people get closer than a set threshold value of minimum permissible distance. A number of experiments on various pre-recorded video streams has been conducted in order to estimate the viability of this method. Through experimental outcomes, it has been found that this YOLO based object detection method with the proposed social distance prediction algorithm produces favourable results for tracking social distancing in public spaces.
{"title":"YOLOv3 based Real Time Social Distance Violation Detection in Public Places","authors":"Chandrika Acharjee, Sumanta Deb","doi":"10.1109/ComPE53109.2021.9752229","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752229","url":null,"abstract":"The prevalent COVID 19 pandemic is incessantly taking toll on the lives of people throughout the world. Moreover, the dearth of effectual remedies has caused an expeditious rise in the total COVID 19 cases. Though vaccines have been developed, the enormous task of vaccinating a large population is still challenging. Also, as new variants emanate, the resilience from infections conceivably decreases. Hence, it’s most unlikely that we’ll achieve herd immunity globally so soon. Thus, since the transmission of COVID causing coronavirus roots mainly to social proximity between people, it is necessary to stringently comply to the non pharmaceutical preventive measures of wearing masks and maintaining physical distancing. Howbeit, it has evidently been found that people are being lethargically ignorant to the social distancing norms with passing time. Hence, an autonomous mechanism intended at social distancing violation detection through monitoring of people is needed to be introduced at an authority level. In this paper, the implementation of YOLO Object detection transfer learning process has been used for accomplishing this aim of real time detection of social distancing violation. Our social distance prediction approach uses a pre-trained YOLOv3 object tracking algorithm for identifying people in an input video stream. A Distance estimation algorithm is further used, that works by computing euclidean distance between the centroids of each pair of detected people. This approach highlights the people violating the social distancing criteria as well as calculates the number of times social distancing gets violated as any two people get closer than a set threshold value of minimum permissible distance. A number of experiments on various pre-recorded video streams has been conducted in order to estimate the viability of this method. Through experimental outcomes, it has been found that this YOLO based object detection method with the proposed social distance prediction algorithm produces favourable results for tracking social distancing in public spaces.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114205054","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752100
Ochin Sharma, K. Mehta, Renuka Sharma
In machine learning, association rule mining is a field with immense opportunity to explore relationships among various attributes and item-sets. However, in Association rule mining, statistically it is the interest measure which play the crucial role to decide these relationships. There exist various types of interest measures based upon the business needs and problem statements. In this paper, a novel interest measure has been proposed to decide the overall importance of an association rule. Statistical comparisons and experimental results have also been embedded to support its potential.
{"title":"Significant Support (SISU): A New Interest Measure in Association Rule Mining","authors":"Ochin Sharma, K. Mehta, Renuka Sharma","doi":"10.1109/ComPE53109.2021.9752100","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752100","url":null,"abstract":"In machine learning, association rule mining is a field with immense opportunity to explore relationships among various attributes and item-sets. However, in Association rule mining, statistically it is the interest measure which play the crucial role to decide these relationships. There exist various types of interest measures based upon the business needs and problem statements. In this paper, a novel interest measure has been proposed to decide the overall importance of an association rule. Statistical comparisons and experimental results have also been embedded to support its potential.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124704147","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752120
S. Bhowmik, A. Mitra, P. Deb
In this proposed work, a mathematical model of transformer has been implemented using Simulink environment to investigate the effect of load switching on the induced e.m.f. due to the presence of leakage inductances of the windings. Estimation of the parameters on a laboratory single-phase transformer has been carried out through Open Circuit (O.C.) and Short Circuit (S.C.) tests and the transient equation for no-load current has been established. This experimental work defines the switching effect of resistive, resistive-inductive (RL) along with a power factor improvement capacitor introduced in parallel with RL load. It has been found in presence of capacitor, the inductive voltage spikes is minimized because of reactive power injection to the system.
{"title":"Effect of Load Switching on Induced e.m.f. of a Transformer","authors":"S. Bhowmik, A. Mitra, P. Deb","doi":"10.1109/ComPE53109.2021.9752120","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752120","url":null,"abstract":"In this proposed work, a mathematical model of transformer has been implemented using Simulink environment to investigate the effect of load switching on the induced e.m.f. due to the presence of leakage inductances of the windings. Estimation of the parameters on a laboratory single-phase transformer has been carried out through Open Circuit (O.C.) and Short Circuit (S.C.) tests and the transient equation for no-load current has been established. This experimental work defines the switching effect of resistive, resistive-inductive (RL) along with a power factor improvement capacitor introduced in parallel with RL load. It has been found in presence of capacitor, the inductive voltage spikes is minimized because of reactive power injection to the system.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123406733","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9751797
Atharva Bankar, Rishabh Shinde, S. Bhingarkar
Computer vision is a top-tier domain of the technological world that is responsible for automating the visual systems from healthcare to self-driving vehicles. With a reputation for surpassing human intelligence, it can be implemented in various trigger systems like wildfire smoke detection where the emission of smoke as a result of wildfire is fairly unpredictable.Low contrast and brightness have a detrimental effect on computer vision tasks. We present a novel approach to detect forest wildfire smoke, using image translation for converting nighttime images to day time which eliminates the confusion between smoke, cloud, and fog. This translation aids the YOLOv5 object detection algorithm to detect the smoke with the same aptness irrespective of time and lighting conditions. This paper demonstrates that the object detection model performs better on the images translated to day time with a better confidence score as compared to the corresponding nighttime images.
{"title":"Impact of Image Translation using Generative Adversarial Networks for Smoke Detection","authors":"Atharva Bankar, Rishabh Shinde, S. Bhingarkar","doi":"10.1109/ComPE53109.2021.9751797","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9751797","url":null,"abstract":"Computer vision is a top-tier domain of the technological world that is responsible for automating the visual systems from healthcare to self-driving vehicles. With a reputation for surpassing human intelligence, it can be implemented in various trigger systems like wildfire smoke detection where the emission of smoke as a result of wildfire is fairly unpredictable.Low contrast and brightness have a detrimental effect on computer vision tasks. We present a novel approach to detect forest wildfire smoke, using image translation for converting nighttime images to day time which eliminates the confusion between smoke, cloud, and fog. This translation aids the YOLOv5 object detection algorithm to detect the smoke with the same aptness irrespective of time and lighting conditions. This paper demonstrates that the object detection model performs better on the images translated to day time with a better confidence score as compared to the corresponding nighttime images.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122767258","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9751830
Abhishek Singh, A. Payal
Recent advancements in various ICT technologies and integration of machine-learning and AI have further extended the Internet of Things (IoT) perspective from ’passive system of interconnected things’ to "an active multi-agent-based system of interconnected everything". However, true realization of future IoT solutions requires addressing critical challenges including limited network coverage and resources. Unmanned Aerial Vehicles (UAVs) have recently gained significant attention due to their acute mobility, equitable operational costs, flexible deployment, and autonomous capabilities. Efforts are being made towards integrating drones in IoT as a solution to the critical challenges. In this paper, we highlighted the key expectations based on the new perspective of IoT and summarized the challenges in IoT due to its inherent nature towards contentment of those expectations. Finally, we investigated the roles of UAVs at various functional layers of IoT-workflow architecture towards addressing the critical issues and enabling key expectations in future-IoT solutions.
{"title":"Roles of UAVs in IoT work-flow architecture","authors":"Abhishek Singh, A. Payal","doi":"10.1109/ComPE53109.2021.9751830","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9751830","url":null,"abstract":"Recent advancements in various ICT technologies and integration of machine-learning and AI have further extended the Internet of Things (IoT) perspective from ’passive system of interconnected things’ to \"an active multi-agent-based system of interconnected everything\". However, true realization of future IoT solutions requires addressing critical challenges including limited network coverage and resources. Unmanned Aerial Vehicles (UAVs) have recently gained significant attention due to their acute mobility, equitable operational costs, flexible deployment, and autonomous capabilities. Efforts are being made towards integrating drones in IoT as a solution to the critical challenges. In this paper, we highlighted the key expectations based on the new perspective of IoT and summarized the challenges in IoT due to its inherent nature towards contentment of those expectations. Finally, we investigated the roles of UAVs at various functional layers of IoT-workflow architecture towards addressing the critical issues and enabling key expectations in future-IoT solutions.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123925361","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9751945
Harsh Walia, J. S.
COVID-19, which is subsequently named as SARS-CoV-2, First Human case was found in the City of Wuhan, from China, in Dec 2019. After that, the World health organization (WHO) has declared Coronavirus as a Pandemic on 11th March 2020. In this study, our primary aim is to Detect the Severe Covid-19 patient in the Early Stages by looking at the information on admission laboratory values, demographics, comorbidities, admission medications, admission supplementary oxygen orders, discharge, and mortality. 4711 patient’s dataset with confirmed SARS-CoV-2 infections are included in the study. Each Patient has total of 85 Features in the Dataset. So, we have Filtered the Top Best 35 features out of 85 features from the Dataset using the seven different feature Selection algorithm and taken the most common features out from the different feature Selection algorithm. After selecting the top most essential features, we have applied around 17 different kinds of ML models like Linear Regression, Logistic regression, SVM, LinearSVC, MLP-Classifier, Decision Tree Classifier, Gradient Boosting Classifier, AdaBoost, Random Forest, XGBoost, LightGBM Classifier, Ridge Classifier, Bagging Classifier, ExtraTreeClassifier, KNN, Naive Bayes, Neural network with Keras, and finally, a Voting Classifier which is the ensemble of all the Top Models from the above-mentioned Models. Finally, all Models are Compared on the basis of Area under the receiver operating characteristic (AUC) & get the best AUC as 0.89.
COVID-19，后来被命名为SARS-CoV-2，于2019年12月在中国武汉市发现了首例人类病例。此后，世界卫生组织(世卫组织)于2020年3月11日宣布冠状病毒为大流行。在这项研究中，我们的主要目的是通过查看入院实验室值、人口统计学、合并症、入院药物、入院补充氧单、出院和死亡率等信息，在早期发现重症Covid-19患者。4711例确诊的SARS-CoV-2感染患者的数据集被纳入研究。每个患者在数据集中共有85个特征。因此，我们使用七种不同的特征选择算法从数据集中的85个特征中筛选出了最好的35个特征，并从不同的特征选择算法中提取了最常见的特征。在选择了最重要的特征后，我们应用了大约17种不同的ML模型，如线性回归，逻辑回归，SVM，线性svc, mlp分类器，决策树分类器，梯度增强分类器，AdaBoost，随机森林，XGBoost, LightGBM分类器，Ridge分类器，Bagging分类器，extratreecclassifier, KNN，朴素贝叶斯，神经网络与Keras，最后，一个投票分类器，它是上述模型中所有顶级模型的集合。最后，根据接收机工作特性下面积(Area under the receiver operating characteristic, AUC)对各模型进行比较，得到最佳AUC为0.89。
{"title":"Early Mortality Risk Prediction in Covid-19 Patients Using an Ensemble of Machine Learning Models","authors":"Harsh Walia, J. S.","doi":"10.1109/ComPE53109.2021.9751945","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9751945","url":null,"abstract":"COVID-19, which is subsequently named as SARS-CoV-2, First Human case was found in the City of Wuhan, from China, in Dec 2019. After that, the World health organization (WHO) has declared Coronavirus as a Pandemic on 11th March 2020. In this study, our primary aim is to Detect the Severe Covid-19 patient in the Early Stages by looking at the information on admission laboratory values, demographics, comorbidities, admission medications, admission supplementary oxygen orders, discharge, and mortality. 4711 patient’s dataset with confirmed SARS-CoV-2 infections are included in the study. Each Patient has total of 85 Features in the Dataset. So, we have Filtered the Top Best 35 features out of 85 features from the Dataset using the seven different feature Selection algorithm and taken the most common features out from the different feature Selection algorithm. After selecting the top most essential features, we have applied around 17 different kinds of ML models like Linear Regression, Logistic regression, SVM, LinearSVC, MLP-Classifier, Decision Tree Classifier, Gradient Boosting Classifier, AdaBoost, Random Forest, XGBoost, LightGBM Classifier, Ridge Classifier, Bagging Classifier, ExtraTreeClassifier, KNN, Naive Bayes, Neural network with Keras, and finally, a Voting Classifier which is the ensemble of all the Top Models from the above-mentioned Models. Finally, all Models are Compared on the basis of Area under the receiver operating characteristic (AUC) & get the best AUC as 0.89.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129590183","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9751990
V. S., Madhusudanan Pillai V, Basil Kuraichen
Tool wear in a milling process affects the finished product's overall quality, which results in rejection. With an increase in tool wear, cutting power decreases that affects the load on the machine. This results in damage of the equipment. Conventional manufacturing system lacks the way of forecasting the tool wear and its effects. Machine Learning (ML) model-based techniques with data-driven prognostics convert conventional manufacturing systems into smart manufacturing systems. This research paper focuses on the comparison of data-driven predictive models that predict tool wear based on the analysis of various sensor signals. In this study, eight algorithms such as Linear Regression (LR), Support Vector Regression (SVR), Naïve Bayesian (NB), Gradient Boost (GB), XG Boost (XGB), CatBoost (CB), Random Forest Regression (RFR), and Artificial Neural Network (ANN) are applied and compared their performance evaluation. The comparative study of regression algorithms provides an overview of tool wear prediction. Evaluation metrics chosen show conclusive evidence that the ANN model performs better than other models. The obtained predictive performance of the ANN model outperforms the existing models reported in the literature. The proposed ANN model for tool wear prediction uses the sensor information and exposes hidden patterns that completely fit the dataset.
{"title":"Data Driven Prognostics of Milling Tool Wear :A Machine Learning Approach","authors":"V. S., Madhusudanan Pillai V, Basil Kuraichen","doi":"10.1109/ComPE53109.2021.9751990","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9751990","url":null,"abstract":"Tool wear in a milling process affects the finished product's overall quality, which results in rejection. With an increase in tool wear, cutting power decreases that affects the load on the machine. This results in damage of the equipment. Conventional manufacturing system lacks the way of forecasting the tool wear and its effects. Machine Learning (ML) model-based techniques with data-driven prognostics convert conventional manufacturing systems into smart manufacturing systems. This research paper focuses on the comparison of data-driven predictive models that predict tool wear based on the analysis of various sensor signals. In this study, eight algorithms such as Linear Regression (LR), Support Vector Regression (SVR), Naïve Bayesian (NB), Gradient Boost (GB), XG Boost (XGB), CatBoost (CB), Random Forest Regression (RFR), and Artificial Neural Network (ANN) are applied and compared their performance evaluation. The comparative study of regression algorithms provides an overview of tool wear prediction. Evaluation metrics chosen show conclusive evidence that the ANN model performs better than other models. The obtained predictive performance of the ANN model outperforms the existing models reported in the literature. The proposed ANN model for tool wear prediction uses the sensor information and exposes hidden patterns that completely fit the dataset.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120925661","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 : 2021-12-01DOI: 10.1109/ComPE53109.2021.9752331
Pachunoori Anusha, Sanjib Patra, Ayanava Roy, D. Saha
This technical write-up emphasizes on combined regulation of frequency - voltage in a restructured renewable energy integrated power system under PoolCo and bilateral transactions. A two area coordinated modeling of area load frequency control (ALFC) and automatic voltage regulator (AVR) is carried out in presence of thermal, hydro, systems. Non linearities are incorporated to get a realistic insight with scheduled delay, rate constraint, and dead band. A powerful algorithm namely Firefly Algorithm based Industrial controllers serve the purpose of classical controller as Secondary control in the considered two area power system. Selection of best secondary controller among integral (I), proportional-integral (PI) and proportional-integral - derivative (PID) controller is carried out based on a fair comparison under Pool Co Transaction Scenario. PID controller serves better. Further investigations are carried out with excitation in AVR loop along with step load perturbation at ALFC in both control areas. PID outperforms I and PI in stabilizing the system responses such as frequency deviation, tie-power deviation and voltage deviations.
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