Radio Modulation Classification is implemented by using the Deep Learning Techniques. The raw radio signals where as inputs and can automatically learn radio features and classification accuracy. The LSTM (Long short-term memory) based classifiers and CNN (Convolutional Neural Network) based classifiers were proposed in this paper. In the proposed work, two CNN based classifiers are implemented such as the LeNet classifier and the ResNet classifier. For visualizing the radio modulation, a class activation vector (w) is used. Finally in the proposed work, it is performed the classification by using the Deep learning models like CNN and LSTM based modulation classifiers. These deep learning models extract the important radio features that are used for classification. Here, the bench mark dataset RadioML2016.10a is used. This is an open dataset which contains the modulated signal I and Q values fewer than ten modulation categories. After evolution of proposed model with bench mark dataset, it is applied with real time data collected through the SDR Dongle receiver. From the obtained real time signal, the modulation categories have been classified and visualized the radio features extracted from the radio modulation classifiers.
{"title":"Deep Learning based Real Time Radio Signal Modulation Classification and Visualization","authors":"S. Rajesh, S. Geetha, Babu Sudarson S, Ramesh S","doi":"10.5815/ijem.2023.05.04","DOIUrl":"https://doi.org/10.5815/ijem.2023.05.04","url":null,"abstract":"Radio Modulation Classification is implemented by using the Deep Learning Techniques. The raw radio signals where as inputs and can automatically learn radio features and classification accuracy. The LSTM (Long short-term memory) based classifiers and CNN (Convolutional Neural Network) based classifiers were proposed in this paper. In the proposed work, two CNN based classifiers are implemented such as the LeNet classifier and the ResNet classifier. For visualizing the radio modulation, a class activation vector (w) is used. Finally in the proposed work, it is performed the classification by using the Deep learning models like CNN and LSTM based modulation classifiers. These deep learning models extract the important radio features that are used for classification. Here, the bench mark dataset RadioML2016.10a is used. This is an open dataset which contains the modulated signal I and Q values fewer than ten modulation categories. After evolution of proposed model with bench mark dataset, it is applied with real time data collected through the SDR Dongle receiver. From the obtained real time signal, the modulation categories have been classified and visualized the radio features extracted from the radio modulation classifiers.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135253026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The failure behavior of beam-to-column connections can be minimized or avoided to some extent by using PET waste fibers. With the change of composition, different seismic performances of concrete joints can be adjusted. FEM analysis was performed in ABAQUS software to compare the performance of concrete beam-to-column connections reinforced with conventional concrete fibers and waste PET under cyclic loading. The concrete mix is designed to achieve a concrete grade of M25. Seven figures of the external beam-to-column connections were modeled as a quarter of the architectural prototype. The first joint is conventional concrete and designed according to IS 1893 (Part 1):2022 and the reinforcement in the joint part are specified according to the ductility requirements of IS 13920:2016. Six other samples were designed to contain different PET waste fibers (0.25% to 1.50%) in the seam area. Beam-to-column connections have 0.75% to 1.25% PET fiber inclusions that have better performance in terms of strength, load-carrying capacity, energy dissipation capacity, joint shear strength, and ductility in the joint area. Incorporating PET waste fibers into concrete can provide the best solution for waste management, and also has the potential to reduce the cost of reinforced concrete by 15%-20% holds economic significance, and concrete with PET waste fibers indeed demonstrates better seismic performance, and could lead to increased safety and longevity of structures in seismic-prone areas. This suggests that experimental work or studies might have explored how these fibers affect the concrete's properties, strength, durability, and other characteristics.
{"title":"Study of PET Fiber Concrete in Beam-Column Joint under Cyclic Loading Using Finite Element Analysis","authors":"Nirav M. Patel, M. N. Patel, Tapsi D. Sata","doi":"10.5815/ijem.2023.05.05","DOIUrl":"https://doi.org/10.5815/ijem.2023.05.05","url":null,"abstract":"The failure behavior of beam-to-column connections can be minimized or avoided to some extent by using PET waste fibers. With the change of composition, different seismic performances of concrete joints can be adjusted. FEM analysis was performed in ABAQUS software to compare the performance of concrete beam-to-column connections reinforced with conventional concrete fibers and waste PET under cyclic loading. The concrete mix is designed to achieve a concrete grade of M25. Seven figures of the external beam-to-column connections were modeled as a quarter of the architectural prototype. The first joint is conventional concrete and designed according to IS 1893 (Part 1):2022 and the reinforcement in the joint part are specified according to the ductility requirements of IS 13920:2016. Six other samples were designed to contain different PET waste fibers (0.25% to 1.50%) in the seam area. Beam-to-column connections have 0.75% to 1.25% PET fiber inclusions that have better performance in terms of strength, load-carrying capacity, energy dissipation capacity, joint shear strength, and ductility in the joint area. Incorporating PET waste fibers into concrete can provide the best solution for waste management, and also has the potential to reduce the cost of reinforced concrete by 15%-20% holds economic significance, and concrete with PET waste fibers indeed demonstrates better seismic performance, and could lead to increased safety and longevity of structures in seismic-prone areas. This suggests that experimental work or studies might have explored how these fibers affect the concrete's properties, strength, durability, and other characteristics.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.
{"title":"An Automated Model for Sentimental Analysis Using Long Short-Term Memory-based Deep Learning Model","authors":"Shashank Mishra, Mukul Aggarwal, Shivam Yadav, Yashika Sharma","doi":"10.5815/ijem.2023.05.02","DOIUrl":"https://doi.org/10.5815/ijem.2023.05.02","url":null,"abstract":"A post, review, or news article's emotional tone can be automatically ascertained using sentiment analysis, a natural language processing approach. Sorting the text into positive, negative, or neutral categories is the aim of sentiment analysis. Many methods, including rule-based systems and machine learning algorithms, can be used to analyse sentiment, or deep learning models. These techniques typically involve analyzing various features of the text, such as word choice, sentence structure, and context, to identify the overall sentiment. Here long short-term memory-based deep learning is applied in this research for the model development purpose. Deeply interconnected neural networks are used in this method. Sentiment analysis can be used in many different applications, such as market research, brand reputation management, customer feedback analysis, and social media monitoring. It shows the use of sentiment analysis in a variety of fields and increases the need of technology to perform it on the existing machines.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The remote real-time patient monitoring system is a healthcare solution that uses ESP32 microcontroller and Blynk IoT cloud platform to monitor the vital signs of patients, including temperature, oxygen saturation, and heartbeat. The system also monitors the environmental factors surrounding the patient, such as temperature and humidity, and determines the GPS location of the patient. Additionally, the system includes an alarm device that alerts healthcare providers in case of emergency. In this paper we design system aims to provide continuous care and monitoring for patients, whether they are in hospitals, at home, or outside. By using Blynk IoT cloud platform, the system aims to reduce the percentage of medical errors and deaths by providing real-time monitoring of the patient's vital signs and environmental conditions, allowing healthcare providers to respond to emergencies quickly and efficiently. The IoT-based patient monitoring system consists of sensors that collect data on the patient's vital signs and environmental factors. The collected data is transmitted wirelessly to the Blynk IoT cloud platform, where it is processed and analyzed. Healthcare providers can access the data through the Blynk mobile app and receive alerts in case of any abnormalities or emergencies.
{"title":"Design Remote Monitoring System for Patients at Real-Time based on Internet of Things (IoT)","authors":"Satar Habib Mnaathr","doi":"10.5815/ijem.2023.05.01","DOIUrl":"https://doi.org/10.5815/ijem.2023.05.01","url":null,"abstract":"The remote real-time patient monitoring system is a healthcare solution that uses ESP32 microcontroller and Blynk IoT cloud platform to monitor the vital signs of patients, including temperature, oxygen saturation, and heartbeat. The system also monitors the environmental factors surrounding the patient, such as temperature and humidity, and determines the GPS location of the patient. Additionally, the system includes an alarm device that alerts healthcare providers in case of emergency. In this paper we design system aims to provide continuous care and monitoring for patients, whether they are in hospitals, at home, or outside. By using Blynk IoT cloud platform, the system aims to reduce the percentage of medical errors and deaths by providing real-time monitoring of the patient's vital signs and environmental conditions, allowing healthcare providers to respond to emergencies quickly and efficiently. The IoT-based patient monitoring system consists of sensors that collect data on the patient's vital signs and environmental factors. The collected data is transmitted wirelessly to the Blynk IoT cloud platform, where it is processed and analyzed. Healthcare providers can access the data through the Blynk mobile app and receive alerts in case of any abnormalities or emergencies.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Najiya Salim Khamis Al Mahrizi, Shaik Mazhar Hussain
The idea behind the paper is to transform the conventional school bag into a smart bag connected to the Internet of Things and aimed at elementary school pupils. Its concept uses GPS to follow the student's location; whenever it detects dangers like gas and smoke around the student, it sends a signal to the user. By lessening the weight on the student with the use of the load sensor, it can also determine the true weight of a bag. It can also be utilized on school buses in case a student is overlooked by notifying the driver of their presence via an LCD on the vehicle that is connected to the gas sensor. The results obtained have shown that the proposed research work successfully developed a prototype that is able to provide security and safety by delivering messages to the user, determining the actual weight of the bag, and tracking the student's location.
{"title":"A Smart Bag for School Students Safety and Security in Oman","authors":"Najiya Salim Khamis Al Mahrizi, Shaik Mazhar Hussain","doi":"10.5815/ijem.2023.05.03","DOIUrl":"https://doi.org/10.5815/ijem.2023.05.03","url":null,"abstract":"The idea behind the paper is to transform the conventional school bag into a smart bag connected to the Internet of Things and aimed at elementary school pupils. Its concept uses GPS to follow the student's location; whenever it detects dangers like gas and smoke around the student, it sends a signal to the user. By lessening the weight on the student with the use of the load sensor, it can also determine the true weight of a bag. It can also be utilized on school buses in case a student is overlooked by notifying the driver of their presence via an LCD on the vehicle that is connected to the gas sensor. The results obtained have shown that the proposed research work successfully developed a prototype that is able to provide security and safety by delivering messages to the user, determining the actual weight of the bag, and tracking the student's location.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-04DOI: 10.1007/s40684-023-00567-8
Chih-Hsing Chu, Jie-Ke Pan
{"title":"A Systematic Review on Extended Reality Applications for Sustainable Manufacturing Across the Product Lifecycle","authors":"Chih-Hsing Chu, Jie-Ke Pan","doi":"10.1007/s40684-023-00567-8","DOIUrl":"https://doi.org/10.1007/s40684-023-00567-8","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135553004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.1007/s40684-023-00563-y
Nicholas Goffin, Lewis C. R. Jones, John R. Tyrer, Jinglei Ouyang, Paul Mativenga, Lin Li, Elliot Woolley
Abstract In an increasingly technological world, energy efficiency in manufacturing is of great importance. While large manufacturing corporations have the resources to commission energy studies with minimal impact on operations, this is not true for small and medium enterprises (SME’s). These businesses will commonly only have a small number of laser processing cells; thus, to carry out an energy study can be extremely disruptive to normal operations. Since rising global energy costs also have the largest impact on small businesses who lack the benefit of economies of scale, they are simultaneously the most in need of improvements to energy efficiency, while also facing the strongest practical barriers to implementing them. In this study, a laser processing energy analysis methodology was designed to run simultaneously with normal operation and applied to a laser shim-cutting cell in a UK-based SME. This paper demonstrates the methodology for identifying operating states in a production environment and Specific Energy Consumption and Scope 2 CO 2 emissions results are analysed. The Processing state itself was the most impactful on overall energy performance, at 55% for single sheets of material, increasing to 71% when batch processing. Generating idealised data in this production environment is challenging with restrictions to isolating variables, these “real-world” limitations for conducting system energy analysis simultaneously with live production are also discussed to present recommendations for further analysis.
{"title":"Industrial Energy Optimisation: A Laser Cutting Case Study","authors":"Nicholas Goffin, Lewis C. R. Jones, John R. Tyrer, Jinglei Ouyang, Paul Mativenga, Lin Li, Elliot Woolley","doi":"10.1007/s40684-023-00563-y","DOIUrl":"https://doi.org/10.1007/s40684-023-00563-y","url":null,"abstract":"Abstract In an increasingly technological world, energy efficiency in manufacturing is of great importance. While large manufacturing corporations have the resources to commission energy studies with minimal impact on operations, this is not true for small and medium enterprises (SME’s). These businesses will commonly only have a small number of laser processing cells; thus, to carry out an energy study can be extremely disruptive to normal operations. Since rising global energy costs also have the largest impact on small businesses who lack the benefit of economies of scale, they are simultaneously the most in need of improvements to energy efficiency, while also facing the strongest practical barriers to implementing them. In this study, a laser processing energy analysis methodology was designed to run simultaneously with normal operation and applied to a laser shim-cutting cell in a UK-based SME. This paper demonstrates the methodology for identifying operating states in a production environment and Specific Energy Consumption and Scope 2 CO 2 emissions results are analysed. The Processing state itself was the most impactful on overall energy performance, at 55% for single sheets of material, increasing to 71% when batch processing. Generating idealised data in this production environment is challenging with restrictions to isolating variables, these “real-world” limitations for conducting system energy analysis simultaneously with live production are also discussed to present recommendations for further analysis.","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-28DOI: 10.1007/s40684-023-00560-1
Zhaojie Chen, Jin Xie, Quanpeng He, Dongsheng Ge, Kuo Lu, Chaolun Feng
{"title":"Study on Strain Energy Transfer and Efficiency in Spatial Micro-forming of Metal","authors":"Zhaojie Chen, Jin Xie, Quanpeng He, Dongsheng Ge, Kuo Lu, Chaolun Feng","doi":"10.1007/s40684-023-00560-1","DOIUrl":"https://doi.org/10.1007/s40684-023-00560-1","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135420763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-27DOI: 10.1007/s40684-023-00553-0
Sungmin Kim, Yunseong Ji, Young-Jun Sohn, Seunghee Woo, Seok-Hee Park, Namgee Jung, Yun Sik Kang, Sung-Dae Yim
{"title":"Performance Analysis of Membrane Electrode Assemblies with Various Compositions Under Non-uniform Large Area Operating Environments of Fuel Cells","authors":"Sungmin Kim, Yunseong Ji, Young-Jun Sohn, Seunghee Woo, Seok-Hee Park, Namgee Jung, Yun Sik Kang, Sung-Dae Yim","doi":"10.1007/s40684-023-00553-0","DOIUrl":"https://doi.org/10.1007/s40684-023-00553-0","url":null,"abstract":"","PeriodicalId":14238,"journal":{"name":"International Journal of Precision Engineering and Manufacturing-Green Technology","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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