Pub Date : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205748
A. Rafli, Muhammad Fauzan, Edy Purnomo, Endah Budi Purnomowati
One of the circular polarization advantages of circular polarized antennas is their ability to minimize the effects of polarization mismatch. By using a circularly polarized antenna, the polarization of the received signal can be effectively matched regardless of the orientation of the receiving antenna. A cross-slot 30-pointed star wideband antenna is implemented in this paper to achieve circularly polarized features. The cross-slot improved the antenna axial ratio bandwidth and gain at WLAN working frequency, giving the antenna circular polarization. The slot’s length and width are observed on its influence on impedance and axial ratio bandwidth.
{"title":"Circular Polarized 30-pointed Star Wideband Antenna with Cross Slot","authors":"A. Rafli, Muhammad Fauzan, Edy Purnomo, Endah Budi Purnomowati","doi":"10.1109/IAICT59002.2023.10205748","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205748","url":null,"abstract":"One of the circular polarization advantages of circular polarized antennas is their ability to minimize the effects of polarization mismatch. By using a circularly polarized antenna, the polarization of the received signal can be effectively matched regardless of the orientation of the receiving antenna. A cross-slot 30-pointed star wideband antenna is implemented in this paper to achieve circularly polarized features. The cross-slot improved the antenna axial ratio bandwidth and gain at WLAN working frequency, giving the antenna circular polarization. The slot’s length and width are observed on its influence on impedance and axial ratio bandwidth.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131675116","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205635
Abdel F. Chabi, Matheus Fontinele de Aguiar, Jordan Kalliure S. Carvalho, Vivianne de Aquino Rodrigues, João Vitor Da S. Campos, Bruna Maira Da S. Fonseca, J. O. D. Sousa
In recent years, Voice over IP (VoIP) call feature has become increasingly accessible to customers due to advancements in internet services and the emergence of voice call applications. However, identifying the variables that impact Voice over Long-Term Evolution (VoLTE) performance, particularly in quantifying end-user experience in the field and the effects of radio conditions and IP impairments on voice quality as measured by the Mean Opinion Score (MOS), presents challenges for carriers. MOS is a widely used metric for evaluating voice quality, and there is a significant commitment from both mobile device manufacturers and carriers to ensure superior voice quality during voice calls. To this end, MOS experiments are performed to evaluate the reliability of VoLTE calls, which is currently the best approach for measuring voice quality. In this study, we present MOS experimentation results in laboratory environments to homologate 146 different smartphone models. As results, we highlight the challenges associated with MOS testing in VoLTE calls under controlled conditions and discuss the primary issues found and how they were addressed. These experimental analyses offer substantial opportunities for enhancing the design and operation of audio quality during VoLTE calls and detail potentially improvements for 5GVoNR calls.
{"title":"Voice Quality Experience Evaluation: MOS Laboratory Test and Spectrum Analysis of Failures in VoLTE Calls","authors":"Abdel F. Chabi, Matheus Fontinele de Aguiar, Jordan Kalliure S. Carvalho, Vivianne de Aquino Rodrigues, João Vitor Da S. Campos, Bruna Maira Da S. Fonseca, J. O. D. Sousa","doi":"10.1109/IAICT59002.2023.10205635","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205635","url":null,"abstract":"In recent years, Voice over IP (VoIP) call feature has become increasingly accessible to customers due to advancements in internet services and the emergence of voice call applications. However, identifying the variables that impact Voice over Long-Term Evolution (VoLTE) performance, particularly in quantifying end-user experience in the field and the effects of radio conditions and IP impairments on voice quality as measured by the Mean Opinion Score (MOS), presents challenges for carriers. MOS is a widely used metric for evaluating voice quality, and there is a significant commitment from both mobile device manufacturers and carriers to ensure superior voice quality during voice calls. To this end, MOS experiments are performed to evaluate the reliability of VoLTE calls, which is currently the best approach for measuring voice quality. In this study, we present MOS experimentation results in laboratory environments to homologate 146 different smartphone models. As results, we highlight the challenges associated with MOS testing in VoLTE calls under controlled conditions and discuss the primary issues found and how they were addressed. These experimental analyses offer substantial opportunities for enhancing the design and operation of audio quality during VoLTE calls and detail potentially improvements for 5GVoNR calls.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125804579","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205863
Yit Hong Choo, Vu Le, Michael Johnstone, Doug Creighton, Himanshu Jindal, Kevin Tan
In line with Industry 4.0, various advanced technologies such as sensors, automation, and artificial intelligence (AI) methods have been leveraged to enhance maintenance processes in the rolling stock industry. In particular, AI techniques are useful for optimising maintenance scheduling and planning tasks for rolling stocks. This study focuses on the use of a metaheuristic method, namely an enhanced multi-objective Harris’ Hawk optimiser (MO-HHO), for optimising competing objectives based on data obtained from a railway maintenance company. The results of MO-HHO are evaluated and compared with those from other competing models. The findings demonstrate the usefulness of MO-HHO in tackling multi-objective train maintenance scheduling tasks in practical environments.
{"title":"Optimisation of Multi-objective Rolling Stock Maintenance Scheduling with Harris’ Hawk Optimiser","authors":"Yit Hong Choo, Vu Le, Michael Johnstone, Doug Creighton, Himanshu Jindal, Kevin Tan","doi":"10.1109/IAICT59002.2023.10205863","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205863","url":null,"abstract":"In line with Industry 4.0, various advanced technologies such as sensors, automation, and artificial intelligence (AI) methods have been leveraged to enhance maintenance processes in the rolling stock industry. In particular, AI techniques are useful for optimising maintenance scheduling and planning tasks for rolling stocks. This study focuses on the use of a metaheuristic method, namely an enhanced multi-objective Harris’ Hawk optimiser (MO-HHO), for optimising competing objectives based on data obtained from a railway maintenance company. The results of MO-HHO are evaluated and compared with those from other competing models. The findings demonstrate the usefulness of MO-HHO in tackling multi-objective train maintenance scheduling tasks in practical environments.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126751218","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205892
Yoga Sagama, A. Alamsyah
Online toxicity detection in Indonesian digital interactions poses a significant challenge due to the complexity and nuances of language. This study aims to evaluate the effectiveness of the BERT and RoBERTa language models, specifically IndoBERTweet, IndoBERT, and Indonesian RoBERTa, for identifying toxic content in Bahasa Indonesia. Our research methodology includes data collection, dataset pre-processing, data annotation, and model fine-tuning for multi-label classification tasks. The model performance is assessed using macro average of precision, recall, and F1-score. Our findings show that IndoBERTweet, fine-tuned under optimal hyperparameters (5e-5 learning rate, a batch size of 32, and three epochs), outperforms the other models with a precision of 0.85, recall of 0.94, and an F1-score of 0.89. These findings indicate that IndoBERTweet performs better in detecting and classifying online toxicity in Bahasa Indonesia. The study ’s implications extend to fostering a safer and healthier online environment for Indonesian users, while also providing a foundation for future research exploring additional models, hyperparameter optimizations, and techniques for enhancing toxicity detection and classification in the Indonesian language.
{"title":"Multi-Label Classification of Indonesian Online Toxicity using BERT and RoBERTa","authors":"Yoga Sagama, A. Alamsyah","doi":"10.1109/IAICT59002.2023.10205892","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205892","url":null,"abstract":"Online toxicity detection in Indonesian digital interactions poses a significant challenge due to the complexity and nuances of language. This study aims to evaluate the effectiveness of the BERT and RoBERTa language models, specifically IndoBERTweet, IndoBERT, and Indonesian RoBERTa, for identifying toxic content in Bahasa Indonesia. Our research methodology includes data collection, dataset pre-processing, data annotation, and model fine-tuning for multi-label classification tasks. The model performance is assessed using macro average of precision, recall, and F1-score. Our findings show that IndoBERTweet, fine-tuned under optimal hyperparameters (5e-5 learning rate, a batch size of 32, and three epochs), outperforms the other models with a precision of 0.85, recall of 0.94, and an F1-score of 0.89. These findings indicate that IndoBERTweet performs better in detecting and classifying online toxicity in Bahasa Indonesia. The study ’s implications extend to fostering a safer and healthier online environment for Indonesian users, while also providing a foundation for future research exploring additional models, hyperparameter optimizations, and techniques for enhancing toxicity detection and classification in the Indonesian language.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130235070","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205930
Faisal Najib, Yusriadi, I. Mustika, S. Sulistyo
The weather has become an important part of people’s daily activities; therefore, many people need faster, more complete, and more accurate information about its condition. Accurate weather predictions can be used to solve problems arising from weather effects. Compared to other methods, the Artificial Neural Network (ANN) method is deemed more efficient in fast computing and is able to handle unstable data in terms of weather forecast data. However, ANN has limitations in studying classification patterns if the dataset has large data and high dimensions. To manage this limitation, a feature selection method is needed to enable the ANN to produce accurate predictions. Several experiments were carried out to obtain the optimal architecture and produce accurate predictions. The proposed method only reduces the accuracy value to less than 1% and the loss value to less than 0.01 in both tested datasets. With these results, it can be said that the proposed method is feasible to be used as an improved method for the ANN algorithm.
{"title":"Rainfall Prediction using Artificial Neural Network with Forward Selection Method","authors":"Faisal Najib, Yusriadi, I. Mustika, S. Sulistyo","doi":"10.1109/IAICT59002.2023.10205930","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205930","url":null,"abstract":"The weather has become an important part of people’s daily activities; therefore, many people need faster, more complete, and more accurate information about its condition. Accurate weather predictions can be used to solve problems arising from weather effects. Compared to other methods, the Artificial Neural Network (ANN) method is deemed more efficient in fast computing and is able to handle unstable data in terms of weather forecast data. However, ANN has limitations in studying classification patterns if the dataset has large data and high dimensions. To manage this limitation, a feature selection method is needed to enable the ANN to produce accurate predictions. Several experiments were carried out to obtain the optimal architecture and produce accurate predictions. The proposed method only reduces the accuracy value to less than 1% and the loss value to less than 0.01 in both tested datasets. With these results, it can be said that the proposed method is feasible to be used as an improved method for the ANN algorithm.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130395643","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205875
Jiann-Liang Chen, Bagus Tri Atmaja, Candra Ahmadi, Jian-Chang Hsu
In the digital era, internet reliance has transformed daily life, potentially exposing security vulnerabilities. In addition, the proliferation of network devices has increased the risk of cyber-attacks, posing threats to individuals and organizations. This study develops a predictive system for Security Functional Requirements (SFRs) and Evaluation Assurance Level (EAL) using machine learning based on the ISO/IEC15408 Common Criteria for Information Technology Security Certification (EUCC), a global ICT product evaluation framework. Utilizing an XML parser, ElementTree, the research focuses on the Common Criteria as the security target and analyzes two datasets: SFRs and EAL. The decision tree algorithm yields an EAL prediction model with 100% accuracy. A random forest algorithm generates an SFR prediction model with 65% accuracy. The lower accuracy is attributed to diverse device specifications. An Expert system manages multiple cases to predict the EAL level. The study also produces a Security Target document with EAL and SFRs predictions, facilitated by a PySide6-developed user interface that integrates the prediction system. This research significantly enhances ICT security, providing a robust tool for improving ICT product security and offering valuable insights for manufacturers and developers through the high accuracy of the EAL prediction model and comprehensive analysis of the SFR dataset
{"title":"Security Document Generation for Common Criteria Using Machine Learning and Rule-based Expert System","authors":"Jiann-Liang Chen, Bagus Tri Atmaja, Candra Ahmadi, Jian-Chang Hsu","doi":"10.1109/IAICT59002.2023.10205875","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205875","url":null,"abstract":"In the digital era, internet reliance has transformed daily life, potentially exposing security vulnerabilities. In addition, the proliferation of network devices has increased the risk of cyber-attacks, posing threats to individuals and organizations. This study develops a predictive system for Security Functional Requirements (SFRs) and Evaluation Assurance Level (EAL) using machine learning based on the ISO/IEC15408 Common Criteria for Information Technology Security Certification (EUCC), a global ICT product evaluation framework. Utilizing an XML parser, ElementTree, the research focuses on the Common Criteria as the security target and analyzes two datasets: SFRs and EAL. The decision tree algorithm yields an EAL prediction model with 100% accuracy. A random forest algorithm generates an SFR prediction model with 65% accuracy. The lower accuracy is attributed to diverse device specifications. An Expert system manages multiple cases to predict the EAL level. The study also produces a Security Target document with EAL and SFRs predictions, facilitated by a PySide6-developed user interface that integrates the prediction system. This research significantly enhances ICT security, providing a robust tool for improving ICT product security and offering valuable insights for manufacturers and developers through the high accuracy of the EAL prediction model and comprehensive analysis of the SFR dataset","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125829406","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205740
K. S. Shanthini, S. N. George, S. George, B. Devassy
Hyperspectral imaging offers the capacity to quickly and noninvasively monitor a food product’s physical, chemical and morphological properties. Specim IQ is a handheld push broom camera with basic data handling and data analysis capabilities within the camera software. However, the recordings of the Specim IQ camera showed a line pattern (stripes) that was evident in all images. Stripes significantly reduce the visual quality of the images and lower the results of further processing. Hence an efficient destriping model is developed, which specifically addresses this issue. The proposed model uses a spatial gradient term to analyze the directional characteristics and group sparsity to describe the structural characteristics of the stripe component. In addition to this, a spatial spectral total variation regularization is used to ensure piecewise smoothness in the spatial and spectral domains and to remove Gaussian noise. The ensuing optimisation problem is solved using the alternating direction method of multipliers (ADMM). The proposed method is tested in real stripe noise environments, and the findings demonstrate that it outperforms some of the best approaches in terms of visual quality and quantitative evaluations. When compared with the other approaches, the proposed method attained the highest noise reduction (NR) and lowest mean relative deviation (MRD) values (NR=1.67, MRD=1.02%).
{"title":"Stripe Removal from Hyperspectral Food Images acquired by Handheld Camera using ℓ2,1 Norm Minimization and SSTV Regularization","authors":"K. S. Shanthini, S. N. George, S. George, B. Devassy","doi":"10.1109/IAICT59002.2023.10205740","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205740","url":null,"abstract":"Hyperspectral imaging offers the capacity to quickly and noninvasively monitor a food product’s physical, chemical and morphological properties. Specim IQ is a handheld push broom camera with basic data handling and data analysis capabilities within the camera software. However, the recordings of the Specim IQ camera showed a line pattern (stripes) that was evident in all images. Stripes significantly reduce the visual quality of the images and lower the results of further processing. Hence an efficient destriping model is developed, which specifically addresses this issue. The proposed model uses a spatial gradient term to analyze the directional characteristics and group sparsity to describe the structural characteristics of the stripe component. In addition to this, a spatial spectral total variation regularization is used to ensure piecewise smoothness in the spatial and spectral domains and to remove Gaussian noise. The ensuing optimisation problem is solved using the alternating direction method of multipliers (ADMM). The proposed method is tested in real stripe noise environments, and the findings demonstrate that it outperforms some of the best approaches in terms of visual quality and quantitative evaluations. When compared with the other approaches, the proposed method attained the highest noise reduction (NR) and lowest mean relative deviation (MRD) values (NR=1.67, MRD=1.02%).","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121709242","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205862
Rehan Mohammed, Vu Le, D. Creighton, Anwar Hosen
Machine learning algorithms are widely used in data-driven predictive maintenance to address prognostics of the condition of lithium-ion batteries over their cycle life. However, selecting relevant features remains a critical issue when predicting the remaining useful life (RUL) of these batteries using data-driven approaches. This issue can significantly affect the performance of machine learning algorithms and lead to time loss. In this paper, we investigate the effectiveness of two feature selection techniques that use the Recursive Feature Elimination (RFE) method for predicting the RUL of fast-charged lithium-ion batteries. We use the RFE-LASSO and RFE-XGB methods for feature selection and the Elastic Net and Relevance Vector Regression models for RUL prediction. Experimental results using Nature Energy’s battery dataset show that the RFEXGB feature selection method can provide stable prediction performance using 33 or more features. Furthermore, when integrated with the Elastic Net model, RFE-XGB achieves the lowest prediction error at a train-test split of 80%-20%.
{"title":"Feature Selection for Cycle Life Prediction of Fast-Charged Lithium-ion Batteries","authors":"Rehan Mohammed, Vu Le, D. Creighton, Anwar Hosen","doi":"10.1109/IAICT59002.2023.10205862","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205862","url":null,"abstract":"Machine learning algorithms are widely used in data-driven predictive maintenance to address prognostics of the condition of lithium-ion batteries over their cycle life. However, selecting relevant features remains a critical issue when predicting the remaining useful life (RUL) of these batteries using data-driven approaches. This issue can significantly affect the performance of machine learning algorithms and lead to time loss. In this paper, we investigate the effectiveness of two feature selection techniques that use the Recursive Feature Elimination (RFE) method for predicting the RUL of fast-charged lithium-ion batteries. We use the RFE-LASSO and RFE-XGB methods for feature selection and the Elastic Net and Relevance Vector Regression models for RUL prediction. Experimental results using Nature Energy’s battery dataset show that the RFEXGB feature selection method can provide stable prediction performance using 33 or more features. Furthermore, when integrated with the Elastic Net model, RFE-XGB achieves the lowest prediction error at a train-test split of 80%-20%.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122452125","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205656
O. Vasilevskyi, V. Didych, O. Zabula, V. Sarana, E. Popovici
Evaluating the measurement accuracy of sensors is one of the most important tasks in the development of support systems for Industry 4.0. The study of accuracy is proposed to be carried out using measurement models by expanding them into a Taylor series. From the components of the Taylor series, equations are obtained that describe the sensitivity, additive and multiplicative errors of the measuring instrument. A mathematical model is also proposed that allows you to recalculate the multiplicative and additive errors of the measuring instrument into the uncertainty. The proposed metrological models are tested on the example of the expansion of the transformation equation, which describes the operation of the means for measuring the activity of ions. In absolute units of measurement of ion activity, the multiplicative and additive errors are 0.047pX each in the measurement range from 0.2 to 7.5pX. Using proposed mathematical model for converting these errors into uncertainty, we obtained the standard type B uncertainty, which is 0.064pX.
{"title":"Developing and using measurement models to assess accuracy: using the example of measurements of the activity of ions","authors":"O. Vasilevskyi, V. Didych, O. Zabula, V. Sarana, E. Popovici","doi":"10.1109/IAICT59002.2023.10205656","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205656","url":null,"abstract":"Evaluating the measurement accuracy of sensors is one of the most important tasks in the development of support systems for Industry 4.0. The study of accuracy is proposed to be carried out using measurement models by expanding them into a Taylor series. From the components of the Taylor series, equations are obtained that describe the sensitivity, additive and multiplicative errors of the measuring instrument. A mathematical model is also proposed that allows you to recalculate the multiplicative and additive errors of the measuring instrument into the uncertainty. The proposed metrological models are tested on the example of the expansion of the transformation equation, which describes the operation of the means for measuring the activity of ions. In absolute units of measurement of ion activity, the multiplicative and additive errors are 0.047pX each in the measurement range from 0.2 to 7.5pX. Using proposed mathematical model for converting these errors into uncertainty, we obtained the standard type B uncertainty, which is 0.064pX.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123292964","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 : 2023-07-13DOI: 10.1109/IAICT59002.2023.10205624
Itsnanta Muhammad Fauzan, D. Gunawan
Post-merger between MNO-1 and MNO-2 become new entity ‘MNO-M’, there are some obligations from government: It is required to make a frequency band return of $2 times 5$ MHz at 2.1 GHz, adding new sites for services until 2025, and to improve its Quality of Service (QoS). On top of those obligations, during the network consolidation, there are some challenges such as big network infrastructure complexity from MNO-1 & MNO-2, network consolidation must be done on the live network which potentially impact to customer experience, and many 3G sites which require to be sunset as part of government compliance. The method that is used in this paper is by analysis secondary data from MNO-M and review of scientific literature as supporting reference. The strategy to be able to face the challenges by building a platform and tool that will provide end-to-end visibility to multi-operator networks. This paper introduces a new digital operation concept and solution named DIAMON (Digital Intelligence Automation Multi-Operator Network). DIAMON integrates all of Network Elements (NEs) multi-vendor end-to-end and provides full visibility for network operations management (network monitoring, performance management, service quality, and customer experience management). A strong digital operation platform and tool through DIAMON is also very important to support the multi-operator network to face network consolidation challenges and provide excellence operational services in Indonesia.
{"title":"Network Consolidation Challenges After MNO-1 & MNO-2 Merger and Strategy for Operational Excellence in Indonesia Using DIAMON","authors":"Itsnanta Muhammad Fauzan, D. Gunawan","doi":"10.1109/IAICT59002.2023.10205624","DOIUrl":"https://doi.org/10.1109/IAICT59002.2023.10205624","url":null,"abstract":"Post-merger between MNO-1 and MNO-2 become new entity ‘MNO-M’, there are some obligations from government: It is required to make a frequency band return of $2 times 5$ MHz at 2.1 GHz, adding new sites for services until 2025, and to improve its Quality of Service (QoS). On top of those obligations, during the network consolidation, there are some challenges such as big network infrastructure complexity from MNO-1 & MNO-2, network consolidation must be done on the live network which potentially impact to customer experience, and many 3G sites which require to be sunset as part of government compliance. The method that is used in this paper is by analysis secondary data from MNO-M and review of scientific literature as supporting reference. The strategy to be able to face the challenges by building a platform and tool that will provide end-to-end visibility to multi-operator networks. This paper introduces a new digital operation concept and solution named DIAMON (Digital Intelligence Automation Multi-Operator Network). DIAMON integrates all of Network Elements (NEs) multi-vendor end-to-end and provides full visibility for network operations management (network monitoring, performance management, service quality, and customer experience management). A strong digital operation platform and tool through DIAMON is also very important to support the multi-operator network to face network consolidation challenges and provide excellence operational services in Indonesia.","PeriodicalId":339796,"journal":{"name":"2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","volume":"376 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123410550","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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