Facing a constantly evolving industry and customers that are becoming more fastidious, companies are seeking to adapt their manufacturing methods to meet market demands [...]
面对不断发展的行业和越来越挑剔的客户，公司正在寻求调整其制造方法以满足市场需求〔…〕
{"title":"New Innovation, Sustainability, and Resilience Challenges in the X.0 Era","authors":"M. Gallab, Mario Di Nardo","doi":"10.3390/asi6020039","DOIUrl":"https://doi.org/10.3390/asi6020039","url":null,"abstract":"Facing a constantly evolving industry and customers that are becoming more fastidious, companies are seeking to adapt their manufacturing methods to meet market demands [...]","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43457619","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}
E. Pietroni, S. Menconero, Carolina Botti, Francesca Ghedini
Commissioned to ALES spa by the Ministry of Culture (MiC), the e-Archeo project was born with the intention of enhancing and promoting knowledge of some Italian archaeological sites with a considerable narrative potential that has not yet been fully expressed. The main principle that guided the choice of the sites and the contents was of illustrating the various cultures and types of settlements present in the Italian territory. Eight sites were chosen, spread across the national territory from north to south, founded by Etruscans, Greeks, Phoenicians, natives and Romans. e-Archeo has developed multimedia, integrated and multi-channel solutions for various uses and types of audiences, adopting both scientific and narrative and emotional languages. Particular attention was paid to multimedia accessibility, technological sustainability and open science. The e-Archeo project was born from a strong synergy between public entities, research bodies and private industries thanks to the collaboration of MiC and ALES with the CNR ISPC, 10 Italian Universities, 12 Creative Industries and the Italian National Television (RAI). This exceptional and unusual condition made it possible to realise all the project’s high-quality contents and several outputs in only one and a half years.
{"title":"e-Archeo: A Pilot National Project to Valorize Italian Archaeological Parks through Digital and Virtual Reality Technologies","authors":"E. Pietroni, S. Menconero, Carolina Botti, Francesca Ghedini","doi":"10.3390/asi6020038","DOIUrl":"https://doi.org/10.3390/asi6020038","url":null,"abstract":"Commissioned to ALES spa by the Ministry of Culture (MiC), the e-Archeo project was born with the intention of enhancing and promoting knowledge of some Italian archaeological sites with a considerable narrative potential that has not yet been fully expressed. The main principle that guided the choice of the sites and the contents was of illustrating the various cultures and types of settlements present in the Italian territory. Eight sites were chosen, spread across the national territory from north to south, founded by Etruscans, Greeks, Phoenicians, natives and Romans. e-Archeo has developed multimedia, integrated and multi-channel solutions for various uses and types of audiences, adopting both scientific and narrative and emotional languages. Particular attention was paid to multimedia accessibility, technological sustainability and open science. The e-Archeo project was born from a strong synergy between public entities, research bodies and private industries thanks to the collaboration of MiC and ALES with the CNR ISPC, 10 Italian Universities, 12 Creative Industries and the Italian National Television (RAI). This exceptional and unusual condition made it possible to realise all the project’s high-quality contents and several outputs in only one and a half years.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42777653","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}
B. Thippeswamy, Mohamed Ghouse, Shanawaz Ahamed Jafarabad, Murtuza Ahamed Khan Mohammed, Ketema Adere, Prabhu Prasad B. M., P. B. N.
Mobile computing is one of the significant opportunities that can be used for various practical applications in numerous fields in real life. Due to inherent characteristics of ubiquitous computing, devices can gather numerous types of data that led to innovative applications in many fields with a unique emerging prototype known as Crowd sensing. Here, the involvement of people is one of the important features and their mobility provides an exclusive opportunity to collect and transmit the data over a substantial geographical area. Thus, we put forward novel idea about Quality of Information (QOI) with unique parameters with opportunistic uniqueness of people’s mobility in terms of sensing and transmission. Additionally, we propose some of the viable improved ideas about the competent opportunistic data collection through efficient techniques. This work also considered some of the open issues mentioned by previous related works.
{"title":"QACM: Quality Aware Crowd Sensing in Mobile Computing","authors":"B. Thippeswamy, Mohamed Ghouse, Shanawaz Ahamed Jafarabad, Murtuza Ahamed Khan Mohammed, Ketema Adere, Prabhu Prasad B. M., P. B. N.","doi":"10.3390/asi6020037","DOIUrl":"https://doi.org/10.3390/asi6020037","url":null,"abstract":"Mobile computing is one of the significant opportunities that can be used for various practical applications in numerous fields in real life. Due to inherent characteristics of ubiquitous computing, devices can gather numerous types of data that led to innovative applications in many fields with a unique emerging prototype known as Crowd sensing. Here, the involvement of people is one of the important features and their mobility provides an exclusive opportunity to collect and transmit the data over a substantial geographical area. Thus, we put forward novel idea about Quality of Information (QOI) with unique parameters with opportunistic uniqueness of people’s mobility in terms of sensing and transmission. Additionally, we propose some of the viable improved ideas about the competent opportunistic data collection through efficient techniques. This work also considered some of the open issues mentioned by previous related works.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44862498","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}
S. S. Sohail, Asfia Aziz, R. Ali, S. H. Hasan, D. Madsen, M. Alam
In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.
{"title":"Human-Centric Aggregation via Ordered Weighted Aggregation for Ranked Recommendation in Recommender Systems","authors":"S. S. Sohail, Asfia Aziz, R. Ali, S. H. Hasan, D. Madsen, M. Alam","doi":"10.3390/asi6020036","DOIUrl":"https://doi.org/10.3390/asi6020036","url":null,"abstract":"In this paper, we propose an approach to recommender systems that incorporates human-centric aggregation via Ordered Weighted Aggregation (OWA) to prioritize the suggestions of expert rankers over the usual recommendations. We advocate for ranked recommendations where rankers are assigned weights based on their ranking position. Our approach recommends books to university students using linguistic data summaries and the OWA technique. We assign higher weights to the highest-ranked university to improve recommendation quality. Our approach is evaluated on eight parameters and outperforms traditional recommender systems. We claim that our approach saves storage space and solves the cold start problem by not requiring prior user preferences. Our proposed scheme can be applied to decision-making problems, especially in the context of recommender systems, and offers a new direction for human-specific task aggregation in recommendation research.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44760743","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}
Kaustubh Mani Tripathi, P. Kamat, S. Patil, Ruchi Jayaswal, Swati Ahirrao, K. Kotecha
This research paper focuses on developing an effective gesture-to-text translation system using state-of-the-art computer vision techniques. The existing research on sign language translation has yet to utilize skin masking, edge detection, and feature extraction techniques to their full potential. Therefore, this study employs the speeded-up robust features (SURF) model for feature extraction, which is resistant to variations such as rotation, perspective scaling, and occlusion. The proposed system utilizes a bag of visual words (BoVW) model for gesture-to-text conversion. The study uses a dataset of 42,000 photographs consisting of alphabets (A–Z) and numbers (1–9), divided into 35 classes with 1200 shots per class. The pre-processing phase includes skin masking, where the RGB color space is converted to the HSV color space, and Canny edge detection is used for sharp edge detection. The SURF elements are grouped and converted to a visual language using the K-means mini-batch clustering technique. The proposed system’s performance is evaluated using several machine learning algorithms such as naïve Bayes, logistic regression, K nearest neighbors, support vector machine, and convolutional neural network. All the algorithms benefited from SURF, and the system’s accuracy is promising, ranging from 79% to 92%. This research study not only presents the development of an effective gesture-to-text translation system but also highlights the importance of using skin masking, edge detection, and feature extraction techniques to their full potential in sign language translation. The proposed system aims to bridge the communication gap between individuals who cannot speak and those who cannot understand Indian Sign Language (ISL).
{"title":"Gesture-to-Text Translation Using SURF for Indian Sign Language","authors":"Kaustubh Mani Tripathi, P. Kamat, S. Patil, Ruchi Jayaswal, Swati Ahirrao, K. Kotecha","doi":"10.3390/asi6020035","DOIUrl":"https://doi.org/10.3390/asi6020035","url":null,"abstract":"This research paper focuses on developing an effective gesture-to-text translation system using state-of-the-art computer vision techniques. The existing research on sign language translation has yet to utilize skin masking, edge detection, and feature extraction techniques to their full potential. Therefore, this study employs the speeded-up robust features (SURF) model for feature extraction, which is resistant to variations such as rotation, perspective scaling, and occlusion. The proposed system utilizes a bag of visual words (BoVW) model for gesture-to-text conversion. The study uses a dataset of 42,000 photographs consisting of alphabets (A–Z) and numbers (1–9), divided into 35 classes with 1200 shots per class. The pre-processing phase includes skin masking, where the RGB color space is converted to the HSV color space, and Canny edge detection is used for sharp edge detection. The SURF elements are grouped and converted to a visual language using the K-means mini-batch clustering technique. The proposed system’s performance is evaluated using several machine learning algorithms such as naïve Bayes, logistic regression, K nearest neighbors, support vector machine, and convolutional neural network. All the algorithms benefited from SURF, and the system’s accuracy is promising, ranging from 79% to 92%. This research study not only presents the development of an effective gesture-to-text translation system but also highlights the importance of using skin masking, edge detection, and feature extraction techniques to their full potential in sign language translation. The proposed system aims to bridge the communication gap between individuals who cannot speak and those who cannot understand Indian Sign Language (ISL).","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49225109","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}
A. Al-Dujaili, A. Humaidi, Ziyad T. Allawi, M. E. Sadiq
This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controller addresses the suppression problem of a single-degree-of-freedom (SDOF) building model, and an earthquake control scenario was conducted and simulated on the basis of earthquake acceleration data recorded from the El Centro Imperial Valley Earthquake. The effectiveness of the adaptive synergetic control was verified and assessed via numerical simulation, and a comparison study was conducted between the adaptive and classical versions of synergetic control (SC). The vibration suppression index was used to evaluate both controllers. The numerical simulation showed the capability of the proposed adaptive controller to stabilize and to suppress the vibration of a building subjected to earthquake. In addition, the adaptive controller successfully kept the estimated viscosity and stiffness coefficients bounded.
本文提出了一种基于协同控制理论的自适应控制方案，用于抑制建筑结构的地震振动。所提出的控制器的控制关键是基于磁流变阻尼器，该阻尼器为建筑物提供支撑。基于李雅普诺夫稳定性分析，建立了振动建筑物在刚度和粘滞系数变化情况下的自适应协同控制策略。为了保证受控结构的稳定性，提出了ASC的控制律和自适应律。所提出的控制器解决了单自由度（SDOF）建筑模型的抑制问题，并根据El Centro Imperial Valley地震记录的地震加速度数据进行了地震控制场景的模拟。通过数值模拟验证和评估了自适应协同控制的有效性，并对自适应协同控制和经典协同控制进行了比较研究。振动抑制指数用于评估两个控制器。数值模拟表明，所提出的自适应控制器具有稳定和抑制地震作用下建筑物振动的能力。此外，自适应控制器成功地使估计的粘度和刚度系数保持有界。
{"title":"Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control","authors":"A. Al-Dujaili, A. Humaidi, Ziyad T. Allawi, M. E. Sadiq","doi":"10.3390/asi6020034","DOIUrl":"https://doi.org/10.3390/asi6020034","url":null,"abstract":"This study presents an adaptive control scheme based on synergetic control theory for suppressing the vibration of building structures due to earthquake. The control key for the proposed controller is based on a magneto-rheological (MR) damper, which supports the building. According to Lyapunov-based stability analysis, an adaptive synergetic control (ASC) strategy was established under variation of the stiffness and viscosity coefficients in the vibrated building. The control and adaptive laws of the ASC were developed to ensure the stability of the controlled structure. The proposed controller addresses the suppression problem of a single-degree-of-freedom (SDOF) building model, and an earthquake control scenario was conducted and simulated on the basis of earthquake acceleration data recorded from the El Centro Imperial Valley Earthquake. The effectiveness of the adaptive synergetic control was verified and assessed via numerical simulation, and a comparison study was conducted between the adaptive and classical versions of synergetic control (SC). The vibration suppression index was used to evaluate both controllers. The numerical simulation showed the capability of the proposed adaptive controller to stabilize and to suppress the vibration of a building subjected to earthquake. In addition, the adaptive controller successfully kept the estimated viscosity and stiffness coefficients bounded.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44516640","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}
K. Deghoum, Mohammed T Gherbi, Hakim S. Sultan, A. N. Jameel Al-Tamimi, A. Abed, O. Abdullah, H. Mechakra, A. Boukhari
In this article, the model of a 5 kW small wind turbine blade is developed and improved. Emphasis has been placed on improving the blade’s efficiency and aerodynamics and selecting the most optimal material for the wind blade. The QBlade software was used to enhance the chord and twist. Also, a new finite element model was developed using the ANSYS software to analyze the structure and modal problems of the wind blade. The results presented the wind blade’s von Mises stresses and deformations using three different materials (Carbon/epoxy, E-Glass/epoxy, and braided composite). The modal analysis results presented the natural frequencies and mode shapes for each material. It was found, based on the results, that the maximum deflections of E-glass, braided composite and carbon fiber were 46.46 mm, 33.54 mm, and 18.29 mm, respectively.
{"title":"Optimization of Small Horizontal Axis Wind Turbines Based on Aerodynamic, Steady-State, and Dynamic Analyses","authors":"K. Deghoum, Mohammed T Gherbi, Hakim S. Sultan, A. N. Jameel Al-Tamimi, A. Abed, O. Abdullah, H. Mechakra, A. Boukhari","doi":"10.3390/asi6020033","DOIUrl":"https://doi.org/10.3390/asi6020033","url":null,"abstract":"In this article, the model of a 5 kW small wind turbine blade is developed and improved. Emphasis has been placed on improving the blade’s efficiency and aerodynamics and selecting the most optimal material for the wind blade. The QBlade software was used to enhance the chord and twist. Also, a new finite element model was developed using the ANSYS software to analyze the structure and modal problems of the wind blade. The results presented the wind blade’s von Mises stresses and deformations using three different materials (Carbon/epoxy, E-Glass/epoxy, and braided composite). The modal analysis results presented the natural frequencies and mode shapes for each material. It was found, based on the results, that the maximum deflections of E-glass, braided composite and carbon fiber were 46.46 mm, 33.54 mm, and 18.29 mm, respectively.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43236551","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}
Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits ovarian follicle development causing symptoms like obesity, acne, infertility, and hirsutism. Artificial Intelligence (AI) has revolutionized healthcare, contributing remarkably to science and engineering domains. Therefore, we have demonstrated an AI approach using heterogeneous Machine Learning (ML) and Deep Learning (DL) classifiers to predict PCOS among fertile patients. We used an Open-source dataset of 541 patients from Kerala, India. Among all the classifiers, the final multi-stack of ML models performed best with accuracy, precision, recall, and F1-score of 98%, 97%, 98%, and 98%. Explainable AI (XAI) techniques make model predictions understandable, interpretable, and trustworthy. Hence, we have utilized XAI techniques such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance with Random Forest for explaining tree-based classifiers. The motivation of this study is to accurately detect PCOS in patients while simultaneously proposing an automated screening architecture with explainable machine learning tools to assist medical professionals in decision-making.
多囊卵巢综合征(PCOS)是一种复杂的疾病，主要表现为生化性雄激素分泌过多、月经少、无排卵，在某些情况下，还会出现卵巢微囊肿。这种内分泌疾病抑制卵巢卵泡发育，导致肥胖、痤疮、不孕症和多毛症等症状。人工智能(AI)已经彻底改变了医疗保健，为科学和工程领域做出了巨大贡献。因此，我们展示了一种人工智能方法，使用异构机器学习(ML)和深度学习(DL)分类器来预测生育患者的PCOS。我们使用了来自印度喀拉拉邦的541名患者的开源数据集。在所有分类器中，最终的多堆栈ML模型表现最好，准确率、精密度、召回率和f1得分分别为98%、97%、98%和98%。可解释的AI (XAI)技术使模型预测可理解、可解释和可信赖。因此，我们利用了XAI技术，如SHapley Additive Values (SHapley Additive Values)、LIME (Local Interpretable Model Explainer)、ELI5、Qlattice和feature importance with Random Forest来解释基于树的分类器。本研究的动机是准确地检测PCOS患者，同时提出一种具有可解释机器学习工具的自动筛查架构，以协助医疗专业人员做出决策。
{"title":"A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome","authors":"Varada Vivek Khanna, Krishnaraj Chadaga, Niranajana Sampathila, Srikanth Prabhu, Venkatesh Bhandage, Govardhan Hegde","doi":"10.3390/asi6020032","DOIUrl":"https://doi.org/10.3390/asi6020032","url":null,"abstract":"Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits ovarian follicle development causing symptoms like obesity, acne, infertility, and hirsutism. Artificial Intelligence (AI) has revolutionized healthcare, contributing remarkably to science and engineering domains. Therefore, we have demonstrated an AI approach using heterogeneous Machine Learning (ML) and Deep Learning (DL) classifiers to predict PCOS among fertile patients. We used an Open-source dataset of 541 patients from Kerala, India. Among all the classifiers, the final multi-stack of ML models performed best with accuracy, precision, recall, and F1-score of 98%, 97%, 98%, and 98%. Explainable AI (XAI) techniques make model predictions understandable, interpretable, and trustworthy. Hence, we have utilized XAI techniques such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance with Random Forest for explaining tree-based classifiers. The motivation of this study is to accurately detect PCOS in patients while simultaneously proposing an automated screening architecture with explainable machine learning tools to assist medical professionals in decision-making.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44720288","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}
Ilyushin Pavel Yurievich, Vyatkin Kirill Andreevich, Kozlov Anton Vadimovich
The modern oil industry is characterized by a strong trend towards the digitalization of all technological processes. At the same time, during the transition of oil fields to the later stages of development, the issues of optimizing the consumed electricity become relevant. The purpose of this work is to develop a digital automated system for distributed control of production wells using elements of machine learning. The structure of information exchange within the framework of the automated system being created, consisting of three levels of automation, is proposed. Management of the extractive fund is supposed to be based on the work of four modules. The “Complications” module analyzes the operation of oil wells and peripheral equipment and, according to the embedded algorithms, evaluates the cause of the deviation, ways to eliminate it and the effectiveness of each method based on historical data. The “Power Consumption Optimization” module allows integrating algorithms into the well control system to reduce energy consumption by maintaining the most energy-efficient operation of pumping equipment or optimizing its operation time. The module “Ensuring the well flow rate” allows you to analyze and determine the reasons for the decrease in production rate, taking into account the parameters of the operation of adjacent wells. The Equipment Anomaly Prediction module is based on machine learning and helps reduce equipment downtime by predicting and automatically responding to potential deviations. As a result of using the proposed system, many goals of the oil company are achieved: specific energy consumption, oil shortages, and accident rate are reduced, while reducing the labor costs of engineering and technological personnel for processing the operation parameters of all process equipment.
{"title":"Development of a Digital Well Management System","authors":"Ilyushin Pavel Yurievich, Vyatkin Kirill Andreevich, Kozlov Anton Vadimovich","doi":"10.3390/asi6010031","DOIUrl":"https://doi.org/10.3390/asi6010031","url":null,"abstract":"The modern oil industry is characterized by a strong trend towards the digitalization of all technological processes. At the same time, during the transition of oil fields to the later stages of development, the issues of optimizing the consumed electricity become relevant. The purpose of this work is to develop a digital automated system for distributed control of production wells using elements of machine learning. The structure of information exchange within the framework of the automated system being created, consisting of three levels of automation, is proposed. Management of the extractive fund is supposed to be based on the work of four modules. The “Complications” module analyzes the operation of oil wells and peripheral equipment and, according to the embedded algorithms, evaluates the cause of the deviation, ways to eliminate it and the effectiveness of each method based on historical data. The “Power Consumption Optimization” module allows integrating algorithms into the well control system to reduce energy consumption by maintaining the most energy-efficient operation of pumping equipment or optimizing its operation time. The module “Ensuring the well flow rate” allows you to analyze and determine the reasons for the decrease in production rate, taking into account the parameters of the operation of adjacent wells. The Equipment Anomaly Prediction module is based on machine learning and helps reduce equipment downtime by predicting and automatically responding to potential deviations. As a result of using the proposed system, many goals of the oil company are achieved: specific energy consumption, oil shortages, and accident rate are reduced, while reducing the labor costs of engineering and technological personnel for processing the operation parameters of all process equipment.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47049729","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}
The rapid increase in urbanization results in an increase in the volume of municipal solid waste produced every day, causing overflow of the garbage cans and thus distorting the city’s appearance; for this and environmental reasons, smart cities involve the use of modern technologies for intelligent and efficient waste management. Smart bins in urban environments contain sensors that measure the status of containers in real-time and trigger wireless alarms if the container reaches a predetermined threshold, and then communicate the information to the operations center, which then sends vehicles to collect the waste from the selected stations in order to collect a significant waste amount and reduce transportation costs. In this article, we will address the issue of the Dynamic Multi-Compartmental Vehicle Routing Problem (DM-CVRP) for selective and intelligent waste collection. This problem is summarized as a linear mathematical programming model to define optimal dynamic routes to minimize the total cost, which are the transportation costs and the penalty costs caused by exceeding the bin capacity. The hybridized genetic algorithm (GA) is proposed to solve this problem, and the effectiveness of the proposed approach is verified by extensive numerical experiments on instances given by Valorsul, with some modifications to adapt these data to our problem. Then we were able to ensure the effectiveness of our approach based on the results in the static and dynamic cases, which are very encouraging.
{"title":"Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection","authors":"Yousra Bouleft, Ahmed Elhilali Alaoui","doi":"10.3390/asi6010030","DOIUrl":"https://doi.org/10.3390/asi6010030","url":null,"abstract":"The rapid increase in urbanization results in an increase in the volume of municipal solid waste produced every day, causing overflow of the garbage cans and thus distorting the city’s appearance; for this and environmental reasons, smart cities involve the use of modern technologies for intelligent and efficient waste management. Smart bins in urban environments contain sensors that measure the status of containers in real-time and trigger wireless alarms if the container reaches a predetermined threshold, and then communicate the information to the operations center, which then sends vehicles to collect the waste from the selected stations in order to collect a significant waste amount and reduce transportation costs. In this article, we will address the issue of the Dynamic Multi-Compartmental Vehicle Routing Problem (DM-CVRP) for selective and intelligent waste collection. This problem is summarized as a linear mathematical programming model to define optimal dynamic routes to minimize the total cost, which are the transportation costs and the penalty costs caused by exceeding the bin capacity. The hybridized genetic algorithm (GA) is proposed to solve this problem, and the effectiveness of the proposed approach is verified by extensive numerical experiments on instances given by Valorsul, with some modifications to adapt these data to our problem. Then we were able to ensure the effectiveness of our approach based on the results in the static and dynamic cases, which are very encouraging.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45034797","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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