Applied System Innovation最新文献_第9页

New Innovation, Sustainability, and Resilience Challenges in the X.0 Era X.0时代的新创新、可持续性和弹性挑战

IF 3.8

Applied System Innovation Pub Date : 2023-03-13 DOI: 10.3390/asi6020039

M. Gallab, Mario Di Nardo

引用次数: 1

e-Archeo: A Pilot National Project to Valorize Italian Archaeological Parks through Digital and Virtual Reality Technologies e-Archeo:通过数字和虚拟现实技术实现意大利考古公园价值的国家试点项目

IF 3.8

Applied System Innovation Pub Date : 2023-03-09 DOI: 10.3390/asi6020038

E. Pietroni, S. Menconero, Carolina Botti, Francesca Ghedini

{"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}

引用次数: 2

QACM: Quality Aware Crowd Sensing in Mobile Computing 移动计算中的质量感知人群感知

IF 3.8

Applied System Innovation Pub Date : 2023-03-08 DOI: 10.3390/asi6020037

B. Thippeswamy, Mohamed Ghouse, Shanawaz Ahamed Jafarabad, Murtuza Ahamed Khan Mohammed, Ketema Adere, Prabhu Prasad B. M., P. B. N.

引用次数: 0

Human-Centric Aggregation via Ordered Weighted Aggregation for Ranked Recommendation in Recommender Systems 基于有序加权聚合的以人为中心的排序推荐

IF 3.8

Applied System Innovation Pub Date : 2023-03-06 DOI: 10.3390/asi6020036

S. S. Sohail, Asfia Aziz, R. Ali, S. H. Hasan, D. Madsen, M. Alam

引用次数: 0

Gesture-to-Text Translation Using SURF for Indian Sign Language 用SURF进行手势到文本的翻译

IF 3.8

Applied System Innovation Pub Date : 2023-03-02 DOI: 10.3390/asi6020035

Kaustubh Mani Tripathi, P. Kamat, S. Patil, Ruchi Jayaswal, Swati Ahirrao, K. Kotecha

{"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}

引用次数: 0

Earthquake Hazard Mitigation for Uncertain Building Systems Based on Adaptive Synergetic Control 基于自适应协同控制的不确定建筑系统的地震减灾

IF 3.8

Applied System Innovation Pub Date : 2023-02-28 DOI: 10.3390/asi6020034

A. Al-Dujaili, A. Humaidi, Ziyad T. Allawi, M. E. Sadiq

{"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}

引用次数: 5

Optimization of Small Horizontal Axis Wind Turbines Based on Aerodynamic, Steady-State, and Dynamic Analyses 基于气动、稳态和动态分析的小型水平轴风力机优化

IF 3.8

Applied System Innovation Pub Date : 2023-02-24 DOI: 10.3390/asi6020033

K. Deghoum, Mohammed T Gherbi, Hakim S. Sultan, A. N. Jameel Al-Tamimi, A. Abed, O. Abdullah, H. Mechakra, A. Boukhari

引用次数: 1

A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome 一个独特的可解释的机器学习框架检测多囊卵巢综合征

IF 3.8

Applied System Innovation Pub Date : 2023-02-23 DOI: 10.3390/asi6020032

Varada Vivek Khanna, Krishnaraj Chadaga, Niranajana Sampathila, Srikanth Prabhu, Venkatesh Bhandage, Govardhan Hegde

{"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}

引用次数: 20

Development of a Digital Well Management System 数字井管理系统的开发

IF 3.8

Applied System Innovation Pub Date : 2023-02-17 DOI: 10.3390/asi6010031

Ilyushin Pavel Yurievich, Vyatkin Kirill Andreevich, Kozlov Anton Vadimovich

{"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}

引用次数: 0

Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection 智能垃圾收集的动态多车厢车辆路径问题

IF 3.8

Applied System Innovation Pub Date : 2023-02-15 DOI: 10.3390/asi6010030

Yousra Bouleft, Ahmed Elhilali Alaoui

{"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}

引用次数: 3