Applied System Innovation最新文献

{"title":"Deep Learning Method to Detect Missing Welds for Joist Assembly Line","authors":"H. Raoofi, Asa Sabahnia, Daniel Barbeau, Ali Motamedi","doi":"10.3390/asi7010016","DOIUrl":"https://doi.org/10.3390/asi7010016","url":null,"abstract":"Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods can now be automated, resulting in time and cost savings, as well as improvements in product quality. This research focuses on the application of computer vision approaches to monitor the quality of welding in prefabricated steel elements. A high-performance network was designed, consisting of a video capturing station, a customized classifier based on a YOLOv4 detector and an IoU tracker, and a user interface software for any interaction with quality control workers. The network demonstrated over 98% accuracy in identifying steel connection types and detecting missed welds on the assembly line in real-time. Extensive validation was conducted using a large dataset from a real production environment. The proposed framework aims to reduce rework, minimize hazards, and enhance product quality. This research contributes to the automation of quality control processes in the construction industry.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839784","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}

{"title":"Deep Learning Method to Detect Missing Welds for Joist Assembly Line","authors":"H. Raoofi, Asa Sabahnia, Daniel Barbeau, Ali Motamedi","doi":"10.3390/asi7010016","DOIUrl":"https://doi.org/10.3390/asi7010016","url":null,"abstract":"Traditional methods of supervision in the construction industry are time-consuming and costly, requiring significant investments in skilled labor. However, with advancements in artificial intelligence, computer vision, and deep learning, these methods can now be automated, resulting in time and cost savings, as well as improvements in product quality. This research focuses on the application of computer vision approaches to monitor the quality of welding in prefabricated steel elements. A high-performance network was designed, consisting of a video capturing station, a customized classifier based on a YOLOv4 detector and an IoU tracker, and a user interface software for any interaction with quality control workers. The network demonstrated over 98% accuracy in identifying steel connection types and detecting missed welds on the assembly line in real-time. Extensive validation was conducted using a large dataset from a real production environment. The proposed framework aims to reduce rework, minimize hazards, and enhance product quality. This research contributes to the automation of quality control processes in the construction industry.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779983","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}

{"title":"The Enhanced Adaptive Grasping of a Soft Robotic Gripper Using Rigid Supports","authors":"Zhikang Peng, Dongli Liu, Xiaoyun Song, Meihua Wang, Yiwen Rao, Yanjie Guo, Jun Peng","doi":"10.3390/asi7010015","DOIUrl":"https://doi.org/10.3390/asi7010015","url":null,"abstract":"Soft pneumatic grippers can grasp soft or irregularly shaped objects, indicating potential applications in industry, agriculture, and healthcare. However, soft grippers rarely carry heavy and dense objects due to the intrinsic low modulus of soft materials in nature. This paper designed a soft robotic gripper with rigid supports to enhance lifting force by 150 ± 20% in comparison with that of the same gripper without supports, which successfully lifted a metallic wrench (672 g). The soft gripper also achieves excellent adaptivity for irregularly shaped objects. The design, fabrication, and performance of soft grippers with rigid supports are discussed in this paper.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784576","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}

{"title":"The Enhanced Adaptive Grasping of a Soft Robotic Gripper Using Rigid Supports","authors":"Zhikang Peng, Dongli Liu, Xiaoyun Song, Meihua Wang, Yiwen Rao, Yanjie Guo, Jun Peng","doi":"10.3390/asi7010015","DOIUrl":"https://doi.org/10.3390/asi7010015","url":null,"abstract":"Soft pneumatic grippers can grasp soft or irregularly shaped objects, indicating potential applications in industry, agriculture, and healthcare. However, soft grippers rarely carry heavy and dense objects due to the intrinsic low modulus of soft materials in nature. This paper designed a soft robotic gripper with rigid supports to enhance lifting force by 150 ± 20% in comparison with that of the same gripper without supports, which successfully lifted a metallic wrench (672 g). The soft gripper also achieves excellent adaptivity for irregularly shaped objects. The design, fabrication, and performance of soft grippers with rigid supports are discussed in this paper.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844140","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}

{"title":"Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation","authors":"Abdulaziz Aldoseri, K. Al-Khalifa, Abdel Magid Hamouda","doi":"10.3390/asi7010014","DOIUrl":"https://doi.org/10.3390/asi7010014","url":null,"abstract":"In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization’s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization’s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139852416","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}

{"title":"Methodological Approach to Assessing the Current State of Organizations for AI-Based Digital Transformation","authors":"Abdulaziz Aldoseri, K. Al-Khalifa, Abdel Magid Hamouda","doi":"10.3390/asi7010014","DOIUrl":"https://doi.org/10.3390/asi7010014","url":null,"abstract":"In an era defined by technological disruption, the integration of artificial intelligence (AI) into business processes is both strategic and challenging. As AI continues to disrupt and reshape industries and revolutionize business processes, organizations must take proactive steps to assess their readiness and capabilities to effectively leverage AI technologies. This research focuses on the assessment elements required to evaluate an organization’s current state in preparation for AI-based digital transformation. This research is based on a literature review and practical insights derived from extensive experience in industrial system engineering. This paper outlines the key assessment elements that organizations should consider to ensure successful and sustainable AI-based digital transformation. This emphasizes the need for a comprehensive approach to assess the organization’s data infrastructure, governance practices, and existing AI capabilities. Furthermore, the research work focuses on the evaluation of AI talent and skills within the organization, considering the significance of fostering an innovative culture and addressing change management challenges. The results of this study provide organizations with elements to assess their current state for AI-based digital transformation. By adopting and implementing the proposed guidelines, organizations can gain a holistic perspective of their current standing, identify strategic opportunities for AI integration, mitigate potential risks, and strategize a successful path forwards in the evolving landscape of AI-driven digital transformation.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139792646","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}

{"title":"Elevating Academic Advising: Natural Language Processing of Student Reviews","authors":"Omiros Iatrellis, Nicholas Samaras, Konstantinos Kokkinos, A. Xenakis","doi":"10.3390/asi7010012","DOIUrl":"https://doi.org/10.3390/asi7010012","url":null,"abstract":"Academic advising is often pivotal in shaping students’ educational experiences and choices. This study leverages natural language processing to quantitatively evaluate reviews of academic advisors, aiming to provide actionable insights on key feedback phrases and demographic factors for enhancing advising services. This analysis encompassed a comprehensive evaluation of 1151 reviews of undergraduate students for academic advisors, which were collected within a European University alliance consisting of five universities, offering a diverse pool of feedback from a wide range of academic interactions. Employing sentiment analysis powered by artificial intelligence, we computed compound sentiment scores for each academic advisor’s reviews. Subsequently, statistical analyses were conducted to provide insights into how demographic factors may or may not influence students’ sentiment and evaluations of academic advisory services. The results indicated that advisor’s gender had no substantial influence on the sentiment of the reviews. On the contrary, the academic advisors’ age showed a notable impact, with younger advisors surprisingly receiving more favorable evaluations. Word frequency analyses, both for positive and negative expressions, were also performed to contextualize the language used in describing academic advisors. The prevalent word combinations in reviews of highly rated academic advisors emphasized attributes like empathy, approachability, and effectiveness in guiding students towards achieving their academic goals. Conversely, advisors with less favorable reviews were often perceived as inadequate in addressing students’ concerns related to their academic journey, revealing persistent challenges in the student–advisor interaction that impacted their evaluation. This analysis of academic advisor reviews contributes to the body of literature by highlighting the significance of managing student expectations and enhancing advisor skills and qualities to foster positive interactions and academic success.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140478008","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}

{"title":"Organizational Processes for Adopting Breakthrough Technology: Text Mining of AI Perception among Japanese Firms","authors":"Yusuke Hoshino, Takashi Hirao","doi":"10.3390/asi7010013","DOIUrl":"https://doi.org/10.3390/asi7010013","url":null,"abstract":"Artificial intelligence (AI) has become popular worldwide after technological breakthroughs in the early 2010s. Accordingly, many organizations and individuals have been using AI for various applications. Previous research has been dominated by case studies regarding the industrial use of AI, although how time-series changes affect users’ perceptions has not been clarified yet. This study analyzes time-series changes in AI perceptions through text mining from nonfinancial information obtained from Japanese firms’ disclosures. The main findings of this study are as follows: first, perceptions of AI vary across industries; second, the business sector has progressed through the stages of recognition, investment, strategization, commercialization, and monetization. This transition is concurrent with each category’s evolving interpretation of the innovator theory proposed by Rogers (2003), to some extent. Third, it took approximately a decade from the breakthrough technology to the monetization by Japanese firms. Our findings underline the importance of speeding up the organizational process through intervention and contribution to the areas regarding “diffusion of innovation” and perceptual characteristics.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472452","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}

{"title":"Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers","authors":"Nils Hütten, Miguel Alves Gomes, Florian Hölken, Karlo Andricevic, Richard Meyes, Tobias Meisen","doi":"10.3390/asi7010011","DOIUrl":"https://doi.org/10.3390/asi7010011","url":null,"abstract":"Quality assessment in industrial applications is often carried out through visual inspection, usually performed or supported by human domain experts. However, the manual visual inspection of processes and products is error-prone and expensive. It is therefore not surprising that the automation of visual inspection in manufacturing and maintenance is heavily researched and discussed. The use of artificial intelligence as an approach to visual inspection in industrial applications has been considered for decades. Recent successes, driven by advances in deep learning, present a possible paradigm shift and have the potential to facilitate automated visual inspection, even under complex environmental conditions. For this reason, we explore the question of to what extent deep learning is already being used in the field of automated visual inspection and which potential improvements to the state of the art could be realized utilizing concepts from academic research. By conducting an extensive review of the openly accessible literature, we provide an overview of proposed and in-use deep-learning models presented in recent years. Our survey consists of 196 open-access publications, of which 31.7% are manufacturing use cases and 68.3% are maintenance use cases. Furthermore, the survey also shows that the majority of the models currently in use are based on convolutional neural networks, the current de facto standard for image classification, object recognition, or object segmentation tasks. Nevertheless, we see the emergence of vision transformer models that seem to outperform convolutional neural networks but require more resources, which also opens up new research opportunities for the future. Another finding is that in 97% of the publications, the authors use supervised learning techniques to train their models. However, with the median dataset size consisting of 2500 samples, deep-learning models cannot be trained from scratch, so it would be beneficial to use other training paradigms, such as self-supervised learning. In addition, we identified a gap of approximately three years between approaches from deep-learning-based computer vision being published and their introduction in industrial visual inspection applications. Based on our findings, we additionally discuss potential future developments in the area of automated visual inspection.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139606690","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}

{"title":"Design and Implementation of Nursing-Secure-Care System with mmWave Radar by YOLO-v4 Computing Methods","authors":"J. Chiu, Guan-Yi Lee, Chi-Yang Hsieh, Qing-You Lin","doi":"10.3390/asi7010010","DOIUrl":"https://doi.org/10.3390/asi7010010","url":null,"abstract":"In computer vision and image processing, the shift from traditional cameras to emerging sensing tools, such as gesture recognition and object detection, addresses privacy concerns. This study navigates the Integrated Sensing and Communication (ISAC) era, using millimeter-wave signals as radar via a Convolutional Neural Network (CNN) model for event sensing. Our focus is on leveraging deep learning to detect security-critical gestures, converting millimeter-wave parameters into point cloud images, and enhancing recognition accuracy. CNNs present complexity challenges in deep learning. To address this, we developed flexible quantization methods, simplifying You Only Look Once (YOLO)-v4 operations with an 8-bit fixed-point number representation. Cross-simulation validation showed that CPU-based quantization improves speed by 300% with minimal accuracy loss, even doubling the YOLO-tiny model’s speed in a GPU environment. We established a Raspberry Pi 4-based system, combining simplified deep learning with Message Queuing Telemetry Transport (MQTT) Internet of Things (IoT) technology for nursing care. Our quantification method significantly boosted identification speed by nearly 2.9 times, enabling millimeter-wave sensing in embedded systems. Additionally, we implemented hardware-based quantization, directly quantifying data from images or weight files, leading to circuit synthesis and chip design. This work integrates AI with mmWave sensors in the domain of nursing security and hardware implementation to enhance recognition accuracy and computational efficiency. Employing millimeter-wave radar in medical institutions or homes offers a strong solution to privacy concerns compared to conventional cameras that capture and analyze the appearance of patients or residents.","PeriodicalId":36273,"journal":{"name":"Applied System Innovation","volume":null,"pages":null},"PeriodicalIF":3.8,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139612424","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}