Journal of Industrial Information Integration最新文献

{"title":"A comprehensive analysis of multi-strategic RIME algorithm for UAV path planning in varied terrains","authors":"Tao Gu ,&nbsp;Yajuan Zhang ,&nbsp;Limin Wang ,&nbsp;Yufei Zhang ,&nbsp;Muhammet Deveci ,&nbsp;Xin Wen","doi":"10.1016/j.jii.2024.100742","DOIUrl":"10.1016/j.jii.2024.100742","url":null,"abstract":"<div><div>Optimizing industrial information integration is fundamental to harnessing the potential of Industry 4.0, driving data-informed decisions that enhance operational efficiency, reduce costs, and improve competitiveness in modern industrial environments. Effective unmanned aerial vehicle (UAV) path planning is crucial within this optimization framework, supporting timely and reliable data collection and transmission for smarter decision-making. This study proposes an enhanced RIME (IRIME) algorithm for three-dimensional UAV path planning in complex urban environments, formulated as a multiconstraint optimization problem aimed at discovering optimal flight paths in intricate configuration spaces. IRIME integrates three strategic innovations into the RIME algorithm: a frost crystal diffusion mechanism for improved initial population diversity, a high-altitude condensation strategy to enhance global exploration, and a lattice weaving strategy to avoid premature convergence. Evaluated on the CEC2017 test set and six realistic urban scenarios, IRIME achieves an 86.21 % win rate across 100 functions. In scenarios 4–6, IRIME uniquely identifies the globally optimal paths, outperforming other algorithms that are limited to locally optimal solutions. We believe these findings demonstrate IRIME's capacity to address complex path-planning challenges, laying a robust foundation for its future application to broader industrial optimization tasks.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"43 ","pages":"Article 100742"},"PeriodicalIF":10.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning assisted prediction of the nitric oxide (NO) solubility in various deep eutectic solvents","authors":"Hulin Jin ,&nbsp;Yong-Guk Kim ,&nbsp;Zhiran Jin ,&nbsp;Chunyang Fan","doi":"10.1016/j.jii.2024.100741","DOIUrl":"10.1016/j.jii.2024.100741","url":null,"abstract":"<div><div>Deep eutectic solvents (DESs) are recently proposed as green materials to remove nitric oxide (NO) from released streams into the atmosphere. The mathematical aspect of this process attracted less attention than it deserved. A straightforward approach in this field will help engineer DES chemistry and optimize the equilibrium conditions to maximize the amount of removed NO. This study covers this gap by constructing a reliable artificial neural network (ANN) to correlate the NO removal capacity of DES with equilibrium pressure/temperature and solvent chemistry. So, firstly, the physical meaningful features are selected to make the DES chemistry quantitative. It was found that the density is the best representative for the hydrogen-bound acceptor and hydrogen-bound donor. Also, the density and viscosity of the DESs exhibit the highest correlation with the NO solubility. Then, the hyperparameters of three famous ANN types (feedforward, recurrent, and cascade) are determined by combining trial-and-error and sensitivity analyzes. Finally, the ranking test distinguishes the ANN type with the lowest uncertainty toward estimating NO dissolution in DESs. The cascade neural network (CNN) with twelve and one neurons in the hidden and output layers equipped with the tangent hyperbolic and radial basis transfer functions is identified as the best ANN type for the given purpose. This model predicts 292 DES-NO equilibrium records collected from the literature with mean absolute errors = 0.033, relative absolute errors = 1.49 %, mean squared errors = 0.002, and coefficient of determination = 0.9998. Also, the present study helps understand the role of DES chemistry and operating conditions on the amount of removable NO by DESs. 1,3-dimethylthioureaP4444Cl (3:1) is recognized as the best DES to separate NO molecules from gaseous streams, respectively. The simulation results show that the unit mass of the best DES is capable of absorbing up to ∼27 mol of NO.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"43 ","pages":"Article 100741"},"PeriodicalIF":10.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancement of industrial information systems through AI models to simulate the vibrational and acoustic behavior of machining operations","authors":"Nisar Hakam ,&nbsp;Khaled Benfriha","doi":"10.1016/j.jii.2024.100744","DOIUrl":"10.1016/j.jii.2024.100744","url":null,"abstract":"<div><div>Advanced simulation tools allow the optimization of processes prior to production implementation. Our study aims to integrate industrial information and data into a digital model based on artificial intelligence (AI) to simulate acoustic and vibration behavior during the production preparation phase. This model integrates real manufacturing conditions with generated vibrations and acoustic waves, creating a comprehensive simulation tool for acoustic and vibration behavior during the production preparation phase. By harnessing Internet of Things (IoT) sensors, Big Data, and Cyber-Physical Systems (CPS), our approach achieves a unified system that consolidates data from diverse sources, facilitating a seamless information flow within an Industry 4.0 framework. Small signal variations made it complex to model manufacturing operations using AI tools, as seen in recent studies. However, the proposed approach overcomes these challenges and has been successfully applied to a numerical lathe using sensors and advanced analytical tools, paving the way for a robust industrial information integration system to optimize and predict operational outcomes.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"43 ","pages":"Article 100744"},"PeriodicalIF":10.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142790141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering robotic training with kinesthetic learning and digital twins in human–centric industrial systems","authors":"Thien Tran ,&nbsp;Quang Nguyen ,&nbsp;Toan Luu ,&nbsp;Minh Tran ,&nbsp;Jonathan Kua ,&nbsp;Thuong Hoang ,&nbsp;Man Dien","doi":"10.1016/j.jii.2024.100743","DOIUrl":"10.1016/j.jii.2024.100743","url":null,"abstract":"<div><div>This paper presents a human-centric mixed reality (MR) collaborative training platform that employs a kinesthetic learning technique in industrial robotic training, specifically focusing on robot pick–and–place (RPP) operations. Collaborating with ABB Robotics Vietnam, we conducted a user study to investigate the user experiences and practical perceptions of university students and novice trainees via the human–centric training assessment. The study compares the traditional training (TT) RPP classroom as a conventional method with a new collaborative MR RPP training approach (N = 50). The MR training features a digital twin (DT) of ABB GoFa™ CRB–15000 collaborative robot in an immersive 360° Digital–Objects–Based Augmented Training Environment (360–ATE) using Microsoft HoloLens devices. The research evaluated the impact of MR and DT on human–robot interaction and collaboration, user experience, task performance, knowledge retention, and interpretation, as well as differences in perceptions between the two novice cohorts under each training condition. The primary research question explores “Whether the MR collaborative training platform with DT integration in 360–ATE can serve as an alternative approach for novice students and industrial trainees in RPP operations?”. The findings indicate that MR training is more engaging and effective in enhancing participant safety, confidence, and task performance, which also augments cognitive capabilities. The virtual contents on HoloLens, especially the DT, captured the attention and stimulated active learning abilities. Overall, participants in the MR cohort find the proposed training platform useful and easy to use. The platform has a positive influence on their intention to use similar 360–ATE–assisted training platforms in the future.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"43 ","pages":"Article 100743"},"PeriodicalIF":10.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal-information-based optimized agricultural prescription recommendation system of crop electronic medical records","authors":"Chang Xu ,&nbsp;Junqi Ding ,&nbsp;Bo Wang ,&nbsp;Yan Qiao ,&nbsp;Lingxian Zhang ,&nbsp;Yiding Zhang","doi":"10.1016/j.jii.2024.100748","DOIUrl":"10.1016/j.jii.2024.100748","url":null,"abstract":"<div><div>Multimodal Crop Electronic Medical Records (CEMRs) contain complex information, including disease symptoms, crop conditions, environmental factors, and diagnostic prescriptions, making them crucial for intelligent prescription recommendations. However, effectively integrating complementary features from different CEMRs modalities has remained a key challenge. Current CEMRs research primarily focuses on unimodal data, and simplistic approaches like feature concatenation struggle to achieve in-depth cross-modal interactions. This study introduces a novel agricultural prescription recommendation model (named AgriPR) based on cross-modal multi-layer feature fusion. The model initially employs task-adaptive pre-trained BERT (TA-BERT) and ConvNeXt to encode text and image unimodal features respectively. Subsequently, it utilizes Bilinear Attention Networks (BAN) to bilinear features and combines them with bimodal encoding features for a multilayer fusion representation. Finally, a dual-layer Transformer performs re-interaction to emphasize key fused features, resulting in precise prescription recommendations. To evaluate AgriPR, we constructed a real CEMRs dataset containing 13 prescription categories from Beijing Plant Clinic. Experimental results demonstrate that AgriPR achieves outstanding performance, with a classification accuracy of 98.88 %, surpassing state-of-the-art models. Furthermore, the study compares and analyzes 8 encoder combinations, 6 feature fusion strategies, and 6 network layer configurations, highlighting the model's design advantages. Lastly, the model's adaptability was also tested with incomplete modality inputs (text-only or image-only) and missing information inputs (e.g., crop, environment, symptoms). The findings confirm AgriPR's practical applicability, providing a high-performance solution for agricultural management systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"43 ","pages":"Article 100748"},"PeriodicalIF":10.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards cognitive intelligence-enabled product design: The evolution, state-of-the-art, and future of AI-enabled product design","authors":"Zuoxu Wang ,&nbsp;Xinxin Liang ,&nbsp;Mingrui Li ,&nbsp;Shufei Li ,&nbsp;Jihong Liu ,&nbsp;Lianyu Zheng","doi":"10.1016/j.jii.2024.100759","DOIUrl":"10.1016/j.jii.2024.100759","url":null,"abstract":"<div><div>Engineering design researchers have increasing interests in leveraging artificial intelligence (AI) techniques to a wide range of product design tasks, such as customer requirement analysis, product concept generation, design synthesis, and decision-making in product design. Indeed, AI techniques perform excellently on well-defined design tasks with clear problem definition, specialized solutions, and abundant training data. However, facing the ever-evolving AI techniques rapidly and radically changing the product design manner, there is still a lack of a systematic summary about the current stage of AI-enabled product design. Besides, although the current AI-enabled product design performs excellently on the well-defined tasks, the other advanced design tasks that need cognitive capability can still hardly be satisfyingly completed by the current product design system. This study systematically reviewed the literature on AI-enabled product design to understand its evolution and state-of-the-arts. To bridge the semantic gap between humans and systems, a novel cognitive intelligence-enabled product design (CIPD) framework is proposed, in which cognitive intelligence is the key enabler. The CIPD's key aspects, including its system architecture, human-like capabilities, enabling technologies, and potential applications, are also systematically discussed. It is hoped that this study could contribute to the future directions of the product design field and offer insightful guidance to the practitioners and researchers in their product design process.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"43 ","pages":"Article 100759"},"PeriodicalIF":10.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-resilient closed-loop supply chain design managed by the 3PL provider: A pick-up strategy and data envelopment analysis","authors":"Beheshteh Moghadaspoor ,&nbsp;Reza Tavakkoli-Moghaddam ,&nbsp;Ali Bozorgi-Amiri ,&nbsp;Tofigh Allahviranloo","doi":"10.1016/j.jii.2024.100763","DOIUrl":"10.1016/j.jii.2024.100763","url":null,"abstract":"<div><div>Population growth and the development of transportation networks have caused the world to face a larger volume of scrap tires, which can cause critical environmental challenges if they are not properly disposed of after being ultimately used. Thus, implementing appropriate recovery practices has developed. The existing challenges in the forward and reverse integration flow motivate leaders to submit a third-party logistics service provider (3PL) as an appropriate option for outsourcing activities. As a result, an inventive closed-loop supply chain (CLSC) network is necessary. A multiple objective, product, and period mathematical model is proposed to develop the CLSC under 3PL management in the tire industry. The data envelopment analysis (DEA) method is applied to choose a better set of manufacturers to coordinate with 3PL. The motivating pricing approach is also considered for appropriate recovery practices, and resiliency was investigated against disruption at crucial levels. This model aims to minimize the costs of diverse processes over scrap products and energy consumption and reach a sufficient level of responsiveness to customers. For solving the multi-objective model, the augmented ε-constraint (AUGMECON2) method leads to Pareto-optimal solutions. The results show that 3PLs improve the supply chain (SC) procedure and increase the responsiveness to customer demand. Also, by planning to increase product recycling, it is possible to save money when purchasing raw materials from suppliers.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100763"},"PeriodicalIF":10.4,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel Pythagorean fuzzy correlation coefficient based on Spearman’s technique of correlation coefficient with applications in supplier selection process","authors":"Paul Augustine Ejegwa ,&nbsp;Nasreen Kausar ,&nbsp;Nezir Aydin ,&nbsp;Muhammet Deveci","doi":"10.1016/j.jii.2024.100762","DOIUrl":"10.1016/j.jii.2024.100762","url":null,"abstract":"<div><div>A Pythagorean fuzzy correlation coefficient (PFCC) is a reliable approach for eliminating ambiguity during the measure of relationships. Numerous Pythagorean fuzzy correlation coefficient methods (PFCCMs) have been constructed using Pearson’s correlation coefficient technique. In this study, a new PFCCM is constructed based on Spearman’s correlation coefficient to eliminate all possible uncertainties that may impede decision-makers from making a dependable selection. To validate the construction of a new PFCCM, we examine the existing PFCCMs and pinpoint their inadequacies. Among the extant PFCCMs, one approach was constructed through Spearman’s correlation coefficient but it does not takes into cognizance the properties of the PFSs. In addition, it sometimes fails the axiomatic conditions of the PFCC, and yields invalid result for PFSs that are defined on a singleton set. These setbacks justify the construction of a new Spearman’s correlation coefficient-like PFCCM, which is shown to overcome the limitations of the extant PFCCMs. Equally, the strength of the new PFCCM is verified by some theoretical results, and it fulfills the conditions of PFCC. Additionally, the use of the novel PFCCM is discussed in the solution of supplier selection problems to eliminate supplier selection ambiguity through the multiple criteria decision-making (MCDM) approach. To unarguably show the intrinsic worth of the new PFCCM, the effectiveness of the new PFCCM is compared with the existing PFCCMs and it is observed that the new PFCCM is reliable, consistent and precise, and in the same way satisfies the axioms of the PFCC. In particular, the existing Spearman’s PFCCM yields <span><math><mi>∞</mi></math></span> in Example 4, while the new PFCCM produces 0.7603, which justifies the construction of a new Spearman’s PFCCM. Finally, it is found that the new approach can suitably handle the hesitancies associated with the art of selection.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100762"},"PeriodicalIF":10.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of generative AI on management innovation","authors":"Caiming Zhang ,&nbsp;Hui Zhang","doi":"10.1016/j.jii.2024.100767","DOIUrl":"10.1016/j.jii.2024.100767","url":null,"abstract":"<div><div>Generative Artificial Intelligence (GAI) demonstrates significant potential in the application of management and organizational innovation. This paper systematically investigates the multifaceted impacts of GAI on management decision-making, management algorithms, information integration, and various specific domains. GAI significantly enhances the accuracy of management decisions through its robust data analysis and predictive capabilities. By effectively integrating internal and external information, it reduces information asymmetry and improves both information transparency and the quality of decisions. In terms of specific application areas, GAI shows broad prospects in multiple fields, including business, education, healthcare, content creation, and game development. As GAI technology continues to advance, it will become more intelligent and adaptive. However, further research and the establishment of relevant ethical guidelines and legal frameworks are necessary to ensure its safety and reliability.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100767"},"PeriodicalIF":10.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142889006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Practical implementation based on histogram of oriented gradient descriptor combined with deep learning: Towards intelligent monitoring of a photovoltaic power plant with robust faults predictions","authors":"Nadji Hadroug ,&nbsp;Amel Sabrine Amari ,&nbsp;Walaa Alayed ,&nbsp;Abdelhamid Iratni ,&nbsp;Ahmed Hafaifa ,&nbsp;Ilhami Colak","doi":"10.1016/j.jii.2024.100760","DOIUrl":"10.1016/j.jii.2024.100760","url":null,"abstract":"<div><div>The increasing complexity of photovoltaic (PV) system monitoring underscores the importance of precise fault detection and energy loss prediction. This paper proposes a deep learning-based framework that integrates multiple advanced techniques to accurately detect, localize, and predict faults in PV panels. A pre-trained Convolutional Neural Network (CNN), based on the AlexNet architecture, processes thermal imaging data for precise fault extraction. This facilitates the classification of faults, contributing to improved decision-making in PV system management.</div><div>To further enhance real-time monitoring, the framework integrates the Histogram of Oriented Gradients (HoG) descriptor with Support Vector Machine (SVM) models, enabling efficient detection and localization of hotspots across the panels. Additionally, the system leverages Long Short-Term Memory (LSTM) networks combined with fuzzy logic to predict panel performance degradation and quantify energy losses caused by detected faults. The learning process relies on the Long-Term Recurrent Convolutional Network (LRCN) to accurately forecast defects by analyzing power efficiency loss rates.</div><div>Experimental results confirm the effectiveness and reliability of the proposed framework. Achieving an accuracy of 95.45%, with a true positive rate of 91.67% and a true negative rate of 100%, the system demonstrates robust fault detection capabilities. These results highlight the framework’s potential to mitigate power losses, ensuring optimal operation of PV systems. This intelligent solution offers a significant advancement in PV system maintenance and monitoring, providing a scalable approach for real-world applications.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"44 ","pages":"Article 100760"},"PeriodicalIF":10.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}