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AI-LCE: Adaptive and Intelligent Life Cycle engineering by applying digitalization and AI methods-An emerging paradigm shift in Life Cycle engineering 28

AI-LCE: Adaptive and Intelligent Life Cycle Engineering by a...

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28th CIRP Conference on Life Cycle engineering, LCE 2021

作者： Sakao, Tomohiko Funk, Peter Matschewsky, Johannes Bengtsson, Marcus Ahmed, Mobyen Uddin Division of Environmental Technology and Management Department of Management and Engineering Linköping University LinköpingSE-581 83 Sweden Division of Intelligent Future Technologies School of Innovation Design and Engineering Mälardalen University VästeråsSE-122 20 Sweden Operations Volvo Construction Equipment Eskilstuna Sweden Division of Computer Science and Software Engineering School of Innovation Design and Engineering Mälardalen University VästeråsSE-122 20 Sweden

This paper presents a vision for a much-needed paradigm shift in Life Cycle engineering (LCE), which is termed Adaptive and Intelligent Life Cycle engineering (AI-LCE). To do so, interdisciplinary analysis of literature in domains of AI and LCE is performed. Needed concepts and methods are described: key enabling technologies are Artificial Intelligence (AI), the Internet of Things, and data lakes, which are becoming cost-effective and increasingly implemented in industry. Both artificial and human intelligence are used in combination. Its advantages compared with the conventional LCE include shorter time for changing activities in the lifecycle and the accuracy of changes made. © 2021 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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Spatial Computing: Concept, Applications, Challenges and Future Directions

arXiv

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arXiv 2024年

作者： Yenduri, Gokul Ramalingam, M. Maddikunta, Praveen Kumar Reddy Gadekallu, Thippa Reddy Jhaveri, Rutvij H. Bandi, Ajay Chen, Junxin Wang, Wei Shirawalmath, Adarsh Arunkumar Ravishankar, Raghav Wang, Weizheng School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology TamilNadu 632014 India Zhongda Group Haiyan County Zhejiang Province Jiaxing City314312 China College of Information Science and Engineering Jiaxing University Jiaxing314001 China Division of Research and Development Lovely Professional University Phagwara India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gandhinagar India School of Computer Science and Information Systems Northwest Missouri State University 800 University Dr MaryvilleMO64468 United States School of Software Dalian University of Technology Dalian116621 China Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University Guangdong Shenzhen518172 China School of Medical Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Vellore Institute of Technology TamilNadu 632014 India Department of Computer Science City University of Hong Kong Hong Kong

Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the potential to become a significant advancement in the field of computing. From GPS and location-based services to healthcare, spatial computing technologies have influenced and improved our interactions with the digital world. The use of spatial computing in creating interactive digital environments has become increasingly popular and effective. This is explained by its increasing significance among researchers and industrial organisations, which motivated us to conduct this review. This review provides a detailed overview of spatial computing, including its enabling technologies and its impact on various applications. Projects related to spatial computing are also discussed. In this review, we also explored the potential challenges and limitations of spatial computing. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of spatial computing, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2024, CC BY.

关键词： Augmented reality

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Cutting Force Similarity Calculation in Milling Process Using Siamese LSTM Structure

Cutting Force Similarity Calculation in Milling Process Usin...

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Mechatronics and Electrical engineering (MEEE), International Conference on

作者： Juheon Kwak Wonkeun Jo Soomin Lee Hyein Kim Jeongin Koo Dongil Kim Division of Artificial Intelligence and Software Ewha Womans University Seoul South Korea Computer Science and Engineering Chungnam National University Daejeon South Korea Manufacturing System R&BD Group Korea Institute of Industrial Technology Cheonan South Korea Department of Data Science Ewha Womans University Seoul South Korea

Cutting force is a key factor in machining processes. Cutting force similarity is required for several important issues, namely: stability evaluation, process control, and process parameter setting. This study employed a long short-term memory (LSTM) with Siamese architecture to measure the similarity of the cutting forces in a milling process. The Siamese LSTM was trained with time series data of the vertical cutting force collected from a cutting tool during the milling process to calculate the similarity. For evaluation, dynamic time warping (DTW), a common approach used to calculate the similarity of time series data, was employed for comparison with the Siamese LSTM. Experimental results showed that the proposed Siamese LSTM outperformed the conventional DTW-based similarity calculation.

关键词： Training Electrical engineering Mechatronics Force measurement Force Time series analysis Process control

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Improving Generalization of Metric Learning via Listwise Self-distillation

arXiv

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arXiv 2022年

作者： Zeng, Zelong Yang, Fan Wang, Zheng Satoh, Shin'ichi The Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan The Digital Content and Media Sciences Research Division National Institute of Informatics Japan The School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China

Most deep metric learning (DML) methods employ a strategy that forces all positive samples to be close in the embedding space while keeping them away from negative ones. However, such a strategy ignores the internal relationships of positive (negative) samples and often leads to overfitting, especially in the presence of hard samples and mislabeled samples. In this work, we propose a simple yet effective regularization, namely Listwise Self-Distillation (LSD), which progressively distills a model's own knowledge to adaptively assign a more appropriate distance target to each sample pair in a batch. LSD encourages smoother embeddings and information mining within positive (negative) samples as a way to mitigate overfitting and thus improve generalization. Our LSD can be directly integrated into general DML frameworks. Extensive experiments show that LSD consistently boosts the performance of various metric learning methods on multiple datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Fractal Pattern Identification from Wearable Inertial and Electromyographic Signals Data during Walking

Fractal Pattern Identification from Wearable Inertial and El...

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International Conference on computer and Information Technology (ICCIT)

作者： Sam Matiur Rahman Md. Abdullah Al Mamun Md. Asraf Ali Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Department of Computer Science American International University-Bangladesh (AIUB) Dhaka Bangladesh Electronics Division Atomic Energy Centre Dhaka Bangladesh

Acceleration, angular velocity and electromyographic (EMG) signal at the lower limb muscles, specially over both leg's Tibialis Anterior muscles are highly non-stationary, even if no perturbing influences can be identified during walking at any speed. This study analyzed the fractal dynamics (i.e., complexity of gait time series) in the walking gait time series of four types of signals obtained from wearable sensors such as IMUs (inertial measurement units), i.e., accelerometer signals which represents the acceleration experienced by the body, gyroscope signals which is the angular velocity, and magnetometer signals which is magnetic field vector, and Electromyographic (EMG) signal from both leg’s Tibialis Anterior muscles. Gait time series from twenty-two healthy participants were analyzed while they performed walking at their comfortable speed. The scaling exponents (i.e., α-values) of the gait dynamics were accomplished by evaluating their fluctuation through detrended fluctuation analysis (DFA), which is most common and widely used non-linear technique for any non-stationary time series. DFA (the scaling exponents α) results established an anti-persistent in EMG and acceleration signal, less persistent pattern in angular velocity and persistent (i.e., long-range or fractal-like correlations) in magnetometer signal. This fractal complexity or noise patterns obtained from the EMG and inertial signals might provide new approaches for assessing and forecasting sudden injury risk during walking.

关键词： Legged locomotion Time series analysis Sociology Muscles Fractals Electromyography Angular velocity

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Positional Encoding-Guided Transformer-Based Multiple Instance Learning for Histopathology Whole Slide Images Classification

SSRN

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SSRN 2024年

作者： Shi, Jun Sun, Dongdong Wu, Kun Jiang, Zhiguo Kong, Xue Wang, Wei Wu, Haibo Zheng, Yushan School of Software Hefei University of Technology Anhui Province Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Anhui Province Hefei230601 China Beijing102206 China Department of Pathology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering China

Whole slide image (WSI) classification is of great clinical significance in computer-aided pathological diagnosis. Due to the high cost of manual annotation, weakly supervised WSI classification methods have gained more attention. As the most representative, multiple instance learning (MIL) generally aggregates the predictions or features of the patches within a WSI to achieve the slidelevel classification under the weak supervision of WSI labels. However, most existing MIL methods ignore spatial position relationships of the patches which is likely to strengthen the discriminative ability of WSI-level features. In this paper, we propose a novel position encoding-guided transformerbased multiple instance learning (PEGTB-MIL) method for histopathology WSI classification. It aims to encode the spatial positional property of the patch into its corresponding semantic features and explore the potential correlation among the patches for improving the WSI classification performance. Concretely, the deep features of the patches in WSI are first extracted and simultaneously a position encoder is used to encode the spatial 2D positional information of the patches into the spatialaware features. After incorporating the semantic features and spatial embeddings, multi-head selfattention (MHSA) is applied to explore the contextual and spatial dependencies of the fused features. Particularly, we introduce an auxiliary reconstruction task to enhance the spatial-semantic consistency and generalization ability of features. The proposed method is evaluated on one public TCGA dataset and two in-house datasets. Experimental results validate it is effective in the tasks of lung cancer subtyping and gene mutation status prediction for lung cancer and gastrointestinal stromal tumor (GIST), and superior to the state-of-the-art MIL-based methods. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval

arXiv

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arXiv 2022年

作者： Zeng, Zelong Wang, Zheng Yang, Fan Satoh, Shin'ichi The School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China The Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan The Digital Content and Media Sciences Research Division National Institute of Informatics Japan

The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks. In this paper, we investigate an extremely challenging task: given a ground-view image of a landmark, we aim to achieve cross-view geo-localization by searching out its corresponding satellite-view images. Specifically, the challenge comes from the gap between ground-view and satellite-view, which includes not only large viewpoint changes (some parts of the landmark may be invisible from front view to top view) but also highly irrelevant background (the target landmark tend to be hidden in other surrounding buildings), making it difficult to learn a common representation or a suitable mapping. To address this issue, we take advantage of drone-view information as a bridge between ground-view and satellite-view domains. We propose a Peer Learning and Cross Diffusion (PLCD) framework. PLCD consists of three parts: 1) a peer learning across ground-view and drone-view to find visible parts to benefit ground-drone cross-view representation learning;2) a patch-based network for satellite-drone cross-view representation learning;3) a cross diffusion between ground-drone space and satellite-drone space. Extensive experiments conducted on the University-Earth and University-Google datasets show that our method outperforms state-of-the-arts significantly. Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

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Integrating Design Thinking into software Process Improvement

Integrating Design Thinking into Software Process Improvemen...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Anum Khalid Amber Sarwar Hashmi Azaz Ahmed Kiani Syeda Mariam Muzammal NZ Jhanjhi Ruqia Bibi Sandeep Kumar Department of Software Engineering Foundation University Islamabad Pakistan Department of Software Engineering Rawalpindi Women University Rawalpindi Pakistan National University of Modern Languages Rawalpindi Pakistan Faculty of Innovation and Technology School of Computer Science Taylor's University Subang Jaya Malaysia University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan Division of Research and Development Lovely Professional University Phagwara KKR and KSR institute of technology and sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The need for software improvement is growing widely due to the demand for technology development. The issues that arise by expanding the software processes in the workplace are expense, lack of understanding, time constrained, and lack user experience. This research paper focuses on proposing a framework for integrating design thinking into software process improvement. The secret to project success may lie in fusing methodological and reusable approaches with human-centric techniques. A mixed-methods research strategy is proposed to compile thorough information and reach conclusions. In addition, it incorporates components of both qualitative and quantitative research (secondary research and descriptive analysis). The assumption is that the existing model significantly affects the user satisfaction variable. This indicates a statistically significant association between the individual's experience (number of years) and design thinking integration into the software process improvement. The variables in the proposed model and the dependent variable user satisfaction accounts for 6.1

关键词： Productivity Pain Employment Organizations Market research software User experience Time factors Optimization software development management

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Accuracy of computer-simulated mandibular autorotation following single-jaw maxillary repositioning surgery using customized surgical cutting guides and fixation plates

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International Journal of Oral and Maxillofacial Surgery 2025年

作者： Vu, C. Hartsfield, J.K. Mian, A. Allan, B. Gebauer, D. Goonewardene, M. Orthodontic Department School of Dentistry The University of Western Australia Nedlands WA Australia Division of Orthodontics Department of Oral Health Science College of Dentistry University of Kentucky Kentucky United States School of Computer Science and Software Engineering The University of Western Australia Crawley WA Australia Oral and Maxillofacial Surgery Department Royal Perth Hospital Perth WA Australia

The literature on computer-aided surgical simulation (CASS) has focused extensively on the precision and accuracy of maxillary and mandibular repositioning surgery. However, the predictability of mandibular autorotation remains understudied. The aim of this study was to evaluate the accuracy of CASS-planned mandibular autorotation in 50 consecutive patients undergoing maxillary repositioning surgery with customized titanium surgical cutting guides and fixation plates. The study patients (32 male and 18 female, mean age 25 years for both sexes) underwent Le Fort I osteotomy, with or without simultaneous genioplasty were performed by one experienced maxillofacial surgeon. Preoperative and postoperative multi-slice computed tomography scans were superimposed and contrasted with the surgical plan to evaluate any discrepancies between the planned and observed mandibular positions after surgery. The translational movements of the maxilla were a median 0.38 mm transversely, mean −5.29 mm anteroposteriorly, and median −1.00 mm vertically. Using a computed mandibular centroid, the absolute linear and angular discrepancies between the planned and observed mandibular autorotation movements were found to be within the clinically significant thresholds of 1 mm and 2°, respectively. It is concluded that CASS-planned mandibular autorotation is predictable when maxillary down-grafting and impaction does not exceed 5 mm and 4 mm, respectively. © 2025 The Authors

关键词： computer-assisted surgery Mandible Maxillofacial surgery Orthognathic surgery Reproducibility of results

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Transmission Opportunity-Based Distributed OBSS/PD Determination Method in IEEE 802.11ax Networks 2

Transmission Opportunity-Based Distributed OBSS/PD Determina...

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2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

作者： Kim, Yonggang Kim, Gyungmin Kim, Taewoon Choi, Wooyeol Samsung Electronics Network Division Korea Republic of School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of School of Software Hallym University Korea Republic of Chosun University Department of Computer Engineering Korea Republic of

ISBN: (纸本)9781728149851

As one of the most widely used wireless networking technologies, the IEEE 802.11 or Wi-Fi has been attracting enormous attention, and as a result, the access points (APs) are becoming densely deployed to support heavy traffic demands. However, such densely deployed APs also cause significant interference problems. For interference management, overlapped basic service set/packet detection (OBSS/PD) is recently added to the IEEE 802.11ax standard. In this paper, we propose a transmission opportunity-based distributed OBSS/PD threshold determination method to enhance the fairness among nodes. The simulation results show that the proposed method improves the fairness performance while mitigating interference. © 2020 IEEE.

关键词： Wi-Fi

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