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30th CIRP Life Cycle engineering Conference, LCE 2023

作者： Formentini, Giovanni Ramanujan, Devarajan Department of Mechanical and Production Engineering - Design and Manufacturing Aarhus University Katrinebjergvej 89 G-F Aarhus N8200 Denmark Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus8200 Denmark

Design for disassembly (DfD) approaches play a vital role in (re)designing products from improving their sustainability and circularity performance. This is particularly true in the case of complex resource-intensive products wherein material recovery and reutilization at their end-of-life (EoL) is dictated by the ease-of-disassembly. Various DfD approaches have been developed for increasing recovery of valuable materials by reducing the overall disassembly time (DT). However, such methods do not consider the actual EoL status of a product while estimating DT. The lack of a systematic approach for adjusting DT based on the EoL product status (e.g., fully operational product, presence of functional failures in specific parts, missing parts) prevents current DfD approaches from accurately assessing DT and can therefore lead designers towards solutions that reduce the overall sustainability and circularity performance of products. To address this gap, the proposed study develops an approach that can account for EoL product status during DT estimation of DfD scenarios during early design. The central idea of this approach is modifying standardized motion sequences for disassembly operations based on potential failure modes in a product's EoL. The proposed approach was tested on a real case study involving an electrical kettle. Results show the proposed approach can generate new insights regarding product disassembly performance. In particular, some Failure Modes can lead to a reduction of DT due to changes in disassembly action type and the corresponding product EoL scenario (i.e., from reuse to disposal). In such cases, a reduction in DT can be counterproductive from a circular economy perspective as the gains result from a diminished ability to recover/reuse products. © 2023 The Authors. Published by Elsevier B.V.

关键词： Sustainable development

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Structuring a Model to Estimate Human Joint Motion from Surface-Electromyography 50

Structuring a Model to Estimate Human Joint Motion from Surf...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Sodenaga, Daiki Shikata, Kosuke Takeuchi, Issei De Silva, Daswin Katsura, Seiichiro Department of System Design Engineering Keio University Yokohama Japan Abstech.Co. Ltd. Saitama Japan Research Center for Data Analytics and Cognition La Trobe University Melbourne Australia

ISBN: (纸本)9781665464543

In this paper, the structure of a model to estimate human motion from surface-electromyography (sEMG) was defined mathematically and physically. Especially, an infinite impulse response (IIR) filter and an element description method were focused to generate the model in this paper. Human motion can be estimated from sEMG. However, the relationship between sEMG and human motion has a complex characteristics such as a non-linear relationship. The cause of the above complex relationship can be thought the muscle contraction velocity. Then, the IIR filter was applied to consider about the previous sEMG value in the estimation. In addition, some conventional models have been a black-box model and it is not easy to interpret the model mathematically and physically. From the above, an element description method (EDM) was applied to generate the model in this paper. EDM is one of the system identification method and it is easy to interpret the model mathematically and physically because EDM can generate the block diagram of the model. In this way, the proposed method could solve two conventional methods about the muscle contraction velocity and the black box model. Then, it was possible to define the structure of the model by the above in this paper. In this paper, the models about a wrist and a fingertip motion were generated and considered about the structure. As the results, the structure of models was defined with a hyperbolic tangent function. It will be possible to apply the model to various motions by just identifying the parameters about the relationship between each joint and muscle because the amount of muscles and range of sEMG are deferent from each muscle. © 2024 IEEE.

关键词： IIR filters

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EXAMINING THE ROLE AND FUTURE POTENTIAL OF DESIGN FOR DISASSEMBLY METHODS TO SUPPORT CIRCULAR PRODUCT DESIGN 24

EXAMINING THE ROLE AND FUTURE POTENTIAL OF DESIGN FOR DISASS...

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24th International Conference on engineering Design, ICED 2023

Design for disassembly (DfD) approaches are crucial in supporting the industrial circular economy transition. In literature, a great amount of DfD methodologies is available, however, it is still not clear how they can be used to improve product circularity. To address this gap, our work proposed a systematic literature review of DfD methodologies applied in the field of product design with the aim to provide an overview of the topic in the last decade (i.e., from 2012 to 2022) in terms of methods applicability (i.e., design phase), required parameters and integration capability with circularity assessment. As a result, the paper shows that DfD methods are mainly used in the later design phase to improve product sustainability performances, but a method that simultaneously considers DfD and CE is currently missing. Based on the obtained results, we outlined the requirements that a new DfX method would need to consider both DfD and CE simultaneously. Finally, we proposed a modified version of the butterfly diagram in which DfD parameters are linked to CE indicators to help visualize the connection between the two areas. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Product design

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MOEA/D With Spatial-Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective Optimization

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wang, Xianpeng Zhao, Yumeng Tang, Lixin Yao, Xin Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Lingnan University Hong Kong China

When solving dynamic multiobjective optimization problems, most evolutionary algorithms attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which may deteriorate the prediction accuracy. To address this problem, this paper proposes a spatial-temporal topological tensor-based prediction method to generate the initial population in a new environment under the decomposition framework of MOEA/D. The method relies on the idea that the population distribution in each environment has topological similarity along the time dimension in the objective space, which makes it efficient to represent the population distribution in terms of a tensor and predict new solutions along each decomposition axis in a new environment by an improved tensor-based multi-short time series prediction method. Experimental results on various benchmark problems and a real-world problem show that the proposed method is competitive or even superior to state-of-the-art dynamic multiobjective evolutionary algorithms based on prediction strategies. IEEE

关键词： Tensors

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Least Squares Projection Onto the Behavior for SISO LTI Models ⁎

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IFAC-PapersOnLine 2024年第15期58卷 336-341页

作者： S. Lagauw B. De Moor Center for Dynamical Systems Signal Processing and Data Analytics (STADIUS) Department of Electrical Engineering (ESAT) KU Leuven Belgium

We consider the least squares projection onto the behavior for linear time-invariant (LTI) single-input single-output (SISO) models, in which the observed input-output data are modified in a least squares (LS) sense by subtracting so-called misfits, so that the modified data satisfy a given linear dynamic relation. We show that the LS-criterion of the projection problem induces an orthogonal decomposition of the ambient data space and we characterize this decomposition by means of banded block-Toeplitz matrices, the elements of which are the coefficients of the difference equation describing the SISO LTI dynamics. We thereby generalize earlier results in the literature on autonomous LTI models to the more complicated SISO case. Additionally, we illustrate that the novel characterization is equivalent (up to a change of model representation) to results derived using (isometric) state-space representations in the literature on behavioral systems theory.

关键词： Modeling identification linear systems errors in variables identification

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A Cross-Sampling Method for Hidden Structure Extraction to Improve Imbalanced Multiclass Classification Accuracy 6

A Cross-Sampling Method for Hidden Structure Extraction to I...

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6th International Conference on Vocational Education and Electrical engineering, ICVEE 2023

作者： Yustanti, Wiyli Iriawan, Nur Irhamah Nuryana, I Kadek Dwi Indriyanti, Aries Dwi Universitas Negeri Surabaya Department of Informatics Faculty of Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Faculty of Science and Data Analytics Department of Statistics Surabaya Indonesia

ISBN: (纸本)9798350326642

Class prediction problems in classification cases are often faced with irregular data conditions. This research contributes to providing a more structured hidden pattern extraction approach to data sets that already have class labels. The process of taking samples from data collection in this study is called the cross-sampling (CS) technique. The basic idea of this technique is to regroup the data using the appropriate clustering method. This study applied the Mini Batch K-Mean and Spectral algorithms to four public datasets with an imbalanced multiclass distribution. The grouping results are then assigned a label based on the original label reference using the pattern-matching concept on the first principal component through the PCA procedure. The labelling results are then used as external validation for the actual data labels to represent all classes. The validation results produce a confusion matrix used as the basis for the cross-sampling method. The dataset before cross-sampling and the results of cross-sampling were compared to the performance of the classification prediction accuracy using the F1-Score and Area Under Curve (AUC) measurements. The statistical hypothesis testing results show a significant difference in performance before and after the cross-sampling procedure. This difference is demonstrated by the accuracy of all the classification algorithms used, which increased significantly from the average performance value of 82.09% to 96.7%. © 2023 IEEE.

关键词： Sampling

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Combined Channel and Spatial Attention-Based Stereo Endoscopic Image Super-Resolution 38

Combined Channel and Spatial Attention-Based Stereo Endoscop...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Hayat, Mansoor Armvith, Supavadee Achakulvisut, Titipat Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand Mahidol University Department of Biomedical Engineering Bangkok Thailand

ISBN: (纸本)9798350302196

Stereo Imaging technology integration into medical diagnostics and surgeries brings a great revolution in the field of medical sciences. Now, surgeons and physicians have better insight into the anatomy of patients' organs. Like other technologies, stereo cameras have limitations, e.g., low resolution (LR) and blurry output images. Currently, most of the proposed techniques for super-resolution focus on developing complex blocks and complicated loss functions, which cause high system complexity. We proposed a combined channel and spatial attention block to extract features incorporated with a specific but very strong parallax attention module (PAM) for endoscopic image super-resolution. The proposed model is trained using the da Vinci dataset on scales 2 and 4. Our proposed model has improved PSNR up to 2.12 dB for scale 2 and 1.29 dB for scale 4, while SSIM is improved by 0.03 for scale 2 and 0.0008 for scale 4. By incorporating this method, diagnosis and treatment for endoscopic images can be more accurate and effective. © 2023 IEEE.

关键词： Geometrical optics

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Automated Crack Analysis and Reporting in Civil Infrastructure using Generative AI 50

Automated Crack Analysis and Reporting in Civil Infrastructu...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Kumar, Sanjay K.J. Nandini, K.L. Amritha Dharshan, S.P. Saran Sowmya, V. Bandaragoda, Tharindu Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Centre for Data Analytics and Cognition La Trobe University Australia

ISBN: (纸本)9781665464543

Maintaining and inspecting infrastructure is crucial due to the safety hazards and economic costs of structural failures. Traditional methods are labor-intensive, time-consuming, and reactive. We propose an automated inspection system leveraging generative AI to enhance efficiency, predictive maintenance capabilities, and comprehensive data analysis. Our framework uses drone-based data acquisition with high-definition cameras and depth sensors, and a custom deep learning model, EyeNet, for precise crack detection. Generative AI techniques, including a Visual Question-Answering (VQA) model and an image-to-image model, are employed for detailed crack analysis and future crack pattern visualization, enabling proactive maintenance. The VQA model achieves an average Root Mean Square Error (RMSE) of 0.394 and an average Symmetric Mean Absolute Percentage Error (SMAPE) of 31.22%. A Large Language Model generates comprehensive reports with visualizations, accessible via a dedicated website. Our system significantly improves the inspection process compared to traditional methods, setting a new benchmark by combining generative AI for detailed crack analysis and predictive maintenance capabilities, creating a comprehensive inspection system for civil infrastructure. © 2024 IEEE.

关键词： Predictive maintenance

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Real-Time Pothole Detection at night to prevent road accidents using YOLOV5 6

Real-Time Pothole Detection at night to prevent road acciden...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Mandal, Gouranga De, Anurag Dey, Arindam Gangarapu, Sudeepthi Chekuru, Sharmila Rani Nandini, Chekuri Vellore Institute of Technology School of Computer Science and Engineering Andhra Pradesh 522237 India Vellore Institute of Technology School of Computer Science and Engineering with Specialization in Data Analytics Andhra Pradesh 522237 India

ISBN: (纸本)9798350394702

This article proposes a technique to create a pothole detection system that can operate both during the day and at night. This model was developed using the real-time object identification YOLO (You Only Look Once) technique. The potholes are located using a pre-trained model with YOLO v5. In its creation, the PyTorch framework is utilized. The first YOLO model created using the PyTorch framework is YOLOv5, which is a lot more user-friendly and lightweight. In a manner similar to real-time object detection systems, the proposed method displays potholes in real time and emphasizes them with boundary boxes for accurate identification. The accuracy achieved with this method is 97% during the day and 92% at night which is found to be satisfactory. © 2023 IEEE.

关键词： Image acquisition

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Edge Detection with multi-scale representation and refined Network 3

Edge Detection with multi-scale representation and refined N...

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3rd International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2022

作者： Elharrouss, Omar Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Department of Computer Science and Engineering Qatar University Qatar Center for Data Analytics and Cybersecurity University of Sharjah Ukraine

ISBN: (纸本)9781839538186

Edge detection is a representation of boundaries between objects and regions in an image. Due to the variations of types, scales, intensities as well as background, the detection of these boundaries represents a challenge for different computer vision algorithms. The exploitation of multi-scale representation using various deep learning backbones make edge detection more realistic. However, edge detection is still not effective in terms of erroneous detection in some scenarios and the quality of detected edges. In this paper, we attempted to overcome these challenges using a high-resolution and refined network by fusing the output of different scales of the network and the output of the first layers. The proposed network used horizontal and vertical blocks, while the horizontal block (HB) contains a set of convolution layers and the vertical block (VB) represent the different scale of the image using convolution and pooling layers. The output of each scale in VR is combined with the corresponding layer in HB. Then, we used an affined batch normalization layer as an erosion operation for the homogeneous region in the image before fusing these outputs for the final edge detection results. The proposed method is evaluated using the most challenging datasets including BSDS500, NYUD, and Multicue. The obtained results outperform the designed edge detection networks in terms of performance metrics and quality of output images. © IET Conference Proceedings 2022.

关键词： Edge detection

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