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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Cai, Da Jia, Tong Wang, Hao Chen, Dongyue College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China Ministry of Education Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Guangdong Foshan528311 China

ISBN: (纸本)9798331506056

X-ray imaging technology is vital in national public security. However, compressing 16-bit high dynamic range X-ray images into 8-bit displayable images often leads to loss of crucial details, hampering effective security inspection. To address this, we propose XTM-GAN, a dual-energy X-ray tone mapping generative adversarial network, to enhance X-ray imaging quality. By leveraging the properties of dual-energy X-rays, we construct a dedicated dataset called DEXray. We design a dual-branch generation network and a discrimination network that combines global and local image information. Through end-to-end generative adversarial training using pairs of dual-energy HDR and LDR X-ray images, which can generate high-quality X-ray security images. We train and compare our algorithm using the DEXray dataset against other state-of-the-art tone mapping algorithms. The results demonstrate the superior performance of our approach. The proposed method has significant practical applications and holds research significance in the fields of security and transportation. © 2024 IEEE.

关键词： Gallium nitride

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Towards Simulation-Based Circular Product Design

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Procedia CIRP 2023年 116卷 690-695页

作者： Gaurav Aher Jalil Boudjadar Devarajan Ramanujan Department of Mechanical & Production Engineering Aarhus University Aarhus 8200 Denmark Department of Electrical & Computer Engineering Aarhus University Aarhus 8200 Denmark Centre for Digitalisation Big Data and Data Analytics Aarhus University Aarhus 8200 Denmark

Assessing the circular economy (CE) performance of products has become an essential consideration in product design processes. While a growing body of literature is exploring the use of CE indicators to guide design processes, they typically assume broad design changes (e.g., material changes, end-of-life treatment) that may not accurately capture trade-offs in CE indicators, technical performance, and design feasibility. This paper aims to address the above gaps with the goal of incorporating CE assessment into simulation-based design. To this end, this paper defines a novel framework for using simulation-based design to analyze the effects of parametric changes in product design attributes on CE performance. First, the proposed framework describes the lifecycle information needed for computing resource-based, product-level CE indicators at the design stage. Depending on the set of selected CE indicators, corresponding simulation models are used to compute the effects of changing design parameters, and setting design/performance constraints, on the product's CE performance. Thus, the proposed framework enables the selection and optimization of product design attributes for achieving a specific level of CE performance, given specific technical performance requirements and other design constraints. The application of this framework is demonstrated using a case study conducted on a flywheel.

关键词： Circular economy Simulation engineering Design

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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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A General Albedo Recovery Approach for Aerial Photogrammetric Images through Inverse Rendering

arXiv

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arXiv 2024年

作者： Song, Shuang Qin, Rongjun Geospatial Data Analytics Laboratory The Ohio State University Columbus United States Department of Civil Environmental and Geodetic Engineering The Ohio State University Columbus United States Department of Electrical and Computer Engineering The Ohio State University Columbus United States Translational Data Analytics Institute The Ohio State University Columbus United States

Modeling outdoor scenes for the synthetic 3D environment requires the recovery of reflectance/albedo information from raw images, which is an ill-posed problem due to the complicated unmodeled physics in this process (e.g., indirect lighting, volume scattering, specular reflection). The problem remains unsolved in a practical context. The recovered albedo can facilitate model relighting and shading, which can further enhance the realism of rendered models and the applications of digital twins. Typically, photogrammetric 3D models simply take the source images as texture materials, which inherently embed unwanted lighting artifacts (at the time of capture) into the texture. Therefore, these "polluted" textures are suboptimal for a synthetic environment to enable realistic rendering. In addition, these embedded environmental lightings further bring challenges to photo-consistencies across different images that cause image-matching uncertainties. This paper presents a general image formation model for albedo recovery from typical aerial photogrammetric images under natural illuminations and derives the inverse model to resolve the albedo information through inverse rendering intrinsic image decomposition. Our approach builds on the fact that both the sun illumination and scene geometry are estimable in aerial photogrammetry, thus they can provide direct inputs for this ill-posed problem. This physics-based approach does not require additional input other than data acquired through the typical drone-based photogrammetric collection and was shown to favorably outperform existing approaches. We also demonstrate that the recovered albedo image can in turn improve typical image processing tasks in photogrammetry such as feature and dense matching, edge, and line extraction. [This work extends our prior work "A Novel Intrinsic Image Decomposition Method to Recover Albedo for Aerial Images in Photogrammetry Processing" in ISPRS Congress 2022]. The code will be made available

关键词： Inverse problems

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Accurate and Efficient Real-World Fall Detection Using Time Series Techniques 9th

Accurate and Efficient Real-World Fall Detection Using Time...

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9th International Workshop on Advanced analytics and Learning on Temporal data, AALTD 2024

作者： Aderinola, Timilehin B. Palmerini, Luca D’Ascanio, Ilaria Chiari, Lorenzo Klenk, Jochen Becker, Clemens Caulfield, Brian Ifrim, Georgiana Insight SFI Research Centre for Data Analytics University College Dublin Dublin Ireland Department of Electrical Electronic and Information Engineering "Guglielmo Marconi" University of Bologna Bologna Italy Department of Clinical Gerontology Robert Bosch Hospital Stuttgart Germany Digital Geriatrics Unit Heidelberg University Hospital Heidelberg Germany

ISBN: (纸本)9783031770654

Falls pose a significant health risk, particularly for older people and those with specific medical conditions. Therefore, timely fall detection is crucial for preventing fall-related complications. Existing fall detection methods often have high false alarm or false negative rates, and many rely on handcrafted features. Additionally, most approaches are evaluated using simulated falls, leading to performance degradation in real-world scenarios. This paper explores a new fall detection approach leveraging real-world fall data and state-of-the-art time series techniques. The proposed method eliminates the need for manual feature engineering and has efficient runtime. Our approach achieves high accuracy, with false alarms and false negatives each as few as one in three days on FARSEEING, a large dataset of real-world falls (mean F1 score: 90.7%). We also outperform existing methods on simulated falls datasets, FallAllD and SisFall. Furthermore, we investigate the performance of models trained on simulated data and tested on real-world data. This research presents a real-time fall detection framework with potential for real-world implementation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fall detection

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Colon Cancer Tissue Classification Using ML 6

Colon Cancer Tissue Classification Using ML

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6th International Conference on Information Systems and Computer Networks, ISCON 2023

作者： Tripathi, Ashish Kumar, Kuldeep Misra, Anuradha Chaurasia, Brijesh Kumar Amity University Lucknow Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Bond University Center for Data Analytics Bond Business School Gold CoastQLD Australia Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350346961

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today's world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world's most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC-VAL-HE-7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.. © 2023 IEEE.

关键词： Image classification

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A real-time temperature prediction method based on CNN-LSTM with MODE in steelmaking process

A real-time temperature prediction method based on CNN-LSTM ...

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IISE Annual Conference and Expo 2024

作者： Wang, Dan Liu, Chang Tang, Lixin Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Ministry of Education Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China

ISBN: (纸本)9781713877851

Prediction of molten steel quality is a key issue in the converter steelmaking process. Due to high temperature and physical-chemical reaction, it is difficult to predict the temperature of molten steel in real time through mechanism analysis. This is a challenging problem with black box characteristics. Here we propose a Convolutional neural networks (CNN) and long short-term memory (LSTM) networks model based on multi-objective differential evolution algorithm (MODE) for the real-time prediction problem in the steelmaking process. Specifically, the CNN is used to extract feature between multiple variables that affect the temperature of molten steel, and the LSTM is suitable for the time series model based on the irregular trends information. The multi-step output CNN-LSTM method is used to construct the temperature prediction model of molten steel, and a knee solution strategy is applied to MODE, which can balance the model accuracy and generalization ability. The experiments were conducted using real data collected from the converter steelmaking process. The results verify the effectiveness of the proposed model on multiple steel grades and outperform other artificial intelligence methods in multi-task prediction. © IISE Annual Conference and Expo *** rights reserved.

关键词： Convolutional neural networks

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Visualization and simulation of steel metallurgy processes

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International Journal of Minerals,Metallurgy and Materials 2021年第8期28卷 1387-1396页

作者： Te Xu Guang Song Yang Yang Pei-xin Ge Li-xin Tang Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China Liaoning Key Laboratory of Manufacturing System and Logistics Shenyang 110819China Institute of Industrial&Systems Engineering Northeastern UniversityShenyang 110819China

Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality(VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials' field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.

关键词： virtual reality steel metallurgy plant-wide visualization

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MMSE-Based Resource Allocation for Clustered Cell-Free Massive MIMO Networks 26

MMSE-Based Resource Allocation for Clustered Cell-Free Massi...

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on Systems, Communications, and Coding, SCC 2023

作者： Mashdour, Saeed de Lamare, Rodrigo C. Schmeink, Anke Lima, João P. S. H. Centre for Telecommunications Studies Pontifical Catholic University of Rio de Janeiro Brazil Department of Electronic Engineering University of York United Kingdom Information Theory and Data Analytics RWTH Aachen University Aachen52062 Germany CPQD Campinas Brazil

ISBN: (纸本)9783800760510

In this paper, a downlink cell-free massive multipleinput multiple-output (CF massive MIMO) system and a network clustering is considered. Closed form sum-rate expressions are derived for CF and the clustered CF (CLCF) networks where linear precoders included zero forcing (ZF) and minimum mean square error (MMSE) are implemented. An MMSE-based resource allocation technique with multiuser scheduling based on an enhanced greedy technique and power allocation based on the gradient descent (GD) method is proposed in the CLCF network to improve the system performance. Numerical results show that the proposed technique is superior to the existing approaches and the computational cost and the signaling load are essentially reduced in the CLCF network. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Mean square error

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Model-Free Decentralized Training for Deep Learning Based Resource Allocation in Communication Networks

Model-Free Decentralized Training for Deep Learning Based Re...

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European Signal Processing Conference (EUSIPCO)

作者： Pourya Behmandpoor Panagiotis Patrinos Marc Moonen Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven

Decentralized deep learning (DL) based resource allocation (RA) in communication networks guarantees scalability and higher communication bandwidth efficiency compared to centralized RA. Although the RA is decentralized in such approaches, the policies are mostly trained in a centralized manner. In this paper, we investigate a decentralized model-free training approach based on zeroth-order optimization methods. Each user trains its individual policy-possibly with a unique structure-to guarantee the maximum global utility, e.g., sum rate (SR) of users. More importantly, during the training, the users need to share only scalar quantities with their neighbors, avoiding a large communication overhead. The training is also robust against a certain level of asynchrony between the users. The proposed approach relaxes the need for a computationally complex central server and offers the possibility for (re)training in dynamic environments in a model-free manner using the computational power at the edge. Numerical experiments show a competitive performance compared to centralized and federated training approaches.

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