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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Zhukovskii, Kirill Ovsiannikova, Polina Jhunjhunwala, Pranay Scarabaggio, Paolo Carli, Raffaele Dotoli, Mariagrazia Vyatkin, Valeriy Luleå Tekniska Universitet Department of Computer Science Computer and Space Engineering Sweden Aalto University Department of Electrical Engineering and Automation Espoo Finland Polytechnic University of Bari Department of Electrical and Information Engineering Bari Italy

ISBN: (纸本)9798350361230

This paper proposes a framework designed to optimise energy consumption in vertical farming. It aims to maximise cost efficiency by balancing between minimising system operations during the electricity price peaks and the ability to trade capacity on the FCR market while also fulfilling constraints on the internal growing process. We consider that the vertical farming system has distributed control with a series of actuators controlled by various spatially distributed PLCs that we refer to as agents, to underline their independence. The paper conducts two experiments for a lO-agent system with a Pareto controller and a lOO-agent system with a Lagrangian approach and shows the balance between more cost-efficient momentary energy consumption control. © 2024 IEEE.

关键词： energy consumption optimisation multi-agent systems vertical farming

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Transfer learning in breast mass detection and classification

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Journal of Ambient Intelligence and Humanized Computing 2024年第10期15卷 3587-3602页

作者： Ryspayeva, Marya Bria, Alessandro Marrocco, Claudio Tortorella, Francesco Molinara, Mario Department of Electrical and Information Engineering University of Cassino and Southern Lazio Viale dell’Università Cassino03043 Italy Department of Computer Engineering Astana IT University Mangilik El block C.1 Astana010000 Kazakhstan Department of Electrical Information Engineering and Applied Mathematics University of Salerno Via Giovanni Paolo II 132 Fisciano84084 Italy

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Mammography

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Class-conditional domain adaptation for semantic segmentation

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Computational Visual Media 2024年第5期10卷 1013-1030页

作者： Yue Wang Yuke Li James H.Elder Runmin Wu Huchuan Lu School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China School of Computer Science Wuhan UniversityWuhan 430072China Department of Electrical Engineering and Computer Science York UniversityToronto M3J 1P3Canada Department of Computer Science the University of Hong KongHong Kong 999077China

Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.

关键词： domain adaptation generative adversarial networks semantic segmentation cityscapes

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A novel deep learning approach for automated grading of knee osteoarthritis severity

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Multimedia Tools and Applications 2024年 1-20页

作者： Kaur, PrabSimran Kohli, Guneet Singh Bedi, Jatin Wasly, Saud Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Electrical and Computer Engineering Department King Abdulaziz University Jeddah22254 Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah22254 Saudi Arabia

Knee Osteoarthritis (OA) is a prevalent musculoskeletal disorder that affects the knee joint that causes pain, stiffness, and reduced mobility. It is also known as "Degenerative Joint Disease" and is caused by the degeneration of cartilage in the knee joint, leading to bone-on-bone contact and further damage. Knee OA is prevalent in the population, affecting around 22% to 39% of people in India, and there is currently no treatment available that can halt the progression of the disease. Therefore, early diagnosis and management of symptoms are essential to reduce its impact on an individual’s quality of life. To address this issue, have introduced a framework that leverages ConvNeXt architecture, a modernization of ResNets (ResNet-50) architecture towards Hierarchical Transformers (Swin Transformers), to provide accurate identification and classification of knee osteoarthritis. The classification of knee osteoarthritis was done using the Kellgren and Lawrence (KL) graded X-ray images. These images of the damaged knees are preprocessed and augmented, creating a scaled, enhanced, and varied version of the features, thus making the data fitter and more significant for classification. The performance estimation of the proposed strategy is conducted on the Osteoarthritis Initiative (OAI), a research project focused on knee osteoarthritis that works in partnership with NIH and other private industries to develop a public domain dataset that can facilitate research and evaluation. It involves training the prepared data using various hyper-tuned versions of ConvNeXt. The different fine-tuned results of the ConvNeXt models on each KL Grade are evaluated against the other state-of-the-art models and vision transformers. The comparative assessment of widely used performance measures shows that the proposed approach outperforms the conventional models by generating the highest score for all the KL grades. Lastly, an approach is employed to statistically confirm the validity of t

关键词： Joints (anatomy)

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GA-Stacking:A New Stacking-Based Ensemble Learning Method to Forecast the COVID-19 Outbreak

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computers, Materials & Continua 2023年第2期74卷 3945-3976页

作者： Walaa N.Ismail Hessah A.Alsalamah Ebtesam Mohamed Department of Management Information Systems College of Business AdministrationAl Yamamah UniversityRiyadh11512Saudi Arabia Faculty of Computers and Information Minia UniversityMinia61519Egypt Information Systems Department College of Computer and Information SciencesKing Saud UniversityRiyadh4545Saudi Arabia Computer Engineering Department College of Engineering and ArchitectureAl Yamamah UniversityRiyadh11512Saudi Arabia

As a result of the increased number of COVID-19 cases,Ensemble Machine Learning(EML)would be an effective tool for combatting this pandemic *** ensemble of classifiers can improve the performance of single machine learning(ML)classifiers,especially stacking-based ensemble *** utilizes heterogeneous-base learners trained in parallel and combines their predictions using a meta-model to determine the final prediction ***,building an ensemble often causes the model performance to decrease due to the increasing number of learners that are not being properly ***,the goal of this paper is to develop and evaluate a generic,data-independent predictive method using stacked-based ensemble learning(GA-Stacking)optimized by aGenetic Algorithm(GA)for outbreak prediction and health decision aided ***-Stacking utilizes five well-known classifiers,including Decision Tree(DT),Random Forest(RF),RIGID regression,Least Absolute Shrinkage and Selection Operator(LASSO),and eXtreme Gradient Boosting(XGBoost),at its first *** also introduces GA to identify comparisons to forecast the number,combination,and trust of these base classifiers based on theMean Squared Error(MSE)as a fitness *** the second level of the stacked ensemblemodel,a Linear Regression(LR)classifier is used to produce the final *** performance of the model was evaluated using a publicly available dataset from the Center for systems Science and engineering,Johns Hopkins University,which consisted of 10,722 data *** experimental results indicated that the GA-Stacking model achieved outstanding performance with an overall accuracy of 99.99%for the three selected ***,the proposed model achieved good performance when compared with existing baggingbased *** proposed model can be used to predict the pandemic outbreak correctly and may be applied as a generic data-independent model 3946 CMC,2023,vol.74,no.2 to pre

关键词： COVID-19 ensemble machine learning genetic algorithm machine learning stacking ensemble unbalanced dataset vaccine

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Advanced Schedulability Testing for Adaptive Mixed-Criticality systems under Weakly Hard Real-Time Constraints 5

Advanced Schedulability Testing for Adaptive Mixed-Criticali...

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5th CPSSI International Symposium on Cyber-Physical systems (Applications and Theory), CPSAT 2024

作者： Rabbiun, Hossein Mohammadi, Jamal Shirazi, Mahmoud Kargahi, Mehdi Department of Computer Science and Information Technology Zanjan Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran

ISBN: (纸本)9798331529284

This paper advances the schedulability analysis of the Adaptive Mixed-Criticality for Weakly Hard Real-Time systems (AMC-WH) which allows a specified number of consecutive low-criticality (LO) jobs of tasks to be skipped when the system operates in the high-criticality (HI) mode. The current AMC-WH schedulability test, based on the skip-over model, in which low tasks are guaranteed to meet m-s out of m consecutive deadlines, where s is the number of allowed skips and m is the cycle length, only calculates the worst-case response times (WCRT) for low tasks under a fixed execution pattern post-mode switch. To address this limitation, we propose a new sufficient schedulability test for AMC-WH using the (m,k)-firm model, which allows various execution patterns for low tasks after a mode switch, enhancing the flexibility and applicability. Additionally, we develop an exact schedulability test for AMC-WH based on response time analysis (RTA). This test modifies the existing exact tests for the AMC algorithm, incorporating updates to state-space exploration and pruning rules to account for low task execution after a mode switch. Simulation results validate the effectiveness of our tests, demonstrating enhanced schedulability analysis and optimized resource allocation. © 2024 IEEE.

关键词： Space research

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HRSpecNET: A Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Improved HAR Classification

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar systems 2024年 2卷 484-497页

作者： Biswas, Sabyasachi Manavi Alam, Ahmed Gurbuz, Ali C. Mississippi State University Department of Electrical and Computer Engineering Information Processing and Sensing Laboratory StarkvilleMS39672 United States

Micro-Doppler signatures (mu -DSs) are widely used for human activity recognition (HAR) using radar. However, traditional methods for generating mu -DS, such as the short-time Fourier transform (STFT), suffer from limitations, such as the tradeoff between time and frequency resolution, noise sensitivity, and parameter calibration. To address these limitations, we propose a novel deep learning (DL)-based approach to reconstruct high-resolution mu -DS directly from a 1-D complex time-domain signal. Our DL architecture consists of an autoencoder (AE) block to improve signal-to-noise ratio (SNR), an STFT block to learn frequency transformations to generate pseudo spectrograms, and, finally, a U-Net block to reconstruct high-resolution spectrogram images. We evaluated our proposed architecture on both synthetic and real-world data. For synthetic data, we generated 1-D complex time-domain signals with multiple time-varying frequencies and evaluated and compared the ability of our network to generate high-resolution mu -DS and perform in different SNR levels. For real-world data, a challenging radar-based American Sign Language (ASL) dataset consisting of 100 words was used to evaluate the classification performance achieved using the mu -DS generated by the proposed approach. The results showed that the proposed approach outperforms the classification accuracy of traditional STFT-based mu -DS by 3.48%. Both synthetic and experimental mu -DSs show that the proposed approach learns to reconstruct higher resolution and sparser spectrograms. © 2023 IEEE.

关键词： Signal to noise ratio

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OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION 12

OUT-OF-DOMAIN UNLABELED DATA IMPROVES GENERALIZATION

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12th International Conference on Learning Representations, ICLR 2024

作者： Saberi, Amir Hossein Movasaghinia, Mohammad Hosein Najafi, Amir Motahari, Seyed Abolfazl Heidari, Alireza Khalaj, Babak H. Department of Electrical Engineering Department of Computer Engineering Sharif Center for Information Systems and Data Science Iran Sharif Institute for Convergence Science & Technology Sharif University of Technology Tehran Iran

We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially robust or ii) non-robust loss functions have been considered. Notably, we allow the unlabeled samples to deviate slightly (in total variation sense) from the in-domain distribution. The core idea behind our framework is to combine Distributionally Robust Optimization (DRO) with self-supervised training. As a result, we also leverage efficient polynomial-time algorithms for the training stage. From a theoretical standpoint, we apply our framework on the classification problem of a mixture of two Gaussians in Rd, where in addition to the m independent and labeled samples from the true distribution, a set of n (usually with n m) out of domain and unlabeled samples are given as well. Using only the labeled data, it is known that the generalization error can be bounded by ∝ (d/m)1/2. However, using our method on both isotropic and non-isotropic Gaussian mixture models, one can derive a new set of analytically explicit and non-asymptotic bounds which show substantial improvement on the generalization error compared to ERM. Our results underscore two significant insights: 1) out-of-domain samples, even when unlabeled, can be harnessed to narrow the generalization gap, provided that the true data distribution adheres to a form of the "cluster assumption", and 2) the semi-supervised learning paradigm can be regarded as a special case of our framework when there are no distributional shifts. We validate our claims through experiments conducted on a variety of synthetic and real-world datasets. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Polynomial approximation

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Rollick Games: A Formal Language Based Platform for Location-Based Pervasive Games 1

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15th International Conference on Interactive Mobile Communication, Technologies, and Learning, IMCL 2023

作者： Anestis, George Gioldasis, Nektarios Karasavvidis, Stefanos Palioudakis, Tzanis Moumoutzis, Nektarios Christodoulakis, Stavros Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Greece School of Electrical and Computer Engineering Technical University of Crete Chania Greece

ISBN: (数字)9783031560750

ISBN: (纸本)9783031560743

In this paper, we present the Rollick Games platform and its Pervasive Game Modeling Language that enables the description of location-based pervasive games using a GUI provided by the Game Studio web app. The runtime environment (Pervasive Game Execution Environment) is responsible for generating the Pervasive Game Execution Language from a game model and loading it to an instance of the game's Execution Engine. The Execution Environment includes a set of agents responsible for monitoring the players’ context and interaction with other game elements and among each other. The Engine follows an event-driven paradigm and uses an event loop waiting for events to be dispatched from the agents to carry out its computations based on its current state, context, and event data. The proposed approach allows for both sharing and reusing game models, significantly reducing time to market and development costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Modeling languages

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Effective NSGA-II Algorithm for a Limited AGV Scheduling Problem in Matrix Manufacturing Workshops with Undirected Material Flow

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Complex System Modeling and Simulation 2025年第1期5卷 68-85页

作者： Xuewu Wang Jianing Zhang Yi Hua Rui Yu School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China Institute for Sustainable Manufacturing and also with the Department of Electrical and Computer EngineeringUniversity of KentuckyLexingtonKY 40506USA

Automatic guided vehicles(AGVs)are extensively employed in manufacturing workshops for their high degree of automation and *** paper investigates a limited AGV scheduling problem(LAGVSP)in matrix manufacturing workshops with undirected material flow,aiming to minimize both total task delay time and total task completion *** address this LAGVSP,a mixed-integer linear programming model is built,and a nondominated sorting genetic algorithm II based on dual population co-evolution(NSGA-IIDPC)is *** NSGA-IIDPC,a single population is divided into a common population and an elite population,and they adopt different evolutionary strategies during the evolution *** dual population co-evolution mechanism is designed to accelerate the convergence of the non-dominated solution set in the population to the Pareto front through information exchange and competition between the two *** addition,to enhance the quality of initial population,a minimum cost function strategy based on load balancing is *** local search operators based on ideal point are proposed to find a better local *** improve the global exploration ability of the algorithm,a dual population restart mechanism is *** tests and comparisons with other algorithms are conducted to demonstrate the effectiveness of NSGA-IIDPC in solving the LAGVSP.

关键词： limited automatic guided vehicle(AGV)scheduling problem nondominated sorting genetic algorithm II(NSGA-II) dual population co-evolution matrix manufacturing workshop

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