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2nd International Congress on Advances in Mechanical and Systems engineering, CAMSE 2021

作者： Srivastava, Manish Pati, Smitarani Verma, Om Prakash Sharma, Tarun Kumar Gupta, Himanshu Arya, Raj Kumar Tiwari, Anurag Kumar Sahu, Deepak Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Computer Science and Engineering Shobhit University Saharanpur India Department of Chemical Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India

ISBN: (纸本)9789811921872

Trim loss is one of the major issues in the paper industry and occurs during the cutting of master and stocked reels to fulfill the ordered demands of reels with smaller widths. The trims (paper waste) produced during this reel cutting need to be reprocessed and follow a heavy chemical process that causes a significant amount of effluent discharge to the water surface which is hazardous as per environmental concern. Hence, this work aims to reduce the trim loss problem by using the flexible width master reels and stocked over-product and usable leftover reels. The minimization is performed with a linear single objective optimization problem with consideration of different operational and technological constraints. Three different solution approaches named simplex method, particle swarm optimization, and genetic algorithm have been proposed to optimize this trim loss problem employed for solving a real-world industrial problem. Again, the obtained solutions are analyzed in contrast with some reported results to prove the validity of the model and check the efficiency of the algorithms. The optimized result produces minimization in trim production due to which a lesser reproduction required that depletes the chemical use and tends to economic saving and reduction in environmental pollution. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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On the Existence of Static Equilibria of a Cable-Suspended Load with Non-stopping Flying Carriers

On the Existence of Static Equilibria of a Cable-Suspended L...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede AE The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This work answers positively the question whether non-stop flights are possible for maintaining constant the pose of cable-suspended objects. Such a counterintuitive answer paves the way for a paradigm shift where energetically efficient fixed-wing flying carriers can replace the inefficient multirotor carriers that have been used so far in precise cooperative cable-suspended aerial manipulation. First, we show that one or two flying carriers alone cannot perform non-stop flights while maintaining a constant pose of the suspended object. Instead, we prove that three flying carriers can achieve this task provided that the orientation of the load at the equilibrium is such that the components of the cable forces that balance the external force (typically gravity) do not belong to the plane of the cable anchoring points on the load. Numerical tests are presented in support of the analytical results.

关键词： Task analysis Aircraft Gravity

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Contrast enhancement of optical videoscope images

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Iran Journal of computer science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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FusionGCNN: An IoT-Based Novel Spatiotemporal Graph Convolutional Network for ECG Arrhythmia Detection

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IEEE Internet of Things Journal 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Alhussein, Musaed Wu, Zongze Aurangzeb, Khursheed Liu, Weixiang Zhang, Yudong Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia Henan Polytechnic University School of Computer Science and Technology Henan Jiaozuo China

Electrocardiogram (ECG) arrhythmia identification is critical for early cardiovascular disease diagnosis and monitoring in Internet of Things (IoT) industry. Still, it is difficult due to complicated waveforms, individual variability, and the requirement for real-time analysis on resource-limited equipment. Traditional approaches sometimes fail to detect complicated spatial-temporal correlations in ECG data, limiting their efficiency in identifying arrhythmias. Furthermore, deploying these models in tinyML contexts, such as edge and IoT devices limited by large computational and memory needs, emphasizes the importance of lightweight, accurate models for real-time applications. Our suggested solution consists of three main components: SigNet, DualGCNN, and FusionGCNN. SigNet uses Separable Convolution layers to effectively extract local spatial features, making it ideal for IoT-based healthcare deployment. DualGCNN combines dual Graph Convolutional layers with spatial attention, allowing the model to capture local and global dependencies for better classification of arrhythmia. FusionGCNN combines the capabilities of GCN and SigNet with an effective feature fusion technique to improve feature representation while remaining computationally economical. Ablation tests show that FusionGCNN improves performance considerably, with greater accuracy (0.9641), lower training error (0.0004), and a higher F1 Score (0.9645) across a variety of ECG patterns. FusionGCNN, with its low training error, high stability, and computational economy, is well-suited to tinyML requirements, allowing implementation on edge and IoT devices for scalable, real-time ECG monitoring in healthcare. © 2014 IEEE.

关键词： Electrocardiograms

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Renewable integration and energy reduction in multiple stage evaporator

Renewable integration and energy reduction in multiple stage...

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作者： Pati, Smitarani Kumar Sharma, Tarun Kumar Goyal, Kapil Prakash Verma, Om Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144011 India Department of Computer Science and Engineering Shobhit University UP Saharanpur-247341 India Department of Industrial and Production Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144011 India

Multiple Effect Evaporator (MEE) is an imperative part of the paper industry. It is used to process the waste byproduct named Black Liquor to enhance its solid concentration that may be further utilized to produce biomass-based energy. This process accounts very high energy consumption which leads to the energy intensiveness of this unit. A substantial amount of energy-saving may be accumulated by integrating various Energy Reduction Schemes (ERSs) with the MEE. Steam splitting, feed splitting, feed liquor preheating, thermo-vapor compressor, etc. are the well-known ERSs that may be incorporated with the MEE to reduce energy utilization. Hence, this study investigates the hybrid ERSs incorporated with MEE to observe their energy efficiency in an optimal manner. The prime focus of this investigation is to achieve enhanced energy efficiency in teams of maximum steam efficiency which is computed by formulating the nonlinear mathematical models of the MEE. To achieve the maximized energy efficiency, an optimization function has been developed under a constraint environment and solved by using metaheuristic-based approach named Flow Direction Algorithm. Another ERSs named flash tanks can also be integrated with this system to encourage waste heat re-utilization and ensures an increase in energy efficiency with constant steam consumption. This work also deals with the incorporation of the Linear Fresnel Reflector with ERSs integrated MEE which is capable of reducing the conventional energy load. The overall integrated MEE model achieves an energy-efficient and renewably operated MEE unit that may enhance the self-sustainability of the industry. © 2022

关键词： Energy efficiency

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： Image segmentation

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A Fully Automatic Visual Attention Estimation Support System for A Safer Driving Experience 9

A Fully Automatic Visual Attention Estimation Support System...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Fiani, Francesca Russo, Samuele Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Department of Psychology Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Drivers’ attention is a key element in safe driving and in avoiding possible accidents. In this paper, we present a new approach to the task of Visual Attention Estimation in drivers. The model we introduce consists of two branches, one which performs Gaze Point Detection to determine the exact point of focus of the driver, and the other which executes Object Detection to recognize all relevant elements on the road (e.g. vehicles, pedestrians, and traffic signs). The combination of the two outputs from the two branches allows us to determine whether the driver is attentive and, eventually, on which element of the road they are focusing. Two models are tested for the gaze detection task: the GazeCNN model and a model consisting of a CNN+Transformer. The performance of both models is evaluated and compared with other state-of-the-art models to choose the best approach for the task. Finally, the results of the Visual Attention Estimation performed on 3761 pairs of images (driver view and corresponding road view) from the DGAZE dataset are reported and analyzed. © 2023 Copyright for this paper by its authors.

关键词： Traffic signs

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A Novel Blockchain-Integrated Distributed Data Storage Model with Built-in Load Balancing 16

A Novel Blockchain-Integrated Distributed Data Storage Model...

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16th IEEE International Conference on Application of Information and Communication Technologies, AICT 2022

作者： Hammoud, Obadah Tarkhanov, Ivan A. National University of Science and Technology Department of Engineering Cybernetics Moscow Russia State Academic University for Humanities Federal Research Center 'Computer Science and Control' of Russian Academy of Sciences Russia

ISBN: (纸本)9781665451628

Most companies relay on centralized servers, which are considered as a fast and easy to deploy solution for file sharing, but they have many downsides, like security and trust issues, which can be solved using DLT (blockchain) solutions. However, DLT-based solutions have many challenges, such as nodes orchestration, adjusting reliability and double-spending, as well as load balancing. This research proposes a DLT (Distributed Ledger Technology) based model for files storage for companies using p2p networks, which takes into account both data reliability and storage space efficiency. More efficient data storage is achieved through the use of the Erasure coding technique. For load balancing, a set of algorithms that can be implemented in the form of a smart contract were proposed. The described model can become the basis of the IT infrastructure for trusted exchange of documents, technical and scientific information, games distributions, mobile applications, updates of scientific, regulatory and technical databases in b2b and b2c format © 2022 IEEE.

关键词： Blockchain

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DEGAN: A Deep Learning Model for SAR Image Segmentation in Aquacultural Raft Monitoring

DEGAN: A Deep Learning Model for SAR Image Segmentation in A...

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Neuromorphic Computing (ICNC), International Conference on

作者： Shuo Lian Jun Wang Min Han Jianchao Fan School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Department of Computer Science and Department of Data Science City University of Hong Kong Kowloon Hong Kong China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

Offshore aquacultural raft information extraction from synthetic aperture radar (SAR) images is crucial for large-scale marine resource exploitation and environmental protection. In this paper, a deep learning model named Detail-Enhancing Generative Adversarial Network (DEGAN) is proposed for SAR image segmentation to monitor aquacultural rafts. DEGAN incorporates several key modules to enhance segmentation performance, including $f$ -divergence to improve the discrim-inator's ability to handle noisy data, and the Convolutional Block Attention Module (CBAM) for more effective spatial and channel attention. Within CBAM, multi-scale feature fusion is employed to capture both fine and coarse details across different resolutions. Additionally, the Simple, Parameter-Free Attention Module (SimAM) is introduced to enhance feature refinement by deriving neuron importance through an energy function. These modules, when integrated into the GAN framework, work together to improve noise suppression and detail preservation, critical for accurate segmentation in challenging SAR images. Experimental results demonstrate that DEGAN outperforms five baseline models in four performance metrics, making it highly effective for aquacultural raft monitoring in complex marine environments.

关键词： Deep learning Image segmentation Generative adversarial networks Feature extraction Radar polarimetry Noise measurement Spatial resolution Monitoring Synthetic aperture radar Aquaculture

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Dorsal Hand Vascular Imaging System

Dorsal Hand Vascular Imaging System

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2024 IEEE Ural-Siberian Conference on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2024

作者： Mizinov, Pavel V. Konnova, Natalia S. Kravchenko, Oleg V. Egorov, Dobroslav P. Osipenko, Gavriil V. Bauman Moscow State Technical University Department Of Information Security Moscow Russia Computer Science And Control Of Ras Federal Research Center Moscow Russia Kotel'nikov Institute Of Radio Engineering And Electronics Of Ras Moscow Russia Moscow Aviation Institute Moscow Russia

ISBN: (纸本)9798350362893

Biometric identification methods using vascular modalities (vein pattern of the finger, wrist, ventral and dorsal part of the metacarpal) use near infrared (NIR) illumination to obtain an image of the vein pattern. The 'classic' attack scenario for this type of biometric system involves the use of a photograph of the user's vein pattern, printed on ordinary office paper and obtained under NIR illumination, as a presentation attack tool. This attack scenario has been well studied by researchers and there are several reliable ways to detect such an attack. New biometric attack vectors on these systems involve the use of attack tools created using NIR reflective compounds. In order to develop effective methods to detect and counter this type of attack, it is necessary to study the characteristics of the attack tool in each of the 750-950 nm NIR sub-bands of the venous channel imaging band. In this paper, the authors present the stages of creating a hardware tool for obtaining an image of the vascular pattern to solve the problem at hand. © 2024 IEEE.

关键词： Biometrics

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