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Proceedings of the Winter Simulation Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (纸本)9798331534202

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

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A Compensation Mechanism for EV Flexibility Services using Discrete Utility Functions

A Compensation Mechanism for EV Flexibility Services using D...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Giraldo, Juan S. Arias, Nataly Banol Duque, Edgar Mauricio Salazar Hoogsteen, Gerwin Hurink, Johann L. University of Twente Mathematics and Computer Science Dept. of Electrical Engineering Netherlands Energy Transition Studies Netherlands Eindhoven University of Technology Electrical Energy Systems Group Netherlands

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Compensation mechanisms are used to counterbalance the discomfort suffered by users due to quality service issues. Such mechanisms are currently used for different purposes in the electrical power and energy sector, e.g., power quality and reliability. This paper proposes a compensation mechanism using EV flexibility management of a set of charging sessions managed by a charging point operator (CPO). Users' preferences and bilateral agreements with the CPO are modelled via discrete utility functions for the energy not served. A mathematical proof of the proposed compensation mechanism is given and applied to a test scenario using historical data from an office building with a parking lot in the Netherlands. Synthetic data for 400 charging sessions was generated using multivariate elliptical copulas to capture the complex dependency structures in EV charging data. Numerical results validate the usefulness of the proposed compensation mechanism as an attractive measure both for the CPO and the users in case of energy not served. © 2022 IEEE.

关键词： Office buildings

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Comparison of DenseNet201, InceptionV3, and Their Fusion Models in Breast Cancer Tissue Image Classification 16

Comparison of DenseNet201, InceptionV3, and Their Fusion Mod...

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16th International Conference on Digital Image Processing, ICDIP 2024

作者： Ge, Ruigang Chen, Guoyue Saruta, Kazuki Terata, Yuki Graduate School of Akita Prefectural University 84-4 Ebinokuchi Tsuchiya Yurihonjo city Akita015-0011 Japan Dept. of Information and Computer Science Faculty of Systems and Technology Akita Prefectural University 84-4 Ebinokuchi Tsuchiya Yurihonjo city Akita015-0011 Japan

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

In this study, we propose a fusion model based on DenseNet201 and InceptionV3 aimed at improving the accuracy of 8-class classification of the BreakHis breast cancer histopathological image dataset. The BreakHis dataset poses significant challenges for automatic classification tasks due to its high feature similarity and class imbalance. Our model leverages the deep connectivity of DenseNet201 and the broad exploratory capabilities of InceptionV3 to capture comprehensive features from local to global scales, enhancing adaptability to various feature scales. This fusion model outperforms individual DenseNet201 or InceptionV3 models in key performance metrics such as precision, recall, and F1 score. It shows marked performance improvements, particularly in categories with high feature similarity and fewer samples, demonstrating its effectiveness in addressing internal dataset imbalances. Additionally, the model exhibits stability across multiple training and testing iterations, further validating its reliability and effectiveness in classifying breast cancer histopathological images. The results indicate that the fusion of DenseNet201 and InceptionV3 models can enhance the accuracy of automatic classification of breast cancer histopathological images, especially in categories with high feature similarity. This finding provides valuable insights for the development and selection of classification models and contributes to future work in medical image analysis. © 2024 SPIE.

关键词： Lung cancer

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On-Chip Sensors Data Collection and Analysis for SoC Health Management

arXiv

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arXiv 2023年

作者： Shibin, Konstantin Jenihhin, Maksim Jutman, Artur Devadze, Sergei Tsertov, Anton Dept. of Computer Systems Tallinn University of Technology Tallinn Estonia Testonica Lab OÜ Tallinn Estonia

Data produced by on-chip sensors in modern SoCs contains a large amount of information such as occurring faults, aging status, accumulated radiation dose, performance characteristics, environmental and other operational parameters. Such information provides insight into the overall health of a system’s hardware as well as the operability of individual modules. This gives a chance to mitigate faults and avoid using faulty units, thus enabling hardware health management. Raw data from embedded sensors cannot be immediately used to perform health management tasks. In most cases, the information about occurred faults needs to be analyzed taking into account the history of the previously reported fault events and other collected statistics. For this purpose, we propose a special structure called Health Map (HM) that holds the information about functional resources, occurring faults and maps relationships between these. In addition, we propose algorithms for aggregation and classification of data received from on-chip sensors. The proposed Health Map contains detailed information on a particular system level (e.g., module, SoC, board) that can be compiled into a summary of hardware health status that in its turn enables distributed hierarchical health management by using this information at a higher level of system hierarchy, thus increasing the system’s availability and effective lifetime. Copyright © 2023, The Authors. All rights reserved.

关键词： System-on-chip

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Remote Motor Rehabilitation: EMG-IMU based Deep Learning Model Improves the Estimate of Wrist Kinematics 46

Remote Motor Rehabilitation: EMG-IMU based Deep Learning Mod...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Siviero, Ilaria Helmhold, Florian Ray, Andreas M. Bibián, Carlos Menegaz, Gloria Murguialday, Ander Ramos Storti, Silvia F. Dept. of Computer Science University of Verona Verona Italy Inst. of Medical Psychology and Behavioral Neurobiology University of Tübingen Tübingen Germany Gtc of Neuroscience Imprs for Cognitive and Systems Neuroscience Tübingen Germany Dept. of Engineering for Innovation Medicine University of Verona Verona Italy Tecnalia Basque Research and Technology Alliance San Sebastian Spain Dept. of Neurology and Stroke University of Tübingen Tübingen Germany Athenea Neuroclinics San Sebastian Spain

ISBN: (纸本)9798350371499

technology for motor rehabilitation faces challenges in uncontrolled settings, such as at home. In these real-world scenarios, robust signals like electromyographic (EMG) and inertial measurement unit (IMU) data are crucial for decoding continuous human actions. Classical modeling methods, such as linear, adaptive, or static filters, lack the capacity to capture complex relationships between surface EMG and kinematics, as well as generalizability across subjects. We propose a deep learning (DL) model for the continuous decoding of hand motion. Custom-made wearable devices acquired EMG-IMU data from 27 healthy subjects performing a wrist flexion/extension task. Two regression models were compared with a hybrid convolutional neural network and a gated recurrent unit. To address inter-subject variability, a leave-one-subject-out cross-validation approach was implemented. The DL model showed a mean R2 increase of 0.18 compared to the polynomial regressor. Our approach enhances wrist kinematics decoding providing a generalized model based on data captured with wearable devices. The findings hold potential for innovative home-based telemedicine solutions in motor rehabilitation. © 2024 IEEE.

关键词： Adaptive filters

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EfficientNetB0-based Rice Disease Classification: A Strategic Fine-Tuning Approach

EfficientNetB0-based Rice Disease Classification: A Strategi...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shivani Mishra Chavali Sai Sreeram Yadav Akansha Raju A Balasundaram Ayesha Shaik Dept. of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India Center for Cyber Physical Systems Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Rice, a major stape food, is crucial for both sustenance and the living of millions of farmers. Detecting rice leaf diseases is vital to prevent widespread crop losses, safeguarding farmers’ incomes and global food supply. While current detection systems face accuracy and robustness challenges, deep learning models, particularly the EfficientNetB0, show promise. Despite their sensitivity to noise and variations in leaf appearance, ongoing research addresses these issues. This study introduces the use of EfficientNetB0 for accurate classification, incorporating a fine-tuning strategy in a brief five-epoch training period. This approach optimally balances model adaptation and prevents overfitting, leveraging the deep neural network’s ability to capture complex hierarchical features. The limited fine-tuning duration addresses potential risks while maximizing the model’s robust features. This unique amalgamation of advanced architecture and tailored training strategy aims to achieve efficient and accurate disease classification. The research signifies a step toward revolutionizing rice disease detection, emphasizing the importance of striking a balance between cutting-edge methodologies and practical considerations in the pursuit of agricultural sustainability. Before Fine Tuning the Training Accuracy was $\mathbf{9 8. 4 6 \%}$, Validation Accuracy was $\mathbf{9 9. 1 5 \%}$ and Test Accuracy was $\mathbf{9 8. 4 4 \%}$.After Fine Tuning there was a significant increase in the accuracy. The Training Accuracy came to be $\mathbf{9 9. 7 7 \%}$, Validation Accuracy is $\mathbf{9 9. 4 3 \%}$ and Test Accuracy: 99.12%

关键词： Training Adaptation models Accuracy Sensitivity Computational modeling Noise Robustness Sustainable development Tuning Diseases

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Comprehensive Security Solution for an Industry 4.0 Garment Manufacturing System 3

Comprehensive Security Solution for an Industry 4.0 Garment ...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Kalhara, Dasunpriya De Alwis, Udara Jinadasa, Anuka Randunu, Dinuwan Nuwanthika, Wellalage Sasini Abeygunawardhana, Pradeep K. W. Sri Lanka Institute of Information Technology Dept. Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Industry 4.0, digital transformation towards Internet of Things (IoT) focuses on productivity rather than security, therefore often face cybersecurity challenges. To overcome such, a centralized security solution focusing on four major areas 1) Standardization, 2) Security configurations with update management, 3) Authentication and physical access control and 4) Intrusion detection was applied to Cyber Physical System (CPS) based garment manufacturing system to overcome the security gap. Security policies based on ISO 27001:2013 and IEC 62443 security standards were applied to the security configuration management system in the design stage to achieve 'security by design' concept. Security and audit configurations were applied using Ansible for the automated tool that can be customized based on requirements. A Raspberry Pi 4 was utilized as IDS (Intrusion Detection System) to evaluate performance. The physical access control prevents unauthorized access and access monitoring was done using logs. The proposed system enhances security, reaching for a cost-effective, efficient, reusable solution, and provides a comprehensive security solution for potential challenges of current and future smart manufacturing. The system is secured in terms of strategy, design, and operations rather than securing the system after deployment. © 2021 IEEE.

关键词： Industry 4.0

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Mobile Health Intervention System in Optimizing Healthcare Delivery for Cardiac Patients

Mobile Health Intervention System in Optimizing Healthcare D...

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Advancements in Computing (ICAC), International Conference on

作者： R.P.K. Prabashana G.A.J.W. Nanayakkara W.K.D.C. Wanniachchi S.B.A.M.B.S. Arampath Samantha Rajapaksha S.M.B. Harshanath Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

The advent of mobile health (mHealth) technologies has ushered in a new era in healthcare delivery, transforming the way cardiac patients receive medical care and support. This research paper explores the role of a mobile health intervention system in optimizing healthcare delivery for cardiac patients. The prevalence of heart attacks has necessitated innovative solutions to enhance patient outcomes and reduce the burden on healthcare systems. The proposed mHealth intervention system leverages the ubiquity of mobile devices to provide real-time monitoring, personalized interventions with environment prediction, and seamless communication between patients and healthcare providers. Through a comprehensive review of existing literature and empirical studies, this paper examines the impact of the mHealth intervention system on patient engagement, adherence to treatment plans, early detection of cardiac events, and overall quality of care. The integration of wearable device, CardioCare mobile application, and telemedicine platforms creates a holistic approach to cardiac care, empowering patients to actively participate in their health management and enabling healthcare professionals to make informed decisions. The potential benefits, challenges, and ethical considerations surrounding the implementation of this system are discussed.

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A Novel Technique to Parameterize Congestion Control in 6TiSCH IIoT Networks

A Novel Technique to Parameterize Congestion Control in 6TiS...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kushal Chakraborty Aritra Kumar Dutta Mohammad Avesh Hussain Syed Raafay Mohiuddin Nikumani Choudhury Rakesh Matam Mithun Mukherjee Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Hyderabad India Dept. of Computer Science & Engineering Indian Institute of Information Technology Guwahati India Nanjing University of Information Science and Technology Nanjing China

The Industrial Internet of Things (IIoT) refers to the use of interconnected smart devices, sensors, and other technologies to create a network of intelligent systems that can monitor and manage industrial processes. 6TiSCH (IPv6 over the Time Slotted Channel Hopping mode of IEEE 802.15.4e) as an enabling technology facilitates low-power and low-latency communication between IoT devices in industrial environments. The Routing Protocol for Low power and lossy networks (RPL), which is used as the de-facto routing protocol for 6TiSCH networks is observed to suffer from several limitations, especially during congestion in the network. Therefore, there is an immediate need for some modifications to the RPL to deal with this problem. Under traffic load which keeps on changing continuously at different instants of time, the proposed mechanism aims at finding the appropriate parent for a node that can forward the packet to the destination through the least congested path with minimal packet loss. This facilitates congestion management under dynamic traffic loads. For this, a new metric for routing using the concept of exponential weighting has been proposed, which takes the number of packets present in the queue of the node into account when choosing the parent at a particular instance of time. Additionally, the paper proposes a parent selection and swapping mechanism for congested networks. Performance evaluations are carried out in order to validate the proposed work. The results show an improvement in the performance of RPL under heavy and dynamic traffic loads.

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A Review of Algorithms's Complexities on Different Valued Sorted and Unsorted Data

A Review of Algorithms's Complexities on Different Valued So...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Majeed, Asad Iftikhar, Mahnoor Ali, Raja Hashim Arshad, Usama Shabbir, Muhammad Zeeshan Zeeshan Ijaz, Ali Ali, Nisar Aftab, Ali Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant. © 2023 IEEE.

关键词： Computational complexity

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