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2013 NASA/ESA Conference on Adaptive Hardware and systems, AHS 2013

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos Santos Coelho, Leandro Ayala-Rincón, Mauricio Electronics Engineering Graduate Program Faculty of Gama University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Departments of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9781467363839

The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times specifically for embedded optimization problems with computational limitations. For reducing execution times we propose a hardware parallel architecture of the FA algorithm that facilitates the implementation in Field programmable Gate Arrays (FPGAs). In addition, this work proposes the application of the opposition-based learning (OBL) approach to the FA algorithm. The respective hardware implementation (HPOFA) was mapped into a Virtex5 FPGA device and numerical experiments using four well-known benchmark problems demonstrate that the opposition-based approach allows the FA algorithm to improve its functionality, preserving the swarm diversity and avoiding the premature convergence problem. Synthesis results point out that the HPOFA architecture is effectively mapped in hardware and is suitable for embedded applications. © 2013 IEEE.

关键词： Parallel architectures

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An efficient heuristic for routing and wavelength assignment in optical WDM networks

An efficient heuristic for routing and wavelength assignment...

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IEEE International Conference on Communications (ICC)

作者： Yongbing Zhang K. Taira H. Takagi S.K. Das Institute of Policy and Planning Sciences University of Tsukuba Tsukuba Ibaraki Japan Doctoral Program in Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki Japan Department of Computer Science and Engineering University of Texas Arlington TX USA

ISBN: (纸本)0780374002

We propose an efficient heuristic algorithm that sets up and releases lightpaths for connection requests dynamically. We partition the routing and wavelength assignment (commonly known as RWA) problem into two subproblems and solves both of them using a well-known shortest path routing algorithm. For solving the routing subproblem, an auxiliary graph is created whereby the nodes and links in the original network are transformed to the edges and vertices, respectively, and the availability of each wavelength on the input and output links of a node as well as the number of available wavelength converters are taken into account in determining the weights of edges. Furthermore, for solving the wavelength assignment subproblem, an auxiliary graph is also utilized and the cost for wavelength conversion is taken into consideration in the edge weight function. A distinguished feature of our algorithm is that it employs more accurate network information on the availability of both the wavelengths and the wavelength converters than the existing algorithms in deciding the routing and the wavelength assignment. Simulation results show that our algorithm performs much better than previously proposed algorithms with comparable computation time, especially when the number of wavelengths is large while the number of converters at each node is limited.

关键词： Wavelength routing Wavelength assignment Optical fiber networks WDM networks Optical wavelength conversion Partitioning algorithms Heuristic algorithms Cost function Availability Computational modeling

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Towards Scalable Automated Grading: Leveraging Large Language Models for Conceptual Question Evaluation in engineering 1

Towards Scalable Automated Grading: Leveraging Large Languag...

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1st Workshop on Large Foundation Models for Educational Assessment

作者： Gao, Rujun Guo, Xiaosu Li, Xiaodi Narayanan, Arun Balajiee Lekshmi Thomas, Naveen Srinivasa, Arun R. J. Mike Walker’66 Department of Mechanical Engineering Texas A&M University United States Computer Science Department University of Texas Dallas United States Intelligent Systems Program University of Pittsburgh United States Department of Artificial Intelligence and Informatics Mayo Clinic United States

This study explores the feasibility of using large language models (LLMs), specifically GPT-4o (ChatGPT), for automated grading of conceptual questions in an undergraduate Mechanical engineering course. We compared the grading performance of GPT-4o with that of human teaching assistants (TAs) on ten quiz problems from the MEEN 361 course at Texas A&M University, each answered by approximately 225 students. Both the LLM and TAs followed the same instructor-provided rubric to ensure grading consistency. We evaluated performance using Spearman’s rank correlation coefficient and Root Mean Square Error (RMSE) to assess the alignment between rankings and the accuracy of scores assigned by GPT-4o and TAs under zero- and few-shot grading settings. In the zero-shot setting, GPT-4o demonstrated a strong correlation with TA grading, with Spearman’s rank correlation coefficient exceeding 0.6 in seven out of ten datasets and reaching a high of 0.9387. Our analysis reveals that GPT-4o performs well when grading criteria are straightforward but struggles with nuanced answers, particularly those involving synonyms not present in the rubric. The model also tends to grade more stringently in ambiguous cases compared to human TAs. Overall, ChatGPT shows promise as a tool for grading conceptual questions, offering scalability and consistency. © 2024 R. Gao1, X. Guo2, X. Li4, A.B.L. Narayanan3, N. Thomas1 & A.R. Srinivasa1.

关键词： Mean square error

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach

Initial Study of Oil Palm Crop Condition Detection Through D...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Winata Nugraha Ermatita Deris Stiawan Faculty of Computer Science Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Information System Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2]

关键词： YOLO Productivity Technological innovation Oils Multimedia systems Plantations Feature extraction Informatics Monitoring Drones

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Improving protein-RNA interface prediction by combining sequence homology based method with a naive bayes classifier: Preliminary results 11

Improving protein-RNA interface prediction by combining sequ...

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2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011

作者： Xue, Li C. Walia, Rasna EL-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Bioinformatics and Computational Biology Program Iowa State University Ames IA 50011 United States Department of Genetics Development and Cell Biology Iowa State University Ames IA 50011 United States Department of Computer Science Iowa State University Ames IA 50011 United States Department of Systems and Computer Engineering Al-Azhar University Cairo Egypt

ISBN: (纸本)9781450307963

Protein-RNA interactions play important roles in cellular processes like protein synthesis, RNA processing, and gene expression regulation. Reliable identification of the interfaces involved in RNA-protein interactions is essential for comprehending the mechanisms and the functional implications of these interactions and provides a valuable guide for rational drug discovery and design. Because the determination of 3D structures of protein-RNA complexes has various technical limitations and is typically costly, reliable in silico interface prediction methods that require only the sequence information are urgently needed. We present HomPRIP, a homologous sequence based method for predicting protein-RNA interfaces, based on our conservation analysis of protein-RNA interfaces. We test Hom-PRIP on a benchmark dataset of 199 proteins and compare it with the state-of-the-art protein-RNA interface prediction methods. Our results show that HomPRIP can reliably identify protein-RNA interface residues in 71% of test proteins with at least one putative sequence homolog passing the similarity thresholds of HomPRIP. Moreover, to facilitate predictions for proteins with no identified homologs, we develop HomPRIP-NB, a method combining the HomPRIP predictor and a Naive Bayes (NB) classifier trained using evolutionary information derived from alignments against the NCBI nr database. Our results suggest that HomPRIP-NB significantly outperforms the state-of-the-art machine learning methods for predicting protein-RNA interface residues. Copyright © 2011 ACM.

关键词： Proteins

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

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A Game-Theoretic Approach for Pricing and Determining Quality Levels of Cybersecurity Products Under an Exogenous Information-Sharing program 19th

A Game-Theoretic Approach for Pricing and Determining Qualit...

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19th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2022

作者： Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Price and quality level of products are two important decisions of any business. This paper provides equilibrium solutions for these decisions of two players for a cybersecurity ecosystem, including a solution provider and an information provider. We assume that end users join a cybersecurity ecosystem based on the prices and qualities of the solution provider’s and information provider’s products;so that, the increasing of the prices or/and decreasing the qualities will reduce the number of end-users of that ecosystem. Also, it is assumed that there is an exogenous information-sharing program under which the effect of quality of the information provider’s product on the cybersecurity level depends on the level of information-sharing. Under this information-sharing program, the equilibrium solutions of prices and qualities of these two players are given. Also, some results and insights are given with a numerical example. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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Prognostics and System Health Management Conference (PHM)

作者： Congrui Cai Zhonghai Lu Embedded Systems Master Program School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Department of Electrical Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms.

关键词： Performance evaluation Semiconductor device modeling Recurrent neural networks Computational modeling Estimation Power electronics Data models

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A Game-Theoretic Approach for Pricing and Determining Virtual Reality Investment and Cybersecurity Level in a Dual-Channel Supply Chain 19th

A Game-Theoretic Approach for Pricing and Determining Virtua...

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19th International Conference on the Economics of Grids, Clouds, systems, and Services, GECON 2022

作者： Alaei, Amir Mohammad Rasti-Barzoki, Morteza Altmann, Jörn Egger, Bernhard Department of Industrial and Systems Engineering Isfahan University of Technology Isfahan84156-83111 Iran Institute of Engineering Research College of Engineering Seoul National University Seoul08826 Korea Republic of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Department of Computer Science and Engineering College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031293146

Virtual reality has become a new option to inform the customers about product before purchasing. However, providing virtual reality may create new challenges. For instance, consumers may obtain essential information about products by using virtual reality option, but eventually they buy their products from the offline channel. This phenomenon is called webrooming. Another challenge is cyber-attacks. E-tailers and their consumers face risks from cyber-attacks. Thus, e-tailers are investing to improve cybersecurity. We consider a dual-channel supply chain consisting of an offline retailer and an e-tailer who purchase the same product from a manufacturer at the same wholesale price. The e-tailer offers a partial refund policy in order to attract the customers. Also, to reduce consumer valuation uncertainty, the e-tailer faces the decision on whether to introduce virtual reality. We analyze three scenarios. Firstly, the e-tailer does not offer virtual reality. Secondly, the e-tailer offers virtual reality, but he does not invest on cybersecurity. Thirdly, the e-tailer offers virtual reality and invests on cybersecurity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Game theory

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A Novel Ensemble XGBoost and Deep Q-Network for Pregnancy Risk Prediction on Multi-Class Imbalanced Datasets

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ICT Express 2025年

作者： Kurnianingsih Sou Nobukawa Melyana Nurul Widyawati Cipta Pramana Nurseno Bayu Aji Afandi Nur Aziz Thohari Dwiana Hendrawati Eri Sato-Shimokawara Naoyuki Kubota Department of Electrical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Department of Computer Science Chiba Institute of Technology 2-17-1 Tsudanuma Narashino 275-0016 Chiba Japan Department of Postgraduate Program Poltekkes Kemenkes Semarang Jl. Tirto Agung Pedalangan Kecamatan Banyumanik Semarang 50268 Central Java Indonesia Department of Obstetrics and Gynecology KRMT Wongsonegoro Hospital Jl. Tembalang Semarang 50272 Central Java Indonesia Department of Mechanical Engineering Politeknik Negeri Semarang H. Soedarto Tembalang Semarang 50275 Central Java Indonesia Faculty of Systems Design Tokyo Metropolitan University Tokyo 1910065 Japan

Addressing imbalanced data is essential for accurate prediction. We propose a novel ensemble method of XGBoost and deep Q-learning networks (DQN) for pregnancy risk prediction. First, we train the majority class utilizing XGBoost. We subsequently utilize DQN to train the minority class into binary classifications. Finally, we use the trained models from DQN and XGBoost in ensemble learning to generate the final classification results. The XGBoost-DQN model achieves high performance with 0.9819 in precision, recall, F1-score, and accuracy, outperforming several baseline classifiers on private data from 5,313 pregnant women in Indonesia and showing superior results on public datasets.

关键词： Imbalanced dataset ensemble learning XGBoost Tree Deep Q-Network pregnancy risk

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