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20th International Joint Conference on Computer Science and Software Engineering, JCSSE 2023

作者： Rasdas, Sherrlyn M. Fajardo, Arnel C. Limbago, Jomel S. Graduate Program Technological Institute of the Philippines Manila Philippines College of Computing Science Information and Communication Technology -CCSICT Isabela State University-Cauayan Philippines Cavite Philippines

ISBN: (纸本)9798350300505

Manila Bay is a significant contributor to the Philippines' fish production, but its resources have been depleted due to overfishing, pollution, and damage, leading to a decline in fish catch and a shift towards less valuable species. Conventional approaches to fisheries stock assessment impose constraints on our comprehension of fish population dynamics. These limitations can be overcome through the utilization of machine learning techniques, which enable the forecasting and modeling of fisheries populations with improved accuracy and understanding. In this study, the commercial fisheries populations data collected from 2018 to 2021 in Manila Bay were used to predict the abundance of species fisheries production data using the K-NN - MLP - Logistic Regression (KNMLPR) model based on the majority voting ensemble approach. Analysis revealed that it is possible to combine the strengths of multiple models and improve overall predictive performance. The results also suggest that the k-nearest neighbors and logistic regression models have the best performance in predicting fish species population dynamics, while the neural network model shows slightly lower accuracy. This study provides valuable insights for fishery management and policymaking to support sustainable fishing practices in the region. Further research could focus on exploring additional machine learning algorithms and incorporating environmental factors to improve the prediction accuracy of the model. © 2023 IEEE.

关键词： Fisheries

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Model Prediction Using Random Forest Algorithm and Time Series Analysis Parts Distribution with in Indonesia Automotive Industry 8

Model Prediction Using Random Forest Algorithm and Time Seri...

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8th International Conference on Informatics and computing, ICIC 2023

作者： Andy Achmad, H. Ramadhan, Arief Warnars Harco Leslie, H. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung40257 Indonesia

ISBN: (纸本)9798350342604

Car spare parts are parts of a car that consist of components that form a certain unity and function, there are some parts that are damaged more often and therefore must be replaced more often. In this research, to predict the availability of spare parts using the times series moving average prediction method with random forest. The dataset used in this research is 19,068 consisting of location, transaction number, transaction date, part number, quantity and odo-km. Distribution of spare parts classified based on recapitulation of spare part distribution is RAE=54.5541% and RSE=63.8888%, distribution of spare parts based on location is RAE=55.5541% and RSE=65.2118%, maintenance based on ODO-KM is RAE=33.0949% and RSE=41.2241 % and distribution quantity RAE=33.749% and RSE=36.2317%. The results of spare part distribution analysis using the Confusion Matrix with probability accuracy are TP Rate=0.903, FP Rate=0.067, Precision=0.911, Recall=0.903, F-Measure=0.893, MCC=0.857, ROC Area=0.983 and PRC Area=0.948. The aim of this research is to determine the factors needed to build a machine learning-based spare parts distribution model, implementation, measurement and prediction in the automotive industry in Indonesia. © 2023 IEEE.

关键词： Automotive industry

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Hardware Design for the Affine Motion Compensation of the VVC Standard

Hardware Design for the Affine Motion Compensation of the VV...

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IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Marcello M. Muñoz Denis Maass Murilo Perleberg Luciano Agostini Marcelo Porto Graduate Program in Computing Federal University of Pelotas Pelotas Brazil

The Affine Motion Estimation (AME) of the Versatile Video Coding (VVC) standard is a high-complexity task. The AME requires Affine Motion Compensation (MC) to be performed for $4\times 4$ subblocks, where the Motion Vector relative to the $4\times 4$ subblock is adopted to define which of the 15 6-tap interpolation filters should be used to interpolate each sample of the $4\times 4$ subblock. This work presents a dedicated hardware implementation for the Affine MC of the VVC standard, where the ASIC synthesis results for this architecture for TSMC 40nm standard cells show an area requirement of 97.5k gates and power dissipation of 11.1mW when targeting the processing of 4K Ultra-High Definition (UHD) video at a frequency of 808MHz. To the best of the author's knowledge, this work presents the first hardware implementation of the VVC Affine MC in the literature.

关键词： Video coding VHDL Motion estimation Logic gates Parallel processing Hardware Motion compensation

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4K UHD@60fps Design For The VVC Affine Motion Estimation Reconstructor

4K UHD@60fps Design For The VVC Affine Motion Estimation Rec...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Marcello M. Muñoz Denis Maass Murilo Perleberg Luciano Agostini Guilherme Correa Marcelo Porto Graduate Program in Computing Federal University of Pelotas Pelotas Brazil

The Versatile Video Coding (VVC) standard adopts the Affine Motion Estimation (AME) as one of its main novelties, which significantly contributed to the coding efficiency gains of VVC over previous standards. On the other hand, AME requires a very high computational effort for processing high-resolution videos in real-time, making dedicated hardware mandatory for battery-powered devices. Considering this scenario, this work presents a dedicated hardware design for the Affine Reconstructor of the VVC standard targeting an ASIC TSMC 40nm standard-cell technology. The synthesis results show 94.4k Gates of area requirements, with 15.36 mW of power dissipation when targeting the processing of 60 fps of 4K Ultra-High Definition (UHD) videos. To the best of the author's knowledge, the presented design is the first in the literature targeting the Affine Reconstructor of the VVC standard.

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Analysis of human mobility behavior in Smart Cities and Smart Environments: A systematic literature review and taxonomy

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Journal of Smart Cities and Society 2024年第PRE-PRESS期Pre-press卷 1-36页

作者： Borges, Carlos Eduardo Liedtke Aranda, Jorge Arthur Schneider Barbosa, Jorge Luis Victória Applied Computing Graduate Program (PPGCA) University of Vale do Rio dos Sinos (UNISINOS) São Leopoldo Brazil

The analysis of human mobility behavior through computational techniques finds applications in various domains and provides valuable insights for urban planning, transportation services, and a deeper understanding of human interactions in Smart Cities and Smart Environments. In this scenario, this study presents a Systematic Literature Review (SLR) with the following main question: How are computational techniques being used to analyse human mobility behavior in Smart Cities and Smart Environments? A total of 5989 articles were initially found and filtered, resulting in 56 articles reviewed. As the main contributions, this study provides responses to 19 research questions. A list of the challenges and the computational techniques identified is provided. The algorithms, machine learning techniques and data-sources used by the reviewed studies are also presented and organized through taxonomies. A comprehensive discussion of the identified techniques is conducted, finishing with a compilation of challenges, open issues and research opportunities. To the best of our knowledge, this is the first study that reviewed human mobility behavior covering a wide range of scenarios, including urban mobility, public transport, points and regions of interest, ridesharing, bike-sharing, traffic analysis, driving behavior, electric vehicle charging stations planning, mobility on demand, crowd analysis and others.

关键词： Smart City Smart Environment behavior mobility crowd analysis

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Spatial Temporal Changes in Mangrove Forests on Tarakan Island, North Kalimantan, Indonesia 3rd

Spatial Temporal Changes in Mangrove Forests on Tarakan Isla...

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3rd International Conference on Radioscience, Equatorial Atmospheric Science and Environment, INCREASE 2023

作者： Giofandi, Eggy Arya Sekarjati, Dhanu Arshad, Mazlan Rahman, Henzulkifli Tjahjono, Boedi Graduate Program of Regional Planning Science Faculty of Agriculture IPB University Bogor Indonesia Geoinformatics Department of Computer Science College of Computing Khon Kaen University Khon Kaen Thailand Graduate Program of Coastal and Ocean Resource Management Faculty of Fisheries and Marine Science IPB University Bogor Indonesia Graduate Program of Tropical Biodiversity Conservation Faculty of Forestry and Environment IPB University Bogor Indonesia Department of Soil Science and Land Resources Faculty of Agriculture IPB University Bogor Indonesia

ISBN: (纸本)9789819707393

Mangroves play an important role in providing essential ecosystem services and resources for fisheries, offering protection against climate change and coastal erosion. Understanding the changes in mangrove forests is essential for the sustainability of the coastal ecosystem. The study aims to analyze the spatiotemporal changes of mangrove forests in Tarakan Island using Sentinel-1 and Sentinel-2 images from 2018 and 2023. The analysis shows changes in land cover, with an increase in aquaculture land and a decrease in other land types. The results of the accuracy assessment show good results, with an accuracy value of 0.82 and a Kappa value of 0.78 in the mangrove areas in 2023. These values indicate that the classification of land cover in the study area was reliable and accurate. The conversion of mangrove forests into aquaculture land poses a challenge for their conservation. Therefore, preserving and conserving mangrove forests in the study area is essential for maintaining the ecological balance and supporting the livelihoods of local communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Aquaculture

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Criteria Selection and Decision-Making Support in IT Governance: A Study via Fuzzy AHP Applied to a Multi-institutional Consortium

Criteria Selection and Decision-Making Support in IT Governa...

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International Conference on Information Technology and Systems, ICITS 2023

作者： de Oliveira, José Fábio de Sousa, Paulo Evelton Lemos Reis, Ana Carla Bittencourt Graduate Program in Applied Computing University of Brasília - UnB PPCA University Campus Darcy Ribeiro - Asa Norte DF CEP: 70910-900 Brasília Brazil

ISBN: (纸本)9783031332579

The formation of consortia between public and private institutions to achieve projects with a common objective is a practice considered usual in Brazil. Due to the different destinations between these institutions, divergences among decision-makers regarding the prioritization of actions may occur with some frequency. With this approach, this work seeks to minimize the impact of subjectivity in the prioritization of actions of a multi-institutional consortium, formed by public and private entities, with the support of Fuzzy logic and multi-criteria analysis to support the decision. Criteria and decision alternatives were identified and analyzed with the support of experts. At the end of the hierarchy, it was possible to present the top management of the Consortium with a proposal of ranked alternatives for optimizing the use of financial resources targeting IT Governance actions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy logic

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A machine learning pipeline for extracting decision-support features from traffic scenes 12

A machine learning pipeline for extracting decision-support ...

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12th International Workshop on Agents in Traffic and Transportation, ATT 2022

作者： Fraga, Vitor A. Schreiber, Lincoln V. Kunst, Rafael Barbosa, Jorge Luis V. Ramos, Gabriel de O. Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos São Leopoldo Brazil

Traffic systems play a key role in modern society. However, these systems are increasingly suffering from problems, such as congestions. A well-known way to efficiently reduce this kind of problem is to perform traffic light control intelligently through reinforcement learning (RL) algorithms. In this context, extracting relevant features from the traffic environment to support decision-making becomes a central concern. Examples of such features include vehicle counting on each queue, identification of vehicles’ origins and destinations, among others. Recently, the advent of deep learning has paved to way to efficient methods for extracting some of the aforementioned features. However, the problem of identifying vehicles and their origins and destinations within an intersection has not been fully addressed in the literature, even though such information has shown to play a role in RL-based traffic signal control. Building against this background, in this work we propose a deep learning pipeline for extracting relevant features from intersections based on traffic scenes. Our pipeline comprises three main steps: (i) a YOLO-based object detector fine-tuned using the UAVDT dataset, (ii) a tracking algorithm to keep track of vehicles along their trajectories, and (iii) an origin-destination identification algorithm. Using this pipeline, it is possible to identify vehicles as well as their origins and destinations within a given intersection. In order to assess our pipeline, we evaluated each of its modules separately as well as the pipeline as a whole. The object detector model obtained 98.2% recall and 79.5% accuracy. The tracking algorithm obtained a MOTA of 72.6% and a MOTP of 74.4%. Finally, the complete pipeline obtained an error of 7.53% in terms of origin and destination counts. © 2021 Copyright for this paper by its authors.

关键词： Object detection

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Defect Recognition on Single Layer Bare Printed Circuit Boards for Quality Control and Visual Inspection: A Low-Sample-Size Deep Transfer Learning Approach 9

Defect Recognition on Single Layer Bare Printed Circuit Boar...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Austria, Yolanda D. Fajardo, Arnel C. Technological Institute of the Philippines Graduate School Program Quezon City Philippines Isabela State University College of Computing Science Information and Communication Technology Isabela Cauayan City Philippines

ISBN: (纸本)9798350369922

Defect detection is a critical component in ensuring the highest quality of printed circuit board manufacture. The work investigates the application of transfer learning to identify YOLOv8 design flaws. The YOLOv8 model performs exceptionally well, with mean Average Precision (mAP) of 98.3%, Precision of 98.6%, and Recall of 96.0%. Numerous datasets provide high detection rates across a range of fault types following thorough testing and inference. This extensive evaluation demonstrates the robustness of the model by reliably identifying Fissure, Mousebite, EC-Short, MC-Spur, Breakout, Pinhole, and Copper defects. The study emphasizes how the YOLOv8 design can identify flaws, providing a dependable and effective way to guarantee the dependability and caliber of printed circuit boards throughout the production process. The foundation for utilizing YOLOv8 in industrial quality control is established by this study, which holds significant potential for improving defect detection and quality control in the manufacturing industry. © 2023 IEEE.

关键词： Printed circuit boards

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Defect Detection and Classification in Printed Circuit Boards using Convolutional Neural Networks 2

Defect Detection and Classification in Printed Circuit Board...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Austria, Yolanda D. Fajardo, Arnel C. Graduate School Program Technological Institute of the Philippines Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Isabela Cauayan City Philippines

ISBN: (纸本)9798350347579

PCB defect detection and classification is vital in electronics manufacturing to ensure high-quality and safe products. Manual inspection is time-consuming, costly, and prone to errors. To address this, automated methods using deep learning models have gained preference. These models learn from diverse datasets of defect images or videos. In this study, a deep learning model was developed and trained using a dataset containing various defect classes, including breakout, copper, crack, cut, excessive conductor, missing conductor, mouse bite, pinhole, spur, and scratch. The model showed promising results in detecting defects across all classes. © 2023 IEEE.

关键词： Image segmentation

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