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2020 IEEE Latin American GRSS and ISPRS Remote Sensing Conference, LAGIRS 2020

作者： Rodrigues, M.L. Korting, T.S. De Queiroz, G.R. Sales, C.P. Da Silva, L.A.R.D. Graduate program in Applied computing National Institute for Space Research Sçao José dos Campos SP Brazil Sçao José dos Campos SP Brazil

ISBN: (数字)9781728143507

ISBN: (纸本)9781728143507

In the last decades, the Brazilian Cerrado biome has undergone major transformations due to the expansion of the agricultural frontier. The region called MATOPIBA acronym for states Maranhão, Tocantins, Piauí, and Bahia can be considered very attractive for agricultural expansion. The Cerrado predominates in the MATOPIBA region (91% of the area), also having small areas of the Amazon and Caatinga biomes to the northeast and east, respectively. In this work, we will present a study to identify center pivot irrigation systems in the MATOPIBA region using remote sensing images from Landsat-8 satellite. The methodology is based on the use of robust edge detection techniques such as Canny, Circular Hough Transform (CHT) and time series extraction through the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13Q1 which has two vegetation indices NDVI and EVI. These time series will be used to filter the detected circles, seeking to eliminate the circles that do not correspond to center pivots. Our approach detected 80% of the center pivots mapped by the Brazilian National Water Agency (ANA) used as a knowledge base. The states with better detection were Piauí and Bahia that showed the accuracy of 90% and 85% respectively, Maranhão obtained 57% and Tocantins 41%. © 2020 IEEE.

关键词： Landsat

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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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Evaluation of an agent-based reinforcement learning framework for model-based testing 7

Evaluation of an agent-based reinforcement learning framewor...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Ramírez-Méndez, Jose Quesada-López, Christian University of Costa Rica Graduate Program in Computing and Informatics San Pedro Costa Rica University of Costa Rica Department of Computer Science and Informatics San Pedro Costa Rica

ISBN: (纸本)9798350364538

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework's performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving the vocabulary learning by personalized proficiency 12

Improving the vocabulary learning by personalized proficienc...

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12th International Conference on Ubi-Media computing, Ubi-Media 2019

作者： Lin, Yi-Zhen Chen, Kai-Hsiang Huang, Jen-Wei Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan

ISBN: (纸本)9781728128207

To improve the vocabulary ability is very important in language learning. Thus, if we can learn and remember a word very effectively, then we will be able to master a language more quickly. Therefore, many scholars began to propose related research. Due to the learning mechanism of human brain, sometimes when people learn a new knowledge they may forgot at a short time. In order to make the consideration more complete, after analyzing Hermann Ebbinghaus's forgetting curve experiment, we added two variables, one is the acceptance of each word by the same person, and the other is the ability of different people to remember vocabulary. With the above two parameters, we want to design a system to help user to review the vocabulary which may be forgetting before. The forgetting curve can be personalized, and it is more accurate to calculate the best time for each user to review the vocabulary. © 2019 IEEE.

关键词： Game design

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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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PPM-HC: A method for helping project portfolio management based on topic hierarchy learnings

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International Journal of Business Intelligence and Data Mining 2021年第3期18卷 364-382页

作者： Pinto, Ricardo A.M. Bernardini, Flavia Marcacini, Ricardo M. Graduate Program in Industrial Engineering and Computing Systems Fluminense Federal University Jardim Bela Vista Rio das OstrasRJ Brazil Computing Institute Fluminense Federal University São Domingos NiteróiRJ Brazil Information Systems Federal University of Mato Grosso do Sul P.O. Box 210 Três LagoasMS Brazil

The projects categorisation is a crucial step in the project portfolio management (PPM). Categorising projects allows the organisation to identify categories with a lack or excess of projects, according to its strategic objectives. In this work, we present a new method for project portfolio management based on hierarchical clustering (PPM-HC) to organise the projects at several levels of abstraction. In the PPM-HC, similar projects are allocated to the same clusters and subclusters. PPM-HC automatically learns an understandable topic hierarchy from the project portfolio dataset, thereby facilitating the (human) task of exploring, analysing and prioritising the projects of the organisation. We also proposed a card sorting-based technique which allows the evaluation of the projects categorisation using an intuitive visual map. We carried out an experimental evaluation based on a benchmark dataset and we also presented a real-world case study. The results show that the proposed PPM-HC method is promising. © 2021 Inderscience Enterprises Ltd.

关键词： Investments

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Indoors Object Location Protocol in a Smart Home

Indoors Object Location Protocol in a Smart Home

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Annual Conference of the IEEE Industrial Electronics Society

作者： Vinicius Prado da Fonseca Paulo F. F. Rosa Systems and Computing Graduate Program Instituto Militar de Engenharia Defense Engineering Graduate Program Instituto Militar de Engenharia

ISBN: (纸本)9781479902255

This article presents a system for tracking the position of objects within a smart home to support a robot assistant in pick-and-place tasks. The current system is capable of estimating the position of an object using the signal strength received by a mobile device in a ZigBee sensor network. Received strength signal indication measurements were done in laboratory for applying a estimation method. Two models were utilized (a) log-distance path loss - model in which signal lost has a random influence with log-normal distribution, and (b) free space decay law - based on the decay law for a signal on a open space. Experiments with satisfactory results were done with a public dataset to benchmark our data.

关键词： Indoors Positioning System RSSI ZigBee

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An IoT Transport Architecture for Passenger Counting: A Real Implementation

An IoT Transport Architecture for Passenger Counting: A Real...

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IFIP/IEEE International Symposium on Integrated Network Management

作者： Janine Kniess Julio Cezar Rutke William Alberto Cruz Castañeda Graduate Program in Applied Computing Santa Catarina State University Brazil

A smart urban mobility considers the technological-based development in transport systems, services and their use to improve the movement of people and goods inter or intra-cities. Cities increasingly face problems caused by traffic and need to find solutions to improve mobility and to reduce congestion and pollution. In this paper, we present a Passenger Counting Scheme into buses. Embedded counting algorithms based on Computer Vision are proposed to count passenger arrivals and departures in real time. Experiments in a real scenario expose that the proposed scheme achieved a high percentage of success in passenger count.

关键词： Training Computer vision Pollution Urban areas Computer architecture Cameras Real-time systems

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Data Classification combining Self-Organizing Maps and Informative Nearest Neighbor

Data Classification combining Self-Organizing Maps and Infor...

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International Joint Conference on Neural Networks

作者： Leandro J. Moreira Leandro A. Silva Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo Brazil Graduate Program in Electrical Engineering & Computing Mackenzie Presbyterian University Sao Paulo Brazil

ISBN: (纸本)9781509006212

The task of classifying data has been addressed in various works, and has been utilized in various areas of application, such as medicine, industry, marketing, financial market and many others. This work will present a data classifier proposal that combines the SOM (Self-Organizing Map) neural network with INN (Informative Nearest Neighbors). The combination of these two algorithms will be called in this work as SOM-INN. The classification will be done in a two steps task: in the first the SOM has a functionality to reduce the dataset through a process that utilizes the winning neuron concept and, in the second step, the dataset objects selected will be utilized as reference for the INN algorithm to decide about the classification utilizing the most informative object of the reduced dataset. Experiments using 14 public datasets will be made comparing classic classification algorithms from the indicators of accuracy and time consumed in the classification process. The results obtained show that the proposed SOM-INN algorithm, when compared with the other classifiers, presents better accuracy rates in databases where the border region does not have the object classes well distributed. However, its main differential is in the classification time, which is extremely important for real applications.

关键词： Classification algorithms Training Neurons Proposals Prototypes Measurement Self-organizing feature maps self-organising feature maps Sorting algorithm Nearest neighbor Neurons PROTOTYPE Functional training Dataset Pharmaceutical Industry pubs data classification GRHL3 gene

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Linguistic Based Steganography Using Lexical Substitution and Syntactical Transformation

Linguistic Based Steganography Using Lexical Substitution an...

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International Conference on IT Convergence and Security (ICITCS)

作者： Ari Barmawi Informatics Graduate Program School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9781509037667

Recently, secure information transmission is necessary such that no one can detect information sent via an open channel. In 2013, Chang and Clark proposed a method using lexical substitution and vertex coding for securing the information exchange between parties, such that no one can detect the exchanged information. However, the maximum payload capacity was still 2 bits. This work proposed a method for increasing the payload capacity of Chang's method by applying additional syntactical transformation instead of only lexical substitution and vertex coding. Based on the implementation, the payload resulted by the proposed method may increase minimum 2 bits, such that the minimum payload size is 4 bits, which is greater than Chang's method.

关键词： Encoding Receivers Pragmatics Payloads Context Natural languages Semantics

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