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15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013

作者： Lopes, Rodolfo A. Pedrosa Silva, Rodrigo C. Campelo, Felipe Guimaraes, Frederico G. Graduate Program in Electrical Engineering Federal University of Minas Gerais UFMG Belo Horizonte - MG Brazil Department of Electrical and Computer Engineering Mcgill University Quebec Canada Department of Electrical Engineering Federal University of Minas Gerais UFMG Belo Horizonte - MG Brazil

ISBN: (纸本)9781450319645

In this paper, a new approach to the topology configuration problem in the Island Model (IM) is proposed. The mechanism proposed works with a pool of candidates for migration and the choice of immigrants is performed using the usual selection techniques of evolutionary algorithms. Computational tests on IM versions of the Differential Evolution show positive effects of the proposed approach in terms of the number of function evaluations required for convergence.

关键词： Evolutionary algorithms

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A Design for Improvement of Visual SLAM in Dynamic Environments Using Feature-Point Removal of Moving Persons

A Design for Improvement of Visual SLAM in Dynamic Environme...

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2023 International Automatic Control Conference, CACS 2023

作者： Song, Kai-Tai Meng, Ching-Hao National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering College of Electrical and Computer Engineering Hsinchu886-3-5731865 Taiwan National Yang Ming Chiao Tung University Graduate Degree Program of Robotics College of Engineering Hsinchu Taiwan

ISBN: (纸本)9798350306354

This paper presents a design to improve the robustness of visual SLAM(vSLAM). A processing step of feature-removal is added to the tracking thread of the conventional ORB-SLAM2 algorithm to improve the localization accuracy of a mobile robot in an environment with moving persons. Instance segmentation and motion tracking are intergrated to identify motion state of people in an image. ORB feature points belonging to moving persons are removed for further processing of the vSLAM pipeline. The advantage of this method is that the vSLAM can remove feature points of moving people, while retain those belonging to static people in the environment, which improves the accuracy of robot pose estimation. The improved ORB-SLAM2 algorithm has been implemented in a NVIDIA Xavier embedded system, which is integrated to a mobile robot. In practical robot navigation experiments, the average positioning error of the proposed method is within 4cm for 22.4m travel distance. Compared with conventional ORB-SLAM2, the average accuracy of our vSLAM method improves 97% in a dynamic environment with moving people. © 2023 IEEE.

关键词： Mobile robots

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Nonlinear Twistronics for EM Waves

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IEEE Transactions on Antennas and Propagation 2025年

作者： Valagiannopoulos, Constantinos Alu, Andrea National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece City University of New York Photonics Initiative Advanced Science Research Center New YorkNY10031 United States City College of The City University of New York Department of Electrical Engineering New YorkNY10031 United States City University of New York Physics Program Graduate Center New YorkNY10016 United States

The electromagnetic (EM) interaction between two anisotropic nonlinear metasurfaces, under arbitrary rotations of their optical axes, is rigorously studied. Multistability conditions with respect to rotation angles between the two surfaces are determined and explored. The examined setup demonstrates opportunities for memory and storage since these in-plane rotations may act as continuous information carriers. The abrupt transitions of the system response, from one solution branch to the other, materialize the concept of twistronics engineering in the classical EM regime, sensitive to small angular changes. These findings can enhance the design of several photonic devices from ultra-sharp sensors and switches to analog information processors and optical memory elements. © 1963-2012 IEEE.

关键词： Optical bistability

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The Effect of Viewpoint Change Strategies on Multi-View Video and Audio Transmission QoE Over ICN/CCN

The Effect of Viewpoint Change Strategies on Multi-View Vide...

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IEEE Region 10 International Conference TENCON

作者： Toshiro Nunome Yoshiyuki Takada Department of En-gineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technol-ogy Nagoya Japan

In this paper, we exploit caches on intermediate nodes for QoE enhancement of multi-view video and audio transmission over ICN/CCN by controlling the content request start timing of consumers. We assume the selected single viewpoint transmission method; a consumer receives video and audio streams of a requested viewpoint. We perform a simple experiment with two consumers. When the consumers play video and audio with the time difference, we assess the effect of cached content by the former consumer's request on the output quality of the latter consumer. We deal with two types of viewpoint change strategies for the former consumer, which affect the efficiency of cache utilization. From the assessment results, we see that cache utilization has an important factor in enhancing QoE.

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Big Data Governance for Building A Smart Cities 10

Big Data Governance for Building A Smart Cities

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10th International Conference on ICT for Smart Society, ICISS 2019

作者： Zulkarnain, Novan Kosala, Raymond Ranti, Benny Supangkat, Suhono Harso Bina Nusantara University Computer Science Department BINUS Graduate Program Doctor of Computer Science Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Computer Science Department Depok16424 Indonesia Institute of Technology Bandung School of Electrical and Informatics Engineering Bandung40132 Indonesia

ISBN: (纸本)9781728148809

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data. © 2019 IEEE.

关键词： Big data

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Downstream Task Performance of Pre-Trained Model For Outdoor Audio Event Classification 9

Downstream Task Performance of Pre-Trained Model For Outdoor...

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

作者： Rochadiani, Theresia Herlina Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wawan Bina Nusantara University Binus Graduate Program- Computer Science Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350369922

Our surroundings' auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (AEDC) systems has a great deal of potential applications in environmental monitoring, security, surveillance, and driverless cars. Even though AEDC has advanced significantly in indoor settings, difficulties still exist outside because of the variable and changing acoustic conditions brought on by elements like the weather, different sound sources, and ambient noise from traffic and industry. This study suggests an outdoor audio event categorization model based on convolutional neural networks (CNNs). The suggested model shows passable accuracy by utilizing adaptation to the downstream task using the ESC-50 dataset and transfer learning from a pre-trained model. The effectiveness of multi-class audio classification models in downstream tasks is analyzed in this paper, with an emphasis on the effect of an increasing number of output classes on accuracy. Three models - three, four, and five classes - with different output class configurations are used in the study, and their training and validation accuracies are assessed. Although the accuracy scores above 80% are commendable, the data show a discernible reduction in performance as the number of classes rises. Notably, the three-class model attains a validation accuracy exceeding 90%, whereas the four-class and five-class models exhibit reduced accuracies, falling below 90% and 85%, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

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IEEE Transactions on Network and Service Management 2023年第3期20卷 3413-3427页

作者： Bhat, Showkat Ahmad Huang, Nen-Fu Hussain, Imtiyaz Sajjad, Uzair National Tsing Hua University College of Electrical and Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Department of Computer Science Hsinchu1300044 Taiwan National Taipei University of Technology Graduate Program in Energy and Opto-Electronic Materials Taipei10608 Taiwan National Taipei University of Technology Department of Energy and Refrigerating Airconditioning Engineering Taipei10608 Taiwan

LoRa's biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel design. In this research, a Bayesian surrogate Gaussian process-based bidirectional LSTM stacked autoencoder model (BSGP-BLSTM-SAE) is proposed to estimate the channel performance indicators such as received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) and to determine the correlation between the ambient weather conditions and performance indicators for the LoRaWAN network. Bayesian optimization algorithm has been used to optimize the hyper-parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evaluation of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental LoRaWAN network. The mean absolute error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real-time optimization of the transmission parameters to enhance the network performance in LoRaWAN-based systems at different ambient conditions. © 2004-2012 IEEE.

关键词： Internet of things

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Improving Candle Direction Classification in Forex Market Using Support Vector Machine with Hyperparameters Tuning 7

Improving Candle Direction Classification in Forex Market Us...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Oetama, Raymond Sunardi Heryadi, Yaya Lukas Suparta, Wayan Computer Science Bina Nusantara University Jakarta Indonesia Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Cognitive Engineering Research Group Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Institut Teknologi Nasional Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Foreign Exchange market is the world's largest daily currency turnover. Two of the popular currencies Euro and Pound sterling traded against the US Dollar. Since the Russia and Ukraine war started in February 2022, their exchange rates decrease to the lowest rate ever. Even though the general trend is bearish, several daily candles increase for some days making challenges for forex analysts. To solve this problem, classification is applied. The data is labeled downward and upward. By utilizing Linear Kernel and Radial Basis Function (RBF) Kernel-based Support Vector Machines (SVM), the candle direction can be classified and optimized by tuning the Hyperparameters. The accuracy of candle direction classifications are highly improved. After tuning, in general, classification using Linear Models can outperform RBF Models. The best accuracy found on the Pound sterling against US Dollar by using the Linear model is 98.11% and the accuracy becomes 100% on data testing at a ratio of 70:30. Whilst for the Euro against the US Dollar, the best accuracy found the same for both Linear and RBF models on a ratio of 80:20 at 97.53%. However, on data testing, it decreases to 94.51% for Linear Model and 93.41% using RBF Model. The implication of this study is SVM can successfully classify candle direction on pairs in the Forex Market that are affected by a big event that comes for such a long period as long as the hyperparameter is tuned. © 2022 IEEE.

关键词： Support vector machines

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Fat percentage evaluation through portable ultrasound in adolescents: A comparison with dual energy X-ray absorptiometry

Fat percentage evaluation through portable ultrasound in ado...

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IEEE EMBS Conference on Biomedical engineering and Sciences (IECBES)

作者： Wagner Luis Ripka Pedro Miguel Gewehr Leandra Ulbricht Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná Curitiba Brazil Graduate Program in Biomedical Engineering Federal University of Technology - Paraná Curitiba Brazil

ISBN: (纸本)9781467377928

Ultrasound has been pointed as a low cost and extremely practical new tool for monitoring body composition and obesity. Thus, this study aims at analyzing the performance of portable US, in comparison with dual energy X-ray absorptiometry (DXA) reference method, in specific equations for predicting fat percentage on male adolescents. 143 male adolescents aged from 12 to 17 years old were evaluated and data on corporal mass, height and fat percentage were collected by US (%BF US) and DXA (%BF). The initial evaluation showed an under estimation of the US method (mean = 10.57 % and standard deviation = 4.45%) in comparison with DXA reference method (mean = 19.94 % and standard deviation = 5.36%). The creation of a specific model, developed from the US measurements, allowed values of R 2 = 0.853 with standard error of the estimate = 1.49. Bland-Altman concordance graphic analysis did not present significant bias. Results suggest that it is possible to use portable US technology to evaluate fat percentage in adolescents, provided that a specific mathematical modeling is used.

关键词： Fats Mathematical model Ultrasonic imaging Obesity Pediatrics Correlation Standards

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Deep Learning Model for Classifying Public Opinions on Energy Sector IHSG Stocks on the Twitter Social Media Platform

Deep Learning Model for Classifying Public Opinions on Energ...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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