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3rd International Conference on computer Science and Computational Intelligence, ICCSCI 2018

作者： Darmadi, Herru Liawatimena, Suryadiputra Abbas, Bahtiar Saleh Trisetyarso, Agung Computer Science Department School of Computer Science Bina Nusantara University Jl. Kebun Jeruk Raya No 27 Jakarta Kemanggisan11480 Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Jl. Kebun Jeruk Raya No 27 JakartaKemanggisan11480 Indonesia Industrial Engineering Department BINUS Graduate Program Doctor Computer Science Bina Nusantara University Jl. Kebun Jeruk Raya No 27 Jakarta Kemanggisan11480 Indonesia Computer Science Department BINUS Graduate Program Doctor Computer Science Bina Nusantara University Jl. Kebun Jeruk Raya No 27 Jakarta Kemanggisan11480 Indonesia

The research purpose is to create a hypermedia driven API for learning object management. The research focuses on conception, design, implementation and evaluation of the learning object management hypermedia API. The hypermedia API was designed based on learning object models, hypermedia concepts, and related studies. Learning object model can be represented and manipulated as a hypermedia resource. It contains page resources with text and links to multimedia elements. The hypermedia API was designed using UML and represented in HTML5 because of its rich affordance. The implementation and evaluation phase were conducted by testing the API using three different client's software to prove the concept of hypermedia as the engine of application state for managing learning objects. The results show that the hypermedia API for learning object management is accessible, consistent, and discoverable by heterogonous client applications. © 2018 The Authors. Published by Elsevier Ltd.

关键词： HTML

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Localization and Perception for Control of a Low Speed Autonomous Shuttle in a Campus Pilot Deployment

arXiv

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arXiv 2024年

作者： Wen, Bowen Graduate Program in Electrical and Computer Engineering The Ohio State University United States

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. Accurate localization is essential for self driving vehicle navigation in GPS inaccessible environments. This thesis concentrates on low speed autonomous shuttles that are mainly utilized for university campus intelligent transportation systems and presents initial results of ongoing work on developing solutions to the localization and perception challenges of a university planned pilot deployment orientated application. The paper treats autonomous driving with real time kinematics GPS (Global Positioning Systems) with an inertial measurement unit (IMU), combined with simultaneous localization and mapping (SLAM) with three-dimensional light detection and ranging (LIDAR) sensor, which provides solutions to scenarios where GPS is not available or a lower cost and hence lower accuracy GPS is desirable. The in-house automated low speed electric vehicle from the Automated Driving Lab is used in experimental evaluation and verification. An improved version of Hector SLAM was implemented on ROS and compared with high resolution GPS aided localization framework in the same hardware architecture. The overall configuration that combines ROS with DSpace controller can be easily transplantable prototype in other hardware architectures for future similar research. Real-world experiments that are reported here have been conducted in a small test area close to the Ohio State University AV pilot test route. are used for demonstrating the feasibility and robustness of this approach to developing and evaluating low speed autonomous shuttle localization and perception algorithms for control and decision making. © 2024, CC BY.

关键词： Global positioning system

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Indoor localization at 5GHz using dynamic machine learning approach (DMLA)

Indoor localization at 5GHz using dynamic machine learning a...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Belay, Abebe Yen, Lei Renu, Sakthidasan Lin, Hsin-Piao Jeng, Shiann-Shiun Department of International Graduate Program in Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering National Dong Hwa University Taipei Taiwan

ISBN: (纸本)9781509048977

In recent years, Wi-Fi based indoor localization using received signal strength (RSS) gets considerable attention. However, RSS based Wi-Fi localization at 2.4GHz is highly susceptible and unstable. We proposed dynamic machine learning approach (DMLA) at 5GHz to effectively localize Wi-Fi users by means of feed-forward neural network algorithm. The simulation result shows that 90% of tested result has less than 0.4m estimation error, and no result shows greater than 0.47m location estimation errors. © 2017 IEEE.

关键词： Feedforward neural networks

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Development of Machine Learning Models to Predict Strip Breakage During the Aluminium Cold Rolling Process

Development of Machine Learning Models to Predict Strip Brea...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tiago Ramos Abreu Alexandre Magno Andrade Maciel Graduate Program in Computer Engineering (PPGEC) University of Pernambuco (UPE) Recife Brazil

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The aluminium processing industry relies on cold rolling as a crucial method for producing materials essential to everyday life. As a consequence of thickness reduction: Strip Breakage, which is one of the main degraders in the aluminum rolling process, arises. In more recent related studies, data were mainly chosen by experts, which can sometimes hide crucial causal factors. Therefore, this work develops Machine Learning models to predict strip breakage, by investigating the relationship between features and the breakage event, using attribute selection techniques to reduce the dimensionality of the problem, comparing classification methods based on trees, such as Decision Trees (DT), Random Forest (RF) and Extra Trees (ET). This work uses the following methodology: first, the models were evaluated for the complete set of data with default initial parameters, then a new evaluation of the models was carried out with the optimized parameters still with high data dimensions, and finally, an evaluation was carried out of the models with the best-selected attributes. The model that performed best was the DT with 21 attributes, with a recall or TPR (True Positive Rate) of 0.863 and an AUC (Compute Area Under of ROC Curve) of 0.926. The models also indicated that there is no unique cause that characterizes the breaks in the aluminium cold mill, but several others, such as the effect of the rolling mill, coil history domain, oil mill and lamination cylinders.

关键词： Industries Radio frequency Performance evaluation Strips Computational modeling Oils Aluminum Predictive models Data models Random forests

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Resume Recommendation using RNN Classification and Cosine Similarity 7th

Resume Recommendation using RNN Classification and Cosine Si...

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7th International Scientific Conference on Intelligent Information Technologies for Industry, IITI 2023

作者： Huseyinov, Ilham Diallo, Issa Raed, Mhd Wasim Software Engineering Department Istanbul Aydin University Istanbul Turkey Graduate Institute of Science Artificial Intelligence and Data Science Program Istanbul Aydin University Istanbul Turkey Computer Engineering Department Istanbul Aydin University Istanbul Turkey

ISBN: (纸本)9783031437885

One way to save time and resources in the human recruitment and hiring process is to post open job positions on the Internet, but the overload of applications creates challenges for hiring managers and companies to select an adequate candidate. One of the solutions is to apply intelligent tools such as Deep Learning and recommender system algorithms to speed up the hiring process and identify the right candidates. In this paper, we propose a two-fold algorithmic approach to 1) building an RNN classifier for resume classification;2) using cosine similarity for resume recommendation to find a candidate that fits job requirements best after selecting all the resumes that belong to the right category. The performance of the proposed RNN classifier is evaluated in terms of accuracy, precision, recall, F1-score, and confusion matrix criteria. The experiment results have shown that the RNN classifier performs better than the other classifiers such as GNB, Linear SVM, RF, and BERT on the same dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Recommender systems

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Bioinformatics

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IEEE Potentials 2004年第1期23卷 24-27页

作者： Doom, Travis Raymer, Michael Krane, Dan IEEE Wright State University United States Department of Computer Science and Engineering Graduate Faculty in Biomedical Sciences Dayton OH United States WSU Biomedical Sciences Ph.D. Program Forensic Bioinformatics Inc. United States Bioinformatics Research Group Computer Science Department Wright State University United States

No abstract available

关键词： Bioinformatics Biology computing Biological information theory Data analysis Computational biology Proteins Biochemistry Sequences Genomics Large-scale systems

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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Patient-specific pipeline to create virtual endoscopic third ventriculostomy scenarios

Patient-specific pipeline to create virtual endoscopic third...

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21st Medicine Meets Virtual Reality Conference, NextMed/MMVR 2014

作者： Armstrong, Ryan Eagleson, Roy De Ribaupierre, Sandrine Biomedical Engineering Graduate Program University of Western Ontario 1151 Richmond St. London ON Canada Department of Clinical Neurological Sciences University of Western Ontario Canada Department of Electrical and Computer Engineering University of Western Ontario Canada

ISBN: (纸本)9781614993742

Graphically realistic, haptics-enabled, virtual reality surgical simulations are becoming increasingly popular training tools for neurosurgical procedures and surgical skills. Compared with traditional training models, virtual simulations are safe, versatile, consistent, and are relatively cost effective. Most simulations are deficient, however, in representing the diverse anatomical variations that occur clinically. In this work, we describe the design and implementation of a pipeline to create patient-specific surgical scenarios for simulator-based training of the endoscopic third ventriculostomy: the procedure of choice for treating obstructive hydrocephalus. © 2014 The authors and IOS Press. All rights reserved.

关键词： Virtual reality

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3D Geometric Shape Assembly via Efficient Point Cloud Matching 41

3D Geometric Shape Assembly via Efficient Point Cloud Matchi...

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41st International Conference on Machine Learning, ICML 2024

作者： Lee, Nahyuk Min, Juhong Lee, Junha Kim, Seungwook Lee, Kanghee Park, Jaesik Cho, Minsu Department of Computer Science and Engineering POSTECH Pohang Korea Republic of Graduate School of Artificial Intelligence POSTECH Pohang Korea Republic of Department of Computer Science and Engineering Seoul National University Seoul Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul Korea Republic of

Learning to assemble geometric shapes into a larger target structure is a pivotal task in various practical applications. In this work, we tackle this problem by establishing local correspondences between point clouds of part shapes in both coarse- and fine-levels. To this end, we introduce Proxy Match Transform (PMT), an approximate high-order feature transform layer that enables reliable matching between mating surfaces of parts while incurring low costs in memory and compute. Building upon PMT, we introduce a new framework, dubbed Proxy Match TransformeR (PMTR), for the geometric assembly task. We evaluate the proposed PMTR on the large-scale 3D geometric shape assembly benchmark dataset of Breaking Bad and demonstrate its superior performance and efficiency compared to state-of-the-art methods. Project page: https://***/pmtr. Copyright 2024 by the author(s)

关键词： 3D modeling

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Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting 3

Single Layer & Multi-layer Long Short-Term Memory (LSTM) Mod...

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3rd International Conference on computer Science and Computational Intelligence, ICCSCI 2018

作者： Salman, Afan Galih Heryadi, Yaya Abdurahman, Edi Suparta, Wayan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Science and Technology Sanata Dharma University Yogyakarta55282 Indonesia

Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled and the wide availability of massive weather observation data have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. The purposes of this research are to build a robust and adaptive statistical model for forecasting univariate weather variable in Indonesian airport area and to explore the effect of intermediate weather variable related to accuracy prediction using single layer Long Short Memory Model (LSTM) model and multi layers LSTM model. The proposed forecasting model is an extension of LSTM model by adding intermediate variable signal into LSTM memory block. The premise is that two highly related patterns in input dataset will rectify the input patterns so make it easier for the model to learn and recognize the pattern from the training dataset. In an effort to achieve a robust model for learning and recognizing weather pattern, this research will also explore various architectures such as single layer LSTM and Multiple Layer LSTM (4 layers LSTM). The dataset is weather variable data collected by Weather Underground at Hang Nadim Indonesia airport. This research used visibility as predicted data and temperature, pressure, humidity, dew point as intermediates data. The best model of LSTM in this experiment is multiple layers LSTM and the best intermediate data is pressure variable. Using the pressure variable this model has gained the validation accuracy 0.8060 and RMSE 0.0775. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Long short-term memory

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