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

作者： Devi Fitrianah Kristien Margi Suryaningrum Noviyanti Tri Maretta Sagala Vina Ayumi Siew Mooi Lim Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Engineering Program Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Computer Science Department Faculty of Computing and Information Technology Tunku Abdul Rahman University College Kuala Lumpur Malaysia

ISBN: (纸本)9781665473286

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%.

关键词： Training Biological system modeling Sociology Ecosystems Fish Statistics Monitoring

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Modified Turnstile Antenna Design for Multi-Band and Broadband Applications

Modified Turnstile Antenna Design for Multi-Band and Broadba...

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USNC-URSI Radio science Meeting (Joint with AP-S Symposium)

作者： I-Fong Chen Chia-Mei Fei-Lung Wu Jian-An Xie Dept. of Computer and Communication School of Electronics and Electrical Engineering Dongguan Polytechnic Jinwen University of Science and Technology New Taipei City Taiwan Department of Communications Engineering Feng Chia University Taichung City Taiwan Ph.D. Program of Electrical and Communications Engineering Feng Chia University Taichung City Taiwan

ISBN: (数字)9789463968119

ISBN: (纸本)9798350359497

The proposed antenna with a size of 90 mm x 90 mm, and the ground portion is 64 mm x 64 mm, which material is FR4 glass epoxy substrate with the thickness of 1.6 mm, relative permittivity of 4.3 and loss tangent of 0.023, consists of four modified dipole elements and four power dividers, as shown in Figure. 1. For the multi-band and broadband operation, three dipole antennas were shunted to be a dipole element. The electrical-length of the dipole element can be determined from the one quarter-wave length at the 2.45 GHz, 5.5 GHz and 6.525 GHz, for covering 2400 MHz-2500 MHz, 5150 MHz-5850 MHz and 5925 MHz-7125 MHz. In order to operates in various modes such as normal mode and axial mode, the modified four port triple-band microstrip series power divider was designed. In normal mode, it radiates horizontally polarized waves where as in axial mode, it radiates circularly polarized waves. The radiated patterns of the proposed antenna as shown in Figure. 2. The features of the proposed antenna are shown in the Table I. Impressive radiated gains and efficiencies are obtained.

关键词： Power dividers Dipole antennas Conferences Glass Broadband communication Broadband antennas Microstrip

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Hybrid polynomial-based trajectory planning for lower limb exoskeleton robots

Hybrid polynomial-based trajectory planning for lower limb e...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Shupei Li Chi Zhang Feng Yi Pingping Lv Ting Yuan Meitong Zhang Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Electrical Engineering Hebei University of Science & Technology Ningbo China

To ensure smooth, continuous, and precise motion of lower limb exoskeleton robots, this paper proposes a 6-5-6 polynomial trajectory planning method (6-5-6 PTPM) that combines 5 th and 6 th polynomials. By imposing corresponding kinematic constraints, this method achieves the trajectory plan of seven sub-phases of the gait cycle on the kinematic model of the lower limb exoskeleton robot. The simulation is conducted and compared to the 4-3-4 polynomial trajectory planning method (4-3-4 PTPM). The comparison results indicate that the 6-5-6 PTPM is shown to have a lower error between the planned trajectory and the actual trajectory. Additionally, it avoids spikes of the acceleration curve that existed in the 4-3-4 polynomial method. The proposed method enables the acceleration curve smoother and mechanical impact lessened, which demonstrates the feasibility and stability of the proposed method.

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A Reinforced Active Learning Sampling for Cybersecurity NER Data Annotation

A Reinforced Active Learning Sampling for Cybersecurity NER ...

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Information Technology (OCIT), OITS International Conference on

作者： Smita Srivastava Deepa Gupta Biswajit Paul Shubhashisa Sahoo Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Centre of Artificial Intelligence and Robotics Défense Research and Development Organization Bangalore India

A vast majority of cybersecurity data comes in the form of unstructured textual data and needs to be annotated proficiently to train supervised machine learning models. The critical question is how much and which subset of data should be annotated for better model performance under budget constraints. Though most of the Machine Learning (ML) research focuses on learning better models using annotated datasets, this paper focuses on data annotation, specifically on suitable subset selection with an emphasis on Named Entity Recognition (NER) for cybersecurity. The proposed method provides an active learning based sampling strategy to select minimal yet most informative samples from a large set. Further, reinforcement learning is combined with the active learning approach to automate the process of sampling. The results on the auto-labelled cyber-NER dataset indicate that the cyber-NER model with Reinforced Active Learning (RAL) based sampling increases F1-Score by +2-7 % and reduces compute time by 90% compared to random sampling based subset selection. Further, the proposed RAL approach achieved an 80% reduction in sample size and, consequently, annotation cost with comparable accuracy to that of complete selection.

关键词： Costs Annotations Computational modeling Reinforcement learning Data models computer security Information technology

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Disclosure Privacy Information Social Media on TikTok

Disclosure Privacy Information Social Media on TikTok

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International Conference on Information Management and Technology (ICIMTech)

作者： Dedy Syamsuar Fazri Fahrezi Ahnaf Favian Edy Irwansyah Achmad Fakhri Irawan Professional Engineer Program Dept. Information System Department Faculty of Engineering School of Information System Bina Nusantara University Jakarta Indonesia Information System Department School of Information System Bina Nusantara University Jakarta Indonesia Computer Science Department School of Information System Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

With the rapid development of technology, the use of social media by the public, especially among young people, is increasing. One of the social media platforms currently used by young people is the TikTok application. It is a video-based TikTok feature accompanied by music, writing, and pictures that are considered attractive, so teenagers like it to show their existence and self-disclosure. Therefore, this study aims to examine the intention of users to disclose their privacy. As the basis of the theory, this study deployed the privacy calculus theory, where the perceived benefits and perceived risks play crucial roles in the intention to disclose their privacy. The study successfully collected 369 responses. The empirical findings indicated that only the relationship between Perceived Personalization (PP) and Perceived Risk (PR) was not supported. However, all the remaining hypotheses were supported. These findings emphasize how several complicated variables influence social media users' intentions to disclose their privacy. When people feel in control of their privacy settings and see significant benefits, they are more likely to divulge personal information; nevertheless, perceived risks can greatly discourage users.

关键词： Privacy Video on demand Social networking (online) Writing Control systems Calculus Information management Web sites

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Multi-scale Feature Fusion with Point Pyramid for 3D Object Detection

arXiv

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arXiv 2024年

作者： Lu, Weihao Zhao, Dezong Premebida, Cristiano Zhang, Li Zhao, Wenjing Tian, Daxin The School of Engineering University of Glasgow University Avenue GlasgowG12 8QQ United Kingdom The Institute of Systems and Robotics University of Coimbra Coimbra3030-290 Portugal The Department of Computer Science Royal Holloway University of London SurreyTW20 0EX United Kingdom The Department of Civil Environmental Engineering Hong Kong Polytechnic University Hong Kong The School of Transportation Science and Engineering Beihang University Haidian District Beijing100191 China

Effective point cloud processing is crucial to LiDAR-based autonomous driving systems. The capability to understand features at multiple scales is required for object detection of intelligent vehicles, where road users may appear in different sizes. Recent methods focus on the design of the feature aggregation operators, which collect features at different scales from the encoder backbone and assign them to the points of interest. While efforts are made into the aggregation modules, the importance of how to fuse these multi-scale features has been overlooked. This leads to insufficient feature communication across scales. To address this issue, this paper proposes the Point Pyramid RCNN (POP-RCNN), a feature pyramid-based framework for 3D object detection on point clouds. POP-RCNN consists of a Point Pyramid Feature Enhancement (PPFE) module to establish connections across spatial scales and semantic depths for information exchange. The PPFE module effectively fuses multi-scale features for rich information without the increased complexity in feature aggregation. To remedy the impact of inconsistent point densities, a point density confidence module is deployed. This design integration enables the use of a lightweight feature aggregator, and the emphasis on both shallow and deep semantics, realising a detection framework for 3D object detection. With great adaptability, the proposed method can be applied to a variety of existing frameworks to increase feature richness, especially for long-distance detection. By adopting the PPFE in the voxel-based and point-voxel-based baselines, experimental results on KITTI and Waymo Open Dataset show that the proposed method achieves remarkable performance even with limited computational headroom. © 2024, CC BY-NC-ND.

关键词： Object detection

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Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning

arXiv

引用

arXiv 2023年

作者： Wang, Mingyang Bing, Zhenshan Yao, Xiangtong Wang, Shuai Su, Hang Yang, Chenguang Huang, Kai Knoll, Alois Department of Informatics Technical University Munich Germany Tencent Robotics X Lab Singapore Dipartimento di Elettronica Politecnico di Milano Italy Bristol Robotics Laboratory University of the West of England United Kingdom School of Computer Science and Engineering Sun Yat-Sen University China Shenzhen Institute Sun Yat-Sen University China

Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this *** extend meta-RL to broad non-parametric task distributions which have never been explored before, and also achieve state-of-the-art results in non-stationary and out-of-distribution tasks. Specifically, MoSS consists of a task inference module and a policy module. We utilize the Gaussian mixture model for task representation to imitate the parametric and non-parametric task variations. Additionally, our online adaptation strategy enables the agent to react at the first sight of a task change, thus being applicable in non-stationary tasks. MoSS also exhibits strong generalization robustness in out-of-distributions tasks which benefits from the reliable and robust task representation. The policy is built on top of an off-policy RL algorithm and the entire network is trained completely off-policy to ensure high sample efficiency. On MuJoCo and Meta-World benchmarks, MoSS outperforms prior works in terms of asymptotic performance, sample efficiency (3-50x faster), adaptation efficiency, and generalization robustness on broad and diverse task distributions. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Mapping and Visualization of Artificial Intelligence for Digital Economy Research: A Bibliometric Approach

Mapping and Visualization of Artificial Intelligence for Dig...

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International Conference of computer science and Information Technology

作者： Fairuz Iqbal Maulana Agung Purnomo Andi Pramono Mohammad Nazir Arifin Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Interior Design Department School of Design Bina Nusantara University Jakarta Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia

ISBN: (纸本)9798350399080

Artificial Intelligence (AI) seems to be a disruptive technology that defines and reshapes the economy, more efficient industrial processes, new business models, and the service sector, becoming the development of different practices than before. The literature shows that it will be included in all fields and people's lives, as is the case in the digital economy. Artificial Intelligence for Digital Economy has not been studied significantly. This study uses bibliometric analysis to graphically map scientific publications and research trends in the Artificial Intelligence for Digital Economy sector around the world in the last ten years. The Scopus database was used to collect metadata information for this study, and VOSViewer was used to demonstrate bibliometric network mapping. We use an article selection procedure starting with the searched keywords and year constraints and then exporting the database to a RIS file format. Over the last ten years, we retrieved 540 scientific publications published between 2012 and 2021 from the Scopus database. From the data obtained, researchers at the Russian Federation have the most published papers indexed by Scopus among the most productive countries (N=131), the most productive authors are Petrenko, S.A. (N=4), and the most subject area is computer science (N=246). We also use VOSViewer to map the Network Theme. This study recommends incorporating research subjects Artificial Intelligence for Digital Economy: Thing, Education, Platform, Global Economy, abbreviated as TEPGE research theme.

关键词： computer science Databases Bibliometrics Taxonomy Education Metadata Market research

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Semantics for robotic mapping, perception and interaction: A survey

arXiv

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arXiv 2021年

作者： Garg, Sourav Sünderhauf, Niko Dayoub, Feras Morrison, Douglas Cosgun, Akansel Carneiro, Gustavo Wu, Qi Chin, Tat-Jun Reid, Ian Gould, Stephen Corke, Peter Milford, Michael QUT Centre for Robotics School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia School of Computer Science University of Adelaide Adelaide Australia College of Engineering and Computer Science Australian National University Canberra Australia

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Multi-Objective Optimization Design for DC-Link Capacitor of Motor Driver

Multi-Objective Optimization Design for DC-Link Capacitor of...

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Energy Storage and Conversion (ISESC), CPSS & IEEE International Symposium on

作者： Qian Liu Kangxing Huang Xueliang Wei Yihong Zheng Hao Xiang School of Automation Wuhan University of Technology Wuhan China Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Hongkong China College of Artificial Intelligence China University of Petroleum-Beijing Beijing China Software Development Department Wuhan iActuator Robotics Co. Ltd Wuhan China

ISBN: (数字)9798350380514

ISBN: (纸本)9798350380521

The capacitor is a common weak point in motor drive system design, necessitating targeted selection, design, and optimization of capacitor banks within these systems. Traditional capacitor design often relies on engineering experience, usually meeting specific capacitance and voltage requirements through simple parallel or series connections of capacitors. This approach often fails to fully optimize volume, cost, and reliability. This paper uses comprehensive theoretical modeling to optimize capacitor module design, effectively balancing capacitor lifespan, cost, and volume. For the design and selection of DC-link capacitors in a 1kW permanent magnet synchronous motor drive system, a multi-objective optimization design method for capacitor banks is proposed. The study treats the individual capacitance values of parallel capacitors as variables, significantly enhancing the overall performance of the capacitor module through precise optimization and the introduction of necessary constraints in the selection design. The proposed method is validated with a 1kW permanent magnet synchronous motor drive system. The optimization results show that under the guidance of a multi-objective optimization framework, the designed DC-link capacitor module meets physical size requirements while achieving overall optimization in terms of volume, cost, and lifespan, significantly improving system reliability.

关键词： Solid modeling Costs Capacitors Voltage Permanent magnet motors Capacitance Stability analysis Optimization Thermal stability System analysis and design

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