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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： F. S. Chagas L. D. P. de Farias A. A. A. Bechina A. L. L. Ramos P. F. F. Rosa Department of Science and Industry Systems Faculty of Technology Natural Sciences and Maritime Sciences University of South-Eastern Norway Kongsberg Norway Department of Mechanical Engineering Military Institute of Engineering (IME) Rio de Janeiro Brazil Graduate Program in Defense Engineering Military Institute of Engineering (IME) Rio de Janeiro Brazil

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper proposes an algorithm to optimize the walking of humanoid robots based on the inverse kinematic model combined with a Genetic Algorithm. The objectives are to improve the sagittal displacement of the robot and reduce possible lateral deviations during a predetermined path. The foot of the humanoid performs a tapered motion, an approximate ellipse. Horizontal and vertical speeds and the angulation of the humanoid trunk are the input parameters of the algorithm. The algorithm utilizes the input information to calculate the inverse kinematics, and then it submits the obtained result to an evaluation function. We develop a virtual simulator and a robotic platform with 14 degrees of freedom to validate the proposed algorithm. We then test a prototype using the best result obtained in the simulations.

关键词： Legged locomotion Humanoid robots Prototypes Kinematics Approximation algorithms Genetics Information technology Biological cells Optimization Genetic algorithms

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Meta-optic accelerators for object classification

Meta-optic accelerators for object classification

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2023 Flat Optics: Components to systems, FLatOptics 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

A meta-optic platform for accelerating object classification is demonstrated. End-to-end design is used to co-optimize the optical and digital systems resulting in high-speed classifiers that are demonstrated for hand... 详细信息

ISBN: (纸本)9781957171289

关键词： Imaging systems Machine vision Neural networks Optical components Optical neural systems Optical systems

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Discretizing Continuous Action Space With Unimodal Probability Distributions for On-Policy Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning systems 2024年第6期36卷 11285-11297页

作者： Yuanyang Zhu Zhi Wang Yuanheng Zhu Chunlin Chen Dongbin Zhao Department of Control Science and Intelligence Engineering School of Management and Engineering and the Laboratory of Data Intelligence and Interdisciplinary Innovation Nanjing University Nanjing China Department of Control Science and Intelligence Engineering School of Management and Engineering and the Research Center for Novel Technology of Intelligent Equipment Nanjing University Nanjing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

For on-policy reinforcement learning (RL), discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between the discrete atomic actions, the explosion in the number of discrete actions can possess undesired properties and induce a higher variance for the policy gradient (PG) estimator. In this article, we introduce a straightforward architecture that addresses this issue by constraining the discrete policy to be unimodal using Poisson probability distributions. This unimodal architecture can better leverage the continuity in the underlying continuous action space using explicit unimodal probability distributions. We conduct extensive experiments to show that the discrete policy with the unimodal probability distribution provides significantly faster convergence and higher performance for on-policy RL algorithms in challenging control tasks, especially in highly complex tasks such as Humanoid. We provide theoretical analysis on the variance of the PG estimator, which suggests that our attentively designed unimodal discrete policy can retain a lower variance and yield a stable learning process.

关键词： Task analysis Probability distribution Aerospace electronics Reinforcement learning Gaussian distribution Technological innovation Learning systems

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Discovery of a Graphene Sibling: The Photonic Topological Quadratic-Node Semimetal

Discovery of a Graphene Sibling: The Photonic Topological Qu...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Zihe Gao Haoqi Zhao Tianwei Wu Xilin Feng Zhifeng Zhang Xingdu Qiao Ching-Kai Chiu Liang Feng Department of Materials Science and Engineering University of Pennsylvania Philadelphia PA USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia PA USA RIKEN Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS) Wako Japan

We report the experimental discovery of the topological quadratic-node semimetal in a photonic microring lattice with a robust second-order nodal point at the Brillouin zone center and two Dirac points at the Brilloui... 详细信息

ISBN: (纸本)9781957171258

关键词： Graphene Nodal points Reflection Scanning electron microscopy Spatial filtering Tunneling

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Towards Understanding the Critical Role of Human Behavior in Smart and Cognitive City Cybersecurity

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Procedia Computer Science 2025年 257卷 1039-1046页

作者： Rawan Alraddadi Mohammad Alshayeb Sajjad Mahmood Mahmood Niazi Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

The rapid development of cognitive cities, driven by IoT and AI technologies, introduces unique cybersecurity challenges aggravated by human-centric vulnerabilities. The objective of this paper is to examine the role of human factors in the cybersecurity landscape of both smart and cognitive cities. Through a literature review, we investigate how human interactions with digital infrastructures impact security and privacy. We highlight the need to address human errors, social engineering attacks, and insider threats while distinguishing how differences in infrastructure, intelligence, and citizen roles between smart and cognitive cities shape the management of human-centric vulnerabilities. Our review of existing research on integrating human behavioral data into AI systems suggests significant threat detection and response improvements. The findings suggest that human-centric AI detection tools not only mitigate risks of human errors and latent failures but also significantly improve protection for vulnerable populations, thereby promoting stronger human-AI collaboration. Ultimately, this paper advocates for advancing these tools to bolster urban cybersecurity frameworks.

关键词： human-centric vulnerabilities smart city cognitive cities AI cybersecurity

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A Security-Constrained Hydrothermal Unit Commitment Model in the Day-Ahead Electricity Market

SSRN

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SSRN 2022年

作者： Lezama-Lope, Uriel I. Ríos-Mercado, Roger Z. Ceciliano-Meza, José Luis Graduate Program in Systems Engineering Mexico Tecnológico de Monterrey Department of Industrial Engineering Mexico

In 2015, the Ministry of Energy created a wholesale electricity market in Mexico. As a result, the country shifted from a vertically integrated regulation to a competitive market framework. In this context, the day-ahead market is essential to plan better the generation and sales of energy. This paper evaluates a hydro-thermal security-constrained unit commitment model and its solution method. The objective function determines the maximum economic surplus of the participants in the market. The problem is modeled as a mixed-integer non-linear programming incorporating real-world constraints from the particular problem and a hydropower generator function non-linear features. A decomposition approach that solves a master problem and a series of subproblems is developed. Furthermore, a new component has been added to deal with the non-linear features of the hydropower generator function using a first-order Taylor's approximation. Simulation results are presented starting by solving a representative case of the electricity market in Mexico, demonstrating its use. Moreover, to evaluate the performance, multiple instances are created from publicly available information on the day-ahead market in Mexico. The model can be helpful in markets with sizable hydropower sources to obtain a more accurate and timely system operation scheduling. © 2022, The Authors. All rights reserved.

关键词： Power markets

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Development of a Lightweight Real-Time Application for Dynamic Hand Gesture Recognition

Development of a Lightweight Real-Time Application for Dynam...

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IEEE International Conference on Mechatronics and Automation

作者： Oluwaleke Yusuf Maki Habib Department of Mechanical Engineering Robotics Control and Smart Systems (RCSS) Program The American University in Cairo (AUC) Cairo Egypt

Hand Gesture Recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have been developed with different combinations of input modalities and network architectures with varying levels of efficacy. Such frameworks maximized performance at the expense of increased hardware and computational requirements. These drawbacks can be tackled by transforming the relatively complex dynamic hand gesture recognition task into a simpler image classification task. This paper presents a skeleton-based HGR framework that implements data-level fusion for encoding spatiotemporal information from dynamic gestures into static representational images. Said static images are subsequently processed by a custom, end-to-end trainable multi-stream CNN architecture for gesture classification. Our framework reduces the hardware and computational requirements of the HGR task while remaining competitive with the state-of-the-art on the CNR, FPHA, LMDHG, SHREC2017, and DHG142S benchmark datasets. We demonstrated the practical utility of our framework by creating a lightweight real-time application that makes use of skeleton data extracted from RGB video streams captured by a standard inbuilt PC webcam. The application operates successfully with minimal CPU and RAM footprint while achieving 93.46% classification accuracy with approximately 2s latency at 15 frames per second.

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Design of Double Integral Sliding Mode Controller for Energy Storage System of a Novel Multisource Hybrid Electric Vehicle

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Transportation Research Procedia 2025年 84卷 551-558页

作者： Saif Ullah Kamran Zeb Muhammad Farooq Siddique Muhammad Khalid Jaeyoung Kim Jong-Myon Kim Department of Electrical Electronic and Computer Engineering University of Ulsan Ulsan 44610 Republic of Korea Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum & Minerals (KFUPM) Dhahran 31261 Saudi Arabia Electrical Engineering Department KFUPM Dhahran 31261 Saudi Arabia Prognosis and Diagnostic Technologies Co. Ulsan 44610 Republic of Korea

The swift depletion of oil, natural gas, and fossil fuels caused by their excessive use serves as a strong motivation to seek potential alternatives. Emissions from traditional vehicles are unhealthy and include greenhouse gases. Therefore, various studies have been carried out on hybrid electric vehicles powered by fuel cells to overcome the problems of depleting natural resources and emissions of harmful gases. In this study, the proposed model consists of four sources: a fuel cell, a solar panel, a battery, and a supercapacitor. The fuel cell is the primary source while the remaining sources are secondary sources. DC converters are used to link the sources to the ssDC bus. A double integral sliding mode non-linear controller is designed and formulated for the hybrid energy storage system. The system’s overall stability has been ensured using Lyapunov criteria. The proposed model simulations are performed in MATLAB/Simulink 2024b to verify the controller’s performance. The results obtained authenticate stability, robustness, and efficient performance under various dynamic conditions while tracking the DC reference bus voltage of 400V.

关键词： Fuel Cell Photovoltaic Battery Supercapacitor Double Integral Sliding Mode Controller Hybrid Electric Vehicle

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Developing an Expert System for Mobile-based Gastroenteritis Detection Using ESDLC and Best First Search Methods

Developing an Expert System for Mobile-based Gastroenteritis...

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International Conference on Informatics and Computing (ICIC)

作者： R. Rizal Isnanto Catur Edi Widodo Ike Pertiwi Windasari Misik Puspajati Saputri Information Systems Doctoral Program Diponegoro University Semarang Indonesia Computer Engineering Department Diponegoro University Semarang Indonesia

Gastroenteritis is a common gastrointestinal disorder with varying degrees of severity, including cases without dehydration, mild dehydration, moderate dehydration, and severe dehydration. This research focuses on the development of a mobile-based expert system for detecting gastroenteritis, capable of distinguishing between these different dehydration levels. The methodology employed integrates the Extreme Structure Development Life Cycle (ESDLC) and the Best First Search algorithm. The expert system is designed to provide swift and accurate responses to users inputting symptoms, categorizing gastroenteritis cases based on the severity of dehydration. The application of ESDLC ensures a structured and organized development process, while the Best First Search method enhances the efficiency of the diagnostic process, facilitating timely and precise recommendations. Accessible through mobile platforms, the system allows users to input symptoms conveniently and receive rapid recommendations. Rigorous testing and evaluation affirm the system's high accuracy in discerning dehydration levels in gastroenteritis patients. In conclusion, the developed mobile-based expert system exhibits promising potential for advancing diagnostic services, particularly in the categorization of dehydration levels in gastroenteritis cases. The successful implementation of ESDLC and Best First Search paves the way for similar solutions in other healthcare domains, contributing positively to early and accurate disease management efforts. This innovation marks a significant stride towards improving healthcare technology for enhanced decision support in gastroenteritis diagnosis.

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Buckling-stretch-buckling dominated hybrid mechanical metamaterials fabricated from 3D-printed photopolymer: Design and investigation for enhanced multifunctional mechanical characteristics

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Journal of Materials Research and Technology 2025年 36卷 8643-8662页

作者： Kashif Azher Sajjad Hussain Suhail Hyder Vattathurvalappil Usman Ali Aamer Nazir Department of Mechanical Engineering King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Kowloon 999077 Hong Kong Special Administrative Region of China Interdisciplinary Research Center for Advanced Materials King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Aerospace Engineering King Fahad University of Petroleum & Minerals Dhahran 31261 Saudi Arabia

With additive manufacturing, lattice structures have become advanced frameworks, and hybrid lattice structures, a type of mechanical metamaterial, can be used for lightweight and impact-resistant materials. In this paper, we present a novel study that solves the global buckling problem by dividing the lattice structure into three layers and combining them based on the mechanical properties that are stretch- and bending-dominated structures. Stretch-dominant structures are in the middle layer, while bending-dominant structures are at the top and bottom. Octets, Iso-trusses, and Truncated cubes are hybridized with benchmark structures like BCC, FCC, and Kelvin, and additively manufactured using stereolithography to create mechanical metamaterials. A support vector machine (SVM) model was used to predict the peak load, force-displacement response, compressive strength, and energy absorption of all lattice structures under uniaxial compression. The hybrid lattice structure (FCC and Truncated cube) increased the peak load, compressive strength, and energy absorption by 15 %, 18 %, and 20 %, respectively, as compared to uniform lattices. Kelvin and Iso-truss hybrid lattice structures endure peak loads better than Kelvin uniform lattices. BCC and Truncated cube hybrid lattice structures absorb more energy and higher compression modulus than BCC. Designed hybrid mechanical metamaterials are multifunctional due to their global buckling resistance as compared to uniform structures. When compared with the analytical model, FCC and Truncated cube hybrid lattice structure exceed compressive modulus by 4 times that of the uniform lattices. SVM made accurate predictions with an average error of 3.2 %, 6.3 %, and 1.5 % for the peak force, compression modulus, and energy absorption, respectively.

关键词： Hybrid lattice structure Stretch dominant Bending dominant DfAM Energy absorption Machine learning

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