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Advances in Systems engineering - Proceedings of the 30th International Conference on Systems engineering, ICSEng 2023

作者： Gaber, Mohab Khamis, Ahmed Zydek, Dawid Egyptian Navy R&D Center Alexandria Egypt Guidance and Control Department Military Technical College Cairo Egypt Electrical and Computer Engineering Department University of Nevada Las Vegas United States

ISBN: (纸本)9783031405785

The global development of electrical power and generation systems has been accelerated by regulations to reduce greenhouse gas emissions and improve ecologically friendly energy sources. For the industry, designing and producing these criteria is extremely challenging. Electrification technologies are the best answer that is selected by the designers. They rely on electricity to run all systems;however, the electricity is generated by burning fossil fuels. The method adopted to address it is the diversification of fossil fuels with renewable energy sources. Technologies used to support this approach are utilizing intelligent energy management strategies. This paper proposes a hybrid power system based on the Adaptive Neuro-Fuzzy Interface System (ANFIS) technology and an intelligent Energy Management System (EMS). The performance of ANFIS is simulated by the SIMULINK/MATLAB system. The Raspberry PI embedded system is used as an intelligent energy management control system on a test bed laboratory bench in order to collect data in real-time. To verify the correctness of the EMS, the simulation results are compared with real-time data from the test bed with a Raspberry PI embedded device. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy management systems

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Enhancing Object Detection Robustness for Cross-Depiction Through Neural Style Transfer 8

Enhancing Object Detection Robustness for Cross-Depiction Th...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Fiani, Francesca Puglisi, Adriano Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Modern neural networks models for computer vision are trained on millions of images. The idea is that models are able to increase generalization when the dataset contains well diversified images, e.g. with varied illumination and environmental conditions of the same objects. Generalization is particularly relevant in object detection, especially for what concerns the cross-depiction problem. In this work we explore the use of Neural Style Transfer as a novel technique to morph the original data, with the aim to enhance model generalization. To verify the effect on performances for object detection models, we selected the Faster R-CNN model to be applied on the Pascal VOC 2012 dataset. A number of tests were performed through style variations on images and by tuning Neural Style Transfer parameters to maintain the content of the original images. The experiments showed promising results, which effectively provide a foundation for future studies on cross-depiction via Neural Style Transfer. © 2023 Copyright for this paper by its authors.

关键词： Object detection

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Synthetic Data Generation for Anomaly Detection on Table Grapes

arXiv

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arXiv 2024年

作者： Motoi, Ionut M. Belli, Valerio Carpineto, Alberto Nardi, Daniele Ciarfuglia, Thomas A. Sapienza University of Rome Department of Computer Control and Management Engineering RM Rome00185 Italy

Early detection of illnesses and pest infestations in fruit cultivation is critical for maintaining yield quality and plant health. computer vision and robotics are increasingly employed for the automatic detection of such issues, particularly using data-driven solutions. However, the rarity of these problems makes acquiring and processing the necessary data to train such algorithms a significant obstacle. One solution to this scarcity is the generation of synthetic high-quality anomalous samples. While numerous methods exist for this task, most require highly trained individuals for setup. This work addresses the challenge of generating synthetic anomalies in an automatic fashion that requires only an initial collection of normal and anomalous samples from the user-a task that is straightforward for farmers. We demonstrate the approach in the context of table grape cultivation. Specifically, based on the observation that normal berries present relatively smooth surfaces, while defects result in more complex textures, we introduce a Dual-Canny Edge Detection (DCED) filter. This filter emphasizes the additional texture indicative of diseases, pest infestations, or other defects. Using segmentation masks provided by the Segment Anything Model, we then select and seamlessly blend anomalous berries onto normal ones. We show that the proposed dataset augmentation technique improves the accuracy of an anomaly classifier for table grapes and that the approach can be generalized to other fruit types. © 2024, CC BY-NC-ND.

关键词： Fruits

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Exploring the Potential of Serious Games: Integrating Technological Innovations in Healthcare and Beyond

Exploring the Potential of Serious Games: Integrating Techno...

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Computing in Natural Sciences, Biomedicine and engineering (COMCONF), International Conference on

作者： Victor Triohin Ionela Samuil Andreea Ionica Monica Leba Department of System Control and Computer Engineering University of Petrosani Petrosani Romania Department of Management and Industrial Engineering University of Petrosani Petrosani Romania

ISBN: (数字)9798350353358

ISBN: (纸本)9798350353365

This paper explores the expansive field of digital games with the objective of identifying a niche for a novel game concept, “Everyday Fantasy,” designed to promote well-being through interactive and immersive gameplay. The game seeks to integrate physical activity, cognitive stimulation, and social engagement by encouraging users to explore their environment, solve puzzles, and interact with others, thereby harnessing the benefits of walking, brain training, and socialization within a singular platform. Employing User Experience design principles within the Software Development Life Cycle framework, this study outlines the development process and assesses the game's market viability through dynamic simulation. By analyzing four distinct market scenarios based on adoption rates, the research identifies key technical components essential for the game's functionality, including a step counter, map generator, database management system, network mirroring technologies, and Unity game mechanics. The findings from this study not only affirm the potential market space for “Everyday Fantasy” but also set a foundational base for future development efforts, highlighting the game's capacity to make a meaningful impact on users' wellbeing.

关键词： Training Legged locomotion Technological innovation Medical services User experience Generators Database systems Serious games Object recognition Software development management

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Proposed Intelligent Energy Management Systems for Hybrid Electric Traction System 30th

Proposed Intelligent Energy Management Systems for Hybrid El...

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Advances in Systems engineering - Proceedings of the 30th International Conference on Systems engineering, ICSEng 2023

作者： Gaber, Mohab Khamis, Ahmed Zydek, Dawid Egyptian Navy R&D Center Alexandria Egypt Guidance & Control Department Military Technical College Cairo Egypt Electrical and Computer Engineering Department University of Nevada Las Vegas United States

ISBN: (纸本)9783031405785

The global development of electrical power and generation systems has been accelerated by regulations to reduce greenhouse gas emissions and improve ecologically friendly energy sources. For the industry, designing and producing systems that meet these criteria is extremely challenging. The best option for electrification technologies is to rely on electricity to run all systems instead of burning fossil fuels. To cut greenhouse gas emissions, the diversification of renewable energy sources is adopted. These electrification strategies are supported by intelligent Energy Management Systems (EMSes). In this paper, hybrid electric traction system with a fuel cell serving as the primary power source is considered, while a battery bank serves as an auxiliary power source. The EMS between the hybrid system and the system load is challenging to implement. This paper proposes an EMS for hybrid electric power system based on Adaptive Neuro-Fuzzy Interface System (ANFIS) technology. The performance of ANFIS is simulated in the MATLAB Simulink. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuel cells

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Human arm motion capture system

Human arm motion capture system

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International Conference on Mathematics and computers in Sciences and in Industry (MCSI)

作者： Marius Leonard Olar Monica Leba Andreea Ionica Department of System Control and Computer Engineering University of Petrosani Petrosani Romania Department of Management and Industrial Engineering University of Petrosani Petrosani Romania

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

The implementation of robotics and human support technologies has opened up new possibilities for recovering the mobility impaired and increasing human productivity in the last few decades. Exoskeletons have been developed as a major component of this progress in order to capture any form of arm motion be it passive or active. Motion capture systems, employing different sensors like IMU sensors, EMG sensors, motion sensors, fiber optic sensors are used to capture arm movements. These sensors provide information on position, movement, muscle contraction, and movement intent of the arm. The use of multiple sensor types allows for redundancy and accurate outcomes. Potentiometer position sensor can be combined with other types of sensors to measure acceleration, orientation, force exerted and arm bendability. The combinations improve accuracy and adaptability for arm motion capture during rehabilitation. Various combinations of adapted sensors can be used for each joint of the hand depending on the peculiarities of application. Sensors enable precise and natural control over recovery devices or exoskeletons which is especially important in terms of rehabilitation process improvement.

关键词： Hands Optical fiber sensors Accuracy Service robots Exoskeletons Potentiometers Manipulators Motion capture Sensor systems Sensors

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Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers (vol 2, pg 1, 2022)

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INFECTION control & HOSPITAL EPIDEMIOLOGY 2023年第10期44卷 1705-1705页

作者： Kamineni, Meghana Otles, Erkin Oh, Jeeheh Rao, Krishna Young, Vincent B. Li, Benjamin Y. West, Lauren R. Hooper, David C. Shenoy, Erica S. Guttag, John G. Wiens, Jenna Makar, Maggie Electrical Engineering and Computer Science Department Massachusetts Institute of Technology Cambridge Massachusetts Medical Scientist Training Program University of Michigan Medical School Ann Arbor Michigan Department of Industrial and Operations Engineering University of Michigan College of Engineering Ann Arbor Michigan Division of Computer Science and Engineering University of Michigan College of Engineering Ann Arbor Michigan Department of Internal Medicine Division of Infectious Diseases University of Michigan Medical School Ann Arbor Michigan Infection Control Unit Massachusetts General Hospital Boston Massachusetts Division of Infectious Diseases Massachusetts General Hospital Boston Massachusetts Harvard Medical School Boston Massachusetts

Many data-driven patient risk stratification models have not been evaluated prospectively. We performed and compared the prospective and retrospective evaluations of 2 Clostridioides difficile infection (CDI) risk-prediction models at 2 large academic health centers, and we discuss the models’ robustness to data-set shifts.

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Uncertainty-aware segmentation for rainfall prediction post processing

arXiv

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arXiv 2024年

作者： Monaco, Simone Monaco, Luca Apiletti, Daniele Department of Control and Computer Engineering Politecnico di Torino Torino Italy Department of Environmental Engineering Politecnico di Torino Torino Italy

Accurate precipitation forecasts are crucial for applications such as flood management, agricultural planning, water resource allocation, and weather warnings. Despite advances in numerical weather prediction (NWP) models, they still exhibit significant biases and uncertainties, especially at high spatial and temporal resolutions. To address these limitations, we explore uncertainty-aware deep learning models for post-processing daily cumulative quantitative precipitation forecasts to obtain forecast uncertainties that lead to a better trade-off between accuracy and reliability. Our study compares different state-of-the-art models, and we propose a variant of the well-known SDE-Net, called SDE U-Net, tailored to segmentation problems like ours. We evaluate its performance for both typical and intense precipitation events. Our results show that all deep learning models significantly outperform the average baseline NWP solution, with our implementation of the SDE U-Net showing the best trade-off between accuracy and reliability. Integrating these models, which account for uncertainty, into operational forecasting systems can improve decision-making and preparedness for weather-related events. © 2024, CC BY.

关键词： Prediction models

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Human-Robot Collaboration using Monte Carlo Tree Search

Human-Robot Collaboration using Monte Carlo Tree Search

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2022 International Conference on Automation, Robotics and computer engineering, ICARCE 2022

作者： Yao, Feng Zhao, Huailin Liu, Huaping Shanghai Institute of Technology University Graduate School of Control Science and Engineering Shanghai China Tsinghua University Department of Computer Department Beijing China

ISBN: (纸本)9781665475488

Human-Robot collaboration as a challenging task has received great attention in the academic research field. Many existing search models are aimed at single agent or multi-agent, but there are some defects in the search efficiency of their task targets. Therefore, we propose a human-computer cooperative search algorithm in the indoor scene, where people and agents cooperate to complete the search of related objects. We have developed a platform for human-robot collaboration, and designed a set of algorithms for agent to integrate scene prior knowledge, target recognition, and path planning. The experimental results that the H-R cooperative search model proposed by us shows good efficiency in target search tasks. © 2022 IEEE.

关键词： Motion planning

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Learning-Based State Estimation for Automated Lane-Changing 27

Learning-Based State Estimation for Automated Lane-Changing

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Ha, Won Yong Chakraborty, Sayan Lin, Xiaoyi Ozbay, Kaan Jiang, Zhong-Ping New York University Control and Networks Lab Department of Electrical and Computer Engineering 370 Jay Street BrooklynNY11201 United States C2SMARTER Center New York University Department of Civil and Urban Engineering 6 MetroTech Center BrooklynNY11201 United States

ISBN: (纸本)9798331505929

This paper introduces a learning-based optimal control strategy enhanced with nonmodel-based state estimation to manage the complexities of lane-changing maneuvers in autonomous vehicles. Traditional approaches often depend on comprehensive system state information, which may not always be accessible or accurate due to dynamic traffic environments and sensor limitations. Our methodology dynamically adapts to these uncertainties and sensor noise by iteratively refining its control policy based on real-time sensor data and reconstructed states. We implemented an experimental setup featuring a scaled vehicle equipped with GPS, IMUs, and cameras, all processed through an Nvidia Jetson AGX Xavier board. This approach is pivotal as it addresses the limitations of simulations, which often fail to capture the complexity of dynamic real-world conditions. The results from real-world experiments demon-strate that our learning-based control system achieves smoother and more consistent lane-changing behavior compared to traditional direct measurement approaches. This paper underscores the effectiveness of integrating Adaptive Dynamic Program-ming (ADP) with state estimation techniques, as demonstrated through small-scale experiments. These experiments are crucial as they provide a practical validation platform that simulates real-world complexities, representing a significant advancement in the control systems used for autonomous driving. © 2024 IEEE.

关键词： State estimation

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