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International conference on advanced Intelligent systems for Sustainable Development, AI2SD 2022

International Conference on Advanced Intelligent Systems for...

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International conference on advanced Intelligent systems for Sustainable Development, AI2SD 2022

ISBN: (纸本)9783031352447

The proceedings contain 250 papers. The special focus in this conference is on advanced Intelligent systems for Sustainable Development. The topics include: Artificial Intelligence for Stroke Prediction;the Role of Uncertainty Avoidance, Trust, and Land Tenure in Predicting the Adoption of Green IoT Irrigation systems in Morocco: An Improved Model;the Complete Mitochondrial Genome of Agapornis roseicollis;cross-Talk Between Intramolecular and Intermolecular Amino Acid Networks Orchestrates the Assembly of the Cholera Toxin B Pentamer via the Residue His94;identification of Responsibilities for the Use of Devices Incorporating Artificial Intelligence in Health: Systemic Review;artificial Intelligence Recognition of Human Body Actions for Bionic applications (New Combination of Indicator Parameters);parkinson’s Disease Recognition from Speech Signal Using Discrete Wavelet Transform, Delta, Delta-Delta, and K-Nearest Neighbor;triple Negative Breast Cancer and Non-Triple Negative Breast Cancer Recurrence Prediction Using Boosting Models;classifiers-Based Personality Disorders Detection;A Word-Based Moroccan Sign Language Dataset for Emergency Situations: Use Case COVID-19;The Use of ISFET for the Measurement of Phosphorus in Moroccan Soils;Exploring the Performance of Traditional Word Embeddings and Contextual Embedding Models for COVID-19 Sentiment Analysis;mycoflora of Dormant Crocus Sativus Corms in Morocco;Optimized DL-Based Model for Hypertrophic Cardiomyopathy CMR Images;low-Cost Water Conductivity sensor Based on a Parallel Plate Capacitor for Precision Agriculture;literature Review of Deep Learning for Tuberculosis Based on Chest Imaging;inventory of Corticolous Lichens and Argan Wood Fungi;detection of Alpha-Cypermethrin in Food Using a sensor Array Coupled to Machine Learning Algorithms: The Case of the Wild Edible Swiss Chard;artificial Intelligence at the Service of Precision Medicine;foreword.

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Improving Coastal and Port Management in Smart Cities with UAVs and Deep Learning 1

Improving Coastal and Port Management in Smart Cities with U...

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1st Mediterranean Smart Cities conference, MSCC 2024

作者： Bakirci, Murat Bayraktar, Irem Unmanned/Intelligent Systems Lab Faculty of Aeronautics and Astronautics Tarsus University Mersin Turkey

ISBN: (纸本)9798350374001

Efficient coast and harbor management is integral to the vitality, sustainability, and resilience of smart cities. With bustling harbors serving as vital hubs of commerce, trade, and tourism, optimizing port operations is paramount for economic growth and prosperity. Smart technologies play a pivotal role in this optimization, leveraging advanced sensor networks, real-time monitoring systems, and predictive analytics to enhance safety, mitigate environmental risks, and improve overall efficiency. Additionally, smart coastal management strategies focus on preserving ecosystems, mitigating climate change impacts, and safeguarding against natural disasters. Aerial imagery, facilitated by Unmanned Aerial Vehicles (UAVs) equipped with high-resolution cameras and sensors, provides comprehensive insights into coastal dynamics, harbor operations, and environmental conditions. These images enable efficient monitoring of coastal areas, ports, and harbors, capturing crucial information for informed decision-making in coastal management and port operations. Object detection, particularly in ship detection, stands as a transformative technology for enhancing coastal and harbor management within smart cities. Leveraging advanced algorithms and high-resolution aerial imagery, ship detection systems offer real-time monitoring crucial for optimizing maritime operations and ensuring port security. Object detection algorithms, particularly Faster R-CNN, have shown promise in accurately detecting ships in aerial imagery, offering valuable insights for harbor planning and infrastructure development. This study focuses on utilizing the Faster R-CNN detection algorithm for ship detection in coastal and harbor environments, highlighting its potential to bolster security applications and contribute to the resilience of smart city infrastructure. Through rigorous evaluation and optimization, this research aims to enhance the effectiveness of ship detection systems in safeguarding coastal and

关键词： Predictive analytics

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Optimizing Agrivoltaic Integration: Spatial Analysis and AHP Assessment 24th

Optimizing Agrivoltaic Integration: Spatial Analysis and AHP...

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24th International conference on Computational Science and Its applications (ICCSA)

作者： Murgante, Beniamino Rahmani, Shiva Scorzelli, Rossella Fattoruso, Grazia Annunziata, Alfonso Univ Basilicata Sch Engn Viale Ateneo Lucano 10 I-85100 Potenza Italy ENEA RC Portici Photovolta & Sensor Applicat Lab Ple E Fermi 1 I-80055 Naples Italy

ISBN: (纸本)9783031653421;9783031653438

The transition towards sustainable energy sources is crucial for mitigating climate change and achieving long-term environmental sustainability goals. Out of these energy options, photovoltaics stands out as a promising choice because it can help cut carbon emissions in various fields. Nevertheless, expanding solar photovoltaic (PV) farms on land can conflict with agricultural activities due to limited space, potentially disrupting ecosystem services. However, a solution called agrivoltaics integrates both solar PV and agriculture on the same land, maintaining or even enhancing these services. It is crucial to investigate the optimal strategies for harnessing solar radiation to produce both energy and food simultaneously. In this study, our objective is to assess the influence of agrivoltaics on ecosystem services beyond conventional agricultural outputs. We employ land suitability analysis as our primary methodological approach to evaluate the compatibility of agrivoltaic systems with diverse ecological services. Our investigation aims to elucidate the broader ecological implications of integrating solar photovoltaic technology with agricultural practices. The case study is conducted in the Basilicata region of Italy.

关键词： Renewable energy sources Ecosystem services Agrivoltaic Land-suitability Sustainability

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From Simulation to Prediction: Enhancing Digital Twins with advanced Generative AI Technologies 18

From Simulation to Prediction: Enhancing Digital Twins with ...

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IEEE 18th International conference on Control and Automation (ICCA)

作者： Huang, Yijun Zhang, Jihan Chen, xi Lam, Alan H. F. Chen, Ben M. Chinese Univ Hong Kong Dept Mech & Automat Engn Shatin Hong Kong Peoples R China

ISBN: (纸本)9798350354416;9798350354409

The integration of Generative Artificial Intelligence (GAI) into Digital Twins (DTs) marks a revolutionary stride in the evolution of virtual replicas for physical systems. This paper explores the cutting-edge advancements brought about by the incorporation of GAI technologies, specifically Large Language Models (LLMs), into DTs. These technologies herald a significant transformation, propelling DTs beyond their current capabilities to become more dynamic, predictive, and interactive tools that can simulate complex scenarios and anticipate future conditions with remarkable accuracy. By systematically examining the levels of GAI integration within DTs, this study delves into the methodologies and strategies for embedding AI capabilities into these virtual models. It outlines how GAI can enhance the functionality of DTs, enabling them to generate synthetic datasets, simulate unprecedented events, and provide actionable insights with LLM-based agents for decision-making. Furthermore, the paper highlights the extended applications of DTs, enriched by GAI, across various domains such as healthcare, urban planning, and beyond. The implications of this integration for operational efficiency, innovation, and decision-making processes are profound. By offering a comprehensive overview of the current state of technology and projecting future trends, this paper aims to provide stakeholders with a deep understanding of the synergistic potential between GAI and DTs. It sets the stage for a new era of DT technologies, where the boundaries of what can be achieved with virtual models are continually expanding.

关键词： Decision making

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SMO Based sensorless Direct Torque Sliding Mode Control of IM for EV applications 3

SMO Based Sensorless Direct Torque Sliding Mode Control of I...

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3rd International conference on advanced Electrical Engineering, ICAEE 2024

作者： Ghezouani, Abdelkader Bousmaha, Imen Souhila Ghouili, Jemal University of Bechar Electrical Engineering Department B.P 417 Bechar08000 Algeria Ecole Supérieure en Sciences Appliquées de Tlemcen ESSAT- Tlemcen BP 165 RP Bel Horizon Tlemcen13000 Algeria Djillali Liabes University of Sidi-Bel-Abbes Intelligent Control and Electrical Power Systems Laboratory Algeria University of Moncton Electrical Engineering Department 18 Avenue Antonine-Maillet Moncton Canada

ISBN: (纸本)9798350349351

This paper presents a study and analysis of a direct torque control strategy associated with a sliding mode control (DTC-SMC) without a speed sensor to control the four induction motors used for the propulsion of a four-wheel drive electric vehicle 4WDEV. The estimation of the rotor speed of each motor is obtained by a sliding mode observer SMO. The main objective of this control method is to increase the robustness and reduce torque and stator flux oscillations. The model is made using the Matlab / Simulink environment. The simulation results obtained show the effectiveness of the control strategy in terms of driving cycle trajectory tracking and disturbance rejection. © 2024 IEEE.

关键词： Disturbance rejection

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A convolutional generative adversarial framework for data augmentation based on a robust optimal transport metric 23

A convolutional generative adversarial framework for data au...

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23rd IEEE International conference on High Performance Computing and Communications, 7th IEEE International conference on Data Science and systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in sensor, Cloud and Big Data systems and applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Su, Liyilei Fu, xianjun Hu, Qingmao Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Zhejiang College of Security Technology Wenzhou325016 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665494571

Enhancement of the vanilla generative adversarial network (GAN) to preserve data variability in the presence of real world noise is of paramount significance in deep learning. In this study, we proposed a new distance metric of cosine distance in the framework of optimal transport (OT), and presented and validated a convolutional neural network (CNN) based GAN framework. In comparison with state-of-the-art methods based on Graphics Processing Units (GPU), the proposed framework could maintain the data diversity and quality best in terms of inception score (IS), Fréchet inception distance (FID) and enhancing the classification network of bone age, and is robust to noise degradation. The proposed framework is independent of hardware and thus could also be extended to more advanced hardware such as specialized Tensor Processing Units (TPU), and could be a potential built-in component of a general deep learning networks for such applications as image classification, segmentation, registration, and object detection. © 2021 IEEE.

关键词： Measurement Deep learning Tensors Smart cities Neural networks Graphics processing units Object detection

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Navigating the Future: Advancing Autonomous Vehicles through Robust Target Recognition and Real-Time Avoidance 4

Navigating the Future: Advancing Autonomous Vehicles through...

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4th International conference on Control Theory and applications, ICoCTA 2024

作者： Hussein, Mohammed A. M. Habib, Maki K. The American University in Cairo Robotics Control and Smart Systems Cairo Egypt The American University in Cairo Department of Mechanical Engineering Cairo Egypt

ISBN: (纸本)9798331506155

This research addresses the critical challenge of enhancing object recognition and real-Time response capabilities in autonomous vehicles (AVs) under varying simulated conditions, which is crucial for ensuring both navigational safety and operational efficiency. Utilizing the Carla 0.9.14 simulator and Unreal Engine 4.26 on Ubuntu 20.04, we focus on improving the detection and classification of key on-road obstacles-vehicles, pedestrians, and cyclists-using the YOLOv7 object detection algorithm. By integrating advanced sensory technologies, specifically stereo vision cameras and LIDAR, we create a dynamic testing environment that simulates diverse urban and rural scenarios. Our methodology enhances the YOLOv7 algorithm's accuracy and speed through extensive training on a meticulously curated dataset of 4,113 images, reflecting a broad spectrum of environmental conditions, including varying lighting and weather conditions. This rigorous approach yielded a significant increase in mean average precision (mAP) from 64.3% to 76.3%, and enhanced the algorithm's reliability, with notable improvements over previous models. The research delineates a clear advancement in AV technology by demonstrating substantial improvements in object detection metrics, contributing foundational insights for future implementations in real-world settings and supporting the further development of real-Time avoidance systems. This study not only progresses the field of AV but also sets a new benchmark for object detection performance, aligning with industry and academic goals to optimize AV systems for complex and unpredictable driving scenarios. © 2024 IEEE.

关键词： Stereo image processing

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Cryogenic temperature sensor based on an optical fiber Fabry-Perot interferometer with high sensitivity 11

Cryogenic temperature sensor based on an optical fiber Fabry...

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conference on advanced sensor systems and applications xi

作者： Yang, Jiahua Dong, xiaopeng Yin, Biao Xiamen Univ Inst Lightwave Technol Dept Elect Engn Xiamen 361005 Peoples R China

ISBN: (数字)9781510646520

ISBN: (纸本)9781510646520;9781510646513

Different from the previously reported cryogenic temperature measurement based on the fiber Bragg grating (FBG) sensor, we propose a novel cryogenic temperature measurement scheme with a fiber interferometric sensor. The proposed sensor is constructed by the extrinsic Fabry-Perot interferometer (EFPI) which is consisted of a ceramic ferrule, a copper sleeve and two fiber ends. Under the cryogenic surrounding, the cavity length of the EFPI will change with the thermal deformation of the copper sleeve. With liquid nitrogen, the surrounding temperature can be changed from 77K to room temperature, and as high as 2.246nm/K of the temperature sensitivity can be achieved at a temperature range from 113K-153K in the experiment, with a 12.38 mu m EFPI cavity. The proposed interferometric sensor will still have relatively high sensitivity at a temperature lower than 77K according to our numerical simulation, so, has good application prospect in cryogenic temperature environment.

关键词： Cryogenic temperature extrinsic Fabry-Perot interferometric temperature sensor

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A wax printed mold for prototyping microfluidic devices 3

A wax printed mold for prototyping microfluidic devices

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3rd International conference on advanced Manufacturing Technology and Manufacturing systems, ICAMTMS 2024

作者： Zhu, Jia Zhang, Shiqiang Xu, Ting Zhang, Quan School of Intelligent Manufacturing and Smart Transportation Suzhou City University Suzhou China Department of Basic Education Suzhou City University Suzhou China School of Advanced Technology Xi'an Jiaotong - Liverpool University Suzhou China

ISBN: (纸本)9781510681804

In this paper, we proposed and demonstrated a facile process that leverages wax printing technology for fabricating high-resolution molds for microfluidic devices. Traditionally, microfluidic devices are made from photolithography-fabricated molds, involving complicated and expensive equipment, reagents, and highly skilled personnel. The process is time-consuming, hindering the widespread use of microfluidic devices. However, wax printing technology can directly print hundreds of micron-wide wax lines with certain heights onto smooth substrates, and these wax lines can directly be used as molds for microfluidic channels, thus completing the microfluidic devices. To date, this process has not been systematically studied and optimized. Here, we report the optimized wax printing process parameters (e.g., grayscale, printing times) and achieved a minimum 300 µm channel width for microfluidic channels. Though the line width cannot compete with the traditional photolithography process, this newly proposed method is inexpensive, fast, and highly automated, holding great potential for widespread use. Moreover, our approach boasts two unique features. Firstly, the half-circular mold shape optimally suits microfluidic channels. Secondly, the mold concurrently deposits a thin wax layer on microfluidic inner channel surfaces, preventing small molecule absorption-crucial for diverse microfluidic applications. © 2024 SPIE.

关键词： Photolithography

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An Easy-portable Displacement-offset-based Trajectory Prediction Method for Autonomous Vehicles 10

An Easy-portable Displacement-offset-based Trajectory Predic...

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10th International conference on Dependable systems and Their applications, DSA 2023

作者： Wei, Cheng Hui, Fei Zhao, xiangmo Li, Shanke Wei, Jie Chang'an University School of Information Engineering Shaanxi Xi'an China Shaanxi Heavy Duty Automobile Co.Ltd Shaanxi Xi'an China

ISBN: (纸本)9798350304770

Trajectory prediction algorithm is an important component of autonomous driving system (ADS) or advanced driver assistance system (ADAS), which enable autonomous vehicles to evaluate critical tasks in advance thus reduce vehicle collisions and improve traffic safety. Most existing trajectory prediction methods suffer from difficulties in portability and application across coordinate systems. To address these difficulties, this study proposes an easy-portable, vehicle displacement-offset-based trajectory prediction model, which can be rapid deployed and reused in different highway road sections and does not require second training. Specifically, first a novel trajectory sampling method to homo-dimension the vehicle data of different lengths is proposed. Second, the traditional neural network used for sequence prediction is modified to develop an enhanced trajectory prediction model, and which is trained and tested using the input reduction method. Finally, the trained model is saved locally and embedded in a co-simulation environment composed of CARLA, SUMO and Keras, afterwards the proposed method is tested in real-time simulation and across coordinate systems. The experimental results show that the proposed method can be quickly ported and deployed for reuse without second training, and has a fairly high prediction accuracy and long prospective time. © 2023 IEEE.

关键词： Autonomous vehicles

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