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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是441-450 订阅

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ieee International conference on Adaptive Science and Technology, ICAST

IEEE International Conference on Adaptive Science and Techno...

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7th ieee International conference on Adaptive Science and Technology, ICAST 2018

ISBN: (纸本)9781538642337

the proceedings contain 44 papers. the topics discussed include: dvelopment of an embedded obstacle avoidance and path planning autonomous solar grass cutting robot for semistructured outdoor environment;sentiment analysis with word embedding;OFDM chirp radar target detection and adaptive design in clutter environment;low complexity clipped frequency shift keying (FSK) for visible light communications;quality of service assessment on some major mobile network operators in Ghana;affordable modular autonomous vehicle development platform;design of power distribution network fault data collector for fault detection, location and classification using machine learning;efficient modeling of a PCB transmission line for high speed digital systems;and methods of chattering reduction in sliding mode control: a case study of ball and plate system.

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Optimization of Traffic and Time control with Sensor-driven Transmission control System using MANET and Machine learning 7

Optimization of Traffic and Time Control with Sensor-Driven ...

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7th International conference on Inventive Computation Technologies, ICICT 2024

作者： Kathirvel, N. Vidyalakshmi, R. Raihana, A. Mohanraj, A. Uma, S. Saranya, N. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science And Technology School of Computing Department of Information Technology India Karpagam College of Engineerng Department of Eee Coimbatore India Sri Krishna College of Engineering And Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science And Engineering India Hindustan College of Engineering And Technology Department of Computer Science And Engineering India

ISBN: (纸本)9798350359299

In the dynamic landscape of modern urban environments, the optimization of transportation and traffic management systems is paramount for ensuring efficiency, sustainability, and the overall well-being of inhabitants. this study introduces an innovative solution through the deployment of a sophisticated network of strategically positioned sensors at crucial locations, encompassing roadway sections, entry and exit points, and parking spaces within a smart city framework. these sensors continuously capture real-time data on traffic flow, vehicle counts, and parking spot occupancy, forming the foundational elements of an intelligent system. Robust machine learning models, notably Convolutional Neural Networks (CNN), VGG19, k-Nearest Neighbors (KNN), and Support Vector Machines (SVM), are employed to process and analyze the acquired sensor data. CNN, renowned for its prowess in image recognition, emerges as the leading performer, achieving an impressive accuracy of 97.6%. this model excels in deciphering complex spatial patterns, significantly enhancing the precision of traffic forecasts. Additionally, performance metrics, encompassing precision, recall, F1 score, and accuracy, underscore the efficacy of each machine learning model. the exceptional precision and recall values across all models showcase their robustness in identifying and predicting traffic situations, while accuracy scores affirm their overall efficiency in optimizing transportation and traffic management within smart city environments. © 2024 ieee.

关键词： Sensor networks

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Fusion Application of Intelligent Unmanned Cluster systems with Large Models

Fusion Application of Intelligent Unmanned Cluster Systems w...

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7th International conference on Electronics Technology, ICET 2024

作者： Chen, Han Joint Operations College National Defense University Shijiazhuang China

ISBN: (纸本)9798350363951

Intelligent unmanned cluster system has become the key technology field of future military transformation. this study discusses the strategy and effectiveness of the integration of intelligent unmanned cluster system and large model, focusing on the research and development of cluster cooperative control algorithm, deep reinforcement learning and adaptive decision-making mechanism, and is committed to improving the autonomy, flexibility and decision-making efficiency of cluster system. By building a multi-level large model, the balance between centralized and distributed control of UAVS swarm intelligence is realized, which promotes the intelligent development of unmanned systems and moves towards typical military applications with high autonomy. the combination of data-driven analysis method and simulation experiments verifies that the cluster system can effectively perform complex tasks in highly uncertain environments, and shows excellent adaptability and robustness. the application prospect of intelligent unmanned cluster system in multi-domain fusion is also discussed, and some strategies are proposed to enhance the robustness and adaptability of the algorithm. this research not only deepens the theoretical basis of the integration application of unmanned cluster system and large model, but also provides strategic guidance and technical support for the future intelligent warfare form. © 2024 ieee.

关键词： Deep reinforcement learning

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AI-driven UAV Distinction: Leveraging Advanced Machine learning 7

AI-Driven UAV Distinction: Leveraging Advanced Machine Learn...

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7th International conference on Internet Applications, Protocols, and Services, NETAPPS 2024

作者： Alzboon, Mowafaq Salem Alqaraleh, Muhyeeddin Wahed, Mutaz Abdel Alourani, Abdullah Bader, Ahmad Fuad Al-Batah, Mohammad Faculty of Information Technology Jadara university Irbid Jordan Faculty of Information Technology Zarqa University Alzarqa Jordan College of Business and Economics Qassim University Buraydah Saudi Arabia Faculty of Engineering Jadara university Irbid Jordan

ISBN: (纸本)9798331519513

this Article addresses the critical task of distinguishing Unmanned Aerial Vehicles (UAVs) from birds in flight, a pressing concern for airspace security in both military and civilian contexts. With the increasing use of UAVs, the need for reliable systems capable of accurately identifying objects in dynamic environments is essential for ensuring operational safety and security. this research evaluates various machine learning models, including k-Nearest Neighbors (kNN), AdaBoost, CN2 Rule Induction, and Support Vector Machines (SVM), to assess their effectiveness in detecting UAVs amidst other airborne objects, such as birds. the findings reveal that kNN, AdaBoost, and CN2 Rule Induction are particularly effective in differentiating UAVs from birds, demonstrating high accuracy with reduced false positives and negatives. these models are well-suited for real-time security applications, offering robust classification capabilities and the potential for integration into existing surveillance systems. their implementation enhances real-time data processing and decision-making in challenging scenarios, improving airspace control and surveillance efficiency. the study also highlights ongoing challenges related to integration, computational efficiency, and the continuous need for algorithmic refinement. Moreover, ethical considerations regarding privacy and surveillance underscore the importance of regulation and transparent implementation. this research contributes significantly to the fields of machine learning and security while also influencing civil aviation safety, environmental monitoring, and wildlife conservation, positioning it as a key element in the future development of airspace management technologies. © 2024 ieee.

关键词： Decision making

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An Deep learning-Based Malicious Code Detection System Combining Graph Convolutional Networks and Bidirectional LSTM 7

An Deep Learning-Based Malicious Code Detection System Combi...

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7th ieee Information Technology, Networking, Electronic and Automation control conference, ITNEC 2024

作者： Su, Yang Wang, Xu'an Engineering University of People's Armed Police Shaanxi Xi'an China

ISBN: (纸本)9798350370782

the rapid evolution of cyber threats demands more effective detection systems that can adapt to complex and dynamic network environments. this paper introduces a novel deep learning-based approach that combines Graph Convolutional Networks (GCN) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the detection of malicious code. By leveraging both the spatial relationships within network data and the temporal dynamics of traffic flows, the proposed model achieves high accuracy in detecting diverse types of malicious activities. Experiments conducted on the CICIDS2018 dataset demonstrate that the hybrid model outperforms traditional deep learning models that rely solely on either graph-based or sequence-based methodologies. the results indicate significant improvements in accuracy, precision, recall, and F1-score, suggesting that the integration of GCN and BiLSTM provides a robust framework for future cybersecurity solutions. © 2024 ieee.

关键词： Contrastive learning

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Robot Skill learning and Generalization based on Human-robot Collaboration and Computer Vision

Robot Skill Learning and Generalization based on Human-robot...

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data driven control and learning systems (ddcls)

作者： Xin Zhang Ruiqing Zhang Deqing Huang Yanan Li School of Electrical Engineering Southwest Jiaotong University Chengdu P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the paper presents a novel approach for human-robot skill transferring. Firstly, we propose a method that combines dynamic time warping (DTW) with the Gaussian mixture model (GMM) to reconstruct the demonstrated skills, including reference path and pose, for each workpiece. Secondly, we integrate hand-eye coordination to facilitate dataset creation and employ YOLOv8 for model training. Finally, we utilize a neural network to obtain the current workpiece's category and pose information and then transform the previous skills into the current workpiece coordinate system. the effectiveness and robustness of our proposed method have been validated on a 7-DOF Sawyer robot equipped with a camera.

关键词： Training Point cloud compression learning systems Robot kinematics Robot vision systems Neural networks Transforms

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Dynamic Transcriptomic data Analysis by Integrating data-driven and Model-guided Approaches

Dynamic Transcriptomic Data Analysis by Integrating Data-dri...

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7th conference on Foundation of systems Biology in Engineering (FOSBE)

作者： Hilliard, Matthew He, Q. Peter Wang, Jin Auburn Univ Dept Chem Engn Auburn AL 36849 USA

the rapid development in "omics" technologies offer unprecedented opportunities to help understand cellular metabolism at genome-scale. Among different "omics" data, RNAseq based transcriptome profiling has been used to enhance the understanding of the genome-scale response of the organism to different stimuli. While these studies have provided insightful findings, they are most often limited to studying two-steady-state conditions. From a control perspective, as cellular metabolism is a highly complex dynamic system, the transient response could offer significantly more information on the system dynamics, especially the internal control mechanism In this work, we present our initial results on analysing the dynamic transcriptomic profiles of Scheffersomyces stipitis during the transition from aerobic growth to oxygen-limited fermentation. Our results confirm that the transient states indeed offer important information that the steady-states cannot, which can be crucial in understanding the cellular metabolism. (C) 2018, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： transcriptome profiling transcriptomic data analysis steady-state transient response Scheffersomyces stipites principal component analysis genome-scale metabolic model

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How Meta-heuristic Algorithms Contribute to Deep learning in the Hype of Big data Analytics 4th

How Meta-heuristic Algorithms Contribute to Deep Learning in...

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4th International conference on Advanced Computing, Networking and Informatics (ICACNI)

作者： Fong, Simon Deb, Suash Yang, Xin-she Univ Macau Dept Comp Informat Sci Macau Peoples R China INNS India Reg Chapter 7th Floor Jorar Ranchi 834010 Jharkhand India Middlesex Univ Sch Sci & Technol London NW4 4BT England

ISBN: (纸本)9789811033735;9789811033728

Deep learning (DL) is one of the most emerging types of contemporary machine learning techniques that mimic the cognitive patterns of animal visual cortex to learn the new abstract features automatically by deep and hierarchical layers. DL is believed to be a suitable tool so far for extracting insights from very huge volume of so-called big data. Nevertheless, one of the three "V" or big data is velocity that implies the learning has to be incremental as data are accumulating up rapidly. DL must be fast and accurate. By the technical design of DL, it is extended from feed-forward artificial neural network with many multi-hidden layers of neurons called deep neural network (DNN). In the training process of DNN, it has certain inefficiency due to very long training time required. Obtaining the most accurate DNN within a reasonable run-time is a challenge, given there are potentially many parameters in the DNN model configuration and high dimensionality of the feature space in the training dataset. Meta-heuristic has a history of optimizing machine learning models successfully. How well meta-heuristic could be used to optimize DL in the context of big data analytics is a thematic topic which we pondered on in this paper. As a position paper, we review the recent advances of applying meta-heuristics on DL, discuss about their pros and cons and point out some feasible research directions for bridging the gaps between meta-heuristics and DL.

关键词： Deep learning Meta-heuristic algorithm Neural network training Nature-inspired computing algorithms Algorithm design

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A Roadside Vehicle Line Pressing Detection Algorithm Based on YOLOv7

A Roadside Vehicle Line Pressing Detection Algorithm Based o...

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data driven control and learning systems (ddcls)

作者： Yuquan Wang Shuhui Feng Li Wang School of Electronics & Control Engineering North China University of Technology Beijing

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to solve the problem of inaccurate vehicle line pressing detection and recognition, a roadside vehicle line pressing detection algorithm based on YOLOv7 is proposed. Firstly, we utilize the YOLOv7 detection method to select the vehicle object in the video, and employ the keypoint estimation method to obtain the vehicle's reference point information. then, the parking line detection method was used to obtain the parking location information. Finally, according to the information about vehicles and parking spaces, we propose an algorithm to judge the vehicle line pressing by comparing the intersection of the vehicle and the parking space. Experiment results show that this algorithm has good detection performance on 294 test images. Compared with the existing algorithm, it does not require collecting the motion trajectory of the vehicle, and can be effectively applied to roadside parking scene.

关键词： Space vehicles Roads Urban areas Estimation Process control Pressing Object detection

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Detection of Anatomical Landmarks During Laparoscopic Cholecystectomy Surgery Based on Improved Yolov7 Algorithm

Detection of Anatomical Landmarks During Laparoscopic Cholec...

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data driven control and learning systems (ddcls)

作者： Zongjin Yang Guang Yang Xingyu Chen Zhenhao Chen Na Qin Deqing Huang Institute of Systems Science and Technology Southwest Jiaotong University Chengdu P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Many adverse events during laparoscopic cholecystectomy surgery occur due to errors in visual perception and judgment by surgeons, leading to misunderstandings of anatomical structures, particularly in surgeries performed by young and inexperienced doctors. this paper proposes a YOLOv7_CA model for detecting anatomical landmarks during laparoscopic cholecystectomy surgery, based on an attention mechanism. the network efficiently detects the gallbladder, Calot's triangle, and common bile duct, which are prone to intraoperative mis-manipulations. We introduce data augmentation and transfer learning methods to address the issue of small datasets. the mAP of YOLOv7_CA is 69.34%, with improvements of 3.22%, 5.59%, and 6.10% in mAP, Precision, and Recall, respectively, compared to YOLOv7. Experimental results demonstrate the network's excellent performance in detecting critical intraoperative anatomical landmarks, thereby achieving real-time assistance for surgeons. It also provides new ideas for the application of deep learning in the surgical field of vision.

关键词： Laparoscopes Deep learning Attention mechanisms Transfer learning Surgery Medical services Object detection

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