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Intelligent topology decision optimization method based on knowledge map enhancement algorithm

Intelligent topology decision optimization method based on k...

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2024 International conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2024

作者： Liu, Yi Tao, Rongming Zhang, Yijing Liu, Jiachen East Branch of State Grid Corporation of China Pudong New Area Shanghai200120 China

ISBN: (纸本)9781510682597

In order to solve the problem of how to effectively optimize the topological structure in complex systems, improve the accuracy and reliability of decision-making, and reduce the false alarm rate and false alarm rate, an intelligent topological structure decision-making optimization method based on knowledge map enhancement algorithm is proposed. Build an algorithm to enhance the knowledge map of intelligent topology decision-making, on this basis, build an intelligent topology and analyze its characteristics;According to the output results of intelligent topology, the decision-making optimization process of intelligent topology is designed. The experimental results show that after the optimization of this method, the storage space usage of each node has changed significantly, the accuracy of intelligent topology decision-making has been improved obviously, and the false alarm rate and false alarm rate have been greatly reduced, which has a good optimization effect. © 2024 SPIE.

关键词： Decision making Mathematical optimization Evolutionary algorithms Data modeling Machine learning Data processing Sampling rates Detection and tracking algorithms Power supplies Signals intelligence

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Development of an Entry-Exit Decision Algorithm for Bus Passenger Counting Using Cameras 16

Development of an Entry-Exit Decision Algorithm for Bus Pass...

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2024 16th International conference on Graphics and Image processing, ICGIP 2024

作者： Nishiura, Tsubasa Yokoyama, Soichiro Yamashita, Tomohisa Kawamura, Hidenori Sato, Yoshimi Hasegawa, Rei Hirasawa, Miyuki Faculty of Information Science and Technology Hokkaido University Kita 14 Nishi 9 Kita-ku Hokkaido Sapporo060-0814 Japan C’s Lab Co. Ltd. 3 Kita 1-jo Nishi 7-chome Chuo-ku Hokkaido Sapporo060-0001 Japan

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

In recent years, entry-exit management technologies have gained attention due to the rise of smart cities and the proliferation of IoT devices. These technologies are used not only in indoor environments like offices and retail stores but also in outdoor settings such as public transportation. Particularly in public transport, there is growing demand for Origin-Destination (OD) data collection to better understand passenger movement. In this study, we developed a system to estimate the number of passengers boarding and alighting from buses by installing cameras at the bus doors and capturing video footage of passengers. The system utilizes person detection and tracking algorithms to estimate passenger trajectories and applies a deep neural network (DNN) model to classify whether a detected individual is boarding or alighting. We introduced a DNN model for binary classification to determine whether individuals are inside or outside the bus, using bounding box and time information. By performing a grid search, we identified optimal model parameters. The system was tested using both YOLOR and the latest YOLOv10 object detectors, and the MPNTrack person tracking algorithm. The combination of YOLOv10 and the DNN model achieved an error rate of 11.7% for the entrance camera, while YOLOR and the DNN model achieved an error rate of 0.803% for the exit camera, significantly improving the accuracy over previous methods. This study demonstrates that integrating object detection, tracking, and DNN-based classification improves the accuracy of passenger counting systems, which is crucial for the effective management of public transportation systems. © 2025 SPIE.

关键词： Deep neural networks

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An effective dynamic SLAM algorithm integrating YOLOv7 model 15

An effective dynamic SLAM algorithm integrating YOLOv7 model

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15th International conference on Graphics and Image processing, ICGIP 2023

作者： Fan, Yisheng Xue, Xiaorong Zhao, Zhiyuan Liu, Zheyu School of Electronics and Information Engineering Liaoning University of Technology Liaoning China

ISBN: (纸本)9781510675018

Dynamic objects are ubiquitous in real-world environments. However, traditional Simultaneous Localization and Mapping (SLAM) algorithms assume static scenes and fail on active tasks. Therefore, we need SLAM systems to achieve a precise and efficient localization and mapping in dynamic environments. Toward this goal, this paper proposes a dynamic SLAM algorithm incorporating a target detection model, YOLOv7. The general process of the algorithm is as follows: Firstly, the real-time target detector YOLOv7 segments the input image and divides it into static and dynamic parts. Secondly, feature information of the image frame is extracted. If the feature information is located in the dynamical part of the image, it will be eliminated for achieving an accurate tracking and mapping in the following steps. Next, tracking and mapping based on the image frame without dynamic objects are performed. Finally, global Bundle Adjustment (BA) optimization is performed to obtain more accurate pose and map information. The experimental results on TUM and KITTI show that the proposed algorithm in this paper has achieved the same accuracy as the DynaSLAM. In real-time, this algorithm has more advanced efficiency advantages on KITTI and TUM than the DynaSLAM algorithm and Dynamic ORB_SLAM. In particular, it has improved the speed by 33.03% on the TUM compared to Dynamic ORB_SLAM. Therefore, the proposed algorithm ensures that the SLAM system can achieve high accuracy in dynamic scenarios while maximizing speed. © 2024 SPIE. All rights reserved.

关键词： Mapping

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Physics-Informed Deep Learning-Based Monocular vision and IMU Fusion for Extracting Position and Heading Data of Multirotors 12

Physics-Informed Deep Learning-Based Monocular Vision and IM...

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12th RSI International conference on Robotics and Mechatronics, ICRoM 2024

作者： Rezaei, Arshia Radfar, Erfan Talaeizadeh, Amin Alasty, Aria Department of Mechanical Engineering Iran

ISBN: (纸本)9798331529734

Multirotor Unmanned Aerial Vehicles (UAV)s flight performance rely on accurate estimation of their attitude and position for stable and robust control, which is essential for precise path following and management. Traditional methods for estimating six Degrees of Freedom (DoF) parameters, such as position, orientation, and velocity, often combine data from Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and vision sensors using complex algorithms for fusion and filtering. This approach can lead to challenges in real-time processing and robustness to environmental variations. In this paper, we propose a novel approach that leverages deep learning and incorporates a physics-informed loss function based on monocular vision data to enhance the accuracy of these sensors by eliciting position and heading data from a trained model of flow detection, enhanced with a mathematical model of optical flow. We implement a fusion of camera, IMU, magnetometer, and ultrasonic sensors for robust and accurate 6 DoF data acquisition in multirotor systems. Our proposed framework utilizes Convolutional Neural Networks (CNN)s to directly learn the mapping between sensor data and 6 DoF parameters. By jointly processing visual and inertial data in an end-to-end manner, our model exploits the complementary information provided by each sensor modality, thereby enhancing estimation accuracy and robustness to external disturbances. We demonstrate the effectiveness of our approach through extensive experiments on real-world multirotor platforms, show-casing significant improvements in pose and motion estimation accuracy compared to conventional methods. Our fusion deep learning framework offers a promising pathway for enhancing the autonomy and performance of multi rotor UAVs. © 2024 IEEE.

关键词： optical flows

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Research on embedded system of image acquisition and real-time processing based on ZYNQ

Research on embedded system of image acquisition and real-ti...

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2022 International conference on Neural Networks, information, and Communication Engineering, NNICE 2022

作者： Zhang, ZongJie Peng, Liu School of Electronic Information Engineering Changchun University of Science and Technology China

ISBN: (纸本)9781510655171

In order to meet the demand for flexible acquisition of video images and real-time processing of images, the speed and efficiency of image processing are improved. This paper is an embedded system based on the ZYNQ-7000 series chip which integrates ARM+FPGA structure for image acquisition and real-time processing. An improved sharpening algorithm that can be used for edge detection in the image processing module is proposed, and the algorithm is integrated into the hardware through the HLS tool that comes with vivado, which realizes the flexible acquisition and real-time processing of video image data. By studying image processing algorithms commonly used in embedded acquisition and processing systems, an improved sharpening algorithm is designed based on threshold segmentation and image filtering and applied in experiments. In terms of hardware, it has the advantages of low power consumption, high performance and flexibility. In terms of algorithm, it not only improves the speed of image optimization processing, but also has a large development space and application value. © 2022 SPIE.

关键词： Embedded systems

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Extracting vitals from ICU Monitor Images: An Insight from Analysis of 10K Patient Data 27th

Extracting Vitals from ICU Monitor Images: An Insight from A...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Rampuria, Akshat Khare, Kushagra Soni, Ayush Dogra, Debi Prosad School of Electrical and Computer Sciences Indian Institute of Technology Bhubaneswar Bhubaneswar752050 India

ISBN: (纸本)9783031781971

In intensive care units (ICUs), continuous monitoring of patient vitals is crucial for timely interventions and optimal outcomes. While traditional methods rely on direct observation by healthcare professionals, technological advancements have led to a growing interest in image-based monitoring systems, particularly utilizing closed-circuit television (CCTV) cameras within ICU environments. However, integrating such systems poses challenges, notably in extracting vital information from monitor images efficiently. Current approaches, including manual interpretation and specialized algorithms, are often laborious and error-prone. In response, this paper presents a robust pipeline for automatic extraction of patient vitals from ICU monitor images. Our approach involves automatic detection and segmentation of monitor screens, followed by extraction of relevant vital signs. By enhancing existing ICU environments with this technology, we aim to improve patient care and resource utilization. Our contributions include a powerful pre-processing pipeline, a comprehensive study of optical character recognition (OCR) frameworks, and a geometry-based heuristic implemented in kornia for vital sign detection. Through this work, we lay the groundwork for innovative solutions that can revolutionize patient monitoring in ICU settings. The method has been tested on a large dataset comprises with 10K ICU monitor images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optical character recognition

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2023 IEEE Open conference of Electrical, Electronic and information Sciences, eStream 2023 - Proceedings

2023 IEEE Open Conference of Electrical, Electronic and Info...

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10th IEEE Open conference of Electrical, Electronic and information Sciences, eStream 2023

ISBN: (纸本)9798350303834

The proceedings contain 19 papers. The topics discussed include: electromagnetic interferences resistant PI/I controller for electrical energy converters;opportunities for automated e-learning path generation in adaptive e-learning systems;development and research of miniature high precision modular rotary encoder kit based on dual optical sensors;dynamic characteristics study of a newly developed suppression system;methodology of low orbit satellites signal processing in the electromagnetically noisy environment;theoretical and experimental research of device for ultrafine particulate matter agglomeration;comparative analysis of the applicability of five clustering algorithms for market segmentation;research and development of information technology for determining shoe size by image;deposition of polydisperse ultrafine particles in a gas flow using an advanced electro-cyclone apparatus;toward bee motion pattern identification on hive landing board;and performance analysis of different ANN-based weight updating algorithms in forecasting short-term load demands in cluster microgrids.

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optical Stealth Algorithm Under Photoelectric Countermeasures and Its Effectiveness Evaluation 18th

Optical Stealth Algorithm Under Photoelectric Countermeasure...

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18th International conference on Intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2022

作者： Yang, Zhonglin Jiang, Yutong Li, Junbao Li, Qiong Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China China North Vehicle Research Institute 4 HuaiShuling Fengtai District Beijing China

ISBN: (纸本)9789819901043

With the diversification of reconnaissance equipment and methods in the military field, optical stealth technology has become a hot spot in military research under the operational needs of optoelectronic confrontation. An improved self-adaptive visible light and infrared waveband stealth algorithm is proposed here, and its effectiveness under photoelectric confrontation is evaluated. The current digital camouflage design method is mainly through background color mixing, image segmentation, spot combination, etc. It has the defects of smooth and clear edge contour, fixed color/texture scene, and cannot adapt to complex texture background or multiple types of scenes. An improved digital camouflage design method based on fuzzy C-means clustering (FCM) image processing technology is proposed to obtain the color characteristics of the background image. Then, the color block types of the camouflage spots are acquired to reduce the loss of texture information of the background image caused by clustering or segmentation to avoid the phenomenon of clear boundaries caused by single region segmentation. The results show that the use of adaptive optical stealth algorithms in the visible and infrared bands can reduce the probability of being discovered in optoelectronic countermeasures, thereby gaining a head start. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Deep learning in the design and application of thoracic medical image-Assisted diagnosis algorithm 7

Deep learning in the design and application of thoracic medi...

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7th International conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xiao, Yineng The Global Intellectual Property Institute Nanjing University Suzhou215163 China Advanced Institute of Information Technology Peking University Hangzhou311200 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

In recent years, with the development of deep learning (DL) technology and the continuous improvement of algorithms, DL-Assisted diagnosis systems based on medical images have rapidly developed. Compared with traditional image processing methods, DL trains models by combining a large amount of relevant data (e.g. clinical data, imaging data, etc.) and then uses the models to predict disease-related information. Compared with traditional medical image processing algorithms, DL has better performance in recognition, segmentation and classification of medical images. In this paper, an AI-Aided diagnosis algorithm is developed for lung cancer, a malignant tumour disease, based on clinical chest CT data and imaging data. The algorithm uses chest CT images as the object of study, and classifies and evaluates patients based on various factors such as their age, gender, tumour volume and location, as well as their knowledge of the disease. A pre-Trained model was first used to establish an algorithm for lung cancer tumour segmentation and recognition. Convolutional NNs are then applied to learn solutions to the feature extraction and classification problems. Finally, the results obtained are used as model output and the system performance is evaluated to complete the diagnostic process for lung cancer as a class of malignant tumour disease. © 2023 SPIE. All rights reserved.

关键词： Diseases

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Application development and technical analysis of rider helmet wearing monitoring system 3

Application development and technical analysis of rider helm...

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3rd International conference on Advanced algorithms and Signal Image processing, AASIP 2023

作者： Liu, Jicheng School of Information Science and Technology Nantong University Nantong China

ISBN: (纸本)9781510668522

More and more people are choosing two-wheeled motor cars as their primary mode of transportation as society develops quickly. As a result, concerns about rider safety have grown over time. Using a helmet may successfully protect the rider's head, lower their chance of suffering a head injury, and lessen their exposure to traffic accidents. The electric bike riders in the images without helmets were found using detection and recognition techniques, and their license plate data was gathered. This technique can successfully identify motorcyclists who disobey the law and give the appropriate authorities crucial information to develop more exact safety standards and enforcement actions. Monitoring the level of helmet use among drivers of motor vehicles is therefore crucial. This study compares and contrasts the key technologies and provides an overview of the current helmet-wearing monitoring systems. The rider helmet wearing monitoring system is first introduced, along with its use cases and importance. The approaches are then distilled into two groups in accordance with the detection techniques' underlying operating principles. The benefits and drawbacks of the two common approaches are then compared, along with potential adaptations. The prospect and summary of the rider helmet wearing monitoring system's use and research directions follows. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computer vision

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