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Chinese Control and Decision Conference, CCDC

作者： Xincheng Han Hongyan Ma Wei He Shun Wang School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

College laboratory buildings have dense population, numerous electrical devices, and high fire risk. However, the existing evacuation A* algorithm has the disadvantages of long time and poor adaptability. In order to effectively solve the problems of long evacuation simulation time, low efficiency, and inability to change evacuation directions according to fire conditions in college laboratory buildings, this paper proposes an improved A* algorithm. This paper improves the heuristic function in the A* algorithm by introducing a logarithmic decay factor. By changing the weight of the estimated cost, it reduces the number of unnecessary search nodes and steers the search algorithm in the correct direction, thereby improving search efficiency. Secondly, it introduces a bidirectional A* search strategy to reduce search nodes and improve the efficiency of the algorithm. Simulation results show that the improved A* algorithm has fewer traversed nodes, shorter search paths, and higher search efficiency, which verifies the effectiveness and feasibility of the improved A* algorithm in experimental building fire evacuation. This study provides a solution for fire evacuation in college laboratory buildings.

关键词： Heuristic algorithms Simulation Buildings Sociology Laboratories Search problems Path planning

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Cerium oxide inclusion nucleation mechanism based on classical nucleation theory and two-step nucleation

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Journal of Rare Earths 2024年第4期42卷 793-802页

作者： Yutang Li Jun Wang Linzhu Wang Chaoyi Chen Shufeng Yang Junqi Li Xiang Li School of Materials and Metallurgy Guizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Metallurgical and Ecological Engineering University of Science and Technology BeijingBeijing100083China Department of Materials&Metallurgy Engineering Guizhou Institute of TechnologyGuiyang550003China

It is challenging to assess the mechanism responsible for the nucleation of inclusions in metals at high *** present work therefore systematically investigates the nucleation of cerium oxide inclusions according to classical nucleation theory and a two-step nucleation *** nucleation rates and nucleation radii of these inclusions are obtained,and the results demonstrate a considerable difference between theoretical and experimental *** the basis of a two-step nucleation mechanism,(CeO_(2))_(n) and(Ce_(2)O_(3))_(n)(n=1-6)clusters were constructed and the thermodynamic properties of both these clusters and of cerium oxide nanoparticles were *** addition,the entropies and heat capacity changes of cerium oxides were determined using first principles calculations and are found to be consistent with literature *** present data indicate that the cerium oxide inclusion nucleation pathway can be summarized as[Ce]+[O]→(CeO_(2))n/(Ce_(2)O_(3))_(n)→(Ce_(2)O_(3))_(n)→(Ce_(2)O_(3))_(2)→core(Ce_(2)O_(3)crystal)-shell((Ce_(2)O_(3))_(2) cluster)nanoparticles→(Ce_(2)O_(3))bulk.

关键词： Cerium oxide inclusion Classical nucleation theory Two-step nucleation mechanism First principles Rare earths

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RGB-D SLAM Method Based on Feature Association in Dynamic Environment

RGB-D SLAM Method Based on Feature Association in Dynamic En...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Baofu Fang Hao Wang School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei China

Simultaneous localization and mapping (SLAM) is one of the current research hotspots. However, in visual SLAM for dynamic environments, inaccurate detection of object motion states and incomplete dynamic region culling will lead to large localization errors. To address these issues, this paper proposes an RGB-D SLAM method based on feature association. The method has strongly correlated features in time and space according to the input image sequence. Using the moving probability of the feature points in the previous frame, the movement of the feature points in the current frame is calculated in combination with the dynamic corner points screened in the current frame. Then, the motion state of the object is determined according to the proportion of different feature points. Then combined with semantic information and object depth information, the fast search method is used to obtain accurate dynamic regions. Finally, the selected effective feature points are used to estimate the camera pose and establish a static map of the environment. This paper evaluates the robustness and accuracy of our method on the TUM dataset and real environment, and the results show that our method can significantly improve the system tracking effect and reduce the system tracking error compared with other SLAM methods in dynamic environments.

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Air quality prediction based on ICEEMDAN-SSA-LSTM

Air quality prediction based on ICEEMDAN-SSA-LSTM

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Chinese Control and Decision Conference, CCDC

作者： Yanling Qin Hongyan Ma Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To address the problem that the Air Quality Index (AQI) is difficult to predict, a prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) is proposed. First, the AQI sequence was decomposed by ICEEMDAN. Second, the initial learning rate, max training epoch and the number of hidden layers of the LSTM model are optimized by the SSA algorithm to establish the SSA-LSTM model. Last, the results decomposed were imported into the SSA-LSTM model to predict AQI. Compared with the prediction results of the LSTM, CEEMD-LSTM, and CEEMDAN-SSA-LSTM model, the accuracy (measured by correlation coefficients) of the CEEMDAN-SSA-LSTM is 98%. The result confirmed that the ICEEMDAN-SSA-LSTM can obtaining an improvement of the correlation coefficients and reduce the mean square error, which is valuable for engineering applications.

关键词： Training Correlation coefficient Adaptation models Accuracy Empirical mode decomposition Atmospheric modeling Predictive models

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Scene-Adaptive Person Search via Bilateral Modulations

arXiv

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arXiv 2024年

作者： Jiang, Yimin Wang, Huibing Peng, Jinjia Fu, Xianping Wang, Yang School of Information Science and Technology Dalian Maritime University Dalian China School of Cyber Security and Computer Hebei University Baoding China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei China

Person search aims to localize specific a target person from a gallery set of images with various scenes. As the scene of moving pedestrian changes, the captured person image inevitably bring in lots of background noise and foreground noise on the person feature, which are completely unrelated to the person identity, leading to severe performance degeneration. To address this issue, we present a Scene-Adaptive Person Search (SEAS) model by introducing bilateral modulations to simultaneously eliminate scene noise and maintain a consistent person representation to adapt to various scenes. In SEAS, a Background Modulation Network (BMN) is designed to encode the feature extracted from the detected bounding box into a multi-granularity embedding, which reduces the input of background noise from multiple levels with norm-aware. Additionally, to mitigate the effect of foreground noise on the person feature, SEAS introduces a Foreground Modulation Network (FMN) to compute the clutter reduction offset for the person embedding based on the feature map of the scene image. By bilateral modulations on both background and foreground within an end-to-end manner, SEAS obtains consistent feature representations without scene noise. SEAS can achieve state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU with 97.1% mAP and PRW with 60.5% mAP. The code is available at https://***/whbdmu/SEAS. © 2024, CC0.

关键词： Embeddings

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STRASS Dehazing:Spatio-Temporal Retinex-Inspired Dehazing by an Averaging of Stochastic Samples

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Journal of Renewable Materials 2022年第5期10卷 1381-1395页

作者： Zhe Yu Bangyong Sun Di Liu Vincent Whannou de Dravo Margarita Khokhlova Siyuan Wu School of Printing Packaging Engineering and Digital Media Xi’an University of TechnologyXi’an710048China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing100048China Key Laboratory of Spectral Imaging Technology CAS Xi’an Institute of Optics and Precision MechanicsChinese Academy of SciencesXi’an710119China LASTIG UniversitéGustave EiffelÉcully69134France

In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing *** novel high-pass filter works by getting each pixel using RSR and computes the average of the *** the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the *** final dehazed image is yielded after iterations of the high-pass *** can be run directly without any machine *** experimental results on datasets prove that STRASS surpass the *** dehazing can be applied in the field of printing and packaging,our method is of great significance for image pre-processing before printing.

关键词： Image dehazing contrast enhancement high-pass filter image reconstruction

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International Conference on Computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing Software

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Image Orientation Estimation Based On Deep Learning - A Survey 11

Image Orientation Estimation Based On Deep Learning - A Surv...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Xu, Ruijie Shi, Yong Qi, Zhiquan School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China College of Information Science and Technology University of Nebraska Omaha68182 United States

In the realm of visual tasks, the estimation of orientation plays a pivotal role yet often remains an understudied aspect in the gamut of image analysis research. This paper endeavors to address this deficiency by conducting a comprehensive review of methods for orientation estimation within 2D imaging contexts. We aim to provide researchers with an exhaustive overview and detailed analysis of the techniques utilized for orientation estimation, thereby enhancing their understanding of this complex field. We introduce an organized classification of orientation estimation methods, segmented into approaches based on classification algorithms and those based on regression models. This classification not only sheds light on the distinct methodologies but also scrutinizes their underlying assumptions, practical applications. Additionally, the study identifies and underscores emerging research opportunities that hold the potential to enrich the discourse and technical advancements in orientation estimation, thereby encouraging deeper academic investigation and technological development. © 2024 The Authors.

关键词： Classification-based Orientation Estimation Regression-based Orientation Estimation

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EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suitable for Natural

EMPA-YOLO:A Lightweight Real-time Weed Detection Method Suit...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuzhe Wang Jiahao Chen Xiaodong Duan Zhuohui Li College of Mechanical and Electronic Engineering Dalian China College of Computer Science and Engineering Dalian China SEAC Key Laboratory of Big Data Applied Technology Dalian China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Weed detection is crucial for the healthy growth of crops, yet existing detection models struggle to perform high-accuracy real-time detection of weeds in natural environments on edge computing devices. This paper introduces EMPA-YOLO, a model designed for rapid, accurate, real-time weed detection on low-performance edge computing devices. It incorporates an efficient multi-scale convolutional structure, C3EMSC, and a lightweight, adaptive weight subsampling layer, LAWDS, into YOLOv5s. Additionally, a logical distillation algorithm, AlignSoftTarget, is proposed for knowledge distillation. Validation on a mixed dataset of crops and weeds showed that EMPA-YOLO improved mAP50 by 11.2%, reduced parameter count by 4.8M, decreased computational load by 8.7GFLOPs, and increased inference frame rate by 58% compared to the original YOLOv5s algorithm. When compared to YOLOv3, YOLOv5s, YOLOv6, YOLOv8s, and RT-DETR, inference speed improved by 89.2%, 60.8%, 77.5%, 76.5%, and 94.7%, respectively, with mean accuracy enhancements of 7.3%, 11.2%, 16.7%, 0.9%, and 1.2%. Real-world testing on edge computing devices met real-time detection requirements, proving its efficacy and practicality in weed detection.

关键词： YOLO Performance evaluation Adaptation models Accuracy Computational modeling Crops Real-time systems Inference algorithms data models Edge computing

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PediaBench: A Comprehensive Chinese Pediatric dataset for Benchmarking Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Qian Chen, Panfeng Li, Jiali Feng, Linkun Liu, Shuyu Zhao, Heng Chen, Mei Li, Hui Wang, Yanhao State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guizhou Engineering Laboratory for Advanced Computing and Medical Information Services Guiyang China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Data Science and Engineering East China Normal University Shanghai China

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pediatrics. Moreover, some of them are limited to objective questions and do not measure the generation capacity of LLMs. Therefore, they cannot comprehensively assess the QA ability of LLMs in pediatrics. To fill this gap, we construct PediaBench, the first Chinese pediatric dataset for LLM evaluation. Specifically, it contains 4,117 objective questions and 1,632 subjective questions spanning 12 pediatric disease groups. It adopts an integrated scoring criterion based on different difficulty levels to thoroughly assess the proficiency of an LLM in instruction following, knowledge understanding, clinical case analysis, etc. Finally, we validate the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs. Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. Our code and data are published anonymously at https://***/ACMISLab/PediaBench. © 2024, CC BY-SA.

关键词： Question answering

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