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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Sachin Gupta Shivali Gupta Shaik Mohammed Raith Anand Kumar Salil Bharany Vishnu Kant Ch.Sandeep Department of Robotics and Control Engineering School of Electronics and Control Engineering Lovely Professional University Phagwara India Department of Chemistry School of Chemical Engineering and Physical Sciences Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science and Artificial Intelligence SR University Warangal Telangana

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Dynamic path planning and collision prevention is one of the key tasks for autonomous vehicles in relation to dynamic movement of obstacles. In this paper, a novel hybrid approach based on integration of Slime Mold Algorithm (SMA), Triangle Inequality Principle (TIP), and Partition Method Strategy (PMS) for solution of this problem statement. The SMA algorithm is very flexible and adaptive by nature due to its biological working mechanism which is associated with TIP and PMS strategies in relation to their optimization and efficient path planning capabilities and robust solutions. This integrated module is tested in virtual environment and the results are compared with other updated algorithms such as A*, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the same static and dynamic environment. It has been observed from the results that this hybrid strategy significantly improves the path planning task several times than other algorithms in terms of multiple aspects. Under testing conditions, the path length was reduced to 25% than other algorithms in a dynamic environment. During this calculation, the computational efficiency of the system has been improved to 60% than conventional methods. The chances of collision have been improved to 34.23% especially in environment where the obstacles are moving. The capability of SMA has been improved due to the synergy between TIP and PMS. The dynamic and adaptive nature of this algorithm is suitable for various applications such as search-and-rescue operations, manufacturing units’ automation and other hazardous environment exploration.

关键词： Navigation Heuristic algorithms Virtual environments Path planning Partitioning algorithms Safety Mobile robots Vehicle dynamics Testing Genetic algorithms

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Relational Prompt-based Pre-trained Language Models for Social Event Detection

arXiv

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arXiv 2024年

作者： Pu, L.I. Xiaoyan, Y.U. Peng, Hao Xian, Yantuan Wang, Linqin Li, S.U.N. Zhang, Jingyun Philip, S.Y.U. Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Cyber Science and Technology Beihang University No. 37 Xue Yuan Road Haidian District Beijing100191 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with missing and noisy edges between messages, affecting the quality of learned message embedding. Moreover, these methods statically initialize node embedding before training, which, in turn, limits the ability to learn from message texts and relations simultaneously. In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM (Relational prompt-based Pre-trained Language Models for Social Event Detection). We first propose a new pairwise message modeling strategy to construct social messages into message pairs with multi-relational sequences. Secondly, a new multi-relational prompt-based pairwise message learning mechanism is proposed to learn more comprehensive message representation from message pairs with multi-relational prompts using PLMs. Thirdly, we design a new clustering constraint to optimize the encoding process by enhancing intra-cluster compactness and inter-cluster dispersion, making the message representation more distinguishable. We evaluate the RPLM on three real-world datasets, demonstrating that the RPLM model achieves state-of-the-art performance in offline, online, low-resource, and long-tail distribution scenarios for social event detection tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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A Single-Cell Clustering Algorithm Based on Structure Perturbation Non-Negative Matrix Factorization

A Single-Cell Clustering Algorithm Based on Structure Pertur...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Meineng Wang Luping Zhang Yu-An Huang Yangyuan Li Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Mathematics and Computer Science Yichun University Yichun China School of Information Engineering East China University of Technology Nanchang China School of Computer Science Northwestern Polytechnical University Xi’an China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The advent of single-cell RNA sequencing (scRNA-seq) has facilitated the acquisition of high-resolution data regarding cell heterogeneity across various tissues. A fundamental and critical step in the analysis of scRNA-seq data is cell type identification. An appropriate graph construction strategy can reflect the topological structure between cells and allows for detailed exploration of the relationships between cells and genes. Given the sparsity of scRNA-seq data and the pronounced noise generated by shallow sequencing, choosing an appropriate graph construction strategy has become a significant challenge. In this paper, we propose a single-cell clustering method, named Sc-PNNMF, based on structural perturbation of nonnegative matrix factorization. Sc-PNNMF employs a structural perturbation algorithm to optimize the gene expression matrix. The optimized matrix guides the matrix factorization process, effectively overcoming the impact of data sparsity and significant noise in gene expression data on the graph construction strategy. We compared the clustering abilities of Sc-PNNMF with five other methods on ten real datasets.

关键词： Sequential analysis Perturbation methods Clustering methods RNA Noise Clustering algorithms Sparse matrices Gene expression Unsupervised learning Periodic structures

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Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

arXiv

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arXiv 2023年

作者： Tucker, Maegan Li, Kejun Ames, Aaron D. The School of Electrical and Computer Engineering The Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30308 United States The Department of Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States The Department of Mechanical and Civil Engineering The Department of Control and Dynamical Systems California Institute of Technology PasadenaCA91125 United States

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness - the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multicontact walking on the Atalante lower-body exoskeleton. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Suppressing the endemic equilibrium in SIS epidemics: A state dependent approach

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IFAC-PapersOnLine 2021年第15期54卷 163-168页

作者： Yuan Wang Sebin Gracy Hideaki Ishii Karl Henrik Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Computer Science Tokyo Institute of Technology Yokohama Japan

This paper considers the susceptible-infected-susceptible (SIS) epidemic model with an underlying network structure and focuses on the effect of social distancing to regulate the epidemic level. We demonstrate that if each subpopulation is informed of its infection rate and reduces interactions accordingly, the fraction of the subpopulation infected stays below half for all time instants. To this end, we first modify the basic SIS model by introducing a state dependent parameter representing the frequency of interactions between subpopulations. Thereafter, we show that for this modified SIS model, the spectral radius of a suitably-defined matrix being not greater than one causes all the agents, regardless of their initial sickness levels, to converge to the healthy state; assuming non-trivial disease spread, the spectral radius being greater than one leads to the existence of a unique endemic equilibrium, which is also asymptotically stable. Finally, by leveraging the aforementioned results, we show that the fraction of (sub)populations infected never exceeds half.

关键词： Epidemic processes Infection reduction Characterization of endemic equilibrium Suppressing endemic equilibrium

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Improved Action Recognition Algorithm for Video Surveillance Based on PoseC3D

Improved Action Recognition Algorithm for Video Surveillance...

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International Conference on computer and Communications (ICCC)

作者： Ruifeng Guo Chao Yang Changyi Deng Ying Bai Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

The precise recognition of human action is of great significance for intelligent monitoring and video analysis. Previous researchers have utilized many methods to detect human action, but the accuracy and robustness of these methods remain to be improved. Skeleton-based action recognition methods have reported high accuracy and robustness, but these methods have not fully utilized multiple modalities for feature fusion. In order to solve the above problems, a multi-modality feature fusion based video surveillance personnel action recognition method is proposed by combining the two methods. The SlowOnly network is used to extract RGB modality features. The Faster R-CNN and Lite-HRNet networks are used to obtain skeleton modality data. The PoseC3D network is used to extract skeleton modality features. The early and late fusion of RGB modality features and skeleton modality features are performed. Then the action recognition results of video surveillance personnel are obtained. The experimental results on the NTU RGB+D public dataset under the X-Sub standard show the following points. In the action recognition model based on a single skeleton modality, PoseC3D has a higher recognition accuracy than GCN methods, reaching 92.8%. In the action recognition model based on RGB modality, recognition accuracy has reached 95.4%. The action recognition model based on multi-modality feature fusion has a higher recognition accuracy than the recognition model based on a single modality, reaching 96.1%.

关键词： Accuracy Computational modeling Feature extraction Video surveillance Skeleton Robustness Personnel Standards Monitoring

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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Electronic Engineering (ICEEM), International Conference on

作者： Mahmoud Elshabrawy Marwa M. Eid Abdelaziz A. Abdelhamid El-Sayed M. El-Kenawy Abdelhameed Ibrahim Dept. of Communications & Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Dep. of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Control Systems Dept. Faculty of Engineering University Mansoura Egypt

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models.

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A Novel DWT-based Encoder for Human Pose Estimation

A Novel DWT-based Encoder for Human Pose Estimation

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2022 Scholar's Yearly Symposium of technology, Engineering and Mathematics, SYSTEM 2022

作者： De Magistris, Giorgio Romano, Matteo Starczewski, Janusz Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Computational Intelligence Czstochowa University of Technology al. Armii Krajowej 36 Częstochowa42-200 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The proposed approach for pose estimation is based on the construction of a Convolutional Neural Network with an encoding-decoding structure and a spatial pyramid based on WASP structure in its bottleneck and a Discrete wavelet transform encoder. These techniques already shown their capabilities to solve the main problems in state of the art related to: different Field of view (FoV) required to analyze the different possible sizes of a specific subject. we want to solve the faulty structure of the modern CNN based Neural Networks in the encoding part using DWT encoder and WASP. This Work also have the objective of demonstrating from a more general point of view which could be the advantages of a Discrete Wavelet Transform (DWT) encoder in any CNN-based approach for Pose Estimation and Object detection in any form, such as for several subjects in the same image or in the internal video due to the almost redundant use of the usual most famous encoding structures for CNN such as ResNet-101, U-Net or VGG16-19. we will do our tests using a U-net Based CNN in order to evaluate the importance of the results of the Discrete Wavelet Transform encoder also in the decoding part through the cropping of theme at the last layers of the network. This is necessary due to the loss of border's pixels during encoding that could be useful for the result's evaluation. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Discrete wavelet transforms

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Research on Real-Time Linux Task Scheduling Algorithm for Multiprocessors Based on Particle Swarm Optimisation

Research on Real-Time Linux Task Scheduling Algorithm for Mu...

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International Conference on computer and Communications (ICCC)

作者： Tong Qiu Zhenyu Yin Jun Wang Zisong Wang Liaoning Provincial Key Laboratory of Industrial Intelligent Technology for Chemical Processes School of Computer Science and Technology Shenyang University of Chemical Technology Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware&Software Shenyang China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware&Software Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Liaoning Provincial Key Laboratory of Industrial Intelligent Technology for Chemical Processes School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

Schedulability analysis in real-time operating systems is one of the hot research topics in the field. While Linux has become the preferred choice for industrial systems due to its excellent portability, it cannot meet the hard real-time requirements of specific industrial environments as a time-sharing operating system. To address this issue, we introduce the Real-Time Application Interface (RTAI) 5.2 real-time operating system kernel patch and combine it with Linux 4.4.176 to form the RTAI-Linux dual-core real-time system. Building upon this foundation, we proposes a Particle Swarm Optimization algorithm based on Earliest Deadline First (PSO-EDF) to improve task scheduling in real-time Linux multiprocessor environments. This method introduces a metric for evaluating processor utilization, when a new task arrives, it considers the allocation of free processor resources. Compared with the original method EDF, PSO-EDF provides a broader search space with better global characteristics. The algorithm finds the globally optimal scheduling scheme through iteration, which effectively improves the processor resource utilisation and alleviates the task deadline missing problem encountered in the original method to a certain extent, thus improving the real-time task completion rate. Through extensive simulation experiments, the PSO-EDF scheduling algorithm proves that the scheduling model can further improve the real-time performance of the RTAI-Linux system compared with the classical EDF algorithm.

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Improved SAR Image Generation with Double Top-K Training Method on Auxiliary Classifier GAN

Improved SAR Image Generation with Double Top-K Training Met...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hongchen Wang Ming Liu Shichao Chen Mingliang Tao Jingbiao Wei School of Computer Science Shaanxi Normal University Xi’an China Xi’an Modern Control Technology Research Institute Xi’an China School of Electronics and Information Northwestern Polytechnical University Xi’an China Army Aviation Research Institute Beijing China

Synthetic aperture radar (SAR) is a critical imaging technique that is widely used for civil and military tasks, as it is featured with an excellent ability for high resolution imaging. However, due to the severe shortage of SAR images, the performance of automatic target recognition (ATR) is greatly sabotaged. Generative adversarial network (GAN) is often applied for data augmentation of small-sized dataset. In this paper, based on auxiliary classifier GAN (ACGAN) and top-k training technique, we propose double top-k training, which implements a modification during training without any further adjustment on model architecture. The proposed method is to enforce generator to only optimize on generated images that perform well in both discriminator and auxiliary classifier, and discard images of poor performance. We evaluate the generated images via recognition on the moving and stationary target acquisition and recognition (MSTAR) dataset. Recognition accuracy and Fréchet inception distance (FID) score indicate better generation results of the proposed method compared with original ACGAN.

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