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Proceedings of the 2023 7th international conference on Electronic Information Technology and Computer Engineering

作者： Jingwen Huang Jiajun Lu Tianyi Jia Xiang Li School of Logistics Beijing Wuzi University China

ISBN: (纸本)9798400708305

In response to the problem of three-dimensional path planning for unmanned aerial vehicles (UAVs) in complex environments, this paper proposes an environmental complexity based variable step Rapidly-exploring Random Tree (RRT) path planning algorithm. Firstly, a model of environmental complexity function is established in a three-dimensional environment to measure the complexity of UAV flight environments. then, an evaluation function for path planning is established. Based on the environmental complexity function and the evaluation function, the optimal step size for the RRT algorithm is found with the genetic algorithm. With a large number of simulated random environmental variables and the corresponding optimal step size, the environmental complexity based RRT step size function is identified and established that represents the relationship between the optimal step size of the RRT algorithm and the complexity of the environment. Based on the sliding window and the environmental complexity based RRT step size function, the variable step RRT path planning for UAVs is constructed. this algorithm can dynamically select the optimal step size according to the local environment, improving the overall performance of UAV path planning ***, comparative experiments are conducted to verify that the proposed algorithm is superior to the traditional on

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Research on Action Recognition Based on Zero-shot Learning 1

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7th international conference on Smart Computing and Communication, SmartCom 2022

作者： Zhao, Hui Tan, Jiacheng Duan, Jiajia Educational Information Technology Lab. Henan University Kaifeng475000 China Software College Henan University Kaifeng475000 China Henan International Joint Laboratory of Theories and Key Technologies on Intelligence Networks Henan University Kaifeng475000 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

At present, the research on human action recognition has achieved remarkable results and is widely used in various industries. Among them, human action recognition based on deep learning has developed rapidly. With sufficient labeled data, supervised learning methods can achieve satisfactory recognition performance. However, the diversification of motion types and the complexity of the video background make the annotation of human motion videos a lot of labor costs. this severely restricts the application of supervised human action recognition methods in practical scenarios. Since the zero-shot learning method can realize the recognition of unseen action categories without relying on a large amount of labeled data. In recent years, action recognition methods based on zero-shot learning have received great attention from researchers. In this paper, we propose an attention-based zero-shot action recognition model ADZSAR. We design a novel attention-based mechanism feature extraction method that introduces the current state-of-the-art semantic embedding model (Word2Vec). Experiments show that this method performs the best among similar zero-shot action recognition methods based on spatio-temporal features. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Zero-shot learning

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On Security Analysis of Periodic Systems: Expressiveness and complexity 7th

On Security Analysis of Periodic Systems: Expressiveness and...

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7th international conference on Information Systems Security and Privacy (ICISSP)

作者： Alturki, Musab A. Kirigin, Tajana Ban Kanovich, Max Nigam, Vivek Scedrov, Andre Talcott, Carolyn KFUPM Dhahran Saudi Arabia Runtime Verificat Inc Urbana IL 61801 USA Univ Rijeka Dept Math Rijeka Croatia UCL London England Natl Res Univ Higher Sch Econ Moscow Russia Fortiss Munich Germany Univ Penn Philadelphia PA 19104 USA SRI Int 333 Ravenswood Ave Menlo Pk CA 94025 USA

ISBN: (纸本)9789897584916

Development of automated technological systems has seen the increase in interconnectivity among its components. this includes Internet of things (IoT) and Industry 4.0 (I4.0) and the underlying communication between sensors and controllers. this paper is a step toward a formal framework for specifying such systems and analyzing underlying properties including safety and security. We introduce automata systems (AS) motivated by I4.0 applications. We identify various subclasses of AS that reflect different types of requirements on I4.0. We investigate the complexity of the problem of functional correctness of these systems as well as their vulnerability to attacks. We model the presence of various levels of threats to the system by proposing a range of intruder models, based on the number of actions intruders can use.

关键词： Formal Methods Verification Security Multiset Rewriting Industry 4.0 complexity

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Challenges’ Strategies in Virtual Robocup Standard Platform League

Challenges’ Strategies in Virtual Robocup Standard Platform...

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Artificial Intelligence Technology (ACAIT), Asian conference on

作者： Zijian Wang Hongjiang Liu Pingchuan Chen Yinuo He Lingyun Zhu Liangjiang International College ChongQing University of Technology ChongQing China College Of Computer Science And Engineering ChongQing University of Technology ChongQing China

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

the RoboCup, an international competition, necessitated an innovative approach in 2022 due to its transition to an online format in China, prompted by an epidemic. this shift demanded the development of more specialized algorithms for the competition. In response to this, we devised a unique algorithm for each challenge. For Challenge 1, we implemented a streamlined greedy algorithm, which rounded the target points to minimize the total time expenditure. In Challenge 2, we adopted an automated obstacle avoidance strategy. For Challenge 3, we calculated the kick angle in real-time to enhance the synergy between the two robots, employing the Sector-angle method to select the optimal shot or pass angle. Our algorithm showed excellent results in the competition, ranking first in the country. In the practical test, our cumulative scores for the three challenges remained consistent at 163.18, 33.65, and 4.8, significantly improving from the pre-optimization scores of 196.2, 51.78, and 3.5, respectively. this indicates the precise and reliable nature of our algorithms. In particular, the sector angle method was notably influential in Challenge 3, swiftly calculating the ideal angle with exceptional accuracy. Before improvements, the algorithm in this project only managed to score 3.5, which was substantially improved post-optimization. Moving forward, these algorithms can be further optimized and adapted to meet the demands of future scenarios.

关键词： Heuristic algorithms Robot sensing systems Real-time systems Reliability Collision avoidance Time complexity Optimization

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conference Report on 2022 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2022) [conference Reports]

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IEEE Computational Intelligence Magazine 2023年第3期18卷 6-9页

作者： Ah-Hwee Tan Dipti Srinivasan Chunyan Miao Singapore Management University Singapore National University of Singapore Singapore Nanyang Technological University Singapore

On behalf of the organizing committee, we are delighted to deliver this conference report for the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), which was held in Singapore from 4th to 7th December 2022. IEEE SSCI is an established flagship annual international series of symposia on computational intelligence (CI) sponsored by the IEEE Computational Intelligence Society (CIS) to promote and stimulate discussions on the latest theory, algorithms, applications, and emerging topics on computational intelligence. After two years of virtual conferences due to the global pandemic, IEEE SSCI returned as an in-person meeting with online elements in 2022.

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SSL VPN over TCP and UDP Tunnels: Performance evaluation with different server-side congestion control 22

SSL VPN over TCP and UDP Tunnels: Performance evaluation wit...

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7th international conference on Cloud Computing and Internet of things, CCIOT 2022

作者： Chua, Choon Hoe Ng, Sok Choo International University of Malaya Wales Malaysia

ISBN: (纸本)9781450396738

One of the key features of SSL VPN software is that they are inherently designed to transport payload data over either TCP or UDP tunnels. However, it is a common refrain to not use a TCP tunnel due to the overhead associated with the protocol and the well-known performance problem caused by the stacking of one layer of TCP on top of another. Even so, in some restrictive network environments where UDP tunnelling may not work, TCP is the only option. With the increasing availability of reliable, high-speed networks and the introduction of new TCP congestion control algorithms, there is an opportunity now to revisit this problem. One such new congestion control algorithm is Google's Bottleneck Bandwidth and Round-trip Propagation Time (BBR). the algorithm has been reported to be superior to older congestion control schemes in performance. In this study, we investigate the use of BBR for the application in SSL VPN and evaluate its' performance in comparison to the default Linux congestion control algorithm Cubic. Our findings showed that the use of BBR led to gains in VPN throughput for both TCP over UDP and TCP over TCP tunnelling. In the latter case, our study did not uncover the undesirable effects commonly associated with the stacking of TCP on top of TCP. © 2022 ACM.

关键词： Bandwidth

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Proceedings of ITNT 2021 - 7th IEEE international conference on Information Technology and Nanotechnology

Proceedings of ITNT 2021 - 7th IEEE International Conference...

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7th IEEE international conference on Information Technology and Nanotechnology, ITNT 2021

ISBN: (纸本)9781665432177

the proceedings contain 284 papers. the topics discussed include: on a lower bound to classification error probability in an ensemble of data sources;comparative analysis of objective quality functions for the stochastic gradient identification method;influence of the relative position of receiving stations on the accuracy of determining the location of the object;comparative analysis of two strategies for forming regions of interest in panorama vision system;comparison of reinforcement learning algorithms for motion control of an autonomous robot in gazebo simulator;theoretical and experimental study of spiral zone plates in aluminum thin film;modeling and data analysis for assessing the sensitivity of competitive performance of territories in the implementation of investment projects;and Bessel beams produced by axicon and spatial light modulator: a brief analysis.

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A Lightweight CNN-Transformer Network with Agent Attention Mechanism for 3D Whole Heart Segmentation

A Lightweight CNN-Transformer Network with Agent Attention M...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Jiatong Li Yifan Wang Dianrong Du Hengfei Cui National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Xi'an China Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China Chongqing Innovation Center Northwestern Polytechnical University Chongqing China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

In recent years, heart diseases have become one of the major global health issues, emphasizing the importance of 3D whole heart segmentation technology for accurate and efficient treatment. Currently, 3D whole heart segmentation algorithms often combine Transformer technologies with 3D Convolutional Neural Networks (3D CNNs), leveraging the self-attention mechanism of Transformers for global information modeling and 3D CNNs for capturing local contextual information. However, the high computational complexity of Transformers leads to huge computational resource wastage. To address this issue, this work proposes a novel whole heart segmentation algorithm named AgentTransBTS, based on an improved 3D Agent Attention mechanism and the TransBTS framework. this algorithm utilizes TransBTS as the baseline model, capitalizing on its deep feature extraction and information integration capabilities to capture global dependencies in images. To reduce computational complexity without sacrificing the model's expressive power, an enhanced 3D Agent Attention mechanism is introduced. Comprehensive experiments were conducted on the Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 dataset to evaluate the proposed algorithm. the experimental results demonstrate that our proposed method achieved an average Dice coefficient of 91.9% on the CT dataset and 79.9% on the MRI dataset of the MM-WHS 2017 challenge. Furthermore, our algorithm's FLOPs are 217G, representing an approximate 9.2% improvement in computational efficiency compared to the baseline network.

关键词： Heart Image segmentation Solid modeling three-dimensional displays Attention mechanisms Computational modeling Magnetic resonance imaging Transformers Computational complexity Biomedical imaging

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Research on the Detection of Abnormal Cervical Cells Based on Improved YOLOv7

Research on the Detection of Abnormal Cervical Cells Based o...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Xuewei Wang Shuang Yuan Lidong Yang Dawei Niu Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Inner Mongolia University of Science and Technology School of Digtial and Intelligence Industry Inner Mongolia China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Cervical cytologic screening is clinically important for the prevention and diagnosis of cervical cancer. Aiming at the many challenges in the detection of abnormal cervical cells, including the difficult detection of small targets of abnormal cervical cells, complex backgrounds, and uneven cell morphology, a detection algorithm for abnormal cervical cells based on improved YOLOv7 was proposed. the algorithm utilizes Deformable Convolutional Networks v2 (DCNv2) that adaptively de-tunes the scale and receptive field size to more effectively cope with the complexity and diversity of cervical cell images. the experimental results show that the improved YOLOv7 algorithm mAP achieves 57.26%, up to 73.88% accuracy, and 46.90% recall on the cervical abnormal cell dataset. Compared with the benchmark model, they increased by 4.07%, 3.39%, and 5.69%, respectively.

关键词： Accuracy Sensitivity Shape Prevention and mitigation Signal processing algorithms Signal processing Feature extraction Convolutional neural networks Detection algorithms Cervical cancer

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XRF Spectral Feature Extraction and Classification Method Based on the EO-CNN Deep Learning Model

XRF Spectral Feature Extraction and Classification Method Ba...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Jiajie Li Qi Zhang Ran Gao Mohai Yue Xiangyu Liu Yi Zhao Lan Rao Feng Tian Yun Teng Fu Wang Qinghua Tian Yongjun Wang State Key Laboratory of Information Photonics and Optical Communications Beijing Key Laboratory of Space-ground Interconnection and Convergence School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Beijing Key Laboratory of Space-ground Interconnection and Convergence School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Guangdong University of Technology Guangzhou China The School of Information and Electronics Beijing Institute of Technology (BIT) Beijing China Beijing Institute of Control and Electronic Technology Beijing China School of Electronic Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Beijing Key Laboratory of Space-ground Interconnection and Convergence BUPT Beijing China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

An XRF spectral feature extraction and classification method based on an EO-CNN model is proposed to address the complexities of soil spectral data and improve classification accuracy. the proposed model is applied to soil classification, achieving an overall accuracy of 95% for five distinct soil categories. the experimental results show robustness in handling imbalanced datasets, as evidenced by a 69% classification accuracy for the category representing only 1% of the dataset.

关键词： Electrical engineering Deep learning Accuracy Automation Soil Feature extraction Robustness Data models complexity theory Classification algorithms

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