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Elbow Joint Classification for Total Elbow Arthroplasty 46

Elbow Joint Classification for Total Elbow Arthroplasty

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Jun Zhang, Yongxing Ding, Xiying Ma, Xiao Peng, Chen Xue, Junxiao Lin, Feng Wu, Yan Zhang, Qing Zhejiang Lab Research Center for Frontier Fundamental Studies Hangzhou China The Second Affiliated Hospital Zhejiang University School of Medicine Department of Orthopedics Hangzhou China Sir Run Run Shaw Hospital Zhejiang University School of Medicine Department of Ultrasound Hangzhou China Zhejiang Lab Research Center for Space Computing System Hangzhou China

ISBN: (纸本)9798350371499

Current elbow prosthesis design and fabrication is not catering for the target population, leading to a prevalent occurrence of prosthesis mismatch in total elbow arthroplasty (TEA) surgeries. To address this challenge, it is crucial to develop an efficient elbow joint classification method that accurately captures specific morphological variations, advancing the design of diverse prostheses for optimal matching. In this paper, we introduce two classification algorithms utilizing shape features extracted from the Riemannian manifold and anatomical features derived from three-dimensional (3D) measurements. Additionally, we quantitatively evaluate these feature-based algorithms using computed tomography (CT) scan images from a randomly recruited sample of 120 individuals registered to a local hospital. This study represents the first endeavor in classifying and evaluating elbow joints through shape and anatomical features. Our results demonstrate that the shape-based classification algorithm not only provide a better understanding of morphological variations with improved cluster compactness and separability, but also captures crucial clinical variations such as gender differences. © 2024 IEEE.

关键词： Computerized tomography

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A Sensitive Image Generation Method based on Improved PatchGAN 12

A Sensitive Image Generation Method Based on Improved PatchG...

引用

12th International Conference of Information and Communication Technology, ICTech 2023

作者： Zhang, Shilin Wang, Hongliang Wang, Liang Shenyang Institute of Computing Technology Shenyang Chemical University Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350332919

Aiming at the problem of less data and unbalanced number of sensitive image samples in the deep learning task of sensitive image detection, this paper proposes a sensitive image generation method SI-GAN based on improved PatchGAN. The SI-GAN generator uses the DUD module, and uses the concat method to increase the number of channels of the feature map in the quantitative dimension, which improves the generation ability of the generator. The SI-GAN discriminator uses the RBL module to improve PatchGAN, extract more advanced feature information, and increase the discriminant's discriminative ability. The experimental results show that SI-GAN has better results in terms of generated image quality. The FID value of SI-GAN is 5.87 lower than that of PatchGAN. The IS value of SI-GAN is 5.09 more than PatchGAN. The SI-GAN proposed in this paper is able to optimize the quality of the generated images and generate images with more details. © 2023 IEEE.

关键词： Deep learning

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A Spatial Registration Method based on Rigid Coherent Point Drift of Key Features 15

A Spatial Registration Method Based on Rigid Coherent Point ...

引用

15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Guo, Ruifeng Zhang, Xiaoxing Wang, Hongliang Bai, Ying Li, Yuening Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China University of Chinese Academy of Sciences Beijing100049 China Research Center Based on Digital Twin Liaoning Province Human-Computer Interaction System Engineering Shenyang China

ISBN: (纸本)9798350329360

Mixed reality and augmented reality technologies have been introduced into the field of surgical navigation for many years. Although many scholars have done a lot of research on holographic-based medical navigation techniques, the registration of preoperative MRI to the patient in a realistic clinical environment is still an existential problem. In this paper, a register method to align the preoperative medical images of patients to the intraoperative patient's body is proposed. The proposed approach simplifies the complex overall registration task of preoperative images of patients into the registration of local critical features. The proposed method extracts the patient's facial key features from the preoperative medical images and 3D information of the clinical environment reconstructed by Intel RealSense, respectively. And then the mapping transformation between the facial key features of preoperative medical images and the patient's key features in the realistic environment was estimated by the fusion of centroid movement and rigid point set registration algorithm of Coherent Point Drift. The centroid movement method performs preliminary alignment by overlapping the centers of mass of two sets of alignment data. For the two sets of alignment data containing noise and outliers, the Coherent Point Drift method is used to refine the alignment results. In the experiment of 4 key features the modified Hausdorff distance value is 3.2403 mm, the RMSE value is 5.8412 mm, and the TRE value is 5.6454 mm. The experimental results show that the proposed method is able to perform a robust and accurate alignment even though the preoperative medical images and the 3D information of the MR/AR environment come from different spatial coordinate systems and that noise and outliers are present in both data. © 2023 IEEE.

关键词： Alignment

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Improved internal voxelization algorithm based on depth buffer 5

Improved internal voxelization algorithm based on depth buff...

引用

5th International Academic Exchange Conference on Science and Technology Innovation, IAECST 2023

作者： Li, Changhao Liu, Jun Wang, Hongliang Xiu, Pengfei Shenyang University of Chemical Technology Shenyang China Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang Institute of Computing Technology Shenyang China

ISBN: (纸本)9798350357738

Aiming to address the inefficiencies in existing internal voxelization algorithms, a solution has been proposed to use a two-dimensional depth buffer table to record the interval range of surface voxels, reducing the number of internal voxels to be checked. The complexity of this algorithm is independent of the model's complexity. the voxel model is stored using a non-uniformly subdivided octree structure, where if a particular layer's node and all of its child nodes are internal voxels, there is no need for further subdivision, reducing storage space. Experimental results indicate that the improved algorithm can run correctly while achieving better time and space efficiency. © 2023 IEEE.

关键词： Computational complexity

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Avionics system Architecture Design of a Manned eVTOL Aircraft

Avionics System Architecture Design of a Manned eVTOL Aircra...

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Chinese Automation Congress (CAC)

作者： Xiaobo Wang Wenyi Liang Fenfen Yu Yiren Hu Libo Cheng Anhuan Xie Research Center for Intelligent Equipment Zhejiang Lab Hangzhou China Online Marketing Service Center China Mobile Communications Co. Ltd. Hangzhou China Space-based Computing System Research Center Zhejiang Lab Hangzhou China Frontier Basic Research Center Zhejiang Lab Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

The avionics system plays a critical role in ensuring the operation of an aircraft, facilitating communication between the aircraft and ground, detecting and displaying aircraft status, as well as coordinating data exchange and processing among various systems. This paper presents an architecture of the avionics system for a manned electric vertical takeoff and landing (eVTOL) aircraft, which mainly includes the flight control system, navigation sensor system, communication system, intelligent onboard computer system, and cloud monitoring system. Detailed descriptions are provided for the redundant flight control system, sensor redundancy design, and intelligent onboard computer system. Various flight tests, including manned flight, cargo transportation, endurance, range, speed, and fault tolerance tests, are conducted on the aircraft equipped with this avionics system. The flight tests have exceeded a thousand. Analysis of flight log data demonstrates that the designed avionics system meets the safety and functionality requirements of the manned eVTOL aircraft.

关键词： Redundancy Fault tolerant systems systems architecture Computer architecture Aerospace electronics Aircraft navigation Sensor systems Safety Aircraft Aerospace control

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Few-shot Object Detection Algorithm based on YOLOv8-Mamba-Like Linear Attention 16

Few-shot Object Detection Algorithm Based on YOLOv8-Mamba-Li...

引用

16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Wang, Hongliang Wang, Kaizhu Ji, Binglong Deng, Changyi 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 Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798331506322

In the production and assembly process, the accuracy of detection is usually low due to limited resources and small quantities. In this paper, we propose a few-shot learning method for object detection based on the combination of YOLOv8 and Mamba-Like Linear Attention (MLLA) module. This method utilizes a high-pixel camera to capture the shape and size of the workpiece. By consistency-based mutual augmentation structure for few-shot samples data augmentation. By introducing the efficient and lightweight MLLA module, the model's speed and accuracy in object detection and segmentation tasks on mobile devices are significantly improved. Specifically, MLLA mainly integrates the two key elements of 'forget gate' and 'block design' in Mamba, and MLLA replaces forget gate by using position coding (RoPE) to provide necessary location information while maintaining parallel computation and fast inference speed. This makes MLLA improves the accuracy of detection and segmentation recognition. Experimental results show that compared to the original YOLOv8 model, the model with the MLLA module exhibits improvements in segmentation precision and recognition accuracy. This method demonstrates high accuracy and robustness in workpiece recognition, making it suitable for real-time industrial applications. © 2024 IEEE.

关键词： Object detection

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Identification and Classification of 5G-R Interference Signals: A Transformer-based Approach

Identification and Classification of 5G-R Interference Signa...

引用

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Ying Zuo Xiaofang Sun Yuan Liu Zhangdui Zhong School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Research Center for Space Computing System Zhejiang Laboratory Hangzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Accurate identification and classification of 5G-R interference signals are crucial for ensuring the safety and reliability of railway communications. This paper proposes a novel approach for the 5th Generation Mobile Communication Technology-Railway(5G-R) interference signal identification and classification utilizing the transformer model. In our scheme, a data preprocessing technique called Locally Weighted Scatterplot Smoothing (LOWESS) is employed to mitigate the effects of noise in the sample spectrum, thus enhancing the model's recognition accuracy. The proposed transformer model, renowned for its exceptional performance in sequence processing tasks, leverages its self-attention mechanism to effectively capture the intricate patterns within the interference signals. Through comparative simulations involving the convolutional neural network (CNN) and long short-term memory (LSTM) models, the results demonstrate that the proposed transformer model achieves superior performance in identifying and classifying 5G-R interference signals.

关键词： Accuracy Smoothing methods Noise Data preprocessing Interference Transformers Data models Convolutional neural networks Reliability Long short term memory

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A Fine-grained Human Gait Recognition Algorithm based on Unordered Video Sequences

A Fine-grained Human Gait Recognition Algorithm Based on Uno...

引用

Information and Communication Technology (ICTech), International Conference of

作者： Dongsheng Yang Ying Bai Hongliang Wang Xiaoxing Zhang Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital

ISBN: (数字)9798350376258

ISBN: (纸本)9798350376265

Gait recognition is an effective and unique biometric recognition pattern, and the performance of the algorithm mainly depends on its ability to extract and distinguish features from spatial temporal sequences. To improve the robustness of fine-grained human gait recognition algorithms, this paper proposes a deep gait recognition algorithm based on unordered sequences. Firstly, this article expands the Casia-C dataset to a new large-scale human gait behavior dataset; Secondly, multi-scale serialized feature extraction is performed on shallow to deep features. The improved Gaitset model enhances the mining of fine-grained sequence level features, while preserving the temporal input independence of the original Gaitset model, achieving fast, flexible, and effective gait recognition tasks. Finally, this article designs and implements the overall process from video capture, human contour segmentation and preprocessing to gait behavior recognition. The experimental results show that the proposed algorithm significantly improves accuracy and robustness in different walking states on the most widely used dataset in the field of gait recognition, CASIA-B (Institute of Automation, Chinese Academy of Sciences) and the expanded CASIA-C dataset.

关键词： Legged locomotion Accuracy Image recognition Video sequences Feature extraction Robustness Behavioral sciences Pattern recognition Information and communication technology Gait recognition

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Improved Object Detection Algorithm for Workshop Environments based on YOLOv5 9

Improved Object Detection Algorithm for Workshop Environment...

引用

9th International Conference on Computer and Communications, ICCC 2023

作者： Tan, Jinting Wang, Hongliang Li, Xuerao Zhang, Xiaoxing Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China Shenyang Ligong University Shenyang China University of Chinese Academy of Sciences Shenyang China Liaoning Prov. Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (纸本)9798350317251

In the context of workshop-restricted environments, this paper presents an improved lightweight detection algorithm named YOLO-M3C to achieve safety helmet wearing detection on embedded devices in real-time while handling multiple video streams efficiently. YOLO-M3C introduces two key enhancements. Firstly, it replaces the backbone network of YOLOv5 with the more lightweight MobileNetV3 network for feature extraction. This improvement significantly reduces the network's parameter count, resulting in enhanced computational speed and a substantial decrease in computational resource requirements. Secondly, the attention mechanism CBAM is introduced to increase the model's focus on important features, leading to further improvements in detection accuracy. CBAM helps the model better comprehend crucial information in the images, thereby enhancing detection algorithm performance. Experimental results demonstrate that YOLO-M3C achieves increased average precision while shortening the training time by 15%. Additionally, the improved algorithm reduces the parameter count of the YOLOv5 model by 52%, effectively reducing model size and training time, while enhancing detection efficiency. The improved YOLOv5 algorithm can be effectively deployed on embedded devices in workshop environments, enabling efficient real-time processing of multiple video streams and reliable safety helmet wearing detection, thus meeting the real-time detection requirements in workshop settings. © 2023 IEEE.

关键词： Object detection

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Investigations on Unclamped-Inductive-Switching Behaviors of p-GaN HEMTs at Cryogenic Temperature 34

Investigations on Unclamped-Inductive-Switching Behaviors of...

引用

34th IEEE International Symposium on Power Semiconductor Devices and ICs, ISPSD 2022

作者： Zhang, Chi Lu, Weihao Li, Sheng Liu, Siyang Ma, Yanfeng Huang, Jingwen Zhang, Long Wei, Jiaxing Sun, Weifeng Zhang, Chunwei Guo, Haijun Southeast University National Asic System Engineering Research Center Jiangsu Nanjing210096 China University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Shandong Jinan250022 China

ISBN: (纸本)9781665422017

In this paper, the unclamped-inductive switching (UIS) behaviors of GaN-based high electron mobility transistors with p-GaN cap (p-GaN HEMTs) at cryogenic temperature (CT) are investigated in details. Unlike the temperature-dependent avalanche process induced failure for Si-based MOSFETs, the withstanding behaviors of p-GaN HEMTs are not affected by temperature. With the help of TCAD simulations and experimental characterizations, the temperature-independent inverse piezoelectric effect induced by high electric field during UIS process is considered as the original failure inducement. Meanwhile, it is demonstrated that p-GaN HEMTs have higher robustness at extremely low temperature scene under repetitive UIS stress. After enduring 5M UIS stress cycles at CT, the threshold voltage (Vth) only increases by 0.06 V and ON-state resistance (Rdson) increases by 4 mΩ, implying higher device reliability at CT. In conclusion, compared with traditional Si-based devices, p-GaN HEMTs are more competitive in extremely low temperature scene. © 2022 IEEE.

关键词： Temperature

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