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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 798 条记录，以下是91-100 订阅

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Classification of finger movement based on EEG phase using deep learning 12

Classification of finger movement based on EEG phase using d...

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Joint 12th international conference on Soft Computing and Intelligent systems / 23rd international Symposium on Advanced Intelligent systems (SCIS and ISIS)

作者： Wenhao, Han Lei, Ma Hashimoto, Kosuke Fukami, Tadanori Yamagata Univ Grad Sch Sci & Engn Yonezawa Yamagata 9928510 Japan

ISBN: (纸本)9781665499248

In recent years, the availability of a huge number of different electroencephalogram (EEG) datasets and the development of deep learning systems have enabled many studies about motor imagery-related EEG classification. However, most of these studies have focused on the movement of relatively large body parts such as the left/right hand, and few have considered the classification of finger movement. Some classification methods have been based on frequency components such as power and amplitude spectra. However, these spectra are less likely to show unique spatial distributions of EEGs on the scalp for different fingers. This is because the regions that control the fingers are close to one another in the motor cortex, leading to similar distributions. We predicted that the arrival timing at electrodes on the scalp of the signal reflecting neural activity would differ depending on the source in the motor cortex. Based on this assumption, we attempted classification using the phase distribution of each EEG frequency component. We used deep learning to classify movement of the thumb and index finger, which are controlled by adjacent regions in the motor area. We collected data during finger movement. Our proposed EEG-phase-based method enabled classification of the movement of two fingers with an accuracy of 70.0%. This is superior to the result of amplitude-based classification, i.e., 62.3%.

关键词： brain computer interface (BCI) finger movement electroencephalogram (EEG) phase deep learning

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Cascaded based Adaptive R-CNN Network for Insulator Defect Detection 23

Cascaded based Adaptive R-CNN Network for Insulator Defect D...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yao, Nan Shan, Guangrui Pan, Jianya Wang, Zhen Zhu, Xueqiong State Grid Jiangsu Electric Power Co. Ltd. Research Institute Nanjing210000 China

ISBN: (纸本)9781665494571

Target detection is a popular direction in computer vision and digital image processing. It is widely used in robot navigation, intelligent video surveillance, industrial detection, aerospace, and many other fields. It has important practical significance to reduce the consumption of human capital through computer vision. Due to the widespread use of deep learning, target detection algorithms have developed relatively rapidly. In this paper, we propose a method for detecting insulator defects Based on Cascaded Fast R-CNN. We use RoIAlign instead of RoIPooling to get a better positioning effect and add a mask branch on the basis of Faster R-CNN to complete the pixel-level segmentation task, which can detect insulator defects in real-time with high detection accuracy. © 2021 IEEE.

关键词： Image segmentation computer vision Service robots Navigation Failure analysis Object detection Insulators

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd international conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Reinforcement learning

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Pose Adaptive Margin Loss for Face Verification 23

Pose Adaptive Margin Loss for Face Verification

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作者： Lei, Jie Zhang, BaiYan Xia, Linglin Ling, HeFei School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Nanchang University Nanchang China

ISBN: (纸本)9781665494571

Face verification is to determine whether two faces belong to the same person, and is widely used in ordinary life. Deep Learning-based Face verification (FV) has made great progress and achieved very high performance on LFW in recent years. And FV is an open-set problem, so is expected to learn discriminative features which intra-class variation of features is small and inter-class difference is large. While at the same time, we notice that the variance in pose greatly degrades the verification performance, due to the intrinsic difficulties in generalization and robust feature extraction from relative few non-frontal face examples. Margin based Softmax loss has achieved outstanding improvements, and we argue that the margin should be adjusted according to different poses. In this paper, we propose an pose adaptive margin loss to improve the generalization ability of margin based Softmax loss in face verification problems, where faces with different poses have different additive margin. Extensive experiments demonstrate that our method achieves better performance than popular margin-based approaches. © 2021 IEEE.

关键词： Training Representation learning Additives Smart cities Benchmark testing Feature extraction Task analysis

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A Transpose-free Three-dimensional FFT Algorithm on ARM CPUs 23

A Transpose-free Three-dimensional FFT Algorithm on ARM CPUs

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作者： Chen, Tun Jia, Haipeng Li, Zhihao Li, Chendi Zhang, Yunquan Skl of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd Shenzhen China

ISBN: (纸本)9781665494571

According to the traditional multi-dimensional FFT, memory layouts of high-dimensional data are discontinuous. Transposition is introduced to keep high-dimensional data continuous in memory. However, transposition increases memory access and is a hot spot for multi-dimensional FFT. This paper proposes an optimization framework to eliminate explicit transpositions and optimize the three-dimensional (3D) FFT. This framework includes three research points. 1) combines the width-first and breadth-first search to optimize the butterfly network of one-dimensional (1D) FFT;2) adopts a column-order algorithm to eliminate data transposition;3) adopts a blocking algorithm of cache-aware to better use the hardware resources of ARM architecture. Based on this optimized framework, a multi-dimensional FFT library named MDFFT is implemented. The experiments demonstrate that MDFFT generally performs better than FFTW and ARMPL on ARM CPUs. © 2021 IEEE.

关键词： Three-dimensional displays Smart cities Layout Parallel processing Libraries Hardware Optimization

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Security Features on and with Documents: A Survey 23th

Security Features on and with Documents: A Survey

引用

23rd international conference on Intelligent systems Design and Applications, ISDA 2023

作者： Yamini, C. Priya, N. Research Department of Computer Science SDNB Vaishnav College for Women Chrompet Madras University Chennai India PG Department of Computer Science SDNB Vaishnav College for Women Chrompet Madras University Chennai India

ISBN: (纸本)9783031646492

In today’s world, Security for any means has become really important. That stands for either hard copy or the soft copy of any document. The concept of online transactions or simply digital transactions has become a rage over a period. To accommodate such stuffs, there arises a need of transforming hard copies of document into soft copy ones. This would further lead to the crisis where there is a need to provide security for these soft copies. The documents when maintained as soft copies are vulnerable to all sorts of attacks either through bugs and/or through human errors. These human errors are the ones that need to be taken care of in a large scale. Some people believe in sharing documents through online with their peers or others of same interest. This may lead to the misuse of these documents when they fall into wrong hands. Then, the security of these documents becomes a topic to dwell on. In this paper, a study was conducted based on the ways to secure data and the techniques or algorithms that can be used to do it. There are many different technologies based on the type of data that is being encrypted. These are being discussed along with the papers that were taken into account for various data Security methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cryptography

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An Improved Bi-LSTM Model for Entity Extraction of Intellectual Property Using Complex Graph 23

An Improved Bi-LSTM Model for Entity Extraction of Intellect...

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作者： Wang, Youning Liu, Qi Wang, Yang School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Intellectual Property Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665494571

The protection of Intellectual Property (IP) has gradually increased in recent years. Traditional intellectual property management service has lower efficiency for such scale of data. Considering that the maturity of deep learning models has led to the development of knowledge graphs. Relevant researchers have investigated the application of knowledge graphs in different domains, such as medical services, social media, etc. However, few studies of knowledge graphs have been undertaken in the domain of intellectual property. In this paper, we introduce the process of building a domain knowledge graph and start from data preparation to conduct the research of named entity recognition. © 2021 IEEE.

关键词： Deep learning Dictionaries Social networking (online) Smart cities Intellectual property Medical services Feature extraction

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A Study on Robust Stability for Multi-Rate Model Predictive Control for Linear systems with Additive Disturbance 23

A Study on Robust Stability for Multi-Rate Model Predictive ...

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23rd international conference on Control, Automation and systems, ICCAS 2023

作者： Kim, Junsoo Park, Gyunghoon School of Electrical and Computer Engineering University of Seoul Seoul02504 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we discuss a model predictive control (MPC) for a multi-rate discrete-time system with additive unknown disturbance. In the multi-rate setting, it is assumed that state measurement and input update are performed less frequently than the system update, which may lead to instability of the overall system when a disturbance enters the system. We here propose a robust stability-guaranteed design for the multi-rate robust MPC (MR-RMPC), by extending the authors' previous work on disturbance-free version to cases of the uncertain systems. To this end, the lifting method is adopted to represent the multi-rate (and thus time-varying) system as an linear time-invariant system with a larger dimension, for which a robust positively invariant set is newly derived. The proposed MR-RMPC is constructed based on the Riccati equation for the lifted system, which directly implies that robust stability condition is carried out. © 2023 ICROS.

关键词： Model predictive control

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Assessing Stroke Patients Movements Using Inertial Measurements Through the Advances of Ensemble Learning Technology 23

Assessing Stroke Patients Movements Using Inertial Measureme...

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作者： Razfar, Najmeh Kashef, Rasha Mohammadi, Farah Electrical and Computer Engineering Ryerson University TorontoON Canada

ISBN: (纸本)9781665494571

Internet of things (IoT) and wearable sensors enabled the possibility of measuring activities of daily living (ADLs). The accuracy and precision of detecting stroke patients' movements, using wearable sensors and analyzing data by implementing the advancement of machine learning and ensemble learning technology can enhance the stroke patients' remote assessment techniques. Therefore, this paper aimed to apply ensemble in addition to the machine learning models on the Xsens sensors dataset derived from wearable sensors and collected from twenty stroke survivors. Then, we compare the performance of the single model with the bagging and boosting ensemble learning methods to detect the affected hand of the stroke survivors from the non-affected hand. The results indicated that the ensemble techniques such as GentleBoost and AdaBoost achieved the highest model performance with 91.2% and 89.6% accuracy, respectively in comparison with other single machine learning techniques as well as other ensembles. It was also noted that the subspace ensemble techniques achieved the lowest accuracy, recall, and specificity compared to the Decision Tree. © 2021 IEEE.

关键词： Learning systems Smart cities Stroke (medical condition) Boosting Sensor systems Internet of Things Motion measurement

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FNCS: Federated Learning Strategy Based on Cosine Similarity under Resource Constraints 23

FNCS: Federated Learning Strategy Based on Cosine Similarity...

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作者： Li, Ruonan Qin, Yang Zang, Lu College of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665494571

Federated learning has been widely applied in healthcare services and real-time object tracking. However, limited by communication resources, such as the server bandwidth, and the impact of client data heterogeneity, the convergence rates and accuracy of federated learning significantly drop. Hence, this study proposes a novel federated normalization learning strategy based on cosine similarity (FNCS). Starting from a new perspective of the relationship between local and global updates of the model, FNCS selects valuable clients to upload updates using cosine similarity. The regularization term is then inserted in the last layer of clients by utilizing cosine distance-based update divergence. Numerous experiments are conducted in PyTorch for accelerated validation. Results show that the high accuracy experiments are conducted on the complex CelebA dataset, and the communication rounds of FNCS are improved by 44.71% and 41.98% compared with FedAvg and FedProx, respectively. © 2021 IEEE.

关键词： Smart cities Wireless networks Medical services Collaborative work Real-time systems Servers Object tracking

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