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4th International Conference on Cloud computing and Security, ICCCS 2018

作者： Zhang, Yongwei Wang, Yonghua Wan, Pin Zhang, Shunchao Li, Nan School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University Ministry of Education Guangzhou510006 China

ISBN: (纸本)9783030000059

In order to improve the sensing performance of spectrum sensing systems in complex environments. This paper proposes a spectrum sensing method based on Null Space Pursuit algorithm (NSP) and fuzzy c-means (FCM) clustering algorithm. The signal sensing by the spectrum system is first pre-processed using a Null Space Pursuit algorithm and the signal is decomposed into sub-signal components with more distinct features. In order to further improve the accuracy of feature estimation the IQ decomposition method is used to process the signal. Then extract the eigenvalues of the signals to form a two dimensional feature vector. Finally, these eigenvectors and the FCM clustering algorithm yield a classifier that uses the classifier to determine the state of the unknown spectrum. In the experimental part, we verify the method in different environments. Experimental results show that the method can effectively improve the sensing performance of spectrum sensing system compared to traditional spectrum sensing methods. © Springer Nature Switzerland AG 2018.

关键词： Eigenvalues and eigenfunctions

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A spectrum sensing algorithm based on information geometry and K-medoids clustering 4th

A spectrum sensing algorithm based on information geometry a...

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4th International Conference on Cloud computing and Security, ICCCS 2018

作者： Wang, Yonghua Chen, Qiang Li, Jiangfan Wan, Pin Pang, Shuiling School of Automation Guangdong University of Technology Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University Ministry of Education Guangzhou510006 China

ISBN: (纸本)9783030000059

In order to improve the performance of existing spectrum sensing methods in cognitive radios and solve the complex problem of decision threshold calculations. This paper uses the information geometry theory and combines the unsupervised learning method of K-medoids clustering to realize the spectrum sensing. Firstly, using the information geometry theory, the statistical characteristics of wireless spectrum signals received by secondary users are analyzed and transformed into geometric characteristics on statistical manifolds. Correspondingly, the sampled signal of the secondary user corresponds to the point on the statistical manifold, and the distance feature between different points is obtained by using a metric method on the manifold. Finally, the K-medoids clustering algorithm is used to classify the distance features and determine whether the primary user signal exists, and achieve the purpose of spectrum sensing. Simulation results show that the proposed method outperforms traditional spectrum sensing algorithms. © Springer Nature Switzerland AG 2018.

关键词： Cognitive radio

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Research on Tibetan Speech Recognition Based on the Am-do Dialect

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Computers, Materials & Continua 2022年第12期73卷 4897-4907页

作者： Kuntharrgyal Khysru Jianguo Wei Jianwu Dang Key Laboratory of Artificial Intelligence Application Technology State Ethnic Affairs Commission Qinghai Minzu UniversityXining810007China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin UniversityTianjin300072China Japan Advanced Institute of Science and Technology IshikawaJapan

In China,Tibetan is usually divided into three major dialects:the Am-do,Khams and Lhasa *** Am-do dialect evolved from ancient Tibetan and is a local variant of modern *** this dialect has its own specific historical and social conditions and development,there have been different degrees of communication with other ethnic groups,but all the abovementioned dialects developed from the same language:*** paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language,which optimizes the problems existing in previous *** data of the Am-do dialect are expanded by data augmentation technology combining noise and reverberation,and the morphological characteristics and characteristics of the Tibetan language are further *** to the particularity of Tibetan grammar,grammatical features are used to optimize grammatical relationships and are combined with a language model,and the Am-do dialect is scored and *** results show that compared with the baseline,our proposed new lexicon and data augmentation technology yields a relative increase of approximately 3%in character error rates(CERs)and a relative increase of 3%-19%in the recognition rate of acoustic models and language models.

关键词： Am-do dialect acoustic model language model rescoring

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Surface formation characteristics in elliptical ultrasonic assisted grinding of monocrystal silicon

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International Journal of Nanomanufacturing 2011年第3-4期7卷 189-198页

作者： Wu, Yongbo Liang, Zhiqiang Fujimoto, Masakazu Lin, Weimin Department of Machine Intelligence and Systems Engineering Akita Prefectural University Akita 015-0055 Japan Key Laboratory of Fundamental Science for Advanced Machining Beijing Institute of Technology Beijing 100081 China

The characteristics of surface formation in elliptical ultrasonic assisted grinding (EUAG) of monocrystal silicon were investigated by grinding and single abrasive scratching experiments. In grinding experiments, an elliptical ultrasonic vibrator, on which a thin sheet shaped monocrystal silicon substrate was attached, was installed on a CNC surface grinder equipped with a diamond grinding wheel. By contrast, the vibrator attached with the substrate was set up on a multi-axis CNC controlled machining centre equipped with a single diamond tool to perform the scratching tests. The obtained results show that the grinding forces are reduced by 30%, the surface quality is improved significantly and the surface roughness is decreased by around 25% during EUAG compared with those in conventional grinding without ultrasonic vibration. There is a transition of material removal mode from the permanent contact to the unsuccessive contact between workpiece and abrasive by increasing the ratio of ultrasonic vibration amplitude to the depth of cut. © 2011 Inderscience Enterprises Ltd.

关键词： Grinding (machining)

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Network Embedding with Topology-Aware Textual Representations 18th

Network Embedding with Topology-Aware Textual Representation...

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18th Pacific Rim International Conference on Artificial intelligence, PRICAI 2021

作者： Chen, Jiaxing Xu, Zenan Su, Qinliang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9783030891879

Textual network embedding aims to learn meaningful low-dimensional representations for vertices with the consideration of the associated texts. When learning the representations for texts in network embedding, existing methods mostly only exploit information from neighboring texts (i.e., contexts), while rarely taking advantages of the valuable network topological (structural) information. To bridge the gap, in this paper, a model based on adaptive-filter convolutional neural networks (CNN) is developed, in which the filters are adapted to local network topologies, rather than clamped to fixed values as in traditional CNNs. The dependency enables the learned text representations to be aware of local network topologies. It is shown that the proposed topology-aware representations can be viewed as a complement to existing context-aware ones. When the two are used together, experimental results on three real-world benchmarks demonstrate that significant performance improvements on the tasks of link prediction and vertex classification. © 2021, Springer Nature Switzerland AG.

关键词： Network topology

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Contextual Heterogeneous Graph Network for Human-Object Interaction Detection 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Wang, Hai Zheng, Wei-shi Yingbiao, Ling School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China Peng Cheng Laboratory Shenzhen518005 China

ISBN: (数字)9783030585204

ISBN: (纸本)9783030585198

Human-object interaction (HOI) detection is an important task for understanding human activity. Graph structure is appropriate to denote the HOIs in the scene. Since there is an subordination between human and object—human play subjective role and object play objective role in HOI, the relations between homogeneous entities and heterogeneous entities in the scene should also not be equally the same. However, previous graph models regard human and object as the same kind of nodes and do not consider that the messages are not equally the same between different entities. In this work, we address such a problem for HOI task by proposing a heterogeneous graph network that models humans and objects as different kinds of nodes and incorporates intra-class messages between homogeneous nodes and inter-class messages between heterogeneous nodes. In addition, a graph attention mechanism based on the intra-class context and inter-class context is exploited to improve the learning. Extensive experiments on the benchmark datasets V-COCO and HICO-DET verify the effectiveness of our method and demonstrate the importance to extract intra-class and inter-class messages which are not equally the same in HOI detection. © 2020, Springer Nature Switzerland AG.

关键词： Graph theory

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FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector 24

FodFoM: Fake Outlier Data by Foundation Models Creates Stron...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Chen, Jiankang Deng, Ling Gan, Zhiyong Zheng, Wei-Shi Wang, Ruixuan School of Computer Science and Engineering Sun Yat-sen University Guangdong Guangzhou China Peng Cheng Laboratory Guangdong Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Guangdong Guangzhou China China United Network Communications Corporation Limited Guangdong Branch Guangdong Guangzhou China School of Computer Science and Engineering Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangdong Guangzhou China

ISBN: (纸本)9798400706868

Out-of-Distribution (OOD) detection is crucial when deploying machine learning models in open-world applications. The core challenge in OOD detection is mitigating the model's overconfidence on OOD data. While recent methods using auxiliary outlier datasets or synthesizing outlier features have shown promising OOD detection performance, they are limited due to costly data collection or simplified assumptions. In this paper, we propose a novel OOD detection framework FodFoM that innovatively combines multiple foundation models to generate two types of challenging fake outlier images for classifier training. The first type is based on BLIP-2's image captioning capability, CLIP's vision-language knowledge, and Stable Diffusion's image generation ability. Jointly utilizing these foundation models constructs fake outlier images which are semantically similar to but different from in-distribution (ID) images. For the second type, GroundingDINO's object detection ability is utilized to help construct pure background images by blurring foreground ID objects in ID images. The proposed framework can be flexibly combined with multiple existing OOD detection methods. Extensive empirical evaluations show that image classifiers with the help of constructed fake images can more accurately differentiate real OOD image from ID ones. New state-of-the-art OOD detection performance is achieved on multiple benchmarks. The code is available at https://***/Cverchen/ACMMM2024-FodFoM. © 2024 ACM.

关键词： Visual languages

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Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

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IET Cyber-Systems and Robotics 2021年第1期3卷 77-88页

作者： Ejay Nsugbe Oluwarotimi Williams Samuel Mojisola Grace Asogbon Guanglin Li Independent Researcher Chinese Academy of Sciences(CAS)Key Laboratory of Human-Machine Intelligence-Synergy System Shenzhen Institutes of Advanced TechnologyShenzhenChina Shenzhen College of Advanced Technology University of Chinese Academy of SciencesShenzhenChina

In recent years,the electroencephalography(EEG)brain-computer interface(BCI)has been researched in the area of upper-limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in the cortex directly from the brain with non-invasive *** is seen as a way of bypassing the limitation posed by acquiring neuromuscular signals predominantly with electromyography(EMG)directly from the stump,which possesses residual limb anatomy *** this study,the sequential forward selection algorithm to form a 10-optimal-channel representation,alongside an extended signal feature vector was applied,to investigate the motion intent decoding performance of EMG-only,EEG-only,and a fused EMG-EEG sensing configuration for four transhumeral amputees with varying stump *** results showed a considerable improvement for the EMG-only configuration with the advanced feature vector,but only a small increase for the EEG-only,and thus a marginal improvement when information from both signals was fused *** is likely due to the EEG requiring a greater number of channels spread across the skull to provide a reliable intent *** work will now involve optimisation studies to find a greater representation of electrode representation and parsimony,to minimise the number of channels while boosting motion intent decoding accuracy.

关键词： anatomy fused motion

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Improving fast segmentation with teacher-student learning 29

Improving fast segmentation with teacher-student learning

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29th British machine Vision Conference, BMVC 2018

作者： Xie, Jiafeng Shuai, Bing Hu, Jian-Fang Lin, Jingyang Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China Nanyang Technological University Singapore Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing MOE Hong Kong

Recently, segmentation neural networks have been significantly improved by demonstrating very promising accuracies on public benchmarks. However, these models are very heavy and generally suffer from low inference speed, which limits their application scenarios in practice. Meanwhile, existing fast segmentation models usually fail to obtain satisfactory segmentation accuracies on public benchmarks. In this paper, we propose a teacher-student learning framework that transfers the knowledge gained by a heavy and better performed segmentation network (i.e. teacher) to guide the learning of fast segmentation networks (i.e. student). Specifically, both zero-order and first-order knowledge depicted in the fine annotated images and unlabeled auxiliary data are transferred to regularize our student learning. The proposed method can improve existing fast segmentation models without incurring extra computational overhead, so it can still process images with the same fast speed. Extensive experiments on the Pascal Context, Cityscape and VOC 2012 datasets demonstrate that the proposed teacher-student learning framework is able to significantly boost the performance of student network. © 2018. The copyright of this document resides with its authors.

关键词： Students

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Exploiting Multiple Features for Hash Codes Learning with Semantic-Alignment-Promoting Variational Auto-encoder 12th

Exploiting Multiple Features for Hash Codes Learning with Se...

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12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Chen, Jiayang Su, Qinliang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Beijing China

ISBN: (纸本)9783031446924

Semantic hashing is an effective technique to empower information retrieval. Currently, considerable efforts have been dedicated to generating high-quality hash codes by modeling document features using generative models and other approaches. However, most of these methods rely solely on a single type of feature, such as TFIDF features, BERT embeddings, etc. As different types of features have distinct but complementary information of documents, e.g. TFIDF mainly contains the keywords information and BERT focuses on the semantics, hash codes generated solely from either may not capture the full essence of the documents. To overcome this challenge, we propose a semantic-alignment-promoting variational auto-encoder to generate hash codes from multiple document features. Specifically, a VAE-based generative model is first developed to model the multiple features. Then, we propose a semantic-alignment-promoting inference network to estimate the parameters of the variational posterior from multiple features. Additionally, the quality of hash codes is further improved by promoting the semantic alignment between the hash codes of connected documents in a constructed connection graph. The results of extensive experiments on three public datasets demonstrate that our proposed model significantly outperforms current state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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