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FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION WITH UNKNOWN ANOMALIES ESTIMATED BY METADATA-ASSISTED AUDIO GENERATION

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arXiv 2023年

作者： Zhang, Hejing Zhu, Qiaoxi Guan, Jian Liu, Haohe Xiao, Feiyang Tian, Jiantong Mei, Xinhao Liu, Xubo Wang, Wenwu Group of Intelligent Signal Processing College of Computer Science and Technology Harbin Engineering University Harbin China National Engineering Laboratory for Modeling and Emulation in E-Government Harbin Engineering University Harbin China Centre for Audio Acoustics and Vibration University of Technology Sydney Ultimo Australia Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availability of normal and abnormal sound data from the target machines. However, due to the lack of anomalous sound data for the target machine types, it becomes challenging when adapting the existing ASD methods to the first-shot task. In this paper, we propose a new framework for the first-shot unsupervised ASD, where metadata-assisted audio generation is used to estimate unknown anomalies, by utilising the available machine information (i.e., metadata and sound data) to fine-tune a text-to-audio generation model for generating the anomalous sounds that contain unique acoustic characteristics accounting for each different machine type. We then use the method of Time-Weighted Frequency domain audio Representation with Gaussian Mixture Model (TWFR-GMM) as the backbone to achieve the first-shot unsupervised ASD. Our proposed FS-TWFR-GMM method achieves competitive performance amongst top systems in DCASE 2023 Challenge Task 2, while requiring only 1% model parameters for detection, as validated in our experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Metadata

A Time-Aware Dynamic Service Quality Prediction Approach for Services

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Tsinghua science and technology 2020年第2期25卷 227-238页

作者： Ying Jin Weiguang Guo Yiwen Zhang Department of Management Hefei UniversityHefei 230601China School of Computer Science and Technology and also with the Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui UniversityHefei 230601China

Dynamic Quality of Service(QoS)prediction for services is currently a hot topic and a challenge for research in the fields of service recommendation and *** paper addresses the problem with a Time-aWare service Quality Prediction method(named TWQP),a two-phase approach with one phase based on historical time slices and one on the current time *** the first phase,if the user had invoked the service in a previous time slice,the QoS value for the user calling the service on the next time slice is predicted on the basis of the historical QoS data;if the user had not invoked the service in a previous time slice,then the Covering Algorithm(CA)is applied to predict the missing *** the second phase,we predict the missing values for the current time slice according to the results of the previous phase.A large number of experiments on a real-world dataset,WS-Dream,show that,when compared with the classical QoS prediction algorithms,our proposed method greatly improves the prediction accuracy.

关键词： dynamic Quality of Service(QoS)prediction time-aware service recommendation

HRTransNet: HRFormer-Driven Two-Modality Salient Object Detection

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arXiv 2023年

作者： Tang, Bin Liu, Zhengyi Tan, Yacheng He, Qian School of Artificial Intelligence and Big Data Hefei University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

The High-Resolution Transformer (HRFormer) can maintain high-resolution representation and share global receptive fields. It is friendly towards salient object detection (SOD) in which the input and output have the same resolution. However, two critical problems need to be solved for two-modality SOD. One problem is two-modality fusion. The other problem is the HRFormer output’s fusion. To address the first problem, a supplementary modality is injected into the primary modality by using global optimization and an attention mechanism to select and purify the modality at the input level. To solve the second problem, a dual-direction short connection fusion module is used to optimize the output features of HRFormer, thereby enhancing the detailed representation of objects at the output level. The proposed model, named HRTransNet, first introduces an auxiliary stream for feature extraction of supplementary modality. Then, features are injected into the primary modality at the beginning of each multi-resolution branch. Next, HRFormer is applied to achieve forwarding propagation. Finally, all the output features with different resolutions are aggregated by intra-feature and inter-feature interactive transformers. Application of the proposed model results in impressive improvement for driving two-modality SOD tasks, e.g., RGB-D, RGB-T, and light field ***://***/liuzywen/HRTransNet Copyright © 2023, The Authors. All rights reserved.

关键词： Global optimization

Dual-State Personalized Knowledge Tracing with Emotional Incorporation

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arXiv 2024年

作者： Wang, Shanshan Yuan, Fangzheng Wang, Keyang Yang, Xun Zhang, Xingyi Wang, Meng Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei230601 China Zhejiang Dahua Technology Co. Ltd China Department of Electronic Engineering and Information Science School of information Science and Technology University of Science and Technology of China Hefei230026 China Key Laboratory of intelligent computing and Signal Processing Ministry of Education school of Computer Science and Technology Anhui University Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model's ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: Firstly, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Secondly, we design an Emotional State Tracing Module to monitor students' personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students' response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learningbased Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

A Crossed-Ellipse Slot Antenna Design with Enhanced Bandwidth using Characterisitc Mode Analysis

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A Crossed-Ellipse Slot Antenna Design with Enhanced Bandwidt...

International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Chao Ni Biyang Wen Yu Zuo Weijun Wu Ping Ren Radar and Signal Processing Laboratory Electronic Information School Wuhan University Science and Technology on Electromagnetic Compatibility Laboratory China Ship Development and Design Center Wuhan China

ISBN: (纸本)9781665418133

This paper presents a novel probe-fed slot antenna design with wide bandwidth characteristic. The antenna consists of two simple crossed-ellipse slots and two square stub slots. The antenna is designed using theory of characteristic mode (TCM). Wide bandwidth characteristic is achieved by combining two typical characteristic modes (CM). Through adjusting the stub slots position and parameters, the total impedance matching can be improved. Simulated results show that the 10dB return loss impedance bandwidth of the designed antenna covers 2.14GHz (4.33GHz~6.47GHz), which demonstrates the design process.

关键词： Antenna measurements Slot antennas Antenna theory Impedance matching Loading Bandwidth Impedance

High-precision DSM-based algorithm for SAR absolute radiation calibration

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High-precision DSM-based algorithm for SAR absolute radiatio...

IET International radar Conference (IRC 2023)

作者： Fanyi Tang Junrui Wang Zhibin Wang Jiahan Lou Huancheng Guo Zhenfang Li National Key Laboratory of Radar Signal Processing Xidian University Xi’an People's Republic of China Shanghai Institute of Aerospace Electronics Technology Shanghai People's Republic of China Country Institute of Remote Sensing Satellite China Academy of Space Technology Beijing People's Republic of China

ISBN: (数字)9781837240982

Radiation calibration is crucial for synthetic aperture radar (SAR) images, but it faces limitations due to the limited availability of corner reflectors. In this paper, we introduce an innovative algorithm to determine the absolute radiation calibration coefficient for SAR images using high-precision Digital Surface Model (DSM). This algorithm adapts to more common observation scenarios, reducing its dependence on reflectors. Firstly, the DSM is employed for target geolocation, providing precise local incidence angle information closely tied to the backscatter coefficient. Secondly, specific land cover types (such as forests, bare soil, or water bodies) are estimated based on a priori scattering model and local incidence angle. Finally, the absolute radiometric calibration coefficient is calculated by comparing observed and theoretical backscatter coefficients. This study utilized the DSM product and SAR image from China's first interferometric spaceborne synthetic aperture radar (InSAR) system, "Tianhui-2," to validate the algorithm's accuracy. Experimental results demonstrate an estimation accuracy exceeding 0.3 dB for the absolute radiation calibration coefficient.

关键词：

Atmospheric Effects on Ultra-High-Resolution Millimeter-Wave ISAR Imagery for Space Targets

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Atmospheric Effects on Ultra-High-Resolution Millimeter-Wave...

2021 CIE International Conference on radar, radar 2021

作者： Wan, Minghui Sheng, Jialian Ding, Xia Xing, Mengdao Shanghai Radio Equipment Research Institute Shanghai201109 China Shanghai Engineering Research Center of Target Identification and Environment Perception Shanghai201109 China Traffic Perception Radar Technology Research&Development Center of CASC Shanghai201109 China Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China

ISBN: (纸本)9781665498142

In order to get more detail information about targets, ultra-high-resolution (UHR) millimeter-wave radar (MMW) is widely used. UHR MMW radar is sensitive to errors, and the atmosphere effect is one of the major factors affecting the final imaging quality during the exploring of space targets. In this paper, high-precision radar signal propagation models of the background troposphere and ionosphere are established. Based on the models, the influence mechanism of the atmospheric effect on radar signal echo is investigated. The simulation results show that the tropospheric effect lead to azimuth defocusing and a large shift in the range direction on the image. The ionospheric effect produces the intra-pulse phase error which is inversely proportional to frequency. © 2021 IEEE.

关键词： inverse synthetic aperture radar (ISAR) ionospheric effect millimeter-wave radar tropospheric effect

Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search

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arXiv 2023年

作者： Yang, Shangshang Ma, Haiping Zhen, Cheng Tian, Ye Zhang, Limiao Jin, Yaochu Zhang, Xingyi The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei230039 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Anhui Hefei230601 China The Faculty of Technology Bielefeld Unversity Bielefeld33619 Germany

Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students’ proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multiobjective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task’s search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models’ variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

Control of the Lateral and Longitudinal Dynamics of UAV Quadcopter 19

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Control of the Lateral and Longitudinal Dynamics of UAV Quad...

19th IEEE International Multi-Conference on Systems, signals and Devices, SSD 2022

作者： Zamoum, Yasmine Baiche, Karim Benkada, Khaled Mohammed Rahou, Mohammed Boushaki, Prof. Razika Kouzou, Prof. Abdellah Systems and Telecommunications Engineering University of Mhamed Bougara Boumerdes Algeria Institute of Electrical and Electronic Engineering University of Mhamed Bougara Boumerdes Algeria Applied Automatics and Signal Processing Laboratory Institute of Electrical and Electronic Engineering University of Mhamed Bougara Boumerdes Algeria Applied Automation and Industrial Diagnostics Laboratory Laadi Faculty of Science and Technology University of Djelfa Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The research Unmanned Aerial Vehicles (UAV) has been considerably advanced. This work is interested principally in dynamic modeling of the four rotor mini aircraft named as a quadrotor. The dynamical model is obtained by Newton-Euler methodology. The robust controller is designed and simulated using a linear and nonlinear control techniques. Proportional Derivative Control and the Backstepping algorithm methods have been implemented in MATLAB/SIMULINK. © 2022 IEEE.

关键词： Controllers