检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Chen, Wenwen Zou, Jie Shan, Liang Zhang, Huanhuan Wang, Wentian Jian, Jiawen Yuan, Tao Zhang, Xin Zhou, Yucun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315000 China Beijing Huairou Laboratory Beijing101400 China School of Materials and Chemistry University of Shanghai for Science and Technology Shanghai200093 China

Selective catalytic reduction (SCR) utilizing urea hydrolysis to generate ammonia (NH3) is the most efficient technology to reduce the emission of nitrogen oxides (NOx). NH3 detection is crucial in the closed-loop feedback system for the precise urea injection. In this work, a high-temperature NH3 sensor using a BaZr0.8Y0.2O3-δ (BZY) proton conductor as the electrolyte and a BaCo0.4Fe0.4Zr0.1Y0.1O3-δ (BCFZY) H+/e–/O2– triple conductor as the sensing electrode (SE) is reported. The effect of the micromorphology of the BCFZY SE on the sensing performance is investigated by varying the sintering temperature of SE. The sensor shows high sensitivity to NH3 with response currents of 0.39, 1.31, 3.03, and 1.81 μA/ppm at 350, 400, 450, and 500 °C, respectively. Furthermore, the sensor shows excellent selectivity and repeatability. This work demonstrates that the BCFZY based sensor has great potential for the high-temperature NH3 detection. © 2024, The Authors. All rights reserved.

关键词： Nitrogen oxides

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Exploring Attention-Aware Network Resource Allocation for Customized Metaverse Services

arXiv

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arXiv 2022年

作者： Du, Hongyang Wang, Jiacheng Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Emerging with the support of computing and communications technologies, Metaverse is expected to bring users unprecedented service experiences. However, the increase in the number of Metaverse users places a heavy demand on network resources, especially for Metaverse services that are based on graphical extended reality and require rendering a plethora of virtual objects. To make efficient use of network resources and improve the Quality-of-Experience (QoE), we design an attention-aware network resource allocation scheme to achieve customized Metaverse services. The aim is to allocate more network resources to virtual objects in which users are more interested. We first discuss several key techniques related to Metaverse services, including QoE analysis, eye-tracking, and remote rendering. We then review existing datasets and propose the user-object-attention level (UOAL) dataset that contains the ground truth attention of 30 users to 96 objects in 1, 000 images. A tutorial on how to use UOAL is presented. With the help of UOAL, we propose an attention-aware network resource allocation algorithm that has two steps, i.e., attention prediction and QoE maximization. Specially, we provide an overview of the designs of two types of attention prediction methods, i.e., interest-aware and time-aware prediction. By using the predicted user-object-attention values, network resources such as the rendering capacity of edge devices can be allocated optimally to maximize the QoE. Finally, we propose promising research directions related to Metaverse services. © 2022, CC BY.

关键词： Eye tracking

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Development of Programmed Autonomous Electric Heavy Vehicle: An Application of IoT

Development of Programmed Autonomous Electric Heavy Vehicle:...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Nagasekhar Reddy P. Umaeswari L. Natrayan Ankur Choudhary Department of Electrical and Electronics Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai India Department of Mechanical Engineering Saveetha School of Engineering Simats India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

The influence of automation in the agriculture and construction industry plays a vital role in the development of the economic backbone of any country. The factors such as power, torque and speed are efficiently controlled by automation devices using Internet of things (IoT). The heavy vehicles are the multi-faced application carrier which is been used in construction, agriculture, mining and other heavy-duty fields. This research focuses on developing a IoT integrated sensor-based obstacle detection system for programmed autonomous heavy vehicles which will improve safety in both on-road and off-road construction. The automation process consists of two main categories: Observing the environment and recognizing the activities. The environment in our case is both highways and rural roads. An intelligent- camera is utilised to capture the activities from the captured video and process the data. A LiDAR sensor performs the obstacle detection process using laser reflection technology and an ultrasonic sensor is used to process the vibration and sound produced by the obstacle or upcoming vehicles. These three sensors are integrated with a controller-based device to be powered by the power distribution system in the programmed autonomous heavy vehicle.

关键词： Vibrations Laser radar Automation Torque Cameras Acoustics Agriculture

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Early Prediction of Student Performance with LSTM-Based Deep Neural Network

Early Prediction of Student Performance with LSTM-Based Deep...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Han Wan Mengying Li Zihao Zhong Xiaoyan Luo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Image Processing Center School of Astronautics Beihang University Beijing China

The shift to hybrid teaching during the COVID-19 pandemic brought about a real challenge to predict student performance and conduct timely interventions on at-risk students. This study proposed a deep neural network supporting the early prediction of student performance. Bidirectional LSTM, Global Average Pooling, and TIME MASK structure were utilized in the improved GritNet model. Subsequently, this study optimized the hyperparameters with the aid of the hill-climbing algorithm. Finally, on-campus data sets were used in experiments to evaluate the model's performance. Data were collected from a course that carried out multiple iterations from Fall 2021 to Fall 2022. In Fall 2022, the proposed model achieved a ROC-AUC value of 95.47% in the 8th week, while the baseline model only achieved 91.44% in the same week. Besides, the proposed model achieved a ROC-AUC value of 89.67% in the 4th week, which meant it had acceptable prediction performance in the early stage. The experimental findings demonstrated that the model was capable of predicting the academic performance of students in hybrid courses early on.

关键词：

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Riding Over Two-Way Carrier: A Dual-Sided RIS-Enabled Symbiotic Backscatter System

Riding Over Two-Way Carrier: A Dual-Sided RIS-Enabled Symbio...

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IEEE Conference on Wireless Communications and Networking

作者： Xiaoyi Huang Haiyang Ding Gang Yang Maged Elkashlan Jules M. Moualeu Chau Yuen School of Information and Communications National University of Defense Technology Wuhan China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China China School of Electronic Engineering and Computer Science Queen Mary University of London London U.K. School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we propose a two-way backscatter communication system assisted by an active and passive dualsided reconfigurable intelligent surface (RIS) where the active RIS element contains an amplifier while the passive one does not. By altering the switch status within each RIS element, different transmission and reflection coefficients can be achieved, enabling a binary backscatter modulation. Moreover, a maximal-ratio-combining (MRC)-based detector is proposed to decode the backscatter signal and the end-user's signal, and the corresponding symbol error rate (SER) and throughput are subsequently analyzed. Numerical results show that by avoiding the additive thermal noise within each RIS element, passive dual-sided RISenabled communications outperform the active dual-sided RISenabled communications in terms of SER, and it is also revealed that the throughput can be significantly improved through backscatter modulation.

关键词： Symbiosis Error analysis Communication systems Modulation Detectors Switches Reconfigurable intelligent surfaces Throughput Thermal noise Backscatter

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Modelling and Optimal Allocation of CCHP-Integrated-Energy-System Considering Low-Carbon and Economic Benefits: Methods and Vision

Modelling and Optimal Allocation of CCHP-Integrated-Energy-S...

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Energy systems and electrical Power (ICESEP), International Conference on

作者： Maodong Li Kengweng Lao Wentao Lu Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City University of Macau Macau S.A.R. China School of Information Science and Technology Fudan University Shanghai China

ISBN: (数字)9798350353563

ISBN: (纸本)9798350353570

Along with the development of smart energy, we take typical integrated energy systems (IES) into research considering various coupling modes and constraints, building a model of the IES for combined cooling, heating and power system, and propose the using of gas micro-turbine as a power supply. On this basis, the three dimension particle swarm optimization algorithm is applied to conduct operational and management research in one typical day, which provides a reference for the economic and low-carbon planning and operation of the IES, moreover demonstrates the superiority and effectiveness of the integrated energy systems compared to the traditional discrete energy systems. Furthermore, the paper gives the effective steps of allocation and optimization methods and vision of integrated energy systems.

关键词： Economics Couplings Power supplies Biological system modeling Buildings Optimization methods Planning

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Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian systems:Tackling Multiplicative Noises and Missing Measurements

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1127-1138页

作者： Shaoying Wang Zidong Wang Hongli Dong Yun Chen Guoping Lu College of Science Shandong University of AeronauticsBinzhou 256603China College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590 Department of Computer Science Brunel University LondonUxbridge UB83PHUK Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Automation Hangzhou Dianzi UniversityHangzhou 310018China School of Electrical Engineering Nantong UniversityNantong 226019China

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission *** multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary *** attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance *** this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented ***,we analyze statistical properties of augmented noises as well as high-order moments of certain random *** the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain ***,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

关键词： filtering quadratic bounds

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Elucidating Spatial Complex Structures from Mass Spectrometry Imaging with Deep Multimodal Model 1

Elucidating Spatial Complex Structures from Mass Spectrometr...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Cui, Jiangnan Xia, Junjie Li, Xuan Wang, Yan Qiu, Fufang Xu, Yaochen Xu, Keren Zuo, Chunman School of Computer Science and Technology Institute of Artificial Intelligence Donghua University Shanghai China College of Computer Science and Technology Jilin University Changchun China Huashan Hospital Department of Neurosurgery Shanghai China Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Cas Key Laboratory of Systems Biology Shanghai China Institute of Artificial Intelligence Donghua University Shanghai China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China

ISBN: (纸本)9798350358780

Spatial mass spectrometry imaging (SMSI) technology has enabled the characterization of biomolecule patterns within the tissue microenvironment, yet its analyses suffer from substantial noise. The lack of effective methods for exploiting multi-view features (e.g., spatial location) has hindered their elucidation ability for tissue heterogeneity. Here, we propose MSG, a multimodal modal that integrates histology, spatial location, and MSI data, in deciphering tissue structure by graph neural network. Specifically, MSG learns low-dimensional representations by aggregating histomorphological features from neighboring tiles via graph attention autoencoder. MSG outperforms other methods in identifying spatial domains and denoising data. The subsequent domain-guided differential expression analysis of the denoised data enables the identification of ions with significantly enriched patterns within the recognized domains. MSG exhibits its versatility in handling SMSI data from various platforms. © 2023 IEEE.

关键词： Data integration

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A Novel Approach for Improving Cloud Liquid Water Content Pro- Filing with Machine Learning

SSRN

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SSRN 2024年

作者： Amaireh, Anas Zhang, Yan Chan, Pak Wai Zrnic, Dusan School of Electrical and Computer Engineering and Advanced Radar Research Center University of Oklahoma 3190 Monitor Ave NormanOK73019 United States Hong Kong Observatory Nathan Rd Sim Sha Tsui 134A Hong Kong NOAA/OAR National Severe Storms Laboratory School of Meteorology the School of Electrical and Computer Engineering University of Oklahoma 120 David L. Boren Boulevard NormanOK73019 United States

Accurate prediction of Cloud Liquid Water Content (CLWC) is important in atmospheric science for understanding and forecasting weather phenomena, particularly in locations with complex microclimates. We cross-validated the Medium-Range Weather Forecasts (ECMWF) ERA5 data with high-precision radiosonde observations from the Hong Kong area. Machine learning (ML) techniques are used for interpolating radiosonde data to enhance data coverage and resolution and estimate CLWC. This is complemented by applying a metaheuristic algorithm for data cleansing, which enhances the correlation between the input features - temperature, pressure levels, relative humidity, and specific humidity - and the CLWC. Our approach, tested against various ML algorithms for robustness and performance, demonstrates marked improvements in the accuracy and reliability of CLWC *** methodology developed is designed to be applicable beyond Hong Kong's unique atmospheric conditions, demonstrating how similar machine learning approaches can be adapted for various climates globally. This enhances weather prediction models worldwide, highlighting the global significance of this work in atmospheric science. © 2024, The Authors. All rights reserved.

关键词： Decision trees

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Is Underwater Image Enhancement All Object Detectors Need?

arXiv

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arXiv 2023年

作者： Wang, Yudong Guo, Jichang He, Wanru Gao, Huan Yue, Huihui Zhang, Zenan Li, Chongyi The School of Electrical and Information Engineering Tianjin University Tianjin300072 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China The School of Computer Science Nankai University Tianjin300071 China

Underwater object detection is a crucial and challenging problem in marine engineering and aquatic robot. The difficulty is partly because of the degradation of underwater images caused by light selective absorption and scattering. Intuitively, enhancing underwater images can benefit high-level applications like underwater object detection. However, it is still unclear whether all object detectors need underwater image enhancement as pre-processing. We therefore pose the questions "Does underwater image enhancement really improve underwater object detection?" and "How does underwater image enhancement contribute to underwater object detection?". With these two questions, we conduct extensive studies. Specifically, we use 18 state-of-the-art underwater image enhancement algorithms, covering traditional, CNN-based, and GAN-based algorithms, to pre-process underwater object detection data. Then, we retrain 7 popular deep learning-based object detectors using the corresponding results enhanced by different algorithms, obtaining 126 underwater object detection models. Coupled with 7 object detection models retrained using raw underwater images, we employ these 133 models to comprehensively analyze the effect of underwater image enhancement on underwater object detection. We expect this study can provide sufficient exploration to answer the aforementioned questions and draw more attention of the community to the joint problem of underwater image enhancement and underwater object detection. The pre-trained models and results are publicly available and will be regularly updated. Project page: https://***/BIGWangYuDong/lqit/tree/main/configs/detection/uw_enhancement_affect_detection. © 2023, CC BY.

关键词： Object detection

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