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SSRN 2024年

作者： Hong, Yutao Han, Dan Duan, Qi Li, Donghui He, Xiuli Li, Bing Liang, Hua Li, Hongwei Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Integrated Circuits Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials Shanxi Medical University School Hospital of Stomatology Shanxi Taiyuan030001 China Shanxi Province Cancer Hospital Shanxi Hospital Affiliated to Cancer Hospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Taiyuan030000 China University of Chicago United States

Effective and accurate detection of NO2 gas is crucial for protecting the environment and public health. In this work, balsam pear-like GaN/MoO3 porous nanosheets (GaN/MoO3) composites were synthesized by in situ assembling, the hydrothermal method, and the nitridation process to detect NO2. A series of characterization methods were used to manifest the successful synthesis and to analyze the morphology and elemental composition features of GaN/MoO3 composites. The results of the gas-sensing experiments show that, at the operating temperature of 225℃, the gas sensor based on the optimal ratio of GaN/MoO3 composites exhibits a 25-fold lower limit of detection for NO2 than that of MoO3, with a reduction of baseline resistance drift. Further, the response value of the sensor based on GaN/MoO3 composites to 200 ppm NO2 (44.74) is 4.36 times higher than that of the sensor based on pure GaN, with an improvement in stability. Meanwhile, the sensor possessed excellent repeatability, selectivity, stability, and splendid anti-humidity ability at 15-70% RH. The improved gas-sensing performance is ascribed to the unique microstructure of both GaN and MoO3 and the heterojunction effect between them. In addition, we constructed a real-time NO2 monitoring device based on the gas-sensing characteristic of GaN/MoO3, which realizes the alarm of excessive concentrations of NO2. Overall, this work provides a way to improve the ability of NO2 monitoring for the sensors of MoO3-based composites. © 2024, The Authors. All rights reserved.

关键词： III-V semiconductors

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Towards ISAC-Empowered Vehicular Networks: Framework, Advances, and Opportunities

arXiv

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arXiv 2023年

作者： Du, Zhen Liu, Fan Li, Yunxin Yuan, Weijie Cui, Yuanhao Zhang, Zenghui Masouros, Christos Ai, Bo School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China

Connected and autonomous vehicle (CAV) networks face several challenges, such as low throughput, high latency, and poor localization accuracy. These challenges severely impede the implementation of CAV networks for immersive metaverse applications and driving safety in future 6G wireless networks. To alleviate these issues, integrated sensing and communications (ISAC) is envisioned as a game-changing technology for future CAV networks. This article presents a comprehensive overview on the application of ISAC techniques in vehicle-to-infrastructure (V2I) networks. We cover the general system framework, representative advances, and a detailed case study on using the 5G New Radio (NR) waveform for sensing-assisted communications in V2I networks. Finally, we highlight open problems and opportunities in the field. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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A Practical and High-Performance Nitrogen Dioxide Gas Sensor that Can Be Rapidly Mass-Produced Based on Gallium Nitride Quantum Dots Film

SSRN

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SSRN 2022年

作者： Han, Dan Chen, Yi Li, Donghui Dong, Hailiang Xu, Bingshe Sang, Shengbo He, Xiuli Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Key Laboratory of Interface Science and Engineering in Advanced Materials Taiyuan University of Technology Ministry of Education Taiyuan030024 China State Key Laboratory of Transducer Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China Shanxi Research Institute of 6D Artificiat Intelligence Biomedical Science Taiyuan030024 China

Gallium nitride (GaN) has some challenges for gas sensing, such as high detection lower limit (LOD) or controlling the epitaxy of nanostructures. To address these issues, we exclusively designed a device to enhance the gas interaction capabilities of GaN quantum dots (QDs) film fabricated by metal-organic chemical vapor deposition (MOCVD). The GaN QDs film gas sensor exhibited a low LOD (5 ppb) to NO2 at room temperature (RT), which has much superior gas performance than other GaN-based sensors. The results manifest that many ultra-small QDs around 2 nm in diameter are stacked to form QDs of larger size, and confirm the existence of vacancies. The sensor displays a ultra-high electron mobility of 1237.37 cm2 V−1s−1, demonstrate that great shallow donor impurities in GaN QDs film that ionize easily at RT, which is greatly beneficial for gas sensing. The sensing mechanisms were analyzed based on its abundant active sites, ultrahigh carrier mobility, high surface energy and chemical activity, grain boundary barrier and good electrical conductivity. Moreover, the sensor processes high selectivity, repeatability, stability and reusability based on MOCVD technology which can realize precise control of structure, morphology and surface energy level, which is expected to achieve rapid industrialization. © 2022, The Authors. All rights reserved.

关键词： Gallium nitride

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Jamming Recognition Based on AC-VAEGAN

Jamming Recognition Based on AC-VAEGAN

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yan Tang Zhijin Zhao Xueyi Ye Shilian Zheng Lijun Wang School of Communication Engineering Hangzhou Dianzi University Hangzhou China State Key Lab of Information Control Technology in Communication System The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China

ISBN: (数字)9781728144801

ISBN: (纸本)9781728144818

To solve the performance deterioration of jamming recognition method based on deep learning in the case of small sample set, a jamming recognition method based on AC-VAEGAN is presented. Interference signal time-frequency diagrams are used as the small sample training set. AC-VAEGAN network is designed by modifying the structure of ACGAN network, that is, adding the core idea of VAE in the ACGAN network, so that the latent space of a continuous and meaningful sample set can be obtained through the encoder. By combining of regularization loss, reconstruction loss, source loss and classification loss, loss functions of three parts of AC-VAEGAN are obtained, and the three parts are trained to make the AC-VAEGAN network more efficient. The simulation results show that when the jamming-to-noise ratio is -10dB~10dB, the AC-VAEGAN network has a higher correct recognition rate for the five kinds of jamming than ACGAN and CNN networks in the case of small sample datasets.

关键词： Jamming Interference Time-frequency analysis Convolution Feature extraction Training Deep learning

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A Hybrid Model for Building-level Day Ahead Power Load Forecasting Based on Gated Recurrent Unit Networks

A Hybrid Model for Building-level Day Ahead Power Load Forec...

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Sustainable Power and Energy Conference (iSPEC)

作者： Hualu Zhang Boya Deng Jie Shi Key Laboratory of Modern Power System Simulation Control and Green Power New Technology of Ministry of Education Northeast Electric Power University Jilin Jilin Nanjing Nanrui Information Communication Technology Co. Ltd. NARI Group Corporation/State Grid Electric Power Research Institute Nanjing

ISBN: (纸本)9781665414401

Accurate forecasting of day-ahead electrical load is a key to optimize the operation of the integrated energy system (IES) and a fundamental function of the cyber-physical system (CPS) of IES. Due to the characteristics of different features, data collected by CPS often have different sampling frequencies, resulting in mining data trends inadequately when making a load forecast. This paper proposes an GRU-based model to combine historical data with different sampling frequencies in forecasting day-ahead building-level load. First, day-level features are clustered by K-means. Hour-levels features are then partitioned according to clustering results and inputted separately into a gated recurrent unit (GRU) based neural network to capture relationships between external features and load fluctuations. The temporal dependencies of minute-level data are captured by the GRU network directly without any external features. Finally, the least square method is used to combine forecasting results at each hour of two models. The simulation results present that the proposed model performs better than a single model when applying to real-world data.

关键词： Load forecasting Simulation Neural networks Training data Predictive models Logic gates Data models

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MRI-guided robot intervention—current state-of-the-art and new challenges

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Med-X 2023年第1期1卷 141-181页

作者： Shaoping Huang Chuqian Lou Ying Zhou Zhao He Xuejun Jin Yuan Feng Anzhu Gao Guang-Zhong Yang Institute of Medical Robotics and School of Biomedical Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy(NERC-AMRT) Shanghai Jiao Tong UniversityShanghai 200240China Materials and Technology Center of Robotics EmpaDübendorf 8600Switzerland State Key Lab of Metal Matrix Composites and School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Institute of Medical Robotics and Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China The Key Laboratory of System Control and Information Processing Ministry of EducationShanghai 200240China

Magnetic Resonance Imaging(MRI)is now a widely used modality for providing multimodal,high-quality soft tissue contrast images with good spatiotemporal resolution but without subjecting patients to ionizing *** addition to its diagnostic potential,its future theranostic value lies in its ability to provide MRI-guided robot intervention with combined structural and functional mapping,as well as integrated instrument localization,target recognition,and in situ,in vivo monitoring of the therapeutic *** of current applications include neurosurgery,breast biopsy,cardiovascular intervention,prostate biopsy and *** applications in targeted drug delivery and MRI-guided chemoembolization are also being *** promising progress has been made in recent years,there are still significant basic science research and engineering *** paper provides a comprehensive review of the current state-of-the-art in MRI-guided robot intervention and allied technologies in actuation,sensing,new materials,interventional instruments,and interactive/real-time *** future research directions and new clinical developments are also discussed.

关键词： MRI-guided robot intervention Actuation Sensing Materials Interventional instruments Interactive/realtime MRI

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Design and Implementation of an Linkage control system for Stage Visual Effect Equipment

Design and Implementation of an Linkage Control System for S...

引用

Culture-oriented Science & technology (ICCST), International Conference on

作者： Shilei Bai Yihan Suo Kai Xu Xiujun Tang Jiaxin Zhang School of Information and Communication Engineering Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism (Communication University of China) Beijing Key Laboratory of Modern Entertainment Technology (Communication University of China) State Key Laboratory of Media Convergence of Communication Beijing China

ISBN: (数字)9781728181387

ISBN: (纸本)9781728181394

In view of the problems of low equipment integration, difficult equipment operation, and high technical threshold existing in the current stage visual effect linkage control system, a highly integrated linkage control system for stage visual effect equipment, which is suitable for small and medium-sized performance venues, is proposed in this paper. This system can control the stage lighting and the playback of multi-channel and multi-format media content, and it is easy to control. The design scheme of the system is described in detail, and the feasibility of the system is verified through tests in this paper.

关键词： Visual effects Lighting Couplings control systems Libraries Software Process control

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Joint Depth Estimation and Semantic Segmentation with Adversarial Multi-task Network

Joint Depth Estimation and Semantic Segmentation with Advers...

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Culture-oriented Science & technology (ICCST), International Conference on

作者： Hui Ren Nan Gao Jia Li State Key Laboratory of Media Convergence of Communication Communication University of China Beijing China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of China Beijing China School of Information and Communication Engineering Communication University of China Beijing China

ISBN: (数字)9781728181387

ISBN: (纸本)9781728181394

Some exiting works have explored the promotion between depth estimation and semantic segmentation. These works are usually based on convolutional neural networks, which extract compact features and map the relationship between input and output according to specific tasks. In this paper, we introduce a novel adversarial training strategy, that is, generator produces the semantic segmentation map and depth map, and then the discriminator can judge the authenticity of the synthesized color image, thereby supervising the output of the network during back propagation. We use the adversarial loss combined with the reconstruction loss function to supervise the model, and find that the adversarial loss function which is seen as a global supervision can further optimize the output. We use 200 color images from Kitti dataset with semantic segmentation ground truth as the training set, and train the network in an end-to-end manner. The experimental results show that the adversarial training method is well applied to the multi-task training combining semantic segmentation and depth estimation, and can further improve the quantitative performance of depth estimation.

关键词： Semantics Estimation Generative adversarial networks Generators Training Task analysis Mathematical model

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A Study on the Relationship Between Timbre Perception Features and Emotion in Musical Sounds

A Study on the Relationship Between Timbre Perception Featur...

引用

Culture-oriented Science & technology (ICCST), International Conference on

作者： Jiying Guo Jingyu Liu Zijin Li Jiaxing Zhu Wei Jiang State Key Laboratory of Media Convergence of Communication Key Laboratory of Acoustic Visual Technology and Intelligent Control System (Communication University of China) Ministry of Culture and Tourism Beijing Key Laboratory of Modern Entertainment Technology (Communication University of China) School of Information and Communication Engineering Communication University of China China Conservatory of Music Beijing China

ISBN: (数字)9781728181387

ISBN: (纸本)9781728181394

This paper mainly studies the relationship between timbre perception features and emotion in musical sounds. Firstly, a five - dimensional emotional space was determined by multidimensional scaling analysis. Then emotional labeling subjective evaluation experiments were carried out for 72 scale materials and 48 melody materials of timbre to obtain emotional data of timbre materials. The correlation between timbre perception features and emotion is studied according to the playing content, temporal characteristics, and musical instrument types. Finally, a set of mathematical models of the relationship between timbre perception features and emotion was established by the method of multiple linear regression. The experimental results show that the features of timbre perception are an important effect on musical emotion, and these models have good predictive ability for the five emotion dimensions.

关键词： Timbre Instruments Correlation Correlation coefficient Standards Aerospace electronics Semantics

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Large-Scale Spatiotemporal Deep Learning Predicting Residential Indoor Pm2.5 Concentration Using a Bayesian Neural Network Model

SSRN

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SSRN 2023年

作者： Dai, Hui Liu, Yumeng Wang, Jianghao Ren, Jun Gao, Yao Dong, Zhaomin Zhao, Bin Department of Building Science School of Architecture Tsinghua University Beijing100084 China Beijing Key Laboratory of Indoor Air Quality Evaluation and Control Tsinghua University Beijing100084 China School of Space and Environment Beihang University Beijing100191 China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China Sustainable Urbanization Lab Department of Urban Studies and Planning Massachusetts Institute of Technology CambridgeMA02139 United States Shenzhen Institute of Building Research Co. Ltd China

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of indoor daily average PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 μg/m3, root-mean-square error of 13.3 μg/m3, and 95% prediction interval coverage of 85%. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 μg/m3, far exceeding the WHO standard. This model can be applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale, making it valuable for assessing and managing indoor air pollution-related health risks. © 2023, The Authors. All rights reserved.

关键词： Health risks

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