检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100080 China Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai200240 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Research Center for Industries of the Future the School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States Department of Electrical Engineering National Tsing Hua University Hsinchu30013 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. Codes will be available at https://***/IVIPLab/FIWHN. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Learning Hierarchical Color Guidance for Depth Map Super-Resolution

arXiv

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arXiv 2024年

作者： Cong, Runmin Sheng, Ronghui Wu, Hao Guo, Yulan Wei, Yunchao Zuo, Wangmeng Zhao, Yao Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Shandong Jinan250061 China Artificial Intelligence Institute Beijing Normal University Beijing100875 China Changsha410073 China School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Lingnan University Hong Kong

Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively. The code and results can be found from the link of https://***/HCGNet_TIM2024. © 2024, CC BY-NC-SA.

关键词： Restoration

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Solarsam: Building-Scale Photovoltaic Potential Assessment Based on Segment Anything Model (Sam) and Remote Sensing for Emerging City

SSRN

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SSRN 2024年

作者： Li, Guanglei Wang, Guohao Luo, Tengqi Hu, Yuxiao Wu, Shouyuan Gong, Guanghui Song, Chenchen Guo, Zhiling Liu, Zhengguang Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan250061 China Department of Power and Electrical Engineering Northwest A&F University Yangling712100 China The Hong Kong Polytechnic University Hong Kong Eastern Institute of Technology Ningbo315200 China Higher Information Industry Technology Research Institute Beijing Information Science and Technology University Beijing100192 China Department of Chemical Engineering The University of Manchester ManchesterM13 9PL United Kingdom

Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source, due to its ease of integration onto building rooftops, facades, and windows. For the emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of building-integrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages remote sensing imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. During the process, SolarSAM segmented various building rooftops using text prompt guided semantic segmentation. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows systems, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city’s total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted, including levelized cost of electricity and carbon reduction calculations, comparing different BIPV systems across various building categories. These findings demonstrated the model’s performance and reveled the potential of BIPV power generation in the future. © 2024, The Authors. All rights reserved.

关键词： Solar power generation

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Energy-Efficient Multi-UAV-Enabled MEC systems with Space-Air-Ground Integrated Networks

arXiv

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arXiv 2024年

作者： Liu, Wenchao Zhang, Xuhui Ren, Jinke Shen, Yanyan Wang, Shuqiang Yang, Bo Guan, Xinping Cui, Shuguang Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Southern University of Science and Technology Guangdong518055 China The Shenzhen Future Network of Intelligence Institute The School of Science and Engineering The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong518055 China Shenzhen University of Advanced Technology Guangdong518055 China The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai200240 China The School of Science and Engineering The Shenzhen Future Network of Intelligence Institute The Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Guangdong Shenzhen518172 China

With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) supported mobile edge computing (MEC) is regarded as an effective solution, offering the MUs multi-tier and efficient computing services. In this paper, we consider an SAGIN supported MEC system, where a low Earth orbit satellite and multiple unmanned aerial vehicles (UAVs) are dispatched to provide computing services for MUs. An energy efficiency maximization problem is formulated, with the joint optimization of the MU-UAV association, the UAV trajectory, the task offloading decision, the computing frequency, and the transmission power control. Since the problem is non-convex, we decompose it into four subproblems, and propose an alternating optimization based algorithm to solve it. Simulation results confirm that the proposed algorithm outperforms the benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

引用

arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Low-latency federated learning over wireless channels with differential privacy

arXiv

引用

arXiv 2021年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Chen, Cailian Jin, Shi Han, Zhu Poor, H. Vincent School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Data61 CSIRO SydneyNSW2015 Australia The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. The performance of uploaded models in such situations can vary widely due to imbalanced data distributions, potential demands on privacy protections, and quality of transmissions. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement. We solve this problem in the framework of multi-agent multiarmed bandit (MAMAB) to deal with the situation where there are multiple clients confornting different unknown transmission environments, e.g., channel fading and interferences. Specifically, we first transform the long-term constraints on both training performance and each client's DP into a virtual queue based on the Lyapunov drift technique. Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach. More importantly, we propose two efficient solutions to this matching problem, i.e., modified Hungarian algorithm and greedy matching with a better alternative (GMBA), in which the first one can achieve the optimal solution with a high complexity while the second one approaches a better tradeoff by enabling a verified low-complexity with little performance loss. In addition, we develop an upper bound on the expected regret of this MAMAB based FL framework, which shows a linear growth over the logarithm of communication rounds, justifying its theoretical feasibility. Extensive experimental results are conducted to validate the effectiveness of our proposed algorithms, and the impacts of various parameters on the FL performance over wireless edge networks are also discussed. Copyright © 2021, The Authors. All rights reserved.

关键词： Multi agent systems

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Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

SSRN

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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 of the 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 and 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°C, 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： Gallium nitride

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Gan/Moo3 N-N Heterojunction Based Anti-Humidity Gas Sensor for No2 Real-Time Monitoring

SSRN

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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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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

arXiv

引用

arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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A Low SAR, Small size, High gain, Circularly polarized Antenna for Wearable Application

引用

Journal of Physics: Conference Series 2020年第1期1651卷

作者： Chao Wang Liang Zhang Yi Cheng XueMei Li Xianliang Wu Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui University Hefei Anhui 230601 China AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei Anhui 230601 China

A planar, small size, high gain, low specific absorption rate (SAR) and circularly-polarized (CP) wearable antenna is proposed in this paper. The antenna is fabricated on a substrate with a dielectric constant of 3.5 with a thickness of 0.5 mm and sized at 42×42 mm only. Both the simulation results show that the overlap bandwidth of impedance and axial ratio of the proposed antenna is 13.7% (5.15-5.91 GHz). To isolate the antenna against the influence of the human body, a artificial magnetic conductor (AMC) plane is added on the reverse side of the radiator. The AMC structure effectively isolates the human body from the radiation of the antenna and reduces the specific absorption rate (SAR) of it by more than 96.1% at 5.5 GHz, which improves the antenna gain and the peak gain reaches 5.09 dBi at 5.5 GHz. The wearable performance of the antenna was analyzed, and it is observed that it can sustain good performance even under human body loading. Beside, the antenna has a small size, which makes it ideal for wearable applications.

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