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arXiv 2022年

作者： Dai, Ming Zheng, Enhui Feng, Zhenhua Zhuang, Jiedong Yang, Wankou Qi, Lei The School of Automation Southeast University Nanjing210096 China The Unmanned System Application Technology Research Institute China Jiliang University Hangzhou310018 China The School of Computer Science and Electronic Engineering University of Surrey GuildfordGU2 7XH United Kingdom Zhejiang University Hangzhou310063 China

Unmanned Aerial Vehicles (UAVs) rely on satellite systems for stable positioning. However, due to limited satellite coverage or communication disruptions, UAVs may lose signals from satellite-based positioning systems. In such situations, vision-based techniques can serve as an alternative, ensuring the self-positioning capability of UAVs. However, most of the existing datasets are developed for the geo-localization tasks of the objects identified by UAVs, rather than the self-positioning task of UAVs. Furthermore, the current UAV datasets use discrete sampling on synthetic data, such as Google Maps, thereby neglecting the crucial aspects of dense sampling and the uncertainties commonly experienced in real-world scenarios. To address these issues, this paper presents a new dataset, DenseUAV, which is the first publicly available dataset designed for the UAV self-positioning task. DenseUAV adopts dense sampling on UAV images obtained in low-altitude urban settings. In total, over 27K UAV-view and satellite-view images of 14 university campuses are collected and annotated, establishing a new benchmark. In terms of model development, we first verify the superiority of Transformers over CNNs in this task. Then, we incorporate metric learning into representation learning to enhance the discriminative capacity of the model and to lessen the modality discrepancy. Besides, to facilitate joint learning from both perspectives, we propose a mutually supervised learning approach. Last, we enhance the Recall@K metric and introduce a new measurement, SDM@K, to evaluate the performance of a trained model from both the retrieval and localization perspectives simultaneously. As a result, the proposed baseline method achieves a remarkable Recall@1 score of 83.05% and an SDM@1 score of 86.24% on DenseUAV. The dataset and code will be made publicly available on https://***/Dmmm1997/DenseUAV. © 2022, CC BY.

关键词： Satellites

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Crsanet: Class Representations Self-Attention Network for the Segmentation of Thyroid Nodules

SSRN

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SSRN 2023年

作者： Sun, Shiyao Fu, Chong Xu, Sen Wen, Yingyou Ma, Tao School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China General Hospital of Northern Theatre Command Shenyang110016 China Medical Imaging Research Department Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang China Dopamine Group Ltd. Auckland1542 New Zealand

Segmenting accurate thyroid nodules in medical ultrasound images is always non-trivial due to the large variation in size, shape and texture of nodules, and also the presence of surrounding tissues and organs with similar intensity values. Existing algorithms solve this by designing multi-scale spatial context modeling modules and introducing the emerging self-attention based paradigm, while failing to learn the class-wise relational context around nodules. To this end, we propose a novel coarse-to-fine framework, namely, CRSANet, which leverages both class-wise relational context and spatial multi-scale context for comprehensive context perception. Specifically, we first introduce the concept of Class Representations, which describe the overall representation of thyroid nodules in ultrasound images from a categorical perspective. Secondly, we propose a backbone-free dual-branch Class Representation Self-Attention Module (CRSA) to refine the coarse segmentation results from segmentation backbones. Concretely, Fine Class Region (FCR) branch is proposed to impose consistency between pixels and their corresponding Class Representations to correct the wrongly classified pixels. Meanwhile, Class Contextual Representation (CCR) branch is introduced to enhance the ability of pixel representations with the corresponding Class Representations. We evaluate the effectiveness and generality of CRSANet using three types of backbone networks. In the experiments, our proposed U-CRSANet achieves a mIoU of 78.86% and 81.35%, an accuracy of 97.11% and 98.63%, a Dice value of 74.26% and 87.57%, respectively, which outperforms most state-of-the-art methods on the public DDTI and TN-SCUI datasets. © 2023, The Authors. All rights reserved.

关键词： Pixels

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Innovative integrated operation planning tool for the current and future 2030+ European power grid

Innovative integrated operation planning tool for the curren...

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CIRED 2020 Berlin Workshop, CIRED 2020

作者： Di Somma, Marialaura Ciavarella, Roberto Graditi, Giorgio Khavari, Ata M. Kosmecki, Michal Papadimitriou, Christina N. Valenti, Maria ENEA Department of Energy Technologies and Renewable Sources Rome Italy DERlab – European Distributed Energy Resources Laboratories Kassel Germany System Analysis Department Institute of Power Engineering Gdansk Poland FOSS Research Centre for Sustainable Energy Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (纸本)9781839535635

To deal with the evolution in place of the power system, innovative solutions are needed to support grid operators for the operation of the electricity system to increase the security of supply accounting for the increasing share of renewable energy sources (RES). The goal of the H2020 INTERPLAN project is to develop an innovative tool for transmission system operators (TSOs) and distribution system operators (DSOs) addressing the operation planning challenges of the ongoing pan-European network, while also fostering the achievement of the low-carbon EU objectives. The contribution of this study is to present the tool, defined as a methodology consisting of a set of functions (grid equivalents, control functions) for the operation planning of the current and future 2030+ EU power grid, by addressing a significant number of operation challenges, from the perspective of the transmission system, the distribution system, and with a focus on the transmission–distribution interface. The tool is aimed to plan the operation of an integrated grid from TSOs and DSOs perspectives, through efficiently handling intermittent RES and emerging technologies such as storage and demand response, by supporting the usage of the flexibility potential coming from these technologies for system services in all network control levels. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Renewable energy resources

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The data synergy effects of time-series deep learning models in hydrology

arXiv

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arXiv 2021年

作者： Fang, Kuai Kifer, Daniel Lawson, Kathryn Feng, Dapeng Shen, Chaopeng Department of Earth System Science Stanford University Stanford United States Department of Civil and Environmental Engineering Pennsylvania State University University ParkPA United States Department of Computer Science and Engineering Pennsylvania State University University ParkPA United States

When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to regionalize - to divide a large spatial domain into multiple regions and study each region separately - instead of fitting a single model on the entire data (also known as unification). Traditional wisdom in these fields suggests that models built for each region separately will have higher performance because of homogeneity within each region. However, by partitioning the training data, each model has access to fewer data points and cannot learn from commonalities between regions. Here, through two hydrologic examples (soil moisture and streamflow), we argue that unification can often significantly outperform regionalization in the era of big data and deep learning (DL). Common DL architectures, even without bespoke customization, can automatically build models that benefit from regional commonality while accurately learning region-specific differences. We highlight an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions. In fact, the performance of the DL models benefited from more diverse rather than more homogeneous training data. We hypothesize that DL models automatically adjust their internal representations to identify commonalities while also providing sufficient discriminatory information to the model. The results here advocate for pooling together larger datasets, and suggest the academic community should place greater emphasis on data sharing and compilation. Copyright © 2021, The Authors. All rights reserved.

关键词： Soil moisture

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Design of concentrated solar power water desalination system (CSPWDS)

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AIP Conference Proceedings 2023年第1期2591卷

作者： Abdulkareem Naser Mahmood Syahrul Ashikin Binti Azmi Reyad El-Khazali 1School of Electrical System Engineering University of Malaysia Perlis (UniMAP) Arau Perlis Malaysia. 2Electrical and Computer Engineering Department Khalifa University Abu Dhabi UAE.

This work focuses on designing a concentrated solar power water desalination system (CSPWDS). A set of four Fresnel lenses (FRL) are used to concentrate the solar power at their corresponding focal points. The objective is to focus the irradiance power at a small area, where the temperature could reach over a 1000 Cͦ at its focal point. This study investigates a design method of a CSPWDS that includes an investigation of the effectiveness of using FRLs for water desalination. The system automatically tracks the sun to improve its production rate. Four spiral copper pipes receivers are designed to convert the saline water into a steam that is directed upward to benefit from the gravitational force. The salt residue in the steam is then filtered out due to both using a special membrane and due to gravity force. A condensation process is then followed to condense the steam into water. The salinity of the condensed steam is tested to ensure a good quality of the desalinated water. The process is continuously monitored to improve the quality of the generated water. If the level of water salinity is still high, a feedback path is implemented to repeat the process until the quality of the desalination is met.

关键词：

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Portfolio Selection Utilizing Electronic Company Stocks During the Enhance Community Quarantine Period in the Philippines

Portfolio Selection Utilizing Electronic Company Stocks Duri...

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IEEE International Conference on Industrial engineering and engineering Management

作者： M. N. Young G. S. Arevalo E. C. Mallari School of Industrial and System Engineering Mapúa University Manila Philippines School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (数字)9781538672204

ISBN: (纸本)9781538672211

This study presents a portfolio selection framework utilizing electric company stocks during the COVID-19 pandemic in the Philippines. A set of criteria is presented to identify the investment pool composed of electric companies. Returns are estimated through historical returns and assumed to be equally likely. Then, equally weighted portfolio strategy is applied to identify the optimal portfolio. The portfolio is then compared to a counterpart portfolio, a benchmark and the market. Back-test shows that the electric company portfolio outperforms its counterpart portfolio in all aspect, it can also outperform the market in terms of returns and standard deviations and be at par with the benchmark. This may lead to a generic portfolio selection framework for individual investors.

关键词： Companies Portfolios Investment History Market research Indexes Electric potential

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High-coherence parallelization in integrated photonics

arXiv

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arXiv 2023年

作者： Zhang, Xuguang Zhou, Zixuan Guo, Yijun Zhuang, Minxue Jin, Warren Shen, Bitao Chen, Yujun Huang, Jiahui Tao, Zihan Jin, Ming Chen, Ruixuan Ge, Zhangfeng Fang, Zhou Zhang, Ning Liu, Yadong Cai, Pengfei Hu, Weiwei Shu, Haowen Pan, Dong Bowers, John E. Wang, Xingjun Chang, Lin State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA United States Frontiers Science Center for Nano-optoelectronics Peking University Beijing China SiFotonics Technologies Co. Ltd. Beijing China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

Coherent optics has profoundly impacted diverse applications ranging from communications, LiDAR to quantum computations. However, building coherent systems in integrated photonics previously came at great expense in hardware integration and energy efficiency: the lack of a power-efficient way to generate highly coherent light necessitates bulky lasers and amplifiers, while frequency and phase recovery schemes require huge digital signal processing resources. In this work, we demonstrate a high-coherence parallelization strategy that facilitates advanced integrated coherent systems at a minimum price. Using a self-injection locked microcomb to injection lock a distributed feedback laser array, we boost the microcomb power by a record high gain of up to 60 dB on chip with no degradation in coherence. This strategy enables tens of highly coherent channels with an intrinsic linewidth down to the 10 Hz level and power of more than 20 dBm. The overall electrical to optical wall-plug efficiency reaches 19%, comparable with that of the state-of-the-art semiconductor lasers. Driven by this parallel source, we demonstrate a silicon photonic communication link with an unprecedented data rate beyond 60 Tbit/s. Importantly, the high coherence we achieve reduces the coherent-related DSP consumption by 99.999% compared with the traditional III-V laser pump scheme. This work paves a way to realizing scalable, high-performance coherent integrated photonic systems, potentially benefiting numerous applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Feedback LSTM Network Based on Attention for Image Description Generator

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computers, Materials & Continua 2019年第5期59卷 575-589页

作者： Zhaowei Qu Bingyu Cao Xiaoru Wang Fu Li Peirong Xu Luhan Zhang Beijing Key Laboratory of Network System and Network Culture Beijing University of Posts and TelecommunicationsBeijing100876China Department of Electrical and Computer Engineering Portland States UniversityPortlandOR 97207-0751USA

Images are complex multimedia data which contain rich semantic *** of current image description generator algorithms only generate plain description,with the lack of distinction between primary and secondary object,leading to insufficient high-level semantic and accuracy under public evaluation *** major issue is the lack of effective network on high-level semantic sentences generation,which contains detailed description for motion and state of the principal *** address the issue,this paper proposes the Attention-based Feedback Long Short-Term Memory Network(AFLN).Based on existing codec framework,there are two independent sub tasks in our method:attention-based feedback LSTM network during decoding and the Convolutional Block Attention Module(CBAM)in the coding ***,we propose an attentionbased network to feedback the features corresponding to the generated word from the previous LSTM decoding *** implement feedback guidance through the related field mapping algorithm,which quantifies the correlation between previous word and latter word,so that the main object can be tracked with highlighted detailed ***,we exploit the attention idea and apply a lightweight and general module called CBAM after the last layer of VGG 16 pretraining network,which can enhance the expression of image coding features by combining channel and spatial dimension attention maps with negligible *** experiments on COCO dataset validate the superiority of our network over the state-of-the-art *** scores and actual effects are *** BLEU 4 score increases from 0.291 to 0.301 while the CIDEr score rising from 0.912 to 0.952.

关键词： Image description generator feedback LSTM network attention CBAM

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Automatically derived stateful network functions including non-field attributes

Automatically derived stateful network functions including n...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Bin Yuan Shengyao Sun Xianjun Deng Deqing Zou Haoyu Chen Shenghui Li Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Research Institute Huazhong University of Science and Technology Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Lab

ISBN: (纸本)9781665416597

The modern network consists of thousands of network devices from different suppliers that perform distinct code-pendent functions, such as routing, switching, modifying header fields, and access control across physical and virtual networks. Because of the network complexity, the network is prone to a wide range of errors, such as false-positive configuration, software errors, or unexpected interactions across protocols. These errors can lead to loops, sub-optimal routing, path leaks, black holes, and access control violations that make services unavailable, vulnerable to exploitation, or prone to attacks (e.g., DDoS attacks). To mitigate these problems, network operators deploy many different stateful network functions, like firewalls, NATs, load balancers, and intrusion-prevention boxes. They have become an important part of networks today, so it is critical to verify that these network functions are the same as expected deployments. All static network verification tools are meant to rigorously check network software or configuration for bugs before deployment. They usually use handwritten models or limited derivation models that are error-prone and ignore the fact that even the same type of network functions (from different vendors) still have different implementation details. In this paper, we propose a tool that can automatically synthesize more realistic and high-fidelity models that include stateful network functions with non-field attributes. We design an inferring algorithm, implement the transformation between data packages and symbolic packages, and obtain a finite state machine that can accurately express the actions of black-box network functions for a given configuration.

关键词： Access control Performance evaluation Privacy Protocols Computational modeling Switches Routing

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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