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Wideband Reconfigurable Instantaneous Microwave Multi-Frequency Measurement System Without Mirror Image Blurring Based on an Optical Frequency Shifter and Optical Frequency Comb

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SSRN 2024年

作者： Zhang, Jiahong Ji, Qian Zhang, Jing Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming650500 China

In this paper, a wideband reconfigurable instantaneous microwave multi-frequency measurement system without mirror image blurring has been proposed, designed and investigated. In this proposed scheme, an optical frequency shift is employed to shift the optical carrier of the input dual-parallel Mach-Zehnder modulator (DPMZM), aligning it with the first comb line of the optical frequency comb. Simultaneously, the central frequencies of the two demultiplexers are designed to ensure that the optical signals output after coupling through a 90° optical hybrid coupler contain only one local oscillator optical carrier. Furthermore, the local oscillator optical carrier is positioned at the initial location of the channel. This configuration allows for the determination of an unknown signal with only one beat frequency, effectively resolving the issue of ambiguous mirror frequencies. Simulation experiments reveal that when the channel bandwidth is set to 2 GHz, the system frequency measurement range is 0.01-50 GHz, with an error of less than ±5 MHz. By adjusting the channel bandwidth and the spacing of the optical frequency comb, the system measurement range can be extended to 0.01-100 GHz, with a frequency measurement error of 5-14.6 MHz. This approach offers advantages such as a wide measurement range, reconfigurability, and low error. © 2024, The Authors. All rights reserved.

关键词： Mirrors

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Adaptive Ensemble Model for Ultra-short-term Wind Power Forecasting 11

Adaptive Ensemble Model for Ultra-short-term Wind Power Fore...

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11th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2022

作者： Li, Yunlong Jin, Huaiping Jin, Huaikang Cao, Yundong Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming650500 China Huaneng Renewables Co. Ltd. Yunnan Branch Kunming650000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Stable and reliable wind power forecasting is of great significance for grid dispatching. However, wind power has strong nonlinear characteristics. Its amount of historical data is very large, and traditional machine learning methods cannot fit the nonlinear relationship well. Furthermore, owing to the seasonality and periodicity of the wind power, the performance of the offline model inevitably deteriorates. To address these problems, this study proposes an adaptive ensemble model for ultra-short-term wind power forecasting. First, a long short-term memory (LSTM) network is established with historical wind power data for an entire year to ensure a strong generalization performance. Second, local weighted partial least squares (LWPLS) were used to obtain LSTM network prediction errors in real time by adaptive modeling and further improving its prediction accuracy. In addition, just-in-time learning (JITL) is used to ensure the adaptability of forecasting using LWPLS. Finally, to fully integrate the prediction advantages of different prediction models, a random forest (RF) was used to transform the determination of the ensemble weight into a three-classification problem. The adaptive fusion of the three different forecasting mechanisms is realized by weighting through the posterior probability of classification. Finally, the effectiveness and superiority of the proposed method are verified in a practical case. © 2022 IEEE.

关键词： Error correction

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Method for Determination Rotor Eccentrity of an Electric Machine

Method for Determination Rotor Eccentrity of an Electric Mac...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Orazovna, Yussupova Assel Neftissov, Alexandr Olegovna, Potapenko Alexandra Sansyzbayevna, Urazalimova Dariya Toraighyrov University Department Of Electrical Engineering And Automation Pavlodar Kazakhstan Research And Innovation Center 'Industry 4.0' Astana It University Nur-Sultan Kazakhstan Toraighyrov University Faculty Of Computer Science Pavlodar Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

Modern diagnostic systems for asynchronous motors are widely used in the power industry. The most promising diagnostic systems for rotor eccentricity of induction motors are systems with capacitive measuring transducers in view of their simplicity, reliability and sufficient sensitivity. In the proposed work, the selection of the information sign of damage for the diagnosis system of rotor eccentricity with open and closed slots of an induction motor with a capacitive measuring transducer. © 2022 IEEE.

关键词： Induction motors

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MATLAB Implementation of Hp Finite Elements on Rectangles Using Hierarchical Basis Functions 1

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14th International Conference on Parallel Processing and Applied Mathematics, PPAM 2022

作者： Moskovka, Alexej Valdman, Jan Department of Mathematics Faculty of Applied Sciences University of West Bohemia Technická 8 Plzeň30100 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic The Czech Academy of Sciences Institute of Information Theory and Automation Pod vodárenskou věží 4 Prague 818208 Czech Republic

ISBN: (数字)9783031304453

ISBN: (纸本)9783031304446

A MATLAB implementation of hierarchical shape functions on 2D rectangles is explained and available for download. Global shape functions are ordered for a given polynomial degree according to the indices of the nodes, edges, or elements to which they belong. For a uniform p-refinement, the hierarchical structure enables an effective assembly of mass and stiffness matrices. A solution to a boundary value problem is approximated for various levels of uniform h and p refinements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Boundary value problems

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Automatic Detection of Tuberculosis with the Help of Image Processing and Machine Learning Classifier 7

Automatic Detection of Tuberculosis with the Help of Image P...

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7th International Conference on Computing in Engineering and Technology, ICCET 2022

作者： Sharma, Anshu Malhotra, Rahul Sharma, Anurag Department of Computer Science & Engineering CT University Ludhiana India Department of Electronics & Communication Engineering CT Group of Institutions Jalandhar India Faculty of Engineering Design and Automation GNA University Phagwara India

ISBN: (纸本)9781839537042

Tuberculosis is the substantial irresistible disease present in human that principally influences lungs, this tuberculosis is one the main ten noticeable reasons for death for human. It has very typical symptoms so without testing it isn't sure that patient has infected with TB. In this paper, an automated framework dependent on image handling is proposed to help specialists and radiologists in deciphering Chest X-beams (CXR) for early identification of lung Tuberculosis (TB). CXR can show the abnormalities in lungs as well as TB which helps to precisely and rapidly distinguish TB on the grounds that early treatment will provide to patients and disease will stop spreading in body. The proposed system comprised of preprocessing, segmentation, features extraction, and classification. In this paper proposed a strategy of image segmentation and AI to conclusion and characterize the tuberculosis (TB). These tool and procedures are utilized to earlier uncover of tuberculosis (TB) illness. Additionally arrange the sort of TB. The framework recognizes the tuberculosis with more prominent precision. © 2022 The Institution of Engineering and Technology.

关键词： Diseases

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Coreference disambiguation based on two-layer label dependence analysis 7

Coreference disambiguation based on two-layer label dependen...

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7th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2023

作者： Xu, Hongfei Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology 727 Jingming South Rd.Chenggong District Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications 727 Jingming South Rd.Chenggong District Kunming650500 China

ISBN: (数字)9781510668089

ISBN: (纸本)9781510668072

A two-layer labeling-based dependency analysis model is proposed to introduce the location features of words for the coreference disambiguation of entities. Firstly, two layers of labels are used for labeling, the second layer labels the location information of words, the features of sentences are learned using a bidirectional long short-Term memory network, the dependency syntax analysis based on deep graph decoding obtains the dependency tree of sentences, and the two layers of labels are fused to improve the performance and accuracy of coreference disambiguation. Experiments are conducted on the text dataset of power security entity relationship extraction, and the results show that the two-layer labeled dependency analysis has an improved effect on co-finger disambiguation, which verifies the effectiveness of the model for cofinger disambiguation on the experimental dataset. © 2023 SPIE. All rights reserved.

关键词： Trees (mathematics)

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Sparse Attention-based Hierarchical Node Representation for Spatial Domain Identification

Sparse Attention-based Hierarchical Node Representation for ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Zhihao Ping Wei Dai Xiaodong Fu Li Liu Lijun Liu Ning Yu Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Department of Computing Sciences The College at Brockport State University of New York Brockport NY

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Using deep learning models on spatial transcriptomics data to identify the spatial domain is crucial for uncovering the spatial distribution of cells and gene expression patterns within tissues, essential for understanding complex biological processes and disease mechanisms. Existing methods for spatial domain partitioning often rely on predefined adjacency relationships at a single scale, overlooking the hierarchical structure and functional characteristics of biological tissues. In this paper, we propose SpaNFM, a novel method that leverages sparse attention-based hierarchical node representation and multi-view contrastive learning for spatial domain identification in spatial transcriptomics data. The SpaNFM first treats each spot as a node and constructs two views using different data augmentation techniques based on tissue image information, gene expression profiles, and spatial coordinates of cells. Subsequently, SpaNFM utilizes a sparse attention-based hierarchical node fusion module to generate coarse-grained node representations. This fine-to-coarse hierarchical structure integrates complementary information from multi-granularity node features and reduces model complexity due to the decreased node size. The model parameters are updated using gene expression reconstruction loss and contrastive loss on the coarse-grained node representations from the two views. Finally, the learned node features are subjected to downstream clustering using the Leiden algorithm. We tested SpaNFM on the human dorsolateral prefrontal cortex dataset. The results demonstrate that SpaNFM outperforms other state-of-the-art methods in most cases. The data and code are available at: https://***/weiba/SpaNFM

关键词： Biological system modeling Biological tissues Transcriptomics Contrastive learning Feature extraction Brain modeling Spatial databases Gene expression Resource management Image reconstruction

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Editorial: Modeling and application of computational intelligence in sustainable energy systems

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Frontiers in Energy Research 2025年 13卷

作者： Wang, Yirui Gao, Shangce Yu, Yang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Faculty of Engineering University of Toyama Toyama Japan College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

In recent years, the study of computational intelligence in sustainable energy systems has gained increasing attention due to its effectiveness and sophistication. Sustainable energy systems are designed, modeled and optimized by various computational intelligence methods, such as evolutionary computation and machine learning. These methods show the great value and significant contributions in energy research. As researchers continue to explore this area, new insights are being uncovered that have the potential to improve the role and significance of computational intelligence methods in modeling and optimizing sustainable energy systems. In this Research Topic, several studies have contributed significantly to the advancement of computational intelligence in sustainable energy systems. In [1], this study focused on optimizing the configuration and scheduling of integrated energy systems with flexible load resources to maximize energy efficiency and reduce environmental impact. By using Weibull and Beta distribution models to account for uncertainties in wind and solar power, and proposing an enhanced Kepler Optimization Algorithm (EKOA), the approach improved search scope and efficiency. A case study in southeastern China demonstrated that this method reduced energy curtailment and costs, while enhancing renewable energy use. Despite the success, the study only considered hydrogen for single-use and did not explore market benefits, leaving room for future research on hydrogen demand response and market integration. In [2], multi-energy microgrids (MEM) integrate various energy sectors to enhance renewable energy use while maintaining balance. This study analyzed the technical challenges of increasing renewable penetration, using an artificial neural network-based model for solar power forecasting, integrated with the EnergyPLAN tool. A case study on Norway revealed that combined heat and power plants hindered renewable integration, while heat pumps enhanced it. A

关键词： aritificial neural networks computational intelligence evolutionary compuation machine learning metaheuristics optimizaiton algorithms sustainable energy systems

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Modeling Defect Formation Processes at Different Stages of Metallurgical Production 3

Modeling Defect Formation Processes at Different Stages of M...

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3rd International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2021

作者： Galkin, Alexander Pimenov, Vladimer Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia Novolipetsk Steel Corporation Lipetsk Russia

ISBN: (纸本)9781665439817

The paper proposes models for predicting the appearance of surface defects in flat-rolled steel production at ferrous metallurgy enterprises. A 'shell' type surface defect is considered. At the complete cycle metallurgical enterprises with flat-rolled steel production, the quality of the finished product is affected by the various stages of production. In particular, the appearance of the 'shell' type defect may influence the quality of the starting material, namely slabs. Therefore, one of the proposed models is a model of the cracks occurrence in continuous steel casting process. The model uses a criterion based on the time the ingot is in the curvilinear zone of the continuous steel casting facility (CSSF) with the crust temperature in the range of the embrittlement temperature of the corresponding steel grades. In the process of cold rolling, the 'shell' type defect is shaped. It can be completely rolled out or appear as a surface defect in cold rolling. Therefore, the next model considered in the paper is a model of defect shaping in the process of cold rolling. The stress-strain state in the defect zone is modeled, and the influence of initial geometrical characteristics and rolling modes on the degree of rolling-out were analyzed. © 2021 IEEE.

关键词： Continuous casting

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A novel fault-tolerant scheduling approach for collaborative workflows in an edge-IoT environment

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Digital Communications and Networks 2022年第6期8卷 911-922页

作者： Tingyan Long Yong Ma Lei Wu Yunni Xia Ning Jiang Jianqi Li Xiaodong Fu Xiangmi You Bo Zhang College of Computer Science Chongqing UniversityChongqing400030China School of Computer and Information Engineering Jiangxi Normal UniversityJiangxi330022China School of Mathematical Sciences University of Electronic Science and Technology of ChinaChengdu611731China Mashang Consumer Finance Co. Ltd.(MSCF)Chongqing401129China Global Energy Interconnection Research Institute Co.Ltd. Beijing102209China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyYunnan650500China CISDI Research&Development CO. LTDChongqing401122China

As a newly emerging computing paradigm, edge computing shows great capability in supporting and boosting 5G and Internet-of-Things (IoT) oriented applications, e.g., scientific workflows with low-latency, elastic, and on-demand provisioning of computational resources. However, the geographically distributed IoT resources are usually interconnected with each other through unreliable communications and ever-changing contexts, which brings in strong heterogeneity, potential vulnerability, and instability of computing infrastructures at different levels. It thus remains a challenge to enforce high fault-tolerance of edge-IoT scientific computing task flows, especially when the supporting computing infrastructures are deployed in a collaborative, distributed, and dynamic environment that is prone to faults and failures. This work proposes a novel fault-tolerant scheduling approach for edge-IoT collaborative workflows. The proposed approach first conducts a dependency-based task allocation analysis, then leverages a Primary-Backup (PB) strategy for tolerating task failures that occur at edge nodes, and finally designs a deep Q-learning algorithm for identifying the near-optimal workflow task scheduling scheme. We conduct extensive simulative case studies on multiple randomly-generated workflow and real-world edge-IoT server position datasets. Results clearly suggest that our proposed method outperforms the state-of-the-art competitors in terms of task completion ratio, server active time, and resource utilization.

关键词： Edge computing Fault tolerance DQN algorithm Primary-backup model

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