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Research progress on Mo2C/C nanocomposites for the application of microwave absorption 5

Research progress on Mo2C/C nanocomposites for the applicati...

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5th international conference on Optoelectronic Science and Materials, ICOSM 2023

作者： Wang, Yahui Li, Zhigang Liu, Yi Chen, Zongsheng Liu, Liping Wang, Junru Zhang, Jinhua Shi, Jiaming Anhui Provincial Laboratory of Advanced Laser Technology College of Electronic Engineering National University of Defense Technology Hefei230037 China Key Laboratory of Infrared and Low Temperature Plasma of Anhui Province College of Electronic Engineering National University of Defense Technology Hefei230037 China

ISBN: (纸本)9781510674547

Microwave absorption materials have been increasingly attracting our attention as an effective method of mitigating electromagnetic pollution. Among these materials, molybdenum carbide/carbon (Mo2C/C) composites have gradually emerged as a prominent candidate in the field of microwave absorption materials, owing to their lightweight characteristics, chemical stability, and exceptional dielectric properties. In this study, the significant advancements in research on Mo2C/C composites are reviewed, encompassing binary Mo2C/C composites and multi-component Mo2C/C composites, as well as exploring the auxiliary attenuation effect of core-shell structures, hollow structures, and three-dimensional porous structures. the microwave absorption mechanism was discussed, and the absorption mechanism of Mo2C/C composites was analyzed and summarized. Ultimately, the current status of Mo2C/C composites has been provided in this study, which will serve as a valuable reference for comprehending the properties and potential applications of this Mo2C/C composites. © 2024 SPIE.

关键词： Dielectric losses

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Research on air interface interference recognition in mobile communication networks based on LSTM autoencoder 5

Research on air interface interference recognition in mobile...

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5th international conference on Signal Processing and Computer Science, SPCS 2024

作者： Sun, Hongwei Hu, Mingwei Wang, Baoji Chen, Yue Fu, Jingwen Huang, Jia Jiangsu Automation Research Institute Lianyungang222006 China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100094 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Beijing Sankuai Online Technology Co. Ltd. Beijing100102 China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

Due to the diverse nature of interference signals in mobile communication frequency bands, feature extraction is challenging, and the rapid and automated identification of behaviors such as illegal frequency usage and malicious interference are difficult. Moreover, evaluating the precise impact of interference signals on communication networks is challenging. In response to this problem, this paper proposes an air interface interference recognition technology for communication links based on base station measurement reporting data. It constructs an interference recognition model to achieve automatic interference identification and evaluates the accuracy of interference recognition. On the base station side, this paper utilizes channel measurement data such as signal strength, signal quality, and signal-to-noise ratio exported by the base station. It uses a Long Short-Term Memory (LSTM) autoencoder model to learn normal signal patterns for interference discrimination and evaluates the impact of interference on communication networks. this method achieves precise identification of fixed-frequency interference on the base station side and evaluates the impact of interference on communication networks. Experimental results show that the base station-side approach achieves an F1 score of 0.99 in identifying fixed-frequency interference, outperforming One-Class Support Vector Machine (OCSVM), Principal Component Analysis (PCA), and Isolation Forest (IForest) methods under similar conditions. © 2025 SPIE.

关键词： Base stations

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Research on Abnormal Detection and Intelligent Diagnosis Algorithm of Reservoir Test data

Research on Abnormal Detection and Intelligent Diagnosis Alg...

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2024 international conference on Power, Electrical engineering, Electronics and Control, PEEEC 2024

作者： Sun, Delan The 7th Operation Area of the 5th Oil Production Plant of Daqing Oilfield Co. Ltd Heilongjiang Daqing163513 China

ISBN: (纸本)9798350378917

As an important basis for evaluating oilfield conditions, predicting productivity and formulating development strategies, the accuracy and reliability of reservoir test data are very important. However, the abnormal test data caused by many factors, if not detected and processed in time, will seriously affect the oilfield production decision, and even bring security risks and economic losses. Aiming at the problem of abnormal detection and intelligent diagnosis of reservoir test data, this study puts forward a solution that comprehensively uses statistics, machine learning and deep learning technology. In the design of anomaly detection algorithm, this study proposes an anomaly detection algorithm based on ensemble learning and adaptive threshold. By combining basic learners such as decision tree and support vector machine (SVM), and using Boosting integrated learning strategy and adaptive threshold mechanism, the algorithm significantly improves the accuracy and stability of anomaly detection. the experimental results show that, compared with the single decision tree and SVM algorithm, the ensemble learning algorithm in this study performs better in accuracy, recall and F1 score, reaching 0.95,0.92 and 0.93 respectively. Further, this study designed an intelligent diagnosis algorithm, combined with abnormal detection results and other related data, such as equipment maintenance records, operation logs, etc., and built a classification model by using the Gradient Boosting Machine (GBM) to realize accurate diagnosis of abnormal causes. the intelligent diagnosis model not only provides targeted solution suggestions, but also improves the accuracy and efficiency of diagnosis through continuous optimization and feedback mechanism. this study not only improves the accuracy and reliability of reservoir test data, but also provides scientific decision support for oilfield staff, effectively improving the production efficiency and economic benefits of the oilfield. © 2024 I

关键词： abnormal detection decision tree intelligent diagnosis reservoir test data support vector machine

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140 GHz Broadband Frequency Doubler Based on GaAs Monolithic 5th

140 GHz Broadband Frequency Doubler Based on GaAs Monolithi...

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5th China and international Young Scientist Terahertz conference, YthZ 2024

作者： Dong, Yazhou Zhou, Jun University of Electronic Science and Technology of China Chengdu China Engineering Center of Integrated Optoelectronic and Radio Meta-Chips University of Electronic Science and Technology Chengdu China

ISBN: (纸本)9789819739127

Based on GaAs monolithic technology, a broadband frequency doubler operating at 140 GHz has been designed. the device is based on a balanced structure, which effectively suppresses odd harmonics and improves the frequency doubling efficiency. Simulation results show that at an input power of 230 mW, the frequency doubling efficiency is greater than 30% over a relative bandwidth of 20.6% from 130 GHz to 160 GHz, and greater than 40% from 134 GHz to 158 GHz, with an output power greater than 90 mW. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Frequency doublers

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Low Lead All-Inorganic Hybrid Perovskite: A Study of Interface Defects using SCAPS-1D 5

Low Lead All-Inorganic Hybrid Perovskite: A Study of Interfa...

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5th international conference on Devices for Integrated Circuit, DevIC 2023

作者： Kaur, Navdeep Madan, Jaya Pandey, Rahul Chitkara University Institute of Engineering and Technology Chitkara University VLSI Centre of Excellence Punjab 140401 India

ISBN: (纸本)9798350347265

Hybrid perovskites are promising materials for photovoltaic applications due to their long diffusion length, high absorption coefficient, longer carrier lifetime, and adjustable bandgaps. However, the toxic nature of lead in hybrid perovskites is still a concern for the environment. In this work, a low-lead, all-inorganic perovskite material (CsP 0.625 Z 0.375 IC12) is proposed as the absorber layer. the addition of zinc reduces lead content, and all-inorganic materials have better stability against temperature and moisture variations. TiO2 is selected as the electron transport layer (ETL) and Spiro-MeOTAD as the hole transport layer (HTL) to efficiently collect the charge carriers at electrodes. the proposed design (TiO2/CsP b 0.625 Z n 0.375IC1 2/SpiroMeOTAD) has been verified in the SCAPS-ID simulator under the influence of interface defect variation. Results show a decrease in current density and quantum efficiency with an increase in interface defect density at the ETL/absorber layer interface. It is observed that the current density decreases from 33.02 mA/cm2 to 29.97 mA/cm2 with an interface defect density of 1 x1010 c -2 and 1 x101S c -2, respectively. However, for the absorber layer/HTL interface, the current density remains constant at 33.02 mA/cm2 on variation of defect density from 1 x1010 c -2 to 1 x101S c -2. © 2023 IEEE.

关键词： Perovskite

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Spatial-temporal change analysis of gross primary productivity of Xilingol grassland based on remote sensing technology 5

Spatial-temporal change analysis of gross primary productivi...

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5th international conference on Geology, Mapping, and Remote Sensing, ICGMRS 2024

作者： Zhao, Jun Cao, Yungang Faculty of Geosciences and Environmental Engineering Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9781510681712

As an important part of China's grassland ecosystem, it is crucial to use remote sensing for high-resolution, long-term ecological monitoring and management of the Xilingol Grassland. this study uses Landsat data from May to September for the years 2013 to 2022 to estimate GPP, with a spatial resolution of 30m, and utilizes Slope and F-test methods to discern the spatial-temporal variations of GPP in Xilingol Grassland. the results indicate that over the past decade: 1) the multi-year average GPP value for Xilingol Grassland stands at 458.55 g C∙m-2∙year-1, with high-value areas mainly located in the northeastern and southern parts of the grassland, and low-value areas mainly located in the southwestern region. 2) the inter-annual average changes in GPP in Xilingol Grassland showed a pattern of initial decline followed by an increase. Among them, the highest proportion without significant change is 54.92%. 3) In particular, the meadow grassland mainly shows an increase or no significant change, accounting for 37.28% and 52.82% respectively. the typical grassland mostly shows no significant change, accounting with 61.8%. And desert steppes mainly showed a decreasing trend with 62.83%. © 2024 SPIE.

关键词： Landsat

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Research on power grid harmonics recognition based on improved YOLOv5 algorithm 5

Research on power grid harmonics recognition based on improv...

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5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Li, Mingjun Sheng, Yanchun Zhang, Xianliang Wu, Guizhou Li, Jihao Yuan, Ye Department of Mechanical and Electrical Engineering Heilongjiang Institute of Construction Technology Harbin China Architecture Department Heilongjiang Institute of Construction Technology Harbin China

ISBN: (纸本)9781510691667

the identification and analysis of harmonics in power grids are crucial for ensuring the stability, reliability, and efficiency of electrical systems. Harmonics, which arise from non-linear loads, can lead to various operational issues such as equipment damage, reduced energy efficiency, and power quality degradation. Traditional methods for harmonic detection often suffer from limitations, including low recognition accuracy, slow processing speed, and challenges in real-time monitoring, making them less suitable for dynamic grid environments. this study addresses these challenges by proposing an enhanced YOLOv5 (You Only Look Once version 5) algorithm for harmonic recognition. the proposed method introduces several key modifications to the YOLOv5 architecture, including improved feature extraction techniques and optimization of the loss function to better capture harmonic patterns in complex power grid data. By leveraging the strengths of YOLOv5 in real-time object detection and combining them with domain-specific improvements, the algorithm is able to achieve superior performance in both harmonic classification accuracy and processing speed. Experimental results demonstrate that the proposed approach significantly outperforms conventional methods, offering a more robust and efficient solution for the detection and analysis of harmonics in power grids. this research presents a promising tool for the development of intelligent monitoring systems that can enhance power grid operation and maintenance, ensuring optimal performance and minimizing potential risks. © 2025 SPIE.

关键词： Smart power grids

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11th and 12th EAI international conference on big data Technologies and Applications, BDTA 2021 and BDTA 2022

11th and 12th EAI International Conference on Big Data Techn...

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11th and 12th EAI international conference on big data Technologies and Applications, BDTA 2021 and BDTA 2022

ISBN: (纸本)9783031336133

the proceedings contain 23 papers. the special focus in this conference is on big data Technologies and Applications. the topics include: Explore the Relationship Between Procedural Score Feedback and Subsequent Time Allocation and Learning Outcomes of Learners in a Massive Open Online Course (MOOC);doS Attacks Detection in the Network of Drones: An Efficient Decision Tree-Based Model;detecting Fake News Spreaders on Twitter through Follower Networks;NODDLE: Node2vec Based Deep Learning Model for Link Prediction;hybrid Coral Reef Optimization Algorithm Employed Local Search Technique for Job Shop Scheduling Problems;efficient Human Activity Recognition Based on Grouped Representations of Multimodal Wearable data;pose+Context: A Model for Recognizing Non-verbal Teaching Behavior of Normal College Student;Dropout Prediction in MOOC Combining Behavioral Sequence Characteristics;CVO: Curriculum Vitae Optimization by Recommending Keywords to Undergraduate Students;a Proposed Keyword-Based Feature Extraction Approach for Labeling and Classifying Egyptian Mobile Apps Arabic Slang User Requirements Reviews;big data in Healthcare Institutions: An Architecture Proposal;analysis of Knowledge Map on Rural Culture Tourism in China;a Visual Analysis of E-Government Research in China Based on Co-word Clustering;research on Virtual Simulation Teaching Platform Based on Convergent Media;a Comparative Study for Anonymizing datasets with Multiple Sensitive Attributes and Multiple Records;a Proposed Framework for Cloud Immunization Information System: Challenges and Opportunities;the Role of Block Chain technology in Reducing Corruption Within the Local Governance in Egypt;a data Brokering Architecture to Guarantee Nonfunctional Requirements in IoT Applications;a Proposed Virtual Learning Model Based on Statistical Analysis of Educational data of Egypt.

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Stage Analysis and Prediction of Green Finance via Sliding Window Decision Tree and Clustering 24

Stage Analysis and Prediction of Green Finance via Sliding W...

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proceedings of the 2024 5th international conference on big data Economy and Information Management

作者： Diya Lei School of Business Southern University of Science and Technology Shenzhen Guangdong China

ISBN: (纸本)9798400711862

this study focuses on the core issue of objectively assessing and categorizing the development stages of green finance in Chinese provinces. Based on green finance-related data from 31 provincial-level administrative regions in mainland China from 1990 to 2021, K-means cluster analysis and decision tree modeling, combined with the sliding window mechanism, are used to scientifically categorize the development stages of green finance, while enhancing the accuracy of prediction in the time dimension. the results show that the development of green finance in China can be classified into four stages: germination, development, deepening and maturity, and the key jump factors between the stages include the proportion of green credit, green insurance and green bonds. these findings provide actionable insights for policymakers to prioritize interventions and optimize green financial policies tailored to regional characteristics. the code and dataset can be found at https://***/leidiya01/SWDTC.

关键词： Decision tree

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Anti-money laundering method based on hierarchical risk control knowledge graph 5

Anti-money laundering method based on hierarchical risk cont...

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5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Tang, Zichen Haihong, E. Sun, Mingzhi Zhao, Lamei Wang, Rongrong Song, Meina School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China Beijing Trusfort Technology Co. Ltd. Beijing100089 China

ISBN: (纸本)9781510668621

With the development of the Internet and advances in technology, financial transactions have become more convenient, but money laundering has also become more concealed, complex, and can be quickly adjusted according to regulatory policies, making traditional methods based on rule libraries and blacklists difficult to cope with the current Anti-Money Laundering (AML) challenges. To improve the efficiency and accuracy of AML work, various machine learning methods have been proposed. However, these methods are often difficult to be widely used due to their complex models and poor interpretability. To overcome the shortcomings of existing methods and adapt to the changing trends in money laundering while ensuring good interpretability, this paper proposes an AML method based on Hierarchical Risk Control Knowledge Graph (HRCKG). Firstly, a simulated AML dataset is generated based on a small amount of real data and money laundering patterns. Next, various transaction features are extracted to construct graded risk control indicators, which are used to automatically search for and filter risk control rules to form a rule library, and then construct an HRCKG. Finally, integrating rule-based reasoning and graph-based reasoning, this paper uses rule matching, node importance measurement, and community discovery algorithms to evaluate the money laundering risks of accounts, identify money laundering accounts, and discover money laundering groups. through comparison with other machine learning methods and analysis of experimental results, the effectiveness and application value of the proposed method is demonstrated. © 2023 SPIE.

关键词： Knowledge graph

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