检索结果-内蒙古大学图书馆

SSRN 2025年

作者： Muğaloğlu, Erhan Kılıç, Edanur Keskin, Hazar Selçuklu, Saltuk Buğra Erciyes University Faculty of Economics and Administrative Sciences Kayseri Turkey Erciyes University Graduate School of Social Sciences Kayseri Turkey Independent Researcher in Economics Hatay Turkey Erciyes University Faculty of Engineering Department of Energy Systems Engineering Kayseri Turkey

This study examines the price volatility dynamics of Türkiye’s day-ahead electricity market from 2018 to 2024, analyzing price variations across moderate, high, and extreme regimes. The Markov Regime-Switching GJR-GARCH model captures sudden regime shifts, proving that the three-regime model outperforms the single-regime model based on Value-at-Risk calculations. Regime 1 accounts for 42% of observations in Türkiye’s electricity market, and Regimes 2 and 3 comprise 45% and 13%, respectively. Transition probabilities indicate that electricity prices tend to stay in more volatile regimes, especially during crucial shocks like the Russia-Ukraine conflict and the COVID-19 pandemic. A linear model is also constructed to assess the impact of different energy sources—solar, wind, hydropower, and natural gas—on price volatility. Results show that solar power reduces volatility in extreme conditions, wind and hydropower can either stabilize or amplify volatility, and natural gas is a key driver of volatility in all regimes. Renewable energy investments increase short-term price volatility due to the Merit Order Effect, but in the long run, they enhance energy security by reducing fossil fuel dependence. This study suggests strategies ranging from risk management techniques to energy policies to strengthen electricity market resilience against sudden price shifts. © 2025, The Authors. All rights reserved.

关键词： Risk management

来源：评论

学校读者我要写书评

暂无评论

Strategic legislation for the promotion of university–industry collaborations: a case study of Taiwan

引用

Journal of Technology Transfer 2025年第1期50卷 304-344页

作者： Truong, Thanh Hue Lin, Bou-Wen Tung, Ching-Pin International Degree Program in Climate Change and Sustainable Development National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan Research Center for Future Earth National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan Graduate Institute of Business Administration College of Management National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan National Applied Research Laboratories No. 106 Sec. 2 Heping E. Rd. Da’an Dist. Taipei City106214 Taiwan Department of Bioenvironmental Systems Engineering National Taiwan University No. 1 Sec. 4 Roosevelt Road Taipei106 Taiwan

The strategic utilization of technology to enhance industrial competitiveness and product quality is pivotal for economic growth. As universities own more inventions, collaborative efforts between industry and academia become essential for regional competitiveness and overall economic advancement. This study explores Taiwan’s systematic approach to upgrading its infrastructure, emphasizing the significance of University–Industry Collaborations (UIC) in realizing Industry 4.0. Government strategies and policy decisions emerge as key drivers in fostering successful collaborative projects between industry and university teams. The relaxation of restrictions, particularly related to part-time teachers and technology transfer, along with the promotion of corporate programs, effective use of public funding, and the development of an innovative digital platform system for UIC, collectively contribute to establishing a robust university–industry cooperation mechanism. This mechanism has contributed to Taiwan’s move towards new digital advances in innovation and entrepreneurship, fostering industrial leadership and efficient governance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Technology transfer

来源：评论

学校读者我要写书评

暂无评论

AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

引用

IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

来源：评论

学校读者我要写书评

暂无评论

Economic and resilient planning of hydrogen-enriched power distribution network with mobile hydrogen energy resources

引用

International Journal of Hydrogen Energy 2025年

作者： Wang, Tengbo Dong, Yuchen Cao, Xiaoyu Xu, Zhanbo Gao, Feng Guan, Xiaohong Systems Engineering Institute School of Automation Science and Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'an710049 China School of Management Xi'an Jiaotong University Shaanxi Xi'an710049 China Center for Intelligent and Networked Systems Department of Automation Tsinghua University Beijing100084 China

This paper presents a risk-averse stochastic mixed-integer programming method to support the economic and resilient planning of hydrogen-enriched power distribution network with mobile hydrogen energy resources (MHERs). Our planning formulation holds a two-stage problem structure. The first-stage problem is to minimize the investment cost for optimally siting and configuring stationary distributed energy resources (DERs) and MHERs. In the second-stage, the operational feasibility of planning alternatives is verified subject to a finite support of stochastic scenarios. Besides of the economic assessment, an additional set of recourse problems based on contingency scenarios is involved to reflect the impact of extreme events (e.g., natural disasters or malicious attacks). To evaluate the risk-hedging capability of long-run system operation, a special objective function based on conditional value-at-risk (CVaR) is introduced. Moreover, various on-emergency corrective measures, e.g., energy storage dispatching, MHERs’ dynamic re-routing, and distribution feeders reconfiguration, are considered to enhance the system resilience. Case studies on a 33-bus test distribution network have validated the economic and resilience benefits of the proposed planning method. Through the proper configuration of stationary and mobile DERs, the load curtailment risks can be reduced by more than 75% given similar levels of system expenses. Besides, the inclusion of CVaR objective provides a flexible and quantitative measure for risk controls by tuning key model parameters. © 2025 Hydrogen Energy Publications LLC

关键词： Power distribution planning

来源：评论

学校读者我要写书评

暂无评论

Methodology for optimizing electrical grounding grids in stratified soils using advanced calculation techniques and evolutionary algorithms

引用

Swarm and Evolutionary Computation 2025年 95卷

作者： Silva, Carlos Leandro Borges da Pires, Thyago Gumeratto Silva Filho, Antonio Marcelino Bulhões, Junio Santos Belo, Orlando M. Oliveira Rodrigues, Clóves Gonçalves Coimbra, Antonio Paulo Calixto, Wesley Pacheco Electrical Mechanical & Computer Engineering School Federal University of Goias Goiania Goias PC 74.605-220 Brazil Institute of Systems and Robotics Coimbra University CoimbraPC 3030-290 Portugal Department of Informatics School of Engineering University of Minho BragaPC 4710-057 Portugal Electrical Power Systems Study Group Federal Institute of Tocantins Palmas TocantinsPC 77.021-090 Brazil Graduate Program in Production and Systems Engineering Pontifical Catholic University of Goias Goiania Goias PC 74605-010 Brazil Mato Grosso Brazil Technology Research and Development Center Federal Institute of Goias Goiania Goias PC 74.055-110 Brazil

This paper presents a practical methodology for optimizing the geometry of electrical grounding grids at industrial frequencies of 50Hz and 60Hz, integrating advanced calculation techniques and evolutionary algorithms to improve the safety and operational performance of electrical grounding systems. The proposed approach is particularly beneficial for industrial automation and control systems, where effective grounding is necessary to maintain system reliability and prevent downtime. This methodology employs mathematical modeling and computational tools to optimize grid parameters, ensuring compliance with safety standards while reducing operational costs, thus contributing to the overall efficiency of automated systems in industrial environments. The study reports a reduction of up to 66% in the number of vertical rods and 40% in horizontal conductors compared to traditional methods. These results indicate that the proposed methodology can significantly reduce material usage and costs while maintaining electrical safety in accordance with regulatory standards, making it applicable to a wide range of industrial settings, including substations and automated facilities. © 2025 The Authors

关键词： Electric grounding

来源：评论

学校读者我要写书评

暂无评论

A Zero Harmonic Distortion Grid-Connected Grid-Forming Converter for Battery Energy Storage System Applications

A Zero Harmonic Distortion Grid-Connected Grid-Forming Conve...

引用

Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Gabriel V. Ramos Thiago M. Parreiras Fangzhou Zhao Xiongfei Wang Braz de J. C. Filho Graduate Program in Electrical Engineering - Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Department of Electrical Engineering Centro Federal de Educação Tecnológica de Minas Gerais Belo Horizonte Brazil Department of Energy Aalborg University Aalborg Denmark Division of Electric Power and Energy Systems KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

The work proposes the Zero Harmonic Distortion Converter (ZHD) as a grid-connected grid-forming (GFM) converter due to its unique sinusoidal characteristic without capacitive filters, low parts count, and off-the-shelf power circuitry with a simple open-loop control structure that does not contribute to possible control interaction and resonances in grid-connected mode. These characteristic are reached by a harmonic cancellation in a three-winding transformer along with harmonic elimination using the Selective Harmonic Elimination Pulse Width Modulation (SHE PWM). The ZHD GFM converter is a compelling alternative for GFM battery energy storage systems (BESS) in medium-voltage applications.

关键词： Filters Battery energy storage system Medium voltage Pulse width modulation Harmonic analysis Harmonic distortion Voltage control Power harmonic filters Grid forming Voltage-source converters

来源：评论

学校读者我要写书评

暂无评论

Valorization of Spondias bahiensis waste: Ultrasound-assisted extraction, in vitro phenolics bioaccessibility, and effect on the stability of lipid-protein oxidation in pre-cooked goat meatballs

引用

Food Bioscience 2025年 69卷

作者： da Silva Ferreira, Valquíria Cardoso de Santana Neto, Deocleciano Cassiano Fernandes Santos, Miriane Moreira da Silva, Anna Júllia Bezerra de Azevedo Moura, Gleyce Swellen Santos Ribeiro, José Evangelista Lima, Marcos dos Santos Magnani, Marciane Silva, Fábio Anderson Pereira da Department of Management and Agro-industrial Technology Federal University of Paraíba Paraíba Bananeiras Brazil Post-graduate Program in Food Engineering Department of Chemical Engineering Federal University of Paraná Brazil Post-graduate Program in Food Science and Technology Department of Food Engineering Technology Center Federal University of Paraíba Paraíba Joao Pessoa 58051-900 Brazil Department of Food Engineering Technology Center Federal University of Paraíba Paraíba Joao Pessoa 58051-900 Brazil

This study aimed to optimize the ultrasound-assisted extraction of phenolic compounds from the bagasse of umbu-cajá (Spondias bahiensis), a native Brazilian fruit, and to assess their bioaccessibility. Additionally, the influence of different concentrations of the umbu-cajá bagasse (SBB) extract on the oxidative stability of pre-cooked goat meatballs was investigated over a 10-day refrigerated storage period by quantifying lipid oxidation products. A central composite rotational design determined the optimal ethanol concentration, sonication time, and bagasse-solvent ratio. The optimized extract was applied to meatballs at two concentrations: EX2 (meatballs with 7.0 g/100 g of added SBB extract) and EX4 (meatballs with 13.7 g/100 g of added SBB extract). A control treatment (no antioxidant) and BHT-treated meatballs (0.02 g/100 g) were included as references. Meatballs were evaluated by measuring instrumental color, peroxide value, TBARS, carbonyl compounds, and through simulated digestion. Optimal extraction conditions were 55 % ethanol, 20 min of sonication, and a 1:36.8 bagasse-to-solvent ratio (m/v). The extract showed high antioxidant activity and bioaccessibility of catechin (1243.75 %), epicatechin gallate (918.75 %), procyanidins B2 (915.38 %), gallic acid (319.76 %) and procyanidins B1 (136.36 %). SBB extract slowed lipid oxidation until day 10 and protein oxidation until day 5 but did not inhibit color change. The meatballs had low content of oxidative compounds and high antioxidant capacity in the intestinal fraction, indicating that SBB extract can partially or totally replace the synthetic preservative. © 2025

关键词： Antioxidant Goat meat In vitro gastrointestinal digestion Oxidation Phenolic compounds Umbu-cajá

来源：评论

学校读者我要写书评

暂无评论

Can AV crash datasets provide more insight if missing information is supplemented? Employing Generative Adversarial Imputation Networks to Tackle Data Quality Issues

引用

Transportation Research Part C: Emerging Technologies 2025年 176卷

作者： Ding, Hongliang Liu, Zhuo Fu, Hanlong Fu, Xiaowen Chen, Tiantian Zhao, Jinhua Institute of Smart City and Intelligent Transportation Southwest Jiaotong University Sichuan Chengdu611756 China School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu611756 China Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Behavior and Knowledge Engineering Research Centre Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology Korea Republic of United States

The growing prevalence of autonomous vehicles (AVs) offers new opportunities for enhancing traffic efficiency. However, AVs still face significant challenges that impact their safety and effectiveness in preventing accidents. Real-world operational data is therefore essential to identifying the factors contributing to AV crashes. Despite this, the analysis of AV crashes is still hampered by a lack of data, missing information, and underreporting, which negatively impacts its accuracy and comprehensiveness. To address this challenge, a method based on Generative Adversarial Networks (GANs) was used for data imputation, leveraging their advantage in handling heterogeneous data. An evaluation of the performance of our proposed data imputation approach was performed by comparing it with two established methods, namely conventional case deletion and Random Forest (RF) imputation. Synthetic data obtained from these three methods were modelled using the random parameters logit model with heterogeneity in means. Data from the California department of Motor Vehicles (DMV) and the National Highway Traffic Safety Administration (NHTSA) covering 2021–2023 were used. Our results showed that the model based on Generative Adversarial Imputation Networks (GAIN)- processing data outperformed other candidate methods in terms of fitting, predictive accuracy, and factor interpretation. Our results suggest that factors including speed limit, roadway types, head-on crashes, and takeover of ADAS-equipped vehicles are positively associated with serious injury crashes. On the other hand, ADS engagement and crashes with fixed objects exhibit a negative association with serious injury crashes. Additionally, heterogeneous effects of posted speed limits and ADS engagement on AV crash severity were captured to provide a deeper insight into implications. © 2025 Elsevier Ltd

关键词： Highway accidents

来源：评论

学校读者我要写书评

暂无评论

Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

引用

2024 7th International Conference on Healthcare Service management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Model for Anomaly Detection in Reducing Water Losses in the Distribution Network of Peru using Machine Learning Algorithms

Model for Anomaly Detection in Reducing Water Losses in the ...

引用

Software engineering and Computer Science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：