检索结果-内蒙古大学图书馆

International Conference on Electrical, Communication, and computer engineering (ICECCE)

作者： Muhammad Ahsan Muhammad Osama Aziz Syed Ahmad Saleem Bokhari Muhammad Haris Mansoor Iqbal Kaleem Ullah Department of Computer Science University of Alabama at Birmingham Birmingham AL USA Buildings Energy Research Center University of Engineering and Technology Peshawar Pakistan Harvard Extension School Harvard University Cambridge MA USA Institute for Insight - J Mack Robinson College of Business Georgia State University Georgia USA Department of Computer Science University of Wah Wah Cantt Pakistan Department of Electrical Energy System CASE UET Peshawar Peshawar Pakistan

ISBN: (数字)9798331529437

ISBN: (纸本)9798331529444

In modern power systems, Short-Term Load Forecasting (STLF) is critical in ensuring the dynamic allocation of operational reserves and accurate prediction of electrical load demand. STLF is pertinent to grid operators to ensure system stability, allocate generation resources efficiently, and provide adequate reserves to deal with unexpected fluctuations. Considering the pivotal role of STLF, this paper introduces a hybrid deep learning model for short-term load forecasting, combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The model features two parallel 32-filter convolutional layers (3×3 kernel, ReLU) for spatial feature extraction, followed by two series-connected GRU layers (42 units) to capture temporal dependencies. A final dense layer with ReLU activation generates the load forecast. This architecture effectively leverages CNNs for local pattern recognition and GRUs for modeling temporal sequences, offering a robust solution for accurate short-term load forecasting. Therefore, the efficacy of the CNN-GRU model is tested and validated using an extensive dataset sourced from the National Transmission & Dispatch Company (NTDC), Pakistan. The performance analysis reveals that hybrid CNN-GRU models achieve a substantial reduction in Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), with values of 400.33, 2.89, 580.95, for single-step forecasts, respectively, and 1031.11, 5.31, and 733.19, for 24-hour forecasts. Furthermore, the simulation results demonstrate notable enhancements in load forecasting accuracy and robustness across various forecast horizons.

关键词： Accuracy Load forecasting Predictive models Power system stability Feature extraction Stability analysis Convolutional neural networks Kernel Forecasting Load modeling

来源：评论

学校读者我要写书评

暂无评论

The light quantum mechanism of PCR efficiency oscillation with gold nanoparticle concentration

arXiv

引用

arXiv 2024年

作者： Fang, Huan-Huan Chen, Yong-Cong Liu, Ze-Fei Zhu, Xiao-Mei Ao, Ping Shanghai Center for Quantitative Life Sciences and Physics Department Shanghai University Shanghai200444 China Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Biomedical Engineering Sichuan University Chengdu610065 China

The widespread application of nanomaterials in polymerase chain reaction (PCR) technology has opened new avenues for improving detection methods in the biomedical field. Recent experiments (Chem. Eur. J. 2023, e202203513) have revealed oscillatory behavior between PCR efficiency and the concentration of gold nanoparticles in the pM range, potentially linked to the long-range Coulomb interactions among charged colloidal particles and the quantum size effect of nanoparticle electronic states. Through Monte Carlo simulation, we discovered that the radial distribution function of gold nanoparticles in solution gradually exhibits peak characteristics with increasing charge, triggering coherent photon behavior in Rayleigh scattering within the solution, thereby influencing the efficiency of reusing released photons in the PCR chain reaction. The study demonstrates that the oscillation period aligns with the wavelength of downstream reaction photons, while their energy matches the width of energy levels near the Fermi level of gold nanoparticles. The latter can absorb and store electron states internally, promoting upstream PCR reactions through subsequent re-release, and compensating for energy deficiencies through the Boltzmann distribution of electrons. This work is poised to advance the application of PCR-specific precise detection methods in the field of quantum biotechnology. © 2024, CC BY.

关键词： Polymerase chain reaction

来源：评论

学校读者我要写书评

暂无评论

A Low-Complexity Coding Scheme for NOMA

TechRxiv

引用

TechRxiv 2022年

作者： Abd-Alaziz, Wael Jebur, Bilal A. Fakhrey, Harih Mei, Zhen Rabie, Khaled The Department of Computer Information System College of computer Science and Information Technology University of Sumer Qalat Sukar64003 Iraq The Department of Computer and Information Engineering Ninevah University Mosul Iraq Kwarizmy School of Engineering Baghdad University Baghdad Iraq The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Engineering Manchester Metropolitan University Manchester United Kingdom

This work focuses on exploiting the constructive interference among different users’ data waveforms to introduce new coding and decoding techniques, which are specifically designed for non-orthogonal multiple access (NOMA) systems. In this paper, a structured coding scheme is devised. In essence, the proposed technique focuses on finding a relationship between the sent users’ data waveforms and then uses this relationship in the decoding process at the receiving destination. It is worth pointing out that the proposed coding and decoding techniques exhibit better performance and reduced the complexity compared with the conventional uncoded NOMA. The complexity order evaluation shows that the proposed scheme attains a reduction in the required number of the floating point operations (FLOPs) of 5N and 6N at the second and third users, respectively, compared with that of the uncoded NOMA. Moreover, we have derived a closed-form expression for the bit-error rate (BER), which is verified using the Monte Carlo simulation. To demonstrate the practicality of the proposed system, the obtained results are compared with those of the uncoded and convolutional coding NOMA systems. Finally, the performance of the proposed system outperformed conventional systems by an average of 5 dB in the case of two users and an average of 15 dB in the case of three users in the same work environment. © 2022-11-28, CC BY.

关键词： Bit error rate

来源：评论

学校读者我要写书评

暂无评论

Predictive Resilience Assessment of Road Networks Based on Dynamic Multi-Granularity Graph Neural Network

SSRN

引用

SSRN 2024年

作者： Zang, Di Ding, Yongjie Zhao, Jiayi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Educaiton Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai China

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: 1) Simulation methods for modeling rainfall impacts lack realism;2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial-temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios. © 2024, The Authors. All rights reserved.

关键词： Roads and streets

来源：评论

学校读者我要写书评

暂无评论

Jigsaw-based Benchmarking for Learning Robotic Manipulation

arXiv

引用

arXiv 2023年

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong Department of Computer Science The University of Hong Kong Hong Kong

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence. © 2023, CC BY.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

AI-Powered Predictive Maintenance for Electric Vehicle Fleets

AI-Powered Predictive Maintenance for Electric Vehicle Fleet...

引用

Intelligent Technologies (ACOIT), Asian Conference on

作者： Mamta Bahheti Vinay Kellengere Shankarnarayan Senthilkumar Durairajan Shravan Chandak T. Gurunathan P. Chandrasekar Department of Computer science Hislop College RTM Nagpur University Nagpur Maharashtra India Department of Business Analytics R V Institute of Management Bengaluru Karnataka India System Engineering Manager Capgemini Engineering Bengaluru India Symbiosis Centre for Management Studies Pune Symbiosis International (Deemed University) Pune India Department of EEE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

For operational effectiveness and cost savings, electric vehicle (EV) fleets must receive proper maintenance. However, reactive repairs and scheduled inspections are frequently the mainstays of traditional maintenance methods, which raises costs and results in more downtime. The predictive maintenance solution for electric vehicle fleets that the research proposes uses artificial intelligence (AI) and real-time data analysis to predict possible faults. The proposed system gathers and analyzes data from different car components to predict maintenance needs ahead of time by integrating IoT devices and machine learning techniques. Comparing the proposed system with existing systems, the study shows that it performs better in terms of accuracy (9 0%), lower rates of false positives (10) and negatives (5), and improved performance metrics (accuracy: 90%, precision: 88%, recall: 85%, F1-score: 86%). The proposed system presents a viable way to improve fleet management in electric vehicle operations, ensuring higher uptime, sustainability, and dependability. Higher cross-validation findings (average accuracy: 88%, average F1-score: 85% and improved maintenance effectiveness further support the claim.

关键词： Training Costs Accuracy Scalability Transforms Electric vehicles Real-time systems Maintenance Sustainable development Predictive maintenance

来源：评论

学校读者我要写书评

暂无评论

Jigsaw-Based Benchmarking for Learning Robotic Manipulation

Jigsaw-Based Benchmarking for Learning Robotic Manipulation

引用

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Xiaobo Liu Fang Wan Sheng Ge Haokun Wang Haoran Sun Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen Guangdong China School of Design Southern University of Science and Technology Shenzhen Guangdong China Robotics & Autonomous Systems Thrust System Hub Hong Kong University of Science and Technology Hong Kong China Department of Computer Science The University of Hong Kong Hong Kong China

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring Text-Guided Single Image Editing for Remote Sensing Images

arXiv

引用

arXiv 2024年

作者： Han, Fangzhou Si, Lingyu Dong, Hongwei Zhang, Lamei Chen, Hao Du, Bo The Department of Information Engineering Harbin Institute of Technology Harbin China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. During the generation stage, consistency in content and details between the original and edited images must be maintained, while in the editing stage, controllability and accuracy of the edits should be ensured. For natural images, these challenges can be tackled by training generative backbones on large-scale benchmark datasets and using text guidance based on vision-language models (VLMs). However, these previously effective approaches become less viable for RSIs due to two reasons: First, existing generative RSI benchmark datasets do not fully capture the diversity of remote sensing scenarios, particularly in terms of variations in sensors, object types, and resolutions. Consequently, the generalization capacity of the trained backbone model is often inadequate for universal editing tasks on RSIs. Second, the large spatial resolution of RSIs exacerbates the problem in VLMs where a single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics when using text to guide RSI editing. To solve above problems, this paper proposes a text-guided RSI editing method that is controllable but stable, and can be trained using only a single image. It adopts a multi-scale training approach to preserve consistency without the need for training on extensive benchmark datasets, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability in the text-guided editing process. High-quality RSI editing results can be achieved by first applying multi-scale training to the target image, followed by text-guided fine-tuning using RSI pre-trained VLMs and PE. Experi

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

来源：评论

学校读者我要写书评

暂无评论

Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water systems in Smart Grids

引用

engineering 2021年第8期7卷 1101-1114页

作者： Junjie Hu Huayanran Zhou Yihong Zhou Haijing Zhang Lars Nordströmd Guangya Yang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University) Beijing 102206China Division of Electric Power and Energy Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholm 10044Sweden Center for Electric Power and Energy Department of Electrical Engineering Technical University of DenmarkKgs Lyngby 2800Denmark

With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency *** flexibility of demand response(DR)resources has become a valuable solution to this ***,existing research indicates that problems on flexibility prediction of DR resources have not been *** study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate *** prediction can generate the size and maintenance time of the aggregated *** accuracy of the flexibility prediction results was verified through simulations of case *** simulation results show that under different maintenance times,the size of the flexibility *** proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

关键词： Load flexibility Electric vehicles Domestic hot water system Temporal convolution network-combined transformer Deep learning

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：