检索结果-内蒙古大学图书馆

18th International conference on Availability, Reliability and Security (ARES)

作者： Da Silva, Mike Puys, Maxime Thevenon, Pierre-Henri Mocanu, Stephane Nkawa, Nelson Univ Grenoble Alpes CEA Leti Grenoble France Univ Grenoble Alpes Lab Informat Grenoble CNRS InriaGrenoble INP Grenoble France

ISBN: (纸本)9798400707728

Industrial control Systems (ICS) are specific systems that combine information technology (IT) and operational technology (OT). Due to their interconnection and remote accessibility, they become a target for cyberattacks. As a result of their complexity and hetero-geneity in terms of devices and communication protocols, specific security controls and risk analysis methods need to be developed. In particular, in order to reduce the effort of deployment of risk analysis on such complex systems, automated methods need to be provided. This paper deals with automation of the risk identification process for ICS using the STRIDE threat modeling framework. We extend the well-known STRIDE modeling tool, namely Microsoft Threat modeling Tool (MTMT), with an incremental template dedi-cated to ICS and provide additional tools to automate the analysis using specific vulnerability extraction from Internet CVE databases.

关键词： MTMT Security Cybersecurity Risk assessment Risk analysis SCADA ICS IT OT

来源：评论

学校读者我要写书评

暂无评论

Research and Application of a Full process control Platform for Power New Energy Engineering Construction Based on AI and Big data 1

Research and Application of a Full Process Control Platform ...

引用

1st IEEE International conference on data Intelligence and Innovative Application, DIIA 2024

作者： Xie, Yixiang Huang, Zhengui Wei, Yuanfei Huang, BaiXue Huang, Chengjia Xia, MeiWen School of Sciences and Engineering Xiangsihu College of Guangxi Minzu University Nanning China Nanning Hongxiang Software Technology Co. Ltd. R&D Department Nanning China Zhongjian Hongtai Communication Engineering Co. Ltd. General Manager Office Nanning China

ISBN: (纸本)9798331504434

Today, with the continuous development of technology, traditional power new energy engineering construction still involves a large number of manual tracking of project progress, manual collection of engineering data, and other traditional management methods. The degree of informatization is low, and there are many loopholes in the construction process, which bring uncertain factors to the progress and quality of the project. This article studies the construction of a full process control platform for power new energy engineering construction based on AI and big data. By studying the use of multimodal big data integration and fusion technology for real-time data collection, analysis, prediction, and decision support during the construction process, utilizing big data technology to efficiently calculate data information at construction nodes, and providing efficiency analysis reports and quality and safety reports for each node throughout the entire construction process. The control platform can monitor construction progress, allocate construction resources, and supervise construction safety in real time. It uses AI prediction models to optimize the allocation of labor, materials, and other resources, reduce waste generated during the construction process, and ensure construction safety. The platform develops a fault diagnosis and warning function for the entire construction process, quickly identifying potential problems through AI algorithms, taking measures in advance, and avoiding construction interruptions. After the system was developed and launched, it significantly improved the construction efficiency and management level of power new energy projects, and reduced implementation costs. This study has important guiding significance for the efficient and safe construction of future new energy projects. © 2024 IEEE.

关键词： AI full process control multimodal big data integration power new energy engineering construction

来源：评论

学校读者我要写书评

暂无评论

data-driven estimation of impedance of inverter-based resources for efficient stability evaluation

引用

ELECTRIC POWER SYSTEMS RESEARCH 2025年 244卷

作者： Mohammed, Nabil Zhou, Weihua Ramasubramanian, Deepak Bahrani, Behrooz Dutta, Sudipta Bello, Mobolaji Monash Univ Clayton Vic Australia Elect Power Res Inst Golden CO USA

Inverter-based resources (IBRs) are key enabling technologies for integrating renewable energy sources and providing ancillary services in modern power systems. However, their dynamic behavior, defined by output impedance models, can pose a threat to power system stability. The primary challenge is that impedance models, typically derived at specific operating points, exhibit limited accuracy under varying conditions. Additionally, the lack of detailed vendor information on commercial IBR control structures complicates the accurate derivation of these models. To address these issues, this paper first investigates the effects of grid parameters and variations in IBR operating points on IBR's impedance model. Afterwards, a data-driven algorithm using Gaussian process regression (GPR) is then proposed to predict impedance models in the dq reference frame, achieving accurate results with a minimal dataset, thus reducing the cost and complexity of data collection for stability evaluation. The proposed approach is validated through case studies that compare predicted impedance models with analytical solutions for various IBR configurations and grid scenarios, including both grid-following and grid-forming inverters. Its superiority over artificial neural network (ANN)- based approaches is demonstrated using the same training dataset. The predicted impedance model is employed to evaluate IBR stability in the frequency domain, with findings validated through time-domain simulations using an electromagnetic transient (EMT) model when connected to grids of varying strengths. A promising application of the proposed GPR-based impedance modeling is its integration into IBR-based power system stability analysis and simulation tools, facilitating the study of emerging low-frequency oscillation phenomena.

关键词： Gaussian process regression Grid-connected inverters Grid impedance Impedance measurement Nyquist Operating point Stability System identification

来源：评论

学校读者我要写书评

暂无评论

ConcVAE: Conceptual Representation Learning

引用

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第4期36卷 7529-7541页

作者： Togo, Ren Nakagawa, Nao Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Fac Informat Sci & Technol Sapporo 0600814 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo 0600814 Japan

Disentangled representation learning aims at obtaining an independent latent representation without supervisory signals. However, the independence of a representation does not guarantee interpretability to match human intuition in the unsupervised settings. In this article, we introduce conceptual representation learning, an unsupervised strategy to learn a representation and its concepts. An antonym pair forms a concept, which determines the semantically meaningful axes in the latent space. Since the connection between signifying words and signified notions is arbitrary in natural languages, the verbalization of data features makes the representation make sense to humans. We thus construct Conceptual VAE (ConcVAE), a variational autoencoder (VAE)-based generative model with an explicit process in which the semantic representation of data is generated via trainable concepts. In visual data, ConcVAE utilizes natural language arbitrariness as an inductive bias of unsupervised learning by using a vision-language pretraining, which can tell an unsupervised model what makes sense to humans. Qualitative and quantitative evaluations show that the conceptual inductive bias in ConcVAE effectively disentangles the latent representation in a sense-making manner without supervision. Code is available at https://***/ganmodokix/concvae.

关键词： Representation learning Task analysis Natural languages Feature extraction Information science data models Computational modeling Deep learning disentanglement interpretable machine learning representation learning vision and language

来源：评论

学校读者我要写书评

暂无评论

Research on PID control method for predictive modeling of oil pipeline infrastructure under industrial internet of things driving

Research on PID control method for predictive modeling of oi...

引用

2024 International conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Liu, Hao Dong, Hongbing Wang, Bo Xu, Yang Cai, Zhongwei Li, Xiang China Kunming China Oil & Gas Pipeline Network Corporation Yunnan Branch 650500 China

ISBN: (纸本)9781510685840

The oil pipeline system faces numerous safety hazards and operational management challenges, such as leaks, blockages, and pressure fluctuations, which may lead to environmental pollution, economic losses, and even casualties. To address these issues, this study proposes a predictive PID control method for oil pipeline infrastructure based on cloud servers and Industrial Internet of Things (IIoT) technology, coupled with advanced data analysis and control algorithms, enabling comprehensive monitoring and management of oil pipeline systems. A risk assessment and control framework based on online model fusion is developed, integrating diverse data sources and optimization model algorithms to achieve intelligent monitoring and control of pipeline systems. Additionally, PID control algorithms are employed for real-time adjustment of flow rates and pressures in the pipeline system to ensure stable operation. Experimental results demonstrate that the proposed risk assessment and control framework effectively identifies anomalies in the pipeline system, while the PID control algorithm enables real-time adjustment of flow rates and pressures, enhancing the safety and stability of the pipeline system. Comparative analysis shows that the proposed control method offers a 28.96% improvement in feedback control effectiveness and can reduce the risk rate by up to 4.2 times through the predictive modeling method. © 2024 SPIE.

关键词： Feedback control

来源：评论

学校读者我要写书评

暂无评论

Research on the Digital Prediction Model of Cigarette Filament Feeding process Based on BP Neural Network 24

Research on the Digital Prediction Model of Cigarette Filame...

引用

3rd International conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Tang, Yaoping Jin, Zhenxun Xi, Lesheng Zhang, Qiang Guo, Miaozhen Guo, Ben China Tobacco Zhejiang Ind Co Lid Hangzhou 310024 Zhejiang Peoples R China

ISBN: (纸本)9798400716959

In order to further achieve the moisture content at the outlet of the feeding process and stabilize the moisture content after cutting, and improve the production line process level, this paper studies a digital prediction model for the cigarette silk feeding process. Based on data and various algorithmic models, a set of process parameter control modeling methods is formed. Establish a digital prediction model for the outlet moisture content of the feeding process, and obtain the minimum error range trained by the Gradient Boosting Decision Tree (GBDT) algorithm. The trend of the predicted value is basically consistent with the actual value. The prediction model for the outlet moisture content of the feeding process can well fit the actual situation. Substitute the test set data for validation and trial operation, with an error range of -0.0906 similar to 0.0942. Based on the prediction model of the Gradient Boosting Decision Tree (GBDT) algorithm, the outlet moisture content of the feeding process has a small error and error range, and the distribution and trend of the predicted values and error values are uniform. The method used in this article for predicting the moisture content at the outlet of the feeding process has good accuracy, stable testing results, and guiding significance. Based on digital prediction, adjust parameters in a timely manner, enhance scientific judgment, reduce complex communication and adjust process data through experience, in order to improve process quality.

关键词： BP neural network Cigarette making silk Number of feeding process prediction model

来源：评论

学校读者我要写书评

暂无评论

Do You Behave Always the Same? A process Mining Approach

Do You Behave Always the Same? A Process Mining Approach

引用

4th International conference on process Mining (ICPM)

作者： Di Federico, Gemma Burattin, Andrea Tech Univ Denmark Lyngby Denmark

ISBN: (纸本)9783031278143;9783031278150

Human behavior could be represented in the form of a process. Existing process modeling notations, however, are not able to faithfully represent these very flexible and unstructured processes. Additional non-process aware perspectives should be considered in the representation. control-flow and data dimensions should be combined to build a robust model which can be used for analysis purposes. The work in this paper proposes a new hybrid model in which these dimensions are combined. An enriched conformance checking approach is described, based on the alignment of imperative and declarative process models, which also supports data dimensions from a statistical viewpoint.

关键词： Behavioral research

来源：评论

学校读者我要写书评

暂无评论

RECIPROFRAME TIMBER GRIDSHELL From CAM data Interface modeling to Operating Industrial Joinery Machine for Scaling up Reusable Timber Structures 29th

RECIPROFRAME TIMBER GRIDSHELL From CAM Data Interface Modeli...

引用

29th International conference of the Association-for-Computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Adelzadeh, Amin Karimian-Aliabadi, Hamed Robeller, Christopher Augsburg Tech Univ Appl Sci Augsburg Germany

ISBN: (纸本)9789887891833

This research extends the work from our previous study on utilizing digital technologies to turn short solid timber elements into framed timber systems designed for the rapid assembly and disassembly of cost-effective, material-efficient, reusable gridshells. In a former paper, we developed an innovative reciprocally-reinforced topology of trivalent polyhedral frames, termed "ReciproFrame", enabled by the development of a CSV file to leverage the precision and speed of multi-axis robotic arms, which was then utilized in the construction of a small-scale, 7.5-meter research demonstrator. Although the multi-objective analysis confirmed the efficiency of the production method in constructing structurally-efficient catenary cross-sections without the need for any steel nodes-a feat not achievable with previous geodesic domes-we realized that the automated construction of larger structures in future timber industry would require an industrial-class production workflow featuring high-performance units equipped with powerful and efficient machining capacities for varied timber processing. As a solution, this paper presents a 24-hour industrial fabrication workflow, enabled by a self-developed data interface plugin that generates XML-based, industry-standard CAM data for the direct instruction of Hundegger K2 machines. It addresses the operational problems and technical challenges related to interoperability between the data interface programming and the operation of industrial joinery machines. Finally, the paper discusses the possible applications and limitations of the production workflow, while presenting the design-to-assembly process of a medium-scale research demonstrator with a maximum span of 15 meters, made of 768 industrially-fabricated Laminated Veneer Lumber (LVL) beams.

关键词： Automated joinery XML-Based CAM data CAMBIUM Hundegger K2 Joinery Machines P-Hex ReciproFrame Laminated Veneer Lumber LVL Reusable Timber Gridshells

来源：评论

学校读者我要写书评

暂无评论

Design of Online Learning Behavior analysis System Based on Big data 2

Design of Online Learning Behavior Analysis System Based on ...

引用

2nd International conference on data Science and Information System, ICDSIS 2024

作者： Gao, Luyun Jinan Preschool Education College Jinan China National University Manila Manila Philippines

ISBN: (纸本)9798350381665

With the rapid development of educational technology in the Internet and digital age, online learning has become increasingly common. As more and more learning activities move online, a wealth of data on learner behaviour is generated which, if effectively collected and analysed, can provide rich insights into understanding the learning process. Therefore, this paper mainly uses big data technology to design an online learning behavior analysis system based on user behavior analysis, content analysis, recommendation algorithm, feedback and optimization modules. This paper focuses on the application of big data in online learning behavior analysis system, and explores three main processes in detail: data collection and preprocessing, user behavior modeling, data analysis and insight extraction. Finally, the results of the simulation experiment are as follows: the user login time of the optimized online learning behavior analysis system is increased by 1.25 h, and the prediction accuracy of the system learning effect is increased by 9.5%. The design of the system not only improves the efficiency and effect of online learning, but also provides rich data support for educational research, which is helpful to understand the complex dynamics of online learning environment. © 2024 IEEE.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Detecting the Impact of Changes in Platelet Demand following the Implementation of PRT Platelets in Canada 19

Detecting the Impact of Changes in Platelet Demand following...

引用

19th International conference on Software Technologies, ICSOFT 2024

作者： Smith, Linden Blake, John Department of Industrial Engineering Dalhousie University HalifaxNS Canada Centre of Innovation Canadian Blood Services OttawaON Canada

ISBN: (纸本)9789897587061

This paper describes tools to detect and estimate demand shifts for platelet products, through inventory monitoring, following the implementation of pathogen reduction (PR) technology at a pilot site in the Canadian Blood Services (CBS) network. A Statistical process control (SPC) framework was constructed to detect change points in inventory signals. A discrete event simulation is used to generate synthetic data for the inventory monitoring process. Both traditional forecasting and machine learning techniques were used to increase sensitivity to change detection and reduce time to detection by supplying the SPC algorithm with projected data. Experiments were run on data representative of changes in demand experienced at the pilot production site. It was found that larger shifts in demand had a higher probability of detection and a lower time to detection. Changes in demand, with an effect on the system larger than 10%, were almost always detected. Detection time varies greatly depending on the level of the demand shift. Typically, shifts greater than 25% have an average detection time of just over a week while shifts of less than 5% have an average detection time of up to 25 weeks. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Statistical process control

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：