检索结果-内蒙古大学图书馆

Novel methodology for developing a safety standard based on clustering of experts' assessments of safety requirements

引用

SAFETY SCIENCE 2021年 140卷 105292-105292页

作者： Aly, Shady Tyrychtr, Jan Kvasnicka, Roman Vrana, Ivan Helwan Univ Fac Engn Helwan Cairo Egypt Czech Univ Life Sci Prague Fac Econ & Management Dept Informat Engn Prague Czech Republic Czech Univ Life Sci Prague Fac Econ & Management Dept Syst Engn Prague Czech Republic

The practice of developing product or system safety standards is currently conducted with high level of subjectivity, which affects the reliability of development outcomes. The decision to adopt specific safety requirements into the standard is usually based on the knowledge, intuition and experience of the technical expert involved in developing the body of the safety standard made up of those requirements or specifications. This is usually carried out without analyzing risk attributes or factors addressed by each safety requirement constituting a safety standard. This can lead to inaccuracy and lack of reliability due to either under-estimation or overestimation of risks involved. This research presents unprecedented, novel, structured and objective methodology, that aims to improve the practice of developing safety standards. In order to accomplish the above end, the proposed methodology acts to prioritize the system' safety requirements of each system's design elements based on a set of commonly identified risk factors they address. We have investigated the currently existing risk prioritization and multi-criterion decision making techniques, and found that machine learning data clustering approaches can be adequate especially in case of relatively large number of assessed decision alternatives (i.e., the assessed safety requirements). We have elaborated the merits of the proposed clustering-based methodology over existing prominent ones. The proposed methodology starts with analyzing product or system safety to identify common risk factors. A numerical scale is used to enable objective quantification of risk factors' values for each safety requirement by multiple experts. The obtained experts' numerical assessments of each safety requirement are then averaged to represent the raw data set containing risk profiles of each safety requirement assessed. This risk factors data set is then used by fuzzy c-means clustering algorithm, to organize them into groups of di

关键词： Safety standards development Safety requirements assessment and prioritization Risk assessment clustering algorithm Fuzzy c-means

来源：评论

学校读者我要写书评

暂无评论

Development of decision support tool for clustering urban regional risk based on R-ArcGIS Bridge

引用

APPLIED SOFT COMPUTING 2021年 110卷 107621-107621页

作者： Zhao, Ming Liu, Xiang Sun, Zengfeng Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Nutrichem Co Ltd Beijing 102206 Peoples R China China Acad Urban Planning & Design Minist Housing & Urban Rural Dev Beijing 100044 Peoples R China

Cities with dense population are susceptible and vulnerable to natural disasters and man-made accidents, making regional risk analysis thus significant for urban public safety. Although risk maps are able to present the broad characteristics of regional risk spatial distribution, quantitative identification of risk clusters in urban areas is still a challenge. In this context, we design and develop a user-friendly customized ArcGIS add-in in this study. In particular, a novel technical routing is proposed for customizing ArcGIS add-in tool based on R-ArcGIS Bridge. The design, architecture and implementation of the tool as well as its core functional modules are introduced. Moreover, R scripts are developed to implement K-means algorithm and Gap statistics validity index for clustering regional risk, respectively. Based on a case study of a typical urban district in China, we introduce the add-in's functionalities as well as its related decision-making procedures. Results successfully obtained provide evidence that the tool is able to partition regional risk clusters and estimate the optimal number of clusters in urban areas. The simple loosely-coupled architecture also suggests a promising future for embedding some novel geospatial clustering algorithms to extend its capabilities in the next step. This work offers new insights on promoting future urban regional risk management with the use of GIS and clustering algorithms, and it provides a valuable demonstration on extending and enhancing the analysis capacity of GIS by harnessing its power with the statistical analysis capability of R packages through R-ArcGIS Bridge. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Regional risk analysis clustering algorithm Add-in R-ArcGIS Bridge Urban public safety

来源：评论

学校读者我要写书评

暂无评论

INTUITIONISTIC FUZZY clustering algorithm BASED ON BOOLE MATRIX AND ASSOCIATION MEASURE

引用

INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2013年第1期12卷 95-118页

作者： Zhao, Hua Xu, Zeshui Wang, Zhong PLA Univ Sci & Technol Coll Sci Nanjing 210007 Jiangsu Peoples R China

In this paper we develop a measure for calculating the association coefficient between Atanassov's intuitionistic fuzzy sets (A-IFSs), and show its desirable axiomatic properties. Then we present an algorithm for clustering A-IFSs. The algorithm first utilizes the association coefficient of A-IFSs to construct an association matrix, and then calculates the lambda-cutting matrix of the association matrix no matter whether it is an equivalent matrix or not. After that, the lambda-cutting matrix is used to cluster A-IFSs (if the lambda-cutting matrix is just only a similarity matrix, then we can easily transform it into an equivalent matrix). Three examples are used to show the effectiveness of the association coefficient and the algorithm for clustering A-IFSs. Furthermore, we extend the algorithm to cluster interval-valued intuitionistic fuzzy sets (IVIFSs), and finally, we use another numerical example to illustrate the latter algorithm.

关键词： Atanassov's intuitionistic fuzzy set interval-valued intuitionistic fuzzy set association coefficients association matrix lambda-cutting matrix clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Energy and task completion time minimization algorithm for UAVs-empowered MEC SYSTEM

引用

SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2022年 35卷

作者： Asim, Muhammad Mashwani, Wali Khan Abd El-Latif, Ahmed A. Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Kohat Univ Sci & Technol Inst Numer Sci Kohat Pakistan Prince Sultan Univ Coll Comp & Informat Sci EIAS Data Sci Lab Riyadh 11586 Saudi Arabia Menoufia Univ Fac Sci Dept Math & Comp Sci Al Minufiyah 32511 Egypt

This paper presents an energy and task completion time minimization scheme for the unmanned aerial vehicles (UAVs)-empowered mobile edge computing (MEC) system, where several UAVs are deployed to serve large-scale users' equipment (UEs). The aim is to minimize the weighted sum of energy consumption and task completion time of the system by planning the trajectories of UAVs. The problem is NP-hard, non-convex, non-linear, and mixed-decision variables. Therefore, it is very challenging to be solved by conventional optimization techniques. To handle this problem, this paper proposes an energy and task completion time minimization algorithm (ETCTMA) that solves the above problem in three steps. In the first step, the deployment updation of stop points (SPs) is handled by adopting a differential evolution algorithm with a variable population size. Then, in the second step, the association between SPs and UAVs is determined. Specifically, a clustering algorithm is proposed to associate SPs with UAVs. Finally, in the third step, a low-complexity tabu search algorithm is adopted to construct the trajectories of all UAVs. The performance of the proposed ETCTMA is tested on seven instances with up to 700 UEs. The results reveal that our proposed algorithm ETCTMA outperforms other variants in terms of minimizing the weighted sum of energy consumption and task completion time of the system.

关键词： Mobile edge computing Unmanned aerial vehicle Differential evolution clustering algorithm Tabu search algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel method for quantitative fault diagnosis of photovoltaic systems based on data-driven

引用

ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期210卷

作者： Guo, Hui Hu, Shan Wang, Fei Zhang, Lijun Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China Univ Lisbon Inst Super Tecn Lisbon Portugal

Photovoltaic (PV) systems are usually exposed outdoors for a long time, easily lead to various faults. Apart from fault location and classification widely studied, accurate and quantitative fault severity diagnosis is vital for the decision making, given the cost of eliminating faults. However, there are scarce investigations into this problem. Therefore, a novel quantitative fault diagnosis method is proposed based on the deviation between the predicted power of fault states and the actual power of normal states. (1) Since the main characteristic of PV faults is the decline of power generation, the prediction model of PV power generation under the normal state is established via clustering algorithm and long short-term memory (LSTM) neural network, which has better processing ability for different weather conditions and long time-series data. (2) In the actual condition of limited fault data, the prediction model is innovatively used to quantitative fault diagnosis through transfer learning, which will provide reasonable and effective guidance for the intelligent operation and maintenance of PV system when the severity of predicted fault reaches the set threshold. Finally, the effectiveness and accuracy of the proposed quantitative fault diagnosis method are verified by experimental results.

关键词： Photovoltaic (PV) system Quantitative fault diagnosis Data-driven clustering algorithm Long short-term memory (LSTM) neural network Transfer learning

来源：评论

学校读者我要写书评

暂无评论

Collaborative Evaluation Method of Multiple Team Membership: Case Study of Engineering Design Company

引用

JOURNAL OF MANAGEMENT IN ENGINEERING 2022年第6期38卷 05022014-05022014页

作者： Jiang, Qi Qiang, Maoshan Lin, Chen Tsinghua Univ State Key Lab Hydrosci & Engn Inst Project Management & Construct Technol Beijing 100084 Peoples R China Beijing Inst Water Dept Technol & Qual Management Landscape Architecture Beijing 100048 Peoples R China

Team members' project collaborative process evaluation is the key link to improve the management effectiveness of an organization. However, there is a lack of evaluation methods from the perspective of multiple team membership (MTM). Based on the data in the project management system (PMS) of engineering design enterprises, this article studies the project participation evaluation method of team members from the perspective of MTM. Using a clustering algorithm, this article developed and verified the participation evaluation index of single projects and multiple projects, a classification and statistics method, and the centrality evaluation method of team members. The research revealed the distribution law of MTM quantity and the influence of team members' knowledge and ability on it. We found that this index can evaluate objectively team members' project participation, which can divided into three levels: high, medium, and low. Collaborative activities that contribute significantly to the project account for 20% of the total. The reasonable range for team members to invest in high participation projects is from 2 to 4. For medium participation projects, the number is from 6 to 8. For low participation projects, the number is from 18 to 25, or even unlimited. This study found four positive and four negative effects of personal characteristics on work efficiency. When individuals have these characteristics, team members can better play to their abilities under the project management mechanism, which provides a reference for enterprises to identify and cultivate team members.

关键词： Multiple team membership (MTM) Project participation Collaborative evaluation method clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Electricity-consuming forecasting by using a self-tuned ANN-based adaptable predictor

引用

ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期210卷

作者： Baba, Abdullatif Univ Turkish Aeronaut Assoc Comp Engn Dept Bahcekapi Quarter Okul St 11 TR-06790 Ankara Turkey

Accurate forecasting of power consumption is an essential and modern approach for planning smart infrastructural projects that are required to overcome the future challenges of power markets. In this context, a new design of a self-tuned ANN-based adaptable predictor is presented in this paper. At first, the main design of the original adaptable predictor is clarified including its new architecture that partially relies on the Hebbian law, its training process is also explained as well as the dataset which is used for training. Then, all the details of the self-tuning-based technique are explained with all the relevant results that prove its high capability to produce more accurate forecasting outcomes. The impact of our suggested approach is introduced by explaining two different practical examples that employ the K-means clustering algorithm, and the genetic algorithm when it is used to optimize the operating/outage schedule for a group of local solar units, respectively.

关键词： Self-tuning Adaptable predictor ANN model Smart grids clustering algorithm Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

A hybrid optimization algorithm-based feature selection for thyroid disease classifier with rough type-2 fuzzy support vector machine

引用

EXPERT SYSTEMS 2022年第1期39卷 e12811-e12811页

作者： Sureshkumar, Vidhushavarshini Balasubramaniam, Sathiyabhama Ravi, Vinayakumar Arunachalam, Ajay Sona Coll Technol Comp Sci & Engn Salem 636005 India Prince Mohammad Bin Fahd Univ Ctr Artificial Intelligence Khobar Saudi Arabia Orebro Univ Ctr Appl Autonomous Sensor Syst Orebro Sweden

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population;thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed-kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

关键词： classification clinical trial clustering algorithm feature selection fuzzy sets hormone machine learning optimization support vector machines thyroid disease

来源：评论

学校读者我要写书评

暂无评论

On representative day selection for capacity expansion planning of power under extreme conditions

引用

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 137卷 107697-107697页

作者： Li, Can Conejo, Antonio J. Siirola, John D. Grossmann, Ignacio E. Carnegie Mellon Univ Dept Chem Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Ohio State Univ Dept Integrated Syst Engn 1971 Neil Ave Columbus OH 43210 USA Ohio State Univ Dept Elect & Comp Engn 2015 Neil Ave Columbus OH 43210 USA Sandia Natl Labs Ctr Res Comp PO 5800 Albuquerque NM 87185 USA

Capacity expansion planning (CEP) of power systems determines the optimal future generation mix and/or transmission lines. Due to the increasing penetration of renewables, CEP has to capture the hourly variations of renewable generator outputs and load demand. Since CEP problems typically involve planning horizons of several years, solving the fullspace models where the operating decisions corresponding to all the days is intractable. Therefore, some "representative days"are selected as a surrogate to the fullspace model. We present an input-based and a cost-based approach in combination with the k-means and the k-medoids clustering algorithms for representative day selection. The mathematical properties of the proposed algorithms are analyzed, including an approach to calculate the "optimality gap"of the investment decisions obtained from the representative day model to the fullspace model, and the relationship between the clustering error and the optimality gap. To capture the extreme operating conditions, two novel approaches, i.e., a "load shedding cost'' approach and a "highest cost'' approach, are proposed to identify the "extreme days". We conclude with a case study based on the Electric Reliability Council of Texas (ERCOT) region, which compares the different approaches and the effects of adding the extreme days.

关键词： Power systems Capacity expansion planning Renewable units Representative day selection clustering algorithm Extreme operating conditions

来源：评论

学校读者我要写书评

暂无评论

A New B-Scan Interpretation Model in Complex Environments

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷

作者： Dai, Qianqi Li, Yibing Jiang, Tao Sun, Qian Harbin Engn Univ Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

In the scenario where the echo hyperbolas of different targets intersect or the opening structure of the hyperbola is destroyed, the current B-scan image interpretation model has the problems of missing detection and unable to make full use of the hyperbolic information. The hyperbola needs to be identified manually or using neural network methods. The recognition accuracy is difficult to guarantee. To solve the above problem, a new ground-penetrating radar (GPR) B-scan interpretation model based on the hyperbolic trend is proposed in this article. The model consists of four parts, including preprocessing method, hyperbolic trend clustering algorithm (HTCA), restricted algebraic distance fitting and cluster center (RADFCC) algorithm, and hierarchical filtered back-projection (HFBP). First, the GPR B-scan image is converted into a binarized image based on the ratio of the main lobe and sidelobe of the echo. Second, the points in the binarized image are classified into hyperbolic column segment clusters based on the hyperbolic trend, and further denoised according to the main lobe width of the Ricker wave. Third, by analyzing the characteristics of the GPR in the ground coupling mode, the restricted hyperbola fitting algorithm is used to fit the classified hyperbola column segment clusters to speed up the convergence of the fit and avoid the result from falling into a local optimum. Fourth, the target's location is preliminarily estimated using the parameters of the hyperbola and the cluster center is established through the location and radius of the target to obtain the accurate estimation of the target and the dielectric constant of the underground. Finally, an imaging algorithm is applied to invert the subsurface image based on the dielectric constant of the subsurface medium. The effectiveness of the proposed model has been demonstrated on both the simulated and real datasets.

关键词： clustering algorithms Fitting Market research Classification algorithms Image edge detection Pipelines Information filters Buried object detection clustering algorithm data processing ground-penetrating radar (GPR) image processing

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：