发布求助
文献互助 智能选刊 最新文献

2008 International Conference on Risk Management & Engineering Management最新文献

筛选
英文 中文
The Realized Volatilities Research on China A-Stock Returns 中国a股收益的已实现波动率研究
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.26
J. Chen, Handong Li
{"title":"The Realized Volatilities Research on China A-Stock Returns","authors":"J. Chen, Handong Li","doi":"10.1109/ICRMEM.2008.26","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.26","url":null,"abstract":"The theory of quadratic variation suggests that, realized volatility is an unbiased and highly efficient estimator of return volatility under suitable conditions. In this article, we compare the realized logarithmic volatilities models VAR-RV and AR-RV computed from high-frequency intra-period data with the traditional daily return evaluation models VAR-R and Daily-GARCH in China A-stock market. The result suggests that the realized volatility do a better and more efficient measure in evaluating and forecasting the volatility characteristic for China stock market.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132757922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Construction Projects Decision Based on Life Cycle Engineering Model 基于生命周期工程模型的建设项目决策研究
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.24
Ting Wang, Zhiwu Hua
{"title":"Research on Construction Projects Decision Based on Life Cycle Engineering Model","authors":"Ting Wang, Zhiwu Hua","doi":"10.1109/ICRMEM.2008.24","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.24","url":null,"abstract":"Construction projects are one-off endeavors with many unique features such as long period, complicated processes, abominable environment, financial intensity and dynamic organization structures and such organizational and technological complexity generates enormous risks. And environmental and economical dimensions throughout the life cycle of the construction project, so the construction project decision is a difficult and important problem. In this paper, a life cycle engineering (LCE) model is proposed to support construction project decision. The LCE model proposed compares a set of candidate construction projects and, obtained a single indicator for each construction project and for each dimension of evaluation (technical, economic, and environmental), allowing the direct incorporation of the technical, economical and environmental performances into a multi-attribute decision making (MADM) problem. Then, we use the LMS neural network method to solve the MADM problem and get the best construction project from the candidate construction projects.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130423191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Study on Intelligent Optimization Model Based on Grey Relational Grade in Long–Medium Term Power Load Rolling Forecasting 基于灰色关联度的中长期电力负荷滚动预测智能优化模型研究
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.32
D. Niu, Jian-rong Jia, Jia-liang Lv, Yuan Zhang
{"title":"Study on Intelligent Optimization Model Based on Grey Relational Grade in Long–Medium Term Power Load Rolling Forecasting","authors":"D. Niu, Jian-rong Jia, Jia-liang Lv, Yuan Zhang","doi":"10.1109/ICRMEM.2008.32","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.32","url":null,"abstract":"According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed that the method in this paper is superior to conventional method, so it is worth to be extended and applied.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"617 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131473761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
The Risk Allocation Method Based on Fuzzy Integrated Evaluation of Construction Projects 基于模糊综合评价的建设项目风险分配方法
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.28
Yun-li Gao, Lei Jiang
{"title":"The Risk Allocation Method Based on Fuzzy Integrated Evaluation of Construction Projects","authors":"Yun-li Gao, Lei Jiang","doi":"10.1109/ICRMEM.2008.28","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.28","url":null,"abstract":"Construction project risk allocation is one of the problems which all the project participants concerning about. On the basis of studying the principles of project risk allocation, this paper establish the evaluating indicator that evaluates the risk carrying capacity of all the project participants. The evaluating factor set in the risk allocation is determined, the integrated risk allocation coefficients of all project participants are calculated in the method of fuzzy integrated evaluation, and the method of the risk loss allocation of each project participant is proposed. This method clears the risk responsibility of each project participant, and points out a new train of thought in the field of quantitative analysis research of project risk allocation.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124320075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 13
Research of Electric Power Industry's Production Logistics Model Based on Hybrid Chaos Immune Evolutionary Optimization Algorithm 基于混合混沌免疫进化优化算法的电力行业生产物流模型研究
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.107
Xing Mian
{"title":"Research of Electric Power Industry's Production Logistics Model Based on Hybrid Chaos Immune Evolutionary Optimization Algorithm","authors":"Xing Mian","doi":"10.1109/ICRMEM.2008.107","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.107","url":null,"abstract":"Inventory control is an important aspect of production logistics management in power system. According to the characteristic of raw material purchase and stock, the paper puts forward an optimal inventory model to minimize the cost. A novel hybrid chaos immune evolutionary optimization algorithm (HCIEOA) of solving the minimal purchasing cost problem is presented. This algorithm integrates space-searching advantages of the chaos optimization algorithm (COA) and immune evolutionary algorithm (IEA). It uses the ergodic property of the chaos system to overcome redundancies, and uses the chaos initial sensitivity to enlarge the searching space. Thus, the diversity of population is retained, the local optimization is avoided, and the rapidity of global optimization is improved. Then, this model is applied to the process of searching the optimization in the purchase and storage model. At last, the example shows that the HCIEOA is effective and reliable.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"15 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115225917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Study on Inventory Early-Warning in Supply Chains Based on Rough Sets and BP Neural Network 基于粗糙集和BP神经网络的供应链库存预警研究
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.118
J. Hua, Ruan Jun-hu
{"title":"Study on Inventory Early-Warning in Supply Chains Based on Rough Sets and BP Neural Network","authors":"J. Hua, Ruan Jun-hu","doi":"10.1109/ICRMEM.2008.118","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.118","url":null,"abstract":"The paper combines rough sets and ANN to analyze inventory early-warning in supply chains. The introduction of Rough sets cuts down the input dimensions of ANN, and the ANN algorithm is improved by adding the momentum factor mc and applying adaptive learning rate. Lastly, according to the inventory data of a manufacturing enterprise in Handan City, the paper proves the validity of the proposed model.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114921784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Evaluation of Competitiveness of Power Plants Based on Optimized SVM Using GA and AIS 基于遗传算法和AIS优化支持向量机的电厂竞争力评价
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.124
Wei Sun, Jie Zhang
{"title":"Evaluation of Competitiveness of Power Plants Based on Optimized SVM Using GA and AIS","authors":"Wei Sun, Jie Zhang","doi":"10.1109/ICRMEM.2008.124","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.124","url":null,"abstract":"With the development of electricity market reformation in China, it is especially important to evaluate the competition competence of power generating enterprises. Based on the characteristics of their, this paper bring forwards an index system to evaluate the competition competence of power generating enterprises. SVMs are widely used in load forecasting and bioinformatics systems. Conventional methods are usually used in the parameter estimation process of SVMs. However, these methods can yield to local optimum parameter values. In this work, we use artificial techniques such as Artificial Immune Systems (AIS) and Genetic Algorithms (GA) to estimate SVM parameters. These techniques are global search optimization techniques inspired from biological systems. Also, the hybrid between genetic algorithms and artificial immune system was used to optimize SVM parameters to evaluate the competitivity of power plants.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126781050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
The Analysis of China Regional Power Demand Cycle Existence 中国区域电力需求周期存在分析
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.16
Shuxia Yang
{"title":"The Analysis of China Regional Power Demand Cycle Existence","authors":"Shuxia Yang","doi":"10.1109/ICRMEM.2008.16","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.16","url":null,"abstract":"Analysis of regional power demand cycle is the foundation of researching the national power demand cycle. Firstly the growth rate data of power demand in China from 1994 to 2005 is dealt with the H-P filter, and the periodicity factors of the nation and provinces power demand are obtained. Secondly the periodicity factors of the provinces power demand are treated with cluster analysis, and the problem whether the regional power demand cycle exist in China is discussed. Subsequently through studying the question which provinces mainly affect the national power demand cycle, the problem that the regional power demand cycle influences the national power demand cycle is analyzed. The result show that regional power demand cycle exists in China, and it plays an important role in effecting the national power demand cycle.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123357396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Combining KPCA with Support Vector Regression Machine for Short-Term Electricity Load Forecasting 结合KPCA和支持向量回归机的短期电力负荷预测
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.84
Cai-qing Zhang, Pan Lu, Zejian Liu
{"title":"Combining KPCA with Support Vector Regression Machine for Short-Term Electricity Load Forecasting","authors":"Cai-qing Zhang, Pan Lu, Zejian Liu","doi":"10.1109/ICRMEM.2008.84","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.84","url":null,"abstract":"Short-term electricity load forecasting is important both from the technological and the economical point of view, but it is also a difficult work because the accuracy of forecasting is influenced by many unpredicted factors whose relationships are commonly complex, implicit and nonlinear. By studying the methods proposed by other scholars, a mew method, KPCA (kernel principal component analysis) -SVRM (support vector regression machine) is proposed by this paper. The first step of this method is to apply KPCA to SVRM for feature extraction. KPCA first maps the original inputs into a high dimensional feature space using the kernel method and then calculates PCA in the high dimensional feature space. These new features are then used as the inputs of SVRM to solve the load forecasting problem. By learning and training, we use the data of this subset to get the solution and find interrelationship of input and output by the SVRM. Practical examples are cited in this paper to illustrate the process. The KPCA-SVRM method can also be used to solve other forecasting problems.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124114670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Rough Programming and Its Application to Production Planning 粗糙规划及其在生产计划中的应用
2008 International Conference on Risk Management & Engineering Management Pub Date : 2008-11-04 DOI: 10.1109/ICRMEM.2008.8
Peng Lv, Peng Chang
{"title":"Rough Programming and Its Application to Production Planning","authors":"Peng Lv, Peng Chang","doi":"10.1109/ICRMEM.2008.8","DOIUrl":"https://doi.org/10.1109/ICRMEM.2008.8","url":null,"abstract":"By rough programming, we mean the optimization theory dealing with rough decision problems. This paper constructs a general framework of rough chance-constrained programming. We also design a spectrum of rough simulations for computing uncertain functions arising in the area of rough programming. To speed up the process of handling uncertain functions, we train a neural network to approximate uncertain functions. Finally, we integrate rough simulation, neural network, and cultural algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving rough programming models and illustrate its effectiveness by example of production planning.","PeriodicalId":430801,"journal":{"name":"2008 International Conference on Risk Management & Engineering Management","volume":"2 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123798991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信