Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Modified condition/decision coverage(MC/DC)oriented compiler optimization for symbolic execution

Symbolic execution is an effective way of systematically exploring the search space of a program,and is often used for automatic software testing and bug finding.The program to be analyzed is usually compiled into a binary or an intermediate representation,on which symbolic execution is carried out.During this process,compiler optimizations influence the effectiveness and efficiency of symbolic execution.However,to the best of our knowledge,there exists no work on compiler optimization recommendation for symbolic execution with respect to(w.r.t.)modified condition/decision coverage(MC/DC),which is an important testing coverage criterion widely used for mission-critical software.This study describes our use of a state-of-the-art symbolic execution tool to carry out extensive experiments to study the impact of compiler optimizations on symbolic execution w.r.t.MC/DC.The results indicate that instruction combining(IC)optimization is the important and dominant optimization for symbolic execution w.r.t.MC/DC.We designed and implemented a support vector machine based optimization recommendation method w.r.t.IC(denoted as auto).The experiments on two standard benchmarks(Coreutils and NECLA)showed that auto achieves the best MC/DC on 67.47%of Coreutils programs and 78.26%of NECLA programs.

MEASURING C PROGRAM COVERAGE BASED ON BINARY DECISION DIAGRAMS

Test coverage analysis is a structural testing technique, which helps to evaluate the sufficiency of software testing. This letter presents two test generation algorithms based on binary decision diagrams to produce tests for the Multiple-Condition Criterion(M-CC) and the Modified Condition/Decision Criterion(MC/DC), and describes the design of the C program Coverage Measurement Tool (CCMT), which can record dynamic behaviors of C programs and quantify test coverage.

Application of decision tree to selection of MTBM for adverse geological conditions

So many potential risks can be identifed for application of trenchless technology especially using microtunneling methods.Unexpected changes in ground conditions,such as encountering boulders,tree roots,ground water and man-made structures such as old foundations are the principal geotechnical risks,which affect the selection of an appropriate microtunnel boring machine.On the other hand,the performance of each microtunneling technique will differ while encountering such conditions.Hence,predicting the potential hazards provides a better safety and risk management plan.In this study,a couple of potentially hazardous situation,which are commonly associated with ground conditions,were identifed and investigated.A decision tree aid methodology was proposed based on geotechnical risk assessment for selection of proper microtunneling technique.Based on the approach the most appropriate microtunneling technique has the minimum risk level either before or after hazards mitigation measures.In order to check the effciency of the approach in practice,selection of microtunnel boring machine for Hamadan sewerage pipeline project was evaluated.Accordingly,an earth pressure balance(EPB)MTBM was selected for the project.

Condition-Based Maintenance Decision-Making for Equipment

At present, condition monitoring and fault diagnosis technology and their application in engineering have been widely studied. Relatively little attention has been paid to condition-based maintenance decision-making for equipment. In this paper,based on the decision-making policy in traditional condition-based maintenance,the connotation of condition-based maintenance for equipment was defined, and its characteristics were analyzed.Working contents of condition-based maintenance for equipment were provided,which were divided into three stages. The influence factors in condition-based maintenance for equipment were analyzed. The key links of equipment maintenance contents and decision-making process were proposed. The condition-based maintenance decision-making policy presented in this paper can provide a practical reference for equipment maintenance.

Conditional Value-at-Risk for Random Immediate Reward Variables in Markov Decision Processes

We consider risk minimization problems for Markov decision processes. From a standpoint of making the risk of random reward variable at each time as small as possible, a risk measure is introduced using conditional value-at-risk for random immediate reward variables in Markov decision processes, under whose risk measure criteria the risk-optimal policies are characterized by the optimality equations for the discounted or average case. As an application, the inventory models are considered.

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

Competitive location problem of multi-level pickup point considering cooperative coverage

To occupy a greater market share in terminal distribution, companies are urged to make full use of cooperative coverage formed with brand effect and information sharing in the layout of pickup points. Based on the diversity of pickup points, the piecewise function, signal intensity function and probability function are introduced. Meanwhile, considering the effect of distance satisfaction and cooperation coverage on customer behavior, the location model of the pickup point under competitive environments is established. The genetic algorithm is used to solve the problem, and the effectiveness of the model and algorithm is verified by a case. The results show that the sensitivity of weighted demand coverages to budget decreases gradually. The maximum weighted demand coverage increases at first and then decreases with the increase of the signal threshold, and there is a positive correlation with the change of the actual demand coverage to the senior customers, but it is negatively related to the intermediate and primary customers. When the number of high-level pickup points in a competitive enterprise is small, the advantage of the target enterprise is more significant. Through comparison, the cooperative coverage model is better than the non-cooperative coverage model, in terms of the weighted demand coverage, the construction cost and the attention paid to the important customers.

Basic Concept and Theoretical Study of Condition-based Maintenance for Power Transmission System

状态检修背景下,为解决电网运行整体与输变电设备个体之间的矛盾和冲突,在已有研究成果基础上,提出电网状态检修的概念,在输变电设备状态检修决策过程中,给出电网故障风险和电网检修风险的定义以及相应的量化表达式。依据设备状态预测,以电网故障风险和电网检修风险的和最小为目标,将设备检修、运行以及电网整体性能在概率引导下相互牵制,形成电网状态检修的数学模型,并给出相应的解决方法。最后,为验证该理论研究的可行性,分别以可靠性测试系统RBTS-BUS6和IEEE-RTS79系统为例,对各种情况进行分析,体现了预期的效果,并期望为电网检修与运行协调决策理论的深入研究提供必要的理论基础。

Analysis of Phased-Mission System Reliability and Importance with Imperfect Coverage

Accounting for static phased-mission systems (PMS) and imperfect coverage (IPC), generalized and integrated algorithm (GPMS-CPR) implemented a synthesis of several approaches into a single methodology whose advantages were in the low computational complexity, broad applicability, and easy implementation. The approach is extended into analysis of each phase in the whole mission. Based on Fussell-Vesely importance measure, a simple and efficient importance measure is presented to analyze component’s importance of phased-mission systems considering imperfect coverage.

RF-CLASS： A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making

Floods often cause significant crop loss in the United States. Timely and objective information on flood-related crop loss, such as flooded acreage and degree of crop damage, is very important for crop monitoring and risk management in ag- ricultural and disaster-related decision-making at many concerned agencies. Currently concerned agencies mostly rely on field surveys to obtain crop loss information and compensate farmers＇ loss claim. Such methods are expensive, labor intensive, and time consumptive, especially for a large flood that affects a large geographic area. The results from such methods suffer from inaccuracy, subjectiveness, untimeliness, and lack of reproducibility. Recent studies have demonstrated that Earth observation （EO） data could be used in post-flood crop loss assessment for a large geographic area objectively, timely, accurately, and cost effectively. However, there is no operational decision support system, which employs such EO-based data and algorithms for operational flood-related crop decision-making. This paper describes the development of an EO-based flood crop loss assessment cyber-service system, RF-CLASS, for supporting flood-related crop statistics and insurance decision-making. Based on the service-orientated architecture, RF-CLASS has been implemented with open interoperability specifications to facilitate the interoperability with EO data systems, particularly the National Aeronautics and Space Administration （NASA） Earth Observing System Data and Information System （EOSDIS）, for automatically fetching the input data from the data systems. Validated EO algorithms have been implemented as web services in the system to operationally produce a set of flood-related products from EO data, such as flood frequency, flooded acreage, and degree of crop damage, for supporting decision-making in flood statistics and flood crop insurance policy. The system leverages recent advances in the remote sensing-based flood monitoring and assessment, the near-real-time availability of EO data, the service-oriented architecture, geospatial interoperability standards, and the standard-based geospatial web service technology. The prototypical system has automatically generated the flood crop loss products and demonstrated the feasibility of using such products to improve the agricultural decision-making. Evaluation of system by the end-user agencies indicates that significant improvement on flood-related crop decision-making has been achieved with the system.

Evaluation of Satisfaction Survey of Decision-Making Meteorological Services in Shaanxi Province, China in 2017

In China, decision-making meteorological services provide meteorological information for the production organization, disaster prevention and mitigation by the CPC committee, government, military leaders and decision-making departments at all levels, as well as scientific decision-making in the areas of rational development and utilization of climate resources and environmental protection. In order to understand the user’s satisfaction with the decision-making meteorological service, the Shaanxi Provincial Meteorological Bureau conducted a statistical survey, and the results showed that: 1) In 2017, the satisfaction level of provincial-level decision-making meteorological services in Shaanxi, China was 92.77%. Among them, the satisfaction index of “Ministry Department Service Personnel Professional Image and Service Awareness” was 94.12%, and the “Weather Forecast Warning Accuracy” satisfaction index was 90.18%. 2) Decision-making users have become an important channel for obtaining meteorological information through meteorological websites, televisions, mobile phone text messages, APP mobile applications, broadcasting, and Meteorological Information Express. Rainstorms, floods, high temperature heat, cold winds, hail, precipitation, and lightning are still the main concerns of decision-makers. 3) The focus on haze and UV intensity is 15% and 8% higher than that of 2016. The next 1 - 3 days weather forecast and 0 - 6 hours short-term forecast are still the most valuable forecast products for decision-making users. Compared with 2016, the next 1 - 3 days weather forecast, future 0 - 6 hours forecast, traffic meteorology, precipitation probability, and air quality forecast increase by 1% to 14% in the year of 2017.

Necessary Conditions and Situational Variables of Payments for Ecosystem Services

Clarifying the necessary conditions for the emergence of payments for ecosystem services (PES) and the situational variables that affect PES is the basis for their interpretation, prediction, and selection. This article proposes an analytical framework for the emergence of PES and argues that the key to determining whether PES can occur and whether a selected PES program is appropriate is to evaluate the net gain. When payers anticipate that a PES program will provide a satisfactory number of ES and a net gain over the opportunity cost and will cover all costs, it is assumed that the program will be implemented. When it is difficult to accurately evaluate the net gain of PES, the situational variables that affect the costs and benefits need to be examined. The group characteristics, ES characteristics, spatial and temporal contacts between the suppliers and demanders, correlation with private goods and additionality are important situational variables that affect the emergence and choice of PES.

Optimal Distribution of N-Team Interacting Decision Makers with Hierarchical Command Inputs That Are Predicated on Order Statistics

The spectral analysis of simulated N-team of interacting decision makers with bounded rationality constraints of Oladejo, which assumes triangular probability density function of command inputs is hereby restructured and analysed, to have hierarchical command inputs that are predicated on order statistics distributions. The results give optimal distributions.

Challenges faced by community health nurses to achieve universal health coverage in Myanmar:A mixed methods study

Objective:This study aimed to identify the challenges of community health nurses(CHNs)in delivering effective community health care to achieve universal health coverage(UHC)in Myanmar.Methods:A total of 30 CHNs from township health centers in the northeastern,southern,and western parts of Myanmar were purposefully recruited for quantitative and qualitative interviews.Quantitative data were processed using Microsoft Excel software,and qualitative data were analyzed using thematic analysis.This study is registered with researchregistry6201.Results:Around the country,30 CHNs uncovered their hardships in implementing primary health care to achieve UHC.Over 90%of the participants agreed to the problem of inadequate health infrastructure,while half of them felt unmotivated when they encountered role conflicts among various cadres of healthcare providers and poor opportunities for career promotion.Major problems arose from the lack of standard professional education at the entry point to community settings because most CHNs did not achieve specialized training in providing public health services.Complications are incapable of evaluating health services for policy-making and the inability to conduct health research to develop evidencebased practices.Insecure work and living conditions,unsupportive community relationships,and undereducation in professional practices were supportive major themes explored by CHNs to achieve a deeper understanding of the barriers to UHC.Not only the health system itself but also the population and other geographical factors have contributed to many challenges to CHNs.Conclusion:Myanmar’s CHNs face many challenges in achieving UHC.These challenges are not confined to the health sector.Some situations,such as geographical barriers and transportation problems,remain persistent challenges for healthcare providers.This study highlights the fact that current health systems should be strengthened by qualified healthcare providers and sufficient infrastructure.Meanwhile,public empowerment plays a critical role in promoting health development.

Research on coal mine safety production management decision system based on VB

Based on safety management appraisal theory, the decision system was divided into 5 function menu module, including system control module, mining coal and the tunneling working surface security evaluation module, the entire ore safety production condition appraisal module, the safety management level appraisal module of main production work area, the withdrawal system module and so on. The system operates through the constitutive procedure, outputs the main operation results by graph and form, and realizes the main function of safety evaluation.

Conditional Coverage Estimation for High-Quality Prediction Intervals

Deep learning has been recently studied to generate high-quality prediction intervals(PIs)for uncertainty quantification in regression tasks,including recent applications in simulation metamodeling.The high-quality criterion requires PIs to be as narrow as possible,whilst maintaining a pre-specified level of data(marginal)coverage.However,most existing works for high-quality PIs lack accurate information on conditional coverage,which may cause unreliable predictions if it is significantly smaller than the marginal coverage.To address this problem,we propose an end-to-end framework which could output high-quality PIs and simultaneously provide their conditional coverage estimation.In doing so,we design a new loss function that is both easy-to-implement and theoretically justified via an exponential concentration bound.Our evaluation on real-world benchmark datasets and synthetic examples shows that our approach not only achieves competitive results on high-quality PIs in terms of average PI width,but also accurately estimates conditional coverage information that is useful in assessing model uncertainty.

基于网络开放数据的区域消防救援总体有效覆盖率评估

高效准确地掌握消防救援有效覆盖率是优化消防站点资源配置的前提。将判断是否有效覆盖的消防救援行程时间阈值定为240 s,建立了基于时间加权的区域消防救援总体有效覆盖率评估模型。基于网络开放数据收集了长沙的50座消防站作为消防救援供给点、5746家被抽查到的社会单位作为消防救援需求点,并调用网络地图应用程序编程接口基于实时路况来仿真消防救援行程时间。在连续7 d内设置197个评估场景,共获得了1131962个有效样本,进而得出:长沙消防救援总体有效覆盖率为21.22%,结果等级为“C”,消防救援水平一般,需加强消防站建设。这种基于网络开放数据的区域消防救援有效覆盖率评估,具备高效和准确的特点,可为优化消防站点资源配置、提升公共安全水平提供关键理论和方法支撑。

我国公路桥梁养护管理系统发展及展望

为促进我国公路桥梁管养智慧化转型,提高管养效率,综述了我国公路桥梁养护管理系统的研究历程与发展趋势。围绕桥梁养护管理系统发展的3个阶段:无纸信息化的桥梁数据库信息管理阶段,改善状态评估与决策方法的桥梁技术状态评估及维护决策阶段,融合可视化、巡检终端及健康监测系统等手段的桥梁高效化管理阶段,总结各阶段系统功能模块与特点、主要解决的问题及存在的弊端;结合当前桥梁智慧管养方面的研究热点,探讨与展望了以减少人为干预、实现经济高效养护为目标的公路桥梁智慧养护管理系统发展方向。结果表明:当代公路桥梁养护管理系统已基本满足桥梁管养部门的精细化管理需求,但仍存在智慧化程度不足、资源配置合理化欠缺等问题,而融合了智慧感知、智慧判别与决策、多源信息融合手段的桥梁智慧管养系统,将通过多种智能技术感知桥梁多源信息,采用大数据分析与挖掘方法评估桥梁状态,指导管理者实现经济高效的管养决策,达到公路桥梁智慧化养护目的。