다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

Statistical Analysis with Missing Data

A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.

High-resolution mapping of global surface water and its long-term changes

Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, variable, etc. Hesitant fuzzy sets suit the modeling of quantitative settings; however, similar situations may occur in qualitative settings so that experts think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. In this paper, the concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms. Then, a multicriteria linguistic decision-making model is presented in which experts provide their assessments by eliciting linguistic expressions. This decision model manages such linguistic expressions by means of its representation using hesitant fuzzy linguistic term sets.

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Hesitant Fuzzy Linguistic Term Sets for Decision Making

Track irregularity fault commonly leads to the abnormal vibration of train which is the main cause of poor ride and even derailment. Based on Evidence Reasoning (ER) rule, this paper presents a fault identification method for determining the dynamic management levels of track irregularity using sample data measured from the accelerometers mounted in the axle-box and car-body of in-service train. Firstly, statistical approach of the sample casting is given to generate diagnosis evidence. Secondly, the ER rule is used to combine the diagnosis evidence coming from two accelerometers. The estimated irregularity displacement can obtained from the combined results. Thirdly, the dynamic levels of track irregularity can be identified according to the estimated displacement and the corresponding management standard. Finally, a representative experiment in Chinese railway line shows the superior identification accuracy of the proposed method by comparing with classical neural network-based approximating methodology.

Track irregularity fault identification Based on Evidence Reasoning rule

The demand is a fundamental variable in economic analysis that measures the needs of goods and services of the consumers. This paper presents a new approach for representing the demand by using aggregation systems that consider the attitudinal character of the consumers and their beliefs regarding the degree of importance of the different variables that may affect them. Several developments are introduced by using multiperson systems, different criterion, attributes, and states of nature. These developments are focused on the use of the weighted average, the ordered weighted average, and the ordered weighted averaging weighted average. Further aggregation systems are suggested by using the concept of the demand growth. The paper ends with an application of the new approach in a forecasting process that considers the attitude of the decision maker and its subjective beliefs.

Demand Analysis with Aggregation Systems

The UK is one of these countries in Europe, which have the highest death rate from asthma, and the rules to identify asthma control steps are vague in the current official guideline of asthma management. In this research, diagnosis rules on asthma control steps are developed to supplement the current guideline and to assist patients to monitor and manage their asthma on daily basis. The main challenge of developing the rules is missing values. Although the data examined have prodigious volumes of records for patients, no one have all and different patients have different information recorded. The large proportion of missing values lead to comparatively limited powers of some techniques like Decision Tree Analysis, Logistic Regression, ANN, Bayes' Rule and SVM. This research explores the Evidential Reasoning (ER) rule to develop prognostic rules for asthma control steps. ER is prior-free probabilistic inference and has not been applied to disease diagnosing and monitoring. The results are represented as probability distributions on asthma control steps given any combination of evidence, even if some combinations are not recorded in the current database. In practice, it could help clinicians to identify asthma control steps of patients, prescribe corresponding treatments, and monitor the effectiveness of the treatment and the progress of patients in asthma control management.

Application of Evidential Reasoning rules to identification of asthma control steps in children

Belief Rule Base Expert Systems and Complex System Modelling

Renewable energy is essential for the world economy and the current and future generations’ welfare, and it contributes in a balanced way to attain the general goal of energy security and environmental protection However, there are also challenges and barriers to the deployment of renewable energy generation, distribution and consumption, including technical, economic, cultural and financial challenges In order to understand and overcome the challenges and barriers of promoting the growth of renewable energy, it is important to model, analyse and assess the cost-effectiveness, and societal and environmental impact of various renewable energy solutions systemically This chapter aims to review relevant performance modelling, impact assessment and decision analysis techniques for renewable energy systems

Dong-Ling Xu

Papers

Track irregularity fault identification Based on Evidence Reasoning rule

Demand Analysis with Aggregation Systems

Application of Evidential Reasoning rules to identification of asthma control steps in children

Belief Rule Base Expert Systems and Complex System Modelling

Multiple Criteria Performance Modelling and Impact Assessment of Renewable Energy Systems—A Literature Review