Due to advances in hardware performance, user-friendly interfaces are becoming one of the major concerns in information systems. Linguistic conversation is a very natural way of human communications. Fuzzy techniques have been employed to liaison the discrepancy between the qualitative linguistic terms and quantitative computerized data. This paper deals with linguistic queries using clustering results on data sets, which are intended to retrieve the close clusters or distant clusters from the clustering results. In order to support such queries, a fuzzy technique-based method is proposed. The method introduces distance membership functions, namely, close and distant membership functions which transform the metric distance between two objects into the degree of closeness or farness, respectively. In order to measure the degree of closeness or farness between two clusters, both cluster closeness measure and cluster farness measure which incorporate distance membership function and cluster memberships are considered. For the flexibility of clustering, fuzzy clusters are assumed to be formed. This allows us to linguistically query close or distant clusters by constructing fuzzy relation based on the measures.
In Vessel Traffic Service (VTS), prediction of ship traffic flow is essential for VTS operator. But, by external force such as tidal current, wind, wave and other regulations, it was difficult to predict ship traffic flow. In this paper, in order to utilize ship trajectory big data by Automatic Identification System (AIS), we propose the method to convert ship speed data to categorical data dividing ship navigating routes into several gate lines. Then experiments to verify model accuracy conduct using multiple input and output variables with artificial neural network.
상황은 단일 사건에 의해 결정되는 경우도 있지만, 많은 경우 일련의 사건이 특정 시간 제약을 만족하면서 발생할 때 상황이 결정된다. 따라서 상황에 대한 추론은 시간 제약 조건 만족 여부와 함께 사건의 발생을 순서를 확인하는 방법으로 수행될 수 있다. 한편, 어떤 상황은 분명하게 정의되는 것이 아니라 애매한 개념을 사용하여 기술되기 때문에, 퍼지 개념을 이용한 상황 기술과 이에 대한 추론이 필요하다. 한편, 유비쿼터스 환경에서와 같이 여러 대상에 대한 상황을 유추하여 서비스를 제공해야 하는 경우에, 대상 간에 동일한 상황이 발생할 수 있기 때문에 이에 대한 고려가 필요하다. 이러한 상황 추론을 위해서 이 논문에서는 Fuzzy Colored Timed Petri net 모델이라는 상황 추론 모델에 대해서 제안한다. 제안한 모델은 Timed Petri net 성질을 이용하여 일련의 사건 발생을 모델링하고, Colored Petri net의 성질을 이용하여 다수 대상에 대한 상황 추론을 허용하며, fuzzy 토큰 개념을 이용하여 애매한 개념을 사용하여 정의된 상환에 대한 추론을 가능하게 한다. In context-aware computing environment, some context is characterized by a single event, but many other contexts are determined by a sequence of events which happen with some timing constraints. Therefore context inference could be conducted by monitoring the sequence of event occurrence along with checking their conformance with timing constraints. Some context could be described with fuzzy concepts instead of concrete concepts. Multiple entities may interact with a service system in the context-aware environments, and thus the context inference mechanism should be equipped to handle multiple entities in the same situation. This paper proposes a context inference model which is based on the so-called fuzzy colored timed Petri net. The model represents and handles the sequential occurrence of some events along with involving timing constraints, deals with the multiple entities using the colored Petri net model, and employs the concept of fuzzy tokens to manage the fuzzy concepts.
Various methods have been developed to detect outliers which are significantly different from others. Most outlier detection methods assume the data lie in Euclidean space in which distances can be easily defined and computed. In reality, we meet many data with both numerical and categorical attributes together, so-called mixed-data, for which it is not easy to define widely-accepted distance metrics. This paper proposes an outlier detection method which can be applied to mixed data. The method focuses on the association among attribute values. It first selects the sets of potentially associated attributes, computes the degrees of outlierness for records with respect to the associated attributes, and then determines a collection of outliers using the degrees. In addition, this paper shows some experiment results of the proposed method and compares with some other methods. Keywords: Data Analysis, Data Quality, Horizontal Consistency, Mixed Data, Outlier Detection
Design is a creative work that demands inspiration, disciplined crafts, and iterative rollouts. To assist such creative works, the generative design technique has been employed, in which design ideas are coded into computer programs and the programs build designs on behalf of human designers. Human designers work on programs, instead of designs themselves, to modify models and to adopt new design ideas. Although human designers do not directly develop models, they need to be kept involved in model development programs. This paper is concerned with a generative design method which does not require human designers to be deeply involved in the design process. The proposed method uses a genetic algorithm approach as an enabling mechanism. The genetic algorithm creates various shapes of models using the predefined genetic operators which are developed with the help of human designers. The method assumes that the customers express their preferences on designs and thus the preferences evaluation is incorporated into the fitness function of the genetic algorithm. The proposed method has been applied to ring design works.
Background/Objectives: In Vessel Traffic Service (VTS), prediction of the flow of vessel traffic is essential to serve safety information and control ship traffic. However, it is difficult to predict a ship’s speed due to many external forces and environmental conditions. This study proposes a data processing method to convert ship speed data to categorical data by dividing ship navigating routes into several gate lines.Methods/Statistical analysis: A ship’s trajectory is converted to each route’s gate line speed. To determine the gate line speed, we convertedthe previous and subsequent gate line speeds into category data. The input and output category data were applied to a multilayer perceptron network using as input variablesthe previous speed variance category, ship type, and ship length, and as output variable the subsequent speed variance.Findings: These results are useful because categorical data can be applied to various neural network models. As a result of the conducted experiments, the accuracy of the model improved when many gate lines are included.Improvements/Applications: The study results can be applied topredict ship traffic flow for VTS operators.
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