• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.380-392
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• 2000

This paper proposes a qualitative reasoning approach for the spatial configuration of mechanisms that could be applied in the early phase of the conceptual design. The spatial configuration problem addressed in this paper involves the relative direction and position between the input and output motion, and the orientation of the constituent primitive mechanisms of a mechanism. The knowledge of spatial configuration of a primitive mechanism is represented in a matrix form called spatial configuration matrix. This matrix provides a compact and convenient representation scheme for the spatial knowledge, and facilitates the manipulation of the relevant spatial knowledge. Using this spatial knowledge of the constituent primitive mechanisms, the overall configuration of a mechanism is described and identified by a spatial configuration state matrix. This matrix is obtained by using a qualitative reasoning method based on sign algebra and is used to represent the qualitative behavior of the mechanism. The matrix-based representation scheme allows handling the involved spatial knowledge simultaneously and the proposed reasoning method enables the designer to predict the spatial behavior of a mechanism without knowing specific dimension of the components of the mechanism.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Korean Journal of Cognitive Science
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• v.15 no.4
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• pp.15-20
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• 2004

To develop a cognitive system with the flexibility and breadth of human, it's very important to construct a large scale knowledge base which include commonsense knowledge as well as expert knowledge. Efficient knowledge representation and reasoning techniques will play a key role for this. This paper introduce a cognitive system which is based on Cyc knowledge base and augmented with our work on qualitative and spatial representation and reasoning. Our system has been implemented and tested on various examples.

• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.19-38
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• 2011

When quantitative representation and reasoning about space is difficult or impossible in a real world, we can use qualitative representation and reasoning. RCC-8 is a well-known qualitative method for 2D space. RCC-8, in which a basic ontological primitive entity for space is a region, shows the connection-based logics and the conceptual neighbors and transitions of topological status between two regions. The transitions happen by changing position or size of regions. However, more aspects have to be considered for representing and reasoning of the world. We propose a modified and extended method QSR-14 for qualitative spatial representation and reasoning of a dynamic physical 2D world in the gravitation field. We mention the need of a global reference frame and describe QSR-14 in detail for representing and reasoning of physical and chemical changes of a real world using the global reference frame. We believe QSR-14 is appropriate for the qualitative spatial representation and reasoning of a dynamic physical world. The usefulness of QSR-14 is shown with several examples.

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.774-779
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• 2015

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner using Apache Spark, an in-memory high speed cluster computing environment, which is effective for sequencing and iterating component reasoning jobs. The proposed reasoner can not only check the integrity of a large-scale spatial knowledge base representing topological and directional relationships between spatial objects, but also expand the given knowledge base by deriving new facts in highly efficient ways. In general, qualitative reasoning on topological and directional relationships between spatial objects includes a number of composition operations on every possible pair of disjunctive relations. The proposed reasoner enhances computational efficiency by determining the minimal set of disjunctive relations for spatial reasoning and then reducing the size of the composition table to include only that set. Additionally, in order to improve performance, the proposed reasoner is designed to minimize disk I/Os during distributed reasoning jobs, which are performed on a Hadoop cluster system. In experiments with both artificial and real spatial knowledge bases, the proposed Spark-based spatial reasoner showed higher performance than the existing MapReduce-based one.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.452-457
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• 2015

In this paper, we propose a spatial query processor, SQUERY, which can derive rich query results through spatial reasoning on the initial knowledge base, as well as, process both qualitative and quantitative queries about the topological and directional relationships between spatial objects. In order to derive richer query results, the query processor expands the knowledge base by applying forward spatial reasoning into the initial knowledge base in a preprocessing step. The proposed query processor uses not only qualitative spatial knowledge describing topological/directional relationship between spatial objects, but also utilizes quantitative spatial knowledge including geometric data of individual spatial objects through geometric computation. The results of an experiment with the OSM(Open Street Map) spatial knowledge base demonstrates the high performance of our spatial query processing system.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1324-1331
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• 1999

물체의 공간에서의 운동을 다루는 공간 추론의 연구에서 충돌을 이해하는 것은 중요하다. 본 논문에서는 정성적 충돌 이론과 추론 기법을 소개하고자 한다. 충돌 해석에 있어 관련된 물체의 공간적 속성과 운동방향의 상호작용을 분석하여 충돌로 인한 힘과 운동 방향의 전달을 계산하였다. 추론 과정에 있어서는 충돌이 갖는 특성인 불연속적인 변화에 대한 분석과 회전 운동으로 인한 변화의 분석이 소개되었다. 제안된 충돌 이론과 추론 기법은 구현되어 자동차 충돌 사고의 충돌에 적용되어 유효성을 입증할 수 있었다. Abstract Understanding collision is important in spatial reasoning problems that study the motions of objects. This paper introduces qualitative collision theory and reasoning techniques. Force and motion transfers are computed by analyzing the interactions of the spatial properties and motions of the objects. This paper also presents inference techniques for handling discontinuous changes and angular changes by rotation. These theories and inference techniques are implemented and applied to real car-to-car collision accidents. The test results verify the reliabilities of our techniques.

• Journal of KIISE
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• v.45 no.3
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• pp.211-223
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• 2018

In this paper, we propose a robot-centered direction relation representation and the relevant reasoning methods for indoor service robots. Many conventional works on qualitative spatial reasoning, when deciding the relative direction relation of the target object, are based on the use of position information only. These reasoning methods may infer an incorrect direction relation of the target object relative to the robot, since they do not take into consideration the heading direction of the robot itself as the base object. In this paper, we present a robot-centered direction relation representation and the reasoning methods. When deciding the relative directional relationship of target objects based on the robot in an indoor environment, the proposed methods make use of the orientation information as well as the position information of the robot. The robot-centered reasoning methods are implemented by extending the existing cone-based, matrix-based, and hybrid methods which utilized only the position information of two objects. In various experiments with both the physical Turtlebot and the simulated one, the proposed representation and reasoning methods displayed their high performance and applicability.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.4
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• pp.61-69
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• 2010

The collapse of a slope or cut embankment brings much damage to life and property. Accordingly, it is very important to analyze the spatial distribution by calculating the landslide susceptibility in the estimation of the risk of landslide occurrence. The heuristic, statistic, deterministic, and probabilistic methods have been introduced to make landslide susceptibility maps. In many cases, however, the reliability is low due to insufficient field data, and the qualitative experience and knowledge of experts could not be combined with the quantitative mechanical?analysis model in the existing methods. In this paper, new modeling method for a probabilistic landslide susceptibility analysis combined Bayesian Network with ontology model about experts' knowledge and spatial data was proposed. The ontology model, which was made using the reasoning engine, was automatically converted into the Bayesian Network structure. Through conditional probabilistic reasoning using the created Bayesian Network, landslide susceptibility with uncertainty was analyzed, and the results were described in maps, using GIS. The developed Bayesian Network was then applied to the test-site to verify its effect, and the result corresponded to the landslide traces boundary at 86.5% accuracy. We expect that general users will be able to make a landslide susceptibility analysis over a wide area without experts' help.

• Journal of the Korean Geographical Society
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• v.47 no.3
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• pp.426-439
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• 2012

The goal of this study is to propose a new method to capture the qualitative person spatial behavior. Beyond tracking or indexing the change of the location of a person, the changes in the relationships between a person and its environment are considered as the main source for the formal model of this study. Specifically, this paper focuses on the movement behavior of a person near the boundary of a region. To capture the behavior of person near the boundary of regions, a new formal approach for integrating an object's scope of influence is described. Such an object, a spatio-temporally extended point (STEP), is considered here by addressing its scope of influence as potential events or interactions area in conjunction with its location. The formalism presented is based on a topological data model and introduces a 12-intersection model to represent the topological relations between a region and the STEP in 2-dimensional space. From the perspective of STEP concept, a prototype analysis results are provided by using GPS tracking data in real world.