• Journal of Mechanical Science and Technology
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• 제16권8호
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• pp.1053-1063
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• 2002

This paper presents a robust nonlinear controller for a f degree of freedom (DOF) parallel manipulator in the task space coordinates. The proposed control strategy requires information on orientations and translations in the task space unlike the joint space or link space control scheme. Although a 6 DOF sensor may provide such information in a straightforward manner, its cost calls for a more economical alternative. A novel indirect method based on the readily available length information engages as a potential candidate to replace a 6 DOF sensor. The indirect approach generates the necessary information by solving the forward kinematics and subsequently applying alpha-beta-gamma tracker With the 6 DOF signals available, a robust nonlinear task space control (RNTC) scheme is proposed based on the Lyapunov redesign method, whose stability is rigorously proved. The performance of the proposed RNTC with the new estimation scheme is evaluated via experiments. First, the results of the estimator are compared with the rate-gyro signals, which indicates excellent agreement. Then, the RNTC with on-line estimated 6 DOF data is shown to achieve excellent control performance to sinusoidal inputs, which is superior to those of a commonly used proportional-plus-integral-plus-derivative controller with a feedforward friction compensation under joint space coordinates and the nonlinear controller under task space coordinates.

• Journal of Computing Science and Engineering
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• 제1권2호
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• pp.177-194
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• 2007

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

• 로봇학회논문지
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• 제4권2호
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• pp.147-154
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• 2009

Visions of ubiquitous robotics and ambient intelligence involve distributing information, knowledge, computation over a wide range of servers and data storage devices located all over the world, and integrating tiny microprocessors, actuators, and sensors into everyday objects as well in order to make them smart. In this paper, we introduce our ongoing research effort aimed at realizing ubiquitous robots in an information structured space. For this, a ubiquitous space and ambient intelligent systems for a librarian robot are introduced and the RFID technology based approach for these systems is described.

• Smart Structures and Systems
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• 제26권1호
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• pp.23-33
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• 2020

Majority of the damage in engineering structures is nonlinear. Damage sensitive features (DSFs) extracted by traditional methods from linear time series models cannot effectively handle nonlinearity induced by structural damage. A new DSF is proposed based on vector space cosine similarity (VSCS), which combines K-means cluster analysis and Bayesian discrimination to detect nonlinear structural damage. A reference autoregressive moving average (ARMA) model is built based on measured acceleration data. This study first considers an existing DSF, residual standard deviation (RSD). The DSF is further advanced using the VSCS, and then the advanced VSCS is classified using K-means cluster analysis and Bayes discriminant analysis, respectively. The performance of the proposed approach is then verified using experimental data from a three-story shear building structure, and compared with the results of existing RSD. It is demonstrated that combining the linear ARMA model and the advanced VSCS, with cluster analysis and Bayes discriminant analysis, respectively, is an effective approach for detection of nonlinear damage. This approach improves the reliability and accuracy of the nonlinear damage detection using the linear model and significantly reduces the computational cost. The results indicate that the proposed approach is potential to be a promising damage detection technique.

• 디지털융복합연구
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• 제14권8호
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• pp.555-564
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• 2016

공간 지각은 일반적으로 물체(형태)를 인식하는 능력에 대한 문제로 여겨진다. 대안적으로, 형태 지각 연구는 시각 공간의 기하학에 관한 논의에 기여한다. 이러한 공간의 기하학은 일반적으로 유클리드가 아닌, 타원, 유사성, 또는 아핀(affine) 기하학으로 알려져 왔다. 다시 말해, 많은 형태 지각 연구들에서 보여 왔듯, 공간은 변형된 기하학으로 지각된다. 이 논문의 목적은 지각된 형태와 시각적으로 유도되는 행동과 관련된 시각 공간의 기하학에 대한 이해를 돕기 위함이다. 따라서 지각과 행위의 관계에 대해 설명하고 있는 두 이론을 비교해 본다. 제한된 인지와 더 나아가서, 인공 지능 연구와의 융합에 있어서 이러한 인간의 기본적인 공간 지각 능력과 시각적으로 유도되는 행위를 먼저 이해하는 것이 중요하다.

• 대한전자공학회논문지TC
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• 제42권10호
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• pp.25-30
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• 2005

본 논문에서는 신호공간 검출의 개념을 일반화하여 하나의 심볼 구간에서 하나 이상의 심볼들의 블록에 대한 검출을 수행하는 고정지연 트리 검색 신호검출기의 구성을 제안한다. 제안된 기법은 고속의 구현에 적합하다. 두 가지의 접근방법이 논의되며 이들은 모두 효율적인 신호공간 분할에 기반을 두고 있다. 첫 번째 방법에서는 심볼의 검출이 다중 클래스 분할에 기반을 둔다. 이 방법은 2개의 클래스에 기반을 둔 이진 심볼 검출방법을 일반화한 접근방법을 사용한다. 두 번째 방법에서는 이진 신호 검출이 look-ahead 기법과 결합된 고도의 병렬처리 신호검출 구조를 활용한다.

• 한국CDE학회논문집
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• 제11권6호
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• pp.455-463
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• 2006

Hidden line removal(HLR) algorithms can be devised either in the image space or in the object space. This paper describes a hidden line removal algorithm in the object space specifically for the CAD viewer data. The approach is based on the Appel's 'Quantitative Invisibility' algorithm and fundamental concept of 'back face culling'. Input data considered in this algorithm can be distinguished from those considered for HLR algorithm in general. The original QI algorithm can be applied for the polyhedron models. During preprocessing step of our proposed algorithm, the self intersecting surfaces in the view direction are divided along the silhouette curves so that the QI algorithm can be applied. By this way the algorithm can be used for any triangulated freeform surfaces. A major advantage of this algorithm is the applicability to general CAD models and surface-based visualization data.

• 한국측량학회지
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• 제37권5호
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• pp.359-366
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• 2019

Indoor spatial data has great importance as the demand for representing the complex urban environment in the context of providing LBS (Location-based Services) is increasing. IndoorGML (Indoor Geographic Markup Language) has been established as the data standard for spatial data in providing indoor navigation, but its definitions and relationships must be expanded to increase its applications and to successfully delivering information to users. In this study, we propose an approach to integrate IndoorGML with Indoor POI (Points of Interest) data by extending the IndoorGML notion of space and topological relationships. We consider two cases of representing Indoor POI, by 3D geometry and by point primitive representation. Using the concepts of the NRS (node-relation structure) and multi-layered space representation of IndoorGML, we define layers to separate features that represent the spaces and the Indoor POI into separate, but related layers. The proposed methodology was implemented with real datasets to evaluate its effectiveness for performing indoor spatial analysis.

• 전자공학회논문지S
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• 제35S권4호
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• pp.58-66
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• 1998

In many situations, it is very important to identify a certain unkown system, it from its input-output data. For this purpose, several system modeling algorithms have been suggested heretofore, and studies regarding the fuzzy modeling based on its nonlinearity get underway as well. Generatlly, fuzzy models have the capability of dividing input space into several subspaces, compared to linear ones. But hitherto subggested fuzzy modeling algorithms do not take into consideration the correlations between components of sample input data and address them independently of each other, which results in ineffective partition of input space. Therefore, to solve this problem, this letter proposes a new fuzzy modeling algorithm which partitions the input space more efficiently that conventional methods by taking into consideration correlations between components of sample data. As a way to use correlation and divide the input space, the method of principal component is ued. Finally, the results of computer simulation are given to demonstrate the validity of this algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권1호
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• pp.1-6
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• 2007

The SVDD (support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the feature space in order to distinguish a set of normal data from all other possible abnormal objects. Recently, with the objective of generalizing the SVDD which treats all training data with equal importance, the so-called D-SVDD (density-induced support vector data description) was proposed incorporating the idea that the data in a higher density region are more significant than those in a lower density region. In this paper, we consider the problem of further improving the D-SVDD toward the use of a partial reference set for testing, and propose an LMI (linear matrix inequality)-based optimization approach to solve the improved version of the D-SVDD problems. Our approach utilizes a new class of density-induced distance measures based on the RSDE (reduced set density estimator) along with the LMI-based mathematical formulation in the form of the SDP (semi-definite programming) problems, which can be efficiently solved by interior point methods. The validity of the proposed approach is illustrated via numerical experiments using real data sets.