• Journal of Computing Science and Engineering
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• v.10 no.2
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• pp.58-67
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• 2016

With the large amount of complex network data that is increasingly available on the Web, link prediction has become a popular data-mining research field. The focus of this paper is on a link-prediction task that can be formulated as a binary classification problem in complex networks. To solve this link-prediction problem, a sparse-classification algorithm called "Truncated Kernel Projection Machine" that is based on empirical-feature selection is proposed. The proposed algorithm is a novel way to achieve a realization of sparse empirical-feature-based learning that is different from those of the regularized kernel-projection machines. The algorithm is more appealing than those of the previous outstanding learning machines since it can be computed efficiently, and it is also implemented easily and stably during the link-prediction task. The algorithm is applied here for link-prediction tasks in different complex networks, and an investigation of several classification algorithms was performed for comparison. The experimental results show that the proposed algorithm outperformed the compared algorithms in several key indices with a smaller number of test errors and greater stability.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.656-664
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• 2016

While anthropomorphic robots are gaining interest, dual-arm robots are widely used in the industrial environment. Many methods exist in order to implement bimanual tasks by dual-arm robot. However, kinesthetic teaching is used in this paper. This paper suggests three different kinesthetic teaching methods that can implement most of the human task by the robot. The three kinesthetic teaching methods are joint level, task level, and contact level teaching. The task introduced in this paper is box packing, which is a popular and complex task in industrial environment. The task is programmed into the dual-arm robot by utilizing the suggested kinesthetic teaching method, and this paper claims that most tasks can be implemented by using the suggesting kinesthetic teaching methods.

• Journal of Industrial Convergence
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• v.1 no.1
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• pp.1-24
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• 2003

The 583 employees as subject for empirical analysis were selected by cluster sampling method in Banwol & Sihwa Industrial complex in Ansan area. This study shows the followings; 1. In the group where the task is unstructured and the subordinate's ability is high, the achievement-oriented and participative leader behavior have positive impact on the subordinate's expectancy. 2. In the group where the task is unstructured and the subordinate's ability is low, the directive leader behavior has positive impact on the subordinate's expectancy. 3. In the group where the task is structured and the subordinate's ability is high, the supportive leader behavior has positive impact on the subordinate's expectancy. 4. In the group where the task is structured and the subordinate's ability is low, only supportive leader behavior has positive impact on the subordinate's expectancy, and the directive leader behavior has no significant impact. 5. The subordinate's need for growth has a strong moderating effect in the relationship between leader behavior and job satisfaction. Finally this study indicates the implication for future theoretical and empirical development.

• Journal of Multimedia Information System
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• v.2 no.3
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• pp.263-274
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• 2015

The subject of this work is the complex discrete systems simulation special features with the aid of dynamic graph models. The proposed simulation technique allows to determine the ways for tasks solutions in terms of discrete systems analysis and synthesis of various complication: one-dimensional and multidimensional, steady and unstable, with the pulse elements abnormal operating mode and others. Often complex control systems analysis and synthesis task solutions, via classical approach comes out to be insolvent, because of the computational problems. The application of graph models allows to perform clear and strict characterization and computer procedures automation. The optimal controls synthesis algorithm presented in this paper, transferring the discrete system from target initial state to target final state within the minimum time, allows to consider the zero initial conditions systems, with the initial potential energy, with the control actions limitations and complex pulse elements operating mode.

• Proceedings of the ESK Conference
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• 1996.04a
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• pp.313-319
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• 1996

Human posture prediction and motion simulaiton methods try to solve inverse kinematic problems based on the optimization concept. It is of great concern to develop an optimization method which soloves complicated optimization models in an efficient way in order for the models to be biomechanically sound. In this study, a new optimization method for posture prediction, which is named the Complex Method, is presented. The Complex Method demonstrates more flexibility in a way that it can deal with various forms of objective functions with constraints. This is because the method is a function-value-based approach. A two-eimensional whole-body lifting task was selected as an example of posture prediction, and a comparison study with te incrementation method was conducted in order to evaluate the accuracy of the Complex Method.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.2
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• pp.303-308
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• 2002

Genetic markers engendered by genome projects drew enormous interest in quantitative genetics, but knowledge on genetic architecture of complex traits is limited. Complexities in genetics will not allow us to easily clarify relationship between genotypes and phenotypes for quantitative traits. Quantitative genetics guides an important way in facing such challenges. It is our exciting task to find genes that affect complex traits. In this paper, landmark research and future prospects are discussed on genetic parameter estimation and quantitative trait locus (QTL) mapping as major subjects of interest.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.412-412
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• 2000

This paper presents a task teaching method for off-line programming of a multi-robot system. Teaching commands were developed in order to simplify a complex teaching process, to shorten the setup time for new working environment and to have flexibility for changes in working environment. Four teaching commands can be used to automatically generate trajectories of an end-effector of the robot in electronics assembly line. The robots used in the work cell are a four-axis SCARA robot and six-axis articulated robot. Each robot is controlled in a independent way while objects, working environment and robots are modeled in corresponding modules, respectively. The off- line programming system developed uses OpenGL for a smooth graphic effect in Window s where three dimensional CAD data can be leaded for graphical modeling.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.148-154
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• 2012

For a successful checkpointing strategy, we should place checkpoints so as to optimize fault-tolerance capability of real-time systems. This paper presents a novel scheme of checkpoint placement for real-time systems with periodic multi-tasks. Under the influence of transient faults, multi-tasks are scheduled by the Rate Monotonic (RM) algorithm. The optimal checkpoint intervals are derived to maximize the probability of task completion. In particular, this paper is concerned about the general case that the deadline of a task is longer than the period. Compared with the special condition that the deadline is equal to or less than the period, this general case causes a more complicate test procedure for schedulability of the RM algorithm with respect to a given set of checkpoint re-execution vectors. The probability of task completion is also derived in a more complex form. A case study is given to show the applicability of the proposed scheme.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.4
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• pp.160-166
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• 2002

Since conventional mobile robot control with one module has limitation to solve complex problems, there have been a variety of works on combining multiple modules for solving them. Recently, many researchers attempt to develop mobile robot controllers using artificial life techniques. In this paper, we develop a mobile robot controller using cellular automata based neural networks, where complex tasks are divided to simple sub-tasks and optimal neural structure of each sub-task is explored by genetic algorithm. Neural network modules are combined dynamically using the action selection mechanism, where basic behavior modules compete each other by inhibition and cooperation. Khepera mobile robot simulator is used to verify the proposed model. Experimental results show that complex behaviors emerge from the combination of low-level behavior modules.

• Korean Journal of Child Studies
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• v.19 no.2
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• pp.185-200
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• 1998

This research examined the development in Korean children of the comprehension of complex sentences. The relative difficulty in comprehension of the temporal conjunctions "before" and "after" was investigated. The order of mention, contextual support, and syntactic appearance was controlled. The role of cognitive strategies and developmental changes in the comprehension of these conjunction was included in this study. Subjects were 90 preschool children between 3 and 5 years of age. The task was a sentence-picture matching problem having 3 types of sentences combining temporally with "before" or "after". The results were that developmental changes in comprehension of the temporal conjunctions "before' and "after" in Korean children depended on the order of mention, contextual support, and such syntactic factors as the position of the subject of the sentence. The importance of the consistency in the occurrence of events and the order of mention in the acquisition of complex sentences among Korean children is similar to the acquisition of complex sentences in other languages.