• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.103-107
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• 2001

In this paper, we have developed a new Force-Display system using wire-tension method. The proposed system is based on the HIR Lab Haptic Rendering library, which calculates the real position and renders the reflecting force data to device rapidly. The system is composed of device based tendon-driven method, controller and Haptic rendering library. The developed system will be used on constructing the dynamical virtual environment. To show the efficiency of our system, we designed simulation program which can display the moving force (attaching, grabbing, rotating) on two virtual point. As the result of the experiment, our proposed system shows much higher resolution and stability than any others.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.308-313
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• 2022

This paper proposes a velocity control method for a permanent magnet synchronous motor (PMSM) based on the Koopman operator that does not require model parameter information except for pole-pair of the motor and external load. First, the Koopman operator is derived using observable functions and observation data. Then, the desired q-axis current corresponding to the desired velocity is generated using the relationship between the continuous-time Koopman operator and the dynamics of PMSM. Also, the dynamic equation of PMSM is expressed as a linear form in observable space using the discrete-time Koopman operator. Finally, it is applied to the linear quadratic regulator (LQR) to derive the final form of control input. To verify the proposed method, the conventional cascade PI controller and the LQR controller configured with the existing technique are compared with the proposed method in the viewpoint of q-axis current generation and velocity tracking performance in an environment with noise and external load.

• ETRI Journal
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• v.38 no.5
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• pp.1019-1029
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• 2016

The probability hypothesis density (PHD) filter is an effective means to track multiple targets in that it avoids explicit data associations between the measurements and targets. However, the target birth intensity as a prior is assumed to be known before tracking in a traditional target-tracking algorithm; otherwise, the performance of a conventional PHD filter will decline sharply. Aiming at this problem, a novel target birth intensity scheme and an improved measurement-driven scheme are incorporated into the PHD filter. The target birth intensity estimation scheme, composed of both PHD pre-filter technology and a target velocity extent method, is introduced to recursively estimate the target birth intensity by using the latest measurements at each time step. Second, based on the improved measurement-driven scheme, the measurement set at each time step is divided into the survival target measurement set, birth target measurement set, and clutter set, and meanwhile, the survival and birth target measurement sets are used to update the survival and birth targets, respectively. Lastly, a Gaussian mixture implementation of the PHD filter is presented under a linear Gaussian model assumption. The results of numerical experiments demonstrate that the proposed approach can achieve a better performance in tracking systems with an unknown newborn target intensity.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.2
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• pp.65-80
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• 2019

With the advent of the ICT-based 4th industrial revolution, the convergence of the manufacturing industry and ICT seems to be the new breakthrough for achieving the company's competitiveness and play a role on the key element for accelerating the revival of the manufacturing industry. When the smart factory is implemented, each plant can analyze the quantity of data collected, build the data-driven operation systems which can make decisions, and ultimately discover the correlation among many events in the manufacturing sites. As the customers' needs become diversified more and more, it is required for the company to change its operating method from large quantity batch production systems to customizable and flexible manufacturing systems. For performing this requirements, it is essential for the company to adopt the smart factory. Based on technology acceptance model (TAM), this study investigates the factors influencing continuous use intention and switching intention of the smart factory. To do so, a questionnaire survey is conducted both online and offline. 122 samples are used for the study analysis. The results of this study will provide many implications with many researchers and practitioners relevant smart factories.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.223-228
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• 2021

As a consequence of sudden outbreak of COVID-19 pandemic worldwide, educational institutes around the globe are forced to switch from traditional learning systems to e-learning systems. This has led to a variety of technology-driven pedagogies in e-teaching as well as e-learning. In order to take the best advantage, an appropriate understanding of the cognitive capability is of prime importance. This paper presents an intelligent cognitive maturity recognition system for confidence-based e-learning. We gather the data from actual test environment by involving a number of students and academicians to act as experts. Then a Genetic Programming based simulation and modeling is applied to generate a generalized classifier in the form of a mathematical expression. The simulation is derived towards an optimal space by carefully designed fitness function and assigning a range to each of the class labels. Experimental results validate that the proposed method yields comparative and superior results which makes it feasible to be used in real world scenarios.

• Smart Structures and Systems
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• v.26 no.4
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• pp.495-506
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• 2020

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the hybrid incremental modelling strategy is applied. Secondly, a generalized fuzzy clustering c-means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing are performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.367-377
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• 2008

As part of study to develop LRFD (Load and Resistance Factor Design) codes for foundation structures in Korea, resistance factors for static bearing capacity of driven steel pipe piles were calibrated in the framework of reliability theory. The 57 data sets of static load tests and soil property tests conducted in the whole domestic area were collected and these load test piles were sorted into two cases: SPT N at pile tip less than 50, SPT N at pile tip equal to or more than 50. The static bearing capacity formula and the Meyerhof method using N values were applied to calculate the expected design bearing capacities of the piles. The resistance bias factors were evaluated for the two static design methods by comparing the representative measured bearing capacities with the expected design values. Reliability analysis was performed by two types of advanced methods: the First Order Reliability Method (FORM), and the Monte Carlo Simulation (MCS) method using resistance bias factor statistics. The target reliability indices are selected as 2.0 and 2.33 for group pile case and 2.5 for single pile case, in consideration of the reliability level of the current design practice, redundancy of pile group, acceptable risk level, construction quality control, and significance of individual structure. Resistance factors of driven steel pipe piles were recommended based on the results derived from the First Order Reliability Method and the Monte Carlo Simulation method.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.17-29
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• 1999

This paper presents a high level synthesis method targeting low power consumption for data-dominated CMOS circuits (e.g., DSP). The high level synthesis is divided into three basic tasks: scheduling, resource and register allocation. For lower power scheduling, we increase the possibility of reusing an input operand of functional units. For a scheduled data flow graph, a compatibility graph for register and resource allocation is formed, and then a special weighted network is then constructed from the compatibility graph and the minimum cost flow algorithm is performed on the network to obtain the minimum power consumption data path assignment. The formulated problem is then solved optimally in polynomial time. This method reduces both the switching activity and the capacitance in synthesized data path. Experimental results show 15% power reduction in benchmark circuits.

• Korean Journal of Computational Design and Engineering
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• v.19 no.4
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• pp.356-367
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• 2014

A robotic sapatial augmented reality (RSAR) system, which combines robotic components with projector-based AR technique, is unique in its ability to expand the user interaction area by dynamically changing the position and orientation of a projector-camera unit (PCU). For a moving PCU mounted on a conventional robotic device, we can compute its extrinsic parameters using a robot kinematics method assuming a link and joint geometry is available. In a RSAR system based on user-created robot (UCR), however, it is difficult to calibrate or measure the geometric configuration, which limits to apply a conventional kinematics method. In this paper, we propose a data-driven kinematics control method for a UCR-based RSAR system. The proposed method utilized a pre-sampled data set of camera calibration acquired at sufficient instances of kinematics configurations in fixed joint domains. Then, the sampled set is compactly represented as a set of B-spline surfaces. The proposed method have merits in two folds. First, it does not require any kinematics model such as a link length or joint orientation. Secondly, the computation is simple since it just evaluates a several polynomials rather than relying on Jacobian computation. We describe the proposed method and demonstrates the results for an experimental RSAR system with a PCU on a simple pan-tilt arm.