• Journal of the Korean Society of Industry Convergence
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• v.14 no.3
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• pp.105-111
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• 2011

Landmark Spots in 2D gel electrophoresis are used in many methods of 2DEG spot matching. Landmark Spots are obtained manually and it is a bottle neck in the entire protein analysis process. Automated landmark spots detection is a very crucial topic in processing a massive amount of 2DGE data. In this paper, Automated landmark spot detection is proposed using point pattern matching and graph theory. Neighbor spots are defined by a graph theory to use and only a centered spot and its neighbor spots are considered for spot matching. Normalized Hausdorff distance is introduced as a criterion for measuring degree of similarity. In the conclusion, the method proposed in this paper can get about 50% of the total spot pairs and the accuracy rate is almost 100%, which the requirements of landmark spots are fully satisfied.

• Journal of Information Technology Application
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• v.1
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• The Korean Journal of Emergency Medical Services
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• v.17 no.3
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• pp.53-60
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• 2013

Purpose: The purpose of the study is to emphasize the importance of out-of-hospital cardiac arrest resuscitation. This resuscitation by paramedic is very effectively performed under the medical direction of the doctors. Methods: The cardiac arrest victim was 4 month old infant. Informed consent from the parents of the infant was received. CPR combined with 150J defibrillation was performed to the 4 month old infant. Results: We reported that the 4 month old infant survived the cardiac arrest. Out-of-hospital cardiac arrest infant survived after 150J automated external defibrillator and CPR performance. Conclusion: Specific operative protocol is important because the paramedic can apply the proper manual defibrillator effectively. It is important to extend the work scpoe of the EMT.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.431-435
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• 2008

Quantitative information of a three dimensional(3D) kinematics of joint is very useful in knee joint surgery, understanding how knee kinematics related to joint injury, impairment, surgical treatment, and rehabilitation. In this paper, an automated 2D/3D image matching technique was developed to estimate the 3D in vivo knee kinematics using dual X-ray images. First, a 3D geometric model of the knee was reconstructed from CT scan data. The 3D in vivo position and orientation of femoral and tibial components of the knee joint could be estimated by minimizing the pixel by pixel difference between the projection images from the developed 3D model and the given X-ray images. The accuracy of the developed technique was validated by an experiment with a cubic phantom. The present 2D/3D image matching technique for the estimation of in vivo joint kinematics could be useful for pre-operative planning as well as post-operative evaluation of knee surgery.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.43-56
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• 2011

An integrated system composed of guidance, navigation and control (GNC) system for autonomous proximity operations and the docking of two spacecraft was developed. The position maneuvers were determined through the integration of the state-dependent Riccati equation formulated from nonlinear relative motion dynamics and relative navigation using rendezvous laser vision (Lidar) and a vision sensor system. In the vision sensor system, a switch between sensors was made along the approach phase in order to provide continuously effective navigation. As an extension of the rendezvous laser vision system, an automated terminal guidance scheme based on the Clohessy-Wiltshire state transition matrix was used to formulate a "V-bar hopping approach" reference trajectory. A proximity operations strategy was then adapted from the approach strategy used with the automated transfer vehicle. The attitude maneuvers, determined from a linear quadratic Gaussian-type control including quaternion based attitude estimation using star trackers or a vision sensor system, provided precise attitude control and robustness under uncertainties in the moments of inertia and external disturbances. These functions were then integrated into an autonomous GNC system that can perform proximity operations and meet all conditions for successful docking. A six-degree of freedom simulation was used to demonstrate the effectiveness of the integrated system.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.126-133
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• 2004

Lots of researches and applications on the automated overhead cranes in shops have been done for some decades, but a few successful results are reported. Integrated crane control systems designed by famous engineering companies are still expensive and are not satisfactory in view of maintenance and reliability. A more reasonable control system fit to requirements of manufacturing industries is suggested in the study. The new deigned system has superior capabilities for anti-sway of rope and position control. The controller for automated operations is composed of a Linux-based PC for non real-time control and a high-speed PLC for hard real-time control. Some algorithms required for coil yard operations as well as main control algorithms such as reference position generation, position control and anti-sway control have been designed and fully tested on the new crane simulator. The designed crane control system showed satisfactory performance on position control accuracy and anti-sway of rope. The maximum positional error is 8mm and the maximum sway error is 0.1 degrees. The suggested control strategies have been successfully applied to the 10-1 crane in No. 4 CGL of in the Kwangyang Steel Works and in commercial operation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.225-232
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• 2004

Recently, there has been a rapid development in computer technology, which has in turn led to develop the automated welding system using Artificial Intelligence (AI). However, the automated welding system has not been achieved duo to difficulties of the control and sensor technologies. In this paper, the classification of the optimized bead geometry such as bead width, height penetration and bead area in the Gas Metal Arc (GMA) welding with fuzzy logic is presented. The fuzzy C-Means algorithm (FCM), which is best known an unsupervised fuzzy clustering algorithm is employed here to analysis the specimen of the bead geometry. Then the quality of the GMA welding can be classified by this fuzzy clustering technique and the choice for obtaining the optimal bead geometry can also be determined.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.12
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• pp.55-64
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• 2008

This paper describes a research work of developing an automated progressive design system for manufacturing the product with multi processes such as piercing, bending, and deep drawing. An approach to the system for progressive working is based on the knowledge-based rules. Knowledge for the system is formulated from plasticity theories, experimental results and the empirical knowledge of field experts. The system consists of three main modules, which are shape treatment, strip layout, and die layout modules. Based on knowledge-based rules, the system is designed considering several factors, such as material and thickness of a product, piercing, bending and deep drawing sequence, and the complexities of the blank geometry and punch profiles. It generates the strip layout drawing for an automobile product. Die design for each process is carried out through the die layout module from the results of the strip layout module. Results obtained using the modules enable the designers for manufacturing products with multi processes to be more efficient in this field.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.90-95
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• 2007

Conventional bridge inspection involves the physical positioning of an inspector by the hydraulic telescoping boom of a "snooper truck" thereby providing visual access to bridge components. The process is time consuming, hazardous, and may be affected by lighting conditions, Therefore, it is of great interest that an automated and/or teleoperated inspection robot be developed to replace the manual inspection procedure. This paper describes the advanced bridge inspection robot system under development and other related activities currently undergoing at the Bridge Inspection Robot Development Interface (BIRDI). BIRDI is a research consortium with its home in the Department of Civil and Environmental System Engineering at Hanyang University at Ansan. Its primary goal is to develop advanced robot systems for bridge inspection and monitoring for immediate field application and commercialization. The research program includes research areas such as advanced inspection robot and motion control system, sensing technologies for monitoring and assessment, and integrated system for bridge maintenance. The center embraces 12 institutions, which consist of 7 universities, 2 research institutes, and 3 private enterprises. Research projects are cross-disciplinary and include experts from structural engineering, mechanical engineering, electronic and control engineering. This research project will contribute to advancement of infrastructure maintenance technology, enhancement of construction industry competitiveness, and promotion of national capacity for technology innovation.

• The Journal of Korean Association of Computer Education
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• v.15 no.6
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• pp.83-89
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• 2012

It becomes important for the countries with ageing populations to prepare coming u-health societies. Surely, the technology of u-health will help the elderly everywhere and everytime because the key concept is based on the combination of the ubiquitous and healthcare technology. In this paper, a design scheme is proposed to construct an automated diagnosis system of body fluid for u-health. Specifically, the system is comprised of embedded system, light generating system and photo sensing system. The system analyzes the diffused lights from the chemical reagent pads under LED lights and diagnoses a variety of diseases according to the predefined color values on specific diseases. As a result, the system is implemented as a hardware and shows the measurement accuracy of 95% in reliability.