• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.321-325
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• 2001

Servo drives with microcomputer control provide the possibility of using modem and sophisticated control algorithms. As an additional feature it is possible to implement parallel and/or redundant software and hardware structures to realise safe motion or similar security functions. Unfortunately microcomputer control also has some impact on the behaviour of servo drives. Control algorithm, cycle time, sensors and interface have to be perfectly synchronised. Special control schemes are necessary on the line side (power supply) to meet the actual requirements concerning EMC. This contribution presents experiences and results obtained from a modem digital drive system pointing out the influences of low and high accuracy position sensors and the interdependencies mentioned above.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.101-106
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• 2004

Subcellular localization is a key functional char acteristic of proteins. With the number of sequences entering databanks rapidly increasing, the importance of developing a powerful tool to identify protein subcellular location has become self-evident. In this paper, we introduce a novel method for predic ting protein subcellular locations from protein sequences. The main idea was motivated from the observation that amino acid pair composition data is redundant. By classifying from multiple feature subsets and using many kinds of amino acid pair composition s, we forced the classifiers to make uncorrelated errors. Therefore when we combined the predictors using a voting scheme, the prediction accuracy c ould be improved. Experiment was conducted on several data sets and significant improvement has been achieve d in a jackknife test.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.756-768
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• 1997

In order to control a dynamic gait of quadrupedal walking robot, the equations of motion of the whole mechanism are required. In this research, the equations of motion are formulated analytically using Kane's dynamic approach. As a dynamic gait model, a trot gait has been adopted. The degree of freedom of whole mechanism could be reduced to 7 by idealizing the kinematic feature of the trot gait. Using the equations of motion formulated, the results of the redundant-joint torque analysis and the simulation of dynamic walking motion are presented.

• Journal of Power Electronics
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• v.2 no.1
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• pp.25-31
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• 2002

Servo drives with microcomputer control provide the possibility of using modern and sophisticated control algorithms. As an additional feature it is possible to implement parallel and/or redundant software and hardware structures to realise safe motion or similar security functions. Unfortunately microcomputer control also has some impact on the behaviour of servo drives. Control algorithm, cycle time, sensors and interface have to be perfectly synchronised. Special control schemes are necessary on the line side (power supply) to meet the actual requirements concerning EMC. This contribution presents experiences and results obtained from a modern digital drive system pointing out the influences of low and high accuracy position sensors and the interdependencies mentioned above.

• Speech Sciences
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• v.10 no.1
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• pp.3-13
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• 2003

The devoicing vowel is a phonological process whose contrast in sonority is lost or reduces in a particular phonetic environment. Phonetically, the vocal fold vibration originates from the abduction/adduction of the glottis in relation to supraglottal articulatory movements. The purpose of this study is to investigate Korean vowel devoicing by means of experimental instruments. The interrelated laryngeal adjustments and aerodynamic effects for this voicing can clarify the redundant articulatory gestures relevant to the distinctive feature of sonority. Five test words were selected, being composed of the high vowel /i/, between the fricative and strong aspirated or lenis affricated consonants. The subjects uttered the test words successively at a normal or at a faster speed. The EMG, the sensing tube Gaeltec S7b and the High-Speech Analysis system and MSL II were used in these studies. Acoustically, three different types of speech waveforms and spectrograms were classified, based on the voicing variation. The intraoral air pressure curves showed differences, depending on the voicing variations. The activity patterns of the PCA and the CT for devoicing vowels appeared differently from those showing the partially devoicing vowels and the voicing vowels.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2153-2155
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• 2001

This thesis presents a development of intelligent digital governing system and it's application on Sumjingang Hydro-Power plant. The developed system consists of hardware, software and governing algorithm. The feature of hardware is triplex-modular fail safe redundant system for a safe turbine running. The software consists of operating system and application program. The operating system has real-time and multi-tasking features. And also, application algorithm is composed to run francis type hydro-turbine. The developed digital governing system is applied to Sumjingang hydro-power plant, Korea Hydro Nuclear Power Corporation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.355-362
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• 2007

Diabetes mellitus can cause devastating complications, which often result in disability and death, and diabetic nephropathy is a leading cause of death in people with diabetes. In this study, we tried to predict the onset of diabetic nephropathy from an irregular and unbalanced diabetic dataset. We collected clinical data from 292 patients with type 2 diabetes and performed preprocessing to extract 184 features to resolve the irregularity of the dataset. We compared several feature selection methods, such as ReliefF and sensitivity analysis, to remove redundant features and improve the classification performance. We also compared learning methods with support vector machine, such as equal cost learning and cost-sensitive learning to tackle the unbalanced problem in the dataset. The best classifier with the 39 selected features gave 0.969 of the area under the curve by receiver operation characteristics analysis, which represents that our method can predict diabetic nephropathy with high generalization performance from an irregular and unbalanced dataset, and physicians can benefit from it for predicting diabetic nephropathy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1456-1463
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• 2010

The safety critical systems in nuclear power plants should be designed to have a high level of fault tolerant capability because those systems are used for protection or mitigation of the postulated accidents of nuclear reactor. Due to increasing of the system complexity of the digital based system in nuclear fields, the reliability of the digital based systems without an auto-test or a self-diagnostic feature is generally lower than those of analog system. To overcome this problem, additional redundant architectures in each redundant channel and self-diagnostic features are commonly integrated into the digital safety systems. The self diagnostic function is a key factor for increasing fault tolerant capabilities in the digital based safety system. This paper presents an availability and safety evaluation model to analyze the effect to the system's fault tolerant capabilities depending on self-diagnostic features when the loss or erroneous behaviors of self-diagnostic function are expected to occur. The analysis result of the proposed model on the several modules of a safety platform shows that the improvement effect on unavailability of each module has generally become smaller than the result of usage of conventional models and the unavailability itself has changed significantly depending on the characteristics of failures or errors of self-diagnostic function.