• Physical Therapy Korea
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• v.6 no.1
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• pp.35-46
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• 1999

The purposes of this study were to evaluate and compare the limits of stability(LOS) at different body positioning(standing and one leg standing) in normal 20 years of age. Fourty subjects participated in the study. Subjects comprised 20 males and 20 females who without neurologic, orthopaedic impairments and balance performance impaired. The LOS was measured at Two Feet Forceplate and One Leg Forceplate with BPM(Balance Performance Monitor) Dataprint Software Version 5.3. The subjects stood 4 inches between the feet at Two Feet Forceplate and stood one legged at One Leg Forceplate. In this study applied the paired t-test and independent t-test to determine the statistical significance of results at 0.01 and 0.05 level of significance. The results of this study were as follows: 1) The anteroposterior LOS significantly increased with one legged stance(p<0.05). 2) The mediolateral LOS significantly decreased with one legged stance(p<0.01). 3) There were significant difference posterior LOS in standing and anterior LOS in one legged stance according to sexual difference(p<0.05). 4) The mediolateral LOS was not significant difference between standing and one legged stance according to sexual difference(p>0.05).

• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.170-178
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• 2005

The purpose of this study is to develop a gait-event detection system, which is necessary for the cycle-to-cycle FES control of locomotion. Proposed gait event detection system consists of a signal measurement part and gait event detection part. The signal measurement was composed of the sensors and the LabVIEW program for the data acquisition and synchronization of the sensor signals. We also used a video camera and a motion capture system to get the reference gait events. Machine learning technique with ANN (artificial neural network) was adopted for automatic detection of gait events. 2 cycles of reference gait events were used as the teacher signals for ANN training and the remnants ($2\sim5$ cycles) were used fur the evaluation of the performance in gait-event detection. 14 combinations of sensor signals were used in the training and evaluation of ANN to examine the relationship between the number of sensors and the gait-event detection performance. The best combinations with minimum errors of event-detection time were 1) goniometer, foot-switch and 2) goniometer, foot-switch, accelerometer x(anterior-posterior) component. It is expected that the result of this study will be useful in the design of cycle-to-cycle FES controller.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.119-125
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• 1999

This paper gives an overview of the various approaches to designing statistical pattern recognition scheme based on Bayes discrimination rule and the artificial neural networks for rotating machine condition classification. Concerning to Bayes discrimination rule, this paper contains the linear discrimination rule applied to classification into several multivariate normal distributions with common covariance matrices, the quadratic discrimination rule under different covariance matrices. Also we discribes k-nearest neighbor method to directly estimate a posterior probability of each class. Five features are extracted in time domain vibration signals. Employing these five features, statistical pattern classifier and neural networks have been established to detect defects on rotating machine. Four different cases of rotation machine were observed. The effects of k number and neural networks structures on monitoring performance have also been investigated. For the comparison of diagnosis performance of these two method, their recognition success rates are calculated form the test data. The result of experiment which classifies the rotating machine conditions using each method presents that the neural networks shows the highest recognition rate.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.1
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• pp.93-99
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• 2008

To improve the performance of plasma-DeNOx catalyst, a research on active system was performed experimentally. Two hydrocarbons, propane and diesel, were used as a reductant in this study. First, using propane, basic performances of plasma-DeNOx catalyst such as the effects of plasma and C/N ratio were measured at the various engine operating conditions. NOx conversion of catalyst was improved as plasma power or C/N ratio was increased. Next, diesel was injected in the exhaust gas flow as a reductant. The first test using diesel as a reductant is spray visualization in a high temperature flow and spray images were utilized for analysis of posterior test results. To evaluate the effect of an injection direction, it was compared with 6 installation methods of diesel injector due to THC concentrations at the inlet of plasma. From the results, injector was installed toward downstream direction below the pipe. Then, basic performances of plasma-DeNOx catalyst with various injection quantities were measured. As an injection quantity was increased, $NO_2$ conversion of plasma reactor was increased but NOx conversion of catalyst was nearly zero. This was because NOx conversion of catalyst had slowed as time goes by due to black particles which had been adhered to the catalyst.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.975-989
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• 2014

Lasso (Tibshirani, 1996) and Elastic Net (Zou and Hastie, 2005) have been widely used in various fields for simultaneous variable selection and coefficient estimation. Bayesian methods using a conditional Laplace and a double Pareto prior specification have been discussed in the form of hierarchical specification. Full conditional posterior distributions with each priors have been derived. We compare the performance of Bayesian lassos with Laplace prior and the performance with double Pareto prior using simulations. We also apply the proposed Bayesian hierarchical models to real data sets to predict the collapse of governments in Asia.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.703-722
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• 2016

Passive wireless devices have increasing civilian and military applications, especially in the scenario with wearable devices and Internet of Things. In this paper, we study indoor localization of a target equipped with radio-frequency identification (RFID) device in ultra-wideband (UWB) wireless networks. With known room layout, deterministic multipath components, including the line-of-sight (LOS) signal and the reflected signals via multipath propagation, are employed to locate the target with one transmitter and a single inaccurate receiver. A factor graph corresponding to the joint posterior position distribution of target and receiver is constructed. However, due to the mixed distribution in the factor node of likelihood function, the expressions of messages are intractable by directly applying belief propagation on factor graph. To this end, we approximate the messages by Gaussian distribution via minimizing the Kullback-Leibler divergence (KLD) between them. Accordingly, a parametric message passing algorithm for indoor passive localization is derived, in which only the means and variances of Gaussian distributions have to be updated. Performance of the proposed algorithm and the impact of critical parameters are evaluated by Monte Carlo simulations, which demonstrate the superior performance in localization accuracy and the robustness to the statistics of multipath channels.

• ETRI Journal
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• v.41 no.6
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• pp.771-781
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• 2019

We propose the VS³-NET model to solve the task of question answering questions with machine-reading comprehension that searches for an appropriate answer in a given context. VS³-NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit-based sentences and self-matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question-type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine-reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS³-NET model has an exact-match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.2
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• Restorative Dentistry and Endodontics
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• v.43 no.4
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• pp.38.1-38.9
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• 2018

The restoration of endodontically treated teeth (ETT) with more than one cusp missing and thin remaining walls is challenging for the general practitioner. The use of posts combined with full coverage restorations is a well-established approach, yet not following the minimal invasive principles of adhesive dentistry. Endocrowns are indirect monoblock restorations that use the pulp chamber of the ETT for retention. In this study the fabrication of 4 endocrowns and their clinical performance will be discussed. Two clinical cases include computer-aided design/computer-aided manufacturing manufactured molar endocrowns (one feldspathic ceramic and one hybrid composite-ceramic restoration) and the other two are dental laboratory manufactured resin composite premolar endocrown restorations. The modified United States Public Health Service criteria were used to assess the clinical behavior of the restorations at different follow up periods. Endocrown restorations present a satisfactory clinical alternative, either by the use of resin composite or glass ceramic and hybrid materials. Specific guidelines with minimal alterations should be followed for an endocrown restoration to be successful. Due to limited evidence regarding the long term evaluation of this restorative technique, a careful selection of cases should be applied.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.51-56
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• 2022

In space based constellation network, users are allowed to enter or leave the network arbitrarily. Hence, the number, identities and transmitted data of active users vary with time and have considerable impacts on the receiver's performance. The so-called problem of multiuser detection means identifying the identity of each active user and detecting the data transmitted by each active user. Traditional methods assume that the number of active users is equal to the maximum number of users that the network can hold. The model of traditional methods are simple and the performance are suboptimal. In this paper a Maximum A Posteriori Probability (MAP) based multiuser detection method is proposed. The proposed method models the activity state of users as Markov chain and transforms multiuser detection into searching optimal path in grid map with BCJR algorithm. Simulation results indicate that the proposed method obtains 2.6dB and 1dB E_b/N₀ gains respectively when activity detection error rate and symbol error rate reach 10^-3, comparing with reference methods.