• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.190-198
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• 2023

Sentiment analysis can be applied to all texts generated from websites, blogs, messengers, etc. The study fulfills an artificial intelligence sentiment analysis estimating visiting evaluation opinions (reviews) and visitor ratings, and suggests a deep learning model which foretells either an affirmative or a negative inclination for new reviews. This study operates review big data about Jeju tourist attractions which are extracted from Google from October 1st, 2021 to November 30th, 2021. The normalization data used in the propensity prediction modeling of this study were divided into training data and test data at a 7.5:2.5 ratio, and the CNN classification neural network was used for learning. The predictive model of the research indicates an accuracy of approximately 84.72%, which shows that it can upgrade performance in the future as evaluating its error rate and learning precision.

• Phonetics and Speech Sciences
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• v.3 no.4
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• pp.63-70
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• 2011

Recent studies on English $L^*$ (low pitch accent) have revealed the difference of changes in acoustic manifestation between utterances produced by Koreans and those produced by native speakers of English. However, not much effort has been made to compare $L^*$ focused constituents and non-focused constituents. At the same time, most previous works on focus realization are lacking in terms of normalization of acoustic measurement. Therefore, this research is dedicated to comparing the $L^*$ focused items and non-focused items realized by Koreans and Americans and to examining the realization of English $L^*$ produced by the two language groups with improved normalization of the acoustic features (F0, intensity and duration). Within-group analysis comparing focused words and non-focused words showed both Americans and Koreans prolonged the $L^*$ focused syllables but the effect size of syllable lengthening made by Koreans was far less than that made by Americans. Furthermore, significant F0 lowering was found in Americans but not in Koreans. However, the effect of intensity change caused by $L^*$ focus was not significant within each group. The effect of focused words was tested between the two groups revealing that Koreans implemented English $L^*$ focus with higher F0, lower intensity and shorter duration than Americans. In the instances in which a significant Group x Focus Location (initial, middle and final of a sentence) interaction was found, further analysis testing the effect of Group on each Focus Location was conducted. The testing showed that the Koreans produced shorter syllables at initial and middle of a sentence and higher F0 at initial of a sentence than Americans. Implications for the intonation training were also discussed.

• Smart Media Journal
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• v.7 no.4
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• pp.17-23
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• 2018

We proposed a LSTM-based RNN model that can effectively perform the automatic spacing characteristics. For those long or noisy sentences which are known to be difficult to handle within Neural Network Learning, we defined a proper input data format and decoding data format, and added dropout, bidirectional multi-layer LSTM, layer normalization, and attention mechanism to improve the performance. Despite of the fact that Sejong corpus contains some spacing errors, a noise-robust learning model developed in this study with no overfitting through a dropout method helped training and returned meaningful results of Korean word spacing and its patterns. The experimental results showed that the performance of LSTM sequence-to-sequence model is 0.94 in F1-measure, which is better than the rule-based deep-learning method of GRU-CRF.

• Physical Therapy Korea
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• v.22 no.3
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• pp.33-40
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• 2015

The purpose of this study was to investigate the effect of multi joint-joint position sense (MJ-JPS) training on joint position sense, balance, and gait ability in stroke patients. A total of 18 stroke patients participated in the study. The subjects were allocated randomly into two groups: an experimental group and a control group. Participants in the experimental group received MJ-JPS training (10 min) and conventional treatment (20 min), but participants in the control group only received conventional treatment (30 min). Both groups received training for five times per week for six weeks. MJ-JPS is a training method used to increase proprioception in the lower extremities; as such, it is used, to position the lower extremities in a given space. MJ-JPS measurement was captured via video using a Image J program to calculate the error distance. Balance ability was measured using Timed Up and Go (TUG) and the Berg Balance Scale (BBS). Gait ability was measured with a 10 m walking test (10MWT) and by climbing four flights of stairs. The Shapiro-Wilk test was used to assess normalization. Within-group differences were analyzed using the paired t-test. Between-group differences were analyzed using the independent t-test. The experimental group showed a significant decrease in error distance (MJ-JPS) compared to the control group (p<.05). Both groups showed a significant difference in their BBS and 10MWT results (p<.05). The experimental group showed a significant decrease in their TUG and climbing results (p<.05), but the control group results for those two tasks were not found to be significant (p>.05). There was significant difference in MJ-JPS and by climbing four flights of stairs on variation of pre and post test in between groups (p<.05), but TUG and BBS and 10MWT was no significantly (p>.05). We suggest that the MJ-JPS training proposed in this study be used as an intervention to help improve the functional activity of the lower extremities in stroke patients.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.603-611
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• 2003

This paper proposes an effective feature compensation scheme based on speech model for achieving robust speech recognition. Conventional model-based method requires off-line training with noisy speech database and is not suitable for online adaptation. In the proposed scheme, we can relax the off-line training with noisy speech database by employing the parallel model combination technique for estimation of correction factors. Applying the model combination process over to the mixture model alone as opposed to entire HMM makes the online model combination possible. Exploiting the availability of noise model from off-line sources, we accomplish the online adaptation via MAP (Maximum A Posteriori) estimation. In addition, the online channel estimation procedure is induced within the proposed framework. For more efficient implementation, we propose a selective model combination which leads to reduction or the computational complexities. The representative experimental results indicate that the suggested algorithm is effective in realizing robust speech recognition under the combined adverse conditions of additive background noise and channel distortion.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.11
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• pp.901-907
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• 2021

The present study introduces an artificial neural network (ANN) that can predict the missile aerodynamic coefficients for various missile nose shapes and flow conditions such as Mach number and angle of attack. A semi-empirical missile aerodynamics code is utilized to generate a dataset comprised of the geometric description of the nose section of the missiles, flow conditions, and aerodynamic coefficients. Data normalization is performed during the data preprocessing step to improve the performance of the ANN. Dropout is used during the training phase to prevent overfitting. For the missile nose shape and flow conditions not included in the training dataset, the aerodynamic coefficients are predicted through ANN to verify the performance of the ANN. The result shows that not only the ANN predictions are very similar to the aerodynamic coefficients produced by the semi-empirical missile aerodynamics code, but also ANN can predict missile aerodynamic coefficients for the untrained nose section of the missile and flow conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.367-369
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• 2004

In this paper we propose an effective and reliable hand detection method using neural network with ICA(Independent Component Analysis) Features. Many algorithms of hand detection have been proposed yet. Among them, ICA is the one of the interesting topics in image processing. ICA can not only separate mixed signals but also efficiently extract low-dimensional features in signals. ICA features are able to represent the characteristic of the images well. The object of this paper is to use effectively ICA that has above advantage. That is, by the proper number of Independent component the arithmetic speed is faster and by normalization of ICA feature the performance of detection is more reliable. For this, we adopt the algorithm, the Proportion of variance, which select the ICA feature by comparing the ratio of variance of ICA feature. By this method, we can extract the feature that is good at classifying hand and non-hand. Our experimental results show that by using ICA features, we obtained a better performance in hand detection than by only training NN on the image. And we can use hand detection system effectively and reliably by our proposal.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.737-742
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• 2013

The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.3
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• pp.117-131
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• 2014

As interests in the management of government-owned corporation rise, in these days, debt reduction and normalization of management of those companies have been proceeding under the lead of government. At this point, it is very important to seek internal marketing method for improvement of internal employees' satisfaction, loyalty and organization performance. This study analyzes impact of factors of internal marketing effect on organization satisfaction, loyalty and organization performance in the context of the domestic public companies, the Korea Electric Power Corporation (KEPCO) and the Korea Water Resources Corporation (K-WATER)'s employees empirically. There are some significant differences between the two publics. It is proved that delegation of the authority influences on internal satisfaction and organization performance in the case of KEPCO, and education and training influence on internal satisfaction and organization performance in the case of K-WATER. On the other hand, in the case of K-WATER, any internal marketing factors don't influence on loyalty. The results of this study show somewhat different characteristics depending on the characteristics of firm, however, it is expected that this study can be very useful in regards to similar public enterprises or businesses.