• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.

• Journal of Agricultural Extension & Community Development
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• v.4 no.1
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• pp.305-315
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• 1997

The propose of this study was to analyze the relationships among the levels of training & development evaluation (reaction, learning, transfer). The study has been conducted on 730 trainees who attended in the basic accounting program in L training and development institution through three incidents of tracked research such as reaction survey right after the conclusion of training, learning evaluation through test, and an evaluation of the transferability after 3 months of training. Questionnaires and test papers for analyses were used after their reliability, validity, difficulty, and discrimination have been verified on a pre-test. The research has been conducted for six months from 4 March 1996 to the end of August 1996, and data have been collected through direct research and survey through mail. The collected data have been worked on at SAS program for Windows with a statistical significance level of 5%. Statistical method that had been used was Pearson's correlation coefficient. The result and conclusion acquired from this study were as follows: Between reaction and learning, learning and transfer of training, only a weak positive correlation exists and explanation or prediction variance showing hierarchical relationship was quite weak with 1%. Thus, this research not only does not strongly support Kirkpatrick(1976)'s hierarchical model of $reaction{\rightarrow}learning{\rightarrow}transfer$, but also indicates that the separate measurement on each levels of training evaluation needs to be done. On the other hand, there was a relatively strong positive correlation between reaction and transfer of training. Based on the result, the conclusion, and the restriction perceived through this study, the following suggestions were made. 1. There is a need to empirically analyze and verify the hierarchy of all levels of training evaluation including the evaluation of the fourth level (result) such as organizational productivity, organizational satisfaction, and separation rate. 2. A great deal of efforts will be needed to systematically analyze what the relationships are among the methods measuring the level of evaluation of the training and development, and to apply this result to the training field.

• Women's Health Nursing
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• v.29 no.3
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• pp.229-238
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• 2023

Purpose: This study analyzed the effects of virtual reality simulation-based problem-based learning on nursing students' critical thinking ability, problem-solving ability, and self-efficacy in the nursing care of women undergoing induction of labor. Methods: A nonequivalent control group pretest and posttest design was employed. The study participants included 52 nursing students (24 in the experimental group and 28 in the control group). The experimental group took a problem-based learning (PBL) class in the first week, and then engaged in self-directed learning using virtual reality simulation. In the second week, lectures about emergency nursing care for induction of labor and drug administration were given. The control group participated in PBL in the first week and lectures in the second week. The study was conducted from April 17 to May 19, 2023. Data were analyzed using the chi-square test, Fisher exact test, analysis of variance, and the independent t-test. Results: Before-and-after differences between the two groups were statistically significant in problem solving ability (t=-5.47, p<.001) and self-efficacy (t=-5.87, p<.001). Critical thinking ability did not show a statistically significant difference between the two groups. The score for satisfaction with the virtual reality simulation program was 3.64±5.88 out of 5 in the experimental group. Conclusion: PBL education using a virtual reality simulation was found to be an effective way of teaching. Although convenience sampling was used, PBL education using virtual reality can be used as an educational strategy to enhance nursing students' problem-solving ability and self-efficacy.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1376-1384
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• 2019

The cosmetic and behavioral aspects of aging have become increasingly evident over the years. Physical aging in people can easily be observed on their face, posture, voice, and gait. In contrast, bone aging only becomes apparent once significant bone degeneration manifests through degenerative bone diseases. Therefore, a more accurate and timely assessment of bone aging is needed so that the determinants and its mechanisms can be more effectively identified and ultimately optimized. This study proposed a deep learning approach to assess the bone age of an adult using whole-body bone scintigraphy. The proposed approach uses multiple inputs deep neural network architectures using a loss function, called mean-variance loss. The data set was collected from Chonnam National University Hwasun Hospital. The experiment results show the effectiveness of the proposed method with a mean absolute error of 3.40 years.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.119-127
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• 2020

The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.1
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• pp.32-40
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• 2013

In this paper, we propose a multi-channel speech enhancement algorithm using beamforming and direction-of-arrival (DOA)-based masking. The proposed algorithm enhances noisy speech basically by the linearly constrained minimum variance (LCMV) algorithm and then a mel-scale Wiener filter designed using DOA-based masking is applied to remove still remaining noises. To improve the performance, we optimize the learning rate of the adaptive filters in LCMV and the DOA threshold to detect target speech spectrum. As performance indices, the perceptual evaluation of speech quality (PESQ) score and output SNRs are measured. Experimantal results show that the proposed algorithm outperforms the conventional LCMV beamformer by 0.09 in PESQ score and 5.75 dB in output SNR, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1975-1988
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• 2019

Short-term traffic flow prediction plays an important role in intelligent transportation systems (ITS) in areas such as transportation management, traffic control and guidance. For short-term traffic flow regression predictions, the main challenge stems from the non-stationary property of traffic flow data. In this paper, we design an ensemble cascading prediction framework based on extremely randomized trees (extra-trees) using a boosting technique called EET to predict the short-term traffic flow under non-stationary environments. Extra-trees is a tree-based ensemble method. It essentially consists of strongly randomizing both the attribute and cut-point choices while splitting a tree node. This mechanism reduces the variance of the model and is, therefore, more suitable for traffic flow regression prediction in non-stationary environments. Moreover, the extra-trees algorithm uses boosting ensemble technique averaging to improve the predictive accuracy and control overfitting. To the best of our knowledge, this is the first time that extra-trees have been used as fundamental building blocks in boosting committee machines. The proposed approach involves predicting 5 min in advance using real-time traffic flow data in the context of inherently considering temporal and spatial correlations. Experiments demonstrate that the proposed method achieves higher accuracy and lower variance and computational complexity when compared to the existing methods.

• Journal of Engineering Education Research
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• v.11 no.4
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• pp.64-75
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• 2008

The purpose of the study was to verify reliability and validity of SDLRS instrument and to prove up possibility of usage as an evaluation method for evaluating life-long learning. Literature review and survey were used to accomplish objectives of the study. 218 students in department of mechanical engineering of A university at Gyunggi province were responded for SDLRS(Guglielmino, 1977) instrument. Data were analyzed using descriptive statistics, factor analysis, t-test, and ANOVA test. 58 items in original version instrument were converted to 23 items. There were 7 factors for assessing the self-directed learning readiness according to this measurement scale with a total variance of about 58%. The total reliability of the final 23 items was $\alpha$. The final 7 factors consisted of love for learning, openness for learning, self-perception, basic learning function and independence, acceptance of responsibility for learning, leadership and future directivity, and creativity and exploration. The result of SDLRS analysis according to individual background, there were significant statistically in the grade, period of employment in industry, entering graduate school or not, and GPA, but no significant statistically in sexual difference, employment in industry or not, final academic level of parent, and income level of the family. In the future, final instrument will be needed to check in the respect of correlation with another ability and skill influencing on life-long learning, and more study will be done for developing life-long learning.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.73-83
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• 2023

This study is a descriptive survey to develop a gamification e-learning class based on self-determination theory and to check its effectiveness. The data collection period was from March 1 to June 15, 2023, and 59 students at G University in G Metropolitan City were surveyed on class participation, learning immersion, and teaching presence before and after the course. IBM SPSS/Win 26.0 was used to analyze the collected data, and descriptive statistics, analysis of variance (ANOVA), and analysis of covariance (ANCOVA) were conducted. The results showed that the self-determination-based gamification class significantly improved students' class participation, learning engagement, and teaching presence (p<.05). An analysis of covariance (ANCOVA) was conducted to determine whether the general characteristics of the participants affected the results of the post-test, and gender affected the post-test results of learning engagement, with an effect of 7.9%. Based on the results of this study, it can be seen that self-determination-based gamification e-learning class is effective in improving learners' class participation, learning engagement, and teaching presence. As the demand for e-learning in universities is expanding, self-determination-based gamification e-learning classes should be developed in various fields of liberal arts and majors.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.73-81
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• 2015

The paper proposes a prototype selection method and evaluates the generalization performance of standard algorithms and prototype based classification learning. The proposed prototype classifier defines multidimensional spheres with variable radii within class areas and generates a small set of training data. The nearest-neighbor classifier uses the new training set for predicting the class of test data. By decomposing bias and variance of the mean expected error value, we compare the generalization errors of k-nearest neighbor, Bayesian classifier, prototype selection using fixed radius and the proposed prototype selection method. In experiments, the bias-variance changing trends of the proposed prototype classifier are similar to those of nearest neighbor classifiers with all training data and the prototype selection rates are under 27.0% on average.