• Korean Journal of Culture and Social Issue
• /
• v.11 no.spc
• /
• pp.117-131
• /
• 2005

The present research integrated various theoretical perspectives of human unlawful behaviors in order to clarify the psychological mechanisms that underly the changes in compliance with and attitude toward law as time goes. Most well-known theories such as classical theory of crime, biosocial and evoluationary theories, and psychological perspectives including psycho-dynamic theory, personality, intellectual/moral development theories, and decision-making were discussed in their unique points in explaining human unlawful behaviors. Finally, social-learning theory and cognitive-dissonance theory has been suggested to explain the psychological mechanism of the phenomena in which people's attitude toward law and compliance with law become weaken through violation experiences of trivial lawful regulations. Especially, the logic of cognitive-dissonance theory (that people committed violation of trivial laws should experience dissonance with their original belief of compliance with law and negative arousal and try to remove the arousal by change their belief along with their behavior) were theoretically convincing to explain the phenomenon and supported by a series of experimental studies. Several practical implications for future constitutional and political activities were discussed in the basis of the cognitive dissonance theory.

• Smart Structures and Systems
• /
• v.32 no.5
• /
• pp.319-338
• /
• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.2
• /
• pp.456-477
• /
• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.20 no.1
• /
• pp.56-65
• /
• 2024

Cast austenitic stainless steels (CASS) and austenitic stainless steel weldments with a ferrite-austenite duplex structure are widely used in nuclear power plants, incorporating ferrite phase to enhance strength, stress relief, and corrosion resistance. Thermal aging at 290-325℃ can induce embrittlement, primarily due to spinodal decomposition and G-phase precipitation in the ferrite phase. This study evaluates the effects of thermal aging by collecting and analyzing various mechanical properties, such as Charpy impact energy, ferrite microhardness, and tensile strength, from various literature sources. Different model expressions, including hyperbolic tangent and phase transformation equations, are applied to calculate activation energy (Q) of room-temperature impact energies, and the results are compared. Additionally, predictive models for Q based on material composition are evaluated, and the potential of machine learning techniques for improving prediction accuracy is explored. The study also examines the use of ferrite microhardness and tensile strength in calculating Q and assessing thermal embrittlement. The findings provide insights for developing advanced prediction models for the thermal embrittlement behavior of CASS and the weldments of austenitic steels, contributing to the safety and reliability of nuclear power plant components.

• Computers and Concrete
• /
• v.34 no.1
• /
• pp.93-122
• /
• 2024

One well-known reason for using Fiber Reinforced Polymer (FRP) composites is to improve concrete strength and strain capacity via external confinement. Hence, various studies have been undertaken to offer a good illustration of the response of FRP-wrapped concrete for practical design intents. However, in such studies, the strength and strain of the confined concrete were predicted using regression analysis based on a limited number of test data. This study presents an approach based on artificial neural networks (ANNs) to develop models to predict the strength and strain at maximum stress enhancement of circular concrete cross-sections confined with different FRP types (Carbone, Glass, Aramid). To achieve this goal, a large test database comprising 493 axial compression experiments on FRP-confined concrete samples was compiled based on an extensive review of the published literature and used to validate the predicted artificial intelligence techniques. The ANN approach is currently thought to be the preferred learning technique because of its strong prediction effectiveness, interpretability, adaptability, and generalization. The accuracy of the developed ANN model for predicting the behavior of FRP-confined concrete is commensurate with the experimental database compiled from published literature. Statistical measures values, which indicate a better fit, were observed in all of the ANN models. Therefore, compared to existing models, it should be highlighted that the newly developed models based on FRP type are remarkably accurate.

• Journal of the Korean Society for information Management
• /
• v.41 no.3
• /
• pp.171-203
• /
• 2024

The purpose of this study is to analyze the factors that influence youth reading habits through exploratory data analysis of various reading variables. Using data from the Korean Children and Youth Panel Survey, we divided participants into a reading group and a non-reading group and analyzed the factors influencing reading habits through a t-test that compared the mean differences between the two groups. The results are as follows: The reading group showed higher scores than the non-reading group in learning-related factors such as study time and academic engagement, positive emotional factors, career-related factors, and social activity factors such as club participation and volunteering. On the other hand, the non-reading group scored higher in key factors such as academic helplessness, emotional problems like aggression and depression, and experiences of delinquent behavior. Additionally, this study suggests further research topics such as the relationship between reading and interactions with parents, cooperation as a social emotion, and physical activity, areas that have not been deeply explored in previous studies.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.4
• /
• pp.221-228
• /
• 2023

Physical and mental changes caused by emotions can affect various behaviors, such as driving or learning behavior. Therefore, recognizing these emotions is a very important task because it can be used in various industries, such as recognizing and controlling dangerous emotions while driving. In this paper, we attempted to solve the emotion recognition task by implementing a multimodal model that recognizes emotions using both audio and video data from different domains. After extracting voice from video data using RAVDESS data, features of voice data are extracted through a model using 2D-CNN. In addition, the video data features are extracted using a slowfast feature extractor. And the information contained in the audio and video data, which have different domains, are combined into one feature that contains all the information. Afterwards, emotion recognition is performed using the combined features. Lastly, we evaluate the conventional methods that how to combine results from models and how to vote two model's results and a method of unifying the domain through feature extraction, then combining the features and performing classification using a classifier.

• Journal of Internet of Things and Convergence
• /
• v.10 no.4
• /
• pp.27-33
• /
• 2024

As of 2023, the number of accident victims in the domestic construction industry is 26,829, ranking second only to other businesses (service industries). The accident types of casualties in all industries were falls (29,229 people), followed by falls (14,357 people). Based on the above data, this study attaches sensors to hard hats and insoles to predict fall accidents that frequently occur at construction sites, and proposes smart safety equipment that applies a random forest algorithm based on the data collected through this. The random forest model can determine fall accidents in real time with high accuracy by generating multiple decision trees and combining the predictions of each tree. This model classifies whether a worker has had a fall accident and the type of behavior through data collected from the MPU-6050 sensor attached to the hard hat. Fall accidents that are primarily determined from hard hats are secondarily predicted through sensors attached to the insole, thereby increasing prediction accuracy. It is expected that this will enable rapid response in the event of an accident, thereby reducing worker deaths and accidents.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.3
• /
• pp.78-98
• /
• 1997

Health education, as the most fundamental concept for national health promotion, alms for developing the self-care ability of the general public. High school days are regarded as the period when most important physical, mental and social developments occur, and most health-related behaviors are formed. School health education is one of the major learning resources influencing health potential in the home and community as well as for the individual student. High school health education in Korea has a fundamental systemic flaw in that health-related subjects are divided and taught under various subjects areas at school. In order to achieve the goal of school health education, it is essential to make a systematic assessment of the learner's concerns connected with his health and life, and the factors affecting them. So far, most of the research projects that had been carried out for improving high school health education were limited in their concerns to a particular aspect of health. Even though some had been done in view of comprehensive school health education, they failed to Include a health assessment of the learner. Therefore, in this study the high school students' concerns related to health and life were investigated in the first place on the basis of the PRECEDE model, developed by Green and others for the purpose of a comprehensive diagnostic research on high school health education. This study was done in two steps : one was the basic study for developing research instrument and the other was the main one. The former was conducted at five high schools in Seoul and Cheongju for 2 months-beginning in March, 1996. The students were asked to respond to questions related to their health and lives in unstructured open-ended question forms. On the basis of analysis of the basic study, the diagnostic instruments for the quality of life, health problems, health behavior and educational factors were constructed to be used for the collection of data for main study. An expert panel and the pilot study were used to improve content validity and reliability of the instruments. The reliability of the instruments was measured at between .7697 and .9611 by the Cronbach $\alpha$. The data for this study were collected from the sample consisted of the junior and senior classes of twenty general and vocational high schools in Seoul and Cheongju for two months period beginning in July, 1996. In analyzing the data, both t-test and $X^2$-test were done by using SAS-$PC^+$ Program to compare data between the sexes of the high school students and the types of high school. A canonical correlation analysis was carried out to determine the relationships among the diagnostic variables, and a multivariate multiple regression analysis was conducted by using LISREL 8.03 to ascertain the influences of variables on the high school students' health and quality of life. The results were as follows : 1) The findings of the hypothesis tests (1) The canonical correlation between the educational diagnosis variables and behavioral, epidemiological, social diagnosis variables was .7221, which was significant at the level of p<.001. (2) The canonical correlation between the educational diagnosis variables and the behavior variables was .6851, which also was significant (p<.001). (3) The canonical correlation between the behavioral diagnosis variables and the epidemiological variables was 4295, which was significant (p<.001). (4) The canonical correlation between the epidemiological diagnosis variables and the social variables was .6005, which was also significant (p<.001). Therefore, the relationship between each diagnosis variable suggested by the PRECEDE model had been experimentally proven to be valid, supporting the conceptual framework of the study as appropriate for assessing the multi-dimensional factors affecting high school students' health and quality of life. Health behavior self-efficacy, the level of parents' interest and knowledge of health, and the level of the perception of school health education, all of which are the educational diagnostic variables, are the most influential variables in students' health and quality of life. In particular, health behavior self-efficacy, a causative factor, was one of the main influential variables in their health and quality of life. Other diagnostic variables suggested in the steps of the PRECEDE model were found to have reciprocal relations rather than a unidirectional causative relationship. The significance of this research is that it has diagnosed the needs of high school health education by the learner-centered assessment of variety of factors related to the health and the life of the students. This research findings suggest an integrated system of school health education to be contrived to enhance the effectiveness of the education by strengthening the influential factors such as self-efficacy to improve the health and quality of the lives of high school students.

• Journal of The Korean Association For Science Education
• /
• v.35 no.2
• /
• pp.247-258
• /
• 2015

Students are engaged in classroom learning, and classroom learning occurs not only through conversation but also through nonverbal behavior. In science classrooms especially, there are meaningful nonverbal behaviors such as practical activities like observation and measurement. But these behaviors have not been properly investigated by existing instruments that try to measure students' engagement. This study aims to develop a new instrument for analyzing students' behavioral engagement especially in science classrooms. The method of developing the instrument was structured along three steps. First, student behaviors have been classified into fourteen categories through literature review and a series of observation of elementary science classroom. Second, based on these, a framework for analyzing student behavioral engagement has been developed. With the framework, every student moment could be labeled as Participatory Speech or Participatory Silence or Non-Participatory Speech or Non-Participatory Silence. Third, an instrument to which the framework is applied has been developed by using Microsoft Excel. As a trial, two fourth-grade students in elementary science class were analyzed with this instrument. The results of the trial analysis shows that the longest period of a science lesson was occupied by Participatory Silence (63% and 72%). Among the participatory silence, 'listening' was the most common (51% and 42% of the trial lesson) and 'observing' which is a specific behavior to science was the fourth position (17% and 17% of the trial lesson). It is expected that the developed instrument could be used in improving our understanding of the patterns of student engagement in science classrooms.