• International Journal of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.141-147
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• 2024

One of the biggest drawbacks of online education using virtual environments is that teachers cannot see students' facial expressions. In offline classes, teachers usually observe students' expressions to determine if they are focused or enjoying the lesson, and they can adjust their teaching accordingly. For example, if a teacher notices that students are losing focus, they can slow down the pace of the lesson or tell an interesting story to regain their attention. However, in a virtual environment, it is impossible to see students' expressions, making it difficult to gather any information about them. As a result, instructors may feel like they are teaching in isolation and are unable to appropriately respond to students' reactions. This can easily lead to a lack of interaction between the teacher and students. This issue has already been raised in other studies, and research has been conducted to measure student engagement and attention. However, existing systems typically measure overall engagement for the entire class or represent the data in numbers or graphs, which doesn't provide impactful real-time feedback to the instructor. This study proposes an online education system that visually displays each student's level of engagement and attention in real time to address this issue. The key advantage of this system is that it allows teachers to quickly and intuitively grasp students' reactions and adjust their teaching in real time accordingly.

• The Research Journal of the Costume Culture
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• v.22 no.2
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• pp.273-289
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• 2014

The purpose of this study is to identify the determinants of the susceptibility of global consumer culture. As determinants, materialism and self monitoring as psychological variables and fashion clothing product knowledge as clothing-related variable were included. It was hypothesized that both psychological variables and clothing-related variable influence susceptibility of global consumer culture. Data were gathered by surveying university students in Seoul metropolitan area, using convenience sampling, and 311 questionnaires were used in the statistical analysis. In analyzing data, exploratory factor analysis using SPSS and confirmatory factor analysis and path analysis using AMOS were conducted. Factor analysis of susceptibility of global consumer culture revealed four dimensions, 'social prestige' factor, 'quality perception' factor, 'conformity to others' factor, and 'conformity to consumption trend' factor. In addition, factor analysis of self monitoring revealed three dimensions, 'center-oriented attention' factor, 'situation-appropriate self-presentation' factor, and 'strategic displays of self-presentation' factor. The results showed that all the fit indices for the variable measures were quite acceptable. In addition, the overall fit of the model suggests that the model fits the data well. Tests of the hypothesized path show that all variables except for the one factor of self monitoring, 'center-oriented attention', and materialism influence all the factors of susceptibility of global consumer culture. The implications of these findings and suggestions for future study are also discussed.

• Nuclear Engineering and Technology
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• v.41 no.3
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• pp.247-270
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• 2009

This paper reviews studies on information searching behavior in process control systems and discusses some implications learned from previous studies for use in human factors studies on nuclear power plants (NPPs) main control rooms (MCRs). Information searching behavior in NPPs depends on expectancy, value, salience, and effort. The first quantitative scanning model developed by Senders for instrument panel monitoring considered bandwidth (change rate) of instruments as a determining factor in scanning behavior. Senders' model was subsequently elaborated by other researchers to account for value in addition to bandwidth. There is also another type of model based on the operator's situation awareness (SA) which has been developed for NPP application. In these SA-based models, situation-event relations or rules on system dynamics are considered the most significant factor forming expectancy. From the review of previous studies it is recommended that, for NPP application, (1) a set of symptomatic information sources including both changed and unchanged symptoms should be considered along with bandwidth as determining factors governing information searching (or visual sampling) behavior; (2) both data-driven monitoring and knowledge-driven monitoring should be considered and balanced in a systematic way; (3) sound models describing mechanisms of cognitive activities during information searching tasks should be developed so as to bridge studies on information searching behavior and design improvement in HMI; (4) the attention-situation awareness (A-SA) modeling approach should be recognized as a promising approach to be examined further; and (5) information displays should be expected to have totally different characteristics in advanced control rooms. Hence much attention should be devoted to information searching behavior including human-machine interface (HMI) design and human cognitive processes.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2184-2189
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• 2009

China is one of the countries in which flood occurs most frequently in the world and with the current economic growth; flood disaster causes more and more economic losses. Chinese government pays more attention to flood monitoring and assessment by space technology. Since1983, NOAA(AVHRR), Landsat-TM, LANDSAT-ETM+, JERS-1, SPOT, ERS-2, Radarsat-1, CBERS-1, Envisat have been used for flood monitoring and assessment. Due to the bad weather conditions during flood, microwave remote sensing is the major tools for flood monitoring. Envisat is one of the best satellite with powerful SAR. Its application for flood monitoring has been studied and its near real time(NRT) application can be realized on the basis of real-time delivery of image. During the 2005, 2006 and 2007 flood seasons, over the 31 NRT flood monitoring based on Envisat, had been carried out in Yangtze, Songua, Huaihe, pearl river basin. The result shows that Envisat SAR is very useful data source for flood disaster monitoring and assessment.

• Journal of Sensor Science and Technology
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• v.24 no.5
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• pp.281-286
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• 2015

The importance of air quality monitoring is rapidly increasing. Even though state-of-the-art air quality monitoring technologies such as mass spectrometry, gas chromatography, and optical measurement enable high-fidelity measurement of air pollutants, they cannot be widely used for portable or personalized platforms because of their high cost and complexity. Recently, personalized and localized environmental monitoring, rather than global and averaged environmental monitoring, has drawn greater attention with the advancement of mobile telecommunication technologies. Here, micro- and nano-technologies enable highly integrated and ultra-compact sensors to meet the needs of personalized environmental monitoring. In this paper, several examples of MEMS-based gas sensors for compact and personalized air quality monitoring are explained. Additionally, the principles and usage of functional nanomaterials are discussed for highly sensitive and selective gas sensors.

• International Journal of High-Rise Buildings
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• v.12 no.4
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• pp.321-333
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• 2023

Structural health monitoring is a technology or research field that extends the service life of structures and contributes to the prevention of disaster accidents by continuously evaluating the safety, stability, and serviceability of structures as well as allowing timely and proper maintenance. However, the contact-type sensors used for it require considerable time, cost, and labor for installation and maintenance. As an alternative, computer vision has attracted attention recently. Computer vision has the potential to make quality, deformation, and damage monitoring for structures contactless and automated. In this study, research cases in which computer vision was utilized for structural health monitoring are introduced, and its effects and limitations are summarized. Therefore, the applicability and future research directions of computer vision-based structural health monitoring are discussed.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.119-128
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• 2022

Recently, due to the development of related technologies for autonomous vehicles, driving work is changing more safely. However, the development of support technologies for level 5 full autonomous driving is still insufficient. That is, even in the case of an autonomous vehicle, the driver needs to drive through forward attention while driving. In this paper, we propose a method to monitor driving tasks by recognizing driver behavior. The proposed method uses pre-trained deep convolutional neural network models to recognize whether the driver's face or body has unnecessary movement. The use of pre-trained Deep Convolitional Neural Network (DCNN) models enables high accuracy in relatively short time, and has the advantage of overcoming limitations in collecting a small number of driver behavior learning data. The proposed method can be applied to an intelligent vehicle safety driving support system, such as driver drowsy driving detection and abnormal driving detection.

• Journal of Technology Innovation
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• v.6 no.1
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• pp.99-127
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• 1998

The purposes of this study are first, to plan communication strategies for promoting the 1st National Science Festival and other events of the April 1997's Science Month in Koreas; second, to monitor communication activities done for those events; third, to evaluate effects of communication activities. Both the Ministry of Science and Technology and the Korea Science Foundation were arranged to execute our planned communication strategies. Basically we utilized the three sequences of human behavioral condition : Exposure, attention, and cognition. For planning, we suggested concrete communication strategies for each sequence, for example, first, those for bringing exposure to every event, second, those for bringing attention to the event, and third, those for bringing cognition of the event. Those communication strategies were suggested to use specifics of newspapers, television programs, radio programs, commercial and corporate magazines, electric visual sign advertisements on the street, and computer communication. For monitoring and evaluation, we used the same three sequences as the criteria. For example, we monitored and evaluated how much exposure, attention or cognition an event got or which specific medium contributed to exposure to, cognition of an event. For monitoring, graduate students were dispatched to examine each event through watching and interviewing. For evaluation, about 950 of event participants and non-participants were surveyed by means of face-to-face interview. Overall, we found that newspaper articles and television programs contributed a lot to people's exposure to events of the April 1997's Science Month. Especially, newspaper played a major role of heightening exposure. However, most events and/or their science and technology content failed to get salient attention and its following active cognition. The 1st National Science Festival attracted much exposure, but had some problems of disorder and commercialism. This sharp increase of exposure and some attention were believed to have reinforced people's, especially event participants' positive opinion of science and technology which is part of scientific culture.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.242-252
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• 2021

Following the intuition that the local information in time instances is hardly incorporated into the posterior sequence in long short-term memory (LSTM), this paper proposes an attention augmented mechanism for fault diagnosis of the complex chemical process data. Unlike conventional fault diagnosis and classification methods, an attention mechanism layer architecture is introduced to detect and focus on local temporal information. The augmented deep network results preserve each local instance's importance and contribution and allow the interpretable feature representation and classification simultaneously. The comprehensive comparative analyses demonstrate that the developed model has a high-quality fault classification rate of 95.49%, on average. The results are comparable to those obtained using various other techniques for the Tennessee Eastman benchmark process.

• Smart Structures and Systems
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• v.30 no.6
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.