• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• The Journal of Korean Society for School & Community Health Education
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• v.23 no.4
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• pp.41-48
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• 2022

Objectives: This study aims to investigate the mediating effect of social support on the relationship between the viewing of sensational videos of idol stars through the mass media and the sexual openness of adolescents. Methods: This study used the 'Study on countermeasures against sexual commodification of adolescents through mass media in the smart era' conducted by the 'Korea Youth Policy Institute' in 2014 for the entire country as the main data. Variables consisted of socio-demographic characteristics of study subjects, video viewing, social support, and sexual openness. For data analysis, SPSS ver 23.0 program was used. Results: As a result of comparing the averages, male students (20.40) had higher sexual openness than female students (18.67), and high school students (20.27) had higher sexual openness than middle school students (18.05) at school level. By grade level, sexual openness increased from the first year of middle school (17.47) to the third year of high school (20.82) (p<0.001). In order to verify the mediating effect of video viewing on the effect of adolescent social support on sexual openness, 3-step mediated regression analysis and Sobel test were conducted. As a result, video viewing had a significant effect on sexual openness through social support (p<0.001). Conclusion: Rather than obscuring the environment of mass media unconditionally, creating an atmosphere according to the tendency of realistic social support and reinforcing emotional education will help prevent the adverse effects of reckless sexualization of adolescents.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1121-1128
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• 2022

Information about the present invention is made available for business use. You are helping to use the LCD, you can't use the LCD screen. During software configuration, Raspbian was used to provide the system environment. We made our way through the menu and made our financial through play. It provides various information such as weather, weather, apps, streamer music, and web browser search function, and it can be charged. Currently, the 'Google Assistant' will be provided through the GUI within a predetermined time.

• Journal of KIBIM
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• v.12 no.4
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• pp.61-69
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• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.61-69
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• 2022

As the delivery industry grew around the restaurant industry along with the COVID-19 situation, the number of delivery workers increased significantly. Along with that, new forms of delivery using personal mobility (PM) also emerged and two-wheeled or PM-related accidents are steadily increasing. This study manufactures a PM's driving analysis device to establish a safe delivery monitoring environment. This system was constructed to process data collected from the driving analysis device and through a cloud server, which would recognize and record special situations (acceleration/deceleration, speed bump) that could occur during the PM's driving situation. As a result, the angular speed, acceleration, and geomagnetic values collected from the IMU in the device were able to determine whether to drive, drive on the sidewalk, and drive on the speed bump. This technology was able to achieve approximately 1600 times more driving information storage efficiency than conventional image-based recording devices.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.95-103
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• 2022

The goal of education and training in military is to foster strong combatants who can fight and defeat enemies. The Korean military is deeply aware of the importance of education & training, and has been introducing various advanced training systems so far. Despite these efforts, however, the military environment to maintain and strengthen the level of training is becoming increasingly difficult. In this study, it was conducted on the effectiveness analysis and utilization of the game system for military education & training through literature review. As a result of literature analysis, the introduction of the game system could be expected to have various effects throughout the cognitive and behavioral areas. Based on this effect analysis, the concept and shape of game system operation for each purpose were derived, and an improved plan using the game system was proposed.

• The Journal of Industrial Distribution & Business
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• v.13 no.4
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• pp.35-49
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• 2022

Purpose - According to the recently changing consumer smart environment and consumer decision-making process, this study investigates the structural relationship between electronic(online) WOM information characteristics and brand community experience value types on specific brand reliability and brand transformation. In particular, the characteristics of word of mouth information and the experience value of brand community users were divided into detailed fac tors and approached. Methodology - In order to proceed with this study, we review previous studies and setting hypotheses. The hypothesis was verified through a survey that was conducted for the consumers with online consumption activities in less than six months. With reference to previous studies, operational definition was made for the questionnaire design. In order to verify the hypothesis, 282 people were statistically analyzed through the survey This data were used for AMOS for confirm hypothesis established. Results - eWOM information characteristics were classified into usefulness, timeliness and un-bias, and online community experience values were classified into interaction, playfulness, and virtuality. In addition, it is to investigate the relationship between the brand reliability and user's experience value in brad community. The main results are as follows. The first result was that usefulness and un-bias, which are the eWOM information characteristics had a positive effect on forming brand reliability. However, the factor of timeliness did not affect brand reliability. Second, in terms of user experience value and brand reliability in the brand community. It was fo und that experience values such as interaction, playfulness, and vituality all had a positive influence on brand reliability. Third, it was found that brand reliability has a positive influence on the on-line conversion activity of users. Conclusions - Through this study, the field of online consumer behavior research is expanding, and this study suggested that careful management is necessary according to the type or characteristics of eWOM information. Additionally, it presents the importance of the user's empirical value in the brand community influencing brand attitude and reliability. In practice, the implementation of the marketing communication mix in digital marketing has recently been underway to enhance the conversion behavior of users. At this level, it also reveals the preceding factors that increase user conversion behavior.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.117-124
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• 2021

Livestock behavioral analysis is a factor that has a great influence on livestock health management and agricultural productivity increase. However, most digital devices introduced for behavioral analysis of livestock do not provide raw data and also provide limited analysis results. Such a closed system makes it more difficult to integrate data and build big data, which are essential for the introduction of advanced IT technologies. Therefore, it is necessary to supply farm-scale data collection devices that can be easily used at low cost. This study presents a data collection system for analyzing the behavior of livestock. The system consists of a number of miniature computing units that operate wirelessly, and collects livestock body temperature and acceleration data, location information, and livestock environment data. In addition, this study presents an algorithm for estimating the behavior of livestock based on the collected acceleration data. For the experiment, a system was built in a Korean cattle farm in Icheon, Gyeonggi-do, and data were collected for 20 Korean cattle, and based on this, the empirical and analysis results were presented.