• Management & Information Systems Review
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• v.29 no.4
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• pp.305-332
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• 2010

This study was searching for elements affecting satisfaction of customers by comparing health management service businesses such as wellbeing care, yoga, and fitness. The discovered elements were analyzed and verified to find which elements are affecting what businesses through case studies. Multidirectional analysis was implemented for each service type using program, physical environment, and provided service drawn from the previous researches with SERVQUAL criteria and measured values on customer satisfactions. According to the analysis, physical environment in forest wellbeing care, program in yoga, and provided service in fitness were the most affecting elements. Thus, each health management service business must consider the lifestyle and trend of customers, and the specialized service corresponding to its uniqueness must be provided to customers. Surely, modernized exercise equipment, personalized program, and comfortable-luxurious settings are must have in order to be competitive. In addition, the business owners have to realize that customers are moving to quality from quantity. This means exercise must be brought up to the level of social value for relationship and links rather than left at the level of simple physical and mental trainings. To achieve these, other programs to support relationship among customers and circulating system with friendly environment must be considered at the same time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1272-1278
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• 2022

The government enacted and promulgated the 'Severe Accident Punishment Act' in January 2021 and is implementing this law. However, the number of occupational accidents in 2021 increased by 10.7% compared to the same period of the previous year. Therefore, safety measures are urgently needed in the industrial field. In this study, BLE Mesh networking technology is applied for safety management of high-risk industrial sites with poor communication environment. The complex sensor AIoT device collects gas sensing values, voice and motion values in real time, analyzes the information values through artificial intelligence LSTM algorithm and CNN algorithm, and recognizes dangerous situations and transmits them to the server. The server monitors the transmitted risk information in real time so that immediate relief measures are taken. By applying the AIoT device and safety management system proposed in this study to high-risk industrial sites, it will minimize industrial accidents and contribute to the expansion of the social safety net.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.587-595
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• 2014

As the number of users of SNS (Social Network Service) and smart devices increases sharply nowadays, many studies on various teaching models and methodologies have been made in order to utilize SNS in education. However, there are not so sufficient studies that explore social media as a learning environment and analyze empirically its relation with the academic achievement. Since various kinds of learning experiences are required in order to foster creative talents, it is necessary to have information sharing, debate and information exchange utilizing SNS. If utilizing SNS for general computer education in a university, it will be possible to collect learners' various thoughts and opinions more effectively. Because real-time feedback can be possible in each individual space through SNS by sharing the information related to the interactions between learner and teacher or between leaner and learner and exchanging opinions each other, the learner's ability to utilize a computer can be improved. Especially SNS can provide a real-time help to solve problems for underachieved students and provide an opportunity to improve the academic achievement.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.259-267
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• 2022

With the rapid development and introduction of digital technologies, both everyday human life and technological processes of any production are changing, which stimulates the transformation of the economy and education. Digital technologies are not only a tool, but also a living environment of modern human, which opens up new opportunities: learning at any convenient time, continuing education, the ability to form individual educational learning trajectories and more. However, the digital environment requires teachers to take a modern approach to the organization of the educational process, the formation of new skills and abilities to work in the digital educational environment. As a result of the study, it was found that the system of vocational education should provide training for masters of industrial training who have a high level of digital competence. The purpose of the article is to single out, theoretically substantiate and determine the level of formation of structural components of digital competence of future masters of agricultural training. The structure of digital competence of agricultural master was analyzed on the basis of domestic and foreign scientists researches. Systematized research results indicate that digital competence consists of four components: motivational-value (combination of internal and external motives for the use of digital technologies in future professional activities), cognitive (a set of theoretical knowledge, skills and abilities of future master of industrial training to effectively build educational process with the use of digital technologies), activity-professional (expansion and deepening of knowledge, skills, necessary skills for effective implementation of digital technologies in the educational process) and evaluative-reflexive (ability to analyze and self-analyze own activities and its results taking into account professional characteristics, self-realization in professional activities through the use of digital technologies). These components are comparable with the indicators that describe the knowledge, skills and abilities needed by the future master of industrial training to organize the modern educational process. A questionnaire was conducted to determine the levels of this competence formation, which allows us to conclude that it is necessary to increase the level of formation of all components of digital competence of future masters of industrial training in agriculture. The results of the study can be used as a basis for the development of disciplines that form the special competencies of masters of industrial training in agriculture and programs of advanced training of teachers.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.123-128
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• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.152-159
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• 2024

Cracks in structural materials present a critical challenge to infrastructure safety and long-term durability. Timely and precise crack detection is essential for proactive maintenance and the prevention of catastrophic structural failures. This study introduces an innovative approach to tackle this issue using U-Net deep learning architecture. The primary objective of the intended research is to explore the potential of U-Net in enhancing the precision and efficiency of crack detection across various concrete crack detection under various environmental conditions. Commencing with the assembling by a comprehensive dataset featuring diverse images of concrete cracks, optimizing crack visibility and facilitating feature extraction through advanced image processing techniques. A wide range of concrete crack images were collected and used advanced techniques to enhance their visibility. The U-Net model, well recognized for its proficiency in image segmentation tasks, is implemented to achieve precise segmentation and localization of concrete cracks. In terms of accuracy, our research attests to a substantial advancement in automated of 95% across all tested concrete materials, surpassing traditional manual inspection methods. The accuracy extends to detecting cracks of varying sizes, orientations, and challenging lighting conditions, underlining the systems robustness and reliability. The reliability of the proposed model is measured using performance metrics such as, precision(93%), Recall(96%), and F1-score(94%). For validation, the model was tested on a different set of data and confirmed an accuracy of 94%. The results shows that the system consistently performs well, even with different concrete types and lighting conditions. With real-time monitoring capabilities, the system ensures the prompt detection of cracks as they emerge, holding significant potential for reducing risks associated with structural damage and achieving substantial cost savings.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Journal of Bio-Environment Control
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• v.29 no.3
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• pp.277-284
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• 2020

To increase the utilization of the intelligent methodology of smart farm management, estimation modeling techniques are required to assess prior examination of crops and environment changes in realtime. A mandatory environmental factor such as CO₂ is challenging to establish a reliable estimation model in time domain accounted for indoor agricultural facilities where various correlated variables are highly coupled. Thus, this study was conducted to develop an artificial neural network for reducing time complexity by using environmental information distributed in adjacent areas from a time perspective as input and output variables as CO₂. The environmental factors in the smart farm were continuously measured using measuring devices that integrated sensors through experiments. Modeling 1 predicted by the mean data of the experiment period and modeling 2 predicted by the day-to-day data were constructed to predict the correlation of CO₂. Modeling 2 predicted by the previous day's data learning performed better than Modeling 1 predicted by the 60-day average value. Until 30 days, most of them showed a coefficient of determination between 0.70 and 0.88, and Model 2 was about 0.05 higher. However, after 30 days, the modeling coefficients of both models showed low values below 0.50. According to the modeling approach, comparing and analyzing the values of the determinants showed that data from adjacent time zones were relatively high performance at points requiring prediction rather than a fixed neural network model.

• Journal of Digital Policy
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• v.1 no.1
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• pp.15-20
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• 2022

Recently, the healthcare field is trying to develop a model that can improve service quality by reflecting the requirements of various industrial fields. In this paper, we propose an Internet of Behavior (IoB) environment model that can process users' healthcare information in real time in a 5G environment to improve healthcare services. The purpose of the proposed model is to analyze the user's healthcare information through deep learning and then check the health status in real time. In this case, the biometric information of the user is transmitted through communication equipment attached to the portable medical equipment, and user authentication is performed through information previously input to the attached IoB device. The difference from the existing IoT healthcare service is that it analyzes the user's habits and behavior patterns and converts them into digital data, and it can induce user-specific behaviors to improve the user's healthcare service based on the collected data.