• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.10
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• pp.1-9
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• 2018

The purpose of this exploratory study is to identify the mobile phone applications (apps) used by foreign tourists visiting South Korea through a pilot study using focus groups and individual interviews. Concentrating on tourist mobile app use in a smart tourism environment and categorized through a taxonomy of mobile applications lays the framework and determines the factors boosting tourism smartphone app trends by foreign tourists visiting South Korea. Researchers collected data through ethnographic methods and analyzed it through qualitative research to uncover major themes within the smart tourism app use phenomenon. The researchers coded, counted, analyzed, and then divided the findings gleaned from a pilot study and interviews into a taxonomy of seven logical smartphone app categories. The labeling and coding of all the data accounting for similarities and differences can be recognized and are logically discussed in the implications of the apps used by tourists to assist tourist destinations. More specifically these findings will assist smart tourism destinations by better understanding foreign tourist smartphone app use behavior. Tourists visiting South Korea interviewed in this study exhibited significant mastery of Internet of Things (IoT) technologies, craved free WiFi access, and utilized smartphone apps for all facets of their travel. Findings show major concentrations of app use in bookings of accommodations, tourist attractions, online shopping, navigation, wayfinding, augmented reality, information searching, language translation, gaming, and online dating while traveling in South Korea.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1001-1009
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• 2016

In LTE networks, the base station transmits control information over the physical downlink control channel (PDCCH) including scheduling grants, which are used to indicate the resources that the user equipment uses to send data to the base station. Because the size of the PDCCH message and the number of the PDCCH transmissions increase in proportion to the number of user equipments, the overhead of the PDCCH may cause serious network congestion problems in the narrowband LTE (NB-LTE) system. This paper proposes the compact PDCCH information bit allocation to reduce the size of the PDCCH message and evaluates the performance of the semi-persistent scheduling (SPS) in the NB-LTE system. The simulation results show that the SPS can significantly reduce the signaling overhead of the PDCCH and therefore increase the system utilization.

• Database Research
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• v.34 no.3
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• pp.86-98
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• 2018

Point-of-Interest(POI) recommendation systems suggest the most interesting POIs to users considering the current location and time. With the rapid development of smartphones, internet-of-things, and location-based social networks, it has become feasible to accumulate huge amounts of user POI visits. Therefore, instant recommendation of interesting POIs at a given time is being widely recognized as important. To increase the performance of POI recommendation systems, several studies extracting users' POI sequential preference from POI check-in data, which is intended for implicit feedback, have been suggested. However, when constructing a model utilizing sequential preference, the model encounters possibility of data distortion because of a low number of observed check-ins which is attributed to intensified data sparsity. This paper suggests refinement of temporal intervals based on data confidence. When building a POI recommendation system using temporal intervals to model the POI sequential preference of users, our methodology reduces potential data distortion in the dataset and thus increases the performance of the recommendation system. We verify our model's effectiveness through the evaluation with the Foursquare and Gowalla dataset.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.11
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• pp.155-162
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• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Journal of Sensor Science and Technology
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• v.31 no.5
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• pp.307-311
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• 2022

With the development of Internet of Things (IoT) technologies, numerous people worldwide connect with various electronic devices via Human-Machine Interfaces (HMIs). Considering that HMIs are a new concept of dynamic interactions, wearable electronics have been highlighted owing to their lightweight, flexibility, stretchability, and attachability. In particular, wearable strain sensors have been applied to a multitude of practical applications (e.g., fitness and healthcare) by conformally attaching such devices to the human skin. However, the stretchable elastomer in a wearable sensor has an intrinsic stretching limitation; therefore, structural advances of wearable sensors are required to develop practical applications of wearable sensors. In this study, we demonstrated a 3-dimensional (3D), porous, and piezoelectric strain sensor for sensing body movements. More specifically, the device was fabricated by mixing polydimethylsiloxane (PDMS) and polyvinylidene fluoride nanoparticles (PVDF NPs) as the matrix and piezoelectric materials of the strain sensor. The porous structure of the strain sensor was formed by a sugar cube-based 3D template. Additionally, mixing methods of PVDF piezoelectric NPs were optimized to enhance the device sensitivity. Finally, it is verified that the developed strain sensor could be directly attached onto the finger joint to sense its movements.

• Journal of Korean Public Health Nursing
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• v.37 no.1
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• pp.5-24
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• 2023

Purpose: The objective of this review was to identify the research trends in Information and Communication Technology (ICT)-based health-related intervention studies for children and adolescents published in South Korea over the past 10 years. Methods: A scoping review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) and the system classification framework for digital health intervention 1.0 of the World Health Organization (WHO) was applied to analyze how technology was being used to support the needs of the health system. Results: A total of 18 studies were included in the final analysis. The participants were mainly children with a variety of diseases. No studies had used innovative technology platforms such as artificial intelligence (AI), the Internet of Things (IoT), and robotics. In addition, the scope of application of the WHO classification criteria was quite limited. Finally, no intervention study considered technical operational indicators, such as the number of website visits and streaming as outcome measurements. Conclusions: Researchers should introduce advanced technology-based strategies to provide customized and professional healthcare services to children and adolescents in South Korea and continue efforts to integrate innovative ICT for various research purposes, subjects, and environments.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.307-312
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• 2018

This paper deals with the role and direction of design in the era of 4th industrial revolution. In addition to understanding the whole of science and technology such as Artificial Intelligence, Internet of Things, Cyber Physical System, 3D printer and Bio which are leading technology of the $4^{th}$ industrial revolution, this thesis is seeking direction of design that makes human life and society comfortable and convenient. The design in the 4th Industrial Revolution must be at the center of society and human being. Therefore, the design using ICT technology is also applied to the emotional design that stimulates human emotion warmly through in-depth study on society and human culture and environment. Through the above research, the design that has continued to evolve along with the development of technology continues to play a role as a design in which human emotion is alive.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.9-18
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• 2020

The research objectives investigates main research topics and trends in the information and communication technology(ICT) field, Korea using LDA(Latent Dirichlet Allocation), one of the topic modeling techniques. The experimental dataset of ICT research and development(R&D) project of 5,200 was acquired through matching with the EZone system of IITP after downloading R&D project dataset from NTIS(National Science and Technology Information Service) during recent five years. Consequently, our finding was that the majority research topics were found as intelligent information technologies such as AI, big data, and IoT, and the main research trends was hyper realistic media. Finally, it is expected that the research results of topic modeling on the national R&D foundation dataset become the powerful information about establishment of planning and strategy of future's research and development in the ICT field.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.573-581
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• 2019

In the era of the 4th Industrial Revolution, the necessity of training advanced engineering personnel with convergent creativity to handle technologies such as artificial intelligence, big data, and the internet of things (IoT) is increasing. In this paper, a new approach of engineering education based on academia-industrial cooperation and design-centered teaching technique for the students who need to learn practicable engineering skill with convergent creativity for the fourth industrial age is presented. It analyzes the strengths and weaknesses of the existing engineering education innovation activities, presents the practical necessities based on the experience of the educational system and the requirements of the educational environment, and analyzes the existing activities and the new roles. In particular, we discuss how to combine student-centered teaching methodology for effective design education, which is a key element of innovative engineering education. Most of the presented methods are verified by the authors' needs and effects in the education field.