• 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.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.35-44
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• 2021

Recent advances in the 4th Industrial Revolution have accelerated the change of the working environment, such that the paradigm of education has been shifted in accordance with career education including the free semester system and the high school credit system. While the purpose of those systems is students' self-motivated career exploration, educational limitations for teachers and students exist due to the rapid change of the information on education. Also, education technology research to tackle these limitations is relatively insufficient. To this end, this study first defines three requirements that education technologies for the career education system should consider. Then, through data-driven artificial intelligence technology, this study proposes a data system and an artificial intelligence recommendation model that incorporates the topics for career exploration, courses, and majors in one scheme. Finally, this study demonstrates that the set-based artificial intelligence model shows satisfactory performances on recommending career education contents such as courses and majors, and further confirms that the actual application of this system in the educational field is acceptable.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.339-345
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• 2021

With the development of online education due to COVID-19, the EduTech market, which combines new technologies such as AI and AR/VR in education is rapidly growing. In addition, the children's industry is steadily growing despite the decreasing birth rate every year as more and more families with one child per household are investing in their children. However, supply of contents to EduTech market is slow compared to demands that are increasing. Therefore, the purpose of this paper is to help solve these problems by developing and supporting AR kids contents with convenience, practicality, and efficiency using AR technology. AR content for supporting vocabulary learning for infants is not just an end to watching and listening, but an observation-driven model that can manipulate content directly, which attracts children's interest and helps children learn words. This paper is intended for infants from 15 months to 36 months old when full-fledged language development occurs.

• Economic and Environmental Geology
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• v.55 no.2
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• pp.183-195
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• 2022

Hydrothermal gold deposits are evidence of intensive fluid flow through fault zones, and the resultant vein structures and textures reflect the fluid redistribution mechanism. This review introduces the suction pump and fault valve models as fluid circulation mechanisms causing hydrothermal gold deposits in the frameworks of the concepts of fault mechanics. The suction pump and fault valve models describe faulting-driven heterogeneous fluid flow and related vein formation mechanisms, accompanied by the cycles of (1) stress accumulation and fluid pressure build-up and (2) seismic rupture and stress/fluid pressure release. The models are available under different geological environments (stress conditions), and the vein structures and textures representing the mechanisms have similarities and differences. The suction pump and fault valve models must help better to interpret the origins of hydrothermal gold deposits in Korea and improve the efficiency of further exploration.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.197-205
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• 2021

Recently, the retail industry has been increasingly demanding information technology convergence and utilization to respond to various external environmental threats such as COVID-19 and to be competitive using AI technologies, but there is a very lack of research and application services. This study is a CCTV video data-driven AI application case study, using CCTV image data collection in retail space, object detection and tracking AI model, time series database to store real-time tracked objects and tracking data, heatmap to analyze congestion and interest in retail space, social access zone.We present the orientation and verify its usability in the direction designed through practical implementation.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.331-337
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• 2022

Smart learning is augmented with digital, context-aware, and adaptable technologies to encourage students to learn better and faster. To ensure that digital learning is successful and that implementation is efficient, it is critical that the dimensions of digital learning are arranged correctly and that interactions between the various elements are merged in an efficient and optimal manner. This paper builds and discusses a basic framework for smart learning in the digital age, aimed to improve students' abilities and performance in learning. The proposed framework consists of five dimensions: Teacher, Technology, Learner, Digital content, and Evaluation. The Teacher and Learner dimensions operate on two levels: (a) an abstract level to fit in knowledge and skills or interpersonal characteristics and (b) a concrete level in the form of digital devices used by teachers and learners. Moreover, this paper proposes asynchronous online course delivery model. An Arabic smart learning platform has been developed, based on these smart learning core dimensions and the asynchronous online course delivery model, because despite the official status of this language in many countries, there is a lack of Arabic platforms to teach Arabic. Moreover, many non-native Arabic speakers around the world have expressed an interest in learning it. The Arabic digital platform consists of over 70 lessons classified into three competence levels: beginner, intermediate, and advanced, delivered by Arabic experts and Arabic linguists from various Arab countries. The five dimensions are described for the Arabic platform in this paper. Learner dimension is the Arabic and non-Arabic speakers, Teacher dimension is Arabic experts and Arabic linguistics, Technology dimension consists of technology for Arabic platform that includes web design, cloud computing, big data, etc. The digital contents dimension consists of web-based video, records, etc. The evaluation dimension consists of Teachers rating, comments, and surveys.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.43-48
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• 2022

Recently, due to the rapid performance improvement of smartphones and the increase in background executions of mobile apps, multitasking has become common on mobile platforms. Unlike traditional desktop and server apps, response time is important in most mobile apps as they are interactive tasks, and some apps are classified as real-time tasks with deadlines. In this paper, we discuss how to meet the requirements of heterogeneous multitasking in managing memory of real-time and interactive tasks when they are executed together on a smartphone. To do so, we analyze the memory requirement of real-time tasks, and propose a model that has the ability of allocating memory to multitasking tasks on a smartphone. Trace-driven simulations with real-world storage access traces captured by heterogeneous apps show that the proposed model provides reasonable performance for interactive tasks while guaranteeing the requirement of real-time tasks.

• Journal of the Korean Geotechnical Society
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• v.25 no.1
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• pp.41-54
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• 2009

The purpose of this research is to introduce the new temporary earth retaining wall system using landslide stabilizing piles. This system is a self-supported retaining wall (SSR) without installing supports such as tiebacks, struts and rakers. The SSR is a kind of gravity structures consisting of twin parallel lines of piles driven below excavation level, tied together at head of soldier piles and landslide stabilizing piles by beams. In order to investigate applicability and safety of this system, a series of experimental model tests were carried out and the obtained results are presented and discussed. Furthermore, the measured data from seven different sites on which the SSR was used for excavation were collected and analyzed to investigate the characteristic behavior lateral wall movements associated with urban excavations in Korea. It is observed that lateral wall movements obtained from the experimental model is in good agreement with the general trend observed by in site measurements.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.685-696
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• 2022

This study explores convergence curriculum for design and data science, and applies data science knowledge on undergraduate design classes for designer's data literacy. First, related studies about data literacy education for non-data science major's, and data driven design project cases are explored, then design competency and data competency based on NCS are studied. Then this study developed 3 step design-data convergence curriculum model for designers' data literacy. The curriculum model is applied on case study classes, which are Big data and UX design(2) classes. The learning results and student's feedback of the case study classes are collected and analyzed to prove the design-data convergence curriculum, and the results provide findings and implications of the design-data convergence class case study.