• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.

• Education of Primary School Mathematics
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• v.14 no.3
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• pp.237-259
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• 2011

Mathematics learning disabilities is a specific learning disorder affecting the normal acquisition of arithmetic and spatial skills. Reported prevalence rates range from 5 to 10 percent and show high rates of comorbid disabilities, such as dyslexia and ADHD. In this study, the characteristics and the causes of this disorder has been examined. The core cause of mathematics learning disabilities is not clear yet: it can come from general cognitive problems, or disorder of innate intuitive number module could be the cause. Recently, researchers try to subdivide mathematics learning disabilities as (1) semantic/memory type, (2) procedural/skill type, (3) visuospatial type, and (4) reasoning type. Each subtype is related to specific brain areas subserving mathematical cognition. Based on these findings, the author has performed a basic research to develop grade specific diagnostic tests: number processing test and math word problems for lower grades and comprehensive math knowledge tests for the upper grades. The results should help teachers to find out prior knowledge, specific weaknesses of students, and plan personalized intervention program. The author suggest diagnostic tests are organized into 6 components. They are number sense, conceptual knowledge, arithmetic facts retrieval, procedural skills, mathematical reasoning/word problem solving, and visuospatial perception tests. This grouping will also help the examiner to figure out the processing time for each component.

• Journal of the Korea Furniture Society
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• v.27 no.4
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• pp.362-374
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• 2016

The ability of 'creative thinking' is one of the most important fact for designers to complete their design process. It is not only the overall concept of the process but also all details related to each steps of the process such as planning product strategies, research, finding ways to solve problems image development, broadening ideas, etc. It is, however, not easy to creative as designers by natural learning through experiences unless they have geniuses for creative designing. Many experienced designers in the field are even in dilemmas being not be able to differentiate from copying other designs. This study is focused on the researches on the works of famous designers, architects and artists who have shown great creativities throw their works or processes. It is aimed to define their ways of thinking which could be fundamental motives for the creativeness, various behavioral methods to solve problems in creative ways, technical approaches to improve creativities, and overall intuition in the design working processes.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.

• The Journal of the Convergence on Culture Technology
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• v.6 no.1
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• pp.63-68
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• 2020

The need for literal education, matching the rapid changes in the media has been increasing in recent years. However, it is true that not enough research has been done on the efficiency of literacy compared to the increasing need. For example, empirical approaches such as studies on the effects of integrated media literacy on youth learning. Thus, this paper took multimedia education, which is different from existing image education, as a subject of research and studied the results of education that fused text and video and sound to teenagers in terms of learning effectiveness. It is an academic quest to see if multimedia education really has positive ㄷ effect for teenagers' learning. As a result of both quantitative and qualitative research as a method of research, multimedia education was concluded to be meaningful in improving learning ability, and based on this, it presented a practical-oriented learner participation education while expanding appearance such as language education through multimedia education. The suggestions for these various education policies will spread to the confidence that multimedia education can be the center of all learning activities as the core of education that achieves an all-round personality, not just an aid to existing education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.813-826
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• 2022

AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.255-263
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• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.87-93
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• 2023

In this paper, we propose a system that accurately and efficiently determines forward head posture based on network learning by analyzing the user's skeletal posture. Forward head posture syndrome is a condition in which the forward head posture is changed by keeping the neck in a bent forward position for a long time, causing pain in the back, shoulders, and lower back, and it is known that daily posture habits are more effective than surgery or drug treatment. Existing methods use convolutional neural networks using webcams, and these approaches are affected by the brightness, lighting, skin color, etc. of the image, so there is a problem that they are only performed for a specific person. To alleviate this problem, this paper extracts the skeleton from the image and learns the data corresponding to the side rather than the frontal view to find the forward head posture more efficiently and accurately than the previous method. The results show that the accuracy is improved in various experimental scenes compared to the previous method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.305-307
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• 2021

Alzheimer's Disease (AD) is a cognitive disorder characterized by memory impairment that can be assessed at early stages based on administering clinical tests. However, the AD pathophysiological mechanism is still poorly understood due to the difficulty of distinguishing different levels of AD severity, even using a variety of brain modalities. Therefore, in this study, we present a hybrid EEG-fNIRS modalities to compensate for each other's weaknesses with the help of Machine Learning (ML) techniques for classifying four subject groups, including healthy controls (HC) and three distinguishable groups of AD levels. A concurrent EEF-fNIRS setup was used to record the data from 41 subjects during Oddball and 1-back tasks. We employed both a traditional neural network (NN) and a CNN-LSTM hybrid model for fNIRS and EEG, respectively. The final prediction was then obtained by using majority voting of those models. Classification results indicated that the hybrid EEG-fNIRS feature set achieved a higher accuracy (71.4%) by combining their complementary properties, compared to using EEG (67.9%) or fNIRS alone (68.9%). These findings demonstrate the potential of an EEG-fNIRS hybridization technique coupled with ML-based approaches for further AD studies.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.487-495
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• 2023

Ammonia is either a crucial resource of fertilizer production for solving the food problem of mankind or an important energy source as both an eco-friendly hydrogen carrier and a carbon-free fuel. Therefore, nowadays ammonia synthesis and decomposition become promising. Then, a catalyst is required to effectively perform the ammonia synthesis and decomposition. In order to design high-performing as well as cheap novel catalysts for ammonia synthesis and decomposition, it is necessary to test huge amount of catalyst candidates, but it is inevitably time-consuming and expensive to search and analyze using only traditional approaches. Recently, new methods using machine learning which is one of the core technologies of the 4th industrial revolution that can quickly and accurately search high-performance catalysts has been emerging. In this paper, we investigate reaction mechanisms of ammonia synthesis and decomposition, and we described recent research and prospects of machine learning-driven methods that can efficiently find high-performing and economical catalysts for ammonia synthesis and decomposition.