• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.99-110
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• 2011

Mobile environments rapidly changing and digital convergence widely employed, mobile devices including smart phones have been playing a critical role that changes users' lifestyle in the areas of entertainments, businesses and information services. The various services using mobile devices are developing to meet the personal needs of users in the mobile environments. Especially, an LBS (Location-Based Service) is combined with other services and contents such as augmented reality, mobile SNS (Social Network Service), games, and searching, which can provide convenient and useful services to mobile users. In this paper, we design and implement the prototype of mobile personal assistant (PA) agents. Our personal assistant agent helps users do some tasks by hiding the complexity of difficult tasks, performing tasks on behalf of the users, and reflecting the preferences of users. To identify user's preferences and provide personalized services, clustering and classification algorithms of data mining are applied. The clusters of the log data using clustering algorithms are made by measuring the dissimilarity between two objects based on usage patterns. The classification algorithms produce user profiles within each cluster, which make it possible for PA agents to provide users with personalized services and contents. In the experiment, we measured the classification accuracy of user model clustered using clustering algorithms. It turned out that the classification accuracy using our method was increased by 17.42%, compared with that using other clustering algorithms.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.1029-1044
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• 2017

The Discrete Element Method (DEM) is one of the useful numerical methods to analyze the behavior of the ground formation by computing the motion and interaction using particles. The DEM has not been applied in civil engineering but also a wide range of industrial fields, such as chemical engineering, pharmacy, material science, food engineering, etc. In this study, to review a performance of the spoke-type earth pressure balance (EPB) shield TBM (Tunnel Boring Machine), the commercial software based on the DEM technology was used. An analysis of the TBM during excavation was conducted according to two pre-defined excavation conditions with the different rotation speed of a cutterhead. During the analysis, the resistant torque at the face of the cutterhead, the compressive force at the cutterhead and shield surface, the muck discharge at the screw auger were measured and compared. Upon the two kinds of excavation conditions, the applicability of the DEM analysis was reviewed as a modelling method for the TBM.

• Journal of Biosystems Engineering
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• v.41 no.4
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• pp.313-318
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• 2016

Purpose: This study measured and analyzed the PTO (power take off) torque of a transplanter according to the planting conditions during field operation. Methods: A torque measurement system was constructed with torque sensors to measure the torque of a PTO shaft, a measurement device to acquire sensor signals, and a power controller to provide power for a laptop computer. The field operation was conducted at four planting distances (26, 35, 43, and 80 cm) and two planting depths using the transplanter on a field with similar soil conditions. One-way ANOVA with planting distance and Duncan's multiple range test at a significance level of 0.05 were used to analyze the PTO torque. The torque ratio was calculated based on the minimum torque using the average PTO torque measured under each planting condition. Results: The average torques on the PTO shaft for planting distances of 26, 35, 43, and 80 cm at a low planting depth were 11.05, 9.07, 7.04, and 3.75 Nm, respectively; the same for planting distances of 26, 35, 43, and 80 cm at a middle planting depth were 12.20, 9.86, 7.94, and 4.32 Nm, respectively. When the planting distance decreased by 43, 35, and 26 cm, the torque ratio at a low planting depth increased by 88, 142, and 195%, respectively. When the planting distance decreased by 43, 35, and 26 cm, the torque ratio at the middle planting depth increased by 84, 128, and 182%, respectively. Conclusions: PTO torque fluctuated by planting distance and depth. Moreover, the PTO torque increased for short planting distances. Therefore, farmers should determine the planting conditions of the transplanter by considering the load and durability of the machine. The results of this study provide useful information pertaining to the optimum PTO design of the transplanter considering the field load.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.469-469
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• 2017

기후 변화 및 환경오염으로 인하여 물부족 국가가 세계적으로 증가하고 있는 추세이며, 특히 집중형 강우의 형태가 많아짐에 따라 홍수피해 및 상수공급의 문제가 사회적으로 큰 이슈가 되고 있다. 최근 20여 년간의 급속한 경제성장과 도시화 과정에서 인도네시아는 인구와 산업의 과도한 도시집중으로 지난 1960-80년대 한국이 산업화 과정에서 겪었던 것보다 훨씬 심각한 환경문제에 직면하고 있으며, 자카르타와 반둥을 포함하는 광역 수도권 지역의 물 부족과 수질 오염, 환경문제가 이미 매우 위험한 수준에 도달하고 있는 실정이다. 특히, 찌따룸강 중상류에 위치한 인도네시아 3대 도시인 반둥시는 고질적인 용수부족 문제를 겪고 있다. 2010년 현재 약 일평균 15 CMS의 용수가 부족한 상황이며, 2030년에는 지속적인 인구증가로 약 23 CMS의 용수가 추가로 더 필요한 것으로 전망된다. 이러한 용수공급 문제 해결을 위해 반둥시 및 찌따룸강 유역관리청은 댐 및 지하수 개발, 유역 간 물이동 등의 구조적인 대책뿐만 아니라 비구조적인 대책으로써 기존 및 신규 저수지 연계운영을 통한 용수이용의 효율성을 높이는 방안을 모색하고 있다. 이에 따라 본 연구에서는 해당유역의 용수공급 부족 문제를 해소할 수 있는 비구조적인 대책의 일환으로써 다양한 댐 및 보, 소수력 발전, 취수장 등 유역 내 수리 시설물의 운영 최적화를 위한 지능형 물관리 시스템 적용 방안을 제시하고자 한다. 본 연구의 지능형 물관리 시스템은 센서 및 사물 인터넷(Internet of Things, IoT), 네트워크 기술을 바탕으로 시설물 및 운영자, 유관기관 간의 양방향 통신을 통해 유기적인 상호연계 체계를 제공 할 수 있다. 또한 유역의 수문상황과 시설물의 운영현황, 용수공급 및 수요 현황을 실시간으로 확인함으로써 수요에 따른 즉각적인 용수공급량의 조절이 가능하다. 또한, 빅데이터 분석 및 기계학습(Machine Learning)을 통해 개별 물관리 시설물에 대한 최적 운영룰을 업데이트할 수 있으며, 유역의 수문상황과 용수 수요 현황을 고려하여 최적의 용수공급 우선순위를 선정할 수 있다. 지능형 물관리 시스템 개발의 목적은 찌상쿠이 유역의 수문현황을 실시간으로 모니터링하고, 하천시설물의 운영을 분석하여 최적의 용수공급 및 배분을 통해 유역의 수자원 활용 효율성을 향상시키는 데 있다. 이를 위해 수문자료의 수집체계를 구축하고 기관간 정보공유체계를 수립함으로써 분석을 위한 기반 인프라를 구성하며, 이를 기반으로 유역 유출을 비롯한 저수지 운영, 물수지 분석을 수행하고, 분석 및 예측결과, 과거 운영 자료를 토대로 새로운 물관리 시설 운영룰 및 시설물 간 연계운영 방안, 용수공급 우선순위 의사결정 등을 지원하고자 한다. 본 연구의 지능형 물관리 시스템은 통합 DB를 기반으로 수리수문 현상의 모의 분석을 통해 하천 시설물 운영의 합리적 기준을 제시함으로써 다양한 관리주체들의 시설물운영에 대한 이견 및 분쟁을 해소하고, 한정된 수자원과 다양한 수요 간의 효율적이고 합리적인 분배 및 시설물 운영문제를 해결하기 위한 의사결정도구로써 활용할 수 있을 것으로 기대된다.

• Tunnel and Underground Space
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• v.28 no.6
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• pp.513-527
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• 2018

The Tunnel Boring Machine(TBM) is relatively insufficient to cope with unpredicted changes in ground conditions as compared with Conventional Tunnelling Methods. Therefore, it is very important to predict the TBM performance at the design stage and estimate the advance rate for the calculation of the construction period. In this study, we added data to 211 TBM databases constructed in the previous study and analyzed the correlation between TBM outer diameter, maximum thrust, maximum cutterhead torque, cutterhead driving power and RPM, which are the main design and manufacturing specifications of TBM. As a result of the analysis from results obtained in the previous studies, it was confirmed that TBM outer diameter is very effective and important in estimating maximum thrust, maximum cutterhead torque, and cutterhead driving power of the TBM. As a result of comparing the regression equations derived from other TBM databases outside the country and the regression equation obtained from the present study results, the maximum thrust showed a similar tendency to each other, but the maximum torque estimated from the regression equation of this study was higher than that of other countries in the case of the large scale TBM.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.21-31
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• 2021

As the COVID-19 pandemic rapidly changes healthcare around the globe, the need for smart healthcare that allows for remote diagnosis is increasing. The current classification of respiratory diseases cost high and requires a face-to-face visit with a skilled medical professional, thus the pandemic significantly hinders monitoring and early diagnosis. Therefore, the ability to accurately classify and diagnose respiratory sound using deep learning-based AI models is essential to modern medicine as a remote alternative to the current stethoscope. In this study, we propose a deep learning-based respiratory sound classification model using data collected from medical experts. The sound data were preprocessed with BandPassFilter, and the relevant respiratory audio features were extracted with Log-Mel Spectrogram and Mel Frequency Cepstral Coefficient (MFCC). Subsequently, a Parallel CNN network model was trained on these two inputs using stacking ensemble techniques combined with various machine learning classifiers to efficiently classify and detect abnormal respiratory sounds with high accuracy. The model proposed in this paper classified abnormal respiratory sounds with an accuracy of 96.9%, which is approximately 6.1% higher than the classification accuracy of baseline model.

• International journal of advanced smart convergence
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• v.11 no.1
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• pp.19-27
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• 2022

Across the world, 'housing' comprises a significant portion of wealth and assets. For this reason, fluctuations in real estate prices are highly sensitive issues to individual households. In Korea, housing prices have steadily increased over the years, and thus many Koreans view the real estate market as an effective channel for their investments. However, if one purchases a real estate property for the purpose of investing, then there are several risks involved when prices begin to fluctuate. The purpose of this study is to design a real estate price 'return rate' prediction model to help mitigate the risks involved with real estate investments and promote reasonable real estate purchases. Various approaches are explored to develop a model capable of predicting real estate prices based on an understanding of the immovability of the real estate market. This study employs the LSTM method, which is based on artificial intelligence and deep learning, to predict real estate prices and validate the model. LSTM networks are based on recurrent neural networks (RNN) but add cell states (which act as a type of conveyer belt) to the hidden states. LSTM networks are able to obtain cell states and hidden states in a recursive manner. Data on the actual trading prices of apartments in autonomous districts between January 2006 and December 2019 are collected from the Actual Trading Price Disclosure System of the Ministry of Land, Infrastructure and Transport (MOLIT). Additionally, basic data on apartments and commercial buildings are collected from the Public Data Portal and Seoul Metropolitan Government's data portal. The collected actual trading price data are scaled to monthly average trading amounts, and each data entry is pre-processed according to address to produce 168 data entries. An LSTM model for return rate prediction is prepared based on a time series dataset where the training period is set as April 2015~August 2017 (29 months), the validation period is set as September 2017~September 2018 (13 months), and the test period is set as December 2018~December 2019 (13 months). The results of the return rate prediction study are as follows. First, the model achieved a prediction similarity level of almost 76%. After collecting time series data and preparing the final prediction model, it was confirmed that 76% of models could be achieved. All in all, the results demonstrate the reliability of the LSTM-based model for return rate prediction.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.855-863
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• 2022

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

• Science of Emotion and Sensibility
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• v.25 no.3
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• pp.77-90
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• 2022

This study seeks to develop a stretch sensor for measuring the wrist movements of people using fishing lures. In order to confirm wrist movement, a stretch sensor was attached to the wrist band, and measurements of the dorsiflexion, plantar flexion, and fishing landing motion were measured using a scale to gauge factor, tensile strength, and elongation recovery rate. A conductive sensor using CNT dispersion was developed and applied to the E-band under the same conditions. A total of 15 sensors of the same size and five types of impregnation once, twice, and three times each were used to measure the gauge factor using UTM. The sensor that was impregnated twice had the best gauge rate, and the prototypes were manufactured with three sensors with high gauge rates and tensile strength. The results of the operation test conducted by connecting to the Arduino showed that Sample 1, which had the highest tensile strength and gauge factor, had a stable graph wavelength in three operations. Samples 2 and 3 showed stable wavelengths in the dorsiflexion and the plantar flexion; however, signal noise appeared in the fishing landing motion. This showed stable wavelengths in the two motions, but the wavelengths of the graphs differ depending on the tensile strength and gauge factor in the fishing landing motion. As a result, it was possible to identify the conditions necessary for manufacturing a stretch sensor for measuring wrist movement. This study will contribute to the development of smart wearable products for lure fishing.