• Transactions on Electrical and Electronic Materials
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• v.8 no.6
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• pp.293-298
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• 2007

The aged are faced with increasing risk for falls. The aged have more fragile bones than others. When falls occur, it is important to detect this emergency state because such events often lead to more serious illness or even death. A implementation of PDA system, for detection of emergency situation, was developed using 3-axis accelerometer in this paper as follows. The signals were acquired from the 3-axis accelerometer, and then transmitted to the PDA through a Bluetooth module. This system can classify human activity, and also detect an emergency state like falls. When the fall occurs, the system generates the alarm on the PDA. If a subject does not respond to the alarm, the system determines whether the current situation is an emergency state or not, and then sends some information to the emergency center in the case of an urgent situation. Three different studies were conducted on 12 experimental subjects, with results indicating a good accuracy. The first study was performed to detect the posture change of human daily activity. The second study was performed to detect the correct direction of fall. The third study was conducted to check the classification of the daily physical activity. Each test lasted at least 1 min. in the third study. The output of the acceleration signal was compared and evaluated by changing various postures after attaching a 3-axis accelerometer module on the chest. The newly developed system has some important features such as portability, convenience and low cost. One of the main advantages of this system is that it is available at home healthcare environment. Another important feature lies in its low cost of manufacture. The implemented system can detect the fall accurately, so it will be widely used in emergency situations.

• Annual Conference of KIPS
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• 2019.10a
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• pp.581-584
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• 2019

Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.7-12
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• 2019

The application of neural networks to stock forecasting has received a great deal of attention because no assumption about a suitable mathematical model has to be made prior to forecasting and they are capable of extracting useful information from data, which is required to describe nonlinear input-output relations of stock forecasting. The paper builds neural network models to forecast daily KOrea composite Stock Price Index (KOSPI), and their performance is demonstrated. MAPEs of NN1 model show 0.427 and 0.627 in its learning and test, respectively. Based on the predicted KOSPI price, the paper proposes an alpha trading for trades in Exchange Traded Funds (ETFs) that fluctuate with the KOSPI200. The alpha trading is tested with data from 125 trade days, and its trade return of 7.16 ~ 15.29 % suggests that the proposed alpha trading is effective.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.533-541
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• 2011

Algal blooms have caused problems for drinking water as well as eutrophication. However it is difficult to control algal blooms by current warning manual in rainy season because the algal blooms happen in a few days. The water quality data, which have high correlations with Chlorophyll-a on Daecheongho station, were analyzed and chosen as input data of Artificial Neural Networks (ANN) for training pattern changes. ANN was applied to early forecasting of algal blooms, and ANN was assessed by forecasting errors. Water temperature, pH and Dissolved oxygen were important factors in the cross correlation analysis. Some water quality items like Total phosphorus and Total nitrogen showed similar pattern to the Chlorophyll-a changes with time lag. ANN model (No. 3), which was calibrated by water temperature, pH and DO data, showed lowest error. The combination of 1 day, 3 days, 7 days forecasting makes outputs more stable. When automatic monitoring data were used for algal bloom forecasting in Daecheong reservoir, ANN model must be trained by just input data which have high correlation with Chlorophyll-a concentration. Modular type model, which is combined with the output of each model, can be effectively used for stable forecasting.

• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1418-1427
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• 2020

The web has become indispensable in all of our daily lives. We communicate, study and get information with others through the web. This behavior also continues in the smart phone environment. The biggest problem is that the small display screen of a smart phone degrades the accuracy in selecting or manipulating content for people with low vision. To compensate for this, voice guidance services that combine touch and voice, such as VoiceOver and Talkback, are currently provided to smart phone devices. However, restrictions arise in GUI, TTS control problems, and content expansion and selection. In addition, unnecessary content is also output by voice, which causes fatigue for low vision people to use. In this study, we propose a mobile web browser interface that selects and enlarges a desired area from web browsers and contents, or outputs it as a voice so that people with low vision can easily use the mobile web browser. In this paper, we propose a context selective focusing function that enables selection for each element of web content. In addition, we intend to develop a mobile voice web browser that can enlarge the selected content or output it by voice.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.57-65
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• 2018

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.38 no.3
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• pp.253-263
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• 1993

Climatological informations have not been fully utilized by agricultural research and extension workers in Korea due mainly to inaccessbilty to the archived climate data. This study was initiated to improve access to historical climate data gathered from 72 weather stations of Korea Meteorological Administration for agricultural applications by using a microcomputer-based methodology. The climatological elements include daily values of average, maximum and minimum temperature, relative humidity, average and maximum wind speed, wind direction, evaporation, precipitation, sunshine duration and cloud amount. The menu-driven, user-friendly data retrieval system(CLIDAS) provides quick summaries of the data values on a daily, weekly and monthly basis and selective retrieval of weather records meeting certain user specified critical conditions. Growing degree days and potential evapotranspiration data are derived from the daily climatic data, too. Data reports can be output to the computer screen, a printer or ASCII data files. CLIDAS can be run on any IBM compatible machines with Video Graphics Array card. To run the system with the whole database, more than 50 Mb hard disk space should be available. The system can be easily upgraded for further expansion of functions due to the module-structured design.

• International Journal of Industrial Entomology and Biomaterials
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• v.1 no.2
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• pp.155-159
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• 2000

In the spring silkworm rearing period of year 1998, some nutritional aspects were studied in the Agricultural Research Station of the Komotini town in Greece, to investigate the feeding behaviour of some Bulgarian silkworm hybrids, suitable for spring rearing, during the fourth and fifth instal under the local climatic conditions. The silkworms were fed by locally cultivated Japanese Kinriu mulberry (Morus alba L.) variety leaves. Eight Bulgarian hybrids had been used, namely Vratza-53xVratza-52, Ukraine-20xVratza-53, Super 1xHessa 2, Merefa 2xVratza 35, as well as their reciprocal crosses. The studies showed out a remarkably higher feed intake and feed utilization by the hybrids Hessa 2xSuper-1, Merefa 2xVratza 35 and Vratza-52xVratza-53. The same hybrids showed an efficient food utilization by means of daily growths cocoon shell ratio and raw silk output. In terms of food to silk conversion efficiency Hessa 2xSuper-1 hybrid gave the best results. Larval stage duration for fourth and fifth instar was not affected by feed intake and utilization. All hybrids showed a good adaptation to the local environment and their feeding performance was equal to the international existing standards. All calculated parameters were found to be around the mean values of other up to date presented results and thus considered as acceptable for the needs of the local production.

• Asian-Australasian Journal of Animal Sciences
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• v.7 no.1
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• pp.137-145
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• 1994

Amino acid (AA) substituted diets had no influence on rumen levels of total volatile fatty acids (VFA), ammonia and ${\alpha}$-amino-N, but tended to increase molar proportions of isovalerate and counts of total viable AA utilizing and celluloytic bacteria in the rumen as compared with the control urea diet. The AA diets did not affect daily flow to the duodenum of dry matter (DM), organic mater (OM) and acid detergent fibre (ADF), and rumen digestibility of these nutrients. However, the AA diets, in particular the 10 essential AA (EAA) diet improved total digestibility of DM, OM and ADF by decreasing faecal output of these fractions. Although N flow to the duodenum and N retention were not affected with the dietary treatments, duodenal bacterial flow appeared to increase by the AA diets when it was estimated by means of 2,6-diaminopimelic acid (DAP) and nucleic acid-purine bases (PB) as markers. The results suggest that AA supplements to a urea diet could improve feed utilization by stimulating microbial activity and proliferation in the rumen but and increased microbial activity per se is not necessarily associated with improvement of feed conversion.