• Journal of the Institute of Convergence Signal Processing
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• v.14 no.3
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• pp.205-211
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• 2013

Recently, the needs of system performances such as working speed and processing accuracy in machine tools have been increased. Especially, the working speed increment generates harmful heat at both moving part of the machine tools and handicrafts. The heat is a main drawback to progress accuracy of the processing. Hence, a oil cooler to control temperature is inevitable for the machine tools. In general, two representative control schemes, hot-gas bypass and variable speed control of a compressor, have been adopted in the oil cooler system. This paper deals with design and implementation method of fuzzy controller for obtaining precise temperature characteristic of HB oil cooler system in machine tools. The opening angle of an electronic expansion valve are controlled to keep reference value and room temperature of temperature at oil outlet. Especially, the fuzzy controller is added to suppress temperature fluctuation under abrupt disturbances. Through some experiments, the suggested method can control the target temperature within steady state error of ${\pm}0.22^{\circ}C$.

• Journal of Platform Technology
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• v.9 no.1
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• pp.23-31
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• 2021

In recent years, in the domestic industry, personal damage has occurred due to sensor malfunction and the emission of harmful gases. But there is a limit to the reliability verification of sensor data because the evaluation of environmental sensors is focused on durability and risk tests. This platform designed a sensor board that measures 10 major substances and a performance verification system for each sensor. In addition, the data collected by the sensor board was transferred to the server for data reliability evaluation and verification using LoRa communication, and a prototype of the sensor data platform was produced to monitor the transferred data. And the collected data is analyzed and predicted by using machine learning techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.2
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• pp.130-135
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• 2013

Recently, the needs of system performances such as working speed and processing accuracy in machine tools have been increased. Especially, the working speed increment generates harmful heat at both moving part of the machine tools and handicrafts. The heat is a main drawback to progress accuracy of the processing. Hence, a oil cooler to control temperature is inevitable for the machine tools. In general, two representative control schemes, hot-gas bypass and variable speed control of a compressor, have been adopted in the oil cooler system. In this paper, the compressor's speed are controlled to keep reference value of temperature at oil outlet. The precision processing of a machine tool is required for an oil cooler guaranteeing ${\pm}0.1^{\circ}C$ temperature control. But the oil cooler with precision temperature control is expensive. Therefore in this paper, instead of a on/off(relay) control method, a PID and phase angle electric power control method is proposed for the precision control of an oil cooler. The proposed controller is implemented and tested at the temperature of $23^{\circ}C$, $24^{\circ}C$ and $25^{\circ}C$.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.400-406
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• 2016

Comparing the past and the present in agriculture, society is losing people who work in farming, and the age of those who remain is increasing. Farmers are interested in special crops if the agricultural products' costs are low and the crops are easy to grow. If the area where a crop grows is bad, the agricultural products' quality gets worse. To overcome this situation, a new approach is being tried with crops. This research offers new technology to the young generation. This paper proposes technology that uses Internet of Things techniques to automatically sparge water and pesticide on orchards and fields using a machine instead of a person. We used the open source Arduino and sensor modules to build the automatic system. In this research, a circuit was simplified, and we constructed the proper size of the system by preventing errors in sensors, keeping distance from objects, and minimizing circuit collision. The machine drives and turns its head to sparge agricultural pesticides. The machine will minimize harmful effects caused by pesticides on humans, and will be helpful to farmers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.2
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• pp.23-33
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• 2004

Because of a rapid growth of internet environment, it is also fast increasing to exchange message using e-mail. But, despite the convenience of e-mail, it is rising a currently bi9 issue to waste their time and cost due to the spam mail in an individual or enterprise. Many kinds of solutions have been studied to solve harmful effects of spam mail. Such typical methods are as follows; pattern matching using the keyword with representative method and method using the probability like Naive Bayesian. In this paper, we propose a classification method of spam mails from normal mails using Support Vector Machine, which has excellent performance in pattern classification problems, to compensate for the problems of existing research. Especially, the proposed method practices efficiently a teaming procedure with a word dictionary including a generated index by the n-Gram. In the conclusion, we verified the proposed method through the accuracy comparison of spm mail separation between an existing research and proposed scheme.

• Journal of the Korean earth science society
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• v.44 no.2
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• pp.105-118
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• 2023

In this study, we predicted the presence of fog with a one-hour delay using the XGBoost DART machine learning algorithm for Andong, which had the highest occurrence of fog among inland stations from 2016 to 2020. We used six datasets: meteorological data, agricultural observation data, additional derived data, and their expanded data. The weather phenomenon numbers obtained through naked-eye observations and the visibility distances measured by visibility meters were classified as fog [1] or no-fog [0]. We set up twelve machine learning modeling experiments and used data from 2021 for model validation. We mainly evaluated model performance using recall and AUC-ROC, considering the harmful effects of fog on society and local communities. The combination of oversampled meteorological data features and the target induced by weather phenomenon numbers showed the best performance. This result highlights the importance of naked-eye observations in predicting fog using machine learning algorithms.

• Journal of the Korean Society of Fashion and Beauty
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• v.4 no.4 s.10
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• pp.33-41
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• 2006

The purpose of this study was to investigate consumer's behaviors on the purchase and the laundering of baby's clothing according to washing machine types using by consumers. The subjects were 255 consumers with babies(0-2 years) in Korea. The data were collected using a self-administered questionnaire and analyzed using Chi-square test and multiple response analysis, and SPSS 12.0 statistics package was used. Consumers were separated into two groups according to washing machine types with general pulsator type and drum-type. Consumer's behaviors between general pulsator type group and drum-type group of washing machines were also examined. The Results of this study were as follows: 1. The purchase rate of baby's clothing was high compare to that of general clothing of family. The consumers with babies had priority on textile materials(cotton 100%) as baby's clothing. As for the consumer's behaviors between the purchase of baby's clothing and the consumer's characteristics, they showed a significant differences according to the consumer's characteristics like education level and washing machine types using by consumers. 2. The removal of soils including various foods was very important on the laundering of baby's clothing. Especially the rinsing of detergents was important on the laundering. Because they considered toxicity of detergents was high and harmful to the baby, and detergent's components such as surfactants, builders, and another additives could not removed completely with normal laundry courses by washing machines. 3. There were significant differences between general pulsator type group and drum-type group of washing machines on the purchase and the laundering of baby's clothing.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.2
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• pp.111-119
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• 2011

Recently, the performances of speed and accuracy are enhanced in machine tools. The high speed of the machine tools usually causes harmful thermal displacements on the objects. To reduce the thermal displacements, machine tools generally adopt oil coolers with precise temperature control function. This study aims at precise control of oil outlet temperature in the oil coolers with hot-gas bypass manner based on PI control logic. The control system was designed for obtaining steady state error within ${\pm}0.1^{\circ}C$ and maximum overshoot with 0.8% even though abrupt disturbances are added to the system. We showed that the PI gains could be easily decided by numerical simulations using practical transfer function which got experiments. Also, transient characteristics could be improved significantly by reflecting the inlet temperature of an evaporator to the output of a controller feedforwardly considering periodic abrupt disturbances. Through some experiments, excellent control performances were established by the suggested control.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1363-1372
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• 2021

Red tide, defined as the major reproduction of harmful birds, has the characteristics of being generated and diffused in a wide area. This has limitations in detection only with the existing investigation method. Therefore, in this study, red tide was detected using a remote sensing technique. In addition, it was intended to increase the accuracy of detection by using optical characteristics, not just the concentration of chlorophyll. Red tide mainly occurs on the southern coast where sea signals are complex, and the main red tide control species on the southern coast is Cochlodinium polykirkoides. Therefore, it was intended to secure objectivity by reflecting features that could not be found depending on the researcher's observation and experience, not limited to visual judgment using machine learning techniques. In this study, support background machines and random forest were used among machine learning models, and as a result of calculating accuracy as performance evaluation indicators of the two models, the accuracy was 85.7% and 80.2%, respectively.