• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.449-457
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• 2024

AIoT, the intelligent Internet of Things, refers to a technology that collects data measured by IoT devices and applies machine learning technology to create and utilize predictive models. Existing research on AIoT technology education focused on building an educational AIoT platform and teaching how to use it. However, there was a lack of case studies that taught the process of automatically creating and utilizing machine learning models from data measured by IoT devices. In this paper, we developed a case study using a machine learning model for AIoT technology education. The case developed in this paper consists of the following steps: data collection from AIoT devices, data preprocessing, automatic creation of machine learning models, calculation of accuracy for each model, determination of valid models, and data prediction using the valid models. In this paper, we considered that sensors in AIoT devices measure different ranges of values, and presented an example of data preprocessing accordingly. In addition, we developed a case where AIoT devices automatically determine what information they can predict by automatically generating several machine learning models and determining effective models with high accuracy among these models. By applying the developed cases, a variety of educational contents using AIoT, such as prediction-based object control using AIoT, can be developed.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2349-2359
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• 2013

The purpose of this study was to predict the factors which are related to the intention and behaviour for breast self-examination (BSE) of middle aged women using the theory of planned behavior (TPB). A survey using a structured questionnaire was conducted wih 217 middle aged women. BSE behaviour was assessed at 1-month follow-up. The overall fit of the structural model to the date was acceptable(${\chi}^2$=1246.6(p<.001), ${\chi}^2$/df=2.72, CFI=.831, TLI=.817, RMSEA=.089). The BSE behavior rate within one month was 56.2%. The TPB explained 43.9% of the variance in BSE intentions and 10.9% of the variance in BSE behavior. The subjective norm(${\beta}$=.364, p<.001) and the perceived behavioral control(${\beta}$=.553, p<.001) both positively influenced the behavioral intention, and the behavioral intention(${\beta}$=.768, p<.01) positively influenced the behavior. This study shows the model's applicability in explaining BSE behavior of middle aged women, and suggests that health intervention programs should focus on strengthening the intention for the promotion of BSE behavior.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.581-588
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• 2017

This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata.

• Korean Journal of Environmental Agriculture
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• v.36 no.4
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• pp.293-298
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• 2017

BACKGROUND: Pyrethroids (PYRs) are a widely used insecticide in agriculture and household area. In mammals, PYRs such as deltamethrin is metabolized to 3-phenoxybenzoic acid (3-PBA) in liver that is mainly excreted in urine. This study is designed to single exposure of deltamethrin to rats in a dose-dependent manner and identify the correlation between deltamethrin exposure and its metabolite (3-PBA) in urine. METHODS AND RESULTS: Exposure levels of deltamethrin were control (0 mg/kg bw), low (0.0705 mg/kg bw), medium (0.705 mg/kg bw) and high (7.05 mg/kg bw) dose. Low concentration was derived by ussing Korea predictive operator exposure model (KoPOEM). Dermal exposure persisted for 6 h, and urine specimens were collected for 24 h. The urine matrix was removed after a series of procedures and 3-PBA was analyzed by gas chromatography/mass spectrometry. CONCLUSION: There was a strong correlation ($R^2=0.83$) between the amount of oral exposure to delta me thrin and urinary levelof3-PBAexcreted. In dermal exposure groups of deltamethrin except high-dose, also there was a good correlation between urinary 3-PBA and deltamethrin exposure, but not stronger than in oral deltamethrin exposure groups. Based on these results, therefore, the amount of 3-PBA in urine can be used as a good monitoring indicator that reflexing the exposure level of deltamethrin to human body.

• Korean Journal of Food Science and Technology
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• v.37 no.3
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• pp.484-491
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• 2005

Quantitative microbial risk assessment (QMRA) analyzes potential hazard of microorganisms on public health and offers structured approach to assess risks associated with microorganisms in foods. This paper addresses specific risk management questions associated with Staphylococcus aureus in kimbab and improvement and dissemination of QMRA methodology, QMRA model was developed by constructing four nodes from retail to table pathway. Predictive microbial growth model and survey data were combined with probabilistic modeling to simulate levels of S. aureus in kimbab at time of consumption, Due to lack of dose-response models, final level of S. aureus in kimbeb was used as proxy for potential hazard level, based on which possibility of contamination over this level and consumption level of S. aureus through kimbab were estimated as 30.7% and 3.67 log cfu/g, respectively. Regression sensitivity results showed time-temperature during storage at selling was the most significant factor. These results suggested temperature control under $10^{\circ}C$ was critical control point for kimbab production to prevent growth of S. aureus and showed QMRA was useful for evaluation of factors influencing potential risk and could be applied directly to risk management.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.