• Korean journal of applied entomology
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• v.61 no.2
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• pp.319-330
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• 2022

Andong is a place to culture the great amount of hot pepper (Capsicum annuum L.) in Korea. This study reports a yearly occurrence (March 31~October 25, 2021) of thrips infesting the hot pepper in Andong. Thrips caught to yellow sticky traps were diagnosed by morphological characters and showed two dominant species: Frankliniella occidentalis and F. intonsa. During this period, a total of 107,874 thrips were caught and included F. occidentalis at about 82%, F. intonsa at about 17%, and the other thrips at about 0.3%. There were two main peaks at May~June and at September~October, respectively, in which the total number of thrips was higher in the second peak and most were F. occidentalis. Interestinly, a low level of thrips occurred during July~August was observed and explained by their susceptibility to high temperatures. A laboratory experiment by exposing thrips to high temperatures showed that thrips were susceptible to temperatures higher than 35℃ and not tolerant to 45℃ for 1 h. Indeed, high temperatures higher than 45℃ were recorded in the greenhouses in Andong during July~August. F. occidentalis was more tolerant to the high temperatures than F. intonsa. On the other hand, the thrips showed the highest occurrence peak at July~August in hot pepper-culturing greenhouse in Kangwon, where the average temperatures were mostly lower than those of Andong and no high temperatures higher than 45℃ were recorded during July~August. A viral disease caused by tomato spotted wilt virus (TSWV) was observed in the hot peppers cultured in Andong greenhouses. Multiplex PCR was used to detect the virus along with identification of thrips. With a high record of about 30%, the virus-infected thrips were detected during all the monitoring period. The virulent thrips were identified to be only F. intonsa. These results suggest that F. occidentalis gives a direct damage especially during harvesting period with their high populations while F. intonsa gives indirect damage by transmitting TSWV.

• 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.