• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.4
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• pp.70-77
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• 2011

This study takes road landscape of traditional Chinese Kangnam gardens in Suzhou as the object. It compares the relations and differences between preferences of Korean and Chinese on road landscapes with different rockery ratios, and studies the differences between Korean and Chinese's adjective visual characteristics of road landscape of traditional gardens and impacts of visual characteristics on preference. The following is the research process: Firstly, the theoretical survey of road landscape of traditional Chinese Kangnam gardens is conducted, pictures of the road landscape of gardens in Suzhou are taken, and 15 pictures are selected based on rockery ratio. Secondly, in order to grasp the visual preference and landscape characteristics of road landscape of garden in Suzhou, 15 pictures and 21 pairs of adjectives are adopted for the questionnaire survey. Thirdly, in order to grasp the differences between preferences of Korean and Chinese on road landscape of traditional Chinese Kangnam gardens, thet-test analysis is conducted. In order to grasp the impacts of rockery ratio on preference, and after the classification of landscape pictures based on rockery occupancy, the average analysis, factor analysis of results of questionnaire survey for Korean and Chinese are conducted respectively. In order to grasp the differences of incentives of landscape preference, the incentive analysis of results of questionnaire survey for Korean and Chinese is carried out. In order to grasp the impacts of various factors on the preference, The results are as follows: The results of analysis of differences between Korean and Chinese's preference on road landscape of traditional Chinese Kangnam gardens show that the overall preference of Chinese is higher than that of Korean. The results of the landscape preference analysis show that the ranking order of average value of Korean and Chinese's preference on rockery ratio categories is: medium ratio, very small ratio, small ratio, large ratio, very large ratio. The results of analysis of relations between rockery ratio of traditional Chinese Kangnam gardens and preference show that the preference increases as the rockery ratio decreases, and the rockery ratio variation causes greater impacts on Korean. Results of the analysis of visual characteristics, factors of visual characteristics of Koreans are "aesthetic factor", "comfort factor", "neat(orderly) factor", and "fun factor". The visual characteristics of Chinese has three factors, namely "psychological factor", "comfort factor", and "neat factor".

• 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.