• Journal of Popular Narrative
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• v.25 no.4
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• pp.429-465
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• 2019

This paper analyzed the ways in which "others" were reproduced in Korean movies in the 1970s. In the midst of the social changes of the era, such as urbanization due to rapid industrial modernization, many people became laborers for industry in order to obtain the fruits of modernization.But the landscape of others, which was inevitably produced in the process of constructing such subjects, has been limited to analysis that is focused on gender and youth discourse. This article aims to extract the landscape of others in the 1970s by adopting a different perspective. The way in which the other is present can be divided into the following two categories. First, in 1970s film, the family community, in contrast with 1960s film, has disintegrated and cracked, due to the inability of others to enter or leave the community. The desperate perception that the family community can no longer function as a stable foundation or center of the constitution, and that it cannot have a sense of security and belonging,is revealed through the way the others are wandering in and out of the community. Second, 'Death' is an element of social life in the violence of the national ideology of the 1970s, and the everyday exceptional state. The way in which the 'other' is completely eliminated from the normal subjectivity requested by the state and is deported in film reflectshow everyday death or potential death is part of life of the 1970s. Normal life pursued through rapid urbanization and industrialization leads to the death of the other beings, but the way of existence of others is the desperate reality of the 1970s, when the boundaries of the state that provide stability and belonging are broken. As a result, the landscape of others in the 1970s reveals a violent reality that destroys the perfect middle class family discourse that industrial modernization was oriented around in the 1970s, and that produced masses of others who caused numerous deaths. In spite of regime censorship, Korean films were popularly revealing the violence of life brought in by the 1970s, following a detour of representation.

• 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.