• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.107-113
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• 2023

This paper proposes a method for detecting smoking behavior in images using artificial intelligence technology. Since smoking is not a static phenomenon but an action, the object detection technology was combined with the posture estimation technology that can detect the action. A smoker detection learning model was developed to detect smokers in images, and the characteristics of smoking behaviors were applied to posture estimation technology to detect smoking behaviors in images. YOLOv8 was used for object detection, and OpenPose was used for posture estimation. In addition, when smokers and non-smokers are included in the image, a method of separating only people was applied. The proposed method was implemented using Google Colab NVIDEA Tesla T4 GPU in Python, and it was found that the smoking behavior was perfectly detected in the given video as a result of the test.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.5
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• pp.63-70
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• 2023

In this paper, we propose a Mixed Reality based rehabilitation exercise solution with the goal of mitigating one of the most common chronic conditions among the elderly, musculoskeletal disorders. In modern society, as the number of elderly increases, more people engage in office work and engage in more sedentary activities. Due to repetitive work in the office, muscle strength decreases and this causes many difficulties in daily life. In this study, we developed a mixed reality based exercise platform to solve these chronic musculoskeletal diseases. VR is not appropriate for elderly because of dizziness. In addition, we developed a wearable sensor based on IMU and attached it to important parts of the upper body to motion tracking. We developed a algorithm synchronize to raw data from wearable sensor with in a vr avatar. Ederly can check in real time whether rehabilitation exercises are being performed accurately through the avatar.

• 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.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Korean Journal of Environment and Ecology
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• v.28 no.2
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• pp.253-262
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• 2014

This study was conducted to find methods of qualitative landscape assessment for vegetational landscapes using ecological analysis. The study site was Seoraksan National Park in Korea. For this study, differing unique landscape resources were categorized and identified according to ecosystems. After identifying the study areas, the relationship between trail visitor preference and the amount of visible overexposure caused by people to the resources was examined. Landscape resources chosen for ecological analysis at Seoraksan National Park were subalpine vegetation community, high mountain rocks, ombrogenous deciduous broadleaf forest in the valley area, edaphic climax community, big tree community, flowering tree dominant community, autumnal tree dominant community and needle-leaf forest in the subalpine area. As a result of the study, it was found that the landscape resources with the highest correlation to visitor trail preference were big tree community area, flowering tree dominant community area, and needle-leaf forest in the subalpine area. As a result of overlapping the analysis of the amount of visible overexposure to the landscape resources and the analysis of preferential use of trails by visitors, guidance for the appropriate season for each trail can be provided. Since a positive correlation exists between certain sections of the natural landscapes and visitor preference, ecological impact on landscape resource ecosystems did not appear to cover wide areas of the trails, but was limited to certain areas preferred by visitors.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.33-39
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• 2013

When the eldery with limited mobility and disabled use a wheelchairs to move, it can cause decreased exercise ability like decline muscular strength in upper limb and lower extremities. The disabled people suffers with spinal cord injuries or post stroke hemiplegia are easily exposed to secondary problems due to limited mobility. In this paper, We designed intelligent wheelchair robot system for upper limb and lower extremities exercise/rehabilitation considering the characteristics of these severely disabled person. The system consists of an electric wheelchair, biometrics module for Identification characteristics of users, upper limb and lower extremities rehabilitation. In this paper, describes the design and configurations and of developed robot. Also, In order to verify the system function, conduct performance evaluation targeting non-disabled about risk context analysis with biomedical signal change and upper limb and lower extremities rehabilitation over wheelchair robot move. Consequently, it indicate sufficient tracking performance for rehabilitation as at about 86.7% average accuracy for risk context analysis and upper limb angle of 2.5 and lower extremities angle of 2.3 degrees maximum error range of joint angle.

• The Korean Journal of Ecology
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• v.28 no.6
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• pp.395-404
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• 2005

A project has recently begun to reintroduce endangered Asiatic black bears to the Jirisan National Park. However, information on home range that is necessary to maintain the Minimum Viable Population (MVP) of those bears does not exist. Based on point data of two bears that were released for trial in Jirisan in 2001, we identified the movement pattern of bears and estimated their home ranges with two different methods Finally, the possibility of conserving the MVP of bears was evaluated by comparing the location and size of the home range with habitats which have been found to be suitable for bears. The frequency of bears' appearance reduced drastically as road densities of both paved roads and legal trails increased. The midpoint of home ranges of the two bears was 376.85 $km^2$ and 50.76 $km^2$ based on 100% MCP (Minimum Convex Polygon) and 95% AK (Adaptive Kernel Home Range Method), respectively, with an overlapped area of 126.0 $km^2$ and 3.99 $km^2$ each. The core areas of their home ranges are located not in the no-entry zone, where major trails were open to the public - despite being designated as no -entry zone - but in areas where most trails were closed to the public. A discrepancy between core areas of home ranges and potentially suitable habitats suggests the effects of vehicles and tracking people through roads within the park. Thus, for the success of in situ conservation of endangered bears, well-planned management of habitats is needed to protect bears and to ensure the home ranges to support the MVP.

• KIISE Transactions on Computing Practices
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• v.21 no.8
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• pp.524-530
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• 2015

A log is for text monitoring and perceiving the issues of a system during the development and operation of a program. Based on the log, system developers and operators can trace the cause of an issue. In the development phase, it is relatively simple for a log to be traced while there are only a small number of personnel uses of a system such as developers and testers. However, it is the difficult to trace a log when many people can use the system in the operation phase. In major cases, because a log cannot be tracked, even tracing is dropped. This study proposed a simplified tracing of a log during the system operation. Thus, the purpose is to create a log on the run time based on an ID/IP, using features provided by the Logback. It saves an ID/IP of the tracking user on a DB, and loads the user's ID/IP onto the memory to trace once WAS starts running. Before the online service operates, an Interceptor is executed to decide whether to load a log file, and then it generates the service requested by a certain user in a separate log file. The load is insignificant since the arithmetic operation occurs in a JVM, although every service must pass through the Interceptor to be executed.

• Korean Journal of Heritage: History & Science
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• v.46 no.2
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• pp.26-45
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• 2013

Sudden increase exceeding 30million in the number of smart phone users, and rising interest in the technology of augmented reality, is now trying to combine it with AR technology in other areas very much. The field of cultural heritage, which has been constructed by the Internet and 3D technology, is not unusual and this field is now rapidly changing thanks to the AR technology which can make users experience cultural heritage with high reality. The Palaces in Seoul, however, use fragmentary tools of information - lack of heritage commentators, leaflet, etc, even though the number of visitors is gradually increasing. Therefore, three-dimensional and comprehensive cultural heritage information service is needed with the guidance in the mobile era. This study utilizes the AR technology for building the Deoksugung Tourist Information Service Application(App.) applying the markerless-based recognition technology which is a more advanced tool than the location-based AR technology. This new AR technology can switch perceived real images such as the tablet of the King in the Palace of in the real world, patterns and pedestals into virtual world, which can reproduce the damaged cultural assets as 3D. This also composes photos of the past with the current buildings, which can increase people's interest and absorption of the contents, and helps them understand and be aware of Korean traditional culture and cultural heritage effectively. In addition, convergence between IT new technology, Augmented Reality(AR) and humanities through storytelling based implementation of cultural heritage in smart phone is attempted to demonstrate that there is strength in which augmented reality technique exerts infinite creativity based on actual reality world.

• Korean Journal of Culture and Social Issue
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• v.21 no.1
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• pp.67-96
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• 2015

Do cultural differences affect moral decisions? Two studies were conducted to investigate whether attitudes of individualism vs. collectivism have an impact on ethical decision making. Study 1 (N=92) showed that utilitarianism was preferred in a situation, in which an intervention resulted in the best outcome (i.e., saving more people's lives), while deontology was preferred in a situation, in which the focus was on negative consequences of the intervention (i.e. personal sacrifices). Additionally, there were differences between the idiocentrics and the allocentrics groups regarding morality aspects. In the idiocentrics group, harm and fairness were regarded as more important than other moral aspects, while in the allocentrics group, not only harm and fairness, but also ingroup and authority were perceived as critical moral aspects. In Study 2 (N=30), after lexical decision tasks were conducted for culture priming, the mouse tracking method was used to explore response dynamics of moral decision processes, while judging appropriateness of interventions in moral dilemmas. In Study 2, in a condition, in which the small number of victims were focused upon, there were more maximal deviations and higher X_flips in the individualism priming group than in the collectivism priming group, which showed that the participants in the individualism condition had more deliberative processes before choosing their answers between utilitarianism and deontology. In addition, the participants in the individualism priming condition showed more maximal deviations in the mouse trajectories regarding ingroup related interventions in moral dilemmas than those in the collectivism priming condition. These results implicated the possibilities that the automatic emotional process and the controlled deliberative process in moral decision making might interact with cultural dispositions of the individuals and the focus of situations.