• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.315-328
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• 2024

Purpose: The purpose of this paper is to present a service construction plan using multiple complex sensor information to detect abnormal situations in urban life safety that are difficult to identify on CCTV. Method: This study selected service scenarios based on actual testbed data and analyzed service importance for local government control center operators, which are main users. Result: Service scenarios were selected as detection of day and night dynamic object, Detection of sudden temperature changes, and Detection of time-series temperature changes. As a result of AHP analysis, walking and mobility collision risk situation services and fire foreshadowing detection services leading to immediate major disasters were highly evaluated. Conclusion: This study is significant in proposing a plan to build an anomaly detection service that can be used in local governments based on real data. This study is significant in proposing a plan to build an anomaly detection service that can be used by local governments based on testbed data.

• Journal of The Korean Society of Integrative Medicine
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• v.9 no.4
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• pp.291-298
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• 2021

Purpose: The Pedisole is a newly developed shoe-mounted wearable assessment system for analyzing balance and gait. This study aimed to determine the reliability and validity of the parameters provided by the system for static balance and gait analysis of healthy adults. Methods: This study included 38 healthy adults (22.4±1.9 years) with no history of injury in the lower limbs. All participants were asked to perform balance and gait tasks for undertaking measurements. For analysis of balance, both the smart Pedisole and Pedoscan systems were concurrently used to analyze the path length of the center of pressure (COP) and the weight ratio of the left and right for 10 s. Gait was measured using the smart Pedisole and GaitRite walkway systems simultaneously. The participants walked at a self-selected preferred gait speed. The cadence, stance time, swing time, and step time were used to analyze gait characteristics. Using the paired t-test, the intra-class coefficient correlation (ICC) was calculated for reliability. The Spearman correlation was used to assess the validity of the measurements. In total, data for balance from 36 participants and the gait profiles of 37 participants were evaluated. Results: There were significant differences between the COP path lengths (p<.050) derived from the two systems, and a significant correlation was found for COP path length (r=.382~.523) for static balance. The ICC for COP path length and weight ratio was found to be greater than .687, indicating moderate agreement in balance parameters. The ICC of gait parameters was found to be greater than .697 except for stance time, and there was significant correlation (r=.678~.922) with the GaitRite system. Conclusion: The newly developed smart insole-type Pedisole system and the related application are useful, reliable, and valid tools for balance and gait analysis compared to the gold standard Pedoscan and the GaitRite systems in healthy individuals.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.593-605
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• 2022

The purpose of this study is to investigate the personalized treacmill exercise analysis using a smart mirror based on Kinect sensor. To evaluate the performance of the development system, 10 health males were used to measure the range of the hip joint, knee joint, and ankle joint using a smart mirror when walking on a treadmill. For the validity and reliability of the development system, the validity and reliability were analyzed by comparing the human movement data measured by the Kinect sensor with the human movement data measured by the infrared motion capture device. As a result of validity verification, the correlation coefficient r=0.871~0.919 showed a high positive correlation, and through linear regression analysis, the validity of the smart mirror system was 88%. Reliability verification was conducted by ICC analysis. As a result of reliability verification, the correlation coefficient r=0.743~0.916 showed high correlation between subjects, and the consistency for repeated measurement was also very high at ICC=0.937. In conclusion, despite the disadvantage that Kinect sensor is less accurate than the motion capture system, Kinect is it has the advantage of low price and real-time information feedback. This means that the Kinect sensor is likely to be used as a tool for evaluating exercise prescription through human motion measurement and analysis.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.145-160
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• 2023

The purpose of this study was to compare and analyze diurnal thermal environments using Unmanned Aerial Vehicles(UAV)-derived physical parameters(NDVI, SVF) and ENVI-met modeling. The research findings revealed significant correlations, with a significance level of 1%, between UAV-derived NDVI, SVF, and thermal environment elements such as S↑, S↓, L↓, L↑, Land Surface Temperature(LST), and Tmrt. In particular, NDVI showed a strong negative correlation with S↑, reaching a minimum of -0.52^** at 12:00, and exhibited a positive correlation of 0.53^** or higher with L↓ at all times. A significant negative correlation of -0.61^** with LST was observed at 13:00, suggesting the high relevance of NDVI to long-wavelength radiation. Regarding SVF, the results showed a strong relationship with long-wave radiative flux, depending on the SVF range. These research findings offer an integrated approach to evaluating thermal comfort and microclimates in urban areas. Furthermore, they can be applied to understand the impact of urban design and landscape characteristics on pedestrian thermal comfort.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.12-28
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• 2017

The core of the transportation service, so-called Mobility 4.0 is the flexibility of the entire mobility and its implementation. By doing so, the most essential element is to build a platform to link a supply and a demand simultaneously. In other word, a comprehensive analytical framework is to be set with a data repository which can be periodically updated. With such circumstances, the entire trip chain including pedestrian movements is required to be thoroughly investigated and constructed at the viewpoint of the intermodal transit station. A few studies, however, have been attempted. In this study, the comprehensive analytical framework with the integrated mobility at intermodal transit station was proposed, which consisted of the three modules; 1) Data Repository Extracting from Smart Card DB, 2) Framework of Analyzing Integrated Mobility, and 3) Interpretation of the Integrated Mobility with GIS information and the other factors. A case study with the seven railway stations (Sadang, Sindorom, Samseong, Gwanghwanoon, Gangnam, Jamsil, Seoul Nat'l Univ. of Education) was conducted. The stations of the case study were clustered by the three groups with the statistical ground, and it is most likely to understand the effect of a variety of factors and a comprehensive data-driven analyses with the entire trip stages.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.4
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• pp.61-75
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• 2021

Roadside greenery in the city is not only a means of reducing fine dust, but also an indispensable element of the city in various aspects such as improvement of urban thermal environment, noise reduction, ecosystem connectivity, and aesthetics. However, in studies dealing with the effect of reducing fine dust through trees in existing urban spaces, microscopic aspects such as the adsorption effect of plants were dealt with, structural changes such as the width of urban buildings and streets, and the presence or absence of trees, Impact studies that reflect the actual form of In this study, the effect of greenery composition applicable to urban space on PM2.5 was simulated through the microclimate epidemiologic model ENVI-met, and field measurements were performed in parallel to verify the results. In addition, by analyzing the results of fine dust background concentration, wind speed, and leaf area index, the sensitivity to major influencing variables was tested. As a result of the study, it was confirmed that the fine dust reduction effect was the highest in the case with a high planting amount, and the reduction effect was the greatest at a low background concentration. Based on this, the cost of planting street green areas and the effect of reducing PM2.5 were compared. The results of this study can contribute as a basis for considering the effect of pedestrian space on air quality when planning and designing street green spaces.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• 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 Korean Society of Forest Science
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• v.108 no.3
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• pp.382-391
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• 2019

This study was conducted to verify whether smartphone hiking apps, which generate social network data including location information, are useful tools for analyzing the use characteristics of a forest recreation area. For this purpose, the study identified the functions and service characteristics of smartphone hiking apps. Also, the use characteristics of the area of Daegwallyoung were analyzed, compared with the results of the field survey, and the applicability of hiking apps was reviewed. As a result, the service types of hiking apps were analyzed in terms of three categories: "information offering," "hiking record," and "information sharing." This study focused on an app that is one of the "hiking record" types with the greatest number of users. Analysis of the data from hiking apps and a field survey in the Daegwallyoung area showed that both hiking apps and the field survey can be used to identify the movement patterns, but hiking apps based on a global positioning system (GPS) are more efficient and objective tools for understanding the use patterns in a forest recreation area, as well as for extracting user-generated photos. Second, although it is advantageous to analyze the patterns objectively through the walking-speed data generated, field surveys and observation are needed as complements for understanding the types of activities in each space. The hiking apps are based on cellphone use and are specific to "hiking" use, so user bias can limit the usefulness of the data. It is significant that this research shows the applicability of hiking apps for analyzing the use patterns of forest recreation areas through the location-based social network data of app users who record their hiking information voluntarily.

• Korean Chemical Engineering Research
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• v.60 no.1
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• pp.86-92
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• 2022

Recently, the bio-healthcare market is enlarging worldwide due to various reasons such as the COVID-19 pandemic. Among them, biometric measurement and analysis technology are expected to bring about future technological innovation and socio-economic ripple effect. Existing systems require a large-capacity battery to drive signal processing, wireless transmission part, and an operating system in the process. However, due to the limitation of the battery capacity, it causes a spatio-temporal limitation on the use of the device. This limitation can act as a cause for the disconnection of data required for the user's health care monitoring, so it is one of the major obstacles of the health care device. In this study, we report the concept of a standalone healthcare monitoring module, which is based on both triboelectric effects and electromagnetic effects, by converting biomechanical energy into suitable electric energy. The proposed system can be operated independently without an external power source. In particular, the wireless foot pressure measurement monitoring system, which is rationally designed triboelectric sensor (TES), can recognize the user's walking habits through foot pressure measurement. By applying the triboelectric effects to the contact-separation behavior that occurs during walking, an effective foot pressure sensor was made, the performance of the sensor was verified through an electrical output signal according to the pressure, and its dynamic behavior is measured through a signal processing circuit using a capacitor. In addition, the biomechanical energy dissipated during walking is harvested as electrical energy by using the electromagnetic induction effect to be used as a power source for wireless transmission and signal processing. Therefore, the proposed system has a great potential to reduce the inconvenience of charging caused by limited battery capacity and to overcome the problem of data disconnection.