• Journal of IKEEE
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• v.23 no.3
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• pp.751-755
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• 2019

This paper introduces a LED CUBE tool interacting with a distance sensor to solve real life problems in the physical computing field. Students can experience interactive education and intuitively understand the cubic operations with 3-dimensional animations obtained from a $3{\times}3{\times}3$ LED CUBE and a distance sensor connecting to Arduino. If the proposed LED CUBE is applied in physical computing section of the Information curriculum of middle school, students are expected to improve their computational thinking ability to solve problems in real life and other areas creatively and efficiently.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Physical Therapy Korea
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• v.30 no.2
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• pp.102-109
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• 2023

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

• Journal of the Korea Society of Computer and Information
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• v.16 no.1
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• pp.117-124
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• 2011

This study was realized the home network system for home care by bio-physical sensor system, to convey for the remote physical signal. The composition condition has four functions of displacement point for a Vision, Somatosensory, Vestibular and CNS that the basic measurement used to a Heart Rate, Temperature, Weight. Physical signal are decided to search a max and min point with adjustment of 0.01 unit in the reference level. There were checked physical condition of body balance to compounded a physical neuroceptor of sensory organ for the measurement such as a Vision, Somatosensory, Vestibular, CNS, BMI. There are to check a health care condition through a combination of physical organ with a posturography of a exercise. The service of home network system can be used to support health care management system through health assistants in health care center and central health care system. It was expected to monitor a physical parameter for the remote control health management system.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.174-176
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• 2006

Sensor network supports data delivery from Physical world to cyber space. Sensors get physical events then wireless network transfers sensor data to service server. We use sensor network technology to light control system for intelligent building. In ubiquitous computing environment. wireless sensor network is basic tool for intelligent service. In this paper, we propose intelligent building light control system based on wireless sensor network. It is implemented using previous light control product, can be adopted to present building light system.

• Journal of the Korea Society of Computer and Information
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• v.12 no.4
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• pp.95-102
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• 2007

Recently, a lot of researchers focus on the sensor middleware that hide the complexity of application developments and provide the abstraction of functions to upper application layer. Although there we several factors to design sensor middleware, the attribute-based naming is considered to be an essential factor among them. However, most existing researches were not designed to reflect the characteristics of sensor networks and have the limitation of attribute-based query extension. This study adopts the concept of Virtual Counterpart to suggest the structure there attribute-based naming is supported by virtual sensor nodes of the middleware on the sink node. Unlike traditional data-centric middleware in which individual sensor nodes process attribute-based query, virtual sensor nodes mapped to physical sensor nodes are running on the middleware of the sink node and process attribute-based query as a proxy of the physical sensor. This approach enables attribute-based naming independent of physical infrastructure and easy extensibility.

• Transactions of The Korea Fluid Power Systems Society
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• v.7 no.2
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• pp.1-6
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• 2010

A new type fiber optic sensing system has been developed as a commercially available standard using the technique of hetero-core spliced fiber optic sensor, for the purposes of monitoring large scaled structures, preserving natural environments and measuring physical phenomenons. The sensing system has been tested and evaluated in a possible outdoor condition in view of the full scaled operation at actual sites to be monitored. Additionally, the developed system in this work conveniently provides us with various options of sensor modules intended to measure such physical quantities as displacement, distortion, pressure, binary states and liquid adhesion. The experiment study has been performed to examine the performance to a pseudo-cracking experiment in the outdoor situation, and to clarify temperature influences to the system in terms of the coupling of optical connectors and the OTDR stability. It has been verified that the sensing system is robust to the temperature change ranging from the general condition to the hard condition. Especially, in this study, the specification and performances of the pressure sensor have been demonstrated to show the capability of inspecting various physical quantities.

• Korean Journal of Geomatics
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• v.2 no.2
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• pp.139-143
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• 2002

The rational polynomial coefficients (RPC) model is a generalized sensor model that is used as an alternative solution for the physical sensor model for IKONOS of the Space Imaging. As the number of sensors increases along with greater complexity, and the standard sensor model is needed, the applicability of the RPC model is increasing. The RPC model has the advantages in being able to substitute for all sensor models, such as the projective, the linear pushbroom and the SAR. This report aimed to generate a RPC model from the physical sensor model of the KOMPSAT-1(Korean Multi-Purpose Satellite) and aerial photography. The KOMPSAT-1 collects 510~730 nm panchromatic imagery with a ground sample distance (GSD) of 6.6 m and a swath width of 17 km by pushbroom scanning. The least square solution was used to estimate the RPC. In addition, data normalization and regularization were applied to improve the accuracy and minimize noise. This study found that the RPC model is suitable for both KOMPSAT-1 and aerial photography.

• Journal of Sensor Science and Technology
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• v.15 no.4
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• pp.269-276
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• 2006

This paper represents development of quartz crystal microbalance (QCM) and usage as a dew point sensor. The temperature of a quartz resonator was controlled precisely from $20^{\circ}C$ to $-30^{\circ}C$ with the ramping rate of $0.1^{\circ}C/s$ by using a custom-made crystal holder housing the quartz resonator associated with a thermoelectric cooler (Peltier cooler), which results in the working range from $15.2^{\circ}C$ to $-24.0^{\circ}C$ based on an accurate holder temperature compensation and temperature effect compensation process. The developed QCM dew point sensor and analysis techniques show very good sensing characteristics at measurement of moist air with the relative humidity from 10 %R.H. to 90 %R.H. generated by a divided-type humidity generator and the dew point temperatures were determined with an accuracy of less than ${\pm}0.18^{\circ}C$, which also showed good agreement with reference values in their error range.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.198-205
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• 2022

Along with a variety of coding education, physical computing education for controlling various sensors is being actively conducted for elementary, middle, and high school students in line with the era of the fourth industrial revolution. A problem with physical computing education using Arduino is pin connection errors between Arduino and various sensors. Most of the students who come into contact with the Arduino for the first time often do not know the purpose of the Arduino pin and the connection position of the pin. Also, hardware built with incorrect pin connections to the Arduino board often does not work properly. If this case continues, students will lose interest in coding education. Therefore, in this paper, we implemented an augmented reality application that informs the connection process of the Arduino board and the sensor during physical computing coding education using Arduino, and designed and implemented educational content for the Arduino pin position and connection process. First, we explain the role of the Arduino board and the sensor and the location of the pins. After that, the students run the educational augmented reality educational content using their smartphones and check the correct pin connection process between the Arduino and the sensor. In the physical computing education, augmented reality content is used to increase the understanding and immersion of the class. It is expected that the educational effect will also increase by inducing fun and interest in physical computing coding education.