• Geophysics and Geophysical Exploration
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• v.8 no.3
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• pp.224-231
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• 2005

In geophysical field surveys, commercial equipments often fail to resolve the subsurface target or even sometimes fail to be applied because they do not fit to the various field situations or the physical properties of the medium or target. We developed a geophysical measurement system, which can be easily adapted for the various field situations and targets. The system based on PXI with A/D converter and some stand alone equipment such as Network Analyzer was applied to borehole radar survey, borehole sonic measurement and electromagnetic noise measurement. The system for borehole radar survey consists of PXI, Network Analyzer, dipole antennas, GPIB interface is used for PXI to control Network Analyzer. The system for borehole sonic measurement consists of PXI, 24 Bit A/D converter, high voltage pulse generator, transmitting and receiving piezoelectric sensors. The electromagnetic noise measurement system consists of PXI, 24 Bit A/D converter, 2 horizontal component electric field sensors and 2 horizontal and 1 vertical component magnetic filed sensors. The borehole radar system has been successfully applied to detect the width of the artificial tunnel through which the borehole pass and to image buried steel pipe, while the commercial borehole radar equipment failed. The borehole sonic system was tested to detect the width of artificial tunnel and showed a reasonable result. The characteristic of electromagnetic noise was grasped at an urban area with the data from the electromagnetic noise measurement system. The system is also applied to characterize the signal distortion by induction between the electric cables in resistivity survey. The system can be applied various geophysical problems with a simple modification of the system and sensors.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.435-444
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• 2008

High energy charged particles are trapped by geomagnetic field in the region named Van Allen Belt. These particles can move to low altitude along magnetic field and threaten even low altitude spacecraft. Space Radiation can cause equipment failures and on occasions can even destroy operations of satellites in orbit. Sun sensors aboard Science and Technology Satellite (STSAT-l) was designed to detect sun light with silicon solar cells which performance was degraded during satellite operation. In this study, we try to identify which particle contribute to the solar cell degradation with ground based radiation facilities. We measured the short circuit current after bombarding electrons and protons on the solar cells same as STSAT-1 sun sensors. Also we estimated particle flux on the STSAT-l orbit with analyzing NOAA POES particle data. Our result clearly shows STSAT-l solar cell degradation was caused by energetic protons which energy is about 700keV to 1.5MeV. Our result can be applied to estimate solar cell conditions of other satellites.

• Journal of Navigation and Port Research
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• v.37 no.6
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• pp.575-580
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• 2013

The IMU(Inertial Measurement Unit) which is the expensive equipment has been used as a special limited area, usually in measurement of posture of applying to the areas of ship, submarine, aircraft and military equipment application. However, in the current situation, MEMS AHRS technology can replace the high-priced IMU in MEMS AHRS selected application field. In this paper, wireless hull motion measurement system was suggested for measuring key elements of ship's movement such as rolling, pitching and yawing using gyro, acceleration and magnetic sensors of AHRS. In order to reduce the error such as instantaneous acceleration, effects and vibration of geomagnetic, we have adopted the sensors equipped with Kalman filtering. The Wireless hull motion measurement system using AHRS sensors was tested in actual ship and it could easily be applied in limited installation circumstances of the ship. In the future, this system can be useful in the navigation safety and marine accident analysis by using with ship equipment such as INS or VDR in the maritime.

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.42.3-43
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• 2009

Triplets of identical cubesats will be built to carry out the following scientific objectives: i) multi-observations of ionospheric ENA (Energetic Neutral Atom) imaging, ii) ionospheric signature of suprathermal electrons and ions associated with auroral acceleration as well as electron microbursts, and iii) complementary measurements of magnetic fields for particle data. Each satellite, a cubesat for ion, neutral, electron, and magnetic fields (CINEMA), is equipped with a suprathermal electron, ion, neutral (STEIN) instrument and a 3-axis magnetometer of magnetoresistive sensors. TRIO is developed by three institutes: i) two CINEMA by Kyung Hee University (KHU) under the WCU program, ii) one CINEMA by UC Berkeley under the NSF support, and iii) three magnetometers by Imperial College, respectively. Multi-spacecraft observations in the STEIN instruments will provide i) stereo ENA imaging with a wide angle in local times, which are sensitive to the evolution of ring current phase space distributions, ii) suprathermal electron measurements with narrow spacings, which reveal the differential signature of accelerated electrons driven by Alfven waves and/or double layer formation in the ionosphere between the acceleration region and the aurora, and iii) suprathermal ion precipitation when the storm-time ring current appears. In addition, multi-spacecraft magnetic field measurements in low earth orbits will allow the tracking of the phase fronts of ULF waves, FTEs, and quasi-periodic reconnection events between ground-based magnetometer data and upstream satellite data.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.95-102
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• 2016

Geomagnetic sensors are widely utilized for sensing heading direction of quadrotors. However, measurement from a geomagnetic sensor is easily corrupted by environmental magnetic field interference and roll/pitch directional motion. In this paper, a measurement method of a quadrotor heading direction is proposed for application to yaw attitude control. In order to eliminate roll/pitch directional motion effect, the geomagnetic sensor data is compensated using the roll/pitch angles measured for stabilization control. In addition, yaw-directional angular velocity data from a gyroscope sensor is fused with the geomagnetic sensor data using a complementary filter which is a simple and intuitive sensor fusion method. The proposed method is applied to experiments, and the results are presented to prove validity and effectiveness of the proposed method.

• Journal of Korea Multimedia Society
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• v.20 no.3
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• pp.562-570
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• 2017

One shot tasks like a MERSC handling policy, a cinema poster, and so on are too small, diverse, and sporadic to make them as apps or web applications. They are usually shared as the form of notes attached in the field or messages in smart phones. In order to support inter-operability with internet web sites, QR/NFC tags are attached to them. What matters in the web technology is that HTML5 standard does not supply the accessability of smart phones' resources like a camera, an audio, magnetic sensors, and etc. In this paper, we propose a universal smart phone application for handling various one-shot tasks in the same UI/UX. One-shot tasks are described with HTML5 web documents, and the URL for the web documents are stored in QR/NFC tags. A smart phone scans a tag, and then the web document is retrieved and presented finally. QR tags can be delivered to other smart phones through messages or SNS. We solve the problem of HTML5 standard supplying a resource access library with javascrippts. We suggested the whole architecture and the internal structure of QR/NFC tags. We show that our scheme is applicable to make variable one-shot tasks.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.1
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• pp.114-120
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• 2013

Recently, navigation systems based on wireless communication have been applied to the internal structures such as building or ship. If a stable azimuth information is obtained, these systems can effectively guide the direction of the user's progress through the information and then can improve the performance of guidance. Since conventional method which has acquired an azimuth information using geomagnetic and acceleration sensor(azimuth sensor hereafter) is sensitive to the effects of the magnetic field, it has unstable error range according to the surrounding environment. In order to improve these problems, this paper presents a new relative azimuth estimation algorithm using the displacement of a mobile node and its rotation angle based on Wireless communication. For the performance assessment of the proposed algorithm, experiments using rotating arm are performed and the results are confirmed that the proposed system can estimate the relative azimuth without using additional sensors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.151-156
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• 2017

In case of massive water leakage, it's crucial for field manager to quickly positioning the problematic valve and related ones. However, it's not easy for the system to find the corresponding valve and even if it's found, it can not respond quickly because it can't know the relevant information immediately. In this paper, we implement the system for identifying sluice valve positions using GPS and AR techniques. The proposed system is composed of hand held android device, remote database server and data acquisition device for DB creation. We utilize the android device's sensors including GPS, gyro, accelerometer, magnetic sensor. The system identifies the valve with matching between the position data from the remote database server, and current GPS locations of device. We use AR techniques to overlay the graphics pattern of valve positions and some additional informations on captured real scene. With this system, it will be fast and accurate for maintenance of sluice valve of municipal water system.

• Journal of Sensor Science and Technology
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• v.16 no.5
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• pp.331-336
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• 2007

In this study, we have fabricated an infrared optical fiber based sensor which can monitor the respiration of a patient. The design of a chalcogenide optical fiber based sensor is suitable for insertion into a high electro-magnetic field environment because the sensor consists of low cost and compact mid-infrared components such as an infrared light source, a chalcogenide optical fiber and a thermopile sensor. A fiber-optic respiration sensor is capable of detecting carbon dioxide ($CO_{2}$) in exhalation of a patient using the infrared absorption characteristics of carbon gases. The modulated infrared radiation due to the presence of carbon dioxide is guided to the thermopile sensor via a chalcogenide receiving fiber. It is expected that a mid-infrared fiber-optic respiration sensor which can be developed based on the results of this study would be highly suitable for respiration measurements of a patient during the procedure of an MRI.