• Journal of Biosystems Engineering
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• v.34 no.6
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• pp.391-396
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• 2009

The percentage of those aged 60 and over is 43.5% among our country's 3,186 thousands farming population, so farm village is getting aging society rapidly. Moreover agricultural competitiveness has being weakened due to labor shortage by degradation in quality of labor configuration from elderly porson. For realisms easy workability, we developed a motor vehicle for agricultural activity. The vehicle has an automatic guidance system which could follows a track of magnetic tape on the floor for easy moving to given working position. We collected data from two guidance sensors, located on front and rear end of the vehicle and calculated displacement and angle deviation from the track. This traveling system was stably controlled with processing information deflection S, angle of deviation, D and angle velocity, Vt = $k_1D$ - $k_2S$ from two guidance sensors attached on front and rear of th motor vehicle. Also this system have been tested under various condition of $k_1$, $k_2$ for comparison on both stepped and turning routes. The results show that traveling performance is best at $k_1$=0.7, $k_2$=3.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.33.3-34
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• 2008

As an activity of building Korean Space Weather Prediction Center (KSWPC), we has studied of radiation effect on the spacecraft components. High energy charged particles trapped by geomagnetic field in the region named Van Allen Belt 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-1) 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-1 orbit with analyzing NOAA POES particle data. Our result clearly shows STSAT-1 solar cell degradation was caused by energetic protons which energy is about 700 keV to 1.5 MeV. Our result can be applied to estimate solar cell conditions of other satellites.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.2
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• pp.1-7
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• 2010

This paper presents empirical equations indicating the high frequency performance characteristics of air-cored induction coil sensors with their constructional parameters. An air-cored induction coil sensor is widely used due to good linearity at low frequency ranges but the sensor has weakness of relatively low sensitivity to the magnetic field. At high frequency ranges, the sensitivity can be dramatically increased, largely depending on the frequency of the injected field, and this property can be a great asset to some electromagnetic inspections, since they utilize the interrogating current with a fixed frequency. The application of this property of the coil sensor requires the estimation of its high frequency performance. We made experiments on the frequency responses of the coil sensors under diverse constructional conditions and, on the basis of the experimental results, the high frequency performance, such as the resonant frequency and the sensitivity at the frequency, was estimated, as a function of the constructional parameters of the coil sensor. The good agreements between experimental and estimated data were reported.

• Journal of Ocean Engineering and Technology
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• v.18 no.4 s.59
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• pp.33-39
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• 2004

This paper presents a hybrid underwater navigation system for unmanned underwater vehicles, using an additional range sonar, where the navigation system is based on inertial and Doppler velocity sensors. Conventional underwater navigation systems are generally based on an inertial measurement unit (IMU) and a Doppler velocity log (DVL), accompanying a magnetic compass and a depth sensor. Although the conventional navigation systems update the bias errors of inertial sensors and the scale effects of DVL, the estimated position slowly drifts as time passes. This paper proposes a measurement model that uses the range sonar to improve the performance of the IMU-DVL navigation system, for extended operation of underwater vehicles. The proposed navigation model includes the bias errors of IMU, the scale effects of VL, and the bias error of the range sonar. An extended Kalman filter was adopted to propagate the error covariance, to update the measurement errors, and to correct the state equation, when the external measurements are available. To illustrate the effectiveness of the hybrid navigation system, simulations were conducted with the 6-d.o.f. equations of motion of an AUV in lawn-mowing survey mode.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.367-374
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• 2006

This research revealed the operation problems of the current crossing control systems through inspecting and testing the obstacle detection systems and gate control systems for the crossings. To resolve the problems of the crossing control systems, this research developed new algorithms of four-quadrant gate system and obstacle detection systems combing the functions of rasar sensors and magnetic sensors and tested the reliability of the systems. Currently, the obstacle detection systems and gate control systems controls approaching and departing traffic by simply detecting vehicles and obstacles but do not consider traffic movements at the crossings. In addition, they do not make signal cooperation for gate controls. As a result, such inefficient crossing controls result in unsafe gate controls for drivers. Therefore, the newly developed crossing control systems through this study will provide more effective crossing control services with more strengthen information cooperation within control systems. Besides they will help to reduce train crashes at the crossings by gate control systems considering various driving behaviors.

• 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 the Korean Society for Nondestructive Testing
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• v.20 no.1
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• pp.38-45
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• 2000

In this research, MFL technique has been studied for the inspection of storage tank floor. The reference specimens having 20%, 40%, 60% and 80% slot's are fabricated using the carbon steel plates of a 6mm and 10mm thick. Powerful permanent magnets and Hall effect sensors are used to this application. It is shown that our system is able to detect metal loss like a slot. Also, it is possible that slot diameter is measured using transverse type of Hall generator. It is demonstrated that MFL can not differentiate between the response from top side and bottom side slot. Flux leakage response from a bottom side indication is significantly lower in amplitude than that from an equivalent top side slot. It is essential to know this sensor lift-off distance because the MFL signal also changes considerably with the sensor lift-off distance.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.22 no.11
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• pp.918-924
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• 2009

In this paper, we designed micro-structured electromagnetic transducers for energy harvesting and verified the performance of proposed transducers using finite element analysis software, COMSOL Multiphysics. To achieve higher energy transduce efficiency, around the magnetic core material, three-dimensional micro-coil structures with high number of turns are fabricated using semiconductor fabrication process technologies. To find relations between device size and energy transduce efficiency, generated electrical power values of seven different sizes of transducers ($3{\times}3\;mm^2$, $6{\times}6\;mm^2$, $9{\times}9\;mm^2$, $12{\times}12\;mm^2$, $15{\times}15\;mm^2$, $18{\times}18\;mm^2$, and $21{\times}21\;mm^2$) are analyzed on various magnetic flux density environment ranging from 0.84 T to 1.54 T and it showed that size of $15{\times}15\;mm^2$ device can generate $991.5\;{\mu}W$ at the 8 Hz of environmental kinetic energy. Compare to other electromagnetic energy harvesters, proposed system showed competitive performance in terms of power generation, operation bandwidth and size. Since proposed system can generate electric power at very low frequency of kinetic energy from typical life environment including walking and body movement, it is expected that proposed system can be effectively applied to various pervasive computing applications including power source of embodied medical equipment, power source of RFID sensors and etc. as an secondary power sources.

• 한국방재학회:학술대회논문집
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• 2011.02a
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• pp.92-92
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• 2011

최근 건설기술의 발전과 함께 강재 케이블을 이용하는 시설물의 시공이 점점 증가하는 추세이다. 특히 현수교, 사장교와 같은 초장대 교량에 사용되는 케이블은 주거더 및 상판에 의한 하중의 대부분을 지지하는 핵심부재이다. 하지만 이러한 케이블 부재는 부식, 파단 등으로 인한 단면손실이 발생할 수 있고, 이로 인한 손상부의 응력집중으로 인해 시설물 전체의 붕괴로 이어질 수 있는 위험성을 가진다. 따라서 조기에 단면손실을 찾아 사고를 미연에 방지할 수 있는 강재 케이블 비파괴 검사 기술기반의 건전성 모니터링이 필수적이다. 이러한 효율적인 건전성 모니터링을 위해 스마트 센서를 활용한 연구가 활발히 이루어지고 있는데, 그중 대표적인 스마트 센서중 하나인 마그네틱 센서는 높은 신뢰도와 어디에나 적용 가능한 재현성 때문에 구조물 건전성 평가에 적용하기 유용한 기술로 그 적용범위가 선박, 항공등으로 점점 넓어지고 있는 추세이다. 마그네틱 센서는 그 적용대상에 따라 다양한 마그네틱 특성을 활용할 수 있는데, 최근에는 투자율 계측을 통해 케이블의 장력 측정이 가능한 Elasto-Magnetic 센서(E/M 센서)가 개발되었고 그 활용성에 대한 연구가 이뤄지고 있다. 이에 본 연구에서는 E/M 센서를 이용한 강재 케이블 모니터링 기술을 제안하고자 한다. E/M 센서는 본래 케이블의 장력측정을 위해 개발되었지만 본 연구에서는 강재 케이블 부재의 단면손실 검색을 위해 적용하였다. 제안된 기술의 실험적 검증을 위해 E/M 센서를 이용하여 4가지의 다른 직경을 가지는 강봉시편을 E/M 센서헤드의 1차 코일을 통해 자화시키고, 각각의 직경에서 출력전압을 2차 코일을 이용하여 계측하였다. 그 결과 강봉의 직경이 감소함에 따라 출력 전압이 감소함을 보였다. 반복실험을 통해 해상도 및 선형성이 확보되는 최적의 입력전압과 출력전압의 워킹포인트를 선정하였고, 선정된 조건에서 강봉시편을 일정 간격으로 스캔한 결과 단면감소에 따른 선형적인 출력전압 감소와 동시에 단면 변화 지점에서는 추세선에서 크게 벗어난 출력전압 계측값을 나타내었다. 본 실험을 통해 제안된 E/M 센서를 이용한 강재 케이블 모니터링 기술의 유용성 및 적용가능성을 확인할 수 있었다.

• Journal of the Korean Society for Precision Engineering
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• v.34 no.1
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• pp.41-46
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• 2017

Magnetically levitated (Maglev) vehicles maintain a constant air gap between guideway and car bogie, and thereby achieves non-contact riding. Since the straightness and the flatness of the guideway directly affect the stability of levitation as well as the ride comfort, it is necessary to monitor the status of the guideway and to alert the train operators to any abnormal conditions. In order to develop a signal processing algorithm that extracts guideway irregularities from sensor data, virtual testing using a simulation model would be convenient for analyzing the exact effects of any input as long as the model describes the actual system accurately. Simulation model can also be used as an estimation model. In this paper, we develop a state-space dynamic model of a maglev vehicle system, running on the guideway that contains jumps. This model contains not only the dynamics of the vehicle, but also the descriptions of the power amplifier, the anti-aliasing filter and the sampling delay. A test rig is built for the validation of the model. The test rig consists of a small-scale maglev vehicle, tracks with artificial jumps, and various sensors measuring displacements, accelerations, and coil currents. The experimental data matches well with those from the simulation model, indicating the validity of the model.