• Journal of Power Electronics
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• v.9 no.1
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• pp.68-73
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• 2009

Automotive inverters may require current sensors for motor torque control, especially, in applications of hybrid electric vehicles or fuel cell vehicles. In this paper, to achieve a compact, integrated and low cost current sensor, a hall current sensor without magnetic core is introduced for integrating an automotive inverter. The compactness of the current sensor is possible by using integrated magnetic concentrators based on the Hall effect. Magnetic fields caused by three-phase currents are analyzed and a magnetic shield design is proposed for decoupling the cross-coupled field. It offers galvanic isolation, wide bandwidth (>100kHz), and accuracy(< 1%). Using 2D FEM analysis, its performance is demonstrated with design parameters at a U-shaped magnetic shield. The proposed coreless current sensor is tested with rated current to validate the linearity and accuracy.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.5
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• pp.827-835
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• 2022

Clothing pressure is considered the essential factor affecting the comfort of clothing, so it is crucial that it is measured precisely. The purpose of this study is to construct a prototype using the Adafruit Flora as the Arduino system, which can be used as a wearable framework for easy, low-cost, and precise clothing pressure measurement. The study also aims to determine how best to conduct the procedure of sensor calibration. To optimize the accuracy of the sensors, the calibration procedure was implemented using mathematical methods that combined polynomial and exponential regression in a hybrid approach. The prototype can easily measure clothing pressure even during active movements, as seen in the detection of stable signals. In addition, since the system was specifically proposed as a wearable patch that can be easily attached and removed as necessary, it can also be used to standardize the value of clothing pressure in each movement.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.119-124
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• 2024

Effective fire sensing is important to protect lives and property from the disaster. In this paper, we present an intelligent visual sensing method for detecting fires based on machine learning techniques. The proposed method involves a two-step process. In the first step, fire and non-fire images are used to train a convolutional neural network (CNN), and in the next step, feature vectors consisting of 256 values obtained from the CNN are used for the learning of a support vector machine (SVM). Linear and nonlinear SVMs with different parameters are intensively tested. We found that the proposed hybrid method using an SVM with a linear kernel effectively increased the recall rate of fire image detection without compromising detection accuracy when an imbalanced dataset was used for learning. This is a major contribution of this study because recall is important, particularly in the sensing of disaster situations such as fires. In our experiments, the proposed system exhibited an accuracy of 96.9% and a recall rate of 92.9% for test image data.

• 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 the Korean Ceramic Society
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• v.50 no.1
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• pp.63-69
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• 2013

Negative-temperature coefficient (NTC) thermistors based on nickel manganite spinel ($NiMn_2O_4$) are widely used for many applications, such as sensors and temperature compensators, due to their good thermistor characteristics and stabilities. However, to achieve thermistors with a high NTC B constant, which is an important figure of merit pertaining to the degree of temperature sensitivity, the activation energy should be high such that high resistivity at ambient temperatures results. To obtain a high B constant and low resistivity, Al and Si modified spinel structured $Ni_{0.6}Si_{0.2}Al_{0.6}Mn_{1.6}O_4$ hybrid thick films with the conducting metal oxide of $LaNiO_3$ were fabricated on a glass substrate by aerosol deposition at room temperature (RT). The NTC-$LaNiO_3$ hybrid thick films showed resistivity as low as < $100k{\Omega}\;cm$ at $90^{\circ}C$, which is one or two orders of magnitude lower than that of the monolithic NTC films, while retaining a high B constant of $NiMn_2O_4$ of over 5500 K when 20 wt% $LaNiO_3$ was added without a post-thermal treatment. These phenomena are explained by the percolation threshold mechanism.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.1
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• pp.39-46
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• 2015

In this paper, a gas sensor using a modified $90^{\circ}$ hybrid coupler structure with conducting polymer which operates at 2.4 GHz is represented. Conducting polymers are used to the gas sensing material in proposed sensors. The conducting polymer varies its electrical property, such as work function and conductivity corresponding to the certain gas. To verify this variation of electrical property of conducting polymer at microwave frequencies, the conducting polymer is incorporated with the $90^{\circ}$ hybrid coupler structure, and this proposed sensor operates as reflection type variable attenuator and variable phase shifter. The conducting polymer is employed as impedence-variable transmission lines that cause a impedance mismatching between the general transmission line and conducting polymer. The experiment was conducted with 100 ppm ethanol gas at temperature of $28^{\circ}C$ and relative humidity of 85 %. As a result, the amplitude deviation of $S_{21}$ is 0.13 dB and the frequency satisfying ${\angle}S_{21}=360^{\circ}$ is shifted about 2.875 MHz.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.828-834
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• 2013

In this paper, TMC (Tracker Motion Controller) as one of the many research methods for condensing efficiency improvements can be condensed into efficient solar system configuration to improve the power generation efficiency of the castle with Concentrated solar silicon and photovoltaic systems (CPV)experiments using PV systems. Microprocessor used on the solar system, tracing the development of solar altitude and latitude of each is calculated in real time. Also accept the value from the sensor, motor control and communication with the central control system by calculating the value of the current position of the sun, there is a growing burden on the applicability. Through the way the program is appropriate for solar power systems and sensors hybrid-type algorithm was implemented in the ARM core with built-in TMC, Concentrated CPV system compared to the existing PV systems, through the implementation of the TMC in the country's power generation efficiency compared and analyzed. Sensor method using existing experimental results Concentrated solar power systems to communicate the value of GPS location tracking method hybrid solar horizons in the coordinate system of the sun's azimuth and elevation angles calculated by the program in the calculations of astronomy through experimental resultslook clear day at high solar irradiation were shown to have a large difference. Stopped after a certain period of time, the sun appears in the blind spot of the sensor, the sensor error that can occur from climate change, however, do not have a cloudy and clear day solar radiation sensor does not keep track of the position of the sun, rather than the sensor of excellence could be found. It is expected that research is constantly needed for the system with ongoing research for development of solar cell efficiency increases to reduce the production cost of power generation, high efficiency condensing type according to the change of climate with the optimal development of the ability TMC.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.38-48
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• 2007

In ubiquitous environment, sensor networks that sense and transmit surrounding data without human intervention will become more important. If sensors are installed for detecting vehicles and measuring their speed in the road and that real-time information is given to drivers, it will be very effective for enhancing safety and controlling traffic in the road. In this paper, we proposed a new reliable and real-time sensor MAC protocol between AP and sensor nodes in order to provide real-time traffic flow information based on ubiquitous sensor networks. The proposed MAC allocates one TDMA slot for each sensor node on the IEEE 802.15.4 based channel structure, introduces relayed communication for distant sensors, and adopts a frame structure that supports retransmission for the case of errors. In addition, the proposed MAC synchronizes with AP by using beacon and adopts a hybrid tracking mode that supports economic power consumption according to various traffic situations, We implemented a simulator for the proposed MAC by using sim++ and evaluated various performances. The simulation results show that the proposed MAC reduces the power consumption and reveals excellent performance in real-time application systems.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.234-252
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• 2020

This paper is to verify the effectiveness of 'Hybrid Disaster Management Strategy' that integrates 'RTM(Real-time Monitoring) based On-line' and 'UAV based Off-line' system. For landslide prone area where sensors were installed, the conventional way of risk management so far has entirely relied on RTM data collected from the field through the instrumentation devices. But it's not enough due to the limitation of'Pin-point sensor'which tend to provide with only the localized information where sensors have stayed fixed. It lacks, therefore, the whole picture to be grasped. In this paper, utilizing 'Digital Photogrammetry Software Pix4D', the possibility of inference for the deformation of ungauged area has been reviewed. For this purpose, actual measurement data from RTM were compared with the estimated value from 3D point cloud outcome by UAV, and the consequent results has shown very accurate in terms of RMSE.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.37-44
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• 2016

Optical fiber temperature sensing systems have incomparable advantages over traditional electrical-cable-based monitoring systems. However, the fiber optic interrogators and sensors have often been rejected as a temperature monitoring technology in real-world industrial applications because of high cost and over-specification. This study proposes a multiplexed fiber optic temperature monitoring sensor system using an economical Optical Time-Domain Reflectometer (OTDR) and Hard-Polymer-Clad Fiber (HPCF). HPCF is a special optical fiber in which a hard polymer cladding made of fluoroacrylate acts as a protective coating for an inner silica core. An OTDR is an optical loss measurement system that provides optical loss and event distance measurement in real time. A temperature sensor array with the five sensor nodes at 10-m interval was economically and quickly made by locally stripping HPCF clad through photo-thermal and photo-chemical processes using a continuous/pulse hybrid-mode laser. The exposed cores created backscattering signals in the OTDR attenuation trace. It was demonstrated that the backscattering peaks were independently sensitive to temperature variation. Since the 1.5-mm-long exposed core showed a 5-m-wide backscattering peak, the OTDR with a spatial resolution of 40 mm allows for making a sensor node at every 5 m for independent multiplexing. The performance of the sensor node included an operating range of up to $120^{\circ}C$, a resolution of $0.59^{\circ}C$, and a temperature sensitivity of $-0.00967dB/^{\circ}C$. Temperature monitoring errors in the environment tests stood at $0.76^{\circ}C$ and $0.36^{\circ}C$ under the temperature variation of the unstrapped fiber region and the vibration of the sensor node. The small sensitivities to the environment and the economic feasibility of the highly multiplexed HPCF temperature monitoring sensor system will be important advantages for use as system-integrated temperature sensors.