• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.11a
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• pp.51-52
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• 2007

This paper compared wired Acoustic Emission (AE) signals with wireless AE signals. According to the material and process condition, each process signal has distinguishable characteristic to show each removal phenomenon. Therefore, wired and wireless AE sensors having different bandwidth are complementary for CMP process monitoring. Especially, the AE sensor was used to investigate abrasive and molecular-scale phenomena during CMP process, which was compatible to acquire high level frequency. In experiment, wireless AE system was used to get signals in rotary system, using bluetooth. But, it is possible to acquire only RMS signals, which can not analyze abrasive and molecular-sale phenomena. Second, wired AE system was installed using mercury slip-ring, which is suitable not only for rotation equipment but also for acquiring original signals. The acquired signals were analyzed by FFT for understanding of abrasive and molecular revel phenomena in CMP process, finally, we verified that two types of AE sensor with different bandwidth were complementary for CMP process monitoring.

• Journal of the Korean Society for Nondestructive Testing
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• v.20 no.6
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• pp.481-489
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• 2000

A time-frequency analysis method was developed to analyze the dispersive waves caused by impact loads in structural members such as beams and plates. Stress waves generated by ball drop and pencil lead break were recorded by ultrasonic transducers and acoustic emission (AE) sensors. Wavelet transform (WT) using Gabor function was employed to analyze the dispersive waves in the time-frequency domain, and then to find the arrival time of the waves as a function of frequency. The measured group velocities in the beam and the plate were compared with the predictions based on the Timoshenko beam theory and Rayleigh-Lamb frequency equations, respectively. The agreements were found to be very good.

• Korean Journal of Food Science and Technology
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• v.38 no.1
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• pp.16-21
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• 2006

Effect of heat (160, 190, and $220^{\circ}C$ for 24 hr) on coconut oil was examined by principal component analysis using electronic nose consisting of six metal oxide sensors. Increase in heating temperature decreased ratio of resistance and first principal component score (from +0.952 to -0.325), indicating rancidity of coconut oil increased at high heating temperature. Result of electronic nose based on GC with surface acoustic wave sensor showed significant changes in volatile profiles of coconut oil. High resolution olfactory imaging $(VaporPrint^{TM})$ was particularly useful for evaluating oil quality. Peak numbers and areas increased with increasing heating time and temperature (160, $220^{\circ}C$). Electronic nose analysis can provide simple, fast, and straightforward results and is best suited for quality control and process monitoring in flavor field of food industry.

• Journal of Ocean Engineering and Technology
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• v.34 no.5
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• pp.351-360
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• 2020

Of late, demand for test sites for marine equipment such as ASV, AUV, ROV, and various underwater sensors is increasing. The authors have focused on an oscillating water column (OWC), which is being constructed near Chagwido Island Jeju, as one of the test-sites. The main objective of the OWC is to produce wave energy and develop technologies. It has been built in the sea approximately 1 km off the coast. It has berth accommodation and some rooms that can be used as laboratories. To investigate the feasibility of its usage as a test site for marine equipment, we acquired bathymetric data around the OWC by using a multi-beam echo sounder and a single-beam scanning sonar. The accessibility of the OWC from nearby ports and the use of support vessels or ships were also investigated. 3D point cloud data from the multi-beam echo sounder and 2D acoustic images from the scanning sonar are expected to be used as references for identifying changes over time. In addition, through these experiments, we derived a procedure to use this facility as a test site by using the IDEF0 functional modelling method. Based on this preliminary investigation and previously reported examples, we determined the general conditions and preferences for evaluating the performance of various marine equipment heuristically. Finally, we developed five applications that were derived from this investigation.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.160-160
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• 2019

Water is a limited source that needs to be properly managed and distributed to the ever-growing population of the world. Rapid urbanization and development have increased the overall water demand of the world drastically. However, there is loss of billions of liters of water every year due to leakages in water distribution systems. Such water loss means significant financial loss for the utilities as well. World bank estimates a loss of $14 billion annually from wasted water. To address these issues and for the development of efficient and reliable leakage management techniques, high efforts have been made by the researchers and engineers. Over the past decade, various techniques and technologies have been developed for leakage management and leak detection. These include ideas such as pressure management in water distribution networks, use of Advanced Metering Infrastructure, use of machine learning algorithms, etc. For leakage detection, techniques such as acoustic technique, and in recent yeats transient test-based techniques have become popular. Smart Water Grid uses two-way real time network monitoring by utilizing sensors and devices in the water distribution system. Hence, valuable real time data of the water distribution network can be collected. Best results and outcomes may be produced by proper utilization of the collected data in unison with advanced detection and management techniques. Long term reduction in Non Revenue Water can be achieved by detecting, localizing and repairing leakages as quickly and as efficiently as possible. However, there are still numerous challenges to be met and future research works to be conducted in this field.

• The Journal of the Convergence on Culture Technology
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• v.5 no.4
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• pp.407-412
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• 2019

In this paper, we propose a technique for processing of speech recognition in korean words. Speech recognition is a technology that converts acoustic signals from sensors such as microphones into words or sentences. Most foreign languages have less difficulty in speech recognition. On the other hand, korean consists of vowels and bottom consonants, so it is inappropriate to use the letters obtained from the voice synthesis system. That improving the conventional structure speech recognition can the correct words recognition. In order to solve this problem, a new algorithm was added to the existing speech recognition structure to increase the speech recognition rate. Perform the preprocessing process of the word and then token the results. After combining the result processed in the Levenshtein distance algorithm and the hashing algorithm, the normalized words is output through the consonant comparison algorithm. The final result word is compared with the standardized table and output if it exists, registered in the table dose not exists. The experimental environment was developed by using a smartphone application. The proposed structure shows that the recognition rate is improved by 2% in standard language and 7% in dialect.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.115-123
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• 2019

The effects of the Froude numbers on turbulent flow patterns downstream of a non-submerged spur dike were investigated in a laboratory flume. Three-dimensional velocities and water depths were measured using Acoustic Doppler Velocimetry and distance sensors under three Froude number conditions ($Fr_d=0.31$, 0.38, and 0.46). The results show that there are marginal differences in the velocity fields downstream of a spur dike due to the change of the Froude number. However, an increase of the Froude number was found to reduce cross-sectional area in the flow and to increase the strength of the jet-like flow. The jet-like flow was observed to displace the location of the maximum turbulence kinetic energy within a cross section toward the inner bank in the transverse direction.

• Journal of Platform Technology
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• v.9 no.3
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• pp.44-51
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• 2021

In general, golden time refers to the most important time in the initial response to accidents such as saving lives or extinguishing fires. The golden time varies from disaster to disaster, but is aimed at five minutes in terms of fire and first aid. However, for the actual site, the average dispatch time for ambulances is 9 minutes and the average transfer time is 17.6 minutes, which is quite large compared to the golden time. There are various causes for this delay, but the main cause is traffic jams. In order to solve the problem, the government has established emergency car concession obligations and secured golden time to prioritize ambulances in places with the highest accident rate, but it is not a solution in rush hour when traffic is increasing rapidly. Therefore, this paper proposed a deep learning-based emergency vehicle priority signal system using collected sound data by installing sound sensors on traffic lights and conducted an experiment to classify frequency signals that differ depending on the distance of the emergency vehicle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.5
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• pp.619-627
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• 2021

Recent advance in low cost and light-weight drones has extended their application areas in both military and private sectors. Accordingly surveillance program against unfriendly drones has become an important issue. Drone detection and classification technique has long been emphasized in order to prevent attacks or accidents by commercial drones in urban areas. Most commercial drones have small sizes and low reflection and hence typical sensors that use acoustic, infrared, or radar signals exhibit limited performances. Recently, artificial intelligence algorithm has been actively exploited to enhance radar image identification performance. In this paper, we adopt machined learning algorithm for high resolution radar imaging in drone detection and classification applications. For this purpose, simulation is carried out against commercial drone models and compared with experimental data obtained through high resolution radar field test.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.110-117
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• 2023

Reconstructing underwater geometry in real time with forward-looking sonar is critical for applications such as localization, mapping, and path planning. Geometrical data must be repeatedly calculated and overwritten in real time because the reliability of the acoustic data is affected by various factors. Moreover, scattering of signal data during the coordinate conversion process may lead to geometrical errors, which lowers the accuracy of the information obtained by the sensor system. In this study, we propose a three-step data processing method with low computational cost for real-time operation. First, the number of data points to be interpolated is determined with respect to the distance between each point and the size of the data grid in a Cartesian coordinate system. Then, the data are processed with a nonlinear interpolation so that they exhibit linear properties in the coordinate system. Finally, the data are transformed based on variations in the position and orientation of the sonar over time. The results of an evaluation of our proposed approach in a simulation show that the nonlinear interpolation operation constructed a continuous underwater geometry dataset with low geometrical error.