• Journal of electromagnetic engineering and science
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• v.18 no.4
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• pp.254-260
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• 2018

In this study, we suggest an emulator for generating multiple heterogeneous interference signals in the Korean radio frequency identification/ubiquitous sensor network (RFID/USN) frequency band. The proposed emulator uses only one universal software radio peripheral to generate multiple heterogeneous interference signals more economically. Moreover, the physical and media access control parameters can be adjusted in real time using the LabVIEW program, thereby making it possible to create various time-varying interference environments easily. As an example showing the capability of the proposed emulator, multiple interference signals consisting of a frequency-hopping RFID signal and two LoRa signals with different spreading factors were generated. The generated signals were confirmed in both frequency and time domains. From the experimental results, we verified that our emulator could successfully generate multiple heterogeneous interference signals with different frequency and time domain characteristics.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.4
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• pp.136-142
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• 1996

Automatic monitoring of cutting process is one of the most important technology in machining. AE sensing technology has been applied to monitoring process and proved to be effective in detecting tool abnor- malities such as tool wear and fracture. In this experimental study. AE signals were detected from the tool holder for continuous and interrupted cutting, which obtained from changing workpice material configuration, under control of constant cutting speed from CNC lathe. From statistical and frequency analysis, the AE signals were analyzed to obtaining the characteristics of continuous and interrupted cutting conditions and tool failure. The Kurtosis values decreased but RMS voltages increased as the cutting speed increased, in both continuous and interrupted cutting. RMS voltage is suddenly increased but Kurtosis value is suddenly decreased when tool failure condition. Power spectrum density of AE signals when tool failure reaches extreme value around 0.065 cycles/ .mu. m.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.522-528
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• 2000

Dynamic characteristics of seated human body were investigated by measuring apparent masses of eight different seating subjects exposed to vertical vibration. Two types of vibration signals - one is random signals over 1 to 30Hz having flat spectral density and the other is signals measured on seat rail in passenger car under driving conditions - were employed. It was found that the apparent masses are highly dependent on vibration level rather than type of the vibration signals. Based on the apparent mass measurements, a mathematical model of the human body in seating posture was developed by using genetic algorithm. Three-degree-of-freedom model was satisfactory in describing apparent mass of seated human body.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1879-1881
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• 2003

In this paper, acoustic signals in $SF_6$ gas were analyzed using wavelet transform. For this, the PD sources in the $SF_6$ gas were divided into corona discharge surface discharge void discharge and crossing particle and acoustic signals were used to detect the PD sources. The measured signals were time-frequency distribution by wavelet transform and the features were extracted from the PD sources. As a result the characteristics of the PD sources were different. And this results is going to be used for basis diagnosis of $SF_6$ gas insulated apparatus.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1076-1094
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• 2022

Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

• Journal of the Korean Society for Railway
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• v.7 no.3
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• pp.232-238
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• 2004

The dynamic characteristics of the current collection process of the high-speed railway are investigated through signals acquired during a test run. The signals are obtained from accelerometers, load cells, and strain gauges attached to various positions of the pantograph, and they are processed in time-and frequency-domains to obtain the dynamic characteristics. The main natural frequency of the pantograph is found to be 8.5Hz. There also are components at low frequencies varying linearly with the train speed. The contact frequency components above 20Hz is attenuated as they pass through the secondary suspension. The main frequency component of the load cell signal is found to be related with the rolling motion of the panhead generated by the stagger in the catenary.

• Journal of Ocean Engineering and Technology
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• v.16 no.2
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• pp.67-71
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• 2002

Damage process of CFRP laminates was characterized by Acoustic Emission (AE). The main objective of this study is to determine if the sources of AE in CERP laminates could be identified from the characteristics of the waveform signals recorded during monotonic tensile test. The time history and power spectrum of each individual wave signal recorded during test were examined and classified according to their special characteristics. The wave from and frequency of AE signal from a specimens is an aid to the determination of the extent of the different fracture mechanism such as matrix crack, debonding, fiber pull-out and fiber fracture as load is increased. Four distinct types of signals were observed regardless of specimen condition. The result showed that the AE method could be effectively used for analysis of fracture mechanism in CFRP laminates.

• Geomechanics and Engineering
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• v.34 no.1
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• pp.1-15
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• 2023

This study conducts uniaxial compression tests on intact and single crack-contained rocks to investigate the time-frequency domain characteristics of acoustic emission (AE) signals monitored during the deformation failure process. A processing approach, short-time Fourier transform (STFT), is performed to obtain the evolution characteristics of time-frequency domain of AE signals. The AE signal modes at different deformation stages of rocks are different. Five modes of AE signal are observed during the cracking process of rocks. The evolution characteristics of time-frequency domain of AE signals processed by STFT can be utilized to evaluate the damage process of rocks. The difference of time-frequency domain characteristics between intact and cracked rocks is comparatively analyzed. The distribution characteristics of frequency changing from a single band-shaped cluster to multiple band-shaped clusters can be regarded as an early warning information of damage and failure of rocks. Meanwhile, the attenuation of frequency enables the exploration of rock failure trends.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2623-2627
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• 2014

The objective of this study is to experimentally investigate the heat transfer characteristics of 200W woofer speaker unit with the input voice signals such as 500 Hz, 1000 Hz, 2000 Hz, and 3000 Hz. The temperature and heat transfer characteristics of the woofer speaker unit were evaluated with the input signals. As results. the temperature of the voice-coil for woofer speaker unit increased with a decrease of the input signals and the temperature differences between parts of the tested speaker unit increased with the decrease of the input voice signals. In addition, the voice-coil temperature for the input signal of 500 Hz showed 48.4 % lower than that of 3000 Hz during 18000 sec.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.386-392
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• 2009

This paper reports on the research which investigates acoustic signals acquired in progressive compressing, hole blanking, shearing and burr compacting process. The work piece is the head pin of the electric connector, whose raw material is the preformed steel bar. An acoustic sensor was set on the bed of hydraulic press. Because the acquired signals include the dynamic characteristics generated for all the processes, it is required to investigate signal characteristics corresponding to unit process. The corresponding dynamic characteristics to the respective process were first studied by analyzing the signals respectively acquired from compressing, blanking and compacting process. The combined signals were then periodically analyzed from the grinding to the grinding in the sound frequency domain and in the ultrasonic wave. The frequency of around 9 kHz in the sound frequency domain was much correlated to the tool wear. The characteristic frequency in the acoustic emission domain between 100 kHz and 500 kHz was not only clearly observed right after tool grinding but its amplitude was also related to the wear. The frequency amplitudes of 160 kHz and 320 kHz were big enough to be classified by the noise. The noise amplitudes are getting bigger, and their energy was much bigger as coming to the next regrinding. The signal analysis was based on the real time data and its frequency spectrum by Fourier Transform. As a result, the acousto-ultrasonic signals were much related to the tool wear progression.