• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.646-654
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• 2020

This paper is to develop an electromagnetic wave-based sensor that can measure the spatial distribution of precipitation, and to a electromagnetic wave rain gauge (hereinafter, "EWRG") capable of simultaneously measuring rainfall, snowfall, and wind field, which are the core of heavy rain observation. Through this study, the LFM transmission and reception signals were theoretically analyzed. In addition, In order to develop a radar transceiver, LFM transceiver design and simulation were conducted. In this paper, we developed a K-BAND pulse-driven 6W SSPA(Solid State Power Amplifiers) transceiver using a small HMIC(Hybrid Microwave Integrated Circuit). It has more than 6W of output power and less than 5dB of receiving NF(Noise Figure) with short duty of 1% in high temperature environment of 65 degrees. The manufactured module emits LFM and Square Pulse waveform with the built-in waveform generator, and the receiver has more than 40dB of gain. The transceiver developed in this paper can be applied to the other small weather radar.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.107-116
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• 2021

This study aims to investigate the effect of multisensory stimulation on relieving the stress experienced by drivers. The photoplethysmograms (PPGs) of 30 healthy subjects were measured, and their subjective response to stressful situations and normal driving were evaluated. The subjects underwent nonstimulation and multisensory stimulation in stressful driving situations. Heart rate estimation from the PPG was collected via an ear-type sensor to reduce movement noise. The signals acquired were sampled at 200 Hz using BIOPAC PPG100C. Heart rate variability (HRV) was analyzed to compare the effect of multisensory stimulation on stress situations. In the multisensory stimulation, blue, green, and yellow were used for the visual sensory system; white, pink, and brown noises were used for the auditory sensory system; and lavender, lemon, and rosemary were used for the olfactory sensory system. No difference was observed in the subjective evaluation; however, the HRV results showed an increased HF (%) and decreased LF (%) and LF/HF (%) in the multisensory stimulation (e.g., green, pink noise, and rosemary) when compared to the nonstimulation.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.679-684
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• 2023

Venous puncture is widely used to obtain blood samples for pathological examination. Because the invasive venipuncture method using a needle is repeatedly performed, the pain suffered by the patient increases, so our research team pre-developed a miniaturized near-infrared (NIR) imaging system in advance. To improve the image quality of the acquired NIR images, this study aims to model the non-local means (NLM) algorithm, which is well known to be efficient in noise reduction, and analyze its applicability in the system. The developed NIR imaging system is based on the principle that infrared rays pass through dichroic and long-pass filters and are detected by a CMOS sensor module. The proposed NLM algorithm is modeled based on the principle of replacing the pixel from which noise is to be removed with a value that reflects the distances between surrounding pixels. After acquiring an NIR image with a central wavelength of 850 nm, the NLM algorithm was applied to segment the final vein area through histogram equalization. As a result, the coefficient of variation of the NIR image of the vein using the NLM algorithm was 0.247 on average, which was an excellent result compared to conventional filtering methods. In addition, the dice similarity coefficient value of the NLM algorithm was improved by 62.91 and 9.40%, respectively, compared to the median filter and total variation methods. In conclusion, we demonstrated that the NLM algorithm can acquire accurate segmentation of veins acquired with a NIR imaging system.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.562-568
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• 2021

Seat reservations and civil complaints in the library reading room have been done face-to-face by managers, and efficient management has been difficult. In addition, there is a problem that it is difficult to take action in the event of a civil complaint due to user inconvenience, such as a noise problem between users in the reading room. In this study, an online reservation system was developed for efficient management of seats in the library reading room so that it could be serviced non-face-to-face. In addition, when using the library reading room, it is possible to apply for non-face-to-face civil complaints when complaints occur due to noise problems between users, loss of belongings, and snoring during the user's sleep. Managers can smoothly manage library reading rooms through non-face-to-face inconvenience reports. It is possible to increase the satisfaction of using the library by resolving the inconvenience of users. The developed service app allows seat reservations and anonymous inconvenience reports. The administrator can check the received inconvenience report and warn the user of the seat with an IoT sensor-based LED. When corrective action is completed, the result of the action may be fed back to the reporter.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.85-93
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• 2024

The monitoring of environmental information (e.g. noise, dust, vibration, temperature, humidity) is crucial to the safe and sustainable operation of a construction site. However, commercial sensors exhibit certain drawbacks when applied on-site. First, the installation cost is prohibitively high. Second, these sensors have been engineered without considering the rugged and harsh conditions of a construction site, resulting in error-prone sensing. Third, construction sites are compelled to allocate additional resources in terms of manpower, expenses, and physical spaces to accommodate individual sensors. This research developed an integrated sensing module to measure the environmental information in construction site. The sensing module slashes the installation cost to 3.3%, is robust enough to harsh and outdoor sites, and consolidates multiple sensors into a single unit. The sensing module also supports GPS, LTE, and real-time sensing. The evaluation showed remarkable results including 97.5% accuracy and 99.9% precision in noise measurement, an 89.7% accuracy in dust measurement, and a 93.5% reliability in data transmission. This research empowers the collection of substantial volumes and high-quality environmental data from construction sites, providing invaluable support to decision-making process. These encompass objective regulatory compliance checking, simulations of environmental data dispersion, and the development of environmental mitigation strategies.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• Journal of IKEEE
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• v.13 no.1
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• pp.41-48
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• 2009

This paper describes an implementation of the analog front end (AFE) incorporated with the image signal processing (ISP) unit in the SoC, dominating the performance of the CCD image sensor system. New schemes are exploited in the high-frequency sampling to reduce the sampling uncertainty apparently as the frequency increases, in the structure for the wide-range variable gain amplifier (VGA) capable of $0{\sim}36\;dB$ exponential gain control to meet the needed bandwidth and accuracy by adopting a new parasitic insensitive capacitor array. Moreover, the double cancellation of the black-level noise was efficiently achieved both in the analog and the digital domain. The proposed topology fabricated in a $0.35-{\mu}m$ CMOS process was proved in a full CCD camera system of 10-bit accuracy, dissipating 80 mA at 15 MHz with a 3.3 V supply voltage.

• Journal of Power Electronics
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• v.15 no.3
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• pp.730-740
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• 2015

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) - based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.

• Journal of the Korean GEO-environmental Society
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• v.14 no.8
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• pp.45-51
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• 2013

In the construction site of underground buildings which have severe environment such as dust, noise, vibration, the technology of rescue the builders in the construction site when accident occurs by tracking the location of the builders and express the mission of supervisor smoothly. In this study, in order to acquire the location information of the builders in the construction site of underground buildings by using MEMS INS and air pressure sensor, we firstly performed the field test in construction site, analyzed the location and the elevation accuracy based on the detected results, and then verified its practicality and rationality after all. As a result, we could acquire worker's position-accuracy within 10m in horizontal direction and 4m in vertical direction. Therefore we could judge availability in construction fields of underground structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.483-503
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• 2014

In this paper, human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning is proposed. First, we accumulate global activities and construct motion history image (MHI) for both RGB and depth channels respectively to encode the dynamics of one action in different modalities, and then different action descriptors are extracted from depth and RGB MHI to represent global textual and structural characteristics of these actions. Specially, average value in hierarchical block, GIST and pyramid histograms of oriented gradients descriptors are employed to represent human motion. To demonstrate the superiority of the proposed method, we evaluate them by KNN, SVM with linear and RBF kernels, SRC and CRC models on DHA dataset, the well-known dataset for human action recognition. Large scale experimental results show our descriptors are robust, stable and efficient, and outperform the state-of-the-art methods. In addition, we investigate the performance of our descriptors further by combining these descriptors on DHA dataset, and observe that the performances of combined descriptors are much better than just using only sole descriptor. With multimodal features, we also propose a collaborative multi-task learning method for model learning and inference based on transfer learning theory. The main contributions lie in four aspects: 1) the proposed encoding the scheme can filter the stationary part of human body and reduce noise interference; 2) different kind of features and models are assessed, and the neighbor gradients information and pyramid layers are very helpful for representing these actions; 3) The proposed model can fuse the features from different modalities regardless of the sensor types, the ranges of the value, and the dimensions of different features; 4) The latent common knowledge among different modalities can be discovered by transfer learning to boost the performance.