• International conference on construction engineering and project management
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• 2022.06a
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• pp.814-822
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• 2022

The deployment of sensors for Structural Health Monitoring requires a complicated network arrangement, ground truthing, and calibration for validating sensor performance periodically. Any conventional sensor on a structural element is also subjected to static and dynamic vertical loadings in conjunction with other environmental factors, such as brightness, noise, temperature, and humidity. A structural model with strain gauges was built and tested to get realistic sensory information. This paper investigates different deep learning architectures and algorithms, including unsupervised, autoencoder, and supervised methods, to benchmark blind drift calibration methods using deep learning. It involves a fully connected neural network (FCNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU) to address the blind drift calibration problem (i.e., performing calibrations of installed sensors when ground truth is not available). The results show that the supervised methods perform much better than unsupervised methods, such as an autoencoder, when ground truths are available. Furthermore, taking advantage of time-series information, the GRU model generates the most precise predictions to remove the drift overall.

• Journal of IKEEE
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• v.27 no.4
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• pp.439-449
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• 2023

In an era marked by the increasing use of drones and the growing demand for indoor surveillance, the development of a robust application for detecting and tracking both drones and humans within indoor spaces becomes imperative. This study presents an innovative application that uses FMCW radar to detect human and drone motions from the cloud point. At the outset, the DBSCAN (Density-based Spatial Clustering of Applications with Noise) algorithm is utilized to categorize cloud points into distinct groups, each representing the objects present in the tracking area. Notably, this algorithm demonstrates remarkable efficiency, particularly in clustering drone point clouds, achieving an impressive accuracy of up to 92.8%. Subsequently, the clusters are discerned and classified into either humans or drones by employing a deep learning model. A trio of models, including Deep Neural Network (DNN), Residual Network (ResNet), and Long Short-Term Memory (LSTM), are applied, and the outcomes reveal that the ResNet model achieves the highest accuracy. It attains an impressive 98.62% accuracy for identifying drone clusters and a noteworthy 96.75% accuracy for human clusters.

• The KIPS Transactions:PartC
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• v.16C no.5
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• pp.637-644
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• 2009

A study about performance analysis of synchronization clock using measured clock noises is required. Therefore this paper executed the study for performance analysis of synchronization clock and acquirement of maximum number of network node with various clock states using measured clock noises in NG-SDH networks. Also this paper generated a suitable clock model using measured clock noises, and carried out simulations with various clock states. Through the simulation results, maximum numbers were 80 or more network nodes in normal state, and were below 37 nodes in short-term phase transient(SPT) state, and were 50 or more in long-term phase transient(LPT) state. Accordingly this study showed that maximum numbers to meet ITU-T specification were below 37 network nodes in three clock states. Also this study showed that when SPT or LPT states occur from NE network before DOTS system, synchronization source must change with other stable synchronization source of normal state.

• Journal of Communications and Networks
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• v.15 no.3
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• pp.312-320
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• 2013

Home evolved Node-B (HeNB), also called a femtocell or a femto base station, is introduced to provide high data rate to indoor users. However, two main problems arise in femtocell networks: (1) Small coverage area of HeNB, which results in limited cell-splitting gain and ping-pong handover (HO) problems and (2) high inter-femtocell interference because HeNBs may be densely deployed in a small region. In this study, an efficient cooperation mechanism called an HeNB-aided virtual-HO (HaVHO) scheme is proposed to expand the coverage area of femtocells and to reduce inter-femtocell interference. The cooperation among neighbor HeNBs is exploited in HaVHO by enabling an HeNB to relay the data of its neighbor HeNB without an HO. The HaVHO procedure is compatible with the existing long term evolution specification, and the information exchange overhead in HaVHO is relatively low. To estimate the signal to interference plus noise ratio improvement, the area average channel state metric is proposed, and the amount of user throughput enhancement by HaVHO is derived. System-level simulation shows that HaVHO has a better performance than the other four schemes, such as lesser radio link failure, lesser ping-pong handover, lesser short-stay handover, and higher user throughput.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.259-259
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• 2019

시계열 자료들을 분석하고자 하는 경우 자료가 정상성(stationarity)을 만족하는 경우는 드물다. 특히 계절성을 제거한 자료들에서는 정량화하기 어려운 주기성이 많이 관찰된다. 즉, 어떤 특정지역에서 나타나는 현상이 다른 기상 현상에 영향을 미칠 것은 자명한 일이나 그 관련성이 선형(linearity)일 가능성은 극히 드물다. 따라서 그들 사이의 관련성이 선형성에 근거한 지표들로 정량화되어야 한다. 이러한 문제점을 해결하기 위해서 다양한 방법이 사용되며 그중에서 웨이블릿 분석을 통해 본 연구를 진행하였다. 웨이블릿 변환(wavelet transforms)은 특수한 함수의 집합으로 구성되어 기존 웨이블릿 신호의 분석을 위해 사용되는 방법이다. 이 변환은 푸리에 변환에서 변형된 방법으로 특정한 기저 함수(base function)를 이용하여 기존의 시계열 자료를 주파수로 바꾸는 변환이다. 웨이블릿 변환에서 기저 함수를 모 웨이블릿이라고 하며 이를 천이, 확대 및 축소 과정을 통해 주파수를 구성한다. 웨이블릿 분석은 모 웨이블릿을 분해하고 재결합하여 시계열 분석을 할 수 있다. 모 웨이블릿 함수에는 Haar, Daubechies, Coiflets, Symlets, Morlet, Mexican Hat, Meyer 등의 여러 가지 종류의 모 웨이블릿 함수가 있으며 모 웨이블릿이 달라지면 결과가 다르게 나타난다. 기존에는 Morlet 웨이블릿을 주로 이용하여 주파수분석에 사용하여 결과를 도출하였다. 그리고 시계열 자료는 크게 백색잡음(White Noise), 장기기억(Long Term Memory), 단기기억(Short Term Memory)으로 나뉜다. 각 시계열 자료의 종류에 따라 임의의 시계열 자료를 산정하여 그에 따른 웨이블릿 분석을 통해 모 웨이블릿의 특성을 도출하였다. 본 연구에서는 웨이블릿 분석을 통해 시계열 자료의 최적 모 웨이블릿을 결정하고자 남방진동지수(SOI), 북극진동지수(AOI)의 자료를 이용하여 웨이블릿 분석을 시도하였다. 웨이블릿 분석은 모 웨이블릿에 따라 달라지는 결과를 토대로 분석하였으며 이를 정상성과 지속성에 따라 분류된 시계열에 적용하여 최적 모 웨이블릿을 결정하고자 하였다. 본 연구에서는 임의의 시계열 자료에서 설정한 최적의 모 웨이블릿을 AOI와 SOI와 같은 실제 시계열 자료에 대입하여 분석을 진행하였다. 본 연구에서는 시계열 자료의 종류를 구분하고 자료의 특성에 따라 가장 적합한 모 웨이블릿을 구하고자 하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1275-1281
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• 2019

The automatic modulation recognition of a radio signal is a major task of an intelligent receiver, with various civilian and military applications. In this paper, we propose a method to recognize the modulation of radio signals in wireless communication based on the deep neural network. We classify the modulation pattern of radio signal by using the LSTM model, which can catch the long-term pattern for the sequential data as the input data of the deep neural network. The amplitude and phase of the modulated signal, the in-phase carrier, and the quadrature-phase carrier are used as input data in the LSTM model. In order to verify the performance of the proposed learning method, we use a large dataset for training and test, including the ten types of modulation signal under various signal-to-noise ratios.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.413-421
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• 2008

The paper derives asymptotic formulas for the MSE distortion and the signal-to-noise ratio of a mismatched fixed-rate minimum MSE Laplacian quantizer. These closed-form formulas are expressed in terms of the number N of quantization points, the mean displacement $\mu$, and the ratio $\rho$ of the standard deviation of the source to that for which the quantizer is optimally designed. Numerical results show that the principal formula is accurate in that, for rate R=$log_2N{\geq}6$, it predicts signal-to-noise ratios within 1% of the true values for a wide range of $\mu$, and $\rho$. The new findings herein include the fact that, for heavy variance mismatch of ${\rho}>3/2$, the signal-to-noise ratio increases at the rate of $9/\rho$ dB/bit, which is slower than the usual 6 dB/bit, and the fact that an optimal uniform quantizer, though optimally designed, is slightly more than critically mismatched to the source. It is also found that signal-to-noise ratio loss due to $\mu$ is moderate. The derived formulas can be useful in quantization of speech or music signals, which are modeled well as Laplacian sources and have changing short-term variances.

• Water for future
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• v.27 no.3
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• pp.45-54
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• 1994

Basic theory of fractal dimension is introduced and performed for the generated time series using the water balance model. The water balance equation over a large area is analyzed at seasonal time scales. In the generation and modification of mesoscale circulation local recycling of precipitation and dynamic effects of soil moisture are explicitly included. Time delay is incorporated in the analysis. Depending on the parameter values, the system showed different senarios in the evolution such as fixed point, limit cycle, and chaotic types of behavior. The stochastic behavior of the generated time series is due to deterministic chaos which arises from a nonlinear dynamic system with a limited number of equations whose trajectories are highly sensitive to initial conditions. The presence of noise arose from the characterization of the incoming precipitation, destroys the organized structure of the attractor. The existence of the attractor although noise is present is very important to the short-term prediction of the evolution. The implications of this nonlinear dynamics are important for the interpretation and modeling of hydrologic records and phenomena.

• Quality Improvement in Health Care
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• v.5 no.2
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• pp.324-334
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• 1998

Background : Strategic planning is an organizationwide or systemwide, ongoing look into the future usually of 2~3 years, based on objective analysis of the current environment and trends, but it can incorporate both short-term and long-term goals. The strategic planning process includes external analysis, internal analysis, issue analysis, development of mission, vision and values, and lastly development of organizational goals and objectives. As a part of the strategic quality planning process, certain service lines, important organizationwide functions, or key processes supporting these functions can be prioritized to expedite and roll out certain strategic goals. This is called strategic quality initiatives. Methods : We organized a quality improvement team, a subgroup of 21st century vision planning corps of our medical center, and pursued QI activities for improvement of healthcare environment, particularly in the admission setting. We developed a strategic quality initiative based on the results of patient satisfaction surveys, and carried out functions of self-directed work team. Results : The strategic goal was to be the benchmark for peer group hospitals in Korea for providing cost-effective best-practice. The QI team included 3 medical doctors, 1 nurse, 1 social worker, and 1 QI consultant as well as many operational members to support services and quality initiatives met every Tuesday for 18 weeks. Outcome objectives were to improve patient satisfaction score. The issues included in the objectives were comfort, temperature, noise, cleanliness of the admission wards, quality and education of patient meals, matters regarding the admission process, and an appurtenant facility such as restaurant or convenience store. Every issue was discussed and recommendations, conclusions and opportunities were implemented. Conclusions : By developing a strategic quality initiative as a part of the strategic quality planning process, and pursuing a self-directed work team, certain sen/ice lines, important organizationwide functions, or key processes supporting these functions can be improved effectively within a short period. Strategic quality initiatives serve to support, or roll out, certain strategic goals that are relevant to performance improvement and development of specific measurable outcome objectives, and associated performance measure for each initiative. Each strategic quality initiative should include a statement of intent outcome objectives, and performance measures. We will come back with follow up of the strategic quality initiative, for improvement of healthcare environment, and results of patient satisfaction re-survey.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.31 no.2
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• pp.71-77
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• 2020

Background and Objectives OperaVOXTM (Oxford Wave Research Ltd.) is a portable voice analysis software package designed for use with iOS devices. As a relatively cheap, portable and easily accessible form of acoustic analysis, OperaVOXTM may be more clinically useful than laboratory-based software in many situations. The aim of this study was to evaluate the agreement between OperaVOXTM and Multi-Dimensional Voice Program (MDVP; Computerized Speech Lab) to assess voice quality before and after laryngeal microsurgery in patient with vocal polyp. Materials and Method Twenty patients who had undergone laryngeal microsurgery for vocal polyp were enrolled in this study. Preoperative and postoperative voices were assessed by acoustic analysis using MDVP and OperaVOXTM. A five-seconds recording of vowel /a/ was used to measure fundamental frequency (F0), jitter, shimmer and noise-to-harmonic ratio (NHR). Results Several acoustic parameters of MDVP and OperaVOXTM related to short-term variability showed significant improvement. While pre-operative value of F0, jitter, shimmer, NHR was 155.75 Hz (male: 125.37 Hz, female: 183.37 Hz), 2.20%, 6.28%, 0.16, post-operative values of these parameter was 164.34 Hz (male: 129.42 Hz, female: 199.26 Hz), 2.15%, 5.18%, 0.14 Hz in MDVP. While pre-operative value of F0, jitter, shimmer, NHR was 168.26 Hz (male: 135.16 Hz, female: 201.37 Hz), 2.27%, 6.95%, 0.26, post-operative values of these parameters was 162.72 Hz (male: 128.267 Hz, female: 197.18 Hz), 1.71%, 5.36%, 0.20 in OperaVOXTM. There was high intersoftware agreement for F0, jitter, shimmer with intraclass correlation coefficient. Conclusion Our results showed that the short-term variability of acoustic parameters in both MDVP and OperaVOXTM were useful for the objective assessment of voice quality in patients who received laryngeal microsurgery. OperaVOXTM is comparable to MDVP and has high intersoftware reliability with MDVP in measuring the F0, jitter, and shimmer