• 한국정보통신학회논문지
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• 제26권8호
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• pp.1222-1230
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• 2022

다양한 변조 기법을 사용하여 저피탐 능력을 갖춘 신호원들이 증가하면서, 신호의 변조 방식을 분류하는 연구가 꾸준히 진행되고 있다. 최근 신호 간섭이나 잡음 환경에서 수신 신호 분류의 성능 개선을 위하여 전처리 과정으로 FFT를 이용하는 CNN(Convolutional Neural Network) 딥러닝 기법이 제안되었다. 하지만 윈도우가 고정되는 FFT의 특성상 탐지 신호의 시간에 따른 변화를 정확히 분류해내지 못한다. 따라서 본 논문에서는 시간 영역과 주파수 영역에서 높은 해상도를 가지고 또한 다양한 유형의 신호를 시간 및 주파수 영역에서 동시에 표현할 수 있는 웨이블릿 변환(wavelet transform)을 전처리 과정으로 사용하는 CNN 모델을 제안한다. 시뮬레이션을 통해 제안하는 웨이블릿 변환 방식이 FFT 변환 방식에 비해 정확도와 학습 속도 측면에서 SNR 변화에 무관하게 우수한 성능을 보이고, 특히 낮은 SNR일 때 더욱 큰 차이를 보임을 입증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1887-1898
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• 2018

In this paper, a method to classify insider threat activity is introduced. The internal threats help detecting anomalous activity in the procedure performed by the user in an organization. When an anomalous value deviating from the overall behavior is displayed, we consider it as an inside threat for classification as an inside intimidator. To solve the situation, Markov Chain Model is employed. The Markov Chain Model shows the next state value through an arbitrary variable affected by the previous event. Similarly, the current activity can also be predicted based on the previous activity for the insider threat activity. A method was studied where the change items for such state are defined by a transition probability, and classified as detection of anomaly of the inside threat through values for a probability variable. We use the properties of the Markov chains to list the behavior of the user over time and to classify which state they belong to. Sequential data sets were generated according to the influence of n occurrences of Markov attribute and classified by machine learning algorithm. In the experiment, only 15% of the Cert: insider threat dataset was applied, and the result was 97% accuracy except for NaiveBayes. As a result of our research, it was confirmed that the Markov Chain Model can classify insider threats and can be fully utilized for user behavior classification.

• Physical Therapy Rehabilitation Science
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• 제8권4호
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• pp.175-186
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• 2019

Objective: Non-specific chronic low back pain (NS-CLBP) has been related to abnormal trunk muscle activations, but literature reported considerable variability in muscle amplitudes of NS-CLBP patients during prolonged sitting periods. Therefore, the purpose of this study was to examine the differences among homogenous NS-CLBP subgroups in muscle activity, using muscle co-contraction indices as a more objective approach, and their roles on pain development during a 1-hour period of prolonged sitting. Design: Cross-sectional study. Methods: Twenty NS-CLBP subjects with motor control impairment (MCI) [10 classified as having flexion pattern disorder, and 10 with active extension pattern disorder], and 10 healthy controls participated in the study. Subjects followed a 1-hour sitting protocol on a standard office chair. Four trunk muscle activities including amplitudes and co-contraction indices were recorded using electromyography over the 1-hour period. Perceived back pain intensity was recorded using a numeric pain rating scale every 10 minutes throughout the sitting period. Results: All study groups presented with no significantly distinctive trunk muscle activities at the beginning of sitting, nor did they change over time when pain increased to a significant level. Both MCI subgroups reported a similarly significant increase in pain behavior through mid-sitting (p<0.001). However, after mid-sitting, they significantly differed from each other in pain (p<0.01) but did not differ in the levels of muscle activation. Conclusions: This study was the first to highlight the similarities in trunk muscle activities among homogenous NS-CLBP patients related to MCI and compared them to healthy controls while sitting for an extended period of time, and the significant increase in pain over the 1-hour sitting might not be attributed to trunk muscle activation.

• 대한교통학회지
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• 제17권4호
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• pp.111-124
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• 1999

본 논문의 목적은 최근에 개발 중에 있는 단일 자석검지기를 이용한 차종인식 알고리즘을 개발하고, 현장실험을 통한 현장 적용성을 검토하는 것이다. 고속도로에 설치되어 이는 자석검지기를 이용하여 자료를 수집하며 분석에 이용되는 자료는 개별차량에 대하여 자속밀도의 변화에 따른 전압 값을 Digital Data값으로 변환한 수치를 사용하였다. 그 수치를 토대로 각 차량의 점유시간을 파악하여 각 차량의 점유시간동안 파형의 특징을 추출하여 각 특징들을 기초로 하여 각 차량이 나타내는 고유의 파형을 식별하는 Template Matching 방법과 신경망기법, 그리고 이들을 상호 보완한 복합기법을 사용하였다. 검지차량에 따른 다양한 점유시간을 일정크기로 수평성분 정규화하고 이에 따른 자속속밀도의 변화에 의한 전압 값을 차종별로 샘플을 취하여 이동평균방법으로 처리를 한 후 위의 세 가지 기법을 사용하여 검지차량의 파형과 기준 파형을 비교하여 차종을 인식하는 방법으로 알고리즘을 개발하였다. 차종의 분류는 3가지 단계로 하였는데 2종분류, 3종분류, 5종분류로 접근하였다. 그리고 각각의 분류에 따라 정규화 크기 및 이동평균간격을 달리하여 적용하여 보았고 2종분류에서 인식율이 82%수준이다.

• 대한원격탐사학회지
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• 제39권4호
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• pp.409-423
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• 2023

North Korea is carrying out reclamation activities in tidal flat areas distributed throughout the west coast. Previousremote sensing research on North Korean tidal flats either failsto reflect recent trends or focuses on identifying and analyzing tidal flats. Thisstudy aimsto quantify the impact of recent reclamation activitiesin North Korea's coastal areas and contribute knowledge useful for determining the best remote sensing methods for coastal areas with limited accessibility, such as those in North Korea. Using Landsat-8 OLI images from 2014-2022, we analyzed land cover changesin an area on the west coast of Pyeonganbuk-do where reclamation activities are underway. Unsupervised classification using the normalized difference water index and the random forest classification technique were each used to divide the study area into classification groups, and changes in their areas over time were analyzed. The resultsshow a clear decrease in the water area and a tendency to increase cultivated area,supporting the evidence that North Korea'sreclamation isfor agricultural land expansion.Along coasts behind seawalls, the water area decreased by nearly half, and the cultivated area increased by over 2,300%, indicating significant changes and highlighting the anthropogenic nature of the cover changes due to reclamation. Both methods demonstrated high accuracy, making them suitable for detecting cover changes caused by reclamation. It is expected that further quality research will be conducted through the use of high-resolution satellite images and by combining data from multiple satellites in the future.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.717-730
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• 2019

The era of digital transformation (or the fourth industrial revolution) has been triggered by the rapid development of software (SW) technologies. In this era, several studies suspected rapid changes in job structures occurring around the world. Thus, there is a growing need for acquiring the skill sets required for the future. However, there are no specific studies on how existing jobs are changing. To cope with this ambiguity of job changes, this paper aims to investigate how the current job structure is changing in response to digital transformation. To identify the dynamic nature of job change over time, we conducted an analysis based on job posting data. As a result, nine job occupations and fifteen jobs were found.

• 대한후두음성언어의학회지
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• 제33권3호
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• pp.142-155
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• 2022

Diagnosis using voice is non-invasive and can be implemented through various voice recording devices; therefore, it can be used as a screening or diagnostic assistant tool for laryngeal voice disease to help clinicians. The development of artificial intelligence algorithms, such as machine learning, led by the latest deep learning technology, began with a binary classification that distinguishes normal and pathological voices; consequently, it has contributed in improving the accuracy of multi-classification to classify various types of pathological voices. However, no conclusions that can be applied in the clinical field have yet been achieved. Most studies on pathological speech classification using speech have used the continuous short vowel /ah/, which is relatively easier than using continuous or running speech. However, continuous speech has the potential to derive more accurate results as additional information can be obtained from the change in the voice signal over time. In this review, explanations of terms related to artificial intelligence research, and the latest trends in machine learning and deep learning algorithms are reviewed; furthermore, the latest research results and limitations are introduced to provide future directions for researchers.

• The Journal of Korean Physical Therapy
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• 제21권4호
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• pp.111-115
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• 2009

Purpose: The aim of this study was to present fundamental information regarding clinical prognosis and clinical criteria for therapeutic intervention in stroke patients with focal pons infarction. Methods: Four stroke patients (male: 2, female: 2) who were diagnosed with pons infarction were recruited. All subjects had motor functions evaluated using methods such as the Motricity Index (MI), the Modified Brunnstrom Classification (MBC), Functional Ambulatory Category (FAC), and the Bathel Index (BI). Evaluations were done at least 4 times over a period that was approximately 8~11 months from stroke onset. We compared the final evaluation with the first evaluation. Results: All patients with focal pons infarction showed improvement with time in motor function. The physical strength of all patients was improved to normal or good grades from zero or trace grades in the Motricity Index test. Also, other motor functions such as ambulatory capacity and activities of daily living (ADL) improved with time. Conclusion: Aspects of functional recovery and clinical prognosis are clearly predictable for specific patients with focal pons infarction. In addition, adequate therapeutic interventions can be provided clinical criterion to patients, according to aspect of functional recovery. Accordingly, patients with pons infarction change for the better over time.

• Asia pacific journal of information systems
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• 제26권4호
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• pp.542-560
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• 2016

This study determines whether or not firms can achieve high productivity growth through external technology acquisition. It also identifies the key factors affecting adopting firms' productivity growth by employing the Malmquist productivity index (MPI) methodology, which features computational ease, low data dependency, and decomposition of productivity growth into technical efficiency change and technical change. Results showed that the effects of productivity growth arising from technology transfer became stronger over time. Moreover, patent transfer guaranteed firms' productivity growth, but no evidence was found that factors such as age and size could increase productivity. Finally, cultural similarity could be another factor conditioning the effectiveness of technology transfer in the productivity of adopting firms.

• 대한원격탐사학회지
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• 제36권1호
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• pp.15-27
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• 2020

Accurate knowledge of land use/land cover (LULC) features and their relative changes over upon the time are essential for sustainable urban management. Urban sprawl growth has been always also a worldwide concern that needs to carefully monitor particularly in a developing country where unplanned building constriction has been expanding at a high rate. Recently, remotely sensed imageries with a very high spatial/spectral resolution and state of the art machine learning approaches sent the urban classification and growth monitoring to a higher level. In this research, we classified the Quickbird satellite imagery by object-based image analysis of Dempster-Shafer (OBIA-DS) for the years of 2002 and 2015 at Karbala-Iraq. The real LULC changes including, residential sprawl expansion, amongst these years, were identified via change detection procedure. In accordance with extracted features of LULC and detected trend of urban pattern, the future LULC dynamic was simulated by using land transformation model (LTM) in geospatial information system (GIS) platform. Both classification and prediction stages were successfully validated using ground control points (GCPs) through accuracy assessment metric of Kappa coefficient that indicated 0.87 and 0.91 for 2002 and 2015 classification as well as 0.79 for prediction part. Detail results revealed a substantial growth in building over fifteen years that mostly replaced by agriculture and orchard field. The prediction scenario of LULC sprawl development for 2030 revealed a substantial decline in green and agriculture land as well as an extensive increment in build-up area especially at the countryside of the city without following the residential pattern standard. The proposed method helps urban decision-makers to identify the detail temporal-spatial growth pattern of highly populated cities like Karbala. Additionally, the results of this study can be considered as a probable future map in order to design enough future social services and amenities for the local inhabitants.