• Journal of Broadcast Engineering
• /
• v.14 no.2
• /
• pp.144-153
• /
• 2009

Recently, as the necessity of a light-weighted video encoding technique has been rising for applications such as UCC(User Created Contents) or Multiview Video, Distributed Video Coding(DVC) where a decoder, not an encoder, performs the motion estimation/compensation taking most of computational complexity has been vigorously investigated. Wyner-Ziv coding reconstructs an image by eliminating the noise on side information which is decoder-side prediction of original image using channel code. Generally the side information of Wyner-Ziv coding is generated by using frame interpolation between key frames. The channel code such as Turbo code or LDPC code which shows a performance close to the Shannon's limit is employed. The noise model of Wyner-Ziv coding for channel decoding is called Virtual Channel Noise and is generally modeled by Laplacian or Gaussian distribution. In this paper, we propose a Wyner-Ziv coding method based on the frequency-adaptive channel noise modeling in transform domain. The experimental results with various sequences prove that the proposed method makes the channel noise model more accurate compared to the conventional scheme, resulting in improvement of the rate-distortion performance by up to 0.52dB.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.10 no.1
• /
• pp.69-79
• /
• 2008

Prediction of ground condition ahead of tunnel face might be the most important factor to prevent collapse during tunnel excavation. In this study, a non-destructive method to evaluate the phase velocity in model rock mass using wavelet transform of surface wave was proposed aiming at ground condition assessment ahead of tunnel face. Model tests using gypsum as a rocklike material composed of two layers were performed. A Piezoelectric actuator with frequencies ranging from 150 Hz to 5 kHz was selected as a harmonic source. The acceleration history was measured with two accelerometers. Wavelet transform analysis was used to obtain the dispersion curves from the measured data. The experimental results showed that the near-field effects can be neglected if the distance between two receivers is chosen to be three times the wavelength. A simple inversion method using weighted factor based on the normal distribution was proposed. The inversion results showed that the predicted phase velocity agreed reasonably well with the measured one when the wavelength influence factor was 0.2. The depth of propagation of surface wave was from 0.42 to 0.63 times the wavelength. The range of wavelength varying with phase velocity in dispersion curve matched well with that estimated by inversion technique.

• Ecology and Resilient Infrastructure
• /
• v.3 no.4
• /
• pp.285-293
• /
• 2016

This study conducted the evaluations of stable channel and physical disturbance improvement for optimal construction of river structures by focusing on Wonju River. A stable slope was analyzed sectionally for stable channel design, and in order to satisfy the stable slope, river structure improvement scenarios were deduced. Accordingly, through physical disturbance improvement evaluation for each scenario, the study extracted the optimal scenario for stable channel design and physical disturbance improvements. The changes in physical habitat were analyzed when river structure improvements were made for stable channel design and physical disturbance improvement. Zacco koreanus, an indicator fish of the soundness of the aquatic ecosystem, was selected as a restoration target species by investigating the community characteristics of fish fauna and river environments in the Wonju River. The habitat suitability was investigated by the PHABSIM model with the habitat suitability index of Zacco koreanus. The results of the prediction evaluation showed a slight decrease in habitat suitability and weighted usable area. However, it was not influenced by the improvements in the river structure. The study suggested river structure arrangement methods that can improve physical soundness and safety of Wonju River, and confirmed that there were no effects to the changes in the physical habitat.

• Journal of Korean Society of Transportation
• /
• v.28 no.1
• /
• pp.135-142
• /
• 2010

The convenient techniques for predicting the bus arrival time have used the data obtained from the buses belong to the same company only. Consequently, the conventional techniques have often failed to predict the bus arrival time at the downstream bus stops due to the lack of the data during congestion time period. The primary objective of this study is to overcome the weakness of the conventional techniques. The estimation model developed based on the data obtained from Bus Information System(BIS) and Bus management System(BMS). The proposed model predicts the bus arrival time at bus stops by using the data of all buses travelling same roadway section during the same time period. In the tests, the proposed model had a good accuracy of predicting the bus arrival time at the bus stops in terms of statistical measurements (e.g., root mean square error). Overall, the empirical results were very encouraging: the model maintains a prediction job during the morning and evening peak periods and delivers excellent results for the severely congested roadways that are of the most practical interest.

• Journal of Environmental Impact Assessment
• /
• v.27 no.3
• /
• pp.291-306
• /
• 2018

This study was designed to predict potential habitat of Japanese evergreen oak (Quercus acuta Thunb.) in Korean Peninsula considering its dispersal ability under climate change. We used a species distribution model (SDM) based on the current species distribution and climatic variables. To reduce the uncertainty of the SDM, we applied nine single-model algorithms and the pre-evaluation weighted ensemble method. Two representative concentration pathways (RCP 4.5 and 8.5) were used to simulate the distribution of Japanese evergreen oak in 2050 and 2070. The final future potential habitat was determined by considering whether it will be dispersed from the current habitat. The dispersal ability was determined using the Migclim by applying three coefficient values (${\theta}=-0.005$, ${\theta}=-0.001$ and ${\theta}=-0.0005$) to the dispersal-limited function and unlimited case. All the projections revealed potential habitat of Japanese evergreen oak will be increased in Korean Peninsula except the RCP 4.5 in 2050. However, the future potential habitat of Japanese evergreen oak was found to be limited considering the dispersal ability of this species. Therefore, estimation of dispersal ability is required to understand the effect of climate change and habitat distribution of the species.

• Journal of the Korean Society for Railway
• /
• v.20 no.2
• /
• pp.275-287
• /
• 2017

During the feasibility study of new rail lines, maintenance cost of railway infrastructure has mostly been estimated based on the track length and on simplified parameters; however, the estimation reliability can be improved by employing the correction factor from UIC 715, as well as the line classification in UIC 714. The correlations between maintenance cost and various parameters such as weighted track length based on line classification, radius of curvature, gradient and worn -out rate have been analyzed according to the case studies. Prediction of the maintenance cost has been carried out using the cost data, which were representative of the whole cost data; as a result, it was demonstrated that a cost model based on the line classification and the correction factor was more reliable than the existing models. Furthermore, possibilities of using data from both the track measurement vehicle and from the maintenance information system, which are under development, have been investigated and, based on this investigation, a combined cost model using line classification, radius of curvature, gradient and worn-out rate, among other factors, will be proposed in the near future.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.4
• /
• pp.287-296
• /
• 2015

Recent navigation systems provide quick guide services, based on processing real-time traffic information and past traffic information by applying predictable pattern for traffic information. However, the current pattern for traffic information predicts traffic information by processing past information that it presents an inaccuracy problem in particular circumstances(accidents and weather). So, this study presented a more precise predictive traffic information system than historical traffic data first by analyzing route search data which the drivers ask in real time for the quickest way then by grasping traffic congestion levels of the route in which future drivers are supposed to locate. First results of this study, the congested route from Yang Jae to Mapo, the analysis result shows that the accuracy of the weighted value of speed of existing commonly congested road registered an error rate of 3km/h to 18km/h, however, after applying the real predictive traffic information of this study the error rate registered only 1km/h to 5km/h. Second, in terms of quality of route as compared to the existing route which allowed for an earlier arrival to the destination up to a maximum of 9 minutes and an average of up to 3 minutes that the reliability of predictable results has been secured. Third, new method allows for the prediction of congested levels and deduces results of route searches that avoid possibly congested routes and to reflect accurate real-time data in comparison with existing route searches. Therefore, this study enabled not only the predictable gathering of information regarding traffic density through route searches, but it also made real-time quick route searches based on this mechanism that convinced that this new method will contribute to diffusing future traffic flow.

• Journal of Digital Convergence
• /
• v.18 no.12
• /
• pp.87-95
• /
• 2020

The Incheon airport is a gateway to and from the Republic of Korea and has a great influence on the image of the country. Therefore, it is necessary to predict the number of airport passengers in the long term in order to maintain the quality of service at the airport. In this study, we compared the predictive performance of various time series models to predict the air passenger demand at Incheon Airport. From 2002 to 2019, passenger data include trend and seasonality. We considered the naive method, decomposition method, exponential smoothing method, SARIMA, PROPHET. In order to compare the capacity and number of passengers at Incheon Airport in the future, the short-term, mid-term, and long-term was forecasted by time series models. For the short-term forecast, the exponential smoothing model, which weighted the recent data, was excellent, and the number of annual users in 2020 will be about 73.5 million. For the medium-term forecast, the SARIMA model considering stationarity was excellent, and the annual number of air passengers in 2022 will be around 79.8 million. The PROPHET model was excellent for long-term prediction and the annual number of passengers is expected to be about 99.0 million in 2024.

• Journal of the Korean Geotechnical Society
• /
• v.38 no.3
• /
• pp.35-42
• /
• 2022

The N-value from the Standard Penetration Test (SPT), which is one of the representative in-situ test, is an important index that provides basic geological information and the depth of the bearing layer for the design of geotechnical structures. In the aspect of time and cost-effectiveness, there is a need to carry out a representative sampling test. However, the various variability and uncertainty are existing in the soil layer, so it is difficult to grasp the characteristics of the entire field from the limited test results. Thus the spatial interpolation techniques such as Kriging and IDW (inverse distance weighted) have been used for predicting unknown point from existing data. Recently, in order to increase the accuracy of interpolation results, studies that combine the geotechnics and deep learning method have been conducted. In this study, based on the SPT results of about 22,000 holes of ground survey, a comparative study was conducted to predict the depth of the bearing layer using deep learning methods and IDW. The average error among the prediction results of the bearing layer of each analysis model was 3.01 m for IDW, 3.22 m and 2.46 m for fully connected network and PointNet, respectively. The standard deviation was 3.99 for IDW, 3.95 and 3.54 for fully connected network and PointNet. As a result, the point net deep learing algorithm showed improved results compared to IDW and other deep learning method.

• Korean Journal of Radiology
• /
• v.23 no.12
• /
• pp.1281-1289
• /
• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.