• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.312-321
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• 2003

In this paper, we implement robot which are ability to recognize obstacles and moving automatically to destination. we present two results in this paper; hardware implementation of image processing board and software implementation of visual feedback algorithm for a self-controlled robot. In the first part, the mobile robot depends on commands from a control board which is doing image processing part. We have studied the self controlled mobile robot system equipped with a CCD camera for a long time. This robot system consists of a image processing board implemented with DSPs, a stepping motor, a CCD camera. We will propose an algorithm in which commands are delivered for the robot to move in the planned path. The distance that the robot is supposed to move is calculated on the basis of the absolute coordinate and the coordinate of the target spot. And the image signal acquired by the CCD camera mounted on the robot is captured at every sampling time in order for the robot to automatically avoid the obstacle and finally to reach the destination. The image processing board consists of DSP (TMS320VC33), ADV611, SAA7111, ADV7l76A, CPLD(EPM7256ATC144), and SRAM memories. In the second part, the visual feedback control has two types of vision algorithms: obstacle avoidance and path planning. The first algorithm is cell, part of the image divided by blob analysis. We will do image preprocessing to improve the input image. This image preprocessing consists of filtering, edge detection, NOR converting, and threshold-ing. This major image processing includes labeling, segmentation, and pixel density calculation. In the second algorithm, after an image frame went through preprocessing (edge detection, converting, thresholding), the histogram is measured vertically (the y-axis direction). Then, the binary histogram of the image shows waveforms with only black and white variations. Here we use the fact that since obstacles appear as sectional diagrams as if they were walls, there is no variation in the histogram. The intensities of the line histogram are measured as vertically at intervals of 20 pixels. So, we can find uniform and nonuniform regions of the waveforms and define the period of uniform waveforms as an obstacle region. We can see that the algorithm is very useful for the robot to move avoiding obstacles.

• Journal of Animal Science and Technology
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• v.46 no.5
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• pp.719-728
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• 2004

The aims of this study were to estimate genetic parameters for carcass traits on Hanwoo(Korean Native Cattle) and to compare two different statistical algorithms for estimating genetic parameters. Data obtained from 1526 steers at Hanwoo Improvement Center and Hanwoo Improvement Complex Area from 1996 to 2001 were used for the analyses. The carcass traits considered in these studies were carcass weight, dressing percent, eye muscle area, backfat thickness, and marbling score. Estimated genetic parameters using EM-REML algorithm were compared to those by Bayesian inference via Gibbs Sampling to find out statistical properties. The estimated heritabilities of carcass traits by REML method were 0.28, 0.25, 0.35, 0.39 and 0.51, respectively and those by Gibbs Sampling method were 0.29, 0.25, 0.40, 0.42 and 0.54, respectively. This estimates were not significantly different, even though the estimated heritabilities by Gibbs Sampling method were higher than ones by REML method. Since the estimated statistics by REML method and Gibbs Sampling method were not significantly different in this study, it is inferred that both mothods could be efficiently applied for the analysis of carcass traits of cattle. However, further studies are demanded to define an optimal statistical method for handling large scale performance data.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.163-179
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• 2010

Recentlythe concept of fast fashion is drawing attention as customer needs are diversified and supply lead time is getting shorter in fashion industry. It is emphasized as one of the critical success factors in the fashion industry how quickly and efficiently to satisfy the customer needs as the competition has intensified. Because the fast fashion is inherently susceptible to trend, it is very important for fashion retailers to make quick decisions regarding items to launch, quantity based on demand prediction, and the time to respond. Also the planning decisions must be executed through the business processes of procurement, production, and logistics in real time. In order to adapt to this trend, the fashion industry urgently needs supports from intelligent quick response(QR) system. However, the traditional functions of QR systems have not been able to completely satisfy such demands of the fast fashion industry. This paper proposes an intelligent quick response system for the fast fashion(IQRS-FF). Presented are models for QR process, QR principles and execution, and QR quantity and timing computation. IQRS-FF models support the decision makers by providing useful information with automated and rule-based algorithms. If the predefined conditions of a rule are satisfied, the actions defined in the rule are automatically taken or informed to the decision makers. In IQRS-FF, QRdecisions are made in two stages: pre-season and in-season. In pre-season, firstly master demand prediction is performed based on the macro level analysis such as local and global economy, fashion trends and competitors. The prediction proceeds to the master production and procurement planning. Checking availability and delivery of materials for production, decision makers must make reservations or request procurements. For the outsourcing materials, they must check the availability and capacity of partners. By the master plans, the performance of the QR during the in-season is greatly enhanced and the decision to select the QR items is made fully considering the availability of materials in warehouse as well as partners' capacity. During in-season, the decision makers must find the right time to QR as the actual sales occur in stores. Then they are to decide items to QRbased not only on the qualitative criteria such as opinions from sales persons but also on the quantitative criteria such as sales volume, the recent sales trend, inventory level, the remaining period, the forecast for the remaining period, and competitors' performance. To calculate QR quantity in IQRS-FF, two calculation methods are designed: QR Index based calculation and attribute similarity based calculation using demographic cluster. In the early period of a new season, the attribute similarity based QR amount calculation is better used because there are not enough historical sales data. By analyzing sales trends of the categories or items that have similar attributes, QR quantity can be computed. On the other hand, in case of having enough information to analyze the sales trends or forecasting, the QR Index based calculation method can be used. Having defined the models for decision making for QR, we design KPIs(Key Performance Indicators) to test the reliability of the models in critical decision makings: the difference of sales volumebetween QR items and non-QR items; the accuracy rate of QR the lead-time spent on QR decision-making. To verify the effectiveness and practicality of the proposed models, a case study has been performed for a representative fashion company which recently developed and launched the IQRS-FF. The case study shows that the average sales rateof QR items increased by 15%, the differences in sales rate between QR items and non-QR items increased by 10%, the QR accuracy was 70%, the lead time for QR dramatically decreased from 120 hours to 8 hours.

• Investigative Magnetic Resonance Imaging
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• v.18 no.4
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• pp.303-313
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• 2014

Purpose : In-vivo conductivity reconstruction using transmit field ($B_1{^+}$) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex $B_1{^+}$ map and provided a parametric model of the conductivity-to-noise ratio value. Materials and Methods: The $B_1{^+}$ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated $B_1{^+}$ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of $B_1{^+}$ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. Results: According to the simulation results, conductivity estimation is more sensitive to statistical noise in $B_1{^+}$ phase than to noise in $B_1{^+}$ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the $B_1{^+}$ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of $B_1{^+}$ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. Conclusion: Through all these relationships, quantitative conductivity estimation error due to statistical noise in $B_1{^+}$ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.

• Progress in Medical Physics
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• v.13 no.4
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• pp.209-217
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• 2002

There have been many studies on the application of the reciprocal advantages of multimodality image to define accurate target volume in the Process of radiation treatment planning. For the proper use of the multimodality images, the registration works between different modality images should be performed in advance. In this study, we selected chamfer matching method and mutual information method as most popular methods in recent image registration studies considering the registration accuracy and clinical practicality. And the two registration methods were analyzed to deduce the optimal registration method according to the characteristics of images. Lung phantom of which multimodality images could be acquired was fabricated and CT, MRI and SPECT images of the phantom were used in this study. We developed the registration program which can perform the two registration methods properly and analyzed the registration results which were produced by the developed program in many different images' conditions. Although the overall accuracy of the registration in both chamfer matching method and mutual information method was acceptable, the registration errors in SPECT images which had lower resolution and in degraded images of which data were removed in some part were increased when chamfer matching method was applied. Especially in the case of degraded reference image, chamfer matching methods produce relatively large errors compared with mutual information method. Mutual information method can be estimated as more robust registration method than chamfer matching method in this study because it did not need the prerequisite works, the extraction of accurate contour points, and it produced more accurate registration results consistently regardless of the images' characteristics. The analysis of the registration methods in this study can be expected to provide useful information to the utilization of multimodality images in delineating target volume for radiation treatment planning and in many other clinical applications.

• Journal of Korea Water Resources Association
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• v.51 no.5
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• pp.439-449
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• 2018

Many potential sources of bias are used in several steps of the radar-rainfall estimation process because the hydrological and meteorological radars measure the rainfall amount indirectly. Previous studies on radar-rainfall uncertainties were performed to reduce the uncertainty of each step by using bias correction methods in the quantitative radar-rainfall estimation process. However, these studies do not provide comprehensive uncertainty for the entire process and the relative ratios of uncertainty between each step. Consequently, in this study, a suitable approach is proposed that can quantify the uncertainties at each step of the quantitative radar-rainfall estimation process and show the uncertainty propagation through the entire process. First, it is proposed that, in the suitable approach, the new concept can present the initial and final uncertainties, variation of the uncertainty as well as the relative ratio of uncertainty at each step. Second, the Maximum Entropy Method (MEM) and Uncertainty Delta Method (UDM) were applied to quantify the uncertainty and analyze the uncertainty propagation for the entire process. Third, for the uncertainty quantification of radar-rainfall estimation at each step, two quality control algorithms, two radar-rainfall estimation relations, and two bias correction methods as post-processing through the radar-rainfall estimation process in 18 rainfall cases in 2012. For the proposed approach, in the MEM results, the final uncertainty (from post-processing bias correction method step: ME = 3.81) was smaller than the initial uncertainty (from quality control step: ME = 4.28) and, in the UDM results, the initial uncertainty (UDM = 5.33) was greater than the final uncertainty (UDM = 4.75). However uncertainty of the radar-rainfall estimation step was greater because of the use of an unsuitable relation. Furthermore, it was also determined in this study that selecting the appropriate method for each stage would gradually reduce the uncertainty at each step. Therefore, the results indicate that this new approach can significantly quantify uncertainty in the radar-rainfall estimation process and contribute to more accurate estimates of radar rainfall.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.

• Korean Journal of Ecology and Environment
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• v.47 no.3
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• pp.186-193
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• 2014

This study aimed to analyze spatio-temporal trends of phenological characteristics in South Korea by using MODIS EVI. For the phenology analysis, we had applied double logistic function to MODIS time-series data. Our results showed that starting date of phenology seems to have a tendency along with latitudinal trends. Starting date of phenology of Jeju Island and Mt. Sobeak went back for 0.38, 0.174 days per year, respectively whereas, Mt. Jiri and Mt. Seolak went forward for 0.32 days, 0.239 days and 0.119 days, respectively. Our results exhibited the fluctuation of plant phonological season rather than the change of phonological timing and season. Starting date of plant phenology by spatial distribution revealed tendency that starting date of mountain area was late, and basin and south foot of mountain was fast. In urban ares such as Seoul metropolitan, Masan, Changwon, Milyang, Daegu and Jeju, the phonological starting date went forward quickly. Pheonoligcal attributes such as starting date and leaf fall in urban areas likely being affected from heat island effect and related warming. Our study expressed that local and regional monitoring on phonological events and changes in Korea would be possible through MODIS data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.5 no.3
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• pp.49-59
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• 1985

Formulation of the geometric optimization for truss structures based on the elasticity theory turn out to be the nonlinear programming problem which has to deal with the Cross sectional area of the member and the coordinates of its nodes simultaneously. A few techniques have been proposed and adopted for the analysis of this nonlinear programming problem for the time being. These techniques, however, bear some limitations on truss shapes loading conditions and design criteria for the practical application to real structures. A generalized algorithm for the geometric optimization of the truss structures which can eliminate the above mentioned limitations, is developed in this study. The algorithm developed utilizes the two-phases technique. In the first phase, the cross sectional area of the truss member is optimized by transforming the nonlinear problem into SUMT, and solving SUMT utilizing the modified Newton-Raphson method. In the second phase, the geometric shape is optimized utilizing the unidirctional search technique of the Rosenbrock method which make it possible to minimize only the objective function. The algorithm developed in this study is numerically tested for several truss structures with various shapes, loading conditions and design criteria, and compared with the results of the other algorithms to examme its applicability and stability. The numerical comparisons show that the two-phases algorithm developed in this study is safely applicable to any design criteria, and the convergency rate is very fast and stable compared with other iteration methods for the geometric optimization of truss structures.