• The Mathematical Education
• /
• v.52 no.1
• /
• pp.1-17
• /
• 2013

The goal of the textbooks of 2009 revised curriculum is to make customized lectures possible considering learning characteristics and understanding level of students. However, it is not easy to find a research result on sensed difficulty of the mathematics textbook, which is able to provide valuable information on the development of the diverse level textbooks. This research suggested criteria in analysing sensed difficulties of field teachers on the textbooks, and analysed sensory difficulty on the selective tracks textbooks based on 2007 revised mathematics curriculum using the criteria. The results of the analysis on 59 mathematics selective tracks textbooks show that all have average sensed difficulty. The criteria and research results are expected to provide valuable information in future mathematics textbook development.

• Korean Journal of Remote Sensing
• /
• v.19 no.5
• /
• pp.341-349
• /
• 2003

The so-called B-method, a simplified surface energy budget, permits calculation of daily actual evapotranspiration (ET) using remotely sensed data, such as NOAA-AVHRR. Even if the use of satellite data allows estimation of the albedo and surface temperature, this model requires meteorological data measured at ground-level to obtain the other inputs. In addition, a difficulty may be occurred by the difference of temporal scales between the net radiation in daily scale and instantaneous measurement at midday of the surface and air temperatures because the data covered whole day are necessary to obtain accumulated daily net radiation. In order to solve these problems, this study attempted a modification of B-method through an extension of hourly ET value calculated using a complete instantaneous inputs. The estimation of the daily apparent ET from newly proposed system showed a root mean square error of 0.26 mm/day as compared the output obtained from the classical model. It is evident that this may offer more rapid estimation and reduced data volume.

• Journal of Korean Elementary Science Education
• /
• v.36 no.4
• /
• pp.356-366
• /
• 2017

The purpose of this study is to analyze whether the required cognitive levels of the current 6th graders' science textbooks conform to the children's cognitive levels and to examine the students' sensed psychological difficulty of the current science textbooks. The eighty five students (boy: 39, girl: 46) from one elementary school were participated in this study. The results of the study are as follows. First, the 2/3 out of the contents in the six graders' science textbooks require concrete level of operation and the 1/3 out of the contents in the textbooks requires the formal level of operation. So the 70% students at the concrete operational level are likely to undergo difficulties with the 1/3 contents in the textbooks. Second, the students' psychological difficulty on the science textbooks is relatively low (approximately two out of five points) and there is not any special pattern between the cognitive level of the textbooks and the students' psychological difficulty of the textbooks.

• Korean Journal of Remote Sensing
• /
• v.10 no.2
• /
• pp.109-120
• /
• 1994

The study is aimed at the extraction of the groundwater potential areas utilizing the remotely sensed data from satellites. The results of the study are summarized as follows. Analyses of the existing operational wells for groundwater supply indicate that 81% of them are related with lineaments and 51% of them are located at the intersections of lineameters. Thus the features of lineaments are considered to be one of the most important parameters to extract a high potertial area of groundwater. Taking into acount features of lineament, high potential points were extracted from Landsat TM data based on the theory developed in this research, then verifications were made through actual drilling. The result of verification indicates that 9 points produces more 200 cubic meter/day which is the amount required from economical point of view for an operational use. Since the actual boring was not made on the recommended points for 4 points due to the difficulty of access to the exact points and of the approval for boring, they did not yield enough output. The result might have been improved if the exact points were bored and if the boring bad been made deeper, since the maximum depth of boring was limited to 62 meters.

• Korean Journal of Remote Sensing
• /
• v.20 no.2
• /
• pp.139-152
• /
• 2004

This paper proposes to improve the results of image classification with spatial region growing segmentation by using an MRF-based classifier. The proposed approach is to re-classify the pixels in the boundary area, which have high probability of having classification error. The MRF-based classifier performs iteratively classification using the class parameters estimated from the region growing segmentation scheme. The proposed method has been evaluated using simulated data, and the experiment shows that it improve the classification results. But, conventional MRF-based techniques may yield incorrect results of classification for remotely-sensed images acquired over the ground area where has complicated types of land-use. A multistage MRF-based iterative class-modification in boundary is proposed to alleviate difficulty in classifying intricate land-cover. It has applied to remotely-sensed images collected on the Korean peninsula. The results show that the multistage scheme can produce a spatially smooth class-map with a more distinctive configuration of the classes and also preserve detailed features in the map.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2002.05a
• /
• pp.43-50
• /
• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.49 no.1
• /
• pp.1-12
• /
• 2021

In this paper, a new algorithm is proposed to estimate the damage location in the composite panel by extracting the elastic wave signal reflected from the damaged area. The guided elastic wave is generated by a piezoelectric actuator and sensed by a piezoelectric sensor. The proposed algorithm adopts a diagnostic approach. It compares the non-damaged signal with the damaged signal, and extract damage information along with sensor network and lamb wave group velocity estimated by signal correlation. However, it is difficult to clearly distinguish the damage location due to the nonlinear properties of lamb wave and complex information composed of various signals. To overcome this difficulty, the cumulative summation feature vector algorithm(CSFV) and a visualization technique are newly proposed in this paper. CSFV algorithm finds the center position of the damage by converting the signals reflected from the damage to the area of distance at which signals reach, and visualization technique is applied that expresses feature vectors by multiplying damage indexes. Experiments are performed for a composite panel and comparative study with the existing algorithms is carried out. From the results, it is confirmed that the damage location can be detected by the proposed algorithm with more reliable accuracy.