• Journal of the Korean association of regional geographers
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• v.6 no.3
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• pp.117-138
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• 2000

This study is, from the view point of constructive social studies which is the foundation of the 7th curriculum, to explore whether there is any viable program and to investigate it by which students, using photo resources in social studies, can organize their knowledge in the way of self-directed thinking. The main results are as follows: If it is a principle of knowledge construction process of constructive social studies that individual construction (cognitive construction) develops into communal construction(social construction) and yet communal construction develops itself, interacting with individual construction, it will be meet the objectives of social studies. In social studies, photos are a powerful communication tool. communicating with photos enables to invoke not only the visual aspects but also invisible aspects of social phenomena from photos. It, therefore, can help develop thinking power through inquiry learning, which is one of the emphasis of the 7th curriculum. Having analyzed photo resources appeared on the regional textbooks in elementary social studies, they have been appeared that even though the importance and amount of space photo resources occupy per page is big with regard to total resources, most of the photos failed to lad to self-directed thinking but just assistant material in stead. Besides, there appeared some problems with the title, variety, size, position, tone of color, visibility of the photos, and further with the combination of the photos. Developing of photo resources for constructive social studies is to overcome some problems inherent in current text books and to reflect the theoretical background of the 7th curriculum. To develop the sort of photo that can realize the point just mentioned, it would be highly preferable to provide photo database to facilitate study with homepage through web-based interaction. To take advantage of constructive photo resources, the instruction is strategized in four stages, intuition, conflict, accommodation, and equilibration stage. With the advancement of the era of image culture, curriculum developers are required to develop dynamic, multidimensional digital photos rather than static photos when develop text books.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.53-65
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• 2019

Object tracking is one of important steps to achieve video-based surveillance systems. Object tracking is considered as an essential task similar to object detection and recognition. In order to perform object tracking, various machine learning methods (e.g., least-squares, perceptron and support vector machine) can be applied for different designs of tracking systems. In general, generative methods (e.g., principal component analysis) were utilized due to its simplicity and effectiveness. However, the generative methods were only focused on modeling the target object. Due to this limitation, discriminative methods (e.g., binary classification) were adopted to distinguish the target object and the background. Among the machine learning methods for binary classification, total error rate minimization can be used as one of successful machine learning methods for binary classification. The total error rate minimization can achieve a global minimum due to a quadratic approximation to a step function while other methods (e.g., support vector machine) seek local minima using nonlinear functions (e.g., hinge loss function). Due to this quadratic approximation, the total error rate minimization could obtain appropriate properties in solving optimization problems for binary classification. However, this total error rate minimization was based on a batch mode setting. The batch mode setting can be limited to several applications under offline learning. Due to limited computing resources, offline learning could not handle large scale data sets. Compared to offline learning, online learning can update its solution without storing all training samples in learning process. Due to increment of large scale data sets, online learning becomes one of essential properties for various applications. Since object tracking needs to handle data samples in real time, online learning based total error rate minimization methods are necessary to efficiently address object tracking problems. Due to the need of the online learning, an online learning based total error rate minimization method was developed. However, an approximately reweighted technique was developed. Although the approximation technique is utilized, this online version of the total error rate minimization could achieve good performances in biometric applications. However, this method is assumed that the total error rate minimization can be asymptotically achieved when only the number of training samples is infinite. Although there is the assumption to achieve the total error rate minimization, the approximation issue can continuously accumulate learning errors according to increment of training samples. Due to this reason, the approximated online learning solution can then lead a wrong solution. The wrong solution can make significant errors when it is applied to surveillance systems. In this paper, we propose an exactly reweighted technique to recursively update the solution of the total error rate minimization in online learning manner. Compared to the approximately reweighted online total error rate minimization, an exactly reweighted online total error rate minimization is achieved. The proposed exact online learning method based on the total error rate minimization is then applied to object tracking problems. In our object tracking system, particle filtering is adopted. In particle filtering, our observation model is consisted of both generative and discriminative methods to leverage the advantages between generative and discriminative properties. In our experiments, our proposed object tracking system achieves promising performances on 8 public video sequences over competing object tracking systems. The paired t-test is also reported to evaluate its quality of the results. Our proposed online learning method can be extended under the deep learning architecture which can cover the shallow and deep networks. Moreover, online learning methods, that need the exact reweighting process, can use our proposed reweighting technique. In addition to object tracking, the proposed online learning method can be easily applied to object detection and recognition. Therefore, our proposed methods can contribute to online learning community and object tracking, detection and recognition communities.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.195-200
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• 2019

The purpose of this study is to present a model for enhancing the academic excellence of adult college students. For this purpose, 408 adult college students attending 2-year and 4-year colleges in Busan, Daegu, and Gyeongbuk were surveyed and analyzed. The components of the model are curriculum, educational methods, evaluation of education, educational administration, educational environment, and institutional support and the results are as follows. First, the curriculum preferred by adult college students was to acquire diverse academic knowledge for a degree, to acquire knowledge and skills to develop skills for the workplace, and to acquire new information and knowledge regarding issues in society as a whole. Second, the professors' qualification among the educational methods preferred by adult college students was professional competence of the professors based on their theoretical and practical skills. The preferred teaching methods were lecture, discussion, action learning, and the project learning method in that order and video and PowerPoint were preferred as effective teaching mediums. Third, the preferred course for adult college students is operated on weekends, and three years was preferred to get a bachelor's degree. The possible hours of learning per day is 3~6 hours, indicating the necessity of e-learning, B-learning, and prior learning experience recognition systems. Fourth, the education evaluation method preferred by adult college students was a compromise method which is a mixture of absolute evaluation and relative evaluation, and it also showed the need for Pass or Non Pass evaluation method. Fifth, the internal factors of college selection preferred by adult college students were the acquisition of new knowledge and skills, and the external factors were desire to receive many opportunities related to employment and job improvement. The classroom, which provides an effective environment, was a fixed seat classroom and an indoor classroom environment was emphasized for desired educational environment. Sixth, institutional support preferred by adult college students was computer-related programs and learning club support services.

• Journal of The Korean Association For Science Education
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• v.41 no.2
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• pp.115-131
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• 2021

As the global climate change emergency is escalating, the need for 'Social Action-Oriented SSI (SAO-SSI) on climate change topics' in science education that can change society through social activity is increasing. By employing sociocultural theory, this study explores the challenges of limiting teacher's agency in implementing SAO-SSI on climate change topics in science education. Data from participant observation for 46 lessons, in-depth interviews with participants, field notes, and teacher reflection notes were analyzed by the structure of into micro- (classrooms), meso- (school), and macro- (Korea society) level. At the micro-level, the teacher's new attempts of SAO-SSI on climate change topics class made it difficult for him to identify students' understanding of climate change, because they have a low sense of perception that climate change is also their problem. In addition, the teacher had difficulties leading students' into an engagement for social action because students were skeptical about the feasibility of planned social behavior by positioning themselves as children or had difficulty in understanding social action and sympathizing with its values. At the meso-level, a school culture that encourages the implementation of a curriculum similar to that of colleagues, it was difficult to implement one's own curriculum. And it was difficult to develop expertise without the support and communications with colleagues who revealed the burden of unfamiliar science topics of climate change. In addition, conflicts arose in the process of implementing out-of-school social actions with the principal's passive support. At the macro-level, the insufficient proper material resources for SAO-SSI on climate change topics class, and negative perceptions on the students' social action in the society were acting as constraints. We offer implications for what kind of structural support and efforts from various subjects in the educational community should be provided to implement SAO-SSI on climate change topics class in science education.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.117-130
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• 2020

Noises can distort acquired geophysical data, leading to their misinterpretation. Potential noises sources include anthropogenic activity, natural phenomena, and instrument noises. Conventional denoising methods such as wavelet transform and filtering techniques, are based on subjective human investigation, which is computationally inefficient and time-consuming. Recently, many researchers attempted to implement neural networks to efficiently remove noise from geophysical data. This study aims to review and analyze different types of neural networks, such as artificial neural networks, convolutional neural networks, autoencoders, residual networks, and wavelet neural networks, which are implemented to remove different types of noises including seismic, transient electromagnetic, ground-penetrating radar, and magnetotelluric surveys. The review analyzes and summarizes the key challenges in the removal of noise from geophysical data using neural network, while proposes and explains solutions to the challenges. The analysis support that the advancement in neural networks can be powerful denoising tools for geophysical data.

• Journal of Bio-Environment Control
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• v.3 no.1
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• pp.42-51
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• 1994

Visual features of a mushromm(Lentinus Edodes L.) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading look simple, it decision making underneath the simple action comes from the result of the complex neural processing of visual image. Recently, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, the neuro -net based computer visual information processing is the promising approach toward the automation in the agricultural field. In this paper, first, the neuro - net based classification of simple geometric primitives were done and the generalization property of the network was tested for degraded primitives. And then the neuro-net based grading system was developed for a mushroom. A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features of sampled mushrooms and their corresponding grades were used as input/output pairs for training the neural network. The grading performance of the trained network for the mushrooms graded previously by the expert were also presented.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.149-155
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• 2020

The Korea Highway Traffic Authority provides statistics that analyze the causes of traffic accidents that occurred since 2015 using the Traffic Accident Analysis System (TAAS). it was reported Through TAAS that the driver's forward carelessness was the main cause of traffic accidents in 2018. As statistics on the cause of traffic accidents, 51.2 percent used mobile phones and watched DMB while driving, 14 percent did not secure safe distance, and 3.6 percent violated their duty to protect pedestrians, representing a total of 68.8 percent. In this paper, we propose a system that has improved the advanced driver assistance system ADAS (Advanced Driver Assistance Systems) by utilizing CNN (Convolutional Neural Network) among the algorithms of Deep Learning. The proposed system learns a model that classifies the movement of the driver's face and eyes using Conv2D techniques which are mainly used for Image processing, while recognizing and detecting objects around the vehicle with cameras attached to the front of the vehicle to recognize the driving environment. Then, using the learned visual steering model and driving environment data, the hazard is classified and detected in three stages, depending on the driver's view and driving environment to assist the driver with the forward and blind spots.

• Journal of the Korean Chemical Society
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• v.64 no.2
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• pp.84-98
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• 2020

In this study, we analyzed the influences of lecture design using CoRe upon the professor's teaching professionalism in the aspects of pedagogical content knowledge (PCK). The participants are three professors from the college of science-engineering located in Chungcheong-do. After collecting their syllabi, we observed their lecture and conducted the orientation. Afterward, we collected the CoRes which they prepared before the lecture. Then we observed their lecture and conducted semi-structured interviews. This process was carried out twice. We analyzed their syllabi, CoRes, videotaped lectures, field notes, the teaching materials, and interview transcripts. The results revealed that professors not only clarified the learning objectives and the characteristics of students but also reflected them in the lecture. In addition, they established the teaching strategies according to the characteristics of contents in the unit. As they recognized the necessity of understanding students' achievement, they selected the assessment method and applied it in the lecture. In some cases, however, they lacked presenting learning objectives specifically and explained students' misconceptions without inducing new concepts. They also presented a shortage of considering students' prior knowledge. They lacked providing students with an opportunity to participate in lectures, and their assessment method was not effective. Based on the results, we discussed implications to improve teaching professionalism using CoRe.

• Korean Journal of Cognitive Science
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• v.7 no.1
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• pp.57-74
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• 1996

This paper presents a comparison of recognition performance of the features used inthe recent handwritten korean character recognition.This research aims at providing the basis for feature selecion in order to improve not only the recognition rate but also the efficiency of recognition system.For the comparison of feature performace,we analyzed the characteristics of theose features and then,classified them into three rypes:global feature(image transformation)type,statistical feature type,and local/ topological feature type.For each type,we selected four or five features which seem more suitable to represent the characteristics of korean alphabet,and performed recongition experiments for the first consonant,horizontal vowel,and vertical vowel of a korean character, respectively.The classifier used in our experiments is a multi-layered perceptron with one hidden layer which is trained with backpropagation algorithm.The training and test data in the experiment are taken from 30sets of PE92. Experimental results show that 1)local/topological features outperform the other two type features in terms of recognition rates 2)mesh and projection features in statical feature type,walsh and DCT features in global feature type,and gradient and concavity features in local/topological feature type outperform the others in each type, respectively.