• The KIPS Transactions:PartA
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• v.9A no.1
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• pp.105-112
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• 2002

The purpose of this paper is to develop a courseware based on GIS (Geographic Information System) for improving visual and spatial learning efficiency of geography learning. The existing coursewares are not easy to encourage the learners in learning motivation, because these provide only the visual information using simple texts or imamges to the learners. To overcome these constraints, our courseware using GIS that can support spatial information can control the attribute information of map. In this paper, we define the courseware as the geography learning system. This courseware system enables the learners to take the perfect learning and the repetitive learning through the feedback after evaluating the learning degree. Also using geography learning application modules we implemented, the learners can participate directly in learning as well as search information in WWW.

• The KIPS Transactions:PartB
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• v.9B no.5
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• pp.523-532
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• 2002

Recently, with the wide spread of the Internet and development of wireless network technology, it has now become possible to access web pages anytime, anywhere through devices with small display such as PDA But, since most existing web pages are optimized for desktop computers, browsing web pages on the small screen through wireless network requires more scrolling and longer loading time. In this paper, we propose a page reconstruction scheme called PageMap to make it feasible to navigate existing web pages through small screen devices even on the wireless connection. Reconstructed pages reduce the file and page size and thus eventually reduce resource requirements. We have Implemented a prototype system and performed several experiments for typical web sites. We report some of the results.

• The KIPS Transactions:PartB
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• v.13B no.2 s.105
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• pp.179-186
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• 2006

A user interface that connects a user to intelligent robots needs to be designed for executing them efficiently. In this paper, it is analyzed how to organize a mobile terminal based user interface according to the function and level of autonomy of intelligent robots and the user interface of PDA (Personal Digital Assistant) and smart phone is developed for controlling intelligent robots remotely. In the image-based user interface, a user can see the motion of a robot directly and control the robot. In the map-based interface, the quantity of transmission information is reduced and therefore a user can control the robot with a small delay of transmission time.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.357-362
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• 2019

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.35-42
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• 2021

In the current context of digitalization of education, the use of modern methods and techniques of data analysis and processing in order to improve students' school results has a very important role. In our paper, we aimed to perform a comparative study of the classification performances of AdaBoost, SVM, Naive Bayes, Neural Network and kNN algorithms to classify the results obtained at the Baccalaureate by students from a college in Suceava, during 2012-2019. To evaluate the results we used the metrics: AUC, CA, F₁, Precision and Recall. The AdaBoost algorithm achieves incredible performance for classifying the results into two categories: promoted / rejected. Next in terms of performance is Naive Bayes with a score of 0.999 for the AUC metric. The Neural Network and kNN algorithms obtain scores of 0.998 and 0.996 for AUC, respectively. SVM shows poorer performance with the score 0.987 for AUC. With the help of the HeatMap and DataTable visualization tools we identified possible correlations between classification results and some characteristics of data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3121-3143
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• 2019

Segmentation plays an important role in the field of image processing and computer vision. Intuitionistic fuzzy C-means (IFCM) clustering algorithm emerged as an effective technique for image segmentation in recent years. However, standard fuzzy C-means (FCM) and IFCM algorithms are sensitive to noise and initial cluster centers, and they ignore the spatial relationship of pixels. In view of these shortcomings, an improved algorithm based on IFCM is proposed in this paper. Firstly, we propose a modified non-membership function to generate intuitionistic fuzzy set and a method of determining initial clustering centers based on grayscale features, they highlight the effect of uncertainty in intuitionistic fuzzy set and improve the robustness to noise. Secondly, an improved nonlinear kernel function is proposed to map data into kernel space to measure the distance between data and the cluster centers more accurately. Thirdly, the local spatial-gray information measure is introduced, which considers membership degree, gray features and spatial position information at the same time. Finally, we propose a new measure of intuitionistic fuzzy entropy, it takes into account fuzziness and intuition of intuitionistic fuzzy set. The experimental results show that compared with other IFCM based algorithms, the proposed algorithm has better segmentation and clustering performance.

• The Korean Journal of Food & Health Convergence
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• v.5 no.2
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• pp.27-34
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• 2019

RNases plays a pivotal role in biological system and different RNases are known for their various functions like angiogenesis, immunological response, antiviral, antitumour activity and apoptosis. In which anti tumour activity of RNase is proved to improve genome stability in normal cells up to some extent. RNases like RNase L shows antiviral and antitumour activities against virus infected cells and cancer cells through 2'-5' oligo adenylate pathway and induces RNaseL dependent apoptosis where as RNase A modulates various proliferative pathways like MAP kinase, JNK, TGF-${\beta}$ and activates apoptosis in cancer cells and promotes immunological response through processing of Ags. IRE1 RNase acts as both tumour suppressor gene and oncogene in normal and cancer cells and involved in both antitumour and tumorigenic activities. RNase III upregulates miRNA in cancer cells there by acting via posttranscriptional level and proven to be effective against colorectal adeno carcinoma. In addition to this IRE1 RNase is a double edged sword through RIDD pathway in ER (18). To some of the cancers expressing c-myc IRE1 acts as tumour suppressor where as in cancers where myc is downregulated IRE1 acts as tumour provoking through RIDD pathway (18). Thus RNases play vital role in regulating the genome stability.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.944-946
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• 2014

Localization of a sensor network node using machine learning has been recently studied. It is easy for Support vector machines algorithm to implement in high level language enabling parallelism. In this paper, we realized Support vector machine using python language and built a sensor network cluster with 5 Pi's. We also established a Hadoop software framework to employ MapReduce mechanism. We modified the existing Support vector machine algorithm to fit into the distributed hadoop architecture system for localization of a sensor node. In our experiment, we implemented the test sensor network with a variety of parameters and examined based on proficiency, resource evaluation, and processing time.

• Journal of the Korea Convergence Society
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• v.10 no.4
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• pp.33-38
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• 2019

Recently, to support location-based services, there have been many researches which consider the spatial network. For this, there are many experimental data for data processing on the road network. However, the data to process the trajectory of moving objects are not suitable. Therefore, we propose index structure to process the trajectory data on the road network and the trajectory data generation algorithm. In addition, to prove efficiency of our index structure and algorithm, we show that edge-based trajectory data are generated through the proposed algorithm using the map data of San Francisco Bay.