• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.776-778
• /
• 1997

We propose a remote controller for a SCARA typed direct drive manipulator with two degrees-of-freedom(DOF). A remote controller system for SCARA robot of DDA is designed using a 2 DSP (TMS320c31) board and Winsock(Internet program class library supplied by Microsoft). The design objective of the system is to implement real time dynamic control algorithms which have been tested only by simulations so far and remote control regardless of the distance between user and robot. Because this system runs on Win95, we developed a VxD program to communicate with DSP controller.

• Journal of the Korean Society for Library and Information Science
• /
• v.55 no.1
• /
• pp.541-565
• /
• 2021

The study is aimed at designing an effective edutech platform based on online remote classes, and clarifying the effect of empathy competence of youths through the operation linked with the empathic reading program focusing on reading. To this end, after constructing the environment for education and drawing a class model based on components of edutech for remote classes on the basis of previous studies and elaborating the empathic reading education program, this study has been conducted for one semester for 107 students in 4 classes in their 1st grade at S middle school, Gyeonggi-do in order to apply them to fields. As a result of the study, the empathy reading program that a remote class model was applied has shown a meaningful difference among groups in cognitive empathy and emotional empathy, and it was found that there was a statistically meaningful difference between the two groups in total scores. Besides, the effect and meaning of adolescents' empathy competence have been verified through the remote empathy reading education and the empirical analysis, and the direction to develop the empathy reading program has been suggested for a solution to settle differences in students' learning due to COVID-19.

• Korean Journal of Remote Sensing
• /
• v.21 no.5
• /
• pp.417-423
• /
• 2005

A fuzzy approach using an EM algorithm for image classification is presented. In this study, a double compound stochastic image process is assumed to combine a discrete-valued field for region-class processes and a continuous random field for observed intensity processes. The Markov random field is employed to characterize the geophysical connectedness of a digital image structure. The fuzzy classification is an EM iterative approach based on mixture probability distribution. Under the assumption of the double compound process, given an initial class map, this approach iteratively computes the fuzzy membership vectors in the E-step and the estimates of class-related parameters in the M-step. In the experiments with remotely sensed data, the MRF-based method yielded a spatially smooth class-map with more distinctive configuration of the classes than the non-MRF approach.

• Korean Journal of Remote Sensing
• /
• v.37 no.4
• /
• pp.719-731
• /
• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.406-412
• /
• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• The Journal of the Korea Contents Association
• /
• v.7 no.1
• /
• pp.215-224
• /
• 2007

The classification of the satellite images is basic part in Remote sensing. In classification of the satellite images, class separability feature is very effective accuracy of the images classified. For improving classification accuracy, It is necessary to study classification methode than analysis of class separability feature deciding classification probability. In this study, IKONOS, SPOT 5, Landsat TM, were resampled to sizes 1m grid. Above images were calculated the class separability prior to the step for classification of pixels. This Study concludes, each image was measured by the rate of class separability, values classified were showed highly about $1,600{\sim}2,000$.

• Korean Journal of Remote Sensing
• /
• v.27 no.3
• /
• pp.289-301
• /
• 2011

In this paper an attempt has been made to classify Asia land cover considering climatic and vegetative characteristics. The sub-class clustering based on the 13 MODIS land cover classes (except water) over Asia was performed with the climate map and the NOVI derived from SPOT 5 VGT D10 data. The unsupervised classification for the sub-class clustering was performed in each land cover class, and total 74 clusters were determined over the study area. Via these clusters, the annual variations (from 1999 to 2007) of precipitation rate and temperature were analyzed as an example by a simple linear regression model. The various annual variations (negative or positive pattern) were represented for each cluster because of the various climate zones and NOVI annual cycles. Therefore, the detailed land cover map as the classification result by the sub-class clustering in this study can be useful information in modelling works for requiring the detailed climatic and vegetative information as a boundary condition.

• Journal of Digital Convergence
• /
• v.19 no.2
• /
• pp.515-522
• /
• 2021

In this study, 186 remote graduate students from J University were surveyed and interviewed in-depth to see if their satisfaction varies depending on their gender, semester, and major, and the results were as follows. First, the gender satisfaction of remote graduate students was shown to be statistical significance differences between men and women in professors, offline activities, and class evaluations, all of which showed that men were significantly higher than women. Second, the satisfaction level of each semester of remote graduate students was shown to be significantly different in curriculum, LMS, lecture content quality, and offline activities, and tended to increase as the school year went up. Third, the satisfaction level of remote graduate students by major was found to have significant differences in other factors except LMS and class contents, with the highest educational administration. The results of this study are expected to be used as basic data to improve elements absolutely necessary for student-centered education, such as curriculum of remote graduate schools, establishment of learning platform (LMS), and development and production of lecture contents.

• Proceedings of the IEEK Conference
• /
• 2003.07c
• /
• pp.2649-2652
• /
• 2003

In this Paper, an Audio-Visual Teaching aid (AVTA) for use in a classroom and with Internet is presented. A system, which was designed and tested, consists of a wireless Microphone system, Text to Speech conversion Software, Noise filtering circuit and a Computer. An IBM compatible PC with sound card and Network Interface card and a Web browser and a voice and text messenger service were used to provide slightly delayed text and also voice over the internet for remote teaming, while providing scrolling text from a real time lecture in a classroom. The motivation for design of this system, was to aid Korean students who may have difficulty in listening comprehension while have, fairly good reading ability of text. This application of this system is twofold. On one hand it will help the students in a class to view and listen to a lecture, and on the other hand, it will serve as a vehicle for remote access (audio and text) for a classroom lecture. The project provides a simple and low cost solution to remote learning and also allows a student to have access to classroom in emergency situations when the student, can not attend a class. In addition, such system allows the student in capturing a teacher's lecture in audio and text form, without the need to be present in class or having to take many notes. This system will therefore help students in many ways.

• 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.