• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제7권9호
• /
• pp.2213-2231
• /
• 2013

Compressed sensing is a novel technology used in the field of wireless communication and sensor networks for channel estimation, signal detection, data gathering, network monitoring, and other applications. It plays a significant role in highly secure, real-time, well organized, and cost-effective data communication in smart-grid (SG) systems, which consist of multi-tier network standards that make it challenging to synchronize in power management communication. In this paper, we present a multi-layer communication model for SG systems and propose compressed-sensing based data transmission at every layer of the SG system to improve data transmission performance. Our approach is to utilize the compressed-sensing procedure at every layer in a controlled manner. Simulation results demonstrate that the proposed monitoring devices need less transmission power than conventional systems. Additionally, secure, reliable, and real-time data transmission is possible with the compressed-sensing technique.

• 한국컴퓨터산업학회논문지
• /
• 제10권5호
• /
• pp.177-186
• /
• 2009

본 논문에서는 적외선 센서와 초음파 센서를 사용하여 넓은 범위의 움직임 감지뿐만 아니라 미세 한 움직임을 감지를 할 수 있는 다중 센서를 내장한 움직임 감지기를 개발하였다. 개발된 움직임 감지기를 전등과 연계시켜 강의실, 사무실 등 넓은 공간에서 사용하면 전기 에너지를 절약할 수 있다.

• 대한원격탐사학회지
• /
• 제37권4호
• /
• 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.

• 한국위성정보통신학회논문지
• /
• 제9권4호
• /
• pp.20-25
• /
• 2014

본 논문에서는 멀티콥터의 원활한 주파수 사용과 에너지 충전을 위하여 효율적인 스펙트럼 센싱기술을 제안한다. 제안된 구조는 Ad-hoc network를 기반으로 한 Spectrum sensing의 성능을 향상시키는데 중점을 둔다. 우선 Cooperative Spectrum sensing, Ad-hoc based Spectrum sensing의 기본원리와 단점을 설명한다. 이러한 점을 보완하여 인지 무선 시스템 기반 ad-hoc 네트워크에서도 신호들의 detection probability를 향상시키기 위하여 2차 사용자의 ad-hoc 단말들에게 보다 높은 1차 사용자의 전송 파워를 실어 주기 위한 beamforming 기법을 이용한다. 실험 결과는 신호검출 성능으로 보여주며, 본 논문의 실험결과는 ad-hoc 기반 인지 무선 시스템에 적용 가능하다.

• Journal of Communications and Networks
• /
• 제12권1호
• /
• pp.19-29
• /
• 2010

Recently, spectrum sensing has been intensively studied as a key technology in realizing the cognitive radio. There have been advances in the performance of spectrum sensing through both multi-antenna and cooperative sensing schemes. In this paper, the performances and complicated scenarios of the latest spectrum sensing schemes are analytically compared and arranged into a technical tree while considering practical concerns. This paper will give a macroscopic view of spectrum sensing and will also provide insight into future spectrum sensing works.

• 대한원격탐사학회지
• /
• 제20권4호
• /
• pp.275-288
• /
• 2004

이 논문은 다중 센서 원격탐사 화상의 분류를 위해 퍼지 논리 융합과 결합된 relaxation labeling 방법을 제안하였다. 다중 센서 원격탐사 화상의 융합에는 퍼지 논리를, 분광정보와 공간정보의 융합에는 반복적인 relaxation labeling 방법을 적용하였다. 특히 반복적 relaxation labeling 방법은 공간정보의 이용에 따른 분류 화소의 변화양상을 얻을 수 있는 장점이 있다. 토지 피복의 감독 분류를 목적으로 광학 화상과 다중 주파수/편광 SAR 화상에 제안 기법을 적용한 결과, 다중 센서 자료를 이용하고 공간정보를 함께 결합하였을 때 향상된 분류 정확도를 얻을 수 있었다.

• 한국멀티미디어학회논문지
• /
• 제24권3호
• /
• pp.355-372
• /
• 2021

Recently, high-performance HMDs (Head-Mounted Display) are becoming wireless due to the growth of virtual reality technology. Accordingly, environmental constraints on the hardware usage are reduced, enabling multiple users to experience virtual reality within a single space simultaneously. Existing multi-user virtual reality platforms use the user's location tracking and motion sensing technology based on vision sensors and active markers. However, there is a decrease in immersion due to the problem of overlapping markers or frequent matching errors due to the reflected light. Goal of this study is to develop a multi-user virtual reality moving platform in a single space that can resolve sensing errors and user immersion decrease. In order to achieve this goal hybrid sensing technology was developed, which is the convergence of vision sensor technology for position tracking, IMU (Inertial Measurement Unit) sensor motion capture technology and gesture recognition technology based on smart gloves. In addition, integrated safety operation system was developed which does not decrease the immersion but ensures the safety of the users and supports multimodal feedback. A 6 m×6 m×2.4 m test bed was configured to verify the effectiveness of the multi-user virtual reality moving platform for four users.

• 대한원격탐사학회지
• /
• 제20권5호
• /
• pp.315-327
• /
• 2004

Nowadays, interests in land cover classification using not only multi-sensor images but also thematic GIS information are increasing. Often, although useful GIS information for the classification is available, the traditional MLE (maximum likelihood estimation techniques) does not allow us to use the information, due to the fact that it cannot handle the GIS data properly. This paper propose two extended MLE algorithms that can integrate both remote sensing images and GIS thematic data for land-cover classification. They include modified MLE and Bayesian predictive likelihood estimation technique (BPLE) techniques that can handle both categorical GIS thematic data and remote sensing images in an integrated manner. The proposed algorithms were evaluated through supervised land-cover classification with Landsat ETM+ images and an existing land-use map in the Gongju area, Korea. As a result, the proposed method showed considerable improvements in classification accuracy, when compared with other multi-spectral classification techniques. The integration of remote sensing images and the land-use map showed that overall accuracy indicated an improvement in classification accuracy of 10.8% when using MLE, and 9.6% for the BPLE. The case study also showed that the proposed algorithms enable the extraction of the area with land-cover change. In conclusion, land cover classification results produced through the integration of various GIS spatial data and multi-spectral images, will be useful to involve complementary data to make more accurate decisions.

• 한국산업융합학회 논문집
• /
• 제21권4호
• /
• pp.167-174
• /
• 2018

Presently, the exploration of an unknown environment is an important task for the development of mobile robots and mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. To fully utilize the strengths of both the sonar and visual sensing systems, In mobile robotics, multi-sensor data fusion(MSDF) became useful method for navigation and collision avoiding. Moreover, their applicability for map building and navigation has exploited in recent years. In this paper, as the preliminary step for developing a multi-purpose autonomous carrier mobile robot to transport trolleys or heavy goods and serve as robotic nursing assistant in hospital wards. The aim of this paper is to present the use of multi-sensor data fusion such as ultrasonic sensor, IR sensor for mobile robot to navigate, and presents an experimental mobile robot designed to operate autonomously within indoor environments. Simulation results with a mobile robot will demonstrate the effectiveness of the discussed methods.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
• /
• pp.570-572
• /
• 2003

In this paper, a method of vegetation spectrum measurement using multi spectrum camera was studied. Each pixel in taken images using multi spectrum camera have spectrum data, the relationship between spectrum data and distribution, structure, etc. are directly turned out. In other words, detailed spectrum data information of object including spatial distribution can be obtained from those images. However, the camera has some problems for applying field measurement and data analysis. In this study, those problems are solved.