• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.7 no.9
• /
• 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.

• Journal of the Korea Computer Industry Society
• /
• v.10 no.5
• /
• pp.177-186
• /
• 2009

In this paper, the device multi-sensing of moving body was developed for sensing of moving body in a wide range and minute moving body. If the device multi-sensing of moving body is connected with electric lamps, it will be able to economize a electric energy in lecture rooms or office rooms.

• 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.

• Journal of Satellite, Information and Communications
• /
• v.9 no.4
• /
• pp.20-25
• /
• 2014

In this paper, we provide efficient spectrum sensing technology for smooth use of frequency and energy charge of multi-copter. The proposed structures focus on improving performance of spectrum sensing that is based on Ad-hoc network. First, we explain basic principles and disadvantages of cooperative spectrum sensing and ad-hoc based spectrum sensing. To solve these problems, in this paper, we employ the beamforming technology that guarantees higher transmit primary users' signal power to secondary users in ad-hoc network. The performance of proposed algorithm is analyzed in terms of detection probabilities, and the results of this paper can be applied to the various ad-hoc based Cognitive Radio system.

• Journal of Communications and Networks
• /
• v.12 no.1
• /
• 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.

• International Journal of Precision Engineering and Manufacturing-Green Technology
• /
• v.9
• /
• pp.323-347
• /
• 2021

Remarkable advances in nanomaterials and nanotechnology have led researchers in various fields. The scale effects imparted by nanomaterials are associated with unexpected macroscale phenomena and properties that find many applications. However, multi-functionalization may be accompanied by physical and commercial limitations. Therefore, research must proceed in several different directions. Here, we define multi-functionalization and the electrical applications thereof in terms of increasing performance, addition of new and valuable properties, and multi-physics in play. We deal with sensors, actuators, energy harvesters, and solar cells and explore research that seeks to increase sensitivity, append "stretchability", and facilitate untethered communication. Furthermore, we analyze research trends in materials use and manufacturing, and highlight useful fabrication methods. With the aim of predicting future research trends, our review presents a roadmap that will aid research on sensing and multi-functional applications.

• Korean Journal of Remote Sensing
• /
• v.20 no.4
• /
• pp.275-288
• /
• 2004

This paper presents a fuzzy relaxation labeling approach incorporated to the fuzzy logic fusion scheme for the classification of multi-sensor remote sensing images. The fuzzy logic fusion and iterative relaxation labeling techniques are adopted to effectively integrate multi-sensor remote sensing images and to incorporate spatial neighboring information into spectral information for contextual classification, respectively. Especially, the iterative relaxation labeling approach can provide additional information that depicts spatial distributions of pixels updated by spatial information. Experimental results for supervised land-cover classification using optical and multi-frequency/polarization images indicate that the use of multi-sensor images and spatial information can improve the classification accuracy.

• Journal of Korea Multimedia Society
• /
• v.24 no.3
• /
• 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.

• Korean Journal of Remote Sensing
• /
• v.20 no.5
• /
• 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.

• Journal of the Korean Society of Industry Convergence
• /
• v.21 no.4
• /
• 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.