• Journal of Multimedia Information System
• /
• v.4 no.4
• /
• pp.233-238
• /
• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• 한국정보디스플레이학회:학술대회논문집
• /
• 2005.07b
• /
• pp.1378-1380
• /
• 2005

The efficiency and the optical properties of the yellow organic light-emitting devices (OLEDs) were significantly affected by the existence of the multiple quantum well (MQW) structures consisting of N, N'- bis-(1-naphthyl)-N, N'-diphenyl-1,1-biphenyl-4,4'- diamine(NPB)/5,6,11,12 - tetraphenylnaphthacene (rubrene). The maximum efficiency and the luminance of OLEDs with 3-periods of the NPB/rubrene MQWs at 41.6 $mA/cm^2$ were 3.66 cd/A and 1524 $cd/m^2$, respectively, and their Commission Internationale de l'Eclairage chromaticity coordinates were (0.34, 0.55), which indicates a yellow color. These results indicate that the efficiencies of the OLEDs by using MQW emitting layers can be improved.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.9
• /
• pp.1824-1837
• /
• 1994

Broadband ISDN will support advanced communication services such as multimedia conference, VOD and electronic news service. Because many of these advanced services require multimedia data delivery, future communication networks must have flexible and efficient multiparty communications capabilities. In this paper we propose a new multicasting method which uses packer filtering capabilities both in a physical network and in a logical network layers. This new scheme has potential to reduce the transmission and packet processing overheads of multicast communications. For the new multicasting method, we develop a group matching algorithm which finds a suitable set of groups that covers multiple target hosts. We show the application of the multicasting method and the group matching algorithm with two simple examoles.

• Journal of the Korean Society for Precision Engineering
• /
• v.24 no.4 s.193
• /
• pp.93-101
• /
• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.

• Korean Journal of Optics and Photonics
• /
• v.16 no.3
• /
• pp.235-238
• /
• 2005

A Multiple electron beam lithography system with arrayed microcolumns has been developed for high throughput applications. The small size of the microcolumn opens the possibility for arrayed operation on a scale commensurate. The arrayed microcolumns based on of Single Column Module (SCM) concept has been fabricated and successfully demonstrated. Low energy microcolumn lithography has been operated in the energy range from 250 eV to 300 eV for the generation of nano patterns. Probe beam current at the sample was measured about >1 nA at a total beam current of $0.5\;{\mu}A$ and a working distance of $\~1\;mm$. The magnitude of probe beam current is strong enough for the low energy lithography. The thin layers of PMMA resist have been employed. The results of nano-patterning by low energy microcolumn lithography will be discussed.

• Bulletin of the Korean Chemical Society
• /
• v.28 no.11
• /
• pp.2069-2074
• /
• 2007

The 27Al multiple quantum magic angle spinning (MQMAS) nuclear magnetic resonance (NMR) spectra of mordenite zeolites were simulated using the point charge model (PCM). The spectra simulated by the PCM considering nearest neighbor atoms only (PCM-n) or including atoms up to the 3rd layer (PCM-m) were not different from those generated by the Hartree-Fock (HF) molecular orbital calculation method. In contrast to the HF and density functional theory methods, the PCM method is simple and convenient to use and does not require sophisticated and expensive computer programs along with specialists to run them. Thus, our results indicate that the spectral simulation of the 27Al MQMAS NMR spectra obtained with the PCM-n is useful, despite its simplicity, especially for porous samples like zeolites with large unit cells and a high volume density of pores. However, it should be pointed out that this conclusion might apply only for the atomic sites with small quadrupole coupling constants.

• Convergence Security Journal
• /
• v.12 no.3
• /
• pp.35-43
• /
• 2012

The explosive growth of Internet traffic cause by smart equipment such as smart phone has led to a dramatic increase in demand for data transmission capacity and network control architecture, which requires high transmission rates beyond the conventional transmission capability. Next generation networks are expected to be controlled by Generalized Multi-Protocol Label Switching(GMPLS) protocol suite and operating at multiple switching layers. In order to ensure the most efficient utilization of multilayer network resources, effective global provisioning that providing the network with the possibility of reacting in advance to traffic changes should be provided. In this paper, we proposes a new path selection scheme in multilayer optical networks based on the vertical PCE architecture and a different approach to efficiently exploit multiple PCE cooperation.

• Korean Journal of Remote Sensing
• /
• v.10 no.2
• /
• pp.49-62
• /
• 1994

Look direction bias in a single look SAR image can often be misinterpreted in the geological application of radar data. This paper investigates digital processing techniques for SAR image data integration and compensation of the SAR data look direction bias. The two important approaches for reducing look direction bias and integration of multiple SAR data sets are (1) principal component analysis (PCA), and (2) wavelet transform(WT) integration techniques. These two methods were investigated and tested with the ERS-1 (VV-polarization) and CCRS*s airborne (HH-polarization) C-SAR image data sets recorded over the Sudbury test site, Canada. The PCA technique has been very effective for integration of more than two layers of digital image data. When there only two sets of SAR data are available, the PCA thchnique requires at least one more set of auxiliary data for proper rendition of the fine surface features. The WT processing approach of SAR data integration utilizes the property which decomposes images into approximated image ( low frequencies) characterizing the spatially large and relatively distinct structures, and detailed image (high frequencies) in which the information on detailed fine structures are preserved. The test results with the ERS-1and CCRS*s C-SAR data indicate that the new WT approach is more efficient and robust in enhancibng the fine details of the multiple SAR images than the PCA approach.

• Smart Structures and Systems
• /
• v.23 no.5
• /
• pp.507-520
• /
• 2019

In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.4
• /
• pp.579-586
• /
• 2023

A wireless sensor network (WSN) has limited battery power because it is used wirelessly using low-cost small sensors. Since the battery cannot be replaced, the lifespan of the sensor node is directly related to the lifespan of the battery, so power must be used efficiently to maximize the lifespan of the network. In this study, based on PEGASIS, a representative energy-efficient routing protocol, we propose a protocol that classifies layers according to the distance from the sink node and configures multiple chains rather than one chain. The proposed protocol can increase network lifespan by reducing the transmission distance between nodes to prevent unnecessary energy consumption.