• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3399-3406
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• 2015

NRD(Network RamDisk) is a scheme which allows a system to use the memory of the remote systems just as his own block device via networking. Basically, it consists of a client requesting an NRD access and server providing the NRD. In this paper, we describe the design, implementation and experiment of the block device driver for accessing the NRD in the Linux kernel(2.6) level. First of all, we have analyzed the flow of processing the requests for accessing the block devices in the traditional Linux kernel and figured out the additional functions required for supporting the NRD. Then we have designed and implemented the device diver of NRD client and NRD server for providing these functions. Finally, we have established a NRD server system, and reviewed its functional feasibility by experimenting the requests of NRD access through the NRD device driver implemented on a NRD client.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.267-284
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• 2023

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve; the root means square error, and the correlation coefficient . Finally, the landslide potential model that was obtained using Gaussian's kernel was selected as the best one for susceptibility of landslides in the Oshvand watershed.

• Environmental and Resource Economics Review
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• v.18 no.1
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• pp.105-138
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• 2009

This study investigates the spatial distribution of the degree of water pollutants in Han River using data obtained by the water pollution observation stations. This study estimates a non -parametric kernel density function for each water pollutants, and tests a significant difference between two estimated distribution functions. Next, Generalized Entropy inequality indices are evaluated and this research tests difference of inequality indices between two years using bootstrapping method. Lastly in a dynamic of view, it is analyzed that there are significant changes in the ranking of water pollution level. Estimation results show that the degree of inequality in spatial distribution of water pollution tends to be stable or decreasing for last 15 years in a great part of water pollutants, and ranking of water pollution level hardly changes in Han River.

• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.179-196
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• 2012

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

• The KIPS Transactions:PartB
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• v.12B no.1 s.97
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• pp.25-30
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• 2005

In this paper, we analyzed and compared the performances of modular FFMLP(feedforward multilayer perceptron) and SVUT(Support Vector Machine) for the large-set classification problems. Overall, SVM dominated modular FFMLP in the correct recognition rate and other aspects Additionally, the recognition rate of SVM degraded more slowly than neural network as the number of classes increases. The trend of the recognition rates depending on the rejection rate has been analyzed. The parameter set of SVM(kernel functions and related variables) has been identified for the large-set classification problems.

• Journal of the Korean Data and Information Science Society
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• v.23 no.6
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• pp.1045-1054
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• 2012

Support vector machine is known to be the very useful statistical method in classification and nonlinear function estimation. In this paper we propose a monotone support vector regression (SVR) for the estimation of monotonically decreasing function. The proposed monotone SVR is applied to smooth the Kaplan-Meier estimate of survival function. Experimental results are then presented which indicate the performance of the proposed monotone SVR using survival functions obtained by exponential distribution.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.2
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• pp.99-105
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• 2007

In this paper, diskless personal multimedia player(PMP) that can access and decode the remote large multimedia data is implemented via wireless network. To implement this, WLAN based NFS protocol is used to connect PMP to the remote server and text image and movie files are decoded and played using ARM9 cored PXA255 embedded processor and Linux OS. The fuction and performance of the PMP is evaluated and verified using variuos types of contents. Linux kernel elements are configured and built in according to the hardware and software on the target board to install on the target board. The confirming result shows the required functions and performances.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.208-216
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• 2020

With the spread of smart manufacturing, one of the key topics of the 4th industrial revolution, manufacturing systems are moving beyond automation to smartization using artificial intelligence. In particular, in the existing automatic machining, a number of machining defects and non-processing occur due to tool damage or severe wear, resulting in a decrease in productivity and an increase in quality defect rates. Therefore, it is important to measure and predict tool life. In this paper, ν-ASVR (ν-Asymmetric Support Vector Regression), which considers the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, was proposed and applied to the tool wear prediction problem. In the case of tool wear, if the predicted value of the tool wear amount is smaller than the actual value (under-estimation), product failure may occur due to tool damage or wear. Therefore, it can be said that ν-ASVR is suitable because it is necessary to overestimate. It is shown that even when adjusting the asymmetry of ⲉ-tube and the asymmetry of penalties for data out of ⲉ-tube, the ratio of the number of data belonging to ⲉ-tube can be adjusted with ν. Experiments are performed to compare the accuracy of various kernel functions such as linear, polynomial. RBF (radialbasis function), sigmoid, The best result isthe use of the RBF kernel in all cases

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.199-207
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• 2021

It is very important to support real-time on the inspection equipment based on Windows. In particular, in the system using MIL-STD-1553B communication, which is widely used in military weapon systems, real-time is required for inspection equipment that uses mostly platforms based on Windows such as Industrial PCs. However, in order to use a complete real-time operating system such as VxWorks, the purchase cost is expensive and the implementation is complicated on the system, so it is not suitable for inspection equipment that requires simple functions to just check go or no-go. Therefore, in this paper, a Real-Time implanted Kernel(RTiK) in the Windows kernel is implanted in order to improve these defects, and real-time performance is implemented for periodically MIL-STD-1553B communication by Deferred Procedure Call(DPC) of Windows. Also, it was verified that the period of up to 2ms was guaranteed with a RDTSC into the EDX:EAX registers for measuring the periodicity.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.33-38
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• 2016

In Android platform, as applications invoke various service functions through IPC (Inter-Process Communication), IPC performance is critical to the responsiveness in Android. However, Android offers long IPC latency of hundreds of micro-seconds due to complicated software stacks between the kernel Binder and the user-level process Context Manager. This separation provides modularity and flexibility, but degrades the responsiveness of services owing to additional context switching and inefficient request handling. In this paper, we anatomize Android IPC mechanisms and observe that 55% of IPC latency comes from the communication overhead between Binder and Context Manager. Based on this observation, this paper proposes a kernel Binder cache that retains a popular subset of service function mappings, thereby reducing the requests transferred to the user-level daemon. The proposed Binder cache is implemented in Android 5.0 and experimental results with various benchmarks show that the proposed cache architecture improves performance by 52.9% on average.