• International Journal of Internet, Broadcasting and Communication
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• v.9 no.1
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• pp.9-17
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• 2017

This paper describes a process of developing commercial real time image recognition system with company. In this paper we will make a system that is combining an empirical kernel map method and conjugate least squares support vector machine in order to represent images in a low-dimensional subspace for real time image recognition. In the traditional approach calculating these eigenspace models, known as traditional PCA method, model must capture all the images needed to build the internal representation. Updating of the existing eigenspace is only possible when all the images must be kept in order to update the eigenspace, requiring a lot of storage capability. Proposed method allows discarding the acquired images immediately after the update. By experimental results we can show that empirical kernel map has similar accuracy compare to traditional batch way eigenspace method and more efficient in memory requirement than traditional one. This experimental result shows that proposed model is suitable for commercial real time image recognition system.

• Proceedings of the KIEE Conference
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• 1998.11a
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• pp.217-219
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• 1998

The need to accommodate much complex algorithm. high communication functionality and easier user interface lays heavy burden on the software developer of the protection relay these days. Using lightweight real-time kernel like uC/OS, the software development process can have much structural and easier feature. And total cost needed to development and maintenance of the software also can be reduced by development based on these real-time kernels.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.2
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• pp.173-186
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• 2000

Real-time embedded systems should accommodate many kinds of hardware platforms and resource management policies that vary depending on their operating environments and purposes. It is not an easy job to adapt a multitasking kernel to new system services and hardware platforms, as the kernel must strictly satisfy constraints on its size and performance. This paper describes the design and implementation of an object-oriented multitasking framework that can be reused for implementing microprocessor-based real-time embedded multitasking kernels, In this kernel framework, those parts that can vary depending on hardware platforms and system resource management policies are separated into the hot spots and encapsulated by abstract classes. Our framework thus can be effectively used to implement microprocessor-based real-time embedded kernels that demand high portability and adaptability.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.547-553
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• 2004

Linux is the one of various RTOS, also embedded linux has being studied with focus on technical stability and commercial utilities. In this paper, the technical trial was discussed on the development of real-time operating system that provides real time capability and extends the network communications ability and would be applied to the maritime mobile communication system through analysis the embedded linux kernel. Some techniques for Analyzing the embedded linux kernel and designing the target board, making the kernel image and porting the kernel are summarized in this paper.

• Journal of the Korea Computer Graphics Society
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• v.21 no.4
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• pp.11-17
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• 2015

As the needs of real-time visual object tracking are increasing in various kinds of application fields such as surveillance, entertainment, etc., kernel-based mean-shift tracking has received more interests. One of major issues in kernel-based mean-shift tracking is to be robust under partial or full occlusion status. This paper presents a real-time mean-shift tracking which is robust in partial occlusion by applying multi-mode local kernel weight. In the proposed method, a kernel is divided into multiple sub-kernels and each sub-kernel has a kernel weight to be determined according to the location of the sub-kernel. The experimental results show that the proposed method is more stable than the previous methods with multi-mode kernels in partial occlusion circumstance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.39-52
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• 2009

Scale Invariant Feature Transform (SIFT) is an effective algorithm in object recognition, panorama stitching, and image matching, however, due to its complexity, real time processing is difficult to achieve with software approaches. This paper proposes using a reconfigurable hardware processor with integer half kernel. The integer half kernel Gaussian reduces the Gaussian pyramid complexity in about half [] and the reconfigurable processor carries out a parallel implementation of a full search Fast SIFT algorithm. We use a low memory, fine grain single instruction stream multiple data stream (SIMD) pixel processor that is currently being developed. This implementation fully exposes the available parallelism of the SIFT algorithm process and exploits the processing and I/O capabilities of the processor which results in a system that can perform real time image and video compression. We apply this novel implementation to images and measure the effectiveness. Experimental simulation results indicate that the proposed implementation is capable of real time applications.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.103-112
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• 2007

In this paper, we implement a hypervisor that runs multiple uC/OS-II real-time kernels on one microprocessor. The hypervisor virtualizes microprocessor and memory that are main resources managed by uC/OS-II kernel. Microprocessor is virtualized by controlling interrupts that uC/OS-II real-time kernel handles and memory is virtualized by partitioning physical memory. The hypervisor consists of three components: interrupt control routines that virtualize timer interrupt and software interrupt, a startup code that initializes the hypervisor and uC/OS-II kernels, and an API that provides communication between two kernels. The original uC/OS-II kernel needs to be modified slightly in source-code level to run on the hypervisor. We performed a real-time test and an independent computation test on Jupiter 32-bit EISC microprocessor and showed that the virtualized kernels run without problem. The result of our research can reduce the hardware cost, the system space and weight, and system power consumption when the hypervisor is applied in embedded applications that require many embedded microprocessors.

• The Journal of the Korea Contents Association
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• v.17 no.10
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• pp.1-8
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• 2017

As the recent advancement of military equipment has been accelerated, it is becoming more important to act as an inspection device that verifies the performance of equipment in real time. Most of the inspection equipments were developed on the Windows OS based system. considering development convenience and development period. However, sice the data communication between these models occurs asynchronously, there is a problem that it is difficult to guarantee real-time performance of the window-based inspection equiment. To solve these problems, we use real-time commercial solutions to guarantee the real-time performance of Windows-based inspection equipment. In this paper, we propose a method of designing and implementing the inspection equipment software based on Real-Time implanted Kernel-Multi Processor (RTiK-MP) operating in Windows environment. In addition, real-time performance data accuracy was measured through a high-speed communication tool and interlocking test to verify the performance of the inspection device based on the real-time porting kernel.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.91-101
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• 2004

The turbulent flow is of fundamental interest because the conservation equations for thermodynamics, mass and momentum are linked together. This turbulent flow consists of some coherent time- and space-organized vortical structures. Research has already shown that some dynamic systems and experimental models still cannot provide a good nonlinear analysis of turbulent time series. In the real turbulent flow, very complicated nonlinear behaviors, which are affected by many vague factors are present. In this paper, a kernel-based machine for fuzzy nonlinear regression analysis is proposed to predict the nonlinear time series of turbulent flows. In order to show the practicality and usefulness of this model, we present an example of predicting the near-wall turbulence time series as a verifiable model and compare with fuzzy piecewise regression. The results of practical applications show that the proposed method is appropriate and appears to be useful in nonlinear analysis and in fuzzy environments to predict the turbulence time series.