• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.472-480
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• 2023

This paper describes the development of a test-equipment for the pre-verification of navigation performance in cluster-based AUVs (Autonomous Underwater Vehicle). In the development of an AUV, conducting hardware and software development sequentially is not efficient due to the limited research and development period. Therefore, in order to reduce the overall development time and achieve successful development results, it is essential to pre-validate the navigation system and navigation algorithms. Accordingly, this paper explains the test-equipment for pre-verification of navigation performance, and ultimately confirms the stability of the navigation system and the performance of the navigation algorithms through the analysis of five types of navigation sensor data stored during real-sea experiments. The results demonstrate that through the development and verification of the test-equipment, it is possible to shorten the overall development period and improvement of product quality in the process of developing multiple AUVs.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.61-70
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• 2024

Cloud Computing is internet-based computing, where the users are provided with whatever service they need from the resources, software, and information. Recently, the security of cloud computing is considered as one of the major issues for both cloud service providers CSP and end-users. Privacy and highly confidential data make many users refuse to store their data within cloud computing, since data on cloud computing is not dully secured. The cryptographic algorithm is a technique which is used to maintain the security and privacy of the data on the cloud. In this research, we applied eight different cryptographic algorithms on Xen and KVM as hypervisors on cloud computing, to be able to measure and compare the performance of the two hypervisors. Response time and CPU utilization while encryption and decryption have been our aspects to measure the performance. In terms of response time and CPU utilization, results show that KVM is more efficient than Xen on average at 11.5% and 11% respectively. While TripleDES cryptographic algorithm shows a more efficient time response at Xen hypervisor than KVM.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.4
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• pp.201-212
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• 2002

This paper proposes a reconfigurable scheduler model that can support various real-time scheduling algorithms. The proposed model consists of two hierarchical upper and lower components, task scheduler and scheduling framework, respectively. The scheduling framework provides a job dispatcher and software timers. The task scheduler implements an appropriate scheduling algorithm, which supports a specific real-time application, based on the scheduling framework. If system developers observe internal kernel interfaces to communicate between two hierarchical components, they can implement a new scheduling algorithm independent of complex low kernel mechanism. Once a task scheduler is developed, it can be reused in a new real-time system in future. In Real-Time Linux (5), we implemented the proposed scheduling framework and several representative real-time scheduling algorithms. Throughout these implementations, we confirmed that a new scheduling algorithm could be developed independently without updates of complex low kernel modules. In order to confirm efficiency of the proposed model, we measured the performance of representative task schedulers. The results showed that the scheduling overhead of proposed model, which has two separated components, is similar to that of a classic monolithic kernel scheduler.

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.773-783
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• 2004

This paper presents a method for real-time verification of Statecharts. Statecharts has been widely used for real-time reactive systems, and supports two time models: synchronous and asynchronous. However, existing real-time verification methods for them are incompatible with the asynchronous time model or increase state space by introducing new variables to the target models. We solved these problems by extending existing model checking algorithms. The extended algorithms can be used with both time models of Statecharts because they consider time increasing transitions only. In addition, they do not increase target state space since they count those transitions internally without additional variables. We extended an existing model checker, NuSMV, based on the proposed algorithms and conducted some experiments to show their advantage.

• Progress in Medical Physics
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• v.28 no.4
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• pp.171-180
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• 2017

The purpose of this study is to install a system that compensated for the respiration motion using an articulated robotic manipulator couch which enables a wide range of motions that a Stewart platform cannot provide and to evaluate the performance of various prediction algorithms including proposed algorithm. For that purpose, we built a miniature couch tracking system comprising an articulated robotic manipulator, 3D optical tracking system, a phantom that mimicked respiratory motion, and control software. We performed simulations and experiments using respiratory data of 12 patients to investigate the feasibility of the system and various prediction algorithms, namely linear extrapolation (LE) and double exponential smoothing (ES2) with averaging methods. We confirmed that prediction algorithms worked well during simulation and experiment, with the ES2-averaging algorithm showing the best results. The simulation study showed 43% average and 49% maximum improvement ratios with the ES2-averaging algorithm, and the experimental study with the $QUASAR^{TM}$ phantom showed 51% average and 56% maximum improvement ratios with this algorithm. Our results suggest that the articulated robotic manipulator couch system with the ES2-averaging prediction algorithm can be widely used in the field of radiation therapy, providing a highly efficient and utilizable technology that can enhance the therapeutic effect and improve safety through a noninvasive approach.

• Journal of KIISE:Software and Applications
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• v.34 no.9
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• pp.857-868
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• 2007

The good classifier ensemble should have a high complementarity among classifiers in order to produce a high recognition rate and its size is small in order to be efficient. This paper proposes a classifier ensemble selection algorithm with coarse-to-fine stages. for the algorithm to be successful, the original classifier pool should be sufficiently diverse. This paper produces a large classifier pool by combining several different classification algorithms and lots of feature subsets. The aim of the coarse selection is to reduce the size of classifier pool with little sacrifice of recognition performance. The fine selection finds near-optimal ensemble using genetic algorithms. A hybrid genetic algorithm with improved searching capability is also proposed. The experimentation uses the worldwide handwritten numeral databases. The results showed that the proposed algorithm is superior to the conventional ones.

• The KIPS Transactions:PartA
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• v.13A no.7 s.104
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• pp.561-572
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• 2006

Performance of the mobile devices greatly depends on the efficient resource management because they are usually resource-restricted. In particular, the dynamic storage allocation algorithms very important part of the mobile device's operating system and OS-like software platform. The existing dynamic storage allocation algorithms did not consider application's execution style and the type, life-time, and characteristics of memory objects that the application uses. Those algorithms, as a result, could not manage memory efficiently Therefore, this Paper analyzes the mobile application's execution characteristics and proposes anew dynamic storage allocation algorithm which saves the memory space and improves mobile application's execution speed. The test result shows that the proposed algorithm works 6.5 times faster than the linked-list algorithm[11], 2.5 times faster better than the Doug. Lea's algorithm[12] and 10.5 times faster than the Brent algorithm[14].

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.45 no.9
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• pp.85-95
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• 2008

Characteristically, 3D graphic algorithms have to perform complex calculations on massive amount of stream data. The vertex and pixel shaders have enabled efficient execution of graphic algorithms by hardware, and these graphic processors may seem to have achieved the aim of "hardwarization of software shaders." However, the hardware shaders have hitherto been evolving within the limits of Z-buffer based algorithms. We predict that the ultimate model for future graphic processors will be an algorithm-independent integrated shader which combines the functions of both vertex and pixel shaders. We design the register file model that supports 3-dimensional computer graphic on the programmable unified shader processor. we have verified the accurate calculated value using FPGA Virtex-4(xcvlx200) made by Xilinx for operating binary files made by the implementation progress based on synthesis results.

• Journal of IKEEE
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• v.20 no.4
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• pp.351-360
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• 2016

Recently, machine learning algorithms, especially deep learning-based algorithms, have been receiving attention due to its high classification performance. Among the algorithms, Convolutional Neural Network(CNN) is known to be efficient for image processing tasks used for Advanced Driver Assistance Systems(ADAS). However, it is difficult to achieve real-time processing for CNN in vehicle embedded software environment due to the repeated operations contained in each layer of CNN. In this paper, we propose a hardware accelerator which enhances the execution time of CNN by parallelizing the repeated operations such as convolution. Xilinx ZC706 evaluation board is used to verify the performance of the proposed accelerator. For $36{\times}36$ input images, the hardware execution time of CNN is 2.812ms in 100MHz clock frequency and shows that our hardware can be executed in real-time.