• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.70-75
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• 2000

This paper examines the performance of adaptive filtering algorithms in relation to the asymptotic relative efficiency (ARE) of estimators. The adaptive filtering algorithms are Hybrid II and modified zero forcing (MZF) algorithms. The Hybrid II and MZF algorithms are simplified forms of the LMS algorithm, which use the polarity of the input signal, and polarities of the error and input signals, respectively. The ARE of estimators for each algorithm is analyzed under the condition of the same convergence speed. Computer simulations for adaptive equalization are performed to check the validity of the theory. The explicit expressions for the ARE values of the Hybrid II and MZF algorithms are derived, and its results have similar values to the results of computer simulation. It also revealed that the ARE values depend on the correlation coefficients between input signal and error signal.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.147-158
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• 2022

Cloud Computing offers flexible, on demand, ubiquitous resources for cloud users. Cloud users are provided computing resources in a virtualized environment. In order to meet the growing demands for computing resources, data centres contain a large number of physical machines accommodating multiple virtual machines. However, cloud data centres cannot utilize their computing resources to their total capacity. Several policies have been proposed for improving energy proficiency and computing resource utilization in cloud data centres. Virtual machine placement is an effective method involving efficient mapping of virtual machines to physical machines. However, the availability of many physical machines accommodating multiple virtual machines in a data centre has made the virtual machine placement problem a non deterministic polynomial time hard (NP hard) problem. Metaheuristic algorithms have been widely used to solve the NP hard problems of multiple and conflicting objectives, such as the virtual machine placement problem. In this context, we presented essential concepts regarding virtual machine placement and objective functions for optimizing different parameters. This paper provides a taxonomy of metaheuristic algorithms for the virtual machine placement method. It is followed by a review of prominent research of virtual machine placement methods using meta heuristic algorithms and comparing them. Finally, this paper provides a conclusion and future research directions in virtual machine placement of cloud computing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.315-318
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• 2001

We propose two new blind LMS and MMSE algorithms called projection-based least mean square (PB-LMS) and projection-based minimum mean square error (PB-MMSE) for smart antennas. Both algorithms employ the finite constellation property of digital signal to transform the conventional LMS and MMSE algorithms into blind algorithms. Computer simulations were carried out in the AWGN channel and Rayleigh fading channel with AWGN in CDMA environment to verify the performance of the two proposed algorithms.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.331-335
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• 2001

In this paper, we propose two novel blind LMS algorithms, called exponential step sire LMS algorithms (ES-LMS), for adaptive array antennas whose convergence speed is increased, hence they are much more capable of tracking the desired signal than the conventional LMS algorithms. Both of the algorithms require neither spatial knowledge nor reference signals since they use the finite symbol property of digital signal. Computer simulations were carried cot in CDMA environment affected by multi-path Rayleigh fading to verify the performance of the two proposed algorithms.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.912-917
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• 2001

In this study, we define the measuring method of crank angle precisely using an event and perform a study on the hardware structure and software algorithms which is applicable for the commercial engine. Also we developed a Computer-ECU(Personal computer based electronic control unit) using a computer and a microprocessor, for performing the ignition at a desire position(angle) and for controlling a duty ratio a pulse for ISC(Idle speed control). We applied these algorithms to the modeling which is induced a concept of event and got a better result than a conventional ECU in the state of transient as a result of performing air fuel ratio control in a commercial engine. This technique can be used for the back to improve ECU performance. It the present type of Hybrid I. C voltage regulator is altered to the new type of regulator, we will be surely able to reduce the production cost as well as simplify the design of alternator\`s rear bracket and rectifier part because of the removal of trio diode. Experiment is taken by MS-R004.

• 한국정보교육학회:학술대회논문집
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• 2010.08a
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• pp.235-241
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• 2010

This paper has been studied about algorithm teaching methods for improving the problem-solving skills and creativity in rapidly changing information society. Especially the algorithms for teaching about computer is very important. Because it is effective learning content for finding the best solution to solve a problem and improve the students' logical thinking. However, teaching algorithms can be monotonous to children on account of using only computer and languages. So It needs to consider about the cognitive structure and level of elementary school students. Therefore, this study has the purpose to acquaint students with the principle of algorithm and improve problem-solving and creativity using games, not computer.

• Journal of information and communication convergence engineering
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• v.12 no.2
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• pp.116-121
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• 2014

This paper discusses computer-generated heuristics for motion planning. Planning with many degrees of freedom is a challenging task, because the complexity of most planning algorithms grows exponentially with the number of dimensions of the problem. A well-designed heuristic may greatly improve the performance of a planning algorithm in terms of the computation time. However, in recent years, with increasingly challenging high-dimensional planning problems, the design of good heuristics has itself become a complicated task. In this paper, we present an approach to algorithmically develop a heuristic for motion planning, which increases the efficiency of a planner in similar environments. To implement the idea, we generalize modern motion planning algorithms to an extent, where a heuristic is represented as a set of random variables. Distributions of the variables are then analyzed with computer learning methods. The analysis results are then utilized to generate a heuristic. During the experiments, the proposed approach is applied to several planning tasks with different algorithms and is shown to improve performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.3
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• pp.178-185
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• 2013

As computer technology continues to develop, computer networks are now widely used. As a result, there are many new intrusion types appearing and information security is becoming increasingly important. Although there are many kinds of intrusion detection systems deployed to protect our modern networks, we are constantly hearing reports of hackers causing major disruptions. Since existing technologies all have some disadvantages, we utilize algorithms, such as the fuzzy C-means (FCM) and the support vector machine (SVM) algorithms to improve these technologies. Using these two algorithms alone has some disadvantages leading to a low classification accuracy rate. In the case of FCM, self-adaptability is weak, and the algorithm is sensitive to the initial value, vulnerable to the impact of noise and isolated points, and can easily converge to local extrema among other defects. These weaknesses may yield an unsatisfactory detection result with a low detection rate. We use a genetic algorithm (GA) to help resolve these problems. Our experimental results show that the combined GA and FCM algorithm's accuracy rate is approximately 30% higher than that of the standard FCM thereby demonstrating that our approach is substantially more effective.

• Journal of the Korea Computer Graphics Society
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• v.9 no.2
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• pp.11-18
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• 2003

For extracting geometric features in 3D meshes according to user-steering with effective interactions. this paper generalizes the 2D algorithms of snapping and wrapping that. respectively. moves a cursor to a nearby feature and constructs feature boundaries. First. we define approximate curvatures and move cost functions that are the numerical values measuring the geometric characteristics of the meshes, By exploiting the measuring values. the algorithms of geometric snapping and geometric wrapping are developed and implemented. We also visualize the results from applying the algorithms to extracting geometric features of general 3D mesh models such as a face model and a tooth model.