• KIPS Transactions on Software and Data Engineering
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• v.3 no.11
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• pp.479-486
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• 2014

MapReduce is a programing model used for parallelly processing a large amount of data. To analyze a large amount data, the data cube is widely used, which is an operator that computes group-bys for all possible combinations of given dimension attributes. When the number of dimension attributes is n, the data cube computes $2^n$ group-bys. In this paper, we propose an efficient method for computing data cubes using MapReduce. The proposed method partitions $2^n$ group-bys into $_nC_{{\lceil}n/2{\rceil}}$ batches, and computes those batches in stages using ${\lceil}n/2{\rceil}$ MapReduce jobs. Compared to the existing methods, the proposed method significantly reduces the amount of intermediate data generated by mappers, so that the cost of sorting and transferring those intermediate data is reduced significantly. Consequently, the total processing time for computing a data cube is reduced. Through experiments, we show the efficiency of the proposed method over the existing methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.28-38
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• 2013

In this paper, we propose a improved turbo equalizer which generates a feedback signal through a simple calculation to improve performance in single carrier system with the LMS(least mean square) algorithm based equalizer and LDPC(low density parity check) codes. LDPC codes can approach the Shannon limit performance closely. However, computational complexity of LDPC codes is greatly increased by increasing the repetition of the LDPC codes and using a long parity check matrix in harsh environments. Turbo equalization based on LDPC code is used for improvement of system performance. In this system, there is a disadvantage of very large amount of computation due to the increase of the repetition number. To less down the amount of this complicated calculation, The proposed improved turbo equalizer adjusts the adoptive equalizer after the soft decision and the LDPC code. Through the simulation results, it's confirmed that performance of improved turbo equalizer is close to the SISO-MMSE(soft input soft output minimum mean square error) turbo equalizer based on LDPC code with the smaller amount of calculation.

• Current Research on Agriculture and Life Sciences
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• v.5
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• pp.134-142
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• 1987

The purpose of this study is to establish more rational and practical planning theory for amusementpark. It analyze and considerate the fluctuation of the people who come and use a Tong-Ch'on amusementpark. The results drawn from this research work are as follows ; There are considerable correlation between use fluctuation and some factors. The factors are season (spring, summer, autumn) as a time, temperature, cloud amount, duration of sunshine, weather (rainy day) as a climate and date (weekday, holiday) as a social system. The important variables are temperature, cloud amount, duration of sunshine and date (week day, holiday) to estimate the user of amusementpark. I can reduce the following two types of regression models. 1.${\log}_eY1=6.9114+0.1135TEM+0.00002_eSUM-0.4068WI+0.4316W3$ ($R^2=0.94$) 2. ${\log}_eY2=7.2069+0.1177TEM-0.0990CLO+0.488W3$ ($R^2=0.95$) Y ; Number of User TEM ; Temperature CLO ; Amount of cloud SUN ; Duration of Sunshine WI ; Weekday W3 ; Holiday Those model is inorder to estimate the user for management of Tong-Ch'on amusementpark and use on the computation of facility size for reconstruction. Besides the amusementpark, city park and outdoor recreation area could estimate of user throuth this method. But, I am not sure about the regression models because I didn't apply the regression models to the other amusementpark, city park or outdoor recreation area. Therefore, I think that this problem needs to study in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.83-88
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• 2022

Internet of Things (hereinafter referred to as IoT) related technologies are continuously developing in line with the recent development of information and communication technologies. IoT system sends and receives unique data through network based on various sensors. Data generated by IoT systems can be defined as big data in that they occur in real time, and that the amount is proportional to the amount of sensors installed. Until now, IoT systems have applied data storage, processing and computation through centralized processing methods. However, existing centralized processing servers can be under load due to bottlenecks if the deployment grows in size and a large amount of sensors are used. Therefore, in this paper, we propose a distributed processing system for applying a data importance-based algorithm aimed at the high availability of the system to efficiently handle real-time sensor data arising in IoT environments.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.3
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• pp.163-172
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• 2022

Typical anti-jamming technologies based on array antennas, Space Time Adaptive Process (STAP) & Space Frequency Adaptive Process (SFAP), are very effective algorithms to perform nulling and beamforming. However, it does not perform equally well for all types of jamming signals. If the anti-jamming algorithm is not optimized for each signal type, anti-jamming performance deteriorates and the operation stability of the system become worse by unnecessary computation. Therefore, jamming classification technique is required to obtain optimal anti-jamming performance. Machine learning, which has recently been in the spotlight, can be considered to classify jamming signal. In general, performing supervised learning for classification requires a huge amount of data and new learning for unfamiliar signal. In the case of jamming signal classification, it is difficult to obtain large amount of data because outdoor jamming signal reception environment is difficult to configure and the signal type of attacker is unknown. Therefore, this paper proposes few-shot jamming signal classification technique using meta-learning and transfer-learning to train the model using a small amount of data. A training dataset is constructed by anti-jamming algorithm input data within the GNSS receiver when jamming signals are applied. For meta-learning, Model-Agnostic Meta-Learning (MAML) algorithm with a general Convolution Neural Networks (CNN) model is used, and the same CNN model is used for transfer-learning. They are trained through episodic training using training datasets on developed our Python-based simulator. The results show both algorithms can be trained with less data and immediately respond to new signal types. Also, the performances of two algorithms are compared to determine which algorithm is more suitable for classifying jamming signals.

• Journal of Advanced Navigation Technology
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• v.5 no.1
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• pp.1-10
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• 2001

Speech has a good characteristics of which car drivers busy to concern with miscellaneous operation can make use in convenient handling and manipulating of devices. By utilizing this, this works proposes a speaker verification method for protecting cars from being stolen and identifying a person trying to access critical on-line services. In this, continuant phonemes recognition which uses language information of speech and MLP(mult-layer perceptron) which has some advantages against previous stochastic methods are adopted. The recognition method, though, involves huge computation amount for learning, so it is somewhat difficult to adopt this in speaker verification application in which speakers should enroll themselves at real time. To relieve this problem, this works presents a solution that introduces speaker cohort models from speaker verification score normalization technique established before, dividing background speakers into small cohorts in advance. As a result, this enables computation burden to be reduced through classifying the enrolling speaker into one of those cohorts and going through enrollment for only that cohort.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.121-127
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• 2016

We propose a algorithm to calculate power factor of fundamental waveform in an environment where the voltage and current have been distorted by harmonics. In the proposed power factor computation algorithm, voltage and current are converted to rotating DQ reference frame, and power factor is calculated from active power and reactive power. We compare the proposed method with the conventional power factor measurement method as mathematically. In a condition that voltage and current are distorted by harmonics, the proposed method accurately measure the power factor of fundamental wave, and it is confirmed by simulation using MATLAB. If the proposed power factor measurement method is applied to an automatic power factor control system, a power factor compensation performance can be maximized in harmonic distortion environment. As a result, it is possible to reduce electricity prices, reduce line loss, increase load capacity, ensure the transmission margin capacity, and reduce the amount of power generation.

• Journal of Digital Convergence
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• v.15 no.11
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• pp.463-470
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• 2017

Currently, virtual reality contents using VR images are spotlighted since they can be easily created and utilized. But because VR images are in a state of lack of interaction, there are limitations in their applications and usability.In order to overcome this problem, we propose a new method in which 360 degree panorama image and game engine are utilized to develop a high resolution of interactive VR service in real time. In particular, since the background image, which is represented by a form of panorama image, is pre-generated through a heavy rendering computation, it can be used to provide a immersive VR service with a relatively small amount of computation in run time on a low performance device. In order to show the effectiveness of our proposed method, an interactive game of a virtual zoo environment was implemented and illustrated showing that it can improve user interaction and immersion experience in a pretty good way.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.18-24
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• 2017

This paper is a part of developing a computer code that can be used to generate synthetic IR images by calculating the outgoing infrared signal from objects. To predict the reflected component that is a part of the outgoing IR signal, such as those components reflected from the target surface by the solar and sky irradiations, it is necessary to calculate the complicated BRDF values for considering the directional surface reflection characteristics. Since the calculation of reflectance using the BRDF requires a large amount of computation time due to the hemispherical integral term, it is frequently restricted in applying for a real-time prediction of IR signal. In this research, the simplified method for calculating IR reflected component has been proposed by replacing the integral terms into two parts, a directionally uniform component and a step function representing the specular component, to reduce computation time. The proposed method is proved to result in very fast calculation of the BRDF (up to 600 times faster calculations) for most of the surfaces with minimal loss of the accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.5039-5061
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• 2017

Cloud storage becomes a new trend that more and more users move their data to cloud storage servers (CSSs). To ensure the security of cloud storage, many cloud auditing schemes are proposed to check the integrity of users' cloud data. However, most of them are based on public key infrastructure, which leads to complex certificates management and verification. Besides, most existing auditing schemes are inefficient when user uploads a large amount of data or a third party auditor (TPA) performs auditing for multiple users' data on different CSSs. To overcome these problems, in this paper, we propose an efficient and secure auditing scheme based on identity-based cryptography. To relieve user's computation burden, we introduce a proxy, which is delegated to generate and upload homomorphic verifiable tags for user. We extend our auditing scheme to support auditing for dynamic data operations. We further extend it to support batch auditing in multiple users and multiple CSSs setting, which is practical and efficient in large scale cloud storage system. Extensive security analysis shows that our scheme is provably secure in random oracle model. Performance analysis demonstrates that our scheme is highly efficient, especially reducing the computation cost of proxy and TPA.