• Journal of IKEEE
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• v.24 no.1
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• pp.46-51
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• 2020

With Wi-Fi and Bluetooth connectivity becoming more common in today's handheld mobile devices, single-chip multi-radio combo-modules, which integrate two or more heterogeneous wireless radios on a single chip, are becoming more and more popular. The key requirement for combo solutions is that the quality of the user experience (QoE) must not be compromised by degrading connectivity performance. Therefore, characterizing and understanding the behaviour of combo-module is of vital importance to ensure this requirement in various environments. In this paper, we investigate the impact of the use of combo-modules on the performance of mobile video streaming over a Wi-Fi network. Our study reveals that the use of combo-modules incurs considerable side effects on QoE for mobile video streaming applications when the Wi-Fi and Bluetooth operate at the same time in the 2.4GHz ISM band. We reveal that rate-based adaptive algorithms, including the most popular adaptive bitrate streaming MPEG-DASH, is more severely affected by this phenomenon than buffer-based adaptive algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2719-2730
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• 2009

In this paper, we implement the measurement functionality for performance measurements of EDGE (Enhanced Data Rates for GSM Evolution) terminal by using software. Generally speaking, the receiving algorithms in normal MODEM cannot be used directly to a measurement system due to the lack of accuracy. Therefore, we propose a new receiver algorithm for precise EDGE signal measurements. In the proposed algorithm, 2-stage (coarse stage, fine stage) parameters estimation (symbol-timing, frequency offset, carrier phase) scheme is used. To improve the estimation accuracy, we increase the number of the received signal samples by interpolation. The proposed EDGE signal measurement algorithm can be used for verifying the hardware measurement system, and also can be used for the commercial systems through software optimization.

• Journal of Satellite, Information and Communications
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• v.7 no.2
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• pp.8-12
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• 2012

Area-based stereo matchng algorithms are widely used for image analysis for stereo vision. SAD (Sum of Absolute Difference) algorithm is one of well known area-based stereo matchng algorithms with the characteristics of data intensive computing application. Therefore, it requires very high computation capabilities and its processing speed becomes very slow with software realization. This paper proposes a fast SAD algorithm utilizing SSE (Streaming SIMD Extensions) instructions based on SIMD (Single Instruction Multiple Data) parallism. CPU supporing SSE instructions has 16 XMM registers with 128 bits. For the performance evaluation of the proposed scheme, we compare the processing speed between SAD with/without SSE instructions. The proposed scheme achieves four times performance improvement over the general SAD, which shows the possibility of the software realization of real time SAD algorithm.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.2
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• pp.57-64
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• 2014

Most previous studies of software fault prediction model which determines the fault-proneness of input modules have focused on supervised learning model using training data set. However, Unsupervised learning model is needed in case supervised learning model cannot be applied: either past training data set is not present or even though there exists data set, current project type is changed. Building an unsupervised learning model is extremely difficult that is why only a few studies exist. In this paper, we build unsupervised models using representative clustering algorithms, EM and DBSCAN, that have not been used in prior studies and compare these models with the previous model using K-means algorithm. The results of our study show that the EM model performs slightly better than the K-means model in terms of error rate and these two models significantly outperform the DBSCAN model.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.49-55
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• 2021

Recently, with the development of database, it is possible to store a lot of data generated in finance, security, and networks. These data are being analyzed through classifiers based on machine learning. The main problem at this time is data imbalance. When we train imbalanced data, it may happen that classification accuracy is degraded due to over-fitting with majority class data. To overcome the problem of data imbalance, oversampling strategy that increases the quantity of data of minority class data is widely used. It requires to tuning process about suitable method and parameters for data distribution. To improve the process, In this study, we propose a strategy to explore and optimize oversampling combinations and ratio based on various methods such as synthetic minority oversampling technique and generative adversarial networks through genetic algorithms. After sampling credit card fraud detection which is a representative case of data imbalance, with the proposed strategy and single oversampling strategies, we compare the performance of trained classifiers with each data. As a result, a strategy that is optimized by exploring for ratio of each method with genetic algorithms was superior to previous strategies.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.17-26
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• 2021

Recently, COVID-19 has spread and time to stay at home has been increasing in accordance with quarantine guidelines of the government such as recommendations to refrain from going out. As a result, the number of single-person households staying at home is also increasingsingle-person households are less likely to be notified to the outside world in times of emergency than multi-person households. This study collects various situations occurring in the home with lidar, image, and voice sensors and analyzes the data according to the sensors through their respective algorithms. Using this method, we analyzed abnormal patterns such as emergency situations and conducted research to detect abnormal signs in humans. Artificial intelligence algorithms that detect abnormalities in people by each sensor were studied and the accuracy of anomaly detection was measured according to the sensor. Furthermore, this work proposes a fusion method that complements the pros and cons between sensors by experimenting with the detectability of sensors for various situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.242-263
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• 2021

This paper presents a method to implement a low-cost driving simulator for developing autonomous driving algorithms. This is implemented by using GTA V, a physical engine-based commercial game software, containing a function to emulate output and data of various sensors for autonomous driving. For this, NF of Script Hook V is incorporated to acquire GT data by accessing internal data of the software engine, and then, various sensor data for autonomous driving are generated. We present an overall function of the developed driving simulator and perform a verification of individual functions. We explain the process of acquiring GT data via direct access to the internal memory of the game engine to build up an autonomous driving algorithm development environment. And, finally, an example applicable to artificial neural network training and performance evaluation by processing the emulated sensor output is included.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.155-168
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• 2020

Open source is a key growth engine of the 4th industrial revolution, and the continuous development and use of various algorithms for image processing is expected. The purpose of this study is to examine the effectiveness of the UAS image processing open source based algorithm by comparing and analyzing the water reproduction and moving object filtering function and the time required for data processing in 3D reproduction. Five matching algorithms were compared based on recall and processing speed through the 'ANN-Benchmarks' program, and HNSW (Hierarchical Navigable Small World) matching algorithm was judged to be the best. Based on this, 108 algorithms for image processing were constructed by combining each methods of triangulation, point cloud data densification, and surface generation. In addition, the 3D reproduction and data processing time of 108 algorithms for image processing were studied for UAS (Unmanned Aerial System) images of a park adjacent to the sea, and compared and analyzed with the commercial image processing software 'Pix4D Mapper'. As a result of the study, the algorithms that are good in terms of reproducing water and filtering functions of moving objects during 3D reproduction were specified, respectively, and the algorithm with the lowest required time was selected, and the effectiveness of the algorithm was verified by comparing it with the result of 'Pix4D Mapper'.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.11-19
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• 2020

Software education is provided for 6th grade students. This study explored the algorithmic composition patterns of elementary school students. After investigating the algorithm for the 6th grade students, the algorithmic pattern of the learner was explored by calculating the cyclomatic complexity of MacCabe based on the structural programming technique. Students often use one or two choice structures to solve problems, which tend to be biased towards the starting and ending points of the problem. It is also passive in the use of selection structures. Algorithm composition depends on visible objects and one's own background. Therefore, in elementary school software education, it is necessary to present the task of thinking about the algorithm structure in the context of the algorithm and the students' experiences in accordance with the algorithm composition pattern.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.105-114
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• 2017

Because the concepts and components of computational thinking included in the Information Education Curriculum and the Software Education Guidelines are different, it has been difficult to establish computational thinking-based software education in schools. Therefore, this study, which is based on the Delphi survey results from 39 experts, we defined computational thinking as 'computing thinking' and separated the components of computational thinking into five main categories: (1) problem definition, (2) data analysis, (3) abstraction, (4) automation, and (5) generalization. In addition, we selected software areas that are strongly related to computational thinking in the KAIE's information Curriculum Standard Model and surveyed experts to decide which computing thinking components are related to the achievement criteria of the software areas.