• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.762-766
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• 2004

This paper introduces a testbed which is suitable for the study of security issues arising in applications involving internet appliances. The testbed implements secure door locks by utilizing the intranet in the building and is composed of two main parts, namely a database server and door locks each of which equipped with a custom-made embedded system. The main objective is to provide a platform for teaching the conflict among real-time specifications, security requirements, and limited-resource constraints. After definitions of threat, vulnerability, and attack are given, we discuss how the testbed can be applied as an education tool for these concepts. Finally, the effects of sequential and multitasking operations are given as a case study.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.769-770
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• 2008

Mani Memory DataBase(MMDB) system store their data in main physical memory and provide very high-speed access. Conventional database system are optimized for the particular characteristics of disk storage mechanism. Memory resident systems, on the other hand, use different optimizations to structure and organize data, as well as to make it reliable. This paper provides a brief overview on MMDBs and the results after evaluating the performance of our simple MMDB based on Embedded system.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.2
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• pp.101-108
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• 2015

In this paper, a robust traffic light detection method is proposed by using vision sensor and DGPS(Difference Global Positioning System). The conventional vision-based detection methods are very sensitive to illumination change, for instance, low visibility at night time or highly reflection by bright light. To solve these limitations in visual sensor, DGPS is incorporated to determine the location and shape of traffic lights which are available from traffic light database. Furthermore the geometric relationship between traffic light and vision sensor is used to locate the traffic light in the image by using DGPS information. The empirical results show that the proposed method improves by 51% in detection rate for night time with marginal improvement in daytime environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.1
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• pp.1-7
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• 2008

In this paper, a MSS-NOVO method that combines the HMM composition method with a noise reduction method is proposed for speech recognition in noisy environments. This combined method starts with noise reduction with modified spectral subtraction (MSS) to enhance the input noisy speech, then the noise and voice composition (NOVO) method is applied for making noise adapted models by using the noise in the non-utterance regions of the enhanced noisy speech. In order to evaluate the effectiveness of our proposed method, we compare MSS-NOVO method with other methods, i.e., SS-NOVO, MWF-NOVO. To set up the noisy speech for test, we add White noise to KLE 452 database with different SNRs range from 0dB to 15dB, at 5dB intervals. From the tests, MSS-NOVO method shows average improvement of 66.5% and 13.6% compared with the existing SS-NOVO method and MWF-NOVO method, respectively. Especially our proposed MSS-NOVO method shows a big improvement at low SNRs.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.329-335
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• 2018

Offline-first strategy is that it allows data to be saved while offline, and when connected online, data is synchronized to ensure that all devices have the same data. Multi-device is a term that shares data through synchronization on various platforms on Android, ios, etc. First, all of the data is stored in the local repository like SQLite and then on the server via HTTP communication. Then, the synchronization is completed by receiving the changed data from the server and storing it in the local repository at the time of the synchronization, and sending the changes to the server from the client. We proposed and implemented a database structure, APIs, and a illustrative application running on PC and Android phone.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.67-76
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• 2022

Monostatic/Bistatic inverse synthetic aperture radar (ISAR) images are two-dimensional radar cross section (RCS) distributions of a target. When there are many targets in a single radar beam, ISAR images are generated with targets overlapped, so it is difficult to perform the targets identification using the trained database. In addition, it is inefficient to perform target identification using only single monostatic and bistatic ISAR images separately because each method has its own advantages and weaknesses. Therefore, this paper analyzes multiple targets identification performances using monostatic/bistatic ISAR images and proposes a method of identification through fusion of two ISAR images. To identify multiple targets, we use image combination technique using trained single target images. Simulation results show effectiveness of proposed method.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.3
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• pp.177-183
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• 2022

To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.101-106
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• 2024

For the commercialization of automated vehicles, it is necessary to create various scenarios that can evaluate driving safety and establish a data system that can verify them. Depending on the vehicle's ODD (Operational Design Domain), there are numerous scenarios with various parameters indicating vehicle driving conditions, but no systematic methodology has been proposed to create and combine scenarios to test them. Therefore, projects are actively underway abroad to establish a scenario library for real-world testing or simulation of autonomous vehicles. However, since it is difficult to obtain data, research is being conducted based on simulations that simulate real road. Therefore, in this study, parameters calculated through individual vehicle trajectory data extracted based on roadside CCTV image-based driving environment DB was proposed through the extracted data. This study can be used as basic data for safety standards for scenarios representing various driving behaviors.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1117-1122
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• 2020

The improving performance and utilizing application fields of mobile devices are getting bigger and wider. With this trend, applications that use database engines on mobile devices are also becoming common. Applications requiring mobile databases include mobile server databases, edge computing, fog computing, and the like. By the way, the most representative and widely used mobile database is SQLite3. In this paper, we test and compare the update performance of SQLite3 by some file systems. The update performance of the file systems in the mobile environment is an important performance factor in the limited H/W environment. The comparison file system was chosen as FAT, Ext2, and NTFS. Under the same conditions, experiments with each file system to test update performance and characteristics were processed. From the experimental results, we could analyze the advantages and disadvantages of each file system for each database update pattern.