• Journal of Navigation and Port Research
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• v.26 no.5
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• pp.529-535
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• 2002

Design and implementation methodologies on the performance evaluation system of Two-Channel Ring-Core Flux-Gate Compass (TCRC FG-Compass) are described, with evaluation procedures and methods based on the polynomial regression models. Performance evaluation system consists of a step motor driving unit, a bearing transmitting unit and evaluation programs derived from polynomial regression formulae. Newly designed performance evaluation system enabled the accuracy of TCRC FG-Compass to be ascertained. It was confirmed that the size of residual deviation of TCRC FG-Compass is $2^{\circ}$, while that of the conventional one is $4^{\circ}$. In addition, the design methodology to the self estimation and correction of residual deviations is also discussed.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.21-27
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• 2019

Recently, embedded systems have been introduced in various fields such as smart factories, industrial drones, and medical robots. Since sensor data collection and IoT functions for machine learning and big data processing are essential in embedded systems, it is essential to port the operating system that is suitable for the function requirements. However, in embedded systems, it is necessary to separate the hard real-time system, which must process within a fixed time according to service characteristics, and the flexible real-time system, which is more flexible in processing time. It is difficult to port the operating system to a low-performance embedded device such as 8BIT MCU to perform simultaneous real-time. When porting a real-time OS (RTOS) to a low-specification MCU and performing a number of tasks, the performance of the real-time and general processing greatly deteriorates, causing a problem of re-designing the hardware and software if a hard real-time system is required for an operating system ported to a low-performance MCU such as an 8BIT MCU. Research on the technology that can process real-time processing system requirements on RTOS (ported in low-performance MCU) is needed.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1911-1923
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• 2022

The availability of high-resolution satellite images provides precise information without direct observation of the research target. Korea Multi-Purpose Satellite (KOMPSAT), also known as the Arirang satellite, has been developed and utilized for earth observation. The machine learning model was continuously proven as a good classifier in classifying remotely sensed images. This study aimed to compare the performance of the support vector machine (SVM) model in classifying the land cover of the Delaware River port area on high and medium-resolution images. Three optical images, which are KOMPSAT-2, KOMPSAT-3A, and Sentinel-2B, were classified into six land cover classes, including water, road, vegetation, building, vacant, and shadow. The KOMPSAT images are provided by Korea Aerospace Research Institute (KARI), and the Sentinel-2B image was provided by the European Space Agency (ESA). The training samples were manually digitized for each land cover class and considered the reference image. The predicted images were compared to the actual data to obtain the accuracy assessment using a confusion matrix analysis. In addition, the time-consuming training and classifying were recorded to evaluate the model performance. The results showed that the KOMPSAT-3A image has the highest overall accuracy and followed by KOMPSAT-2 and Sentinel-2B results. On the contrary, the model took a long time to classify the higher-resolution image compared to the lower resolution. For that reason, we can conclude that the SVM model performed better in the higher resolution image with the consequence of the longer time-consuming training and classifying data. Thus, this finding might provide consideration for related researchers when selecting satellite imagery for effective and accurate image classification.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.154-155
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• 2023

The International Convention on Standards for Training, Certification, and Watchkeeping of Seafarers (STCW) outlines the qualifications that maritime cadets must meet in order to serve as merchant marine officers. Maritime English is one of the most essential qualifications for STCW, and each national authority is implementing Maritime English education that complies with national and international regulations. In this study, an English proficiency background survey was conducted to investigate the factors related to the Maritime English skills and competencies. In line with this, maritime cadets utilized the Standard Maritime English Communication Phrases (SMCP) learning platform to track their learning progress and its efficacy. This study examined the applicability of the automatic scoring platform for Maritime English education, as well as its future potential for widespread use in the maritime education field.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.388-389
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• 2022

Recently, AI (Artificial Intelligence) has been applied to various technologies such as automatic driving, robot and smart communication. Currently, AI system is developed by software-based method using tensor flow, and GPU (Graphic Processing Unit) is employed for processing unit. However, if software-based method employing GPU is used for AI applications, there is a problem that we can not change the internal circuit of processing unit. In this method, if high-level jobs are required for AI system, we need high-performance GPU, therefore, we have to change GPU or graphic card to perform the jobs. In this work, we developed a CNN-based FPGA (Field Programmable Gate Array) chip to solve this problem.

• Smart Media Journal
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• v.5 no.4
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• pp.69-74
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• 2016

Recently, parallel computing using GPGPU(General-Purpose computing on Graphics Processing Units) according to the development of the graphics processing unit is expanding. This can be achieved through the processing speeds faster than traditional computing environments across many fields, including science, medicine, engineering, and analysis. However, in using the GPU technology to implement the a parallel program there are many constraints. In this paper, we port a CPU-based program(Video Quality Measurement Program) to use technology. The program ported to GPU-based show about 1.83 times the execution speed than CPU-based program. We study on the acceleration of the GPU-based program. Also we discuss the technical constraints and problems that occur when you modify the CPU to the GPU-based programs.

• Smart Media Journal
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• v.1 no.2
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• pp.47-53
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance procedures. Condition Monitoring System(CMS) can be used to aid plant owners in achieving these goals. In this work, systematic design procedure for artificial neural network based normal behavior model which can be applied for fault detection of various devices is proposed. Furthermore, to verify the design method SCADA(Supervisor Control and Data Acquisition) data from 850kW wind turbine system installed in Beaung port were utilized.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.5
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• pp.527-536
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• 2018

In this paper, we developed a prototype model accident management SMART-Navigation project. In order to develop a prototype model, we analyzed the status of maritime data exchange standard and procedure. We developed accident management prototype application schema, feature catalog and portrayal catalog in accordance with S-100 standard data model development procedure by collecting requirements related services and referring to related standards. In order to verify accident management prototype model, we test data set based on Gwang-yang Port. The prototype model and test data verified verification software, and it was confirmed that the designated symbol was displayed at the correct position through the S-100 simple viewer.

• Journal of the Semiconductor & Display Technology
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• v.10 no.4
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• pp.7-13
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• 2011

In this paper, the IC(Integrated Circuit) for multi-channel synchronous communication was designed by using FPGA and VHDL language. The existing chips for synchronous communication that has been used commercially are composed for one to two channels. Therefore, when communication system with three channels or more is made, the cost becomes high and it becomes complicated for communication system to be realized and also has very little buffer, load that is placed into Microprocessor increases heavily in case of high speed communication or transmission of high-capacity data. The designed IC was improved the function and performance of communication system and reduced costs by designing 8 synchronous communication channels with only one IC, and it has the size of transmitter/receiver buffer with 1024 bytes respectively and consequently high speed communication became possible. It was designed with a communication signal of a form various encoding. To detect errors of communications, the CRC-ITU-T logic and channel MUX logic was designed with hardware logics so that the malfunction can be prevented and errors can be detected more easily and input/output port regarding each communication channel can be used flexibly and consequently the reliability of system was improved. In order to show the performance of designed IC, the test was conducted successfully in Quartus simulation and experiment and the excellence was compared with the 85C3016VSC of ZILOG company that are used widely as chips for synchronous communication.

• Smart Media Journal
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• v.11 no.11
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• pp.25-31
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• 2022

Deep learning-based image processing is essential for autonomous vehicles. To process road images in real-time in a System-on-Chip (SoC) environment, we need to execute deep learning models on a NPU (Neural Procesing Units) specialized for deep learning operations. In this study, we imported seven open-source image processing deep learning models, that were developed on GPU servers, to Texas Instrument Deep Learning (TIDL) NPU environment. We confirmed that the models imported in this study operate normally in the SoC virtual environment through performance evaluation and visualization. This paper introduces the problems that occurred during the migration process due to the limitations of NPU environment and how to solve them, and thereby, presents a reference case worth referring to for developers and researchers who want to port deep learning models to SoC environments.