• Journal of the Korean Magnetics Society
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• v.14 no.6
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• pp.201-206
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• 2004

The AC-DC magnetic field standard systems were constructed for the calibration of magnetometers for low magnetic field and the tests for low magnetic field characteristics of sensors and materials. In the range of 1 mT, the expanded uncertainty of dc is 8${\times}$10$\^$-6/, ac uncertainties are 0.16% in 0.1～1 kHz, 0.26% in 1～5 kHz, and 0.44% in 5～20 kHz. We have been participated in international key comparison(KC) to achieve the equality and the mutual agreement between standard institutes for the results of calibrations and tests. KRISS participating in ac-dc magnetic flux density of KC got equal level of uncertainty results compare with the advanced nations. It confirm that measurement ability of magnetic flux density is high level in the world.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.1-9
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• 2010

This paper describes parallel event-driven remote IT convergence applications which are a combination of traditional industry and IT Technology including advanced communication. In IT convergence system, events can occur currently from many sensors of devices or users. And IT convergence system must have a parallel processing method. In this paper, the parallel processing method was implemented using a thread and we developed a connection method between a device and a mode of communication which is a wireless communication or a power line communication. In addition to that, we developed object modeling, device, user and event modeling, based on XML (eXtensible Markup Language) using object-oriented modeling method. To efficiently show results in real time, systems provide various graphic user interfaces such as a bar graph, a table, and a combination of the two.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.89-93
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• 2003

The Korea Research Institute of Ships and Ocean Engineering (KRISO), the ocean engineering branch of KORDI, has designed and manufactured a model of an autonomous underwater vehicle (AUV) to test underwater docking. This paper introduces the AUV model, ASUM, equipped with a visual servo control system to dock into an underwater station with a camera and motion sensors. To make a visual servoing AUV, this paper implemented the visual servo control system designed with an augmented state equation, which was composed of the optical flow model of a camera and the equation of the AUV's motion. The system design and the hardware configuration of ASUM are presented in this paper. A small long baseline acoustic positioning system was developed to monitor and record the AUV's position for the experiment in the Ocean Engineering Basin of KRISO, KORDI. ASUM recognizes the target position by processing the captured image for the lights, which are installed around the end of the cone-type entrance of the duct. Unfortunately, experiments are not yet conducted when we write this article. The authors will present the results for the docking test of the AUV in near future.

• Journal of Ocean Engineering and Technology
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• v.32 no.5
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• pp.402-409
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• 2018

This paper describes the plan and procedure of a development test and evaluation that will be performed to verify the performance and technology of multi-mission unmanned surface vehicles (MMUSVs). In order to verify the design requirement of MMUSVs, we designed and manufactured the common platform of MMUSVs, which have an overall length of8.4 m, a displacement of 3,100 kg, and a speed of more than35 kts. The platform is equipped with several sub-systems, including radar and an EOTS/IRS. The EOTS/IRS, along with the search radar, is used for effective detection, identification, and targeting. The core technologies of MMUSV for DT&E will be investigated. The common platform design technologies, remote operating and control system technologies, autonomous navigation technologies, and unmanned operational technology of sensors and equipment will be studied for the development of the MMUSV's core technologies. The system will be able to make precise observations and track targets both manually and automatically during day and night conditions. Currently, the verification tests for each of the technologies and for the integrated system are in the pipeline for DT&E, which will be performed next year. Also, software reliability and life tests will be performed.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.4
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• pp.231-237
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• 2018

In this study, two important requirements for the home production of a robot to detect and remove improvised explosive devices (IEDs) are presented in terms of the total cost for robot system development and the performance improvement of the mine detection technology. Firstly, cost analyses were performed in order to provide a reasonable solution following an engineering estimate method. As a result, the total cost for a mass production system without the mine detection system was estimated to be approximately 396 million won. For the case including the mine detection system, the total cost was estimated to be approximately 411 million won, in which labor costs and overhead charges were slightly increased and the material costs for the mine detection system were negligible. Secondly, a method for fabricating the carbon nanotube (CNT) based gas detection sensor was studied. The detection electrodes were formed by a photolithography process using a photosensitive CNT paste. As a result, this method was shown to be a scalable and expandable technology for producing excellent mine detection sensors. In particular, it was found that surface treatments by using adhesive taping or ion beam bombardment methods are effective for exposing the CNTs to the ambient air environment. Fowler-Nordheim (F-N) plots were obtained from the electron-emission characteristics of the surface treated CNT paste. The F-N plot suggests that sufficient electrons are available for transport between CNT surfaces and chemical molecules, which will make an effective chemiresistive sensor for the advanced IED detection system.

• Journal of Sensor Science and Technology
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• v.14 no.5
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• pp.322-330
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• 2005

A new type of transducer, piezoelectric floating mass transducer (PFMT) which has advantages of piezoelectric and electromagnetic transducer has been proposed and implemented for the implantable middle ear hearing devices. By the uneven bonding of piezoelectric material to the inner bottom of transducer case, the PFMT can vibrate back-and-forth along the longitudinal axis of the transducer even though the piezoelectric material within the cylindrical case produces only the bilateral expansion and contraction according to the applied electrical signal. To improve efficiency of the PFMT, the multi-layered piezoelectric material has been adapted. The small number of components in the PFMT enables the simple manufacturing and the easy implanting into the middle ear. In order to examine the characteristics of vibration, mechanical modeling and finite element analyses of the proposed transducer have been performed. From the result of theoretical analyses and the measured data from the experiment, it is verified that the implemented PFMT can be used in implantable middle ear hearing devices.

• Journal of Sensor Science and Technology
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• v.15 no.4
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• pp.223-230
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• 2006

An FIR filter for digital voice signal processing has been designed and implemented using a microcontroller in implantable middle ear hearing devices (IMEHDs). The designed digital voice signal processing filter which has fast and accurate filtering operation and controllable filter characteristics has been implemented using a hardware multiplier and a direct memory access (DMA) in the low power microcontroller, MSP430F169. It has been confirmed that each of the implemented 6-orders Remez FIR filters with 1 channel and 2 channels can be applied to the voice signal processing module of IMEHDs based on the evaluation results of the filtering performance experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.395-398
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• 2017

The Smart Ship is considered one of the most discussed and novel topics in developing technological period. In this reason, the amount of running researches on it is evolving so fast. As a proof, the faced drawbacks such as the departure of ships, their safety, exchanging data, traffic and data monitoring system are being solved by presenting advanced technologies and innovations like Cloud, BigData, IoT and etc. Expanding the utilization of these technologies in the Marine world emphasizes not only the departure of the ships in the water but also they focus on solving the problems of the ships connected with the communication to the ports. In this paper, we present an IoT based smart reporting and mooring system for vessels and ports. In the proposed system, the ships automatically send all the data about themselves to the port and after getting the data, ports automatically send the information about possible spaces to moor for the ships using the sensors at the port. The intended system gives an amenity to minimize the time, effort and the cost while mooring the vessels.

• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.481-486
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• 2019

Smart Factory requires real-time monitoring and analysis of all process processes for optimal production environment. Monitoring system for data collection from various sensors is necessary to make all production processes automatic. By storing and analyzing the collected data, we can check whether there are any signs of abnormalities in any machine or equipment. Thus, in this paper, an integrated monitoring system for smart factory incorporating a working environment monitoring system and an automatic storage system of measurement values was implemented. By using the automatic storage system of measurement values, it is possible to carry out reliable inspection in any place without misentry. Also, through working environment monitoring system using LoRa, production environments such as temperature, humidity and atmospheric pressure can be monitored in real time.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.