• Smart Structures and Systems
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• v.23 no.4
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• pp.359-371
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• 2019

As part of a structural health monitoring system, the relative geometric relationship between a ship and bridge has been recognized as important for bridge authorities and ship owners to avoid ship-bridge collision. This study proposes a novel computer vision method for the real-time geometric parameter identification of moving ships based on a single shot multibox detector (SSD) by using transfer learning techniques and monocular vision. The identification framework consists of ship detection (coarse scale) and geometric parameter calculation (fine scale) modules. For the ship detection, the SSD, which is a deep learning algorithm, was employed and fine-tuned by ship image samples downloaded from the Internet to obtain the rectangle regions of interest in the coarse scale. Subsequently, for the geometric parameter calculation, an accurate ship contour is created using morphological operations within the saturation channel in hue, saturation, and value color space. Furthermore, a local coordinate system was constructed using projective geometry transformation to calculate the geometric parameters of ships, such as width, length, height, localization, and velocity. The application of the proposed method to in situ video images, obtained from cameras set on the girder of the Wuhan Yangtze River Bridge above the shipping channel, confirmed the efficiency, accuracy, and effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4385-4399
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• 2021

The Internet of Things (IoT) eHealth systems composed by Wireless Body Area Network (WBAN) has emerged recently. Sensor nodes are placed around or in the human body to collect physiological data. WBAN has many different applications, for instance health monitoring. Since the limitation of the size of the battery, besides speed, reliability, and accuracy; design of WBAN protocols should consider the energy efficiency and time delay. To solve these problems, this paper adopt the end-edge-cloud orchestrated network architecture and propose a transmission based on reinforcement algorithm. The priority of sensing data is classified according to certain application. System utility function is modeled according to the channel factors, the energy utility, and successful transmission conditions. The optimization problem is mapped to Q-learning model. Following this online power control protocol, the energy level of both the senor to coordinator, and coordinator to edge server can be modified according to the current channel condition. The network performance is evaluated by simulation. The results show that the proposed power control protocol has higher system energy efficiency, delivery ratio, and throughput.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.218-221
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• 2006

This paper reports on a Realtime OS based motor control system for laparoscopic surgery robot which enables telesurgery and overcomes shortcomings with conventional laparoscopic surgery. The system has a conventional master-slave robot configuration and the control system consists of joint controllers, host controllers, and power units. The robot features (1) a compact slave robot with 5 DOF (Degree Of Freedom) expanding the workspace of each tool and increasing the number of tools operating simultaneously, and (2) direct 1:1 correspondence in the joint of master and slave robot that simplifies control algorithm and enhances reliability. Each master, slave and GUI (Graphical User Interface) host has a dedicated RTOS (RealTime OS), RTLinux-Pro (FSMLabs Inc., U.S.A.) Each master and slave controller set pair has a dedicated CAN (Controller Area Network) channel for control and monitoring signal communication. Total 4 pairs of the master/slave manipulators as current are monitored by one host controller for operation monitoring and higher level motion control. The system showed acceptable performance in both position control precision and master-slave motion synchronization and is now under further development for better safety and control fidelity for clinically applicable prototype.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.797-806
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• 2012

Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.147-156
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• 2023

Because the behavior of cable bridges is dominated by dynamic response and is relatively complex, short- and long-term field monitoring are often required to evaluate the bridge condition. If a permanent SHMS (Structural Health Monitoring System) is not installed, a portable monitoring system is needed for the checking of bridge condition. In this case, it can be difficult to operate the portable monitoring system due to limited conditions such as power and communication according to the location and type of the bridge. In this study, the portable-based smart structural response monitoring system is developed that can be effectively used for short- and long-term monitoring of cable bridges in Korea and Southeast Asia. The developed system is a multi-channel portable data acquisition and analyzer that can be operated for a long time in the field using its own power supply system, and is included with the automated analysis algorithm for the dynamic characteristics of cable bridges using real-time data. In order to evaluate the field applicability of the developed system, field demonstration was conducted on cable bridges in Korea and Vietnam. Through the demonstration, the reliability and efficiency of field operation of the developed system were confirmed, and additionally, the possibility of application to overseas markets was confirmed in cable bridge monitoring field.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.315-316
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• 2002

Electrochemical discharge machining is a very recent technique for non-conducting materials such as ceramics and glasses. ECDM is conducted in the NaOH solution and the cathode electrode is separated from the solution by $H_2$ gas bubble. Then the discharge is appeared and the non-conductive material is removed by spark and some chemical reactions. In the ECDM technology, the $H_2$ bubble control is the most important factor to stabilize the discharging condition. In this paper, we proposed the discharge peak monitoring/ discharging duty feedback algorithms for the discharge stabilization and the feasibility of this algorithm is verified by various pattern machining in the constant preload conditions for the cathode electrode.

• Journal of KSNVE
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• v.6 no.3
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• pp.325-332
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• 1996

One of the greatest shortcoming in today's predictive maintenance program is the ability to diagnose the mechanical and electrical problems within the machine when the vibration exceeds preset overall and spectral alarm levels. In this study, auto-diagnosis system is constructed by using A/D converter to convert analog to digital singal. With this device the system analyses input signal to diagonosis machine condition. Many plots, which display machine condition, and input values of every channel are calculated in this system. If the falut is found, the system diagnoses automatically using fuzzy algorithm and trend monitoring. Prediction is also performed by the grey system theory. Operator finds out eh machine operating condition intuitively based on with personal computer CRT in using this system.

• KSTLE International Journal
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• v.3 no.2
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• pp.95-100
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• 2002

Electrochemical discharge machining is a very recent technique for non-conducting materials such as ceramics and glasses. ECDM is conducted in the NaOH solution and the cathode electrode is separated from the solution by H$_2$ gas bubble. Then the discharge is appeared and the non-conductive material is removed by spark and some chemical reactions. In the ECDM technology, the H$_2$ bubble control is the most important factor to stabilize the discharging condition. In this paper we proposed the discharge peak monitoring/discharging duty feedback algorithms fur the discharge stabilization and the feasibility of this algorithm is verified by various pattern machining in the constant preload conditions for the cathode electrode.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.97-104
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• 2010

In this paper, we provide an embedded type non-contact bio-radar heart and respiration rate monitoring system. We implemented the rate finding algorithm into the embedded system. The high-speed and reliable real-time signal processor is then tested. To avoid null-point data loss problem, we applied quadrature demodulation. Among several other combining techniques, we suggest arctangent demodulation for quadrature channel combining and DSP is used for real-time signal processing. We also suggest DC-offset compensation technique to preserve the wanted DC components of the IQ signals for accurate demodulation while keeping the dynamic range of the ADC lower. Using Texas Instrument C6711 series DSP and external 12Bit ADC, we implemented proper elliptic digital filter and autocorrelation detection algorithm for robust commercial hand held device.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.