• Smart Structures and Systems
• /
• v.19 no.1
• /
• pp.1-10
• /
• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• Aerospace Engineering and Technology
• /
• v.13 no.2
• /
• pp.29-37
• /
• 2014

Telemetry, satellite event and error information are used to check the satellite status in ground station. Different from telemetry which only informs the parameter value, event and error gives explicit information of a certain operation or status. Event also contains ground action information because every command execution is logged as event. Currently, those information is gathered and applied only for monitoring of the satellite. However, the load of the operation is getting grown because of the excessively increased information of the satellite with the number of satellite increasement. Also, the process of reporting problem to developer (or an expert) induce time delay for satellites fault management. In this paper, we propose a satellite operation assistant system which collects event sequence and stores in different group by its feature, and then recommends or executes an appropriate action for the identified abnormal state. This system is applicable to on board system for resolving LEO-satellite autonomous fault situation since is has limited contact time.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.14 no.5
• /
• pp.798-804
• /
• 2011

A number of navigation improvements are envisaged : Differential GPS - WAAS, LAAS, and Performance Based Navigation. The GPS receiver verifies the integrity(usability) of the signals received from the GPS constellation through a process called receiver autonomous integrity monitoring(RAIM) to determine if a satellite is providing corrupted information. This paper describe the RAIM function and Performance-Based Navigation implementation of Mission Equipment Package(MEP) for Korean Utility Helicopter.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.104.3-104
• /
• 2002

A quadruped stair robot introduced here plays a role in monitoring and moving some place where an operator can not reach or when he may not keep watching. It has several features that travels and poses variable position by four caterpillars and quadruped typed arms, transmits an image and command data via RF wireless and network communication. The robot can balance itself when it moves up and down on a slope by using the quadruped mechanism. The robot vision scans ahead before it moves, and the captured image is transferred to a main computer via a RF image module. The main computer analyzes the obstacle, and when it is found the obstacle, the robot avoids from the obstacle and keep moving f...

• IEMEK Journal of Embedded Systems and Applications
• /
• v.9 no.3
• /
• pp.165-171
• /
• 2014

In this paper, we propose the hardware and software of a test-bed of a hovering AUV (autonomous underwater vehicle). Test-bed to develop as the underwater robot for the hovering -type is planning to apply for marine resource development and exploration for deep sea. The RTU that controls a azimuth thruster and a vertical thruster of test-bed is a intergrated-type thruster. The main control unit that collects sensor's data and performs high-speed processing and controls a movement of test-bed is a underwater hybrid navigation system. Also it transfers position, posture, state information of test-bed to the host PC of user using a wireless communication. The host PC checks a test-bed in real time by using a realtime monitoring system that is implemented by LabVIEW.

• International Journal of Ocean System Engineering
• /
• v.3 no.1
• /
• pp.16-21
• /
• 2013

This paper presents a design of fuzzy PD depth controller for the autonomous underwater vehicle entitled KAUV-1. The vehicle is shaped like a torpedo with light weight and small size and used for marine exploration and monitoring. The KAUV-1 has a unique ducted propeller located at aft end with yawing actuation acting as a rudder. For depth control, the KAUV-1 uses a mass shifter mechanism to change its center of gravity, consequently, can control pitch angle and depth of the vehicle. A design of classical PD depth controller for the KAUV-1 was presented and analyzed. However, it has inherent drawback of gains, which is their values are fixed. Meanwhile, in different operation modes, vehicle dynamics might have different effects on the behavior of the vehicle. In this reason, control gains need to be appropriately changed according to vehicle operating states for better performance. This paper presents a self-tuning gain for depth controller using the fuzzy logic method which is based on the classical PD controller. The self-tuning gains are outputs of fuzzy logic blocks. The performance of the self-tuning gain controller is simulated using Matlab/Simulink and is compared with that of the classical PD controller.

• Solar Energy
• /
• v.18 no.3
• /
• pp.147-151
• /
• 1998

Several PV-diesel hybrid systems were built in isolated islands in Korea, where they are far from the inland to be supplied the electric power to a utility level from it. A lot of efforts has been concentrated to find a cost-effective electric supply system with higher reliability and minimum maintenance when compared with a diesel generator. For this purpose, an autonomous PV-diesel hybrid system with multi-channel remote monitoring system was investigated to supply electric power under minimum operating cost and maintenance in a small isolated island. In this report, the economic analysis was performed for comparison with photovoltaic system and diesel generator by computer simulation. And it was proven that a PV system is more cost-effective than an internal combustion engine for the remote island with less than 150 households. Especially, in the case of islands with less than 50 households, the initial construction cost of the PV system is comparable to and its operating cost is about 70% less than the diesel generator.

• Korean Journal of Construction Engineering and Management
• /
• v.12 no.6
• /
• pp.22-30
• /
• 2011

In the construction operation, foundation work should be done in advance for the building structure to be installed. This foundation work include a number of activities such as excavation, ground water prevention, piling, wale installation, etc. Caution should be taken in the operation because the dynamics of earth movement can cause a significant failure in the temporary structure. The temporary structure, therefore, should be constantly monitored to understand its behavior. This paper introduces the USN-based monitoring system to automatically identify the behavior of the temporary structure in addition to visual inspection. The autonomous capability of the monitoring system can increase the safety in the construction operation by providing the detailed structural changes of temporary structures.

• Smart Structures and Systems
• /
• v.32 no.5
• /
• pp.309-318
• /
• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.77-91
• /
• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.