• Proceedings of the KIPE Conference
• /
• 2012.07a
• /
• pp.619-620
• /
• 2012

This paper proposes a model of the real time monitoring system based on Ubiquitous Sensor Network (USN) for the detection and prediction of landslides. For this purpose, the real time monitoring system with tilting sensor and USN was set up and the performance was conducted. The performance was accomplished by conducting both field examinations and the experimental evaluation of the monitoring system. The results of this study show that the movements detected by the sensor module coincide with the actual displacement of field and the data measured from the sensor modules through USN transfer to the monitoring system without errors.

• Journal of Auto-vehicle Safety Association
• /
• v.13 no.4
• /
• pp.114-122
• /
• 2021

In this paper, an automated vehicle intersection collision accident was analyzed through simulation. Recently, the more automated vehicles are distributed, the more accidents related to automated vehicles occur. Accidents may show different trends depending on the sensor characteristics of the automated vehicle and the performance of the accident prevention system. Based on NASS-CDS (National Automotive Sampling System-Crashworthiness Data System) and TAAS (Traffic Accident Analysis System), four scenarios are derived and simulations are performed. Automated vehicles are applied with a virtual system consisting of an autonomous emergency braking system and algorithms that predict the route and avoid collisions. The simulations are conducted by changing the sensor angle, vehicle speed, the range of the sensor and vehicle speed range. A range of variables considered vehicle collision were derived from the simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.7
• /
• pp.3150-3170
• /
• 2016

In Intelligent Transportation Systems (ITS), logistics distribution and mobile e-commerce, the real-time, accurate and reliable vehicle trajectory prediction has significant application value. Vehicle trajectory prediction can not only provide accurate location-based services, but also can monitor and predict traffic situation in advance, and then further recommend the optimal route for users. In this paper, firstly, we mine the double layers of hidden states of vehicle historical trajectories, and then determine the parameters of HMM (hidden Markov model) by historical data. Secondly, we adopt Viterbi algorithm to seek the double layers hidden states sequences corresponding to the just driven trajectory. Finally, we propose a new algorithm (DHMTP) for vehicle trajectory prediction based on the hidden Markov model of double layers hidden states, and predict the nearest neighbor unit of location information of the next k stages. The experimental results demonstrate that the prediction accuracy of the proposed algorithm is increased by 18.3% compared with TPMO algorithm and increased by 23.1% compared with Naive algorithm in aspect of predicting the next k phases' trajectories, especially when traffic flow is greater, such as this time from weekday morning to evening. Moreover, the time performance of DHMTP algorithm is also clearly improved compared with TPMO algorithm.

• The Journal of Korea Robotics Society
• /
• v.11 no.4
• /
• pp.277-284
• /
• 2016

A new prediction scheme has been proposed for the robust teleoperation in a non-visible environment. The positioning error caused by the time delay in the non-visible environment has been compensated for by the Smith predictor and the sensory data have been estimated by the Grey model. The Smith predictor is effective for the compensation of the positioning error caused by the time delay with a precise system model. Therefore the dynamic model of a mobile robot has been used in this research. To minimize the unstable and erroneous states caused by the time delay, the estimated sensor data have been sent to the operator. Through simulations, the possibility of compensating the errors caused by the time delay has been verified using the Smith predictor. Also the estimation reliability of the measurement data has been demonstrated. Robust teleoperations in a non-visible environment have been performed with a mobile robot to avoid the obstacles effective to go to the target position by the proposed prediction scheme which combines the Smith predictor and the Grey model. Even though the human operator is involved in the teleoperation loop, the compensation effects have been clearly demonstrated.

• Journal of Korea Water Resources Association
• /
• v.57 no.7
• /
• pp.461-470
• /
• 2024

This study aimed to predict total phosphorus (T-P) to address early eutrophication caused by nutrient influx from various human activities. Traditional T-P monitoring systems are labor-intensive and time-consuming, leading to a global trend of using direct reading sensors. Therefore, this study utilized water quality parameters obtained from direct reading sensors in a two-stage T-P prediction process. The importance of turbidity (Tur) in T-P prediction was examined, and an analysis was conducted to determine if T-P prediction is possible using only direct reading sensor parameters by adding automatic water quality analyzer parameters. The study found that T-P concentrations were higher in the mid-lower reaches of the Nakdong River basin compared to the upper reaches. Pearson correlation analysis identified water quality parameters highly correlated with T-P at each site, which were then used in multiple linear regression analysis to predict T-P. The analysis was conducted with and without the inclusion of Tur, and the performance of models incorporating automatic water quality analyzer parameters was compared with those using only direct reading sensor parameters. The results confirmed the significance of Tur in T-P prediction, suggesting that it can be used as a foundational element in the development of measures to prevent eutrophication.

• Journal of the Korea Convergence Society
• /
• v.11 no.11
• /
• pp.47-53
• /
• 2020

There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

• Smart Structures and Systems
• /
• v.26 no.5
• /
• pp.591-603
• /
• 2020

Dynamic deflection monitoring is an essential and critical part of structural health monitoring for high-speed railway bridges. Two critical problems need to be addressed when using inclinometer sensors for such applications. These include constructing a general representation model of inclination-deflection and addressing the ill-posed inverse problem to obtain the accurate dynamic deflection. This paper provides a dynamic deflection monitoring method with the placement of optimal inclinometer sensors for high-speed railway bridges. The deflection shapes are reconstructed using the inclination-deflection transformation model based on the differential relationship between the inclination and displacement mode shape matrix. The proposed optimal sensor configuration can be used to select inclination-deflection transformation models that meet the required accuracy and stability from all possible sensor locations. In this study, the condition number and information entropy are employed to measure the ill-condition of the selected mode shape matrix and evaluate the prediction performance of different sensor configurations. The particle swarm optimization algorithm, genetic algorithm, and artificial fish swarm algorithm are used to optimize the sensor position placement. Numerical simulation and experimental validation results of a 5-span high-speed railway bridge show that the reconstructed deflection shapes agree well with those of the real bridge.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.2
• /
• pp.276-283
• /
• 2018

Climate changes consistently cause coastal accidents such as coastal flooding, so the studies on monitoring the marine environments are progressing to prevent and reduce the damage from coastal accidents. In this paper, we propose a new method to predict the sea level which can be applied to coastal monitoring systems to observe the variation of sea level and warn about the dangers. Existing sea level models are very complicated and need a lot of tidal data, so they are not proper for real-time prediction systems. On the other hand, the proposed algorithm is very simple but precise in short period such as one or two hours since we use the measured data from the sensor. The proposed method uses Kalman filter algorithm for harmonic analysis and double exponential smoothing for additional error correction. It is shown by experimental results that the proposed method is simple but predicts the sea level accurately.

• Journal of the Society of Naval Architects of Korea
• /
• v.47 no.4
• /
• pp.565-572
• /
• 2010

It is difficult to predict the accurate fatigue life of the ship structure because of load uncertainty and load redistribution at the ship structure members. As one of studies for accurate evaluation and prediction of fatigue life, it is a promising way to detect the crack previously by attaching the Fatigue Indicator Sensor (FIS) at the crack prediction region. In order to predict the fatigue life of the ship structure by using FIS, it is required to know previously the crack propagation characteristics according to pre-notch shapes. In this study, we obtained the stress distribution phase, stress concentration factors and stress intensity factor of various pre-notch shapes through FEA. Additionally, we conducted the fatigue test and obtained the characteristics of crack propagation according to the pre-notch shapes through comparison between the fatigue test and the FEA. Consequently, we classified the pre-notch shape into 3 categories: Long, Medium, and Short life type. On the basis of the numerical and experimental results, the FIS can be developed.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.10 no.11
• /
• pp.1265-1270
• /
• 2015

For the sensor node or collection node operating with a battery in a wireless sensor network, MAC protocols with improved energy efficiency are important performance factors. In this paper, in order to improve the restrictive capability in accordance with the fixed activity period of the duty cycle technology in the MAC protocol for wireless sensor networks, we propose a periodic adaptive wakeup technique based on receive prediction. The proposed technique is through a performance evaluation using the CC2500 RF transceiver and C8051F330 microcontroller based wireless node, to analyze the minimum active period. As a result, it was confirmed that it is possible to improve energy efficiency by adaptively changing the sleep period in accordance with the change of period.