• Smart Structures and Systems
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• v.20 no.5
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• pp.549-562
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• 2017

The US railroad network carries 40% of the nation's total freight. Railroad bridges are the most critical part of the network infrastructure and, therefore, must be properly maintained for the operational safety. Railroad managers inspect bridges by measuring displacements under train crossing events to assess their structural condition and prioritize bridge management and safety decisions accordingly. The displacement of a railroad bridge under train crossings is one parameter of interest to railroad bridge owners, as it quantifies a bridge's ability to perform safely and addresses its serviceability. Railroad bridges with poor track conditions will have amplified displacements under heavy loads due to impacts between the wheels and rail joints. Under these circumstances, vehicle-track-bridge interactions could cause excessive bridge displacements, and hence, unsafe train crossings. If displacements during train crossings could be measured objectively, owners could repair or replace less safe bridges first. However, data on bridge displacements is difficult to collect in the field as a fixed point of reference is required for measurement. Accelerations can be used to estimate dynamic displacements, but to date, the pseudo-static displacements cannot be measured using reference-free sensors. This study proposes a method to estimate total transverse displacements of a railroad bridge under live train loads using acceleration and tilt data at the top of the exterior pile bent of a standard timber trestle, where train derailment due to excessive lateral movement is the main concern. Researchers used real bridge transverse displacement data under train traffic from varying bridge serviceability levels. This study explores the design of a new bridge deck-pier experimental model that simulates the vibrations of railroad bridges under traffic using a shake table for the input of train crossing data collected from the field into a laboratory model of a standard timber railroad pile bent. Reference-free sensors measured both the inclination angle and accelerations of the pile cap. Various readings are used to estimate the total displacements of the bridge using data filtering. The estimated displacements are then compared to the true responses of the model measured with displacement sensors. An average peak error of 10% and a root mean square error average of 5% resulted, concluding that this method can cost-effectively measure the total displacement of railroad bridges without a fixed reference.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.4
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• pp.63-70
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• 2011

In this paper, we proposed a method for estimating energy expenditure using the unfixed axis of the accelerometer. Most studies adopted waist-placement because of the fact that the waist is close to the center of mass of a whole human body. But we adopted pocket-placement, which is capable of using unfixed axis of sensor, that is more convenient than conventional methods. To evaluate the proposed method, 28 male subjects performed walking and running on a motor driven treadmill. All of subject put on the indirect calorimeter and fixed accelerometer, then data were simultaneously measured during exercise. The regression analysis was performed using the test group(n=20) and the regression equation was applied to the control group(n=8). A strong linear relationship between energy expenditure and unfixed accelerometer signal was found. Futhermore, the coefficient of determination was significantly reliable($R^2$=0.98) and showed zero of p-value. The error of energy expenditure estimation between indirect calorimeter and two types of accelerometer was 15.0%(fixed) and 17.0%(unfixed) respectively. These results show the possibilities that the unfixed accelerometer can be used in estimating the energy expenditure during exercise.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.61-70
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• 2020

In this study, the IOT-based desk layout method was proposed to complement the existing seating method and to improve the work efficiency. The IoT system for the desk layout needs determining the function, type and network protocol of the sensor to find out the working status of the desk to reasonably assist the worker's seat placement. A collection method was proposed. The algorithm used in Block Stacking was used when deciding how to allocate seats using the acquired data. As a result, we could suggest an arithmetic basis for rational desk layout in IoT environment and show that it can be applied to an advanced flexible seating system based on working type in addition to the preferences of employees in the future.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.11
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• pp.247-249
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• 2020

In the era of the 4th industrial revolution, the demand for convergence between ICT technologies is increasing in various fields. Accordingly, a new term that combines data, network, and artificial intelligence technology, DNA (Data, Network, AI) is in use. and has recently become a hot topic. DNA has various potential technology to be able to develop intelligent application in the real world. Therefore, this paper introduces the reviewed papers on the service image placement mechanism based on the logical fog network, the mobility support scheme based on machine learning for Industrial wireless sensor network, the prediction of the following BCI performance by means of spectral EEG characteristics, the warning classification method based on artificial neural network using topics of source code and natural language processing model for data visualization interaction with chatbot, related on DNA technology.

• Composites Research
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• v.28 no.6
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• pp.383-388
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• 2015

Ionic Polymer-Metal Composite (IPMC) consisting of soft membrane plated by platinum electrode layers on both surfaces generates electric energy when subjected to various mechanical stimuli. The paper proposes a circuit model that describes the physical composition of IPMC to predict the voltage generation characteristic corresponding to bending motion. The parameter values in the model are identified to minimize the RMS error between the real and simulated outputs. Following the design of IPMC circuit model, the state observer of the model is designed by using pole placement technique which improves the model accuracy. State observer design technique is also applied to find the inverse model which estimates the input bending angles from the output voltage data. The results show that the inverse model estimates input bending angles fairly well enough for the further applications of IPMC not only as an energy harvester but also as a bending sensor.

• Physical Therapy Korea
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• v.28 no.3
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• pp.208-214
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• 2021

Background: The CKCUES test evaluates the functional performance of the shoulder joint. The CKCUES test scores CKC exercises of the upper limbs to examine shoulder stability. Although the CKCUES test provides quantitative data on functional ability and performance, no study has determined the relationship between CKCUES scores and SA and TB muscle strength. Objects: The objective of this study is to determine the relationship between the CKCUES test scores and the strength of the SA and TB muscles in the CKCUES and unilateral CKCUES tests. Methods: Sixty-six healthy male volunteers participated in the study. A Smart KEMA strength sensor measured SA and TB muscle strength. Two parallel lines on the floor indicated the initial hand placement to start CKCUES tests. For 15 seconds, the subject raises one hand and reaches over to touch the supporting hand, then returns to the starting position. Results: The correlation between the CKCUES test scores and the strength of the SA was strong (r = 0.650, p < 0.001), and the TB was moderate (r = 0.438, p < 0.001). The correlation between the unilateral CKCUES test and the strength of the SA of the supporting side was strong (r = 0.605, p < 0.001), and swing side was strong (r = 0.681, p < 0.001). The correlation between the unilateral CKCUES test and the strength of the TB of the supporting side was moderate (r = 0.409, p < 0.001), and swing side was moderate (r = 0.482, p < 0.001). Conclusion: Our study showed that the CKCUES test had a strong association with isometric strength of SA and moderate association with that of TB. These findings suggest that the CKCUES test can evaluate the function of the SA. Moreover, the unilateral CKCUES test can evaluate unilateral shoulder function.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.419-425
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• 2022

This paper aims to evaluate the acoustic detection performance for the deployment of the source and receiver positions of a bi-static sonar. In contrast with a mono-static sonar, a bi-static sonar has a large amount of computation and complexity for deployment. Therefore, in this study, we propose an assessment method that reduces the amount of computation while considering the variability of the ocean environment as a method to apply to the placement of the source and receiver of a bi-static sonar. First, we assume the representative ocean environment in the shallow and deep water. The signal excess is calculated with the source to receiver ranges and sensor depths. And the result is compared with the proposed assessment method of acoustic detection performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.117-125
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• 2015

In this paper, we propose a classification algorithm based on the obtained EEG(Electroencephalogram) signal for the control of 'left' and 'right' turnings of which a driving system composed of EEG sensor, Labview, DAQ, Matlab and driving robot. The proposed algorithm uses features extracted from frequency band information obtained by DWT (Discrete Wavelet Transform) and selects features of high discrimination by using Fisher score. We, also propose the number of feature vectors for the best classification performance by using SVM(Support Vector Machine) classifier and propose a decision pending algorithm based on MLD (Maximum Likelihood Decision) to prevent malfunction due to misclassification. The selected four feature vectors for the proposed algorithm are the mean of absolute value of voltage and the standard deviation of d5(2-4Hz) and d2(16-32Hz) frequency bands of P8 channel according to the international standard electrode placement method. By using the SVM classifier, we obtained 98.75% accuracy and 1.25% error rate. Also, when we specify error probability of 70% for decision pending, we obtained 95.63% accuracy and 0% error rate by using the proposed decision pending algorithm.