• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2642-2649
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• 2023

A real-time unmeasured dynamic response prediction process for the nuclear power plant pressure pipeline is proposed and its performance is tested in the test-loop system (KAERI). The aim of the process is to predict unmeasurable or unreachable dynamic responses such as acceleration, velocity, and displacement by using a limited amount of directly measured physical responses. It is achieved by combining a well-constructed finite element model and robust inverse force identification algorithm. The pressure pipeline system is described by using the displacement-pressure vibro-acoustic formulation to consider fully filled liquid effect inside the pipeline structure. A robust multiphysics modal projection technique is employed for the real-time sensor synchronized prediction. The inverse force identification method is also derived and employed by using Bathe's time integration method to identify the full-field responses of the target system from the modal domain computation. To validate the performance of the proposed process, an experimental test is extensively performed on the nuclear power plant pressure pipeline test-loop under operation conditions. The results show that the proposed identification process could well estimate the unmeasured acceleration in both frequency and time domain faster than 32,768 samples per sec.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.21-28
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• 2024

Radar is an essential sensor component in autonomous vehicles, and the market for radar applications in this context is steadily expanding with a growing variety of products. In this study, we aimed to enhance the stability and performance of radar systems by developing and evaluating a radar performance prediction model that can predict radar defects. We selected seven machine learning and deep learning algorithms and trained the model with a total of 49 input data types. Ultimately, when we employed an ensemble of 17 models, it exhibited the highest performance. We anticipate that these research findings will assist in predicting product defects at the production stage, thereby maximizing production yield and minimizing the costs associated with defective products.

• Journal of Hydrogen and New Energy
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• v.25 no.5
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• pp.491-499
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• 2014

It is important to accurately measure and control the relative humidity of humidified gas entering a PEM (polymer electrolyte membrane) fuel cell stack because the level of humidification strongly affects the performance and durability of the stack. Humidity measurement devices can be used to directly measure the relative humidity, but they cost much to be equipped and occupy spaces in a fuel cell system. We present soft sensors for predicting the relative humidity without actual humidity measuring devices. By combining FIR (finite impulse response) model with PLS (partial least square) and SVM (support vector machine) regression models, DPLS (dynamic PLS) and DSVM (dynamic SVM) soft sensors were developed to correctly estimate the relative humidity of humidified gases exiting a planar-type membrane humidifier. The DSVM soft sensor showed a better prediction performance than the DPLS one because it is able to capture nonlinear correlations between the relative humidity and the input data of the soft sensors. Without actual humidity sensors, the soft sensors presented in this work can be used to monitor and control the humidity in operation of PEM fuel cell systems.

• Advances in robotics research
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• v.2 no.2
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• pp.129-140
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• 2018

With the increasing demand for the development of human pose estimation, such as human-computer interaction and human activity recognition, there have been numerous approaches to detect the 2D poses of people in images more efficiently. Despite many years of human pose estimation research, the estimation of human poses with images remains difficult to produce satisfactory results. In this study, we propose a robust 2D human body pose estimation method using an RGB camera sensor. Our pose estimation method is efficient and cost-effective since the use of RGB camera sensor is economically beneficial compared to more commonly used high-priced sensors. For the estimation of upper-body joint positions, semantic segmentation with a fully convolutional network was exploited. From acquired RGB images, joint heatmaps accurately estimate the coordinates of the location of each joint. The network architecture was designed to learn and detect the locations of joints via the sequential prediction processing method. Our proposed method was tested and validated for efficient estimation of the human upper-body pose. The obtained results reveal the potential of a simple RGB camera sensor for human pose estimation applications.

• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• Journal of Navigation and Port Research
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• v.28 no.6
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• pp.573-578
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• 2004

The most important effects on algae multiplication are coming from maintaining the growth environment such as necessary nutrients and proper temperature, but it is difficult to adjust for every species individually. In this study, therefore, the environment is obtained using the local water where target organisms live, and their growth is promoted by raising the water temperature. A sensor to count the organism population is developed here. Because the early stage of a sudden increase of the algae population is detected using the sensor, it is available to predict the sudden increase of algae, a source of red tide.

• Science of Emotion and Sensibility
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• v.22 no.3
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• pp.55-64
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• 2019

Stroke is a health problem experienced by many elderly people around the world. Stroke has a devastating effect on quality of life, causing death or disability. Hemiplegia is clearly an early sign of a stroke and can be detected through patterns of body balance and gait. The goal of this study was to determine various feature vectors of foot pressure and gait parameters of patients with stroke through the use of a wearable sensor and to compare the gait parameters with those of healthy elderly people. To monitor the participants at all times, we used a simple measuring device rather than a medical device. We measured gait data of 220 healthy people older than 65 years of age and of 63 elderly patients who had experienced stroke less than 6 months earlier. The center of pressure and the acceleration during standing and gait-related tasks were recorded by a wearable insole sensor worn by the participants. Both the average acceleration and the maximum acceleration were significantly higher in the healthy participants (p < .01) than in the patients with stroke. Thus gait parameters are helpful for determining whether they are patients with stroke or normal elderly people.

• Journal of Advanced Navigation Technology
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• v.13 no.5
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• pp.699-704
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• 2009

The requirements of disaster prevention have been constantly increasing on highly disaster frequency by Global warming and environmental destruction. The damage occur more highly, especially when it's on the localized change of weather. It requires that we have methods of disaster prevention locally. In this paper, we design and implement a breakwater disaster prevention system integrated wireless sensor technique for the shore breakwater of East Sea that is raised anxiety about an accident occurrence due to stormy weather. The provided disaster prevention system perceive the seriousness of the situation that is chance of that happening by the information of realtime remote situation and a prediction system so that it could be of some help to reduce the damage of disaster and the cost of recovery.

• Journal of the Semiconductor & Display Technology
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• v.12 no.4
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• pp.39-44
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• 2013

In the semiconductor heat-treatment process, the temperature uniformity determines the film quality of a wafer. This film quality effects on the overall yield rate. The heat transfer of the wafer surface in the heat-treatment process equipment is occurred by convection and radiation complexly. Because of this, there is the nonlinearity between the wafer temperature and reactor. Therefore, the accurate prediction of temperature on the wafer surface is difficult without the direct measurement. The thermal camera and the T/C wafer are general ways to confirm the temperature uniformity on the heat-treatment process. As above ways have limit to measure the temperature in the precise domain under the micro-scale. In this study, we developed the thin film type temperature sensor using the MEMS technology to establish the system which can measure the temperature under the micro-scale. We combined the experiment and numerical analysis to verify and calibrate the system. Finally, we measured the temperature on the wafer surface on the semiconductor process using the developed system, and confirmed the temperature variation by comparison with the commercial T/C wafer.

• Journal of KIISE
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• v.44 no.7
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• pp.726-732
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• 2017

In practical distributed sensing and prediction applications over wireless sensor networks (WSN), environmental sensing activities are highly dynamic because of noisy sensory information from moving source signals. The recent distributed online convex optimization frameworks have been developed as promising approaches for solving approximately stochastic learning problems over network of sensors in a distributed manner. Negligence of mobility consequence in the original distributed saddle point algorithm (DSPA) could strongly affect the convergence rate and stability of learning results. In this paper, we propose an integrated sliding windows mechanism in order to stabilize predictions and achieve better convergence rates in cooperative detection of a moving source signal scenario.