• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• International journal of advanced smart convergence
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• v.11 no.3
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• pp.187-196
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• 2022

In this paper, we propose an example of designing and constructing a roof-type solar power plant structure equipped with a Pseudo-BIPV (Building-Integrated Photovoltaic) shape suitable for use as a roof of a small warehouse with a sandwich-type panel structure. As the characteristics of the roof-type solar power generation facility to be installed in the small warehouse proposed in this study, the shape of the roof is not a general A type, but a right-angled triangle shape with the slope is designed to face south. We chose a structure in which an inverter for one power plant and a control facility are linked by grouping several roofs of buildings. In addition, the height of the roof structure is less than 20 cm from the floor, and it has a shape similar to that of the BIPV, so it is building-friendly because it is almost in close contact with the roof. At the same time, the roof creates a reflective light source due to the white color. By linking this roof with a double-sided solar panel, we designed it to obtain both the advantage of the roof-friendliness and the advantage of efficiency improvement for the electric power generation based on the double-sided panel. Compared to the existing solar power generation facilities using A-shaped cross-sectional modules, the power generation efficiency of roofs in this case is increased by more than 11%, which we can confirm, through the comparison analysis of monitoring data between power plants in the same area. Therefore, if the roof-type solar structure suitable for the small warehouse we have presented in this paper is used, the facilities of electric power generation is eco-friendly. Further it is easier to obtain facility certification compared to the BIPV, and improved capacity of the power generation can be secured at low material cost. It is believed that the roof-type solar power generation facility we proposed can be usefully used for warehouse or factory-based smart housing. Sensor devices for monitoring, CCTV monitoring, or safety and environment management, operating in connection with the solar power generation facilities, are linked with the Internet of Things (IoT) solution, so they can be monitored and controlled remotely.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.294-305
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• 2022

Recently, drones have been widely used to solve environmental problems such as environmental protection and natural disaster monitoring. This study focuses on the problem of the search altitude of drones when using drones to search for air pollution in order to maintain the urban air environment. In particular, when exploring air pollution in cities using drones, various experiments are conducted to determine the appropriate search altitude for each air pollution source and each communication module. Through the experiment, the maximum measurable altitude for the most common air pollutants, such as CO (carbon monoxide), NO₂ (nitrogen dioxide), O₃ (ozone), and P10, P2.5 (fine dust), was identified, and the effective search altitude for each air pollution source was determined. As a result of the experiment, three types of drone search altitudes including legally measurable altitudes were suggested. The communication module measurable altitude was 60m to 120m depending on the communication module, and the effective measurable altitude was analyzed from 10m to 100m.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.107-114
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• 2022

In this paper, proposes a delivery robot that can be autonomous driving learning. The proposed robot is designed to be used in park-type apartments without ground parking facilities. Compared to the existing apartments with complex ground and underground routes, park-type apartments have a standardized movement path, allowing the robot to run stably, making it suitable for students' initial education environment. The delivery robot is configured to enable delivery of parcels through machine learning technology for route learning and autonomous driving using cameras and LiDAR sensors. In addition, the control MCU was designed by separating it into three parts to enable learning by level, and it was confirmed that it can be used as a delivery robot for learning through operation tests such as autonomous driving and obstacle recognition. In the future, we plan to develop it into an educational delivery robot for various delivery services by linking with the precision indoor location information recognition technology and the public technology platform of the apartment.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.8
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• pp.241-248
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• 2022

In this paper, the PID controller and Kalman filter are applied to improve the automobile cruise control in the environment with disturbance and noise, and the performance is verified through diverse simulation. First, a mathematical model for a automobile cruise control system is introduced. Second, the performance degradation due to disturbance in the basic open-loop control based cruise control system is shown and then PID controller-based feedback control system to resolve this problem is verified. Third, to improve the performance degradation due to sensor noise that may occur during the feedback process, a Kalman filter is applied and verified. Ultimately, it is verified that the designed cruise control system with PID controller and Kalman filter not only satisfies all performance conditions but also has the ability for disturbance rejection and noise reduction.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.111-118
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• 2023

For the implementation of a smart factory, it is necessary to collect data by connecting various sensors and devices in the manufacturing environment and to diagnose or predict failures in production facilities through data analysis. In this paper, to predict the residual useful lifetime of milling insert used for machining products in CNC machine, weight k-NN algorithm, Decision Tree, SVR, XGBoost, Random forest, 1D-CNN, and frequency spectrum based on vibration signal are investigated. As the results of the paper, the frequency spectrum does not provide a reliable criterion for an accurate prediction of the residual useful lifetime of an insert. And the weighted k-nearest neighbor algorithm performed best with an MAE of 0.0013, MSE of 0.004, and RMSE of 0.0192. This is an error of 0.001 seconds of the remaining useful lifetime of the insert predicted by the weighted-nearest neighbor algorithm, and it is considered to be a level that can be applied to actual industrial sites.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.802-806
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• 2004

For the protection of the local air quality and the global atmosphere, the emissions of trace species including nitric oxides (NO and NO$_2$) from gas turbines are regulated by local governments and by the International Civil Aviation Organization. In-situ measurements of such species are needed not only for the development of advanced low-emission combustion concepts but also for providing emissions data required for the sound assessment of the effects of the emissions on environment. We have been developing a laser absorption system that has a capability of simultaneous determination of NO and NO$_2$concentrations in the exhaust jets from aero gas turbines. A diode laser operating near 1.8 micrometer is used for the detection of NO while a separated visible tunable diode laser operating near 676 nanometers is used for NO$_2$. The sensitivities at elevated temperature conditions were determined for simulated gas mixtures heated up to 500K in a heated cell of a straight 0.5 m optical path. Sensitivity limits estimated as were 30 ppmv-m and 3.7 ppmv-m for NO and NO$_2$, respectively, at a typical exhaust gas temperature of 800K. Experiments using the simulated exhaust flows have proven that $CO_2$ and $H_2O$ vapor - both major combustion products - do not show any interference in the NO or NO$_2$ measurements. The measurement system has been applied to the NO/NO$_2$ measurements in NO and NO$_2$ doped real combustion gas jets issuing from a rectangular nozzle having 0.4 m optical path. The lower detection limits of the system were considerably decreased by using a multipass optical cell. A pair of off-axis parabola mirrors successfully suppressed the beam steering in the combustion gas jets by centralizing the fluctuating beam in sensor area of the detectors.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.209-218
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• 2010

The aim of the study on the tactical object representation techniques in synthetic environment is on acquiring fundamental techniques for detection and tracking of tactical objects, and evaluating the strategic situation in the virtual ground. In order to acquire these techniques, there need the tactical objects' position tracking and evaluation, and an inter-sharing technique between tactical models. In this paper, we study the algorithms on the sensor data fusion and coordinate conversion, proportional navigation guidance(PNG), and pursuit-evasion technique for engineering and higher level models. Additionally, we simulate the position evaluation of tractical objects using the pursuit and evasion maneuvers between a submarine and a torpedo.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.625-631
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• 2023

The important requirement of industrial dynamic X-ray detector operating under high tube voltage up to 450 kVp for 24 hours and 7 days is to obtain significantly high radiation resistance. This study presents the radiation resistance characteristics of various thin film transistors (TFTs) with a-Si, poly-Si and IGZO semiconducting layers. IGZO TFT offering dozens of times higher field effect mobility than a-Si TFT was processed with highly hydrogenated plasma in between IGZO semiconducting layer and inter-layered dielectric. The hydrogenated IGZO TFT showed most sustainable radiation resistance up to 10,000Gy accumulated, thus, concluded that it is a sole switching device in X-ray imaging sensor offering dynamic X-ray imaging at high frame rate under extremely severe radiation environment such as automated X-ray inspection.