• Smart Structures and Systems
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• v.34 no.2
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• pp.129-143
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• 2024

In recent years, with the vigorous development of visual algorithms, a large amount of research has been conducted on blade surface defect detection methods represented by deep learning. Detection methods based on deep learning models must rely on a large and rich dataset. However, the geographical location and working environment of wind turbines makes it difficult to effectively capture images of blade surface defects, which inevitably hinders visual detection. In response to the challenge of collecting a dataset for surface defects that are difficult to obtain, a multi-class blade surface defect generation method based on the StyleGAN3 (Style Generative Adversarial Networks 3) deep learning model and PBGMs (Physics-Based Graphics Models) method has been proposed. Firstly, a small number of real blade surface defect datasets are trained using the adversarial neural network of the StyleGAN3 deep learning model to generate a large number of high-resolution blade surface defect images. Secondly, the generated images are processed through Matting and Resize operations to create defect foreground images. The blade background images produced using PBGM technology are randomly fused, resulting in a diverse and high-resolution blade surface defect dataset with multiple types of backgrounds. Finally, experimental validation has proven that the adoption of this method can generate images with defect characteristics and high resolution, achieving a proportion of over 98.5%. Additionally, utilizing the EISeg annotation method significantly reduces the annotation time to just 1/7 of the time required for traditional methods. These generated images and annotated data of blade surface defects provide robust support for the detection of blade surface defects.

• Smart Structures and Systems
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• v.33 no.4
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• pp.291-300
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• 2024

Uncertainty of the model, system delay and drive dynamics can be considered as normal uncertainties, and the main source of uncertainty in the seismic control system is related to the nature of the simulated seismic error. In this case, optimizing the management strategy for one particular seismic record will not yield the best results for another. In this article, we propose a framework for online management of active structural management systems with seismic uncertainty. For this purpose, the concept of reinforcement learning is used for online optimization of active crowd management software. The controller consists of a differential controller, an unplanned gain ratio, the gain of which is enhanced using an online reinforcement learning algorithm. In addition, the proposed controller includes a dynamic status forecaster to solve the delay problem. To evaluate the performance of the proposed controllers, thousands of ground motion data sets were processed and grouped according to their spectrum using fuzzy clustering techniques with spatial hazard estimation. Finally, the controller is implemented in a laboratory scale configuration and its operation is simulated on a vibration table using cluster location and some actual seismic data. The test results show that the proposed controller effectively withstands strong seismic interference with delay. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results is believed to achieved in the near future by the ongoing development of AI and control theory.

• Science of Emotion and Sensibility
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• v.18 no.4
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• pp.25-34
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• 2015

TThis study explored the requirement of fabric sensor that can measure the motion of the joint effectively by measuring and analyzing the variation in electric resistance of a sensor in accordance with bending and stretching motion of the arm by the implementation of a motion sensor utilizing conductive fabric. For this purpose, on both sides of two kinds of knitted fabric, namely 'L' fabric and 'W' fabric Single Wall Carbon Nano-Tube(SWCNT) was coated, fabric sensor was developed by finishing them in a variety of ways, and the sensor was attached to the arm band. The fabric sensor consisted of total 48 cases, namely background fabric for coating, the method of sensor attachment, the number of layer of sensors, the length of sensor, and the width of sensor. The performance of fabric motion sensors in terms of a dummy arm, that is, a Con-Trex MJ with 48 arm bands around it was evaluated. For each arm band, a total of 48, fastened around the dummy arm, it was adjusted to repeat the bending and stretching at the frequency : 0.5Hz, ROM : $20^{\circ}{\sim}120^{\circ}$, the voltage was recorded for each case after conducting three sets of repeat measurement for a total of 48 cases. As a result of the experiment, and as a consequences of the evaluation and analysis of the voltage based on the uniformity of the base line of the peak-to-peak voltage(Vp-p), the uniformity of Vp-p within the same set, and the uniformity of the Vp-p among three sets, the fabric sensors that have been configured in SWCNT coated 'L' fabric / welding / two layers / $50{\times}5mm$, $50{\times}10mm$, $100{\times}10mm$, and SWCNT coated 'W' fabric / welding / two layers / $50{\times}10mm$ exhibited the most uniform and stable signal value within 5% of the total variation rate. Through all these results of the experiment, it was confirmed that SWCNT coated fabric was suitable for a sensor that can measure the human limb operation when it was implemented as a fabric sensor in a variety of forms, and the optimal sensor types were identified.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Economic and Environmental Geology
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• v.53 no.5
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• pp.609-617
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• 2020

After introducing the industry 4.0 policy, Korean government announced 'Digital New Deal' and 'Green New Deal' as 'Korean New Deal' in 2020. We analyzed Korea Institute of Geoscience and Mineral Resources (KIGAM)'s research projects related to that policy and conducted markets analysis focused on Digital Twin and environmental monitoring technologies. Regarding 'Data Dam' policy, we suggested the digital geo-contents with Augmented Reality (AR) & Virtual Reality (VR) and the public geo-data collection & sharing system. It is necessary to expand and support the smart mining and digital oil fields research for '5th generation mobile communication (5G) and artificial intelligence (AI) convergence into all industries' policy. Korean government is suggesting downtown 3D maps for 'Digital Twin' policy. KIGAM can provide 3D geological maps and Internet of Things (IoT) systems for social overhead capital (SOC) management. 'Green New Deal' proposed developing technologies for green industries including resource circulation, Carbon Capture Utilization and Storage (CCUS), and electric & hydrogen vehicles. KIGAM has carried out related research projects and currently conducts research on domestic energy storage minerals. Oil and gas industries are presented as representative applications of digital twin. Many progress is made in mining automation and digital mapping and Digital Twin Earth (DTE) is a emerging research subject. The emerging research subjects are deeply related to data analysis, simulation, AI, and the IoT, therefore KIGAM should collaborate with sensors and computing software & system companies.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.1-8
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• 2016

Current sensors are used widely in the fields of current control, monitoring, and measuring. They have become more popular with the increasing demand for smart grids in a power network, generation of renewable energy, electric cars, and hybrid cars. Although open loop Hall effect current sensors have merits, such as low cost, small size, and weight, they have low accuracy. This paper describes the design and fabrication of a 300A open loop current sensor that has high accuracy and temperature performance. The core of the current sensor was calculated numerically and the signal conditioning circuits were designed using circuit analysis software. The characteristics of the manufactured open loop current sensor of 300 A class was measured at currents up to 300 A. According to the test of the current sensor, the accuracy error and linearity error were 0.75% and 0.19%, respectively. When the temperature compensation was carried out with the relevant circuit, the temperature coefficients were less than $0.012%/^{\circ}C$ at temperatures between $-25^{\circ}C$ and $85^{\circ}C$.

• Journal of the Korea Convergence Society
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• v.4 no.2
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• pp.29-33
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• 2013

From one elementary school of last 2005 Pusan was started "the school police" system is being applied the effect in the elementary school of the human nature receiving entire country. But "with school police system " have a limit about manpower that is insufficient. So we try to make "Baumter-Gikimy " that is extended scollo police in seoul. Despite being like that at 2010 and 2011 cannot forget the elementary student suicide events which are shocking or sexual assault events occur from the campus.From elementary school 4 grade the students between junior high school 1 grade crime occurrence ratio are highest. These people the group leaving out mainly, suicide, with group violence and sexual assault problem gets up same problems. In order to solve like this problems stands the countermeasure of the dimension which is national certainly is necessary. But so far only the damage student and the parents have an interest in about like this problems and they hurt. We the smart phone of Android bases it will be able to solve like this problem points [ayp] from under planning and development boil the elementary student safe helper it will can use. When it uses the elementary student safe helper system which we propose before the accident occurs, it will grasp the state information of the student and it will be able to prevent an accident in advance. We will try to more efficient safe guard to elementary school student by real-time monitoring skill soon.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.859-864
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• 2017

IoT is a new emerging technology to lead the $4^{th}$ industry renovation and has been widely used in industry and home to increase the quality of human being. In this paper, IoT based face detection and recognition system for a smart elevator is developed. Haar cascade classifier is used in a face detection system and a proposed PCA algorithm written in Python in the face recognition system is implemented to reduce the execution time and calculates the eigenfaces. SVM or Euclidean metric is used to recognize the faces detected in the face detection system. The proposed system runs on RaspberryPi 3. 200 sample images in ORL face database are used for training and 200 samples for testing. The simulation results show that the recognition rate is over 93% for PP+EU and over 96% for PP+SVM. The execution times of the proposed PCA and the conventional PCA are 0.11sec and 1.1sec respectively, so the proposed PCA is much faster than the conventional one. The proposed system can be suitable for an elevator monitoring system, real time home security system, etc.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.577-582
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• 2016

This Study focused on the development of SMPS (Switching Mode Power Supply) to supply the constant votage and current nevertheless LED fluorescent Light generated the electric noise (with Harmonics) and Inrush current at instant time of turn-on and off. Recently, according to the Green policy in government, the LED fluorescent Lighter showed the rapidly increasing tend as indoor and outdoor Lighter. But, because of costs, LED fluorescent Light not considered and neglected the following items; power factor, efficiency, Harmonics and Inrush current. So, we are developed the SMPS about 3 key issues as follows: 1st, power factor and efficiency is 85%. 2nd, the switching noisy by harmonic is minimized. 3rd, the Inrush current at turn on and off time is reduced the minimum 0.3 A after $100{\mu}sec$ on turnon time. The proposed SMPS adjusted by LNK 409 driver (included the high frequency modulation function). Although, the developed SMPS maintained the about 85% of power factor and efficiency. but, the SMPS must be generated low heat by the variation of minute load current at switching timing. To improve the above weak point, the developed SMPS have the feedback monitoring circuit between input side and output side to maintain the power factor and efficiency. Also, we are studied the time-constant of control circuit to output the constant voltage and current nevertheless the load disturbance of LED lighting. The LED fluorescent Light of 46W is checked the above items.