• Composites Research
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• v.35 no.4
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• pp.283-287
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• 2022

Stretchable strain sensors have been developed for potential future applications including wearable devices and health monitoring. For practical implementation of stretchable strain sensors, their stability and repeatability are one of the important aspects to be considered. In this work, we utilized 3D printed polymer structures having kirigami patterns to improve the stretchability and reduce the hysteresis. The polymer structures were coated with graphene/carbon nanofiber hybrids to make a robust electrical network. The stretchable strain sensors showed a high gauge of 36 at a strain of 32%. Because of the kirigami structures and the robust graphene/carbon nanofiber coating, the sensors also exhibited stable resistance responses at various strains ranging from 1% to 30%.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.356-367
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• 2021

With the recent development of IoT and artificial intelligence technology, research and applications for optimization of real-world problems by collecting and analyzing data in real-time have increased in various fields such as manufacturing and smart city. Representatively, the digital twin platform that supports real-time synchronization in both directions with the virtual world digitized from the real world has been drawing attention. In this paper, we define a digital twin concept and propose a digital twin platform prototype that links real objects and predicted results from the virtual world in real-time by utilizing the oneM2M-based IoT platform. In addition, we implement an application that can predict accidents from object collisions in advance with the prototype. By performing predefined test cases, we present that the proposed digital twin platform could predict the crane's motion in advance, detect the collision risk, perform optimal controls, and that it can be applied in the real environment.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.183-189
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• 2022

COVID-19 is also influencing the coffee industry. This will increase untact consumption, a new consumption trend. Consumption utilizing online channels and delivery service applications that represent untact consumption is becoming commonplace. The coffee industry is also increasingly using coffee shops with drive-through and smart ordering systems that can be ordered with minimal contact. While most of the untact services are preempted at franchise stores, many independent coffee shops still offer differentiated services by communicating directly with customers. However, along with the prolonged COVID-19 infection, coffee shops in the present era, which cannot be free from infectious diseases, have no choice but to worry about delivery services. Therefore, this study analyzed the factors that influence coffee delivery services. Research results due to the influence of COVID-19, regular delivery services have increased along with coffee delivery services. Regular delivery services will play a central role in coffee delivery services due to increased use of home cafes by consumers who want to enjoy coffee in various ways.

• Structural Engineering and Mechanics
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• v.82 no.1
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• pp.107-120
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• 2022

The bracing members capable of active control against seismic loads to reduce earthquake damage have been widely utilized in construction projects. Effectively reducing the structural damage caused by earthquake events, bracing systems equipped with retrofitting damper devices, which take advantage of the energy dissipation and impact absorption, have been widely used in practical construction sites. Shape Memory Alloys (SMAs) are a new generation of smart materials with the capability of recovering their predefined shape after experiencing a large strain. This is mainly due to the shape memory effects and the superelasticity of SMA. These properties make SMA an excellent alternative to be used in passive, semi-active, and active control systems in civil engineering applications. In this research, a new system in diagonal braces with slit damper combined with SMA is investigated. The diagonal element under the effect of tensile and compressive force turns to shear force in the slit damper and creates tension in the SMA. Therefore, by creating shear forces in the damper, it leads to yield and increases the energy absorption capacity of the system. The purpose of using SMA, in addition to increasing the stiffness and strength of the system, is to create reversibility for the system. According to the results, the highest capacity is related to the case where the ratio of the width of the middle section to the width of the end section (b₁/b) is 1.0 and the ratio of the height of the middle part to the total height of the damper (h₁/h) is 0.1. This is mainly because in this case, the damper section has the highest cross-section. In contrast, the lowest capacity is related to the case where b₁/b=0.1 and the ratio h₁/h=0.8.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Smart Structures and Systems
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• v.29 no.1
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• pp.167-179
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• 2022

This article develops a long-term condition assessment method for stay cables in cable stayed bridges using the monitored cable tension forces under operational condition. Based on the concept of influence surface, the matched cable tension ratio of two cables located at the same side (either in the upstream side or downstream side) is theoretically proven to be related to the condition of stay cables and independent of the positions of vehicles on the bridge. A sensor grouping scheme is designed to ensure that reliable damage detection result can be obtained even when sensor fault occurs in the neighbor of the damaged cable. Cable forces measured from an in-service cable-stayed bridge in China are used to demonstrate the accuracy and effectiveness of the proposed method. Damage detection results show that the proposed approach is sensitive to the rupture of wire damage in a specific cable and is robust to environmental effects, measurement noise, sensor fault and different traffic patterns. Using the damage sensitive feature in the proposed approach, the metrics such as accuracy, precision, recall and F1 score, which are used to evaluate the performance of damage detection, are 97.97%, 95.08%, 100% and 97.48%, respectively. These results indicate that the proposed approach can reliably detect the damage in stay cables. In addition, the proposed approach is efficient and promising with applications to the field monitoring of cables in cable-stayed bridges.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Journal of Ocean Engineering and Technology
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• v.36 no.3
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• pp.153-160
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• 2022

Moored floating platforms have great potential in ocean engineering applications because a mooring system is necessary to keep the platform in station, which is directly related to the operational efficiency and safety of the platform. This paper briefly introduces the technical and operational details of an optical sensor for measuring the tension of mooring lines of a moored platform in waves. In order to check the performance of optical sensors, an experiment with a moored floating platform in waves is carried out in the wave tank at Changwon National University. The experiment is performed in regular waves and irregular waves with a semi-submersible and triangle platform. The performance of the optical sensor is confirmed by comparing the results of the tension of the mooring lines by the optical sensor and tension gauges. The maximum tension of the mooring lines is estimated to investigate the mooring dynamics due to the effect of the wave direction and wavelength in the regular waves. The significant value of the tension of mooring lines in various wave directions is estimated in the case of irregular waves. The results show that the optical sensor is effective in measuring the tension of the mooring lines.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.63-70
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• 2023

Due to the spread of high-definition black boxes and the introduction of mobile applications such as 'Smart Citizens Report' and 'Safety Report', the number of public interest reports for violations of Traffic Law has increased rapidly, resulting in shortage of police personnel to handle them. In this paper, we describe the development of a system that can automatically detect lane violations which account for the largest proportion of public interest reporting videos for violations of traffic laws, using deep learning algorithms. In this study, a method for recognizing a vehicle and a solid line object using a YOLO model and a Lanenet model, a method for tracking an object individually using a deep sort algorithm, and a method for detecting lane change violations by recognizing the overlapping range of a vehicle object's bounding box and a solid line object are described. Using this system, it is expected that the shortage of police personnel in charge will be resolved.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.173-182
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• 2023

The UAE government has set its sights on creating a smart, electronic-based government system that utilizes AI. The country's collaboration with India aims to bring substantial returns through AI innovation, with a target of over $20 billion in the coming years. To achieve this goal, the UAE launched its AI strategy in 2017, focused on improving performance in key sectors and becoming a leader in AI investment. To ensure public safety as the role of AI in government grows, the country is working on developing integrated cyber security solutions for SCADA systems. A questionnaire-based study was conducted, using the AI IQ Threat Scale to measure the variables in the research model. The sample consisted of 200 individuals from the UAE government, private sector, and academia, and data was collected through online surveys and analyzed using descriptive statistics and structural equation modeling. The results indicate that the AI IQ Threat Scale was effective in measuring the four main attacks and defense applications of AI. Additionally, the study reveals that AI governance and cyber defense have a positive impact on the resilience of AI systems. This study makes a valuable contribution to the UAE government's efforts to remain at the forefront of AI and technology exploitation. The results emphasize the need for appropriate evaluation models to ensure a resilient economy and improved public safety in the face of automation. The findings can inform future AI governance and cyber defense strategies for the UAE and other countries.