• 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.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.134-138
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• 2023

This study focuses on the analysis of the results of computational fluid dynamics simulations of mist-chemical vapor deposition for the growth of an epitaxial wafer in power semiconductor technology using artificial intelligence techniques. The conventional approach of predicting the uniformity of the deposited layer using computational fluid dynamics and design of experimental takes considerable time. To overcome this, artificial intelligence method, which is widely used for optimization, automation, and prediction in various fields, was utilized to analyze the computational fluid dynamics simulation results. The computational fluid dynamics simulation results were analyzed using a supervised deep neural network model for regression analysis. The predicted results were evaluated quantitatively using Euclidean distance calculations. And the Bayesian optimization was used to derive the optimal condition, which results obtained through deep neural network training showed a discrepancy of approximately 4% when compared to the results obtained through computational fluid dynamics analysis. resulted in an increase of 146.2% compared to the previous computational fluid dynamics simulation results. These results are expected to have practical applications in various fields.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.205-211
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• 2022

Performing the rituals of Hajj and Umrah is an obligation of Allah Almighty to all Muslims from all over the world. Millions of Muslims visit the holy mosques in Makkah every year to perform Hajj and Umrah. One of the most important pillars in Performing Hajj/Umrah is Tawaf and Sa'ay. Tawaf finished by seven rounds around the holy house (Al-Kabaa) and Sa'ay is also seven runs between As-Safa and Al-Marwa. Counting/knowing the number of runs during Tawaf/Sa'ay is one of the difficulties that many pilgrims face. The pilgrim's confusing for counting (Tawaf/Sa'ay) rounds finished at a specific time leads pilgrims to stay more time in Mataff bowl or Masa'a run causing stampedes and more crowded as well as losing the desired time for prayers to get closer to Almighty Allah in this holy place. These issues can be solved using effective crowd management systems for Tawaf/Sa'ay pillars, which is the topic of this research paper. While smart devices and their applications are gaining popularity in helping pilgrims for performing Hajj/Umrah activities efficiently, little has been dedicated for solving these issues. We present an autonomous Mobile-based framework for guiding pilgrims during Tawaf/Sa'ay pillars with the aid of GPS for points tracking and rounds counting. This framework is specially designed to prevent and manage stampedes during Tawaf/Sa'ay pillars, by helping pilgrims automatically counting the rounds during Tawaf/Sa'ay with supported Supplications (in written/audio form with different languages) from the Quran and Sunna'a.

• Smart Structures and Systems
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• v.31 no.4
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• pp.311-323
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• 2023

Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based post-earthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based post-earthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.125-135
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• 2010

Mobile office is becoming common as advances in mobile devices such as PDAs, Smart-phones, or wireless Internet. In this paper, we construct a mobile office environment on a geotechnical information system(GIS). Especially, web services and XML technology combined with SOA (service oriented services) are adopted for various types of mobile devices and services in a minimum cost. Web service and XML can provide an excellent SW reusability, extensibility, and interoperability even for heterogeneous distributed systems. Applications can exploit web services by just knowing server's address. Prototype system integrates a client in Visual Basic.Net and server in Java via the web services and XML data exchange. We verify effectiveness of the approach through the implementation of prototype system.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.655-661
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• 2023

Big data, an important element of the 4th industrial revolution, is actively opening public data in public institutions and local governments. In the public data portal, everyone can conveniently search for data and check related data, but only those in ICT-related fields are using public data. Although data held by public institutions is open to citizens, it is difficult for anyone to easily utilize public data to develop applications. In this paper, data provided in open API format from public data portals has XML and JSON formats. In this study, we are a method of modularizing public data in XML format into a part that can be easily developed by linking it to a GUI interface. Based on the necessary public data, we propose a way to easily develop mobile programs and promote the use of public data.