• Smart Structures and Systems
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• v.25 no.6
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• pp.733-749
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• 2020

This study presents a computer vision-based approach for representing time evolution of structural damages leveraging a database of inspection images. Spatially incoherent but temporally sorted archival images captured by robotic cameras are exploited to represent the damage evolution over a long period of time. An access to a sequence of time-stamped inspection data recording the damage growth dynamics is premised to this end. Identification of a structural defect in the most recent inspection data set triggers an exhaustive search into the images collected during the previous inspections looking for correspondences based on spatial proximity. This is followed by a view synthesis from multiple candidate images resulting in a single reconstruction for each inspection round. Cracks on concrete surface are used as a case study to demonstrate the feasibility of this approach. Once the chronology is established, the damage severity is quantified at various levels of time scale documenting its progression through time. The proposed scheme enables the prediction of damage severity at a future point in time providing a scope for preemptive measures against imminent structural failure. On the whole, it is believed that the present study will immensely benefit the structural inspectors by introducing the time dimension into the autonomous condition assessment pipeline.

• Journal of Drive and Control
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• v.19 no.4
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• Journal of Auto-vehicle Safety Association
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• v.9 no.1
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• pp.19-24
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• 2017

This paper present a performance evaluation scenarios to assess the safety performance of autonomous emergency braking (AEB) system for cyclist collision. To guarantee the safety performance of AEB for cyclist, AEB system should be tested in various scenarios which can be occurred in real driving condition. For this, real-traffic car-to-cyclist collision data are analyzed to classify the real traffic collision scenarios. Using this information, typical car-to-cyclist collision scenarios are selected. Also, in order to develop the detail features of these collision scenarios, several accident cases related with these scenarios are explained. Based on these information, test scenarios which can describe the car-to-cyclist collisions occurred in Korea are proposed. For practicality and feasibility of the test scenarios, proposed scenarios should be designed to assess the safety performance of AEB system effectively. For this, some test scenarios are combined or removed based on the consideration about the effectiveness of each scenario to the assessment of the performance of AEB system. To confirm that the proposed test scenarios are realistic and physically meaningful, simulation is conducted using simple AEB system in proposed test scenarios.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.641-652
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• 2021

Drivers have willing to do secondary tasks in situations deemed safe autonomous driving. I studied that positive notifications for secure areas could improve situation awareness and driving performance after TOR(Take over request) in autonomous driving. Comparing TOR alert only and monitoring alert conditions, participants in the positive notification condition showed higher situation awareness and driving performance. Also, in emotional assessment, the positive notification condition showed higher positive evaluation than other conditions. Due to Covid-19, I designed experiments separate online with driving videos in experiment 1 and offline using a driving simulator in experiment 2. This study has implications that presented a different perspective on autonomous driving notification design.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.555-562
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• 2023

The Railway Safety Act of 2014 strengthened the 'Railway Safety Management System' to establish autonomous safety management for railway operators and railway facility managers. Accordingly, it is required to establish and implement risk assessment and safety measures for risk management. However, the current risk assessment system is carried out at the fragmented safety management level within individual fields, which has caused difficulties in establishing and implementing risk assessment and safety measures. In addition, the technical standards of the safety management system stipulate that risk assessment of railway operators is mandatory, so standardized standards for risk assessment of railway facilities and railway vehicle maintenance are needed. Therefore, in this paper, we first verified railway risks by analyzing railway accident data for the last 10 years, and proposed a standardized framework to effectively assess and manage risks through a case study of a condition-based smart maintenance system developed based on railway vehicle maintenance data.

• Computers and Concrete
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• v.33 no.4
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• pp.341-348
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• 2024

Ultra-high-performance concrete (UHPC) has received remarkable attentions in civil infrastructure due to its unique mechanical characteristics and durability. UHPC gains increasingly dominant in essential structural elements, while its unique properties pose challenges for traditional inspection methods, as damage may not always manifest visibly on the surface. As such, the need for robust inspection techniques for detecting cracks in UHPC members has become imperative as traditional methods often fall short in providing comprehensive and timely evaluations. In the era of artificial intelligence, computer vision has gained considerable interest as a powerful tool to enhance infrastructure condition assessment with image and video data collected from sensors, cameras, and unmanned aerial vehicles. This paper presents a computer vision-based approach employing deep learning to detect cracks in UHPC beams, with the aim of addressing the inherent limitations of traditional inspection methods. This work leverages computer vision to discern intricate patterns and anomalies. Particularly, a convolutional neural network architecture employing transfer learning is adopted to identify the presence of cracks in the beams. The proposed approach is evaluated with image data collected from full-scale experiments conducted on UHPC beams subjected to flexural and shear loadings. The results of this study indicate the applicability of computer vision and deep learning as intelligent methods to detect major and minor cracks and recognize various damage mechanisms in UHPC members with better efficiency compared to conventional monitoring methods. Findings from this work pave the way for the development of autonomous infrastructure health monitoring and condition assessment, ensuring early detection in response to evolving structural challenges. By leveraging computer vision, this paper contributes to usher in a new era of effectiveness in autonomous crack detection, enhancing the resilience and sustainability of UHPC civil infrastructure.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.51-58
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• 2023

Geofencing supports unmanned aerial vehicle (UAV) operation by defining stay-in and stay-out regions. National Aeronautics and Space Administration (NASA) has developed a prototype of the geofencing function, SAFEGUARD, which prevents stayout region violation by utilizing position estimates. Thus, SAFEGUARD depends on navigation system performance, and the safety risk associated with the navigation system uncertainty should be considered. This study presents a methodology to compute the safety risk assessment-based along-track position error bound under nominal and Global Navigation Satellite Systems (GNSS) failure conditions. A Kalman filter system using pseudorange measurements as well as pseudorange rate measurements is considered for determining the position uncertainty induced by velocity uncertainty. The worst case pseudorange and pseudorange rate fault-based position error bound under the GNSS failure condition are derived by applying a Receiver Autonomous Integrity Monitor (RAIM). Position error bound simulations are also conducted for different GNSS fault hypotheses and constellation conditions with a GNSS/INS integrated navigation system. The results show that the proposed along-track position error bounds depend on satellite geometries caused by UAV attitude change and are reduced to about 40% of those of the single constellation case when using the dual constellation.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.5 no.2
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• pp.225-236
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• 1998

The literature review about bladder management method given to maintain and improve health of neurogenic bladder patients was done. Because the study of neurogenic bladder patients in nursing field is not enough, I tried to find report the study tendency through literature review, 1. There are five types of neurogenic bladder such as uninhibited neurogenic bladder, reflex neurogenic bladder, autonomous neurogenic bladder, sensory paralytic neurogenic bladder, and motor paralytic neurogenic bladder. 2. The accurate assessment of neurogenic bladder is done mainly through urodynamics and especially cystometrogram and urethrogram are used. 3. As the study of therapeutic management, the effect of Desmopressin, bladder auto-augmentation, incision of external urethral sphincter muscle, subarachnoid block and pudendal never block using phenol was studied. 4. For the study of general management, the effect of bladder training progam, intermittent catheterization and infection control has been studied but there has not been any obvious study in nursing field. Reviewed the study condition, it is necessary to develope bladder training program in order to increase life quality of neurogenic bladder patients.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.19-34
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• 2015

One of the main causes of a limited use of nondestructive evaluation (NDE) technologies in bridge deck assessment is the speed of data collection and analysis. The paper describes development and implementation of the RABIT (Robotics Assisted Bridge Inspection Tool) for data collection using multiple NDE technologies. The system is designed to characterize three most common deterioration types in concrete bridge decks: rebar corrosion, delamination, and concrete degradation. It implements four NDE technologies: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW) method. The technologies are used in a complementary way to enhance the interpretation. In addition, the system utilizes advanced vision to complement traditional visual inspection. Finally, the RABIT collects data at a significantly higher speed than it is done using traditional NDE equipment. The robotic system is complemented by an advanced data interpretation. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. This paper concentrates on the validation and field implementation of two NDE technologies. The first one is IE used in the delamination detection and characterization, while the second one is the USW method used in the assessment of concrete quality. The validation of performance of the two methods was conducted on a 9 m long and 3.6 m wide fabricated bridge structure with numerous artificial defects embedded in the deck.