• Science of Emotion and Sensibility
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• v.25 no.4
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• pp.129-138
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• 2022

In the context of the increasing applications of voice assistants in vehicles, we focused on the association between the visual appeal of the cars and the acoustic characteristics of the voice assistants. This study aimed to investigate the relationship between the visual appeal of the vehicle and the voice assistant based on their emotional characteristics. A total of 15 adjectives were used to assess the emotional characteristics of 12 types of cars and six types of voices. An online interview was carried out, instructing participants to match three adjectives with the presented car images or voices. This was followed with a brief interview to allow the participants to reflect on the adjective matches. Based on the assessments, we performed principal component analysis (PCA) to determine factors. We aimed to deploy the cars and voices and analyze the patterns of clustering. The PCA analysis revealed two factors profiled as "Light-Heavy" and "Comfortable-Radical." Both car and voice stimuli were deployed in a two-dimensional space showing the internal relationship within and between the two substances. Based on the coordination data, a hierarchical cluster grouped the 18 stimuli into four groups labeled as challenge, elegance, majesty, and vigor. This study identified two latent factors describing the emotional characteristics of both car images and voice types clustered into four groups based on their emotional characteristics. The coherent matches between car style and voice type are expected to address the design concept more successfully.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.443-451
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• 2010

This paper proposes the methodology about advisory safety speed based on real-time vehicular data collected from highway. The proposed model is useful information to drivers by appling seamless wireless communication and being collected from ECU(Engine Control Unit) equipment in every vehicle. Furthermore, this model also permits the use of realtime sensing data like as adverse weather and road-surface data. Here, the advisory safety speed is defined "the safety speed for drivers considering the time-dependent traffic condition and road-surface state parameter at uniform section", and the advisory safety speed model is developed by considering the parameters: inter-vehicles safe stopping distance, statistical vehicle speed, and real-time road-surface data. This model is evaluated by using the simulation technique for exploring the relationships between advisory safety speed and the dependent parameters like as traffic parameters(smooth condition and traffic jam), incident parameters(no-accident and accident) and road-surface parameters(dry, wet, snow). A simulation's results based on 12 scenarios show significant relationships and trends between 3 parameters and advisory safety speed. This model suggests that the advisory safety speed has more higher than average travel speed and is changeable by changing real-time incident states and road-surface states. The purpose of the research is to prove the new safety related services which are applicable in SMART Highway as traffic and IT convergence technology.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.63-65
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• 2023

BIM and AR technologies have been assessed as a means of enhancing productivity within the construction industry, through the provision of effortless access to critical data on site, achieved via the projection of 3D models and associated information onto actual structures. However, most of the previous researches for applying AR technology in construction quality management has been performed for construction projects in general, resulting in only overall on-site management solutions. Also, a few previous researches for the application of AR in the quality management of specific elements like reinforcements focused only on simple projection, so conducting specific quality inspection was impossible. Hence, this study aimed to develop a practically applicable BIM-based AR quality management system targeted for reinforcements. For the development of this system, the reinforcement inspection items on the quality checklist used at railway construction sites were analyzed, and four types of AR functions that can effectively address these items were developed and installed. The validation result of the system for the actual railway bridge showed a degradation of projection stability. This problem was solved through model simplification and enhancement of the AR device's hardware performance, and then the normal operation of the system was validated. Subsequently, the final developed reinforcement quality inspection system was evaluated for practical applicability by on-site quality experts, and the efficiency of inspection would significantly increase when using the AR system compared to the current inspection method for reinforcements.

• Journal of Korea Port Economic Association
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• v.39 no.3
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• pp.33-46
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• 2023

The Autonomous ships are equipped with a function to judge and navigate the sea conditions on their own, so the job of the ship officer who operates it changes. The educational curriculum to nurture ship officer with the ability to operate and manage autonomous ships must also be changed. This study aimed to develop the curriculum for training autonomous ship officer by using the Delphi survey method suitable for predicting the uncertain future. Among the current 61 subjects for training ship officer identified in the Delphi survey, 32 subjects with high importance should be maintained in the training for autonomous ship officer, and subjects with low importance should be abolished or integrated into other subjects. These subjects were collectively referred to as 'general courses'. The expert panel of the Delphi survey suggested 42 items as new subjects, with 18 items of 'high', 14 items of 'middle', and 10 items of 'low'. Through in-depth analysis of these items by experts, 27 subjects were adjusted and three courses were proposed : 1)'Basic course(10 courses)' for developing basic capabilities such as basic theories for understanding advanced technology and information applied to autonomous ships, 2)'Job course(10 courses)' for practical competency directly related to autonomous ship operation, 3)'Intensive course(7 subjects)' for fostering land remote operators of autonomous ships. Since the introduction and spread of autonomous ships will progress rapidly, research to develop and supplement autonomous ship pilot training courses should be continued by reflecting the level of autonomous navigation of autonomous ships.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.729-741
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• 2023

The Operational Linescan System (OLS)sensor is a sensor aboard satellites launched through the Defense Meteorological Satellite Program (DMSP) that detects light in the visible and infrared bands emitted at night. Studies by several researchers have shown a high correlation between nighttime light data from OLS sensors and gross domestic product values. In this study, we investigated the correlation of nighttime light data with the total amount of individual land prices, which is one of the various indicators related to economic development. The study found that most cities and provinces showed a high correlation with a correlation coefficient of more than 0.7, and the correlation coefficient of 0.7837 between the total amount of individual land price and nighttime light data for the entire South Korea was also high. However, unlike other cities and provinces, Seoul has a low correlation coefficient of 0.5648 between nighttime light and the total amount of individual land price, which is analyzed as a reason that the digital number value of the OLS sensor is close to the maximum value and cannot show further brightness changes. This study is expected to help identify announced land prices in areas where announced land prices are not systematically organized and to analyze land use changes in such areas.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.849-858
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• 2023

Investigating disaster sites such as earthquakes and landslides involves significant risks due to potential secondary disasters like facility collapse. In situations where direct access is challenging, there is a need to develop methods for safely acquiring high-precision 3D disaster information using light detection and ranging (LiDAR) equipped drone survey systems. In this study, the feasibility of using drone LiDAR in disaster scenarios was examined, focusing on the collapse accident at Jeongja Bridge in Bundang-gu, Seongnam City, in April 2023. High-density point clouds for the accident bridge were collected, and the bridge's 3D terrain information was reconstructed and compared to the measurement performance of 10 ground control points. The results showed horizontal and vertical root mean square error values of 0.032 m and 0.055 m, respectively. Additionally, when compared to a point cloud generated using ground LiDAR for the same target area, a vertical difference of approximately 0.08 m was observed, but overall shapes showed minimal discrepancies. Moreover, in terms of overall data acquisition and processing time, drone LiDAR was found to be more efficient than ground LiDAR. Therefore, the use of drone LiDAR in disaster sites with significant risks allows for safe and rapid onsite investigations.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.404-411
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• 2022

The main function in operating the satellite navigation system is to accurately determine the orbit of the navigation satellite and transmit it as a navigation message. In this study, we developed software to determine the orbit of a navigation satellite by combining an extended Kalman filter and an accurate dynamic model. Global positioning system (GPS) and quasi-zenith satellite system (QZSS) orbit determination was performed using international gnss system (IGS) ground station observations and user range error (URE), a key performance indicator of the navigation system, was calculated by comparison with IGS precise ephemeris. When estimating the clock error mounted on the navigation satellite, the radial orbital error and the clock error have a high inverse correlation, which cancel each other out, and the standard deviations of the URE of GPS and QZSS are small namely 1.99 m and 3.47 m, respectively. Instead of estimating the clock error of the navigation satellite, the orbit was determined by replacing the clock error of the navigation message with a modeled value, and the regional correlation with URE and the effect of the ground station arrangement were analyzed.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.827-835
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• 2023

Drone light detection and ranging (LiDAR) is a state-of-the-art surveying technology that enables close investigation of the top of the mountain slope or the inaccessible slope, and is being used for field surveys in mountainous terrain. To build topographic information using Drone LiDAR, a preprocessing process is required to effectively separate ground and non-ground points from the acquired point cloud. Therefore, in this study, the point group data of the mountain topography was acquired using an aerial LiDAR mounted on a commercial drone, and the application and accuracy of the cloth simulation filtering algorithm, one of the ground separation techniques, was verified. As a result of applying the algorithm, the separation accuracy of the ground and the non-ground was 84.3%, and the kappa coefficient was 0.71, and drone LiDAR data could be effectively used for landslide field surveys in mountainous terrain.