• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.233-239
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• 2019

An unmanned underwater vehicle is a major weapon system that allows surveillance and reconnaissance missions in border areas or threatening areas where enemy submarines are present. Unmanned underwater vehicles can be used to explore underwater resources, predict disasters, and survey the topography of the ocean floor in the civilian fields, while in the defense fields, it can be used for anti-submarine reconnaissance and mine countermeasures. In this paper, we first investigate the main classification of unmanned underwater vehicles, and foreign R&D trends are analyzed based on the main classification criteria by weight, such as portable, light, heavy and large-scale unmanned underwater vehicles. Then we examine the trends in the development of domestic unmanned underwater vehicles. Finally, through the analysis of both domestic and foreign unmanned underwater vehicles, we present future development trends of unmanned underwater vehicles in order to set defense goals to counter the anticipated threats and diversified potential environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.47-52
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• 2011

Vehicle classification data are considerably used in the almost all fields of transportation planning and engineering. Highway agencies use a large number of vehicle classification schemes. Vehicles on the national highway are classified by 12-Category classification system, using number of axles, distances between axles, vehicle length, overhang, and other factors. In the case of using existing axle-sensor-based classification counters (that is, 12-category classification system), two-axle vehicles(Class 1 to 4) can be erroneously classified because a passenger vehicle becomes larger and similar with class 3 and 4. In this reason, this study proposes the vehicle classification scheme based on using vehicle height profiles obtained by a laser sensors. Also, the accuracy of the proposed method are tested through a field study.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.61-73
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• 1996

The objective of this paper is to estimate the PCE(Passenger Car Equivalents) of various vehicle types on expressway work zones. Headway samples of 5,359 vehicles were collected in 6 work zones in the Kyungbu Expressway between September and November of 1995. Average headways of 8 vehicle types based on the vehicle classification method of the Department of Construction and Transportation were calculated. A statistical test of effects of the types of the preceding vehicles were performed for the average headways between a vehicle type preceded by other vehicle types. The results show that the effects of the type of preceding vehicles are significant (exceeded 5% and 10% significance levels) and the PCEs of heavy vehicles on expressway work zones are higher than that of basic expressway section. Therefore, different adjustment factors should be applied for heavy vehicles in estimating saturation flow rates of expressway work zones. The study also derives an equation to determine PCEs of these vehicle types.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.3955-3971
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• 2020

Due to the significant increase in the use of autonomous car technology, it is essential to integrate this technology with high-precision digital map data containing more precise and accurate roadway information, as compared to existing conventional map resources, to ensure the safety of self-driving operations. While existing map technologies may assist vehicles in identifying their locations via Global Positioning System, it is however difficult to update the environmental changes of roadways in these maps. Roadway vision algorithms can be useful for building autonomous vehicles that can avoid accidents and detect real-time location changes. We incorporate a hybrid architectural design that combines unsupervised classification of vision data with supervised joint fusion classification to achieve a better noise-resistant algorithm. We identify, via a deep learning approach, an intelligent hybrid fusion algorithm for fusing multimodal vision feature data for roadway classifications and characterize its improvement in accuracy over unsupervised identifications using image processing and supervised vision classifiers. We analyzed over 93,000 vision frame data collected from a test vehicle in real roadways. The performance indicators of the proposed hybrid fusion algorithm are successfully evaluated for the generation of roadway digital maps for autonomous vehicles, with a recall of 0.94, precision of 0.96, and accuracy of 0.92.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.12
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• pp.1237-1244
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• 2016

Vehicle classification system in domestic tollgates is usually to use treadle sensor for calculating wheel width and tread of the vehicle. Due to the impact that occurs when the wheels of the vehicle contact, treadle sensor requires high durability. Recently, KHC(Korea Highway Corporation) began operating high-speed lane for cargo truck. High-speed cargo truck generate more impact the design criteria of previous treadle. Therefore, an increase in the maintenance and management costs of the treadle damage is concerned. In this paper, we propose an algorithm to classify vehicles using non-contact treadle sensors for improving durability from physical impacts. This was based on the KHC's classification criteria and showed a classification accuracy of 99.5 % in one experiment with 1892 vehicles through Changwon tollgate in 1020 local road. Therefore, it shows that vehicle classification system using non-contact treadle sensor could be applied to domestic toll tollgates, effectively.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.5
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• pp.436-443
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• 2011

Recognition of structures in urban environments is a fundamental ability for unmanned ground vehicles. In this paper we propose the geometrical featured voxel which has not only 3-D coordinates but also the type of geometrical properties of point cloud. Instead of dealing with a huge amount of point cloud collected by range sensors in urban, the proposed voxel can efficiently represent and save 3-D urban structures without loss of geometrical properties. We also provide an urban structure classification algorithm by using the proposed voxel and machine learning techniques. The proposed method enables to recognize urban environments around unmanned ground vehicles quickly. In order to evaluate an ability of the proposed map representation and the urban structure classification algorithm, our vehicle equipped with the sensor system collected range data and pose data in campus and experimental results have been shown in this paper.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1D
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• pp.45-51
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• 2010

Traffic volume is essential data for traffic control or maintenance and rehabilitation planning. The volume especially with respect to the type of vehicles can facilitate to those road operations. In this research, a method for vehicle classification was developed using skewed sensors which can generate traffic signatures. In order to characterize vehicle types, the method investigates whether the second axle of each vehicle consists of dual tires. The presence of dual tire is determined by the discriminate function obtained from discriminant analysis. The validation using 1,878 vehicles recorded from a highway using a CCTV camera indicated significantly accurate results: 96.92% for class 1, 82.91% for class 3 and 79.13% for class 4.

• Journal of Digital Convergence
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• v.18 no.2
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• pp.249-259
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• 2020

This study presents a recently obtained social media data set based upon the case study of Electric Vehicles (EV) and looks to implement a sentiment analysis (SA) in order to gain insights. This study uses two methods in order to fully analyze the public's sentiment on EVs. First, we implement a SA tool in which we used to extract the sentiment of comments. Next we labeled the data with these sentiments obtained and classified them. While performing classification we found the problem of dimensionality and also explored the use of feature selection (FS) models in order to reduce the data set's dimensionality. We found that the use of three FS models (Chi Squared, Information Gain and ReliefF) showed the most promising results when used alongside a logistic and support vector machines classification algorithm. the contributions of this paper are in providing an real-world example of social media text analytics which can be adopted in many other areas of research and business. Moving forward researchers can use the methodological approach in this paper to further refine and improve their own case uses in text analytics.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.32-39
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• 2023

This paper provides a comprehensive overview of UAV classification, tracking, and detection, offering researchers a clear understanding of these fundamental concepts. It elucidates how classification categorizes UAVs based on attributes, how tracking monitors real-time positions, and how detection identifies UAV presence. The interconnectedness of these aspects is highlighted, with detection enhancing tracking and classification aiding in anomaly identification. Moreover, the paper emphasizes the relevance of simulations in the context of drones and UAVs, underscoring their pivotal role in training, testing, and research. By succinctly presenting these core concepts and their practical implications, the paper equips researchers with a solid foundation to comprehend and explore the complexities of UAV operations and the role of simulations in advancing this dynamic field.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.91-112
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• 2019

In accordance with the advent of the 5th generation (5G) communication technology, we are having a change in various communication services which converge with high technologies related to the 4th Industrial Revolution. To utilize the upcoming 5G technology effectively and practically, we analyzed the technologies which have the most potential in convergence under the introduction of 5G technology and as a result, it is a autonomous vehicle that we'll discuss the core technologies of the 4th Industrial Revolution, which can lead to service activation by being combined with 5G technology. In addition, we developed an industry-service classification of 5G-based autonomous vehicle, we provided a basis for supporting a new business and its new business model converged with 5G communication technology. Furthermore, we will create a linkage matrix with the industry-service classification system of a new autonomous vehicles. This matrix will service as a guideline for industry-service development where autonomous vehicles can be utilized actively in the next generation.