• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.126-132
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• 2015

Technology forecasting is about understanding a status of a specific technology in the future, based on the current data of the technology. It is useful when planning technology management strategies. These days, it is common for countries, companies, and researchers to establish R&D directions and strategies by utilizing experts' opinions. However, this qualitative method of technology forecasting is costly and time consuming since it requires to collect a variety of opinions and analysis from many experts. In order to deal with these limitations, quantitative method of technology forecasting is being studied to secure objective forecast result and help R&D decision making process. This paper suggests a methodology of technology forecasting based on quantitative analysis. The methodology consists of data collection, principal component analysis, and technology forecasting by logistic regression, which is one of the data mining techniques. In this research, patent documents related to autonomous vehicle are collected. Then, the texts from patent documents are extracted by text mining technique to construct an appropriate form for analysis. After principal component analysis, logistic regression is performed by using principal component score. On the basis of this result, it is possible to analyze R&D development situation and technology forecasting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.178-187
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• 2016

The purpose of this study is to analyze governance in the business processes of Jeju's proposed 'Happy Living Area.' This study found Jeju's 'Happy Living Area' plans for governance were realized and conflicts may frequently occur in future business processes. Furthermore, roles and support systems were measured for the development council of 'Happy Living Area.' This paper reports findings from a case study on community planning for a public art project. This study recommends public-private governance in terms of process factors as well as role considerations. With respect to process factors, administrative agencies and citizen participation are examined. Second, the Living Area Council should play a mediating role between central and local governments as well as residents. Third, Happy Living Zones' Advisory Centre should undertake an advisory role. Fourth, consultation between public and private sectors is needed to establish evaluation criteria for reviewing proposals from subordinate administrative agencies. Fifth, local government systems should be managed by autonomous municipalities. Concerning role considerations, a new 'Personality for Governance' position should be established for performing different roles in the project implementation stage.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.186-201
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• 2021

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.2
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• pp.95-111
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• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• Journal of Korean Philosophical Society
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• v.123
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• pp.309-346
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• 2012

The trust toward the researchers and their study activities in society has faltered, in the wake of the paper fabrication event of researcher, Hwang Woo-Suk's doctor research team. After the event, researcher community and scientific community have experienced many changes through the self-reflection or the process of insight meditation. Until now, we have experienced that when researcher community leads the way to try to show their efforts to eliminate the raised doubts throughly, the public support toward researcher community and the trust in its study activity have not faltered. Nevertheless, the path for the researchers to go is still far and rough because the opposite cases coping with research misconducts passively are much more. Therefore It's urgent that misconducts in the research and learning should be avoided from unnecessary overinterpretation. To practice it, above all it's important how well researcher or learner should be equipped with a system where decision is made autonomously and reasonably, regardless of the interests from all fields including politic, economic and social etc. It's also required that their systems should be meticulous enough to prevent such irrationality in advance before the misconduct instances are depreciated. In this context, I will investigate the reason why research and development on norms in research ethics and learning ethics is meaningful, not in a posteriori but a priori dimension, as the way to have researcher and learner prepare autonomous self-purification systems. It's essential that for the progress of an obvious argument, first, what research ethics and learning ethics are should be established and defined distinctly(2). Then in the process, it is also examined why research ethics and learning ethics need norms(3). Subsequently I will conclude the paper, arguing the reason why research ethics and learning ethics should be justified(4), if the norms in research ethics and learning ethics can be formulated(5).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.173-184
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• 2018

As the portable traffic information collection system using probe vehicles spreads, it is becoming possible to collect road hazard information such as portholes, falling objects, and road surface freezing using in-vehicle sensors in addition to existing traffic information. In this study, we developed a integration and decision algorithm that integrates time and space in real time when multiple probe vehicles detect events such as road hazard information based on GPS coordinates. The core function of the algorithm is to determine whether the road hazard information generated at a specific point is the same point from the result of detecting multiple GPS probes with different GPS coordinates, Generating the data, (3) continuously determining whether the generated event data is valid, and (4) ending the event when the road hazard situation ends. For this purpose, the road risk information collected by the probe vehicle was processed in real time to achieve the conditional probability, and the validity of the event was verified by continuously updating the road risk information collected by the probe vehicle. It is considered that the developed hybrid processing algorithm can be applied to probe-based traffic information collection and event information processing such as C-ITS and autonomous driving car in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• Journal of Sensor Science and Technology
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• v.30 no.3
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• pp.154-164
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• 2021

In this study, the IoT sensor technology required for improving the survival rate and high-density productivity of individual shrimp in smart shrimp farming (which involves the usage of recirculating aquaculture systems and biofloc technology) was analyzed. The principles and performances of domestic and overseas water quality monitoring IoT sensors were compared. Furthermore, the drawbacks of existing aquaculture monitoring technologies and the countermeasures for future aquaculture monitoring technologies were examined. In particular, for farming white-legged shrimp, an IoT sensor was employed to collect measurement indicators for managing the water quality environment in real-time, and the IoT sensor-based real-time monitoring technology was then analyzed for implementing the optimal farming environment. The results obtained from this study can potentially contribute to the realization of an autonomous farming platform that can improve the survival rate and productivity of shrimp, achieve feed reduction, improve the water quality environment, and save energy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.84-99
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• 2021

As the micro-mobility market grows, the demand for route guidance, that includes uphill information as well, is increasing. Since the climbing angle depends on the electric motor uesed, it is necessary to establish an uphill road DB according to the threshold standard. Although road alignment information is a very important element in the basic information of the roads, there is no information currently on the longitudinal slope in the road digital map. The High Definition(HD) map which is being built as a preparation for the era of autonomous vehicles has the altitude value, unlike the existing standard node link system. However, the HD map is very insufficient because it has the altitude value only for some sections of the road network. This paper, hence, intends to propose a method to generate the road longitudinal slope using currently available data. We developed a method of computing the longitudinal slope by combining the digital elevation model and the standard link system. After creating an altitude at the road link point divided by 4m based on the Seoul road network, we calculated individual slope per unit distance of the road. After designating a representative slope for each road link, we have extracted the very steep road that cannot be climbed with personal mobility and the slippery roads that cannot be used during heavy snowfall. We additionally described errors in the altitude values due to surrounding terrain and the issues related to the slope calculation method. In the future, we expect that the road longitudinal slope information will be used as basic data that can be used for various convergence analyses.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.115-131
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• 2022

Carbon dioxide (CO₂) emissions from the transportation sector in South Korea accounts for 16.5% of all CO₂ emissions, and road transportation accounts for 96.5% of this sector's emissions in South Korea. Hence, constant research is being carried out on methods to reduce CO₂ emissions from this sector. With the emerging use of smart crossings, attempts to monitor individual vehicles are increasing. Moreover, the potential commercial deployment of autonomous vehicles increases the possibility of obtaining individual vehicle data. As such, CO₂ emission research was conducted at five signalized intersections in the Gangnam District, Seoul, using data such as vehicle type, speed, acceleration, etc., obtained from image detectors located at each intersection. The collected data were then applied to the MOtor Vehicle Emission Simulator (MOVES)-Matrix model-which was developed to obtain second-by-second vehicle activity data and analyze daily CO₂ emissions from the studied intersections. After analyzing two large and three small intersections, the results indicated that 3.1 metric tons of CO₂ were emitted per day at each intersection. This study reveals a new possibility of analyzing CO₂ emissions using actual individual vehicle data using an improved analysis model. This study also emphasizes the importance of more accurate CO₂ emission analyses.