• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.208-217
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• 2016

This paper describes a simulation method to generate sensor measurements for location estimation of an underwater robot. Field trial of a navigation method of an underwater robot takes much time and expenses and it is difficult to change the environment of the field trial as desired to test the method in various situations. Therefore, test and verification of a navigation method through simulation is inevitable for underwater environment. This paper proposes a method to generate sensor measurements of range, depth, velocity, and attitude taking the uncertainties of measurements into account through simulation. The uncertainties are Gaussian noise, outlier, and correlation between the measurement noise. Also, the method implements uncertainty in sampling time of measurements. The method is tested and verified by comparing the uncertainty parameters calculated statistically from the generated measurements with the designed uncertainty parameters. The practical feasibility of the measurement data is shown by applying the measurement data for location estimation of an underwater robot.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.3
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• pp.25-35
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• 2011

Traffic volume increases due to diversification of industry. Also, Automobile ownerships also increase steadily. It is estimated that the registered number of vehicle is expected to be 20 milion in the year 2015. These trends may result in increasing the number of woman drivers and elderly drivers. Therefore, this study aims to identify routes that reflect characteristics of each driver's preferences. A survey was conducted on different routes attributes for variances drivers. Driver types were classified by gender, age, and driving career. Accordingly, a weight for road composition attribute such as number of lanes, number of accidents, slope was estimated by using Swing Weighting technique in Multi-Attribute Utility Theory. In addition, a case study was conducted and identified weights were applied to routes. In result, drivers commonly prefer short route when they considered their routes. Also, male drivers prefer speedy and shorter route than that of female drivers. Elderly drivers prefer safe routes that represent low accidents rate. Moreover driving career under a year drivers prefer safe and easy routes. Therefore, we may conclude that the necessity of diversified route information is essential in the future car navigation system.

• Journal of information and communication convergence engineering
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• v.5 no.2
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• pp.171-176
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• 2007

To give a guarantee a consistently high level of quality and reliability of Telematics traffic service, traffic flow forecasting is very important issue. In this paper, we proposed an adaptable integrated prediction model to predict the traffic flow in the future. Our model combines two methods, short-term prediction model and long-term prediction model with different combining coefficients to reflect current traffic condition. Short-term model uses the Kalman filtering technique to predict the future traffic conditions. And long-term model processes accumulated speed patterns which means the analysis results for all past speeds of each road by classifying the same day and the same time interval. Combining two models makes it possible to predict future traffic flow with higher accuracy over a longer time range. Many experiments showed our algorithm gives a better precise prediction than only an accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.74-80
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• 2009

Path tracking of unmanned vehicle is a basis of autonomous driving and navigation. For the path tracking, it is very important to find the exact position of a vehicle. GPS is used to get the position of vehicle and a direction sensor and a velocity sensor is used to compensate the position error of GPS. To detect path lines in a road image, the bird's eye view transform is employed, which makes it easy to design a lateral control algorithm simply than from the perspective view of image. Because the driving speed of vehicle should be decreased at a curved lane and crossroads, so we suggest the speed control algorithm used GPS and image data. The control algorithm is simulated and experimented from the basis of expert driver's knowledge data. In the experiments, the results show that bird's eye view transform are good for the steering control and a speed control algorithm also shows a stability in real driving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4103-4117
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• 2014

Image processing and computer vision algorithms are gaining larger concern in a variety of application areas such as robotics and man-machine interaction. Vision allows the development of flexible, intelligent, and less intrusive approaches than most of the other sensor systems. In this work, we determine the location and orientation of a mobile robot which is crucial for performing its tasks. In order to be able to operate in real time there is a need to speed up different vision routines. Therefore, we present and evaluate a method for introducing parallelism into the multiple non-overlapping camera pose estimation algorithm proposed in [1]. In this algorithm the problem has been solved in real time using multiple non-overlapping cameras and the Extended Kalman Filter (EKF). Four cameras arranged in two back-to-back pairs are put on the platform of a moving robot. An important benefit of using multiple cameras for robot pose estimation is the capability of resolving vision uncertainties such as the bas-relief ambiguity. The proposed method is based on algorithmic skeletons for low, medium and high levels of parallelization. The analysis shows that the use of a multiprocessor system enhances the system performance by about 87%. In addition, the proposed design is scalable, which is necaccery in this application where the number of features changes repeatedly.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.151-160
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• 2009

The aim of this study is to propose economical driving speed index which those are geometric road status; assess the levels of which those cost-benefit of driving energy consumption and emission; are search road safety design and operational technology for driving simulator. For the objective, we analyzed the current status of driving energy consumption and driving scenarios by the road alignments, and reviewed driving and technical specifications by the geometric types of road according to the implementation, and extended completion. Throughout the result of this study, diverse related driving information provision service, efficiently navigation driving module is expected to be implemented in the national highway design system.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.61-83
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• 2012

A knowledge map describes the network of related knowledge into the form of a diagram, and therefore underpins the structure of knowledge categorizing and archiving by defining the relationship of the referential navigation between knowledge. The referential navigation between knowledge means the relationship of cross-referencing exhibited when a piece of knowledge is utilized by a user. To understand the contents of the knowledge, a user usually requires additionally information or knowledge related with each other in the relation of cause and effect. This relation can be expanded as the effective connection between knowledge increases, and finally forms the network of knowledge. A network display of knowledge using nodes and links to arrange and to represent the relationship between concepts can provide a more complex knowledge structure than a hierarchical display. Moreover, it can facilitate a user to infer through the links shown on the network. For this reason, building a knowledge map based on the ontology technology has been emphasized to formally as well as objectively describe the knowledge and its relationships. As the necessity to build a knowledge map based on the structure of the ontology has been emphasized, not a few researches have been proposed to fulfill the needs. However, most of those researches to apply the ontology to build the knowledge map just focused on formally expressing knowledge and its relationships with other knowledge to promote the possibility of knowledge reuse. Although many types of knowledge maps based on the structure of the ontology were proposed, no researches have tried to design and implement the referential navigation-enabled knowledge map. This paper addresses a methodology to build the ontology-based knowledge map enabling the referential navigation between knowledge. The ontology-based knowledge map resulted from the proposed methodology can not only express the referential navigation between knowledge but also infer additional relationships among knowledge based on the referential relationships. The most highlighted benefits that can be delivered by applying the ontology technology to the knowledge map include; formal expression about knowledge and its relationships with others, automatic identification of the knowledge network based on the function of self-inference on the referential relationships, and automatic expansion of the knowledge-base designed to categorize and store knowledge according to the network between knowledge. To enable the referential navigation between knowledge included in the knowledge map, and therefore to form the knowledge map in the format of a network, the ontology must describe knowledge according to the relation with the process and task. A process is composed of component tasks, while a task is activated after any required knowledge is inputted. Since the relation of cause and effect between knowledge can be inherently determined by the sequence of tasks, the referential relationship between knowledge can be circuitously implemented if the knowledge is modeled to be one of input or output of each task. To describe the knowledge with respect to related process and task, the Protege-OWL, an editor that enables users to build ontologies for the Semantic Web, is used. An OWL ontology-based knowledge map includes descriptions of classes (process, task, and knowledge), properties (relationships between process and task, task and knowledge), and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. Therefore a knowledge network can be automatically formulated based on the defined relationships, and the referential navigation between knowledge is enabled. To verify the validity of the proposed concepts, two real business process-oriented knowledge maps are exemplified: the knowledge map of the process of 'Business Trip Application' and 'Purchase Management'. By applying the 'DL-Query' provided by the Protege-OWL as a plug-in module, the performance of the implemented ontology-based knowledge map has been examined. Two kinds of queries to check whether the knowledge is networked with respect to the referential relations as well as the ontology-based knowledge network can infer further facts that are not literally described were tested. The test results show that not only the referential navigation between knowledge has been correctly realized, but also the additional inference has been accurately performed.

• Journal of Korean Port Research
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• v.13 no.2
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• pp.333-350
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• 1999

Today freeway is thought to be a very important transportation facility carrying tremendous traffic flow as the main corridor within the area of between the areas. However freeway is experiencing severe congestion and accidents by increased entrance ramp flow especially at peak time period. Ramp meters on the freeway entrance ramps that supply traffic to the freeway in a measured or appropriately regulated amount are needed for alleviating freeway congestion. Because ramp meters can be operated to discharge traffic at a measured or regulated rate thus maintaining more uniform speed on the mainline section maximizing the throughput to the freeway within the capacity of a downstream bottleneck and reducing the congestion related accidents. Thus the objectives in this study were to analyze the traffic characteristics on the freeway I-94 with ramp metering system before/after ITS technology in Detroit (Michigan) area compare shifts of the traffic characteristics on the freeway I-94 before/after ITS technology and finally suggest a better ramp metering strategy for the freeway system The following results were obtained: i)Flow occupancies and speeds on the mainline merge section of freeway were shown to be a big difference depending on the peak periods areas and directions based on the distribution of traffic flow characteristics on the freeway. ii)Reduced speed was shown to be more than 5 mph and ramp flow was also shown to be more than 240 vph at peak periods if there was the ramp metering system constructed on the freeway. iii)Ramp metering system was shown to be optimally operated on the freeway if ramp flow could be maximized within the range of over 900 vph and reduced occupancy could be also maximized by no more than 2 percent at peak periods. iv)The average flows on the freeway after the ITS technology were shown to be a decrease of over 20% depending on the peak periods areas and directions when compared with those flow on the freeway before the ITS technology. over 20% depending on the peak periods areas and directions when compared with those speeds on the freeway before the ITS technology. vi)The average metering rates on the freeway after the ITS technology were shown to be an increase of over 10% depending on the peak periods areas and directions when compared with those metering rates on the freeway before the ITS technology.

• Journal of IKEEE
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• v.22 no.1
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• pp.29-37
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• 2018

In recent years, there have been increasing interests in the mobile crowdsourcing that exploits multiple sensors, communication and user interfaces, and the computation power of widespread smartphones. In this paper, we present a novel mobile crowdsourcing-based driver assistance systems (MC-DAS) that crowdsource the sensor data of smartphone app having already passed a road segment, generate its profile information through a massive data processing, and forward this profile to the smartphone app of vehicle entering the road segment. Based on the MC-DAS platform, we also design and implement a new navigation system that advices the vehicle speed depending on the speedbump and on the road curvature profile. We expect that the proposed MC-DAS platform will be used as a platform for emerging new mobile crowdsourcing applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.12
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• pp.2355-2362
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• 2016

Seg fog removal is an important issue concerned by both computer vision and image processing. Sea fog or haze removal is widely used in lots of fields, such as automatic control system, CCTV, and image recognition. Color image dehazing techniques have been extensively studied, and expecially the dark channel prior(DCP) technique has been widely used. This paper propose a fast and efficient image prior - dark channel prior to remove seg-fog from a single digital image based on the GPU. We implement the basic parallel program and then optimize it to obtain performance acceleration with more than 250 times. While paralleling and the optimizing the algorithm, we improve some parts of the original serial program or basic parallel program according to the characteristics of several steps. The proposed GPU programming algorithm and implementation results may be used with advantages as pre-processing in many systems, such as safe navigation for ship, topographical survey, intelligent vehicles, etc.