• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.4
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• pp.47-54
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• 1983

The performance of an intra-frame DCT/DPCM hybrid coding is investigated with the criteria of normalized mean square error and subjective test for various system parameters. It includes the prediction coefficient in transform domain, normalization factor and bit-map in block quantizer, and adaptive coding. It is shown that the generalized covariance model of image is a convenient tool for bit-map and adaptive coding, and for a fast low bit-rate coding.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.109-124
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• 2015

A novel approach, hybrid surface rainfall (KNU-HSR) technique developed by Kyungpook Natinal University, was utilized for improving the radar rainfall estimation. The KNU-HSR technique estimates radar rainfall at a 2D hybrid surface consistings of the lowest radar bins that is immune to ground clutter contaminations and significant beam blockage. Two HSR techniques, static and dynamic HSRs, were compared and evaluated in this study. Static HSR technique utilizes beam blockage map and ground clutter map to yield the hybrid surface whereas dynamic HSR technique additionally applies quality index map that are derived from the fuzzy logic algorithm for a quality control in real time. The performances of two HSRs were evaluated by correlation coefficient (CORR), total ratio (RATIO), mean bias (BIAS), normalized standard deviation (NSD), and mean relative error (MRE) for ten rain cases. Dynamic HSR (CORR=0.88, BIAS= $-0.24mm\;hr^{-1}$, NSD=0.41, MRE=37.6%) shows better performances than static HSR without correction of reflectivity calibration bias (CORR=0.87, BIAS= $-2.94mm\;hr^{-1}$, NSD=0.76, MRE=58.4%) for all skill scores. Dynamic HSR technique overestimates surface rainfall at near range whereas it underestimates rainfall at far ranges due to the effects of beam broadening and increasing the radar beam height. In terms of NSD and MRE, dynamic HSR shows the best results regardless of the distance from radar. Static HSR significantly overestimates a surface rainfall at weaker rainfall intensity. However, RATIO of dynamic HSR remains almost 1.0 for all ranges of rainfall intensity. After correcting system bias of reflectivity, NSD and MRE of dynamic HSR are improved by about 20 and 15%, respectively.

• Plant Breeding and Biotechnology
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• v.7 no.3
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• pp.272-286
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• 2019

Developing elite hybrid rice varieties is one important objective of rice breeding programs. Several genes related to male sterilities, restores, and pollinators have been identified through map-based gene cloning within natural variations of rice. These identified genes are good targets for introducing genetic traits in molecular breeding. This study was conducted to breed elite hybrid lines with major genes related to hybrid traits and disease/insect resistance in 240 genetic resources and F₁ hybrid combinations of rice. Molecular markers were reset for three major hybrid genes (S5, Rf3, Rf4) and thirteen disease/insect resistant genes (rice bacterial blight resistance genes Xa3, Xa4, xa5, Xa7, xa13, Xa21; blast resistance genes Pita, Pib, Pi5, Pii; brown planthopper resistant genes Bph18(t) and tungro virus resistance gene tsv1). Genotypes were then analyzed using molecular marker-assisted selection (MAS). Biological assay was then performed at the Red River Delta region in Vietnam using eleven F₁ hybrid combinations and two control vatieties. Results showed that nine F₁ hybrid combinations were highly resistant to rice bacterial blight and blast. Finally, eight F₁ hybrid rice varieties with resistance to disease/insect were selected from eleven F₁ hybrid combinations. Their characteristics such as agricultural traits and yields were then investigated. These F₁ hybrid rice varieties developed with major genes related to hybrid traits and disease/insect resistant genes could be useful for hybrid breeding programs to achieve high yield with biotic and abiotic resistance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1605-1614
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• 2009

The technique of simultaneous localization and mapping is the most important research topic in mobile robotics. In the process of building a map in its available memory, the robot memorizes environmental information on the plane of grid or topology. Several approaches about this technique have been presented so far, but most of them use mapping technique as either grid-based map or topology-based map. In this paper we propose a frame of solving the SLAM problem of linking map covering, map building, localizing, path finding and obstacle avoiding in an automatic way. Some algorithms integrating grid and topology map are considered and this make the SLAM performance faster and more stable. The proposed scheme uses an occupancy grid map in representing the environment and then formulate topological information in path finding by A${\ast}$ algorithm. The mapping process is shown and the shortest path is decided on grid based map. Then topological information such as direction, distance is calculated on simulator program then transmitted to robot hardware devices. The localization process and the dynamic obstacle avoidance can be accomplished by topological information on grid map. While mapping and moving, pose of the robot is adjusted for correct localization by implementing additional pixel based image layer and tracking some features. A laser range finer and electronic compass systems are implemented on the mobile robot and DC geared motor wheels are individually controlled by the adaptive PD control method. Simulations and experimental results show its performance and efficiency of the proposed scheme are increased.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.3
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• pp.173-186
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• 2013

Web-based map services provide various geographic information that users want to get by continuous updating of data. Those map services provide different information for a geographic object respectively. It causes several problems, and most of all various information cannot be integrated and provided. To resolve the problem, this paper proposes a system which can integrate diverse geographic information and provide users rich geographic information. In this paper, a hybrid schema matching (HSM) algorithm is proposed and the algorithm is a mixture of the adapter-based semantic processing method, static semantic management-based approach, and dynamic semantic management-based approach. A comparative evaluation is described to show effectiveness of the proposed algorithm. The proposed algorithm in this paper improves the accuracy of schema matching because of registration and management of schemas of new semantic information. The proposal enables vocabulary-based schema matching using various schemas, and it thus also supports high usability. Finally, the proposed algorithm is cost-effective by providing the progressive extension of relationships between schema meanings.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.326-335
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• 2015

In this paper, is a study that analyzed the fuel consumption of hybrid tractor. Testing and analysis in order to evaluate the fuel consumption was performed. Analysis model was developed by using the SimulationX that is a commercial software. Also, map of the analysis model was modeled on the basis of test data. Test was performed using a dynamo device. The engine was tested the fuel consumption in accordance with the conditions on the load and throttle opening. The battery was tested the discharge and charge in accordance with the current amount. We verified the reliability of the analysis model by comparing the analysis results with the rest results. After considering the reliability of each analysis model was extended to the entire hybrid tractor system. To evaluate the efficiency using the analysis model, compared the fuel consumption of general tractor with hybrid tractor in the same load conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.101-107
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• 2006

In this research, we propose a hybrid inference architecture for medical diagnosis based on dynamic knowledge map (DKM) and relational database (RDB). Conventional expert systems (ES) and developing tools of ES has some limitations such as, 1) time consumption to extend the knowledge base (KB), 2) difficulty to change the inference path, 3) inflexible use of inference functions and operators. To overcome these Limitations, we use DKM in extracting the complex relationships and causal rules from human expert and other knowledge resources. The DKM also can help the knowledge engineers to change the inference path rapidly and easily. Then, RDB and its management systems help us to transform the relationships from diagram to relational table.

• Advances in robotics research
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• v.2 no.1
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• pp.1-11
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• 2018

To monitor the environment and determine the source of a pollutant gradient using a multiple robot swarm, we propose a hybrid algorithm that combines two bio-inspired algorithms mimicking chemotaxis and pheromones of bacteria. The algorithm is implemented in virtual robot agents in a simulator to evaluate their feasibility and efficiency in gradient maps with different sizes. Simulation results show that the chemotaxis controller guided robot agents to the locations with higher pollutant concentrations, while the pheromone marked in a virtual field increased the efficiency of the search by reducing the visiting redundancy. The number of steps required to reach the target point did not increase proportionally as the map size increased, but were less than those in the linear whole-map search method. Furthermore, the robot agents could function with simple sensor composition, minimum information about the map, and low calculation capacity.

• Korean Journal of Microbiology
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• v.29 no.4
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• pp.226-229
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• 1991

In our laboratory a R plasmid pKU10 was isolated from Pseudomonas and its characteristics were investigated. In this study, as a basic work to improve its utility as a cloning vehicle, restriction patterns of pKU10 were analyzed for other various restriction enzymes in addition to restriction evdonucleases previously examined. As a result, pKU10 DNA has two cleavage sites for ClaI and HpaI, and three sites for AvaI. The restriction map of pKU10 was supplemented with AvaI, ClaI, and HpaI. From the result of this experiment, the usefulness of PKU10 as a cloning vector in Pseudomonas will be enhanced by constructions of mini-plasmid or hybrid plasmids.