• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2477-2484
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• 1997

Best-first heuristic search algorithm, such as $A^{\ast}$ algorithm, are one of the most important techniques used to solve many problems in artificial intelligence. A common feature of heuristic search is its high computational complexity, which prevents the search from being applied to problems is practical domains such as route-finding in road map with significantly many nodes. In this paper, several heuristic search algorithms are concerned. A new dynamic weighting heuristic method called the pat-sensitive heuristic is proposed. It is based on a dynamic weighting heuristic, which is used to improve search effort in practical domain such as admissible heuristic is not available or heuristic accuracy is poor. It's distinctive feature compared with other dynamic weighting heuristic algorithms is path-sensitive, which means that ${\omega}$(weight) is adjusted dynamically during search process in state-space search domain. For finding an optimal path, randomly scattered road-map is used as an application area.

• Economic and Environmental Geology
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• v.38 no.1
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• pp.33-43
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• 2005

The purpose of this study is to make and validate landslide susceptibility map using artificial neural network and GIS in Kangneung area. For this, topography, soil, forest, geology and land cover data sets were constructed as a spatial database in GIS. From the database, slope, aspect, curvature, water system, topographic type, soil texture, soil material, soil drainage, soil effective thickness, wood type, wood age, wood diameter, forest density, lithology, land cover, and lineament were used as the landslide occurrence factors. The weight of the each factor was calculated, and applied to make landslide susceptibility maps using artificial neural network. Then the maps were validated using rate curve method which can predict qualitatively the landslide occurrence. The landslide susceptibility map can be used to reduce associated hazards, and to plan land use and construction as basic data.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.55-61
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• 2020

In this paper, we present a novel approach to efficiently control the location of NPC(Non-playable characters) in the interactive virtual world such as game, virtual reality. To control the NPC's movement path, we first calculate the main trajectory based on the user's path, and then move the NPC based on the weight map. Our method constructs automatically a navigation mesh that provides new paths for NPC by referencing the user trajectories. Our method enables adaptive changes to the virtual world over time and provides user-preferred path weights for smartagent path planning. We have tested the usefulness of our algorithm with several example scenarios from interactive worlds such as video games, virtual reality. In practice, our framework can be applied easily to any type of navigation in an interactive world.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.36 no.3
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• pp.113-120
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• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.4
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• pp.32-48
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• 2022

This study presented a spatial data serialization technique that can efficiently store and transmit large amounts of spatial data for precision road maps was designed and implemented. For efficient serialization, a binary spatial data structure is defined, and a coordinate value encoding technique without loss of information is designed using the Zigzag-Z-order curve. The spatial data serialization technique designed for precision road maps was tested, and the data size and encoding/decoding speed after encoding were compared with Protocol buffer and Geobuff. As a result, it was confirmed that the designed serialization method was excellent in data weight reduction performance and encoding speed. However, the decoding speed was inferior to other serialization techniques in linestring and polygon type spatial data. Through this study, it was confirmed that spatial data can be efficiently encoded, stored, and transmitted using binary serialization techniques.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.153-163
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• 2023

This study is situated in the context of intelligent transport systems, where in-vehicle devices assist drivers to avoid accidents and therefore improve road safety. The vehicles present in a given area form an ad' hoc network of vehicles called vehicular ad' hoc network. In this type of network, the nodes are mobile vehicles and the messages exchanged are messages to warn about obstacles that may hinder the correct driving. Node mobilities make it impossible for inter-node communication to be end-to-end. Recognizing this characteristic has led to delay-tolerant vehicular networks. Embedded devices have small buffers (memory) to hold messages that a node needs to transmit when no other node is within its visibility range for transmission. The performance of a vehicular delay-tolerant network is closely tied to the successful management of the nodes' transit buffer. In this paper, we propose a message transit buffer management model for nodes in vehicular delay tolerant networks. This model consists in setting up, on the one hand, a policy of dropping messages from the buffer when the buffer is full and must receive a new message. This drop policy is based on the concept of intermediate node to destination, queues and priority class of service. It is also based on the properties of the message (size, weight, number of hops, number of replications, remaining time-to-live, etc.). On the other hand, the model defines the policy for selecting the message to be transmitted. The proposed model was evaluated with the ONE opportunistic network simulator based on a 4000m x 4000m area of downtown Bouaké in Côte d'Ivoire. The map data were imported using the Open Street Map tool. The results obtained show that our model improves the delivery ratio of security alert messages, reduces their delivery delay and network overload compared to the existing model. This improvement in communication within a network of vehicles can contribute to the improvement of road safety.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.182-189
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• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.

• Journal of Animal Science and Technology
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• v.45 no.6
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• pp.939-948
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• 2003

The objective of this study was to identify the quantitative traits loci(QTL) for economically important traits such as growth, carcass and meat quality on pig chromosome 6. A three generation resource population was constructed from cross between Korean native boars and Landrace sows. A total of 240 F$_2$ animals were produced using intercross between 10 boars and 31 sows of F$_1$ animals. Phenotypic data including body weight at 3 weeks, backfat thickness, muscle pH, shear force and crude protein level were collected from F$_2$ animals. Animals including grandparents(F$_0$), parents(F$_1$) and offspring(F$_2$) were genotyped for 29 microsatellite markers and PCR-RFLP marker on chromosome 6. The linkage analysis was performed using CRI-MAP software version 2.4(Green et al., 1990) with FIXED option to obtain the map distances. The total length of SSC6 linkage map estimated in this study was 169.3cM. The average distance between adjacent markers was 6.05cM. For mapping of QTL, we used F$_2$ QTL Analysis Servlet of QTL express, a web-based QTL mapping tool(http://qtl.cap.ed.ac.uk). Five QTLs were detected at 5% chromosome-wide level for body weight of 3 weeks of age, shear force, meat pH at 24 hours after slaughtering, backfat thickness and crude protein level on SSC6.

• Journal of Bio-Environment Control
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• v.19 no.1
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• pp.49-54
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• 2010

The study was conducted to compare the postharvest physiology and storability of fresh cut paprika fruits classified by normal, blossom end rot(BER), and misshapen (or knots) fruit. Some disordered paprika fruits that were produced frequently during high temperature season in highland, were sorted out to non-marketable products. These fruits are mostly wasted, but some of them may be used for fresh cut. The respiration rate of fresh cut paprika fruits was lower and ethylene production rate was higher in normal fruits than in disordered fruits, but there was no significant difference. The fresh-cut paprika fruits were stored in MAP conditions at $4^{\circ}C$, $9^{\circ}C$ and room temperature in 25 ${\mu}m$ and 50 ${\mu}m$ thickness ceramic film packaging. The fresh weight of fresh cut paprika fruits decreased below to 1.1% regardless of fruit types, but the fresh weight loss increased in thinner packaging materials and lower storage temperatures. There were not significant different carbon dioxide and oxygen contents in MAP of all fruit types, while $4^{\circ}C$ storage temperature treatment and 25 ${\mu}m$ thickness ceramic film treatment had lower carbon dioxide and higher oxygen contents. Moreover, the carbon dioxide and oxygen contents were changed rapidly at 9 days in $4^{\circ}C$ storage and at 6 days in $9^{\circ}C$ storage when the visual quality of fresh cut decreased dramatically. The ethylene concentration of packages was below 7 ${\mu}l{\cdot}l^{-1}$ in all treatments during storage, while the treatments of thinner packaging material and lower storage temperature showed lower ethylene concentration. The fresh cut of disordered fruits showed less visual quality than normal fruit treatment in both $4^{\circ}C$ and $9^{\circ}C$ storage temperatures, but there was no significant difference. The value of $4^{\circ}C$ treatment that measured 12 days in storage was higher than $9^{\circ}C$ treatment that measured 9 days in storage. The results suggest that the disordered fruits may be used to fresh cut product without any concerns that they will decreased the value of commodities more quickly than the fresh cut made of marketable paprika fruits. As the fresh cut paprika fruits stored in MAP condition, the more effective storage temperature is $4^{\circ}C$ that may have induced chilling injury a whole fruit of the paprika.