• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.59-67
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• 2011

This paper proposes a model to analyze the fault factors of air-to-ground weapon delivery utilizing software fault localization methods. In the previous study, to figure out the factors to affect the accuracy of air-to-ground weapon delivery, the FBEL (Factor-based Error Localization) method had been proposed and the fault factors were analyzed based on the method. But in the study, the correlation between weapon delivery accuracy and the fault factors could not be revealed because the firing accuracy among several factors was fixed. In this paper we propose a more precise fault analysis model driven through a study of the correlation among the fault factors of weapon delivery, and a method to estimate the possibility of faults with the limited number of test cases utilizing the model. The effectiveness of proposed method is verified through the simulation utilizing real delivery data. and weapons delivery testing in the evaluation of which element affecting the accuracy of analysis that was available to be used successfully.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.45-50
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• 2023

In software development, the elicitation and analysis of requirements is a crucial phase, and it involves considerable time and effort due to the involvement of various stakeholders. ChatGPT, having been trained on a diverse array of documents, is a large language model that possesses not only the ability to generate code and perform debugging but also the capability to be utilized in the domain of software analysis and design. This paper proposes a method of requirements engineering that leverages ChatGPT's capabilities for eliciting software requirements, analyzing them to align with system goals, and documenting them in the form of use cases. In software requirements engineering, it suggests that stakeholders, analysts, and ChatGPT should engage in a collaborative model. The process should involve using the outputs of ChatGPT as initial requirements, which are then reviewed and augmented by analysts and stakeholders. As ChatGPT's capability improves, it is anticipated that the accuracy of requirements elicitation and analysis will increase, leading to time and cost savings in the field of software requirements engineering.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.55-62
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• 2024

In this paper, we propose a thin-film hydroponic plant cultivator using HMI display and IoT technology. Existing plant cultivators were difficult to manage due to soil-based cultivation, and it was difficult to optimize environmental conditions due to the open cultivation environment. In addition, there are problems with plant cultivation as immediate control is difficult and growth of plants is delayed. To solve this problem, a cultivation environment was established by connecting the MCU and sensors, and the environment information could be checked and quickly controlled by linking with the HMI display. Additionally, a case was applied to minimize changes in environmental information. Implementation of a thin-film hydroponic cultivation system made soil management easier, improved functionality through operation and control, and made it easy to understand environmental information through the display. The effectiveness of rapid growth was confirmed through crop cultivation experiments in existing growers and hydroponic growers. Future research directions will include optimizing growth information by transmitting and storing cultivation environment information and linking and comparing growth information using vision cameras. It is expected that this will enable efficient and stable plant cultivation.

• Journal of Korea Water Resources Association
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• v.51 no.4
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• pp.347-354
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• 2018

In urban areas, runoff flow is drained through sewer networks as well as surface areas. Therefore, it is very important to consider sewer networks as a component of hydrological drainage processes when conducting urban inundation modelling. However, most researchers who have implemented urban inundation/flood modelling, instinctively simplified the sewer networks without the appropriate criteria. In this research, a 1D-2D fully coupled urban inundation model is applied to estimate the influence of sewer network simplification on urban inundation modelling based on the dendritic network classification. The one-dimensional (1D) sewerage system analysis model, which was introduced by Lee et al. (2017), is used to simulate inlet and overflow phenomena by interacting with surface flow. Two-dimensional (2D) unstructured meshes are also applied to simulate surface flow and are combined with the 1D sewerage analysis model. Sewer network pipes are simplified based on the dendritic network classification method, namely the second and third order, and all cases of pipes are conducted as a control group. Each classified network case, including a control group, is evaluated through their application to the 27 July 2011 extreme rainfall event, which caused severe inundation damages in the Sadang area in Seoul, South Korea. All cases are compared together regarding inundation area, inflow discharge and overflow discharge. Finally, relevant criterion for the simplification method is recommended.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.29-41
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• 2010

Due to the recent technological advance, new types of AS/RS which can handle containers are being developed, and it is expected that they will be applied to related industries before long. Some companies and institutes in our country have constructed pilot systems for high-density-high-stacking systems and tested them to develop AS/RS-typed warehouses for containers. Along with this kind of construction efforts, development of rules to operate such systems efficiently and safely is also important. When outward-bound shipment is scheduled in container port, re-marshalling which rearranges containers in the yard to make shipment easy is conducted. In this paper, operating rules for the re-marshalling as well as simulation experiments to evaluate the performance of the rules are presented. We suggested two kinds of alternative sets of operating rules for re-marshalling and described the relevant logics corresponding to all possible cases for each alternative of operating rules. Through various simulation experiments, we found that each alternative has the merits and demerits at the same time and we could not say the one is always superior to the other. As a useful strategy, changing the applying operating rule is recommended from moment to moment depending on the expected number of operations at the landside input/output position.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.4
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• pp.450-456
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• 2022

In this study, as the preliminary research on the development of heating concrete members, compressive strength and accelerated chloride diffusion behavior in the mortar specimens containing C₁₂A₇ based binder and anhydrite was evaluated. Also, the effect of the mixing ratio of the citric acid based retarder was quantitatively evaluated by considering 4 levels of mixing cases. The compressive strength tests of the mortar specimen were performed referred to KS L ISO 679, and the accelerated chloride diffusion tests were performed according to NT BUILD 492 and ASTM C 1202. In the mortar with 0.3 % of retarder, the highest compressive strength was evaluated, which showed the strength development ratio of 127.6 % compared to the control case. It was considered that engineering performance was improved by effectively securing setting and curing time with 0.3 % of citric acid based retarder. As the result of the evaluation of the passed charge and the accelerated chloride diffusion coefficient, the evaluation results had similar behavior with the results of compressive strength. According to the previous study, the strength behavior and the chloride diffusion behavior had a linear relationship. The mixture showing the highest strength performance had the highest durability performance for chloride ingress, and the heating concrete development from this study will be performed in the future.

• Land and Housing Review
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• v.15 no.3
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• pp.61-77
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• 2024

This study analyzed the characteristics of land use changes around urban railway stations in peri-urban mixed-use cities. The research focused on four station areas along the Gyeonggang Line in Gwangju City, Gyeonggi Province, categorized into three phases based on their opening dates to analyze land use changes. The analysis method utilized building permit registry data from 2012 to 2020 within a 1 km radius of each station, at 250-meter intervals, examining temporal, spatial, and functional distributions. Statistical analysis employed SPSS for weighted cross-tabulation to explore differences in building permits and concentration levels among various building types. The findings revealed: firstly, peri-urban mixed-use city station areas exhibited the highest number of building permits at the time of opening; secondly, significant land use changes were observed within the 500-meter radius from the station; thirdly, residential buildings dominated, reflecting a trend towards housing supply-oriented land use changes; fourthly, cross-tabulation indicated significant differences in building permits across time, distance, and type (p < 0.01). Lastly, the concentration analysis revealed that residential buildings were distributed most evenly, while buildings for educational, social, and agricultural and fisheries purposes were distributed unevenly.

• Progress in Medical Physics
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• v.24 no.2
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• pp.127-132
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• 2013

The position of the internal organs can change continually and periodically inside the body due to the respiration. To reduce the respiration induced uncertainty of dose localization, one can use a respiratory gated radiotherapy where a radiation beam is exposed during the specific time of period. The main disadvantage of this method is that it usually requests a long treatment time, the massive effort during the treatment and the limitation of the patient selection. In this sense, the combination of the real-time position management (RPM) system and the volumetric intensity modulated radiotherapy (RapidArc) is promising since it provides a short treatment time compared with the conventional respiratory gated treatments. In this study, we evaluated the accuracy of the respiratory gated RapidArc treatment. Total sic patient cases were used for this study and each case was planned by RapidArc technique using varian ECLIPSE v8.6 planning machine. For the Quality Assurance (QA), a MatriXX detector and I'mRT software were used. The results show that more than 97% of area gives the gamma value less than one with 3% dose and 3 mm distance to agreement condition, which indicates the measured dose is well matched with the treatment plan's dose distribution for the gated RapidArc treatment cases.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.205-225
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• 2018

Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.