• Journal of the Korean Society of Safety
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• 제34권1호
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• pp.82-89
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• 2019

The importance of aviation safety has been emphasized recently due to the development of aviation industry. Despite the efforts of each country and the improvement of screening equipment, screening tasks are still difficult and detection failures are frequent. The purpose of this study was to examine the effect of feedback on improving signal detection performance applying a Simulated Baggage Screening Program(SBSP) for improving aviation safety. SBSP consists of three parts: image combination, option setting and experiment. The experimental images were color-coded to reflect the items' transmittance of the x-rays and could be combined as researchers' need. In the option, the researcher could set up the information, incentive, and comments needed for training to be delivered on a number of tasks and times. Experiment was conducted using SBSP and participant's performance information (hit, missed, false alarms, correct rejection, reaction time, etc.) was automatically calculated and stored. A total of 50 participants participated and each participant was randomly assigned to feedback and non-feedback group. Participants performed a total of 200 tasks and 20(10%) contained target object(gun and knife). The results showed that when the feedback was provided, the hit, correct rejection ratio and d′ were increased, however, the false alarms and miss decreased. However, there was no significant difference in response criteria(${\beta}$). In addition, implications, limitations of this study and future research were discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4420-4438
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• 2021

Image dehazing is an ill-posed problem which is far from being solved. Traditional image dehazing methods often yield mediocre effects and possess substandard processing speed, while modern deep learning methods perform best only in certain datasets. The haze removal effect when processed by said methods is unsatisfactory, meaning the generalization performance fails to meet the requirements. Concurrently, due to the limited processing speed, most dehazing algorithms cannot be employed in the industry. To alleviate said problems, a lightweight fast dehazing network based on a multiple scale-patch framework (MSP) is proposed in the present paper. Firstly, the multi-scale structure is employed as the backbone network and the multi-patch structure as the supplementary network. Dehazing through a single network causes problems, such as loss of object details and color in some image areas, the multi-patch structure was employed for MSP as an information supplement. In the algorithm image processing module, the image is segmented up and down for processed separately. Secondly, MSP generates a clear dehazing effect and significant robustness when targeting real-world homogeneous and nonhomogeneous hazy maps and different datasets. Compared with existing dehazing methods, MSP demonstrated a fast inference speed and the feasibility of real-time processing. The overall size and model parameters of the entire dehazing model are 20.75M and 6.8M, and the processing time for the single image is 0.026s. Experiments on NTIRE 2018 and NTIRE 2020 demonstrate that MSP can achieve superior performance among the state-of-the-art methods, such as PSNR, SSIM, LPIPS, and individual subjective evaluation.

• Journal of Korea Multimedia Society
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• 제25권8호
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• pp.1167-1174
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• 2022

With the sustainable development and transformation of the city, public art as a business card of the famous city of culture has become a hot topic of research. The intervention of public art in public space not only brings users a sense of space experience, but also becomes a unique carrier of urban and rural image making. Although there is much research on the classification, aesthetics and function of public art, there is few quantitative research on user satisfaction. This paper takes the basic features of airport public art as a research object and the basic features of airport public art as the theoretical basis to study the impact of the basic characteristics of airport public art on user satisfaction. Research methods were based on questionnaire data of 247 people, in which models and hypotheses were tested using SPSS 21.0 software, based on the induction and extraction of nine influential factors in the basic characteristics of public art. The study found that public interpretation, media patterns, color perception, modeling form, place perception, city image and memory have significant positive effects on user satisfaction. The sharedness of public art, cognition and communication in public culture and spatial relations do not affect satisfaction. Conclusion, inspiration and prospect provide suggestions for designers and reference data and theoretical support for public art evaluation.

• Korea Science and Art Forum
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• 제19권
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• pp.601-612
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• 2015

Postmodernism has blurred the line between design and art. We would like to suggest a design case that took diversity in the modern era that harmonizes functionality and shape of the top board of a furniture and trend of the role of furniture and objet into consideration. This study aims to project a new role into space via convergence of objet that has aesthetic function and furniture design that plays practical role in space. Thus, furniture design attempts to combine ceramic and carpentry and demonstrate the value and potential the combination possesses. By creating a distinct design from previous furniture that had visual limitations with ceramic bridge that adopted existing piling method in ceramic design, we expect a fresh blend of furniture and space that encompasses a different sensation from color and texture of soil and glaze, unable to attain from simple wood.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2021년도 춘계학술대회
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• pp.463-465
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• 2021

Currently, the inspection method printed on food packages and boxes is to sample only a few products and inspect them with human eyes. Such a sampling inspection has the limitation that only a small number of products can be inspected. Therefore, accurate inspection using a camera is required. This paper proposes a deep learning object recognition technology model, which is an artificial intelligence technology, as a method for detecting the defects of expiration date printed on the product packaging. Using the Faster R-CNN (region convolution neural network) model, the color images, converted gray images, and converted binary images of the printed expiration date are trained and then tested, and each detection rates are compared. The detection performance of expiration date printed on the package by the proposed method showed the same detection performance as that of conventional vision-based inspection system.

• Journal of the Korean Society of Food Culture
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• 제25권1호
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• pp.70-75
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• 2010

The object of this study was to investigate the effects of rice particle size on the physicochemical properties of beef-rice porridge. The pH of beef-rice porridge was decreased as compared to that of the control, while the redness of beef porridge increased according to rice particle size. The viscosity of flour in the beef-rice porridge was the highest among three porridges, at $40^{\circ}C$. The protein content of beef-rice porridge was increased 3-fold over that of rice porridges. The total amino acid content of the beef-rice porridge was 3071.2 mg/100 g, and that of rice porridge was 1147.5 mg/100 g. As compared to rice porridge, the maximum amounts of the amino acids Lys and Thr were increased beef-rice porridge. Sensory evaluation results showed that the beef-rice porridge with a particle size half that of rice had the highest scores in color, taste, texture, and overall preference. Based on these results, it is suggested that beef-rice porridge with a particle size half (0.7-2.5 mm) that of rice has optimal quality in terms of both physicochemical and sensory properties.

• Journal of IKEEE
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• 제28권1호
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• pp.65-71
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• 2024

This paper proposes a neural network that can reduce the number of parameters while reducing matching errors in occluded regions to increase the accuracy of depth maps in stereo matching. Stereo matching-based object recognition is utilized in many fields to more accurately recognize situations using images. When there are many objects in a complex image, an occluded area is generated due to overlap between objects and occlusion by background, thereby lowering the accuracy of the depth map. To solve this problem, existing research methods that create context information and combine it with the cost volume or RoIselect in the occluded area increase the complexity of neural networks, making it difficult to learn and expensive to implement. In this paper, we create a depthwise seperable neural network that enhances regional feature extraction before cost volume generation, reducing the number of parameters and proposing a neural network that is robust to occlusion errors. Compared to PSMNet, the proposed neural network reduced the number of parameters by 30%, improving 5.3% in color error and 3.6% in test loss.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.311-326
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• 2024

The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.

• Journal of KIISE:Software and Applications
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• 제31권10호
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• pp.1332-1344
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• 2004

Upon development of information super-highway and multimedia-related technoiogies in recent years, more efficient technologies to transmit, store and retrieve the multimedia data are required. Among such technologies, firstly, it is common that the semantic-based image retrieval is annotated separately in order to give certain meanings to the image data and the low-level property information that include information about color, texture, and shape Despite the fact that the semantic-based information retrieval has been made by utilizing such vocabulary dictionary as the key words that given, however it brings about a problem that has not yet freed from the limit of the existing keyword-based text information retrieval. The second problem is that it reveals a decreased retrieval performance in the content-based image retrieval system, and is difficult to separate the object from the image that has complex background, and also is difficult to extract an area due to excessive division of those regions. Further, it is difficult to separate the objects from the image that possesses multiple objects in complex scene. To solve the problems, in this paper, I established a content-based retrieval system that can be processed in 5 different steps. The most critical process of those 5 steps is that among RGB images, the one that has the largest and the smallest background are to be extracted. Particularly. I propose the method that extracts the subject as well as the background by using an Image, which has the largest background. Also, to solve the second problem, I propose the method in which multiple objects are separated using RGB channel selection techniques having optimized the excessive division of area by utilizing Watermerge's threshold value with the object separation using the method of RGB channels separation. The tests proved that the methods proposed by me were superior to the existing methods in terms of retrieval performances insomuch as to replace those methods that developed for the purpose of retrieving those complex objects that used to be difficult to retrieve up until now.

• Horticultural Science & Technology
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• 제33권1호
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• pp.1-10
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• 2015

This study was conducted in three years (7-9 years after planting) to investigate vegetative growth, yield, fruit quality, and return bloom for optimum crop load based on different planting densities of adult 'Fuji'/M.9 apple trees. As plant materials, 'Fuji'/M.9 apple trees planted at $3.5{\times}1.5m$ (190 trees per 10 a), $3.5{\times}1.2m$ (238 trees per 10 a), and $3.2{\times}1.2m$ (260 trees per 10 a) spacing and trained as slender spindles were used. The crop load was assigned to five different object ranges as follows: 55-64, 65-74, 75-84, 85-94, and 95-104 fruit per tree. TCA increment, total shoot growth, return bloom, yield per tree, and yield efficiency tended to increase as planting density decreased, and fruit weight and soluble solid content tended to increase as the object range of crop load decreased. Fruit red color tended to increase as shoot growth decreased. For apple trees planted with 238 trees and 260 trees per 10a, biennial bearing occurred when the crop load was over 85-94 and 75-84 fruits, respectively. However, biennial bearing did not occur when the crop load was 95-104 fruits in apple trees planted with 190 trees per 10a. Accumulated yield tended to increase as planting density and crop load increased, but that of biennial bearing did not show such a difference. Based on our results, optimum crop load recommendations are to set 95-104 fruits per tree in 'Fuji'/M.9 mature apple trees planted at 190 trees per 10a, 75-84 fruits per tree at 238 trees per 10a, and 65-74 fruits per tree at 260 trees per 10a.