• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.7
• /
• pp.233-240
• /
• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• Journal of Navigation and Port Research
• /
• v.46 no.3
• /
• pp.280-288
• /
• 2022

This study examined the diagnostics of abnormalities and faults of equipment, whose rotational speed changes even during regular operation. The purpose of this study was to suggest a procedure that can properly apply machine learning to the time series data, comprising non-stationary characteristics as the rotational speed changes. Anomaly and fault diagnosis was performed using machine learning: k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest. To compare the diagnostic accuracy, an autoencoder was used for anomaly detection and a convolution based Conv1D was additionally used for fault diagnosis. Feature vectors comprising statistical and frequency attributes were extracted, and normalization & dimensional reduction were applied to the extracted feature vectors. Changes in the diagnostic accuracy of machine learning according to feature selection, normalization, and dimensional reduction are explained. The hyperparameter optimization process and the layered structure are also described for each algorithm. Finally, results show that machine learning can accurately diagnose the failure of a variable-rotation machine under the appropriate feature treatment, although the convolution algorithms have been widely applied to the considered problem.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.8
• /
• pp.241-251
• /
• 2022

Diagnosis and management of customer's skin condition is an important essential function in the cosmetics and beauty industry. As the social media environment spreads and generalizes to all fields of society, the interaction of questions and answers to various and delicate concerns and requirements regarding the diagnosis and management of skin conditions is being actively dealt with in the social media community. However, since social media information is very diverse and atypical big data, an intelligent skin condition diagnosis system that combines appropriate skin condition information analysis and artificial intelligence technology is necessary. In this paper, we developed the skin condition diagnosis system SCDIS to intelligently diagnose and manage the skin condition of customers by processing the text analysis information of social media into learning data. In SCDIS, an artificial neural network model, AnnTFIDF, that automatically diagnoses skin condition types using artificial neural network technology, a deep learning machine learning method, was built up and used. The performance of the artificial neural network model AnnTFIDF was analyzed using test sample data, and the accuracy of the skin condition type diagnosis prediction value showed a high performance of about 95%. Through the experimental and performance analysis results of this paper, SCDIS can be evaluated as an intelligent tool that can be used efficiently in the skin condition analysis and diagnosis management process in the cosmetic and beauty industry. And this study can be used as a basic research to solve the new technology trend, customized cosmetics manufacturing and consumer-oriented beauty industry technology demand.

• Journal of the Korean Geotechnical Society
• /
• v.22 no.9
• /
• pp.45-59
• /
• 2006

The successful performance of any numerical geotechnical simulation depends on the accuracy and efficiency of the numerical implementation of constitutive model used to simulate the stress-strain (constitutive) response of the soil. The corner stone of the numerical implementation of constitutive models is the numerical integration of the incremental form of soil-plasticity constitutive equations over a discrete sequence of time steps. In this paper a well known two-surface soil plasticity model is implemented using a generalized implicit return mapping algorithm to arbitrary convex yield surfaces referred to as the Closest-Point-Projection method (CPPM). The two-surface model describes the nonlinear behavior of coarse-grained materials by incorporating a bounding surface concept together with isotropic and kinematic hardening as well as fabric formulation to account for the effect of fabric formation on the unloading response. In the course of investigating the performance of the CPPM integration method, it is proven that the algorithm is an accurate, robust, and efficient integration technique useful in finite element contexts. It is also shown that the algorithm produces a consistent tangent operator $\frac{d\sigma}{d\varepsilon}$ during the iterative process with quadratic convergence rate of the global iteration process.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.5A
• /
• pp.841-848
• /
• 2006

There is a complex variation of stress in PSC anchorage zones and box girder diaphragms because of large concentrated load by prestress. According to the AASHTO LFRD design code, three-dimensional effects due to concentrated jacking loads shall be investigated using three-dimensional analysis procedures or may be approximated by considering separate submodels for two or more planes. In this case, the interaction of the submodels should be considered, and the model loads and results should be consistent. However, box girder diaphragms are 3-dimensional disturbed region which requires a fully three-dimensional model, and two-dimensional models are not satisfactory to model the flow of forces in diaphragms. In this study, the strengths of the prestressed box girder diaphragms are predicted using the 3-dimensional grid strut-tie model approach, which were tested to failure in University of Texas. According to the analysis results, the 3-dimensional strut-tie model approach can be possibly applied to the analysis and design of PSC box girder anchorage zones as a reasonable computer-aided approach with satisfied accuracy.

• Asia Marketing Journal
• /
• v.8 no.1
• /
• pp.1-17
• /
• 2006

A couple of previous studies have warned against the use of homogeneous choice models in assessing the effect of reference price since unaccounted for response heterogeneity may result in spurious reference price effects(Chang, Siddarth and Weinberg 1999; Bell and Lattin 2000). According to Meyer and Kahn(1991), not accounting for consideration set heterogeneity may also bias the effect parameters in the choice model. Therefore, failure to account for these two sources of bias, in fact, have cast doubt on the empirical support for reference price effects in general. In view of aforementioned potential sources of bias, the author investigates the robustness of loss aversion effect in the reference-dependent model after accounting for heterogeneity in response as well as consideration set. The proposed model defines individual household's consideration set based on the posterior distribution of preference obtained from the Empirical Bayes approach. In addition, the same posterior distribution is used to form household-specific reference prices. Response heterogeneity correction is carried out via the Latent Class approach. The proposed model outperforms the Reference-Dependent model that includes the reference price measure most often employed in the previous studies. This implies that as a way of simplifying decision task, consumers restrict their consideration set to a subset of available brands not only in making a brand choice but also in forming reference prices.

• The Journal of Korean Academy of Prosthodontics
• /
• v.61 no.4
• /
• pp.367-378
• /
• 2023

With the recent development of computer-aided design-computer-aided manufacturing technology and 3D printing technology, and the introduction of various digital techniques, the accuracy and efficiency of top-down definitive prosthetic restoration are increasing. In this clinical case, stable occlusion support was obtained through the placement of a total of 9 maxillary and mandibular posterior implants in patient with anterior-posterior crossed occlusion. The edentulous area of the maxillary anterior teeth, which showed a tendency of high resorption of the residual alveolar bone, was restored with a Kennedy Class IV implant assisted removable partial denture to restore soft tissue esthetics. Computed tomography guided surgery was used to place implants in the planned position, double scan technique was used to reflect the stabilized occlusion in the interim restoration stage to the definitive prostheses, and metal 3D printing was used to manufacture the coping and framework. This clinical case reports that efficient and predictable top-down full mouth rehabilitation was achieved using various digital technologies and techniques.

• The Journal of Engineering Geology
• /
• v.34 no.1
• /
• pp.39-49
• /
• 2024

The construction of underground infrastructure is garnering growing increasing research attention owing to population concentration and infrastructure overcrowding in urban areas. An important associated task is establishing a monitoring system to evaluate stability during infrastructure construction and operation, which relies on developing techniques for ground investigation that can evaluate ground stability, verify design validity, predict risk, facilitate safe operation management, and reduce construction costs. The method proposed here uses satellite imaging in a cost-effective and accurate ground investigation technique that can be applied over a wide area during the construction and operation of infrastructure. In this study, analysis was performed using Synthetic Aperture Radar (SAR) data with the time-series radar interferometric technique to observe surface displacement during the construction of urban underground roads. As a result, it was confirmed that continuous surface displacement was occurring at some locations. In the future, comparing and analyzing on-site measurement data with the points of interest would aid in confirming whether displacement occurs due to tunnel excavation and assist in estimating the extent of excavation impact zones.

• Horticultural Science & Technology
• /
• v.31 no.4
• /
• pp.473-482
• /
• 2013

Molecular markers, as an efficient selection tool, have been and is being used for practical breeding program in chili pepper (Capsicum annuum L.). Recently, a lot of researches on inheritance and genetic analysis for quantitative traits including capsaicinoids, carotenoids, and sugar content in pepper are being performed worldwide. It has been also reported that QTL mapping is a necessary tool to develop molecular markers associated with the quantitative traits. In this study, we suggested a new method to construct a pepper genetic map using SNP (HRM) markers generated from NGS resequencing of female and male parents. Plant materials were C. annuum 'NB1' (female parent), C. chinense 'Jolokia' (male parent), and their $F_2$ population consisting of 94 progenies. Sequences of 4.6 Gbp and 6.2 Gbp were obtained from NGS resequencing of 'NB1' and 'Jolokia', respectively. Totally, 4.29 million SNPs between 'NB1' and 'Jolokia' were detected and the 1.76 million SNPs were clearly identified. Among them, total 145 SNP (HRM) primer pairs covering pepper genetic map were selected, and the 116 SNP (HRM) markers of them were located on this map. Total distance of the map, which consisted of 12 linkage groups and matched with basic chromosome numbers of pepper, was 1,167.9 cM. According to the mapping result, we concluded that our mapping method was suitable to construct a pepper genetic map fast and accurately. In addition, the genetic map could be directly used for QTL analysis of traits different between both parents.

• Journal of Korean Society of Transportation
• /
• v.36 no.2
• /
• pp.155-168
• /
• 2018

In this study, we studied the directional pattern of entering the intersection from the intersection upstream link prior to predicting short future (such as 5 or 10 minutes) intersection direction traffic volume on the interrupted flow, and examined the possibility of traffic volume prediction using traffic assignment model. The analysis method of this study is to investigate the similarity of patterns by performing cluster analysis with the ratio of traffic volume by intersection direction divided by 2 hours using taxi DTG (Digital Tachograph) data (1 week). Also, for linking with the result of the traffic assignment model, this study compares the impact area of 5 minutes or 10 minutes from the center of the intersection with the analysis result of taxi DTG data. To do this, we have developed an algorithm to set the impact area of intersection, using the taxi DTG data and traffic assignment model. As a result of the analysis, the intersection entry pattern of the taxi is grouped into 12, and the Cubic Clustering Criterion indicating the confidence level of clustering is 6.92. As a result of correlation analysis with the impact area of the traffic assignment model, the correlation coefficient for the impact area of 5 minutes was analyzed as 0.86, and significant results were obtained. However, it was analyzed that the correlation coefficient is slightly lowered to 0.69 in the impact area of 10 minutes from the center of the intersection, but this was due to insufficient accuracy of O/D (Origin/Destination) travel and network data. In future, if accuracy of traffic network and accuracy of O/D traffic by time are improved, it is expected that it will be able to utilize traffic volume data calculated from traffic assignment model when controlling traffic signals at intersections.