• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.103-111
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• 2024

Axial-flow fans are used to transport fluids in relatively low-pressure flow regimes, and a variety of design variables are employed. The tip geometry of an axial fan plays a dominant role in its flow and noise performance, and two of the most prominent flow phenomena are the tip vortex and the tip leakage vortex that occur at the tip of the blade. Various studies have been conducted to control these three-dimensional flow structures, and winglet geometries have been developed in the aircraft field to suppress wingtip vortices and increase efficiency. In this study, a numerical and experimental study was conducted to analyze the effect of winglet geometry applied to an axial fan blade for an air conditioner outdoor unit. The unsteady Reynolds-Averaged Navier-Stokes (RANS) equation and the FfocwsWilliams and Hawkings (FW-H) equation were numerically solved based on computational fluid dynamics techniques to analyze the three-dimensional flow structure and flow noise numerically, and the validity of the numerical method was verified by comparison with experimental results. The differences in the formation of tip vortex and tip leakage vortex depending on the winglet geometry were compared through a three-dimensional flow field, and the resulting aerodynamic performance was quantitatively compared. In addition, the effect of winglet geometry on flow noise was evaluated by numerically simulating noise based on the predicted flow field. A prototype of the target fan model was built, and flow and noise experiments were conducted to evaluate the actual performance quantitatively.

• The Transactions of the Korea Information Processing Society
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• v.13 no.7
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• pp.326-334
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• 2024

Extra-oral imaging techniques such as Panoramic X-rays (PXs) and Cone Beam Computed Tomography (CBCT) are the most preferred imaging modalities in dental clinics owing to its patient convenience during imaging as well as their ability to visualize entire teeth information. PXs are preferred for routine clinical treatments and CBCTs for complex surgeries and implant treatments. However, PXs are limited by the lack of third dimensional spatial information whereas CBCTs inflict high radiation exposure to patient. When a PX is already available, it is beneficial to reconstruct the 3D oral structure from the PX to avoid further expenses and radiation dose. In this paper, we propose 3DentAI - an U-Net based deep learning framework for 3D reconstruction of oral structure from a PX image. Our framework consists of three module - a reconstruction module based on attention U-Net for estimating depth from a PX image, a realignment module for aligning the predicted flattened volume to the shape of jaw using a predefined focal trough and ray data, and lastly a refinement module based on 3D U-Net for interpolating the missing information to obtain a smooth representation of oral cavity. Synthetic PXs obtained from CBCT by ray tracing and rendering were used to train the networks without the need of paired PX and CBCT datasets. Our method, trained and tested on a diverse datasets of 600 patients, achieved superior performance to GAN-based models even with low computational complexity.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.664-672
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• 2004

Widespread popularity of wireless data communication devices, coupled with the availability of higher bandwidths, has led to an increased user demand for content-rich media such as images and videos. Since such content often tends to be private, sensitive, or paid for, there exists a requirement for securing such communication. However, solutions that rely only on traditional compute-intensive security mechanisms are unsuitable for resource-constrained wireless and embedded devices. In this paper, we propose a selective partial image encryption scheme for image data hiding , which enables highly efficient secure communication of image data to and from resource constrained wireless devices. The encryption scheme is invoked during the image compression process, with the encryption being performed between the quantizer and the entropy coder stages. Three data selection schemes are proposed： subband selection, data bit selection and random selection. We show that these schemes make secure communication of images feasible for constrained embed-ded devices. In addition we demonstrate how these schemes can be dynamically configured to trade-off the amount of ded devices. In addition we demonstrate how these schemes can be dynamically configured to trade-off the amount of data hiding achieved with the computation requirements imposed on the wireless devices. Experiments conducted on over 500 test images reveal that, by using our techniques, the fraction of data to be encrypted with our scheme varies between 0.0244％ and 0.39％ of the original image size. The peak signal to noise ratios (PSNR) of the encrypted image were observed to vary between about 9.5㏈ to 7.5㏈. In addition, visual test indicate that our schemes are capable of providing a high degree of data hiding with much lower computational costs.

• Archives of design research
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• v.13 no.1
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• pp.209-218
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• 2000

The purpose of this study was to improve the curriculum for industrial design department in the junior colleges. In order to achieve the purpose, two methodologies were carried out. First is job analysis of the industrial designers who have worked in the small & medium manufacturing companies, second is survey for the opinions of professors in the junior colleges. Some results were as follows: 1. The period of junior college for industrial designers is 2 years according to present. But selectively 1 year of advanced course can be established. 2. The practice subjects same as computational formative techniques needed to product development have to be increased. In addition kinds of selection subjects same as foreign language, manufacturing process, new product information and consumer behavior investigation have to be extended. 3. The next subjects need to adjust the title, contents and hours. (1) The need of 3.D related subjects same as computer modeling, computer rendering, 3.D modeling was high. The use of computer is required to design presentation subjects. (2)The need of advertising and sale related subjects same as printing, merchandise, package, typography, photography was low, the need of presentation techniques of new product development was high. (3) The need of field practice, special lecture on practice and reading original texts related subjects was same as at present, but these are not attached importance to form. As the designers feel keenly the necessity of using foreign language, the need of language subject was high.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.14 no.12
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• pp.1102-1139
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• 2002

A review on the papers published in the Korean Journal of Air-Conditioning and Refrigerating Engineering in 2000 and 2001 has been done. Focus has been put on current status of research in the aspect of heating, cooling, ventilation, sanitation and building environment. The conclusions are as follows. (1) Most of fundamental studies on fluid flow were related with heat transportation of facilities. Drop formation and rivulet flow on solid surfaces were interesting topics related with condensation augmentation. Research on micro environment considering flow, heat, humidity was also interesting for comfortable living environment. It can be extended considering biological aspects. Development of fans and blowers of high performance and low noise were continuing topics. Well developed CFD technologies were widely applied for developing facilities and their systems. (2) Most of papers related with heat transfer analysis and heat exchanger shows dealt with convection, evaporation, and channel flow for the design application of heat exchanger. The numerical heat transfer simulation studies have been peformed and reported to show heat transfer characteristics. Experimental as well as numerical studies on heat exchanger were reported, while not many papers are available for the system analysis including heat exchanger. (3) A review of the recent studies on heat pump system shows that performance analysis and control of heat pump have been peformed by various simulations and experiments. The research papers on multi-type heat pump system increased significantly. The studies on heat pipe have been examined experimently for change of working characteristics and strut lure. Research on the phase change has been carried out steadily and operation strategies of encapsulated ice storage tank are reported experimentally in several papers. (4) A review of recent studies on refrigeration/air conditioning system have focused on the system performance and efficiency for new alternative refrigerants. Evaporation and condensation heat transfer characteristics are investigated for tube shapes and new alternative refrigerants. Studies on components of refrigeration/air conditioning system are carried to examine efficiency for various compressors and performance of new expansion devices. In addition to thermophysical properties of refrigerant mixtures, studies on new refrigerants are also carried out, however research works on two-phase flow seemed to be insufficient. (5) A review of the recent studies on absorption cooling system indicates that heat and mass transfer phenomena have been investigated to improve absorber performance. Various experimental data have been presented and several simulation models have been proposed. A review of the recent studies on duct and ventilation shows that ventilation indices have been proposed to quantify the ventilation performance in buildings and tunnels. Main efforts have been focused on the applications of ventilation effectiveness in practice, either numerically using computational fluid dynamics or experimentally using tracer gas techniques. (6) Based on a review of recent studies on indoor thermal environment and building service systems, research issues have mainly focused on many innovative ideas such as underfloor air-conditioning system, personal environmental modules, radiant floor cooling and etc. Also, the new approaches for minimizing energy consumption as well as improving indoor environmental conditions through predictive control of HVAC systems, various activities of building energy management and cost-benefit analysis for economic evaluation were highlighted.

• Asia pacific journal of information systems
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• v.20 no.2
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• pp.125-155
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• 2010

Ontologies have been adopted in various business and scientific communities as a key component of the Semantic Web. Despite the increasing importance of ontologies, ontology developers still perceive construction tasks as a challenge. A clearly defined and well-structured methodology can reduce the time required to develop an ontology and increase the probability of success of a project. However, no reliable knowledge-engineering methodology for ontology development currently exists; every methodology has been tailored toward the development of a particular ontology. In this study, we developed a Graduation Screen Ontology (GSO). The graduation screen domain was chosen for the several reasons. First, the graduation screen process is a complicated task requiring a complex reasoning process. Second, GSO may be reused for other universities because the graduation screen process is similar for most universities. Finally, GSO can be built within a given period because the size of the selected domain is reasonable. No standard ontology development methodology exists; thus, one of the existing ontology development methodologies had to be chosen. The most important considerations for selecting the ontology development methodology of GSO included whether it can be applied to a new domain; whether it covers a broader set of development tasks; and whether it gives sufficient explanation of each development task. We evaluated various ontology development methodologies based on the evaluation framework proposed by G$\acute{o}$mez-P$\acute{e}$rez et al. We concluded that METHONTOLOGY was the most applicable to the building of GSO for this study. METHONTOLOGY was derived from the experience of developing Chemical Ontology at the Polytechnic University of Madrid by Fern$\acute{a}$ndez-L$\acute{o}$pez et al. and is regarded as the most mature ontology development methodology. METHONTOLOGY describes a very detailed approach for building an ontology under a centralized development environment at the conceptual level. This methodology consists of three broad processes, with each process containing specific sub-processes: management (scheduling, control, and quality assurance); development (specification, conceptualization, formalization, implementation, and maintenance); and support process (knowledge acquisition, evaluation, documentation, configuration management, and integration). An ontology development language and ontology development tool for GSO construction also had to be selected. We adopted OWL-DL as the ontology development language. OWL was selected because of its computational quality of consistency in checking and classification, which is crucial in developing coherent and useful ontological models for very complex domains. In addition, Protege-OWL was chosen for an ontology development tool because it is supported by METHONTOLOGY and is widely used because of its platform-independent characteristics. Based on the GSO development experience of the researchers, some issues relating to the METHONTOLOGY, OWL-DL, and Prot$\acute{e}$g$\acute{e}$-OWL were identified. We focused on presenting drawbacks of METHONTOLOGY and discussing how each weakness could be addressed. First, METHONTOLOGY insists that domain experts who do not have ontology construction experience can easily build ontologies. However, it is still difficult for these domain experts to develop a sophisticated ontology, especially if they have insufficient background knowledge related to the ontology. Second, METHONTOLOGY does not include a development stage called the "feasibility study." This pre-development stage helps developers ensure not only that a planned ontology is necessary and sufficiently valuable to begin an ontology building project, but also to determine whether the project will be successful. Third, METHONTOLOGY excludes an explanation on the use and integration of existing ontologies. If an additional stage for considering reuse is introduced, developers might share benefits of reuse. Fourth, METHONTOLOGY fails to address the importance of collaboration. This methodology needs to explain the allocation of specific tasks to different developer groups, and how to combine these tasks once specific given jobs are completed. Fifth, METHONTOLOGY fails to suggest the methods and techniques applied in the conceptualization stage sufficiently. Introducing methods of concept extraction from multiple informal sources or methods of identifying relations may enhance the quality of ontologies. Sixth, METHONTOLOGY does not provide an evaluation process to confirm whether WebODE perfectly transforms a conceptual ontology into a formal ontology. It also does not guarantee whether the outcomes of the conceptualization stage are completely reflected in the implementation stage. Seventh, METHONTOLOGY needs to add criteria for user evaluation of the actual use of the constructed ontology under user environments. Eighth, although METHONTOLOGY allows continual knowledge acquisition while working on the ontology development process, consistent updates can be difficult for developers. Ninth, METHONTOLOGY demands that developers complete various documents during the conceptualization stage; thus, it can be considered a heavy methodology. Adopting an agile methodology will result in reinforcing active communication among developers and reducing the burden of documentation completion. Finally, this study concludes with contributions and practical implications. No previous research has addressed issues related to METHONTOLOGY from empirical experiences; this study is an initial attempt. In addition, several lessons learned from the development experience are discussed. This study also affords some insights for ontology methodology researchers who want to design a more advanced ontology development methodology.

• Journal of the Korean Institute of Gas
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• v.24 no.2
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• pp.1-8
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• 2020

Centrifugal pump is a facility that transfers energy to fluid through centrifugal force, which is usually generated by rotating the impeller at high speed, and is a major process facility used in many LNG production bases such as vaporization seawater pump, industrial water and fire extinguishing pump using seawater. to be. Currently, pumps in LNG plant sites are subject to operating conditions that vary depending on the amount of supply desired by the customer for a long period of time. Pumps in particular occupy a large part of the consumption strategy at the plant site, and if the optimum operation condition is not available, it can incur enormous energy loss in long term plant operation. In order to solve this problem, it is necessary to identify the performance deterioration factor through the flow analysis and the result analysis according to the fluctuations of the pump's operating conditions and to determine the optimal operation efficiency. In order to evaluate operation efficiency through experimental techniques, considerable time and cost are incurred, such as on-site operating conditions and manufacturing of experimental equipment. If the performance of the pump is not suitable for the site, and the performance of the pump needs to be reduced, a method of changing the rotation speed or using a special liquid containing high viscosity or solids is used. Especially, in order to prevent disruptions in the operation of LNG production bases, a technology is required to satisfy the required performance conditions by processing the existing impeller of the pump within a short time. Therefore, in this study, the rotation difference of the pump was applied to the ANSYS CFX program by applying the modified 3D modeling shape. In addition, the results obtained from the flow analysis and the curve fitting toolbox of the MATLAB program were analyzed numerically to verify the outer diameter correction theory.