• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.65-70
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• 2024

As the interest in AI education grows, researchers have made efforts to implement AI education programs. However, research targeting pre-service teachers has been limited thus far. Therefore, this study was conducted to develop an AI literacy education program for preservice secondary teachers. The research results revealed that the weekly topics included the definition and applications of AI, analysis of intelligent agents, the importance of data, understanding machine learning, hands-on exercises on prediction and classification, hands-on exercises on clustering and classification, hands-on exercises on unstructured data, understanding deep learning, application of deep learning algorithms, fairness, transparency, accountability, safety, and social integration. Through this research, it is hoped that AI literacy education programs for preservice teachers will be expanded. In the future, it is anticipated that follow-up studies will be conducted to implement relevant education in teacher training institutions and analyze its effectiveness.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.101-106
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• 2023

Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]

• Nuclear Medicine and Molecular Imaging
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• v.43 no.2
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• pp.143-149
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• 2009

Purpose: It is not known whether bone scan is useful for the prediction of the prognosis of patients with temporomandibular disorders(TMD). The aim of the present study was to identify useful prognostic markers on bone scan for the pre-therapeutic assessment of patients with unilateral TMD. Materials and Methods: Between January 2005 and July 2007, 55 patients(M:F=9:46; mean age, $34.7{\pm}14.1$ y) with unilateral TMD that underwent a pre-therapeutic bone scan were enrolled. Uptake of Tc-99m HDP in each temporomandibular joint(TMI) was quantitated using a $13{\times}13$ pixel-square region-of-interest over TMJ and parietal skull area as background. TMJ uptake ratios and asymmetric indices were calculated. TMD patients were classified as improved or not improved and the bone scan findings associated with each group were investigated. Results: Forty-six patients were improved, whereas 9 patients were not improved. There was no significant difference between the two groups of patients regarding the TMJ uptake ratio of the involved joint, the TMJ uptake ratio of the non-involved joint, and the asymmetric index(p>0.05). However, in a subgroup analysis, the patients with an increased uptake of Tc-99m HDP at the disease-involved TMJ, by visual assessment, could be easily identified by the asymmetric index; the patients that improved had a higher asymmetric index than the patients that did not improve($1.32{\pm}0.35$ vs. $1.08{\pm}0.04$, p=0.023), Conclusion: The Tc-99m HDP bone scan may help predict the prognosis of patients with unilateral TMD after splint therapy when the TMD-involved joint reveals increased uptake by visual assessment.

• Journal of IKEEE
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• v.27 no.1
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• pp.116-121
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• 2023

In this paper, we develop a deep learning structure for a complex microbial incubator that applies deep learning prediction result information. The proposed complex microbial incubator consists of pre-processing of complex microbial data, conversion of complex microbial data structure, design of deep learning network, learning of the designed deep learning network, and GUI development applied to the prototype. In the complex microbial data preprocessing, one-hot encoding is performed on the amount of molasses, nutrients, plant extract, salt, etc. required for microbial culture, and the maximum-minimum normalization method for the pH concentration measured as a result of the culture and the number of microbial cells to preprocess the data. In the complex microbial data structure conversion, the preprocessed data is converted into a graph structure by connecting the water temperature and the number of microbial cells, and then expressed as an adjacency matrix and attribute information to be used as input data for a deep learning network. In deep learning network design, complex microbial data is learned by designing a graph convolutional network specialized for graph structures. The designed deep learning network uses a cosine loss function to proceed with learning in the direction of minimizing the error that occurs during learning. GUI development applied to the prototype shows the target pH concentration (3.8 or less) and the number of cells (10⁸ or more) of complex microorganisms in an order suitable for culturing according to the water temperature selected by the user. In order to evaluate the performance of the proposed microbial incubator, the results of experiments conducted by authorized testing institutes showed that the average pH was 3.7 and the number of cells of complex microorganisms was 1.7 × 10⁸. Therefore, the effectiveness of the deep learning structure for the complex microbial incubator applying the deep learning prediction result information proposed in this paper was proven.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.434-441
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• 2023

Determining the size or area of a plant's leaves is an important factor in predicting plant growth and improving the productivity of indoor farms. In this study, we developed a convolutional neural network (CNN)-based model to accurately predict the length and width of lettuce leaves using photographs of the leaves. A callback function was applied to overcome data limitations and overfitting problems, and K-fold cross-validation was used to improve the generalization ability of the model. In addition, ImageDataGenerator function was used to increase the diversity of training data through data augmentation. To compare model performance, we evaluated pre-trained models such as VGG16, Resnet152, and NASNetMobile. As a result, NASNetMobile showed the highest performance, especially in width prediction, with an R_squared value of 0.9436, and RMSE of 0.5659. In length prediction, the R_squared value was 0.9537, and RMSE of 0.8713. The optimized model adopted the NASNetMobile architecture, the RMSprop optimization tool, the MSE loss functions, and the ELU activation functions. The training time of the model averaged 73 minutes per Epoch, and it took the model an average of 0.29 seconds to process a single lettuce leaf photo. In this study, we developed a CNN-based model to predict the leaf length and leaf width of plants in indoor farms, which is expected to enable rapid and accurate assessment of plant growth status by simply taking images. It is also expected to contribute to increasing the productivity and resource efficiency of farms by taking appropriate agricultural measures such as adjusting nutrient solution in real time.

• IMMUNE NETWORK
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• v.15 no.2
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• pp.66-72
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• 2015

Currently, detecting biochemical differences before and after allogeneic stem cell transplantation (SCT) for improved prediction of acute graft-versus-host disease (aGVHD) is a major clinical challenge. In this pilot study, we analyzed the kinetics of circulating adipokine levels in patients with or without aGVHD before and after allogeneic SCT. Serum samples were obtained and stored at $-80^{\circ}C$ within 3 hours after collection, prior to conditioning and at engraftment after transplantation. A protein array system was used to measure the levels of 7 adipokines of patients with aGVHD (n=20) and without aGVHD (n=20). The resistin level at engraftment was significantly increased (p<0.001) after transplantation, regardless of aGVHD occurrence. In the non-aGVHD group, the concentrations of the hepatocyte growth factor (HGF) (mean values${\pm}$SD; $206.6{\pm}34.3$ vs. $432.3{\pm}108.9pg/ml$, p=0.040) and angiopoietin-2 (ANG-2) (mean values${\pm}$SD; $3,197.2{\pm}328.3$ vs. $4,471.8{\pm}568.4pg/ml$, p=0.037) at engraftment were significantly higher than those of the pre-transplant period, whereas in the aGVHD group, the levels of adipokines did not change after transplantation. Our study suggests that changes in serum HGF and ANG-2 levels could be considered helpful markers for the subsequent occurrence of aGVHD.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.1-11
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• 2020

Article 325 (Prevention of Fire Explosion due to Electrostatic) of the Rule for Occupational Safety and Health Standard specifies that in order to prevent the risk of disasters caused by static electricity, fire, explosion and static electricity in the production process, However, in order to do this, it is absolutely necessary to use a pre-detection technology and a detector for antistatic discharge prediction, which is a precautionary measure by static electricity in a fire / explosion hazard place, but in Korea, And there is no technical standard for the application of the technology of the explosion proof structure of the related equipment. Research methods include domestic and overseas electrostatic discharge detection technology and literature investigation of related equipment explosion proofing technology, domestic and foreign electrostatic discharge detection device production and use situation investigation, advanced foreign technology data analysis and benchmarking. In particular, we sought to verify the results of empirical experiments using electrostatic discharge detection technology through sample purchase and analysis of related major products, development of optimization technology through prototype production, evaluation, and supplementation, and expert knowledge through expert consultation. The results of this study were developed and fabricated two prototypes of electrostatic discharge detector based on the technology / standard related to electrostatic discharge detection technology in Korea and abroad through development of electrostatic discharge detection technology and development and production of detector. In addition, based on the development of electrostatic discharge detection technology, we developed an intrinsic safety explosion proof ib class explosion proof technology applicable to the process of using and handling flammable gas and flammable liquid vapor and combustible dust. In the case of the over voltage and minimum voltage are supplied to the explosion-proof structure ESD detector, check the state of the circuit and the transient and transient currents generated by the coil and capacitor elements during the input and standby of the signal pulse voltage. Explosion-proof equipment-Part 11: Intrinsically safe explosion proof structure The comparative evaluation with the reference curve in Annex A of "i" confirms that the characteristics of the intrinsically safe explosion protection structure are met.

• Journal of Korea Water Resources Association
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• v.33 no.6
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• pp.745-755
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• 2000

The dam operation system of KOWACO for flood control doesn't have capability to account for the downstream hydrologic conditions and any feasible index to decide the pre-release from the forecasted rainfall and inflow. In this study, a dam operation model for flood control was developed to account for the flood flow condition of its downstream to give users the dam release schedules. Application test of EV ROM to Keum River showed that EV ROM is superior to the Rigid ROM and Technical ROM which are currently used by KOWACO. EV ROM developed in this study provides a release schedule accounting for the cumulative lateral flow hydrograph at the downstream control points where the discharge does not depend only on the dam operation. but also on lateral inflow from the tributaries. In order to reduce the peak discharge at the control points, it suggests the preliminary release during the early rising phase of the predicted hydrograph, holding the flood flow inside the dam during a peak phase, and afterward resuming the release. Three case studies of flood control by the operation of Daechung Multipurpose Dam in Geum River Basin show that the EV ROM is superior to the Rigid ROM and Technical ROM. This must be due to its nature to account for the downstream flow condition as well as the inflow and water level of the dam. It was also conceived that further case studies of EV ROM and more accurate rainfall prediction would improve the dam operation for flood control.ontrol.

• Journal of the Korean Geotechnical Society
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• v.20 no.5
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• pp.99-107
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• 2004

Anisotropic characteristics of deformation are important to understand the particular behavior in the pre-failure state of soils. Recent experiments show that cross-anisotropic moduli of granular soils can be expressed by functions of normal stresses in the corresponding directions, which is closely linked to micromechanical characteristics of particles. Granular soils are composed of a number of particles so that the force-displacement relationship at each contact point governs the macroscopic stress-strain relationship. Therefore, the micromechanical approach in which the deformation of granular soils is regarded as a mutual interaction between particle contacts is one of the best ways to investigate the anisotropic elastic deformation of soils. In this study, a numerical program based on the theory of micromechanics is developed. Generalized contact model for the irregular contact surface of soil particles is adopted to represent the force-displacement relationship in each contact point far the realistic prediction of anisotropic moduli. To evaluate the model parameters, a set of analytical solutions of anisotropic elastic moduli is derived in the isotropic stress condition. A detailed procedure to determine the model parameters is proposed with emphasis on the practical applicability of micromechanical program to analyze the elastic behavior of the granular soils.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.6
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• pp.2527-2530
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• 2015

Background: The cancer of the prostate risk assessment (CAPRA) score has been defined to predict prostate cancer recurrence based on the pre-clinical data, then pathological data have also been incorporated. Thus, CAPRA post-surgical (CAPRA-S) score has been developed based on six criteria (prostate specific antigen (PSA) at diagnosis, pathological Gleason score, and information on surgical margin, seminal vesicle invasion, extracapsular extension and lymph node involvement) for the prediction of post-surgical recurrences. In the present study, biochemical recurrence (BCR)-free probabilities after open retropubic radical prostatectomy (RP) were evaluated by the CAPRA-S scoring system and its three-risk level model. Materials and Methods: CAPRA-S scores (0-12) of our 240 radical prostatectomies performed between January 2000-May 2011 were calculated. Patients were distributed into CAPRA-S score groups and also into three-risk groups as low, intermediate and high. BCR-free probabilities were assessed and compared using Kaplan-Meier analysis and Cox proportional hazards regression. Ability of CAPRA-S in BCR detection was evaluated by concordance index (c-index). Results: BCR was present in 41 of total 240 patients (17.1%) and the mean follow-up time was $51.7{\pm}33.0$ months. Mean BCR-free survival time was 98.3 months (95% CI: 92.3-104.2). Of the patients in low, intermediate and high risk groups, 5.4%, 22.0% and 58.8% had BCR, respectively and the difference among the three groups was significant (P = 0.0001). C-indices of CAPRA-S score and three-risk groups for detecting BCR-free probabilities in 5-yr were 0.87 and 0.81, respectively. Conclusions: Both CAPRA-S score and its three-risk level model well predicted BCR after RP with high c-index levels in our center. Therefore, it is a clinically reliable post-operative risk stratifier and disease recurrence predictor for prostate cancer.