• Biomolecules & Therapeutics
• /
• v.20 no.5
• /
• pp.477-481
• /
• 2012

The rhizomes of Acorus gramineus have frequently been used in traditional medicine mainly for sedation as well as enhancing brain function. In this study, the anti-allergic activity of A. gramineus was investigated. The 70% ethanol extract of the rhizomes of A. gramineus was found to inhibit the allergic response against 5-lipoxygenase (5-LOX)-catalyzed leukotriene (LT) production from rat basophilic leukemia (RBL)-1 cells and ${\beta}$-hexosaminidase release from RBL-2H3 cells with $IC_{50}$'s of 48.9 and > $200{\mu}g/ml$, respectively. Among the 9 major constituents isolated, ${\beta}$-asarone, (2R,3R,4S,5S)-2,4-dimethyl-1,3-bis (2',4',5'-trimethoxyphenyl)tetrahydrofuran (AF) and 2,3-dihydro-4,5,7-trimethoxy-1-ethyl-2-methyl-3-(2,4,5-trimethoxyphenyl)indene (AI) strongly inhibited 5-LOX-catalyzed LT production in A23187-treated RBL-1 cells, AI being the most potent ($IC_{50}=6.7{\mu}M$). Against ${\beta}$-hexosaminidase release by antigen-stimulated RBL-2H3 cells, only AI exhibited strong inhibition ($IC_{50}=7.3{\mu}M$) while ${\beta}$-asarone and AF showed 26.0% and 39.9% inhibition at $50{\mu}M$, respectively. In addition, the ethanol extract of A. gramineus showed significant inhibitory action against the hapten-induced delayed hypersensitivity reaction in mice by oral administration at 200 mg/kg. Therefore, it is suggested that A. gramineus possesses anti-allergic activity and the constituents including ${\beta}$-asarone and AI certainly contribute to the anti-allergic activity of the rhizomes of A. gramineus.

• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.241-265
• /
• 2023

A corporate insolvency prediction model serves as a vital tool for objectively monitoring the financial condition of companies. It enables timely warnings, facilitates responsive actions, and supports the formulation of effective management strategies to mitigate bankruptcy risks and enhance performance. Investors and financial institutions utilize default prediction models to minimize financial losses. As the interest in utilizing artificial intelligence (AI) technology for corporate insolvency prediction grows, extensive research has been conducted in this domain. However, there is an increasing demand for explainable AI models in corporate insolvency prediction, emphasizing interpretability and reliability. The SHAP (SHapley Additive exPlanations) technique has gained significant popularity and has demonstrated strong performance in various applications. Nonetheless, it has limitations such as computational cost, processing time, and scalability concerns based on the number of variables. This study introduces a novel approach to variable selection that reduces the number of variables by averaging SHAP values from bootstrapped data subsets instead of using the entire dataset. This technique aims to improve computational efficiency while maintaining excellent predictive performance. To obtain classification results, we aim to train random forest, XGBoost, and C5.0 models using carefully selected variables with high interpretability. The classification accuracy of the ensemble model, generated through soft voting as the goal of high-performance model design, is compared with the individual models. The study leverages data from 1,698 Korean light industrial companies and employs bootstrapping to create distinct data groups. Logistic Regression is employed to calculate SHAP values for each data group, and their averages are computed to derive the final SHAP values. The proposed model enhances interpretability and aims to achieve superior predictive performance.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.28 no.6
• /
• pp.628-637
• /
• 2004

The present study investigates the performance of dynamic mixed model (DMM; Zang et ai.) in a channel with strong wall injection through a Large eddy simulation (LES) technique. The DMM results are compared with those of DNS and the results obtained with popular dynamic Smagorinsky model (DSM). Better agreement is achieved when using the DMM with box filter than DSM and coarse DNS in predicting the first and second order statistics as well as large-scale structures of velocity and temperature fields. Such favorable features of DMM are attributed to the fact that it explicitly calculates the modified Leonard stress term and only models the remaining cross and the SGS Reynolds stress terms and, thus, it reduces the excessive burden put on the model coefficient of DSM. Also it is demonstrated that the DMM can be successfully extended to the prediction of temperature (passive scalar) field where strong streamwise inhomogeneity exists.

• The Journal of the Korea Contents Association
• /
• v.19 no.9
• /
• pp.37-46
• /
• 2019

Humans have anxieties as well as expectations for artificial intelligence. This study attempted to identify the expectation and anxiety factors affecting the attitude toward artificial intelligence innovation and to ascertain how much influence they have on current artificial intelligence innovation. This study considered that attitudes toward artificial intelligence may be different for each generation sharing a similar technology change culture. Therefore, the researchers limited the research subjects to I generation, which is the main users of artificial intelligence in the future. As a main result, the factors of expectiation of 'performance gain', 'positive social impact', and the factor of anxiety of 'threat to human-oriented social value' were drawn, and these factors influenced weak and strong artificial intelligence respectively. The results of this study suggests that artificial intelligence should develop into a pleasant relationship with humankind.

• Advances in nano research
• /
• v.12 no.2
• /
• pp.185-195
• /
• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Korean Journal of Cognitive Science
• /
• v.8 no.1
• /
• pp.37-56
• /
• 1997

Can the appropriately programmed computer think?I analyse,in this paper, argugments for and against strong AI-thesis,basically Turing's argument and Searle's chinese room argument.Through a cirtical review of these arguments, I try to show that the supportes of Al-thesis like Turing fail to explain the subjective nature of human consciousness.However,I do not think that subjective consciousness is a necessary condition for the ability to understand language.(In this respect my views are different from Searle's). But when we consider the conditions of humans as language users,we should presuppose that a human being is the unity of body and mind (or consciousness). Therefore, our subjective consciousness,together with human body(thus,way of our behavior and life). serve as a mark of person.

• Electronics and Telecommunications Trends
• /
• v.34 no.4
• /
• pp.43-54
• /
• 2019

Currently, a majority of artificial intelligence is used to secure big data; however, it is concentrated in a few of major companies. Therefore, automatic data augmentation and efficient learning algorithms for small-scale data will become key elements in future artificial intelligence competitiveness. In addition, it is necessary to develop a technique to learn meanings, correlations, and time-related associations of complex modal knowledge similar to that in humans and expand and transfer semantic prediction/knowledge inference about unknown data. To this end, a neural memory model, which imitates how knowledge in the human brain is processed, needs to be developed to enable knowledge expansion through modality cooperative learning. Moreover, declarative and procedural knowledge in the memory model must also be self-developed through human interaction. In this paper, we reviewed this essential methodology and briefly described achievements that have been made so far.

• Physical Therapy Rehabilitation Science
• /
• v.12 no.3
• /
• pp.251-258
• /
• 2023

Objective: The practice of breathing exercises involves altering the depth and frequency of respiration. Strengthening respiratory muscles plays a crucial role in maintaining overall health and well-being. The efficiency of the respiratory system affects not only physical activity but also various physiological processes including cardiovascular health, lung function, and cognitive abilities. The study evaluated the reliability of the developed device for inspiratory/expiratory training using pressure sensors and Bluetooth connectivity with a smartphone application. Design: Design & development research Methods: The research methodology involved connecting a custom-made respiratory sensor to an IMT-PEP BIC Breath device. Various pressure conditions were measured, and statistical analyses were performed to assess reliability and consistency. Results showed high Intraclass Coefficient Correlation (ICC) values for both inspiratory and expiratory pressures, indicating strong test-retest reliability. The device was designed for ease of use and wireless monitoring through a smartphone app. Results: This study conducted at expiratory pressure confirmed the proper operation of the IMT/PEP breathing trainer at the specified pressure setting in the product. The pressure sensor demonstrated high test-retest reliability with an ICC value of 0.999 for both expiratory and inspiratory pressure measurements. Conclusions: The developed respiratory training device measured and monitored inspiratory and expiratory pressures, demonstrating its reliability for respiratory training. The system could be utilized to record training frequency and intensity, providing potential benefits for patients requiring respiratory interventions. Further research is needed to assess the full potential of the device in diverse populations and applications.

• Korean Journal of Food Science and Technology
• /
• v.24 no.2
• /
• pp.142-148
• /
• 1992

Certain parts of 95 species of edible and medical plants were extracted with water and 75% of ethyl alcohol. After addition of those extracts to palm oil, lard and soybean oil at different level, their antioxidative activities were compared by Rancimat test. Six species among them seemed to have rather strong antioxidative activity and high extracting yields(i.e. Taraxacum platycarpum, Plantago asiatica, Rhus javanica L., Lycopus lucidus, Astragalus membranaceus, Taraxacum platycarpumH). Among them, the Rhus javanica L. ethanol extract retarded greatly the induction period of palm oil and lard. When 600 ppm of Rhus javanica L. extract were added to palm oil and lard, AI(antioxidant index was expressed as induction period of oil containing various plant extracts/induction period of control oil) of each was 1.35 and 3.03 respectively. This result indicated that the Rhus javanica L. extract was more effective on lard than the other oils.

• Proceedings of the Korean Society of Propulsion Engineers Conference
• /
• 2008.03a
• /
• pp.16-21
• /
• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.