• Bulletin of the Korean Chemical Society
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• v.16 no.5
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• pp.427-432
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• 1995

A model based on two coupled generalized Langevin equations is proposed to investigate the trans-stilbene photoisomerization dynamics. In this model, a system which has two independent coordinates is considered and these two system coordinates are coupled to the same harmonic bath. The direct coupling between the system coordinates is assumed negligible and these two coordinates influence each other through the frictional coupling mediated by solvent molecules. From the Hamiltonian which is equivalent to the coupled generalized Langevin equations, we obtain the transition state theory rate constants of the stilbene photoisomerization. The rates obtained from this model are compared to experimental results in n-alkane solvents.

• Earthquakes and Structures
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• v.22 no.6
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• pp.609-623
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• 2022

To study the empirical seismic fragility of a reinforced concrete girder bridge, based on the theory of numerical analysis and probability modelling, a regression fragility method of a rapid fragility prediction model (Gaussian first-order regression probability model) considering empirical seismic damage is proposed. A total of 1,069 reinforced concrete girder bridges of 22 highways were used to verify the model, and the vulnerability function, plane, surface and curve model of reinforced concrete girder bridges (simple supported girder bridges and continuous girder bridges) considering the number of samples in multiple intensity regions were established. The new empirical seismic damage probability matrix and curve models of observation frequency and damage exceeding probability are developed in multiple intensity regions. A comparative vulnerability analysis between simple supported girder bridges and continuous girder bridges is provided. Depending on the theory of the regional mean seismic damage index matrix model, the empirical seismic damage prediction probability matrix is embedded in the multidimensional mean seismic damage index matrix model, and the regional rapid prediction matrix and curve of reinforced concrete girder bridges, simple supported girder bridges and continuous girder bridges in multiple intensity regions based on mean seismic damage index parameters are developed. The established multidimensional group bridge vulnerability model can be used to quantify and predict the fragility of bridges in multiple intensity regions and the fragility assessment of regional group reinforced concrete girder bridges in the future.

• Journal of the Korean Society of Radiology
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• v.11 no.7
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• pp.555-564
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• 2017

The current radiation risk assessment for occupational exposure is based on the measured exposure dose and health checkups of workers. This people-centered risk assessment may occur errors because absence of using personal dosimeter or unrelated health symptoms of individuals lead to difficulties in obtaining accurate data from workers. In addition, although the established legal upper dose limit was used as a reference for the assessment, it does not imply that this limit is the optimal dose of radiation workers should get; ALARA principle should always be appreciated. Therefore, a new risk assessment model that can take account of all the important factors and implement optimization of radiation protection is required at the national level. In this paper, based on the KOSHA Risk Assessment, we studied on the workplace-centered risk assessment model for radiation field rather than the people-centered. The result of the study derived a right model for radiation field through the analysis of the risk assessment methods in various fields and also found data acquisition methods and procedures for applying to the model. Multidimensional model centering on the workplace will enables more accurate radiation risk assessment by using a risk index and radar plot, and consequently contribute to the efficient worker management, preemptive worker protection and implementation of optimization of radiation protection.

• 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.

• 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]

• Bulletin of the Korean Chemical Society
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• v.27 no.12
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• pp.1940-1960
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• 2006

Theoretical descriptions of two-dimensional (2D) vibrational and electronic spectroscopy are presented. By using a coupled multi-chromophore model, some examples of 2D spectroscopic studies of peptide solution structure determination and excitation transfer process in electronically coupled multi-chromophore system are discussed. A few remarks on perspectives of this research area are given.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1847-1852
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• 2004

This article provides the connection between feedback stabilization and interpolation conditions for n-D linear systems (n > 1). In addition to internal stability, if one demands performance as a design goal, then there results an n-D matrix Nevanlinna-Pick interpolation problem. Application of recent work on Nevanlinna-Pick interpolation on the polydisk yields a solution of the problem for the 2-D case. The same analysis applies in the n-D case (n > 2), but leads to solutions which are contractive in a norm (the "Schur-Agler norm") somewhat stronger than the $H^{\infty}$ norm. This is an analogous version of the connection between the standard $H^{\infty}$ control problem and an interpolation problem of Nevanlinna-Pick type in the classical 1-D linear time-invariant systems.

• Journal of Korean Society of Transportation
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• v.4 no.1
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• pp.12-27
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• 1986

This study examined user preferences toward transportation modes in Seoul. Two multidimensional scaling models, the ideal point and vector models, were applied to data on mode preferences of 114 adults in the metropolitan area. While both models produced fairly similar results, the vector model performed slightly better than the other in terms of interpretability of the results. The transport attributes elicited are comfort, flexibility, travel cost, travel time, privacy, and safety; among which comfort is salient most. The comfort variable is a multi-faceted attribute in nature. The variations of attribute preferences are most significant between the gender groups as well as worker/nonworker groups. In particular, male workers, female workers and female nonworkers form three distinctive market segments. An unidimensional scaling of the preference data reveals that subway, auto-driver, and subscription bus modes are preferred most, whereas motorcycle and bicycle least. The other modes of express bus, taxt, auto-passenger, bus and walk rank intermediately. An examination of how preference orders vary among modal groups hints that users align their stated attitudes to their choice in order to reduce cognitive dissonance.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.399-404
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• 2008

사용자 중심의 메뉴 기반 인터페이스를 설계하기 위해서는 인간의 지식 구조를 이해하는 것이 중요하다. 인간의 지식 구조를 이해하게 되면, 인터페이스를 통해서 전달된 자극들이 만들어낸 개념들이 어떠한 관계를 가지고 정신 모형(mental model)을 형성하고 있는지 알 수 있다. 인간의 지식 구조는 MDS (Multidimensional Scaling)과 Trajectory Mapping을 이용하여 Visual Concept Map 으로 나타낼 수 있고, 이것을 바탕으로 인간의 지식구조를 시각적으로 이해할 수 있다. MDS 는 인간의 머릿속에 자리잡고 있는 개념들의 상대적 위치를 알려주고, Trajectory Mapping 은 개념들 간의 연결 상태를 보여준다. 즉, Trajectory Mapping 을 통하여 개념들 간악 인지적 정보를 알 수 있다. 본 연구에서는 MDS 와 Trajectory Mapping 을 이용하여 핸드폰 메뉴로부터 전달 받은 시각적 자극들에 악해 형성된 개념들에 대한 인간의 지식 구조를 Visual Concept Map 으로 시각화하였다. 그리고 이렇게 시각화된 지식 구조를 바탕으로 메뉴 구조를 개발하였다. 본 연구 결과, MDS 와 Trajectory Mapping 을 이용한 인간의 지식 구조의 시각화는 사용자 중심의 메뉴 기반 인터페이스를 설계하는데 유용하게 쓰일 수 있을 것으로 보인다.

• Korean Journal of Computational Design and Engineering
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• v.10 no.5
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• pp.314-327
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• 2005

This paper describes the volumetric data modeling and analysis methods that employ volumetric NURBS or VNURBS that represents heterogeneous objects or fields in multidimensional space. For volumetric data modeling, we formulate the construction algorithms involving the scattered data approximation and the curvilinear grid data interpolation. And then the computational algorithms are presented for the geometric and mathematical analysis of the volume data set with the VNURBS model. Finally, we apply the modeling and analysis methods to various field applications including grid generation, flow visualization, implicit surface modeling, and image morphing. Those application examples verify the usefulness and extensibility of our VNUBRS representation in the context of volume modeling and analysis.