• Bulletin of the Korean Chemical Society
• /
• v.25 no.8
• /
• pp.1165-1170
• /
• 2004

The applicability of the combined nearly ideal binary solvent/Redlich-Kister (CNIBS/R-K) equation for correlation of various solvatochromic parameters (SP) with composition is shown employing 84 experimental data sets for aqueous and organic binary solvent systems at temperatures ranging 15 to $75^{\circ}C$. The model provides a simple computational model to correlate/predict different SP values in various binary solvent systems. In proposed equations, $MPD_s$ (mean percentage deviations) are between 0.0500% and 6.9591% in mixtures of dimethyl sulfoxide with 2-methylpropan-2-ol and benzene with 2-methylpropan-2-ol, respectively. Correlation of the calculated and experimental values of various SP give an equation with an overall mean percentage deviation (OMPD) of 1.1900, $R^2$ = 0.99692, s.e = 0.01223 and F = 341925.51. Approximately 70% of the calculated SP values have IPD (individual percentage deviation) lower than one and it is possible to predict unmeasured SP values by using only eight experimental data.

• Wind and Structures
• /
• v.16 no.4
• /
• pp.393-409
• /
• 2013

This paper uses ten years of on-site monitoring data for the Confederation Bridge to derive wind loads and investigate whether the bridge has experienced its design wind force effects since its completion in 1997. The load effects derived using loads from the on-site monitoring data are compared to the load effects derived using loads from the 1994 and 2009 wind tunnel aerodynamic model tests. The research shows, for the first time, that the aerodynamic model-based methodology originally developed in 1994 is a very accurate method for deriving wind loads for structural design. The research also confirms that the bridge has not experienced its specified (i.e., unfactored) wind force effects since it was opened to traffic in 1997, even during the most severe event that has occurred during this period.

• Journal of the Korean Society of Systems Engineering
• /
• v.17 no.2
• /
• pp.91-97
• /
• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• Nuclear Engineering and Technology
• /
• v.56 no.5
• /
• pp.1895-1901
• /
• 2024

The fission model, G4ParaFissionModel, was enhanced in this study, mainly focusing on refining the energy dependence of the peak-to-valley ratio in the mass distribution and the energy dependence of the average total kinetic energy (TKE). The enhanced model was employed to investigate the characteristics of fission products from ²³⁵U(n, f) reaction. The calculated results, including fission yield, TKE distribution, prompt fission neutron and gamma spectra, were compared with both evaluated and experimental data. The comparison shows that these physical observables related nuclear data, which are of importance for developments of the nuclear power and physics, can be reasonably well reproduced.

• Computers and Concrete
• /
• v.20 no.5
• /
• pp.583-593
• /
• 2017

Applications of fibre-reinforced polymer (FRP) composites for retrofitting, strengthening and repairing concrete structures have been expanded dramatically in the last decade. FRPs have high specific strength and stiffness compared to conventional construction materials, e.g., steel. Ease of preparation and installation, resistance to corrosion, versatile fabrication and adjustable mechanical properties are other advantages of the FRPs. However, there are major concerns about long-term performance, serviceability and durability of FRP applications in concrete structures. Therefore, structural health monitoring (SHM) and damage detection in FRP-retrofitted concrete structures need to be implemented. This paper presents a study on investigating the application of Rayleigh wave for detecting debonding defect in FRP-retrofitted concrete structures. A time-of-flight (ToF) method is proposed to determine the location of a debonding between the FRP and concrete using Rayleigh wave. A series of numerical case studies are carried out to demonstrate the capability of the proposed debonding detection method. In the numerical case studies, a three-dimensional (3D) finite element (FE) model is developed to simulate the Rayleigh wave propagation and scattering at the debonding in the FRP-retrofitted concrete structure. Absorbing layers are employed in the 3D FE model to reduce computational cost in simulating the practical size of the FRP-retrofitted structure. Different debonding sizes and locations are considered in the case studies. The results show that the proposed ToF method is able to accurately determine the location of the debonding in the FRP-retrofitted concrete structure.

• Proceedings of the SAREK Conference
• /
• 2008.11a
• /
• pp.239-243
• /
• 2008

In many literatures the researchers pointed out that the using metal foam will significantly enhance the performance of heat exchanger. This paper focuses on theory study of metal foam, including calculation method of properties of foam (permeability ��K, inertial coefficient f, fiber diameter $d_f$, and effective conductivity $k_e$), model of pressure drop and model of heat transfer. Theory analysis on the performance of heat exchanger will be presented here. Finally the optimal material will be obtained from theory calculation.

• International Journal of Computer Science & Network Security
• /
• v.24 no.2
• /
• pp.89-94
• /
• 2024

Indian subcontinent is a birthplace of multilingual people where documents such as job application form, passport, number plate identification, and so forth is composed of text contents written in different languages/scripts. These scripts may be in the form of different indic numerals in a single document page. Due to this reason, building a generic recognizer that is capable of recognizing handwritten indic digits written by diverse writers is needed. Also, a lot of work has been done for various non-Indic numerals particularly, in case of Roman, but, in case of Indic digits, the research is limited. Moreover, most of the research focuses with only on MNIST datasets or with only single datasets, either because of time restraints or because the model is tailored to a specific task. In this work, a hybrid model is proposed to recognize all available indic handwritten digit images using the existing benchmark datasets. The proposed method bridges the automatically learnt features of Capsule Network with hand crafted Bag of Feature (BoF) extraction method. Along the way, we analyze (1) the successes (2) explore whether this method will perform well on more difficult conditions i.e. noise, color, affine transformations, intra-class variation, natural scenes. Experimental results show that the hybrid method gives better accuracy in comparison with Capsule Network.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.4
• /
• pp.9-19
• /
• 2024

Indonesia ranks fifth as the country of origin for spammers. Attention is urgently needed to tackle spam, especially in Bahasa Indonesia (Indonesian language), which can be achieved by building the best spam detection model. This study aims to compare machine learning models for spam detection, study spam email modeling topics, and design the implementation on the REST API. Spam detection is carried out using machine learning algorithms, i.e., Long Short Term Memory (LSTM), K-Nearest Neighbours (KNN), Naive Bayes, Random Forest, Adaboost, and Support Vector Machine (SVM) combined with slang preprocessing convert and translate. Furthermore, Latent Dirichlet Allocation (LDA) is used for topic modeling of spam emails. The results show that slang processes convert and translate can improve accuracy and f1-score, Long Short Term Memory (LSTM) was the best method with accuracy 93.15% and f1-score of 93.01%, compared to the other methods. In addition, there were five main topics on data categorized as spam: promotions, job vacancies, educational offers, bulletins and news, and investment and finance. A REST API model was successfully developed to separate spam categories based on promotional and other topics.

• Cartoon and Animation Studies
• /
• s.40
• /
• pp.337-365
• /
• 2015

The objective of this study proposes the new user's addiction model in 'Social Network Games' (SNGs). Research model is derived from the separation of two characteristics. First one is logical characteristics that includes 'Functional' (F), 'Keystroke' (K), and 'Goal' (G). Second one is feeling characteristics that consists a few factors such as 'Emotion' (E), 'Social' (S), and 'Affection' (A). For the pre-test, a total of 30 participants responded to survey in order to inspect the fitness of research questionnaire, roughly validity of the proposed model, and the direction of this reseach. After that for the main test, a total 300 users participated in this research. The final number of effective participants were 261 because 39 were insincere respondents and without playing SNGs who were excluded. Then we examined the measurement model by performing 'Partial Least Squares - Structural Equation Modeling' (PLS-SEM) analysis to test the research hypothesis empirically. The results of the measurement and structural model test lend support to the proposed research model by providing a good fit to the construct data. Interestingly, the model showed the significant effects of the interaction between eleven hypothesis(H1,H2,H3,H4,H5,H6,H7,H8,H9,H10, H12). Only one hypothesis decision t-value not supported that is involved the relationship between SNGs Addiction and Keystroke, H11(1.193). This research expect to contributes to an exploratory SNGs research to clarify the base of addition and will aids understanding of users' behavior associated with SNGs development.

• Applied Microscopy
• /
• v.47 no.3
• /
• pp.110-120
• /
• 2017

Non-isothermal thermogravimetry (TG) measurements on melt-quenched $Bi_xSe_{100-x}$ specimens (x=0, 2.5, 7.5 at%) were made at a heating rate ${\beta}=10^{\circ}C/min$ in the range $T=35^{\circ}C{\sim}950^{\circ}C$. The as-measured TG curves confirm that $Bi_xSe_{100-x}$ samples were thermally stable with minor loss at $T{\leq}400^{\circ}C$ and mass loss starts to decrease up to $600^{\circ}C$, beyond which trivial mass loss was observed. These TG curves were used to estimate molar (Se/Bi)-ratios of $Bi_xSe_{100-x}$ samples, which were not in accordance with initial composition. Shaping features of conversion curves ${\alpha}(T)-T$ of $Bi_xSe_{100-x}$ samples combined with a reliable flow chart were used to reduce kinetic mechanisms that would have caused their thermal mass loss to few nth-order reaction models of the form $f[{\alpha}(T)]{\propto}[1-{\alpha}(T)]^n$ (n=1/2, 2/3, and 1). The constructed ${\alpha}(T)-T$ and $(d{\alpha}(T)/dT)-T$ curves were analyzed using Coats-Redfern (CR) and Achar-Brindley-Sharp (ABS) kinetic formulas on basis of these model functions, but the linearity of attained plots were good in a limited ${\alpha}(T)-region$. The applicability of CR and ABS methods, with model function of kinetic reaction mechanism R0 (n=0), was notable as they gave best linear fits over much broader ${\alpha}(T)-range$.