• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.154-158
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• 2007

As the technology developed, the system is being developed as the structure that is adapted to the intelligent environment. Therefore, the existing situation information system couldn't provide satisfactory service to the user as it provides service only by the information which it received from the sensor. This paper analyzed the problems of the existing user intention awareness system and suggested user intention awareness system to provide a stable and efficient service that fits to the intention of the user compensating this. This paper has collected the behavior data based on the scenario of the sequential behavior course of the user that occurs at breakfast time in the kitchen which is the home domain environment thai is closely related to our lives. This scenario course also showed the flow that the goal intentional user intention awareness system acted that it suggested, and showed the sequential course processing the user behavior data by tables and charts.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.132-137
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• 1993

In this paper, a fish drying process control system is proposed, which predicts the proper change with time in weight of the material fish and the drying conditions in advance, based on the performance of skilled worker. In order to implement a human expertise into an automated fish drying process control system, an experimental analysis is made and a model for the process is built. The proposed system divided into two procedures: The procedure before drying and the one during drying. The procedure before drying is for the prediction of necessary drying time. To estimate the necessary drying time, first, the proper change in weight for the product is obtained by using fuzzy reasoning. The condition part of the production rule consists of the factors of fish body and the expected degree of dryness. Kext, the necessary drying time is obtained by regression models. The variables employed in the models are the factors, inferred change in weight and drying conditions. The model for the procedure during drying is also proposed for more accurate estimation, which is described by a system of linear-differential equations.

• IE interfaces
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• v.9 no.3
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• pp.257-268
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• 1996

This paper illustrates the process of developing and configuring the prototype Computer Analysis System for Controlling Occupational WORKload (CAS-COWORK). The software interface between the database and expert system was attempted. The database is used for storing and retrieving series of data entered by general users and the expert system is used for identifying and solving occupational problem areas. Two theories were applied in developing the algorithm base of CAS-COWORK that were used to calculate overall workload stress level. The fuzzy set theory was introduced to capture the subject‘s workload stress perception. The Analytic Hierarchy Process (AHP) was introduced to estimate the importance of the task and workplace variables. The purpose of the system development is for future prediction and problem solving which would be highly valuable to the industrial engineer.

• Structural Engineering and Mechanics
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• v.45 no.3
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• pp.323-336
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• 2013

With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection, the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.

• International Journal of Naval Architecture and Ocean Engineering
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• v.4 no.2
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• pp.112-122
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• 2012

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.291-304
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• 2022

Maximum shear modulus (G_max or G₀) is an important soil property useful for many engineering applications, such as the analysis of soil-structure interactions, soil stability, liquefaction evaluation, ground deformation and performance of seismic design. In the current study, bender element (BE) tests are used to evaluate the effect of the void ratio, effective confining pressure, grading characteristics (D₅₀, C_u and C_c), anisotropic consolidation and initial fabric anisotropy produced during specimen preparation on the G_max of sand-gravel mixtures. Based on the tests results, an empirical equation is proposed to predict G_max in granular soils, evaluated by the experimental data. The artificial neural network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models were also applied. Coefficient of determination (R²) and Root Mean Square Error (RMSE) between predicted and measured values of G_max were calculated for the empirical equation, ANN and ANFIS. The results indicate that all methods accuracy is high; however, ANFIS achieves the highest accuracy amongst the presented methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.137-145
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• 2012

According to the National Police Agency, the total number of traffic accidents which occurred in 2010 was 226,878. Intersection accidents accounts for 44.8%, the largest portion of the entire traffic accidents. An research on the signalized intersection is constantly made, while an research on the unsignalized intersection is yet insufficient. This study selected traffic volume, road width, and sight distance as the input variables which affect unsignalized intersection accidents, and number of accidents as the output variable to build a model using ANFIS(Adaptive Neuro-Fuzzy Inference System). The forecast performance of this model is evaluated by comparing the actual measurement value with the forecasted value. The compatibility is evaluated by R2, the coefficient of determination, along with Mean Absolute Error (MAE) and Mean Square Error (MSE), the indicators which represent the degree of error and distribution. The result shows that the $R^2$ is 0.9817, while MAE and MSE are 0.4773 and 0.3037 respectively, which means that the explanatory power of the model is quite decent. This study is expected to provide the basic data for establishment of safety measure for unsignalized intersection and the improvement of traffic accidents.

• Journal of the military operations research society of Korea
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• v.23 no.2
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• pp.120-134
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• 1997

This paper illustrates the process of developing and configuring the prototype computer-assisted analysis system named as Work-Expert for analyzing occupational stress. A Work-Expert was developed to allow the nonexperts or line manager to utilize the existing knowledge in the area of occupational stress estimation, and to provide intelligent and computer-aided problem solving. The purpose of the system development is for future prediction and problem solving. Creating preventive measures, such as early detection of stress, proper placement and promotion of employees, job enlargement, employee identification, employee involvement, communication, and training of managers will be possible by using this system effectively.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.199-206
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• 2004

Epileptic seizures result from a temporary electrical disturbance of the brain. In this paper, a method of discriminating EEG for diagnoses of temporal lobe epilepsy is proposed. The proposed method for classification of epilepsy and sleep EEG is based on the wavelet transform and the fuzzy c-means. The magnitude and mean of wavelet coefficients for each EEG band are applied to the cluster of the FCM classifier. The proposed system show a little more accurate diagnosis for EEG by analysis of frequency for Wavelet and the success rate of 95% classification using FCM. From the simulation results by the implemented system, we demonstrated this research can be reduce doctor's labors and realize quantitative diagnosis of EEG.