• Journal of the Ergonomics Society of Korea
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• v.19 no.2
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• pp.87-99
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• 2000

This study aims to examine a fuzzy logic-based human expert EMG prediction model (FLHEPM) for predicting electromyographic responses of trunk muscles due to manual lifting based on two task (control) variables. The FLHEPM utilizes two variables as inputs and ten muscle activities as outputs. As the results, the lifting task variables could be represented with the fuzzy membership functions. This provides flexibility to combine different scales of model variables in order to design the EMG prediction system. In model development, it was possible to generate the initial fuzzy rules using the neural network, but not all the rules were appropriate (87% correct ratio). With regard to the model precision, the EMG signals could be predicted with reasonable accuracy that the model shows mean absolute error of 8.43% ranging from 4.97% to 13.16% and mean absolute difference of 6.4% ranging from 2.88% to 11.59%. However, the model prediction accuracy is limited by use of only two task variables which were available for this study (out of five proposed task variables). Ultimately, the neuro-fuzzy approach utilizing all five variables to predict either the EMG activities or the spinal loading due to dynamic lifting tasks should be developed.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.10
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• pp.529-535
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.81-86
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• 2019

Deep learning is emerging as one of the best tool in processing data related to medical imaging. In our research work, we have proposed a deep learning based framework CNN (Convolutional Neural Network) for the classification of dysplastic tissue images. The CNN has classified the given images into 4 different classes namely normal tissue, mild dysplastic tissue, moderate dysplastic tissue and severe dysplastic tissue. The dataset under taken for the study consists of 672 tissue images of epithelial squamous layer of oral cavity captured out of the biopsy samples of 52 patients. After applying the data pre-processing and augmentation on the given dataset, 2688 images were created. Further, these 2688 images were classified into 4 categories with the help of expert Oral Pathologist. The classified data was supplied to the convolutional neural network for training and testing of the proposed framework. It has been observed that training data shows 91.65% accuracy whereas the testing data achieves 89.3% accuracy. The results produced by our proposed framework are also tested and validated by comparing the manual results produced by the medical experts working in this area.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.3
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• pp.257-263
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• 2022

The objective of this study was to see how artificial intelligence applications affected organizational innovation in Jordanian commercial banks. Both independent and dependent variables were measured in three dimensions: expert systems, neural network systems, and fuzzy logic systems for artificial intelligence applications variable. Product innovation, process innovation, and management innovation for the organizational innovation variable. To achieve study objectives, a questionnaire was developed and distributed to a sample of one hundred fifty-three managers in Jordanian commercial banks, who were selected according to the simple random sampling method. Except for the neural network systems dimension, which comes in at an average level, the study indicated that there is a high level of organizational innovation and artificial intelligence applications. Furthermore, the findings revealed that artificial intelligence applications have a significant impact on organizational innovation in Jordanian commercial banks, with the most important artificial intelligence application being a fuzzy logic system. The study suggested keeping track of technological advancements in the field of artificial intelligence applications and incorporating them into banking operations by benchmarking with the best commercial bank practices and allocating a portion of the budget to technological applications and infrastructure development, as well as balancing between technology use and information security risks to ensure client privacy is protected.

• Tunnel and Underground Space
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• v.9 no.1
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• pp.48-55
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• 1999

One of the major roles of concrete lining is the supplementary support of ground load. Therefore, if there are cracks or deformation found in the lining, the causes should be carefully examined. Tunnel Soundness Evaluation System (DW-TSES) was developed to meet such requirements. Main facility of the system was intended to find the probable causes on the basis of the apparent changes in lining and the environmental conditions. It also includes facilities for evaluating the soundness of a tunnel and indicating the method for repair or reinforcement. The characteristic feature of damages is used for reasoning in case of deterioration and leakage, and artificial neural network is used in external pressure. This process depends on the results of the case analyses and FDM, which have a collection of the typical features of different types of damages as well as the unusual changes caused by the external pressure. The comparison of the outputs of this system with those of expert's diagnoses draws the following conclusions. 1) Artificial neural network was a suitable tool to find to causes of damages by external pressure. 2) The environmental conditions improved the accuracy in reasoning. 3) The result of finding causes and evaluating soundness was helpful to suggest effective methods concerning tunnel maintenance.

• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.83-93
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• 2001

Prediction of corporate failure using past financial data is well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks (NNs) can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. Although numerous theoretical and experimental studies reported the usefulness or neural networks in classification studies, there exists a major drawback in building and using the model. That is, the user can not readily comprehend the final rules that the neural network models acquire. We propose a genetic algorithms (GAs) approach in this study and illustrate how GAs can be applied to corporate failure prediction modeling. An advantage of GAs approach offers is that it is capable of extracting rules that are easy to understand for users like expert systems. The preliminary results show that rule extraction approach using GAs for bankruptcy prediction modeling is promising.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.563-564
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• 2006

In silicon wafer manufacturing process, the grinding process has been adopted to improve the flatness of water. The grinding of wafer is usually used by the infeed grinding machine. Grinding conditions are spindle speed, feed speed, rotation speed, grinding stone etc. But grinding condition selection and analysis is so difficult in grinding machine. In the intelligent grinding system based on knowledge many researchers have studied expert system, neural network, fuzzy etc. In this paper we deal grinding condition selection method, Taguchi method and Genetic Analysis.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.161-164
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• 1999

Estimation system of aging diagnosis using partial discharge(PD) is being highlighted as a research area for the residual lifetime pridiction of industrial equipment. But the application of PD requires complicated analysis method as expert system because the PD has complex progressing forms according to external stress. In this paper, it has been investigated the statistical distribution to express the 2D PD patterns of the diagnosis system using neural network(NN).

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2492-2494
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• 2000

Design of Fuzzy cotroller consists of intuition of human expert, and any other information about how to control system. If the rules adequately control the system, the design work is done well. If the rules are inadequate, the designer must modify the rules. Through this procedure, the system can be controlled. In this paper, we designed simply a fuzzy controller based on human knowledge, but it has errors showing some vibrations. So we updated the optimal parameters of fuzzy controller using Neural Network algorithm.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.