• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.715-721
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• 2000

In this research, rule and neuro net based boundary extraction algorithm was developed. Extracting boundary of the interest, lean tissue, is essential for the quality evaluation of the beef based on color machine vision. Major quality features of the beef are size, marveling state of the lean tissue, color of the fat, and thickness of back fat. To evaluate the beef quality, extracting of loin parts from the sectional image of beef rib is crucial and the first step. Since its boundary is not clear and very difficult to trace, neural network model was developed to isolate loin parts from the entire image input. At the stage of training network, normalized color image data was used. Model reference of boundary was determined by binary feature extraction algorithm using R(red) channel. And 100 sub-images(selected from maximum extended boundary rectangle 11${\times}$11 masks) were used as training data set. Each mask has information on the curvature of boundary. The basic rule in boundary extraction is the adaptation of the known curvature of the boundary. The structured model reference and neural net based boundary extraction algorithm was developed and implemented to the beef image and results were analyzed.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.70-75
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• 1997

This paper proposes a new approach to the design of fuzzy-neuro control for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Journal of Biosystems Engineering
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• v.20 no.4
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• pp.376-382
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• 1995

This study was intended to develop more reliable fruit sorting algorithm regardless of the feeding positions of fruits by using the neural network in which various information could be included as input data. Specific objectives of this study were to select proper input units in the neural network by investigating the features of input image, to analyze the sorting accuracy of the algorithm depending on the feeding positions of Fuji apple and to evaluate the performance of the algorithm for practical usage. the average value in color grading accuracy was 90%. Based on the computing time required for color grading, the maximum sorting capacity was estimated to approximately 10, 800 apples per hours. Finally, it is concluded that the neuro-net based color sorting algorithm developed in this study has feasibility for practical usage.

• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.585-594
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• 2007

The back propagation algorithm took a long time to learn the input patterns and was difficult to train the additional or repeated learning patterns. So Aleksander proposed the binary neural network which could overcome the disadvantages of BP Network. But it had the limitation of repeated learning and was impossible to extract a generalized pattern. In this paper, we proposed a dynamic 3 dimensional Neuro System which was consisted of a learning network which was based on weightless neural network and a feedback module which could accumulate the characteristic. The proposed system was enable to train additional and repeated patterns. Also it could be produced a generalized pattern by putting a proper threshold into each learning-net's discriminator which was resulted from learning procedures. And then we reused the generalized pattern to elevate the recognition rate. In the last processing step to decide right category, we used maximum response detector. We experimented using the MNIST database of NIST and got 99.3% of right recognition rate for training data.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.494-499
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• 2013

Based on the state-feedback adaptive neuro-control algorithm for a SISO nonaffine pure-feedback nonlinear system proposed in [15], an output-feedback controller is proposed in this paper. The output-feedback adaptive neural-net controller for the considered nonlinear system has not been previously proposed in any other literatures yet. The proposed output-feedback controller inherits all the advantages of [15] such that it does not adopt backstepping and this results in relatively simple control and adapting laws. Only one neural network is required for the proposed adaptive controller. The proposed neural-net control scheme expands the applicable class of nonlinear systems.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.310-317
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• 2022

The mass attenuation coefficient is the primary physical parameter to model narrow beam gamma-ray attenuation. A new machine learning based approach is proposed to model gamma-ray shielding behavior of composites alternative to theoretical calculations. Two fuzzy logic algorithms and a neural network algorithm were trained and tested with different mixture ratios of vanadium slag/epoxy resin/antimony in the 0.05 MeV-2 MeV energy range. Two of the algorithms showed excellent agreement with testing data after optimizing adjustable parameters, with root mean squared error (RMSE) values down to 0.0001. Those results are remarkable because mass attenuation coefficients are often presented with four significant figures. Different training data sizes were tried to determine the least number of data points required to train sufficient models. Data set size more than 1000 is seen to be required to model in above 0.05 MeV energy. Below this energy, more data points with finer energy resolution might be required. Neuro-fuzzy models were three times faster to train than neural network models, while neural network models depicted low RMSE. Fuzzy logic algorithms are overlooked in complex function approximation, yet grid partitioned fuzzy algorithms showed excellent calculation efficiency and good convergence in predicting mass attenuation coefficient.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• Journal of Biosystems Engineering
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• v.26 no.2
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• pp.147-154
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• 2001

In Korea and Japan, dried oak mushrooms are classified into 12 to 16 different categories based on its external visual quality. And grading used to be done manually by the human expert and is limited to the randomly sampled oak mushrooms. Visual features of dried oak mushrooms dominate its quality and are distributed over both sides of the gill and the cap. The 2nd prototype computer vision based automatic grading and sorting system for dried oak mushrooms was developed based on the 1st prototype. Sorting function was improved and overall system for grading was simplified to one stage grading instead of two stage grading by inspecting both front and back sides of mushrooms. Neuro-net based side(gill or cap) recognition algorithm of the fed mushroom was adopted. Grading was performed with both images of gill and cap using neural network. A real time simultaneous discharge algorithm, which is good for objects randomly fed individually and for multi-objects located along a series of discharge buckets, was developed and implemented to the controller and the performance was verified. Two hundreds samples chosen from 10 samples per 20 grade categories were used to verify the performance of each unit such as feeding, reversing, grading, and discharging unites. Test results showed that success rates of one-line feeding, reversing, grading, and discharging functions were 93%, 95%, 94%, and 99% respectively. The developed prototype revealed successful performance such as the approximate sorting capability of 3,600 mushrooms/hr per each line i.e. average 1sec/mushroom. Considering processing time of approximate 0.2 sec for grading, it was desired to reduce time to reverse a mushroom to acquire the reversed surface image.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.608-616
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• 2022

Power peaking factors (PPF) is an important parameter for safe and efficient reactor operation. There are several methods to calculate the PPF at TRIGA research reactors such as MCNP and TRIGLAV codes. However, these methods are time-consuming and required high specifications of a computer system. To overcome these limitations, artificial intelligence was introduced for parameter prediction. Previous studies applied the neural network method to predict the PPF, but the publications using the ANFIS method are not well developed yet. In this paper, the prediction of PPF using the ANFIS was conducted. Two input variables, control rod position, and neutron flux were collected while the PPF was calculated using TRIGLAV code as the data output. These input-output datasets were used for ANFIS model generation, training, and testing. In this study, four ANFIS model with two types of input space partitioning methods shows good predictive performances with R² values in the range of 96%-97%, reveals the strong relationship between the predicted and actual PPF values. The RMSE calculated also near zero. From this statistical analysis, it is proven that the ANFIS could predict the PPF accurately and can be used as an alternative method to develop a real-time monitoring system at TRIGA research reactors.

• Korea Journal of Hospital Management
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• v.8 no.1
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• pp.135-164
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• 2003

The operating room is the major facility that costs the highest investment per unit area in a hospital. It requires commitment of hospital resources such as manpower, equipments and material. The quantity of these resources committed actually differs from one type of operation to another. Because of this, it is not an easy task to allocate the operating cost to individual clinical departments that share the operating room. A practical way to do so may be to collect and add the operating costs incurred by each clinical department and charge the net cost to the account of the corresponding clinical department. It has been customary to allocate the cost of the operating room to the account of each individual department on the basis of the ratio of the number of operations of the department or the total revenue by each operating room. In an attempt to set up more rational cost allocation method than the customary method, this study proposes a new cost allocation method that calls for itemizing the operation cost into its constituent expenses in detail and adding them up for the operating cost incurred by each individual department. For comparison of the new method with the conventional method, the operating room in the main building of hospital A near Seoul is chosen as a study object. It is selected because it is the biggest operating room in hospital A and most of operations in this hospital are conducted in this room. For this study the one-month operation record performed in January 2001 in this operating room is analyzed to allocate the per-month operation cost to six clinical departments that used this operating room; the departments of general surgery, orthopedic surgery, neuro-surgery, dental surgery, urology, and obstetrics & gynecology. In the new method(or method 1), each operation cost is categorized into three major expenses; personnel expense, material expense, and overhead expense and is allocated into the account of the clinical department that used the operating room. The method 1 shows that, among the total one-month operating cost of 814,054 thousand wons in this hospital, 163,714 thousand won is allocated to GS, 335,084 thousand won to as, 202,772 thousand won to NS, 42,265 thousand won to uno, 33,423 thousand won to OB/GY, and 36.796 thousand won to DS. The allocation of the operating cost to six departments by the new method is quite different from that by the conventional method. According to one conventional allocation method based on the ratio of the number of operations of a department to the total number of operations in the operating room(method 2 hereafter), 329,692 thousand won are allocated to GS, 262,125 thousand won to as, 87,104 thousand won to NS, 59,426 thousand won to URO, 51.285 thousand won to OB/GY, and 24,422 thousand won to DS. According to the other conventional allocation method based on the ratio of the revenue of a department(method 3 hereafter), 148,158 thousand won are allocated to GS, 272,708 thousand won to as, 268.638 thousand won to NS, 45,587 thousand won to uno, 51.285 thousand won to OB/GY, and 27.678 thousand won to DS. As can be noted from these results, the cost allocation to six departments by method 1 is strikingly different from those by method 2 and method 3. The operating cost allocated to GS by method 2 is about twice by method 1. Method 3 makes allocations of the operating cost to individual departments very similarly as method 1. However, there are still discrepancies between the two methods. In particular the cost allocations to OB/GY by the two methods have roughly 53.4% discrepancy. The conventional methods 2 and 3 fail to take into account properly the fact that the average time spent for the operation is different and dependent on the clinical department, whether or not to use expensive clinical material dictate the operating cost, and there is difference between the official operating cost and the actual operating cost. This is why the conventional methods turn out to be inappropriate as the operating cost allocation methods. In conclusion, the new method here may be laborious and cause a complexity in bookkeeping because it requires detailed bookkeeping of the operation cost by its constituent expenses and also by individual clinical department, treating each department as an independent accounting unit. But the method is worth adopting because it will allow the concerned hospital to estimate the operating cost as accurately as practicable. The cost data used in this study such as personnel expense, material cost, overhead cost may not be correct ones. Therefore, the operating cost estimated in the main text may not be the same as the actual cost. Also, the study is focused on the case of only hospital A, which is hardly claimed to represent the hospitals across the nation. In spite of these deficiencies, this study is noteworthy from the standpoint that it proposes a practical allocation method of the operating cost to each individual clinical department.