• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.131-134
• /
• 1990

A back-propagation neural network based system for a fault diagnosis of a chemical process is developed. Training data are acquired from FCD(Fault-Consequence Digraph) model. To improve the resolution of a diagnosis, the system is decomposed into 6 subsystems and the training data are composed of 0, 1 and intermediate values. The feasibility of this approach is tested through case studies in a real plant, a naphtha furnace, which has been used to develop a knowledge based expert system, OASYS (Operation Aiding expert SYStem).

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.45 no.3
• /
• pp.444-450
• /
• 1996

A digital hardware architecture for artificial neural network with learning capability is described in this paper. It is a modified hardware architecture known as HANNIBAL(Hardware Architecture for Neural Networks Implementing Back propagation Algorithm Learning). For implementing an efficient neural network hardware, we analyzed various type of multiplier which is major function block of neuro-processor cell. With this result, we design a efficient digital neural network hardware using serial/parallel multiplier, and test the operation. We also analyze the hardware efficiency with logic level simulation. (author). refs., figs., tabs.

• Korean Journal of Plant Resources
• /
• v.4 no.1
• /
• pp.23-27
• /
• 1991

This experiment was conducted to establish shade-method suited to on tile artificial propagation of Lycoris radiata HERB. The ligilt intensity in the native habitatwas 30 kux in March and 3.2 Klux from June to sept. The average light intensityin tile open area was 137 Klux in June. The plant height and number of Ieaves wasgood at the 35% to 55% shadeing net. Bloonling time of Lycoris radiata HERB was inthe last of Sept. Yeild of bulb was increased in 35% to 55% of shadeing net.

• The Journal of Engineering Geology
• /
• v.15 no.1
• /
• pp.67-76
• /
• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.

• Computers and Concrete
• /
• v.6 no.3
• /
• pp.253-268
• /
• 2009

This study aims to develop a cost-based high-performance concrete (HPC) mix optimization system based on an integrated approach using artificial neural networks (ANNs) and genetic algorithms (GA). ANNs are used to predict the three main properties of HPC, namely workability, strength and durability, which are used to evaluate fitness and constraint violations in the GA process. Multilayer back-propagation neural networks are trained using the results obtained from experiments and previous research. The correlation between concrete components and its properties is established. GA is employed to arrive at an optimal mix proportion of HPC by minimizing its total cost. A system prototype, called High Performance Concrete Mix-Design System using Genetic Algorithm and Neural Networks (HPCGANN), was developed in MATLAB. The architecture of the proposed system consists of three main parts: 1) User interface; 2) ANNs prediction models software; and 3) GA engine software. The validation of the proposed system is carried out by comparing the results obtained from the system with the trial batches. The results indicate that the proposed system can be used to enable the design of HPC mix which corresponds to its required performance. Furthermore, the proposed system takes into account the influence of the fluctuating unit price of materials in order to achieve the lowest cost of concrete, which cannot be easily obtained by traditional methods or trial-and-error techniques.

• Tunnel and Underground Space
• /
• v.3 no.1
• /
• pp.63-79
• /
• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• Proceedings of the KSME Conference
• /
• 2007.05a
• /
• pp.136-141
• /
• 2007

This paper introduces an artificial neuron which is a nano composite continuous sensor. The continuous nano sensor is fabricated as a thin and narrow polymer film sensor that is made of carbon nanotubes composites with a PMMA or a silicone matrix. The sensor can be embedded onto a structure like a neuron in a human body and it can detect deteriorations of the structure. The electrochemical impedance and dynamic strain response of the neuron change due to deterioration of the structure where the sensor is located. A network of the long nano sensor can form a structural neural system to provide large area coverage and an assurance of the operational health of a structure without the need for actuators and complex wave propagation analyses that are used with other methods. The artificial neuron is expected to effectively detect damage in large complex structures including composite helicopter blades and composite aircraft and vehicles.

• International Journal of Precision Engineering and Manufacturing
• /
• v.8 no.4
• /
• pp.27-32
• /
• 2007

Gears, crucial components in modern precision machinery for power transmission mechanisms, are required to have low contacting noise with high torque transmission, which makes the use of gear-tooth profile modifications and gear-tooth surface crowning extremely efficient and valuable. Due to the shortcomings of current techniques, such as manual rectification, mechanical modification, and numerically controlled rectification, we propose a novel electrochemical gear-tooth profile modification method based on an artificial neural network control technique. The fundamentals of electrochemical tooth-profile modifications based on real-time control and a mathematical model of the process are discussed in detail. Due to the complex and uncertain relationships among the machining parameters of electrochemical tooth-profile modification processes, we used an artificial neural network to determine the required processing electric current as the tooth-profile modification requirements were supplied. The system was implemented and a practical example was used to demonstrate that this technology is feasible and has potential applications in the production of precision machinery.

• The Korean Journal of Malacology
• /
• v.24 no.1
• /
• pp.51-57
• /
• 2008

With the purpose to estimate the possibility of short-term storage and cryopreservation for sperm of Charonia sauliae, which is a potential preparation for its artificial reproduction and further research, in this study, protocols for short-term storage and cryopreservation of trumpet shell sperm was optimized. The effects of different immobilizing solutions, dilution ratios were estimated for short-term storage. And the effects of different cryoprotectant extenders and freezing rates were estimated for cryopreservation in terms of motility and survival of sperm. The results indicated that the artificial sea water of 350 mOsmol/kg is a better immobilizing solution and sperm which was diluted at a ratio of 1:1 (v/v) had higher motility and survival rate during short-term storage. The effect of 5% dimethyl sulfoxide was significantly better than those of other cryoprotectant extenders. And a freezing rate of $-20^{\circ}C\;min^{-1}$ showed better effect than other freezing rates. In conclusion, this study optimized some key factors of the short-term and cryopreservation of C. sauliae sperm, which can provide valuable data for germ-plasm conservation and artificial propagation of C. sauliae.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.54 no.4
• /
• pp.65-71
• /
• 2012

The objective of this study was to estimate surface runoff from rice paddy plots using an artificial neural network (ANN). A field experiment with three treatment levels was conducted in the NICS saemangum experimental field located in Iksan, Korea. The ANN model with the optimal network architectures, named Paddy1901 with 19 input nodes, 1 hidden layer with 16 neurons nodes, and 1 output node, was adopted to predict surface runoff from the plots. The model consisted of 7 parameters of precipitation, irrigation rate, ponding depth, average temperature, relative humidity, wind speed, and solar radiation on the daily basis. Daily runoff, as the target simulation value, was computed using a water balance equation. The field data collected in 2011 were used for training and validation of the model. The model was trained based on the error back propagation algorithm with sigmoid activation function. Simulation results for the independent training and testing data series showed that the model can perform well in simulating surface runoff from the study plots. The developed model has a main advantage that there is no requirement for any prior assumptions regarding the processes involved. ANN model thus can be a good tool to predict surface runoff from rice paddy fields.