• Journal of the Korea Concrete Institute
• /
• v.14 no.4
• /
• pp.457-466
• /
• 2002

An artificial neural network was applied to predict compressive strength, slump value and mix proportion of a concrete. Standard mixed tables were trained and estimated, and the results were compared with those of the experiments. To consider variabilities of material properties, the standard mixed fables from two companies of Ready Mixed Concrete were used. And they were trained with the neural network. In this paper, standard back propagation network was used. The mix proportion factors such as water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate and air entraining admixture were used. For the arrangement on the approval of prediction of mix proportion factor, the standard compressive strength of $180kgf/cm^2{\sim}300kgf/cm^2$, and target slump value of 8 cm, 15 cm were used. For the arrangement on the approval of prediction of compressive strength and slump value, the standard compressive strength of $210kgf/cm^2{\sim}240kgf/cm^2$, and target slump value of 12 cm and 15 cm wore used because these ranges are most frequently used. In results, in the prediction of mix proportion factor, for all of the water cement ratio, sand aggregate ratio, unit water, unit cement, unit weight of sand, unit weight of crushed sand, unit coarse aggregate, air entraining admixture, the predicted values and the values of standard mixed tables were almost the same within the target error of 0.10 and 0.05, regardless of two companies. And in the prediction of compressive strength and slump value, the predicted values were converged well to the values of standard mixed fables within the target error of 0.10, 0.05, 0.001. Finally artificial neural network is successfully applied to the prediction of concrete mixture and compressive strength.

• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.127-142
• /
• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Korean journal of applied entomology
• /
• v.12 no.3
• /
• pp.93-107
• /
• 1973

Garlic (Allium sativum L.) is an important vegetable crop for the Korean people and has long been cultivated extensively in Korea. More recently it has gained importance as a source of certain pharmaceuticals. This additional use has also contributed to the increasing demand for Korean garlic. Garlic has been propagated vegetatively for a long time without control measures against virus diseases. As a result it is presumed that most of the garlic varieties in Korea may have degenerated. The production of virus-free plants offers the most feasible way to control the virus diseases of garlic. However, little is known about garlic viruses both domestically and in foreign countries. More basic information regarding garlic viruses is needed before a sound approach to the control of these diseases can be developed. Currently garlic mosaic disease is most prevalent in plantings throughout Korea and is considered to be the most important disease of garlic in Korea. Because of this importance, studies were initiated to isolate and characterize the garlic mosaic virus. Symptom expression in test plants, physical properties, purification, serological reaction and morphological characteristics of the garlic mosaic virus were determined. Results of these studies are summarized as follows. 1. Surveys made throughout the important garlic growing areas in Korea during 1970-1972 revealed that most of the garlic plants were heavily infected with mosaic disease. 2. A strain of garlic mosaic virus was obtained from infected garlic leaves and transmitted mechanically to Chenopodium amaranticolor by single lesion isolation technique. 3. The symptom expression of this garlic mosaic virus isolate was examined on 26 species of test plants. Among these, Chenopodium amaranticolor, C. quince, C. album and C. koreanse expressed chlorotic local lesions on inoculated leaves 11-12 days after mechanical inoculation with infective sap. The remaining 22 species showed no symptoms and no virus was recovered from them whet back-inoculated to C. amaranticolor. 4. Among the four species of Chtnopodium mentioned above, C. amaranticolor and C. quinoa appear to be the most suitable local lesion test plants for garlic mosaic virus. 5. Cloves and top·sets originating from mosaic infected garlic plants were $100\%$ infected with the same virus. Consequently the garlic mosaic virus is successively transmitted through infected cloves and top-sets. 6. Garlic mosaic virus was mechanically transmitted to C, amaranticolor when inoculations were made with infective sap of cloves and top-sets. 7. Physical properties of the garlic mosaic virus as determined by inoculation onto C. amaranticolor were as follows. Thermal inactivation point: $65-70^{\circ}C$, Dilution end poiut: $10^-2-10^-3$, Aging in vitro: 2 days. 8. Electron microscopic examination of the garlic mosaic virus revealed long rod shaped particles measuring 1200-1250mu. 9. Garlic mosaic virus was purified from leaf materials of C. amaranticolor by using two cycles of differential centrifugation followed by Sephadex gel filtration. 10. Garlic mosaic virus was successfully detected from infected garlic cloves and top-sets by a serological microprecipitin test. 11 Serological tests of 150 garlic cloves and 30 top-sets collected randomly from seperated plants throughout five different garlic growing regions in Korea revealed $100\%$ infection with garlic mosaic virus. Accordingly it is concluded that most of the garlic cloves and top-sets now being used for propagation in Korea are carriers of the garlic mosaic virus. 12. Serological studies revealed that the garlic mosaic virus is not related with potato viruses X, Y, S and M. 13. Because of the difficulty in securing mosaic virus-free garlic plants, direct inoculation with isolated virus to the garlic plants was not accomplished. Results of the present study, however, indicate that the virus isolate used here is the causal virus of the garlic mosaic disease in Korea.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.73-85
• /
• 2013

Nowadays, in today's information society, the importance of the knowledge service using the information to creative value is getting higher day by day. In addition, depending on the development of IT technology, it is ease to collect and use information. Also, many companies actively use customer information to marketing in a variety of industries. Into the 21st century, companies have been actively using the culture arts to manage corporate image and marketing closely linked to their commercial interests. But, it is difficult that companies attract or maintain consumer's interest through their technology. For that reason, it is trend to perform cultural activities for tool of differentiation over many firms. Many firms used the customer's experience to new marketing strategy in order to effectively respond to competitive market. Accordingly, it is emerging rapidly that the necessity of personalized service to provide a new experience for people based on the personal profile information that contains the characteristics of the individual. Like this, personalized service using customer's individual profile information such as language, symbols, behavior, and emotions is very important today. Through this, we will be able to judge interaction between people and content and to maximize customer's experience and satisfaction. There are various relative works provide customer-centered service. Specially, emotion recognition research is emerging recently. Existing researches experienced emotion recognition using mostly bio-signal. Most of researches are voice and face studies that have great emotional changes. However, there are several difficulties to predict people's emotion caused by limitation of equipment and service environments. So, in this paper, we develop emotion prediction model based on vision-based interface to overcome existing limitations. Emotion recognition research based on people's gesture and posture has been processed by several researchers. This paper developed a model that recognizes people's emotional states through body gesture and posture using difference image method. And we found optimization validation model for four kinds of emotions' prediction. A proposed model purposed to automatically determine and predict 4 human emotions (Sadness, Surprise, Joy, and Disgust). To build up the model, event booth was installed in the KOCCA's lobby and we provided some proper stimulative movie to collect their body gesture and posture as the change of emotions. And then, we extracted body movements using difference image method. And we revised people data to build proposed model through neural network. The proposed model for emotion prediction used 3 type time-frame sets (20 frames, 30 frames, and 40 frames). And then, we adopted the model which has best performance compared with other models.' Before build three kinds of models, the entire 97 data set were divided into three data sets of learning, test, and validation set. The proposed model for emotion prediction was constructed using artificial neural network. In this paper, we used the back-propagation algorithm as a learning method, and set learning rate to 10%, momentum rate to 10%. The sigmoid function was used as the transform function. And we designed a three-layer perceptron neural network with one hidden layer and four output nodes. Based on the test data set, the learning for this research model was stopped when it reaches 50000 after reaching the minimum error in order to explore the point of learning. We finally processed each model's accuracy and found best model to predict each emotions. The result showed prediction accuracy 100% from sadness, and 96% from joy prediction in 20 frames set model. And 88% from surprise, and 98% from disgust in 30 frames set model. The findings of our research are expected to be useful to provide effective algorithm for personalized service in various industries such as advertisement, exhibition, performance, etc.