• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.316-329
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• 2008

Recently the increased attention pays on the processing of multiple, relatively low quantity, high value-added products resulted in adoption of batch process in the chemical process industry such as pharmaceuticals, polymers, bio-chemicals and foods. As there are more possibilities of the improvement of operations in batch process than continuous processes, a lot of effort has been made to enhance the productivity and operability of batch processes. But the chemical process industry faces a range of uncertainties factors such as demands for products, prices of product, lead time for the supply of raw materials and in the production, and the distribution of product. And global competition has made it imperative for the process industries to manage their supply chains optimally. Supply chain management aims to integrate plants with their supplier and customers so that they can be managed as a single entity and coordinate all input/output flows (of materials, information) so that products are produced and distributed in the right quantities, to the right locations, and at the right time.The objective of this study is to solve the purchase, distribution, production planning and scheduling problem, which minimizes the total costs of production, inventory, and transportation under uncertainty. And development of SCM model in chemical industry including batch mode operations. Through that, the enterprise can respond to uncertainty. Also integrated process optimal planning and scheduling model for manufacturing supply chain. The result shows that, the advantage of supply chain integration are quality matters seen by customers and suppliers, order schedules, flexibility, cost reduction, and increase in sales and profits. Also, an integration of supply chain (production and distribution system) generates significant savings by trading off the costs associated with the whole, rather than minimizing supply chain costs separately.

• Journal of Digital Convergence
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• v.16 no.7
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• pp.223-229
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• 2018

Recently, the necessity of safety diagnosis of electrical devices has been increasing as the fire caused by electric devices has increased rapidly. This study is concerned with the safety diagnosis of electric equipment using intelligent Fuzzy technology. It is used as a diagnostic input for the multiple electrical safety factors such as the use current, cumulative use time, deterioration and arc characteristics inherent to the equipment. In order to extract these information in real time, a device composed of various sensor circuits, DSP signal processing, and communication circuit is implemented. The fuzzy logic algorithm using the Gaussian function for each information is designed and compiled to be implemented on a small DSP board. The fuzzy logic receives the four diagnostic information, deduces it by the fuzzy engine, and outputs the overall safety status of the device as a 100-step analog fuzzy value familiar to human sensibility. By experiments of a device that combines hardware and fuzzy algorithm implemented in this study, it is verified that it can be implemented in a small DSP board with human-friendly fuzzy value, diagnosing real-time safety conditions during operation of electric equipment. In the future, we expect to be able to study more intelligent diagnostic systems based on artificial intelligent with AI dedicated Micom.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.344-350
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• 2003

In this paper, we propose a novel sharing method ordering the events occurring between users collaborated with the common telecomputing environment. We realize the sharing method with multimedia data to improve the coworking effect using teleprocessing network. This sharing method advances the efficiency of communicating projects such as remote education, tele-conference, and co-authoring of multimedia contents by offering conveniences of presentation, group authoring, common management, and transient event productions of the users. As for the conventional sharing white board system, all the multimedia contents segments should be authored by the exclusive program, and we cannot use any existing contents or program. Moreover we suffer from the problem that ordering error occurs in the teleprocessing operation because we do not have any line-up technology for the input ordering of commands. Therefore we develop a method of retrieving input and output events from the windows system and the message hooking technology which transmits between programs in the operating system In addition, we realize the allocation technology of the processing results for all sharing users of the distributed computing environment without any error. Our sharing technology should contribute to improve the face-to-face coworking efficiency for multimedia contents authoring, common blackboard system in the area of remote educations, and presentation display in visual conference.

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

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.15-24
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• 2007

The recognition of postal address images is indispensable for the automatic sorting of postal envelopes. The process of the address image recognition is composed of three steps-address image preprocessing, character recognition, address interpretation. The extracted character images from the preprocessing step are forwarded to the character recognition step, in which multiple candidate characters with reliability scores are obtained for each character image extracted. aracters with reliability scores are obtained for each character image extracted. Utilizing those character candidates with scores, we obtain the final valid address for the input envelope image through the address interpretation step. The envelope sorting rate depends on the performance of all three steps, among which character recognition step could be said to be very important. The good character recognizer would be the one which could produce valid candidates with very reliable scores to help the address interpretation step go easy. In this paper, we propose the method of generating character candidates with reliable recognition scores. We utilize the existing MLP(multilayered perceptrons) neural network of the address recognition system in the current automatic postal envelope sorters, as the classifier for the each image from the preprocessing step. The MLP is well known to be one of the best classifiers in terms of processing speed and recognition rate. The false alarm problem, however, might be occurred in recognition results, which made the address interpretation hard. To make address interpretation easy and improve the envelope sorting rate, we propose promising methods to reestimate the recognition score (confidence) of the existing MLP classifier: the generation method of the statistical recognition properties of the classifier and the method of the combination of the MLP and the subspace classifier which roles as a reestimator of the confidence. To confirm the superiority of the proposed method, we have used the character images of the real postal envelopes from the sorters in the post office. The experimental results show that the proposed method produces high reliability in terms of error and rejection for individual characters and non-characters.