• Journal of Drive and Control
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• v.17 no.4
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• pp.8-14
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• 2020

Intelligent devices using ICT technology have been introduced in the field of construction machinery to improve productivity and stability. Among the intelligent devices, Machine Guidance is a device that provides real-time posture, location, and work range to drivers by installing various sensors, controllers, and satellite navigation systems on construction machines. Conversely, the efficiency of equipment that requires location information, such as machine guidance, will be greatly reduced in buildings, and tunnels in the GPS blind spots. Thus, the other high-precision positioning technologies are required in the GPS blind spot zone. In this study, we will develop a relative position measurement system that provides precise location information such as construction machinery and robots in a local area where the GPS reception is difficult. A relative position measurement system tracks a marker in the form of a sphere installed on a vehicle by using the image base tracking technology, and measures the distance and direction information to the marker to calculate a position.

• The Korean Journal of Archival Studies
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• no.68
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• pp.41-72
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• 2021

In order to analyze big data, documents should be converted to a open standard format to increase machine readability. It also need natural language processing tools. This study focused on the background of intelligent document processing and the status of research in the public sector, and predicted the changes in work that intelligent document processing would bring. This study noted the changes that intelligent document processing would bring to the archival work, and also considered changes in the role of archivist and their required competencies. Changes in archival work could be anticipated across a wide range of Records Management work and Archives Management work. In particular, it was expected to have a significant impact on the automation of repetitive archival tasks or the description and utilization of records. This study proposed the need to prepare new archival work procedures, methods, and necessary competencies in response to these change in archival work.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.427-432
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• 2003

EEG is an electrical signal, which occurs during information processing in the brain. These EEG signals have been used clinically, but nowadays we ate mainly studying Brain-Computer Interface (BCI) such as interfacing with a computer through the EEG, controlling the machine through the EEG. The ultimate purpose of BCI study is specifying the EEG at various mental states so as to control the computer and machine. This research makes the controlling system of directions with the artifact that are generated from the subject s will, for the purpose of controlling the machine correctly and reliably We made the system like this. First, we select the particular artifact among the EEG mixed with artifact, then, recognize and classify the signals pattern, then, change the signals to general signals that can be used by the controlling system of directions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.95-99
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect. locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal. Rising time. Half period. and Global time, they are used as the inputs to neural network . Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising clime. Half Period amplitude. The result shored that the neural network would be applied to LPMS. Also, applying the neural network to thin practical false alarm data during startup and impact test signal at nuclear power plant, the false alarms are reduced effectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.505-509
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• 2001

In this paper, we evaluate the learning ability of non-monotonic DMM(Deterministic Boltzmann Machine) network through numerical simulations. The simulation results show that the proposed system has higher performance than monotonic DBM network model. Non-monotonic DBM network also show an interesting result that network itself adjusts the number of hidden layer neurons. DBM network can be realized with fewer components than other neural network models. These results enhance the utilization of non-monotonic neurons in the large scale integration of neuro-chips.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.56-58
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• 2006

Camera calibration in machine vision is the process of determining the intrinsic cameara parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.13-23
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• 2001

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Hence, these systems are inflexible in the sense that they cannot easily be adapted to other applications. In this paper, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify \\\"defects\\\" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning if knowledge that allows concurrent parallel processing during recognition.cognition.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.27-35
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• 2016

Black-box classifiers, such as artificial neural network and support vector machine, are a popular classifier because of its remarkable performance. They are applied in various fields such as inductive inferences, classifications, or regressions. However, by its characteristics, they cannot provide appropriate explanations how the classification results are derived. Therefore, there are plenty of actively discussed researches about interpreting trained black-box classifiers. In this paper, we propose a method to make a fuzzy logic-based classifier using extracted rules from the artificial neural network and support vector machine in order to interpret internal structures. As an object of classification, an anomalous propagation echo is selected which occurs frequently in radar data and becomes the problem in a precipitation estimation process. After applying a clustering method, learning dataset is generated from clusters. Using the learning dataset, artificial neural network and support vector machine are implemented. After that, decision trees for each classifier are generated. And they are used to implement simplified fuzzy logic-based classifiers by rule extraction and input selection. Finally, we can verify and compare performances. With actual occurrence cased of the anomalous propagation echo, we can determine the inner structures of the black-box classifiers.