• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.149-156
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• 2014

NGE(Nested Generalized Exemplar Method) can increase the performance of the noisy data at the same time, can reduce the size of the model. It is the optimal distance-based classification method using a matching rule. NGE cross or overlap hyperrectangles generated in the learning has been noted to inhibit the factors. In this paper, We propose the DHGen(Dominant Hyperrectangle Generation) algorithm which avoids the overlapping and the crossing between hyperrectangles, uses interval weights for mixed hyperrectangles to be splited based on the mutual information. The DHGen improves the classification performance and reduces the number of hyperrectangles by processing the training set in an incremental manner. The proposed DHGen has been successfully shown to exhibit comparable classification performance to k-NN and better result than EACH system which implements the NGE theory using benchmark data sets from UCI Machine Learning Repository.

• The KIPS Transactions:PartD
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• v.8D no.2
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• pp.166-174
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• 2001

Lately computing environment is changing into integrating open system. This paper proposes Integrated Management Model to improve productivity about new software development. The model is divided by Management Model to deal with the rapidly changing environment effectively into three layers: the first layer classifies and displays information to users, the users, the second layer controls function, the integration and management layer, and the last layer manages data, the objects management storage layer. So it designs of Efficient Object Management Repository Using Integration Management Model. This might support afterward prototyping in maximizing the reuse of software, which is advantage to the integration of the system, and in promoting its productivity.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.1-5
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• 2009

This paper suggests to apply mirror-reflected method based 2D emotion recognition database to 3D application. Also, it makes facial expression of fuzzy modeling using probability of emotion. Suggested facial expression function applies fuzzy theory to 3 basic movement for facial expressions. This method applies 3D application to feature vector for emotion recognition from 2D application using mirror-reflected multi-image. Thus, we can have model based on fuzzy nonlinear facial expression of a 2D model for a real model. We use average values about probability of 6 basic expressions such as happy, sad, disgust, angry, surprise and fear. Furthermore, dynimic facial expressions are made via fuzzy modelling. This paper compares and analyzes feature vectors of real model with 3D human-like avatar.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.228-234
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• 2023

As the increasing demand for high-quality software, there is huge requiring for quality certification of international standards, industrial functional safety (IEC 61508), automotive (ISO 26262), embedded software guidelines for weapon systems, etc., in the industry. Software companies are very difficult to systematically acquire the quality certification in terms of cost and manpower of Startup, venture small-sized companies. For their companies one test case automatic generation is considered as a core technique to evaluate or improve software quality. This paper proposes a test case automatic generation method based on the design decision table for system and software design verification. We apply the proposed method with OMG's standard techniques of metamodel and model transformation for automatically generating test cases. To do this, we design the metamodels of design decision table (Model) and test case document (Text) and define model transformation to automatically generate test cases, which will expect to easily work MC/DC coverage.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.389-396
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• 2024

Generative AI has recently been utilized across all fields, achieving expert-level advancements in deep data analysis. However, identifying regional names in scientific literature remains a challenge due to insufficient training data and limited AI application. This study developed a standardized dataset for effectively classifying regional names using address data from Korean institution-affiliated authors listed in the Web of Science. It tested and evaluated the applicability of machine learning and deep learning models in real-world problems. The BERT model showed superior performance, with a precision of 98.41%, recall of 98.2%, and F1 score of 98.31% for metropolitan areas, and a precision of 91.79%, recall of 88.32%, and F1 score of 89.54% for city classifications. These findings offer a valuable data foundation for future research on regional R&D status, researcher mobility, collaboration status, and so on.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.127-136
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• 2024

This paper presents a novel method for minting hierarchical Non-Fungible Tokens(NFTs) via a parent-child structure. In contrast to existing NFT structures, our proposed model enables an NFT to act as a parent, creating child NFTs and distributing ownership stakes among them. These child NFTs are recursively structured, allowing them to generate their own descendants. The existing structure of NFTs does not inherently allow for fractional ownership. However, our proposed hierarchical model provides a feasible solution to this restriction. By dividing an NFT into multiple child NFTs, each with its own unique identity, we facilitate the detailed division of an asset, thereby making fractional ownership possible. In conclusion, the hierarchical NFT model proposed in this paper offers a promising solution to the challenges of fractional ownership in the digital asset arena. By enabling the detailed division of NFTs through a parent-child structure, we anticipate a future where digital assets can be owned and traded more flexibly and transparently.

• Geophysics and Geophysical Exploration
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• v.24 no.2
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• pp.53-66
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• 2021

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.3
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• pp.64-69
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• 2007

Motion control of the system is a position control of motor. Motion control of an uncertain robot system is considered as one of the most important and fundamental research directions in the robotics. Some distinguished works using linear control, adaptive control, robust control strategies based on computed torque methodology have been reported. However, it is generally recognized within the control community that these strategies suffer from the following problems : the exact robot dynamics are needed and hard to implement, the adaptive control cannot guarantee the performance during the transient period for adaptation under the variation, the robust control algorithms such as the sliding mode control need information on the bounds of the possible uncertainty and disturbance. And it produces a large control input as well. In this dissertation, a motion control for the unmanned intelligent robot system using disturbance observer is studied. This system is affected with an impact vibration disturbance. This paper describes a stable motion control of the system with the consideration of external disturbance. To obtain the stable motion independently against the external disturbance, the disturbance rejection is strongly required. To address the above issue, this paper presents a Disturbance OBserver(DOB) control algorithm. The validity of the suggested DOB robust control scheme is confirmed by several computer simulation results. And the experiments with a motor system is performed to give the validity of applicability in the industrial field. This results make the easier implementation of the controller possible in the field.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.140-153
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• 2019

Cloase-range image information from drones and ground-based camera has been frequently used in the field of disaster mitigation with 3D modeling and mapping. In addition, the utilization of virtual reality(VR) is being increased by implementing realistic 3D models with the VR technology simulating disaster circumstances in large scale. In this paper, we created a VR training program by extracting realistic 3D models from close-range images from unmanned aircraft and digital camera on hand and observed several issues occurring during the implementation and the effectiveness in the case of a VR application in training for disaster mitigation. First of all, we built up a scenario of disaster and created 3D models after image processing with the close-range imagery. The 3D models were imported into Unity, a software for creation of augmented/virtual reality, as a background for android-based mobile phones and VR environment was created with C#-based script language. The generated virtual reality includes a scenario in which the trainer moves to a safe place along the evacuation route in the event of a disaster, and it was considered that the successful training can be obtained with virtual reality. In addition, the training through the virtual reality has advantages relative to actual evacuation training in terms of cost, space and time efficiencies.

• Journal of Digital Contents Society
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• v.8 no.1
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• pp.93-106
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• 2007

Web Applications are rapidly increasing and the structure becomes very complicated. However, when users explore such complex Web applications, they cannot often grasp the current location and get the information that they want. Therefore, a novel approach to model the navigation of Web application contents is required. In this study, a framework has been presented for modeling adaptive context-based contents navigation of Web applications. The framework performs activities including navigation analysis, navigation design, and navigation realization. first, in navigation analysis domain is analyzed by using use case, focusing on navigation. Next, in navigation design three models have been produced: a navigation information model, a profile, and a navigation interface model. Finally, in navigation realization a Webpage navigation model and a component navigation model have been produced. In this work, several formal definitions and rules for checking validity of navigation model have also been provided.