• Journal of Information Processing Systems
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• 제13권4호
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• pp.893-913
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• 2017

The handwriting based person identification systems use their designer's perceived structural properties of handwriting as features. In this paper, we present a system that uses those structural properties as features that graphologists and expert handwriting analyzers use for determining the writer's personality traits and for making other assessments. The advantage of these features is that their definition is based on sound historical knowledge (i.e., the knowledge discovered by graphologists, psychiatrists, forensic experts, and experts of other domains in analyzing the relationships between handwritten stroke characteristics and the phenomena that imbeds individuality in stroke). Hence, each stroke characteristic reflects a personality trait. We have measured the effectiveness of these features on a subset of handwritten Devnagari and Latin script datasets from the Center for Pattern Analysis and Recognition (CPAR-2012), which were written by 100 people where each person wrote three samples of the Devnagari and Latin text that we have designed for our experiments. The experiment yielded 100% correct identification on the training set. However, we observed an 88% and 89% correct identification rate when we experimented with 200 training samples and 100 test samples on handwritten Devnagari and Latin text. By introducing the majority voting based rejection criteria, the identification accuracy increased to 97% on both script sets.

• Asia pacific journal of information systems
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• 제32권2호
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• pp.226-248
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• 2022

The existing model for sentiment analysis of product reviews learned from past data and new data was labeled based on training. But new data was never used by the existing system for making a decision. The proposed Aspect-based multi-agent Deep Reinforcement learning Sentiment Analysis (ADRSA) model learned from its very first data without the help of any training dataset and labeled a sentence with aspect category and sentiment polarity. It keeps on learning from the new data and updates its knowledge for improving its intelligence. The decision of the proposed system changed over time based on the new data. So, the accuracy of the sentiment analysis using deep reinforcement learning was improved over supervised learning and unsupervised learning methods. Hence, the sentiments of premium customers on a particular site can be explored to other customers effectively. A dynamic environment with a strong knowledge base can help the system to remember the sentences and usage State Action Reward State Action (SARSA) algorithm with Bidirectional Encoder Representations from Transformers (BERT) model improved the performance of the proposed system in terms of accuracy when compared to the state of art methods.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.1187-1192
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• 2003

The computer is an important tool to design an engineering system and CAD systems are widely used for various design practice. To meet the market requirements, the old mass production system is being changed into the mass customization system. As for CAD systems, it is increasingly required to extend, automate, and customize a wide range of functionality. This article describes the state-of-the-art of the principal technologies for customizing CAD systems. And we have implemented an application that enables the parametric design by directly inputting numerical values of parameters for a CAD model. Based on this application, we have developed another system that makes it possible sharing of part family data between SolidEdge and Pro/Engineer. Through customization of CAD systems, it is possible to improve the product quality using external knowledge-based systems or to integrate with external system such as CAE tools. This paper can be a guide for engineering designers who want to customize CAD systems.

• Asia pacific journal of information systems
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• 제8권3호
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• pp.51-73
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• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• Preventive Nutrition and Food Science
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• 제21권1호
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• pp.1-8
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• 2016

Increasing knowledge on the link between diet and human health has generated a lot of interest in the development of functional foods. However, several challenges, including discovering of beneficial compounds, establishing optimal intake levels, and developing adequate food delivering matrix and product formulations, need to be addressed. A number of new processes and materials derived from nanotechnology have the potential to provide new solutions in many of these fronts. Nanotechnology is concerned with the manipulation of materials at the atomic and molecular scales to create structures that are less than 100 nm in size in one dimension. By carefully choosing the molecular components, it seems possible to design particles with different surface properties. Several food-based nanodelivery vehicles, such as protein-polysaccharide coacervates, multiple emulsions, liposomes and cochleates have been developed on a laboratory scale, but there have been very limited applications in real food systems. There are also public concerns about potential negative effects of nanotechnology-based delivery systems on human health. This paper provides an overview of the new opportunities and challenges for nanotechnology-based systems in future functional food development.

• 한국지능시스템학회논문지
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• 제2권2호
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• pp.3-33
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• 1992

The successful application of fuzzy reasoning models to fuzzy control systems depends on a number of parameters, such as fuzzy membership functions, that are usually decided upon subjectively. It is shown ill this paper that the performance of fuzzy control systems call be improved if the fuzzy reasoning model is supplemented by a genetic-based learning mechanism. The genetic algorithm enables us to generate all optimal set of parameters for the fuzzy reasoning model based either on their initial subjective selection or on a random selection. It is shown that if knowledge of the domain is available, it is exploited by the genetic algorithm leading to an even better performance of the fuzzy controller.

• 한국지능시스템학회논문지
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• 제5권3호
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• pp.3-10
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• 1995

This paper proposedan on-line learning controller which can be applied to nonlinear systems. The proposed on-line learning controller is based on the universal approximation by the local affine mapping-based neural networks. It has self-organizing and learning capability to adapt itself to the new environment arising from the variation of operating point of the nonlinear system. Since the learning controller retains the knowledge of trained dynamics, it can promptly adapt itself to situations similar to the previously experienced one. This prompt adaptability of the proposed control system is illustrated through simulations.

• 경영과정보연구
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• 제24권
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• pp.25-44
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• 2008

Small business have very different characteristics from large business; notable, small business suffer from resource and capacity poverty. Without knowing the relative importance of limited resources and capacity in small business, it have limited practical contribution to e-business implementation success. This paper develops an exploratory resource-based model of e-business implementation for small business with 42 sample companies. The results show that three types of resource factors; IS/IT vender supports, the level of IS/IT knowledge, and financial support. The implication for research is that the resource-based view of the firm and the test an exploratory framework of resource factors are useful theories to ground future work on e-business implementation in small business.

• Journal of Information Processing Systems
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• 제10권3호
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• pp.335-354
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• 2014

The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other training-based approaches.

• 한국CDE학회논문집
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• 제17권2호
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• pp.123-131
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• 2012

The requirements for computational resources to perform the structural analysis are increasing rapidly. The size of the current analysis problems that are required from practical industry is typically large-scale with more than millions degrees of freedom (DOFs). These large-scale analysis problems result in the requirements of high-performance analysis codes as well as hardware systems such as supercomputer systems or cluster systems. In this paper, the pre- and post-processing system for supercomputing based large-scale structural analysis is presented. The proposed system has 3-tier architecture and three main components; geometry viewer, pre-/post-processor and supercomputing manager. To analyze large-scale problems, the ADVENTURE solid solver was adopted as a general-purpose finite element solver and the supercomputer named 'tachyon' was adopted as a parallel computational platform. The problem solving performance and scalability of this structural analysis system is demonstrated by illustrative examples with different sizes of degrees of freedom.