• Journal of information and communication convergence engineering
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• 제5권4호
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• pp.346-350
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• 2007

Almost all network systems provide an authentication mechanism based on user ID and password. In such system, it is easy to obtain the user password using a sniffer program with illegal eavesdropping. The one-time password and challenge-response method are useful authentication schemes that protect the user passwords against eavesdropping. In client/server environments, the one-time password scheme using time is especially useful because it solves the synchronization problem. It is the stability that is based on Square Root Problem, and we would like to suggest PBSI(Password Based Secure Identification), enhancing the stability, for all of the well-known attacks by now including Off-line dictionary attack, password file compromise, Server and so on. The PBSI is also excellent in the aspect of the performance.

• Journal of the Korean Wood Science and Technology
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• 제44권4호
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• pp.505-514
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• 2016

To study mechanical properties of fiberglass reinforced wood-based composite (FRWC), fiberglass with a diameter of $20{\mu}m$ was selected to prepare test specimens. Mechanical properties of fiberglass reinforced wood-based composite were determined by three-point-bending test while its microstructure was characterizes by scanning electron microscopy (SEM). The results showed that mechanical properties of fiberglass reinforced wood-based composite were superior to that of the wood fiberboard based on the contrasting mechanical curves and the analysis of fracture mechanism. It is believed that the material design with this "sandwich" structure brings a unique buffering capacity of fiberglass into play in the composites. So the specimen did not produce a sudden fracture failure at high level of applied loads because it had a bearing ability. The SEM analysis showed that the working strength of PVAc adhesive was high; under a bearing force, it could properly transfer a load. In addition, glass fiber mesh and wood fiber board combined well.

• Journal of information and communication convergence engineering
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• 제3권4호
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• pp.213-216
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• 2005

Almost all network systems provide an authentication mechanism based on user ID and password. In such system, it is easy to obtain the user password using a sniffer program with illegal eavesdropping. The one-time password and challenge-response method are useful authentication schemes that protect the user passwords against eavesdropping. In client/server environments, the one-time password scheme using time is especially useful because it solves the synchronization problem. It is the stability that is based on Square Root Problem, and we would like to suggest PBI(password Based Identification), enhancing the stability, for all of the well-known attacks by now including Off-line dictionary attack, password file compromise, Server and so on. The PBI is also excellent in the aspect of the performance.

• 한국응용곤충학회지
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• 제32권4호
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• pp.373-381
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• 1993

Resistance evolution to organophosphate-based pesticides in apple and pear inhabiting arthropods of western North America extends to many classes of pest and some beneficial species. Resistance management programs to minimize resistance in pests while exploiting it in natural enemies have met with mixed success. Among beneficials, resistances have been exploited mostly among predators of pest mites. Evolution of resistant mites, leafminers, leafhopper, aphids, leafrollers and some internal fruit feeders have led to development of new monitoring methods and means to delay or avoid resistance. But it is resistance to azinphosmethyl in codling moth (Cydia pomonella) that is changing the pest control system and moving it from chemical to biologically-based means. Newly merging IPM system will depend more on use of biological, cultural, behavior and genetic controls. But more selective pesticides also will be needed to augment pheromones, resistant host plants and genetically altered organisms. These more biologically-based tactics will be prone to resistance evolution in pests as well, if used too unilaterally and/or too extensively.

• 산업경영시스템학회지
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• 제29권2호
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• pp.13-19
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• 2006

The instance-based learning is a machine learning technique that has proven to be successful over a wide range of classification problems. Despite its high classification accuracy, however, it has a relatively high storage requirement and because it must search through all instances to classify unseen cases, it is slow to perform classification. In this paper, we have presented a new data reduction method for instance-based learning that integrates the strength of instance partitioning and attribute selection. Experimental results show that reducing the amount of data for instance-based learning reduces data storage requirements, lowers computational costs, minimizes noise, and can facilitates a more rapid search.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.2603-2605
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• 2001

In this paper, we introduce the concept and construction of a web-based bidirectional remote laboratory system. In this configuration, students build and test circuits using internet at home, and the circuit components and experimental equipments at the school are interconnected following student's design. This means that it is possible to choose components, build circuits, connect experiment equipments, control the equipment. and receive measurement results at home in real-time. Labview and Java are used to control this system efficiently and to provide user-friendly environments. Computer-based function generator, DMM, and oscilloscope are used and switch module is used to change interconnections. It was found that a system adopting computer-based instruments, which include PXI/CompactPCI modules and PC plug-in board, provide better performance than that adopting stand-alone instruments connected with GPIB. It is believed that, if this system is installed, the real experiments, not the virtual experiments based on simulation, can be done at home effectively, resulting in revolutional change in engineering education.

• 한국통신학회논문지
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• 제22권8호
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• pp.1691-1697
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• 1997

This paper presents a new motion compensated error coding method suitable for region based image coding system. Compared with block based conding, the region based coding improves subjective quality as it estimates and compensates 2D (or 3D) translantional, rotational, and scaling motion for each regions. although the region based coding has this advantage, its merit is reduced as bock-DCT (2D-DCT) is used to encode motion-compensated error. To overcome this problem, a new region adaptive motion compensated error coding technique which improver subjective and objective quality in the region boundary is proposed in this paper. In the proposed method, regions with large error are estimated using contour of the regions and contrast between the regions. The regions estiated as those with large error are coded by arbitrarily shaped image segment coding method. The mask information of the coded regions is not transmitted because it is estimated as the same algorithm in the encoder and the decoder. The proposed region adaptive motion conpensated error coding method improves about 0.5dB when it is compared with conventional block based method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2447-2449
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• 2003

In this paper, optimal fuzzy systems using vector quantization and fuzzy logic controllers are designed for vision-based robot control systems. The complexity of the optimal fuzzy system for vision-based control systems is so great that it can not be applied to real vision-based control systems or it can not be useful, because there are so many input-output pairs. Therefore, we generally use the clustering of input-output pairs, in order to reduce the complexity of optimal fuzzy systems. To increase the effectiveness of the clustering, a vector quantization clustering method is proposed. In order to verify the effectiveness of the proposed method experimentally, it is applied to a vision-based arm robot control system.

• 한국IT서비스학회지
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• 제6권1호
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• pp.17-30
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• 2007

The rapid development of information and communication technologies has resulted in rapid increase in the qualitative and quantitative information that can be accessed by information technology. Furthermore an enormous amount of information is provided in various formats. However, there are a group of people who are not able to even approach to targeted information due to physical or environmental problems. Visually handicapped users constitute a class of these users. In this paper, a method of developing user interface for web-based education service is described for the visually handicapped users to access information more efficiently. First, this paper analyzed the problems of visually handicapped users to access information targeting representative domestic sites that supply education services. And based on the results of the above analyses, Nest, this paper explains the web-based education service interface implementation method according to types of the content to be delivered. Lastly, the validity of this method is provided by testing a web page based on the implementation method according to the types of content.

• ETRI Journal
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• 제44권3호
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• pp.438-449
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• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.