• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.3
• /
• pp.55-65
• /
• 2004

When a neural network is used to solve a given problem it is necessary to match the complexity of the network to that of the problem because the complexity of the network significantly affects its learning capability and generalization performance. Thus, it is desirable to have an algorithm that can find appropriate network structures in a self-organizing way. This paper proposes algorithms which automatically organize feed forward multi-layer neural networks with sigmoid hidden neurons for given problems. Using both constructive procedures and pruning procedures, the proposed algorithms try to find the near optimal network, which is compact and shows good generalization performance. The performances of the proposed algorithms are tested on four function regression problems. The results demonstrate that our algorithms successfully generate near-optimal networks in comparison with the previous method and the neural networks of fixed topology.

• Journal of KIISE:Databases
• /
• v.29 no.5
• /
• pp.358-366
• /
• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.3
• /
• pp.231-240
• /
• 2001

Conventional expert systems has been criticized due to its lack of capability to adapt to the changing decision-making environments. In literature, many methods have been proposed to make expert systems more environment-adaptive by incorporating fuzzy logic and neural networks. The objective of this paper is to propose a new approach to building a self-evolving expert system inference mechanism by integrating fuzzy neural network and fuzzy rule extraction technique. The main recipe of our proposed approach is to fuzzify the training data, train them by a fuzzy neural network, extract a set of fuzzy rules from the trained network, organize a knowledge base, and refine the fuzzy rules by applying a pruning algorithm when the decision-making environments are detected to be changed significantly. To prove the validity, we tested our proposed self-evolving expert systems inference mechanism by using the bankruptcy data, and compared its results with the conventional neural network. Non-parametric statistical analysis of the experimental results showed that our proposed approach is valid significantly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.434-437
• /
• 2010

NIPS(Network Intrusion Prevention System) is in line at the end of the external and internal networks which performed two kinds of action: Signature-based filtering and anomaly detection and prevention-based on self-learning. Among them, a signature-based filtering is well known to defend against attacks. By using signature-based filtering, intrusion prevention system passing a payload of packets is compared with attack patterns which are signature. If match, the packet is discard. However, when there is packet delay, it will increase the required pattern matching time as the number of signature is increasing whenever there is delay occur. Therefore, to ensure the performance of IPS, we needed more efficient pattern matching algorithm for high-performance ISP. To improve the performance of pattern matching the most important part is to reduce the number of comparisons signature rules and the packet whenever the packets arrive. In this paper, we propose an improve signature hashing-based pattern matching method. We use tuple pruning algorithm with Bloom filters, which effectively remove unnecessary tuples. Unlike other existing signature hashing-based IPS, our proposed method to improve the performance of IPS.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.4 no.6
• /
• pp.1273-1293
• /
• 2010

Immune-inspired intrusion detection is a promising technology for network security, and well known for its diversity, adaptation, self-tolerance, etc. However, scalability and coverage are two major drawbacks of the immune-inspired intrusion detection systems (IIDSes). In this paper, we propose an IIDS framework, named GEP-IIDS, with improved basic system elements to address these two problems. First, an additional bio-inspired technique, gene expression programming (GEP), is introduced in detector (corresponding to detection rules) representation. In addition, inspired by the avidity model of immunology, new avidity/affinity functions taking the priority of attributes into account are given. Based on the above two improved elements, we also propose a novel immune algorithm that is capable of integrating two bio-inspired mechanisms (i.e., negative selection and positive selection) by using a balance factor. Finally, a pruning algorithm is given to reduce redundant detectors that consume footprint and detection time but do not contribute to improving performance. Our experimental results show the feasibility and effectiveness of our solution to handle the scalability and coverage problems of IIDS.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.18 no.2
• /
• pp.122-132
• /
• 2008

The purpose of this study is to present the basic materials of a developmental counterplan by assessing prevalence of musculoskeletal symptoms and characteristics of work-related ergonomic risk factors. The prevalence of musculoskeletal symptoms of 64 vineyard workers was investigated by using a self-reported questionnaire. And the vineyard workers' tasks were also collected and analyzed during 9-month period by observing and videotaping methods to identify the ergonomic risk factors. The prevalence rate of musculoskeletal symptoms of female(84.4%) was much higher than that of male(62.5%) and a total prevalence rate was 73.4%. There was no different prevalence of musculoskeletal symptoms between age over 60 years and under 60 years. The prevalence rates of musculoskeletal complaints for each body part were 48.4%, 42.2%, and 35.9% for the wrist, legs/knee, and shoulder, respectively. In postural risk analysis, 7 works(REBA score) represented the high risk tasks such as delivering harvest boxed(12), picking cluster(11) and so on. The main works(exposure score) were orderly ranked as picking cluster(1590), pruning branch(388), and cluster thinning(327). The risk factors of vineyard work were identified as follows: shoulder flexion(${\geq}45^{\circ}$), wrist Flex./Ext.(${\geq}15^{\circ}$), hand force(power/pinch-grip), and prolonged standing(${\geq}4hr$). The engineering solutions including an improvement of hand tools, working process, and working environment should be applied to the high risky tasks in order to resolve the ergonomic problems. The administrative solutions such as improving a distribution of resting time, an exercise cure, an early recognition of symptoms and rehabilitation might be another solution for reducing musculoskeletal symptoms in vineyard workers.

• Journal of Korean Society of Forest Science
• /
• v.79 no.2
• /
• pp.196-204
• /
• 1990

Pinus densiflora is not only widely distributed but also one of the most economically important timber species in Korea. In Korea, this species is classified into four local forms according to tree forms, as it has widely geographical variations. Trees of Ankang form are dwarfish nearly like shrubs, and those of Keumkang form are very tall and straight. Those of flatland and highland forms are exhibited in-between Ankang and Keumkang forms. This study was carried out to examine biomass, net production and production efficiency among four local forms of Pirzus densiflora forests growing in Korea. For the study, dimension analysis was used for trees and harvest method for shrubs and herbs in four 30-40 year-old stands showing the typical tree forms. Stand biomass of Ankang, flatland, highland and Keumkang forms were 29.87, 110.89, 133.53 and 205.42 t/ha, respectively. As going in order of Ankang, flatland, highland and Keumkang forms, the proportions of stem woods to total tree biomass increased while the proportions of stem barks, live branches, needles and roots showed a tendency to decrease. Stand net productions of Ankang, flatland, highland and Keumkang forms were 3.716, 10.796, 13.097 and 16.500 t/ha yr., respectively. As going in order of Ankang, flatland, highland and Keumkang forms, the proportions of stem woods and lire branches to total tree net production increased while the proportion of needles decreased. In case of live branches, the opposite trend of biomss and net production proportion may be resulted from the differences in the ratio of self-pruning. Net assimilation ratios of Ankang, flatland, highland and Keumkang forms were 1.406, 1.920, 1.487 and 1.677, respectively. Efficiency of needles to produce stems in those forms were 0.239, 0.714, 0.572 and 0.771, respectively. Considering the difference in measuring time, net assimilation ratio and efficiency of needles to produce stems may increase as going in order of Ankang, flatland, highland and Keumkang forms.