• Bulletin of the Society of Naval Architects of Korea
• /
• v.4 no.1
• /
• pp.59-65
• /
• 1967

Since the screw propellers were adopted as ship propulsion devices, the replacement of propeller shaft due to damage was mostly of fatigue failure due to the alternative stresses [1],[2]. To prevent such a failure, hence, it is suggested that careful attention should be paid to account of the alternative stresses on the design stage of the propeller shafts. In connection with this fact the Ship Classification Societies' Rules are regarded simply as guidance for preliminary determination of the shaft diameter. In this paper, limiting the topic to the small and medium-sized motor ships, an evaluation of the Rules formulae to a theoretical based on Soderberg's correlation [5] between the factor of safety and the resultant stresses obtained by application of the maximum shear theory is done. For this purpose eleven (11) ships built recently in Korea were taken as a species(refer to table 2. in text). In the end the following conclusions are made: (1) In general the Rules formulae give considerably larger size of the propeller shaft diameter than that derived from theoretical calculation, that is, about 7% more in AB and BV Rules, and about 20% more in LR and KR-NK Rules. (2) LR Rule gives the largest size of all, and AB Rule is mostly closed value to the theoretical. (3) The formular of the AB Rule is considered to be of the simplest in utilization and of the reasonable.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.6
• /
• pp.792-798
• /
• 2007

In this paper, we propose a hierarchical fuzzy-rough classification method based on statistical information for maximizing the performance of pattern classification and reducing the number of rules without learning approaches such as neural network, genetic algorithm. In the proposed method, statistical information is used for extracting the partition intervals of antecedent fuzzy sets at each layer on hierarchical fuzzy-rough classification systems and rough sets are used for minimizing the number of fuzzy if-then rules which are associated with the partition intervals extracted by statistical information. To show the effectiveness of the proposed method, we compared the classification results(e.g. the classification accuracy and the number of rules) of the proposed with those of the conventional methods on the Fisher's IRIS data. From the experimental results, we can confirm the fact that the proposed method considers only statistical information of the given data is similar to the classification performance of the conventional methods.

• Journal of Korean Library and Information Science Society
• /
• v.21
• /
• pp.459-483
• /
• 1994

The purpose of the study is to design and implement a prototype expert system for library classification in the literature field of the DDC 20. The system was largely consisted of a knowledge base, an inference engine, a knowledge acquisition facility, an explanation facility and an user interface facility. The knowledge base was represented by inference rules and frames. The name file for authors and titles was designed separately. The forward chaining technique was chosen for the inference engine and the menu-driven dialog technique was also taken for the user interface. The conclusions of the study can be summarized as follows: 1) The difficulty of document classification work is due to the complex and stringent classification rules. Such problems can be considerably alleviated by using the present system. 2) Even the novice with a knowledge about the DDC 20 can easily access the system. And also librarian other than the professional classifier can easily be accustomed to the classification work. 3) The system can be used as an online classification scheme. 4) By adding any local language other than English or Hangeul on the menu screen, the language problem relating classification can be overcome. 5) The system can be employed as the intensification tool for the education of classification as well as library automation.

• Journal of Information Processing Systems
• /
• v.19 no.2
• /
• pp.258-266
• /
• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.2 no.2
• /
• pp.42-60
• /
• 1992

An adaptive fuzzy system can efficiently classify subimages into four categories according to image activity level for image data compression. The system estimates fuzzy rules by clustering input-output data generated from a given adaptive transform image coding process. The system encodes different images without modification and reduces side information when encoding multiple images. In the second part, a fuzzy system estimates optimal bit maps for the four subimage classes in noisy channels assuming a Gauss-Markov image model. The fuzzy systems respectively estimate the sampled subimage classification and the bit-allocation processes without a mathematical model of how outputs depend on inputs and without rules articulated by experts.

• Proceedings of the IEEK Conference
• /
• 2002.06d
• /
• pp.153-156
• /
• 2002

The image labeling and classification is one of the important tasks for a content-based image retrieval and an image understanding. This paper propose a new technique to label and classify natural images with a color-spatial histogram and production rules. We show that our proposed method is very efficient for a natural image composed of a few regions.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.2
• /
• pp.145-152
• /
• 2020

In this paper, we propose the Crop Classification Management System (CCMS) that detects changes in growth environment to improve crop production rate. The CCMS consists of two modules. First, the Crop Classification Module (CCM) classifies crops through CNN. Second, the Farm Anomaly Detection Module (FADM) detects abnormal crops by comparing accumulated data of farms. The CCM recognizes crops currently grown on farms and sends them to the FADM, and the FADM picks up the weather data from the past to the present day of the farm growing the crops and applies them to the Nelson rules. The FADM uses the Nelson rules to find out weather data that has occurred and adjust farm conditions through IoT devices. The performance analysis of CCMS showed that the CCM had a crop classification accuracy of about 90%, and the FADM improved the estimated yield by up to about 30%. In other words, managing farms through the CCMS can help increase the yield of smart farms.

• Journal of KIISE:Software and Applications
• /
• v.32 no.12
• /
• pp.1229-1237
• /
• 2005

Combining multiple classifiers has been actively exploited to improve classification performance. It is required to construct a pool of accurate and diverse base classifier for obtaining a good ensemble classifier. Conventionally ensemble learning techniques such as bagging and boosting have been used and the diversify of base classifiers for the training set has been estimated, but there are some limitations in classifying gene expression profiles since only a few training samples are available. This paper proposes an ensemble technique that analyzes the diversity of classification rules obtained by genetic programming. Genetic programming generates interpretable rules, and a sample is classified by combining the most diverse set of rules. We have applied the proposed method to cancer classification with gene expression profiles. Experiments on lymphoma cancer dataset, prostate cancer dataset and ovarian cancer dataset have illustrated the usefulness of the proposed method. h higher classification accuracy has been obtained with the proposed method than without considering diversity. It has been also confirmed that the diversity increases classification performance.

• Proceedings of the IEEK Conference
• /
• 2004.06a
• /
• pp.171-174
• /
• 2004

Packet classification is an essential architectural component in implementing the quality-of-service (QoS) in today's Internet which provides a best-effort service to ail of its applications. Multiple header fields of incoming packets are compared against a set of rules in packet classification, the highest priority rule among matched rules is selected, and the packet is treated according to the action of the rule. In this Paper, we proposed a new packet classification scheme based on parallel multiple hashing on tuple spaces. Simulation results using real classifiers show that the proposed scheme provides very good performance on the required number of memory accesses and the memory size compared with previous works.

• Journal of Korean Institute of Industrial Engineers
• /
• v.40 no.4
• /
• pp.375-381
• /
• 2014

Data mining is a process of discovering useful patterns or information from large amount of data. Decision tree is one of the data mining algorithms that can be used for both classification and prediction and has been widely used for various applications because of its flexibility and interpretability. Decision trees for classification generally generate a number of rules that belong to one of the predefined category and some rules may belong to the same category. In this case, it is necessary to determine the significance of each rule so as to provide the priority of the rule with users. The purpose of this paper is to propose a rule selection method in classification tree models that accommodate the umber of observation, accuracy, and effectiveness in each rule. Our experiments demonstrate that the proposed method produce better performance compared to other existing rule selection methods.