• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.3
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• pp.140-151
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• 2020

It is important to identify the location of ship hull blocks with exact block identification number when scheduling the shipbuilding process. The wrong information on the location and identification number of some hull block can cause low productivity by spending time to find where the exact hull block is. In order to solve this problem, it is necessary to equip the system to track the location of the blocks and to identify the identification numbers of the blocks automatically. There were a lot of researches of location tracking system for the hull blocks on the stockyard. However there has been no research to identify the hull blocks on the stockyard. This study compares the performance of 5 Convolutional Neural Network (CNN) models with multi-view image set on the classification of the hull blocks to identify the blocks on the stockyard. The CNN models are open algorithms of ImageNet Large-Scale Visual Recognition Competition (ILSVRC). Four scaled hull block models are used to acquire the images of ship hull blocks. Learning and transfer learning of the CNN models with original training data and augmented data of the original training data were done. 20 tests and predictions in consideration of five CNN models and four cases of training conditions are performed. In order to compare the classification performance of the CNN models, accuracy and average F1-Score from confusion matrix are adopted as the performance measures. As a result of the comparison, Resnet-152v2 model shows the highest accuracy and average F1-Score with full block prediction image set and with cropped block prediction image set.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.15-34
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• 2014

Overall, accuracy as a performance measure does not fully consider modular accuracy: the accuracy of classifying 1 (or true) as 1 is not same as classifying 0 (or false) as 0. A smarter classification algorithm would optimize the classification rules to match the modular accuracies' goals according to the nature of problem. Correspondingly, smarter algorithms must be both more generalized with respect to the nature of problems, and free from decretization, which may cause distortion of the real performance. Hence, in this paper, we propose a novel vertical boosting algorithm that improves modular accuracies. Rather than decretizing items, we use simple classifiers such as a regression model that accepts continuous data types. To improve the generalization, and to select a classification model that is well-suited to the nature of the problem domain, we developed a model selection algorithm with smartness. To show the soundness of the proposed method, we performed an experiment with a real-world application: predicting the intellectual properties of e-transaction technology, which had a 47,000+ record data set.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.1-8
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• 2008

Generally, moving objects in surveillance video are extracted by background subtraction or frame difference method. However, moving cast shadows on object distort extracted figures which cause serious detection problems. Especially, analyzing vehicle information in video frames from a fixed surveillance camera on road, we obtain inaccurate results by shadow which vehicle causes. So, Shadow Elimination is essential to extract right objects from frames in surveillance video. And we use shadow removal algorithm for vehicle classification. In our paper, as we suppress moving cast shadow in object, we efficiently discriminate vehicle types. After we fit new object of shadow-removed object as three dimension object, we use extracted attributes for supervised learning to classify vehicle types. In experiment, we use 3 learning methods {IBL, C4.5, NN(Neural Network)} so that we evaluate the result of vehicle classification by shadow elimination.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.341-346
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• 2022

Due to global warming and abnormal climate, the frequency and damage of floods are increasing, and the number of people exposed to flood-prone areas has increased by 25% compared to 2000. Floods cause huge financial and human losses, and in order to reduce the losses caused by floods, it is necessary to predict the flood in advance and decide to evacuate quickly. This paper proposes a flood risk determination model using a CNN-based classification model so that timely evacuation decisions can be made using rainfall and water level data, which are key data for flood prediction. By comparing the results of the CNN-based classification model proposed in this paper and the DNN-based classification model, it was confirmed that it showed better performance. Through this, it is considered that it can be used as an initial study to determine the risk of flooding, determine whether to evacuate, and make an evacuation decision at the optimal time.

• Nuclear Engineering and Technology
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• v.44 no.3
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• pp.225-236
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• 2012

The Fukushima-Daiichi accident shook the world, as a well-known plant design, the General Electric BWR Mark I, was heavily damaged in the tsunami, which followed the Great Japanese Earthquake of 11 March 2011. Plant safety functions were lost and, as both AC and DC failed, manoeuvrability of the plants at the site virtually came to a full stop. The traditional system of Emergency Operating Procedures (EOPs) and Severe Accident Management Guidelines (SAMG) failed to protect core and containment, and severe core damage resulted, followed by devastating hydrogen explosions and, finally, considerable radioactive releases. The root cause may not only have been that the design against tsunamis was incorrect, but that the defence against accidents in most power plants is based on traditional assumptions, such as Large Break LOCA as the limiting event, whereas there is no engineered design against severe accidents in most plants. Accidents beyond the licensed design basis have hardly been considered in the various designs, and if they were included, they often were not classified for their safety role, as most system safety classifications considered only design basis accidents. It is, hence, time to again consider the Design Basis Accident, and ask ourselves whether the time has not come to consider engineered safety functions to mitigate core damage accidents. Associated is a proper classification of those systems that do the job. Also associated are safety criteria, which so far are only related to 'public health and safety'; in reality, nuclear accidents cause few casualties, but create immense economical and societal effects-for which there are no criteria to be met. Severe accidents create an environment far surpassing the imagination of those who developed EOPs and SAMG, most of which was developed after Three Mile Island - an accident where all was still in place, except the insight in the event was lost. It requires fundamental changes in our present safety approach and safety thinking and, hence, also in our EOPs and SAMG, in order to prevent future 'Fukushimas'.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1401-1408
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• 2000

Critical prediction models that determine whether a design entity is fault-prone or non fault-prone play an important role in reducing system development cost because the problems in early phases largely affected the quality of the late products. Real-time systems such as telecommunication system are so large that criticality prediction is more important in real-time system design. The current models are based on the technique such as discriminant analysis, neural net and classification trees. These models have some problems with analyzing cause of the prediction results and low extendability. In this paper, we propose a criticality prediction model using fuzzy rulebase constructed by genetic algorithm. This model makes it easy to analyze the cause of the result and also provides high extendability, high applicability, and no limit on the number of rules to be found.

• Journal of The Korean Association For Science Education
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• v.18 no.4
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• pp.635-641
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• 1998

The purpose of this study was to suggest a new direction of learning method in botany based on the analysis of a classification system and concepts in Science(Biology) textbooks of elementary and secondary school. Elementary and secondary school textbooks of Biology have been analyzed for plant classification system and concepts. Findings are summarized as belows. 1. In textbook of elementary school, the organization of life is grouped into Plantae and Animalia. Learning contents of plant are divided by the size and habitat. However, this system of classification might cause false concepts. Therefore, learning contents should be organized as whether they are flowering plants or not. 2. In a textbook of middle school, the organization of life is grouped into Plantae and Animalia. For a textbook of high school, it is grouped into three kingdoms; Plantae, Animalia and Prorista. With the idea of new age of Life Science, we should change the standards to 5 kingdoms; Plantae, Animalia, Proristae, Fungi and Monera. Moreover, it would be desirable if the concept of plant classification could be explained with a general outline, not by an individual interpretation focusing on characters of species only. In addition to the above indications, a learning course should provide present a standard classification according to a cognitive developemental level. It also has to teach students how to classify plant, in secondary school. Learning materials focusing on algae of the present system, but should be organized based on Seed plants.

• Journal of the Korean Society of Safety
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• v.26 no.5
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• pp.110-117
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• 2011

The paper tries to understand the other side of characteristics on occupational injuries and diseases in forestry. Occupational injuries and diseases in forestry seems to be greatly influenced by the environmental characteristics of the mountain district and individual's ability of workers. A traditional method on the analysis of occupational injuries and diseases data may show that the main cause of occupational injuries and diseases is the material factors significantly. To identify the other side of occupational injuries and diseases in forestry, the occupational injuries and diseases data of 3,091 workers in forestry was analyzed. The data in forestry, 2009 shows certain characteristics among the recent occupational injuries and diseases data. The first step is to classify the data according to standard of classification of original cause materials. Material factors are 72.3% and human factors (included managerial factors) and environmental factors are 27.0%. The next step is to reclassify the first step data by using the concept of influence factors which caused and influenced occupational injuries and diseases. The result is that material factors are 2.4%, human factors(included managerial factors) and environmental factors are 97.0%. Also, an aging degree of workers in forestry is higher than other categories of business. It is true that an aging degree of injured or diseased workers in forestry is higher than that of other categories of business. However, relevance with increase of occupational injuries and diseases could not be explained. An injury and disease rate in forestry is remarkably increased recently than other categories of business. One of the reason why an injury and disease rate increased remarkably in 2009 could be considered as the increase of the number of workers and related budget. Therefore, this study proposes important measures or means to prevent occupational injuries and diseases in forestry.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.709-718
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• 2019

The international nuclear industry has undergone a lot of changes since the Fukushima, Chernobyl and TMI nuclear power plant accidents. However, there are still large and small component deficiencies at nuclear power plants in the world. There are many causes of electrical equipment defects. There are also factors that cause component failures due to human errors. This paper analyzed the root causes of failure and types of human error in 300 cases of electrical component failures. We analyzed the operating experience of electrical components by methods of root causes in K-HPES (Korean-version of Human Performance Enhancement System) and by methods of human error types in HuRAM+ (Human error-Related event root cause Analysis Method Plus). As a result of analysis, the most electrical component failures appeared as circuit breakers and emergency generators. The major causes of failure showed deterioration and contact failure of electrical components by human error of operations management. The causes of direct failure were due to aged components. Types of human error affecting the causes of electrical equipment failure are as follows. The human error type group I showed that errors of commission (EOC) were 97%, the human error type group II showed that slip/lapse errors were 74%, and the human error type group III showed that latent errors were 95%. This paper is meaningful in that we have approached the causes of electrical equipment failures from a comprehensive human error perspective and found a countermeasure against the root cause. This study will help human performance enhancement in nuclear power plants. However, this paper has done a lot of research on improving human performance in the maintenance field rather than in the design and construction stages. In the future, continuous research on types of human error and prevention measures in the design and construction sector will be required.