• Journal of Acupuncture Research
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• v.32 no.4
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• pp.91-102
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• 2015

Objectives : The Oriental medical device industry is expected to continue to experience significant growth. It should increase its global market share rather than focusing on the domestic market. Countries around the world self-regulate their domestic market, so this study aims to aid in the development of a particular overseas market by introducing the U.S.(the largest market) medical device registration and approval process. Methods : For an understanding of the US medical device licensing process, we researched the relevant regulatory organization (FDA), the history, definition and classification of medical devices, the approval and 510(k) submission process related to substantial equivalence, IEC 60601-1 Edition 3, usability tests, and so on. Results : Medical devices in the United States are assigned to one of three regulatory classes: Class I, Class II and Class III, based on the level of control necessary to assure the safety and effectiveness of the device. If a company's device is classified as Class II and if it is not exempt, a 510k will be required for marketing. 1) A 510(k) is a premarket submission made to the FDA to demonstrate that the new device to be marketed is "substantially equivalent" to a legally marketed device (predicate device) 2) The IEC 60601-1 Edition 3 preparation process, which contains information related to usability, is expensive and time-consuming but a critical requirement. Conclusions : Although the U.S. market has high barriers to entry, access to this, large overseas market will encourage development of the Oriental medical device industry and commercial value enhancement is expected.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.112-120
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• 2009

Face membership authentication is to decide whether an incoming person is an enrolled member or not using face recognition, and basically belongs to two-class classification where support vector machine (SVM) has been successfully applied. The previous SVMs used for face membership authentication have been trained and tested using image feature vectors extracted from member face images of each class (enrolled class and unenrolled class). The SVM so trained using image feature vectors extracted from members in the training set may not achieve robust performance in the testing environments where configuration and size of each class can change dynamically due to member's joining or withdrawal as well as where testing face images have different illumination, pose, or facial expression from those in the training set. In this paper, we propose an effective class discriminating feature vector-based SVM for robust face membership authentication. The adopted features for training and testing the proposed SVM are chosen so as to reflect the capability of discriminating well between the enrolled class and the unenrolled class. Thus, the proposed SVM trained by the adopted class discriminating feature vectors is less affected by the change in membership and variations in illumination, pose, and facial expression of face images. Through experiments, it is shown that the face membership authentication method based on the proposed SVM performs better than the conventional SVM-based authentication methods and is relatively robust to the change in the enrolled class configuration.

• Journal of the Korean earth science society
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• v.32 no.1
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• pp.84-98
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• 2011

The study investigated the linguistic characteristics of middle school students' writing on the themes of earth science through analysis of its genre and register. Data for analysis included $7^{th}$ grade and $9^{th}$ grade students' writings about 'global warming' and 'classification of rocks'. The results of this study include: First, many students were not accustomed to writing in genre, especially exposition genre. Second, in terms of ideational meaning, the material verbs representing action or doing were more dominant than relational verbs that are related to the attribute or definition of things, and additional logical relations were predominant. Third, regarding interpersonal meaning, agents, emotions, subjective opinions appeared in the writings and students did not express their ideas conclusively and revealed feelings of doubt and uncertainty about their knowledge. Fourth, as for textual meaning, most students listed fragments of information using additional conjunctions in simple structures and were not accustomed to writing texts with organizing structures, logical patterns, cohesion, and coherence. From these results, we argued that the scientific writings should be emphasized in science learning that aims to foster scientific literacy. In addition, we discussed the necessity of improving science teachers' perceptions on scientific writing as well as setting up a specific plan in the national curriculum.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.4
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• pp.193-205
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• 2003

There has been so many research activities about robot soccer system in the many research fields, for example, intelligent control, communication, computer technology, sensor technology, image processing, mechatronics. Especially researchers research strategy for attacking in the field of strategy, and develop intelligent strategy. Then, soccer robots cannot defense completely and efficiently by using simple defense strategy. Therefore, intention extraction of attacker is needed for efficient defense. In this thesis, intention extractor of soccer robots is designed and developed based on FMMNN(Fuzzy Min-Max Neural networks ). First, intention for soccer robot system is defined, and intention extraction for soccer robot system is explained.. Next, FMMNN based intention extractor for soccer robot system is determined. FMMNN is one of the pattern classification method and have several advantages: on-line adaptation, short training time, soft decision. Therefore, FMMNN is suitable for soccer robot system having dynamic environment. Observer extracts attack intention of opponents by using this intention exactor, and this intention extractor is also used for analyzing strategy of opponent team. The capability of developed intention extractor is verified by simulation of 3 vs. 3 robot succor simulator. It was confirmed that the rates of intention extraction each experiment increase.

• Journal of Bio-Environment Control
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• v.3 no.1
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• pp.42-51
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• 1994

Visual features of a mushromm(Lentinus Edodes L.) are critical in sorting and grading as most agricultural products are. Because of its complex and various visual features, grading and sorting of mushrooms have been done manually by the human expert. Though actions involved in human grading look simple, it decision making underneath the simple action comes from the result of the complex neural processing of visual image. Recently, an artificial neural network has drawn a great attention because of its functional capability as a partial substitute of the human brain. Since most agricultural products are not uniquely defined in its physical properties and do not have a well defined job structure, the neuro -net based computer visual information processing is the promising approach toward the automation in the agricultural field. In this paper, first, the neuro - net based classification of simple geometric primitives were done and the generalization property of the network was tested for degraded primitives. And then the neuro-net based grading system was developed for a mushroom. A computer vision system was utilized for extracting and quantifying the qualitative visual features of sampled mushrooms. The extracted visual features of sampled mushrooms and their corresponding grades were used as input/output pairs for training the neural network. The grading performance of the trained network for the mushrooms graded previously by the expert were also presented.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.33-41
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• 2018

Recently, Internet technology has developed, various programs are being created and therefore various codes are being made through many authors. On this aspect, some author deceive a program or code written by other particular author as they make it themselves and use other writers' code indiscriminately, or not indicating the exact code which has been used. Due to this makes it more and more difficult to protect the code. In this paper, we propose author identification framework using Authorship Analysis theory and Natural Language Processing(NLP) based on Convolutional Neural Network(CNN). We apply Authorship Analysis theory to extract features for author identification in the source code, and combine them with the features being used text mining to perform author identification using machine learning. In addition, applying CNN based natural language processing method to source code for code author classification. Therefore, we propose a framework for the identification of authors using the Authorship Analysis theory and the CNN. In order to identify the author, we need special features for identifying the authors only, and the NLP method based on the CNN is able to apply language with a special system such as source code and identify the author. identification accuracy based on Authorship Analysis theory is 95.1% and identification accuracy applied to CNN is 98%.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.4
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• pp.1621-1626
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• 2014

Background: Breast cancer is the most common malignant tumor in females worldwide. Many differences exist in clinico-pathological characteristics of breast cancer patients between China and Western countries. This study aimed to analyze clinico-pathological characteristics of breast cancer from central China. Methods: Clinico-pathological information on breast cancer from three hospitals in central China was collected and analyzed. Results: From 1994 to 2012, 2,525 patients with a median age 50 years were included in this study. The 45-49-year age group and invasive ductal carcinoma not otherwise specified accounted for the highest proportions (19.1%, 480/2,525 and 81.0%, 1,982/2,446). Stages 0-I, II and III accounted for 28.0% (682/2,441), 48.4% (1,180/2,441), and 23.7% (578/2,441), respectively. Distribution of N stage showed that N0 accounted for 53.2% (1,344/2,525), and proportion of N0 rose from 51.1% (157/307) in 30-39-year age group to 64.3% (110/171) in ${\geq}$ 70-year age group, with an average increase of 2.1% in each age group. Modified radical mastectomy, radical mastectomy, breast-conserving surgery and simple mastectomy were performed for 71.8% (1,812/2,525), 18.0% (454/2,525), 5.2% (131/2,525) and 2.6% (66/2,525), respectively. Proportions of breast-conserving surgery in age ${\leq}$ 44-year group (68/132, 51.5%) and simple mastectomy in age ${\geq}$ 60-year group (57/89, 64.0%) were higher than in the other age groups. Breast cancers positive for estrogen receptor accounted for 53.0% (1,107/ 2,112). The comparisons among this study and other reports showed higher proportion of younger patients, lower proportion of breast-conserving surgery and positive estrogen receptor patients in China than western countries. Conclusions: Clinico-pathological characteristics in this study demonstrated clear differences between the center of China than Western countries. Additional classification systems should be developed to guide grading of early breast cancer more accurately, especially for N0 patients. Invasive ductal carcinoma is a focus for intensive research.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.83-96
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• 2018

Extracting keywords representing documents is very important because it can be used for automated services such as document search, classification, recommendation system as well as quickly transmitting document information. However, when extracting keywords based on the frequency of words appearing in a web site documents and graph algorithms based on the co-occurrence of words, the problem of containing various words that are not related to the topic potentially in the web page structure, There is a difficulty in extracting the semantic keyword due to the limit of the performance of the Korean tokenizer. In this paper, we propose a method to select candidate keywords based on semantic similarity, and solve the problem that semantic keyword can not be extracted and the accuracy of Korean tokenizer analysis is poor. Finally, we use the technique of extracting final semantic keywords through filtering process to remove inconsistent keywords. Experimental results through real web pages of small business show that the performance of the proposed method is improved by 34.52% over the statistical similarity based keyword selection technique. Therefore, it is confirmed that the performance of extracting keywords from documents is improved by considering semantic similarity between words and removing inconsistent keywords.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.55-62
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• 2004

In this paper, we have developed a portable acceleration measurement system to measure acceleration signals during walking and a gait analysis algorithm which can evaluate gait regularity and symmetry and estimate gait parameters automatically. Portable acceleration measurement system consists of a biaxial accelerometer, amplifiers, lowpass filter with cut-off frequency of 16Hz, one-chip microcontroller, EEPROM and RF(TX/RX) module. The algerian includes FFT analysis, filter processing and detection of main peaks. In order to develop the algorithm, eight hemiplegic patients for training set and the other eight hemiplegic patients for test set are participated in the experiment. Acceleration signals during 10m walking were measured at 60 samples/sec from a biaxial accelerometer mounted between L3 and L4 intervertebral area. The algorithm, detected foot contacts and classified right/left steps, and then calculated gait parameters based on these informations. Compared with video data and analysis by manual, algorithm showed good performance in detection of foot contacts and classification of right/left steps in test set perfectly. In the future, with improving the reliability and ability of the algerian so that calculate more gait Parameters accurately, this system and algerian could be used to evaluate improvement of walking ability in hemiplegic patients in clinical practice.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.