• 중국학논총
• /
• no.66
• /
• pp.105-126
• /
• 2020

Complement is a special grammatical phenomenon in Chinese. There is no complement in Korean, so complement teaching is a difficulty. There are some similarities between the degree complement with "de" and the modality complement. For a long time, there has been a phenomenon of fuzzy classification in the academic circle, which also affects the teaching of Chinese as a foreign language. There are several different views on these two kinds of complements in the current textbooks. This makes students and teachers feel very confused. From the perspective of Chinese teaching, the author analyzes the characteristics, classification and grammatical significance of these two kinds of complements, and investigates the four sets of current teaching materials. It is found that: some teaching materials are confused in terms of terms and classification; some teaching materials are not fully reproduced in terms of knowledge points; the students are prone to have partial errors, and the situation of the clutch words with modal complements is ignored by most teaching materials. Some textbooks overemphasize Verb Copying in the arrangement of modal complements. Most textbooks are lack of context. The author puts forward some teaching suggestions. In class, teachers should concentrate on speaking and practicing, and focus on the types of errors that students are prone to make. The textbooks should be arranged according to the outline, the two kinds of "similar" complements should be clearly classified, the context should be added appropriately, and the textbooks compiled in South Korea can be nationalized. Students should listen carefully, participate in classroom activities and interact with teachers. Ask for advice in case of any problem you don't understand..

• Communications for Statistical Applications and Methods
• /
• v.13 no.3
• /
• pp.587-605
• /
• 2006

Recent results in applied statistics have shown that the presence of periodicities in time series may influence the estimation and testing of the fractional differencing parameter. In this article, we provide further evidence on the issue by using several procedures of fractional integration. The results show that in the presence of periodicities, the order of integration can be erroneously detected. An empirical application in the context of seasonal data is also carried out at the end of the article.

• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.470-472
• /
• 2006

Sensor networks are the results of convergence of very important technologies such as wireless communication and micro electromechanical systems. In recent years, sensor networks found a wide applicability in various fields such as environment and health, industry scene system monitoring, etc. A very important step for these many applications is pattern classification and recognition of data collected by sensors installed or deployed in different ways. But, pattern classification and recognition are sometimes difficult to perform. Systematic approach to pattern classification based on modem learning techniques like Multivariate Gaussian mixture models, can greatly simplify the process of developing and implementing real-time classification models. This paper proposes a new recognition system which is hierarchically composed of many sensor nodes having the capability of simple processing and wireless communication. The proposed system is able to perform context classification of sensed data using the Multivariate Gaussian function. In order to verify the usefulness of the proposed system, it was applied to intelligent dust collecting system.

• Journal of information and communication convergence engineering
• /
• v.21 no.1
• /
• pp.9-16
• /
• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• International journal of advanced smart convergence
• /
• v.12 no.3
• /
• pp.32-39
• /
• 2023

This paper provides a comprehensive overview of UAV classification, tracking, and detection, offering researchers a clear understanding of these fundamental concepts. It elucidates how classification categorizes UAVs based on attributes, how tracking monitors real-time positions, and how detection identifies UAV presence. The interconnectedness of these aspects is highlighted, with detection enhancing tracking and classification aiding in anomaly identification. Moreover, the paper emphasizes the relevance of simulations in the context of drones and UAVs, underscoring their pivotal role in training, testing, and research. By succinctly presenting these core concepts and their practical implications, the paper equips researchers with a solid foundation to comprehend and explore the complexities of UAV operations and the role of simulations in advancing this dynamic field.

• The Journal of Society for e-Business Studies
• /
• v.12 no.2
• /
• pp.111-132
• /
• 2007

Developing and managing Web applications are more complex than ever because of their growing functionalities, advancing Web technologies, increasing demands for integration with legacy applications, and changing content and structure. All these factors call for a more inclusive and comprehensive Web application design method. In response, we propose a context-based Web application design methodology that is based on several classification schemes including a Webpage classification, which is useful for identifying the information delivery mechanism and its relevant Web technology; a link classification, which reflects the semantics of various associations between pages; and a software component classification, which is helpful for pinpointing the roles of various components in the course of design. The proposed methodology also incorporates a unique Web application model comprised of a set of information clusters called compendia, each of which consists of a theme, its contextual pages, links, and components. This view is useful for modular design as well as for management of ever-changing content and structure of a Web application. The proposed methodology brings together all the three classification schemes and the Web application model to arrive at a set of both semantically cohesive and syntactically loose-coupled design artifacts.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.15 no.2
• /
• pp.37-52
• /
• 2005

Traditional access control models like role-based access control model are insufficient in security needs in ubiquitous computing environment because they take no thought of access control based on user's context or environment condition. In these days, although researches on context-aware access control using user's context or environment conditions based on role-based access control are emerged, they are on the primary stage. We present context definitions md an access control model to provide more flexible and dynamic context-aware access control based on role-based access control. Specially, we describe the conflict problems occurred in the middle of making an access decision. After classifying the conflict problems, we show some resolutions to solve them. In conclusion, we will lay the foundations of the development of security policy and model assuring right user of right object(or resource) and application service through pre-defined context and context classification in ubiquitous computing environments. Beyond the simplicity of access to objects by authorized users, we assure that user can access to the object, resource, or service anywhere and anytime according to right context.

• International Journal of Computer Science & Network Security
• /
• v.22 no.5
• /
• pp.143-148
• /
• 2022

Brain tumor classification is an important process that allows doctors to plan treatment for patients based on the stages of the tumor. To improve classification performance, various CNN-based architectures are used for brain tumor classification. Existing methods for brain tumor segmentation suffer from overfitting and poor efficiency when dealing with large datasets. The enhanced CNN architecture proposed in this study is based on U-Net for brain tumor segmentation, RefineNet for pattern analysis, and SegNet architecture for brain tumor classification. The brain tumor benchmark dataset was used to evaluate the enhanced CNN model's efficiency. Based on the local and context information of the MRI image, the U-Net provides good segmentation. SegNet selects the most important features for classification while also reducing the trainable parameters. In the classification of brain tumors, the enhanced CNN method outperforms the existing methods. The enhanced CNN model has an accuracy of 96.85 percent, while the existing CNN with transfer learning has an accuracy of 94.82 percent.

• Communications for Statistical Applications and Methods
• /
• v.15 no.4
• /
• pp.543-549
• /
• 2008

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

• International conference on construction engineering and project management
• /
• 2015.10a
• /
• pp.333-335
• /
• 2015

A computer vision-based scene recognition algorithm is proposed for monitoring construction sites. The system analyzes images acquired from a surveillance camera to separate regions and classify them as building, ground, and hole. Mean shift image segmentation algorithm is tested for separating meaningful regions of construction site images. The system would benefit current monitoring practices in that information extracted from images could embrace an environmental context.