• Journal of Service Research and Studies
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• v.5 no.2
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• pp.103-110
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• 2015

Recently, Classification of characters in a 3D FPS game has emerged as a very significant issue. In this study, We propose the game character Classification method using Image Region Segmentation of the extracting meaningful object in a simple operation. In this method, first used a non-linear RGB color model and octree color quantization scheme. The input image represented a less than 20 quantized color and uses a small number of meaningful color histogram. And then, the image divided into small blocks, calculate the degree of similarity between the color histogram intersection and adjacent block in block units. Because, except for the block boundary according to the texture and to extract only the boundaries of the object block. Set a region by these boundary blocks as a game object and can be used for FPS game play. Through experiment, we obtain accuracy of more than 80% for Classification method using each feature. Thus, using this property, characters could be classified effectively and it draws the game more speed and strategic actions as a result.

• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.904-911
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• 2003

In this paper, we propose an effective method for the distinction between machine-printed and handwritten Korean address images. It is important to know whether an input image is handwritten or machine-printed, because methods for handwritten image are quite different from those of machine-printed image in such applications as address reading, form processing, FAX routing, and so on. Our method consists of three blocks: valid connected components grouping, feature extraction, and classification. Features related to width and position of groups of valid connected components are used for the classification based on a neural network. The experiment done with live Korean address images has demonstrated the superiority of the proposed method. The correct classification rate for 3,147 testing images was about 98.85%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.830-841
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• 2000

We extend the sue of the method of least square to develop a recursive algorithm for the design of adaptive transversal filters such that, given the least-square estimate of this vector of the filter at iteration n-1, we may compute the updated estimate of this vector at iteration a upon the arrival of new data. We begin the development of the RLS algorithm by reviewing some basic relations that pertain to the method of least squares. Then, by exploiting a relation in matrix algebra known as the matrix inversion lemma, we develop the RLS algorithm. An important feature of the RLS algorithm is that it utilizes information contained in the input data, extending back to the instant of time when the algorithm is initiated. In this paper, we propose new tap weight updated RLS algorithm in adaptive transversal filter with data-recycling buffer structure. We prove that convergence speed of learning curve of RLS algorithm with data-recycling buffer is faster than it of exiting RL algorithm to mean square error versus iteration number. Also the resulting rate of convergence is typically an order of magnitude faster than the simple LMS algorithm. We show that the number of desired sample is portion to increase to converge the specified value from the three dimension simulation result of mean square error according to the degree of channel amplitude distortion and data-recycle buffer number. This improvement of convergence character in performance, is achieved at the (B+1)times of convergence speed of mean square error increase in data recycle buffer number with new proposed RLS algorithm.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.573-582
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• 2020

Fuzzing is a method of testing software by randomly generating test cases. Since its introduction, a variety of fuzzing techniques have been studied. Among them, mutation-based fuzzing is an efficient method that finds real-world bugs even though it uses a simple approach such as probabilistic bit-flipping and character substitution. However, the interpreter fuzzing has difficulty in applying general mutation techniques because the interpreter requires grammar and semantic correctness input values. In this paper, we present a novel mutation-based fuzzing on JavaScript interpreters with a dynamic data flow analysis. To this end, we implement JMFuzzer that can generate various types of mutated test cases that operate normally without runtime errors in JavaScript interpreter considering syntax and semantics. As a result, we found numerous unknown vulnerabilities in the latest JavaScript interpreters. We reported all of them to the vendors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.556-563
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• 2006

We should extract lines and data which consist of the table in order to classify the table region and analyze its structure in document image. But it is difficult to extract lines and data exactly because the lines are cut and their lengths are changed, or characters or noises are merged to the table lines. These problems result from the error of image input device or image reduction. In this paper, we propose the better method of extracting lines and data for table region classification and structure analysis than the previous ones including commercial softwares. The prposed method extracts horizontal and vertical lines which consist of the table by the use of one dimensional median filter. This filter not only eliminates the noises which attach to the line and the lines which are orthogonal to the filtering direction, but also connects the cut line of which the gap is shorter than the length of the filter tap in the process of extracting lines to the filtering direction. Furthermore, texts attached to the line are separated in the process of extracting vertical lines. This is an example of ABSTRACT format.

• The Journal of Korean Physical Therapy
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• v.5 no.1
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• pp.109-114
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• 1993

The originator of the proprioceptive neuromuscular facilitation method was Dr. Herman Kabat, a man who received the bachelor of science degree from New York University in 1932. In 1936 he moved to the University of Minnesota where he served as instructor in physiology and also studies medicine. He received his medical doctorate in 1942. When Dr. Karbat meet Sister Kenny suggested that certain change. She does not receptive his ideas. So that he decided to pursure the treatment of patients. Upon the establishment of the Karbat- kaiser Institute to be opened in 1946. Margaret Knott, the first physical therapist to be employed by him and to become his head physical therapist. In 1948 Vallejo center was opened. Dr. Kabat developed the PNF method combined motions to ascertain the effectiveness of maximal resistance and stretch in facilitating the response of a weak distal muscle. He identified mass movement patterns that were spiral and diagonal in character in 1965. Margaret Knott presented lecture at tile APTA Annual Conference in Las Vegas. The title was In the groove. On December 18, 1978 she passed away at her home in Vallejo. Marie-Louise Mangold is director of the Kaiser Foundation Rehabilitation Center now. She is the Vice President of International Proprioceptive Neuromuscular facilitation Association. About 20 physical therapist working teaching and study at KFRC in Vallejo. PNF neuromuscular mechanism becomes integrated and efficient without awareness of individual muscle action, reflex and a multitude of other neurophysiological reactions. The principles of PNF are visual consideration, verbal consideration, and proprioceptive input consideration with tactile stimulation, joint receptors, appropriate facilitation, stretch reflex normal timing, irradiation, pattern of movement.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.71-80
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• 1999

Sign-language communication can be a useful way of exchanging message between people who using different languages. In this paper, we report an experimental survey on the construction of a Korean-Japanese sign-language communication system using 3D model. For real-time communication, we introduced an intelligent communication method and built the system as a client-server architecture on the Internet. A character model is stored previously in the clients and a series of animation parameters are sent instead of real image data. The input-sentence is converted into a series of parameters of Korean sign language or Japanese sign language at server. The parameters are transmitted to clients and used for generating the animation. We also employ the emotional expressions, variable frames allocation method, and a cubic spline interpolation for the purpose of enhancing the reality of animation. The proposed system is implemented with Visual $C^{++}$ and Open Inventor library on Windows platform. Experimental results show a possibility that the system could be used as a non-verbal communication means beyond the linguistic barrier.

• Smart Media Journal
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• v.10 no.2
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• pp.30-37
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• 2021

Recently, 3D contents used in various fields have been attracting people's attention due to the development of virtual reality and augmented reality technology. In order to produce 3D contents, it is necessary to model the objects as vertices. However, high-quality modeling is time-consuming and costly. In order to convert a 2D character into a 3D model, it is necessary to express it as line drawings through feature line extraction. The extraction of consistent line drawings from 2D cartoon cartoons is difficult because the styles and techniques differ depending on the designer who produces them. Therefore, it is necessary to extract the line drawings that show the geometrical characteristics well in 2D cartoon shapes of various styles. This study proposes a method of automatically extracting line drawings. The 2D Cartoon shading image and line drawings are learned by using adversarial network model, which is artificial intelligence technology and outputs 2D cartoon artwork of various styles. Experimental results show the proposed method in this research can be obtained as a result of the line drawings representing the geometric characteristics when a 2D cartoon painting as input.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3991-4010
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• 2021

The Question Similarity Measurement of Chinese Crop Diseases and Insect Pests (QSM-CCD&IP) aims to judge the user's tendency to ask questions regarding input problems. The measurement is the basis of the Agricultural Knowledge Question and Answering (Q & A) system, information retrieval, and other tasks. However, the corpus and measurement methods available in this field have some deficiencies. In addition, error propagation may occur when the word boundary features and local context information are ignored when the general method embeds sentences. Hence, these factors make the task challenging. To solve the above problems and tackle the Question Similarity Measurement task in this work, a corpus on Chinese crop diseases and insect pests(CCDIP), which contains 13 categories, was established. Then, taking the CCDIP as the research object, this study proposes a Chinese agricultural text similarity matching model, namely, the AgrCQS. This model is based on mixed information extraction. Specifically, the hybrid embedding layer can enrich character information and improve the recognition ability of the model on the word boundary. The multi-scale local information can be extracted by multi-core convolutional neural network based on multi-weight (MM-CNN). The self-attention mechanism can enhance the fusion ability of the model on global information. In this research, the performance of the AgrCQS on the CCDIP is verified, and three benchmark datasets, namely, AFQMC, LCQMC, and BQ, are used. The accuracy rates are 93.92%, 74.42%, 86.35%, and 83.05%, respectively, which are higher than that of baseline systems without using any external knowledge. Additionally, the proposed method module can be extracted separately and applied to other models, thus providing reference for related research.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.30-36
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• 2000

The hidden node plays a role of the functional units that classifies the features of input pattern in the given question. Therefore, a neural network that consists of the number of a suitable optimum hidden node has be on the rise as a factor that has an important effect upon a result. However there is a problem that decides the number of hidden nodes based on back-propagation learning algorithm. If the number of hidden nodes is designated very small perfect learning is not done because the input pattern given cannot be classified enough. On the other hand, if designated a lot, overfitting occurs due to the unnecessary execution of operation and extravagance of memory point. So, the recognition rate is been law and the generality is fallen. Therefore, this paper suggests a method that decides the number of neural network node with feature information consisted of the parameter of learning algorithm. It excludes a node in the Pruning target, that has a maximum value among the feature value obtained and compares the average of the rest of hidden node feature value with the feature value of each hidden node, and then would like to improve the learning speed of neural network deciding the optimum structure of the multi-layer neural network as pruning the hidden node that has the feature value smaller than the average.