• The KIPS Transactions:PartB
• /
• v.9B no.3
• /
• pp.375-382
• /
• 2002

Handwritten character recognition is classified into on-line handwritten character recognition and off-line handwritten character recognition. On-line handwritten character recognition has made a remarkable outcome compared to off-line hacdwritten character recognition. This method can acquire the dynamic written information such as the writing order and the position of a stroke by means of pen-based electronic input device such as a tablet board. On the contrary, Any dynamic information can not be acquired in off-line handwritten character recognition since there are extreme overlapping between consonants and vowels, and heavily noisy images between strokes, which change the recognition performance with the result of the preprocessing. This paper proposes a method that effectively extracts the stroke including dynamic information of characters for off-line Korean handwritten character recognition. First of all, this method makes improvement and binarization of input handwritten character image as preprocessing procedure using watershed algorithm. The next procedure is extraction of skeleton by using the transformed Lu and Wang's thinning: algorithm, and segment pixel array is extracted by abstracting the feature point of the characters. Then, the vectorization is executed with a maximum permission error method. In the case that a few strokes are bound in a segment, a segment pixel array is divided with two or more segment vectors. In order to reconstruct the extracted segment vector with a complete stroke, the directional component of the vector is mortified by using right-hand writing coordinate system. With combination of segment vectors which are adjacent and can be combined, the reconstruction of complete stroke is made out which is suitable for character recognition. As experimentation, it is verified that the proposed method is suitable for handwritten Korean character recognition.

• Journal of Korean Society for Geospatial Information Science
• /
• v.25 no.1
• /
• pp.9-17
• /
• 2017

Hyperspectral imagery is used in the land cover classification with the principle component analysis and minimum noise fraction to reduce the data dimensionality and noise. Recently, studies on the supervised classification using various features having spectral information and spatial characteristic have been carried out. In this study, principle component bands and normalized difference vegetation index(NDVI) was utilized in the supervised classification for the land cover classification. To utilize additional information not included in the principle component bands by the hyperspectral imagery, we tried to increase the classification accuracy by using the NDVI. In addition, the extended attribute profiles(EAP) generated using the morphological filter was used as the input data. The random forest algorithm, which is one of the representative supervised classification, was used. The classification accuracy according to the application of various features based on EAP was compared. Two areas was selected in the experiments, and the quantitative evaluation was performed by using reference data. The classification accuracy of the proposed algorithm showed the highest classification accuracy of 85.72% and 91.14% compared with existing algorithms. Further research will need to develop a supervised classification algorithm and additional input datasets to improve the accuracy of land cover classification using hyperspectral imagery.

• Journal of KIISE:Information Networking
• /
• v.32 no.3
• /
• pp.318-326
• /
• 2005

This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.16 no.4 s.95
• /
• pp.389-396
• /
• 2005

In this paper, we propose a pre-distortion method using the NLMS(Normalized Least Mean Square) algorithm to cope with hish PAPR(Peak to Average Power Ratio) problem in OFDM communication system. This proposed scheme estimates the distortion characteristics of HPA, and changes the characteristic against the distortion. Therefore, it can be shown that the adaptive characteristic of the NLMS pre-distorter is good to track the various nonlinear characteristic of HPA, even though HPA characteristic is changed by temperature variation or aging. From the performance analysis, SNR efficiency of NLMS pre-distorter is about $0.5\;\cal{dB}$ less than that of common numerical non-adaptive pre-distorter, when IBO(Input Back Off) is $0\;\cal{dB}$. However, the NLMS pre-distorter is better than the common numerical pre-distorter, because these two pre-distorters have similar performance in higher than $3\;\cal{dB}$ IBO, and the NLMS pre-distorter maintains the constant performance even though characteristic of HPA is changed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.5
• /
• pp.960-966
• /
• 2017

In this paper, we proposed an algorithm that can extract lesion by inputting a medical image. Feature points are extracted using SIFT algorithm to extract genetic training of medical image. To increase the intensity of the feature points, the input image and that raining image are matched using vector similarity and the lesion is extracted. The vector similarity match can quickly lead to lesions. Since the direction vector is generated from the local feature point pair, the direction itself only shows the local feature, but it has the advantage of comparing the similarity between the other vectors existing between the two images and expanding to the global feature. The experimental results show that the lesion matching error rate is 1.02% and the processing speed is improved by about 40% compared to the case of not using the feature point intensity information.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.5D
• /
• pp.639-647
• /
• 2008

The objective of this research is to establish the relationship between the important variables necessary for real-time available parking space presumption algorithm for a parking lot information provision. So it investigated the variables that come relate to the algorithm and collected parking information for an algorithm plan. It analyzed variables inciuding average number of parked cars, average parked time, rate of turnover, occupancy, cumulative number of parked cars, and etc. of the parking lot. And it collected data regarding illegal parking, double space parking, searching car and it presumed the number of available parking spaces. As a result of the research, It appeared that the achieved accuracy is superier to existing provision system which only take the total input and output numbers. This will help drivers' judgement, and also the parking lot operation and management.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.5
• /
• pp.1225-1231
• /
• 2018

FPGA-based system development is being developed as a form of outsourcing that shortens the development time and reduces the cost. Through the process, the risk of letting the hardware Trojan, which causes malfunctions, seep into the system also increases. Various detection methods are proposed for the issue; however, such type of hardware Trojans is inserted by modifying a bitstream directly and therefore, it is hard to detect with the suggested methods. To detect the type of hardware Trojans, it is essential to reverse-engineer the electric circuit implemented by bitstream to a distinguishable level. Specifically, it is important to reverse-engineer the routing information of the circuit that can identify the input-output flow of the signal. In this paper, we analyze the BIL bitstream reverse-engineering tool-chain that uses the algorithm, which retrieves the routing information from FPGA bitstream, and suggest the method to improve the tool-chain.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.12
• /
• pp.3836-3854
• /
• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.4
• /
• pp.1025-1034
• /
• 1997

Inference process of resolution-based automated reasoning easily consumes the memory of computer without giving any useful result by priducing lots of fruioless information which are not necessary for the conslusion. This paper suggests a control strategy for saving the space of computer memory and reducing the inference time. The strategy uses a restriction that comparatively irrelevant axioms do mot take pare in the resoluition. In order to analyze and determine the priorities of the input axioms of joning the inference process, the system employs the fuzzy relational products.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.758-761
• /
• 2003

This paper introduces a method that can detect obstacles and corridor environments from the images captured by a CCD camera in an automobile or mobile robot is proposed. Processing the input dynamic images in real time requires high performance hardware as well as efficient software. In order to relieve these requirements for detecting the useful information from the images in real time, a "Moving Window" scheme is proposed. Therefore, detecting the useful information, it becomes possible to search the obstacles within the driving corridor of an automobile or mobile robot. The feasibility of the proposed algorithm is demonstrated through the simulated experiments of the corridor driving.