• Journal of Intelligence and Information Systems
• /
• v.12 no.1
• /
• pp.1-15
• /
• 2006

In Korea, a resident registration card has various personal information such as a present address, a resident registration number, a face picture and a fingerprint. A plastic-type resident card currently used is easy to forge or alter and tricks of forgery grow to be high-degree as time goes on. So, whether a resident card is forged or not is difficult to judge by only an examination with the naked eye. This paper proposed an automatic recognition method of a resident card which recognizes a resident registration number by using a refined ART2-based RBF network newly proposed and authenticates a face picture by a template image matching method. The proposed method, first, extracts areas including a resident registration number and the date of issue from a resident card image by applying Sobel masking, median filtering and horizontal smearing operations to the image in turn. To improve the extraction of individual codes from extracted areas, the original image is binarized by using a high-frequency passing filter and CDM masking is applied to the binaried image fur making image information of individual codes better. Lastly, individual codes, which are targets of recognition, are extracted by applying 4-directional contour tracking algorithm to extracted areas in the binarized image. And this paper proposed a refined ART2-based RBF network to recognize individual codes, which applies ART2 as the loaming structure of the middle layer and dynamicaly adjusts a teaming rate in the teaming of the middle and the output layers by using a fuzzy control method to improve the performance of teaming. Also, for the precise judgement of forgey of a resident card, the proposed method supports a face authentication by using a face template database and a template image matching method. For performance evaluation of the proposed method, this paper maked metamorphoses of an original image of resident card such as a forgey of face picture, an addition of noise, variations of contrast variations of intensity and image blurring, and applied these images with original images to experiments. The results of experiment showed that the proposed method is excellent in the recognition of individual codes and the face authentication fur the automatic recognition of a resident card.

• The Transactions of the Korea Information Processing Society
• /
• v.1 no.2
• /
• pp.225-236
• /
• 1994

Many research result conducted by neural network researchers claimed that the degree of generalization of the neural network system is higher or at least equal to that of statistical methods. However, those successful results could be brought only if the neural network was trained by appropriately sound data, having a little of noisy data and being large enough to control noisy data. Real data used in a lot of fields, especially business fields, were not so sound that the network have frequently failed to obtain satisfactory prediction accuracy, the degree of generalization. Enhancing the degree of generalization with noisy data is discussed in this study. The suggestion, which was obtained through a series of experiments, to enhance the degree of generalization is to remove inconsistent data by checking overlapping and inconsistencies. Furthermore, the previous conclusion by other reports is also confirmed that the learning mechanism of neural network takes average value of two inconsistent data included in training set［2］. The interim results of on-going research project are reported in this paper These are ann architecture of the neural network adopted in this project and the whole idea of developing on-line credit evaluation system,being intergration of the expert(resoning)system and the neural network(learning system.Another definite result is corroborated through this study that quickprop,being agopted as a learing algorithm, also has more speedy learning process than does back propagation even in very noisy environment.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.23 no.4
• /
• pp.415-420
• /
• 2017

In this paper, an algorithm for object counting via a conveyor system using machine vision is suggested. Object counting systems using image processing have been applied in a variety of industries for such purposes as measuring floating populations and traffic volume, etc. The methods of object counting mainly used involve template matching and machine learning for detecting and tracking. However, operational time for these methods should be short for detecting objects on quickly moving conveyor belts. To provide this characteristic, this algorithm for image processing is a region-based method. In this experiment, we counted young abalone shells that are similar in shape, size and color. We applied a characteristic conveyor system that operated in one direction. It obtained information on objects in the region of interest by comparing a second frame that continuously changed according to the information obtained with reference to objects in the first region. Objects were counted if the information between the first and second images matched. This count was exact when young shells were evenly spaced without overlap and missed objects were calculated using size information when objects moved without extra space. The proposed algorithm can be applied for various object counting controls on conveyor systems.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.8
• /
• pp.727-739
• /
• 2003

In this paper, we propose a generation method of regression classes for adaptation in the HM-Net (Hidden Markov Network) system. The MLLR (Maximum Likelihood Linear Regression) adaptation approach is applied to the HM-Net speech recognition system for expressing the characteristics of speaker effectively and the use of HM-Net in various tasks. For the state level sharing, the context domain state splitting of PDT-SSS (Phonetic Decision Tree-based Successive State Splitting) algorithm, which has the contextual and time domain clustering, is adopted. In each state of contextual domain, the desired phoneme classes are determined by splitting the context information (classes) including target speaker's speech data. The number of adaptation parameters, such as means and variances, is autonomously controlled by contextual domain state splitting of PDT-SSS, depending on the context information and the amount of adaptation utterances from a new speaker. The experiments are performed to verify the effectiveness of the proposed method on the KLE (The center for Korean Language Engineering) 452 data and YNU (Yeungnam Dniv) 200 data. The experimental results show that the accuracies of phone, word, and sentence recognition system increased by 34∼37%, 9%, and 20%, respectively, Compared with performance according to the length of adaptation utterances, the performance are also significantly improved even in short adaptation utterances. Therefore, we can argue that the proposed regression class method is well applied to HM-Net speech recognition system employing MLLR speaker adaptation.

• The Journal of the Korea Contents Association
• /
• v.15 no.4
• /
• pp.29-44
• /
• 2015

Digital Signage, a way of advertising or delivering information to viewers through digital displays, has expanded from being just an advertising channel in public places. Recently, it has become widely prevalent in restaurants and retail stores. Despite its wide expansion, digital signage is limited to specific usages and services and the devices it uses are also quite expensive. This study introduces a stick-type digital signage product that operates on Android OS, which addresses all the weaknesses of digital signage with much more reasonable pricing and stable operation. For stability, performance tests were executed on the hardware and applications. The results for hardware performance were extremely promising, as each scenario's maximum performance results, measured by Load Runner programs, reached target indexes. Also, as a result of the usability test, all participants, including non-digital signage system users (novices), were able to easily learn all the tasks. As a result of user satisfaction survey, positive responses were exhibited for ease of learning and usability (LEU), helpfulness and problem solving capabilities (HPSC), affective aspect and multimedia properties (AAMP), commands and minimal memory load (CMML), and control and efficiency (CE).

• Journal of the Korean Society for information Management
• /
• v.37 no.1
• /
• pp.51-78
• /
• 2020

Recently, the emphasis on self-directed learning and lifelong education is increasing the importance of school libraries in the curriculum. Accordingly, various studies have been conducted mainly from a structural, institutional and operational point of view. However, more research is necessary on the micro topics such as school library users' autonomous intrinsic motivations in the sense that school libraries play key roles in autonomy-based self-directed education. This study aims at finding out what types of school library use motivations are more important and the degree to which the use motivations affect the school library service quality based on the self-determination theory. In addition, this study examines how the use motivations and the perceived service quality vary depending on the school grade of the library users. Based on a focus-group-interview pilot study, a questionnaire survey was administered on the effects of school library motivations on perceived library service quality to 588 students from 5 high schools and 2 middle schools in Seoul. When the service quality and its components, service affect, information control, and library as place were set as dependent variables, in all these four cases, intrinsic motivations were more significant than extrinsic motivations. In addition, when middle school students and high school students were selected as separate analysis target groups, the results of both analyses show that the higher the intrinsic motivations were, the higher the perceived service quality was. The contribution of this study is that it applies the self-determination theory to school library service, measures the influence of motivation type based on the theoretical basis, and focuses on micro aspects to improve school library services.

• Journal of The Korean Association For Science Education
• /
• v.36 no.2
• /
• pp.325-335
• /
• 2016

Software (SW) education is guided by the government to operate not only computer subject matter but also related subject matter. SW education is highlighted in the 2015 Revised Curriculum and Guide for Operating SW Education. SW education is related with science education. For example, education on algorithms employing SW and activities using sensors/output control can be an effective strategy for scientific inquiry. The method can also be applied in developing Computational Thinking (CT) in students. In this study, we designed lessons to solve everyday scientific problems using Educational Programming Language (EPL) SW and physical computing materials and applied them to high school students. We conducted surveys that were modified from questionnaires of Internet application capability and based on the standard of accomplishment of SW education as well as elements of CT to find out the change in perceptions on programming and CT of students. We also conducted a survey on students' attitude toward science learning after an SW inquiry activity. In the results, perceptions on programming and CT of students were improved through lessons using unplugged activity, EPL SW, and physical computing. In addition, scores for interest, self-directed learning ability, and task commitment were high.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.31 no.5B
• /
• pp.405-414
• /
• 2011

The adaptive network-based fuzzy inference system (ANFIS) which had a success for time series prediction and system control was applied for modeling the hourly runoff in the Gapcheon watershed. The ANFIS used the antecedent rainfall and runoff as the input. The ANFIS was trained by varying the various simulation factors such as mean areal rainfall estimation, the number of input variables, the type of membership function and the number of membership function. The root mean square error (RMSE), mean peak runoff error (PE), and mean peak time error (TE) were used for validating the ANFIS simulation. The ANFIS predicted runoff was in good agreement with the measured runoff and the applicability of ANFIS for modelling the hourly runoff appeared to be good. The forecasting ability of ANFIS up to the maximum 8 lead hour was investigated by applying the different input structure to ANFIS model. The accuracy of ANFIS for predicting the hourly runoff was reduced as the forecasting lead hours increased. The long-term predictability of ANFIS for forecasting the hourly runoff at longer lead hours appeared to be limited. The ANFIS might be useful for modeling the hourly runoff and has an advantage over the physically based models because the model construction of ANFIS based on only input and output data is relatively simple.

• The Journal of the Korea Contents Association
• /
• v.13 no.2
• /
• pp.19-31
• /
• 2013

Since the high readable source codes help us to understand and modify the program, it is much easy to maintain them. The readability of source code is not only affected by the complexity of algorithms such as control structures but also affected by the coding styles such as naming and indentation. Although various coding standards have been presented for promoting the readability of source codes, it has been usually lost or ignored in a programming course. One of the reasons is that the coding standard is not a hard-and-false rule since it does not contribute to the performance of software. In this paper, we propose a simple automatic system, namely StyleJigsaw, which checks the style of the source codes written by C/C++ or Java. In this system, the coding style score is calculated and visualized as a jigsaw puzzle. To measure the educational effectiveness of StyleJigsaw, several experiments have been conducted on a class students in C++ programming course. According to the experimental results, the coding style score increased about 8.0 points(10.9%) on average using StyleJigsaw. Further, according to a questionnaire survey targeting the students who attended the programming course, about 88.5% of the students responded that StyleJigsaw was of help to learn the coding standards. We expect that the StyleJigsaw can be effectively used to encourage the students to obey the coding standards, resulting in high readable programs.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.3
• /
• pp.43-54
• /
• 1997

Knowledge acquisition is one of the most difficult problems in designing fuzzy systems. As application domains of fuzzy systems become larger and more complex, it is more difficult to find the relations among the system's input- outpiit variables. Moreover, it takes a lot of efforts to formulate expert's knowledge about complex systems' control actions by linguistic variables. Another difficulty is to define and adjust membership functions properly. Soin conventional fuzzy systems, the membership functions should be adjusted to improve the system performance. This is time-consuming process. In this paper, we suggest a new approach to design a fuzzy system. We design a fuzzy system using two neural networks, Kohonen neural network and backpropagation neural network, which generate fuzzy rules automatically and construct inference network. Since fuzzy inference is performed based on fuzzy relation in this approach, we don't need the membership functions of each variable. Therefore it is unnecessary to define and adjust membership functions and we can get fuzzy rules automatically. The design process of fuzzy system becomes simple. The proposed approach is applied to a simulated automatic car speed control system. We can be sure that this approach not only makes the design process of fuzzy systems simple but also produces appropriate inference results.