• Convergence Security Journal
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• v.12 no.3
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• pp.99-106
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• 2012

Today, the typical web hacking attacks are cross-site scripting(XSS) attacks, injection vulnerabilities, malicious file execution and insecure direct object reference included. Web hacking security systems, access control solutions, access only to the web service and flow inside but do not control the packet. So you have been illegally modified to pass the packet even if the packet is considered as a unnormal packet. The defense system is to fail to appropriate controls. Therefore, in order to ensure a successful web services diagnostic system development is necessary. Web application diagnostic system is real and urgent need and alternative. The diagnostic system development process mu st be carried out step of established diagnostic systems, diagnostic scoping web system vulnerabilities, web application, analysis, security vulnerability assessment and selecting items. And diagnostic system as required by the web system environment using tools, programming languages, interfaces, parameters must be set.

• Smart Media Journal
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• v.5 no.1
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• pp.24-29
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• 2016

To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.10
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• pp.487-492
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• 2017

Clustering is a technique which is used to measure similarities between data in big data analysis and data mining field. Among various clustering methods, k-Modes algorithm is representatively used for categorical data. To increase the performance of iterative-centric tasks such as k-Modes, a distributed and concurrent framework Spark has been received great attention recently because it overcomes the limitation of Hadoop. Spark provides an environment that can process large amount of data in main memory using the concept of abstract objects called RDD. Spark provides Mllib, a dedicated library for machine learning, but Mllib only includes k-means that can process only continuous data, so there is a limitation that categorical data processing is impossible. In this paper, we design RDD for k-Modes algorithm for categorical data clustering in spark environment and implement an algorithm that can operate effectively. Experiments show that the proposed algorithm increases linearly in the spark environment.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.43-52
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• 2014

In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.882-897
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• 2020

Recently, the use of immersive media contents representing Panorama and 360° video is increasing. Since the viewing angle is limited to generate the content through a general camera, image stitching is mainly used to combine images taken with multiple cameras into one image having a wide field of view. However, if the parallax between the cameras is large, parallax distortion may occur in the stitched image, which disturbs the user's content immersion, thus an image stitching overcoming parallax distortion is required. The existing Seam Optimization based image stitching method to overcome parallax distortion uses energy function or object segment information to reflect the location information of objects, but the initial seam generation location, background information, performance of the object detector, and placement of objects may limit application. Therefore, in this paper, we propose an image stitching method that can overcome the limitations of the existing method by adding a weight value set differently according to the type of object to the energy value using object detection based on deep learning.

• (The)Study of the Eastern Classic
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• no.36
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• pp.85-104
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• 2009

This study examines a study of communication factor in Lunyu, as a communication text, in terms of communicator, audience, message, communication factor, communication text, interdisciplinary research. In many respects, it is generally accepted that Lunyu have been the generic references of the Oriental culture. Lunyu consider ethics, logic, and practicability as the qualifying requirement of communicator, asserting that communicator should speak true language, like a "chuntzu"(君子) does, and should also put their language into practice. The audience's attitude and method as contained in Lunyu are that hearers should have sharp ears for language, hear selectively the right language, and use the language suitable to the situation. It is also emphasized that the Hearer should actively lead in the situation of transactional communications. In Lunyu, one property of message is that language, which determines the rise and fall of a nation and is also the basis of judgement for other people, should comply with ethics and reasons and sould also be put into practice. In other words, credible message, as the practice of language, is the practical requirement of ethics and the qualification of a "chuntzu"(君子, superior man) in ruling the nation or conducting one's life.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.807-812
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• 2021

In order to be able to interact with the user to experience it as if it were real in virtual reality contents, input/output devices that make them feel the five senses of humans are required . In virtual reality (VR), devices that stimulate sight and hearing are the most representative. For a more realistic experience, suits and gloves that stimulate the sense of touch have recently been released, but there are not many cases applied to actual contents due to the limitation of device . In this paper, we analyze a virtual reality glove that can detect hand movement and touch in a virtual world. Based on the analysis, we propose an algorithm that can sense the intensity of collision with a VR object by tactile sense by improving the UI/UX using the vibration of the feedback method used in the existing virtual reality glove. In addition, the system implemented by the algorithm is applied to an actual case.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.389-396
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• 2022

Deep Learning is based on a perceptron, and is currently being used in various fields such as image recognition, voice recognition, object detection, and drug development. Accordingly, a variety of learning algorithms have been proposed, and the number of neurons constituting a neural network varies greatly among researchers. This study analyzed the learning characteristics according to the number of neurons of the currently used SGD, momentum methods, AdaGrad, RMSProp, and Adam methods. To this end, a neural network was constructed with one input layer, three hidden layers, and one output layer. ReLU was applied to the activation function, cross entropy error (CEE) was applied to the loss function, and MNIST was used for the experimental dataset. As a result, it was concluded that the number of neurons 100-300, the algorithm Adam, and the number of learning (iteraction) 200 would be the most efficient in deep learning learning. This study will provide implications for the algorithm to be developed and the reference value of the number of neurons given new learning data in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.571-576
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• 2022

Due to the epochal development of the unmanned technology, the importance of LDT(: Location Detection Technology), which accurately estimates the location of a user or object, is dramatically increased. TOA(: Time of Arrival), which calculates a location by measuring the arrival time of signals, and TDOA(: Time Difference of Arrival) which calculates it by measuring the difference between two arrival times, are representative LDT methods. Based on the signals received from three or more base stations, TOA calculates an intersection point by drawing circles and TDOA calculates it by drawing hyperbolas. In order to improve the radio shadow area problem, a huge number of repeaters have been installed in the urban area, but the signals received through these repeaters may cause the serious error for estimating a location. In this paper, we propose an efficient location estimation technique using the signal received through the repeater. The proposed approach estimates the location of MS(: Mobile Station) employing TOA and TDOA methods, based on signals received from one repeater and two BS(: Base Station)s.