• KIPS Transactions on Computer and Communication Systems
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• v.7 no.7
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• pp.183-194
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• 2018

With the rapid development of IoT(Internet of Things) technology, various convenient services such as smart home and smart city have been realized. However, IoT devices in unmanned environments are exposed to various security threats including eavesdropping and data forgery, information leakage due to unauthorized access. To build a secure IoT environment, it is necessary to use proper cryptographic technologies to IoT devices. But, it is impossible to apply the technologies applied in the existing IT environment, due to the limited resources of the IoT devices. In this paper, we survey the classification of IoT devices according to the performance and analyze the security requirements for IoT devices. Also we survey and analyze the use of cryptographic technologies in the current status of IoT open standard platform such as AllJoyn, oneM2M, IoTivity. Based on the research of cryptographic usage, we examine whether each platform satisfies security requirements. Each IoT open platform provides cryptographic technology for supporting security services such as confidentiality, integrity, authentication an authorization. However, resource constrained IoT devices such as blood pressure monitoring sensors are difficult to apply existing cryptographic techniques. Thus, it is necessary to study cryptographic technologies for power-limited and resource constrained IoT devices in unattended environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.124-131
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• 2009

A reliable detection of regions in radiography is one of the most important task before the evaluation of defects on welded joints. The extracted features is to be classified into distinctive clusters for each segmented region. But conventional segmentation techniques give unsatisfactory results for this task due to the spatial superposition of intensity and low signal-to-ratio(SNR) in radiographic images. The usage of global or local processes not only provide the necessary noise resistance but also fail in classification of regions. In this paper, we presents an appropriate approach for segmentation of region-based indications in industrial Computed Radiography(CR) images. The geometric differences between welded and non-welded area which is generated on radiography as the representative regions(background, thickness, middle and welded region in steel tube image) have constructed the hierarchical structure. Although this structure is contaminated by noise, the scheme between regions can be selected by the help of local clustering based on distinctive geometric property of each region. Because of the geometric nature of the considered region and so that the region is selected layer by layer, and that the real class represents the boundary between regions, the vertical and horizontal clustering process in each layer must be judicious. In order to show the effectiveness of this approach, a comparative experiment of various segmentation method is performed on industrial steel tube CR images.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.215-229
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• 2020

In response to the recent 4th industrial revolution, the demand for 3D object models in the latest fields of digital twin, autonomous driving, and VR/AR, as well as the existing fields such as city, construction, transportation, and energy has increased significantly. It is expected that the demand for 3D object models with various precision from LOD1 to LOD4 will increase more and more in various industry fields. However, the Ministry of Land, Infrastructure and Transport, and the local government and the private sector have partially built 3D object models of different precisions for some specific regions because of the huge cost. Therefore, this study proposes a feasible plan that can solve the cost problem in generating 3D object models for the whole territory. For our purpose, we first analyzed usage, demand, generation technology and generation cost for 3D object models. Afterwards, we proposed LOD3 model generation plan for all territory using automatic 3D object model generation technology based on image matching. Additionally, we supplemented the proposed plan by using LOD4 generation plan for landmarks and LOD2 generation plan non-urban area. In the near future, we expect this would be a great help in establishing a feasible and effective 3D object model generation plan for the whole country.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.7
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• pp.1075-1081
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• 2013

Twitter, Facebook, and KakaoStory are the major SNS in Korea. Social knowledge production is being produced by those services from numerous collaboration and co-participation in those SNS. Wikipedia or Naver JishikIN service was regarded as the representative product of collective knowledge production during the wired internet era. However now at the wireless internet era centered with smart phones, various forms of collective knowledge production would be achieved by connecting to SNS in real-time. In this thesis, the survey data of collective knowledge production for users of three SNS have been compared and analyzed. The difference of the collective knowledge production mechanism among Twitter, Facebook and KakaoStory has been studied and compared through three variables: the motivation of collective knowledge production, the preference of collective knowledge production model, and collective knowledge production cultural perception. As a result of the analysis of the discriminant factors for three SNS user groups, it turns out that the diversity-toward usage motivation, personal contribution motivation, and collective knowledge production tendency perception are the most influential variables. This thesis is of significance in that it unites the value of social science such as social capital and collective knowledge production from the viewpoint of computer science and opens the new chapter of collective knowledge production with the real-time SNS of wireless internet from the wired internet.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.12
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• pp.287-296
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• 2019

Due to the development and increased usage of Internet services such as IoT and cloud computing, a large number of packets are being generated on the Internet. In order to create a safe Internet environment, malicious data that may exist among these packets must be processed and detected quickly. In this paper, we apply MongoDB, which is specialized for unstructured data analysis and big data processing, to intrusion detection system for rapid processing of big data security events. In addition, building the intrusion detection system(IDS) using some of the private cloud resources which is the target of protection, elastic and dynamic reconfiguration of the IDS is made possible as the number of security events increase or decrease. In order to evaluate the performance of MongoDB - based IDS proposed in this paper, we constructed prototype systems of IDS based on MongoDB as well as existing relational database, and compared their performance. Moreover, the number of virtual machine has been increased to find out the performance change as the IDS is distributed. As a result, it is shown that the performance is improved as the number of virtual machine is increased to make IDS distributed in MongoDB environment but keeping the overall system performance unchanged. The security event input rate based on distributed MongoDB was faster as much as 60%, and distributed MongoDB-based intrusion detection rate was faster up to 100% comparing to the IDS based on relational database.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.819-822
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• 2008

As the IT and computer network technology is developed very rapidly, the quantity of digital contents is increased and disseminated more widely. The digital contents is generally expressed in 2 or 3 dimensional multimedia format and the visible human image that is taken from human body is very important because of its variety of usefulness. The KISTI(Korea Institute of Science and Technology Information) is now constructing various Korean human informations such as visible Korean, digital Korean, human bone property and human models. These informations are accessable through the internet. However, these human images are not easily understandable for general users because they are specialized in medical image field and there is no detailed explanation data. In this study, we designed the annotation database and searching interface for KISTI's visible Korean database. This annotation database involved the detailed explanation and special note of visible Korean data and it can connect the image and text data of visible Korean with each other. By studying this database and interface design, the KISTI's visible Korean database is more easily accessable and understandable to general users and it can promote the usage of visible Korean data more widely.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.4
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• pp.95-103
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• 2009

In this paper, a generation method of transmit and receive beamforming vectors based on base station cooperation is proposed which maximizes the user SINR in mobile cellular multi-user MIMO systems. There are two main sources of interference which deteriorate the performance of the system, i.e. the inter-user interference caused by the usage of the same radio resource for multiple users in the system, and the inter-cluster interference from neighboring base stations which are not participating in cooperative transmission. The proposed scheme cancels out the inter-user interference by using the block diagonalization(BD) method, and mitigate the inter-cluster interference by using optimal transmit and receive beamforming vectors based on optimal combining(OC) with the statistic information of inter-cluster interference. We perform computer simulations to verify the performance of the proposed scheme, and compare the result to the conventional performance obtained from utilizing the receiver side information only or utilizing the information from neither sides. The performance evaluations are conducted not only over the independent MIMO channels, but over correlated MIMO channels to demonstrate the robustness of the proposed scheme over the channels with correlation among antennas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.2
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• pp.369-377
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• 2008

Though the Hough transform(HT) is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the HT, the one-to-many mapping from an image spare to a Hough space, causes the innate problem, the sensitivity to noise. This basic problem also deteriorates the quality of detected lines and makes the detected line deviated from the real one or generates some bogus, multiple lines where only one real line exists. The size of Hough space also affects the quality of detected lines. In this paper, we analyzed the line distortions in the traditional Hough transform and showed that the distortions are relieved in the edge strength Hough transform(ESHT), which is a modified HT. However the usage of expanded edge and edge strength in ESHT can cause some new line distortions which do not exist in the HT. These new ones can be solved by a proper setting of decreasing and broadening parameter values and the optimal values can be determined only by some pre-determined values. We also illustrated several examples to show the distortion-decreasing property of ESHT.

• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.229-238
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• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.