• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.49-54
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• 2022

Rapid changes in modern society after the COVID-19 have highlighted artificial intelligence talent as a major influencing factor in determining national competitiveness. Accordingly, the Ministry of Education planned a large-scale SW·AI camp education project to develop the digital capabilities of 4th to 6th grade elementary school students and middle and high school students who are in a vacuum in artificial intelligence education. Therefore, this study aims to develop a camp-type SW·AI education program for students in grades 4-6 of elementary school so that students in grades 4-6 of elementary school can acquire basic knowledge in artificial intelligence. For this, the meaning of SW·AI education in elementary school is defined and SW·AI contents to be dealt with in elementary school are: understanding of SW AI, 'principle and application of SW AI', and 'social impact of SW AI' was set. In addition, an attempt was made to link the set elements of elementary school SW AI education and learning with related subjects and units of textbooks currently used in elementary schools. As for the program used for education, entry, a software coding learning tool based on block coding, is designed to strengthen software programming basic competency, and all programs are designed to be operated centered on experience and experience-oriented participants in consideration of the developmental characteristics of elementary school students. In order for SW·AI education to be organized and operated as a member of the regular curriculum, it is suggested that research based on the analysis of regular curriculum contents and in-depth analysis of SW·AI education contents is necessary.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.149-155
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• 2021

In this study, we intend to develop a defective road surface object recognition model that automatically detects road surface defects that restrict the movement of the transportation handicapped using electric mobile devices with deep learning. For this purpose, road surface information was collected from the pedestrian and running routes where the electric mobility aid device is expected to move in five areas within the city of Busan. For data, images were collected by dividing the road surface and surroundings into objects constituting the surroundings. A series of recognition items such as the detection of breakage levels of sidewalk blocks were defined by classifying according to the degree of impeding the movement of the transportation handicapped in traffic from the collected data. A road surface object recognition deep learning model was implemented. In the final stage of the study, the performance verification process of a deep learning model that automatically detects defective road surface objects through model learning and validation after processing, refining, and annotation of image data separated and collected in units of objects through actual driving. proceeded.

• Maritime Security
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• v.3 no.1
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• pp.45-72
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• 2021

North Korea's cyber capabilities represent a relatively new threat to global financial institutions and foreign governments, particularly the U.S and South Korean governments. Based primarily on publicly available sources, such as journalistic accounts and scholarly publications, this qualitative paper analyzes the ways in which North Korean leader Kim Jong Un has bolstered his country's asymmetric power and advanced his line of byungjin (dual development in the economy and military). Particularly by merging the cyber and maritime domains, North Korean operatives generate more revenue for the regime and helps keep the heavily sanctioned leadership in power. Despite the increased international attention to North Korean hackers, few analysts have examined the important role of cyber in the DPRK's internal political culture, specifically in advancing Kim Jong Un's byungjin line. Cyber fits into the DPRK's longstanding tradition of irregular warfare and guerilla-based armed struggle. Cyber also further advances Kim's personal reputation in the DPRK as an economic innovator and military strategist. This paper pays particular attention to the role of the DPRK's cyber operations in both ideological and maritime contexts. Recently, North Korean hackers have targeted South Korean shipbuilding industries and developed a blockchain scam, known as Marine Chain. North Korean cyber agents have increasingly paid attention to the nexus of cyber and maritime domains in their activities.

• Trans-
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• v.13
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• pp.1-16
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• 2022

Recently, the method of contacting and purchasing NFT art has been popularly evolving, and various prospects and predictions for NFT art have emerged. The discussion of NFT art is mainly a hot topic in terms of market size, profit, and sustainability. As the transition to digital accelerated after COVID-19, almost all genres of culture and arts faced a forced transition with the topic of combining digital and technology, but in terms of profitability, it was generally difficult to guarantee positive continuity. On the other hand, the combination of art and blockchain, and the new distribution method called NFT, attracted a lot of attention by causing fundamental changes that lead to technological transformation, continuity through profit creation, and even genre expansion of visual art. The discourse on NFT and the art market is a recent phenomenon and is being discussed focusing on statistics and numerical reports through data from newspapers and art-related research institutes rather than academic analysis or research. However, discussions related to NFT art should be considered in various aspects, such as the incorporation of technology and art, changes in distribution methods, and changes in creative methods according to media changes, not just short stories of phenomena such as high prices and winning bids. In this paper, we would like to examine the impact of changes in creators and distribution methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.177-185
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• 2022

A method of restoring the occluded area was proposed by referring to images taken with the same types of sensors on high-resolution optical satellite images through deep learning. For the natural continuity of the simulated image with the occlusion region and the surrounding image while maintaining the pixel distribution of the original image as much as possible in the patch segmentation image, CycleGAN (Cycle Generative Adversarial Network) method with ConvNeXt block applied was used to analyze three experimental regions. In addition, We compared the experimental results of a training patch size of 512*512 pixels and a 1024*1024 pixel size that was doubled. As a result of experimenting with three regions with different characteristics,the ConvNeXt CycleGAN methodology showed an improved R² value compared to the existing CycleGAN-applied image and histogram matching image. For the experiment by patch size used for training, an R² value of about 0.98 was generated for a patch of 1024*1024 pixels. Furthermore, As a result of comparing the pixel distribution for each image band, the simulation result trained with a large patch size showed a more similar histogram distribution to the original image. Therefore, by using ConvNeXt CycleGAN, which is more advanced than the image applied with the existing CycleGAN method and the histogram-matching image, it is possible to derive simulation results similar to the original image and perform a successful simulation.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1404-1415
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• 2022

The 2-dimensional arrangement method of nodes has been used in most of RF (Radio Frequency) based communication network simulations. However, this method is not useful for the an none-obstacle 3-dimensional space networks in which the propagation delay speed in communication is very slow and, moreover, the values of performance factors such as the communication speed and the error rate change on the depth of node. Such a typical example is an underwater communication network. The 2-dimensional arrangement method is also not useful for the RF based network like some WSNs (Wireless Sensor Networks), IBSs (Intelligent Building Systems), or smart homes, in which the distance between nodes is short or some of nodes can be arranged overlapping with their different heights in similar planar location. In such cases, the 2-dimensional network simulation results are highly inaccurate and unbelievable so that they lead to user's erroneous predictions and judgments. For these reasons, in this paper, we propose a method to place uniformly and randomly communication nodes in 3-dimensional network space, making the wireless link with neighbor node possible. In this method, based on the communication rage of the node, blocks are generated to construct the 3-dimensional network and a node per one block is generated and placed within a block area. In this paper, we also introduce an algorithm based on this method and we show the performance results and evaluations on the average time in a node generation and arrangement, and the arrangement time and scatter-plotted visualization time of all nodes according to the number of them. In addition, comparison with previous studies is conducted. As a result of evaluating the performance of the algorithm, it was found that the processing time of the algorithm was proportional to the number of nodes to be created, and the average generation time of one node was between 0.238 and 0.28 us. ultimately, There is no problem even if a simulation network with a large number of nodes is created, so it can be sufficiently introduced at the time of simulation.

• The Journal of Engineering Geology
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• v.32 no.3
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• pp.351-361
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• 2022

Numerical analysis performed to predict the behavior of Ipseok-dae columnar joints in Mudeungsan National Park to understand their stability and movement. The numerical analysis technique, 3DEC, is based on the discrete element method that can analysis discontinuities. The analysis used data for material properties derived from laboratory tests, which found that average density was 2.68 kN/m³, average normal stiffness was 3.15 GPa/m, average shear stiffness was 1.00 GPa/m, average cohesion was 0.51 MPa, and the average friction angle was 33°. The Ipseok-dae columnar joints were modeled on the basis of the field survey data for 15 joints located between the observation platform and the hiking trail. The numerical analysis assessed the behavior of each columnar joint by interpreting the displacement of the edges of its upper and lower surfaces. The greatest maximum displacement was found in columnar joint No. 6, and the greatest minimum displacement was found in joint No. 11. Analyzing the movements of five discontinuities in joint No. 11 indicated that the maximum displacement occurred at the 2nd level. The other levels were ordered 5th, 4th, 1st, and 3rd in terms of subsequent greatest displacements. Considering the total displacement in the 15 studied joints, the Ipseok-dae columnar joints are judged to be stable. However, considering the cultural and historical value of Mudeungsan National Park, it is regarded that the currents slope stability should be maintained by monitoring the individual rock blocks of the joints.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.33-42
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• 2022

In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time. In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.

• Journal of the Korea Society of Computer and Information
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• v.28 no.3
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• pp.189-196
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• 2023

This paper analyzes the level of satisfaction of two groups of teachers who were educated about artificial intelligence using App Inventor. The participants were 13 pre-service and 9 in-service elementary school teachers and the data was collected using a questionnaire. As a result of the study, in-service teachers were all more satisfied than pre-service teachers in terms of interest, difficulty, and participation in the education. In addition, the questions investigating whether education helped motivate learning of artificial intelligence and whether there is a willingness to apply it to elementary classes in the future were also more positive for in-service teachers than for pre-service teachers. In general, pre-service teachers had somewhat more negative views than in-service teachers, but they were more positive than in-service teachers in terms of whether the education helped improve their understanding of artificial intelligence and whether they were willing to participate in additional education. Analysis of the Mann-Whitney test to see if there was a significant difference in satisfaction between the two groups showed no significance. This may be because most of the students in the two groups already had block-type or text-type programming experience, so they were able to participate in the education without any special resistance or difficulty with App Inventor, resulting in high levels of satisfaction from both groups. The results of this study can provide basic data for the future development and operation of programs for artificial intelligence education for both pre-service and in-service elementary school teachers.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.453-460
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• 2022

As high-performance quantum computers are expected to be developed, studies are being actively conducted to build a post-quantum security system that is safe from potential quantum computer attacks. When the Grover's algorithm, a representative quantum algorithm, is used to search for a secret key in a symmetric key cryptography, there may be a safety problem in that the security strength of the cipher is reduced to the square root. NIST presents the post-quantum security strength estimated based on the cost of the Grover's algorithm required for an attack of the cryptographic algorithm as a post-quantum security requirement for symmetric key cryptography. The estimated cost of Grover's algorithm for the attack of symmetric key cryptography is determined by the quantum circuit complexity of the corresponding encryption algorithm. In this paper, the quantum circuit of the SCHWAEMM algorithm, AEAD family of SPARKLE, which was a finalist in NIST's lightweight cryptography competition, is efficiently implemented, and the quantum cost to apply the Grover's algorithm is analyzed. At this time, the cost according to the CDKM ripple-carry adder and the unbounded Fan-Out adder is compared together. Finally, we evaluate the post-quantum security strength of the lightweight cryptography SPARKLE SCHWAEMM algorithm based on the analyzed cost and NIST's post-quantum security requirements. A quantum programming tool, ProjectQ, is used to implement the quantum circuit and analyze its cost.