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Development of a Software for Synthetic IR Image Generation
Chanuk Kyeong, Junyoung Shim, Youngann Woo, Sewon Kim, Wonsik Lee
http://doi.org/10.5626/JOK.2024.51.12.1088
As the importance of seekers, a critical factor in the hit rate of guided missiles, has become more prominent, interest of IR(Infrared) seekers with high object detection performance is increasing. To improve the hit rate of IR seekers, it is essential to acquire IR images in various environments. However, capturing images with real cameras is costly and has difficulty in collecting the images in diverse environments. To address these issues, methods for generating IR images by calculating and analyzing IR signals have been developed. Recently, the demand for the development of domestic IR signal analysis software has been led to prevent the leakage of technology overseas and anlayze IR signals suitable for domestic conditions. In this paper, a domestic synthetic IR image software, which can generate IR images with IR signal analysis algorithm is propsed. The software's IR signal analysis algorithm and the method for generating synthetic IR image are described in detail, and a parallel processing method to increase the speed of synthetic IR image generation is discussed. From the simulation result, the synthetic IR images generated by the proposed software are confirmed.
A Comparative Analysis of the Motion Recognition Rate by Direction of Push-up Activity Using ELM Algorithm
Sangwoong Kim, Jaeyeong Ryu, Jiwoo Jeong, Dongyeong Kim, Youngho Chai
http://doi.org/10.5626/JOK.2023.50.12.1031
In this paper, we propose a motion recognition system for each direction of push-up activity using ELM algorithm. In the proposed system, a recognized motion consists of three parts. The first part is the process of reading motion data. In the process, the data acquired from the motion capture system is entered into the system"s memory. Then, the system extracts a feature vector from the motion data. The 3D position data converted from the quaternion data value of the motion data is projected onto the X-Y plane, Y-Z plane and Z-X plane of the system, and the values are used as the final feature vector. Feature vectors projected on each plane train different ELM, and a total of three ELM are learned. Finally, by inputting test data to each learned ELM, the final recognition result value is derived. First, before obtaining motion data, as the data set to be trained, general push-ups performed in the correct posture were selected. Second, the upper chest did not go down all the way. Third, only the buttocks came up when bending and lifting. Four, when bending your elbows move away from your upper chest. Finally, mix these motions to build a test dataset.
An Improved Algorithm of Finding a Maximal Common Subsequence
Hyeonjun Shin, Joong Chae Na, Jeong Seop Sim
http://doi.org/10.5626/JOK.2023.50.9.737
A maximal common subsequence (MCS) of two strings is a common subsequence that cannot be extended by inserting any character. Unlike the longest common subsequence (LCS), the length of MCS can vary as the longest MCS is an LCS. Although LCS is commonly used to compare similarities of two sequences, computing can take a significant amount of time. Hence, finding a longer MCS is important, as it can be computed faster than the LCS. An algorithm was proposed to compute one of the MCSs of two strings X and Y of total length n using O(kn) space and O(n√(logn/loglogn)) time. Improved algorithms were also proposed. In this paper, we present an algorithm that can check for more characters to compute an MCS. The algorithm proposed in this paper runs in O(kn) space and O(n√(logn/loglogn)) time for a given constant k. Experimental results using both real and randomly generated data showed that the length of the MCS computed by the algorithm proposed in this paper could be up to 6.31 times longer than those computed by previous algorithms.
Dynamic Group Management to Improve the Scalability of PBFT
Jinsung Cho, Gwangyong Kim, Geunmo Kim, Bongjae Kim, Min Choi
http://doi.org/10.5626/JOK.2023.50.5.369
A consensus algorithm that affects the reliability and performance of a blockchain is used for identical decision-making of nodes participating in the consensus. PBFT(Practical Byzantine Falut Tolerance) is a voting-based consensus algorithm with an O(n2) time complexity. The scalability of PBFT is generally poor. This paper proposes a scheme for grouping the nodes participating in a PBFT-based blockchain network and dynamically managing each group by layering it. In addition, we create a mathematical model for estimating the expected time required for consensus of the proposed scheme. Afterwards, we propose a dynamic consensus algorithm for dynamically adjusting the structures of groups and layers based on the model for estimating the expected time of a consensus. As a result of the experiment, the proposed scheme improves the performance of the consensus time by about 97% on average compared to the group-based PBFT without hierarchical structures.
A Bottom-up Algorithm to Find the Densest Subgraphs Based on MapReduce
Finding the densest subgraphs from social networks, such that people in the subgraph are in a particular community or have common interests, has been a recurring problem in numerous studies undertaken. However, these algorithms focused only on finding the single densest subgraph. We suggest a heuristic algorithm of the bottom-up type, which finds the densest subgraph by increasing its size from a given starting node, with the repeated addition of adjacent nodes with the maximum degree. Furthermore, since this approach matches well with parallel processing, we further implement a parallel algorithm on the MapReduce framework. In experiments using various graph data, we confirmed that the proposed algorithm finds the densest subgraphs in fewer steps, as compared to other related studies. It also scales efficiently for many given starting nodes.
A Video Quality Adaptation Algorithm to Improve QoE for HTTP Adaptive Streaming Service
HTTP adaptive streaming has recently emerged to handle the rapidly growing traffic and to provide high quality multimedia contents. To improve the QoE (Quality of Experience) for HTTP adaptive streaming service, the average video bitrate should be maximized, and the video switching frequency (difference of bitrate between adjacent segments) and video stalling events need to be minimized. The recently proposed quality adaptation algorithms for HTTP adaptive streaming do not provide high QoE, since detailed QoE factors such as video switching frequency and bitrate difference of adjacent segments, are not considered. In this paper, we propose a SQA (Smooth Quality Adaptation) algorithm to improve the user QoE. The proposed algorithm provides the smoothed QoE, such that it minimizes the unnecessary video switching events by maintaining the quality in a certain period, thus minimizing the bitrate difference of adjacent segments. Through simulation, we confirm that the proposed algorithm reduces the unnecessary switching events, and prevents the sudden decrease in video quality.
The Design of Object-of-Interest Extraction System Utilizing Metadata Filtering from Moving Object
Taewoo Kim, Hyungheon Kim, Pyeongkang Kim
The number of CCTV units is rapidly increasing annually, and the demand for intelligent video-analytics system is also increasing continuously for the effective monitoring of them. The existing analytics engines, however, require considerable computing resources and cannot provide a sufficient detection accuracy. For this paper, a light analytics engine was employed to analyze video and we collected metadata, such as an object’s location and size, and the dwell time from the engine. A further data analysis was then performed to filter out the target of interest; as a result, it was possible to verify that a light engine and the heavy data analytics of the metadata from that engine can reject an enormous amount of environmental noise to extract the target of interest effectively. The result of this research is expected to contribute to the development of active intelligent-monitoring systems for the future.
kNN Query Processing Algorithm based on the Encrypted Index for Hiding Data Access Patterns
Hyeong-Il Kim, Hyeong-Jin Kim, Youngsung Shin, Jae-woo Chang
In outsourced databases, the cloud provides an authorized user with querying services on the outsourced database. However, sensitive data, such as financial or medical records, should be encrypted before being outsourced to the cloud. Meanwhile, k-Nearest Neighbor (kNN) query is the typical query type which is widely used in many fields and the result of the kNN query is closely related to the interest and preference of the user. Therefore, studies on secure kNN query processing algorithms that preserve both the data privacy and the query privacy have been proposed. However, existing algorithms either suffer from high computation cost or leak data access patterns because retrieved index nodes and query results are disclosed. To solve these problems, in this paper we propose a new kNN query processing algorithm on the encrypted database. Our algorithm preserves both data privacy and query privacy. It also hides data access patterns while supporting efficient query processing. To achieve this, we devise an encrypted index search scheme which can perform data filtering without revealing data access patterns. Through the performance analysis, we verify that our proposed algorithm shows better performance than the existing algorithms in terms of query processing times.
An Automated Technique for Illegal Site Detection using the Sequence of HTML Tags
Since the introduction of BitTorrent protocol in 2001, everything can be downloaded through file sharing, including music, movies and software. As a result, the copyright holder suffers from illegal sharing of copyright content. In order to solve this problem, countries have enacted illegal share related law; and internet service providers block pirate sites. However, illegal sites such as pirate bay easily reopen the site by changing the domain name. Thus, we propose a technique to easily detect pirate sites that are reopened. This automated technique collects the domain names using the google search engine, and measures similarity using Longest Common Subsequence (LCS) algorithm by comparing the tag structure of the source web page and reopened web page. For evaluation, we colledted 2,383 domains from google search. Experimental results indicated detection of a total of 44 pirate sites for collected domains when applying LCS algorithm. In addition, this technique detected 23 pirate sites for 805 domains when applied to foreign pirate sites. This experiment facilitated easy detection of the reopened pirate sites using an automated detection system.
A Distributed Vertex Rearrangement Algorithm for Compressing and Mining Big Graphs
Namyong Park, Chiwan Park, U Kang
How can we effectively compress big graphs composed of billions of edges? By concentrating non-zeros in the adjacency matrix through vertex rearrangement, we can compress big graphs more efficiently. Also, we can boost the performance of several graph mining algorithms such as PageRank. SlashBurn is a state-of-the-art vertex rearrangement method. It processes real-world graphs effectively by utilizing the power-law characteristic of the real-world networks. However, the original SlashBurn algorithm displays a noticeable slowdown for large-scale graphs, and cannot be used at all when graphs are too large to fit in a single machine since it is designed to run on a single machine. In this paper, we propose a distributed SlashBurn algorithm to overcome these limitations. Distributed SlashBurn processes big graphs much faster than the original SlashBurn algorithm does. In addition, it scales up well by performing the large-scale vertex rearrangement process in a distributed fashion. In our experiments using real-world big graphs, the proposed distributed SlashBurn algorithm was found to run more than 45 times faster than the single machine counterpart, and process graphs that are 16 times bigger compared to the original method.
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