• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1730-1747
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• 2018

Multi-view video plus depth (MVD) is a mainstream format of 3D scene representation in free viewpoint video systems. The advanced 3D extension of the high efficiency video coding (3D-HEVC) standard introduces new prediction tools to improve the coding performance of depth video. However, the depth video in 3D-HEVC is time consuming. To reduce the complexity of the depth video inter coding, we propose a fast coding unit (CU) size and mode decision algorithm. First, an off-line trained Bayesian model is built which the feature vector contains the depth levels of the corresponding spatial, temporal, and inter-component (texture-depth) neighboring largest CUs (LCUs). Then, the model is used to predict the depth level of the current LCU, and terminate the CU recursive splitting process. Finally, the CU mode search process is early terminated by making use of the mode correlation of spatial, inter-component (texture-depth), and inter-view neighboring CUs. Compared to the 3D-HEVC reference software HTM-10.0, the proposed algorithm reduces the encoding time of depth video and the total encoding time by 65.03% and 41.04% on average, respectively, with negligible quality degradation of the synthesized virtual view.

• Journal of KIISE:Software and Applications
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• v.29 no.1_2
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• pp.30-41
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• 2002

We focus on the mobile agents which are considered as new paradigm to solve information retrieval of large volumes of data in the distributed and heterogeneous environment. The mobile agent moves the computation to data instead of large volumes of data to computations. In this paper, we propose an information retrieval model, which can effectively search data in the distributed and heterogeneous environment, using mobile agents. Our model is applied to the design and implementation of an Q&A(Question and Answer) retrieval system. Our Q&A retrieval system, called QASSMA(Q&A Search System using Mobile Agents), uses mobile agents to retrieve articles from Q&A boards and newsgroups that exist in the heterogeneous and distributed environment. QASSMA has the following features and advantages. First, the mobile retrieval agent moves to the destination server to retrieve articles to reduce the retrieval time by eliminating data traffics from the server to the client host. Also it can reduce the traffic that was occurred in the centralized network system, and reduce the usage of resources by sending its agent and running in the destination host. Finally, the mobile retrieval agent of QASSMA can add and update dynamically the class file according to its retrieval environment, and support other retrieval manner. In this paper, we have shown that our Q&A retrieval system using mobile agents is more efficient than the retrieval system using static agents by our experiments.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1270-1281
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• 1999

피라미드 기법 1 은 d-차원의 공간을 2d개의 피라미드들로 분할하는 특별한 공간 분할 방식을 이용하여 고차원 데이타를 효율적으로 색인할 수 있는 새로운 색인 방법으로 제안되었다. 피라미드 기법은 고차원 사각형 형태의 영역 질의에는 효율적이나, 유사성 검색에 많이 사용되는 고차원 구형태의 영역 질의에는 비효율적인 면이 존재한다. 본 논문에서는 고차원 데이타를 많이 사용하는 유사성 검색에 효율적인 새로운 색인 기법으로 구형 피라미드 기법을 제안한다. 구형 피라미드 기법은 먼저 d-차원의 공간을 2d개의 구형 피라미드로 분할하고, 각 단일 구형 피라미드를 다시 구형태의 조각으로 분할하는 특별한 공간 분할 방법에 기반하고 있다. 이러한 공간 분할 방식은 피라미드 기법과 마찬가지로 d-차원 공간을 1-차원 공간으로 변환할 수 있다. 따라서, 변환된 1-차원 데이타를 다루기 위하여 B+-트리를 사용할 수 있다. 본 논문에서는 이렇게 분할된 공간에서 고차원 구형태의 영역 질의를 효율적으로 처리할 수 있는 알고리즘을 제안한다. 마지막으로, 인위적 데이타와 실제 데이타를 사용한 다양한 실험을 통하여 구형 피라미드 기법이 구형태의 영역 질의를 처리하는데 있어서 기존의 피라미드 기법보다 효율적임을 보인다.Abstract The Pyramid-Technique 1 was proposed as a new indexing method for high- dimensional data spaces using a special partitioning strategy that divides d-dimensional space into 2d pyramids. It is efficient for hypercube range query, but is not efficient for hypersphere range query which is frequently used in similarity search. In this paper, we propose the Spherical Pyramid-Technique, an efficient indexing method for similarity search in high-dimensional space. The Spherical Pyramid-Technique is based on a special partitioning strategy, which is to divide the d-dimensional data space first into 2d spherical pyramids, and then cut the single spherical pyramid into several spherical slices. This partition provides a transformation of d-dimensional space into 1-dimensional space as the Pyramid-Technique does. Thus, we are able to use a B+-tree to manage the transformed 1-dimensional data. We also propose the algorithm of processing hypersphere range query on the space partitioned by this partitioning strategy. Finally, we show that the Spherical Pyramid-Technique clearly outperforms the Pyramid-Technique in processing hypersphere range queries through various experiments using synthetic and real data.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.398-404
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• 2015

In this paper, we describe the design and performance of a prototype multi-rotor unmaned aerial vehicle( UAV) platform featuring an inertial measurement unit(IMU) based autonomous-flying for use in bluetooth communication environments. Although there has been a fair amount of study of free-flying UAV with multi-rotors, the more recent trend has been to outfit hexarotor helicopter with gimbal to support various services. This paper introduces the hardware and software systems toward very compact and autonomous hexarotors, where they can perform search, rescue, and surveillance missions without external assistance systems like ground station computers, high-performance remote control devices or vision system. The proposed system comprises the construction of the test hexarotor platform, the implementation of an IMU, mathematical modeling and simulation in the helicopter. Furthermore, the hexarotor helicopter with implemented IMU is connected with a micro controller unit(MCU)(ARM-cortex) board. The micro-controller is able to command the rotational speed of the rotors and to get the measurements of the IMU as input signals. The control simulation and experiment on the real system are implemented in the test platform, evaluated and compared against each other.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.7
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• pp.1277-1282
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• 2016

In the next generation wireless communication systems, an energy harvesting from radio frequency signals is considered as a method to solve the lack of power supply problem for sensors. In this paper, we try to propose an efficient algorithm for simultaneous wireless information and power transfer in energy harvesting networks with channel estimation error. At first, we find an optimal channel training interval using one-dimensional exhaustive search, and estimate a channel using MMSE channel estimator. Based on the estimated channel, we propose a power allocation and splitting algorithm for maximizing the data rate while guaranteeing the minimum required harvested energy constraint, The simulation results confirm that the proposed algorithm has an insignificant performance degradation less than 10%, compared with the optimal scheme which assumes a perfect channel estimation, but it can improve the data rate by more than 20%, compared to the conventional scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.11
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• pp.6856-6862
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• 2014

Web usage mining is a method to extract meaningful patterns based on the web users' log data. Most existing patterns of web usage mining, however, do not consider the users' diverse inclination but create general models. Web users' keywords can have a variety of meanings regarding their tendency and background knowledge. This study evaluated the extraction web-user's pattern after collecting and analyzing the web usage information on the users' keywords of interest. Web-user's pattern can supply a web page network with various inclination information based on the users' keywords of interest. In addition, the Web-user's pattern can be used to recommend the most appropriate web pages and the suggested method of this experiment was confirmed to be useful.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.499-506
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• 2017

Chatter bots are normally used in task-oriented dialogue systems to support free conversations. However, there is not much research on how chatter bots as auxiliary system should be different from independent ones. In this paper, we have developed a chatter bot for a dialogue-based computer assisted language learning (DB-CALL) system. We compared the chatter bot in two different cases: as an independent bot, and as an auxiliary system. The results showed that, the chatter bot as an auxiliary system showed much lower satisfaction than the independent one. A discussion is held about the difference between an auxiliary chatter bot and an independent bot. In addition, we evaluated a search-based chatter bot and a deep learning based chatter bot. The advantages and disadvantages of both methods are discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.236-239
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• 2021

With the growth of AI technology, the scale of Knowledge Graphs continues to be expanded. Knowledge Graphs are mainly expressed as RDF representations that consist of connected triples. Many RDF storages compress and transform RDF triples into the condensed IDs. However, if we try to transform a large scale of RDF triples, it occurs the high processing time and memory overhead because it needs to search the large ID mapping table. In this paper, we propose the method of converting RDF triples using Hash-based ID mapping tables with MapReduce, which is the software framework with a parallel, distributed algorithm. Our proposed method not only transforms RDF triples into Integer-based IDs, but also improves the conversion speed and memory overhead. As a result of our experiment with the proposed method for LUBM, the size of the dataset is reduced by about 3.8 times and the conversion time was spent about 106 seconds.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.8 no.4
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• pp.110-121
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• 2009

It is essential to embed product quality in the design process to win the global competition. Many components found in many products including automobiles and electronic devices are fabricated using sheet metal forming processes. Wrinkle and fracture are two types of defects frequently found in the sheet metal forming process. Reducing such defects is a hard problem as they are affected by many uncontrollable factors. Attempts to solve the problem based on traditional deterministic optimization theories are often led to failures. Furthermore, the wrinkle and fracture are conflicting defects in such a way that reducing one defect leads to increasing the other. Hence, it is a difficult task to reduce both of them at the same time. In this research, a new design method for reducing the rates of conflicting defects under uncontrollable factors is presented by using operating window and a sequential search procedure. A new SN ratio is proposed to overcome the problems of a traditional SN ratio used in the operating window technique. The method is applied to optimizing the robust design of a sheet metal forming process. To show the effectiveness of the proposed method, a comparison is made between the traditional and the proposed methods using simulation software, applied to a design of particular sheet metal forming process problem. The results show that the proposed method always gives a more robust design that is less sensitive to noises than the traditional method.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.