• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.7
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• pp.685-691
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• 2011

The aim of this study is to point out the uniqueness of Doppler optical coherence tomography (DOCT) for use in a probe station for (in vivo) visualization of microscale flow and structure and to maximize the effectiveness of DOCT by overcoming its limitations. Conventional DOCT produces images of only one of the velocity components that is parallel to the incident light. In this study, a multi-angle DOCT to quantify a velocity vector field is proposed; this is an extension from a velocity scalar field to a vector field. Quantifying an instantaneous three-dimensional velocity field in a pulsating flow is another challenge because of its limited frame rate. The in-vivo pulsating blood flow is measured by using an electrocardiogram-gated multi-angle DOCT in a hamster cheek pouch model. It is shown that the aliasing problem caused by a relatively low frame rate is resolved by using this method of measurement.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.550-556
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• 2017

Due to the rapid growth and development of Internet of Things (IoT), various devices are now accessible and controllable anytime from anywhere. However, the current IoT system requires a series of complex steps (e.g., launch an application, choose a space and thing, control the thing, etc.) to control the IoT devices; therefore, IoT suffers from a lack of efficient and intuitive methods of interacting with users. To address this problem, we propose a novel IoT control framework based on IoT tags and social messages. The proposed system provides an intuitive and efficient way to control the device based on the device ownership: 1) users can easily control the device by IoT tagging, or 2) users can send an IoT social message to the device owner to request control of the tagged device. Through the development of the prototype system, we show that the proposed system provides an efficient and intuitive way to control devices in the IoT environment.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.557-563
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• 2017

When large-scale gene expression data are analyzed using lasso regression, the estimation of regression coefficients may be unstable due to the highly correlated expression values between associated genes. This irregularity, in which the coefficients are reduced by L1 regularization, causes difficulty in variable selection. To address this problem, we propose a regression model which exploits the repetitive bootstrapping of gene expression values prior to lasso regression. The genes selected with high frequency were used to build each regression model. Our experimental results show that several genes were consistently selected in all regression models and we verified that these genes were not false positives. We also identified that the sign distribution of the regression coefficients of the selected genes from each model was correlated to the real dependent variables.

• Journal of KIISE
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• v.44 no.8
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• pp.774-781
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• 2017

Mention detection systems use nouns or noun phrases as a head and construct a chunk of text that defines any meaning, including a modifier. The term "mention detection" relates to the extraction of mentions in a document. In the mentions, a coreference resolution pertains to finding out if various mentions have the same meaning to each other. A pointer network is a model based on a recurrent neural network (RNN) encoder-decoder, and outputs a list of elements that correspond to input sequence. In this paper, we propose the use of mention detection using pointer networks. Our proposed model can solve the problem of overlapped mention detection, an issue that could not be solved by sequence labeling when applying the pointer network to the mention detection. As a result of this experiment, performance of the proposed mention detection model showed an F1 of 80.07%, a 7.65%p higher than rule-based mention detection; a co-reference resolution performance using this mention detection model showed a CoNLL F1 of 52.67% (mention boundary), and a CoNLL F1 of 60.11% (head boundary) that is high, 7.68%p, or 1.5%p more than coreference resolution using rule-based mention detection.

• Journal of KIISE
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• v.45 no.2
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• pp.134-140
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• 2018

A chat system is a computer program that understands user's miscellaneous utterances and generates appropriate responses. Sometimes a chat system needs to answer users' simple information-seeking questions. However, previous generative chat systems do not consider how to embed knowledge entities (i.e., subjects and objects in triple knowledge), essential elements for question-answering. The previous chat models have a disadvantage that they generate same responses although knowledge entities in users' utterances are changed. To alleviate this problem, we propose a knowledge entity embedding method for improving question-answering accuracies of a generative chat system. The proposed method uses a Siamese recurrent neural network for embedding knowledge entities and their synonyms. For experiments, we implemented a sequence-to-sequence model in which subjects and predicates are encoded and objects are decoded. The proposed embedding method showed 12.48% higher accuracies than the conventional embedding method based on a convolutional neural network.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1083-1092
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• 2016

In this paper, the performance of the optical code-division multiple access (OCDMA) technology on a plastic optical fiber (POF) access network, which had received much attention due to its low weight, large core diameter, flexibility, easy installation, and especially its high bandwidth, is analyzed. Recently, POF was a very attractive candidate for transmission media in an access network based on OCDMA technology. But the conventional OCDMA system only allows finite units to transmit and access simultaneously according to the number of channels which are restricted by BER, and so, in this paper, to resolve this problem a novel multi-priority reservation protocol is also proposed. By using this reservation scheme and a distributed arbitration algorithm, channel collision and destination conflict could be avoided. And this protocol can efficiently support the transmission of multimedia messages that require the different time-delay. The network throughput and average delay using various system parameters have been investigated by numerical analysis and simulation experiments. These results shows that the multi-priority reservation protocol in this POF access network based on OCDMA technology is valid and efficient.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.8
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• pp.921-931
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• 2011

This paper presents a data-fitting technique in which a B-spline hypervolume is used to approximate a given data set of scattered data samples. We describe the implementation of the data structure of a B-spline hypervolume, and we measure its memory size to show that the representation is compact. The proposed technique includes two algorithms. One is for the determination of the knot vectors of a B-spline hypervolume. The other is for the control points, which are determined by solving a linear least-squares minimization problem where the solution is independent of the data-set complexity. The proposed approach is demonstrated with various data-set configurations to reveal its performance in terms of approximation accuracy, memory use, and running time. In addition, we compare our approach with existing methods and present unconstrained optimization examples to show the potential for various applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1431-1437
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• 2016

In mobile healthcare device, when to measure the heart rate by using the PPG signal, its performance is reduced according to the motion artifacts that is the movement of user. This is because the frequency range of motion (0.01-10 Hz) and that of PPG signals overlap. Also, the motion artifacts cannot be rectified by general filters. To solve the problem, this paper proposes a method using filter banks and independent component analysis (ICA). To evaluate the performance of the proposed method, we were artificially applied various movements and compared heart rate errors of the moving average filter and ICA. In the experimental results, heart rate error of the proposed method showed very low than moving average filter and ICA. In this way, it is possible to measure stable heart rate if the proposed method is applied to the healthcare terminal design.

• Journal of KIISE
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• v.44 no.7
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• pp.703-709
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• 2017

Outsourcing databases is to offload storage and computationally intensive tasks to the third party server. Therefore, data owners can manage big data, and handle queries from clients, without building a costly infrastructure. However, because of the insecurity of network systems, the third-party server may be untrusted, thus the query results from the server may be tampered with. This problem has motivated significant research efforts on authenticating various queries such as range query, kNN query, function query, etc. Although aggregation queries play a key role in analyzing big data, authenticating aggregation queries has not been extensively studied, and the previous works are not efficient for data with high dimension or a large number of distinct values. In this paper, we propose the AMR-tree that is a data structure, applied to authenticate aggregation queries. We also propose an efficient proof construction method and a verification method with the AMR-tree. Furthermore, we validate the performance of the proposed algorithm by conducting various experiments through changing parameters such as the number of distinct values, the number of records, and the dimension of data.

• Journal of KIISE
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• v.45 no.1
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• pp.30-35
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• 2018

GuessWhat?! is a game in which two machine players, composed of questioner and answerer, ask and answer yes-no-N/A questions about the object hidden for the answerer in the image, and the questioner chooses the correct object. GuessWhat?! has received much attention in the field of deep learning and artificial intelligence as a testbed for cutting-edge research on the interplay of computer vision and dialogue systems. In this study, we discuss the objective function and characteristics of the GuessWhat?! game. In addition, we propose a simple solver for GuessWhat?! using a simple rule-based algorithm. Although a human needs four or five questions on average to solve this problem, the proposed method outperforms state-of-the-art deep learning methods using only two questions, and exceeds human performance using five questions.