• KIPS Transactions on Software and Data Engineering
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• v.2 no.7
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• pp.487-494
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• 2013

Medical image watermarking has received extensive attention as wide security services in the healthcare information system. This paper proposes a blind medical image watermarking approach on the segmented gray-matter (GM) images by utilizing discrete wavelet transform (DWT) and discrete cosine transform (DCT) along with enhanced suppressed fuzzy C-means (EnSFCM) for the optimal selection of sub-blocks position to insert a watermark. Experimental results show that the proposed approach outperforms other methods in terms of peak signal to noise ratio (PSNR) and M-SVD. In addition, the proposed approach shows better robustness than other methods in normalized correlation (NC) values against several attacks, such as noise addition, filtering, JPEG compression, blurring, histogram equalization, and cropping.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.547-553
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• 2008

We propose a two-stage document layout segmentation method. At the first stage, as top-down segmentation, morphological distance map algorithm extracts a collection of rectangular regions from a given input image. This preliminary result from the first stage is employed as input parameters for the process of next stage. At the second stage, a machine-learning algorithm is adopted RBF network, one of neural networks based on statistical model, is selected. In order for constructing the hidden layer of RBF network, a data clustering technique bared on the self-organizing property of Kohonen network is utilized. We present a result showing that the supervised neural network, trained by 300 number of sample data, improves the preliminary results of the first stage.

• Journal of Korean Neurosurgical Society
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• v.42 no.3
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• pp.205-210
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• 2007

Objective : Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels mediate the hyperpolarization-activated currents (Ih) that participate in regulating neuronal membrane potential and contribute critically to pacemaker activity, promoting synchronization of neuronal networks. However, distinct regional and cellular localization of HCN channels in the brain have not been precisely defined. Aim of this study was to verify the precise cellular location of HCN1 channels in rat cerebellum to better understand the physiological role these channels play in synaptic transmission between CNS neurons. Methods : HCN1 expression in rat brain was analyzed using immunohistochemistry and electron-microscopic observations. Postsynaptic density-95 (PSD-95), otherwise known as locating and clustering protein, was also examined to clarify its role in the subcellular location of HCN1 channels. In addition, to presume the binding of HCN1 channels with PSD-95, putative binding motifs in these channels were investigated using software-searching method. Results : HCN1 channels were locally distributed at the presynaptic terminal of basket cell and exactly corresponded with the location of PSD-95. Moreover, nine putative SH3 domain of PSD-95 binding motifs were discovered in HCN1 channels from motif analysis. Conclusion : Distinct localization of HCN1 channels in rat cerebellum is possible, especially when analyzed in conjunction with the SH3 domain of PSD-95. Considering that HCN1 channels contribute to spontaneous rhythmic action potentials, it is suggested that HCN1 channels located at the presynaptic terminal of neurons may play an important role in synaptic plasticity.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.155-165
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• 2020

In this paper, we propose a smart thermostat temperature set-point control method based on machine learning and rule engine, which controls thermostat's temperature set-point so that it can achieve energy savings as much as possible without sacrifice of occupants' comfort while users' preference usage pattern is respected. First, the proposed method periodically mines data about how user likes for heating (winter)/cooling (summer) his or her home by learning his or her usage pattern of setting temperature set-point of the thermostat during the past several weeks. Then, from this learning, the proposed method establishes a weekly schedule about temperature setting. Next, by referring to thermal comfort chart by ASHRAE, it makes rules about how to adjust temperature set-points as much as low (winter) or high (summer) while the newly adjusted temperature set-point satisfies thermal comfort zone for predicted humidity. In order to make rules work on time or events, we adopt rule engine so that it can achieve energy savings properly without sacrifice of occupants' comfort. Through experiments, it is shown that the proposed smart thermostat temperature set-point control method can achieve better energy savings while keeping human comfort compared to other conventional thermostat.

• Molecular & Cellular Toxicology
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• v.4 no.4
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• pp.267-277
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• 2008

Benzene and ethylbenzene (BE), the volatile organic compounds (VOCs) are common constituents of cleaning and degreasing agents, paints, pesticides, personal care products, gasoline and solvents. VOCs are evaporated at room temperature and most of them exhibit acute and chronic toxicity to human. Chronic exposure of benzene is responsible for myeloid leukemia and also ethylbenzene is also recognized as a possible carcinogen. To evaluate the BE effect on human, whole human genome 35 K oligonucleotide microarray were screened for the identification of the differential expression profiling. We identified 280 up-regulated and 201 down-regulated genes changed by more than 1.5 fold by BE exposure. Functional analysis was carried out by using DAVID bioinformatics software. Clustering of these differentially expressed genes were associated with immune response, cytokine-cytokine receptor interaction, toll-like signaling pathway, small cell lung cancer, immune response, apoptosis, p53 signaling pathway and MAPKKK cascade possibly constituting alternative or subordinate pathways of hematotoxicity and immune toxicity. Gene ontology analysis methods including biological process, cellular components, molecular function and KEGG pathway thus provide a fundamental basis of the molecular pathways through BEs exposure in human lymphoma cells. This may provides a valuable information to do further analysis to explore the mechanism of BE induced hematotoxicity.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.373-379
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• 2015

In this paper, we propose an online VQ Codebook generation method for updating an existing VQ Codebook in real-time and adding to an existing cluster with newly created text data which are news paper, web pages, blogs, tweets and IoT data like sensor, machine. Without degrading the performance of the batch VQ Codebook to the existing data, it was able to take advantage of the newly added data by using a triangle inequality which modifying the VQ Codebook progressively show a high degree of accuracy and speed. The result of applying to test data showed that the performance is similar to the batch method.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.157-162
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• 2008

This paper presents a learning method of tagging ontology using large tagging data such as a folksonomy, which stands for classification structure informally created by the people. There is no common agreement about the semantics of a tagging, and most social web sites internally use different methods to represent tagging information, obstructing interoperability between sites and the automated processing by software agents. To solve this problem, we need a tagging ontology, defined by analyzing intrinsic attributes of a tagging. Through several machine learning for tagging data, tag groups and similar user groups are extracted, and then used to learn the tagging ontology. A recommender system adopting the tagging ontology is also suggested as an applying field.

• Journal of KIISE:Software and Applications
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• v.27 no.2
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• pp.97-107
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• 2000

Incremental learning generally reduces training time and increases the generalization of a neural network by selecting training data incrementally during the training. However, the existing methods of incremental learning repeatedly evaluate the importance of training data every time they select additional data. In this paper, an incremental learning algorithm is proposed for pattern classification problems. It evaluates the importance of each piece of data only once before starting the training. The importance of the data depends on how close they are to the decision boundary. The current paper presents an algorithm which orders the data according to their distance to the decision boundary by using clustering. Experimental results of two artificial and real world classification problems show that this proposed incremental learning method significantly reduces the size of the training set without decreasing generalization performance.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.4
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• pp.59-67
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• 2013

Compared to the conventional Hardware-oriented base stations, Software Defined Radio (SDR)-based base station provides various advantages especially in flexibility and expandability. It enables the multimode capability required in 4th-generation (4G) environment which aims at a convergence network of various kinds of communication standards. However, since a single base station processes all data required in various multiple waveforms, the SDR base station faces a problem of data processing speed. In this paper, we propose a new concept of SDR base station system which adopts a parallel processing technology of clustering environment. We implemented a WiMAX system with SDR concept which adopts the Message Passing Interface (MPI) technology which enables the speed-up operations. In order to maximize the efficiency of parallel processing in signal processing, we analyze how the algorithm at each of modules is related to data to be processed. Through the implemented system, we show a drastic improvement in operation time due to parallel processing using the proposed MPI technology. In addition, we demonstrate a feasibility of SDR system for 4G or even beyond-4G as well.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.731-738
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• 2013

We analyze public opinion via a sentiment analysis of tweets collected by using recent topic keywords extracted from newspaper articles. Newspaper articles collected within a certain period of time are clustered by using K-means algorithm and topic keywords for each cluster are extracted by using term frequency. A sentiment analyzer learned by a machine learning method can classify tweets according to their polarity values. We have an assumption that tweets collected by using these topic keywords deal with the same topics as the newspaper articles mentioned if the tweets and the newspapers are generated around the same time. and we tried to verify the validity of this assumption.