• Journal of Advanced Navigation Technology
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• v.18 no.5
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• pp.468-475
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• 2014

The recommend system is getting more difficult real time recommend by lager preference data set, computing power and recommend algorithm. For this reason, recommend system is proceeding actively one's studies toward distribute processing method of large preference data set. This paper studied distribute processing method of large preference data set using hadoop distribute processing platform and mahout machine learning library. The recommend algorithm is used Co-occurrence Matrix similar to item Collaborative Filtering. The Co-occurrence Matrix can do distribute processing by many node of hadoop cluster, and it needs many computation scale but can reduce computation scale by distribute processing. This paper has simplified distribute processing of co-occurrence matrix by changes over from four stage to three stage. As a result, this paper can reduce mapreduce job and can generate recommend file. And it has a fast processing speed, and reduce map output data.

• Journal of KIISE
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• v.44 no.4
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• pp.383-391
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• 2017

As we enter a new era of Big Data, the amount of semantic data has rapidly increased. In order to derive meaningful information from this large semantic data, studies that utilize the SWRL(Semantic Web Rule Language) are being actively conducted. SWRL rules are based on data extracted from a user's empirical knowledge. However, conventional reasoning systems developed on single machines cannot process large scale data. Similarly, multi-node based reasoning systems have performance degradation problems due to network shuffling. Therefore, this paper overcomes the limitations of existing systems and proposes more efficient distributed inference methods. It also introduces data partitioning strategies to minimize network shuffling. In addition, it describes a method for optimizing the incremental reasoning process through data selection and determining the rule order. In order to evaluate the proposed methods, the experiments were conducted using WiseKB consisting of 200 million triples with 83 user defined rules and the overall reasoning task was completed in 32.7 minutes. Also, the experiment results using LUBM bench datasets showed that our approach could perform reasoning twice as fast as MapReduce based reasoning systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2827-2848
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• 2012

Previously, we described a social media cloud computing service environment (SMCCSE). This SMCCSE supports the development of social networking services (SNSs) that include audio, image, and video formats. A social media cloud computing PaaS platform, a core component in a SMCCSE, processes large amounts of social media in a parallel and distributed manner for supporting a reliable SNS. Here, we propose a Hadoop-based multimedia system for image and video transcoding processing, necessary functions of our PaaS platform. Our system consists of two modules, including an image transcoding module and a video transcoding module. We also design and implement the system by using a MapReduce framework running on a Hadoop Distributed File System (HDFS) and the media processing libraries Xuggler and JAI. In this way, our system exponentially reduces the encoding time for transcoding large amounts of image and video files into specific formats depending on user-requested options (such as resolution, bit rate, and frame rate). In order to evaluate system performance, we measure the total image and video transcoding time for image and video data sets, respectively, under various experimental conditions. In addition, we compare the video transcoding performance of our cloud-based approach with that of the traditional frame-level parallel processing-based approach. Based on experiments performed on a 28-node cluster, the proposed Hadoop-based multimedia transcoding system delivers excellent speed and quality.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.207-214
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• 2011

In this paper, using Zigbee-based wireless sensor networks and Lego MindStorms NXT robot, a remote monitoring and navigation system for mobile robot has been developed. Mobile robot can estimate its position using encoder values of its motor, but due to the existing friction and shortage of motor power etc., error occurs. To fix this problem and obtain more accurate position of mobile robot, a ultrasound module on wireless sensor networks has been used in this paper. To overcome disadvantages of ultrasound which include straightforwardness and narrow detection coverage, we rotate moving node attached to mobile robot by $360^{\circ}$ to measure each distance from four fixed nodes. Then location of mobile robot is estimated by triangulation using measured distance values. In addition, images are sent via a network using a USB Web camera to smart phone. On smart phones we can see location of robot, and images around places where robot navigates. And remote monitoring and navigation is possible by just clicking points at the map on smart phones.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.13
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• pp.5445-5451
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• 2015

Fascin-1 (FSCN1) is an actin-bundling protein that induces cell membrane protrusions, increases cell motility, and is overexpressed in various human epithelial cancers, including esophageal squamous cell carcinoma (ESCC). We analyzed various protein-protein interactions (PPI) of differentially-expressed genes (DEGs), in fascin knockdown ESCC cells, to explore the role of fascin overexpression. The node-degree distributions indicated these PPI sub-networks to be characterized as scale-free. Subcellular localization analysis revealed DEGs to interact with other proteins directly or indirectly, distributed in multiple layers of extracellular membrane-cytoskeleton/ cytoplasm-nucleus. The functional annotation map revealed hundreds of significant gene ontology (GO) terms, especially those associated with cytoskeleton organization of FSCN1. The Random Walk with Restart algorithm was applied to identify the prioritizations of these DEGs when considering their relationship with FSCN1. These analyses based on PPI network have greatly expanded our comprehension of the mRNA expression profile following fascin knockdown to future examine the roles and mechanisms of fascin action.

• Genomics & Informatics
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• v.20 no.3
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• pp.27.1-27.13
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• 2022

Oral squamous cell carcinoma (OSCC) is the most prevalent head and neck malignancy, with frequent cervical lymph-node metastasis, leading to a poor prognosis in OSCC patients. The present study aimed to identify potential markers, including microRNAs (miRNAs) and genes, significantly involved in the etiology of early-stage OSCC. Additionally, the main OSCC's dysregulated Gene Ontology annotations and significant signaling pathways were identified. The dataset GSE45238 underwent multivariate statistical analysis in order to distinguish primary OSCC tissues from healthy oral epithelium. Differentially expressed miRNAs (DEMs) with the criteria of p-value < 0.001 and |Log2 fold change| > 1.585 were identified in the two groups, and subsequently, validated targets of DEMs were identified. A protein interaction map was constructed, hub genes were identified, significant modules within the network were illustrated, and significant pathways and biological processes associated with the clusters were demonstrated. Using the GEPI2 database, the hub genes' predictive function was assessed. Compared to the healthy controls, main OSCC had a total of 23 DEMs. In patients with head and neck squamous cell carcinoma (HNSCC), upregulation of CALM1, CYCS, THBS1, MYC, GATA6, and SPRED3 was strongly associated with a poor prognosis. In HNSCC patients, overexpression of PIK3R3, GIGYF1, and BCL2L11 was substantially correlated with a good prognosis. Besides, "proteoglycans in cancer" was the most significant pathway enriched in the primary OSCC. The present study results revealed more possible mechanisms mediating primary OSCC and may be useful in the prognosis of the patients with early-stage OSCC.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.2
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• pp.451-457
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• 2012

One decade early onset of the breast cancer in Iranian females was reported but the basis of the observed difference has remained unclear and difference in gene silencing by epigenetic processes is suggested. Hence, this study was sought to map the methylation status of estrogen receptor (ER) gene CpG islands and its impact on clinicopathological factors of triple negative and non-triple negative ductal cell carcinoma of the breast in Iranian females. Surgically resected formalin-fixed paraffin-embedded breast tissues from sixty Iranian women with confirmed invasive ductal carcinoma were assessed by methylation-specific PCR using primer sets encompassing some of the 29 CpGs across the ER gene CpG island. The estrogen and progesterone receptors, Her-$2^+$ overexpression, and nuclear accumulation of P53 were examined using immunohistochemistry (IHC). Methylated ER3, ER4, and ER5 were found in 41.7, 11.3, and 43.3% of the samples, respectively. Significantly higher methylation of ER4 was found in the tumors with nuclear accumulation of P53, and significantly higher methylation of ER5 was found in patients with lymph node involvement and tumor with bigger size or higher grades. Furthermore, significantly higher rate of ER5 methylation was found in patients with Her-$2^+$ tumors and in postmenopausal patients with $ER^-$, $PgR^-$, or $ER^-/PgR^-$ tumors. However, no significant difference in ERs methylation status was found between triple negative and non-triple negative tumors in pre- and postmenopausal patients. Findings revealed that aberrant hypermethylation of the ER-alpha gene frequently occurs in Iranian women with invasive ductal cell carcinoma of the breast. However, methylation of different CpG islands produced a diverse impact on the prognosis of breast cancer, and ER5 was found to be the most frequently methylated region in the Iranian women, and could serve as a marker of poor prognosis.

• The KIPS Transactions:PartB
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• v.19B no.3
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• pp.189-194
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• 2012

In this paper, we propose a query expansion method based on word graphs using pseudo-relevant and pseudo non-relevant documents to achieve performance improvement in information retrieval. The initially retrieved documents are classified into a core cluster when a document includes core query terms extracted by query term combinations and the degree of query term proximity. Otherwise, documents are classified into a non-core cluster. The documents that belong to a core query cluster can be seen as pseudo-relevant documents, and the documents that belong to a non-core cluster can be seen as pseudo non-relevant documents. Each cluster is represented as a graph which has nodes and edges. Each node represents a term and each edge represents proximity between the term and a query term. The term weight is calculated by subtracting the term weight in the non-core cluster graph from the term weight in the core cluster graph. It means that a term with a high weight in a non-core cluster graph should not be considered as an expanded term. Expansion terms are selected according to the term weights. Experimental results on TREC WT10g test collection show that the proposed method achieves 9.4% improvement over the language model in mean average precision.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.6
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• pp.97-104
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• 2010

In this paper, ICT (in circuit tester) inspection system and inspection algorithm is proposed and detects whether inferiority exists or not in the mounted device on the flexible PCB in cell phones or mobile display devices. The system is composed of PD (pin-driver) and GGM (ground guarding method). The structural characteristics of these flexible PCB are analyzed, which is needed to input or output the test signal. Test signal to investigate the characteristics of passive components is generated using modified circuit diagram and proposed inspection algorithm. PM (pin-map) is decided on the basis of circuit diagram and has the information about the kind of test signal to be applied and the pad number for the test signal to be connected. PD is designed to load a proper test signal for a specific pad and is adjusted according to PM so that the reconstructed circuit has minimum node and mash. The proposed ICT inspection system is realized using PD and GGM. Using the system, an experiment for each passive component is done to investigate the measurement accuracy of the developed system and an experiment for real flexible PCB model is done to verity the effectiveness of the system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.12
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• pp.874-880
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• 2019

This paper introduces a method which can be effectively used for the path planning of UAV in a realistic map which has mountainous terrains, air defense networks and radars based on the Visibility Graph. Existing studies of Visibility Graph have been studied mainly for simple shape obstacles in 2-dimensional environment such as self-driving cars which avoid buildings. However, for UAV, Visibility Graph must be used in 3-dimensional environment for the variance of altitude. This occurs significant elapsed time increase because of the increase of the amount of the visibility of node sets. To solve this problem, this paper decrease the number of nodes which consists the complex terrain environments using convex hull based on Layered Visibility Graph. With convex hull method, this paper confirmed that the elapsed time is decreased about 99.5% compared to the case which has no decrease of the number of nodes.