• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1171-1178
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• 2019

This study proposes a deep neural network model based on an encoder-decoder structure for visual dialogs. Ongoing linguistic understanding of the dialog history and context is important to generate correct answers to questions in visual dialogs followed by questions and answers regarding images. Nevertheless, in many cases, a visual understanding that can identify scenes or object attributes contained in images is beneficial. Hence, in the proposed model, by employing a separate person detector and an attribute recognizer in addition to visual features extracted from the entire input image at the encoding stage using a convolutional neural network, we emphasize attributes, such as gender, age, and dress concept of the people in the corresponding image and use them to generate answers. The results of the experiments conducted using VisDial v0.9, a large benchmark dataset, confirmed that the proposed model performed well.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.45-48
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• 2013

In this paper, we proposed an Indexing Structure System (ISS) based on Apache Hadoop in Wireless Sensor Network (WSN). Nowadays sensors data continuously keep growing that need to control. Data constantly update in order to provide the newest information to users. While data keep growing, data retrieving and storing are face some challenges. So by using the ISS, we can maximize processing quality and minimize data retrieving time. In order to design ISS, Indexing Types have to be defined depend on each sensor type. After identifying, each sensor goes through the Indexing Structure Processing (ISP) in order to be indexed. After ISP, indexed data are streaming and storing in Hadoop Distributed File System (HDFS) across a number of separate machines. Indexed data are split and run by MapReduce tasks. Data are sorted and grouped depend on sensor data object categories. Thus, while users send the requests, all the queries will be filter from sensor data object and managing the task by MapReduce processing framework.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.187-192
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• 2023

With development of information technology and necessity for high security, using different identification methods has become very important. Each biometric feature has its own advantages and disadvantages and choosing each of them depends on our usage. Retinal scanning is a bio scale method for identification. The retina is composed of vessels and optical disk. The vessels distribution pattern is one the remarkable retinal identification methods. In this paper, a new approach is presented for identification via retinal images using LBP and hog methods. In the proposed method, it will be tried to separate the retinal vessels accurately via machine vision techniques which will have good sustainability in rotation and size change. HOG-based or LBP-based methods or their combination can be used for separation and also HSV color space can be used too. Having extracted the features, the similarity criteria can be used for identification. The implementation of proposed method and its comparison with one of the newly-presented methods in this area shows better performance of the proposed method.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.477-486
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• 2010

This Study suggests security directions to reconstruct separate network of Smart Grid and information communication network as one communications system and implement Smart Grid integrated information communication network. In addition, it suggests prevention directions to prevent future cyber attacks by reorganizing network as the key three-stage network and separating TCP/IP four layers that consist of existing information communication network from Smart Grid. Moreover, it suggests the foundation for the study and the test by providing current problems of Smart Grid, weak points, and three security models. This study is meaningful to suggest development directions and situations as a technology of future-oriented electric industries, integrate attacks and preventions of TCP/IP Layers with Smart Grid, and seek for a new technology of Smart Grid and future tasks for Smart Grid information security.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.5998-6008
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• 2015

The value of social network and entrepreneurial orientation has widely been acknowledged as a critical resource of business success. However, the previous studies did not specify each role and conceptually have examined them as separate antecedents of firm performance. This study develops propositions concerning network capabilities and investigates the moderating effects of network structures (degree centrality, closeness centrality, and betweenness centrality) on the relationship between entrepreneurial orientation and firm performance. This research conducted with a sample of 115 small consulting companies in Korea. The findings indicate that closeness centrality moderates the relationship between proactiveness and performance. Also, the combination of betweenness centrality and proactiveness exerts a positive impact on performance. Accordingly, degree centrality enhances the influence of innovativeness on performance. In particular, betweenness centrality exacerbates the influence of risk-taking on performance against our expectation. Overall, these findings conclude the synergistic effects of network capability and entrepreneurial orientation on firm performance. Finally, practical implications and academic propositions are presented regarding the way firms could most effectively utilize entrepreneurial orientation and network capability to maximize firm performance.

• Asia Marketing Journal
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• v.15 no.3
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• pp.1-14
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• 2013

Our research brings managerial insights for developing new digital convergence of devices and services. To explain the phenomenon of device and service convergence, we combine two different approaches from separate research fields: a perceptual mapping technique generally used for segmentation in marketing and associative network analysis mobilized to understanding network structure of core and peripheral as well as the information mediating role of nodes in network science. By combining these two approaches, we provide an in-depth analysis of the associations among devices and services by assessing the centrality of device and service nodes in an associative network. This is done by examining the connections between these services and devices as well as investigating the role of mediation in the combined device-service associative network. Our results based on bi-partite network analysis of survey responses from 250 Internet Protocol (IP) television viewers show which device and which service will play the major role in future device and service convergence as well as which characteristics and functionalities have to be incorporated into future convergence. Among the devices, the mobile handset with the betweenness centrality of 0.26 appears to be the device that would lead future device convergence. Among the services, wireless broadband with the betweenness centrality of 0.276 appears to be the service on which future service convergence needs to be developed. This result is quite unexpected, since wireless broadband has a lower penetration rate than other services, such as fixed broadband and cable TV. In addition, we indicate the possibility of converging devices, such as personal digital assistant (PDA) and mobile handset, and services, such as IPTV and mobile Internet, into wireless broadband services in the future.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1A
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• pp.73-81
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• 2011

IEEE 802.11 PHY and MAC layer provide multiple channels and data rates. To improve the performance of IEEE 802.11 multi-radio ad hoc networks, it is required to utilize available channels and data rates efficiently. However, in IEEE 802.11 multi-rate networks, the rate anomaly (RA) problem occurs that the network performance is severely degraded as low-rate links affect high-rate links. Hence, in this paper, we propose a model-based rate separation (MRS) algorithm that uses multiple channels to separate different data rate links so that the RA problem is mitigated. MRS algorithm utilizes an existing throughput model that estimates the throughput of IEEE 802.11 single-hop networks to separate low-rate links and high-rate links. Through simulations, we demonstrate that the MRS algorithm shows improved network performance compared with existing algorithms in multi-radio ad hoc networks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.2
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• pp.166-171
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• 2015

Recently, the international standard RS-422 serial communication, which was primarily designed for the communications between micro-controllers, has been used as a communication method on the ship. However, as the NMEA (National Marine Electronics Association) 2000 standardization progresses recently, a high-speed Ethernet-based communication environment is expected to be an alternative communication method instead of the RS-422 gradually. In addition, the ship's main instrumentation equipment with versatility and convenience is implemented to control devices as monitoring and controlling tools. This paper describes an implementation of wireless ultrasonic anemometer and anemoscope data processing and logger system using PC network, where the control processing equipment such as a separate operational console, data-logger, and indicator is required for the system. For these, the related element technologies to implement ultrasonic anemometer and anemoscope data processing system are expressed as a functional modules, and the NMEA 2000 standard certified data-logger system is implemented without the separate operational console.

• Geosystem Engineering
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• v.21 no.5
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• pp.262-272
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• 2018

The U-statistic method is one of the most important structural methods to separate the anomaly from background. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and determine anomalous areas. In the present study, 3D U-statistic method has been applied for the first time through the three-dimensional (3D) modeling of an ore deposit. In order to achieve this purpose, 3D U-statistic is applied on the data (Fe grade) resulted from the drilling network in Baghak mine, central part of the Sangan iron mines (in Khorassan Razavi Province, Iran). Afterward, results from applying 3D U-statistic method are used for 3D modeling of the iron mineralization. Results show that the anomalous values are well separated from background so that the determined samples as anomalous are not dispersed and according to their positioning, denser areas of anomalous samples could be considered as anomaly areas. And also, final results (3D model of iron mineralization) show that output model using this method is compatible with designed model for mining operation. Moreover, seen that U-statistic method in addition for separating anomaly from background, could be very efficient for the 3D modeling of different ore type.