• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.5
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• pp.836-858
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• 2010

Web Services allow a user to integrate applications from different platforms and languages. Since mobile applications often run on heterogeneous platforms and conditions, Web Service becomes a popular solution for integrating with server applications. However, because of its verbosity, XML based SOAP messaging gives the possible overhead to the less powerful mobile devices. Based on the mobile client's behavior that it usually exchanges messages with Web Service continuously in a session, we design the Handheld Flexible Representation architecture. Our proposed architecture consists of three main components: optimizing message representation by using a data format language (Simple_DFDL), streaming communication channel to reduce latency and the Context-store to store context information of a session as well as redundant parts of the messages. In this paper, we focus on the Context-store and describe the architecture with the Context-store for improving the performance of mobile Web Service messaging. We verify our approach by conducting various evaluations and investigate the performance and scalability of the proposed architecture. The empirical results show that we save 40% of transit time between a client and a service by reducing the message size. In contrast to solutions for a single problem such as the compression or binarization, our architecture addresses the problem at a system level. Thus, by using the Context-store, we expect reliable recovery from the fault condition and enhancing interoperability as well as improving the messaging performance.

• Journal of Biomedical Engineering Research
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• v.40 no.5
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• pp.179-188
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• 2019

In ultrasound telemedicine, it is important to reduce the size of the data by compressing the ultrasound image when sending it. Ultrasound images can be divided into image context and other information consisting of patient ID, date, and several letters. Between them, ultrasound context is very important information for diagnosis and should be securely preserved as much as possible. In several previous papers, ultrasound compression methods were proposed to compress ultrasound context and other information into different compression parameters. This ultrasound compression method minimized the loss of ultrasound context while greatly compressing other information. This paper proposed the method of automatic segmentation of ultrasound context to overcome the limitation of the previously described ultrasound compression method. This algorithm was designed to robust for various ultrasound device and to enable real-time operation to maintain the benefits of ultrasound imaging machine. The operation time of extracting ultrasound context through the proposed segmentation method was measured, and it took 311.11 ms. In order to optimize the algorithm, the ultrasound context was segmented with down sampled input image. When the resolution of the input image was reduced by half, the computational time was 126.84 ms. When the resolution was reduced by one-third, it took 45.83 ms to segment the ultrasound context. As a result, we verified through experiments that the proposed method works in real time.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.4
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• pp.145-152
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• 2019

Unlike a single value from a sensor device, a massive data set has characteristics for various processing aspects; input data may be formed in a different format, the size of input data varies, and the processing time of analyzing input data is not predictable. Therefore, context aware systems may contain complex modules, and these modules can be implemented and used in different ways. In order to solve these problems, we propose a method to handle context information from the result of analyzing massive data. The proposed method considers analysis work as a different type of abstracting context and suggests the way of representing context information. In experiment, we demonstrate how the context processing engine works properly in a couple of steps with healthcare services.

• MALSORI
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• v.55
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• pp.103-118
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• 2005

In this paper, we describe a morpheme-based pronunciation lexicon useful for Korean LVCSR. The phonemic-context-dependent multiple pronunciation lexicon improves the recognition accuracy when cross-morpheme pronunciation variations are distinguished from within-morpheme pronunciation variations. Since adding all possible pronunciation variants to the lexicon increases the lexicon size and confusability between lexical entries, we have developed a lexicon pruning scheme for optimal selection of pronunciation variants to improve the performance of Korean LVCSR. By building a proposed pronunciation lexicon, an absolute reduction of $0.56\%$ in WER from the baseline performance of $27.39\%$ WER is achieved by cross-morpheme pronunciation variations model with a phonemic-context-dependent multiple pronunciation lexicon. On the best performance, an additional reduction of the lexicon size by $5.36\%$ is achieved from the same lexical entries.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.371-381
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• 2017

The performance of the statistical context-sensitive spelling error correction depends on the quality and quantity of the data for statistical language model. In general, the size and quality of data in a statistical language model are proportional. However, as the amount of data increases, the processing speed becomes slower and storage space also takes up a lot. We suggest the improved statistical language model to solve this problem. And we propose an effective spelling error candidate generation method based on a new statistical language model. The proposed statistical model and the correction method based on it improve the performance of the spelling error correction and processing speed.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.542-546
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• 2007

In the vision of ubiquitous computing environment, smart objects would communicate each other and provide many kinds of information about user and their surroundings in the home. This information enables smart objects to recognize context and to provide active and convenient services to the customers. However in most cases, context-aware services are available only with expert systems. In this paper, we present generalized activity recognition application in the smart home based on a naive Bayesian network(BN) and fuzzy clustering. We quantize continuous sensor data with fuzzy c-means clustering to simplify and reduce BN's conditional probability table size. And we apply mutual information to learn the BN structure efficiently. We show that this system can recognize user activities about 80% accuracy in the web based virtual smart home.

• Journal of Korean Society of Rural Planning
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• v.11 no.1 s.26
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• pp.9-15
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• 2005

Preservation of geometrical context of terrains in a digitized format is useful in handling and making modification to the data. Digitization of three-dimensional terrain still proves a great challenge due to heavy load of context required to retain details of topological and geometrical information. Methods of simplification, restoration and multi-level terrain generation are often employed to transform the original data into a compressed digital format. However, reduction of the stored data size comes at an expense of loss of details in the original data set. This article reports on an alternative scheme for simplification and restoration of terrain data. The algorithm utilizes the fact that the terrain formation and patterns can be predicted and modeled through the fractal algorithm. This method was used to generate multi-level terrain model based on NGIS digital maps with preserving geometrical context of terrains.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6A
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• pp.550-557
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• 2007

We predict a context of various structures by using Bayesian Networks Algorithms, Three-Dimensional Structures Algorithms and Genetic Algorithms. However, these algorithms have unavoidable problems when providing a context-aware service in reality due to a lack of practicality and the delay of process time in real-time environment. As far as context-aware system for specific purpose is concerned, it is very hard to be sure about the accuracy and reliability of prediction. This paper focuses on reasoning and prediction technology which provides a stochastic mechanism for context information by incorporating various context information data. The objective of this paper is to provide optimum services to users by suggesting an intellectual reasoning and prediction based on hierarchical context information. Thus, we propose a design of user location prediction algorithm using sequential matching with n-size flexible window scheme by taking user's habit or behavior into consideration. This algorithm improves average 5.10% than traditional algorithms in the accuracy and reliability of prediction using the Flexible Window Scheme.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.249.1-249.1
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• 2015

Quantum dots (QDs) are attracting growing attention for photovoltaic device applications because of their unique electronic, optical and physical properties. Lead sulfide (PbS) QDs are one of the most widely studied materials for the devices and known to have size-tunable properties. In this context, we investigated the relationship between the size of PbS QDs and two synthesizing conditions, a concentration of ligand, oleic acid in this work, and injection temperature. The inverted colloidal quantum dot solar cells based on the heterojunction of n-type zinc oxide layer and p-type PbS QDs were also fabricated. The size of the QDs and cell properties were observed to depend on both the QD synthesizing conditions, and hence the overall efficiency of the cell could vary even though the size of QDs used was same. The QD synthesizing conditions were finally optimized for the maximum cell efficiency.