• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.1
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• pp.45-53
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• 2017

The Internet of Things (IoT) technology allows devices connected to the Internet to exchange information without human intervention, and to provide useful services to people. Currently, garbage trucks are regularly dispatched to collect garbage. In such a case, garbage may be less than half of the garbage collection capacity in some area, and garbage may be exceeded in another area so that garbage trucks can not collect all at once. In this paper, we have studied the method of estimating the amount of garbage to be collected and describe the development contents of the product and management system. The prediction of garbage volume was made possible by using IoT technology to measure the volume of garbage in real time. In addition, the measurement values are visibly displayed through the dashboard, so that the amount of garbage generated can be predicted and managed. This will allow IoT technology to help keep street hygiene.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.35-48
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• 2017

There are many and various devices like sensors, displays, smart phone, etc. in smart environment. And contents can be provided by using these devices. Vast amounts of contents are provided to users, but in most environments, there are no regard for user or some simple elements like location and time are regarded. So there's a limit to provide meaningful contents to users. In this paper, I suggest the contents recommendation system that can recommend contents to users by reasoning context of users, devices and contents. The contents recommendation system suggested in this paper recommend the contents by calculating the user preferences using the situation reasoned with the contextual data acquired from various devices and the user profile received from the user directly. To organize this process, the method on how to model ontology with domain knowledge and how to design and develop the contents recommendation system are discussed in this paper. And an application of the contents recommendation system in Centum City, Busan is introduced. Then, the evaluation methods how the contents recommendation system is evaluated are explained. The evaluation result shows that the mean absolute error is 0.8730, which shows the excellent performance of the proposed contents recommendation system.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.124-130
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• 2017

Character encoding is the method used to represent characters or symbols on a computer, and there are many encoding detection software tools. For the widely used encoding detection software"uchardet", the accuracy of encoding detection of unmodified normal text document is 91.39%, but the accuracy of language detection is only 32.09%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 3.55% and the accuracy of language detection is 0.06%. Therefore, in this paper, we propose encoding and language detection of text document using the deep learning algorithm called LSTM(Long Short-Term Memory). The results of LSTM are better than encoding detection software"uchardet". The accuracy of encoding detection of normal text document using the LSTM is 99.89% and the accuracy of language detection is 99.92%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 99.26%, the accuracy of language detection is 99.77%.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.66-76
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• 2017

In this study, we investigated how to protect personal healthcare information when constructing OMOP (Observational Medical Outcomes Partnership) CDM (Common Data Model). There are two proposed methods; to restrict data corresponding to HIPAA (Health Insurance Portability and Accountability Act) PHI (Protected Health Information) to be extracted to CDM or to disable identification of it. While processing sensitive information is restricted by Korean Personal Information Protection Act and medical law, there is no clear regulation about what is regarded as sensitive information. Therefore, it was difficult to select the sensitive information for protecting personal healthcare information. In order to solve this problem, we defined HIPAA PHI as restriction criterion of Article 23 of the Personal Information Protection Act and maps data corresponding to CDM data. Through this study, we expected that it will contribute to the spread of CDM construction in Korea as providing solutions to the problem of protection of personal healthcare information generated during CDM construction.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3203-3208
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• 2007

The performance study shows the result using two different methods which are used to control missile or aircraft. One is the Thrust Vector Control(TVC) method for the aviation of next generation and the other is the present effective Shroud Jet-vane System(SJVS) method for the satellite effector development. The research was done through the performance estimation using the numerical simulation analysis, the modelling, the performance measuring using the model, the investigation of the flow visualization and aerodynamic performance with the enforced power to the vane and the result comparison.

• JSTS:Journal of Semiconductor Technology and Science
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• v.14 no.5
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• pp.566-571
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• 2014

Channel-stacked 3D NAND flash memory is very promising candidate for the next-generation NAND flash memory. However, there is an inherent issue on cell size variation between stacked channels due to the declined etch slope. In this paper, the effect of the cell variation on the incremental step pulse programming (ISPP) characteristics is studied with 3D TCAD simulation. The ISPP slope degradation of elliptical channel is investigated. To solve that problem, a new programming method is proposed, and we can alleviate the $V_T$ variation among cells and reduce the total programming time.

• Transactions on Electrical and Electronic Materials
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• v.15 no.6
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• pp.324-327
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• 2014

Germanium (Ge) is a promising material for next generation nanoelectronics and multiple junction solar cells. This work investigated the electrical properties in Cu/p-type Ge Schottky diodes, using current-voltage (I-V) measurements. The Schottky barrier heights were 0.66, 0.59, and 0.70 eV from the forward ln(I)-V, Cheung, and Norde methods, respectively. The ideality factors were 1.92 and 1.78 from the forward ln(I)-V method and Cheung method, respectively. Such high ideality factor could be associated with the presence of an interfacial layer and interface states at the Cu/p-Ge interface. The reverse-biased current transport was dominated by the Poole-Frenkel emission rather than the Schottky emission.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.11a
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• pp.103-104
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• 2006

PRAM (Phase change Random Access Memory) is one of the most promising candidates for next generation Non-volatile Memories. The Phase change material has been researched in the field of optical data storage media. However, the characteristics required in solid state memory are quite different from optical ones. In this study, the reset current and temperature profile of PRAM cells with bottom electrode were calculated by the numerical method.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.759-762
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• 1999

In this paper, we introduce the concept of a HAPS(High Altitude Platform Station) system which is expected to be a next generation communication system and suggest several error correcting codes to provide high quality services. Since a HAPS system encounters serious signal attenuation due to rain and scattering in the air, concatenated codes which have a high coding gain is considered to be a proper error correcting method. In this paper, we provide performance analyses result of two candidate coding schemes for a HAPS. The first one is a conventional concatenated coding scheme, and the second one is a iterative decoding method known as Turbo Codes.