• Advances in aircraft and spacecraft science
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• v.10 no.6
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• pp.545-554
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• 2023

The widespread introduction of unmanned aircrafts has led to the understanding of the need to organize a common information space for manned and unmanned aircrafts, which is reflected in the Russian Unmanned aircraft system Traffic Management (RUTM) project. The present article deals with the issues of spatial information database (DB) organization, which is the core of RUTM and provides storage of various data types (spatial, aeronautical, topographical, meteorological, vector, etc.) required for flight safety management. Based on the analysis of functional capabilities and types of work which it needs to ensure, the architecture of spatial information DB, including the base of source information, base of display settings, base of vector objects, base of tile packages and also a number of special software packages was proposed. The issues of organization of these DB, types and formats of data and ways of their display are considered in detail. Based on the analysis it was concluded that the optimal construction of the spatial DB for RUTM system requires a combination of different model variants and ways of organizing data structures.

• Journal of Power Electronics
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• v.11 no.4
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• pp.401-407
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• 2011

In order to enhance torque density of five-phase permanent magnetic synchronous motor with third harmonic injection for electric vehicles (EVs) applications, optimum seeking method for injection ratio of third harmonic was proposed adopting theoretical derivation and finite element analysis method, under the constraint of same amplitude for current and air-gap flux. By five-dimension space vector decomposition, the mathematic model in two orthogonal space plane, $d_1-q_1$ and $d_3-q_3$, was deduced. And the corresponding dual-plane vector control method was accomplished to independently control fundamental and third harmonic currents in each vector plane. A five-phase PMSM prototype with quasi-trapezoidal flux pattern and its fivephase voltage source inverter were designed. Also, the dual-plane vector control was digitized in a single XC3S1200E FPGA. Simulation and experimental results prove that using the proposed optimum seeking method, the torque density of five-phase PMSM is enhanced by 20%, without any increase of power converter capacity, machine size and iron core saturation.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.120-125
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• 2000

We propose a semi-automatic and dynamic indexing algorithm for multi-media database(e.g. movie files, audio files), which are difficult to create indexes expressing their emotional or abstract contents, according to user's sensitivity by using user's histories of access to database. In this algorithm, we simply categorize data at first, create a vector space of each user's interest(user model) from the history of which categories the data belong to, and create vector space of each data(title model) from the history of which users the data had been accessed from. By continuing the above method, we could create suitable indexes, which show emotional content of each data. In this paper, we define the recurrence formulas based on the proposed algorithm. We also show the effectiveness of the algorithm by simulation result.

• Asia pacific journal of information systems
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• v.14 no.2
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• pp.115-130
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• 2004

This paper proposes how to build an expert profile database in KMS, which provides the information of expertise that each expert possesses in the organization. To manage tacit knowledge in a knowledge management system, recent researches in this field have shown that it is more applicable in many ways to provide expert search mechanisms in KMS to pinpoint experts in the organizations with searched expertise so that users can contact them for help. In this paper, we develop a framework to automate expert classification using a text categorization technique called Vector Space Model, through which an expert database composed of all the compiled profile information is built. This approach minimizes the maintenance cost of manual expert profiling while eliminating the possibility of incorrectness and obsolescence resulted from subjective manual processing. Also, we define the structure of expertise so that we can implement the expert classification framework to build an expert database in KMS. The developed prototype system, "Knowledge Portal for Researchers in Science and Technology," is introduced to show the applicability of the proposed framework.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Power Electronics
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• v.18 no.6
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• pp.1791-1804
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• 2018

To achieve a fast dynamic response and to solve the multi-objective control problems of the output currents, capacitor voltages and system constraints, this paper proposes a deadbeat and hierarchical predictive control with space-vector modulation (DB-HPC-SVM) for five-level nested neutral point piloted (NNPP) converters. First, deadbeat control (DBC) is adopted to track the reference currents by calculating the deadbeat reference voltage vector (DB-RVV). After that, all of the candidate switching sequences that synthesize the DB-RVV are obtained by using the fast SVM principle. Furthermore, according to the redundancies of the switch combination and switching sequence, a hierarchical model predictive control (MPC) is presented to select the optimal switch combination (OSC) and optimal switching sequence (OSS). The proposed DB-HPC-SVM maintains the advantages of DBC and SVM, such as fast dynamic response, zero steady-state error and fixed switching frequency, and combines the characteristics of MPC, such as multi-objective control and simple inclusion of constraints. Finally, comparative simulation and experimental results of a five-level NNPP converter verify the correctness of the proposed DB-HPC-SVM.

• Earthquakes and Structures
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• v.18 no.4
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• pp.493-506
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• 2020

In this paper, a new opposition based charged system search (CSS) is proposed to be used as a parameter identification of highly nonlinear semi-active magneto-rheological damper. By replacing the opposition particles with current solutions, the mentioned strategy is used to enhance the search space and to increase the exploration of CSS. To investigate the effectiveness of the proposed method, a nonlinear modified Bouc-Wen model of MR damper is considered to find its parameters, and compare it with those achieved from experimental model of MR damper. Also, by exploiting the sensitivity analysis and using the importance vector, the less importance parameters in the Bouc-Wen model are eliminated which makes the MR damper model simpler. Results demonstrate the new proposed algorithm (OBLCSS) has a high ability to tackle highly nonlinear problems. Based on the results of the α importance vector, a simplified model is proposed and its parameters are identified by using the presented OBLCSS algorithm. The simplified proposed model also has a high capability of estimating damper responses.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.405-410
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• 1994

The purpose of this paper is to present the details of design procedure of a nonlinear regulator by Riemannian geometric approach and to applied it to the case of a double-effect evaporator. A nonlinear geometric model is proposed on a direct sum space of a state vector and a control vector as well as in the previous parers by the authors. The geometric model is derived by replacing the orthogonal straight coordinate axes of a linear system on the direct sum space with the curvilinear coordinate axes. The integral manifold of the geometric model becomes homeomorphic to that of fictitious linear system. For the geometric model a nonlinear regulator with a performance index is designed renewedly by the procedure of optimization. The construction method of the curvilinear coordinate axes on which the nonlinear system behaves as a linear system is discussed. To apply the above regulator theory to double-effect evaporators especially to the pilot plant at the University of Alberta, a suitable nonlinear model is determined by the plant dynamics. The optimal control law is derived through the calculation of the homeomorphism. As a result it is confirmed that the regulator is effective and superior to that of the conventional control.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.687-692
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• 2016

Conventional way to search documents is keyword-based queries using vector space model, like tf-idf. Searching process of documents which is based on keywords can make some problems. it cannot recogize the difference of lexically different but semantically same words. This paper studies a scheme of document search based on document queries. In particular, it uses centrality vectors, instead of tf-idf vectors, to represent query documents, combined with the Word2vec method to capture the semantic similarity in contained words. This scheme improves the performance of document search and provides a way to find documents not only lexically, but semantically close to a query document.

• Journal of Astronomy and Space Sciences
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• v.22 no.3
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• pp.273-282
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• 2005

A star sensor daytime observation model is developed in order to test the performance of the star sensor useful for daylight application. The centroid errors of the star sensor in the day time application are computed by using the model. The standard atmospheric model (LOWTRAN7) is utilized to calculate the physical quantities of the daylight atmospheric environments where the star sensor is immersed. This observation model takes the separation angles between the sun and star, the centroid algorithm and the various system specifications of the star sensor into the account. The developed star sensor model will provide more realistic measurement errors in estimating the performance of the attitude determination from the vector observations.