• International Journal of Concrete Structures and Materials
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• v.10 no.2
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• pp.189-203
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• 2016

This paper investigates on the use of advanced optical measurement methods, i.e. 3D coordinate measurement machines (3D CMM) and stereo-vision digital image correlation (3D DIC), for the mechanical analysis of shear deficient prestressed concrete members. Firstly, the experimental program is elaborated. Secondly, the working principle, experimental setup and corresponding accuracy and precision of the considered optical measurement techniques are reported. A novel way to apply synthesised strain sensor patterns for DIC is introduced. Thirdly, the experimental results are reported and an analysis is made of the structural behaviour based on the gathered experimental data. Both techniques yielded useful and complete data in comparison to traditional mechanical measurement techniques and allowed for the assessment of the mechanical behaviour of the reported test specimens. The identified structural behaviour presented in this paper can be used to optimize design procedure for shear-critical structural concrete members.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.138.6-138
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• 2001

In recent years, as the battery-powered portable systems such as cellular phone, personal digital assistant (PDA) are widely used, power consumption comes to be a top-priority design concerns. Because those embedded systems become more and more complex than ever and they are operated under severe power and energy constrains, long battery lifetime with a limited energy is very critical. Even though there are various levels of energy optimization techniques, system level techniques are mainly focused on, for their stronger impact on power consumption of the overall system than traditional techniques : circuit level, switch level, architecture level, etc. In this technique, operating system (OS) plays the most important role in the system because it controls ...

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.71-73
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• 2000

Considering that there is the lack of standards for storing genome-related on-line documents, the techniques in Natural Language Processing (NLP) is likely to become more and more important. It is necessary to extract useful information from the raw text and to store it in a computer-readable database format. Recent advances in NLP technologies raise new challenges and opportunities for tackling genome-related on-line text for information extraction task, For example, we can obtain many useful information related to genetic network or metabolic pathways simply by analyzing verbs such as 'activate'or 'inhibit'in Medline abstracts in a fully automatic way, Thus, combining NLP techniques with genome informatics extends beyond the traditional realms of either technology to a variety of emerging applications.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.84-89
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• 2007

MIL-STD-461E, EMI military standard for the equipment and subsystems, requires reception characteristic tests to verify the susceptibility of a receiver against the jntermodulation and spurious signals. Because the CS103 and CS104 of MIL-STD-461E show the test configuration of the equipment unit like a traditional receiver, it is possible to verify the susceptibility only for the reception signal through analog filters. However, at present when software programming techniques make a progress, the CS103 and CS104 tests need to evaluate the reception signal of the subsystem which includes both the digital filtering effects and analog filter characteristics. These test and evaluation techniques applied to a surveillance radar subsystem. This paper researched the EMI test methods in order to confirm feasibility of these test and evaluation techniques. Also the test results are compared and analyzed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.887-890
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• 1993

By introducing the notion of constraint-oriented fuzzy inference, we will show that it provides us ways of fuzzy control methods that has abilities of adaptation, learning and self-organization. The basic supporting techniques behind these abilities are“hard”processing by Artificial Intelligence or traditional computational framework and“soft”processing by Neural Network or Genetic Algorithm techniques. The reason that these techniques can be incorporated to fuzzy control systems is that the notion of“constraint”itself has two fundamental properties, that is, the“modularity”property due to its declarativeness and the“logicality”property due to its two-valuedness. From the former property, the modularity property, decomposing and integrating constraints can be done easily and efficiently, which enables us to carry out the above“soft”processing. From the latter property, the logicality property, Qualitative Reasoning and Instance Generalization by Symbolic Reasoning an be carried out, thus enabling the“hard”processing.

• Transactions on Electrical and Electronic Materials
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• v.7 no.4
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• pp.184-188
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• 2006

This work describes efforts in the fabrication and testing of robust microelectromechanical systems (MEMS). Robustness is typically achieved by investigating non-silicon substrates and materials for MEMS fabrication. Some of the traditional MEMS fabrication techniques are applicable to robust MEMS, while other techniques are drawn from other technology areas, such as electronic packaging. The fabrication technologies appropriate for robust MEMS are illustrated through laminated polymer membrane based pressure sensor arrays. Each array uses a stainless steel substrate, a laminated polymer film as a suspended movable plate, and a fixed, surface micromachined back electrode of electroplated nickel. Over an applied pressure range from 0 to 34 kPa, the net capacitance change was approximately 0.14 pF. An important attribute of this design is that only the steel substrate and the pressure sensor inlet is exposed to the flow; i.e., the sensor is self-packaged.

• The Korean Journal of Financial Studies
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• v.3 no.2
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• pp.267-279
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• 1996

The prediction of future demand is a vital task in managing business operations. To this end, traditional approaches often focused on statistical techniques such as exponential smoothing and moving average. The need for better accuracy has led to nonlinear techniques such as neural networks and case based reasoning. In addition, experimental design techniques such as orthogonal arrays may be used to assist in the formulation of an effective methodology. This paper investigates a multistrategy approach involving neural nets, case based reasoning, and orthogonal arrays. Neural nets and case based reasoning are employed both separately and in combination, while orthoarrays are used to determine the best architecture for each approach. The comparative evaluation is performed in the context of an application relating to the prediction of Treasury notes.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.11
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• pp.1424-1431
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• 2004

With traditional experimental methods such as the secondary fluid (e.g., water) calorimetric method, it is very difficult to accurately test the local condensation heat transfer inside mini-channels. Hence, there are large discrepancies between the results of previous studies. The experimental methods as well as unidentified sources of uncertainties could be reasons for such discrepancies. In this study, innovative experimental techniques were developed to measure the in-tube condensation heat transfer coefficient. With these techniques, very low heat dissipation rates such as several watts from the mini-channel could be estimated and low mass flow rates below the 0.1 ㎏/h could be measured with reasonable uncertainties. To the authors' knowledge, these techniques provide a unique experimental apparatus for measuring the condensation heat transfer coefficients inside the sub-millimeter hydraulic diameter single channels.

• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.76-80
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• 2021

Post-concussion syndrome (PCS) following mild traumatic brain injury (mTBI) is a major factor that contributes to the increased socioeconomic burden caused by TBI. Myelin loss has been implicated in the development of PCS following mTBI. Diffusion tensor imaging (DTI), a traditional imaging modality for the evaluation of axonal and myelin integrity in mTBI, has intrinsic limitations, including its lack of specificity and its time-consuming and labor-intensive post-processing analysis. More recently, various fast MR techniques based on multicomponent relaxometry (MCR), including QRAPMASTER, mcDESPOT, and MDME sequences, have been developed. These MCR-based sequences can provide myelin water fraction/myelin volume fraction, a quantitative parameter more specific to myelin, which might serve as a surrogate marker of myelin volume, in a clinically feasible time. In this review, we summarize the clinical application of the MCR-based fast MR techniques in mTBI patients.

• The Journal of Information Systems
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• v.30 no.4
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• pp.247-275
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• 2021

Purpose This study attempted to predict corporate innovation and business performance using ensemble learning. Design/methodology/approach The ensemble techniques uses weak learning to create robust learning, which combines several weak models to derive improved performance. In this study, XGboost, LightGBM, and Catboost were used among ensemble techniques. It was compared and evaluated with traditional machine learning methods. Findings The summary of the research results is as follows. First, the type of innovation is expanding from technical innovation to non-technical areas. Second, it was confirmed that LightGBM performed best for radical innovation prediction, and XGboost performed best for incremental innovation prediction. Third, Catboost performed best for firm performance prediction. Although there was no significant difference in predictive power between ensemble techniques, we found that comparative analysis was necessary to confirm better prediction performance.