• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.493-498
• /
• 2000

The magnetic bearing systems are intrinsically unstable, and need the feedback control of electromagnetic forces with measured displacements. So the controller design plays an important role in constructing high performance magnetic bearing system. In case of magnetic bearing systems, the order of identified model is high because of unknown dynamics included in closed loop systems - such as sensor dynamics, actuator dynamics-and non-linearity of magnetic bearings itself. "Identification for control" - joint optimization of system identification and controller design- is proposed to get the limited-order model which is suited for the design of high-performance controller. We applied the joint identification/controller design scheme to MIMO rigid rotor system supported by magnetic bearings. Firs, we designed controller of a nonlinear simulation model of MIMO magnetic bearing system with this scheme and proved its feasibility. Then, we performed experiments on MIMO rigid rotor system supported by magnetic bearings, and the performance of closed-loop system is improved gradually during the iteration.

• Nuclear Engineering and Technology
• /
• v.54 no.5
• /
• pp.1635-1643
• /
• 2022

STPA (System-Theoretic Process Analysis) is a widely used safety analysis technique to identify UCAs (Unsafe Control Actions) resulting in potential losses. It is totally dependent on the experience and ability of analysts to construct an information model called Control Structures, upon which analysts try to identify unsafe controls between system components. This paper proposes a formal approach to support the manual identification of UCAs, effectively and systematically. It allows analysts to mechanically extract Process Model, an important element that makes up the Control Structures, from a formal requirements specification for a software controller. It then concisely constructs the contents of Context Tables, from which analysts can identify all relevant UCAs effectively, using a software fault tree analysis technique. The case study with a preliminary version of a Korean nuclear reactor protections system shows the proposed approach's effectiveness and applicability.

• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.129-134
• /
• 1998

In this paper, a simple method is presented to synthesize a transfer function from experimentally obtained gain and phase data. The method we offer here is based on the previous method given by M.Hassul etc. [1], where they proposed relevant formulas in a straightforward manner so that undergraduate students could follow the development more easily. This method, however, inevitably is accompanied by a significant difference between the real and identified model especially in the low frequency region. We solve this problem by introducing a new weighting function that can be determined by using the additive uncertainty of the Identified transfer function.

• International Journal of Control, Automation, and Systems
• /
• v.2 no.3
• /
• pp.298-309
• /
• 2004

In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.

• 제어로봇시스템학회:학술대회논문집
• /
• 1999.10a
• /
• pp.45-48
• /
• 1999

Model predictive control algorithm requires a relevant model of the system to be controlled. Unfortunately, the first principle model describing a polymerization reaction system has a large number of parameters to be estimated. Thus there is a need for the identification and control of a polymerization reactor system by using available input-output data. In this work, the polynomial auto-regressive moving average (ARMA) models are employed as the input-output model and combined into the nonlinear model predictive control algorithm based on the successive linearization method. Simulations are conducted to identify the continuous styrene polymerization reactor system. The input variables are the jacket inlet temperature and the feed flow rate whereas the output variables are the monomer conversion and the weight-average molecular weight. The polynomial ARMA models obtained by the system identification are used to control the monomer conversion and the weight-average molecular weight in a continuous styrene polymerization reactor It is demonstrated that the nonlinear model predictive controller based on the polynomial ARMA model tracks the step changes in the setpoint satisfactorily. In conclusion, the polynomial ARMA model is proven effective in controlling the continuous styrene polymerization reactor.

• Journal of Information Processing Systems
• /
• v.20 no.3
• /
• pp.399-408
• /
• 2024

The moisture content of crude oil notably affects various aspects of oil production, storage, transportation, and exploration. However, accurately measuring this moisture content is challenging because of numerous influencing factors, leading to a lack of precision in existing detection methods. This inadequacy hinders the progress of China's petroleum industry. To overcome these challenges, this paper proposes a conductivity-based method for measuring crude oil moisture content. By employing an embedded system, we designed a sensor comprising five electrodes. Additionally, we developed signal excitation and signal processing circuits. Moreover, a software program was designed to analyze and compute the output signal using fast Fourier transform operations. This facilitated the identification of flow patterns, computation of relevant flow rates, and establishment of correlation rates based on frequency spectral characteristics. Based on experimental results, we established a functional relationship between measurement parameters and crude oil moisture content. This study enhanced the precision of moisture content measurement, thereby addressing existing limitations and fostering the advancement of China's petroleum industry.

• Korean Journal of Otorhinolaryngology-Head and Neck Surgery
• /
• v.62 no.2
• /
• pp.75-81
• /
• 2019

The objective of this study was to perform a systematic review of the literature for application of intranasal sodium citrate in the patients with olfactory dysfunction to help determine the sodium citrate treatments for this condition. Two authors independently searched the data base (Medline, Scopus, and the Cochrane database) for relevant studies from inception to January 2018. Included studies were randomized controlled studies published in English comparing topical sodium citrate application (treatment group) with saline (control group) in patients who had olfactory dysfunction. Outcomes of interest included the change of olfactory identification and threshold during 2 hours post-treatment. Three studies were enrolled in the meta-analysis. Compared with control group, treatment group did not increase posttreatment score of olfactory identification [standardized mean difference (SMD)=-0.03; 95% confidence interval (CI)=-0.29-0.24; I²=0%] and olfactory threshold (SMD=0.18; 95% CI=-0.09-0.45; I²=0%) significantly. In the degree of pre-post improvement of two outcomes, although treatment group statistically showed the significant improvement in olfactory threshold (SMD=0.30; 95% CI=0.05-0.55; I²=17%), the clinical significance of this outcome was meaningless. Similarly, there was no significant difference in olfactory identification between two groups (SMD=0.17; 95% CI=-0.11-0.45; I²=22%). Unlike the recent favorable results, our summated results presented the uselessness for the local application of sodium citrate in improving patient's olfactory function. However, we also had some limitation such as small sample size and inconsistent application methods. Therefore, larger trials and standardized methodology are needed to reach more stronger and exact results.

• International journal of advanced smart convergence
• /
• v.13 no.2
• /
• pp.48-60
• /
• 2024

With the exponential growth of satellite data utilization, machine learning has become pivotal in enhancing innovation and cybersecurity in satellite systems. This paper investigates the role of machine learning techniques in identifying and mitigating vulnerabilities and code smells within satellite software. We explore satellite system architecture and survey applications like vulnerability analysis, source code refactoring, and security flaw detection, emphasizing feature extraction methodologies such as Abstract Syntax Trees (AST) and Control Flow Graphs (CFG). We present practical examples of feature extraction and training models using machine learning techniques like Random Forests, Support Vector Machines, and Gradient Boosting. Additionally, we review open-access satellite datasets and address prevalent code smells through systematic refactoring solutions. By integrating continuous code review and refactoring into satellite software development, this research aims to improve maintainability, scalability, and cybersecurity, providing novel insights for the advancement of satellite software development and security. The value of this paper lies in its focus on addressing the identification of vulnerabilities and resolution of code smells in satellite software. In terms of the authors' contributions, we detail methods for applying machine learning to identify potential vulnerabilities and code smells in satellite software. Furthermore, the study presents techniques for feature extraction and model training, utilizing Abstract Syntax Trees (AST) and Control Flow Graphs (CFG) to extract relevant features for machine learning training. Regarding the results, we discuss the analysis of vulnerabilities, the identification of code smells, maintenance, and security enhancement through practical examples. This underscores the significant improvement in the maintainability and scalability of satellite software through continuous code review and refactoring.

• Steel and Composite Structures
• /
• v.20 no.6
• /
• pp.1345-1368
• /
• 2016

This paper presents the probabilistic-based assessment of composite steel-concrete structures through an innovative framework. This framework combines model identification and reliability assessment procedures. The paper starts by describing current structural assessment algorithms and the most relevant uncertainty sources. The developed model identification algorithm is then presented. During this procedure, the model parameters are automatically adjusted, so that the numerical results best fit the experimental data. Modelling and measurement errors are respectively incorporated in this algorithm. The reliability assessment procedure aims to assess the structure performance, considering randomness in model parameters. Since monitoring and characterization tests are common measures to control and acquire information about those parameters, a Bayesian inference procedure is incorporated to update the reliability assessment. The framework is then tested with a set of composite steel-concrete beams, which behavior is complex. The experimental tests, as well as the developed numerical model and the obtained results from the proposed framework, are respectively present.

• Bulletin of the Korean Chemical Society
• /
• v.24 no.3
• /
• pp.279-284
• /
• 2003

The One Pass Method (OPM) previously presented for the identification of single input single output systems is used to estimate the parameters of a Decentralized Control System (DCS). The OPM is a linear and therefore a simple estimation method. All of the calculations are performed in one pass, and no initial parameter guess, iteration, or powerful search methods are required. These features are of interest especially when the parameters of multi input-output model are estimated. The benefits of the OPM are revealed by comparing its results against those of two recently published methods based on pulse testing. The comparison is performed using two databases from the literature. These databases include single and multi input-output process transfer functions and relevant disturbances. The closed loop responses of these processes are roughly captured by the previous methods, whereas the OPM gives much more accurate results. If the parameters of a DCS are estimated, the OPM yields the same results in multi or single structure implementation. This is a novel feature, which indicates that the OPM is a convenient and practice method for the parameter estimation of multivariable DCSs.