• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.5
• /
• pp.710-715
• /
• 2010

As wind has become one of the fastest growing renewable energy sources, the key issue of wind energy conversion systems is how to efficiently operate the wind turbines in a wide range of wind speeds. The wind speed has a huge impact on the dynamic response of wind turbine. For this purpose, many control algorithms are in need for a method to measure wind speed to increase performance. Unfortunately, no accurate measurement of the effective wind speed is online available from direct measurements, which means that it must be estimated in order to make such control methods applicable in practice. In this paper, a new method based on Kalman filter and artificial neural network is presented for the estimation of the effective wind speed. To verify the performance of the proposed scheme, some simulation studies are carried out.

• Journal of Korea Water Resources Association
• /
• v.41 no.10
• /
• pp.1059-1065
• /
• 2008

Rainfall data is the most basic input data to analyze the hydrological phenomena and can be missing due to various reasons. In this research, a neural network based model to estimate missing rainfall data as approximate values was developed for 12 rainfall stations in the Soyang river basin to improve existing methods. This approach using neural network has shown to be useful in many applications to deal with complicated natural phenomena and displayed better results compared to the popular offline estimating methods, such as RDS(Reciprocal Distance Squared) method and AMM(Arithmetic Mean Method). Additionally, we proposed automated data reconciliation systems composed of a neural network learning processer to be capable of real-time reconciliation to transmit reliable hydrological data online.

• Journal of Power Electronics
• /
• v.19 no.5
• /
• pp.1224-1234
• /
• 2019

Hybrid rotor position estimation methods that integrate a fundamental model and high frequency (HF) signal injection are widely used for the wide speed-range sensorless control of interior permanent-magnet synchronous machines (IPMSMs). However, the direct transition of two different schemes may lead to system fluctuations or system instability since two estimated rotor positions based on two different schemes are always unequal due to the effects of parameter variations, system delays and inverter nonlinearities. In order to avoid these problems, a seamless transition strategy to define and construct a virtual q-axis inductance is proposed in this paper. With the proposed seamless transition strategy, an estimated rotor position based on a fundamental model is forced to track that based on HF signal injection before the transition by adjusting the constructed virtual q-axis inductance. Meanwhile, considering that the virtual q-axis inductance changes with rotor position estimation errors, a new observer with a two-phase phase-locked loop (TP-PLL) is developed to accurately obtain the virtual q-axis inductance online. Furthermore, IPMSM sensorless control with maximum torque per ampere (MTPA) operations can be tracked automatically by selecting the proper virtual q-axis inductance. Finally, experimental results obtained from an IPMSM demonstrate the feasibility of the proposed seamless transition strategy.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.8
• /
• pp.2529-2551
• /
• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.2
• /
• pp.176-182
• /
• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• Advances in aircraft and spacecraft science
• /
• v.4 no.1
• /
• pp.53-64
• /
• 2017

Aerospace Launch Vehicles (ALV) are generally designed with high reliability to operate in complete security through fault avoidance practices. However, in spite of such precaution, fault occurring is inevitable. Hence, there is a requirement for on-board fault recovery without significant degradation in the ALV performance. The present study develops an advanced fault recovery strategy to improve the reliability of an Aerospace Launch Vehicle (ALV) navigation system. The proposed strategy contains fault detection features and can reconfigure the system against common faults in the ALV navigation system. For this purpose, fault recovery system is constructed to detect and reconfigure normal navigation faults based on the sliding mode observer (SMO) theory. In the face of pitch channel sensor failure, the original gyro faults are reconstructed using SMO theory and by correcting the faulty measurement, the pitch-rate gyroscope output is constructed to provide fault tolerant navigation solution. The novel aspect of the paper is employing SMO as an online tuning of analytical fault recovery solution against unforeseen variations due to its hardware/software property. In this regard, a nonlinear model of the ALV is simulated using specific navigation failures and the results verified the feasibility of the proposed system. Simulation results and sensitivity analysis show that the proposed techniques can produce more effective estimation results than those of the previous techniques, against sensor failures.

• Smart Structures and Systems
• /
• v.11 no.1
• /
• pp.19-34
• /
• 2013

The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified $1^{st}$ mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of $2^{nd}$ mode slope ratio could be used as another feature to indicate imminent pier settlement.

• Smart Structures and Systems
• /
• v.6 no.1
• /
• pp.1-16
• /
• 2010

This paper presents a novel smart MEMS accelerometer which employs a hybrid control algorithm and an estimator. This scheme is realized by adding a sliding-mode controller to a conventional PID closed loop system to achieve higher stability and higher dynamic range and to prevent pull-in phenomena by preventing finger displacement from passing a maximum preset value as well as adding an adaptive nonlinear observer to a conventional PID closed loop system. This estimator is used for online estimation of the parameter variations for MEMS accelerometers and gives the capability of self testing to the system. The analysis of convergence and resolution show that while the proposed control scheme satisfies these criteria it also keeps resolution performance better than what is normally obtained in conventional PID controllers. The performance of the proposed hybrid controller investigated here is validated by computer simulation.

• The Journal of Industrial Distribution & Business
• /
• v.11 no.4
• /
• pp.7-18
• /
• 2020

Purpose: This research investigates the impact of technology adoption on organisation productivity. The framework has three independent variables viz. technological change, information technology (IT) infrastructure, and IT knowledge management and one dependent variable as organisational productivity. Research design, data and methodology: An explanatory research design with a quantitative research method was employed, and data was collected using a self-administered questionnaire using online as well as an offline survey. The sample consisted of 300 IT managers and senior-level executives (production as well as service team) in leading IT companies in Malaysia selected using snowball sampling. Normality and reliability assessment was performed in the first stage utilising SPSS 22, and Confirmatory Factory Analysis (CFA) was performed with maximum likelihood estimation to assess the internal consistency, convergent validity, and discriminant validity. Finally, Structural Equation Model (SEM) and path analysis are conducted using AMOS 22. Results: The research findings demonstrated that technological change and IT infrastructure positively and significantly impact the organisation's productivity while IT knowledge management has significant but negative impact on organizational productivity of IT companies in Malaysia. Conclusion: The research concludes that all three factors plays important role in deciding organizational producvity. Recommendations, implications, limitations and future research avenues are discussed.

• Journal of Korea Multimedia Society
• /
• v.15 no.11
• /
• pp.1391-1398
• /
• 2012

Time synchronization is crucial in wireless sensor networks such as Wireless USB and WBAN for diverse purposes from the MAC to the application layer. This paper proposes online clock skew estimators to achieve energy-efficient time synchronization for wireless sensor networks. By using recursive least squares estimators, we not only reduce the amount of data which should be stored locally in a table at each sensor node, but also allow offset and skew compensations to be processed simultaneously. Our skew estimators can be easily integrated with traditional offset compensation schemes. The results of simulation and experiment show that the accuracy of time synchronization can be greatly improved through our skew compensation algorithm.