• Environmental Engineering Research
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• v.24 no.4
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• pp.559-565
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• 2019

In order to alleviate the environmental pressure caused by production process of thermal power plants, the application of cleaner production is imperative. To estimate the implementation effects of cleaner production in thermal plants and optimize the strategy duly, it is of great significance to take a comprehensive evaluation for sustainable development. In this paper, a hybrid model that integrated the analytic hierarchy process (AHP) with least squares support vector machine (LSSVM) algorithm optimized by grid search (GS) algorithm is proposed. Based on the establishment of the evaluation index system, AHP is employed to pre-process the data and GS is introduced to optimize the parameters in LSSVM, which can avoid the randomness and inaccuracy of parameters' setting. The results demonstrate that the combined model is able to be employed in the comprehensive evaluation of the cleaner production in the thermal power plants.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.261-273
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• 1997

This paper presents an EMQ model which determines an optimal production cycle and inspection schedules simultaneously in a deteriorating machine. It is assumed that a machine is subject to a random deterioration from an in-control state to an out-of-control state and thus producing some proportion of defective items. Optimal solutions and minimum average cost as well as some unique properties are derived. Numerical experiments are carried out to examine the behavior of the proposed model and compare the proposed model to the existing models. Several mistakes in the previous research are found and discussed.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• The KIPS Transactions:PartD
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• v.19D no.4
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• pp.281-292
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• 2012

A wide variety of smartphone applications is increasing the usage time of smartphone. Due to the increased time, it becomes difficult to providing stable services to users with limited battery capacity. The past works have been performed the power management of mobile device toward long-lasting battery development or low-power electric devices. However as the complexity of software embedded into system are increased, the research interests of the software power analysis is also increased. Among these studies on the software power analysis, model-based analysis technique is one of major interests because it can be able to analyze the power consumption before the development of source codes, then the analysis result can be used in the development of the software system, This paper suggests a model-based power analysis technique using UML state machine diagram. Our proposed technique estimates the power consumption by the simulation of Perti-net which is transformed from the state machine diagram.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.64-69
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• 2004

The work presented here deals with controller design using error model constructed with proportional control ramp response. The design aims at the improvement of transient response, steady-state error reduction with stability preservation, generation of the consistent contour error through the proportional gain regulation of a mismatched system. The first step is to generate tracking-error curve with proportional control only and decide the added error signal shape on the error curve. The next is to construct a table for the steady-state loop gain with step input. The table is used for selecting the proportional gain. The effectiveness of the proposed controller is confirmed through the simulation and experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4950-4966
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• 2015

Cloud providers now face the problem of estimating the amount of computing resources required to satisfy a future workload. In this paper, a virtual machine provisioning (VMP) mechanism is designed to adapt workload fluctuation. The arrival rate of forthcoming jobs is predicted for acquiring the proper service rate by adopting an exponential smoothing (ES) method. The proper service rate is estimated to guarantee the service level agreement (SLA) constraints by using a diffusion approximation statistical model. The VMP problem is formulated as a facility location problem. Furthermore, it is characterized as the maximization of submodular function subject to the matroid constraints. A greedy-based VMP algorithm is designed to obtain the optimal virtual machine provision pattern. Simulation results illustrate that the proposed mechanism could increase the average profit efficiently without incurring significant quality of service (QoS) violations.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.245-248
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• 1998

This paper proposes a sensorless velocity estimator using the reduced-order state equation of induction motor based on Kalman Filter. The electrical transients in the stator voltage equations of induction motor are neglected in the reduced-order model. The advantage of using the reduced-order model is to reduce the required number of numerical integrations for filtering the rotor speed. As changing the operating points and the parameters of the induction motor in simulation studies, the behavior of the sensorless velocity estimator as predicted by the reduced-order state equation of induction machine is compared with the behavior predicted by the complete state equation of induction machine.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.863-868
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• 2005

Hardware and software codesign framework called PeaCE(Ptolemy extension as a Codesign Environment) was developed. It allows to express both data flow and control flow which is described as fFSM which extends traditional finite state machine. While the fFSM model provides lots of syntactic constructs for describing control flow, it has a lack of their formality and then difficulties in verifying the specification. In order to define the formal semantics of the fFSM, in this paper, firstly the hierarchical structure in the model is flattened and then the step semantics is defined. As a result, some important bugs such as race condition, ambiguous transition, and circulartransition can be formally detected in the model.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.523-531
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• 2014

Brushless Dual-Mechanical-Port Electrical Machine (BLDMPEM) is a new type of motor designed for Hybrid Electric Vehicle (HEV), which contains two mechanical ports and two electric ports. Compared with Dual-Mechanical-Port Electrical Machine (DMPEM), the brushless structure brings higher reliability and easier maintenance. In this paper, the model of BLDMPEM is discussed. In Chapter 2, the energy flow and mathematical model of BLDMPEM are analyzed. Then a novel three-phase half-bridge controlled rectifier topology and its control strategy for cruising mode of HEV based on BLDMPEM are proposed in Chapter 3. Compared with the Field Oriented Control (FOC) strategy of BLDMPEM, the proposed method does not require accurate motor parameters, and it is much simpler and easier to be implemented. At last, simulation and experiment results show the feasibility and validity of the proposed strategy.