• Journal of Advanced Marine Engineering and Technology
• /
• v.24 no.3
• /
• pp.99-105
• /
• 2000

For multi-body dynamic problems. especially coalescent eigenvalue problems with multiobjective optimization, the design sensitivity analysis is too much complicated mathematically and numerically. Therefore, this article proposes a new technique for structural dynamic modification using a mode modification and homologous structures design method with Genetic Algorithm(GA). In this work, the homologous structure of the resiliently mounted multi-body for marine diesel generator systems is studied and the problem is treated as a combinational optimization problem using the GA. In GA formulation, fitness is defined based on penalty function approach. That include homology, allowable stress and minimum weight of common plate.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.26 no.1
• /
• pp.16-24
• /
• 2021

Solar array simulator (SAS) is essential equipment in testing and evaluating the power processing performance of a power conditioning system. However, the nonlinearity in the current(I)-voltage(V) characteristic makes the control loop design of SAS a challenging task. Conventionally, only the inner loop is usually considered in the control design approach. However, this study proves that the reference generation loop also interacts with the inner loop and plays a key role in the overall performance of the SAS. In this paper, the performance of voltage-mode control and impedance control, which are two of the most popular architectures for the SAS system, are reviewed and compared by multi-loop analysis.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.18 no.8
• /
• pp.1985-1996
• /
• 1994

A new design method of sliding hyperplanes is proposed in the synthesis of a variable structure controller for robust tracking of general nonlinear multi-input-output(MIMO) uncertain systems of relative degree higher than two. Input/ output(I/O) linearzation is firstly undertaken by employing the concept of relative degree and minimum phase followed by the construction of sliding mode controllers. Sliding hyperplanes are then derived from the inherent properties of companion matrix and ideal sliding mode characterized in I/O linearized system. Subsequently, the gradient magnitudes of the sling hyperplanes are determined in an optimal manner by considering a quadratic performance index to be evaluated at two phases; a reaching phase and a sliding phase. The proposed design methodology is relatively straightforward and systematic compared with conventional strategies such as geometric approach or pole assignment technique. A nonlinear governor and exciter control problem for a power system is adopted herein in order to demonstrate the design efficiency and also favorable and robust control performances.

• International Journal of Fluid Machinery and Systems
• /
• v.3 no.4
• /
• pp.352-359
• /
• 2010

In recent years, the market has shown increasing interest in pump-turbines. The prompt availability of pumped storage plants and the benefits to the power system achieved by peak lopping, providing reserve capacity, and rapid response in frequency control are providing a growing advantage. In this context, there is a need to develop pumpturbines that can reliably withstand dynamic operation modes, fast changes of discharge rate by adjusting the variable diffuser vanes, as well as fast changes from pumping to turbine operation. In the first part of the present study, various flow patterns linked to operation of a pump-turbine system are discussed. In this context, pump and turbine modes are presented separately and different load cases are shown in each operating mode. In order to create modern, competitive pump-turbine designs, this study further explains what design challenges should be considered in defining the geometry of a pump-turbine impeller. The second part of the paper describes an innovative, staggered approach to impeller development, applied to a low head pump-turbine project. The first level of the process consists of optimization strategies based on evolutionary algorithms together with 3D in-viscid flow analysis. In the next stage, the hydraulic behavior of both pump mode and turbine mode is evaluated by solving the full 3D Navier-Stokes equations in combination with a robust turbulence model. Finally, the progress in hydraulic design is demonstrated by model test results that show a significant improvement in hydraulic performance compared to an existing reference design.

• Smart Structures and Systems
• /
• v.2 no.2
• /
• pp.141-154
• /
• 2006

Recently, the Hilbert-Huang transform (HHT) has gained considerable attention as a novel technique of signal processing, which shows promise for the system identification and damage detection of structures. This study investigates the effectiveness and accuracy of the HHT method for the system identification and damage detection of structures through a series of experiments. A multi-degree-of-freedom (MDOF) structural model has been constructed with modular members, and the columns of the model can be replaced or removed to simulate damages at different locations with different severities. The measured response data of the structure due to an impulse loading is first decomposed into modal responses using the empirical mode decomposition (EMD) approach with a band-pass filter technique. Then, the Hilbert transform is subsequently applied to each modal response to obtain the instantaneous amplitude and phase angle time histories. A linear least-square fit procedure is used to identify the natural frequencies and damping ratios from the instantaneous amplitude and phase angle for each modal response. When the responses at all degrees of freedom are measured, the mode shape and the physical mass, damping and stiffness matrices of the structure can be determined. Based on a comparison of the stiffness of each story unit prior to and after the damage, the damage locations and severities can be identified. Experimental results demonstrate that the HHT method yields quite accurate results for engineering applications, providing a promising tool for structural health monitoring.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.93-107
• /
• 2014

In this paper, we report what we have observed with regard to user-perspective issue clustering based on multi-layered two-mode network analysis. This work is significant in the context of data collection by companies about customer needs. Most companies have failed to uncover such needs for products or services properly in terms of demographic data such as age, income levels, and purchase history. Because of excessive reliance on limited internal data, most recommendation systems do not provide decision makers with appropriate business information for current business circumstances. However, part of the problem is the increasing regulation of personal data gathering and privacy. This makes demographic or transaction data collection more difficult, and is a significant hurdle for traditional recommendation approaches because these systems demand a great deal of personal data or transaction logs. Our motivation for presenting this paper to academia is our strong belief, and evidence, that most customers' requirements for products can be effectively and efficiently analyzed from unstructured textual data such as Internet news text. In order to derive users' requirements from textual data obtained online, the proposed approach in this paper attempts to construct double two-mode networks, such as a user-news network and news-issue network, and to integrate these into one quasi-network as the input for issue clustering. One of the contributions of this research is the development of a methodology utilizing enormous amounts of unstructured textual data for user-oriented issue clustering by leveraging existing text mining and social network analysis. In order to build multi-layered two-mode networks of news logs, we need some tools such as text mining and topic analysis. We used not only SAS Enterprise Miner 12.1, which provides a text miner module and cluster module for textual data analysis, but also NetMiner 4 for network visualization and analysis. Our approach for user-perspective issue clustering is composed of six main phases: crawling, topic analysis, access pattern analysis, network merging, network conversion, and clustering. In the first phase, we collect visit logs for news sites by crawler. After gathering unstructured news article data, the topic analysis phase extracts issues from each news article in order to build an article-news network. For simplicity, 100 topics are extracted from 13,652 articles. In the third phase, a user-article network is constructed with access patterns derived from web transaction logs. The double two-mode networks are then merged into a quasi-network of user-issue. Finally, in the user-oriented issue-clustering phase, we classify issues through structural equivalence, and compare these with the clustering results from statistical tools and network analysis. An experiment with a large dataset was performed to build a multi-layer two-mode network. After that, we compared the results of issue clustering from SAS with that of network analysis. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The sample dataset contains 150 million transaction logs and 13,652 news articles of 5,000 panels over one year. User-article and article-issue networks are constructed and merged into a user-issue quasi-network using Netminer. Our issue-clustering results applied the Partitioning Around Medoids (PAM) algorithm and Multidimensional Scaling (MDS), and are consistent with the results from SAS clustering. In spite of extensive efforts to provide user information with recommendation systems, most projects are successful only when companies have sufficient data about users and transactions. Our proposed methodology, user-perspective issue clustering, can provide practical support to decision-making in companies because it enhances user-related data from unstructured textual data. To overcome the problem of insufficient data from traditional approaches, our methodology infers customers' real interests by utilizing web transaction logs. In addition, we suggest topic analysis and issue clustering as a practical means of issue identification.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.3
• /
• pp.107-114
• /
• 2017

In beacon-enabled mode, beacon collision is a significant problem for the scalability of cluster-tree wireless sensor networks. In this paper, multi-channel time division scheduling (MCTS) is proposed to prevent beacon collisions and provide scalability. A coordinator broadcasts a beacon frame, including information on allocated channels and time-slots, and a new node determines its own channel and time-slot. The performance of the proposed method is evaluated by comparing the proposed approach with a typical ZigBee. MCTS prevents beacon collisions in cluster-tree wireless sensor networks. It enables large-scale wireless sensor networks based on a cluster tree to be scalable and effectively constructed.

• KIPS Transactions on Computer and Communication Systems
• /
• v.4 no.11
• /
• pp.369-382
• /
• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(quality of service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to activate only the minimum number of servers needed to handle current user requests. Previous studies on energy aware server cluster put efforts to reduce power consumption or heat dissipation, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management method to improve not only performance per watt but also QoS of the existing server power mode control method based on autonomous learning. Our proposed method is to adjust server power mode based on a hybrid approach of autonomous learning method with multi level thresholds and power consumption prediction method. Autonomous learning method with multi level thresholds is applied under normal load situation whereas power consumption prediction method is applied under abnormal load situation. The decision on whether current load is normal or abnormal depends on the ratio of the number of current user requests over the average number of user requests during recent past few minutes. Also, a dynamic shutdown method is additionally applied to shorten the time delay to make servers off. We performed experiments with a cluster of 16 servers using three different kinds of load patterns. The multi-threshold based learning method with prediction and dynamic shutdown shows the best result in terms of normalized QoS and performance per watt (valid responses). For banking load pattern, real load pattern, and virtual load pattern, the numbers of good response per watt in the proposed method increase by 1.66%, 2.9% and 3.84%, respectively, whereas QoS in the proposed method increase by 0.45%, 1.33% and 8.82%, respectively, compared to those in the existing autonomous learning method with single level threshold.

• Steel and Composite Structures
• /
• v.20 no.4
• /
• pp.777-799
• /
• 2016

One of the major components for performance based seismic design is accurate estimation of critical seismic demand parameters. While nonlinear seismic analysis is the most appropriate analysis method for estimation of seismic demand parameters, this method is very time consuming and complex. Single mode pushover analysis method, N2 method and multi-mode pushover analysis method, modal pushover analysis (MPA) are two nonlinear static methods that have recently been used for seismic performance evaluation of few lateral load-resisting systems. This paper further investigates the applicability of N2 and MPA methods for estimating the seismic demands of ductile unstiffened steel plate shear walls (SPSWs). Three different unstiffened SPSWs (4-, 8-, and 15-storey) designed according to capacity design approach were analysed under artificial and real ground motions for Vancouver. A comparison of seismic response quantities such as, height-wise distribution of floor displacements, storey drifts estimated using N2 and MPA methods with more accurate nonlinear seismic analysis indicates that both N2 and MPA procedures can reasonably estimates the peak top displacements for low-rise SPSW buildings. In addition, MPA procedure provides better predictions of inter-storey drifts for taller SPSW. The MPA procedure has been extended to provide better estimate of base shear of SPSW.

• Journal of the Korean Society of Safety
• /
• v.22 no.5
• /
• pp.1-6
• /
• 2007

Vibro-impacts in manual transmissions result due to several nonlinearities such as multi-staged clutch characteristics and gear backlashes. For the sake of understanding the torsional system, one specific manual transmission with front engine and front wheel drive configuration is investigated with a linear model under the several assumptions substituting the nonlinear factors. First, this system is examined with the mathematical approaches by expressing the governing equations to find out the torsional motions. Second, this system is analyzed using the linear model in order to understand its modal and frequency response characteristics using eigensolution method and the FRF(Frequency Responses Function) analysis. Third, with the given results from the eigensolutions, several mode shapes are investigated related to the torsional motion characteristics. Fourth, the system characteristics with the FRFs are studied with the basic approach, with which the several key parameters will be suggested based upon the results in the further studies.