• Journal of the Korea institute for structural maintenance and inspection
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• v.21 no.5
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• pp.108-116
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• 2017

In this study, the prototype structure of seismically designed steel moment frame was analyzed statically and dynamically in order to demonstrate the applicability of energy-based approximate analysis with the dynamic effect of sudden column loss in the evaluation of the collapse resistance and a method for assessing the sensitivity to progressive collapse was proposed. For the purpose of comparing the structural behavior of buildings with different structural systems, the sensitivity of the structure to the sudden removal of vertical members can be used as a significant measure. The energy-based approximate analysis prediction for the prototype structure considered in the study showed good agreement with the dynamic analysis result. In the sensitivity evaluation, the structural robustness index that indicates the ability of a structure to resist collapse induced by abnormal loads was used. It was confirmed that the proposed methods can be used conveniently and rationally in progressive collapse analysis and design.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.909-937
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• 2011

Energy efficiency is one of the most critical issues in the design of wireless sensor networks. In object-tracking sensor networks, the data storage and query processing should be energy-conserving by decreasing the message complexity. In this paper, a Prediction-based Energy-conserving Approximate StoragE schema (P-EASE) is proposed, which can reduce the query error of EASE by changing its approximate area and adopting predicting model without increasing the cost. In addition, focusing on reducing the unnecessary querying messages, P-EASE enables an optimal query algorithm to taking into consideration to query the proper storage node, i.e., the nearer storage node of the centric storage node and local storage node. The theoretical analysis illuminates the correctness and efficiency of the P-EASE. Simulation experiments are conducted under semi-random walk and random waypoint mobility. Compared to EASE, P-EASE performs better at the query error, message complexity, total energy consumption and hotspot energy consumption. Results have shown that P-EASE is more energy-conserving and has higher location precision than EASE.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.5
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• pp.1155-1165
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• 1990

A new method of predicting effect of rolling parameters on roll pressure, roll force, and power and energy consumptions in hot strip rolling is presented. The method is based on approximate solutions for velocity, strain rate, and stress distributions in the roll gap. The degree of approximation was examined by the finite element solutions. The theoretical predictions were compared with experimental data from hot rolling of steel strip and steel plate.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.1
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• pp.1-9
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• 2003

This paper deals with the nodal admittance approach steady-state frequency domain analysis of the three-phase self-excited induction generator (SEIG) driven by the variable speed prime mover as the wind turbine. The steady-state performance analysis of this power conditioner designed for the renewable energy is based on the principle of equating the input mechanical power of the three-phase SEIG to the output mechanical power of the variable speed prime mover mentioned above. Us-ing the approximate frequency domain based equivalent circuit of the three-phase SEIG. The main features of the present algorithm of the steady-state performance analysis of the three-phase SEIG treated here are that the variable speed prime mover characteristics are included in the approximate equivalent circuit of the three-phase SEIG under the condition of the speed changes of the prime mover without complex computations processes. Furthermore, a feedback closed-loop voltage regulation of the three-phase SEIG as a power conditioner which is driven by variable speed prime movers such as the wind turbine(WT) employing the static VAR compensator(SVC) circuit composed of the thyristor phase controlled reactor(TCR) and the thyristor switched capacitor(TSC) controlled by the PI controller is designed and considered for wind-turbine driving power conditioner.

• ETRI Journal
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• v.36 no.1
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• pp.175-178
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• 2014

We introduce the Energy-Based Blind Separating (EBS) algorithm for extremely fast separation of mixed speech signals without loss of quality, which is performed in two stages: iterative-form separation and closed-form separation. This algorithm significantly improves the separation speed simply due to incorporating only some specific frequency bins into computations. Simulation results show that, on average, the proposed algorithm is 43 times faster than the independent component analysis (ICA) for speech signals, while preserving the separation quality. Also, it outperforms the fast independent component analysis (FastICA), the joint approximate diagonalization of eigenmatrices (JADE), and the second-order blind identification (SOBI) algorithm in terms of separation quality.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.7
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• pp.2167-2173
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• 1996

An approximate weight function technique using the indirect boundary integral equation has been presented for the analysis of stress intensity foactors(SIFs) of a thick pipe. One-term boundary integral was introduced to represent the crack surface displacement field for the displacement based weight function technique. An explicit closed-form SIF solution applicable to symmetric cracked pipes without any modification of the solution including both circumferential and radial cracks has been derived. The necessary information in the analysis is two or three reference SIFs. In most cases the SIF solution were in good agreement with those available in the literature.

• Structural Engineering and Mechanics
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• v.73 no.1
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• pp.97-108
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• 2020

This study presents a 4 node, 11 DOF/node plate element based on higher order shear deformation theory for lamina composite plates. The theory accounts for parabolic distribution of the transverse shear strain through the thickness of the plate. Differential field equations of composite plates are obtained from energy methods using virtual work principle. Differential field equations of composite plates are obtained from energy methods using virtual work principle. These equations were transformed into the operator form and then transformed into functions with geometric and dynamic boundary conditions with the help of the Gâteaux differential method, after determining that they provide the potential condition. Boundary conditions were determined by performing variational operations. By using the mixed finite element method, plate element named HOPLT44 was developed. After coding in FORTRAN computer program, finite element matrices were transformed into system matrices and various analyzes were performed. The current results are verified with those results obtained in the previous work and the new results are presented in tables and graphs.

• Structural Engineering and Mechanics
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• v.29 no.2
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• pp.187-201
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• 2008

Earthquake induced hysteretic energy demand for a structure can be used as a limiting value of a certain performance level in seismic design of structures. In cases where it is larger than the hysteretic energy dissipation capacity of the structure, failure will occur. To be able to select the limiting value of hysteretic energy for a particular earthquake hazard level, it is required to define the variation of hysteretic energy in terms of probabilistic terms. This study focuses on the probabilistic evaluation of earthquake induced worst failure probability and approximate confidence intervals for inelastic single-degree-of-freedom (SDOF) systems with a typical steel moment connection based on hysteretic energy. For this purpose, hysteretic energy demand is predicted for a set of SDOF systems subject to an ensemble of moderate and severe EQGMs, while the hysteretic energy dissipation capacity is evaluated through the previously published cyclic test data on full-scale steel beam-to-column connections. The failure probability corresponding to the worst possible case is determined based on the hysteretic energy demand and dissipation capacity. The results show that as the capacity to demand ratio increases, the failure probability decreases dramatically. If this ratio is too small, then the failure is inevitable.