• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.4 no.2
• /
• pp.20-28
• /
• 2008

According to IAEA regulations, a transportation package of radioactive material should perform its intended function of containing the radioactive contents after the drop test, which is one of hypothetical accident conditions. Impact limiters attached to a transport cask absorb the most of impact energy. So, it is appreciated to determine properly the shape, size and material of impact limiters. A material data needed in this determination is a dynamic one. In this study, several materials considered as those of impact limiters were tested by a drop weight facility to acquire dynamic material characteristics data. Impact absorbing volume of the impact limiter was derived mathematically for each drop condition. A size optimization of impact limiter was conducted. The derived impact absorbing volumes were applied as constraints. These volumes should be less than critical volumes generated based on the dynamic material characteristics. The derived procedure to decide the shape of impact limiter can be useful at the preliminary design stage when the transportation package's outline is roughly determined and applied as input value.

• Journal of the Korea Society of Computer and Information
• /
• v.8 no.1
• /
• pp.156-165
• /
• 2003

The purpose of this study is concerned with possibilistic decision making model (PDMM) that can be used to help CEO and information systems managers decide which information systems should be selected. The application of IT which has influence on rapidly changed environment of enterprise plays an important role in enterprise's activity. When enterprise outsource IT, it is very important to select vendors that reflect goals and constraints of organization. For this purpose, mathematical model in which possibilistic programming is applied is suggested in this study. Although many researches have conducted in conventional programming and stochastic programming. they are still limited in solving practical problems and imprecise/uncertain situations. Multiple decision making model in which impreciseness of input variable is considered can be constructed in PDMM.

• Smart Structures and Systems
• /
• v.5 no.4
• /
• pp.415-426
• /
• 2009

This study focuses on the concept of multi-scale wireless sensor networks for damage detection in civil infrastructure systems by first over viewing the general network philosophy and attributes in the areas of data acquisition, data reduction, assessment and decision making. The data acquisition aspect includes a scalable wireless sensor network acquiring acceleration and strain data, triggered using a Restricted Input Network Activation scheme (RINAS) that extends network lifetime and reduces the size of the requisite undamaged reference pool. Major emphasis is given in this study to data reduction and assessment aspects that enable a decentralized approach operating within the hardware and power constraints of wireless sensor networks to avoid issues associated with packet loss, synchronization and latency. After over viewing various models for data reduction, the concept of a data-driven Bivariate Regressive Adaptive INdex (BRAIN) for damage detection is presented. Subsequent examples using experimental and simulated data verify two major hypotheses related to the BRAIN concept: (i) data-driven damage metrics are more robust and reliable than their counterparts and (ii) the use of heterogeneous sensing enhances overall detection capability of such data-driven damage metrics.

• Journal of the Society of Naval Architects of Korea
• /
• v.47 no.2
• /
• pp.233-241
• /
• 2010

Scaffolding system in the membrane LNG carrier is a steel structure composed of various pipe-shaped members connected by specific coupling devices. In this study, the automatic design program for scaffolding system in membrane LNG carrier has been developed. It enables user to arrange members easily considering design constraints and input variables such as size of tank, position of legs, level height and so on. In addition to that, it creates finite element analysis model with loading and boundary conditions automatically and carries out structural analysis. With post processor based a state-of-the-art computer graphics, users can easily check the results of structural analysis and make a report for structural safety of scaffolding system.

• The Journal of Korea Robotics Society
• /
• v.15 no.3
• /
• pp.212-220
• /
• 2020

This paper deals with a strategy of gain optimization for the kinematic control algorithm of a wire-driven surgical robot. The proposed controller consists of the closed-loop inverse kinematics with the back-calculation method. The closed-loop inverse kinematics has 18 PID control gains, and the back-calculation method has 6 gains. An efficient strategy is designed to optimize 18 values first and then the remaining 6 values. The optimal gain sets are searched under the step input with performance indices. In this gain optimization, the objective function is defined as the minimum value of signal-to-noise ratio of the performance indices for 6 DoF (Degree-of-Freedom) motion that is based on the Taguchi method, and the constraints are applied to obtain stable responses for each motion evenly. The gain sets obtained are verified by simulations using the test trajectories. In comparative results, the optimal gain value based on the performance index combined with ISE (integral of square error) and settling time showed the best control performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.13 no.4
• /
• pp.171-177
• /
• 2013

Due to the demand for high speed 3D graphic rendering, video file format conversion, compression, encryption and decryption technologies, the importance of digital signal processor system is growing rapidly. In order to satisfy the real-time constraints, high performance digital signal processor is required. Therefore, as in general purpose computer systems, digital signal processor should be designed as multicore architecture as well. Using UTDSP benchmarks as input, the trace-driven simulation has been performed and analyzed for the 2 to 16-core digital signal processor architectures with the cores from simple RISC to in-order and out-of-order superscalar processors for the various window sizes, extensively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.12 no.2
• /
• pp.69-76
• /
• 1998

This paper presents a novel algorithm for determining the optimal operation of a cogeneration plant with back-pressure and extraction-condensing turbine/generators. The proposed algorithm determines the optimum load of boilers and turbine/generators, using only one parameter, the steam mass flow rate, which can be obtained directly from on-line measurement during plant operation. The proposed algorithm consists of the non -linear operating cost function, and its correlated constraints. Furthermore, it has been successfully applied to an actual industrial cogeneration plant, with satisfactory results. Comparison of these results with actual operating data has revealed that using the proposed algorithm results in at least 1.2~4.5[%] operating cost saving, depending on the process steam load. Furthermore the proposed algorithm can be easily installed in a process control computer because the required input data can be easily obtained from information available on-line.n-line.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.297-298
• /
• 2007

Repetitive systems stand for a kind of systems that perform a simple task on a fixed pattern repetitively and are widely spread in industrial fields. Hence, those systems have been of much interests by many researchers, especially in the field of iterative learning control (ILC). In this paper, we propose a finite-horizon tracking control scheme for linear time-varying repetitive systems with uncertain initial conditions. The scheme is derived both analytically and numerically for state-feedback systems and only numerically for output-feedback systems. Then, it is extended to stable systems with input constraints. All numerical schemes are developed in the forms of linear matrix inequalities. A distinguished feature of the proposed scheme from the existing iterative learning control is that the scheme guarantees the tracking performance exactly even under uncertain initial conditions. The simulation results demonstrate the good performance of the proposed scheme.

• Proceedings of the KIEE Conference
• /
• 2007.10a
• /
• pp.231-232
• /
• 2007

In this paper, a fault diagnosis and control integrated system (FDCIS) was developed to control the thermoelectric (TE) power in the SP-100 space reactor. The objectives of the proposed model predictive control were to minimize both the difference between the predicted TE power and the desired power, and the variation of control drum angle that adjusts the control reactivity. Also, the objectives were subject to maximum and minimum control drum angle and maximum drum angle variation speed. A genetic algorithm was used to optimize the model predictive controller. The model predictive controller was integrated with a fault detection and diagnostics algorithm so that the controller can work properly even under input and output measurement faults. With the presence of faults, the control law was reconfigured using online estimates of the measurements. Simulation results of the proposed controller showed that the TE generator power level controlled by the proposed controller could track the target power level effectively even under measurement faults, satisfying all control constraints.

• Industrial Engineering and Management Systems
• /
• v.9 no.1
• /
• pp.10-19
• /
• 2010

This paper presents an extended computing procedure for the global optimization of the triple response system (TRS) where the response functions are nonconvex (nonconcave) quadratics and the input factors satisfy a radial region of interest. The TRS arising from response surface modeling can be approximated using a nonlinear mathematical program involving one primary (objective) function and two secondary (constraints) functions. An optimization algorithm named triple response surface algorithm (TRSALG) is proposed to determine the global optimum for the nondegenerate TRS. In TRSALG, the Lagrange multipliers of target (secondary) functions are computed by using the Hooke-Jeeves search method, and the Lagrange multiplier of the radial constraint is located by using the trust region (TR) method at the same time. To ensure global optimality that can be attained by TRSALG, included is the means for detecting the degenerate case. In the field of numerical optimization, as the family of TR approach always exhibits excellent mathematical properties during optimization steps, thus the proposed algorithm can guarantee the global optimal solution where the optimality conditions are satisfied for the nondegenerate TRS. The computing procedure is illustrated in terms of examples found in the quality literature where the comparison results with a gradient-based method are used to calibrate TRSALG.