• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3438-3448
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• 2021

Dynamic seals are one of the critical components of Absorber Rod Drive Mechanism of Fast Breeder Reactors, requiring separate experimental development. Their significance can't be overemphasized considering that the availability and re-usability of Control Rod Drive Mechanisms of Fast Breeder Test Reactor is governed by the failure rate of dynamic seals (bellows). For prototype and subsequent Fast Breeder Reactors in India, choice of the dynamic seal is changed to an in-house designed & developed labyrinth type V-ring seal. The present work is related to the numerical investigations carried out to gain insights into the sealing mechanism and the thermal behaviour of these seals. The results indicate that the seal geometry is very important for obtaining optimum performance. By changing the geometry of the present seal, performance enhancement by more than 50% in important indices is obtained. Further, the results point out that caution shall be exercised when the seal material & its operating temperature are changed. Also, the numerical model developed in this study will be useful for developing more robust dynamic seals in future.

• Journal of Computing Science and Engineering
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• v.2 no.4
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• pp.375-393
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• 2008

The dynamic cast operation allows flexibility in the design and use of data management facilities in object-oriented programs. Dynamic cast has an important role in the implementation of the Data Management Services (DMS) of the Mission Data System Project (MDS), the Jet Propulsion Laboratory's experimental work for providing a state-based and goal-oriented unified architecture for testing and development of mission software. DMS is responsible for the storage and transport of control and scientific data in a remote autonomous spacecraft. Like similar operators in other languages, the C++ dynamic cast operator does not provide the timing guarantees needed for hard real-time embedded systems. In a recent study, Gibbs and Stroustrup (G&S) devised a dynamic cast implementation strategy that guarantees fast constant-time performance. This paper presents the definition and application of a cosimulation framework to formally verify and evaluate the G&S fast dynamic casting scheme and its applicability in the Mission Data System DMS application. We describe the systematic process of model-based simulation and analysis that has led to performance improvement of the G&S algorithm's heuristics by about a factor of 2. In this work we introduce and apply a library for extracting semantic information from C++ source code that helps us deliver a practical and verifiable implementation of the fast dynamic casting algorithm.

• Proceedings of the KSME Conference
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• 2001.06d
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• pp.927-932
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• 2001

It is intended to develope an algorithm for dynamic simulation of fast-acting solenoid valves. The coupled equations of the electric, magnetic, and mechanical systems should be solved simultaneously in a transient nonlinear manner. The transient nonlinear electromagnetic field is analyzed by the Finite Element Method (FEM), which is coupled with nonlinear electronic circuitry. The dynamic movement of the solenoid valve is analyzed at every time step from the force balances acting on the plunger, which include the electromagnetic force calculated from the Finite Element analysis as well as the elastic force by a spring and the hydrodynamic pressure force along the flow passage. Dynamic responses of the solenoid valves predicted by this algorithm agree well with the experimental results including bouncing effects.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.7
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• pp.959-965
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• 2002

It is intended to develop an algorithm for dynamic simulation of a fast-acting solenoid valve. The coupled equations of electric, magnetic, and mechanical systems should be solved simultaneously in a transient nonlinear manner. The transient nonlinear electromagnetic field is analyzed by the Finite Element Method (FEM), which is coupled with nonlinear electronic circuitry. The dynamic movement of the solenoid valve is analyzed at every time step from the force balance acting on the plunger, which includes the electromagnetic force calculated from the Finite Element analysis as well as the elastic force by a spring and the hydrodynamic pressure force along the flow passage. Dynamic responses of the solenoid valves predicted by this algorithm agree well with the experimental results including bouncing effects.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.512-515
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• 2009

A 90-nm CMOS motion estimation (ME) processor was developed by employing dynamic voltage and frequency scaling (DVFS) to greatly reduce the dynamic power. To make full use of the advantages of DVFS, a fast ME algorithm and a small on-chip DC/DC converter were also developed. The fast ME algorithm can adaptively predict the optimum supply voltage ($V_D$) and the optimum clock frequency ($f_c$) before each block matching process starts. Power dissipation of the ME processor, which contained an absolute difference accumulator as well as the on-chip DC/DC converter and DVFS controller, was reduced to $31.5{\mu}W$, which was only 2.8% that of a conventional ME processor.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.540-546
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• 2003

A fast pattern classification algorithm with Cellular Nonlinear Network-based dynamic programming is proposed. The Cellular Nonlinear Networks is an analog parallel processing architecture and the dynamic programing is an efficient computation algorithm for optimization problem. Combining merits of these two technologies, fast pattern classification with optimization is formed. On such CNN-based dynamic programming, if exemplars and test patterns are presented as the goals and the start positions, respectively, the optimal paths from test patterns to their closest exemplars are found. Such paths are utilized as aggregating keys for the classification. The algorithm is similar to the conventional neural network-based method in the use of the exemplar patterns but quite different in the use of the most likely path finding of the dynamic programming. The pattern classification is performed well regardless of degree of the nonlinearity in class borders.

• Ocean Systems Engineering
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• v.6 no.1
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• pp.1-21
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• 2016

The global performance of the 5 MW OC4 semisubmersible floating wind turbine in random waves with or without steady/dynamic winds is numerically simulated by using the turbine-floater-mooring fully coupled dynamic analysis program FAST-CHARM3D in time domain. The numerical simulations are based on the complete second-order diffraction/radiation potential formulations along with nonlinear viscous-drag force estimations at the body's instantaneous position. The sensitivity of hull motions and mooring dynamics with varying wave-kinematics extrapolation methods above MWL(mean-water level) and column drag coefficients is investigated. The effects of steady and dynamic winds are also illustrated. When dynamic wind is added to the irregular waves, it additionally introduces low-frequency wind loading and aerodynamic damping. The numerically simulated results for the 5 MW OC4 semisubmersible floating wind turbine by FAST-CHARM3D are also extensively compared with the DeepCWind model-test results by Technip/NREL/UMaine. Those numerical-simulation results have good correlation with experimental results for all the cases considered.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.280-285
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• 1991

Neural network control has many innovative potentials for fast, accurate and intelligent adaptive control. In this paper, a learning control architecture for the dynamic control of a robot manipulator is developed using inverse dynamic neurocontroller and linear neurocontroher. The inverse dynamic neurocontrouer consists of a MLP (multi-layer perceptron) and the linear neurocontroller consists of SLPs (single layer perceptron). Compared with the previous type of neurocontroller which is using an inverse dynamic neurocontroller and a fixed PD gain controller, proposed architecture shows the superior performance over the previous type of neurocontroller because linear neurocontroller can adapt its gain according to the applied task. This superior performance is tested and verified through the control of PUMA 560. Without any knowledge on the dynamic model, its parameters of a robot , (The robot is treated as a complete black box), the neurocontroller, through practice, gradually and implicitly learns the robot's dynamic properties which is essential for fast and accurate control.

• Journal of the Korean Society of Safety
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• v.35 no.5
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• pp.39-48
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• 2020

This study proposes a fast estimation method of dynamic reliability indices or failure probability for SDOF structure subjected to earthquake excitations. The proposed estimation method attempts to derive coefficient function for correcting dynamic effects from static reliability analysis in order to estimate the dynamic reliability analysis results. For this purpose, a total of 60 cases of structures with various characteristics of natural frequency and damping ratio under various allowable limits were taken into account, and various types of approximation coefficient functions were considered as potential candidate models for dynamic effect correction. Each reliability index was computed by directly performing static and dynamic reliability analyses for the given 60 cases, and nonlinear curve fittings for potential candidate models were performed from the computed reliability index data. Then, the optimal estimation model was determined by evaluating the accuracy of the dynamic reliability analysis results estimated from each candidate model. Additional static and dynamic reliability analyses were performed for new models with different characteristics of natural frequency, damping ratio and allowable limit. From these results, the accuracy and numerical efficiency of the optimal estimation model were compared with the dynamic reliability analysis results. As a result, it was confirmed that the proposed model can be a very efficient tool of the dynamic reliability estimation for seismically excited SDOF structure since it can provide very fast and accurate reliability analysis results.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.