A method for evaluating fault coverage using simulated fault injection for digitalized systems in nuclear power plants

The fault coverage for digital system in nuclear power plants is evaluated using a simulated fault injection method. Digital systems have numerous advantages, such as hardware elements share and hardware replication of the needed number of independent channels. However, the application of digital systems to safety-critical systems in nuclear power plants has been limited due to reliability concerns. In the reliability issues, fault coverage is one of the most important factors. In this study, we propose an evaluation method of the fault coverage for safety-critical digital systems in nuclear power plants. The system under assessment is a local coincidence logic processor for a digital plant protection system at Ulchin nuclear power plant units 5 and 6. The assessed system is simplified and then a simulated fault injection method is applied to evaluate the fault coverage of two fault detection mechanisms. From the simulated fault injection experiment, the fault detection coverage of the watchdog timer is 44.2% and that of the read only memory (ROM) checksum is 50.5%. Our experiments show that the fault coverage of a safety-critical digital system is effectively quantified using the simulated fault injection method.     

Controlled density of vertically aligned carbon nanotubes in a triode plasma chemical vapor deposition system

We report on the growth mechanism and density control of vertically aligned carbon nanotubes using a triode plasma enhanced chemical vapor deposition system. The deposition reactor was designed in order to allow the intermediate mesh electrode to be biased independently from the ground and power electrodes. The CNTs grown with a mesh bias of + 300 V show a density of ∼ 1.5 μm^− 2 and a height of ∼ 5 μm. However, CNTs do not grow when the mesh electrode is biased to − 300 V. The growth of CNTs can be controlled by the mesh electrode bias which in turn controls the plasma density and ion flux on the sample.     

Preparation of CuInSe2 thin films through metal organic chemical vapor deposition method by using di-μ-methylselenobis(dimethylindium) and bis(ethylisobutyrylacetato) copper(II) precursors

Highly polycrystalline copper indium diselenide (CuInSe₂) thin films on molybdenum substrate were successfully grown at 330 °C through two-stage metal organic chemical vapor deposition (MOCVD) method by using two precursors at relatively mild conditions. First, phase pure InSe thin film was prepared on molybdenum substrate by using a single-source precursor, di-μ-methylselenobis(dimethylindium). Second, on this InSe/Mo film, bis(ethylisobutyrylacetato) copper(II) designated as Cu(eiac)₂ was treated by MOCVD to produce CuInSe₂ films. The thickness and stoichiometry of the product films were found to be easily controlled in this method by adjusting the process conditions. Also, there were no appreciable amounts of carbon and oxygen impurities in the prepared copper indium diselenide films.     

Improvement in the volume efficiency of multilayered ceramic capacitors (MLCCs)

An attempt was made to improve the conventional lamination process in the multilayered ceramic capacitors (MLCCs) fabrication by adapting a solvent treatment on the BaTiO₃ green sheets. During the lamination process, additional particle rearrangement in the BaTiO₃ green block was stimulated by solvent treatment. This rearrangement resulted in a more compact structure of the BaTiO₃ green sheets compared to that obtained through the conventional lamination process. The overall thickness of BaTiO₃/Ni/BaTiO₃ laminates was also reduced after solvent treatment. It is suggested that miniaturization in the fabrication of MLCCs can be improved by adapting a solvent treatment during the lamination process.     

Effect of Hot Band Annealing on Magnetic Properties in 3% Si Grain‐oriented Electrical Steels

Hot band annealing is known to be a prerequisite for good magnetic properties irrespective of manufacturing methods in grain‐oriented Fe‐3 wt.% Si electrical steels. In this study, the effects of hot band annealing on magnetic properties were investigated in 3% grain‐oriented electrical steels of low soluble AI contents and one‐stage cold rolling. Microstructure and precipitate distribution were compared with hot band annealing conditions. Secondary recrystallization behaviour with hot band annealing condition was also discussed.     

Phase evolution and dielectric properties of MgTi2O5 ceramic sintered with lithium borosilicate glass

Phase evolution, densification, and dielectric properties of MgTi₂O₅ dielectric ceramic, sintered with lithium borosilicate (LBS) glass, were studied. Reaction between LBS glass and MgTi₂O₅ was significant in forming secondary phases such as TiO₂ and (Mg,Ti)₂(BO₃)O. The glass addition was not necessarily deleterious to the dielectric properties due to the formation of TiO₂: permittivity increased and temperature coefficient of resonance frequency could be tuned to zero with the addition of LBS glass, although the inevitable glass-induced decrease of quality factor was not retarded by the formation of TiO₂. The sintered specimen with 10 wt% LBS fired at 950 °C for 2 h showed permittivity of 19.3, quality factor of 6800 GHz, and τ_f of −16 ppm/°C.     

A simple receding horizon control for state delayed systems and its stability criterion

In this paper, a simple receding horizon (or model predictive) control for state delayed systems is presented and its solution is given in a closed form by a reduction method. While the control for a time-delay system is usually complex, the proposed controller is simple to construct and therefore can be simply implemented in real applications. To check the closed-loop stability of the proposed controller, a sufficient condition is provided by linear matrix inequalities. In addition, a numerical algorithm is presented for computing the eigenvalues of systems with distributed time delays, which can be used as a necessary and sufficient condition to check closed-loop stability. It is shown by simulation that this simple control can be a stabilizing control for time-delay systems.     

Information processing using chaos with application to mobile robot navigation problems

Throughout this study on information processing using an artificial neural network (ANN) and chaos we are attempting to devise a memory model that resembles human behavioral characteristics. For that purpose we construct a framework of the macroscopic model of the responding process in biological systems. Incoming stimuli are applied to the sensory receptors and preprocessed. A pattern-matching block allows one of the chaotic memories to find a feasible response in an associative way. After the chaotic memory is stabilized on one of the stable equilibrium points or limit cycles, its performance is evaluated. Since chaotic memory and the performance evaluation block form a feedback loop, they can handle features of the information blocks and store newly updated information blocks. Two kinds of chaotic memories are established in this paper: one is a 1-D map in which many information blocks can be stored as unstable periodic orbits, and the other is the famous Lozi attractor with rich dynamics. Simulations are performed for the mobile robot navigation problem in each case. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

Hybrid accident simulation methodology using artificial neural networks for nuclear power plants

A hybrid accident simulation methodology for nuclear power plants is proposed to enhance the capabilities of compact simulator by introducing artificial neural networks. Two neural networks are trained with the target values obtained from the analyses of detailed computer codes and trained results are combined with the compact simulator to perform the following roles: (i) compensation for inaccuracies of a compact simulator occurring from simplified governing equation and reduced number of physical control volumes, and (ii) prediction of the critical parameter usually calculated from the sophisticated computer code: the autoassociative neural network improves the computational results of the compact simulator up to the accuracy level of detailed best estimate computer code, while the backpropagation neural network predicts the minimum departure from nucleate boiling ratio (DNBR). Simulations are carried out to verify the applicability of the proposed methodology for the loss of flow accidents and the results show that the neural networks can be used as a complementary tool to improve the results of a compact simulator.     

An agent for selecting optimal order set in EC marketplace

For most small manufacturing companies, the selection and completion of orders placed by buyers are closely linked to the load status of their production lines. The decision to accept an order, or the selection of optimal order set, critically depends on the production schedule when orders exceed production capacity. However, production scheduling is mainly performed by human experts, and small companies lack resources to implement such a task. As a result, most small manufacturers suffer from the difficulty to meet due dates or to make proper decision in accepting new orders. To address this problem, this paper develops an automatic agent that selects an optimal set of orders using commonly available Internet technology. The main engine of the selection agent is based on a typical job shop scheduling model, formulating and implementing it as an Integer Program (IP) model. We also translate IP into Genetic Algorithm (GA) to address its NP-hard problem. We conclude with a suggestion for an agent architecture that tackles Web-based order selection problems.