• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.10
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• pp.767-770
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• 2010

In this work, we investigated the static characteristics of 4H-SiC vertical metal-oxidesemiconductor field effect transistors (VMOSFETs) by adjusting the doping level of n-epilayer and the effect of a current spreading layer (CSL), which was inserted below the p-base region with highly doped n+ state ($5{\times}10^{17}cm^{-3}$). The structure of SiC VMOSFET was designed by using a 2-dimensional device simulator (ATLAS, Silvaco Inc.). By varying the n-epilayer doping concentration from $1{\times}10^{16}cm^{-3}$ to $1{\times}10^{17}cm^{-3}$, we investigated the static characteristics of SiC VMOSFETs such as blocking voltages and on-resistances. We found that CSL helps distribute the electron flow more uniformly, minimizing current crowding at the top of the drift region and reducing the drift layer resistance. For that reason, silicon carbide VMOSFET structures of highly intensified blocking voltages with good figures of merit can be achieved by adjusting CSL and doping level of n-epilayer.

• Journal of Communications and Networks
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• v.18 no.2
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• pp.201-209
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• 2016

In wireless sensor networks (WSNs) duty cycling has been an imperative choice to reduce idle listening but it introduces sleep delay. Thus, the conventional WSN medium access control protocols are bound by the energy-latency tradeoff. To break through the tradeoff, we propose a radio wave sensor called radio frequency (RF) wakeup sensor that is dedicated to sense the presence of a RF signal. The distinctive feature of our design is that the RF wakeup sensor can provide the same sensitivity but with two orders of magnitude less energy than the underlying RF module. With RF wakeup sensor a sensor node no longer requires duty cycling. Instead, it can maintain a sleep state until its RF wakeup sensor detects a communication signal. According to our analysis, the response time of the RF wakeup sensor is much shorter than the minimum transmission time of a typical communication module. Therefore, we apply duty cycling to the RF wakeup sensor to further reduce the energy consumption without performance degradation. We evaluate the circuital characteristics of our RF wakeup sensor design by using Advanced Design System 2009 simulator. The results show that RF wakeup sensor allows a sensor node to completely turn off their communication module by performing the around-the-clock carrier sensing while it consumes only 0.07% energy of an idle communication module.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.149-161
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• 2023

Internet of Things (IoT) is a relatively new concept that has gained immense popularity in a short period of time due to its wide applicability in making human life more convenient and automated. As an illustration: the development of smart homes, smart cities, etc. However, it is also accompanied by a substantial number of risks and flaws. IoT makes use of low-powered devices, so secure, less time-consuming and energy-intensive transmission (routing) of messages due to the limited availability of energy is one of the many and most significant concerns for IoT developers. The following paper presents a trust-based routing scenario for the Internet of Things (IoT) that exploits the past transmission record from the cupcarbon simulator's log files. Artificial Neural Network is used to quantify knowledge of trust, calculate the value of trust, and share this information with other network devices. As a human behavioural pattern, trust provides a superior method for making routing decisions. If there is a tie in the trust values and no other path is available, the remaining battery power is used to break the tie and make a forwarding decision; this is also seen as a more efficient use of the available resources. The proposed algorithm is observed to have superior energy consumption and routing decisions compared to conventional routing algorithms, and it improves the communication pattern.

• Science of Emotion and Sensibility
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• v.11 no.3
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• pp.365-374
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• 2008

Using flight simulator tasks with 48 air force cadets, this research examined the effects of pilots' flight skill and self-esteem on risk-taking behavior in the context of social comparison with their cohorts. Flight skill and self-esteem were assessed for individual cadets and three conditions of social comparison (upward-, downward-, and no-comparison) were devised. Flight simulator situations inappropriate for further approach or landing were designed to assess pilots' risk-taking behavior. Weather conditions in the simulator were inadequate to make a landing and the recommended strategy was to break off the approach and attempt a go-around. In this experiment, pilots' risk taking was measured in terms of their approach altitudes; the lower approach altitudes indicative of the higher risk-taking. Our results showed interaction effects of flight skill, self-esteem, and social comparison on risk-taking behaviors. For pilots who were either high or low in both self-esteem and flight skill, social comparison had no effect on risk-taking behavior. However, pilots with high self-esteem but low flight skill showed more risk-taking behaviors in social comparison conditions. And, pilots with low self-esteem but high flight skill showed risk-aversive behaviors in the downward-comparison condition.

• Journal of Energy Engineering
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• v.13 no.1
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• pp.51-59
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• 2004

The NSSS (Nuclear Steam Supply System) thermal-hydraulic programs adopted in the domestic full-scope power plant simulators were provided in early 1980s by foreign vendors. Because of limited compulsational capability at that time, they usually used very simplified physical models for a real-time simulation of NSSS thermal-hydraulic transients, which entails inaccurate results and, thus, the possibility of so-called "negative training", especially for complicated two-phase flows in the reactor coolant system. In resolve the problem, KEPRI developed a realistic NSSS T/H program ARTS which was based on the RETRAN-3D code for the improvement of the Nuclear Power Plant full-scope simulator. The ARTS (based on the RETRAN-3D code) guarantees the real-time calculations of almost all transients and ensures the robustness of simulations. However, there is some possibility of failing to calculate in the case of large break loss of coolant accident (LBLOCA) and low-pressure low-flow transient. In this case, the backup calculation system cover automatically the ARTS. The backup calculation system was expected to provide substantially more accurate predictions in the analysis of the system transients involving LBLOCA. The results were reasonable in terms of accuracy, real-time simulation, robustness and education of operators, complying with FSAR and the AMSI/ANS-3.5-1998 simulator software performance criteria.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.73-80
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• 2007

In conventional on-demand mobile ad hoc routing algorithms, an alternate path is sought only after an active path is broken. It incurs a significant cost in terms of money and time in detecting the disconnection and establishing a new route. In this thesis, we propose proactive route selection and maintenance to conventional mobile ad hoc on-demand routing algorithms. The key idea for this research is to only consider a path break to be likely when the signal power of a received packet drops below an optimal threshold value and to generate a forewarning packet. In other words, if a path is lost with high probability, the neighboring node that may easily be cut off notifies the source node by sending a forewarning packet. Then the source node can initiate route discovery early and switched to a reliable path potentially avoiding the disconnection altogether. For the simulational study, network simulator(NS2) is used. The result of simulation shows that the algorithm significantly improves the performance of networks comparing to conventional on-demand routing protocols based on DSR and AODV in terms of packet delivery ratio, packet latency and routing overhead.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.5
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• pp.1358-1364
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• 2016

Maritime accidents caused by a ship include collisions, sinking, stranding and fire etc. This study is intending to consider fire accidents among such diverse marine accidents. It is much likely that various sorts of fires break out because crewmen are living in a narrow space for long periods of time consequent on the ship's characteristic of sailing on the sea. According to the ship fire survey, about 50% of the total fire accidents occurred at an engine room, and the main fire origin was analyzed to be oil. In addition, ship fire breaks out in the order of baggage racks and living quarter. In short, the survey indicates that all sorts of fires belonging to A, B, C and D-class have occurred. This study, targeting an actual passenger ship 'A', found the response time to evacuation, during which the people on board a ship recognize the outbreak of fire, and act, and the travel time for evacuation which is the actual travel time. In addition, this study carried out a simulation through the special program for fire analysis - FDS (Fire Dynamics Simulator) in order to find the effective evacuation time, i.e. life survival time. Particularly, this study did comparative analysis of the influence on the survival of passengers and crew based on the collected simulation data by fire size and sort. As a result of the analysis, it was found that when examining the only actual evacuation movement time excepting the response time to evacuation, people are safe by completing evacuation before the effective evacuation time only in case fire size is 100Kw among all sorts of fires. In other words, in case of the outbreak of fire more than 1 MW, it was found to fail to meet evacuation safety regardless of fire size.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.2
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• pp.380-385
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• 2017

Maritime accidents caused by a ship include collisions, sinking, stranding and fire etc. This study is intending to consider fire accidents among such diverse marine accidents. It is much likely that various sorts of fires break out because crews are living in a narrow space for long periods of time consequent on the ship's characteristic of sailing on the sea. This study carried out a simulation through the special program for fire analysis - FDS (Fire Dynamics Simulator) in order to find the effective evacuation time, i.e. life survival time. Particularly, this study did comparative analysis of the influence on the survival of cadets based on the collected simulation data by fire size and sort. As a result of the analysis, It was analyzed the Evacuation Allowable Limit Temperature $60^{\circ}C$ and resulted that there is no influence in evacuation by temperature. In case of visibility analysis, it reached to 5m which is the Evacuation Allowable Limit at 117 seconds under the condition of wood fire in 1MW. When there is Kerosene in 1MW, it took 92.4 seconds to reach by 5m which is the Evacuation Allowable Limit. Theoretical evacuation time for the non-tilted ship was 118.8 seconds in 1MW sized fire so it is shown that the most passengers are met the evacuation safety in case of wood fire. However, the majority of passengers could not be ensured the evacuation safety in Kerosene case.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.349-356
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• 2005

Artificial Organism-used application areas are expanding at a break-neck speed with a view to getting things done in a dynamic and Informal environment. A use of general programming or traditional hi methods as the representation of Artificial Organism behavior knowledge in these areas can cause problems related to frequent modifications and bad response in an unpredictable situation. Strategies aimed at solving these problems in a machine-learning fashion includes Genetic Programming and Evolving Neural Networks. But the learning method of Artificial-Organism is not good yet, and can't represent life in the environment. With this in mind, this research is designed to come up with a new behavior evolution model. The model represents behavior knowledge with Classification Rules and Enhanced Backpropation Neural Networks and discriminate the denomination. To evaluate the model, the researcher applied it to problems with the competition of Artificial-Organism in the Simulator and compared with other system. The survey shows that the model prevails in terms of the speed and Qualify of learning. The model is characterized by the simultaneous learning of classification rules and neural networks represented on chromosomes with the help of Genetic Algorithm and the consolidation of learning ability caused by the hybrid processing of the classification rules and Enhanced Backpropagation Neural Network.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.