• Journal of Korea Multimedia Society
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• v.16 no.1
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• pp.75-86
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• 2013

This paper presents a technique to implement embedded systems using interrupts of the one-chip microcontroller with many peripherals based on a multiple finite state machines model. The multiple finite state machine model utilizes the structure of FSMD used for hardware design and the features of flow control by interrupts. The main finite state machine corresponds to the main program and the sub-state machines corresponds to the interrupt subroutines. Therefore, interrupts from the peripherals can be processed immediately in the sub-state machines. The request and reply variables are used to interface between the finite state machines. Additional operating system is not necessary for the context switching between the main state machine and the sub-state machine, because the flow-control caused by interrupt can be replaced with the switching. An embedded system modeled on multiple finite state machine with ASM charts can be easily implemented by the conversion of ASM charts into C-language programs. This implementation technique can be easily adopted to the hardware oriented embedded systems because of the detail description of the model and the fast response to the interrupt events in the sub-state machine.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.61.2-61.2
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• 2005

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.863-868
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• 2005

Hardware and software codesign framework called PeaCE(Ptolemy extension as a Codesign Environment) was developed. It allows to express both data flow and control flow which is described as fFSM which extends traditional finite state machine. While the fFSM model provides lots of syntactic constructs for describing control flow, it has a lack of their formality and then difficulties in verifying the specification. In order to define the formal semantics of the fFSM, in this paper, firstly the hierarchical structure in the model is flattened and then the step semantics is defined. As a result, some important bugs such as race condition, ambiguous transition, and circulartransition can be formally detected in the model.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.4
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• pp.127-137
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• 1989

This paper describes hardware flow-chart and SDL-II, which are register-transfer level, to automate logic design. Hardware flow-chart specifies behavioral and structural charaterstics of generalized FSMs (Finite State Machine) usin the modified ASM (Algorithmic State Machnine) design techniques. SDL-II describes the hardware flow-chat which specifies the control and the data path of ASIC(Application Specific IC). Also many examples are enumerated to illustrate the features of hardware flow-chart and SDL-II.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1796-1809
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• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

• Journal of KIBIM
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• v.9 no.1
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• pp.22-31
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• 2019

In this paper, we construct a prototype model for city data prediction by using time series data of floating population, and use machine learning to analyze urban data of complex structure. A correlation prediction model was constructed using three of the 10 data (total flow population, male flow population, and Monday flow population), and the result was compared with the actual data. The results of the accuracy were evaluated. The results of this study show that the predicted model of the floating population predicts the correlation between the predicted floating population and the current state of commerce. It is expected that it will help efficient and objective design in the planning stages of architecture, landscape, and urban areas such as tree environment design and layout of trails. Also, it is expected that the dynamic population prediction using multivariate time series data and collected location data will be able to perform integrated simulation with time series data of various fields.

• Journal of Electrical Engineering and Technology
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• v.9 no.2
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• pp.523-531
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• 2014

Brushless Dual-Mechanical-Port Electrical Machine (BLDMPEM) is a new type of motor designed for Hybrid Electric Vehicle (HEV), which contains two mechanical ports and two electric ports. Compared with Dual-Mechanical-Port Electrical Machine (DMPEM), the brushless structure brings higher reliability and easier maintenance. In this paper, the model of BLDMPEM is discussed. In Chapter 2, the energy flow and mathematical model of BLDMPEM are analyzed. Then a novel three-phase half-bridge controlled rectifier topology and its control strategy for cruising mode of HEV based on BLDMPEM are proposed in Chapter 3. Compared with the Field Oriented Control (FOC) strategy of BLDMPEM, the proposed method does not require accurate motor parameters, and it is much simpler and easier to be implemented. At last, simulation and experiment results show the feasibility and validity of the proposed strategy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.457-462
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• 1995

This paper describes diagnosis strategy of the Flexible Multiple Fault Diagnosis Module for forecasting faults in system and deciding current machine state form sensor information. Most studydeal with diagnosis control stategy about single fault in a system, this studies deal with multiple fault diagnosis. This strategy is consist of diagnosis control module such as backward tracking expert system shell, various neural network, numerical model to predict machine state and communication module for information exchange and cooperate between each model. This models are used to describe structure, function and behavior of subsystem, complex component and total system. Hierarchical structure is very efficient to represent structural, functional and behavioral knowledge. FT(Fault Tree). ST(Symptom Tree), FCD(Fault Consequence Diagrapy), SGM(State Graph Model) and FFM(Functional Flow Model) are used to represent hierachical structure. In this study, IA(Intelligent Agent) concept is introduced to match FT component and event symbol in diagnosed system and to transfer message between each event process. Proposed diagnosis control module is made of IPC(Inter Process Communication) method under UNIX operating system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1572-1581
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• 1993

DFSM(Deterministic Finite State Machine) is used because it easily represents the control flow of a protocol in the protocol specification. Real protocols contain problem of nondeterminisms that have more than one enabled transition in the same state by same input. But DFSM does not process nondeterminism. So, in this paper, we first specify a protocol with NFSM (Nonderministic FSM) that may show the characteristics of nondeterminism, and propose an algorithm which converts NFSM to DFSM.

• Computers and Concrete
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• v.25 no.6
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.