• Nuclear Engineering and Technology
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• v.52 no.6
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• pp.1188-1200
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• 2020

In this paper, we consider a SDMS (Self-Diagnostic Monitoring System) for a reciprocating pump for the purpose of not only diagnosis but also prognosis. We have replaced a multi class estimator that selects only the most probable one with a multi label estimator such that we are able to see the state of each of the components. We have introduced a measure called certainty so that we are able to represent the symptom and its state. We have built a flow loop for a reciprocating pump system and presented some results. With these changes, we are not only able to detect both the dominant symptom as well as others but also to monitor how the degree of severity of each component changes. About the dominant ones, we found that the overall recognition rate of our algorithm is about 99.7% which is slightly better than that of the former SDMS. Also, we are able to see the trend and to make a base to find prognostics to estimate the remaining useful life. With this we hope that we have gone one step closer to the final goal of prognosis of SDMS.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.613-618
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• 2023

In this paper, a monitoring program that can automatically determine whether a patient admitted to an isolation room acts out of a stable state through a screen photographed in real time is described. The motion recognition model of this program was built by learning through transfer learning. 900 images were used for the three movements, and this program was developed for the web to support all environments. The model was determined with high accuracy to determine the state of the subject hospitalized in the isolation room, and can be applied by applying it to the existing isolation room monitoring system.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.547-553
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• 2021

Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.904-914
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• 2008

An action-selection-mechanism(ASM) has been proposed to work as a fully connected finite state machine to deal with sequential behaviors as well as to allow a state in the task program to migrate to any state in the task, in which a primitive node in association with a state and its transitional conditions can be easily inserted/deleted. Also, such a primitive node can be learned by a shortest path-finding-based reinforcement learning technique. Specifically, we define a behavioral motivation as having state-dependent value as a primitive node for action selection, and then sequentially construct a network of behavioral motivations in such a way that the value of a parent node is allowed to flow into a child node by a releasing mechanism. A vertical path in a network represents a behavioral sequence. Here, such a tree for our proposed ASM can be newly generated and/or updated whenever a new behavior sequence is learned. To show the validity of our proposed ASM, experimental results of a mobile robot performing the task of pushing- a- box-in to- a-goal(PBIG) will be illustrated.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.2
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• pp.137-144
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• 2001

In this paper, a state assignment algorithm was proposed to reduce power consumption in control-flow oriented finite state machines. The Markov chain model is used to reduce the switching activities, which closely relate with dynamic power dissipation in VLSI circuits. Based on the Markov probabilistic description model of finite state machines, the hamming distance between the codes of neighbor states was minimized. To express the switching activities, the cost function, which also accounts for the structure of a machine, is used. The proposed state assignment algorithm is tested with Logic Synthesis Benchmarks, and reduced the cost up to 57.42% compared to the Lakshmikant's algorithm.

• The KSFM Journal of Fluid Machinery
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• v.11 no.5
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• pp.50-56
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• 2008

Metal cutting operations involve generation of heat due to friction between the tool and the pieces. This heat needs to be carried away otherwise it creates white spots. To reduce this abnormal heat cutting fluid is used. Cutting fluid also has an important role in the lubrication of the cutting edges of machine tools and the pieces they are shaping, and in sluicing away the resulting swarf. As a cutting fluid, water is a great conductor of heat but is not stable at high temperatures, so to improve stability an emulsion type mixed fluid with water and oil is often used. It is pumped over the cutting site of cutting machines as a state of atomized water droplet coated with oil by using jet. In this paper, to develop cutting fluid supplying nozzle to obtain ultra thin oil film for coating water droplet, a numerical analysis of three dimensional mixed fluid Jet through multi-stage nozzle was carried out by using a finite volume method. Jet flow characteristics such as nozzle exit velocity, development of mixing region, re-entrance and jet intensity were analyzed. Detailed mixing process of fluids such as air, water and oil in the nozzle were also investigated. It is easy to understand complex flow pattern in multi-stage nozzle. Important flow Information for advance design of cutting fluid supplying nozzle was drawn.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.117-124
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• 2006

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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.07a
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• pp.297-301
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• 2014

게임에는 많은 종류의 장르가 있다. 어떤 장르의 게임이 되었건 플레이어와 많은 상호작용을 하는 A.I는 게임에 있어 중요한 요소 이며 어드벤처 게임(Adventure Game) 장르도 예외는 아니다. A.I(Artificial Intelligence)I의 행동이나 상황에 따른 플레이어와의 상호작용은 게임에 있어 플레이어에게 몰입감을 주며 게임을 좀 더 현실감 있게 해주는 게임의 수많은 요소 중 하나다. 본 논문에서는 FSM(Finite-State Machine) 기법을 사용하여 어드벤처 게임에서플레이어에게 '몰입'을 유발 시키는 방법으로 FSM 기법의 NPC(None-Player Character) A.I 패턴을 디자인을 통해 플레이어의 '몰입'을 유발 하였다.

• Journal of KIISE
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• v.42 no.1
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• pp.44-53
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• 2015

Control flow information is useful in the analysis and comparison of programs. Virtualization-obfuscation hides control structures of the original program by transforming machine instructions into bytecode. Direct examination of the resulting binary reveals only the structure of the interpreter. Recovery of the original instructions requires knowledge of the virtual machine architecture, which is randomly generated and hidden. In this paper, we propose a method to reconstruct original control flow using only traces generated from the obfuscated binary. We consider traces as strings and find an automaton that represents the strings. State transitions in the automaton correspond to the control transfers in the original program. We have shown the effectiveness of our method with commercial obfuscators.