• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 춘계학술대회 논문집
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• pp.388-392
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• 2003

As the machine tool industry progresses, the performance of cutting tools also needs to be of good quality and specialized. If the existing metal cutting tool tip is coated properly, its life would be longer and the machining of a difficult-to-cut material also could be possible. For the development of the high quality cutting tool, the applicabilities of TiAIN coating deposited by PVD on the cutting insert were experimentally investigated. The various measurements, such as, SEM micrograph, XRD pattern, AFM surface morphology, and hardness were performed to cross-check the possibility and availability of TiAIN coated tool in the field. The effects of it is successful and we hope to see good results in many fields.

• 디지털산업정보학회논문지
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• 제9권1호
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• pp.1-7
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• 2013

The cloud computing is technology which gives flexible and solid infrastructure to IT environment. With this technology multiple computing environment can be consolidated in to a single server so that maximize system resource utilization. Better processing power can be achieved with less system resource. IT manager can cope with increasing unnecessary cost for additional server and management cost as well. This means a enterprise is able to provide services with better quality and create new services with surplus resource. The time required for recovery from system failure will be reduced from days to minutes. Enhanced availability and continuity of enterprise business minimize the codt and the risk produced by service discontinuity. In this paper, we propose framework architecture that is strong against denial-of-service attack.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2007년도 하계학술대회 논문집
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• pp.467-469
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• 2007

In this paper, design of PMA-RSM(permanent magnet assisted reluctance synchronous motor) for washing machine using FEM(finite element method) is presented and algorithms to maximize electromagnetic torque is introduced. FEM has been approached to show the effects of motor parameters on the developed average torque and maximum torque. The designed motor is a combination of salient poles, which is making reluctance torque, and permanent magnet which are located on the air-gap of rotor to get a enough torque during low speed resign. And to verify availability of the proposed PMA-RSM, various simulation are done as compared with bldc motor which is used for washing machine.

• 소음진동
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• 제9권2호
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• pp.371-376
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• 1999

The purpose of this study is to identify the dynamic characteristics of a motor-die in washing machine and provide quantitative design information needed for reduction of radiated sound from the motor-die. To perform the design analysis, dynamic characteristics are identified by motor-die modeling and the availability of model is verified by experimental modal analysis. Numerical approach using MSC/NASTRAN and SYSNOISE predicted sound attenuation effects according to the change of design parameters, such as thickness, concentrated mass and rib. The numerical results due to the rib attachment showed the significant noise attenuation effects over 15 dB in the frequency range of 450∼700 Hz.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 추계학술대회논문집
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• pp.23-32
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• 2000

The purpose of this study is to identify the dynamic characteristics of a motor-die in washing machine and provide quantitative design information needed for the reduction of radiated sound from the motor-die. To perform the design analysis, dynamic characteristics are identified by motor-die modeling and the availability of model is verified by experimental modal analysis. Numerical approach using MSC/NASTRAN and SYSNOISE predicted sound attenuation effects according to the change of design parameters, such as thickness, concentrated mass and rib. The numerical results due to the rib attachment showed the significant noise attenuation effects over 15dB in the frequency range of 450-700Hz.

• Advances in nano research
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• 제15권6호
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• pp.513-520
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• 2023

The exponential proliferation of cutting-edge computing technologies has spurred organizations to outsource their data and computational needs. In the realm of cloud-based computing environments, ensuring robust security, encompassing principles such as confidentiality, availability, and integrity, stands as an overarching imperative. Elevating security measures beyond conventional strategies hinges on a profound comprehension of malware's multifaceted behavioral landscape. This paper presents an innovative paradigm aimed at empowering cloud service providers to adeptly model user behaviors. Our approach harnesses the power of a Particle Swarm Optimization-based Probabilistic Neural Network (PSO-PNN) for detection and recognition processes. Within the initial recognition module, user behaviors are translated into a comprehensible format, and the identification of malicious nano-structures behaviors is orchestrated through a multi-layer neural network. Leveraging the UNSW-NB15 dataset, we meticulously validate our approach, effectively characterizing diverse manifestations of malicious nano-structures behaviors exhibited by users. The experimental results unequivocally underscore the promise of our method in fortifying security monitoring and the discernment of malicious nano-structures behaviors.

• Computers and Concrete
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• 제33권2호
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• pp.175-193
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• 2024

Researchers have embarked on an active investigation into the feasibility of adopting alternative materials as a solution to the mounting environmental and economic challenges associated with traditional concrete-based construction materials, such as reinforced concrete. The examination of concrete's mechanical properties using laboratory methods is a complex, time-consuming, and costly endeavor. Consequently, the need for models that can overcome these drawbacks is urgent. Fortunately, the ever-increasing availability of data has paved the way for the utilization of machine learning methods, which can provide powerful, efficient, and cost-effective models. This study aims to explore the potential of twelve machine learning algorithms in predicting the tensile strength of geopolymer concrete (GPC) under various curing conditions. To fulfill this objective, 221 datasets, comprising tensile strength test results of GPC with diverse mix ratios and curing conditions, were employed. Additionally, a number of unseen datasets were used to assess the overall performance of the machine learning models. Through a comprehensive analysis of statistical indices and a comparison of the models' behavior with laboratory tests, it was determined that nearly all the models exhibited satisfactory potential in estimating the tensile strength of GPC. Nevertheless, the artificial neural networks and support vector regression models demonstrated the highest robustness. Both the laboratory tests and machine learning outcomes revealed that GPC composed of 30% fly ash and 70% ground granulated blast slag, mixed with 14 mol of NaOH, and cured in an oven at 300°F for 28 days exhibited superior tensile strength.

• Nuclear Engineering and Technology
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• 제52권10호
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• pp.2262-2273
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• 2020

Digital twin technology can provide significant value for the prognostics and health management (PHM) of critical plant components by improving insight into system design and operating conditions. Digital twinning of systems can be utilized for anomaly detection, diagnosis and the estimation of the system's remaining useful life in order to optimize operations and maintenance processes in a nuclear plant. In this regard, a conceptual framework for the application of digital twin technology for the prognosis of Control Element Drive Mechanism (CEDM), and a data-driven approach to anomaly detection using coil current profile are presented in this study. Health management of plant components can capitalize on the data and signals that are already recorded as part of the monitored parameters of the plant's instrumentation and control systems. This work is focused on the development of machine learning algorithm and workflow for the analysis of the CEDM using the recorded coil current data. The workflow involves features extraction from the coil-current profile and consequently performing both clustering and classification algorithms. This approach provides an opportunity for health monitoring in support of condition-based predictive maintenance optimization and in the development of the CEDM digital twin model for improved plant safety and availability.

• 한국소음진동공학회논문집
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• 제26권6_spc호
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• pp.714-720
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• 2016

Rubbing and misalignment are the most usual faults that occurs in rotating machinery and with them severe effect on power plant availability. Especially blade rubbing is hard to detect on FFT spectrum using the vibration signal. In this paper, the possibility of feature analysis of vibration signal is confirmed under blade rubbing and misalignment condition. And the lab-scale rotor test device provides the blade rubbing and shaft misalignment modes. Feature selection based on GA (genetic algorithm) is processed by the extracted feature of the time domain. Then, classification of the features is analyzed by using SVM (support vector machine) which is one of the machine learning algorithm. The results of features selection based on GA compared with those based on PCA (principal component analysis). According to the results, the possibility of feature analysis is confirmed. Therefore, blade rubbing and shaft misalignment can be diagnosed by feature of vibration signal.

• Journal of Medicine and Life Science
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• 제20권4호
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.