• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.29-41
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• 2022

Recently, with the development of deep learning technology, a variety of huge models with excellent performance have been devised by pre-training massive amounts of text data. However, in order for such a model to be applied to real-life services, the inference speed must be fast and the amount of computation must be low, so the technology for model compression is attracting attention. Knowledge distillation, a representative model compression, is attracting attention as it can be used in a variety of ways as a method of transferring the knowledge already learned by the teacher model to a relatively small-sized student model. However, knowledge distillation has a limitation in that it is difficult to solve problems with low similarity to previously learned data because only knowledge necessary for solving a given problem is learned in a teacher model and knowledge distillation to a student model is performed from the same point of view. Therefore, we propose a heterogeneous knowledge distillation method in which the teacher model learns a higher-level concept rather than the knowledge required for the task that the student model needs to solve, and the teacher model distills this knowledge to the student model. In addition, through classification experiments on about 18,000 documents, we confirmed that the heterogeneous knowledge distillation method showed superior performance in all aspects of learning efficiency and accuracy compared to the traditional knowledge distillation.

• The Mathematical Education
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• v.61 no.3
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• pp.477-497
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• 2022

The rapidly changing world calls for reform in mathematics education from lifelong learning perspectives. This study examines adults' perception of mathematics by reflecting on their experiences of mathematics in and out of school in order to understand what the current needs of adults are. With the two questions: "what experiences do participants have during their learning of mathematics in schools?" and "how do they perceive mathematics in their current life?", we analyzed the semi-structured interviews with 10 adults who have different sociocultural backgrounds using narrative inquiry methodology. As a result, participants tended to accept school mathematics as simply a technique for solving computational problems, and when they had not known the usefulness of mathematical knowledge, they experienced frustration with mathematics in the process of learning mathematics. After formal education, participants recognized mathematics as the basic computation skill inherent in everyday life, the furniture of their mind, and the ability to efficiently express, think, and judge various situations and solve problems. Results show that adults internalized school education to clearly understand the role of mathematics in their lives, and they were using mathematics efficiently in their lives. Accordingly, there was a need to see school education and adult education on a continuum, and the need to conceptualize the mathematical abilities required for adults as mathematical literacy.

• The Journal of the Acoustical Society of Korea
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• v.42 no.1
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• pp.7-15
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• 2023

Recently, as interest in air quality in vehicles increases, the use of fine dust detection sensors for air quality measurement is becoming common. An axial-flow fan is inserted in the fine dust sensor installed in the air conditioning system in the vehicle to prevent dust from sinking directly on the sensor. When the sensor operates, the flow noise caused by the rotation of the axial-flow fan acts as a major noise source of the fine dust sensor. flow noise is recognized as one of the product competitiveness of fine dust sensors. In this study, the noise was gradually reduced at the same flow rate by improving the flow performance of the small axial flow fan. First, a virtual fan performance tester consisting of about 20 million grids was developed to analyze the aerodynamic performance of the target small axial-flow fan. In addition, the flow field was simulated by using compressible Large Eddy Simulation for direct computation of flow noise as well as high-accurate prediction of flow rate. The validity of numerical method are confirmed through the comparison of predicted results with experimental ones. After the effects of pitch angle on flow performance were analyzed using the verified numerical method, the pitch angle was determined to maximize the flow rate. It was found that the flow rate was increased by 8.1 % and noise was reduced by 0.8 dBA when the axial-flow fan with the optimum pitch angle was used.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1244-1251
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• 2022

Marine accidents due to a loss of stability, have been gradually increasing over the last decade. Measures must be taken on the roll reduction of a ship. Amongst the measures, building an anti-roll tank in a ship is recognized as the most simple and effective way to reduce the roll motion. Therefore, this study aims to develop a computational model for a U-tube type anti-roll tank and to validate it by experiment. In particular, to validate the developed computational model, the height of the free surface in the tank was measured in the experiment. To develop a computational model, the mesh dependency test was carried out. Further, the effects of a turbulence model, time step size, and the number of iterations on the numerical solution were analyzed. In summary, a U-tube type anti-roll tank simulation had to be performed accurately with conditions of a realizable k-𝜖 turbulence model, 10^-2s time step size, and 15 iterations. In validation, the two cases of measured data from the experiment were compared with the numerical results. In the present study, STAR-CCM+ (ver. 17.02), a RANS-based commercial solver was used.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.4
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• pp.23-32
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• 2023

The seismic performance of buried PE pipes is reported to be favorable due to their exceptional elongation capacity at break. Although a seismic performance evaluation procedure based on the response displacement method has been summarized in Korea for fusion-bonded PE pipes, there is currently no procedure available for mechanically jointed PE pipes. This article aims to present a seismic performance evaluation procedure based on the response displacement method specifically designed for mechanically jointed PE pipes in Korea. When employing the mechanical joining method for PE pipes, it is recommended to adhere to the evaluation procedure established for segment-type pipes. This involves assessing the stress induced by the pipe, the expansion and contraction strain of the joint, and the bending angle of the pipe joint. Furthermore, the coefficient of inhomogeneity of the soil, which is necessary for estimating the axial strain of the ground, is introduced. Additionally, a computation method for determining lateral displacement and reconsolidation settlement in soil susceptible to liquefaction is proposed. As a result of the sensitivity analysis considering the typical soil condition in Korea, the mechanically jointed PE pipe with a certain quality was shown to have good structural seismic safety when soil liquefaction was not considered. This procedure serves as a valuable tool for seismic design and evaluating the seismic performance of mechanically joined buried PE pipes, which are primarily utilized for connecting small-diameter pipes.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.241-248
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• 2022

Shear Reinforcement of Dual Anchorage(SRD) is used to reinforce the safety of reinforced concrete structures at construction sites. Welding is used to make shear reinforcement, and welding plays an important role in determining productivity and competitiveness of products. Therefore, a weld bead detection inspection is required. In this paper, we suggest an algorithm for inspecting welding beads using image data of welding beads. First, the proposed algorithm calculates a brightness value in a vertical direction in an image, and then divides a welding bead in a vertical direction by finding a position corresponding to a 50% height point of the brightness value distribution in the image. The welding bead area is also divided in the same way for the horizontal direction, and then the segmentation image is analyzed if there is a welding bead. The proposed algorithm reduced the amount of computation by performing analysis after specifying the region of interest. In addition, accuracy could be improved by using all brightness values in the vertical and horizontal directions using the difference of brightness between the base metal and the welding bead region in the SRD image. The experiment compared the analysis results using five algorithms, such as K-mean and K-neighborhood, as a method to detect if there is a welding bead, and the experimental result proved that the proposed algorithm was the most accurate.

• Industry Promotion Research
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• v.8 no.2
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• pp.21-27
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• 2023

This study studies the policies and use cases of the government and the financial sector for artificial intelligence, and the future policy tasks of the financial sector. want to derive According to Gartner, noteworthy technologies leading the financial industry in 2022 include 'generative AI', 'autonomous system', 'Privacy Enhanced Computation (PEC) was selected. The financial sector is developing new technologies such as artificial intelligence, big data, and blockchain. Developments are spurring innovation in the financial sector. Data loss due to the spread of telecommuting after the corona pandemic As interests in sharing and personal information protection increase, companies are expected to change in new digital technologies. Global financial companies also utilize new digital technology to develop products or manage and operate existing businesses. I n order to promote process innovation, I T expenses are being expanded. The financial sector utilizes new digital technology to prevent money laundering, improve work efficiency, and strengthen personal information protection. are applying In the era of Big Blur, where the boundaries between industries are disappearing, the competitive edge in the challenge of new entrants In order to preoccupy the market, financial institutions must actively utilize new technologies in their work.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.