• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.475-484
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• 2021

The optimal design for water distribution system (WDS) is not only satisfying the minimum required water pressure of the nodes, but also minimizing pipe cost, etc. The number of designs of WDS increases exponentially due to the arrangement of various pipes. Various optimization algorithms were applied to propose an optimized design of WDS. In this study, Modified Hybrid Vision Correction Algorithm (MHVCA) with improved self-adapting parameter was applied to optimal design of WDS. The performance was improved by changing the Hybrid Rate (HR) of the existing Hybrid Vision Correction Algorithm (HVCA) to nonlinear HR. To verify the performance of the proposed MHVCA, it applied to mathematical problems consisting of 2 and 30 decision variables and constrained mathematical problems. In order to review the application results of MHVCA, it was compared with Harmony Search (HS), Improved Harmony Search (IHS), Vision Correction Algorithm (VCA) and HVCA. Finally, MHVCA was applied to the optimal design problem of WDS and the results were compared with other algorithms. MHVCA showed better results than other algorithms in mathematical problems and WDS problem. MHVCA will be able to show good results by applying to various water resource engineering problems as well as problems applied in this study.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.6
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• pp.1086-1091
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• 2022

The International Maritime Organization is establishing international agreements on maritime safety and security to prepare for the introduction of autonomous ships. In Korea, the industry is focusing on autonomous navigation system technology development, and to reduce accidents involving coastal ships, research on autonomous ship technology application plans for coastal ships is in progress. Interest in autonomously operated ships is increasing worldwide, and maritime demonstrations for verification of developed technologies are being pursued. In this study, a basic investigation was conducted on the design of a demonstration ship and an onshore platform (remote support center) using digital twin technology for application to coastal ships. To apply digital twin technology, an 8-m small battery-powered electric propulsion ship was selected as the target. The basic design of the twin-integrated platform was developed. The ship navigation and operation data were stored on a server system, and remote-control commands of the electric propulsion ship was achieved through communication between the ship and the onshore platform. Ship performance management, operation and operation optimization, and predictive control are possible using this digital twin technology. This safe and economical digital twin technology is applicable to ships responding to crisis scenarios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1627-1634
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• 2021

This paper introduces a low-power compact ADC circuit for analog Convolutional filter for low-power neural network accelerator SOC. While convolutional neural network accelerators can speed up the learning and inference process, they have drawback of consuming excessive power and occupying large chip area due to large number of multiply-and-accumulate operators when implemented in complex digital circuits. To overcome these drawbacks, we implemented an analog convolutional filter that consists of an analog multiply-and-accumulate arithmetic circuit along with an ADC. This paper is focused on the design optimization of a low-power 8bit SAR ADC for the analog convolutional filter accelerator We demonstrate how to minimize the capacitor-array DAC, an important component of SAR ADC, which is three times smaller than the conventional circuit. The proposed ADC has been fabricated in CMOS 65nm process. It achieves an overall size of 1355.7㎛², power consumption of 2.6㎼ at a frequency of 100MHz, SNDR of 44.19 dB, and ENOB of 7.04bit.

• Journal of the Korea Organic Resources Recycling Association
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• v.29 no.4
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• pp.47-54
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• 2021

In this study, the performance of a honeycomb-type hydrogen oxidation catalyst to remove hydrogen in a hydrogen economy society to secure leaking hydrogen. The Pd/TiO₂ catalyst was prepared based on a liquid phase reduction method that is not exposed to a heat source, and it was showed through H₂-chemisorption analysis that it existed as very small active particles of 2~4 nm. In addition, it was found that the metal dispersion decreased and the active particle size increased as the reduction reaction temperature increased. It was meant that the active metal particle size and the hydrogen oxidation performance were in a proportional correlation, so that it was consistent with the hydrogen oxidation performance reduction result. The prepared catalyst was coated on a support in the form of a honeycomb so that it could be applied to the hydrogen industrial process. When 20 wt% or more of the AS-40 binder was coated, oxidation performance of 90% or more was observed under low-concentration hydrogen conditions. It was showed through SEM analysis that long-term catalytic activity can be expected by enhancing the adhesion strength of the catalyst and preventing catalyst desorption. It is a basic research that can secure safety in a hydrogen society such as gasification, organic resource, and it can be utilized as a system that can respond to unexpected safety accidents in the future.

• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.119-125
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• 2022

Materials used for education are materials such as SM20C, Al6061, and acrylic. SM20C materials are carbon steel and are often used in certification tests and functional competitions, but are also widely used in industrial sites. The Al6061 material is said to be a material that has lower hardness and stronger flexibility than carbon steel, so it is a material that generates a lot of compositional selection of tools. If students are taught practical training using acrylic materials, vibration occurs due to excessive cutting in some parts and damage to the tool occurs. In this process, we examine to what extent the impact on the 2NC head, which is a five-axis equipment, can affect precision control. The weakest part of the five-axis equipment can be said to be the weakest part of the head that controls the AC axis. When the accuracy and cumulative tolerance of this part occur, the accuracy of all products decreases. Therefore, the core part of the 2NC head, the spindle housing, was carried out using an Al7075 T6 (Alcoa, USA) material. In the process of vibration and cutting applied to this material, the analysis was conducted to find out the value applied to the finite element analysis under extreme conditions. It is hoped that this analysis data will help students see and understand the structure of 5-axis machining rather than 5-axis cutting.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.213-220
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• 2023

This paper presents a model simulating the folding process and collision dynamics of "ddakji", a traditional Korean game played using paper tiles (which are also referred to as ddakji). The model uses two A4 sheets as the base materials for ddakji. The folding process involves a series of boundary conditions that transform the wing part of the paper structure into a twisted configuration. A rigid plate boundary condition is also adopted for squeezing, establishing the shape and stress state of the game-ready ddakji through dynamic relaxation analysis. The gaming process analysis involves a forced displacement of the striking ddakji to a predetermined collision position. Collision analysis then follows at a given speed, with the objective of overturning the struck ddakji--a winning condition. A genetic algorithm-based optimization analysis identifies the optimal collision conditions that result in the overturning of the struck ddakji. For efficiency, the collision analysis is divided into two stages, with the second stage carried out only if the first stage predicts a possible overturn. The fitness function for the genetic algorithm during the first stage is the direction cosine of the struck ddakji, whereas in the second stage, it is the inverse of the speed, thus targeting the lowest overall collision speed. Consequently, this analysis provides optimal collision conditions for various compression thicknesses.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1211-1222
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• 2022

Abnormal condition inevitably occurs during operation of water distribution system (WDS) and requires the isolation of certain areas using isolation valves. In general, the determination of the optimal location of isolation valves considered minimization of hydraulic failures as isolation of certain areas causes a change in hydraulic states (e.g., flow direction, velocity, pressure, etc.). Water quality failure can also be induced by changes in hydraulics, which have not been considered for isolation valve system design. Therefore, this study proposes a new isolation valve system design methodology to prevent unexpected water quality failure events. The new methodology considers flow direction change ratio (FDCR), which accounts for flow direction changes after isolation of the area, as a constraint while reliability is used as the objective function. The optimal design model has been applied to a synthetic grid network and the results are compared with the traditional design approach. Results show that considering FDCR can eliminate flow direction changes while average pressure and coefficient of variation of pressure, velocity, and hydraulic geodesic index (HGI) outperform compared to the traditional design approach. The proposed methodology is expected to be a useful approach to minimizing unexpected consequences by traditional design approaches.

• Advances in concrete construction
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• v.13 no.3
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• pp.215-221
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• 2022

Considering their similar mass densities, an attempt was made to optimize the mix design of micro-concrete that used barite sand as an aggregate by substituting marble powder (5%, 10%, 20%, 30%, 40%, 50%, 70%), clay brick powder (30%, 50%, 70%), and fly ash (30%, 50%, 70%) for the concrete (by mass) to form specimens for shaking table tests. The test results showed that for these three groups of materials, the substitutions had little effect on the density. The barite sand played a decisive role in the density, and the overall density of the specimens reached approximately 2.9 g/cm³. The compressive strength and elastic modulus decreased with an increase in the substitution rates for the three types of materials. Among them, the 28 day compressive strength values of the 40% and 50% marble powder groups were 11.73 MPa and 8.33 MPa, respectively, which were 58.7% and 70.7% lower than the control group, respectively. Their elastic modulus values were 1.33×10⁴ MPa and 1.42×10⁴ MPa, respectively, which were 39.1% and 35% lower than those of the control group, respectively. The 28 day compressive strength values of the 50% and 70% clay brick powder groups were 13.13 MPa and 5.8 MPa, respectively, which were 53.8% and 79.6% lower than the control group, respectively. Their elastic modulus values were 1.54×10⁴ MPa and 1.19×10⁴ MPa, respectively, which were 29.7% and 45.4% lower than those of the control group, respectively. The 28 day compressive strength values of the 50% and 70% fly ash groups were 13.5 MPa and 7.1 MPa, respectively, which were 52.5% and 75% lower than those of the control group, respectively. Their elastic modulus values were 1.36×10⁴ MPa and 0.95×10⁴ MPa, respectively, which were 37.9% and 56.6% lower than those of the control group, respectively. There was a linear relationship between the 28 day compressive strength and elastic modulus, with the correlation coefficient reaching a value higher than 0.88. The test results showed that the model materials met the high density, low compressive strength, and low elastic modulus requirements for shaking table tests, and the test data of the three groups of different alternative materials were compared and analyzed to provide references and assistance for relevant model testers.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.