• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.305-312
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• 2023

Physics-Informed Neural Network (PINN) is used to invert bubble size distributions from attenuation losses. By considering a linear system for the bubble population inversion, Adaptive Learned Iterative Shrinkage Thresholding Algorithm (Ada-LISTA), which has been solved linear systems in image processing, is used as a neural network architecture in PINN. Furthermore, a regularization based on the linear system is added to a loss function of PINN and it makes a PINN have better generalization by a solution satisfying the bubble physics. To evaluate an uncertainty of bubble estimation, deep ensemble is adopted. 20 Ada-LISTAs with different initial values are trained using the same training dataset. During test with attenuation losses different from those in the training dataset, the bubble size distribution and corresponding uncertainty are indicated by average and variance of 20 estimations, respectively. Deep ensemble Ada-LISTA demonstrate superior performance in inverting bubble size distributions than the conventional convex optimization solver of CVX.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.75-84
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• 2020

Modular construction is a construction method whereby prefabricated volumetric units are produced in a factory and are installed on site to form a building block. The construction productivity can be substantially improved by the manufacturing and assembly of standardized modular units. 3D printing is a computer-controlled fabrication method first adopted in the manufacturing industry and was utilized for the automated construction of small-scale houses in recent years. Implementing 3D printing in the fabrication of modular units brings huge benefits to modular construction, including increased customization, lower material waste, and reduced labor work. Such implementation also benefits the large-scale and wider adoption of 3D printing in engineering practice. However, a critical issue for 3D printed modules is the loading capacity, particularly in response to horizontal forces like wind load, which requires a deeper understanding of the building structure behavior and the design of load-bearing modules. Therefore, this paper presents the state-of-the-art literature concerning recent achievement in 3D printing for buildings, followed by discussion on the opportunities and challenges for examining 3D printing in modular construction. Promising 3D printing techniques are critically reviewed and discussed with regard to their advantages and limitations in construction. The appropriate structural form needs to be determined at the design stage, taking into consideration the overall building structural behavior, site environmental conditions (e.g., wind), and load-carrying capacity of the 3D printed modules. Detailed finite element modelling of the entire modular buildings needs to be conducted to verify the structural performance, considering the code-stipulated lateral drift, strength criteria, and other design requirements. Moreover, integration of building information modelling (BIM) method is beneficial for generating the material and geometric details of the 3D printed modules, which can then be utilized for the fabrication.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.467-474
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• 2013

Construction contactors must continuously seek to improve their cash flows, which reside at the heart of their financial success. They require careful planning, analysis, and optimization to avoid the risk of bankruptcy, remain profitable, and secure long-term growth. Sources of cash include bank loans and retained earnings, which are conceptually similar in that they both incur a cost of capital. Financial management therefore requires accurate yet customizable modeling capabilities that can quantify all expenses, including said cost of capital. However, currently existing cash flow models in construction engineering and management have strongly simplified the manner in which interest is assessed, which may even lead to overstating it at a disadvantage to contractors. The variable nature of cash balances, especially in the early phases of construction projects, contribute to this challenging issue. This research therefore extends a new cash flow model with an accurate interest calculation. It utilizes singularity functions, so called because of their ability to flexibly model changes across any number of different ranges. The interest function is continuous for activity costs of any duration and allows the realistic case that activities may begin between integer time periods, which are often calendar months. Such fractional interest calculation has hitherto been lacking from the literature. It also provides insights into the self-referential behavior of compound interest for variable cash balances. The contribution of this study is twofold; augmenting the corpus of financial analysis theory with a new interest formula, whose strengths include its generic nature and that it can be evaluated at any fractional value of time, and providing construction managers with a tool to help improve and fine-tune the financial performance of their projects.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.139-147
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• 2023

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

• Membrane Journal
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• v.33 no.6
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• pp.344-351
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• 2023

The membrane separation process for carbon dioxide capture from hydrogen reformer exhaust gas has been developed. Using a commercial membrane module, a multi-stage process was developed to achieve 90% of carbon dioxide purity and 90% of recovery rate for ternary mixed gas. Even if a membrane module with being well-known properties such as material selectivity and permeability, the process performance of purity and recovery widely varies depending on the stage-cut, the pressure at feed and permeate side. In this study, we verify the limits of capture efficiency at single-stage membrane process under various operating conditions and optimized the two-stage recovery process to simultaneously achieve high purity and recovery rate.

• Smart Structures and Systems
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• v.32 no.5
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• pp.281-295
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• 2023

The purpose of this study is to demonstrate and verify the application of phase-control absolute-acceleration-feedback active tuned mass dampers (PCA-ATMD) to multiple-degree-of-freedom (MDOF) building structures. In addition, servo speed control technique has been developed as a replacement for force control in order to mitigate the negative effects caused by friction and inertia. The essence of the proposed PCA-ATMD is to achieve a 90° phase lag for a structure by implementing the desired control force so that the PCA-ATMD can receive the maximum power flow with which to effectively mitigate the structural vibration. An MDOF building structure with a PCA-ATMD and a real-time filter forming a complete system is modeled using a state-space representation and is presented in detail. The feedback measurement for the phase control algorithm of the MDOF structure is compact, with only the absolute acceleration of one structural floor and ATMD's velocity relative to the structure required. A discrete-time direct output-feedback optimization method is introduced to the PCA-ATMD to ensure that the control system is optimized and stable. Numerical simulation and shaking table experiments are conducted on a three-story steel shear building structure to verify the performance of the PCA-ATMD. The results indicate that the absolute acceleration of the structure is well suppressed whether considering peak or root-mean-square responses. The experiment also demonstrates that the control of the PCA-ATMD can be decentralized, so that it is convenient to apply and maintain to real high-rise building structures.

• Annual Conference of KIPS
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• 2008.11a
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• pp.882-884
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• 2008

통상 하드웨어 캐시의 크기보다 수십에서 수백배 큰 크기의 데이타를 랜덤하게 접근하는 경우 낮은 메모리 접근 지역성(locality)에 기인하여 캐시 메모리 성능이 급격히 저하되는 문제를 야기한다. 예를 들면, 현재 보편적으로 사용되고 있는 차량용 General Positioning System (GPS) 프로그램의 경우 최대 32개의 위성으로부터 데이터를 받아 수신단의 위치를 계산하는 부분이 핵심 모듈중의 하나 이며, 이는 전체 성능의 50% 이상을 차지한다. 이러한 모듈에서는 위성 신호를 실시간으로 받아 버퍼 메모리에 저장하며, 이때 필요한 데이터가 순차적으로 저장되지 못하기 때문에 랜덤하게 데이터를 읽어 사용하게 된다. 결과적으로 낮은 지역성에 기인하여 실시간 (realtime)안에 데이터 처리를 하기 어려운 문제에 직면하게 된다. 통상의 통신 응용의 알고리즘 상에 내재된(inherited) 낮은 메모리 접근 지역성을 개선하는 것은 알고리즘 상에서의 접근을 요구한다. 이는 높은 비용이 필요함으로 본 연구에서는 사용되는 데이터 구조를 변환하여 지역성을 높이는 방향으로 접근하였다. 결과적으로 핵심 모듈에서 2배, 전체 시스템 성능에서 14%를 개선할 수 있었다.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Clean Technology
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• v.29 no.4
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• pp.321-326
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• 2023

Recently, there has been a growing interest in clean hydrogen energy that does not emit carbon dioxide during combustion due to the increasing focus on carbon neutral. Research related to hydrogen production continues, and in this study, we applied waste-derived synthesis gas to the water-gas shift reaction to simultaneously treat waste and produce high-purity hydrogen. To enhance catalytic activity in the high-temperature water-gas shift (HT-WGS) reaction, magnesium was used as a support material alongside cerium. Cu-CeO₂-MgO catalysts were synthesized, with copper acting as the active component for the HT-WGS reaction. A study on the catalytic activity based on the preparation method was conducted, and the Cu-CeO₂-MgO catalyst prepared by impregnation method exhibited the highest activity in the HT-WGS reaction. The observed superior performance of the Cu-CeO₂-MgO catalyst prepared through the impregnation method can be attributed to its significantly higher oxygen storage capacity and amount of active Cu species.