• 한국습지학회지
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• 제23권1호
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• pp.7-13
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• 2021

급속한 산업발전과 인구증가, 생활 수준의 향상으로 영양염류를 포함한 하·폐수 발생량이 매년 증가하고 있다. 증가하는 하·폐수 발생량과 더불어 미처리 연계처리수(분뇨, 가축분뇨, 산업폐수, 침출수, 음폐수)또한 발생량이 증가하고 있고, 연계처리수 상당 수가 인근 하수처리장으로 연계하여 연계처리되고 있다. 연계처리수는 기존 유입하수와 비교했을 때 저유량 고농도의 특징을 가지고 있기때문에 하수처리장에서는 방류수 수질 악화, 처리량 증가, 오염부하 증가, 충격부하 또는 중금속 유입 등과 같은 많은 문제점을 가지게 된다. 이와같은 문제를 해결하기 위해서 하수처리장에 연계 유입되는 연계처리수의 양 및 수질 등의 조사가 필요하고, 하수처리에 미치는 영향등에 관한 연구가 필요하다. 따라서 본 논문은 연계처리수의 영향을 살펴보기 위해 4계절에 따른 수온조건을 고려했을 때 연계처리수의 영향 및 연계처리수 유입 유량 분배 등 영향인자를 고려하여 시나리오를 만들고 최적의 효율을 나타내는 조건을 분석하고자 한다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제10권4호
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• pp.101-106
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• 2021

양자 알고리즘과 양자 컴퓨터는 우리가 현재 사용하고 있는 많은 암호들의 안전성을 깨뜨릴 수 있다. 그루버 알고리즘을 n-bit 보안레벨을 가지는 대칭키 암호에 적용한다면 보안레벨을 (n/2)-bit 까지 낮출 수 있다. 그루버 알고리즘을 적용하기 위해서는 오라클 함수에 대칭키 암호가 양자 회로로 구현되어야 하기 때문에 대상 암호를 양자 회로로 최적화하는 것이 가장 중요하다. 이에 AES 또는 경량 블록암호를 양자 회로로 구현하는 연구들이 최근 활발히 진행되고 있다. 본 논문에서는 국산 경량 블록암호 LEA를 양자 회로로 최적화하여 구현 하였다. 기존의 LEA 양자회로 구현과 비교하여 양자 게이트는 더 많이 사용하였지만, 큐빗을 획기적으로 줄일 수 있었으며 이러한 트레이드오프 문제에 대한 성능 평가를 수행하였다. 마지막으로 제안하는 LEA 양자 회로에 그루버 알고리즘을 적용하기 위한 양자 자원들을 평가하였다.

• 산업경영시스템학회지
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• 제44권4호
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• pp.43-52
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• 2021

In weapon assignment studies to defend against threats such as ballistic missiles and long range artillery, threat assessment was partially lacking in analysis of various threat attributes, and considering the threat characteristics of warheads, which are difficult to judge in the early flight stages, it is very important to apply more reliable optimal solutions than approximate solution using LP model, Meta heuristics Genetic Algorithm, Tabu search and Particle swarm optimization etc. Our studies suggest Generic Rule based threat evaluation and weapon assignment algorithm in the basis of various attributes of threats. First job of studies analyzes information on Various attributes such as the type of target, Flight trajectory and flight time, range and intercept altitude of the intercept system, etc. Second job of studies propose Rule based threat evaluation and weapon assignment algorithm were applied to obtain a more reliable solution by reflection the importance of the interception system. It analyzes ballistic missiles and long-range artillery was assigned to multiple intercept system by real time threat assessment reflecting various threat information. The results of this study are provided reliable solution for Weapon Assignment problem as well as considered to be applicable to establishing a missile and long range artillery defense system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.3172-3193
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• 2022

Cognitive radio-simultaneous wireless information and power transfer (CR-SWIPT) has attracted much interest since it can improve both the spectrum and energy efficiency of wireless networks. This paper focuses on the resource sharing between a point-to-point primary system (PRS) and a multiuser multi-antenna cellular cognitive radio system (CRS) containing a large number of cognitive users (CUs). The resource sharing optimization problem is formulated by jointly scheduling CUs and adjusting the transmit power at the cognitive base station (CBS). The effect of accessing CUs' spatial channel correlation on the possible transmit power of the CBS is investigated. Accordingly, we provide a low-complexity suboptimal approach termed the semi-correlated semi-orthogonal user selection (SC-SOUS) algorithm to enhance the spectrum efficiency. In the proposed algorithm, CUs that are highly correlated to the information decoding primary receiver (IPR) and mutually near orthogonal are selected for simultaneous transmission to reduce the interference to the IPR and increase the sum rate of the CRS. We further develop a spatial correlation-based resource sharing (SC-RS) strategy to improve energy sharing performance. CUs nearly orthogonal to the energy harvesting primary receiver (EPR) are chosen as candidates for user selection. Therefore, the EPR can harvest more energy from the CBS so that the energy utilization of the network can improve. Besides, zero-forcing precoding and power control are adopted to eliminate interference within the CRS and meet the transmit power constraints. Simulation results and analysis show that, compared with the existing CU selection methods, the proposed low-complex strategy can enhance both the achievable sum rate of the CRS and the energy sharing capability of the network.

• 한국컴퓨터정보학회논문지
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• 제26권4호
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• pp.47-53
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• 2021

본 논문에서는 초해상도(Super-Resolution, SR)을 계산하는데 필요한 물리 기반 시뮬레이션 데이터를 효율적으로 분류하고 분할하여 빠르게 SR연산을 가능하게 하는 쿼드트리 기반 최적화 기법을 제안한다. 제안하는 방법은 입력 데이터로 사용하는 연기 시뮬레이션 데이터를 다운스케일링(Downscaling)하여 쿼드트리 연산 소요 시간을 대폭 감소시킨다. 이 과정에서 연기의 밀도를 이진화함으로써, 다운스케일링 과정에서 밀도가 수치 손실되는 문제를 완화하며 쿼드트리를 구축한다. 학습에 사용된 데이터는 COCO 2017 데이터 셋이며, 인공신경망은 VGG19 기반 네트워크를 사용한다. 컨볼루션 계층을 거칠 때 데이터의 손실을 막기 위해 잔차(Residual) 보완 방식과 유사하게 이전 계층의 출력 값을 더해주며 학습을 진행한다. 실험결과가 연기의 경우 제안된 방법은 이전 접근법에 비해 약 15~18배 정도의 속도향상을 얻었다.

• Nuclear Engineering and Technology
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• 제52권12호
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• pp.2789-2802
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• 2020

Small Modular Reactors (SMRs) are attracting wide attention due to their outstanding performance, extensive studies have been carried out for lead-based fast reactors (LFRs) that cooled with Lead or Lead-bismuth (LBE), and small modular natural circulation LFR is one of the promising candidates for SMRs and LFRs development. One of the challenges for the design small modular natural circulation LFR is to master the natural circulation thermal-hydraulic performance in the reactor primary circuit, while the natural circulation characteristics is a coupled thermal-hydraulic problem of the core thermal power, the primary loop layout and the operating state of secondary cooling system etc. Thus, accurate predicting the natural circulation LFRs thermal-hydraulic features are highly required for conducting reactor operating condition evaluate and Thermal hydraulic design optimization. In this study, a thermal-hydraulic analysis code is developed for small modular natural circulation LFRs, which is based on several mathematical models for natural circulation originally. A small modular natural circulation LBE cooled fast reactor named URANUS developed by Korea is chosen to assess the code's capability. Comparisons are performed to demonstrate the accuracy of the code by the calculation results of MARS, and the key thermal-hydraulic parameters agree fairly well with the MARS ones. As a typical application case, steady-state analyses were conducted to have an assessment of thermal-hydraulic behavior under nominal condition, and several parameters affecting natural circulation were evaluated. What's more, two characteristics parameters that used to analyze natural circulation LFRs natural circulation capacity were established. The analyses show that the core thermal power, thermal center difference and flow resistance is the main factors affecting the reactor natural circulation. Improving the core thermal power, increasing the thermal center difference and decreasing the flow resistance can significantly increase the reactor mass flow rate. Characteristics parameters can be used to quickly evaluate the natural circulation capacity of natural circulation LFR under normal operating conditions.

• Nuclear Engineering and Technology
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• 제54권5호
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.

• Steel and Composite Structures
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• 제44권2호
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• pp.271-281
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• 2022

This study discusses challenges of running expandable profile liners (EPLs) to isolate trouble zones in directional section of a deep well, and summary the expandable profile liner technology (EPLT) field trial experience. Technically, the trial result reveals that it is feasible to apply the EPLT solving lost-circulation control problem and wellbore instability in the deep directional section. Propose schemes for optimizing the EPLT operation procedure to break through the existing bottleneck of EPLT in the deep directional section. Better-performing transition joints are developed to improve EPL string reliability in high borehole curvature section. High-performing and reliable expanders reduce the number of trips, offer excellent mechanical shaping efficiency, simplify the EPLT operation procedure. Application of the expansion and repair integrated tool could minimize the risk of insufficient expansion and increase the operational length of the EPL string. The new welding process and integrated automatic welding equipment improve the welding quality and EPL string structural integrity. These optimization schemes and recent new advancements in EPLT can bring significant economic benefits and promote the application of EPLT to meet future challenges.

• Coupled systems mechanics
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• 제11권1호
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• pp.55-78
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• 2022

The main aim of this paper is the identification of the model parameters for the constitutive model of concrete and concrete-like materials capable of representing full set of 3D failure mechanisms under various stress states. Identification procedure is performed taking into account multi-scale character of concrete as a structural material. In that sense, macro-scale model is used as a model on which the identification procedure is based, while multi-scale model which assume strong coupling between coarse and fine scale is used for numerical simulation of experimental results. Since concrete possess a few clearly distinguished phases in process of deformation until failure, macro-scale model contains practically all important ingredients to include both bulk dissipation and surface dissipation. On the other side, multi-scale model consisted of an assembly micro-scale elements perfectly fitted into macro-scale elements domain describes localized failure through the implementation of embedded strong discontinuity. This corresponds to surface dissipation in macro-scale model which is described by practically the same approach. Identification procedure is divided into three completely separate stages to utilize the fact that all material parameters of macro-scale model have clear physical interpretation. In this way, computational cost is significantly reduced as solving three simpler identification steps in a batch form is much more efficient than the dealing with the full-scale problem. Since complexity of identification procedure primarily depends on the choice of either experimental or numerical setup, several numerical examples capable of representing both homogeneous and heterogeneous stress state are performed to illustrate performance of the proposed methodology.

• Smart Structures and Systems
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• 제29권1호
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.