• 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.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.45-52
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• 2021

The article proves the relevance of developing conceptual frameworks for managing the quality assurance of logistics activities in the context of socio-economic interaction of their participants. It is established that the fundamental difference of the logistic approach in management from traditional approaches is the allocation of a single management function of previously separated, disparate material flows, as well as economic, technological, information integration of chain links into a single system capable of effective management of these flows. It is substantiated that the functioning of the enterprise as a logistics system can be represented in the form of a triad of logistics components, namely: supply logistics, production logistics, sales logistics. Management of quality assurance processes of logistics activities in the context of socio-economic interaction of their participants is a functional component of the entire logistics system due to the quality of work and interaction of all participants in the implementation of certain activities. The quality of logistics activities will affect the level of economic potential, rationalization and optimization of all logistics flows. It is proved that the management of quality assurance processes of logistics activities in the context of socio-economic interaction of their participants involves the following main areas: the introduction of a quality system of logistics processes; development and implementation of the general strategy of quality improvement at the enterprise; internal integration; controlling. Management of quality assurance processes of logistics activities in the context of socio-economic interaction of its participants requires compliance with the following requirements: systematic and comprehensive management of all flow processes; coordination of criteria and indicators for assessing the effectiveness of the entire logistics system; dissemination of the use and application of information technology; ensuring partnerships and close interaction of all participants in sales networks.

• Journal of Animal Science and Technology
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• 제63권6호
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• pp.1453-1463
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• 2021

Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaning-based method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

• 한국안전학회지
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• 제37권6호
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• pp.166-173
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• 2022

A variety of input parameters are taken into consideration while performing a Level 3 PSA. Some parameters related to plume segments, spatial grids, and particle size distribution have flexible input formats. Fine modeling performed by splitting a number of segments or grids may enhance the accuracy of analysis but is time-consuming. Analysis speed is highly important because a considerably large number of calculations is required to handle Level 2 PSA scenarios for a single-unit or multi-unit Level 3 PSA. This study developed a sensitivity analysis supporting interface called MACCSsense to compare the results of the trials of plume segmentation with the results of the base case to determine its impact (in terms of time and accuracy) and to support the development of a modeling approach, which saves calculation time and improves accuracy. MACCSense is an automation tool that uses a large amount of plume segmentation analysis results obtained from MUST Converter and Mr. Manager developed by KAERI to generate a sensitivity report that includes impact (time and accuracy) by comparing them with the base-case result. In this study, various plume segmentation approaches were investigated, and both the accuracy and speed of offsite consequence analysis were evaluated using MACCS as a consequence analysis tool. A simultaneous evaluation revealed that execution time can be reduced using multi-threading. In addition, this study can serve as a framework for the development of a modeling strategy for plume segmentation in order to perform accurate and fast offsite consequence analyses.

• 품질경영학회지
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• 제50권4호
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• 한국BIM학회 논문집
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• 제12권4호
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• pp.61-69
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• 2022

Earthmoving operations account for approximately 25% of construction cost, generally executed prior to the construction of buildings and structures with heavy equipment. For the successful completion of earthwork projects, it is crucial to constantly monitor earthwork equipment (e.g., trucks), estimate productivity, and optimize the construction process and equipment on a construction site. Traditional methods however require time-consuming and painstaking tasks for the manual observations of the ongoing field operations. This study proposed the use of a GPS sensor embedded in a smartphone for the tracking and visualization of equipment locations, which are in turn used for the estimation and simulation of cycle times and production rates of ongoing earthwork. This approach is implemented into a digital platform enabling real-time data collection and simulation, particularly in a 2D (e.g., maps) or 3D (e.g., point clouds) virtual environment where the spatial and temporal flows of trucks are visualized. In the case study, the digital platform is applied for an earthmoving operation at the site development work of commercial factories. The results demonstrate that the production rates of various equipment usage scenarios (e.g., the different numbers of trucks) can be estimated through simulation, and then, the optimal number of tucks for the equipment fleet can be determined, thus supporting the practical potential of real-time sensing and simulation for onsite equipment management.

• 인간식물환경학회지
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• 제23권5호
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• pp.537-544
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• 2020

Background and objective: Artemisia argyi has a long history as an effective treatment for various diseases. The detection of environmental pollutant benzo(a)pyrene, a known human carcinogen, in the leaves of Artemisia argyi is cause for concern. For medicinal plant extracts, both a reduction of benzo(a)pyrene as well as the maintained effectiveness of the compound are important. Therefore, in this study, we propose an optimized process for the addition and filtration of activated carbon to reduce benzo(a)pyrene and change the contents of the indicating substance(jaceosidine and eupatilin). Methods: Artemisia argyi EtOH extract containing 36 ppb of benzo(a)pyrene was added to 0.1, 0.5, 1.0, and 1.5% (w/w) of activated carbon for 120 min and filtered using an activated carbon filter 1, 2, 3, and 5 times respectively. The content of benzo(a)pyrene and indicating substances in Artemisia argyi extract were then measured with high performance liquid chromatography (fluorescence and UV detectors). Results: As the amounts of activated carbon powder and filtering cycles increased, the content of benzo(a)pyrene in the Artemisia argyi extract decreased. However, when activated carbon powder 1.5% was added to the extract, and when the activated carbon filter was filtered five times, the results were reduced by 15% and 30~40% respectively. The optimal extraction condition for reducing benzo(a)pyrene was adding 1.5% of activated carbon powder. This resulted in reducing benzo(a)pyrene by 83% and indicating substances by about 4%. Conclusions: Here we present a process for reducing benzo(a)pyrene in Artemisia argyi extract using activated carbon to reduce toxicity and minimize the loss of active ingredients. This approach has potential application within a manufacturing process of various medicinal plant extracts.

• 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.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.37-44
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• 2022

Plug-in Hybrid electric vehicles (PHEV) show great potential to reduce gas emission, improve fuel efficiency and offer more driving range flexibility. Moreover, PHEV help to preserve the eco-system, climate changes and reduce the high demand for fossil fuels. To address this; some basic components and energy resources have been used, such as batteries and proton exchange membrane (PEM) fuel cells (FCs). However, the FC remains unsatisfactory in terms of power density and response. In light of the above, an electric storage system (ESS) seems to be a promising solution to resolve this issue, especially when it comes to the transient phase. In addition to the FC, a storage system made-up of an ultra-battery UB is proposed within this paper. The association of the FC and the UB lead to the so-called Fuel Cell Battery Electric Vehicle (FCBEV). The energy consumption model of a FCBEV has been built considering the power losses of the fuel cell, electric motor, the state of charge (SOC) of the battery, and brakes. To do so, the implementing a reinforcement-learning energy management strategy (EMS) has been carried out and the fuel cell efficiency has been optimized while minimizing the hydrogen fuel consummation per 100km. Within this paper the adopted approach over numerous driving cycles of the FCBEV has shown promising results.