• 한국강구조학회 논문집
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• 제19권2호
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• pp.171-179
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• 2007

본 논문은 유전자 알고리즘을 근거한 이산최적설계 알고리즘에 의한 온실구조용 최적설계 프로그램을 개발하였다. 최적기법은 이산화 최적기법에 효과적인 유전자알고리즘을 근거로 하였다.본 연구에서 목적함수는 온실 구조물의 중량이고, 제약조건식은 한계상태설계기준에 대한 설계 제한식이다. 설계변수는 KSD 3760 농업용 아연도강관이다. 온실구조의 경제적인 구조설계나 안정성평가에 대한 기준을 제시하고자 하였다. 또한 온실구조자재의 표준화 및 규격화연구에 기여하고자 하였다. 본 연구의 유전자 알고리즘에 의한 온실구조용 이산화 최적설계 프로그램의 적용을 위해 설계 예를 들었다.

• Agribusiness and Information Management
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• 제10권1호
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• pp.1-15
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• 2018

Many paddy cultivating farmers in the country are forced to use their limited resources to produce adequate food for their family, leading to the degradation and reduction in potential of these resources. The yield levels of paddy at the farmers' level and in the Front Line Demonstrations (FLDs) conducted in the farmers' fields is not at par with potential yield of the paddy variety. The gap between potential yield of crop variety and yield realized in FLDs refers to Research gap and the yield gap between FLDs and due to farmers' practice refers to Extension gap. The earlier studies conducted in India in general and in Andhra Pradesh in particular highlighted the existence of both research and extension gaps with reference to paddy. It is essential that, the narrowing of both research and extension gaps is not static, but dynamic considering the influence of technological interventions in boosting paddy yields at FLDs level and at farmers' level and also with the improvement of the yield potential of paddy varieties. This calls for integrated and holistic approaches to address these two gaps and with this background, the researcher aimed at this in depth study. The findings revealed that, research gaps are high with reference to weed management and pest management and extension gaps are high with reference to farm mechanization followed by fertilizer management. Reliable source of seed, capital use and frequency of meetings with Scientists or Agricultural Officers significantly influence the extension gaps in paddy. Farmers also prioritized socio-economic and technical constraints and the analysis infers that, it is high time now for the farmers to adopt the planned technological interventions on scientific scale to minimize the extension gaps to the extent possible. As the enabling environment in the State of Andhra Pradesh is highly encouraging for the farmers with relevant policy instruments in the form of subsidized inputs, free power, credit at concessional rates of interest, constructing irrigation projects etc., the adoption of the proposed technological interventions significantly contribute to minimizing both research and extension gaps in paddy cultivation in Kurnool district of Andhra Pradesh.

• 천문학회보
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• 제44권1호
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• pp.78.2-78.2
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• 2019

We develop an Alcock-Paczynski (AP) test method that uses the evolution of redshift-space two-point correlation function (2pCF) of galaxies. The method improves the AP test proposed by Li et al. (2015) in that it uses the full two-dimensional shape of the correlation function. Similarly to the original method, the new one uses the 2pCF in redshift space with its amplitude normalized. Cosmological constraints can be obtained by examining the redshift dependence of the normalized 2pCF. This is because the 2pCF should not change apart from the expected small non-linear evolution if galaxy clustering is not distorted by incorrect choice of cosmology used to convert redshift to comoving distance. Our new method decomposes the redshift difference of the 2-dimensional correlation function into the Legendre polynomials whose amplitudes are modelled by radial fitting functions. The shape of the normalized 2pCF suffers from small intrinsic time evolution due to non-linear gravitational evolution and change of type of galaxies between different redshifts. It can be accurately measured by using state of the art cosmological simulations. We use a set of our Multiverse simulations to find that the systematic effects on the shape of the normalized 2pCF are quite insensitive to change of cosmology over \Omega_m=0.21 - 0.31 and w=-0.5 - -1.5. Thanks to this finding, we can now apply our method for the AP test using the non-linear systematics measured from a single simulation of the fiducial cosmological model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• 한국컴퓨터정보학회논문지
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• 제28권3호
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• pp.1-9
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• 2023

본 논문에서는 양자컴퓨팅 기술을 활용해 유의미한 수준의 현실 문제를 해결하는 데 있어서 응용을 어렵게 하는 요인이 무엇인지 도출하고, 관련 연구 동향과 방향성을 제시한다. 이를 위해, 양자컴퓨팅 기술을 응용하는 일의 어려움을 이해하는 데 필요한 양자 역학의 기본 지식을 컴퓨터공학의 관점에서 정리하고, 현재 상용화된 양자 컴퓨터와 양자 프로그래밍 계층을 문헌을 통해 분석한다. 또한 클라우드 기반 상용 양자컴퓨팅 서비스의 현황과 활용 방안에 대한 분석과정을 통해, 현시점에서 양자컴퓨팅의 실용적 응용을 어렵게 하는 요인으로 양자 컴퓨터 프로그래밍에 대한 진입장벽과 노이즈가 존재하는 중규모 양자 컴퓨터에 대한 제약사항, 아직 성장 중인 오픈소스 생태계, 현실 문제 크기에 대한 시뮬레이션의 어려움의 네 가지 요인을 도출하고 관련 연구에 대한 동향과 방향성을 제시한다. 이를 통해, 양자컴퓨팅 기술의 실용적 응용을 위한 토대를 마련하는 데 기여할 것으로 기대된다.

• 대한토목학회논문집
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• 제42권6호
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• pp.887-892
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• 2022

국제적인 철도 사업은 개도국의 재정 부족 등 다양한 대외적 환경변화로 인해 재정사업에서 민간 투자사업(PPP)으로의 변경이 보편화되고 있다. 하지만, 철도 시설물은 도로 등 다른 인프라 시설물 보다는 철도 생애주기 특성상 사업개발, 건설 및 운영단계까지 막대한 건설자금 및 O&M 비용이 소요된다. 따라서 투자 대비 일정 수준의 수익률이 보장되지 않는다면 해외 특히 예측불허한 상황들이 발생하는 개발도상국에서의 해외철도 민간투자사업의 수행은 매우 어려운 실정이다. 본 연구에서는 글로벌 철도시장 동향 정보 및 기존 문헌 고찰을 통해 국내기업의 해외철도 민간투자사업 진출시 취약점 및 위험요인들을 살펴보고 그 의미와 대응 방안들에 대한 논의를 통하여 해외철도사업 진출시 국내기업의 경쟁력을 강화하는 방향을 제시하고자 한다.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Steel and Composite Structures
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• 제44권2호
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• pp.199-210
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• 2022

This study build finite element analysis (FEA) models describing the bending events of coiled tubing (CT) at the wellhead and trips into the hole, accurately provide the state of stress and strain while the CT is in service. The bending moment and axial force history curves are used as loads and boundary conditions in the diametrical growth models to ensure consistency with the actual working conditions in field operations. The simulation diametrical growth results in this study are more accurate and reasonable. Analysis the factors influencing fatigue and diametrical growth shows that the internal pressure has a first-order influence on fatigue, followed by the radius of the guide arch, reel and the CT diameter. As the number of trip cycles increase, fatigue damage, residual stress and strain cumulatively increase, until CT failure occurs. Significant residual stresses remain in the CT cross-section, and the CT exhibits a residual curvature, the initial residual bending configuration of CT under wellbore constraints, after running into the hole, is sinusoidal. The residual stresses and residual bending configuration significantly decrease the buckling load, making the buckling and buckling release of CT in the downhole an elastic-plastic process, exacerbating the helical lockup. The conclusions drawn in this study will improve CT models and contribute to the operational and economic success of CT services.

• International Journal of Computer Science & Network Security
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• 제22권11호
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• pp.87-92
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• 2022

Today the system of higher education needs significant reforms. Intellectualization of the educational process in HEIs aims to improve the quality of educational services. Intellectual information technologies are information technologies that help a person to accelerate the analysis of the political, economic, social, and technical situation, as well as the synthesis of management decisions. The basis for their mastery is information and communication technologies. The purpose of the research work is to identify the relationship between the introduction of information and communication technologies and the increase in the level of intellectualization of higher education. The article substantiates the expediency of introducing information and communication technologies in order to improve the intellectualization of the educational process in higher education. An empirical study of the variables that characterize the level of intellectualization of higher education through the proposed techniques has been conducted. The tendencies characteristic of pedagogical conditions of implementation of information and communication model in the educational process were revealed. It is proved that the level of intellectualization of higher education depends on the implemented pedagogical conditions. The effectiveness of the proposed information and communication model is also confirmed. Given the data obtained during the study and the low constraints that may affect the results of further research on this issue should focus on the study of other variables that characterize the state of intellectualization of the educational process.