• Journal of Information Processing Systems
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• 제17권4호
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• pp.721-736
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• 2021

For the mobile edge computing (MEC) system supporting dense network, a joint allocation algorithm of computing and communication resources based on reinforcement learning is proposed. The energy consumption of task execution is defined as the maximum energy consumption of each user's task execution in the system. Considering the constraints of task unloading, power allocation, transmission rate and calculation resource allocation, the problem of joint task unloading and resource allocation is modeled as a problem of maximum task execution energy consumption minimization. As a mixed integer nonlinear programming problem, it is difficult to be directly solve by traditional optimization methods. This paper uses reinforcement learning algorithm to solve this problem. Then, the Markov decision-making process and the theoretical basis of reinforcement learning are introduced to provide a theoretical basis for the algorithm simulation experiment. Based on the algorithm of reinforcement learning and joint allocation of communication resources, the joint optimization of data task unloading and power control strategy is carried out for each terminal device, and the local computing model and task unloading model are built. The simulation results show that the total task computation cost of the proposed algorithm is 5%-10% less than that of the two comparison algorithms under the same task input. At the same time, the total task computation cost of the proposed algorithm is more than 5% less than that of the two new comparison algorithms.

• 한국가정과교육학회지
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• 제33권3호
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• pp.85-105
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• 2021

이 연구의 목적은 중학교 가정 교과 수업에서 디자인씽킹을 활용한 협력적 소비 교육 프로그램을 개발하고 실행하여 학생들의 협력적 문제해결역량에 미치는 영향을 알아보는데 있다. 이에 ADDIE 모형에 따라 디자인씽킹에 기반 한 협력적 소비 교육 프로그램을 개발하고, 총 25명의 학생을 대상으로 실행한 후 평가를 실시하였으며, 연구 결과는 다음과 같다. 첫째, 선행연구를 바탕으로 협력적 소비의 실천을 위한 '공유학교 만들기'를 주제로 D. school의 디자인씽킹 프로세스에 기반한 소비교육 프로그램을 개발하였다. 8차시의 교수·학습 과정안과 워크북을 전문가 타당도 검증한 결과, 문항 평균 4.72(5점 만점), CVI 평균 0.93으로 내용 타당도와 현장 적합성이 우수하다고 검증받았다. 둘째, 협력적 소비 교육 프로그램을 실행하고, 수정·보완된 협력적 문제해결역량 도구를 활용한 사전-사후검사와 개방형 설문조사를 실시한 결과를 종합해볼 때, 학생들이 협력적 소비에 대한 지식과 필요성을 인식하여 실천의식이 향상됨과 함께 개발한 프로그램이 협력적 문제해결역량을 향상시키는 데 유의미한 효과가 있음을 확인하였다. 후속연구로 연구 대상의 확대, 디자인씽킹을 적용한 가정 교과 프로그램 및 소비자 역량 강화를 위한 프로그램 연구, 개발을 제언하였다. 이 연구는 디자인씽킹을 활용한 협력적 소비 교육 프로그램이 청소년의 협력적 문제해결역량 향상에 효과가 있음을 확인하였으며, 가정교과에서 변화하는 소비환경에 따른 소비자 교육 변화 요구에 부응하여 '협력적 소비'를 주제로 소비 교육 프로그램을 개발했다는 점에서 의의가 있다.

• Management Science and Financial Engineering
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• 제22권1호
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• pp.13-20
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• 2016

We investigate the optimal consumption and investment problem when a working debtor has an option to file for bankruptcy. By applying the duality approach, the closed-form solutions are obtained for the case of CRRA utility function. The optimal bankruptcy time is determined by the first hitting time when the financial wealth hits the wealth threshold derived from the optimal stopping time problem. Moreover, the numerical results show that the investment increases as the wealth approaches the threshold and the value gain from the bankruptcy option is vanished as wealth increases.

• 가정과삶의질연구
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• 제7권2호
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• pp.31-43
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• 1989

The purposes of this study were to identify the overall levels of consumer problem, consumer competencies and purchase pattern of urban low-income consumers and to examine the factors affecting the consumer problem and the subareas-market environment problem(MEP) and transaction relation problem(TRP). The related factors, that is, independent variables were competencies-related factors(consumption-oriented attitude, attitude on consumerism, consumer knowledge), purchase pattern-related factors (search pattern, credit pattern, peddler pattern) and socio-demorgraphic factors(age, educational level, family size). For this purpose, a survey was conducted by interview using questionaires on 198 homemakers that lived in the poor areas of Seoul. Statistics used for data analysis were Frequency Distribution, Percentile, Mean, Pearson's Correlation, One-way ANOVA, Scheffe-test, Breakdown and Multiple Classification Analysis. Major findings were as follows: 1) In the level of consum r problem were in the middle level and the level of MEP were higher than that of TRP. The attitude on consumption-orientation was so negative, while attitude on consumerism was positive. The level of consumer knowledge was in the middle level. The urban low-income consumers searched a little and depended on credit and peddler in the low level. 2) Consumer problem perceived by urban low-income consumers differed significantly according to attitude on consumerism, credit pattern, monthly charge of peddler purchase. The MEP depended on attitude on consumerism and monthly charge of peddler purchase, and the TRP was affected by credit pattern and attitude on consumerism. Resulting from MCA, the most influencial variable was attitude on consumerism and credit pattern in the consumer problem, and attitude on consumerism in the MEP, and credit pattenr in the TRP.

• 한국통신학회논문지
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• 제38A권6호
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• pp.528-534
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• 2013

센서 네트워크에서 기본적으로 고려되어야 하는 것은 센서 노드의 에너지 소모 문제이다. 이를 해결하기 위해 많은 연구들이 진행되어 왔지만 에너지 소모 문제와 더불어 트레이드오프 관계를 갖는 지연 문제도 간과할 수 없는 부분이다. 본 논문은 생체시스템을 모방하여 무선 센서망에서 에너지의 소모와 지연시간을 줄이기 위한 BISA(Bio-inspired Scheduling Algorithm)를 제안한다. BISA는 에너지 효율성이 높은 라우팅 경로를 탐색하고 다중채널을 이용하여 데이터 전송의 경로를 다중화하여 데이터 전송을 위한 에너지 소모와 지연시간을 최소화한다. 모의실험을 통해 제안한 방식이 효율적으로 에너지를 소모함과 동시에 요구지연시간을 보장함을 확인한다.

• Journal of Communications and Networks
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• 제13권1호
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• pp.17-25
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• 2011

A convergecast is a popular routing scheme in wireless sensor networks (WSNs) in which every sensor node periodically forwards measured data along configured routing paths to a base station (BS). Prolonging lifetimes in energy-limited WSNs is an important issue because the lifetime of a WSN influences on its quality and price. Low-energy adaptive clustering hierarchy (LEACH) was the first attempt at solving this lifetime problem in convergecast WSNs, and it was followed by other solutions including power efficient gathering in sensor information systems (PEGASIS) and power efficient data gathering and aggregation protocol (PEDAP). Our solution-chain routing with even energy consumption (CREEC)-solves this problem by achieving longer average lifetimes using two strategies: i) Maximizing the fairness of energy distribution at every sensor node and ii) running a feedback mechanism that utilizes a preliminary simulation of energy consumption to save energy for depleted Sensor nodes. Simulation results confirm that CREEC outperforms all previous solutions such as LEACH, PEGASIS, PEDAP, and PEDAP-power aware (PA) with respect to the first node death and the average lifetime. CREEC performs very well at all WSN sizes, BS distances and battery capacities with an increased convergecast delay.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2003-2021
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• 2020

Today's computer systems are widely integrated with CPU and GPU to achieve considerable performance, but energy consumption of such system directly affects operational cost, maintainability and environmental problem, which has been aroused wide concern by researchers, computer architects, and developers. To cope with energy problem, we propose a task-scheduling framework to reduce energy under performance constraint by rationally allocating the tasks across the CPU and GPU. The framework first collects the estimated energy consumption of programs and performance information. Next, we use above information to formalize the scheduling problem as the 0-1 knapsack problem. Then, we elaborate our experiment on typical platform to verify proposed scheduling framework. The experimental results show that our proposed algorithm saves 14.97% energy compared with that of the time-oriented policy and yields 37.23% performance improvement than that of energy-oriented scheme on average.

• 한국생활과학회지
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• 제24권1호
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• pp.25-38
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• 2015

This paper focused to classify the consumption stress coping types among married women consumers and to investigate the differences of socio-economic variables, social class, perceived health status, and consumption stress among coping types. Data were collected from 500 married women through online surveys in South Korea. Two factors of consumption stress(consumption stress before purchase, consumption stress after purchase), and three factors of consumption stress coping(Social support coping, problem solving focused coping, Passive avoidance coping) were identified. K-mean cluster analysis classified into 4 coping types with consumption stress coping. 15% of the sample were included to the passive coping type, and 25% were classified into the ambivalent coping type. 26.8% of the sample were identified to the active coping type, and 35.2% were maladaptive coping type. There were significant differences among the consumption stress coping types on education, family income, social class, health status, consumption stress after purchase. Consumer education programs should develop and implement especially for passive coping type and maladaptive coping type to cope effectively with consumption stress.

• 융합신호처리학회논문지
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• 제12권1호
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• pp.38-43
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• 2011

센서 네트워크에서 사용되는 동적 클러스터링 방식은 주기적으로 클러스터 구조가 바뀌는 셋업과정으로 인한 에너지 소모가 크다. 셋업과정은 보안적용을 해야 할 경우 보안 키가 주기적으로 재 생성되는 등 클러스터 구성 이외에 추가적인 에너지 낭비가 발생한다. 본 논문은 최초에 구성된 클러스터 알고리즘과 이후 반복적으로 발생되는 클러스터 재셋업 알고리즘을 달리하는 하이브리드 방식을 제안한다. 재 셋업에서는 고정된 클러스터 내에서 순환적으로 클러스터 헤드노드를 선출하는 순환적 클러스터 헤드선정(RRCH: Round-Robin Cluster Header)방식을 이용하여 에너지 소모를 줄인다. 보안키 생성 및 적용으로 추가되는 에너지 소모는 클러스터가 지속적으로 고정되기 때문에 최초 클러스터 형성 때 사전 배포하는 방식으로 해결된다. 본 논문에서 제안한 방식의 타당성을 확인하기 위해 모의실험을 실시하였다. 라운드 구간을 100번 반복하여 클러스터 구성과 데이터 전송을 포함한 전체 에너지 소모량을 측정하였다. 결과는 제안한 방식이 LEACH방식보다 평균 26.5%, HEED방식보다 평균 20% 적게 소모되는 것을 확인하였다.

• 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.