• Journal of Information Processing Systems
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• v.17 no.4
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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.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.85-105
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• 2021

The purpose of this study is to develop and implement cooperative consumption education programs using design thinking in middle school home economics education classes to understand the impact on students' cooperative problem solving competency. Accordingly, a cooperative consumption education program based on design thinking was developed according to the ADDIE model, and the evaluation was conducted on a total of 25 students. The results of the study were as follows. First, based on prior research, we developed a consumption education program based on D. school's design thinking process under the theme of 'Creating a Shared School' for the practice of cooperative consumption. As a result of expert validity verification of the teaching/learning course plan and workbook for the eight sessions, the average question was 4.72 (out of 5 points) and the average CVI was 0.93, indicating that the content validity and field suitability were excellent. Second, to summarize the results achieved from the implementation of the cooperative consumption education program, the pre-/post-test using the revised and supplemented cooperative problem-solving competency tool, and the open-ended survey, It was confirmed that the developed program had a significant effect on improving not only the students' knowledge and perceived necessity for cooperative consumption along with the awareness of practice, but also the cooperative problem-solving competency. As a follow-up study, we propose to expand the research to a wider audience, and to further conduct research and develop programs applied with design thinking in home economics curriculum and in consumer competency development. This study confirmed that cooperative consumption education programs using design thinking are effective in improving youth's cooperative problem-solving competency and is meaningful in that they developed consumption education programs under the theme of 'cooperative consumption' in response to changing consumer education needs.

• Management Science and Financial Engineering
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• v.22 no.1
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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.

• Journal of Families and Better Life
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• v.7 no.2
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.6
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• pp.528-534
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• 2013

The energy consumption problem should be taken into consideration in wireless sensor network. Many studies have been proposed to address the energy consumption and delay problem. In this paper, we propose BISA(Bio-inspired Scheduling Algorithm) to reduce the energy consumption and delay in wireless sensor networks based on biological system. BISA investigates energy-efficient routing path and minimizes the energy consumption and delay using multi-channel for data transmission by multiplexing data transmission path. Through simulation, we confirm that the proposed scheme guarantees the efficient energy consumption and delay requirement.

• Journal of Communications and Networks
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• v.13 no.1
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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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• v.14 no.5
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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.

• Korean Journal of Human Ecology
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• v.24 no.1
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.38-43
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• 2011

Cluster-based routing is high energy consumption of cluster head nodes. A recent approach to resolving the problem is the dynamic cluster technique that periodically re-selects cluster head nodes to distribute energy consumption of the sensor nodes. However, the dynamic clustering technique has a problem that repetitive construction of clustering consumes the more energies. This paper proposes a solution to the problems described above from the energy efficiency perspective. The round-robin cluster header(RRCH) technique, which fixes the initially structured cluster and sequentially selects cluster head nodes, is suggested for solving the energy consumption problem regarding repetitive cluster construction. A simulation result were compared with the performances of two of the most widely used conventional techniques, the LEACH(Low Energy Adaptive Clustering Hierarchy) and HEED(Hybrid, Energy Efficient, Distributed Clustering) algorithms, based on energy consumption, remaining energy for each node and uniform distribution. The evaluation confirmed that in terms of energy consumption, the technique proposed in this paper was 26.5% and 20% more efficient than LEACH and HEED, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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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.