• ETRI Journal
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• v.44 no.5
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• pp.746-758
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• 2022

This paper focuses on a mobile edge-computing-enabled heterogeneous network. A battery level-aware task-scheduling framework is proposed to improve the energy efficiency and prolong the operating hours of battery-powered mobile devices. The formulated optimization problem is a typical mixed-integer nonlinear programming problem. To solve this nondeterministic polynomial (NP)-hard problem, a decomposition-based task-scheduling algorithm is proposed. Using an alternating optimization technology, the original problem is divided into three subproblems. In the outer loop, task offloading decisions are yielded using a pruning search algorithm for the task offloading subproblem. In the inner loop, closed-form solutions for computational resource allocation subproblems are derived using the Lagrangian multiplier method. Then, it is proven that the transmitted power-allocation subproblem is a unimodal problem; this subproblem is solved using a gradient-based bisection search algorithm. The simulation results demonstrate that the proposed framework achieves better energy efficiency than other frameworks. Additionally, the impact of the battery level-aware scheme on the operating hours of battery-powered mobile devices is also investigated.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.515-520
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• 2009

According to the operating characteristics of pump applications, they should exhibit high efficiency and energy saving capabilities throughout the whole operating process. A novel efficiency optimization control strategy is presented here to meet the high efficiency demand of a variable speed Permanent Magnet Synchronous Motor (PMSM). The core of this strategy is the excellent integration of mended maximum torque to the current control algorithm, based on the losses model during the dynamic and the grade search method with changed step by fuzzy logic during the steady. The performance experiments for the control system of a variable speed high efficiency PMSM have been completed. The test results verified that the system can reliably operate with a different control strategy during dynamic and steady operation, and the system exhibits better performance when using the efficiency-optimization control.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.17 no.3
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• pp.13-20
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• 2021

The capacity optimization of the heat exchanger of the TEM dehumidifier was performed through numerical analysis. If the ratio of the size of heat exchangers on the cold and hot surfaces of the TEM is not appropriate, the larger the size of the heat exchanger results the lower performance and efficiency. Optimizing the ratio of heat exchangers on the cold surface of TEM can improve the performance and the efficiency compared to when the ratio is 50%. The optimal proportion of cold surface heat exchangers is inversely proportional to the sum of the size of the heat exchangers on the cold and hot surfaces. When the optimum ratio of cold surface heat exchanger was applied, the larger the sum of size of the two heat exchangers results the greater the improvement of the performance and efficiency, compared to when the ratio of cold surface heat exchangers is 50%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2873-2892
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• 2019

Reducing the interference to the licensed mobile users and obtaining the energy efficiency are key issues in cognitive heterogeneous networks. A corresponding rate loss constraint is proposed to be used for the sensing-based spectrum sharing (SBSS) model in cognitive heterogeneous networks in this paper. Resource allocation optimization strategy is designed for the maximum energy efficiency under the proposed interference constraint together with average transmission power constraint. An efficiency algorithm is studied to maximize energy efficiency due to the nonconvex optimal problem. Furthermore, the relationship between the proposed protection criterion and the conventional interference constraint strategy under imperfect sensing condition for the SBSS model is also investigated, and we found that the conventional interference threshold can be regarded as the upper bound of the maximum rate loss that the primary user could tolerate. Simulation results have shown the effectiveness of the proposed protection criterion overcome the conventional interference power constraint.

• Journal of Communications and Networks
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• v.18 no.4
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• pp.629-638
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• 2016

In this paper, we investigate an energy-efficient resource allocation problem in a two-way relay (TWR) network consisting of multiple user pairs and an amplify-and-forward (AF) relay. As the users and relay have individual energy efficiencies (EE), we formulate a multi-objective optimization problem (MOOP). A single-objective optimization problem (SOOP) of the MOOP is introduced using a weighted-sum method, which achieves a single Pareto optimal point of the MOOP. To derive the algorithm for the SOOP, we propose a more tractable equivalent problem using the Karush-Kuhn-Tucker conditions of the SOOP, which guarantees convergence at the local optimal points. The proposed equivalent problem can be efficiently solved by the proposed iterative algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm in achieving the optimal EE in multi-user AF TWR networks.

• Journal of Drive and Control
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• v.21 no.1
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• pp.46-52
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• 2024

The advancement of autonomous driving technology has heightened the importance of Autonomous Mobile Robotics (AMR) within smart factories. Notably, in tasks involving the transportation of heavy objects, the consideration of weight in route optimization and path planning has become crucial. There is ongoing research on local path planning, such as Dijkstra, A^*, and RRT^*, focusing on minimizing travel time and distance within smart factory warehouses. Additionally, there are ongoing simultaneous studies on route optimization, including TSP algorithms for various path explorations and on minimizing energy consumption in mobile robotics operations. However, previous studies have often overlooked the weight of the objects being transported, emphasizing only minimal travel time or distance. Therefore, this research proposes route planning that accounts for the maximum payload capacity of mobile robotics and offers load-optimized path planning for multi-destination transportation. Considering the load, a genetic algorithm with the objectives of minimizing both travel time and distance, as well as energy consumption is employed. This approach is expected to enhance the efficiency of mobility within smart factories.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.565-571
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• 2013

This paper presents an effective method to improve efficiency of switched reluctance motor by optimizing energy conversion loop. A nonlinear analytical model which takes saturation account is developed to calculate inductance and flux-linkage. The flux-linkage curve is studied to calculate the co-energy increment to obtain the optimum exciting current. For a given cross-section, the exciting current at which co-energy increment is maximum was found to be constant while stack length varies. Then the energy conversion loop was optimized by varying the stack length and turns of windings. The constraints during optimization were that motor was excited by the maximum increment co-energy current and the energy in the loop was determined by rated power of motor. Dynamic finite element analysis was used to evaluate the efficiency of various models and the comparison of results shows promising effects of the proposed method. Experiment was also conducted to validate the simulation result.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5614-5633
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• 2018

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_3
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• pp.1205-1213
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• 2023

This study delves into methods for enhancing the power efficiency of air compressor systems, with the primary objective of significantly impacting industrial energy consumption and environmental preservation. The paper scrutinizes Shinhan Airro Co., Ltd.'s power efficiency optimization technology and employs machine learning ensemble models to simulate power efficiency optimization. The results indicate that Shinhan Airro's optimization system led to a notable 23.5% increase in power efficiency. Nonetheless, the study's simulations, utilizing machine learning ensemble techniques, reveal the potential for a further 51.3% increase in power efficiency. By continually exploring and advancing these methodologies, this research introduces a practical approach for identifying optimization points through data-driven simulations using machine learning ensembles.

• Journal of Communications and Networks
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• v.12 no.2
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• pp.150-157
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• 2010

Understanding and optimizing the energy consumption of wireless devices is critical to maximize the network lifetime and to provide guidelines for the design of new protocols and interfaces. In this work, we first provide an accurate analysis of the energy performance of an IEEE 802.11 WLAN, and then we derive the configuration to optimize it. We further analyze the impact of the energy configuration of the stations on the throughput performance, and we discuss under which circumstances throughput and energy efficiency can be both jointly maximized and where they constitute different challenges. Our findings are that, although an energy-optimized configuration typically yields gains in terms of throughput as compared against the default configuration, it comes with a reduction in performance as compared against the maximum-bandwidth configuration, a reduction that depends on the energy parameters of the wireless interface.