• ETRI Journal
• /
• v.39 no.3
• /
• pp.353-363
• /
• 2017

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms.

• Journal of the Korean Society for Precision Engineering
• /
• v.34 no.2
• /
• pp.133-138
• /
• 2017

Inverter-Type refrigerators are known to consume less energy by varying the inverter frequency according to indoor temperatures and refrigerant pressure through indoor-outdoor communication. However, many commercial operators cannot afford to replace indoor units with ones capable of communication. In a non-communication configuration, indoor units are connected with an inverter-type outdoor unit without intercommunication abilities. The research goal is finding appropriate operating parameters to achieve energy efficiency. Thus, an operation algorithm with two modes is proposed, i.e., one to search the best operating parameters and one for normal operation with the best parameters. The experimental evaluation showed 11.27% reduction in energy consumption, indicating a good applicability of the algorithm.

• International Journal of Control, Automation, and Systems
• /
• v.6 no.4
• /
• pp.468-476
• /
• 2008

This paper presents a novel approach to economic dispatch (ED) with nonconvex fuel cost function as combinatorial optimization problems (COP) while most of the conventional researches have been developed as function optimization problems (FOP). One nonconvex fuel cost function can be divided into several convex fuel cost functions, and each convex function can be regarded as a generation type (G-type). In that case, ED with nonconvex fuel cost function can be considered as COP finding the best case among all feasible combinations of G-types. In this paper, a genetic algorithm is applied to solve the COP, and the $\lambda$-P table method is used to calculate ED for the fitness function of GA. The $\lambda$-P table method is reviewed briefly and the GA procedure for COP is explained in detail. This paper deals with three kinds of ED problems, namely ED considering valve-point effects (EDVP), ED with multiple fuel units (EDMF), and ED with prohibited operating zones (EDPOZ). The proposed method is tested for all three ED problems, and the test results show an improvement in solution cost compared to the results obtained from conventional algorithms.

• Journal of KIBIM
• /
• v.5 no.4
• /
• pp.11-22
• /
• 2015

Government efforts for green growth policy initiatives demand low-carbon technologies in the road construction industry. The purpose of this paper is to develop an algorithm of a road alignment design solution for establishing the multi-dimensional information, and to calculate carbon emission quantity due to the geometric design elements in the planning phase of road alignment. The paper developed a calculation method for carbon emission quantity by drawing a speed profile reflected in the operating speed, acceleration and deceleration, which are majors factor of carbon emissions while driving and by applying a carbon emission factor. From this effort, it enabled alignment planning to reduce carbon emission. Object-based parametric design methods of the cross-sections were proposed for alignment planning, and the paper demonstrated a BIM-based road alignment planning solution. The proposed solutions can provide multi-dimensional information on carbon emission quantity and cross section elements through driving simulation. It is expected to allow construction of eco-friendly roads by deriving optimal road alignment to minimize environmental costs.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2006.05a
• /
• pp.321-326
• /
• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the copper and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Journal of Electrical Engineering and Technology
• /
• v.2 no.2
• /
• pp.178-187
• /
• 2007

One important issue in power systems is dynamic instability due to loosing balance relation between electrical generation and a varying load demand that justifies the necessity of stabilization. Moreover, Power System Stabilizer (PSS) must have capability of producing appropriate stabilizing signals over a wide range of operating conditions and disturbances. To overcome these drawbacks, this paper proposes a new method for robust design of PSS by using an auto-tuning fuzzy control in combination with Real-Coded Genetic Algorithm (RCGA). This method includes two fuzzy controllers; internal fuzzy controller and supervisor fuzzy controller. The supervisor controller tunes the internal one by on-line applying of nonlinear scaling factors to inputs and outputs. The RCGA-based method is used for off-line training of this supervisor controller. The proposed PSS is tested in three operational conditions; nominal load, heavy load, and in the case of fault occurrence in transmission line. The simulation results are provided to compare the proposed PSS with conventional fuzzy PSS and conventional PSS. By evaluating the simulation results, it is shown that the performance and robustness of proposed PSS in different operating conditions is more acceptable

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.6
• /
• pp.1107-1112
• /
• 2008

Clustering techniques, which are algorithm to increase the network lifetime in wireless sensor networks, is developed to minimize the energy consumption of nodes. Existing clustering techniques by to increase the network lifetime with equalizing each node's the energy consumption by rotating the role of CH(Cluster Head), but these algorithms did not present the solution that minimizes the energy consumption of neighboring nodes with sink. In this paper, we propose the clustering algorithm that prolongs the network lifetime by not including a part of nodes in POS(Personal Operating Space) of the sink in a cluster and communicating with sink directly to reduce the energy consumption of CH closed to sink.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.17 no.1
• /
• pp.1-6
• /
• 2021

In this paper, to expand the use of standalone street lights, an algorithm for controlling LED energy consumption was proposed. The proposed method uses an LED module of a standalone street light divided into n zones. This is a method of reducing total power consumption by preventing the increase in power consumption due to high heat generation by weakly operating the entire LED according to the illuminance. When the amount of sunlight decreases, the whole LED operates weakly and then brightens, and unlike streetlight that act as streetlight, a method of dividing LEDs by area and limiting the number of LEDs operating according to illumination intensity was proposed. This is a way to use a lot of time with limited battery capacity by reducing the generation of heat that consumes the most power in streetlight. It is also a method of continuously changing the initial usage area to improve the total usage time of the LED substrate. As a result of the experiment, it was found that the proposed method extends the service time because it generates less heat than the conventional stand-alone streetlight.

• Nuclear Engineering and Technology
• /
• v.54 no.4
• /
• pp.1271-1287
• /
• 2022

When abnormal operating conditions occur in nuclear power plants, operators must identify the occurrence cause and implement the necessary mitigation measures. Accordingly, the operator must rapidly and accurately analyze the symptom requirements of more than 200 abnormal scenarios from the trends of many variables to perform diagnostic tasks and implement mitigation actions rapidly. However, the probability of human error increases owing to the characteristics of the diagnostic tasks performed by the operator. Researches regarding diagnostic tasks based on Artificial Intelligence (AI) have been conducted recently to reduce the likelihood of human errors; however, reliability issues due to the black box characteristics of AI have been pointed out. Hence, the application of eXplainable Artificial Intelligence (XAI), which can provide AI diagnostic evidence for operators, is considered. In conclusion, the XAI to solve the reliability problem of AI is included in the AI-based diagnostic algorithm. A reliable intelligent diagnostic assistant based on a merged diagnostic algorithm, in the form of an operator support system, is developed, and includes an interface to efficiently inform operators.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.4
• /
• pp.425-433
• /
• 2022

This paper studies the method of delivering munitions using both drones and UGVs that are developing along with the 4th Industrial Revolution. While drones are more mobile than UGVs, their loading capacity is small, and UGVs have relatively less mobility than drones, but their loading capacity is better. Therefore, by simultaneously operating these two delivery means, each other's shortcomings may be compensated. In addition, on actual battlefields, time constraints are an important factor in delivering munitions. Therefore, assuming an actual battlefield environment with a time limit, we establish delivery routes that minimize delivery time by operating both drones and UGVs with different capacities and speeds. If the delivery is not completed within the time limit, penalties are imposed. We devised the hybrid genetic algorithm to find solutions to the proposed model, and as results of the experiment, we showed the algorithm we presented solved the actual size problems in a short time.