• Industrial Engineering and Management Systems
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• v.15 no.3
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• pp.259-267
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• 2016

In Wireless Sensor Network, various routing protocols were employed by our Research and Development community to improve the energy efficiency of a network as well as to control the traffic by considering the terms, i.e. Packet delivery rate, the average end-to-end delay, network routing load, average throughput, and total energy consumption. While maintaining network connectivity for a long-term duration, it's necessary that routing protocol must perform in an efficient way. As we discussed Optimized Link State Routing protocol between all of them, we find out that this protocol performs well in the large and dense networks, but with the decrease in network size then scalability of the network decreases. Whenever a link breakage is encountered, OLSR is not able to periodically update its routing table which may create a redundancy problem. To resolve this issue in the OLSR problem of redundancy and predict link breakage, an enhanced protocol, i.e. S-OLSR (More Scalable OLSR) protocol has been proposed. At the end, a comparison among different existing protocols, i.e. DSR, AODV, OLSR with the proposed protocol, i.e. S-OLSR is drawn by using the NS-2 simulator.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.47-54
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• 2024

The global decrease in traditional energy resources has prompted increasing energy demand, necessitating efforts to replace and optimize energy sources. This study focuses on enhancing energy efficiency in manufacturing plants, known for their high energy consumption. Through simulations and analyses, the study proposes a temperature-based control system for HVAC (Heating, Ventilating, and Air Conditioning) operations, utilizing machine learning algorithms to predict and optimize factory temperatures. The results indicate that this approach, particularly the prediction-based free cooling algorithm, can achieve over 10% energy savings compared to existing systems. This paper presents that implementing an efficient HVAC control system can significantly reduce overall factory energy consumption, with plans to apply it to real factories in the future.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.4
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• pp.85-94
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• 2015

Recently, research on the smart grid that combines ICT(Information & Communication technology) to the power system has been actively progressed. If the occupancy of the EV(Electric vehicle) is increased. the V2G(Vehicle to grid) system is available which constitutes the micro-grid through battery of EV. V2G system performs load leveling and efficient energy consumption by battery operation considering load condition. But, if the battery is used only depending on the electricity rates, it doses not consider the life of the battery. The ACC(Achievable cycle) and the total transferable energy of battery varies corresponding to the selected DOD(Depth of discharge). In this paper, the optimal DOD selection method of V2G system considering battery wear cost and average driving distance of EV. Also, the total revenue prediction of various nation is presented considering the actual electricity costs per hour.

• KIEAE Journal
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• v.17 no.4
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• pp.83-88
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• 2017

Purpose: The objective of this study is to develop a predictive model for calculating the amount of cooling load for the different setback temperatures during the setback period. An artificial neural network (ANN) is applied as a predictive model. The predictive model is designed to be employed in the control algorithm, in which the amount of cooling load for the different setback temperature is compared and works as a determinant for finding the most energy-efficient optimal setback temperature. Method: Three major steps were conducted for proposing the ANN-based predictive model - i) initial model development, ii) model optimization, and iii) performance evaluation. Result:The proposed model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results (Mi) and the predicted results (Si) under generally accepted levels. In conclusion, the ANN model presented its applicability to the thermal control algorithm for setting up the most energy-efficient setback temperature.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.12
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• pp.419-430
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• 2020

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model.

• Proceeding of EDISON Challenge
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• 2017.03a
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• pp.677-687
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• 2017

Caffeine has a long history of human consumption but the consumption of caffeine due to caffeinated energy drinks(CEDs) is rapidly growing. Marketing targets of CED sales are children, adolescents and young adults, possibly caffeine-sensitive groups and its effect for them can be significantly different from healthy adults. Caffeine-related toxicities among these groups are growing in number and a number of countries are recognizing severity of caffeine toxicities. Previous research showed prediction of maximal plasma caffeine concentration profiles after the single CED ingestion and the primary aim of this study is to visually predict plasma caffeine concentration after the single and multiple ingestion of standard servings of CED. Based on the population pharmacokinetic model using Monte Carlo simulation, prediction of caffeine concentration leading to caffeine intoxication in the sensitive groups is quantitatively presented and visualized. This research also broadens the perspective by creating and utilizing diverse open science tools including R package, Edison Science App and Shiny apps.

• Journal of muscle and joint health
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• v.5 no.1
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• pp.83-109
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• 1998

This study was carried out to identify the Important modifiable risk factors for reduced bone mass and to construct prediction model which can classify women with either low or high bone mass. Through the literature review, individual characteristics such as age, body weight, height, education level, family history, age of menarche, postmenopausal period, gravity, parity, menopausal status, and breast feeding period were identified and factors of life style such as past milk consumption, past physical activity, present daily activity, present calcium intake, alcohol intake, cigarette smoking, coffee consumption were identified as influencing factors of reduced bone mass in women. Four hundred and eighty women aged between 28 and 76 who had given measurement bone mineral density by dual energy x-ray absortiometry in lumbar vertebrae and femur from July to October, 1997 at 4 general hospitals in Seoul and Pusan were selected for this study. Women were excluded if they had a history of any chronic illness such as rheumatoid arthritis, diabetes mellitus, hyperthroidism, & gastrointestinal disorder and any medication such as calcium supplements, calcitonin, estrogen, thyroxine, antacids, & corticosteroids known affect bone. As a result of these exclusion criteria, four hundred and seventeen women were used for analysis. Multiple logistic regression model was developed for estimating the likelihood of the presence or absence of reduced bone mass. A SAS procedure was used to estimate risk factor coefficient. The results are as follows For lumbar spine, the variables significant were age, body weight, menopause status, daily activity, past milk consumption, and past physical activity(p<0.01), while for femoral Ward's triangle, age, body weight, level of education, past milk consumption, past physical activity(p<0.001). Past physical activity, present daily activity and past milk consumption are the most powerful modifiable predictors in vertebrae and femur among the predictors. When the model performance was evaluated by comparing the observed outcome with predicted outcome, the model correctly identified 74.1% of persons with reduced bone mass and 84.5% of persons with normal bone mass in the lumbar vertebrae and 82.9% of persons with reduced bone mass and 75.0% of persons with normal bone mass in the femoral Ward's triangle. On the basis of these results, a number of recommendations for the management of reduced bone mass may be made : First, those woman who are classified as high risk group of the reduced bone mass in the prediction model should examine the bone mineral density to further examine the usefulness of this model. Second, the optimal amount of milk consumption and a regular weight bearing exercise in childhood, adolescence, and early adult should be ensured.

• Journal of the Korean Solar Energy Society
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• v.32 no.5
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• pp.75-82
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• 2012

This study aims to investigate the usage of the renewable energy systems installed in buildings and make suggestions for the effective management. In this regard, a questionnaire survey was conducted on 1)design and construction, 2) operation and management, 3) user satisfaction and improvements about the renewable energy systems in buildings. Findings from this study can be summarized as follows; a lack of the basic information about systems, non-use of energy management systems, the differences in the features by energy source, and a lack of expertise of managers. The requirements to resolved these problems include the integrated management of various electric heat sources including a renewable energy source, an operation schedule based on the prediction of production and consumption, and so on. Furthermore the necessity of multiplex energy sources management system was confirmed and the basic data needed to establish the targets of this system were obtained.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.11B
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• pp.1200-1206
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• 2009

In this paper, we propose an energy-efficient MAC(Medium Access Control) protocol for processing context information in ubiquitous sensor network environments. CASMAC(Context Aware Sensor MAC) use context information for energy-efficient operation and its operation principle is as follows. First, we make scenarios with possible prediction for CASMAC. And then we save setted context information in server. When event occur at specific sensor node, and then it send three times sample data to server. According to context information, server process sample data. If server process sample data with event, it receive continuous data from event occur node by a transmission request signal. And then server send data transmission stop signal to event occur node when it do not need to data. If server process sample data with no event, it have not reply. Through we make energy consumption tables and an energy consumption model, we simulate analysis of CASMAC performance. In a result, we gains about 5.7 percents energy reduction compared to SMAC.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.