• Korean Journal of Community Nutrition
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• v.20 no.2
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• pp.87-95
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• 2015

Objectives: Maintaining a balanced diet and thus health is crucial for adolescents, and the first step for balanced diet practice is meal planning. Adolescents, however, find it difficult to plan their meals. This study thus was set out to design an easier way of planning meals for adolescent girls. Methods: A dish-based target pattern for adolescent girls was tabulated, and validity of this was examined. Meal plan applying a dish-based target pattern was prepared by 150 female middle school students, and nutritional adequacies of those meal plans were examined. Validity and adequacy were tested by energy content, energy contribution ratio, nutrient adequacy ratio (NAR), probability of nutrient inadequacy, index of nutritional quality (INQ) calculation. Results: A dish-based target pattern with 11 dish groups was validated for nutritional adequacy. Though the NAR of calcium was 0.96, the INQ of calcium was 1.00. The average energy supply from the meal plans was 2,379 kcal, higher than the estimated energy requirement of a female middle school student, but the energy contribution ratio of carbohydrates, proteins, and fats were all adequate according to the acceptable macronutrient distribution range (AMDR). NAR of all nutrients examined were 1.0, except for calcium. The NAR and INQ of calcium were 0.87 and 0.75, respectively, and the meal plans at risk for calcium inadequacy was 19.30%. Conclusions: A dish-based target pattern proposed for adolescent girls was valid, but the meal plan prepared by female middle school students using this approach was high in energy and low in calcium supply. To cut down the energy supply from the meal plan, it is necessary to recommend dishes low in fat and use low fat cooking methods. To increase the calcium supply, it is important to recommend seaweed and legume group dishes with higher Ca INQ food items.

• Earthquakes and Structures
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• v.16 no.4
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• pp.487-499
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• 2019

Energy-based seismic design of structures has gradually become prominent in today's structural engineering investigations because of being more rational and reliable when it is compared to traditional force-based and displacement-based methods. Energy-based approaches have widely taken place in many previous studies and investigations and undoubtedly, they are going to play more important role in future seismic design codes, too. This paper aims to compute the maximum earthquake energy input to elastic single-degree-of-freedom (SDOF) systems for selected real ground motion records. A data set containing 100 real ground motion records which have the same site soil profiles has been selected from Pacific Earthquake Research (PEER) database. Response time history (RTH) analyses have been conducted for elastic SDOF systems having a constant damping ratio and natural periods of 0.1 s to 3.0 s. Totally 3000 RTH analyses have been performed and the maximum mass normalized earthquake input energy values for all records have been computed. Previous researchers' approaches have been compared to the results of RTH analyses and an approach which considers the pseudo-spectral velocity with Arias Intensity has been proposed. Graphs of the maximum earthquake input energy versus the maximum pseudo-spectral velocity have been obtained. The results show that there is a good agreement between the maximum input energy demands of RTH analysis and the other approaches and the maximum earthquake input energy is a relatively stable response parameter to be used for further seismic design and evaluations.

• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Journal of Construction Engineering and Project Management
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• v.3 no.3
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• pp.17-22
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• 2013

In many urban cities, super-tall buildings have been being constructed around New York and Chicago as the center since 1930 to improve the efficiency of land use and respond to new residential type. In terms of energy consumption, super-tall buildings are classified as a top energy consumption building. Also, as time passed, the degradation of energy performance occurs in super-tall buildings like general things so that these cannot show the initial performance planned in the design phase. Accordingly, building owners need to make a plan to apply energy saving measures to existing building during the operation phase. In order to select energy saving measures, calculus-based methods and enumerative schemes have been typically used. However, these methods are time-consuming and previous studies which used these methods have problems with not considering the initial construction cost. Consequently, this study proposes a model for selecting an optimal combination of energy saving measures which derives maximum energy saving within allowable cost using genetic algorithms. As a contribution of this research, it would be expected that a model is utilized as one of the decision-making tools during the planning stage for energy saving.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.294-299
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• 2013

In many urban cities, super-tall buildings have been being constructed around New York and Chicago as the center since 1930 to improve the efficiency of land use and respond to new residential type. In terms of energy consumption, super-tall buildings are classified as a top energy consumption building. Also, as time passed, the degradation of energy performance occurs in super-tall buildings like general things so that these cannot show the initial performance planned in the design phase. Accordingly, building owners need to make a plan to apply energy saving measures to existing building during the operation phase. In order to select energy saving measures, calculus-based methods and enumerative schemes have been typically used. However, these methods are time-consuming and previous studies which used these methods have problems with not considering the initial construction cost. Consequently, this study proposes a model for selecting an optimal combination of energy saving measures which derives maximum energy saving within allowable cost using genetic algorithms. As a contribution of this research, it would be expected that a model is utilized as one of the decision-making tools during the planning stage for energy saving.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3834-3857
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• 2021

The design of cluster-based routing protocols is necessary for Wireless Sensor Networks (WSN). But, due to the lack of features, the traditional methods face issues, especially on unbalanced energy consumption of routing protocol. This work focuses on enhancing the security and energy efficiency of the system by proposing Energy Efficient Based Secure Routing Protocol (EESRP) which integrates trust management, optimization algorithm and key management. Initially, the locations of the deployed nodes are calculated along with their trust values. Here, packet transfer is maintained securely by compiling a Digital Signature Algorithm (DSA) and Elliptic Curve Cryptography (ECC) approach. Finally, trust, key, location and energy parameters are incorporated in Particle Swarm Optimization (PSO) and meta-heuristic based Harmony Search (HS) method to find the secure shortest path. Our results show that the energy consumption of the proposed approach is 1.06mJ during the transmission mode, and 8.69 mJ during the receive mode which is lower than the existing approaches. The average throughput and the average PDR for the attacks are also high with 72 and 62.5 respectively. The significance of the research is its ability to improve the performance metrics of existing work by combining the advantages of different approaches. After simulating the model, the results have been validated with conventional methods with respect to the number of live nodes, energy efficiency, network lifetime, packet loss rate, scalability, and energy consumption of routing protocol.

• International Journal of Contents
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• v.11 no.2
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• pp.1-8
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• 2015

In this paper, we describe a novel method for document layout analysis that is based on a Fuzzy Energy Matrix (FEM). A FEM is a two-dimensional matrix that contains the likelihood of text and non-text and is generated through the use of Fuzzy theory. The key idea is to define an Energy map for the document to categorize text and non-text. The proposed mechanism is designed for execution with a low-resolution document image, and hence our method has a fast processing speed. The proposed method has been tested on public ICDAR 2009 datasets to conduct a comparison against other state-of-the-art methods, and it was also tested with Korean documents. The results of the experiment indicate that this scheme achieves superior segmentation accuracy, in terms of both precision and recall, and also requires less time for computation than other state-of-the-art document image analysis methods.

• Journal of Integrative Natural Science
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• v.12 no.1
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• pp.20-23
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• 2019

Demand response is usually operated through using the power rates and incentives. Demand management based on power charges is the most rational and efficient demand management method, and such methods include rolling base charges with peak time, sliding scaling charges depending on time, sliding scaling charges depending on seasons, and nighttime power charges. Search for other methods to stimulate resources on demand by actively deriving the demand reaction of loads to increase the energy efficiency of loads. In this paper, ESS algorithm for saving energy based on predicting the amount of solar power generation that can be used for buildings with small loads not under electrical grid.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1925-1932
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• 2016

One of the major challenges in the design of wireless sensor network (WSN) is to reduce the energy consumption of sensor nodes for prolonging the network lifetime. In the sensor network, communication is the most energy consuming event. Therefore, most of the energy saving techniques conserve energy by adjusting different parameters of the trans-receiver. Among them, one of the promising methods is the transmission power control (TPC). In this paper, we investigated the effects of the link quality based TPC scheme employed to the IEEE 802.15.4 standard for energy saving. The simulation results demonstrated that the link quality based TPC scheme works effectively in conserving energy as compared to the conventional IEEE 802.15.4.

• Journal of Electrochemical Science and Technology
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• v.15 no.1
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• pp.14-31
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• 2024

The text highlights the growing need for eco-friendly energy storage and the potential of metal-organic frameworks (MOFs) to address this demand. Despite their promise, challenges in MOF-based energy storage include stability, reproducible synthesis, cost-effectiveness, and scalability. Recent progress in supercapacitor materials, particularly over the last decade, has aimed to overcome these challenges. The review focuses on the morphological characteristics and synthesis methods of MOFs used in supercapacitors to achieve improved electrochemical performance. Various types of MOFs, including monometallic, binary, and tri-metallic compositions, as well as derivatives like hybrid nanostructures, sulfides, phosphides, and carbon composites, are explored for their energy storage potential. The review emphasizes the quest for superior electrochemical performance and stability with MOF-based materials. By analyzing recent research, the review underscores the potential of MOF-based supercapacitors to meet the increasing demands for high power and energy density solutions in the field of energy storage.