• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1143-1154
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• 2021

The selection of activation function has a great influence on the groundwater level prediction performance of artificial neural network (ANN) model. In this study, five activation functions were applied to ANN model for two groundwater level observation wells in the middle mountainous area of the Pyoseon watershed in Jeju Island. The results of the prediction of the groundwater level were compared and analyzed, and the optimal activation function was derived. In addition, the results of LSTM model, which is a widely used recurrent neural network model, were compared and analyzed with the results of the ANN models with each activation function. As a result, ELU and Leaky ReLU functions were derived as the optimal activation functions for the prediction of the groundwater level for observation well with relatively large fluctuations in groundwater level and for observation well with relatively small fluctuations, respectively. On the other hand, sigmoid function had the lowest predictive performance among the five activation functions for training period, and produced inappropriate results in peak and lowest groundwater level prediction. The ANN-ELU and ANN-Leaky ReLU models showed groundwater level prediction performance comparable to that of the LSTM model, and thus had sufficient potential for application. The methods and results of this study can be usefully used in other studies.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.173-179
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• 2016

This study was conducted to provide basic information for future Anseong local food policy and local food activation by finding the key factor determining consumer purchasing for Anseong local food. First, we conducted a survey and derived consumer purchasing attributes for the local food. Logistic regression analysis was performed to find the main factors that determine the consumers' purchase intention for Anseong local food out of such seven attributes as 'excellent quality', 'safety', 'good for health', 'activation of local economy', 'low price', 'accessibility', 'variety of items'. The results showed that the most influencing attributes on consumers' purchase decisions for Anseong local food were 'excellent quality' and 'low price' followed by 'accessibility' and 'activation of local economy'.

• Journal of the Ergonomics Society of Korea
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• v.26 no.2
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• pp.103-112
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• 2007

This study was designed to validate Spreading Activation Theory (SAT) for an evaluation methodology for menu structure through Eye-Tracking approach. When a visual search is on the way, more eye fixations and time are necessary to visually process complex and vague area. From the aspect of recognition, well-designed menu structures were hypothesized to have fewer numbers of fixations and shorter duration because well-designed menu structures reflecting the users' mental model would be well matched with the product's menu structure, resulting in reducing the number of fixations and duration time. The results show that the shorter reaction times for SAT had significantly fewer numbers of fixation and shorter duration time as the hypothesis for this study stated. In conclusion, SAT was proved to be an effective evaluation methodology for menu structure with the eye tracking equipment. In addition, using SAT instead of the real performance experiment would be useful for designing user-centered systems and convenient information structures because SAT was proven to be the theoretical background for design and evaluation of menu structures.

• 대한임상약리학회:학술대회논문집
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• 2003.12a
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• pp.14-14
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• 2003

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.1
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• pp.57-62
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• 2014

Most analytical models of the human body have focused on conscious responses. A patellar tendon reflex, a representative example of spinal reflexes, occurs without a neural command. Muscle forces and activation of the quadriceps femoris muscles in healthy adults during patellar tendon reflex are identified in this study. The model is assumed to move in the sagittal plane, and the thigh and the trunk are assumed to be fixed in a sitting position so that the shank can move similar to a pendulum. The knee joint is modeled as a revolute joint, and the ankle joint is modeled as a fixed joint so that the shank and the foot can be regarded as one rigid body. Muscle forces are calculated following the inverse dynamic approach. Kinematic data obtained from an experiment (Mamizuka, 2007) are used as input data. Muscle activations are identified using a Hill-type muscle model. The obtained simulation results are compared with experimental results for validating the model and the underlying assumptions.

• Fire Science and Engineering
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• v.34 no.3
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• pp.35-42
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• 2020

The accurate prediction of fire detector activation time is required to ensure the reliability of fire modeling during the safety assessment of performance-based fire safety design. The main objective of this study is to determine the activation temperature and the response time index (RTI) of a fixed heat detector, which are the main input factors of a fixed-temperature heat detector applied to the fire dynamics simulator (FDS), a typical fire model. Therefore, a fire detector evaluator, which is a fire detector experimental apparatus, was applied, and 10 types of domestic fixed-temperature heat detectors were selected through a product recognition survey. It was found that there were significant differences in the activation temperature and RTI among the detectors. Additionally, the detector activation time of the FDS with the measured DB can be predicted more accurately. Finally, the DB of the activation temperature and RTI of the fixed-temperature heat detectors with reliability was provided.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10a
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• pp.173-178
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• 2000

New prediction model is investigated estimating splitting tensile strength and modulus of elasticity with curing temperature and aging. New prediction model is based on the model which was proposed to predict compressive strength, and splitting tensile strength and modulus of elasticity calculated by this model are compared with experimental values. New prediction model well estimated splittinge tensile strength and elastic modulus as well as compressive strength.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1263-1279
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• 2020

Activating appropriate roles for a session in the role-based access control (RBAC) model has become challenging because of the so-called role explosion. In this paper, factors and issues related to user-driven role management are analysed, and a session role activation (SRA) problem based on reasonable assumptions is proposed to describe the problem of such role management. To solve the SRA problem, we propose an extended RBAC model with context-based role filtering. When a session is created, context conditions are used to filter roles that do not need to be activated for the session. This significantly reduces the candidate roles that need to be reviewed by the user, and aids the user in rapidly activating the appropriate roles. Simulations are carried out, and the results show that the extended RBAC model is effective in filtering the roles that are unnecessary for a session by using predefined context conditions. The extended RBAC model is also implemented in the Apache Shiro framework, and the modifications to Shiro are described in detail.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.4
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• pp.692-700
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• 2017

In this paper, we propose the structural design of Interval Type-2 FCM based RBFNN. Proposed model consists of three modules such as condition, conclusion and inference parts. In the condition part, Interval Type-2 FCM clustering which is extended from FCM clustering is used. In the conclusion part, the parameter coefficients of the consequence part are estimated through LSE(Least Square Estimation) and WLSE(Weighted Least Square Estimation). In the inference part, final model outputs are acquired by fuzzy inference method from linear combination of both polynomial and activation level obtained through Interval Type-2 FCM and acquired activation level through Interval Type-2 FCM. Additionally, The several parameters for the proposed model are identified by using differential evolution. Final model outputs obtained through benchmark data are shown and also compared with other already studied models' performance. The proposed algorithm is performed by using Iris and Vehicle data for pattern classification. For the validation of regression problem modeling performance, modeling experiments are carried out by using MPG and Boston Housing data.