• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.310-314
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• 2023

The purpose of this study is to predict the remaining capacity of lithium-ion batteries and evaluate their performance using five artificial intelligence models, including linear regression analysis, decision tree, random forest, neural network, and ensemble model. We is in the study, measured Excel data from the CS2 lithium-ion battery was used, and the prediction accuracy of the model was measured using evaluation indicators such as mean square error, mean absolute error, coefficient of determination, and root mean square error. As a result of this study, the Root Mean Square Error(RMSE) of the linear regression model was 0.045, the decision tree model was 0.038, the random forest model was 0.034, the neural network model was 0.032, and the ensemble model was 0.030. The ensemble model had the best prediction performance, with the neural network model taking second place. The decision tree model and random forest model also performed quite well, and the linear regression model showed poor prediction performance compared to other models. Therefore, through this study, ensemble models and neural network models are most suitable for predicting the remaining capacity of lithium-ion batteries, and decision tree and random forest models also showed good performance. Linear regression models showed relatively poor predictive performance. Therefore, it was concluded that it is appropriate to prioritize ensemble models and neural network models in order to improve the efficiency of battery management and energy systems.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.1
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• pp.1-11
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• 2022

The study incorporates model uncertainty into the private equity (PE) valuation model (SWY model) (Sorensen et al., 2014) to evaluate how model uncertainty distorts the leverage and valuations of PE funds. This study applies a continuous-time model to PE project valuation, modeling the LPs' goal as multiplier preferences provided by Anderson et al. (2003), and assuming that LPs' aversion to model uncertainty causes endogenous belief distortions with entropy as a measure of model discrepancies. Concerns regarding model uncertainty, according to the theoretical model, have an unclear effect on LPs' risk attitude and GPs' decision, which is based on the value of the PE asset. It also demonstrates that model uncertainty lowers the certainty-equivalent valuation of the LPs. Finally, we compare the outcomes of the Full-spanning risk model with the Non-spanned risk model, and they match the intuitive economic reasoning. The most important implication is that model uncertainty will have negative effects on the LPs' certainty-equivalent valuation but has ambiguous effects on the portfolio allocation choice of liquid wealth. Our works contribute to two literature streams. The first is the literature that models the PE funds. The second is the literature introduces model uncertainty into standard finance models.

• Journal of the Korean earth science society
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• v.27 no.1
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• pp.6-14
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• 2006

The study investigates mental models of scientifically gifted, middle school students when it comes to tides. This was done by analyzing facet of conceptual types for two contexts. We carried out two performance tasks of tide with different context. A large number of students showed different conceptual types by context. As a result of analyzing facet of conceptual types by context, there was a slight difference in content-specific facet, but a remarkable one in strategic facet. We classified four mental models about tide by configuring facets of conceptual types: (1) Tide model (2) Force model (3) Phase model (4) Hybrid model. The Tide model is scientifically accepted model, but Force model and Phase model are incorrect models, and Hybrid model is mixed model. In cases of Force model and Phase model, conceptual types concur with each other, but these types of students comprehend tides as a result of joined forces of Moon & Sun and phase change of Moon, respectively. Arranging low mental models in proportional order, Tide model (45.0%), Hybrid model (30.0%), Force model (12.5%), and Phase model (7.5%).

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.87-97
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• 2000

Signal optimization model is divided bandwidth-maximizing model and delay-minimizing model. Bandwidth-maximizing model express model formulation as MILP(Mixed Integer Linear Programming) and delay-minimizing model like TRANSYT-7F use "hill climbing" a1gorithm to optimize signal times. This study Proposed optimization model using genetic algorithm one of evolution algorithm breaking from existing optimization model This Proposed model were tested by several scenarios and evaluated through NETSIM with TRANSYT-7F\`s outputs. The result showed capability that can obtain superior solution.

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.107-109
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• 2003

We propose a wall distance free one-equation turbulence model. The model is organized in an extremely simple form. Only a few model constants were introduced into the model. The model is numerically tough and easy-of-use. The model also demonstrated the ability to simulate the laminar to turbulent flow transition. The model has been applied to the channel flow, the plane jet, the backward facing step flow, the flat plate boundary layer, as well as the flow around the 2D airfoil at large angles of attack, which obtained satisfactory results.

• Anayses & Alternatives
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• v.5 no.1
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• pp.3-24
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• 2021

This paper introduces a multilevel item-response-theory (IRT) model as a unifying model for hypothesis testing using legislative voting data. This paper shows that a probit or logit model is a special type of multilevel IRT model. In particular, it is demonstrated that, when a probit or logit model is applied to multiple votes, it makes unrealistic assumptions and produces incorrect coefficient estimates. The advantages of a multilevel IRT model over a probit or logit model are illustrated with a Monte Carlo experiment and an example from the U.S. House. Finally, this paper provides a practical guide to fitting this model to legislative voting data.

• The Journal of Korean Academy of Prosthodontics
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• v.34 no.3
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• pp.415-430
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• 1996

The purpose of this study was to evaluate the stress distributions in the surrounding tissues of the teeth seated by indirect retainers in three different teeth of unilateral distal extension partial denture when the dislodging forces were applied on denture bases. Three dimensional photoelastic models were made. The teeth on which indirect retainers were seated were mandibular left lateral incisor (Model I), canine (Model II), and first premolar (Model III). The dislodging force with 860mg at $45^{\circ}$ angulation to occlusal plane was applied to each model. Three dimensional photoelastic stress analysis was done, and the records were diagramed and analysed. The results were as follows : The compressive stresses were shown the most on neck portions of buccal, mesial, and distal sides in all three models. Slight tensile stresses were shown on neck portions of lingual sides in all three models. The compressive stresses on buccal side were shown in strength in such order as model I, model II, and model III. The compressive stresses were shown on neck portion of mesial and distal sides of model I and mode II, with model I more than Model II. The compressive stresses were shown only on neck portion of mesial side on Model III. The general overall magnitude of compressive stresses were shown in strength in such order as Model I, Model II, and Model III.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.178-187
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• 1999

In order to identify a transfer function model with noise, penalty function method has been widely used. In this method, estimation process for possible model parameters from low to higher order proceeds the model identification process. In this study, based on linear estimation method, a new approach unifying the estimation and the identification of ARMAX model is proposed. For the parameter estimation of a transfer function model with noise, linear estimation method by noise separation is suggested instead of nonlinear estimation method. The feasibility of the proposed model identification and estimation method is verified through simulations, namely by applying the method to time series model. In the case of time series model with noise, the proposed method successfully identifies the transfer function model with noise without going through model parameter identification process in advance. A new algorithm effectively achieving model identification and parameter estimation in unified frame has been proposed. This approach is different from the conventional method used for identification of ARMAX model which needs separate parameter estimation and model identification processes. The consistency and the accuracy of the proposed method has been verified through simulations.

• The Journal of Information Systems
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• v.7 no.1
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• pp.181-208
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• 1998

In many literatures of model management, various schemes for representing model base schema have proposed. Ultimately, the goal is to arrive at a set of mutually supportive and synergistic methodologies and tools for the modeling problem domain and model base design. This paper focus on how best to structure and represent conceptual model of problem domain and schema of model base. Semantic concepts and modeling constructs are valuable conceptual tools for understanding the structural relationships and constraints involved in an model management environment. To this end, we reviewed the model management literature, and analyzed the constructs of modeling tools of data model management graph-based approach. Although they have good tools but most of them are not enough for the representation of structural relationships and constraints. So we wanted more powerful tools which can represent diverse constructs in a decision support modeling and model base schema design. For the design of a model base, we developed object modeling framework which uses Object Modeling Techniques (OMT). In Object Modeling Framework, model base schema are classified into conceptual schema, logical schema, and physical schema. The conceptual schema represents the user's view of problem domain, and the logical schema represents a model formatted by a particular modeling language. The schema design, this paper proposes an extension of Object Model to overcome some of the limitations exhibited by the OMT. The proposed tool, Extended Object Model(EOM) have diverse constructs for the representation of decision support problem domain and conceptual model base schema.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.1-7
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• 2022

This paper aims to compare the mean object size of M/G/1/PS model with that of M/BP/1 model used in the web service. The mean object size is one of important measure to control and manage web service economically. M/G/1/PS model utilizes the processor sharing in which CPU rotates in round-robin order giving time quantum to multiple tasks. M/BP/1 model uses the Bounded Pareto distribution to describe the web service according to file size. We may infer that the mean waiting latencies of M/G/1/PS and M/BP/1 model are equal to the mean waiting latency of the deterministic model using the round robin scheduling with the time quantum. Based on the inference, we can find the mean object size of M/G/1/PS model and M/BP/1 model, respectively. Numerical experiments show that when the system load is smaller than the medium, the mean object sizes of the M/G/1/PS model and the M/BP/1 model become the same. In particular, when the shaping parameter is 1.5 and the lower and upper bound of the file size is small in the M/BP/1 model, the mean object sizes of M/G/1/PS model and M/BP/1 model are the same. These results confirm that it is beneficial to use a small file size in a web service.