H.7. Simulation, Modeling, and Visualization
Ju Xiaolin; Vaskar Chakma; Misbahul Amin; Arkhid Chakma Joy
Abstract
This research examines the key factors influencing house prices, focusing on how size, condition, and structural features contribute to property valuation. Using a dataset from Washington State, USA, covering the year 2014 with over 4,600 entries, a multivariate analysis was conducted with a Linear Regression ...
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This research examines the key factors influencing house prices, focusing on how size, condition, and structural features contribute to property valuation. Using a dataset from Washington State, USA, covering the year 2014 with over 4,600 entries, a multivariate analysis was conducted with a Linear Regression model to assess the relationships between crucial features such as square footage, number of bedrooms, bathrooms, floors, and additional structural elements like garage presence and yard size. The analysis revealed that square footage and bathrooms exhibit the strongest positive correlations with house prices (both with correlation values of 0.76, statistically significant at p < 0.05), indicating their substantial impact on property valuation. In contrast, factors like condition and view demonstrated weaker correlations, suggesting a more limited influence. The Linear Regression model explained 75% of the variation in house prices (R2 = 0.75), with validation conducted using a holdout test set to ensure generalizability. While the model effectively highlights key price determinants, its limitations in handling non-linear relationships and sensitivity to outliers were addressed through data transformation and outlier removal. Compared to prior studies, this research reinforces established findings on square footage and bathrooms while providing new insights into the comparatively lower impact of property condition. Future work could explore advanced predictive models, such as non-linear regression and machine learning techniques, to better capture complex relationships and improve forecasting accuracy. These findings offer valuable insights for buyers, sellers, and industry professionals, emphasizing the importance of a data-driven approach to understanding house price dynamics.
H.7. Simulation, Modeling, and Visualization
A.R. Ebrahimi; Gh. Barid Loghmani; M. Sarfraz
Abstract
In this paper, a new technique has been designed to capture the outline of 2D shapes using cubic B´ezier curves. The proposed technique avoids the traditional method of optimizing the global squared fitting error and emphasizes the local control of data points. A maximum error has been determined ...
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In this paper, a new technique has been designed to capture the outline of 2D shapes using cubic B´ezier curves. The proposed technique avoids the traditional method of optimizing the global squared fitting error and emphasizes the local control of data points. A maximum error has been determined to preserve the absolute fitting error less than a criterion and it administers the process of curve subdivision. Depending on the specified maximum error, the proposed technique itself subdivides complex segments, and curve fitting is done simultaneously. A comparative study of experimental results embosses various advantages of the proposed technique such as accurate representation, low approximation errors and efficient computational complexity.
H.7. Simulation, Modeling, and Visualization
J. Peymanfard; N. Mozayani
Abstract
In this paper, we present a data-driven method for crowd simulation with holonification model. With this extra module, the accuracy of simulation will increase and it generates more realistic behaviors of agents. First, we show how to use the concept of holon in crowd simulation and how effective it ...
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In this paper, we present a data-driven method for crowd simulation with holonification model. With this extra module, the accuracy of simulation will increase and it generates more realistic behaviors of agents. First, we show how to use the concept of holon in crowd simulation and how effective it is. For this reason, we use simple rules for holonification. Using real-world data, we model the rules for joining each agent to a holon and leaving it with random forests. Then we use this model in simulation. Also, because we use data from a specific environment, we test the model in another environment. The result shows that the rules derived from the first environment exist in the second one. It confirms the generalization capabilities of the proposed method.
H.7. Simulation, Modeling, and Visualization
R. Ghanizadeh; M. Ebadian
Abstract
This paper presents a new control method for a three-phase four-wire Unified Power Quality Conditioner (UPQC) to deal with the problems of power quality under distortional and unbalanced load conditions. The proposed control approach is the combination of instantaneous power theory and Synchronous Reference ...
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This paper presents a new control method for a three-phase four-wire Unified Power Quality Conditioner (UPQC) to deal with the problems of power quality under distortional and unbalanced load conditions. The proposed control approach is the combination of instantaneous power theory and Synchronous Reference Frame (SRF) theory which is optimized by using a self-tuning filter (STF) and without using load or filter currents measurement. In this approach, load and source voltages are used to generate the reference voltages of series active power filter (APF) and source currents are used to generate the reference currents of shunt APF. Therefore, the number of current measurements is reduced and system performance is improved. The performance of proposed control system is tested for cases of power factor correction, reducing source neutral current, load balancing and current and voltage harmonics in a three-phase four-wire system for distortional and unbalanced loads. Results obtained through MATLAB/SIMULINK software show the effectiveness of proposed control technique in comparison to the conventional p-q method.
