• Soft Computing and Machine Intelligence
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• v.1 no.1
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• pp.11-23
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• 2021

House price prediction is a significant financial decision for individuals working in the housing market as well as for potential buyers. From investment to buying a house for residence, a person investing in the housing market is interested in the potential gain. This paper presents machine learning algorithms to develop intelligent regressions models for House price prediction. The proposed research methodology consists of four stages, namely Data Collection, Pre Processing the data collected and transforming it to the best format, developing intelligent models using machine learning algorithms, training, testing, and validating the model on house prices of the housing market in the Capital, Islamabad. The data used for model validation and testing is the asking price from online property stores, which provide a reasonable estimate of the city housing market. The prediction model can significantly assist in the prediction of future housing prices in Pakistan. The regression results are encouraging and give promising directions for future prediction work on the collected dataset.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.350-355
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• 2005

This paper presents a new approach for hysteresis modeling of a magnetorheological (MR) fluid. The field-dependent hysteresis of MR fluid is investigated using the Preisach model. The commercial MR Product (MRF-132LD, Lord Corporation) is employed. Its field-dependent shear stress is then obtained using a rheometer (MCR 300, Physica). In order to show the applicability of the Preisach model to the MR fluid, two significant Properties; the minor loop property and the wiping-out property are experimentally examined. Subsequently, the Preisach model for the MR fluid is identified using experimental first order descending (FOD) curves in discrete manner. The effectiveness of the identified hysteresis model is verified in the time domain by comparing the predicted field-dependent shear stress with the measured one. In addition, the hysteresis model proposed in this work is compared to Bingham model.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.1 s.106
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• pp.3-11
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• 2006

This paper presents a new approach for hysteresis modeling of a magnetorheological (MR) fluid. The field-dependent hysteresis of MR fluid is investigated using the Preisach model. The commercial MR product (MRF-132LD, Lord Corporation) is employed. Its field-dependent shear stress is then obtained using a rheometer (MCR 300, Physica). In order to show the applicability of the Preisach model to the MR fluid, two significant properties; the minor loop property and the wiping-out. property are experimentally examined. Subsequently, the Preisach model for the MR fluid is identified using experimental first order descending (FOD) curves in discrete manner. The effectiveness of the identified hysteresis model is verified in the time domain by comparing the predicted field-dependent shear stress with the measured one. In addition, the hysteresis model proposed in this work is compared to Bingham model.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.49-61
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• 2010

The time series dependencies of Financial volatility are frequently measured by the autocorrelation function of power-transformed absolute returns. It is known as the Taylor property that the autocorrelations of the absolute returns are larger than those of the squared returns. Hass (2009) developed a simple method for detecting the Taylor property in absolute-value-GAROH(1,1) (AVGAROH(1,1)) model. In this article, we fitted AVGAROH(1,1) model for various Korean financial time series and observed the Taylor property.

• Journal of Korea Multimedia Society
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• v.11 no.8
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• pp.1146-1159
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• 2008

This paper proposes a method for determining material property of a haptic model which represents the haptic behavior of a target object. This paper also presents a haptic rendering framework. We adapt elastography to obtain the physical property of a target object. One of the key differences between the proposed framework and a traditional method is that the physical property of the target object can be easily set into a haptic model. For evaluating the proposed method, we construct a real-time palpation prototype simulator. In our work, a human liver is selected as a target object and the liver is represented by Shape-retaining Chain Linked Model(S-chain model) for satisfying the real-time performance. We conduct experiments whether a user easily distinguishes abnormal portions from normal portions. From the experimental results, we evaluate that the proposed method provides the discriminable force to users in real-time.

• Journal of the Korean Ceramic Society
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• v.52 no.2
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• pp.92-102
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• 2015

Current plans for legacy nuclear wastes stored in underground tanks at the U.S. Department of Energy's Hanford Site in Washington are that they will be separated into high-level waste and low-activity waste fractions that will be vitrified separately. Formulating optimized glass compositions that maximize the waste loading in glass is critical for successful and economical treatment and immobilization of these nuclear wastes. Glass property-composition models have been developed and applied to formulate glass compositions for various objectives for the past several decades. Property models with associated uncertainties combined with composition and property constraints have been used to develop preliminary glass formulation algorithms designed for vitrification process control and waste-form qualification at the planned waste vitrification plant. This paper provides an overview of the current status of glass property-composition models, constraints applicable to Hanford waste vitrification, and glass formulation approaches that have been developed for vitrification of hazardous and highly radioactive wastes stored at the Hanford Site.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.418-425
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• 2022

For a commercial building, property managers play an important role in maximizing the benefit by reducing cost and increasing revenue in the operation and maintenance phase of the building. However, most of property managers are spending their time in monitoring facility managers who have little impact on cost reduction and maximization of operating profit. The industry practitioners have difficutlty in increasing the efficiency of thier work due to this work environment. In addition, both property managers(PMr) and facility managers(FMr) are dependent on the paper drawings and manuals, which can worsen the inefficiency and human errors are inevitable. This study aims to contribute to improvement of the current practice by developing a novel algorithm that autmatically links the facility information with 3D model, which can provide an efficient property management for commercial buildings.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.243-248
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• 1995

In this article we show that a class of random coefficient autoregressive processes including the NEAR (New exponential autoregressive) process has the strong mixing property in the sense of Rosenblatt with mixing order decaying to zero. The result can be used to construct model free prediction interval for the future observation in the NEAR processes.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.587-592
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• 1994

To realize artificial device with sensing ability of the human skin, a mono-material tactile sensor with three sensing functions made of some elastic thin electro-conductive rubber sheet with eight latticed patch elements is proposed. This trial sensor provides the information of three kinds of model material characteristics such as thermal property, hardness property and the surface situation of materials by setting up three kinds of surface models as test materials. It can be finally expected to estimate unknown model materials by analyzing the data of the sensor.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.233-237
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• 2003

An asymptotic property of the ordinary least squares estimator of the disturbance variance is considered in the regression model with correlated errors. It is shown that the convergence in probability of S$^2$ is equivalent to the asymptotic unbiasedness. Beyond the assumption on the design matrix or the variance-covariance matrix of disturbances error, the result is quite general and simplify the earlier results.