Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics

To the best knowledge of authors, the use of Random forest as a potential technique for residential estate mass appraisal has been attempted for the first time. In the empirical study using data on residential apartments the method performed better than such techniques as CHAID, CART, KNN, multiple regression analysis, Artificial Neural Networks (MLP and RBF) and Boosted Trees. An approach for automatic detection of segments where a model significantly underperforms and for detecting segments with systematically under- or overestimated prediction is introduced. This segmentational approach is applicable to various expert systems including, but not limited to, those used for the mass appraisal.     

电子设备的建模研究

本文首先介绍了开关电源的基本工作原理和电路构成,在此基础上,通过试验研究,建立了计算机和电视机等电子设备的静态负荷模型。所得结论,为电力系统稳定分析计算提供了依据。     

对前后数据进行融合处理的灰色双重建模测法

提出一种基于原始数据序列,构建一个新的数据序列,分别两次建立灰色建模GM(1,1),并对秘得的两组数据进行算术平均融合处理。实例证明,这种预测方法提高了预测精度。     

组合式空调系统设计中机组选型方法探讨

对目前组合式空调机组在选型设计及使用中出现的问题进行了分析，运用空气处理设备选型的有关理论和计算方法，对舒适性空调系统中机组选型的一些问题和数据进行了简化和近似处理，提出了能够满足工程设计的要求的简便实用的选型方法。     

核事故条件下的大气扩散模式及应用

本文介绍一个适用于核事故条件的大气扩散模式(粒子-烟团模式)的建立与调试。模式在模拟大气扩散的粒子随机游走方法中引入类似于扩散烟团的核函数概念，用以反映单个随机游走粒子所代表的污染物质的空间影响范围。模式解决了随机游走粒子与核函数“烟团”耦合后反映扩散物理真实过程的一致性问题，在约200km的范围内模拟结果与理论结果相一致。与代表实际大气典型扩散状况的高斯烟羽模式的结果进行定量比较，表明本模式结果在10km的范围内总体合理，但在近源处有系统偏小。模式瞬时浓度的涨落相对于平均轴线浓度大体控制在20％～30％的范围内。将模式应用于秦山地区的实际地形和气象条件进行实例测试表明，其结果可以反映当地复杂的大气扩散过程和特性。     

A new approach to adaptive control: no nonlinearities

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability. However, a typical adaptive controller does not track time-varying parameters very well, and it is often highly nonlinear, which can result in undesirable behaviour, such as large transients or a large control signal. Furthermore, most adaptive controllers provide only asymptotic tracking, with no ability to design for a pre-specified settling time.Here we propose an alternative approach, which yields a linear periodic controller. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters were known. In this paper we argue the utility of this approach and then examine the first order case in detail, including a simulation. We also explore the benefits and limitations of the approach.     

SATCHMOREBID: SATCHMO(RE) with BIDirectional relevancy

SATCHMORE was introduced as a mechanism to integrate relevancy testing with the model-generation theorem prover SATCHMO. This made it possible to avoid invoking some clauses that appear in no refutation, which was a major drawback of the SATCHMO approach. SATCHMORE relevancy, however, is driven by the entire set of negative clauses and no distinction is accorded to the query negation. Under unfavorable circumstances, such as in the presence of large amounts of negative data, this can reduce the efficiency of SATCHMORE. In this paper we introduce a further refinement of SATCHMO called SATCHMOREBID: SATCHMORE with BIDirectional relevancy. SATCHMOREBID uses only the negation of the query for relevancy determination at the start. Other negative clauses are introduced on demand and only if a refutation is not possible using the current set of negative clauses. The search for the relevant negative clauses is performed in a forward chaining mode as opposed to relevancy propagation in SATCHMORE which is based on backward chaining. SATCHMOREBID is shown to be refutationally sound and complete. Experiments on a prototype SATCHMOREBID implementation point to its potential to enhance the efficiency of the query answering process in disjunctive databases. Donald Loveland, Ph.D.: He is Emeritus Professor of Computer Science at Duke University. He received his Ph.D. in mathematics from New York University and taught at NYU and CMU prior to joining Duke in 1973. His research in automated deduction includes defining the model elimination proof procedure and the notion of linear resolution. He is author of one book and editor/co-editor of two other books on automated theorem proving. He has done research in the areas of algorithms, complexity, expert systems and logic programming. He is an AAAI Fellow, ACM Fellow and winner of the Herbrand Award in Automated Reasoning. Adnan H. Yahya, Ph.D.: He is an associate professor at the Department of Electrical Engineering, Birzeit University, Palestine. He received his Diploma and PhD degrees from St. Petersburg Electrotechnical University and Nothwestern University in 1979 and 1984 respectively. His research interests are in Artificial Intelligence in general and in the areas of Deductive Databases, Logic Programming and Nonmonotonic Reasoning in particular. He had several visiting appointments at universities and research labs in the US, Germany, France and the UK. Adnan Yahya is a member of the ACM, IEEE and IEEE Computer Society.     

电容式两相流层析成象系统仿真研究

以油料在管道中输送为例进行电容层析成象仿真.利用有限元方法对电场建立数学模型,在SGI indigo 2 工作站上,使用AVS可视化软件和反投影算法,对3种典型流态模型反演及显示其流体成份分布的图象.     

基於稳态映照记忆模型的自学习控制

本文介绍了基於系统集的通用智能控制器的设计思想。作为示例，我们对一类具有相同特征与约束的系统集给出了基於稳态映照记忆模型的自学习算法，证明了它的可学习性与收敛性，在不同的对象上验证了它的通用性。     

JAVA新类库中Swing的事件传送机制

ＳＵＮ公司最新发布的一套ＡＰＩ中的Ｓｗｉｎｇ组件集采用了ＭＶＣ结构，其中Ｍ、Ｖ、Ｃ三者间的事件传送及与外界的通信机制相当重要。本文从Ｓｗｉｎｇ的组件如何响应外部事件、如何处理自身的变化及如何把自身变化传递给外界三个方面详细阐述Ｓｗｉｎｇ的事件传送机制。