Strength and Deformation Characteristics of Fly Ash Mixed with Randomly Distributed Plastic Waste

In order to explore the potential use of fly ash and plastic waste in bulk quantities in civil engineering applications, it is necessary to understand the behavior of fly ash and fly ash mixed with plastic waste. These materials are considered as wastes and in this study, it is shown that combination of fly ash and plastic waste is very useful. In this regard, various tests such as classification tests, unconfined compressive strength and compressibility tests, consolidated undrained tests, and California bearing ratio tests were conducted. The results indicated that the inclusion of plastic waste in fly ash is effective in improving the engineering properties of fly ash in terms of compressive strength, shear strength parameters, and CBR values. In order to understand the effect of sample size on the shear strength parameters of fly ash and fly ash mixed with plastic waste, consolidated undrained tests were conducted with sample sizes of 38x76mm and 50x100mm. The results of the tests indicate that the shear strength increases with the increase in sample size. The implication of the use of fly ash mixed with plastic waste in unpaved roads is presented in terms of reduction of carbon print.

Thickness of the near-interface regions and central bulk ohmic resistivity in lead lanthanum zirconate titanate ferroelectric thin films

Thickness of the near-interface regions (NIR) and central bulk ohmic resistivity in lead lanthanum zirconate titanate ferroelectric thin films were investigated. A method to separate the low-resistive near-interface regions (NIRs) from the high-resistive central bulk region (CBR) in ferroelectric thin films was presented. Results showed that the thickness of the NIRs depended on the electrode materials in use and the CBR resistivity depended on the impurity doping levels.

船体分段装配计算机辅助工艺设计系统研究与开发

为促进计算机辅助工艺设计系统向着集成化的方向发展，本文提出采用国际上先进的基于范例的推理（CBR）方法实现装配工艺的思路。本文重点研究利用CBR方法生成装配工艺中的关键技术，并以船体分段装配为背景利用基于范例推理的方法，开发出渤海造船厂计算机集成制造系统中的计算机辅助工艺设计系统，为船厂船体工艺设计提供有效的手段。在计算机辅助装配工艺设计子系统中，重点介绍了装配工艺在计算机内的表示和存储方法以及基于特征的索引和检索机制的建立（利用关系型数据库对范例分层存贮，建立工艺范例库）；根据相应的工艺特征，建立了最近邻法和归纳法两种索引方式；运用相联检索和层次检索相结合的检索方式，提供了方便灵活的检索功能；对于不满足用户需求的工艺范例，在生成装配工艺过程中系统提供批量统一修改或单个修改的方式，实现一定程度地机器自动修改。同时对维护工艺范例库提供了友好的人机界面。并且由于系统能够扩充工艺范例数据库，系统的适应性将逐渐增强。对于工艺计划子系统采用基于规则的方法表示工艺知识，结合工时定额和材料定额计算公式，自动生成工艺计划和工艺项目文件。至今，在国内只有几例采用CBR方法的CAPP系统，无更多的经验可以借鉴，本系统是对CBR方法在CAPP中的应用的一种尝试。

Efficiently and Fairly Allocating Bandwidth at a Highly Congested Link

We consider the problem of efficiently and fairly allocating bandwidth at a highly congested link to a diverse set of flows, including TCP flows with various Round Trip Times (RTT), non-TCP-friendly flows such as Constant-Bit-Rate (CBR) applications using UDP, misbehaving, or malicious flows. Though simple, a FIFO queue management is vulnerable. Fair Queueing (FQ) can guarantee max-min fairness but fails at efficiency. RED-PD exploits the history of RED's actions in preferentially dropping packets from higher-rate flows. Thus, RED-PD attempts to achieve fairness at low cost. By relying on RED's actions, RED-PD turns out not to be effective in dealing with non-adaptive flows in settings with a highly heterogeneous mix of flows. In this paper, we propose a new approach we call RED-NB (RED with No Bias). RED-NB does not rely on RED's actions. Rather it explicitly maintains its own history for the few high-rate flows. RED-NB then adaptively adjusts flow dropping probabilities to achieve max-min fairness. In addition, RED-NB helps RED itself at very high loads by tuning RED's dropping behavior to the flow characteristics (restricted in this paper to RTTs) to eliminate its bias against long-RTT TCP flows while still taking advantage of RED's features at low loads. Through extensive simulations, we confirm the fairness of RED-NB and show that it outperforms RED, RED-PD, and CHOKe in all scenarios.

Combining and choosing case base maintenance algorithms

Case-Based Reasoning (CBR) uses past experiences to solve new problems. The quality of the past experiences, which are stored as cases in a case base, is a big factor in the performance of a CBR system. The system's competence may be improved by adding problems to the case base after they have been solved and their solutions verified to be correct. However, from time to time, the case base may have to be refined to reduce redundancy and to get rid of any noisy cases that may have been introduced. Many case base maintenance algorithms have been developed to delete noisy and redundant cases. However, different algorithms work well in different situations and it may be difficult for a knowledge engineer to know which one is the best to use for a particular case base. In this thesis, we investigate ways to combine algorithms to produce better deletion decisions than the decisions made by individual algorithms, and ways to choose which algorithm is best for a given case base at a given time. We analyse five of the most commonly-used maintenance algorithms in detail and show how the different algorithms perform better on different datasets. This motivates us to develop a new approach: maintenance by a committee of experts (MACE). MACE allows us to combine maintenance algorithms to produce a composite algorithm which exploits the merits of each of the algorithms that it contains. By combining different algorithms in different ways we can also define algorithms that have different trade-offs between accuracy and deletion. While MACE allows us to define an infinite number of new composite algorithms, we still face the problem of choosing which algorithm to use. To make this choice, we need to be able to identify properties of a case base that are predictive of which maintenance algorithm is best. We examine a number of measures of dataset complexity for this purpose. These provide a numerical way to describe a case base at a given time. We use the numerical description to develop a meta-case-based classification system. This system uses previous experience about which maintenance algorithm was best to use for other case bases to predict which algorithm to use for a new case base. Finally, we give the knowledge engineer more control over the deletion process by creating incremental versions of the maintenance algorithms. These incremental algorithms suggest one case at a time for deletion rather than a group of cases, which allows the knowledge engineer to decide whether or not each case in turn should be deleted or kept. We also develop incremental versions of the complexity measures, allowing us to create an incremental version of our meta-case-based classification system. Since the case base changes after each deletion, the best algorithm to use may also change. The incremental system allows us to choose which algorithm is the best to use at each point in the deletion process.

An intelligent assistant for design and material engineers in the submarine cable industry

This paper describes an industrial application of case-based reasoning in engineering. The application involves an integration of case-based reasoning (CBR) retrieval techniques with a relational database. The database is specially designed as a repository of experiential knowledge and with the CBR application in mind such as to include qualitative search indices. The application is for an intelligent assistant for design and material engineers in the submarine cable industry. The system consists of three components; a material classifier and a database of experiential knowledge and a CBR system is used to retrieve similar past cases based on component descriptions. Work has shown that an uncommon retrieval technique, hierarchical searching, well represents several search indices and that this techniques aids the implementation of advanced techniques such as context sensitive weights. The system is currently undergoing user testing at the Alcatel Submarine Cables site in Greenwich. Plans are for wider testing and deployment over several sites internationally.

A case based reasoning system capturing fire modelling expertise

This paper describes the architecture of the case based reasoning (CBR) component of Smartfire, a fire field modelling tool for use by members of the Fire Safety Engineering community who are not expert in modelling techniques. The CBR system captures the qualitative reasoning of an experienced modeller in the assessment of room geometries so as to set up the important initial parameters of the problem. The system relies on two important reasoning principles obtained from the expert: 1) there is a natural hierarchical retrieval mechanism which may be employed; and 2) much of the reasoning on a qualitative level is linear in nature, although the computational solution of the problem is non-linear. The paper describes the qualitative representation of geometric room information on which the system is based, and the principles on which the CBR system operates.

Maintenance of a case-base for the retrieval of rotationally symmetric shapes for the design of metal castings

In this paper, we discuss the problem of maintenance of a CBR system for retrieval of rotationally symmetric shapes. The special feature of this system is that similarity is derived primarily from graph matching algorithms. The special problem of such a system is that it does not operate on search indices that may be derived from single cases and then used for visualisation and principle component analyses. Rather, the system is built on a similarity metric defined directly over pairs of cases. The problems of efficiency, consistency, redundancy, completeness and correctness are discussed for such a system. Performance measures for the CBR system are given, and the results for trials of the system are presented. The competence of the current case-base is discussed, with reference to a representation of cases as points in an n-dimensional feature space, and a Gramian visualisation. A refinement of the case base is performed as a result of the competence analysis and the performance of the case-base before and after refinement is compared.

Intelligent design assistant (IDA): a case base reasoning system for material and design

This paper describes the approach to the modelling of experiential knowledge in an industrial application of Case-Based Reasoning (CBR). The CBR involves retrieval techniques in conjunction with a relational database. The database is especially designed as a repository of experiential knowledge, and includes qualitative search indices. The system is intended to help design engineers and material engineers in the submarine cable industry. It consists of three parts: a materials database; a database of experiential knowledge; and a CBR system used to retrieve similar past designs based upon component and material qualitative descriptions. The system is currently undergoing user testing at the Alcatel Submarine Networks site in Greenwich.

A knowledge-based system to represent spatial reasoning for fire modelling

This paper describes the architecture of the knowledge based system (KBS) component of Smartfire, a fire field modelling tool for use by members of the fire safety engineering community who are not expert in modelling techniques. The KBS captures the qualitative reasoning of an experienced modeller in the assessment of room geometries, so as to set up the important initial parameters of the problem. Fire modelling expertise is an example of geometric and spatial reasoning, which raises representational problems. The approach taken in this project is a qualitative representation of geometric room information based on Forbus’ concept of a metric diagram. This takes the form of a coarse grid, partitioning the domain in each of the three spatial dimensions. Inference over the representation is performed using a case-based reasoning (CBR) component. The CBR component stores example partitions with key set-up parameters; this paper concentrates on the key parameter of grid cell distribution.

Case-based retrieval of 3-dimensional shapes for the design of metal castings

This paper describes research into retrieval based on 3-dimensional shapes for use in the metal casting industry. The purpose of the system is to advise a casting engineer on the design aspects of a new casting by reference to similar castings which have been prototyped and tested in the past. The key aspects of the system are the orientation of the shape within the mould, the positions of feeders and chills, and particular advice concerning special problems and solutions, and possible redesign. The main focus of this research is the effectiveness of similarity measures based on 3-dimensional shapes. The approach adopted here is to construct similarity measures based on a graphical representation deriving from a shape decomposition used extensively by experienced casting design engineers. The paper explains the graphical representation and discusses similarity measures based on it. Performance measures for the CBR system are given, and the results for trials of the system are presented. The competence of the current case-base is discussed, with reference to a representation of cases as points in an n-dimensional feature space, and its principal components visualization. A refinement of the case base is performed as a result of the competence analysis and the performance of the case-base before and after refinement is compared.

Enhancing the usability of numerical models with case-based reasoning

Numerical models are important tools used in engineering fields to predict the behaviour and the impact of physical elements. There may be advantages to be gained by combining Case-Based Reasoning (CBR) techniques with numerical models. This paper considers how CBR can be used as a flexible query engine to improve the usability of numerical models. Particularly they can help to solve inverse and mixed problems, and to solve constraint problems. We discuss this idea with reference to the illustrative example of a pneumatic conveyor problem. The paper describes example problems faced by design engineers in this context and the issues that need to be considered in this approach. Solution of these problems require methods to handle constraints in both the retrieval phase and the adaptation phase of a typical CBR cycle. We show approaches to the solution of these problesm via a CBR tool.

CBE-conveyor: a case-based reasoning system to assist engineers in designing conveyor systems

In this paper, we address the use of CBR in collaboration with numerical engineering models. This collaborative combination has a particular application in engineering domains where numerical models are used. We term this domain “Case Based Engineering” (CBE), and present the general architecture of a CBE system. We define and discuss the general characteristics of CBE and the special problems which arise. These are: the handling of engineering constraints of both continuous and nominal kind; interpolation over both continuous and nominal variables, and conformability for interpolation. In order to illustrate the utility of the method proposed, and to provide practical examples of the general theory, the paper describes a practical application of the CBE architecture, known as CBE-CONVEYOR, which has been implemented by the authors.Pneumatic conveying is an important transportation technology in the solid bulks conveying industry. One of the major industry concerns is the attrition of powders and granules during pneumatic conveying. To minimize the fraction of particles during pneumatic conveying, engineers want to know what design parameters they should use in building a conveyor system. To do this, engineers often run simulations in a repetitive manner to find appropriate input parameters. CBE-Conveyor is shown to speed up conventional methods for searching for solutions, and to solve problems directly that would otherwise require considerable intervention from the engineer.