Frequency Domain Characterization Of Sliding Mode Control Of An Inverter Used In DSTATCOM Application

In this paper, the commonly used switching schemes for sliding mode control of power converters is analyzed and designed in the frequency domain. Particular application of a distribution static compensator (DSTATCOM) in voltage control mode is investigated in a power distribution system. Tsypkin's method and describing function is used to obtain the switching conditions for the two-level and three-level voltage source inverters. Magnitude conditions of carrier signals are developed for robust switching of the inverter under carrier-based modulation scheme of sliding mode control. The existence of border collision bifurcation is identified to avoid the complex switching states of the inverter. The load bus voltage of an unbalanced three-phase nonstiff radial distribution system is controlled using the proposed carrier-based design. The results are validated using PSCAD/EMTDC simulation studies and through a scaled laboratory model of DSTATCOM that is developed for experimental verification

Application of micro CT and computation modeling in bone tissue engineering

Computer aided technologies, medical imaging, and rapid prototyping has created new possibilities in biomedical engineering. The systematic variation of scaffold architecture as well as the mineralization inside a scaffold/bone construct can be studied using computer imaging technology and CAD/CAM and micro computed tomography (CT). In this paper, the potential of combining these technologies has been exploited in the study of scaffolds and osteochondral repair. Porosity, surface area per unit volume and the degree of interconnectivity were evaluated through imaging and computer aided manipulation of the scaffold scan data. For the osteochondral model, the spatial distribution and the degree of bone regeneration were evaluated. In this study the versatility of two softwares Mimics (Materialize), CTan and 3D realistic visualization (Skyscan) were assessed, too.

Clonal characterization of bone marrow derived stem cells and their application for bone regeneration

Tissue engineering allows the design of functionally active cells within supportive bio-scaffolds to promote the development of new tissues such as cartilage and bone for the restoration of pathologically altered tissues. However, all bone tissue engineering applications are limited by a shortage of stem cells. The adult bone marrow stroma contains a subset of nonhematopoietic cells referred to as bone marrow mesenchymal stem cells (BMSCs). BMSCs are of interest because they are easily isolated from a small aspirate of bone marrow and readily generate single- cell-derived colonies. These cells have the capacity to undergo extensive replication in an undifferentiated state ex vivo. In addition, BMSCs have the potential to develop either in vitro or in vivo into distinct mesenchymal tissues, including bone, cartilage, fat, tendon, muscle, and marrow stroma. Thus, BMSCs are an attractive cell source for tissue engineering approaches. However, BMSCs are not homo- geneous and the quantity of stem cells decreases in the bone marrow in aged population. A sequential loss of lineage differentiation potential has been found in the mixed culture of bone marrow stromal cells due to a heterogenous popu- lation. Therefore, a number of studies have proposed that homogenous bone marrow stem cells can be generated from clonal culture of bone marrow cells and that BMSC clones have the greatest potential for the application of bone regeneration in vivo

Application of K-tree to document clustering

Digital collections are growing exponentially in size as the information age takes a firm grip on all aspects of society. As a result Information Retrieval (IR) has become an increasingly important area of research. It promises to provide new and more effective ways for users to find information relevant to their search intentions. Document clustering is one of the many tools in the IR toolbox and is far from being perfected. It groups documents that share common features. This grouping allows a user to quickly identify relevant information. If these groups are misleading then valuable information can accidentally be ignored. There- fore, the study and analysis of the quality of document clustering is important. With more and more digital information available, the performance of these algorithms is also of interest. An algorithm with a time complexity of O(n2) can quickly become impractical when clustering a corpus containing millions of documents. Therefore, the investigation of algorithms and data structures to perform clustering in an efficient manner is vital to its success as an IR tool. Document classification is another tool frequently used in the IR field. It predicts categories of new documents based on an existing database of (doc- ument, category) pairs. Support Vector Machines (SVM) have been found to be effective when classifying text documents. As the algorithms for classifica- tion are both efficient and of high quality, the largest gains can be made from improvements to representation. Document representations are vital for both clustering and classification. Representations exploit the content and structure of documents. Dimensionality reduction can improve the effectiveness of existing representations in terms of quality and run-time performance. Research into these areas is another way to improve the efficiency and quality of clustering and classification results. Evaluating document clustering is a difficult task. Intrinsic measures of quality such as distortion only indicate how well an algorithm minimised a sim- ilarity function in a particular vector space. Intrinsic comparisons are inherently limited by the given representation and are not comparable between different representations. Extrinsic measures of quality compare a clustering solution to a “ground truth” solution. This allows comparison between different approaches. As the “ground truth” is created by humans it can suffer from the fact that not every human interprets a topic in the same manner. Whether a document belongs to a particular topic or not can be subjective.

Nitrogen fertilizer management for nitrous oxide (N2O) mitigation in intensive corn (Maize) production: an emissions reduction protocol for US Midwest agriculture

Nitrous oxide (N2O) is a major greenhouse gas (GHG) product of intensive agriculture. Fertilizer nitrogen (N) rate is the best single predictor of N2O emissions in row-crop agriculture in the US Midwest. We use this relationship to propose a transparent, scientifically robust protocol that can be utilized by developers of agricultural offset projects for generating fungible GHG emission reduction credits for the emerging US carbon cap and trade market. By coupling predicted N2O flux with the recently developed maximum return to N (MRTN) approach for determining economically profitable N input rates for optimized crop yield, we provide the basis for incentivizing N2O reductions without affecting yields. The protocol, if widely adopted, could reduce N2O from fertilized row-crop agriculture by more than 50%. Although other management and environmental factors can influence N2O emissions, fertilizer N rate can be viewed as a single unambiguous proxy—a transparent, tangible, and readily manageable commodity. Our protocol addresses baseline establishment, additionality, permanence, variability, and leakage, and provides for producers and other stakeholders the economic and environmental incentives necessary for adoption of agricultural N2O reduction offset projects.

The application of neural networks to induction machine control

Neural networks (NNs) are discussed in connection with their possible use in induction machine drives. The mathematical model of the NN as well as a commonly used learning algorithm is presented. Possible applications of NNs to induction machine control are discussed. A simulation of an NN successfully identifying the nonlinear multivariable model of an induction-machine stator transfer function is presented. Previously published applications are discussed, and some possible future applications are proposed.

Construction, application and validation of selection evaluation model (SEM) for intelligent HVAC control system

Design teams are confronted with the quandary of choosing apposite building control systems to suit the needs of particular intelligent building projects, due to the availability of innumerable ‘intelligent’ building products and a dearth of inclusive evaluation tools. This paper is organised to develop a model for facilitating the selection evaluation for intelligent HVAC control systems for commercial intelligent buildings. To achieve these objectives, systematic research activities have been conducted to first develop, test and refine the general conceptual model using consecutive surveys; then, to convert the developed conceptual framework into a practical model; and, finally, to evaluate the effectiveness of the model by means of expert validation. The results of the surveys are that ‘total energy use’ is perceived as the top selection criterion, followed by the‘system reliability and stability’, ‘operating and maintenance costs’, and ‘control of indoor humidity and temperature’. This research not only presents a systematic and structured approach to evaluate candidate intelligent HVAC control system against the critical selection criteria (CSC), but it also suggests a benchmark for the selection of one control system candidate against another.

Modeling crash outcome probabilities at rural intersections: Application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.

Effects of transportation accessibility on residential property values: Application of spatial hedonic price model in Seoul, South Korea, metropolitan area

A number of studies have focused on estimating the effects of accessibility on housing values by using the hedonic price model. In the majority of studies, estimation results have revealed that housing values increase as accessibility improves, although the magnitude of estimates has varied across studies. Adequately estimating the relationship between transportation accessibility and housing values is challenging for at least two reasons. First, the monocentric city assumption applied in location theory is no longer valid for many large or growing cities. Second, rather than being randomly distributed in space, housing values are clustered in space—often exhibiting spatial dependence. Recognizing these challenges, a study was undertaken to develop a spatial lag hedonic price model in the Seoul, South Korea, metropolitan region, which includes a measure of local accessibility as well as systemwide accessibility, in addition to other model covariates. Although the accessibility measures can be improved, the modeling results suggest that the spatial interactions of apartment sales prices occur across and within traffic analysis zones, and the sales prices for apartment communities are devalued as accessibility deteriorates. Consistent with findings in other cities, this study revealed that the distance to the central business district is still a significant determinant of sales price.

Further notes on the application of zero-inflated models in highway safety

The intent of this note is to succinctly articulate additional points that were not provided in the original paper (Lord et al., 2005) and to help clarify a collective reluctance to adopt zero-inflated (ZI) models for modeling highway safety data. A dialogue on this important issue, just one of many important safety modeling issues, is healthy discourse on the path towards improved safety modeling. This note first provides a summary of prior findings and conclusions of the original paper. It then presents two critical and relevant issues: the maximizing statistical fit fallacy and logic problems with the ZI model in highway safety modeling. Finally, we provide brief conclusions.

Research and application of Building Information Modelling (BIM) in the Architecture, Engineering and Construction (AEC) industry : a review and direction for future research

With an increasing level of collaboration amongst researchers, software developers and industry practitioners in the past three decades, building information modelling (BIM) is now recognized as an emerging technological and procedural shift within the architect, engineering and construction (AEC) industry. BIM is not only considered as a way to make a profound impact on the professions of AEC, but is also regarded as an approach to assist the industry to develop new ways of thinking and practice. Despite the widespread development and recognition of BIM, a succinct and systematic review of the existing BIM research and achievement is scarce. It is also necessary to take stock on existing applications and have a fresh look at where BIM should be heading and how it can benefit from the advances being made. This paper first presents a review of BIM research and achievement in AEC industry. A number of suggestions are then made for future research in BIM. This paper maintains that the value of BIM during design and construction phases is well documented over the last decade, and new research needs to expand the level of development and analysis from design/build stage to postconstruction and facility asset management. New research in BIM could also move beyond the traditional building type to managing the broader range of facilities and built assets and providing preventative maintenance schedules for sustainable and intelligent buildings

Application of reinforcement learning in an open railway access market price negotiation

In an open railway access market price negotiation, it is feasible to achieve higher cost recovery by applying the principles of price discrimination. The price negotiation can be modeled as an optimization problem of revenue intake. In this paper, we present the pricing negotiation based on reinforcement learning model. A negotiated-price setting technique based on agent learning is introduced, and the feasible applications of the proposed method for open railway access market simulation are discussed.

Application of reliability-centred maintenance technology on electric trains

This paper demonstrates the application of the reliability-centred maintenance (RCM) process to analyse and develop preventive maintenance tasks for electric multiple units (EMU) in the East Rail of the Kowloon-Canton Railway Corporation (KCRC). Two systems, the 25 kV electrical power supply and the air-conditioning system of the EMU, have been chosen for the study. RCM approach on the two systems is delineated step by step in the paper. This study confirms the feasibility and effectiveness of RCM applications on the maintenance of electric trains.

Parallel linear system solution and its application to railway power network simulation

The Streaming SIMD extension (SSE) is a special feature embedded in the Intel Pentium III and IV classes of microprocessors. It enables the execution of SIMD type operations to exploit data parallelism. This article presents improving computation performance of a railway network simulator by means of SSE. Voltage and current at various points of the supply system to an electrified railway line are crucial for design, daily operation and planning. With computer simulation, their time-variations can be attained by solving a matrix equation, whose size mainly depends upon the number of trains present in the system. A large coefficient matrix, as a result of congested railway line, inevitably leads to heavier computational demand and hence jeopardizes the simulation speed. With the special architectural features of the latest processors on PC platforms, significant speed-up in computations can be achieved.