The wildspace™ decision support system

A system architecture was developed for the wildspace decision support system (DSS) to provide a better understanding of complex wildlife and habitat problems. The system makes use of two key concepts, SPECIES and SPACES, to define the study domain. wildspace DSS’s flexible user interface allows users to select SPECIES through a number of different approaches, including direct selection and selection using information such as avian life history and project metadata. On the SPACES side, the system uses the raison™ object system (ROS) for mapping functions and spatial analysis. The key element in wildspace DSS is its knowledge-based database manager that provides intelligent support to various components of the system. It keeps track of all the legitimate databases, provides intelligence within the SPECIES and SPACES selection process and, more importantly, interfaces with the knowledge templates which are sets of operations implementing pre-defined analysis routines used for integrated analysis. This integrated decision support approach allows users to combine a diverse set of tools within a common framework. wildspace DSS is used to study complex wildlife problems involving multiple projects and data that are temporally and spatially heterogeneous. A case study about a relevant wildlife conservation question is presented using a series of queries and analyses performed within wildspace DSS. The system also serves as the repository for all past, current and future wildlife data collected by the Canadian Wildlife Service—Ontario Region.     

Cloudlet-based Efficient Data Collection in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have developed as an effective solution for a wide range of healthcare, military and sports applications. Most of the proposed works studied efficient data collection from individual and traditional WBANs. Cloud computing is a new computing model that is continuously evolving and spreading. This paper presents a novel cloudlet-based efficient data collection system in WBANs. The goal is to have a large scale of monitored data of WBANs to be available at the end user or to the service provider in reliable manner. A prototype of WBANs, including Virtual Machine (VM) and Virtualized Cloudlet (VC) has been proposed for simulation characterizing efficient data collection in WBANs. Using the prototype system, we provide a scalable storage and processing infrastructure for large scale WBANs system. This infrastructure will be efficiently able to handle the large size of data generated by the WBANs system, by storing these data and performing analysis operations on it. The proposed model is fully supporting for WBANs system mobility using cost effective communication technologies of WiFi and cellular which are supported by WBANs and VC systems. This is in contrast of many of available mHealth solutions that is limited for high cost communication technology, such as 3G and LTE. Performance of the proposed prototype is evaluated via an extended version of CloudSim simulator. It is shown that the average power consumption and delay of the collected data is tremendously decreased by increasing the number of VMs and VCs.     

Planned further development of an integrated climatological data system for building energy modelling

Detailed hourly data for a number of meteorological variables is typically needed as inputs for dynamic simulation of building performance. The variables input vary with the aspect of performance being considered. This paper explains the meteorological data structure being developed by the Department of Building Science, University of Sheffield, UK on the SRC Rutherford interactive facility, to help modellers in different institutions to get more effective access to the specific meteorological data they need for their specific problem. A two stage facility is envisaged — an interactive search facility based on daily and monthly data to help users establish the data likely to be of value and a batch data transmission facility to transfer requested data to users for subsequent use in their own computing environment.     

基于优先使用权的温贮备可修重试系统可靠性与期望成本模型

针对具有两种不同类型部件的温贮备可修系统,基于优先使用权规则,将部件的重试特性和不可靠修理设备引入系统,建立一种新的系统可靠性和成本模型.失效部件由一个不可靠修理设备维修,当正常的修理设备维修失效部件时会发生不完全故障,此时,它或以一定的概率立即被维修,或以一定的概率继续低效维修失效部件.在不完全故障下,修理设备有可能发生完全性故障,完全性故障后会立即被维修.鉴于维修环境的随机波动性,利用PH分布对修理设备的修理时间进行建模,这在一定程度上拓展了模型的适用性.通过概率讨论得到稳态下系统的一些主要性能指标,构建单位时间的总期望成本函数,并就总期望成本关于各参数的敏感性进行分析.通过数值实例展示系统参数对系统稳态性能指标和系统成本的影响,为系统可靠性设计者和决策者提供理论支撑和参考.     

A 3D visualized expert system for maintenance and management of existing building facilities using reliability-based method

Facility maintenance and management (FMM) is an emerging issue in civil engineering. Decisions involving maintenance-related tasks are generally made based on various sources of accumulated historical data, such as design drawings, inspection records, and sensing data. Systems are developed for storing and maintaining such maintenance-related data electronically in a database. However, the data-accessing method of these systems is based mainly on text input in Web form, which is occasionally insufficiently intuitive to interpret retrieved information for decision making. Besides simple data management practices, the feasibility of implementing analysis on FMM-related data to provide estimated or predictive information for decision making should be examined. This paper presents an expert system model for the maintenance and management of existing facilities. A prototype system was developed for concept proofing. A 3D facility model is introduced in the system as the interface for accessing various maintenance-related data intuitively. Various maintenance-related data and analysis results should be presented visually on the model as much as possible to provide users with an intuitive understanding of the facility status in many aspects. Behind the 3D visualized interface is a database that integrates and stores various maintenance-related data systematically. This database information should be accumulated continuously via input from users and sensors in appropriate formats. Moreover, a reliability-based module should analyze the accumulated data periodically to provide predictive forecast information, subsequently facilitating decision making during maintenance.     

Group technology applications in manufacturing operations through the computer system

The typical manufacturing facility is constantly developing new product designs and related manufacturing processes. The increased volume of new designs and processes causes rapid and inefficient construction of product designs and manufacturing processes. Many parts and manufacturing processes are developed over the life cycle of a production facility with no organized means of cataloging this past and present data. This procedure is extremely ineffective because there is no way to determine if a part or process has been previously developed. The constant “reinventing of the wheel” creates a tremendous waste of manpower and cost.

One approach to solving this problem is through the use of group technoogy. Group technology is the identification and grouping of similar parts and processes in order to take avantage of their similarities in the design and manufacturing process. Parts and processes can be grouped under a classification and implemented with a coding system. Concurrently, the number of parts and processes can be reduced by putting them in a “family.” This “family” has common characteristics such as shape, size, color, tolerance or production operations.

For handling and manipulation of this data, a computer system has been developed. The computer system would set up a reporting format that would classify, code and group the parts and processes, so the user can analyze if a previously designed process or part can be used in the current system and/or if a better layout can be feasible.

Many advantages such as reduced inventory cost, increased facility space and better utilization of manpower are but a few of the benefits from this system.     

基于模型代数的DSS建模与实现方法研究

对DSS模型代数在实际系统中的建模方法进行研究,并结合一个实际应用系统介绍DSS模型代数的应用。在应用系统中采用Java技术对DSS模型之间的各种通信方式编程,充分体现了DSS模型代数的实用性。所形成的设计模式可以有效地应用到具体的DSS系统的开发中。     

Non-Cooperative Cost Sharing Games via Subsidies

We consider a cost sharing system where users are selfish and act according to their own interest. There is a set of facilities and each facility provides services to a subset of the users. Each user is interested in purchasing a service, and will buy it from the facility offering it at the lowest cost. The overall system performance is defined to be the total cost of the facilities chosen by the users. A central authority can encourage the purchase of services by offering subsidies that reduce their price, in order to improve the system performance. The subsidies are financed by taxes collected from the users.     

A decision support model for warranty servicing of repairable items

This paper addresses the question of servicing warranties for repairable items. Each time a unit under warranty fails, the manufacturer is obliged to restore it to operating condition either by repairing it or by replacing it with a new unit. The decision to replace or repair depends on a variety of factors such as cost of immediate repair, age of the unit, the extent of usage, the condition of the unit, and the remaining warranty period. We consider products with phase type lifetime distributions, where the phase describes the condition of the unit, and develop a decision support system (DSS) for the repair/replace decisions in servicing the warranty using the criterion of expected cost of servicing the remaining warranty. A comprehensive example is presented to illustrate the DSS. Effectiveness of the proposed DSS is verified by simulation.     

Sensitivity analysis in logic-based models

An important feature of the causal models used in most DSS is that they allow their users to perform sensitivity analyses. Most of these analyses are performed on continuous variables, which are the principal types of variables found in causal models. However, some of the models derived from artificial intelligence that are increasingly being used in DSS contain logic (or Boolean) variables. In this paper we exploit the notion of a ‘Boolean derivative’ developed for circuit design and apply it to the calculation of sensitivity measures in logic-based models.     

A new approach to water quality modelling and environmental decision support systems

The concepts and technology of environmental decision support systems (EDSS) have developed considerably over recent decades, although core concepts such as flexibility and adaptability within a changing decision environment remain paramount. Much recent EDSS theory has focussed on model integration and re-use in decision support system (DSS) tools and for design and construction of ‘DSS generators’. Many current specific DSS have architectures, tools, models and operational characteristics that are either fixed or difficult to change in the face of changing management needs. This paper reports on development and deployment of an EDSS that encompasses a new approach to DSS tools, generators and specific DSS applications. The system, named E2, is built upon a conceptualisation of terrestrial and aquatic environmental systems that has resulted in a robust and flexible system architecture. The architecture provides a set of base classes to represent fundamental concepts, and which can be instantiated and combined to form DSS generators of varying complexity. A DSS generator is described within which system users are able to select and link models, data, analysis tools and reporting tools to create specific DSS for particular problems, and for which new models and tools can be created and, through software reflection (introspection), discovered to provide expanded capability where required. This system offers a new approach within which environmental systems can be described in the form of specific DSS at a scale and level of complexity suited to the problems and needs of decision makers.     

Cost-minimization analysis of a working vacation queue with N-policy and server breakdowns

This paper investigates the N-policy M/M/1 queueing system with working vacation and server breakdowns. As soon as the system becomes empty, the server begins a working vacation. The server works at a lower service rate rather than completely stopping service during a vacation period. The server may break down with different breakdown rates during the idle, working vacation, and normal busy periods. It is assumed that service times, vacation times, and repair times are all exponentially distributed. We analyze this queueing model as a quasi-birth–death process. Furthermore, the equilibrium condition of the system is derived for the steady state. Using the matrix-geometric method, we find the matrix-form expressions for the stationary probability distribution of the number of customers in the system and system performance measures. The expected cost function per unit time is constructed to determine the optimal values of the system decision variables, including the threshold N and mean service rates. We employ the particle swarm optimization algorithm to solve the optimization problem. Finally, numerical results are provided, and an application example is given to demonstrate the applicability of the queueing model.     

Improvement of transportation cost estimation for prefabricated construction using geo-fence-based large-scale GPS data feature extraction and support vector regression

In panelized construction, transportation is an essential process linking a manufacturing facility to a project’s jobsite using hauling equipment (e.g., trucks and trailers). Accordingly, the cost associated with transportation operations is considerable compared to a traditional stick build. Nevertheless, transportation cost estimation has often relied on a fixed-cost approach, regarding the cost as part of the overhead cost, rather than conducting detailed estimation of actual transportation operations. This is because operation-level data might be challenging to collect and analyze in practice. In this regard, the prevalent use of GPS devices for construction equipment may provide an automated means of monitoring the operations of transportation equipment, and large and detailed spatial and temporal data can be generated from multiple pieces of equipment in multiple construction projects on a daily basis or even in real time. This study thus proposes a spatial and temporal data filtering and abstracting approach to transportation cost estimation using fleet GPS data which extracts equipment activities from the GPS data and accordingly predicts the transportation demands required for an individual project. From large-scale GPS data, key operation information, such as the number of trailers and durations required (i.e., transportation demands), is extracted using a geo-fence and a rule-based equipment operation analysis algorithm. Then, the extracted transportation demand information, along with related project specifications, is used to train support vector regression (SVR) models for the purpose of predicting the transportation demand in new projects, which is in turn utilized to estimate the transportation cost using the relevant transportation unit cost of the equipment. To evaluate the performance, GPS datasets collected from 221 panelized residential projects over a period of 8 months are used to train the prediction model and are compared with actual transportation costs estimated in practice. The results show that the SVR model has an accuracy of 86% and 88% in predicting the number of trailers and the duration, respectively. For the cost estimation performance, the results reveal that the average cost difference of 57% between the fixed cost and the actual transportation cost was reduced to 14% by implementing the GPS-data-based method in various project locations and for projects of various sizes. The GPS-data-based estimation approach thus is found to provide a more accurate transportation cost estimation result for various panelized construction projects, and the method improves the understanding of large-scale spatial and temporal equipment data while increasing the utilization of the GPS data already available.     

Analyzing the feasibility of building a new mass storage system on distributed resources

The average PC now contains a large and increasing amount of storage with an ever greater amount left unused. We believe there is an opportunity for organizations to harness the vast unused storage capacity on their PCs to create a very large, low‐cost, shared storage system. What is needed is the proper storage system architecture and software to exploit and manage the unused portions of existing PC storage devices across an organization and make it reliably accessible to users and applications. We call our vision of such a storage system Storage@desk (SD). This paper describes our first step towards the realization of SD—a study of machine and storage characteristics and usage in a model organization. We studied 729 PCs in an academic institution for 91 days, monitoring the configuration, load and usage of the major machine subsystems, i.e. disk, memory, CPU and network. To further analyze the availability characteristics of storage in an SD system, we performed a trace‐driven simulation of some basic storage allocation strategies. This paper presents the results of our data collection efforts, our analysis of the data, our simulation results and our conclusion that an SD system is indeed feasible and holds promise as a cost‐effective way to create massive storage systems. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimizing the Location of a Production Firm

A facility needs to be located in the plane to sell goods to a set of demand points. The cost for producing an item and the actual transportation cost per unit distance are given. The planner needs to determine the best location for the facility, the price charged at the source (mill price) and the transportation rate per unit distance to be charged to customers. Demand by customers is elastic and assumed declining linearly with the total charge. For each customer two parameters are given: the demand at charge zero and the decline of demand per unit charge. The objective is to find a location for the facility in the plane, the mill price charged to customers and the unit transportation rate charged to customers such that the company’s profit is maximized. The problem is formulated and an algorithm that finds the optimal solution is designed and tested on randomly generated problems.     

An artificial neural network based decision support system for energy efficient ship operations

Reducing fuel consumption of ships against volatile fuel prices and greenhouse gas emissions resulted from international shipping are the challenges that the industry faces today. The potential for fuel savings is possible for new builds, as well as for existing ships through increased energy efficiency measures; technical and operational respectively. The limitations of implementing technical measures increase the potential of operational measures for energy efficient ship operations. Ship owners and operators need to rationalise their energy use and produce energy efficient solutions. Reducing the speed of the ship is the most efficient method in terms of fuel economy and environmental impact. The aim of this paper is twofold: (i) predict ship fuel consumption for various operational conditions through an inexact method, Artificial Neural Network ANN; (ii) develop a decision support system (DSS) employing ANN-based fuel prediction model to be used on-board ships on a real time basis for energy efficient ship operations. The fuel prediction model uses operating data – ‘Noon Data’ – which provides information on a ship’s daily fuel consumption. The parameters considered for fuel prediction are ship speed, revolutions per minute (RPM), mean draft, trim, cargo quantity on board, wind and sea effects, in which output data of ANN is fuel consumption. The performance of the ANN is compared with multiple regression analysis (MR), a widely used surface fitting method, and its superiority is confirmed. The developed DSS is exemplified with two scenarios, and it can be concluded that it has a promising potential to provide strategic approach when ship operators have to make their decisions at an operational level considering both the economic and environmental aspects.     

View Definition and Generalization for Database Integration in a Multidatabase System

Access to a heterogeneous distributed collection of databases can be simplified by providing users with a logically integrated interface or global view. There are two aspects to database integration. Firstly, the local schemas may model objects and relationships differently and, secondly, the databases may contain mutually inconsistent data. This paper identifies several kinds of structural and data inconsistencies that might exist. It describes a versatile view definition facility for the functional data model and illustrates the use of this facility for resolving inconsistencies. In particular, the concept of generalization is extended to this model, and its importance to database integration is emphasized. The query modification algorithm for the relational model is extended to the semantically richer functional data model with generalization.     

A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy

This paper develops an integrated neural-network-based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics     

Fault tolerance design in JPEG 2000 image compression system

The JPEG 2000 image compression standard is designed for a broad range of data compression applications. The new standard is based on wavelet technology and layered coding in order to provide a rich feature compressed image stream. The implementations of the JPEG 2000 codec are susceptible to computer-induced soft errors. One situation requiring fault tolerance is remote-sensing satellites, where high energy particles and radiation produce single event upsets corrupting the highly susceptible data compression operations. This paper develops fault tolerance error-detecting capabilities for the major subsystems that constitute a JPEG 2000 standard. The nature of the subsystem dictates the realistic fault model where some parts have numerical error impacts whereas others are properly modeled using bit-level variables. The critical operations of subunits such as discrete wavelet transform (DWT) and quantization are protected against numerical errors. Concurrent error detection techniques are applied to accommodate the data type and numerical operations in each processing unit. On the other hand, the embedded block coding with optimal truncation (EBCOT) system and the bitstream formation unit are protected against soft-error effects using binary decision variables and cyclic redundancy check (CRC) parity values, respectively. The techniques achieve excellent error-detecting capability at only a slight increase in complexity. The design strategies have been tested using Matlab programs and simulation results are presented.     

An optimization algorithm for production systems

As the scale of rule-based expert systems increases, the efficiency of production systems becomes a pressing concern. Recently developed production systems thus enable users to specify an appropriate ordering or clustering of join operations. Various efficiency heuristics have been introduced to optimize production rules manually. However, since the heuristics often conflict With each other, users have to proceed by trial and error. The problem addressed in this paper is how to automatically determine efficient join structures for production system programs. Our algorithm does not directly apply efficiency heuristics to programs, but rather enumerates possible join structures under various constraints and selects the best one. For this purpose, the cost model for production systems is introduced to estimate the run-time cost of join operations. Evaluation results demonstrate that the proposed algorithm can generate programs that are as efficient as those obtained by manual optimization, and thus can reduce the burden of manual optimization