During the past decade, building energy consumption has risen significantly. Meanwhile, the building area is being increased at a high speed. The conflict between high building energy consumption and low energy efficiency has attracted great attention in the construction industry. HVAC system contributes to most of the whole building energy consumption. Thus, it is imperative to study and analyze the means of HVAC system’s energy conservation. This study aims at addressing two specific challenges: (1) the lack of knowledge to know the kind of HVAC performance that can be evaluated as the criteria for decision making; and (2) the lack of efficient methods for collecting HVAC system and equipment data to comprehend the information used by decision makers. An effective way to minimize these challenges is to predict the HVAC performance of a new building at the conceptual design stage through the application of energy simulation tools. However, the development process of these tools is usually isolated, which results in having the information of a building model that is created by other tools cannot be shared. On another side, there is a need to establish an energy conservation expert system to use during the design of the HVAC systems for buildings. Based on the above, this study integrates Building Information Modeling (BIM) and decision-making system to select HVAC systems for buildings. First, the basic of HVAC components and systems are collected and stored in specific database that will be used for the optimization of HVAC design. Various types of heating/cooling equipment are presented based on ASHRAE standards. Second, the environmental, economic, technical performance and green building rating system are summarized as the criteria for evaluating HVAC performance. Then a combined AHP (Analytic Hierarchy Process) and Entropy structure for HVAC system is introduced as the Decision-making method. Finally, the interoperability of BIM tool is developed to bridge the connection between BIM tool and the HVAC decision making systems through the whole life cycle of buildings. The entire model is coded in Visual Studio via C#. The model is tested through a project to prove its workability and dependency.

Thesis (MPhil) -- AUT University, 2008. / Includes bibliographical references. Also held in print (xi, 165 leaves : col. ill. ; 30 cm.) in the Archive at the City Campus (T 658.403 CRO)

Diploma thesis deals with design of a model for currency rate prediction by using artificial intelligence as a tool for decision making process in business and public administration. Concrete usage of this prediction is applied in company TechPlasty s.r.o. The thesis focuses on analysis of input data, optimization of a prediction model and evaluation of the results and their profit for the selected company.

As a beneficial component with critical impact, computer-aided decision making systems have infiltrated many fields, such as economics, medicine, architecture and agriculture. The latent capabilities for facilitating human work propel high-speed development of such systems. Effective decisions provided by such systems greatly reduce the expense of labor, energy, budget, etc. The computer-aided decision making system for traumatic injuries is one type of such systems that supplies suggestive opinions when dealing with the injuries resulted from accidents, battle, or illness. The functions may involve judging the type of illness, allocating the wounded according to battle injuries, deciding the severity of symptoms for illness or injuries, managing the resources in the context of traumatic events, etc. The proposed computer-aided decision making system aims at estimating the severity of blood volume loss. Specifically speaking, accompanying many traumatic injuries, severe hemorrhage, a potentially life-threatening condition that requires immediate treatment, is a significant loss of blood volume in process resulting in decreased blood and oxygen perfusion of vital organs. Hemorrhage and blood loss can occur in different levels such as mild, moderate, or severe. Our proposed system will assist physicians by estimating information such as the severity of blood volume loss and hemorrhage , so that timely measures can be taken to not only save lives but also reduce the long-term complications as well as the cost caused by unmatched operations and treatments. The general framework of the proposed research contains three tasks and many novel and transformative concepts are integrated into the system. First is the preprocessing of the raw signals. In this stage, adaptive filtering is adopted and customized to filter noise, and two detection algorithms (QRS complex detection and Systolic/Diastolic wave detection) are designed. The second process is to extract features. The proposed system combines features from time domain, frequency domain, nonlinear analysis, and multi-model analysis to better represent the patterns when hemorrhage happens. Third, a machine learning algorithm is designed for classification of patterns. A novel machine learning algorithm, as a new version of error correcting output code (ECOC), is designed and investigated for high accuracy and real-time decision making. The features and characteristics of this machine learning method are essential for the proposed computer-aided trauma decision making system. The proposed system is tested agasint Lower Body Negative Pressure (LBNP) dataset, and the results indicate the accuracy and reliability of the proposed system.

A comercialização de produtos agropecuários tem grande importância para o Brasil, sendo responsável por altos índices de produção e financeiro. E como para todo grande segmento, o acesso à informação tem se tornado essencial para manter a competitividade no ambiente globalizado. Apesar de o agronegócio possuir um grande volume de informações, as mesmas muitas vezes estão inconsistentes ou inacessíveis. Nesse contexto, ainda se observam a necessidade de ampliação de pesquisas que busquem minimizar essa falta de dados que podem auxiliar produtores, agentes de mercado e principalmente pesquisadores para que desenvolvam análises para o mercado agropecuário para servir de apoio para as tomadas de decisões sobre a comercialização diária desses produtos. A técnica usada por diversos segmentos que visam a melhorar e a permitir acesso a dados para tomada de decisão é conhecida como Data Warehouse, pois provê um ambiente propício para análises e acesso a informações consolidadas e históricas. O objetivo do estudo foi demonstrar o processo de análise efetuado por pesquisadores para obtenção de dados, e aplicar dois estudos de casos usando técnicas de Data Warehouse, sendo para dados de comercialização de pecuária e para dados de grãos (soja e milho), gerando assim o modelo dimensional, a matriz de barramento e aplicação da ferramenta de processamento analítico online. O resultado do estudo de pecuária permitiu gerar os modelos iniciais para a cadeia, e podem ser expandidos com a inclusão de novos produtos, bem como auxiliar o pesquisador na geração de análises com a utilização das ferramentas para acesso ao Data Mart. Assim como foi possível gerar análises de comercialização de grãos a partir de dados armazenados no Data Mart. Portanto, pode-se afirmar que a aplicação e expansão da técnica de Data Warehouse é viável para outras cadeias do agronegócio possibilitando a ampliação e a melhoria da oferta de informações. / The agricultural and livestock markets in Brazil play important roles both for the production and the financial sectors. However, like other business, information is essential to maintain the competitiveness in the global market. Although the agribusiness sector processes high amount of information on a daily basis, it is almost always inconsistent or inaccessible. Moreover, there is little research aimed at minimizing the lack of agribusiness information available for the producers, market agents and researchers in order to develop data analyses on agribusiness, to help in the decision process. The technique commonly used in other segments, such as industries, is called Data Warehouse, which provides an environment to provide access to data and consistent information. The goals of this research were to present what researchers what they can do to perform agribusiness analysis, how to apply Data Warehouse modeling and the application of the tool to this process. To reach these goals, we evaluated two case studies. The first study used data from the livestock market, as it allowed to generate initial data for the chain and because it can be applied to other products, as well as to help the researcher to analyze using the OLAP tool. At the same line, the study about grain market was developed, and models and OLAP tools were developed to help with the particular points for this chain. Concluding, the research shows that it is viable to use Data Warehouse techniques to create an agribusiness data environment, consistent and organized, which can be expanded to new agricultural products.

A comercialização de produtos agropecuários tem grande importância para o Brasil, sendo responsável por altos índices de produção e financeiro. E como para todo grande segmento, o acesso à informação tem se tornado essencial para manter a competitividade no ambiente globalizado. Apesar de o agronegócio possuir um grande volume de informações, as mesmas muitas vezes estão inconsistentes ou inacessíveis. Nesse contexto, ainda se observam a necessidade de ampliação de pesquisas que busquem minimizar essa falta de dados que podem auxiliar produtores, agentes de mercado e principalmente pesquisadores para que desenvolvam análises para o mercado agropecuário para servir de apoio para as tomadas de decisões sobre a comercialização diária desses produtos. A técnica usada por diversos segmentos que visam a melhorar e a permitir acesso a dados para tomada de decisão é conhecida como Data Warehouse, pois provê um ambiente propício para análises e acesso a informações consolidadas e históricas. O objetivo do estudo foi demonstrar o processo de análise efetuado por pesquisadores para obtenção de dados, e aplicar dois estudos de casos usando técnicas de Data Warehouse, sendo para dados de comercialização de pecuária e para dados de grãos (soja e milho), gerando assim o modelo dimensional, a matriz de barramento e aplicação da ferramenta de processamento analítico online. O resultado do estudo de pecuária permitiu gerar os modelos iniciais para a cadeia, e podem ser expandidos com a inclusão de novos produtos, bem como auxiliar o pesquisador na geração de análises com a utilização das ferramentas para acesso ao Data Mart. Assim como foi possível gerar análises de comercialização de grãos a partir de dados armazenados no Data Mart. Portanto, pode-se afirmar que a aplicação e expansão da técnica de Data Warehouse é viável para outras cadeias do agronegócio possibilitando a ampliação e a melhoria da oferta de informações. / The agricultural and livestock markets in Brazil play important roles both for the production and the financial sectors. However, like other business, information is essential to maintain the competitiveness in the global market. Although the agribusiness sector processes high amount of information on a daily basis, it is almost always inconsistent or inaccessible. Moreover, there is little research aimed at minimizing the lack of agribusiness information available for the producers, market agents and researchers in order to develop data analyses on agribusiness, to help in the decision process. The technique commonly used in other segments, such as industries, is called Data Warehouse, which provides an environment to provide access to data and consistent information. The goals of this research were to present what researchers what they can do to perform agribusiness analysis, how to apply Data Warehouse modeling and the application of the tool to this process. To reach these goals, we evaluated two case studies. The first study used data from the livestock market, as it allowed to generate initial data for the chain and because it can be applied to other products, as well as to help the researcher to analyze using the OLAP tool. At the same line, the study about grain market was developed, and models and OLAP tools were developed to help with the particular points for this chain. Concluding, the research shows that it is viable to use Data Warehouse techniques to create an agribusiness data environment, consistent and organized, which can be expanded to new agricultural products.

TransCanada Corporation has proposed the Keystone XL pipeline project to transfer crude bitumen from the oil sand fields in northern Alberta, Canada, to oil refineries located in the southern part of the United States. This project has created controversy at the national level in the US and Canada and at the international level. The existence of various stakeholders with differing wants and needs has embroiled the Keystone XL in a complicated strategic dispute. This dispute was initially ignited by the potential project’s negative environmental impacts. However, economic and political issues have also played a critical role in further complicating the decision process. The objective of this study is to design a strategic decision-making system for use in assessing the Keystone XL conflict with standard and perceptual graph model methods. Standard graph model analysis consists of various steps. After identifying the decision makers (DMs) subjectively, their options and preferences are determined. Then, possible scenarios or combinations of options for these DMs are evaluated. In the next step, based on rules called solution concepts, a standard stability analysis is conducted. The perceptual graph model technique, on the other hand, considers the emotions and perceptions of DMs in a conflict to assess the existing dynamics among them. Although this technique takes its basic structure from the standard graph model technique, it presents unique insights into each DM’s perspectives toward the conflict and other DMs. This technique has been used in this study to understand how the awareness of one DM regarding other DMs’ perceptions can change reactions and strategies under different conditions regarding the Keystone XL conflict.

Telefonrådgivning är en av arbetsuppgifterna för ögonsjuksköterskor, men studier om telefonrådgivning inom ögonsjukvård är begränsat. Syftet var att beskriva ögonsjuksköterskors upplevelser av användandet av beslutsstöd vid telefonrådgivning. Metoden var kvalitativ beskrivande. Sex ögonsjuksköterskor vid tre olika ögonkliniker intervjuades. Data analyserades enligt kvalitativ innehållsanalys. Resultatet gav huvudkategorierna kunskapskälla vid behov, stöd i yrkesrollen, brister i beslutsstödet och god vårdkvalitet ur ett patientperspektiv. Ögonsjuksköterskorna var positiva till användandet av beslutsstöd vid oerfarenhet av arbetsuppgiften samt vid sällan förekommande och komplexa situationer. Användandet av beslutsstöd upplevdes underlätta prioritering och samordning av vårdinsatser vilket upplevdes leda till god vårdkvalitet ur ett patientperspektiv och en känsla av trygghet. Beslutsstödet underlättade kommunikationen med andra vårdenheter och vårdsökande. Det upplevdes inte täcka alla situationer utan uttrycktes behövdes utvecklas och uppdateras kontinuerligt. Aktivt lyssnande och tänkande i kombination av erfarenhet och kunskap ansågs vara viktiga komponenter vid telefonrådgivning. Vidareutveckling och förbättring av de beslutsstöd som används idag är av betydelse. / Telephone counseling is one of the tasks in ophthalmic nursing and research within this area was found inadequate. The aim was to describe the ophthalmic nurses’ experiences of using a decision making system in when counseling. The method was a descriptive qualitative de sign. Six ophthalmic nurses at three different eye clinics were interviewed. The data were analyzed according to qualitative content analysis. The result was four categories: a source of knowledge source if needed, supportive to the professional role, deficiencies in decision system and good quality of care from a patient perspective. Ophthalmic nurses were positive using a decision making system if inexperienced and in infrequent and complex situations. The use of decision making system facilitated the prioritization and coordination of the care taken, high quality of care from a patient perspective and a sense of security. Decision making system facilitated communication with other health care providers and patients. The system though was not useful in every situation and there was a need of a continuous development and update. Active listening and thinking in the combination of experience and knowledge were considered to be important components in the telephone counseling. Further development of the system in use today is needed.

There is a recognised need for the continued improvement of both the techniques and technology for spatial decision support in infrastructure site selection. Many authors have noted that current methodologies are inadequate for real-world site selection decisions carried out by heterogeneous groups of decision-makers under uncertainty. Nevertheless despite numerous limitations inherent in current spatial problem solving methods, spatial decision support systems have been proven to increase decision-maker effectiveness when used. However, due to the real or perceived difficulty of using these systems few applications are actually in use to support decision-makers in siting decisions. The most common difficulties encountered involve standardising criterion ratings, and communicating results. This research has focused on the use of Approximate Reasoning to improve the techniques and technology of spatial decision support, and make them easier to use and understand. The algorithm developed in this research (ARAISS) is based on the use of natural language to describe problem variables such as suitability, certainty, risk and consensus. The algorithm uses a method based on type II fuzzy sets to represent problem variables. ARAISS was subsequently incorporated into a new Spatial Decision Support System (InfraPlanner) and validated by use in a real-world site selection problem at Australia's Brisbane Airport. Results indicate that Approximate Reasoning is a promising method for spatial infrastructure planning decisions. Natural language inputs and outputs, combined with an easily understandable multiple decision-maker framework created an environment conducive to information sharing and consensus building among parties. Future research should focus on the use of Genetic Algorithms and other Artificial Intelligence techniques to broaden the scope of existing work.

The main objective of this study is to develop requisite models for information and communication technology (ACT) adoption and diffusion in the banking industry in Thailand. The research, combining two study areas of multiple criteria decision-making (MCDM) and system dynamics (SD), is conducted using two research methodologies: system development and a case study of the Siam Commercial Bank PCL in Thailand.The study shows how to combine the two decision-making tools of MCDM and system dynamics effectively. The requisite group models of ICT adoption and diffusion provide ways to select the most preferred technology and to allow forward planning to diffuse the adopted technology more effectively. With an embedded decision support tool, decision-makers are able to apply the models with their available information, intuition, knowledge, and experience to improve their decision-making and enhance their learning.Initially, the research revealed that the Siam Commercial Bank currently employs various types of information and communication technologies (ICT) to facilitate work processes, fulfil customers' requirements, and retain its competitive advantage. However, the bank still confronts problems relating to technology adoption and diffusion.A requisite group model of ICT adoption was developed using MCDM as a decision making tool. The model illustrated how to select the most preferable technological alternative that fulfilled the mission of the bank. Results from the MCDM analysis revealed that the preferred technology was Extranet banking followed by a data warehouse. The requisite group model of ICT diffusion was further developed using the system dynamics approach in order to enhance understanding of system behaviour of the selected technology and then provide ways to diffuse it more effectively. The model analyses were divided into three sub-models of information ++ / and communication technologies (ICT), a data warehouse, and Extranet banking.The generic model of ICT can be applied to any particular technology. Results revealed that the pattern of technology diffusion follows the S curve and the dominant variables that may impact on technology diffusion are training, a backlog of problems, and market potential. Furthermore economic returns are obtained only after spending substantially on technological investment. Thus, it is necessary to balance between technological investment and economic returns. The model of diffusion of a data warehouse was developed highlighting the necessity of quality and quantity of knowledge workers. Therefore, training support is an important factor to diffuse this technology. On the other hand, the model of diffusion of Extranet banking revealed that the success of this technology comes from the acceptance by customers. Thus, perceived relative advantages, positive features of the technology and promotional advertising should be taken into consideration. The S curve pattern of technology diffusion is also confirmed by the two technologies.The policy for technology adoption involves the selection of technology, which best fits with identified criteria. The policy analyses of the three technologies confirm that the core important policies that increase technology diffusion and economic gains are increasing positive features of technology, decreasing perceived complexity, increased perceived relative advantages and increasing co-operation between IT people and users. If technology is to support the work performance in an organisation, training support is the dominant policy, whereas if technology facilitates customers directly, marketing strategy such as promotional advertising is vital.The study implied that the banking industry in Thailand is able to use ICT as levers for competitive advantage. ++ / However, technological investment in each bank differs depending on size, objectives and readiness in terms of capital and human resources.All the findings have implications for the bank and could be applied to other banks and general policy makers in various business enterprises.