This thesis work was carried out at a manufacturing plant in Sweden and they are producing a different kind of components for the company. This thesis work was about an assembly line in the factory where they needed a simulation model in Plant Simulation. The main goal of the thesis work was to break down different losses, building a simulation model and compare it if the results are the same as the real assembly line with similar characteristics. The assembly line consists of several workers and AGVs with three u-shaped lines. Frame of reference describes topics that are related to the project. It consists of theories about simulation, collection of data, the bottleneck of the production line, integrated manufacturing systems and flexible workers. The chapter of literature review presents researches on similar studies, in this case, simulation, bottleneck, and workers in the assembly line. In the chapter Simulation model, there will be an explanation of how data got collected, the transformation of raw data and building a simulation model of it. Also the way the model was programmed and built. Result and analysis, the focus is to analyze the throughput, lead time and work in process parameters to get a steady state graph and confidence interval. This chapter also shows how long time does the model need for warm up and how many replications it needs to be more accurate. Discussion part reviews the whole project and its progress. It covers problems that authors got through, at the same time learning and getting a new experience of tools and methods that were used in this project.

Modern civil structures as well as loads on them are still too complex to be accurately modeled or simulated. Therefore, structural failures and structural defects are NOT uncommon! More and more full-scale structural monitoring systems have been deployed in order to monitor how structures behave under various loading conditions. This research focuses on how to maximise benefits from such full-scale measurements by employing advanced digital signal processing techniques. This study is based on accelerometer and GPS data collected on three very different structures, namely, the steel tower in Tokyo, the long and slender suspension bridge in Hong Kong, and the tall office tower in Sydney, under a range of loading conditions, i.e., typhoon, earthquake, heavy traffic, and small scale wind. Systematic analysis of accelerometer and GPS data has demonstrated that the two sensors complement each other in monitoring the static, quasi-static and dynamic movements of the structures. It has also been confirmed that the Finite Element Model could under-estimate the natural frequencies of structures by more than 40% in some case. The effectiveness of using wavelet to de-noise GPS measurement has been demonstrated. The weakness and strengths of accelerometer and GPS have been identified and framework has been developed on how to integrate the two as well as how to optimize the integration. The three-dimensional spectral analysis framework has been developed which can track the temporal evolution of all the frequency components and effectively represents the result in the 3D spectrogram of frequency, time and magnitude. The dominant frequency can also be tracked on the 3D mesh to vividly illustrate the damping signature of the structure. The frequency domain coherent analysis based on this 3D analysis framework can further enhance the detection of common signals between sensors. The developed framework can significantly improve the visualized performance of the integrated system without increasing hardware costs. Indoor experiments have shown the excellent characteristics of the optical fibre Bragg gratings (FBGs) for deformation monitoring. Innovative and low-cost approach has been developed to measure the shift of FBG???s central wavelength. Furthermore, a schematic design has been completed to multiplex FBGs in order to enable distributed monitoring. In collaboration with the University of Sydney, the first Australian full-scale structural monitoring system of GPS and accelerometer has been deployed on the Latitude Tower in Sydney to support current and future research.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2005. / Bibliography: leaves 110-113.

Efficient project planning and project management is crucial to complete the software projects in expected time and requirements. The most critical stage in project planning is estimation of the software size, time and budget. In this stage, effort data is used for benchmarking data sets, effort estimation, project monitoring and controlling. However, there are some problems related to effort data collection in the software industry. In this thesis, a pilot study and survey study are conducted to observe common practices and problems in effort data collection in the industry and results are analyzed. These problems are explained in terms of tool, process and people factors and solution suggestions are presented according to these problems. In accordance with the findings, a method and a tool which can facilitates to provide more accurate data are developed. A case study is performed in order to validate the method and applicability of the tool in the industry.

Despite the significant advances that have been made in traffic sensor technologies, there are only a few systems that provide measurements at the trip level and fewer yet that can do so for all travel modes. On the other hand, traditional methods of collecting individual travel behavior (i.e. manual or web-based travel diaries) are resource intensive and prone to a wide range of errors. Moreover, although dedicated GPS loggers provide the ability to collect detailed travel behavior data with less effort, their use still faces several challenges including the need to distribute and retrieve the logger; the potential need to have the survey participants upload data from the logger to a server; and the need for survey participants to carry another device with them on all their trips. The widespread adoption of smart phones provides an opportunity to acquire travel behavior data from individuals without the need for participants to record trips in a travel diary or to carry dedicated recording devices with them on their travels. The collected travel data can then be used by municipalities and regions for forecasting the travel demand or for analyzing the travel behavior of individuals. In the current research, a smart phone based travel behavior surveying system is designed, developed, and pilot tested. The custom software written for this study is capable of recording the travel characteristics of individuals over the course of any period of time (e.g. days or weeks) and across all travel modes. In this system, a custom application on the smart phone records the GPS data (using the onboard GPS unit) at a prescribed frequency and then automatically transmits the data to a dedicated server. In the server, the data are stored in a dedicated database to be then processed using trip characteristics inference algorithms. The main challenge with the implemented system is the need to reduce the amount of energy consumed by the device to calculate and transmit the GPS fixes. In order to reduce the power consumption from the travel behavior data acquisition software, several techniques are proposed in the current study. Finally, in order to evaluate the performance of the developed system, first the accuracy of the position information obtained from the data acquisition software is analyzed, and then the impact of the proposed methods for reducing the battery consumption is examined. As a conclusion, the results of implemented system shows that collecting individual travel behavior data through the use of GPS enabled smart phones is technically feasible and would address most of the limitations associated with other survey techniques. According to the results, the accuracy of the GPS positions and speed collected through the implemented system is comparable to GPS loggers. Moreover, proposed battery reduction techniques are able to reduce the battery consumption rate from 13.3% per hour to 5.75% per hour (i.e. 57% reduction) when the trip maker is non-stationary and from 5.75% per hour to 1.41% per hour (i.e. 75.5% reduction) when the trip maker is stationary.

In this thesis, a data collection application based on MapReduce programming is described. This application aims to collect tempera- ture data stream continuously from a specied set of sensors. Instead of collecting the temperature information of all the sensors by one machine, the sensors are divided into several subsets each of which is handled as a Map task. In each Map task, the temperature data stream of the assigned sensors is collected continuously and stored in a predened database. All the Map tasks can run simultaneously on several machines. This method can reduce the delay time and improve the eciency of the data collection service, especially in the case of having a huge number of sensors monitored remotely by a data center through Internet. We can use the value of remote sensors to predict the next value of remote sensors by some methods such as linear regres- sion and K-means. And, we can use it to predict the system alarm. Experimental results show that the proposed method is eective in temperature data collection,and eective in carbon reduction.

This thesis analyzes data collection tools and methods in domestic environments for smart product development processes. The aim of the study is to create a method for supporting data collection studies in user research of smart products at domestic environments. The study examines the utilization of information and communication technologies in ethnographic data collection methods at domestic environments with a qualitative approach. Two case studies are conducted to understand and analyze the effects of custom designed data collection tools in user research studies conducted at domestic environments. The results of the study revealed that utilization of data collection equipment and methods that are customized to the characteristics of aims and objectives of user research studies, product characteristic and study participants&rsquo / own environment might lead to the possibility to increase number of study participants and decrease researchers&rsquo / presence in domestic environments. Additionally, it was found that, sensor kits and internal device logs are capable of supporting user research studies for the evaluation of products, however due to technical complexity and unpredictable contextual factors, triangulation of data collection methods and redundancy of data collection equipment are necessary.

Diss. (sammanfattning) Uppsala : Sveriges lantbruksuniv., 2009. / Härtill 4 uppsatser.

Diss. (sammanfattning) SLU : Sveriges lantbruksuniv., 2009. / Härtill 4 uppsatser.

With the recent development of smart-well technology, the reservoir community now faces the challenge of developing robust and efficient techniques for reservoir characterization by means of data inversion. Unfortunately, classical history-matching methodologies do not possess computational efficiency and robustness needed to assimilate data measured almost in real time. Therefore, the reservoir community has started to explore techniques previously applied in other disciplines. Such is the case of the representer method, a variational data assimilation technique that was first applied in physical oceanography. The representer method is an efficient technique for solving linear inverse problems when a finite number of measurements are available. To the best of our knowledge, a general representer-based methodology for nonlinear inverse problems has not been fully developed. We fill this gap by presenting a novel implementation of the representer method applied to the nonlinear inverse problem of identifying petrophysical properties in reservoir models. Given production data from wells and prior knowledge of the petrophysical properties, the goal of our formulation is to find improved parameters so that the reservoir model prediction fits the data within some error given a priori. We first define an abstract framework for parameter identification in nonlinear reservoir models. Then, we propose an iterative representer-based scheme (IRBS) to find a solution of the inverse problem. Sufficient conditions for convergence of the proposed algorithm are established. We apply the IRBS to the estimation of absolute permeability in single-phase Darcy flow through porous media. Additionally, we study an extension of the IRBS with Karhunen-Loeve (IRBS-KL) expansions to address the identification of petrophysical properties subject to linear geological constraints. The IRBS-KL approach is compared with a standard variational technique for history matching. Furthermore, we apply the IRBS-KL to the identification of porosity, absolute and relative permeabilities given production data from an oil-water reservoir. The general derivation of the IRBS-KL is provided for a reservoir whose dynamics are modeled by slightly compressible immiscible displacement of two-phase flow through porous media. Finally, we present an ad-hoc sequential implementation of the IRBS-KL and compare its performance with the ensemble Kalman filter. / text