Adaptive optimization schemes for mobile VoIP applications Battery life and call quality aspects

In this thesis programmatic, application-layer means for better energy-efficiency in the VoIP application domain are studied. The work presented concentrates on optimizations which are suitable for VoIP-implementations utilizing SIP and IEEE 802.11 technologies. Energy-saving optimizations can have an impact on perceived call quality, and thus energy-saving means are studied together with those factors affecting perceived call quality. In this thesis a general view on a topic is given. Based on theory, adaptive optimization schemes for dynamic controlling of application's operation are proposed. A runtime quality model, capable of being integrated into optimization schemes, is developed for VoIP call quality estimation. Based on proposed optimization schemes, some power consumption measurements are done to find out achievable advantages. Measurement results show that a reduction in power consumption is possible to achieve with the help of adaptive optimization schemes.

Optimization of short term power trading between Russia, Finland and Baltic countries

Russian and Baltic electricity markets are in the process of reformation and development on the way for competitive and transparent market. Nordic market also undergoes some changes on the way to market integration. Old structure and practices have been expired whereas new laws and rules come into force. The master thesis describes structure and functioning of wholesale electricity markets, cross-border connections between different countries. Additionally methods of cross-border trading using different methods of capacity allocation are disclosed. The main goal of present thesis is to study current situation at different electricity markets and observe changes coming into force as well as the capacity and electricity balances forecast in order to optimize short term power trading between countries and estimate the possible profit for the company.

Optimization of a heatset dryer

The objective of the thesis was to examine the possibilities in designing better performing nozzles for the heatset drying oven in Forest Pilot Center. To achieve the objective, two predesigned nozzle types along with the replicas of the current nozzles in the heatset drying oven were tested on a pilot-scale dryer. During the runnability trials, the pilot dryer was installed between the last printing unit and the drying oven. The two sets of predesigned nozzles were consecutively installed in the dryer. Four web tension values and four different impingement air velocities were used and the web behavior during the trial points was evaluated and recorded. The runnability in all trial conditions was adequate or even good. During the heat transfer trials, each nozzle type was tested on at least two different nozzle-to-surface distances and four different impingement air velocities. In a test situation, an aluminum plate fitted with thermocouples was set below a nozzle and the temperature measurement of each block was logged. From the measurements, a heat transfer coefficient profile for the nozzle was calculated. The performance of each nozzle type in tested conditions could now be rated and compared. The results verified that the predesigned simpler nozzles were better than the replicas. For runnability reasons, there were rows of inclined orifices on the leading and trailing edges of the current nozzles. They were believed to deteriorate the overall performance of the nozzle, and trials were conducted to test this hypothesis. The perpendicular orifices and inclined orifices of a replica nozzle were consecutively taped shut and the performance of the modified nozzles was measured as before, and then compared to the performance of the whole nozzle. It was found out, that after a certain nozzle-to-surface distance the jets from the two nozzles would collide, which deteriorates the heat transfer.

Optimization of energy in a pulp mill

This study presents examination of ways to increase power generation in pulp mills. The main purpose was to identify and verify the best ways of power generation growth. The literature part of this study presented operation of energy pulp mill departments, energy consumption and generation by the recovery and power boilers. The second chapter of this part described the main directions for increase of electricity generation rise of black liquor dry solid content, increase of main steam parameters, flue gas heat recovery technologies, feed water and combustion air preheating. The third chapter of the literature part presented possible technical, environment and corrosion risks appeared from described alternatives. In the experimental part of this study, calculations and results of possible models with alternatives was presented. The possible combinations of alternatives were generated in 44 `models of energy pulp mill. The target of this part was define extra electricity generation after alternatives using and estimate profitability of generated models. The calculations were made by computer programme PROSIM. In the conclusions, the results were estimated on the basis of extra electricity generation and equipment design data of models. The profitability of cases was verified by their payback periods and additional incomes.

ContinuousMultimodal Global Optimization with Differential Evolution-Based Methods

Metaheuristic methods have become increasingly popular approaches in solving global optimization problems. From a practical viewpoint, it is often desirable to perform multimodal optimization which, enables the search of more than one optimal solution to the task at hand. Population-based metaheuristic methods offer a natural basis for multimodal optimization. The topic has received increasing interest especially in the evolutionary computation community. Several niching approaches have been suggested to allow multimodal optimization using evolutionary algorithms. Most global optimization approaches, including metaheuristics, contain global and local search phases. The requirement to locate several optima sets additional requirements for the design of algorithms to be effective in both respects in the context of multimodal optimization. In this thesis, several different multimodal optimization algorithms are studied in regard to how their implementation in the global and local search phases affect their performance in different problems. The study concentrates especially on variations of the Differential Evolution algorithm and their capabilities in multimodal optimization. To separate the global and local search search phases, three multimodal optimization algorithms are proposed, two of which hybridize the Differential Evolution with a local search method. As the theoretical background behind the operation of metaheuristics is not generally thoroughly understood, the research relies heavily on experimental studies in finding out the properties of different approaches. To achieve reliable experimental information, the experimental environment must be carefully chosen to contain appropriate and adequately varying problems. The available selection of multimodal test problems is, however, rather limited, and no general framework exists. As a part of this thesis, such a framework for generating tunable test functions for evaluating different methods of multimodal optimization experimentally is provided and used for testing the algorithms. The results demonstrate that an efficient local phase is essential for creating efficient multimodal optimization algorithms. Adding a suitable global phase has the potential to boost the performance significantly, but the weak local phase may invalidate the advantages gained from the global phase.

Production Cycle Optimization: Defining the Market Potential for a New Planning Tool

An optimization tool has been developed to help companies to optimize their production cycles and thus improve their overall supply chain management processes. The application combines the functionality that traditional APS (Advanced Planning System) and ARP (Automatic Replenishment Program) systems provide into one optimization run. A qualitative study was organized to investigate opportunities to expand the product’s market base. Twelve personal interviews were conducted and the results were collected in industry specific production planning analyses. Five process industries were analyzed to identify the product’s suitability to each industry sector and the most important product development areas. Based on the research the paper and the plastic film industries remain the most potential industry sectors at this point. To be successful in other industry sectors some product enhancements would be required, including capabilities to optimize multiple sequential and parallel production cycles, handle sequencing of complex finishing operations and to include master planning capabilities to support overall supply chain optimization. In product sales and marketing processes the key to success is to find and reach the people who are involved directly with the problems that the optimization tool can help to solve.

Line filter optimization in elevator use

The purpose of this thesis was to create design a guideline for an LCL-filter. This thesis reviews briefly the relevant harmonics standards, old filter designs and problems faced with the previous filters. This thesis proposes a modified design method based on the “Liserre’s method” presented in the literature. This modified method will take into account network parameters better. As input parameters, the method uses the nominal power, allowed ripple current in converter and network side and desired resonant frequency of the filter. Essential component selection issues for LCL-filter, such as heating, voltage strength, current rating etc. are also discussed. Furthermore, a simulation model used to verify the operation of the designed filter in nominal power use and in transient situations is included in this thesis.

Optimization of wind blade design including its energetic characteristics

The goal of the Master’s thesis is to develop and to analyze the optimization method for finding a geometry shape of classical horizontal wind turbine blades based on set of criteria. The thesis develops a technique that allows the designer to determine the weight of such factors as power coefficient, sound pressure level and the cost function in the overall process of blade shape optimization. The optimization technique applies the Desirability function. It was never used before in that kind of technical problems, and in this sense it can claim to originality of research. To do the analysis and the optimization processes more convenient the software application was developed.

Optimization of the power cable for low voltage direct current system

The demand for electricity is constantly growing in contemporary world and, in the same time, quality and reliability requirements are becoming more rigid. In addition, renewable sources of energy have been widely introduced for power generation, and they create specific challenges for the network. Consequently, new solution for distribution system is required, and Low Voltage Direct Current (LVDC) system is the proposed one. This thesis focuses on the investigation of specific cable features for low voltage direct current (LVDC) distribution system. The LVDC system is public ±750 VDC distribution system, which is currently being developed at Lappeen-ranta University of Technology. The aspects, considered in the thesis, are reliable and economic power transmission in distribution networks and possible power line communication in the LVDC cable.

Complexity Optimization in Product Development with Modularization

A company’s competence to manage its product portfolio complexity is becoming critically important in the rapidly changing business environment. The continuous evolvement of customer needs, the competitive market environment and internal product development lead to increasing complexity in product portfolios. The companies that manage the complexity in product development are more profitable in the long run. The complexity derives from product development and management processes where the new product variant development is not managed efficiently. Complexity is managed with modularization which is a method that divides the product structure into modules. In modularization, it is essential to take into account the trade-off between the perceived customer value and the module or component commonality across the products. Another goal is to enable the product configuration to be more flexible. The benefits are achieved through optimizing complexity in module offering and deriving the new product variants more flexibly and accurately. The developed modularization process includes the process steps for preparation, mapping the current situation, the creation of a modular strategy and implementing the strategy. Also the organization and support systems have to be adapted to follow-up targets and to execute modularization in practice.

Algorithmic Solutions for Combinatorial Problems in Resource Management of Manufacturing Environments

This thesis studies the use of heuristic algorithms in a number of combinatorial problems that occur in various resource constrained environments. Such problems occur, for example, in manufacturing, where a restricted number of resources (tools, machines, feeder slots) are needed to perform some operations. Many of these problems turn out to be computationally intractable, and heuristic algorithms are used to provide efficient, yet sub-optimal solutions. The main goal of the present study is to build upon existing methods to create new heuristics that provide improved solutions for some of these problems. All of these problems occur in practice, and one of the motivations of our study was the request for improvements from industrial sources. We approach three different resource constrained problems. The first is the tool switching and loading problem, and occurs especially in the assembly of printed circuit boards. This problem has to be solved when an efficient, yet small primary storage is used to access resources (tools) from a less efficient (but unlimited) secondary storage area. We study various forms of the problem and provide improved heuristics for its solution. Second, the nozzle assignment problem is concerned with selecting a suitable set of vacuum nozzles for the arms of a robotic assembly machine. It turns out that this is a specialized formulation of the MINMAX resource allocation formulation of the apportionment problem and it can be solved efficiently and optimally. We construct an exact algorithm specialized for the nozzle selection and provide a proof of its optimality. Third, the problem of feeder assignment and component tape construction occurs when electronic components are inserted and certain component types cause tape movement delays that can significantly impact the efficiency of printed circuit board assembly. Here, careful selection of component slots in the feeder improves the tape movement speed. We provide a formal proof that this problem is of the same complexity as the turnpike problem (a well studied geometric optimization problem), and provide a heuristic algorithm for this problem.

Bayesian methods for estimation, optimization and experimental design

Mathematical models often contain parameters that need to be calibrated from measured data. The emergence of efficient Markov Chain Monte Carlo (MCMC) methods has made the Bayesian approach a standard tool in quantifying the uncertainty in the parameters. With MCMC, the parameter estimation problem can be solved in a fully statistical manner, and the whole distribution of the parameters can be explored, instead of obtaining point estimates and using, e.g., Gaussian approximations. In this thesis, MCMC methods are applied to parameter estimation problems in chemical reaction engineering, population ecology, and climate modeling. Motivated by the climate model experiments, the methods are developed further to make them more suitable for problems where the model is computationally intensive. After the parameters are estimated, one can start to use the model for various tasks. Two such tasks are studied in this thesis: optimal design of experiments, where the task is to design the next measurements so that the parameter uncertainty is minimized, and model-based optimization, where a model-based quantity, such as the product yield in a chemical reaction model, is optimized. In this thesis, novel ways to perform these tasks are developed, based on the output of MCMC parameter estimation. A separate topic is dynamical state estimation, where the task is to estimate the dynamically changing model state, instead of static parameters. For example, in numerical weather prediction, an estimate of the state of the atmosphere must constantly be updated based on the recently obtained measurements. In this thesis, a novel hybrid state estimation method is developed, which combines elements from deterministic and random sampling methods.