Multifactor Performance Study on Complex Systems for the Benefit of Management

The primary objective is to identify the critical factors that have a natural impact on the performance measurement system. It is important to make correct decisions related to measurement systems, which are based on the complex business environment. The performance measurement system is combined with a very complex non-linear factor. The Six Sigma methodology is seen as one potential approach at every organisational level. It will be linked to the performance and financial measurement as well as to the analytical thinking on which the viewpoint of management depends. The complex systems are connected to the customer relationship study. As the primary throughput can be seen in a new well-defined performance measurement structure that will also be facilitated as will an analytical multifactor system. These critical factors should also be seen as a business innovation opportunity at the same time. This master's thesis has been divided into two different theoretical parts. The empirical part consists of both action-oriented and constructive research approaches with an empirical case study. The secondary objective is to seek a competitive advantage factor with a new analytical tool and the Six Sigma thinking. Process and product capabilities will be linked to the contribution of complex system. These critical barriers will be identified by the performance measuring system. The secondary throughput can be recognised as the product and the process cost efficiencies which throughputs are achieved with an advantage of management. The performance measurement potential is related to the different productivity analysis. Productivity can be seen as one essential part of the competitive advantage factor.

Performance and Methodological Aspects in Positron Emission Tomography

Performance standards for Positron emission tomography (PET) were developed to be able to compare systems from different generations and manufacturers. This resulted in the NEMA methodology in North America and the IEC in Europe. In practices, the NEMA NU 2- 2001 is the method of choice today. These standardized methods allow assessment of the physical performance of new commercial dedicated PET/CT tomographs. The point spread in image formation is one of the factors that blur the image. The phenomenon is often called the partial volume effect. Several methods for correcting for partial volume are under research but no real agreement exists on how to solve it. The influence of the effect varies in different clinical settings and it is likely that new methods are needed to solve this problem. Most of the clinical PET work is done in the field of oncology. The whole body PET combined with a CT is the standard investigation today in oncology. Despite the progress in PET imaging technique visualization, especially quantification of small lesions is a challenge. In addition to partial volume, the movement of the object is a significant source of error. The main causes of movement are respiratory and cardiac motions. Most of the new commercial scanners are in addition to cardiac gating, also capable of respiratory gating and this technique has been used in patients with cancer of the thoracic region and patients being studied for the planning of radiation therapy. For routine cardiac applications such as assessment of viability and perfusion only cardiac gating has been used. However, the new targets such as plaque or molecular imaging of new therapies require better control of the cardiac motion also caused by respiratory motion. To overcome these problems in cardiac work, a dual gating approach has been proposed. In this study we investigated the physical performance of a new whole body PET/CT scanner with NEMA standard, compared methods for partial volume correction in PET studies of the brain and developed and tested a new robust method for dual cardiac-respiratory gated PET with phantom, animal and human data. Results from performance measurements showed the feasibility of the new scanner design in 2D and 3D whole body studies. Partial volume was corrected, but there is no best method among those tested as the correction also depends on the radiotracer and its distribution. New methods need to be developed for proper correction. The dual gating algorithm generated is shown to handle dual-gated data, preserving quantification and clearly eliminating the majority of contraction and respiration movement

The Detector Control Systems for the CMS Resistive Plate Chamber at LHC

The RPC Detector Control System (RCS) is the main subject of this PhD work. The project, involving the Lappeenranta University of Technology, the Warsaw University and INFN of Naples, is aimed to integrate the different subsystems for the RPC detector and its trigger chain in order to develop a common framework to control and monitoring the different parts. In this project, I have been strongly involved during the last three years on the hardware and software development, construction and commissioning as main responsible and coordinator. The CMS Resistive Plate Chambers (RPC) system consists of 912 double-gap chambers at its start-up in middle of 2008. A continuous control and monitoring of the detector, the trigger and all the ancillary sub-systems (high voltages, low voltages, environmental, gas, and cooling), is required to achieve the operational stability and reliability of a so large and complex detector and trigger system. Role of the RPC Detector Control System is to monitor the detector conditions and performance, control and monitor all subsystems related to RPC and their electronics and store all the information in a dedicated database, called Condition DB. Therefore the RPC DCS system has to assure the safe and correct operation of the sub-detectors during all CMS life time (more than 10 year), detect abnormal and harmful situations and take protective and automatic actions to minimize consequential damages. The analysis of the requirements and project challenges, the architecture design and its development as well as the calibration and commissioning phases represent themain tasks of the work developed for this PhD thesis. Different technologies, middleware and solutions has been studied and adopted in the design and development of the different components and a big challenging consisted in the integration of these different parts each other and in the general CMS control system and data acquisition framework. Therefore, the RCS installation and commissioning phase as well as its performance and the first results, obtained during the last three years CMS cosmic runs, will be

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Creating generation Y sources of motivation for enhanced performance

New challenges have been created in the modern work environment as the diversity of the workforce is greater than ever in terms of generations. There will become a large demand of generation Y employees as the baby boomer generation employees retire at an accelerated rate. The purpose of this study is to investigate Y generation specific characteristics and to identify motivational systems to enhance performance. The research questions are: 1. What are Y generation characteristics? 2. What motivational systems organizations can form to motivate Y generation employees and in turn, create better performance? The Y generation specific characteristics identified from the literature include; achievement oriented; confident; educated; multitasking; having a need for feedback; needing management support; sociable and tech savvy. The proposed motivational systems can be found in four areas of the organization; HRM, training and development, communication and decision making policies. Three focus groups were held to investigate what would motivate generation Y employees to achieve better performance. Two of these focus groups were Finnish natives and the third consisted of international students. The HRM systems included flexibility and a culture of fun. It was concluded that flexibility within the workplace and role was a great source of motivation. Culture of fun was not responded to as favorably although most focus group participants rated enjoyableness as one of their top motivating factors. Training and development systems include training programs and mentoring as sources of potential motivation. Training programs were viewed as a mode to gain a better position and were not necessarily seen as motivational systems. Mentoring programs were not concluded to have a significant effect on motivation. Communication systems included keeping up with technology, clarity and goals as well as feedback. Keeping up with technology was seen as an ineffective tool to motivate. Clarity and goal setting was seen as very important to be able to perform but not necessarily motivating. Feedback had a highly motivating effect on these focus groups. Decision making policies included collaboration and teamwork as well as ownership. Teams were familiar and meet the social needs of Y generation employees and are motivating. Ownership was equated with trust and responsibility and was highly valued as well as motivating to these focus group participants.

Maintaining supply performance during an ERP system implementation phase : case Cloetta Suomi Oy

Implementing an enterprise resource planning (ERP) system often means a major change to an organization and involves significant risks. It is typical that many of the ERP system implementations fail resulting in tremendous damage to the business. Moreover, running normal business operations during an ERP system implementation is far more complicated than normally. This thesis focuses on how an organization should manage the ERP system implementation process in order to maintain supply performance during the implementation phase. The theoretical framework in this thesis focuses on ERP system implementations with a critical success factor approach. Critical success factors can be divided into strategic and tactical level success factors. By considering these critical success factors, ERP system implementation project’s timeline and best practices of an ERP implementation, a critical success factor based ERP system implementation management framework is presented. The framework can be used as a theoretical framework when the goal is to avoid ERP system implementation phase issues that are driven by the ERP system implementation project and that may decrease organization’s supply performance. This thesis is a case study that was written on an assignment to a confectionary company Cloetta Suomi Oy. In order to collect data, interviews of the case company personnel were conducted. In addition, several other data collection methods were used throughout the research process. These data collection methods include examination of presentations and archival records as well as direct observations in case company meetings and in various work duties. The results of this thesis indicate that there are several factors that may decrease organization’s supply performance during the ERP system implementation. These issues are categorized under external and internal issues and further into six risk drivers that are suppliers, customers, products, staff, information systems and other projects. After the description and categorization of each issue, the thesis focuses on finding solutions on how to avoid or mitigate the impact of these issues on the organization’s supply performance. This examination leads to several operational activities that are also practical to business practitioners. It is also stated that a successful ERP system implementation that also causes minimal disturbance to organization’s supply performance during the ERP system implementation, is achieved by considering three levels of actions.

Development of Measurement Systems in Scientific Research: Case Study

In recent years, technological advancements in microelectronics and sensor technologies have revolutionized the field of electrical engineering. New manufacturing techniques have enabled a higher level of integration that has combined sensors and electronics into compact and inexpensive systems. Previously, the challenge in measurements was to understand the operation of the electronics and sensors, but this has now changed. Nowadays, the challenge in measurement instrumentation lies in mastering the whole system, not just the electronics. To address this issue, this doctoral dissertation studies whether it would be beneficial to consider a measurement system as a whole from the physical phenomena to the digital recording device, where each piece of the measurement system affects the system performance, rather than as a system consisting of small independent parts such as a sensor or an amplifier that could be designed separately. The objective of this doctoral dissertation is to describe in depth the development of the measurement system taking into account the challenges caused by the electrical and mechanical requirements and the measurement environment. The work is done as an empirical case study in two example applications that are both intended for scientific studies. The cases are a light sensitive biological sensor used in imaging and a gas electron multiplier detector for particle physics. The study showed that in these two cases there were a number of different parts of the measurement system that interacted with each other. Without considering these interactions, the reliability of the measurement may be compromised, which may lead to wrong conclusions about the measurement. For this reason it is beneficial to conceptualize the measurement system as a whole from the physical phenomena to the digital recording device where each piece of the measurement system affects the system performance. The results work as examples of how a measurement system can be successfully constructed to support a study of sensors and electronics.

Energy aware software for many-core systems

Many-core systems provide a great potential in application performance with the massively parallel structure. Such systems are currently being integrated into most parts of daily life from high-end server farms to desktop systems, laptops and mobile devices. Yet, these systems are facing increasing challenges such as high temperature causing physical damage, high electrical bills both for servers and individual users, unpleasant noise levels due to active cooling and unrealistic battery drainage in mobile devices; factors caused directly by poor energy efficiency. Power management has traditionally been an area of research providing hardware solutions or runtime power management in the operating system in form of frequency governors. Energy awareness in application software is currently non-existent. This means that applications are not involved in the power management decisions, nor does any interface between the applications and the runtime system to provide such facilities exist. Power management in the operating system is therefore performed purely based on indirect implications of software execution, usually referred to as the workload. It often results in over-allocation of resources, hence power waste. This thesis discusses power management strategies in many-core systems in the form of increasing application software awareness of energy efficiency. The presented approach allows meta-data descriptions in the applications and is manifested in two design recommendations: 1) Energy-aware mapping 2) Energy-aware execution which allow the applications to directly influence the power management decisions. The recommendations eliminate over-allocation of resources and increase the energy efficiency of the computing system. Both recommendations are fully supported in a provided interface in combination with a novel power management runtime system called Bricktop. The work presented in this thesis allows both new- and legacy software to execute with the most energy efficient mapping on a many-core CPU and with the most energy efficient performance level. A set of case study examples demonstrate realworld energy savings in a wide range of applications without performance degradation.

Power and Performance Management Strategies on NoC-Based Multicore Platforms, Using DVFS, Adaptive Algorithm and Task Parallelism

This work presents synopsis of efficient strategies used in power managements for achieving the most economical power and energy consumption in multicore systems, FPGA and NoC Platforms. In this work, a practical approach was taken, in an effort to validate the significance of the proposed Adaptive Power Management Algorithm (APMA), proposed for system developed, for this thesis project. This system comprise arithmetic and logic unit, up and down counters, adder, state machine and multiplexer. The essence of carrying this project firstly, is to develop a system that will be used for this power management project. Secondly, to perform area and power synopsis of the system on these various scalable technology platforms, UMC 90nm nanotechnology 1.2v, UMC 90nm nanotechnology 1.32v and UMC 0.18 μmNanotechnology 1.80v, in order to examine the difference in area and power consumption of the system on the platforms. Thirdly, to explore various strategies that can be used to reducing system’s power consumption and to propose an adaptive power management algorithm that can be used to reduce the power consumption of the system. The strategies introduced in this work comprise Dynamic Voltage Frequency Scaling (DVFS) and task parallelism. After the system development, it was run on FPGA board, basically NoC Platforms and on these various technology platforms UMC 90nm nanotechnology1.2v, UMC 90nm nanotechnology 1.32v and UMC180 nm nanotechnology 1.80v, the system synthesis was successfully accomplished, the simulated result analysis shows that the system meets all functional requirements, the power consumption and the area utilization were recorded and analyzed in chapter 7 of this work. This work extensively reviewed various strategies for managing power consumption which were quantitative research works by many researchers and companies, it's a mixture of study analysis and experimented lab works, it condensed and presents the whole basic concepts of power management strategy from quality technical papers.

Applying software performance engineering methods to development of IT device management systems

Ohjelmiston suorituskyky on kokonaisvaltainen asia, johon kaikki ohjelmiston elinkaaren vaiheet vaikuttavat. Suorituskykyongelmat johtavat usein projektien viivästymisiin, kustannusten ylittymisiin sekä joissain tapauksissa projektin täydelliseen epäonnistumiseen. Software performance engineering (SPE) on ohjelmistolähtöinen lähestysmistapa, joka tarjoaa tekniikoita suorituskykyisen ohjelmiston kehittämiseen. Tämä diplomityö tutkii näitä tekniikoita ja valitsee niiden joukosta ne, jotka soveltuvat suorituskykyongelmien ratkaisemiseen kahden IT-laitehallintatuotteen kehityksessä. Työn lopputuloksena on päivitetty versio nykyisestä tuotekehitysprosessista, mikä huomioi sovellusten suorituskykyyn liittyvät haasteet tuotteiden elinkaaren eri vaiheissa.

Viologen based electroactive polymers and composites for durable electrochromic systems

Electrochromism, the phenomenon of reversible color change induced by a small electric charge, forms the basis for operation of several devices including mirrors, displays and smart windows. Although, the history of electrochromism dates back to the 19th century, only the last quarter of the 20th century has its considerable scientific and technological impact. The commercial applications of electrochromics (ECs) are rather limited, besides top selling EC anti-glare mirrors by Gentex Corporation and airplane windows by Boeing, which made a huge commercial success and exposed the potential of EC materials for future glass industry. It is evident from their patents that viologens (salts of 4,4ʹ-bipyridilium) were the major active EC component for most of these marketed devices, signifying the motivation of this thesis focusing on EC viologens. Among the family of electrochromes, viologens have been utilized in electrochromic devices (ECDs) for a while, due to its intensely colored radical cation formation induced by applying a small cathodic potential. Viologens can be synthesized as oligomer or in the polymeric form or as functionality to conjugated polymers. In this thesis, polyviologens (PVs) were synthesized starting from cyanopyridinium (CNP) based monomer precursors. Reductive coupling of cross-connected cyano groups yields viologen and polyviologen under successive electropolymerization using for example the cyclic voltammetry (CV) technique. For further development, a polyviologen-graphene composite system was fabricated, focusing at the stability of the PV electrochrome without sacrificing its excellent EC properties. High electrical conductivity, high surface area offered by graphene sheets together with its non-covalent interactions and synergism with PV significantly improved the electrochrome durability in the composite matrix. The work thereby continued in developing a CNP functionalized thiophene derivative and its copolymer for possible utilization of viologen in the copolymer blend. Furthermore, the viologen functionalized thiophene derivative was synthesized and electropolymerized in order to explore enhancement in the EC contrast and overall EC performance. The findings suggest that such electroactive viologen/polyviologen systems and their nanostructured composite films as well as viologen functionalized conjugated polymers, can be potentially applied as an active EC material in future ECDs aiming at durable device performances.

Performance Degradation Modelling and Techno-Economic Analysis of Lithium-Ion Battery Energy Storage Systems

Transmission system operators and distribution system operators are experiencing new challenges in terms of reliability, power quality, and cost efficiency. Although the potential of energy storages to face those challenges is recognized, the economic implications are still obscure, which introduce the risk into the business models. This thesis aims to investigate the technical and economic value indicators of lithium-ion battery energy storage systems (BESS) in grid-scale applications. In order to do that, a comprehensive performance lithium-ion BESS model with degradation effects estimation is developed. The model development process implies literature review on lifetime modelling, use, and modification of previous study progress, building the additional system parts and integrating it into a complete tool. The constructed model is capable of describing the dynamic behavior of the BESS voltage, state of charge, temperature and capacity loss. Five control strategies for BESS unit providing primary frequency regulation are implemented, in addition to the model. The questions related to BESS dimensioning and the end of life (EoL) criterion are addressed. Simulations are performed with one-month real frequency data acquired from Fingrid. The lifetime and cost-benefit analysis of the simulation results allow to compare and determine the preferable control strategy. Finally, the study performs the sensitivity analysis of economic profitability with variable size, EoL and system price. The research reports that BESS can be profitable in certain cases and presents the recommendations.