The purpose of this thesis is to find a proper positioning strategy for China self-owned car brands in the Chinese market. For this purpose, the authors used the theory about brand positioning, target market and segmentation, brand image, differentiation and general positioning process. What’s more, the authors conducted a questionnaire research, studied the Geely acquisition case, and collected information from consumers’ perspectives to understand the current positioning situation of the Chinese automakers. In the methodology part, limitations of the quantitative and qualitative data are discussed, and the authors presented suggestions for further studies. After gathering empirical data, the authors analyzed the strengths and weaknesses of Chinese car, Geely and Volvo, introduced competitor’s performance to make clear the current situation of China self-owned car brands. From the analysis, the present brand image of Chinese car is low-price and bad-quality in the consumers’ mind. The authors tried to figure out a proper brand image for Chinese car to increase the market share in the domestic market. In conclusion, developing safe car of high quality and targeting the middle-class market is the optimal choice for current China self-owned car brands.

When simulating target seekers, there is a great need for computationally efficient, target models. This report considers a study of radar target modelling based on Inverse Synthetic Aperture Radar (ISAR) measurements of generic aircraft. The results underlie future modelling of full-size air targets. A method is developed for two-dimensional modelling of aspect-dependent target scattering. The approach taken is to generate point-scatterer models of two targets, where each point scatterer is defined according to its position and radar cross section (RCS), estimated from ISAR images. The scattered energy contributions from all point scatterers are summed to simulate a radar return signal. To validate the models, the modelled radar target centre is compared to the true radar target centre, which is determined from ISAR images. The method is presented to be promising for modelling air targets with large, persistent radar cross section.

Wireless sensor networks are made up of a large number of sensors deployed randomly in an ad-hoc manner in the area/target to be monitored. Due to their weight and size limitations, the energy conservation is the most critical issue. Energy saving in a wireless sensor network can be achieved by scheduling a subset of sensor nodes to activate and allowing others to go into low power sleep mode, or adjusting the transmission or sensing range of wireless sensor nodes. In this thesis, we focus on improving the lifetime of wireless sensor networks using both smart scheduling and adjusting sensing ranges. Firstly, we conduct a survey on existing works in literature and then we define the sensor network lifetime problem with range assignment. We then propose two completely localized and distributed scheduling algorithms with adjustable sensing range. These algorithms are the enhancement of distributed algorithms for fixed sensing range proposed in the literature. The simulation results show that there is almost 20 percent improvement of network lifetime when compare with the previous approaches.

The United Kingdom’s Civil Aviation Authority published the national aviation forecast in 2008. The forecast predicts that domestic traffic will increase by 3.5% per year, and that international traffic will grow, on average, by 4.5% during 2010-2020. Based on this prediction, the traffic density will increase dramatically in the future, and airspace will be more and more congested. Usually, there are two potential solutions to deal with this situation: improving the ability of air traffic flow management is one solution; reducing the separation minimum of aircraft is another solution. However, this thesis focuses on the second solution, based on constraints of communication, navigation and surveillance systems (CNS). Cont?d.

The problem considered in this thesis is the development of an autonomous prototype robot capable of gathering sensory information from its environment allowing it to provide feedback on the condition of specific targets to aid in maintenance of hydro equipment. The context for the solution to this problem is based on the power grid environment operated by the local hydro utility. The intent is to monitor power line structures by travelling along skywire located at the top of towers, providing a view of everything beneath it including, for example, insulators, conductors, and towers. The contribution of this thesis is a novel robot design with the potential to prevent hazardous situations and the use of rough coverage feedback modified reinforcement learning algorithms to establish behaviours. / October 2007

This thesis presents a rough set approach to reinforcement learning. This is made possible by considering behaviour patterns of learning agents in the context of approximation spaces. Rough set theory introduced by Zdzisław Pawlak in the early 1980s provides a ground for deriving pattern-based rewards within approximation spaces. Learning can be considered episodic. The framework provided by an approximation space makes it possible to derive pattern-based reference rewards at the end of each episode. Reference rewards provide a standard for reinforcement comparison as well as the actor-critic method of reinforcement learning. In addition, approximation spaces provide a basis for deriving episodic weights that provide a basis for a new form of off-policy Monte Carlo learning control method. A number of conventional and pattern-based reinforcement learning methods are investigated in this thesis. In addition, this thesis introduces two learning environments used to compare the algorithms. The first is a Monocular Vision System used to track a moving target. The second is an artificial ecosystem testbed that makes it possible to study swarm behaviour by collections of biologically-inspired bots. The simulated ecosystem has an ethological basis inspired by the work of Niko Tinbergen, who introduced in the 1960s methods of observing and explaining the behaviour of biological organisms that carry over into the study of the behaviour of interacting robotic devices that cooperate to survive and to carry out highly specialized tasks. Agent behaviour during each episode is recorded in a decision table called an ethogram, which records features such as states, proximate causes, responses (actions), action preferences, rewards and decisions (actions chosen and actions rejected). At all times an agent follows a policy that maps perceived states of the environment to actions. The goal of the learning algorithms is to find an optimal policy in a non-stationary environment. The results of the learning experiments with seven forms of reinforcement learning are given. The contribution of this thesis is a comprehensive introduction to a pattern-based evaluation of behaviour during reinforcement learning using approximation spaces. / May 2006

The visibility-aware path planner addresses the problem of path planning for target visibility. It computes sequences of motions that afford a line of sight to a stationary visual target for sensors on a robotic platform. The visibility-aware planner uses a model of the visible region, namely, the region of the task space in which a line of sight exists to the target. The planner also takes the orientation of the sensor into account, utilizing a model of the field of view frustum. The planner applies a penalty to paths that cause the sensor to lose target visibility by exiting the visible region or rotating so the target is not in the field of view. The planner applies these penalties to the edges in a probabilistic roadmap, providing weights in the roadmap graph for graph-search based planning algorithms. This thesis presents two variants on the planner. The static multi-query planner precomputes penalties for all roadmap edges and performs a best-path search using Dijkstra's algorithm. The dynamic single-query planner uses an iterative test-and-reject search to find paths of acceptable penalty without the benefit of precomputation. Four experiments are presented which validate the planners and present examples of the path planning for visibility on 6-DOF robot manipulators. The algorithms are statistically tested with multiple queries. Results show that the planner finds paths with significantly lower losses of target visibility than existing shortest-path planners.

In order to understand the migration patterns of migrating birds, it is necessary to understand whenand where to they migrate. Many of these birds are very small and thus cannot carry heavy sensors;hence it is necessary to be able to perform positioning using a very small sensor. One way to do this isto use a light-intensity sensor. Since the sunrise and sunset times are known given time and position onthe earth, it is possible to determine the global position using light intensity. Light intensity increasesas the sun rises. Data sets from several calibration sensors, mainly from different locations in Sweden, have been examinedin different ways in order to get an understanding of the measurements and what affects them. Inorder to perform positioning, it is necessary to know the solar elevation angle, which can be computedif the time and position are known, as is the case for the calibration sensors. This has been utilized toidentify a mapping from measured light intensity to solar elevation angle, which is used to computepseudo-measurements for target tracking, described below. In this thesis, positioning is performed using methods from the field of target tracking. This is doneboth causally (filtering) and non-causally (smoothing). There are certain problems that arise; firstly,the measured light intensity can be attenuated due to weather conditions such as cloudiness, which ismodelled as a time-varying offset. Secondly, the sensor can be shadowed causing outliers in the data.Furthermore, birds are not always in a migratory state, they oftentimes stay in one place. The lattertwo phenomena are modelled using an Interacting Multiple Model (IMM) where they are representedas discrete states, corresponding to different models.

No description available.

<p>MicroRNAs (miRNAs) are small RNAs that have important roles in post-transcriptional gene regulation in a wide range of species. This regulation is controlled by having miRNAs directly bind to a target messenger RNA (mRNA), causing it to be destabilized and degraded, or translationally repressed. Identifying miRNA targets has been a large area of focus for study; however, a lack of generally high-throughput experiments to validate direct miRNA targeting has been a limiting factor. To overcome these limitations, computational methods have become crucial for understanding and predicting miRNA-gene target interactions.</p><p>While a variety of computational tools exist for predicting miRNA targets, many of them are focused on a similar feature set for their prediction. These commonly used features are complementarity to 5'seed of miRNAs and evolutionary conservation. Unfortunately, not all miRNA target sites are conserved or adhere to canonical seed complementarity. Seeking to address these limitations, several studies have included energy features of mRNA:miRNA duplex formation as alternative features. However, different independent evaluations reported conflicting results on the reliability of energy-based predictions. Here, we reassess the usefulness of energy features for mammalian target prediction, aiming to relax or eliminate the need for perfect seed matches and conservation requirement.</p><p>We detect significant differences of energy features at experimentally supported human miRNA target sites and at genome-wide interaction sites to Argonaute (AGO) protein family members, which are essential parts of the miRNA machinery complex. This trend is confirmed on data sets that assay the effect of miRNAs on mRNA and protein expression changes, where a statistically significant change in expression is noted when compared to the control. Furthermore, our method also allows for prediction of strictly imperfect sites, as well as non-conserved targets.</p><p>Recently, new methods for identifying direct miRNA binding have been developed, which provides us with additional sources of information for miRNA target prediction. While some computational target predictions tools have begun to incorporate this information, they still rely on the presence of a seed match in the AGO-bound windows without accounting for the possibility of variations. </p><p>We investigate the usefulness of the site level direct binding evidence in miRNA target identification and propose a model that incorporates multiple different features along with the AGO-interaction data. Our method outperforms both an ad hoc strategy of seed match searches as well as an existing target prediction tool, while still allowing for predictions of sites other than a long perfect seed match. Additionally, we show supporting evidence for a class of non-canonical sites as bound targets. Our model can be extended to predict additional types of imperfect sites, and can also be readily modified to include additional features that may produce additional improvements.</p> / Dissertation