Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Spatial multicriteria decision analysis for the siting of on-shore wind power in Kemiönsaari

Wind power is a low-carbon energy production form that reduces the dependence of society on fossil fuels. Finland has adopted wind energy production into its climate change mitigation policy, and that has lead to changes in legislation, guidelines, regional wind power areas allocation and establishing a feed-in tariff. Wind power production has indeed boosted in Finland after two decades of relatively slow growth, for instance from 2010 to 2011 wind energy production increased with 64 %, but there is still a long way to the national goal of 6 TWh by 2020. This thesis introduces a GIS-based decision-support methodology for the preliminary identification of suitable areas for wind energy production including estimation of their level of risk. The goal of this study was to define the least risky places for wind energy development within Kemiönsaari municipality in Southwest Finland. Spatial multicriteria decision analysis (SMCDA) has been used for searching suitable wind power areas along with many other location-allocation problems. SMCDA scrutinizes complex ill-structured decision problems in GIS environment using constraints and evaluation criteria, which are aggregated using weighted linear combination (WLC). Weights for the evaluation criteria were acquired using analytic hierarchy process (AHP) with nine expert interviews. Subsequently, feasible alternatives were ranked in order to provide a recommendation and finally, a sensitivity analysis was conducted for the determination of recommendation robustness. The first study aim was to scrutinize the suitability and necessity of existing data for this SMCDA study. Most of the available data sets were of sufficient resolution and quality. Input data necessity was evaluated qualitatively for each data set based on e.g. constraint coverage and attribute weights. Attribute quality was estimated mainly qualitatively by attribute comprehensiveness, operationality, measurability, completeness, decomposability, minimality and redundancy. The most significant quality issue was redundancy as interdependencies are not tolerated by WLC and AHP does not include measures to detect them. The third aim was to define the least risky areas for wind power development within the study area. The two highest ranking areas were Nordanå-Lövböle and Påvalsby followed by Helgeboda, Degerdal, Pungböle, Björkboda, and Östanå-Labböle. The fourth aim was to assess the recommendation reliability, and the top-ranking two areas proved robust whereas the other ones were more sensitive.

JNK regulates dendrite and spine architecture in the central nervous system influencing spatial learning and motor tasks

JNK1 is a MAP-kinase that has proven a significant player in the central nervous system. It regulates brain development and the maintenance of dendrites and axons. Several novel phosphorylation targets of JNK1 were identified in a screen performed in the Coffey lab. These proteins were mainly involved in the regulation of neuronal cytoskeleton, influencing the dynamics and stability of microtubules and actin. These structural proteins form the dynamic backbone for the elaborate architecture of the dendritic tree of a neuron. The initiation and branching of the dendrites requires a dynamic interplay between the cytoskeletal building blocks. Both microtubules and actin are decorated by associated proteins which regulate their dynamics. The dendrite-specific, high molecular weight microtubule associated protein 2 (MAP2) is an abundant protein in the brain, the binding of which stabilizes microtubules and influences their bundling. Its expression in non-neuronal cells induces the formation of neurite-like processes from the cell body, and its function is highly regulated by phosphorylation. JNK1 was shown to phosphorylate the proline-rich domain of MAP2 in vivo in a previous study performed in the group. Here we verify three threonine residues (T1619, T1622 and T1625) as JNK1 targets, the phosphorylation of which increases the binding of MAP2 to microtubules. This binding stabilizes the microtubules and increases process formation in non-neuronal cells. Phosphorylation-site mutants were engineered in the lab. The non-phosphorylatable mutant of MAP2 (MAP2- T1619A, T1622A, T1625A) in these residues fails to bind microtubules, while the pseudo-phosphorylated form, MAP2- T1619D, T1622D, Thr1625D, efficiently binds and induces process formation even without the presence of active JNK1. Ectopic expression of the MAP2- T1619D, T1622D, Thr1625D in vivo in mouse brain led to a striking increase in the branching of cortical layer 2/3 (L2/3) pyramidal neurons, compared to MAP2-WT. The dendritic complexity defines the receptive field of a neuron and dictates the output to the postsynaptic cells. Previous studies in the group indicated altered dendrite architecture of the pyramidal neurons in the Jnk1-/- mouse motor cortex. Here, we used Lucifer Yellow loading and Sholl analysis of neurons in order to study the dendritic branching in more detail. We report a striking, opposing effect in the absence of Jnk1 in the cortical layers 2/3 and 5 of the primary motor cortex. The basal dendrites of pyramidal neurons close to the pial surface at L2/3 show a reduced complexity. In contrast, the L5 neurons, which receive massive input from the L2/3 neurons, show greatly increased branching. Another novel substrate identified for JNK1 was MARCKSL1, a protein that regulates actin dynamics. It is highly expressed in neurons, but also in various cancer tissues. Three phosphorylation target residues for JNK1 were identified, and it was demonstrated that their phosphorylation reduces actin turnover and retards migration of these cells. Actin is the main cytoskeletal component in dendritic spines, the site of most excitatory synapses in pyramidal neurons. The density and gross morphology of the Lucifer Yellow filled dendrites were characterized and we show reduced density and altered morphology of spines in the motor cortex and in the hippocampal area CA3. The dynamic dendritic spines are widely considered to function as the cellular correlate during learning. We used a Morris water maze to test spatial memory. Here, the wild-type mice outperformed the knock-out mice during the acquisition phase of the experiment indicating impaired special memory. The L5 pyramidal neurons of the motor cortex project to the spinal cord and regulate the movement of distinct muscle groups. Thus the altered dendrite morphology in the motor cortex was expected to have an effect on the input-output balance in the signaling from the cortex to the lower motor circuits. A battery of behavioral tests were conducted for the wild-type and Jnk1-/- mice, and the knock-outs performed poorly compared to wild-type mice in tests assessing balance and fine motor movements. This study expands our knowledge of JNK1 as an important regulator of the dendritic fields of neurons and their manifestations in behavior.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.

Spatial and temporal variation in fish populations and assemblages in coastal waters of the northern Baltic Proper

The distribution and traits of fish are of interest both ecologically and socio-economically. In this thesis, phenotypic and structural variation in fish populations and assemblages was studied on multiple spatial and temporal scales in shallow coastal areas in the archipelago of the northern Baltic Proper. In Lumparn basin in Åland Islands, the fish assemblage displayed significant seasonal variation in depth zone distribution. The results indicate that investigating both spatial and temporal variation in small scale is crucial for understanding patterns in fish distribution and community structure in large scale. The local population of Eurasian perch Perca fluviatilis L displayed habitat-specific morphological and dietary variation. Perch in the pelagic zone were on average deeper in their body shape than the littoral ones and fed on fish and benthic invertebrates. The results differ from previous studies conducted in freshwater habitats, where the pelagic perch typically are streamlined in body shape and zooplanktivorous. Stable isotopes of carbon and nitrogen differed between perch with different stomach contents, suggesting differentiation of individual diet preferences. In the study areas Lumparn and Ivarskärsfjärden in Åland Islands and Galtfjärden in Swedish east coast, the development in fish assemblages during the 2000’s indicated a general shift towards higher abundances of small-bodied lower-order consumers, especially cyprinids. For European pikeperch Sander lucioperca L., recent declines in adult fish abundances and high mortalities (Z = 1.06–1.16) were observed, which suggests unsustainably high fishing pressure on pikeperch. Based on the results it can be hypothesized that fishing has reduced the abundances of large predatory fish, which together with bottom-up forcing by eutrophication has allowed the lower-order consumer species to increase in abundances. This thesis contributes to the scientific understanding of aquatic ecosystems with new descriptions on morphological and dietary adaptations in perch in brackish water, and on the seasonal variation in small-scale spatial fish distribution. The results also demonstrate anthropogenic effects on coastal fish communities and underline the urgency of further reducing nutrient inputs and regulating fisheries in the Baltic Sea region.