Table tennis playing involves complex spatial movement of the racket and human body. It takes much effort for the novice players to better mimic expert players. The evaluation of motion patterns during table tennis training, which is usually achieved by coaches, is important for novice trainees to improve faster. However, traditional coaching relies heavily on coaches qualitative observation and subjective evaluation. While past literature shows considerable potential in applying biomechanical analysis and classification for motion pattern assessment to improve novice table tennis players, little published work was found on table tennis biomechanics. To attempt to overcome the problems and fill the gaps, this research aims to quantify the movement of table tennis strokes, to identify the motion pattern differences between experts and novices, and to develop a model for automatic evaluation of the motion quality for an individual. Firstly, a novel method for comprehensive quantification and measurement of the kinematic motion of racket and human body is proposed. In addition, a novel method based on racket centre velocity profile is proposed to segment and normalize the motion data. Secondly, a controlled experiment was conducted to collect motion data of expert and novice players during forehand strokes. Statistical analysis was performed to determine the motion differences between the expert and the novice groups. The experts exhibited significantly different motion patterns with faster racket centre velocity and smaller racket plane angle, different standing posture and joint angular velocity, etc. Lastly, a support vector machine (SVM) classification technique was employed to build a model for motion pattern evaluation. The model development was based on experimental data with different feature selection methods and SVM kernels to achieve the best performance (F1 score) through cross-validated and Nelder-Mead method. Results showed that the SVM classification model exhibited good performance with an average model performance above 90% in distinguishing the stroke motion between expert and novice players. This research helps to better understand the biomechanical mechanisms of table tennis strokes, which will ultimately aid the improvement of novice players. The phase segmentation and normalization methods for table tennis strokes are novel, unambiguous and straightforward to apply. The quantitative comparison identified the comprehensive differences in motion between experts and novice players for racket and human body in continuous phase time, which is a novel contribution. The proposed classification model shows potential in the application of SVM to table tennis biomechanics and can be exploited for automatic coaching.

The analysis of categorical data often leads to the analysis of a contingency table. For large samples, asymptotic approximations are sufficient when calculating p-values, but for small samples the tests can be unreliable. In these situations an exact test should be considered. This bases the test on the exact distribution of the test statistic. Sampling techniques can be used to estimate the distribution. Alternatively, the distribution can be found by complete enumeration. A new algorithm is developed that enables a model to be defined by a model matrix, and all tables that satisfy the model are found. This provides a more efficient enumeration mechanism for complex models and extends the range of models that can be tested. The technique can lead to large calculations and a distributed version of the algorithm is developed that enables a number of machines to work efficiently on the same problem.

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Water table control by subsurface drainage has been shown to affect leaching losses of nitrate-nitrogen: a concern both for economic use of fertilizer, and for maintenance of water quality. The effect of water table height on leaching of NO₃⁻-N was investigated in this study in nineteen 15cm x 100cm undisturbed cores of silty clay loam. The experiment simulated fertilization followed by rainfall, then rapid water table rise and fall, under conditions similiar to those experienced in the early spring in the Lower Fraser Valley. In the first part of the experiment, a concentrated solution of KNO₃ and KG (equivalent to 35 kg/ha of N and 22 kg/ha of Cl) was applied to the columns, followed by intermittent leaching with distilled water. Leachate from two depths in each column was collected before and after a period of static water table, and analyzed for NO₃⁻, No₂⁻, NH₄⁺, and Cl⁻. This procedure was repeated without nutrient addition in the second part of the experiment. Chloride was used an inert tracer to follow anion movement and retention within the columns. There was no significant difference in the leachate NO₃⁻ concentration or leachate N/CI ratio from any of the four water table heights tested (15, 35, 55, and 75 cm above drain depth). The NO₃⁻ concentrations and N/CI ratios decreased with depth in the soil columns, indicating removal of N from the percolating soil solution, either by denitrification or immobilization. The variability in leachate concentrations among all columns was very high (eg. for a typical sample time, NO₃⁻-N ranged from 0.01 to 15.72 mg/L, and Cl⁻ ranged from 4.8 to 14.5 mg/L), as was the variability in constant head satiated hydraulic conductivities (range: 1 to 1468 cm/day; CV = 181%), and drainable porosity (range: 2.7 to 10.4%; CV = 39%). Cross sections of columns leached with 1% methylene blue solution did not reveal differences in patterns of water transmission between low and high conductivity columns. Indications were that penetration of dye was greater in columns with higher conductivities, and that preferential flow occurred in all columns examined. Leachate concentrations and N/CI ratios correlated significantly with hydraulic conductivity: Spearman's correlation coefficients were always > 0.8 for samples obtained from the bottom of the columns. However, even when the conductivity was included as a covariate in an analysis of covariance, there was no significant effect of water table height on nitrate leaching. / Applied Science, Faculty of / Chemical and Biological Engineering, Department of / Graduate

The advancements of AI methodologies and computing power enables automation and propels the Industry 4.0 phenomenon. Information and data are digitized more than ever, millions of documents are being processed every day, they are fueled by the growth in institutions, organizations, and their supply chains. Processing documents is a time consuming laborious task. Therefore automating data processing is a highly important task for optimizing supply chains efficiency across all industries. Document analysis for data extraction is an impactful field, this thesis aims to achieve the vital steps in an ideal data extraction pipeline. Data is often stored in tables since it is a structured formats and the user can easily associate values and attributes. Tables can contain vital information from specifications, dimensions, cost etc. Therefore focusing on table analysis and recognition in documents is a cornerstone to data extraction. This thesis applies deep learning methodologies for automating the two main problems within table analysis for data extraction; table detection and table structure detection. Table detection is identifying and localizing the boundaries of the table. The output of the table detection model will be inputted into the table structure detection model for structure format analysis. Therefore the output of the table detection model must have high localization performance otherwise it would affect the rest of the data extraction pipeline. Our table detection improves bounding box localization performance by incorporating a Kullback–Leibler loss function that calculates the divergence between the probabilistic distribution between ground truth and predicted bounding boxes. As well as adding a voting procedure into the non-maximum suppression step to produce better localized merged bounding box proposals. This model improved precision of tabular detection by 1.2% while achieving the same recall as other state-of-the-art models on the public ICDAR2013 dataset. While also achieving state-of-the-art results of 99.8% precision on the ICDAR2017 dataset. Furthermore, our model showed huge improvements espcially at higher intersection over union (IoU) thresholds; at 95% IoU an improvement of 10.9% can be seen for ICDAR2013 dataset and an improvement of 8.4% can be seen for ICDAR2017 dataset. Table structure detection is recognizing the internal layout of a table. Often times researchers approach this through detecting the rows and columns. However, in order for correct mapping of each individual cell data location in the semantic extraction step the rows and columns would have to be combined and form a matrix, this introduces additional degrees of error. Alternatively we propose a model that directly detects each individual cell. Our model is an ensemble of state-of-the-art models; Hybird Task Cascade as the detector and dual ResNeXt101 backbones arranged in a CBNet architecture. There is a lack of quality labeled data for table cell structure detection, therefore we hand labeled the ICDAR2013 dataset, and we wish to establish a strong baseline for this dataset. Our model was compared with other state-of-the-art models that excelled at table or table structure detection. Our model yielded a precision of 89.2% and recall of 98.7% on the ICDAR2013 cell structure dataset.

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No / The complexity of automotive systems has increased dramatically, driven by the requirement to address environmental and safety concerns and the pressure to offer higher level consumer technologies. This places a great challenge on product development organizations to manage the multidisciplinary systems integration in a reliable and robust manner. Engineering changes, which are integral part of the iterative automotive product development process, need to be managed in a way that efficiently addresses the integration requirements of complex multidisciplinary systems. The aim of this paper is to present a structured approach for engineering change management which is based on an enhanced interface analysis method which aims to identify comprehensively the system integration functional requirements as the basis for both engineering change prediction and support of robust engineering change design. The framework will be illustrated with an industrial case study on the development of an electric vehicle powertrain. The effectiveness of the proposed approach will be discussed in contrast with other methods for engineering change management.