Statistics Decomposition and Monitoring in Original Variable Space

The traditional process monitoring method first projects the measured process data into the principle component subspace (PCS) and the residual subspace (RS), then calculates $$\mathrm T^2$$ T 2 and $$\mathrm SPE$$ S P E statistics to detect the abnormality. However, the abnormality by these two statistics are detected from the principle components of the process. Principle components actually have no specific physical meaning, and do not contribute directly to identify the fault variable and its root cause. Researchers have proposed many methods to identify the fault variable accurately based on the projection space. The most popular is contribution plot which measures the contribution of each process variable to the principal element (Wang et al. 2017; Luo et al. 2017; Liu and Chen 2014). Moreover, in order to determine the control limits of the two statistics, their probability distributions should be estimated or assumed as specific one. The fault identification by statistics is not intuitive enough to directly reflect the role and trend of each variable when the process changes.

Tree-Structured Regression Model Using a Projection Pursuit Approach

In this paper, we propose a new tree-structured regression modelthe projection pursuit regression tree.a new tree-structured regression model—the projection pursuit regression tree—is proposed. It combines the projection pursuit classification tree with the projection pursuit regression. The main advantage of the projection pursuit regression tree is exploring the independent variable space in each range of the dependent variable. Additionally, it retains the main properties of the projection pursuit classification tree. The projection pursuit regression tree provides several methods of assigning values to the final node, which enhances predictability. It shows better performance than CART in most cases and sometimes beats random forest with a single tree. This development makes it possible to find a better explainable model with reasonable predictability.

Generative models for capturing and exploiting the influence of process conditions on process curves

Variations of dedicated process conditions (such as workpiece and tool properties) yield different process state evolutions, which are reflected by different time series of the observable quantities (process curves). A novel method is presented, which firstly allows to extract the statistical influence of these conditions on the process curves and its representation via generative models, and secondly represents their influence on the ensemble of curves by transformations of the representation space. A latent variable space is derived from sampled process data, which represents the curves with only few features. Generative models are formed based on conditional propability functions estimated in this space. Furthermore, the influence of conditions on the ensemble of process curves is represented by estimated transformations of the feature space, which map the process curve densities with different conditions on each other. The latent space is formed via Multi-Task-Learning of an auto-encoder and condition-detectors. The latter classifies the latent space representations of the process curves into the considered conditions. The Bayes framework and the Multi-task Learning models are used to obtain the process curve probabilty densities from the latent space densities. The methods are shown to reveal and represent the influence of combinations of workpiece and tool properties on resistance spot welding process curves.

Improving Estimates of Natural Resources Using Model-Based Estimators: Impacts of Sample Design, Estimation Technique, and Strengths of Association

Natural resource managers need accurate depictions of existing resources to make informed decisions. The classical approach to describing resources for a given area in a quantitative manner uses probabilistic sampling and design-based inference to estimate population parameters. While probabilistic designs are accepted as being necessary for design-based inference, many recent studies have adopted non-probabilistic designs that do not include elements of random selection or balance and have relied on models to justify inferences. While common, model-based inference alone assumes that a given model accurately depicts the relationship between response and predictors across all populations. Within complex systems, this assumption can be difficult to justify. Alternatively, models can be trained to a given population by adopting design-based principles such as balance and spread. Through simulation, we compare estimates of population totals and pixel-level values using linear and nonlinear model-based estimators for multiple sample designs that balance and spread sample units. The findings indicate that model-based estimators derived from samples spread and balanced across predictor variable space reduce the variability of population and unit-level estimators. Moreover, if samples achieve approximate balance over feature space, then model-based estimates of population totals approached simple expansion-based estimates of totals. Finally, in all comparisons made, improvements in estimation were achieved using model-based estimation over design-based estimation alone. Our simulations suggest that samples drawn from a probabilistic design, that are spread and balanced across predictor variable space, improve estimation accuracy.

AWSMOTE: An SVM-Based Adaptive Weighted SMOTE for Class-Imbalance Learning

In class-imbalance learning, Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to tackle class-imbalance problems from the data level, whereas SMOTE blindly selects neighboring minority class points when performing an interpolation among them and inevitably brings collinearity between the generated new points and the original ones. To combat these problems, we propose in this study an adaptive-weighting SMOTE method, termed as AWSMOTE. AWSMOTE applies two types of SVM-based weights into SMOTE. A kind of weight is used in variable space to combat the drawbacks of collinearity, while another weight is utilized in sample space to purposefully choose those support vectors from the minority class as the neighboring points in the interpolation. AWSMOTE is compared with SMOTE and its improved versions with six simulated datasets and 22 real-world datasets. The results demonstrate the effectiveness and advantages of the proposed approach.

Reversible Pebble Games and the Relation Between Tree-Like and General Resolution Space

We show a new connection between the clause space measure in tree-like resolution and the reversible pebble game on graphs. Using this connection, we provide several formula classes for which there is a logarithmic factor separation between the clause space complexity measure in tree-like and general resolution. We also provide upper bounds for tree-like resolution clause space in terms of general resolution clause and variable space. In particular, we show that for any formula F, its tree-like resolution clause space is upper bounded by space$$(\pi)$$ ( π ) $$(\log({\rm time}(\pi))$$ ( log ( time ( π ) ) , where $$\pi$$ π is any general resolution refutation of F. This holds considering as space$$(\pi)$$ ( π ) the clause space of the refutation as well as considering its variable space. For the concrete case of Tseitin formulas, we are able to improve this bound to the optimal bound space$$(\pi)\log n$$ ( π ) log n , where n is the number of vertices of the corresponding graph

PENGARUH SETTING RUANG TERBUKA TERHADAP SEBARAN TERITORI PKL DI WATERFRONT KOTA PONTIANAK

Pontianak waterfront city merupakan salah satu program yang terus dilakukan oleh pemerintah Kota Pontianak. Salah satu kawasan waterfront yang menarik untuk dikunjungi adalah kawasan Waterfront Seng Hie. Keberadaan waterfront Seng Hie memberikan dampak yang positif membantu meningkatkan citra Kota Pontianak sebagai Kota Tepian air, disisi lain ternyata memberikan dampak negatif, yaitu menjadi magnet kegiatan PKL yang tidak terencana sebelumnya. Kondisi ini jika tidak mendapat perhatian khusus, maka berpotensi munculnya konflik penggunaan ruang antara pengunjung dan para PKL. Penelitian ini bertujuan untuk mengetahui faktor-faktor Setting ruang yang mempengaruhi pola sebaran teritori PKL di Waterfront Kota Pontianak. Secara umum, hasil penelitian ini akan menjadi masukan Pemerintah Kota Pontianak dalam upaya memperbaiki kualitas ruang terbuka di tepian air dan akan bersinergi dengan keberadaan PKL. Metode digunakan dalam penelitian ini adalah pemetaan perilaku, yang akan terkait dengan variabel Setting ruang. Hasil penelitian ini ditemukan faktor utama yang mempengaruhi pola distribusi PKL di Waterfront Kota Pontianak yaitu keberadaan seting Fix di waterfront seperti Pagar, Bangku Taman, Perkerasan Beton yang menjadi media PKL untuk berjualan, yang dibedakan atas lima pola teritori sebaran PKL (1) disekitar bangku taman, 2) di sekitar plaza, 3) di sekitar pagar, 4) di sekitar reling tangga, 5) di sekitar anak tangga. THE EFFECT OF SETTING OPEN SPACE ON THE SPREAD OF PKL TERRITORY IN THE WATERFRONT OF PONTIANAK CITY Pontianak waterfront city is one of the programs that the Pontianak City government continues to carry out. One of the interesting waterfront areas to visit is the Seng Hie Waterfront area. The existence of Seng Hie's waterfront has a positive impact helping to improve the image of Pontianak City as a waterfront city; on the other hand, it has a negative effect, namely becoming a magnet for previously unplanned street vendors activities. If this condition does not get special attention, then the potential for conflict in the use of space between visitors and street vendors. This study aims to determine the spatial setting factors that affect the distribution patterns of street vendors at the Waterfront of Pontianak City. In general, the results of this research will be used as input for the Pontianak City Government to improve the quality of open spaces on the water's edge. They will synergize with the existence of street vendors. The method used in this research is behavior mapping, which will be related to the variable space setting. The results of this study found that the main factors that influence the distribution pattern of street vendors at the Waterfront of Pontianak City are the presence of Fix settings on the waterfront such as fences, park benches, concrete pavers which become the media for street vendors to sell, which are divided into five territorial patterns of street vendors (1) around park benches, 2) around the plaza, 3) around the fence, 4) around the stair rail, 5) around the steps.

Spatially and temporally distributed data foraging decisions in disciplinary field science

How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions.