Automatic Shrinkage Research Articles

EFNN-NullUni: An evolving fuzzy neural network based on null-uninorm

Interpretability in intelligent models becomes a challenge in academic research and approaches that facilitate understanding the responses obtained in models based on artificial intelligence and machine learning. This paper presents a new logical fuzzy neuron based on the concept of null-uninorm, thus called null-unineuron to compose the architecture of an evolving neuro-fuzzy model. This new structural neuron can extract advanced fuzzy rules allowing AND and OR-connections of antecedents to better interpret and understand the analyzed problem. This three-layer model uses an evolving weighted fuzzification approach based on incremental data partitioning concepts for knowledge extraction through null-unineurons whose training procedure suits the classification of binary and multiclass patterns in an online and incremental way. The weights integrated in the evolving data partitioning algorithm belong to feature importance levels and achieve an automatic shrinkage of distance calculations along unimportant input directions (features), which in turn accounts for a soft dimension reduction and the likelihood to decrease over-fitting. The new architecture proposed in this model was, subject to pattern classification tests, being more efficient compared to related (evolving) neuro-fuzzy models in the literature. Finally, experiments on various real-world data sets proved that the evolving neuro-fuzzy model proposed in this paper can act in a simplified way in the extraction of knowledge from data while providing answers with a high degree of accuracy for pattern classification problems.

Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso

Water demand is a highly important variable for operational control and decision making. Hence, the development of accurate forecasts is a valuable field of research to further improve the efficiency of water utilities. Focusing on probabilistic multi-step-ahead forecasting, a time series model is introduced, to capture typical autoregressive, calendar and seasonal effects, to account for time-varying variance, and to quantify the uncertainty and path-dependency of the water demand process. To deal with the high complexity of the water demand process a high-dimensional feature space is applied, which is efficiently tuned by an automatic shrinkage and selection operator (lasso). It allows to obtain an accurate, simple interpretable and fast computable forecasting model, which is well suited for real-time applications. The complete probabilistic forecasting framework allows not only for simulating the mean and the marginal properties, but also the correlation structure between hours within the forecasting horizon. For practitioners, complete probabilistic multi-step-ahead forecasts are of considerable relevance as they provide additional information about the expected aggregated or cumulative water demand, so that a statement can be made about the probability with which a water storage capacity can guarantee the supply over a certain period of time. This information allows to better control storage capacities and to better ensure the smooth operation of pumps. To appropriately evaluate the forecasting performance of the considered models, the energy score (ES) as a strictly proper multidimensional evaluation criterion, is introduced. The methodology is applied to the hourly water demand data of a German water supplier.

Development of High-Acceleration and Ultra-Precision Linear Motor Driven Machining Center and its Characteristics

We have developed machining centers, equipped with damping mechanism, for precision molds, which can not only suppress vibrations during high-acceleration driving of the machine but also perform high-precision and high-efficiency machining in micro regions. We have also achieved automatic exchanges of tools by employing ultrafast rotating main shafts with air hydrostatic bearings and automatic shrinkage fitting methods, as well as we have realized automated operations of the machine by integrating work with tool-measuring system.

Shrinkage movement analysis of reinforced concrete floors constructed in stages

Reinforced concrete floors constructed between movement restraints often crack seriously due to shrinkage after completion. One common mitigation measure is to construct the concrete floors in stages to allow part of the shrinkage movement to take place before completion. However, shrinkage movement analysis of concrete floors constructed in stages is quite cumbersome, as the structural configuration changes during construction, thus necessitating reanalysis of the partially completed structure at each stage. Herein, a finite element method for shrinkage movement analysis of concrete floors constructed in stages is developed. It analyses the whole structure, including the completed and uncompleted portions, at all stages. The same mesh is used all the time and therefore re-meshing and location matching are no longer necessary. This is achieved by giving negligibly small stiffness to the uncompleted portions, which in reality do not exist yet. In the analysis, the locked-in strains due to increase in elastic modulus as the concrete hardens and the creep of the hardened concrete are taken into account. Most important of all, this method would enable fully automatic shrinkage movement analysis for the purpose of construction control.

Automatic measurement of shrinkage during sintering

1. Universal gauges have been developed for the continuous, automatic monitoring and recording of the shrinkage of metal powder parts during sintering. The gauges can be used in conjunction with furnaces of all types, at temperatures up to 3000°C, with an error of not more than 2%. 2. Automatic shrinkage determination makes it possible to conduct thorough investigations into the sintering kinetics of various materials with the aim of establishing the laws governing sintering processes. 3. Using data yielded by automatic shrinkage measurements, it is possible to determine the density of metal powder parts during sintering when the initial compact density γ0 and an experimental coefficient K are known.