Manufacturers are faced with many challenges, such as globalization, disparate enterprise systems and a lack of platform maturity. These will only multiply as technology advances, offshore manufacturing increases and new ways of working emerge. To overcome these challenges, many manufacturers have embraced product lifecycle management (PLM) or have expanded the use of PLM processes and tools to manage product data effi ciently, with increased visibility and control over the life cycle of the product.

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Product lifecycle management

Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition

An Adaptive Polyploid Memetic Algorithm for scheduling trucks at a cross-docking terminal

Soil moisture forecasts are vital for environmental monitoring, the health of ecological systems, hydrology, agriculture and understanding the soil characteristics. In this study, we design a new multivariate sequential predictive model that utilizes the ensemble empirical mode decomposition (EEMD) algorithm hybridized with extreme learning machines (ELM) to forecast soil moisture (SM) over weekly horizons. The EEMD data pre-processing utility is a self-adaptive tool, which does not require a predefined basis function and avoids frequency-mode mixing issues. The proposed multivariate sequential EEMD model is designed to sequentially demarcate model predictor variables and the target (SM) into analogous intrinsic mode functions (IMFs) and a residue component using the EEMD process, to address the complexities and associated non-linearities in hydrologic-based inputs. To validate the new approach, four diversely characterized sites in Australia's Murray-Darling Basin are purposely selected where 13 weekly hind-casted predictors are collated from the Australian Water Availability Project WaterDyn physical model. After the sequential EEMD transformation process, a two-stage feature selection employing cross-correlation and random forest based Boruta wrapper algorithm is adopted to extract pertinent features from the hydro-meteorological predictor series to construct a hybridized multivariate sequential EEMD-Boruta-ELM model. Comprehensive model evaluation using statistical metrics and diagnostic plots against alternative methods: hybrid multivariate adaptive regression splines (MARS) (EEMD-Boruta-MARS) and classical MARS and ELM, establish the superiority of hybrid EEMD-Boruta-ELM model, yielding relatively low errors and high performance. The study ascertains that the EEMD-Boruta-ELM hybrid model can be explored as a pertinent data-driven tool for relatively short-term soil moisture forecasts, thus advocating its practical use in near real-time hydrological and pedological applications.

Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach

Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms

A Data Mining System for Real Time Soil Moisture Prediction

A two-level solution approach for solving the generalized minimum spanning tree problem

In the context of technical and economic challenges, the last decade stands out through a continuous preoccupation to develop integrated design models, based on ontological structures of information submission. Tracking the life cycle of a product by using PLM systems requires the analysis and integration of knowledge resulted from product development ontology creation and use.

Broadening the Use of Product Development Ontology for One-Off Products

Predictions in data mining are a difficult process but useful in various areas. The purpose of this article is to make a parallel between the classical data mining process and two new approaches in the process of data mining: collaborative data mining and context-aware data mining. Data gathered from seven meteorological stations in Transylvania served as baseline for the research. Processes for predicting the air humidity were designed and analyzed using the same machine learning algorithms and data. The results obtained prove that collaborative and context-aware data mining approaches bring better results than the standalone approach and highlight some of the algorithms that are more suitable for each approach. The combination of the two notions could be another example of a successful approach for future research.

Performance Analysis of Collaborative Data Mining vs Context Aware Data Mining in a Practical Scenario for Predicting Air Humidity

Within the development of products under PLM applications, ontology holds an important role. Ontology with its instances constitutes a knowledge base. In the development applications of the product, the ontology-based approaches for machine and equipment manufacturing are very few, predominant being the approaches in the field of IT. The experience of a designer in machine manufacturing can be structured in an ontology and its instances, becoming a knowledge base, which can be reused by another designer, who has never designed such a product. On the other hand, the design duration are part of the manufacturing cycle of the product. We propose that through ontology we should be able to specify the sub-assemblies in such a way that the duration of acquisition (contracting-manufacturing-delivery) overlaps the cycle of designing and manufacturing of the product which uses them. We’re trying to demonstrate that that the manufacturing cycle can be shortened and that a family of products can be developed based on this, by developing an ontological application, which combines ontologies in the field of engineering, all these by using Protege.

Adrian Petrovan

Papers

A Data Mining System for Real Time Soil Moisture Prediction

A two-level solution approach for solving the generalized minimum spanning tree problem

Broadening the Use of Product Development Ontology for One-Off Products

Performance Analysis of Collaborative Data Mining vs Context Aware Data Mining in a Practical Scenario for Predicting Air Humidity

Overview on Equipment Development Ontology