Digital twin-based analysis of volumetric error mapping procedures

The evaluation of the volumetric accuracy of a machine tool is an open challenge in the industry, and a wide variety of technical solutions are available in the market and at research level. All solutions have advantages and disadvantages concerning which errors can be measured, the achievable uncertainty, the ease of implementation, possibility of machine integration and automation, the equipment cost and the machine occupation time, and it is not always straightforward which option to choose for each application. The need to ensure accuracy during the whole lifetime of the machine and the availability of monitoring systems developed following the Industry 4.0 trend are pushing the development of measurement systems that can be integrated in the machine to perform semi-automatic verification procedures that can be performed frequently by the machine user to monitor the condition of the machine. Calibrated artefact based calibration and verification solutions have an advantage in this field over laser based solutions in terms of cost and feasibility of machine integration, but they need to be optimized for each machine and customer requirements to achieve the required calibration uncertainty and minimize machine occupation time.This paper introduces a digital twin-based methodology to simulate all relevant effects in an artefact-based machine tool calibration procedure, from the machine itself with its expected error ranges, to the artefact geometry and uncertainty, artefact positions in the workspace, probe uncertainty, compensation model, etc. By parameterizing all relevant variables in the design of the calibration procedure, this simulation methodology can be used to analyse the effect of each design variable on the error mapping uncertainty, which is of great help in adapting the procedure to each specific machine and user requirements. The simulation methodology and the analysis possibilities are illustrated by applying it on a 3-axis milling machine tool.     

Development of high pressure membraneless alkaline electrolyzer

A technology for cyclic generation of hydrogen and oxygen using electrodes made of variable valency material that does not need the use of separating ion-exchange membranes is presented. The technological solution enables to fabricate electrolyzers for uninterrupted producing high-pressure hydrogen with reduced energy intensity of the production. The total work for compressing 1 m³ of hydrogen and 0.5 m³ of oxygen has been estimated. Results of investigation of influence of discrete supply of DC current to the electrolysis cell, in order to improve the processes of gas evolution and to simplify the power systems of the electrolysis plant, have been considered. There is also considered an electrolysis installation equipped with a thermosorption compressor in which LaNi₅ is used as a hydride-forming compound. The comparative characteristics of the developed electrolyzer and the currently used hydrogen generators are given.     

Noncentrosymmetric Tetrel Pnictides RuSi4P4 and IrSi3P3: Nonlinear Optical Materials with Outstanding Laser Damage Threshold

Noncentrosymmetric (NCS) tetrel pnictides have recently generated interest as nonlinear optical (NLO) materials due to their second harmonic generation (SHG) activity and large laser damage threshold (LDT). Herein nonmetal-rich silicon phosphides RuSi₄P₄ and IrSi₃P₃ are synthesized and characterized. Their crystal structures are reinvestigated using single crystal X-ray diffraction and ²⁹Si and ³¹P magic angle spinning NMR. In agreement with previous report RuSi₄P₄ crystallizes in NCS space group P1, while IrSi₃P₃ is found to crystallize in NCS space group Cm, in contrast with the previously reported space group C2. A combination of DFT calculations and diffuse reflectance measurements reveals RuSi₄P₄ and IrSi₃P₃ to be wide bandgap (E_g) semiconductors, E_g = 1.9 and 1.8 eV, respectively. RuSi₄P₄ and IrSi₃P₃ outperform the current state-of-the-art infrared SHG material, AgGaS₂, both in SHG activity and laser inducer damage threshold. Due to the combination of high thermal stabilities (up to 1373 K), wide bandgaps (≈2 eV), NCS crystal structures, strong SHG responses, and large LDT values, RuSi₄P₄ and IrSi₃P₃ are promising candidates for longer wavelength NLO materials.     

人工智能在智慧海洋建设中的应用

围绕人工智能与智慧海洋建设这条主线，论述人工智能、智慧海洋的概念，列举当前较为成熟的人工智能与海洋科技在海洋观测方面融合的切入点，初步展现一种海洋技术与装备智能化的发展路径，提出加快人工智能技术向智慧海洋建设赋能的几点建议。     

Context-free grammars to detect straight segments and a novel polygonal approximation method

A new method for the polygonal approximation is presented. The method is based on the search for break points through a context-free grammar, that accepts digital straight segments with loss of information, as well as the decrease in the error committed employing the comparison of a tolerable error. We present an application of our method to different sets of objects widely used, as well as a comparison of our results with the best results reported in the literature, proving that our method achieves better values of error criteria. Besides, a new way to find polygonal approximations, with context-free grammars to recognize digital straight segments without loss of pixels, it is also addressed.     

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

A PV installation framework concerning electricity variable rates

This paper assesses building integrated photovoltaic (BIPV) installation parameters based on the profit generated by a photovoltaic system. It takes into consideration a home building case study and it investigates its monthly energy demand based on a specific location and a typical occupancy. The capability of a photovoltaic (PV) system to generate more profit occurs when solar intensity is maximum while the electric energy price is at its highest rate. The paper traces a framework that encompasses different aspects such as energy demand, energy price, and solar intensity. This framework identifies profit alternatives according to different installation parameters. A tool that predicts a PV installation hourly electric energy production is developed. The profit generated is simulated for home buildings located in Beirut (Lebanon) and Xihua (China), both at 33.8° latitude north. The paper highlights a new approach for BIPV installations, taking into account weather conditions, energy demand, and electric energy utility rates.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Automatic generation of variants depending on changes of product properties in a flexible manufacturing environment

Competition forces manufacturing systems to be flexible and to increase product variety and process complexity. These tasks depend on the flexible design of a bill of materials (BOM), one of the most important inputs in manufacturing planning and control systems. Product variety forces systems to generate BOMs with regard to product properties through a BOM pattern. A variant bill of materials provides a structure to manage product variability. In this study, an algorithm is designed to build a BOM pattern using computer-aided design and computer-aided manufacturing (CAD/CAM) data, and another algorithm is designed to generate variants with regard to product specifications. Genetic algorithm is used to generate new products to provide high product variability for testing algorithms. After the test, both algorithms are applied to a real industry problem. The BOM pattern is built automatically using CAD/CAM data, and variants are generated with regard to the pattern, and the results are discussed.