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This paper presents an original approach for learning models, partially known, of particular interest when performing source identification or structural health monitoring. The proposed procedures employ some amount of knowledge on the system under scrutiny as well as a limited amount of data efficiently assimilated.

Two different formulations are explored. The first, based on the use of informed neural networks, leverages data collected at specific locations and times to determine the unknown source term of a parabolic partial differential equation. The second procedure, more challenging, involves learning the unknown model from a single measured field history, enabling the localization of a region where material properties differ.

Both procedures assume some kind of sparsity, either in the source distribution or in the region where physical properties differ. This paper proposed two different neural approaches able to learn models in order to perform efficient inverse analyses.

Two original methodologies are explored to identify hidden property that can be recovered with the right usage of data. Both methodologies are based on neural network architecture.

The development of social networks enhances the interaction between people, which brings new challenges to the research of group decision-making (GDM). This study aims at the problem that the synergy and redundancy due to interaction among decision-makers are ignored in the previous GDM, a trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences is proposed.

Firstly, confidence level is introduced to improve the hesitation phenomenon that should be considered when calculating trust degree; Secondly, a new trust propagation operator is developed to deal with indirect trust relationships; Thirdly, trust degree is transformed into interaction index to quantify the synergy and redundancy in decision-making. Fuzzy capacities of decision-makers are used to replace traditional weights, and the final scores of alternatives are obtained through Choquet integral.

The proposed model using fuzzy capacity can reflect the synergy or redundancy among decision-makers and improve the accuracy of final ranking result and reduce the loss of information.

This study proposes a trust-enhanced consensus reaching model, which develops a new trust propagation operator to ensure the continuous attenuation of trust in propagation process. And the proposed model uses fuzzy capacity to improve the enhancement or attenuation on the scores of alternatives.

The electrical properties of piezoelectric vibrators have a crucial influence on the operating state of ultrasonic motors. In order to solve the problem that the current piezoelectric vibrator generates a large amount of heat during vibration to degrade its performance, which in turn affects the normal operation of ultrasonic motors, this paper prepares a novel piezoelectric vibrator and tests its maximum vibration velocity under the working condition, which is more than twice as much as that of the current commercial PZT-8.

The crystal structures of the samples were analyzed by using an X-ray diffractometer. For microstructure observation, samples were observed by scanning electron microscope (SEM). The quasi-static piezoelectric coefficient meter (ZJ-3AN) was used for piezoelectric measurement. Dielectric properties were measured by utilizing an impedance analyzer (Agilent 4294A) with a laboratory heating unit. Ferroelectric hysteresis loops were obtained using a ferroelectric analyzer (Radiant, Multiferroic 100). A Doppler laser vibrometer (Polytec PSV-300F, Germany) and a power amplifier were used for piezoelectric vibration measurements, during which the temperature rise was determined by an infrared radiation thermometer (Victor 303, China).

The ceramics exhibit enhanced piezoelectric performance at 0.1–0.4 mol% of Yb doping contents. The ceramic of 0.4 mol% Yb reaches the maximal internal bias field and presents a larger mechanical quality factor of 1,692 compared with that of 0.2 mol% Yb-doped ceramic, in spite of a slightly decreased dielectric constant of 439 pC/N, the unit of the piezoelectric constant, which is the ratio of the local charge (pC) to the frontal force (N) and electromechanical coupling coefficient of 0.63. The vibrator with this large mechanical quality factor ceramic displays a vibration velocity of up to 0.81 m/s under the constraint of 20 °C temperature rising, which is much higher than commercial high-power piezoelectric ceramics PZT-8.

The enhanced high-power properties of the piezoelectric vibrator by Yb doping may provide a potential application for the high-performance USM and offer the possibility of long-term stable operation under high power for special equipment like USM. In the subsequent phase of research, the novel PZT-based high-power piezoelectric vibrator can be utilized in the USM, and the motor's performance will be evaluated under aerospace conditions to objectively assess the reliability of the piezoelectric vibrator.

Process capability indices (PCI) are frequently used in order to measure the performance of production processes. In their 2005 article, Castagliola and Castellanos proposed a new approach for the estimation of bivariate PCIs in the case of a bivariate normal distribution and a rectangular tolerance region. This paper proposes extending Castagliola and Garcia‐Castellanos's paper to the estimation of bivariate PCIs in the case of non‐normal bivariate distributions.

The proposed method is based on the use of Johnson's System of distributions/transformations in order to transform the bivariate non normal distribution into an approximate bivariate normal distribution. Numerical examples are presented and some criteria are given in order to choose the appropriate Johnson's distribution.

The proposed method is only dedicated to the case of two quality characteristics and a rectangular tolerance region (the most common case).

The proposed method allows the evaluation of bivariate capability indices irrespective of the distribution of the data and thus allows obtaining more reliable estimates for these values.

The main originality of the method presented in this paper is its ability to compute bivariate capability indices when the distribution of the data is not a bivariate normal distribution, i.e. the general case.

The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution.

In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm.

The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods.

The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts.

A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

This article aims to present a methodology applied to the transition between the “as-is” and “to-be” stages of the Business Process Management (BPM) life cycle, supporting its implementation and maintenance for the organizational stability, using techniques from Operations Research and Information and Decision Theories, applied by a gamified system.

The study used Design Science Research, considering the following methodological elements: (1) artifact model, after initial analysis of the organization; (2) problem relevance, incorporating components to the Markov transition matrix and the integer programming model for resource optimization; (3) model evaluation, establishing mechanisms to validate the methodology created; (4) research contributions, showing benefits found; (5) systematic approach, detailing methods used; (6) model's research process, revealing the means for execution; and (7) final presentation of results.

After planning three scenarios for the company, containing zero, one or two implemented processes, the matrix of states in the Markov chain effectively identified the states of greater and lesser transition uncertainty. At the same time, the optimization model guided the organization toward a stable change in its operational and financial areas.

The company's planning capacity has increased, as its managers now have a methodology to promote rational decisions about the development of plans. Before, managers believed that the methodology used was only for large companies. However, this view changed with the results, showing a structured view of the ability to absorb new customers, relocate established ones, increase the comfort level for employees and increase profitability for the company's business.

The study showed that the combination of techniques opens a new perspective to the incorporation of BPM in organizations, allows a smooth change between the current and future state, making it possible to predict the evolution of transition scenarios.