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This study aims to use resonant surface acoustic wave (SAW) sensors, which have advantages in the harsh application environments, to measure different physical parameters such as temperature, pressure and force. For SAW sensors, the locality in measurement resolution by the effective time is poor, it cannot give the detailed results of SAW echoes.

To promote the application of SAW sensor, this paper proposes a convex program-based super-resolution measurement method to recover the missing spectral line and enhance frequency resolution.

The proposed method reduces the reliance on effective time and improves the measurement resolution of SAW sensors. The performance was validated by experiments.

The limited resolution capability restricts the benefit of SAW technology in harsh environments. The proposed method shed light on SAW measurement resolution increase, exploiting its full potential and leading to commercial applications.

This paper aims to propose a new solution for real-time 3D perception with monocular camera. Most of the industrial robots’ solutions use active sensors to acquire 3D structure information, which limit their applications to indoor scenarios. By only using monocular camera, some state of art method provides up-to-scale 3D structure information, but scale information of corresponding objects is uncertain.

First, high-accuracy and scale-informed camera pose and sparse 3D map are provided by leveraging ORB-SLAM and marker. Second, for each frame captured by a camera, a specially designed depth estimation pipeline is used to compute corresponding 3D structure called depth map in real-time. Finally, depth map is integrated into volumetric scene model. A feedback module has been designed for users to visualize intermediate scene surface in real-time.

The system provides more robust tracking performance and compelling results. The implementation runs near 25 Hz on mainstream laptop based on parallel computation technique.

A new solution for 3D perception is using monocular camera by leveraging ORB-SLAM systems. Results in our system are visually comparable to active sensor systems such as elastic fusion in small scenes. The system is also both efficient and easy to implement, and algorithms and specific configurations involved are introduced in detail.

The purpose of this paper is to establish a version of a theorem that originated from population genetics and has been later adopted in evolutionary computation theory that will lead to novel Monte‐Carlo sampling algorithms that provably increase the AI potential.

In the current paper the authors set up a mathematical framework, state and prove a version of a Geiringer‐like theorem that is very well‐suited for the development of Mote‐Carlo sampling algorithms to cope with randomness and incomplete information to make decisions.

This work establishes an important theoretical link between classical population genetics, evolutionary computation theory and model free reinforcement learning methodology. Not only may the theory explain the success of the currently existing Monte‐Carlo tree sampling methodology, but it also leads to the development of novel Monte‐Carlo sampling techniques guided by rigorous mathematical foundation.

The theoretical foundations established in the current work provide guidance for the design of powerful Monte‐Carlo sampling algorithms in model free reinforcement learning, to tackle numerous problems in computational intelligence.

Establishing a Geiringer‐like theorem with non‐homologous recombination was a long‐standing open problem in evolutionary computation theory. Apart from overcoming this challenge, in a mathematically elegant fashion and establishing a rather general and powerful version of the theorem, this work leads directly to the development of novel provably powerful algorithms for decision making in the environment involving randomness, hidden or incomplete information.

The inverse problem in the eddy current (EC) imaging of metallic parts is an ill‐posed problem. The purpose of the paper is to compare the performances of regularized algorithms to estimate the 3D geometry of a surface breaking defect.

The forward problem is solved using a mesh‐free semi‐analytical model, the distributed point source method, which allows EC data to be simulated according to the shape of the considered defect. The inverse problem is solved using two regularization methods, namely the Tikhonov (l2) and the 3D total variation (tv) methods, implemented with first‐ and second‐order algorithms. The inversion performances were evaluated in terms of both mean square error (MSE) and computation time, while considering additive white and colored noise, respectively, standing for acquisition errors and model errors.

In presence of colored noise, the authors found out that first‐ and second‐order methods provide approximately the same result according to the SEs obtained while estimating the defect voxels. Nevertheless, in comparison with (l2), the (tv) regularization was proved to decrease the MSE by 10 voxels, at the cost of less than twice the computational effort.

In this paper, an easy to implement mesh‐free model, based on virtual defect current sources, was used to generated EC data relative to a defect positioned at the surface of a metallic part. A 3D total variation regularization approach was used in combination with the proposed model, which appears to be well suited to the reconstruction of volumic defects.

Visual quality control on raw textile fabrics is a vital process in weaving factories to ensure their exterior quality (visual defects or imperfection) satisfying customer requirements. Commonly, this critical process is manually conducted by human inspectors, which can hardly provide a fast and reliable inspection results due to fatigue and subjective errors. To meet modern production needs, it is highly demanded to develop an automated defect inspection system by replacing human eyes with computer vision.

As a structural texture, fabric textures can be effectively represented by a linearly summation of basic elements (dictionary). To create a robust representation of a fabric texture in an unsupervised manner, a smooth constraint is imposed on dictionary learning model. Such representation is robust to defects when using it to recover a defective image. Thus an abnormal map (likelihood of defective regions) can be computed by measuring similarity between recovered version and itself. Finally, the total variation (TV) based model is built to segment defects on the abnormal map.

Different from traditional dictionary learning method, a smooth constraint is introduced in dictionary learning that not only able to create a robust representation for fabric textures but also avoid the selection of dictionary size. In addition, a TV based model is designed according to defects' characteristics. The experimental results demonstrate that (1) the dictionary with smooth constraint can generate a more robust representation of fabric textures compared to traditional dictionary; (2) the TV based model can achieve a robust and good segmentation result.

The major originality of the proposed method are: (1) Dictionary size can be set as a constant instead of selecting it empirically; (2) The total variation based model is built, which can enhance less salient defects, improving segmentation performance significantly.

Magnetic induction tomography (MIT) is a tomographic imaging technique with a wide range of potential industrial applications. Planar array MIT is a convenient setup but unable to access freely from the entire periphery as it only collects measurements from one surface, so it remains challenging given the limited data. This study aims to assess the use of sparse regularization methods for accurate position and depth detection in planar array MIT.

The most difficult challenges in MIT are to solve the inverse and forward problems. The inversion of planar MIT is severely ill-posed due to limited access data. Thus, this paper posed a total variation (TV) problem and solved it efficiently with the Split Bregman formulation to overcome this difficulty. Both isotropic and anisotropic TV formulations are compared to Tikhonov regularization with experimental MIT data.

The results show that Tikhonov method failed or underestimated the object position and depth. Both isotropic and anisotropic TV led to accurate recovery of depth and position.

There are numerous potential applications for planar array MIT where access to the materials under testing is restrict. Sparse regularization methods are a promising approach to improving depth detection for limited MIT data.

The purpose of the study was to apply the theory of planned behavior to predict Danish adolescents’ behavioral intention for healthy eating.

A cluster sample survey of 410 students aged 11 to 16 years studying in Grade 6 to Grade 10 was conducted in Denmark.

Perceived behavioral control followed by attitudes were the most important factors in predicting behavioral intention. Females and adolescents with a higher Body Mass Index were also found to have a stronger behavioral intention. Healthy eating was perceived to be beneficial and useful, and, to a lesser extent, interesting and desirable. Family, TV programs, and teachers were influential socialization agents.

The survey responses may be affected by a social desirability bias. The survey includes a non‐probability sample and results may not be generalized to all adolescents, even in Denmark.

The results may inform educators and policy makers in designing health communication interventions, particularly in making socializing agents aware of their role in fostering healthy eating behaviors in adolescents. As perceived behavioral control was the strongest predictor of behavioral intention, interventions and messages communicated to adolescents on healthy eating should aim to empower them with knowledge, ability and determination to eat more healthily.

The study uses a predictive, theoretical framework (TPB) to investigate healthy eating, whereas previous efforts among Danish adolescents have primarily used descriptive approaches.

The traditional total variation (TV) models in wavelet domain use thresholding directly in coefficients selection and show that Gibbs' phenomenon exists. However, the nonzero coefficient index set selected by hard thresholding techniques may not be the best choice to obtain the least oscillatory reconstructions near edges. This paper aims to propose an image denoising method based on TV and grey theory in the wavelet domain to solve the defect of traditional methods.

In this paper, the authors divide wavelet into two parts: low frequency area and high frequency area; in different areas different methods are used. They apply grey theory in wavelet coefficient selection. The new algorithm gives a new method of wavelet coefficient selection, solves the nonzero coefficients sort, and achieves a good image denoising result while reducing the phenomenon of “Gibbs.”

The results show that the method proposed in this paper can distinguish between the information of image and noise accurately and also reduce the Gibbs artifacts. From the comparisons, the model proposed preserves the important information of the image very well and shows very good performance.

The proposed image denoising model introducing grey relation analysis in the wavelet coefficients selecting and modifying is original. The proposed model provides a viable tool to engineers for processing the image.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

The purpose of this paper is to explore the effect of organizational psychological ownership (OPO) and organization-based self-esteem (OBSE) on positive organizational behaviors (POBs).

Based on empirical survey, 2,566 employees from 45 production enterprises in China were surveyed by a self-designed questionnaire on OPO, OBSE and POB. Then, the methods of correlation analysis, multiple regressions, impact effect and path analysis were used to verify the research hypothesis.

The results showed that POB is positively related to OPO and OBSE, and that OPO and OBSE are positive predictors of POBs. The results also demonstrated that OBSE has partial mediating effects on OPO and POB. In particular, psychological ownership has a significant impact on each sub-factor of POB, while OBSE has a remarkable effect on the behavior of devotion and interpersonal harmony.

This is a non-experimental field study and as such inferences about causality are limited, and there is a possibility that the results may be influenced by common method variance.

The findings of the present study reveal that to strengthen employees’ POBs, manager should enhance employees’ OPO and OBSE, and therefore the organizational performance and the individual efficacy will be improved.

This is the first research which studies the relationship among OPO, POB and OBSE under the background of China.