Big data and machine learning: A roadmap towards smart plants

Industry 4.0 aims to transform chemical and biochemical processes into intelligent systems via the integration of digital components with the actual physical units involved. This process can be thought of as addition of a central nervous system with a sensing and control monitoring of components and regulating the performance of the individual physical assets (processes, units, etc.) involved. Established technologies central to the digital integrating components are smart sensing, mobile communication, Internet of Things, modelling and simulation, advanced data processing, storage and analysis, advanced process control, artificial intelligence and machine learning, cloud computing, and virtual and augmented reality. An essential element to this transformation is the exploitation of large amounts of historical process data and large volumes of data generated in real-time by smart sensors widely used in industry. Exploitation of the information contained in these data requires the use of advanced machine learning and artificial intelligence technologies integrated with more traditional modelling techniques. The purpose of this paper is twofold: a) to present the state-of-the-art of the aforementioned technologies, and b) to present a strategic plan for their integration toward the goal of an autonomous smart plant capable of self-adaption and self-regulation for short- and long-term production management.     

Fuzzy cell Hough transform for curve detection

In this paper a new variation of Hough Transform is proposed. It can be used to detect shapes or contours in an image, with better accuracy, especially in noisy images. The parameter space of Hough Transform is split into fuzzy cells which are defined as fuzzy numbers. This fuzzy split provides the advantage to use the uncertainty of the contour point location which is increased when noisy images are used. By using fuzzy cells, each contour point in the spatial domain contributes in more than one fuzzy cell in the parameter space. The array that is created after the fuzzy voting process is smoother than in the crisp case and the effect of noise is reduced. The curves can now be detected with better accuracy. The computation time that is slightly increased by this method, can be minimized in comparison with classical Hough Transform, by using recursively the fuzzy voting process in a roughly split parameter space, to create a multiresolution fuzzily split parameter space.     

On the evaluation of illumination compensation algorithms

This paper presents a comparison framework for algorithms that can diminish the effects of illumination in images. Its main objective is to reveal the positive and negative characteristics of such algorithms, allowing researchers to select the most appropriate one for their target application. The proposed framework utilizes artificial illumination degradations on real images, which are then processed by the tested algorithms. The results are evaluated by an ensemble of performance metrics, highlighting the various characteristics of the algorithms across a range of different image attributes. The proposed framework represents a useful tool for the selection of illumination compensation algorithms due to a) its quantitative nature, b) its multifaceted analysis and c) its easy reproducibility. The validity of the proposed framework is tested by applying it to the enhancement results of four illumination compensation algorithms, which are used as preprocessing in two classic computer vision applications. The improvements brought about by the algorithms are in accordance with the predictions of the proposed framework.     

An optimal feedback control framework for grasping objects with position uncertainty

As we move, the relative location between our hands and objects changes in uncertain ways due to noisy motor commands and imprecise and ambiguous sensory information. The impressive capabilities humans display for interacting and manipulating objects with position uncertainty suggest that our brain maintains representations of location uncertainty and builds compensation for uncertainty into its motor control strategies. Our previous work demonstrated that specific control strategies are used to compensate for location uncertainty. However, it is an open question whether compensation for position uncertainty in grasping is consistent with the stochastic optimal feedback control, mainly due to the difficulty of modeling natural tasks within this framework. In this study, we develop a stochastic optimal feedback control model to evaluate the optimality of human grasping strategies. We investigate the properties of the model through a series of simulation experiments and show that it explains key aspects of previously observed compensation strategies. It also provides a basis for individual differences in terms of differential control costs-the controller compensates only to the extent that performance benefits in terms of making stable grasps outweigh the additional control costs of compensation. These results suggest that stochastic optimal feedback control can be used to understand uncertainty compensation in complex natural tasks like grasping.     

Toward identifying behavioral risk markers for mental health disorders: an assistive system for monitoring children’s movements in a preschool classroom

Mental health disorders are a leading cause of disability in North America. An important aspect in treating mental disorders is early intervention, which dramatically increases the probability of positive outcomes; however, early intervention hinges upon knowledge and detection of risk markers for particular disorders. Ideally, the screening of these risk markers should occur in a community setting, but this is time-consuming and resource-intensive. Assistive systems could greatly aid in the detection of risk markers in a hectic environment like a preschool classroom. This paper presents a multi-sensor system consisting of 5 RGB-D sensors that detects and tracks the location of occupants in a preschool classroom and computes a measure of activity level and proximity between individuals, an index of social functioning. This assistive system operates in near real-time and is able to track occupants and deal with difficult situations both with occupants (children sitting and laying on the ground, hugging, playing dress-up, etc) and their environment (i.e., changing light levels from artificial and natural sources). The system is installed at, and validated on recordings taken from, the Shirley G. Moore Lab School, a research preschool classroom at the University of Minnesota. The work described herein provides the initial groundwork for monitoring basic elements of child behavior; future efforts will be geared toward identifying and tracking more sophisticated behavioral signatures relevant to mental health.     

Aquafaba from commercially canned chickpeas as potential egg replacer for the development of vegan mayonnaise: recipe optimisation and storage stability

Aquafaba, the viscous liquid recovered from canned chickpeas, was used as egg replacer for the development of vegan mayonnaise. The textural, microstructural and physicochemical properties of mayonnaise were determined during cold storage to optimise the aquafaba-to-oil ratio (A/O) of the formulation (15%–25%/80%–70%). Aquafaba was capable to form a stable emulsion with an average value of droplet size distribution below 4 μm. The physical stability determined by the Turbiscan Stability Index (TSI) was unaffected by the A/O ratio during 21 days of storage at 4 °C. The lowest droplet size distribution was obtained for samples with a low A/O ratio (15/80%). Firmness, adhesive force and adhesiveness decreased (P < 0.05) with increasing the A/O ratio, whereas consistency remained unaffected. The oxidative stability of the oil phase was similar for all formulations and remained unaffected during storage. Aquafaba can be used effectively to replace egg in mayonnaise formulations containing oil at standard levels.     

Quality of service differentiation in heterogeneous CDMA networks: a mathematical modelling approach

Next-generation cellular networks are expected to enable the coexistence of macro and small cells, and to support differentiated quality-of-service (QoS) of mobile applications. Under such conditions in the cell, due to a wide range of supported services and high dependencies on efficient vertical and horizontal handovers, appropriate management of handover traffic is very crucial. Furthermore, new emerging technologies, such as cloud radio access networks (C-RAN) and self-organizing networks (SON), provide good implementation and deployment opportunities for novel functions and services. We design a multi-threshold teletraffic model for heterogeneous code division multiple access (CDMA) networks that enable QoS differentiation of handover traffic when elastic and adaptive services are present. Facilitated by this model, it is possible to calculate important performance metrics for handover and new calls, such as call blocking probabilities, throughput, and radio resource utilization. This can be achieved by modelling the cellular CDMA system as a continuous-time Markov chain. After that, the determination of state probabilities in the cellular system can be performed via a recursive and efficient formula. We present the applicability framework for our proposed approach, that takes into account advances in C-RAN and SON technologies. We also evaluate the accuracy of our model using simulations and find it very satisfactory. Furthermore, experiments on commodity hardware show algorithm running times in the order of few hundreds of milliseconds, which makes it highly applicable for accurate cellular network dimensioning and radio resource management.     

Electrodynamic multiple-scattering method for the simulation of optical trapping atop periodic metamaterials

We present a new theoretical tool for simulating optical trapping of nanoparticles in the presence of an arbitrary metamaterial design. The method is based on rigorously solving Maxwell’s equations for the metamaterial via a hybrid discrete-dipole approximation/multiple-scattering technique and direct calculation of the optical force exerted on the nanoparticle by means of the Maxwell stress tensor. We apply the method to the case of a spherical polystyrene probe trapped within the optical landscape created by illuminating of a plasmonic metamaterial consisting of periodically arranged tapered metallic nanopyramids. The developed technique is ideally suited for general optomechanical calculations involving metamaterial designs and can compete with purely numerical methods such as finite-difference or finite-element schemes.     

Dual ramptime nonlinear current control applied to a single phase five level cascaded inverter

This paper reports on the application of a zero average current error (ZACE) control method in single-phase five level cascaded inverter based systems. The dual ramptime method and its adaptation to the five level cascaded converter are described in detail. Recommendations on the choice of control parameters and how to improve noise immunity are included. Experimental results demonstrate the suitability of the control method, since the lower order harmonics are negligible and the ripple current frequency is kept within a narrow band.