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Featured Application: Specific application or a potential application of the work includes novel solutions in exoskeletons technology. An aging population, the effects of pandemics and civilization-related conditions, and limited leapfrogging in the number of rehabilitation and physiotherapy specialists are driving demand for modern assistive technologies, especially upper and lower limb exoskeletons. Patient-tailored devices are a rapidly developing group of technologies, both from a biomechanics, informatics, and materials engineering perspective. In particular, the technological development of 3D printing, the expanding range of available materials and their properties (including contact with living tissue and bodily fluids), and the possibility of selecting and optimizing them using artificial intelligence (including machine learning) are encouraging the emergence of new concepts, particularly within the Industry 4.0 paradigm. The article provides an overview of what is available in this area, including an assessment of as yet untapped research and industrial and, in part, clinical potential. [ABSTRACT FROM AUTHOR]

Featured Application: Potential applications of the findings presented in this article include low-cost, fast, and simple gait analysis systems for use by primary care physicians, orthopedists, neurologists, and geriatricians, enabling the screening of patients who require a further more accurate gait diagnosis, including as part of preventive medicine. Research on gait function assessment is important not only in terms of the patient's mobility, but also in terms of the patient's current and future quality of life, ability to achieve health goals, family life, study and/or work, and participation in society. The main methods used herein include a literature review and an analysis of our own original research and concepts. This study used the historical data of 92 ischemic stroke patients (convenience trial) undergoing two kinds of rehabilitation. An artificial neural network, fractal analysis, and fuzzy analysis were used to analyze the results. Our findings suggest that artificial neural networks, fuzzy logic, and multifractal analysis are useful for building simple, low-cost, and efficient computational tools for gait analysis, especially in post-stroke patients. The novelty lies in the simultaneous application of the three aforementioned technologies to develop a computational model for the analysis of a patient's post-stroke gait. The contribution of this work consists not only in its proposal of a new and useful clinical tool for gait assessment, even in the most severe post-stroke cases, but also in its attempt to offer a comprehensive computational explanation of observed gait phenomena and mechanisms. We conclude by anticipating more advanced and broader future applications of artificial intelligence (AI) in gait analysis, especially in post-stroke patients. [ABSTRACT FROM AUTHOR]

Featured Application: Potential application of the work concerns AI-supported eco-design of a novel family of products. Creative thinking is an inherent process in the creation of innovations. Imagination is employed to seek creative solutions. This article presents research results on the use of inventive methods to develop an eco-friendly product. A household appliance was selected as the object of research. The article deals with issues relating to eco-design, eco-innovation, and inventory. The process of selecting inventive methods was presented. Selected inventive methods used to develop the product concept were briefly characterized. Creativity sessions were conducted using the methods of brainstorming, stimulating, reverse brainstorming, word games, and superpositions. The effect of these activities is the concept for an eco-innovative product. A product design was developed that is highly recyclable and environmentally friendly. An ecological analysis of the designed product, including AI-based (artificial neural networks), was carried out, which showed the legitimacy of the actions taken to develop an environmentally friendly product. The novelty of the proposed approach consists of combining the use of research data, with new methods for their analysis using both traditional and artificial intelligent tools, to create a transparent and scalable product design. To date, this approach is unique and has no equivalent in the literature. Despite higher manufacturing costs, the more environmentally friendly refrigerator is cheaper in operation (consumes less energy) due to the ecological solutions incorporated into its design. [ABSTRACT FROM AUTHOR]

Featured Application: The potential application of the research results presented in this paper relates to artificially intelligent (deep learning-based) techniques for the design and production of 3D printed chainmails with personalized mechanical properties, including for medical exoskeletons (wearable robots). The aim of this paper is to refine a scientific solution to the problem of automated or semi-automated efficient and practical design of 3D printed chainmails of exoskeletons with pre-programmed properties (variable stiffness/flexibility depending on direction) reflecting individual user needs, including different types and degrees of deficit. We demonstrate this with the example of using chainmail in a hand exoskeleton, where 3D printed chainmail components can be arranged in a single-layer structure with adjustable one- or two-way bending modulus. The novelty of the proposed approach consists in combining the use of real data from research on the exoskeleton of the hand, new methods of their analysis using deep neural networks, with a clear and scalable design of a 3D printed fabric product that can be personalized (mechanical parameters such as stiffness and bend angles in various directions) to the needs and goals of therapy in a particular patient. So far, this approach is unique, having no equivalent in the literature. This paves the way for a wider implementation of adaptive chainmails based on machine learning, more efficient for more complex chainmail designs. [ABSTRACT FROM AUTHOR]

The central objective of the present author's research is to develop a system supporting the design of a technological process (a computer-aided process planning system) that functions similarly to a human expert in the field in question. The use of neural networks makes the creation of such a system possible. The proposed method uses a system of three blocks of neural networks, and involves the creation of neural networks to be used for the selection of machines, tools, and machining parameters. These networks are built for each process operation separately; that is, a set of neural networks is created for each selection. For the construction of models, different types of neural networks (multilayer networks with error backpropagation, radial basis function, and Kohonen) with different structures were employed, and the networks that made the best selections were identified. A method was also developed for the elimination of defects occurring during the production process. When a defect comes to light, this method suggests changes to the technological process, thus improving the quality of that process. Guidelines for the elimination of defects are produced in the form of decision rules. Such a computer-aided process planning system will be especially useful for process engineers who do not yet have sufficient experience in the design of technological processes, or who have only recently joined a particular manufacturing enterprise and are not fully familiar with its machines and other means of production (tools and instrumentation). It should be emphasized that such a system performs an advisory role, and it is always the process engineer who makes the final decision. The neural network models were tested on real data from an enterprise. A computer-aided process planning system based on rules and neural network models enables the intelligent design of technological processes. [ABSTRACT FROM PUBLISHER]

This paper presents neural networks in process quality control. The control is a very important step in the production process. There are different methods for checking the quality of the process. In some manufacturing companies quality is checked by machine operators or quality control departments. Here can be used method for manually checking the quality or tools of statistical control in the form of specialized computer programs. Another approach is to treat the control card as an image instead of a set of points. We have developed the models of analysis of process control charts. The models were created in the form of multilayer networks with error back propagation (MLP neural networks). The models were tested on real data from the production company. [ABSTRACT FROM AUTHOR]

The failures location algorithms for water-supply network by use the neural networks were presented in the article. The algorithms have to be the part of monitoring and management system of water-supply network. The present utilization of the monitoring systems does not answer their possibilities. At present the monitoring systems provide as autonomic programs to gathering the information about flows and pressures of water in source pumping stations, the zone of hydrophore stations and the pipes of water-supply network, giving general knowledge about state of her work, when simultaneously they can and should be used as elements of systems of network management, in this particularly in the range of detection and location of water leakages. The models of network failure location are created relatively by use simple neural networks in the form of MLP, RBF, Kohonen networks and hybrid neural networks. [ABSTRACT FROM AUTHOR]

The paper presents neural networks as performance improvement models in intelligent computer aided process planning systems (CAPP systems). For construction of these models three types of neural networks were used: linear network, multi-layer network with error backpropagation, and the Radial Basis Function network (RBF). The models were compared. Due to the comparison, we can say which type of neural network is the best for selection of tools for manufacturing operations. Tool selection for manufacturing operation is a classification problem. Hence, neural networks were built as classification models, meant to improve tool selection for manufacturing. The study was done for selected manufacturing operations: turning, milling and grinding. Models for the milling operation were presented in detail. [ABSTRACT FROM AUTHOR]

3D printing of assistive devices requires optimization of material selection, raw materials formulas, and complex printing processes that have to balance a high number of variable but highly correlated variables. The performance of patient-specific 3D printed solutions is still limited by both the increasing number of available materials with different properties (including multi-material printing) and the large number of process features that need to be optimized. The main purpose of this study is to compare the optimization of 3D printing properties toward the maximum tensile force of an exoskeleton sample based on two different approaches: traditional artificial neural networks (ANNs) and a deep learning (DL) approach based on convolutional neural networks (CNNs). Compared with the results from the traditional ANN approach, optimization based on DL decreased the speed of the calculations by up to 1.5 times with the same print quality, improved the quality, decreased the MSE, and a set of printing parameters not previously determined by trial and error was also identified. The above-mentioned results show that DL is an effective tool with significant potential for wide application in the planning and optimization of material properties in the 3D printing process. Further research is needed to apply low-cost but more computationally efficient solutions to multi-tasking and multi-material additive manufacturing. [ABSTRACT FROM AUTHOR]

While the intensity, complexity, and specificity of robotic exercise may be supported by patient-tailored three-dimensional (3D)-printed solutions, their performance can still be compromised by non-optimal combinations of technological parameters and material features. The main focus of this paper was the computational optimization of the 3D-printing process in terms of features and material selection in order to achieve the maximum tensile force of a hand exoskeleton component, based on artificial neural network (ANN) optimization supported by genetic algorithms (GA). The creation and 3D-printing of the selected component was achieved using Cura 0.1.5 software and 3D-printed using fused filament fabrication (FFF) technology. To optimize the material and process parameters we compared ten selected parameters of the two distinct printing materials (polylactic acid (PLA), PLA+) using ANN supported by GA built and trained in the MATLAB environment. To determine the maximum tensile force of the exoskeleton, samples were tested using an INSTRON 5966 universal testing machine. While the balance between the technical requirements and user safety constraints requires further analysis, the PLA-based 3D-printing parameters have been optimized. Additive manufacturing may support the successful printing of usable/functional exoskeleton components. The network indicated which material should be selected: Namely PLA+. AI-based optimization may play a key role in increasing the performance and safety of the final product and supporting constraint satisfaction in patient-tailored solutions. [ABSTRACT FROM AUTHOR]

The last decade has seen the development of complex IT systems to support city management, i.e., the creation of so-called intelligent cities. These systems include modules dedicated to particular branches of municipal economy, such as urban transport, heating systems, energy systems, telecommunications, and finally water and sewage management. In turn, with regard to the latter branch, IT systems supporting the management of water supply and sewage networks and sewage treatment plants are being developed. This paper deals with the system concerning the urban water supply network, and in particular, with the subsystem for detecting and locating leakages on the water supply network, including so-called hidden leakages. These leaks cause the greatest water losses in networks, especially in old ones, with a very diverse age and material structure. In the proposed concept of the subsystem consisting of a GIS (Geographical Information System), SCADA (Supervisory Control and Data Acquisition) system and hydraulic model of the water supply network, an algorithm of leak detection and location based on the neural networks' MLP (multi-layer perceptron) and Kohonen was developed. The algorithm has been tested on the hydraulic models of several municipal water supply networks. [ABSTRACT FROM AUTHOR]