This paper details the development of a polymer-based miniaturized spectrometer as part of the "Minispectral" project at Jade University of Applied Sciences. The spectrometer's design leverages advanced injection molding techniques to create a polymer dome, which forms the core structure of the device. A concave diffraction grating is incorporated on the dome's surface to disperse incoming light into its constituent wavelengths. The dome and grating are designed with a generalized Rowland circle radius ratio in three-dimensional space, producing a lens effect for focused detection and analysis. Zemax ray tracing simulations are used to model the optical setup, while wave optics simulations, utilizing Rigorous Coupled-Wave Analysis (RCWA) implemented through Dynamic Link Libraries (DLLs) in Zemax, simulate critical parameters such as dome alignment, blazing, and the line density of the concave diffractive optical element (DOE). These simulations are vital for optimizing the spectrometer's performance. This paper presents the basic design, simulations, and the first test grating masters.
In short-range communication 1 mm PMMA Step Index Polymeric Optical Fibers established itself as a reasonable. alternative to the traditional data communication media such as glass fibers, copper cables and wireless systems. Due to multiple advantages such as a large core diameter, tolerance to fiber facet damages and low installation costs, the SI-POF is typically used for network systems in homes, vehicles and industrial automation. For wavelength division multiplex an optical demultiplexer is a key component. The paper concentrates on the demultiplexing techniques employing thin-film interference filters used in Polymeric Fiber Transmission Systems. An interference filter-based Step Index Polymeric Optical Fibers demultiplexer was realized using a precisely adjustable. opto-mechanical setup, which allowed maximization of the optical throughput in the individual channels. Intermediate setups with two and three channels were first established. In addition, a serial and a two-stage configuration of a target setup with four channels were realized. It was shown that the latter configuration outperformed the former one in terms of Insertion Loss (IL) and IL uniformity. Furthermore, the demultiplexer with two-stage configuration provided low insertion loss (< 5.7 dB) and high channel isolation (> 30 dB). It outperformed other interference filter- based SI-POF demultiplexers reported so far, and was well suited for implementation in high- speed POF WDM transmission experiments. To demonstrate experimentally the feasibility and potential of a high-speed POF WDM concept, a four-channel data transmission setup was realized.
In this article, we contrast laser ablation propulsion with photon pressure propulsion. LAP must use repetitive short pulses (~100 ps) for best performance, while PPP requires GW-level continuous (CW) lasers and a lightsail to receive the beam and drag the payload to relativistic speeds.
In the context of Industry 4.0, this paper examines the integration of four key design principles – Interconnection, Information Transparency, Decentralized Decisions, and Technical Assistance – into the Reference Architecture Model Industry 4.0 (RAMI 4.0). While RAMI 4.0 offers a robust foundation, its abstract nature hinders practical application. The analysis reveals that RAMI 4.0 partially incorporates the design principles, however because of its abstract nature companies often fail to instantiate it. In an industrial case study of a plastic housing production system, a more specific reference architecture is developed based on RAMI 4.0 which incorporates all30 four design principles more explicitly. The paper underscores the importance of more detailed reference architectures for effective system design in Industry 4.0, offering actionable insights for companies navigating the complexities of this evolving industrial landscape.
This study focuses on the role of autonomous control systems in robotics, focusing on how robot controls the actuator movements after meticulous information processing and decision-making within the robotic framework ROS. To go on this experimental challenge, a diesel tractor was modified into a versatile experimental platform capable of autonomous navigation and control. At the center of this tractor is the sensory module term1ed "Sentry," which consists of a network of interconnected sensors that have been methodically integrated to enable comprehensive ambient perception. The sensors use advanced technologies like 3D 360º LiDAR for spatial mapping, thermal cameras for object detection, RGBD cameras for visual perception, a microcontroller for control, GPS+RTK for precise positioning and a Jetson Xavier for high-performance computing. The experimental assessments done in this work covered a wide range of scenarios, from simulated environments with controlled variables to real-world terrains rife with uncertainty and variability. Valuable insights were gained by analyzing the resulting data, revealing light on the system's operation, performance, and efficacy under various scenarios.
Industrial robots (IR) are a cost-effective and highly flexible alternative to machining centres. Nevertheless, their use for separating processes in production technology is limited because of their low structural rigidity, which results in comparatively low accuracies. The vibration behaviour of the robots is a major challenge to increasing accuracy, as it can vary significantly depending on the position of the tool centre point (TCP) in the workspace. To apply vibration compensation, it is necessary to be able to describe the vibration behaviour of the robot with sufficient accuracy. This article presents a new approach, utilising parametric FEM simulation to generate a state space description of the robot. The simulation captures the pose-dependent vibration behaviour of each axis, which is then integrated into a downstream system simulation, resulting in a comprehensive description of the robot's vibration behaviour. After being finely tuned, the model demonstrates excellent alignment with the experimentally determined behaviour. Hence, the robot's vibration behaviour in the workspace can be holistically described, which is a decisive advantage over the limited possibilities of experimental identification.
The purpose of this article is the simulation of energy efficiency performance using a mathematical model of a unit process. A dynamic model of the process is used for its control. As the process is used for different production quantities, energy efficiency is investigated. Specific Energy Consumption (SEC) is one of the key performance measures of a plant or equipment. It consists of calculating energy consumed for producing a unit product. Hence, the model is used together with an observer and a controller to iteratively compute the specific consumption. For different trajectory to track (product type) and for different capacity usage, the iteration permits to get the corresponding specific energy consumption curve versus capacity usage. This characterization can be used later when operating to diagnose overconsumption of energy compared to initial equipment setup (equipment and control system). Finally, an industrial example is provided as a comparison to the simulation case.
This paper introduces an integrated simulation framework designed to advance the research and development of cooperative multi-robot systems. In contrast to the co-simulation approaches dominant in the state of the art, an architecture combining co-modelling and co-simulation approaches is employed. It allows the synergistic combination of physics-based models, wireless network simulations, and virtual machine environments to facilitate comprehensive software-in-the-loop simulation of complex system behaviors. The reference implementation of this architecture is applied to an industrial setting, simulating multiple Autonomous Transport Vehicles (ATVs) in a factory including raytracing-based lidar sensors and path loss models for network simulation of wireless networks.
The swift advancement of technology is fundamentally transforming the industrial landscape, steering it towards Industry 4.0, a digitized and interconnected framework crucial for ensuring production resilience, especially in the wake of global challenges like the Covid-19 pandemic. The far-reaching consequences of this crisis, marked by disruptions, temporary closures, and economic downturns across sectors, underscore the imperative for Industry 4.0 adoption. This new industrial paradigm not only addresses challenges but also brings forth advantages such as heightened productivity, personalized production processes, and streamlined information management. Nevertheless, it introduces complexities, including technology and cybersecurity risks, necessitating continuous workforce training. The vulnerabilities of traditional industrial models have been glaringly emphasized by the Covid-19 pandemic, highlighting the u rgent need for digital transformation. Industry 4.0 has emerged not just as a technological evolution but as a strategic lifeline for companies navigating unprecedented disruptions. In response, companies have embraced Industry 4.0 practices, employing remote collaboration, robust document management, and advanced assistance tools. A case study involving 68 Moroccan companies illustrates the positive impact of these practices during the Covid-19 crisis. Remarkably, over 50 % of these companies reported maintaining or increasing their sales by strategically leveraging digital solutions. This case study serves as compelling evidence of the pivotal role of Industry 4.0 in reinforcing business resilience amid challenging circumstances.
Hill-type muscle models play a crucial role in biomechanics, aiding in the prediction and understanding of muscle behavior, particularly when direct force measurement is challenging. They are instrumental in evaluating anatomical changes resulting from external loads affecting internal forces on anatomical structures. This study aims to develop a modeling framework based on the Hill model to predict and preempt changes in internal forces caused by various loads. Factors such as increased desk work during the pandemic, musculoskeletal deformations in earthquake victims, injuries from wars, athlete rehabilitation, and cumulative musculoskeletal injuries in daily work are considered. A mathematical model based on the Hill muscle model was designed, verified experimentally and numerically, focusing on the Biceps Brachii muscle and its associated structures. EMG signals were collected using the Biopac Device and simulated in Matlab. This approach offers a platform for assessing current and future states of internal forces, predicting musculoskeletal disorders, and investigating factors affecting muscle performance. Furthermore, the study suggests potential applications in improving rehabilitation processes through prosthetic and orthosis design, as well as enhancing the quality of life for Parkinson's patients.
The Global Navigation Satellite System (GNSS) has been widely utilized as a method to accurately determine the position of a vehicle, but at present, the accuracy may be degraded depending on the radio wave reception conditions from the satellite. There have also been growing concerns about cyber-attacks on GNSS. We have been developing a method for estimating the position of a traveling vehicle using MEMS sensor data acquired using acceleration, gyro, and geomagnetic sensors. This algorithm has been evaluated in several fixed courses and obtained good results with a position error of less than 1 m. In the current study, we present a solution to extend this vehicle localization algorithm with MEMS sensor data by combining it with a map-matching algorithm in order to associate reference sensor data with road links. This will greatly reduce the computational time by avoiding exhaustive cross-correlation calculation to vast amount of sensor data.
Wireless communication is a key issue in many industrial and research areas. On one hand cables are not necessary anymore, and this reduces costs while it is getting much more flexible and information can be selected from any device. On the other hand, safety and especially security are becoming even more important as data can be read by everyone and can be more easily perturbed or intentionally falsified. This article defines protocols that changes its safety attributes according to the continually calculated probability of error per hour value. A grey (gray) channel is defined that is not ignoring any information from the communication channel (black-channel) and uses the observed information to adapt the safety attributes. The protocols use short data-lengths and several different cyclic redundancy checks (CRCs) which are changed depending on the current safety integrity level, the observed channel characteristics and the amount of data to be sent.
Embedded systems are at the core of modern industrial applications that interface with the environment through sensors and actuators. As software-defined modelling revolutionises the development of safety-critical systems, such as self-driving cars in the automotive industry, rapid turnaround and integration into cloud-based software development services are increasingly becoming critical. Recent parts shortages and supply-chain constraints have highlighted the importance of dynamic, cross-platform codebases that allow scalability and quick re-deployment to different hardware. In this paper, we show the ability of our development framework to create decomposable, embedded systems that consist of software that factors out hardware dependencies and can thus be easily ported and deployed to multiple hardware architectures. We demonstrate how our embedded cross framework allows us to decouple hardware-specifics from the requirements for the software that implements the behaviour of the system. To this end, we show how to design and build scalable software that integrates with multiple hardware architectures, operating systems, and middleware for embedded systems. For the first time, we not only show how such systems can be developed for microcontrollers, but how the same embedded cross framework can be utilised for Field-Programmable Gate Arrays (FPGAs). We demonstrate how software systems that utilise our framework can seamlessly integrate with continuous integration and continuous deployment (CI/CD) processes. This allows the flexibility of testing and integration using local and cloud-based systems, as well as end-to-end hardware-in-the-loop approaches.
Gripping is an important hand function required for successful daily living activities. Grip strength measurements are one of the regional strength assessment methods and are used as the basic element in the evaluation of arm function. The scope of this study is to analyze biceps brachii muscle strength levels by taking noninvasive muscle strength measurements from individuals. The biceps brachii muscle plays an important role in upper body movements. There are various studies in the literature on measuring the EMG signal and muscle strength of the Biceps Brachii muscle during arm wrestling movement, but no study has been found on measuring biceps muscle strength during a movement similar to arm wrestling movement, in other words, during dynamic movement, with a hand dynamometer. In this context, an EMG device (EMG) was used to examine muscle electrical signals and measure the electrical currents produced during muscle contraction. By taking into account the force values obtained from the EMG signal in line with the analyzes performed at the end of the study, it was possible to determine whether individuals' cumulative trauma disorders (CTD) and various muscle diseases (paralysis, multiple sclerosis, etc.) are a risky situation for the biceps brachii muscle. This framework reveals a promising potential in the design of prosthetics and orthoses, in the evaluation of rehabilitation processes, and in improving the quality of life of individuals struggling with movement and coordination disorders in the studied muscle.
Sustaining optimal task engagement is becoming vital in smart factories, where manufacturing operators' roles are increasingly shifting from hands-on machinery tasks to supervising complex automated systems. However, because monitoring tasks are inherently less engaging than manual operation tasks, operators may have a growing difficulty in keeping the optimal levels of engagement required to detect system errors in highly automated environments. Addressing this issue, we created an adaptive task engagement feedback system designed to enhance manufacturing operators’ engagement while working with highly automated systems. Utilizing real-time acceleration, heart rate, and respiration rate data, our system provides an intuitive visual representation of an operator's engagement level through a color gradient, ensuring operators can stay informed of their engagement levels in real-time and make prompt adjustments if required. This article elaborates on the six-step process that guided the development of this adaptive feedback system. We developed a task engagement index by leveraging the physiological distinctions between more and less engaging manufacturing scenarios and using automation to induce lower engagement. This index demonstrates a prediction accuracy rate of 80.95 % for engagement levels, as demonstrated by a logistic regression model employing leave-one-out cross-validation. The implications of deploying this adaptive system include enhanced operator engagement, higher productivity and improved safety measures.
This research explores the use of Q-Learning to enhance energy efficiency and data transmission in RF-powered Internet of Things (IoT) networks. We present a novel Q-Learning strategy integrated with a Hybrid Access Point to significantly improve error correction, polling rounds, network capacity, and message delivery speeds. The study reveals Q-Learning's effectiveness in reducing errors and boosting network performance, outperforming traditional methods like Aloha with Successive Interference Cancellation (Aloha-SIC) and Time Division Multiple Access with Successive Interference Cancellation (TDMA-SIC). Utilizing the Independent Learner paradigm within a distributed Q-Learning framework, we enable sensor devices to dynamically adjust their transmission power based on network conditions, enhancing network efficiency and device energy management. Our findings highlight Q-Learning's success in overcoming the challenges of existing network protocols, enhancing the reliability and performance of RF-powered IoT networks. Additionally, the research illustrates the practical advantages of integrating Q-Learning into IoT systems, including consistent network performance under various conditions and the potential for energy savings. We conclude with a call for the wider adoption of intelligent learning systems in IoT networks to address the demands of connectivity and sustainability, emphasizing Q-Learning's role in advancing IoT connectivity and energy management for the future.
For the automation of industrial production, the variety of sensors plays a key role. The sensors in question are position sensors, touch sensors, temperature sensors, photo sensors, chemical sensors, etc. Recently, THz sensors joined the sensor family due to their specific property. The most common application of THz sensors is in security, as they have the ability to penetrate the clothes without harming the person. Low-power THz radiation does not damage the cells in the body and does not ionize as X-rays do. Consequently, THz rays can replace X-rays in many applications. This is happening rapidly due to the fast development of the THz wave sources and detectors. This article describes an antenna-coupled nano-bolometer for a THz sensor operating at room temperature. It was developed and manufactured in our laboratory. The key features we focused on are high sensitivity, compact size, and affordable production cost. Some practical examples of the novel THz sensor use are also presented.
The rapid advancements in artificial intelligence (AI) and robotics have sparked concerns about their potential impact on productivity in small and medium enterprises (SMEs) in the Western Balkans region. One of the major concerns is the possible displacement of the workforce, which necessitates the need for psychological adaptation to these technologies. Despite this, the adoption of AI in SMEs may bring a multitude of benefits, including improved financial performance, increased employee engagement, efficient data management, and enhanced marketing strategies, as highlighted by Kumar and Kalse. The importance of AI adoption for SMEs was highlighted during the challenges posed by the COVID-19 pandemic. This research examines the impact of AI and robotics on SMEs in the Western Balkans region, focusing on the necessary psychological adjustments for employees and the economic outcomes associated with technology adoption. The study aims to assess the current level of integration of AI and robotics in SMEs in the Western Balkans, exploring the challenges faced by these enterprises in adopting these technologies.
Industrial communication at the field level is highly dependent on the standards and their implementation on industrial PCs and Programmable Logic Controllers. The integration of industrial sensors and actuators requires manual configuration by plant operators and automation engineers. Nowadays interoperability plays an important role in Industry 4.0. For this the OPC UA Foundation and Platform Industrie 4.0 organization have published the Field Device eXchange and Asset Administration Shell standards, respectively, to define interoperable metadata models. However, there is no single way to define a field device metadata model and reuse it with other systems. This leads to heterogeneous data models and a lack of agreement on a generic semantic model for field devices. In this paper, we propose the Industry 4.0 Field Device Ontology to enable an interoperable semantic definition of field devices. The goal of this ontology is to reuse existing information from field devices, such as device description files, device profiles, and their application data. This paper covers the design of the ontology to enable semantic interoperability of field devices, the generalization of application data, and its implementation with the OWL 2 Web Ontology Language. The main contribution of our work is to provide the basic building blocks to enable the development of interoperable field device applications and integration with Industry 4.0 information model standards.
This article presents a method of optimizing dynamic systems governed by integro-differential equations of the second kind with weakly singular kernels in such systems, the time range is infinite and the controls may be delayed. Performance assessments involve measuring the square distance between tracking functions and states and measuring the energy of controls, and measuring the deviation of the final states from the tracking functions. We report the results of typical examples.
