Search

Food unit operations refer to the engineering processes involved in transforming raw materials into desirable food products, taking into account the main laws and principles that govern the physical, chemical, and biochemical changes related to these processes. Drying is one of the most common unit operations used in the food sector to reduce food water content, thereby extending shelf-life, reducing weight and volume, and decreasing inventory and transportation costs. Traditionally, food materials are dried using conventional methods, such as natural solar drying and hot air drying. However, recent years have witnessed the introduction of several emerging technologies (e.g., infrared drying, microwave drying, and freeze drying) that have promising potential to overcome challenges, such as uneven drying, poor sensory properties and nutrient loss, and large energy consumption. More interestingly, recent developments and advancements in digital, physical, and biological technologies, spurred by the Fourth Industrial Revolution (Industry 4.0), have significantly impacted various food manufacturing operations, including food drying. Growing evidence shows that diverse Industry 4.0 technologies (notably artificial intelligence, the Internet of Things, smart sensors, digital twins, and big data) can be harnessed to improve the modelling, monitoring, prediction, and optimization of various parameters in food drying. These technological advancements are not only accelerating the pace of innovation but also enhancing process efficiency and overall performance in intelligent food drying, ushering in the era of "Food Drying 4.0." [ABSTRACT FROM AUTHOR]

Copyright of Food & Fermentation Industries is the property of Food & Fermentation Industries and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors. [ABSTRACT FROM AUTHOR]

Smart sensors and networks have spread worldwide over the past few decades. In the industry field, these concepts have found an increasing quantity of applications. The omnipresence of smart sensor networks and smart devices, especially in the industrial world, has contributed to the emergence of the concept of Industry 4.0. In a world where everything is interconnected, communication among smart devices is critical to technological development in the field of smart industry. To improve communication, many engineers and researchers implemented methods to standardize communication along the various levels of the ISO-OSI model, from hardware design to the implementation and standardization of different communication protocols. The objective of this paper is to study and implement an unconventional type of communication, exploiting acoustic wave propagation on metallic structures, starting from the state of the art, and highlighting the advantages and disadvantages found in existing literature, trying to overcome them and describing the progress beyond the state of the art. The proposed application for acoustic communication targets the field of smart industries, where implementing signal transmission via wireless or wired methods is challenging due to interference from the widespread presence of metallic structures. This study explores an innovative approach to acoustic communication, with a particular focus on the physical challenges related to acoustic wave propagation. Additionally, communication performance is examined in terms of noise rejection, analyzing the impact of injected acoustic noise on communication efficiency. [ABSTRACT FROM AUTHOR]

Smart grids (SGs) are crucial to the efficiency and sustainability of modern energy systems. As the world’s population continues to increase, so does the need for energy, and traditional energy systems are struggling to keep up. In this context, this study reviews the possibilities of deploying machine learning (ML) on wireless sensor networks (WSNs) in smart grid systems. In several ways, SGs may gain from combining WSNs with ML, including enhance system reliability, sustainability, improve fault detection, and increase energy efficiency. This paper offers an extensive review of pertinent research emphasizing the use of supervised, unsupervised, and reinforcement learning approaches. The evaluation contains 234 peer reviewed articles from highly regarded academic journals and conferences covering the years 2017 through 2024 which depict the effectiveness of supervised techniques on WSNs in the field of SGs. In addition the paper presents set of the most usable datasets in the field of WSNs and SGs, and introduces a comparison between our paper and relevant surveys. The study also analyses the opportunities and challenges related to the application of WSNs and ML in SGs and offers possible research directions. Overall, the study makes it clear that combining WSNs with ML may significantly contribute to the creation of smart grid systems that are more effective, dependable, and sustainable.

In the last few years, the agricultural field has undergone a digital transformation, incorporating artificial intelligence systems to make good employment of the growing volume of data from various sources and derive value from it. Within artificial intelligence, Machine Learning is a powerful tool for confronting the numerous challenges of developing knowledge-based farming systems. This study aims to comprehensively review the current scientific literature from 2017 to 2023, emphasizing Machine Learning in agriculture, especially viticulture, to detect and predict grape infections. Most of these studies (88%) were conducted within the last five years. A variety of Machine Learning algorithms were used, with those belonging to the Neural Networks (especially Convolutional Neural Networks) standing out as having the best results most of the time. Out of the list of diseases, the ones most researched were Grapevine Yellow, Flavescence Dorée, Esca, Downy mildew, Leafroll, Pierce's, and Root Rot. Also, some other fields were studied, namely Water Management, plant deficiencies, and classification. Because of the difficulty of the topic, we collected all datasets that were available about grapevines, and we described each dataset with the type of data (e.g., statistical, images, type of images), along with the number of images where they were mentioned. This work provides a unique source of information for a general audience comprising AI researchers, agricultural scientists, wine grape growers, and policymakers. Among others, its outcomes could be effective in curbing diseases in viticulture, which in turn will drive sustainable gains and boost success. Additionally, it could help build resilience in related farming industries such as winemaking. [ABSTRACT FROM AUTHOR]

This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe. [ABSTRACT FROM AUTHOR]

Invasive sea lamprey (Petromyzon marinus) has historically inflicted considerable economic and ecological damage in the Great Lakes and continues to be a major threat. Accurately monitoring sea lampreys are critical to enabling the deployment of more targeted and effective control measures to minimize the impact associated with this species. This paper presents the first stand-alone system for real-time detection of sea lamprey attachment on underwater surfaces through the use of classifier models deployed on a microcontroller system. A range of low-complexity models was explored: single-layer artificial neural networks, logistic regression, Gaussian Naive-Bayes, decision trees, random forest, and Scalable, Efficient, and Fast classifieR (SEFR). Threshold models tuned using a multi-objective optimization formulation were also considered. Classifier models were trained with a dataset generated through live animal testing and presented accuracies between 80 and 86%. The models were deployed on an Arduino microcontroller platform and compared in classification accuracy, detection performance, time complexity, and memory size using real-time detection testing. Classification accuracies between 65 and 75% were observed during validation. Models demonstrated good capture rates for lamprey attachments (63–85%), and average detection delays ranging from 9 to 36 s. A video demonstrating the operation of the system during a real-time validation test is also included in this work. While there is room for improving the accuracy of the system, this research presents the first step toward an electronic sea lamprey monitoring system that can provide a detailed view of sea lamprey activity enhancing control and conservation efforts across its entire range. [ABSTRACT FROM AUTHOR]

Remote sensing technology is a vital component of disaster management, poised to revolutionize how we safeguard lives and property through enhanced prediction, mitigation, and recovery efforts. Disaster management hinges on continuous monitoring of various environments, from urban areas to forests and farms. Data from these observations are relayed to servers, where sophisticated processing algorithms forecast impending disasters. Remote sensing technology operates through a layered framework. The sensing layer acquires raw data, the network layer facilitates data transmission, and the data processing layer extracts meaningful insights. The application layer then leverages these insights to make informed decisions. Elevating the intelligence of remote sensing technology necessitates advancements across these layers. This paper delves into disaster management concepts and highlights the pivotal role played by remote sensing technology. It offers a comprehensive exploration of each layer within the remote sensing technology framework, detailing foundational principles, tools, and methodologies for enhancing intelligence. Addressing challenges inherent to this technology, the paper also presents future-oriented solutions. Furthermore, it examines the influence of wireless network infrastructure, alongside emerging technologies like the Internet of Things, cloud computing, virtual machines, and low-power wireless networks, in nurturing the evolution and sustainability of remote sensing technology. [ABSTRACT FROM AUTHOR]

Ice formation on aircraft surfaces poses significant safety risks, and current detection systems often struggle to provide accurate, real-time predictions. This paper presents the development and comprehensive evaluation of a smart ice control system using a suite of machine learning models. The system utilizes various sensors to detect temperature anomalies and signal potential ice formation. We trained and tested supervised learning models (Logistic Regression, Support Vector Machine, and Random Forest), unsupervised learning models (K-Means Clustering), and neural networks (Multilayer Perceptron) to predict and identify ice formation patterns. The experimental results demonstrate that our smart system, driven by machine learning, accurately predicts ice formation in real time, optimizes deicing processes, and enhances safety while reducing power consumption. This solution holds the potential for improving ice detection accuracy in aviation and other critical industries requiring robust predictive maintenance. [ABSTRACT FROM AUTHOR]

A fast and precise convolutional neural network (CNN) framework was built for classifying tea species using low-cost electrochemical profiles of samples. The model achieved an outstanding 96.3% accuracy in categorizing 9 major tea taxa from sensor input data alone. The approach combines simplicity with reliability gains over conventional analytical chemistry, thereby enabling field verification of labels along supply chains. Precision of 96% and recall exceeding 94% per class substantiate robust deployability. Charting of loss values and accuracy over training epochs validates learning efficiency. Comparative evaluation proves deep learning superiority over other models with up to 11% higher prediction rates. Normalization of input signatures was also found vital for inter-sample variability reduction and amplification of subtle inter-species differences that aid discrimination. Practical validation can entrench viability for scalable commercial adoption targeting small producers and vendors globally. In conclusion, an AI-powered rapid, low-cost sensor prototype allows tea sector modernization with data-driven quality assessments. [ABSTRACT FROM AUTHOR]

The assessment of production plant efficiency is crucial for optimizing the operational performance of manufacturing systems. In traditional facilities, automated data collection is limited and information primarily relies on operators declarations, which are prone to inaccuracy. There is therefore a need for readily accessible digital alternatives. This paper introduces a cost-effective method for classifying the status of machine tools using smart sensors to monitor their primary doors with minimal integration, and a streamlined algorithm for efficient data processing. The innovative algorithm was conceived using data collected in over 3 months in a manufacturing plant comprising 50 diverse machine tools engaged in batch production for the automotive industry, and is based on non-dimensional thresholds, making it suitable for generic applications requiring classification of repetitive patterns. Also, a realistic simulator was developed to provide reliable data for algorithm accuracy evaluation. The classification performance was fully tested using synthetic data, showing very good accuracy. In addition, the performance of the algorithm was compared to basic machine learning approaches further proving the validity of the proposed method. Ultimately, the classification algorithm was employed to assess the Overall Equipment Effectiveness (OEE) of the real plant machines, which were closely aligned with the estimates provided by the enterprise management. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the impact of the implementation of an experimental program with combined plyometric and coordination exercises for a time interval of 6 months aimed at improving the jump shots of U12 junior players through the use of information technologies. One hundred seventeen female basketball players, aged between 10 and 12 years (U12), participated in this study. The study subjects were divided into two groups: the experimental group (EG), with 60 (51.3%) subjects, and the control group (CG), with 57 subjects (48.7%). The 6-month experiment program implemented in the experimental group included exercises that combined coordination exercises with plyometric exercises in the execution of throwing skills and skills specific to the basketball game by using the MyVert portable smart sensor. This study included an initial test and a final test, in which three motor tests adapted to the specifics of the basketball game were applied in order to evaluate jump shots: a throw-after-step test, a standing shot test and a shot-after-dribbling test. Only the results of the experimental group showed statistically significant progress (p < 0.05) between the final and initial testing in all three motor tests for the following parameters: maximum jump height (cm), average jump height (cm), power (watts/kg) and successful shots (no). The gains of the control group were not statistically significant in any test. It should be noted that the number of throws scored in the basket of the experimental group increased significantly, a fact highlighted by the very large size of Cohen's value > 3 in all the tests of this study. The results of the experimental group as a result of the implementation of the experimental training program using MyVert technology were superior to the results of the control group. The practical implications of the present study will contribute to the optimization of the athletes' training methodology in order to improve the physical and technical levels in relation to the peculiarities of age and training level. [ABSTRACT FROM AUTHOR]

Along with the revolution of the Internet of Things (IoT), smart, flexible, easily fabricated, and highly sensitive gas sensors are essential for many portable electronics. In this study, a wearable NO2 sensor based on a nanomesh structure woven with Ti3C2Tx microflakes and Bi2S3 nanobelts operated at room temperature was successfully designed and prepared. The effects of the mass ratio between two materials on the performance were evaluated. Bi2S3 nanobelts embedded inside accordion-like Ti3C2Tx provide large specific surface areas and abundant active sites for the adsorption of NO2 molecules. Attributed to the high conductivity of Ti3C2Tx, the carriers generated in the sensitive layers are easily transmitted during sensing, thus significantly reducing the response and recovery times. The sensor based on the nanomesh containing 30 wt % Ti3C2Tx exhibits excellent sensing performance, fast response/recovery rates, and high stability even in arbitrary deformed shape conditions. Upon exposure to 1 ppm NO2, the response of the sensor is 12.67, and the response/recovery times are 5/28 s, respectively. A stable sensing performance can be maintained after 1500 cycles of deformation. This work demonstrates a simple but reliable manufacturing strategy of wearable gas sensors for the next generation of IoT monitoring network-linking human activities. [ABSTRACT FROM AUTHOR]

Reliable testing of aviation components depends on the quality and configuration flexibility of measurement systems. In a typical approach to test instrumentation, there are tens or hundreds of sensors on the test head and test facility, which are connected by wires to measurement cards in control cabinets. The preparation of wiring and the setup of measurement systems are laborious tasks requiring diligence. The use of smart wireless transducers allows for a new approach to test preparation by reducing the number of wires. Moreover, additional functionalities like data processing, alarm-level monitoring, compensation, or self-diagnosis could improve the functionality and accuracy of measurement systems. A combination of low power consumption, wireless communication, and wireless power transfer could speed up the test-rig instrumentation process and bring new test possibilities, e.g., long-term testing of moving or rotating components. This paper presents the design of a wireless smart transducer dedicated for use with sensors typical of aviation laboratories such as thermocouples, RTDs (Resistance Temperature Detectors), strain gauges, and voltage output integrated sensors. The following sections present various design requirements, proposed technical solutions, a study of battery and wireless power supply possibilities, assembly, and test results. All presented tests were carried out in the Components Test Laboratory located at the Łukasiewicz Research Network–Institute of Aviation. [ABSTRACT FROM AUTHOR]

Cerebrospinal fluid (CSF) is a body fluid that can be used for the diagnosis of various diseases. However, CSF collection requires an invasive and painful procedure called a lumbar puncture (LP). This procedure is applied to any patient with a known risk of central nervous system (CNS) damage or neurodegenerative disease, regardless of their age range. Hence, this can be a very painful procedure, especially in infants and elderly patients. On the other hand, the detection of disease biomarkers in CSF makes diagnoses as accurate as possible. This review aims to explore novel electrochemical biosensing platforms that have impacted biomedical science. Biosensors have emerged as techniques to accelerate the detection of known biomarkers in body fluids such as CSF. Biosensors can be designed and modified in various ways and shapes according to their ultimate applications to detect and quantify biomarkers of interest. This process can also significantly influence the detection and diagnosis of CSF. Hence, it is important to understand the role of this technology in the rapidly progressing field of biomedical science. [ABSTRACT FROM AUTHOR]

In the contemporary healthcare landscape, various intelligent automated approaches are revolutionizing healthcare tasks. Learning concepts are pivotal for activities like comprehending acquired data and monitoring patient behavior. Among patient-centric concerns, addressing data heterogeneity, extraction, and prediction challenges is crucial. To enhance patient monitoring using care indicators like cost and length of stay at healthcare centers, many researchers found a model for automated tools, but do not have the artificial intelligence (AI) based models as of now. Therefore, this research study will propose an AI and internet of things (IoT) integrated automated approach with smart sensors called the "PatientE" framework with heterogeneity and patient data. Employing certain rules for data extraction to form a distinct representation, our model integrates pretreatment information and employs a modified combined random forest, long-short term memory (LSTM), and bidirectional long-short term memory (BiLSTM) algorithm for predictive post-treatment monitoring. This framework, synergizing AI, IoT, and advanced neural networks, facilitates real-time health monitoring, especially focusing on breast cancer patients. Embracing pre-treatment, in-treatment, and post-treatment phases, our model aims for accurate diagnosis, improved cost-efficiency, and extended stays. The evaluation underscores scalability, reliability enhancement, and validates the framework's efficacy in transforming healthcare practices. [ABSTRACT FROM AUTHOR]

This paper reports the results of an investigation into the use of magnetic sensors for measuring AC currents and subsequently, estimating AC current phasors in low‐ and medium voltage AC power systems. Tunnelling magnetoresistive (TMR) sensor of high sensitivity and a wide range was used for the magnetic field measurement around AC conductor. The sensor was calibrated to overcome the inequality in the sensed magnetic field due to various aspects such as the distance from the source, minute structural variations, the magnitude of the source current, and presence of harmonics. Performance was tested for accuracy at lower frequencies such as 1, 2, 5 and 10 Hz as well as at higher frequencies such as 2nd, 3rd, 4th and 5th harmonics of the fundamental frequency. The percentage total vector error (TVE) was calculated for current phasors with input current magnitudes varying from 5 to 1500 A of various frequencies and was compared with the actual current as well as with the outputs of a high accuracy conventional core‐wound donut type current transformer (CT). The measurement accuracy corresponding to magnitude, phase and TVE during laboratory and field applications validated the suitability of TMR sensor for contactless and non‐invasive AC current measurement. [ABSTRACT FROM AUTHOR]

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices' performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement. [ABSTRACT FROM AUTHOR]

Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model's performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model's ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model's performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement. [ABSTRACT FROM AUTHOR]

This research aims to conduct a systematic literature review (SLR) on intelligent sensors and the Internet of Things (IoT) in assistive devices for the deaf and hard of hearing. This study analyzes the current state and promise of intelligent sensors in improving the daily lives of those with hearing impairments, addressing the critical need for improved communication and environmental interaction. We investigate the functionality, integration, and use of sensor technologies in assistive devices, assessing their impact on autonomy and quality of life. The key findings show that many sensor-based applications, including vibration detection, ambient sound recognition, and signal processing, lead to more effective and intuitive user experiences. The study emphasizes the importance of energy efficiency, cost-effectiveness, and user-centric design in developing accessible and sustainable assistive solutions. Moreover, it discusses the challenges and future directions in scaling these technologies for widespread adoption, considering the varying needs and preferences of the end-users. Finally, the study advocates for continual innovation and interdisciplinary collaboration in advancing assistive technologies. It highlights the importance of IoT and intelligent sensors in fostering a more inclusive and empowered environment for the deaf and hard-of-hearing people. This review covers studies published between 2011 and 2024, highlighting advances in sensor technologies for assistive systems in this timeframe.

The fourth industrial revolution (Industry 4.0) is driving significant changes across multiple sectors, including the food industry. This review examines how Industry 4.0 technologies, such as smart sensors, artificial intelligence, robotics, and blockchain, among others, are transforming unit operations within the food sector. These operations, which include preparation, processing/transformation, preservation/stabilization, and packaging and transportation, are crucial for converting raw materials into high-quality food products. By incorporating advanced digital, physical, and biological innovations, Industry 4.0 technologies are enhancing precision, productivity, and environmental responsibility in food production. The review highlights innovative applications and key findings that showcase how these technologies can streamline processes, minimize waste, and improve food product quality. The adoption of Industry 4.0 innovations is increasingly reshaping the way food is prepared, transformed, preserved, packaged, and transported to the final consumer. The work provides a valuable roadmap for various sectors within agriculture and food industries, promoting the adoption of Industry 4.0 solutions to enhance efficiency, quality, and sustainability throughout the entire food supply chain.

Smart sensors and networks have spread worldwide over the past few decades. In the industry field, these concepts have found an increasing quantity of applications. The omnipresence of smart sensor networks and smart devices, especially in the industrial world, has contributed to the emergence of the concept of Industry 4.0. In a world where everything is interconnected, communication among smart devices is critical to technological development in the field of smart industry. To improve communication, many engineers and researchers implemented methods to standardize communication along the various levels of the ISO-OSI model, from hardware design to the implementation and standardization of different communication protocols. The objective of this paper is to study and implement an unconventional type of communication, exploiting acoustic wave propagation on metallic structures, starting from the state of the art, and highlighting the advantages and disadvantages found in existing literature, trying to overcome them and describing the progress beyond the state of the art. The proposed application for acoustic communication targets the field of smart industries, where implementing signal transmission via wireless or wired methods is challenging due to interference from the widespread presence of metallic structures. This study explores an innovative approach to acoustic communication, with a particular focus on the physical challenges related to acoustic wave propagation. Additionally, communication performance is examined in terms of noise rejection, analyzing the impact of injected acoustic noise on communication efficiency.

The applications of the permanent magnet synchronous motor (PMSM) are the most seen in the elevator industry due to their high efficiency, low losses and the potential for high energy savings. The Internet of Things (IoT) is a modern technology which is being incorporated in various industrial applications, especially in electrical machines as a means of control, monitoring and preventive maintenance. This paper is focused on reviewing the use PMSM in lift systems, the application of various condition monitoring techniques and real-time data collection techniques using IoT technology. In addition, we focus on different categories of industrial sensors, their connectivity and the standards they should meet for PMSMs used in elevator applications. Finally, we analyze various secure ways of transmitting data on different platforms so that the transmission of information takes into account possible unwanted instructions from exogenous factors.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies. [ABSTRACT FROM AUTHOR]

Rotary machines commonly use rolling element bearings to support rotation of the shafts. Most machine performance imperfections are related to bearing defects. Thus, reliable bearing condition monitoring systems are critically needed in industries to provide early warning of bearing fault so as to prevent machine performance degradation and reduce maintenance costs. The objective of this paper is to develop a smart monitoring system for real-time bearing fault detection and diagnostics. Firstly, a smart sensor-based data acquisition (DAQ) system is developed for wireless vibration signal collection. Secondly, a modified variational mode decomposition (MVMD) technique is proposed for nonstationary signal analysis and bearing fault detection. The proposed MVMD technique has several processing steps: (1) the signal is decomposed into a series of intrinsic mode functions (IMFs); (2) a correlation kurtosis method is suggested to choose the most representative IMFs and construct the analytical signal; (3) envelope spectrum analysis is performed to identify the representative features and to predict bearing fault. The effectiveness of the developed smart sensor DAQ system and the proposed MVMD technique is examined by systematic experimental tests. [ABSTRACT FROM AUTHOR]

Through the use of Underwater Smart Sensor Networks (USSNs), Marine Observatories (MOs) provide continuous ocean monitoring. Deployed sensors may not perform as intended due to the heterogeneity of USSN devices' hardware and software when combined with the Internet. Hence, USSNs are regarded as complex distributed systems. As such, USSN designers will encounter challenges throughout the design phase related to time, complexity, sharing diverse domain experiences (viewpoints), and ensuring optimal performance for the deployed USSNs. Accordingly, during the USSN development and deployment phases, a few Underwater Environmental Constraints (UECs) should be taken into account. These constraints may include the salinity level and the operational depth of every physical component (sensor, server, etc.) that will be utilized throughout the duration of the USSN information systems' development and implementation. To this end, in this article we present how we integrated an Artificial Intelligence (AI) Database, an extended ArchiMO meta-model, and a design tool into our previously proposed Enterprise Architecture Framework. This addition proposes adding new Underwater Environmental Constraints (UECs) to the AI Database, which is accessed by USSN designers when they define models, with the goal of simplifying the USSN design activity. This serves as the basis for generating a new version of our ArchiMO design tool that includes the UECs. To illustrate our proposal, we use the newly generated ArchiMO to create a model in the MO domain. Furthermore, we use our self-developed domain-specific model compiler to produce the relevant simulation code. Throughout the design phase, our approach contributes to the handling and controling of the uncertainties and variances of the provided quality of service that may occur during the performance of the USSNs, as well as reducing the design activity's complexity and time. It provides a way to share the different viewpoints of the designers in the domain of USSNs. [ABSTRACT FROM AUTHOR]

Considering that the fusion of education and technology has delivered encouraging outcomes, things are becoming more challenging for higher education as students seek experiences that bridge the gap between theory and their future professional roles. Giving priority to the above issue, this study presents methods and results from activities assisting engineering students to utilize recent machine-learning techniques for tackling the challenge of water resource preservation. Cost-effective, innovative hardware and software components were incorporated for monitoring the proper operation of the corresponding agricultural equipment (such as electric pumps or water taps), and suitable educational activities were developed involving students of agricultural engineering. According to the evaluation part of the study being presented, the implementation of a machine-learning system with sufficient performance is feasible, while the outcomes derived from its educational application are significant, as they acquaint engineering students with emerging technologies entering the scene and improve their capacity for innovation and cooperation. The study demonstrates how emerging technologies, such as IoT, ML, and the newest edge-AI techniques can be utilized in the agricultural industry for the development of sustainable agricultural practices. This aims to preserve natural resources such as water, increase productivity, and create new jobs for technologically efficient personnel. [ABSTRACT FROM AUTHOR]

Development of smart nanocomposites by reinforcing carbon nanomaterials into polymer composite materials has gained significant attention due to their extensive potential applications, especially in the field of monitoring of mechanical, aerospace and infrastructure systems. These smart nanocomposites exhibit excellent electro-mechanical sensing capability. Carbon nanotubes (CNTs) with outstanding electrical, mechanical, and thermal properties can be reinforced into polymer composite materials by appropriate dispersion to produce polymer/CNTs-based smart nanocomposites with excellent sensitivity. Understanding the processing, characterization and properties of the nanocomposites will help in yielding a deeper insight into the effect of incorporation of carbon nanotubes (CNTs) into the matrix of the polymer composites. This study provides a comprehensive overview (primarily in the last 2 decades) of the major influencing factors on the underlying mechanisms involved in the fabrication and performance of nanocomposites which can cause a paradigm shift in instrumentation and sensing for structural health monitoring (SHM). Therefore, this review article presents the recent advancement in the development of CNTs-reinforced polymer nanocomposites-based sensors encompassing the mechanism for electrical conductivity/piezo properties, fabrication, applications for various types of sensing, and the futuristic application for smart sensing through appropriate tailoring of nanocomposites using CNTs. [ABSTRACT FROM AUTHOR]

This study introduces an innovative numerical approach for polylinear approximation (polylinearization) of non-self-intersecting compact sensor characteristics (transfer functions) specified either pointwise or analytically. The goal is to partition the sensor characteristic optimally, i.e., to select the vertices of the approximating polyline (approximant) along with their positions, on the sensor characteristics so that the distance (i.e., the separation) between the approximant and the characteristic is rendered below a certain problem-specific tolerance. To achieve this goal, two alternative nonlinear optimization problems are solved, which differ in the adopted quantitative measure of the separation between the transfer function and the approximant. In the first problem, which relates to absolutely integrable sensor characteristics (their energy is not necessarily finite, but they can be represented in terms of convergent Fourier series), the polylinearization is constructed by the numerical minimization of the L 1 -metric (a distance-based separation measure), concerning the number of polyline vertices and their locations. In the second problem, which covers the quadratically integrable sensor characteristics (whose energy is finite, but they do not necessarily admit a representation in terms of convergent Fourier series), the polylinearization is constructed by numerically minimizing the L 2 -metric (area- or energy-based separation measure) for the same set of optimization variables—the locations and the number of polyline vertices. [ABSTRACT FROM AUTHOR]

Speaker recognition is a challenging problem in behavioral biometrics that has been rigorously investigated over the last decade. Although numerous supervised closed-set systems inherit the power of deep neural networks, limited studies have been made on open-set speaker recognition. This paper proposes a self-supervised open-set speaker recognition that leverages the geometric properties of speaker distribution for accurate and robust speaker verification. The proposed framework consists of a deep neural network incorporating a wider viewpoint of temporal speech features and Laguerre–Voronoi diagram-based speech feature extraction. The deep neural network is trained with a specialized clustering criterion that only requires positive pairs during training. The experiments validated that the proposed system outperformed current state-of-the-art methods in open-set speaker recognition and cluster representation. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent research shows that deep neural networks (DNNs) are capable of achieving accurate STLP since they are effective in predicting nonlinear and complicated time-series data. To perform STLP, existing DNNs use time-varying dynamics of either past load consumption or past power correlated features such as weather, meteorology or date. However, the existing DNN approaches do not use the time-invariant features of users, such as building spaces, ages, isolation material, number of building floors or building purposes, to enhance STLF. In fact, those time-invariant features are correlated to user load consumption. Integrating time-invariant features enhances STLF. In this paper, a fuzzy clustering-based DNN is proposed by using both time-varying and time-invariant features to perform STLF. The fuzzy clustering first groups users with similar time-invariant behaviours. DNN models are then developed using past time-varying features. Since the time-invariant features have already been learned by the fuzzy clustering, the DNN model does not need to learn the time-invariant features; therefore, a simpler DNN model can be generated. In addition, the DNN model only learns the time-varying features of users in the same cluster; a more effective learning can be performed by the DNN and more accurate predictions can be achieved. The performance of the proposed fuzzy clustering-based DNN is evaluated by performing STLF, where both time-varying features and time-invariant features are included. Experimental results show that the proposed fuzzy clustering-based DNN outperforms the commonly used long short-term memory networks and convolution neural networks. [ABSTRACT FROM AUTHOR]

AI has been gaining incredible momentum in the public perception throughout the past year. The main trigger has been the media effective presentation of ChatGPT, presumably. Suddenly, a use case for AI graspable for each and everyone had been created, making the topic directly available to the public mind. Plenty of diverse discussions are ongoing since, be it fantasizing on future use cases, experimenting with the technology (sometimes seemingly "throwing" AI on pretty much everything, treating AI as the "Holy Grail" of technological progress), critical voices full of emotions (especially various subjects towards anxiety) or profound neutral analysis of potential use cases from a scientific point of view. However, both enthusiastic and critical discussions very often lack fundamental background knowledge of the technology behind AI along with what potentially is realistic to implement. Generally, a comprehensive discussion of AI needs to be carried out on various levels, including a more socio-economic overview of the impact of AI on the public, a summary of the technology behind AI to a depth naturally limited by the nature of an article like this, and finally a review of current real use cases. The purpose of this article is to discuss the aforementioned fields in the context of the brewing industry. The main focus will be on a basic introduction of the technology behind current AI trends in the relevant field along with non-classic sensors used to implement new use cases and applications. This summary is followed by a presentation and critical discussion of already existing industrial use cases. It will be shown: AI has already arrived in the brewing industry - longer than the publicly perceived existence of ChatGPT. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)