Set Of Sensors Research Articles

Optimization of upper respiratory tract cancer diagnosis method based on exhaled breath gas analysis

Introduction. Head and neck tumors comprise about 7 % of all malignant neoplasms. In the head and neck area, tumors are usually located on the tongue (25–40 %) and floor of mouth (15–20 %). In the majority of cases, diagnosis, especially at early disease stages, is based on clinical and histopathological evaluation of tumor process. However, recently development and implementation of non-invasive techniques of early diagnosis of upper respiratory tract tumors through detection of pathognomonic volatile tumor markers in the exhaled air has become topical.Aim. To evaluate diagnostic accuracy of sensory gas analysis device and artificial neural network for examination of exhaled gas samples from patients with oropharyngeal, laryngeal, laryngopharyngeal cancer and to establish the optimal conditions for sampling.Materials and methods. The study included 28 patients with oropharyngeal, laryngeal, laryngopharyngeal cancers and 25 healthy volunteers. The proposed technique is based on analysis of samples of exhaled gas from the studied individuals using a diagnostic device developed by the authors. The device detects volatile compounds in the exhaled air using a set of semiconductor sensors with subsequent analysis by a neural network. The exhaled air was sampled using two methods: in the morning in the fasted state before daily hygienic procedures and physical activity (prepared samples) and in the context of everyday life, nutrition and hygiene without restrictions before sampling (non-prepared samples).Results. Based on the signals from the sensors, the neural network classified and detected patients with malignant tumors. Accuracy of the prepared samples from healthy volunteers and patients with oropharyngeal, laryngeal, laryngopharyngeal cancers was 79.17 %, of non-prepared – 84.09 %.Conclusion. High diagnostic accuracy of the developed technique of non-invasive diagnosis of malignant tumors of the oropharyngeal, laryngeal, laryngopharyngeal areas using exhaled air samples which does not require special preparation of the studied samples was demonstrated.

Development and Validation of Concept of Innovative Method of Computer-Aided Monitoring and Diagnostics of Machine Components

The monitoring and diagnostic system has been suggested as a non-destructive diagnostic method. The structure and operation of the suggested system can be described by the concept of digital shadow (DS). One of the main DS subsystems is a set of sensors properly placed on the monitored object and coupled with a discrete data processing model created in Matlab/Simulink. The discrete model, as another important DS subsystem of the monitored facility, transfers information about its technical condition to the operator based on data recorded by the sensor system. The digital monitoring model processes the recorded data in the form of the object’s response to actions caused by its operating conditions. This work formalized a mathematical model determining the coupling of the digital model with the sensors placed on the monitored object. The formulated method using DS, due to its sensitivity, enables the detection of the damage in the object at an early stage. The tests allowed for detecting the regularities enabling the determination of the area of damage to the shaft and its size.

Human-Robot Interaction (HRI) using Machine Learning (ML): a Survey and Taxonomy

Human-robot interaction (HRI) which has become the fundamental need of the hour is born out of the necessity for studying the relation between humans and robots. This cutting-edge discipline is a multidisciplinary field that draws from computer science, robotics along with human-computer interaction and psychology. It focuses mainly on designing and programming machines, best known as automated machines or robots, which are used by humans to perform specific tasks in a timely manner and with higher quality. The key problem in HRI is to realize, shape, tune, and modelling the humanrobot interaction in a flexible manner. For the sake of reflecting and shaping the interactions between humans and robots, HRI is based on the fusion of the two areas: the people's behaviour and attitudes towards using these robots, as well as the physical, technological, and interactive features of the robots. As the robot has tightly integrated from a set of sensors that collect the data from the environment and send them to the processor which in turn translates the collected data into information that can be used in the robot itself, machine learning (ML) is a well-known research area that focuses on the building of well-stocked knowledge systems by using supervised and unsupervised algorithms. From a conceptual standpoint, this research survey and taxonomy pursue to present an in-depth evaluation and review of the most current state-of-the-art papers that have already been published so far and encompass the use of ML algorithms in the HRI field. Thus, a total of 30 research papers devoted to HRI were examined and analysed to give the most ML algorithms implemented in the field of HRI. Evidently, this study shows that the Neural and Reinforcement learning machine algorithms that are used mostly in the recent studies that have an interest in HRI use a machine learning algorithm with a supervised technique in a physical application. There are many challenges facing HRI using ML algorithms, which reduce the use of other ML algorithms such as deep and SVM learning algorithm. Unfortunately, these challenges limit use in social and mobile applications.

At-home wearable-based monitoring predicts clinical measures and biological biomarkers of disease severity in Friedreich’s Ataxia

BackgroundFriedreich ataxia (FRDA) results in progressive impairment in gait, upper extremity coordination, and speech. Currently, these symptoms are assessed through expert examination at clinical visits. Such in-clinic assessments are time-consuming, subjective, of limited sensitivity, and provide only a limited perspective of the daily disability of patients.MethodsIn this study, we recruited 39 FRDA patients and remotely monitored their physical activity and upper extremity function using a set of wearable sensors for 7 consecutive days. We compared the sensor-derived metrics of lower and upper extremity function as measured during activities of daily living with FRDA clinical measures (e.g., mFARS and FA-ADL) and biological biomarkers of disease severity (guanine-adenine-adenine (GAA) and frataxin (FXN) levels), using Spearman correlation analyses.ResultsThe results show significant correlations with moderate to high effect sizes between multiple sensor-derived metrics and the FRDA clinical and biological outcomes. In addition, we develop multiple machine learning-based models to predict disease severity in FRDA using demographic, biological, and sensor-derived metrics. When sensor-derived metrics are included, the model performance enhances 1.5-fold and 2-fold in terms of explained variance, R², for predicting FRDA clinical measures and biological biomarkers of disease severity, respectively.ConclusionsOur results establish the initial clinical validity of using wearable sensors in assessing disease severity and monitoring motor dysfunction in FRDA.

Integration of an IoT sensor with angle-of-arrival-based angle measurement in AGV navigation: A reliability study

Automated guided vehicle (AGV), which was initially designed for indoor operations in industry, has been increasingly applied in outdoor heavy-duty logistics tasks. In typical navigation tasks, such as the autonomous tracking of a designated object or a person, relative angle and relative distance between AGV and the target is required. To obtain the necessary information, various on-board sensors are extensively integrated. In this paper, the reliability of measuring the relative angle with the Angle-of-Arrival (AoA) method with two different Internet of Things (IoT) sensor sets from Texas Instrument (TI) and u-blox, according to Bluetooth 5.1 was investigated. The performance of IoT sensors was validated with angle accuracy parameters and received signal strength indicator (RSSI). The better IoT sensor was then integrated into the AGV navigation system, and the information gathered from IoT sensor enabled the AGV to turn toward the direction of the target. The process of AGV turning to the targeted direction based on IoT sensor information was respectively tested in the simulation and actual environment and evaluated by the disparity between the real relative angle and the rotation angle of the AGV. The results showed that this disparity was within ±5° in both simulated and actual environments, and methods for higher accuracy were proposed. In this way, the reliability and performance of Angle of Arrival (AoA) sensors in measuring the relative angle, which remains unexplored by other researchers, was systematically assessed contributing to extending the usability of AoA sensors in complex, real-world applications.

Point-of-care breath sample analysis by semiconductor-based E-Nose technology discriminates non-infected subjects from SARS-CoV-2 pneumonia patients: a multi-analyst experiment.

Metal oxide sensor-based electronic nose (E-Nose) technology provides an easy to use method for breath analysis by detection of volatile organic compound (VOC)-induced changes of electrical conductivity. Resulting signal patterns are then analyzed by machine learning (ML) algorithms. This study aimed to establish breath analysis by E-Nose technology as a diagnostic tool for severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pneumonia within a multi-analyst experiment. Breath samples of 126 subjects with (n=63) or withoutSARS-CoV-2 pneumonia (n=63) were collected using the ReCIVA® Breath Sampler, enriched and stored on Tenax sorption tubes, and analyzed using an E-Nose unit with 10 sensors. ML approaches were applied by three independent data analyst teams and included a wide range of classifiers, hyperparameters, training modes, and subsets of training data. Within the multi-analyst experiment, all teams successfully classified individuals as infected or uninfected with an averaged area under the curve (AUC) larger than 90% and misclassification error lower than 19%, and identified the same sensor as most relevant to classification success. This new method using VOC enrichment and E-Nose analysis combined with ML can yield results similar to polymerase chain reaction (PCR) detection and superior to point-of-care (POC) antigen testing. Reducing the sensor set to the most relevant sensor may prove interesting for developing targeted POC testing.

A multispectral camera in the VIS–NIR equipped with thermal imaging and environmental sensors for non invasive analysis in precision agriculture

Multispectral imaging (MSI) is a technique used to inspect materials properties in different domains, ranging from industrial to medical and cultural heritage and, recently, precision agriculture. Even though several MSI solutions are already commercially available, the research community is working to optimize multispectral cameras in terms of performance and cost. Systems for the agricultural field are usually very compact, combined with drones for large areas acquisition. In this work, we detail the implementation of an innovative, modular and low-cost solution of a multispectral camera based on three core camera systems in the optical (VIS–NIR) and thermal (LWIR) range. Multispectral imaging is performed with a rotating wheel of interchangeable band-pass filters. The system is also equipped with a set of environmental sensors to acquire CO2 concentration values, light intensity, temperature, and relative humidity of the surrounding environment. The technology and the measurement protocol were experimentally validated in laboratory and in open field. Advantages with respect to the available MSI cameras mounted on UAV is the integrated imaging in both the reflectance and the thermal emissive band in a close-up imaging setup and the use of environmental sensors. From the multispectral stack the spectral signature of the plants can be obtained and various vegetation indices (e.g., NDVI, NDRE) can be calculated for investigating the health status of the plant, while thermography provide additional monitoring. Close-up multispectral imaging is expected to tackle the new challenges of precision agriculture by enabling the acquisition of high-quality dataset on single plants.

Acceptability, validity and responsiveness of inertial measurement units for assessing motor recovery after gene therapy in infants with early onset spinal muscular atrophy: a prospective cohort study

BackgroundOnasemnogene abeparvovec gene replacement therapy (GT) has changed the prognosis of patients with spinal muscular atrophy (SMA) with variable outcome regarding motor development in symptomatic patients. This pilot study evaluates acceptability, validity and clinical relevance of Inertial Measurement Units (IMU) to monitor spontaneous movement recovery in early onset SMA patients after GT.MethodsClinical assessments including CHOPINTEND score (the gold standard motor score for infants with SMA) and IMU measurements were performed before (M0) and repeatedly after GT. Inertial data was recorded during a 25-min spontaneous movement task, the child lying on the back, without (10 min) and with a playset (15 min) wearing IMUs. Two commonly used parameters, norm acceleration 95th centile (||A||_95) and counts per minute (||A||_CPM) were computed for each wrist, elbow and foot sensors.Results23 SMA-patients were included (mean age at diagnosis 8 months [min 2, max 20], 19 SMA type 1, three type 2 and one presymptomatic) and 104 IMU-measurements were performed, all well accepted by families and 84/104 with a good child participation (evaluated with Brazelton scale). ||A||_95 and ||A||_CPM showed high internal consistency (without versus with a playset) with interclass correlation coefficient for the wrist sensors of 0.88 and 0.85 respectively and for the foot sensors of 0.93 and 0.91 respectively. ||A||_95 and ||A||_CPM were strongly correlated with CHOPINTEND (r for wrist sensors 0.74 and 0.67 respectively and for foot sensors 0.61 and 0.68 respectively, p-values < 0.001). ||A||_95 for the foot, the wrist, the elbow sensors and ||A||_CPM for the foot, the wrist, the elbow sensors increased significantly between baseline and the 12 months follow-up visit (respective p-values: 0.004, < 0.001, < 0.001, 0.006, < 0.001, < 0.001).ConclusionIMUs were well accepted, consistent, concurrently valid, responsive and associated with unaided sitting acquisition especially for the elbow sensors. This study is the first reporting a large set of inertial sensor derived data after GT in SMA patients and paves the way for IMU-based follow-up of SMA patients after treatment.

G.A.I.T: gait analysis interactive tool a pipeline for automatic detection of gait events across different motor impairments

We introduce an open-access tool capable of automatically extracting the timing of gait events during unconstrained locomotion across different neuromotor impairments. The gait analysis interactive tool is conceived as an assistant for gait assessment studies, both in healthy participants or in people with motor impairments affecting gait symmetry, regularity, or balance, as usually encountered in patients with neurological disorders. Our open-access pipeline makes it possible to automatically identify the time of key gait events (heel strike, toe off) from a single gyroscope axis (lateral mid-axis), simplifying experimental protocols, and can easily be used in everyday life conditions. The code is user-friendly and interactive. At each stage of analysis, it allows for possible adjustments and manual corrections of undetected or mismatched events. To implement, test, and validate our algorithm, we used three different databases of gait recordings that span from healthy subjects to patients affected by Parkinson’s disease. The pipeline consists of three main sections that allow us to segment, identify, and eventually correct the events within the gait cycle. The algorithm achieved an average accuracy of 99.23% over healthy participants, either with average weight or overweight, and a performance of 94.84% over patients with Parkinson’s disease. Even if gait analysis is a widely studied problem, so far, no open-source algorithm is available. The present work provides an easy tool capable of working with a minimum set of sensors and without any expensive platform or camera-based system. Employing three databases widely different for the environment, and for the subjects’ age and motor impairments highlights the versatility of our approach.

Quantifying the impact of vertical greenery systems (VGS) on Mediterranean urban microclimate during heat wave events

This research investigates the potential of vertical greenery systems (VGS) to modulate the outdoor microclimate in a real-scale Mediterranean climate street canyon with respect to a non-vegetated control during four heat waves and summer conditions. The cooling effect of VGS on air temperature was assessed using an identical set of sensors in both canyons. The effect on thermal comfort was evaluated using radiation fluxes, Mr.T tool, and the ENVI-met model that was validated against measured data.The VGS cooling effect during the heat waves was up to 0.36–2.04°C, and its duration was significantly extended. The correlation (R2 = 0.75) between the cooling effect, relative humidity, and wind direction, highlights that warmer and drier conditions increase the VGS cooling effect during heat waves.VGS improved the mean radiant temperature (Tmrt) above the canyon (3.9–4.1°C) and at the pedestrian level (2.21–2.8°C), while the physiological equivalent temperature (PET) and universal thermal climate index (UTCI) decreased by an average of 0.67–1.07 °C and 0.63–0.8 °C, respectively. Shaded walls improved the average Tmrt (11.7°C), PET (3.2°C), and UTCI (2.7°C), reflecting the importance of casting shadow in the urban environment.

Development of the neutral gas diagnostic system for neutral pressure measurements and gas analysis on the SPARC tokamak.

A suite of plasma diagnostics will be installed on the SPARC tokamak to allow for real-time plasma control, an investigation of high-field tokamak physics, and to de-risk the design of ARC, a compact fusion power plant with the aim to supply electricity to the grid. Among these diagnostics is the neutral gas diagnostics system (NTGS), a set of pressure sensors and gas analyzers used to monitor neutral pressure and gas composition for plasma control, optimization of wall conditioning, and helium ash removal, among other measurement functions linked to operational and scientific research needs. While reliable measurements of neutral pressure and gas composition have been fielded on existing magnetic-confinement fusion devices, SPARC represents a step increase in challenge due to its larger power density, higher field, high vacuum vessel bake temperatures, and higher neutron flux environment, as well as a step decrease in the accessibility for maintenance of in-vessel sensors. Multiple sensor types will be employed to have defense-in-depth and mitigate common failure modes. The NTGS system is currently progressing through final design, working to close out decisions using prototyping and analysis, and then moving on to procuring sensors for assembly and installation on SPARC. This paper outlines the current status of the system design and the diagnostic requirements that motivate neutral gas measurements on SPARC, as well as highlights the planned prototyping activities.

Data Quality Based Intelligent Instrument Selection with Security Integration

We propose a novel Data Quality with Security (DQS) integrated instrumentation selection approach that facilitates aggregation of multi-modal data from heterogeneous sources. As our major contribution, we develop a framework that incorporates multiple levels of integration in finding the best DQS-based instrument selection: data fusion from multi-modal sensors embedded into heterogeneous platforms, using multiple quality and security metrics and knowledge integration. Our design addresses the security aspect in the instrumentation design, which is commonly overlooked in real applications, by aggregating it with other metrics into an integral DQS calculus. We develop DQS calculus that formalizes the problem of finding the optimal DQS value. We then propose a Genetic Algorithm–based solution to find an optimal set of sensors in terms of the DQS they provide, while maintaining the level of platform security desirable by the user. We show that our proposed algorithm demonstrates optimal real-time performance in multi-platform instrument selection. To facilitate the framework application by the instrumentation designers and users, we develop and make available multiple Android applications.

Feasibility Study of Capacitor-Based Smart Washer Sensor Systems for Highway Sign, Luminaire/Light, and Traffic Signal Structures

This paper aims to provide an improvement on the current inspection methods for anchor bolts used in highway sign, luminaire/light, and traffic signal (SLTS) support structures to reduce the risk of structural failure as well as human labor costs. A feasibility study on a capacitance-based smart washer system was conducted. The capacitor-based sensor system design combined with customized polylactic acid three-dimensional printed attachments was proposed and tested. Laboratory tests on multiple sets of sensors were performed through initial testing and tightening–loosening testing; results have shown that the performance of the sensor system follows the working principle of the capacitor and, with a less than 5% error tolerance, a capacitance–pretension relationship was developed in the laboratory. Then the sensor system was installed at a sign structure specimen for long-term evaluation; the 12-month consecutive monitoring results show that the sensor system was able to perform steadily with a proper waterproof cover applied. The change of the capacitance was found at ±5% of the reference value, and torques were applied to verify the measurement results from the sensor system, indicating that the minor capacitance change was caused by a change of temperature. The results have proven the feasibility of this capacitor-based smart washer system that detects the pretension change inside the anchor bolts and performs steadily in a controlled on-site environment with a high durability. In summary, this study provides the possibility of improving the monitoring methods for anchor bolts in highway SLTS structures.

Low-cost and convenient screening of disease using analysis of physical measurements and recordings

In recent years, there has been substantial work in low-cost medical diagnostics based on the physical manifestations of disease. This is due to advancements in data analysis techniques and classification algorithms and the increased availability of computing power through smart devices. Smartphones and their ability to interface with simple sensors such as inertial measurement units (IMUs), microphones, piezoelectric sensors, etc., or with convenient attachments such as lenses have revolutionized the ability collect medically relevant data easily. Even if the data has relatively low resolution or signal to noise ratio, newer algorithms have made it possible to identify disease with this data. Many low-cost diagnostic tools have been created in medical fields spanning from neurology to dermatology to obstetrics. These tools are particularly useful in low-resource areas where access to expensive diagnostic equipment may not be possible. The ultimate goal would be the creation of a “diagnostic toolkit” consisting of a smartphone and a set of sensors and attachments that can be used to screen for a wide set of diseases in a community healthcare setting. However, there are a few concerns that still need to be overcome in low-cost diagnostics: lack of incentives to bring these devices to market, algorithmic bias, “black box” nature of the algorithms, and data storage/transfer concerns.

A spatio-temporal methodology for greenhouse microclimatic mapping

Greenhouse internal microclimate has been proven to be non-homogeneous in many aspects. However, this variability is only sometimes considered by greenhouse models, which often calculate climatic variables without any spatial reference. Farmers, on the other hand, may wish to have these differences highlighted as they could lead to aimed actions only for a specific area of the greenhouse, while at the same time, they are not willing to invest in sensors to be installed everywhere. This paper presents a data-driven methodology to generate a virtual 2D map of a greenhouse, which allows farmers to control any critical parameter they desire with minimum investment, as monitoring is done via soft sensing with only a few actual sensors. The proposed flow starts with a set of temporary sensors placed in the points of interest; then, a model for each of them is developed via linear regression and, finally, a map of the entire area can be derived by interpolating values from these models. This allows the generation of accurate models at a reduced cost as temporary sensors can be reused at other locations. The methodology has been tested on adjacent greenhouses and in two farms, where temperature and other climatic variables have been monitored. Experimental results show that the proposed methodology can reach an adjusted R2 value of 98% for predicting values in different greenhouse locations.

An Industrial Internet-of-Things (IIoT) Open Architecture for Information and Decision Support Systems in Scientific Field Campaigns.

Information and decision support systems are essential to conducting scientific field campaigns in the atmospheric sciences. However, their development is costly and time-consuming since each field campaign has its own research goals, which result in using a unique set of sensors and various analysis procedures. To reduce development costs, we present a software framework that is based on the Industrial Internet of Things (IIoT) and an implementation using well-established and newly developed open-source components. This framework architecture and these components allow developers to customize the software to a campaign's specific needs while keeping the coding to a minimum. The framework's applicability was tested in two scientific field campaigns that dealt with questions regarding air quality by developing specialized IIoT applications for each one. Each application provided the online monitoring of the acquired data and an intuitive interface for the scientific team to perform the analysis. The framework presented in this study is sufficiently robust and adaptable to meet the diverse requirements of field campaigns.

Detecting floating litter in freshwater bodies with semi-supervised deep learning

Researchers and practitioners have extensively utilized supervised Deep Learning methods to quantify floating litter in rivers and canals. These methods require the availability of large amount of labeled data for training. The labeling work is expensive and laborious, resulting in small open datasets available in the field compared to the comprehensive datasets for computer vision, e.g., ImageNet. Fine-tuning models pre-trained on these larger datasets helps improve litter detection performances and reduces data requirements. Yet, the effectiveness of using features learned from generic datasets is limited in large-scale monitoring, where automated detection must adapt across different locations, environmental conditions, and sensor settings. To address this issue, we propose a two-stage semi-supervised learning method to detect floating litter based on the Swapping Assignments between multiple Views of the same image (SwAV). SwAV is a self-supervised learning approach that learns the underlying feature representation from unlabeled data. In the first stage, we used SwAV to pre-train a ResNet50 backbone architecture on about 100k unlabeled images. In the second stage, we added new layers to the pre-trained ResNet50 to create a Faster R-CNN architecture, and fine-tuned it with a limited number of labeled images (≈1.8k images with 2.6k annotated litter items). We developed and validated our semi-supervised floating litter detection methodology for images collected in canals and waterways of Delft (the Netherlands) and Jakarta (Indonesia). We tested for out-of-domain generalization performances in a zero-shot fashion using additional data from Ho Chi Minh City (Vietnam), Amsterdam and Groningen (the Netherlands). We benchmarked our results against the same Faster R-CNN architecture trained via supervised learning alone by fine-tuning ImageNet pre-trained weights. The findings indicate that the semi-supervised learning method matches or surpasses the supervised learning benchmark when tested on new images from the same training locations. We measured better performances when little data (≈200 images with about 300 annotated litter items) is available for fine-tuning and with respect to reducing false positive predictions. More importantly, the proposed approach demonstrates clear superiority for generalization on the unseen locations, with improvements in average precision of up to 12.7%. We attribute this superior performance to the more effective high-level feature extraction from SwAV pre-training from relevant unlabeled images. Our findings highlight a promising direction to leverage semi-supervised learning for developing foundational models, which have revolutionized artificial intelligence applications in most fields. By scaling our proposed approach with more data and compute, we can make significant strides in monitoring to address the global challenge of litter pollution in water bodies.

Optimization of Chemical Engineering Processes in the Mining and Metal Industry: A Review

Process optimization is acutely vital: mining and a metal industries research. Through applying modelling and optimization tools, businesses see tremendous improvement in process efficiencies, declining costs of production, and ensuring high quality. The valuable of simulation tools have also been reflected in the design of the virtual plants which make possible to conduct test runs of different process scenarios thereby providing the opportunity to optimize the performance of the plants. So, even if these issues are close to being resolved, some challenges must still be tackled to approach the full field of process optimization enjoyment. One of the main drawbacks is the integration of sustainable development and environmental evaluation into optimization phase. This can be achieved by developing models that are amended to an environmental impact of each process parameters and afterwards an optimization of processes will be based on environment factors. The second challenge is for the research community to come up with more advanced and sophisticated mathematical models that are capable of not only capturing but also depicting the diverse and complex interactions that take place between different processes variables. Such an approach indeed requires implementation of more precise data analysis tools and techniques, including neural networks and genetic algorithms. While there are many difficulties in it way, the process optimization of a bright future is foreseeable. The fast growth of the technological sphere, such as machine-2-machine and big data analytics, enables process optimization which was unavailable before. By implementation of a set of sensors and real-time data analysis plant operators are being provided with necessary information that enables this way to exercise real-time decisions for the sake of the increase of the plant performance. Minimizing energy consumption, increasing the product yield, and less damaging to the environment is a potential focus for the research. Through the application of mathematical models, optimization algorithms, and simulation-based approaches, one can compare the different methods of each process and the produced product yields.

On-Site Sensor Sensitivity Adjustment Technique for a Maintenance-Free Heat Flow Monitoring in Building Systems

In this paper, an advanced solution for measuring heat flow through opaque building elements is presented. The solution is based on the implementation of a computer-aided technique for continuous monitoring of heat flow sensor performance and silent checking of their accuracy. In principle, the technique provides an ex-post compensation of potential deviations and inaccuracies detected during the measurement, which can be done without interfering with the ongoing experiment. As a consequence, traditional ‘non-smart’ sensors can be turned into advanced sensors with self-sensing or self-adjustment features at nearly zero additional costs. The high efficiency of the proposed approach was validated against experimental data obtained from an independent set of advanced high-sensitive sensors. Considering the validation results, the proposed technique brings an entirely new potential for maintenance-free applications for thermal performance monitoring in the building sector, typically for long-term experiments or measurements under dynamic environments.

FPID-RCP: A Control Method for a Swing-Type Wave Compensation Platform System

With the rapid development of marine engineering in recent years, offshore operations have become increasingly common, making wave compensation platforms crucial for safe operations at sea. This paper presents a pendulum-type wave compensation platform specifically designed for wave compensation applications. The main components of this wave compensation platform include a chassis, support base, hydraulic cylinders, telescopic rods, upper platform, three sets of balancing mechanisms, three sets of tilt angle sensors, and a control system. Firstly, to thoroughly understand the compensatory motion of the pendulum-type three-degree-of-freedom wave compensation platform, kinematic analysis of the entire system was conducted, and the motion inverse solution curves of the mechanism were obtained through simulation using motion simulation software. Secondly, to enhance the compensatory response performance of the platform, a fuzzy PID control algorithm was employed to control the system and achieve attitude control of the platform. Finally, through control system simulation, compared to PID control, fuzzy PID reduces system response delay and successfully meets the expected technical requirements and application needs.