Sensor Data Processing Research Articles

Современные подходы к подготовке карт растительности Арктики

The emergence of multispectral and hyperzonal satellite imagery of the Earth’s surface has opened wide opportunities for vegetation mapping of remote and inaccessible territories of the Arctic. Availability of high and ultra-high resolution satellite images in the conditions of fine-grained mosaic of tundra landscapes allows distinguishing and recognizing the physiognomically similar and small territorial units of vegetation. The results of application of some modern approaches to vegetation mapping related to GIS-technologies and satellite imagery processing methods are presented on the example of key areas of Eastern European tundra. Despite the development and active usage of remote sensing data, GIS and other latest technologies, the vegetation maps creation is based on field work and geobotanical relevés. Map’s accuracy and informativeness is determined, first of all, by the completeness of coverage of different vegetation types by field descriptions and the degree of revealing its syntaxa composition. Currently, the geodatabase for the Eastern European tundra contains more than 10,000 relevés within about 70 key areas (Fig. 1). Unmanned aerial vehicles DJI Phantom 4, DJI Mavic Pro and DJI Mavic 2 Pro in combination with geobotanical relevés were used to study the structure and composition of complex territorial patches. To date tundra vegetation image bank includes more than 500,000 scenes. These images most fully reflect the physiognomic features, spatial structure of different syntaxa and their distribution according to relief (Fig. 2). This makes it possible to use them to diagnose the content and composition of contours identified on satellite imagery. Among the most important elements that we use in the preparation of geobotanic maps is the digital elevation model. It allows to visually assess the distribution of communities of different syntaxa across relief elements (Fig. 3), and on the other hand, it is used as an additional layer to spectral channels when processing satellite images. Nowadays, object based image analysis (OBIA) is widely used in remote sensing data processing (Srifitriani et al., 2019; Mikula et al., 2021; Sari et al., 2021; Tzepkenlis et al., 2023; etc.). Within OBIA, we perform image segmentation, which allows us to move from representing data as a set of pixels with different brightness indices to describing the image as a combination of objects (segments) each characterized by shape, area, mutual location, brightness, texture, and other characteristics. In our work we used segmentation of satellite images, after which classification processes were performed at the segment level. The paper presents an example of image segmentation for a fragment of a key area in the Severnaya River basin (Fig. 4) and geobotanical map (Fig. 5) prepared with the help of training sample by the method of supervised classification. A promising direction to solve the problems facing the study and mapping of vegetation, which is currently being actively developed, is related to the use of convolutional neural networks (CNNs) (Watanabe et al., 2020; Kislov, Korznikov, 2020; Kislov et al., 2021; Kattenborn et al., 2021; Korznikov et al., 2021, 2023; and many others). Analysis of publications on the application of these methods has shown that at this stage most of the current research in this area is devoted to recognizing individual categories of vegetation cover (tree species, agricultural crops, wetland and tropical forest types, etc.) in images. Works describing the processes of creating vegetation maps using SNA are still few in number (Langford et al., 2016, 2019; Kattenborn et al., 2019; Wagner et al., 2020; etc.), which is quite understandable — the processes of model training based on the analysis of multispectral and hyperspectral characteristics, texture of selected units, their shape, geomorphological, edaphic and other indicators, including materials of field studies, are under development. Based on CNNs, a new approach called semantic segmentation has emerged in the field of object recognition in images. The task of semantic segmentation differs significantly from conventional segmentation: it is the process of dividing an image into segments and simultaneously classifying these segments into various attributes. Thus, the application of a number of modern approaches to vegetation mapping shows their high efficiency for the Arctic territories. Work is currently underway to create large-scale maps of vegetation and habitats of the Eastern European tundra, which is planned to be carried out using convolutional neural network (CNN) technology.

AoI Optimization in the UAV-Aided Traffic Monitoring Network Under Attack: A Stackelberg Game Viewpoint

Intelligent Vehicle Systems (IVSs) devote to integrating the data sensing, processing, and transmission in the Vehicle to Everything (V2X) scenarios, where the Unnamed Aircraft Vehicle (UAV)-aided traffic monitoring network is one of the most significant applications. Moreover, since the central premise to support the IVS is timely and effectively sensing data processing, Age of Information (AoI) can precisely reflect the timeliness and effectiveness of the communication process in the UAV-aided traffic monitoring network. However, recent researches pay little attention to AoI minimization issue, especially when the malicious attacker attempts to deteriorate the network performance. The accurately modelling of the adversarial relationship between legitimate UAVs and attacker is not fully investigated. To make up this research gap, we start from the Stackelberg game viewpoint to investigate the AoI optimization problem in the UAV-aided traffic monitoring network under attack. Firstly, the system model and three-layer Stackelberg game-based optimization goal are established. Secondly, based on the Backward Induction (BI) analysis, the follower’s data sensing rate, transmission power, and the leader’s attacking power are determined by the Lagrange duality optimization technology successively. Moreover, the sub-gradient update-based optimization technology is used to achieve the Stackelberg Equilibrium (SE). Finally, simulations are performed under various parameters. The evaluation results present better performance of our proposed approach when compared with the typical baselines.

Modular Hardware/Software Architecture for Edge Units in Highly Flexible Manufacturing Systems

With the increasing demand for individualized production and swift adaptability to changing market needs, new manufacturing system concepts emerge. Aiming for high flexibility and scalability, they rely on universal reconfigurable machines that can be used in various production steps by automatically changing tools and sensors. Consequently, the data processing infrastructure is required to support the frequent exchange of process-specific software applications, ranging from control and monitoring tasks to complex sensor data processing. This can be a major challenge when computation-intensive software is supported by hardware acceleration to achieve the desired performance or latency requirements. In this paper, we present an edge unit architecture that allows for hosting such applications while meeting the needs for modularity and reconfigurability. It builds upon a hypervisor to partition fixed processing resources among the applications. We present a mechanism that extends this partitioning to field-programmable gate arrays (FPGA) by using dynamic partial reconfiguration. This provides the option to deploy application-specific hardware accelerators with computation-intensive applications while maintaining modularity and on-line reconfigurability. We conclude the paper by pointing out the potential of the proposed architecture based on a use case for automated disassembly in a reconfigurable robotic cell.

Matrix data analysis methods for applications in laser beam position measurement modules

This paper addresses the problem of selecting an optimal algorithm suitable for real-time processing of matrix sensor data in laser beam positioning applications. We compare four different algorithms and prove that the chosen one provides the same results in time by several orders of magnitude lower and with negligible memory consumption, making it suitable for use in microcontroller-based sensing modules. Full Text: PDF ReferencesS. Donati, Electro-Optical Instrumentation-Sensing and Measuring with Lasers (Prentice Hall, Upper Saddle River, NJ, 2004). DirectLink K.N. Joo, J.D. Ellis, E.S. Buice, J.W. Spronck, R.H. Munning Schmidt, "High resolution heterodyne interferometer without detectable periodic nonlinearity", Optics Expr. 18(2), 1159 (2010). CrossRef G. Budzyń, J. Rzepka, "Study on Noises Influencing the Accuracy of CNC Machine Straightness Measurements Methods Based on Beam Position Detection", J. Machine Engin. 20(3), 76 (2020). CrossRef Reference materials for PSD and CMOS sensors, , referenced 7-11-2022 CrossRef S. Das, A. Saha, "Laser Beam Position-Dependent PSD-Based Calibrated Self-Vibration Compensated Noncontact Vibration Measurement System", IEEE Trans. Instr. Meas. 68(9), 3308 (2019). CrossRef P. Zhang, J. Liu, H. Yang, L. Yu, "Position Measurement of Laser Center by Using 2-D PSD and Fixed-Axis Rotating Device", IEEE Access 7, 140319 (2019). CrossRef J. Rzepka, G. Budzyn, "Laser measurement system for machine tools", Proc. SPIE 5144, 840 (2003). CrossRef S. Xiang, H. Li, M. Deng et al., "Geometric error identification and compensation for non-orthogonal five-axis machine tools", Int. J. Adv. Manuf. Technol. 96, 2915 (2018). CrossRef N. Hagen, E. Dereniak, "Gaussian profile estimation in two dimensions", App. Opt. 47(36), 6842 (2008). CrossRef S.M. Ross, Introduction to Probability and Statistics for Engineers and Scientists (Academic Press 2014). CrossRef

DETERMINATION OF THE TOURIST POSITION OF LAKES OF WESTERN AND CENTRAL KAZAKHSTAN BY SPACE SURVEY

The article reviews the methodology for assessing water bodies in the territories of Western and Central Kazakhstan. The concept of sustainable development of the region for recreational activities of this study is relevant. Literary sources of research of lakes of Western, Northern, and Central Kazakhstan are analyzed. The study area has excellent opportunities for the development of certain types of tourism. As a result of topographic and bathymetric surveys of water resources of geosystems, the most reliable morphometric data were obtained. Analyzing of the results of remote sensing data processing and index mapping made it possible to assess the area of lakes and determine their main metric characteristics. The article also evaluates the accuracy of the results obtained using various remote sensing materials. The geographical position of the lakes causes differences in the factors of formation of their hydrological regime. The altitude position of the studied lakes is also manifested in the peculiarity of the course of long-term changes in their characteristics. These received and processed research materials will be used for tourist and recreational activities. Recommendations on the tourist and recreational use of lake systems of Kazakhstan are given.

Monitoring ecological status of wetlands using linked fuzzy inference system- remote sensing analysis

The present study develops an applicable model to simulate the ecological status of saltwater lakes in which depth and total dissolved solids are selected as the effective factors on aquatic habitats. First, spectral images of the operational land imager of Landsat 8 were used to simulate distribution of depth and total dissolved solids by applying two feed forward neural networks. Next, a Mamdani fuzzy inference system was used to develop habitat suitability rules of Artemia and Flamingo as the selected target species in which expert opinions were considered. Finally, habitat suitability maps of target species were generated by linking distribution maps of selected effective parameters and fuzzy inference system. Based on the results in the Urmia lake as a case study, the Nash–Sutcliffe efficiency coefficients of depth and total dissolved solids are 0.88 and 0.5 which indicates the proposed method for simulating distribution of these parameters is reliable. Average depth in the simulated date is 227 cm, while average simulated total dissolved solids is 264 g per litre. Simulation of habitat suitability maps demonstrated that average habitat suitability of Artemia is less than 30% in the most areas of the lake. Moreover, average habitat suitability of the Flamingo is less than 10% which implies the ecological status of the lake is critical and ecological restoration is necessary. The main advantage of the proposed method is to develop a framework for combining the expert opinions with remote sensing data processing to generate habitat suitability maps in lakes.

Thermal Management System of the UNICARagil Vehicles—A Comprehensive Overview

The collaboration project UNICARagil aiming to develop new autonomous battery electric vehicle concepts has progressed and the four vehicle prototypes have been built up. All seven universities and six industrial partners have worked towards this milestone. At the time of writing the four vehicles are operational and can be driven by a safety driver using a sidestick. The automated driving functions are being applied on the test track and first demonstrations are carried out. This paper gives an overview of the results, which have been achieved within the work package of the thermal onboard network. The thermal management system including the heating, ventilation and air conditioning system and its development process is explained in detail. Furthermore, climate chamber measurements with prototype hardware of a sensor data processing computer and the integration of the air conditioning control unit into the vehicle’s automotive service-oriented architecture framework are described. A coupled simulation approach to predict occupant thermal comfort in one vehicle variant is presented. Simulation results using environmental conditions typical for a European summer show a comfortable environment for all six occupants. In addition, the simulation and development process of a thermoelectric heat pump is shown. First measurement results with the heat pump on a test bench are highlighted which show an achievable coefficient of performance greater than two.

Kontrol Nutrisi Tanaman Hidroponik Berbasis Monitoring Internet of Things

Hydroponics is a way of farming that does not use soil, but only uses water that contains nutrients. The growth of hydroponic plants can be influenced by various factors, one of which is a lack of water nutrition in hydroponic plants. Therefore, this study aims to create a system that functions to control water nutrition, as well as the availability of sufficient water in hydroponic reservoirs. This control process uses NodeMCU esp8266 and processes sensor data and then sends it to firebase which will be displayed to Android with Internet of Thing (IoT) technology. If the nutritional condition being monitored is reduced, this nutrition control device will receive input from the sensor to add nutrients automatically. The addition of nutrients uses an on-off control system, when the TDS sensor reads the water nutrients are reduced the nutrient pump will be on until it reaches the initial nutrient setpoint, then the nutrient pump will be off. The result of reading sensor data obtains the percentage error for the Total Dissolve Solid (TDS) sensor with a comparison of TDS meter of 5.746%.

Long-Range Technology-Enabled Smart Communication: Challenges and Comparison

Among other technologies, the Internet of Things (IoT) is one of the promising technologies that will help us to make our environment smarter and gather, analyze and process sensor data without any human intervention. Low Power Wide Area Network (LPWAN) is a technology that is part of the IoT, in which is the long-range (LoRa) technology that can provide wide coverage at a cost of very low power and that can boost data communication in our smart environment. The ease of their integration with the electronic system has opened a lot of new scope in the field of low-power wide-area networks in IoT applications. This paper provides a well-rounded review of LPWAN technology and in-depth details of LoRa technology. A good insight is given into the implementation of LoRa technology to design smart cities and the types of antenna technology used in LoRa data communications. Finally, a few open issues, possible solutions and future development are pointed out.

Remote sensing image scene classification by transfer learning to augment the accuracy

Efficiently executing image categorization with high spatial quality imagery from remote sensing can bring great benefits to scene classification. Effective feature representation is critical in the development of high-performance scene categorization techniques because sensor data processing is tough. Remote sensing as well as deep learning abilities have made it easier to extract spatiotemporal information for classification. Furthermore, other scientific disciplines, together with remote sensing, have made significant advances in image categorization by convolutional neural networks (CNNs), and transfer learning is being combined. Image categorization in this article was performed to enrich the accuracy of Scene classification using Transfer learning utilizing pre-trained Alex Net, and Visual Geometry Group (VGG) networks and compared with feature extraction methods. First, features were retrieved from the pre-trained network's second fully-connected layer and employed in SVM classification. Second, substituting the last layers of pre-trained networks with the notion of transfer learning was used to categorize new datasets. It is executed on the UCM Dataset as well as the SIRI-WHU Dataset. The proposed methodologies produced improved accuracy of 95% for UCM, 93% for SIRI-WHU Datasets.

R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC

In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.

The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics

This study presents vegetation land cover mapping based on Remote Sensing (RS) data processing, including satellite imagery scenes from Sentinel-2A, ASTER, and Landsat, in order to show the spatio-temporal correlation between the climate and environmental setting in Morocco. The main objective of this study is to contribute on land use planning and ecosystem protection. The distribution of vegetation in the study area was analyzed based on the satellite image processing and computing Normalized Difference Vegetation Index (NDVI). The study was conducted on different satellite imagery (Sentinel-2A, ASTER and the Landsat TM5, ETM+, and OLI) using SAGA GIS and SNAP software, to achieve two specific objectives: (1) conducting a comparative analysis of the NDVI calculation results from three satellite images (i.e., sentinel-2A, ASTER, Landsat 8); (2) highlighting the dynamics of the NDVI index from 1984 to 2020 by processing 35 satellite images from Landsat data Archive using SAGA GIS software, and then correlating these changes with other vegetation indices for the Marrakech-Haouz region provided by NOAA, namely: Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and NDVI indices. This study shows a repetitive fluctuation in the decrease of NDVI mean value from 1984 to 2020, due to the contrasting climate setting of the study area and the influence of the alternation of humid and dry periods in the High Atlas of Marrakech. In addition, the presented results underline that open-source software, such as SAGA GIS and SNAP can provide satisfactory results for vegetation coverage dynamics.

Lean-logistics in constructive geography: theoretical and methodological foundations

Research problem introduction. The main research goal of this paper is to provide the lean-logistics geosystem research concept as a contemporary approach of constructive geography closely connected with industry as a part of the service sector based on leading scientific principles, GIS-technologies and remote sensing data processing software involvement, and to give some details about this concept applied implementation. All these arguments allow us to specify and to study in-depth the peculiarities, key types, and specificities of the lean-logistics. In this paper, Lean-logistics is understood as an environmental service that helps to improve biodiversity and a stable environment. Lean-logistics itself can be developed as a type of modelling land use and land management activities. This activity can be even thought of as educational innovation with nature-based solutions. The key geographic-centered ideas of the concept of Lean-logistics are integration, engineering, economization, ecologization, socialization, and humanization of modern human activities. The methodological basis of Lean-logistics is presented by three sections, such as Section I. Theory of logistics (for instance, such aspects as general characteristics of logistics and its key features, Varieties of logistics and its classification, Lean logistics as a science: its place in logistics, key distinct features, etc.), Section II. Applied logistics (with such aspects as Military logistics, Business logistics, Environmental logistics, Urban logistics, and Logistics services), and Section III. Logistical construction (Lean-construction of commodity flows and Lean-construction in the field of services). The practical basis of Lean-logistics is formed by logic modelling (studios). Logic modelling in Lean-logistics are called logistics studios and are understood as a form of active practical activities of researchers. Summarizing and generalizing key ideas discussed in the text, the authors underline that the conceptual framework of the lean-logistics geosystem research is based on the implementation of four cross-cutting lines such as environmental security and sustainable development, health and safety, civic responsibility, business undertaking, and financial literacy. All of them aimed at forming a conscious citizen, all areas of activity of which are permeated by logistics chains. Concluding the research, the authors emphasize that the lean-logistics concept may be quite useful within the proposed content of Lean-logistics as a page navigator E-collection in the form of a book trailer. Some pages of it contain examples of the multimedia scrapbook of interactive exercises (deal with some definite sections and/or topics) which are presented using the Print Screen-visualization of the logistics lotto. The use of such tools, forms, technologies, techniques in the study of the content of “Lean-logistics in geography”, as E-collection, book-trailer, hotlist, multimedia scrapbook, mind mapping software, QR-coding, Print Screen-visualization, with active use of GIS, educational Internet services, modernizes the learning process and with systematic use in practice makes them necessary and appropriate in the knowledge about geography and constructive geography.

Healthcare Monitoring System for Diabetic Patients Using Machine Learning

Abstract: Present-day technology provides quite an efficient way of monitoring an individual’s health. Bluetooth Low Energy (BLE)-based sensors can be considered as one of the solutions for checking personal vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG). In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device dataset, data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. The proposed real-time data processing system utilizes machine Learning algorithms to train the model. Machine learning–based classification methods were tested on a diabetes dataset in order to show that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. Furthermore, the proposed diabetes classification and prediction might be integrated with individualized diet and physical activity recommendations to improve patients' health quality and avert severe circumstances in the future.

Branched Neural Network based model for cutter wear prediction in machine tools

Cutter wear has a great impact on machining quality, which is particularly true when demand for machining accuracy is high. Therefore, cutter wear analysis is critical in assuring high machining quality and long tool life. However, it is highly dangerous and difficult to monitor and determine tool wear conditions during machining. This paper proposes a method of real-time machining status monitoring using the data collected by external sensors without interfering with the machining process. A tool wear forecast model is introduced in this article. Multiple process parameters and sensor data are collected. Due to missing data, however, data preprocessing is done applying the interpolation or extrapolation approach and data are standardized in order to create an artificial intelligence-based model. The said model will then be used to forecast tool wear during different processing stages and be compared with other different models, such as: AdaBoost, Support Vector Machine, Decision Tree, and Random Forest. The model developed in this study is based on a Branched Neural Network, which generates the best prediction results among all publicly available algorithms. This approach helps reduce the mean absolute error and root-mean-square error values and can improve by 0.11 in R2.

RETRACTED: Activation‐based recurrent learning method for wearable sensor data processing in human activity recognition

AbstractHuman activity recognition using wearable sensors is a universal application for providing additional real‐time support in AAL and medical healthcare systems. Input signals and sensor data are analyzed for the features, followed by correlation analysis to achieve better recognition accuracy. In this article, activation‐based recurrent learning for human activity detection is introduced. This method is based on recurrent neural networks that pre‐classifies the features and extract them for time and rate based on the input signals. Following the pre‐classification, the repetitive learning estimates the features for the correlation and activities to reduce false‐negative errors in processing. This recurrent process is validated using a conditional activation function and rectified linear unit for joint and concurrent layer processing. The combined processing feature of this proposed method helps to improve the accuracy by 92% and recall by 94.5% by reducing the computation time. The performance of the proposed method is analyzed for six activities, varying iterates, and at time intervals.

Views and experiences on the use of voice assistants by family and professionals supporting people with cognitive impairments.

The use of voice assistants (e.g., Amazon Alexa, Google Home) is being widely advocated as part of supporting people living with dementia at home. The development of this technology is largely driven by industry, and there is little research to determine how family carers and professionals use voice assistants with people with dementia. This paper presents the findings from further analysis of data from two studies: Study 1-a qualitative study that aimed to explore the views and expectations of family carers and professionals who use voice assistants to support people with a cognitive impairment at home, and Study 2-a qualitative enquiry aiming to identify the views and barriers on using voice assistants by family carers of people with dementia and professionals, together with a pilot case study evaluating a prototype that addresses barriers identified during the enquiry, entitled IntraVox. Based on processing of smart home sensor data, IntraVox uses a personalised human voice to send prompts and reminders to end-users to conduct daily life activities and to activate smart home processes using voice assistants. The results of the qualitative studies indicate that family carers and professionals use voice assistants in their caring role for home automation, skills maintenance and development, prompts and reminders, behaviour and environment monitoring, and for leisure and social interaction support. The findings also show that family carers and professionals have specific challenges that need to be overcome for them to realise the benefits that may be gained through the use of voice assistants within technology enabled care. The pilot case study also provided a useful demonstration that interoperability can be achieved to enable exchanges between IntraVox and voice assistants, with the aim of providing customised and personalised technological solutions that address some of the barriers that people with dementia and their carers face in the use of this technology.

Study of CNN deep learning model for temporal remote sensing data processing to map rabi crops

Convolution Neural Network (CNN) is a deep learning approach that has become an area of interest to the researchers for solving complex problems. With the evaluation of CNN, extraction of deep features for accurate classification of remotely sensed images has gained lot of momentum. This research work uses CNN deep learning model for mapping rabi crops (mustard and wheat) using temporal remote sensing data. The mappings of mustard and wheat crops have been conducted using multispectral temporal images obtained from Sentinel 2A/2B between the dates 1st Nov 2019 and 24th Feb 2020 of Banasthali, Rajasthan region. The CNN model created in this research work uses several layers along with 5 activation functions (relu, sigmoid, tanh, elu and selu) for finding out which activation function gave the best result for the proposed study. Batch size has been examined from 1 to 50 in the multiple of 5 and epochs have been tested from 1 to 10 for a training data of 200 samples for each class. The optimal value with a batch size of 5 and epochs of 30 has been calculated as best suited in this study as the accuracy was getting constant. The implementation of CNN model for classification shows better results as compared to the traditional approach as the CNN algorithms are learning algorithms. This also helps in handling the heterogeneity within a class. A comparison has been conducted using Modified Possibilistic c-Means (MPCM) fuzzy algorithm for the classification of the same set of classes. F-Score, Kappa and Overall Accuracy have been calculated to show how the proposed approach has been outperformed and the level of classification accuracy achieved.

Measurement of Cable Force through a Fiber Bragg Grating-Type Thin Rod Vibration Sensor and Its Application.

The key to evaluating the health status of cable-stayed bridges lies in the accuracy of cable force measurement. When measuring the cable force using the conventional frequency method, the clearance between the bracing cable and the protective tube is typically disregarded. Moreover, due to their large size, existing vibration sensors are difficult to install into protective tubes for steel strand-type bracing cables to measure the cable force. To address the above difficulties, a type of thin rod vibration sensor only 5 mm in diameter was designed based on the high sensitivity of Fiber Bragg grating (FBG), and high-throughput data processing software for engineering calculation (EC) was self-developed. Then, the recognition principle of the thin rod vibration sensor was theoretically analyzed and a step-by-step tension test was carried out. The results demonstrated that the relative error of the cable force measured by the thin rod vibration sensor within 12.865 Hz was less than 5% and the sensitivity reached 28.7 pm/Hz, indicating its high measurement precision. Upon subsequent application of the thin rod vibration sensor to a monitoring test in the field, the relative error of the fundamental frequency between artificial and natural excitations was less than 4%. In addition, the error relative to both the theoretical frequency and the third-party sampling frequency was less than 5%, further verifying the accuracy and applicability for monitoring the cable force of bridges under natural excitation. Compared with the traditional cantilever FBG sensor, the improved sensor with supporting data processing software has the advantages of small cross-section, high reliability, and good sensitivity. The research results can provide a reference for the subsequent accurate measurement of cable force and the development of a supporting sensor data processing system.

Collaborative Damage Detection Framework for Rail Structures Based on a Multi-Agent System Embedded with Soft Multi-Functional Sensors.

With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.