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Wijaya, Tomi, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Bobby K. Pappachan, Arthur Wee, and Muhammad Izzat Roslan. "A Review on Sensors for Real-time Monitoring and Control Systems on Machining and Surface Finishing Processes." MATEC Web of Conferences 159 (2018): 02034. http://dx.doi.org/10.1051/matecconf/201815902034.
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One of the key components in real-time monitoring and control on machining and surface finishing processes are sensors. The advances of such system have triggered interesting questions on sensor selection that act as the fundamental before starting a project. This paper is made to review and answer the questions surrounding sensor selection. The paper first explains on the type of sensors commonly used in practice for real-time monitoring and control systems. After which, the paper discusses on how often the sensors are used on several machining and surface finishing processes and what are the reasons for the sensor selection. Thereafter, a review on the type features commonly analysed through these sensors is discussed. The paper expects reader would decide better upon selecting sensors and has a better direction in their project. Thus the paper works to guide reader to improve based on what has been completed before.
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Juboor, Saed Sa’deh, Sook-Ling Chua, and Lee Kien Foo. "Informative sensor selection on clustered sensors." Journal of Physics: Conference Series 1192 (March 2019): 012057. http://dx.doi.org/10.1088/1742-6596/1192/1/012057.
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Wentao, Shi, Chen Dong, Zhou Lin, Bai Ke, and Jin Yong. "Sensor Selection Scheme considering Uncertainty Disturbance." Journal of Sensors 2022 (February 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/2488907.
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In multisensor cooperative detection network, some random disturbances, energy carried by sensor, distance between target and sensor node, and so on all affect the sensor selection scheme. To effectively select some sensors for detecting the target, a novel sensor selection method considering uncertainty disturbance is proposed under constraints of estimation accuracy and energy consumption. Firstly, the sensor selection problem is modeled as a binary form optimization problem with a penalty term to minimize the number of sensors. Secondly, some factors (precision, energy, and distance, etc.) affecting the sensor selection scheme are analyzed and quantified, and energy consumption matrix and estimation precision threshold are given by matrix tra‘nsformation. Finally, the problem of minimizing sensor number after relaxation is solved by convex optimization method, obtaining sensor selection scheme by discretization and legitimization of the suboptimal solution after convex relaxation. Simulation results show that the proposed algorithm can ensure the minimum number of sensors, improving accuracy of state estimation and saving network energy.
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Reeves, J., R. Remenyte-Prescott, and J. Andrews. "Sensor selection for fault diagnostics using performance metric." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 4 (2018): 537–52. http://dx.doi.org/10.1177/1748006x18804690.
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As technology advances, modern systems are becoming increasingly complex, consisting of large numbers of components, and therefore large numbers of potential component failures. These component failures can result in reduced system performance, or even system failure. The system performance can be monitored using sensors, which can help to detect faults and diagnose failures present in the system. However, sensors increase the weight and cost of the system, and therefore, the number of sensors may be limited, and only the sensors that provide the most useful system information should be selected. In this article, a novel sensor performance metric is introduced. This performance metric is used in a sensor selection process, where the sensors are chosen based on their ability to detect faults and diagnose failures of components, as well as the effect the component failures have on system performance. The proposed performance metric is a suitable solution for the selection of sensors for fault diagnostics. In order to model the outputs that would be measured by the sensors, a Bayesian Belief Network is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the Bayesian Belief Network. The results of the Bayesian Belief Network can then be used to rank the component failures in order of likelihood of causing the sensor readings. To illustrate the proposed approach, a simple flow system is used in this article.
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Mehmood, Zahid, Ibraheem Haneef, and Florin Udrea. "Material selection for optimum design of MEMS pressure sensors." Microsystem Technologies 26, no. 9 (2019): 2751–66. http://dx.doi.org/10.1007/s00542-019-04601-1.
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Abstract Choice of the most suitable material out of the universe of engineering materials available to the designers is a complex task. It often requires a compromise, involving conflicts between different design objectives. Materials selection for optimum design of a Micro-Electro-Mechanical-Systems (MEMS) pressure sensor is one such case. For optimum performance, simultaneous maximization of deflection of a MEMS pressure sensor diaphragm and maximization of its resonance frequency are two key but totally conflicting requirements. Another limitation in material selection of MEMS/Microsystems is the lack of availability of data containing accurate micro-scale properties of MEMS materials. This paper therefore, presents a material selection case study addressing these two challenges in optimum design of MEMS pressure sensors, individually as well as simultaneously, using Ashby’s method. First, data pertaining to micro-scale properties of MEMS materials has been consolidated and then the Performance and Material Indices that address the MEMS pressure sensor’s conflicting design requirements are formulated. Subsequently, by using the micro-scale materials properties data, candidate materials for optimum performance of MEMS pressure sensors have been determined. Manufacturability of pressure sensor diaphragm using the candidate materials, pointed out by this study, has been discussed with reference to the reported devices. Supported by the previous literature, our analysis re-emphasizes that silicon with 110 crystal orientation [Si (110)], which has been extensively used in a number of micro-scale devices and applications, is also a promising material for MEMS pressure sensor diaphragm. This paper hence identifies an unexplored opportunity to use Si (110) diaphragm to improve the performance of diaphragm based MEMS pressure sensors.
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Kulkarni, Amol, Janis Terpenny, and Vittaldas Prabhu. "Sensor Selection Framework for Designing Fault Diagnostics System." Sensors 21, no. 19 (2021): 6470. http://dx.doi.org/10.3390/s21196470.
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In a world of rapidly changing technologies, reliance on complex engineered systems has become substantial. Interactions associated with such systems as well as associated manufacturing processes also continue to evolve and grow in complexity. Consider how the complexity of manufacturing processes makes engineered systems vulnerable to cascading and escalating failures; truly a highly complex and evolving system of systems. Maintaining quality and reliability requires considerations during product development, manufacturing processes, and more. Monitoring the health of the complex system while in operation/use is imperative. These considerations have compelled designers to explore fault-mechanism models and to develop corresponding countermeasures. Increasingly, there has been a reliance on embedded sensors to aid in prognosticating failures, to reduce downtime, during manufacture and system operation. However, the accuracy of estimating the remaining useful life of the system is highly dependent on the quality of the data obtained. This can be enhanced by increasing the number of sensors used, according to information theory. However, adding sensors increases total costs with the cost of the sensors and the costs associated with information-gathering procedures. Determining the optimal number of sensors, associated operating and data acquisition costs, and sensor-configuration are nontrivial. It is also imperative to avoid redundant information due to the presence of additional sensors and the efficient display of information to the decision-maker. Therefore, it is necessary to select a subset of sensors that not only reduce the cost but are also informative. While progress has been made in the sensor selection process, it is limited to either the type of the sensor, number of sensors or both. Such approaches do not address specifications of the required sensors which are integral to the sensor selection process. This paper addresses these shortcomings through a new method, OFCCaTS, to avoid the increased cost associated with health monitoring and to improve its accuracy. The proposed method utilizes a scalable multi-objective framework for sensor selection to maximize fault detection rate while minimizing the total cost of sensors. A wind turbine gearbox is considered to demonstrate the efficacy of the proposed framework.
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Abbas, Jabbar, Amin Al-Habaibeh, and Dai Zhong Su. "Sensor Fusion for Condition Monitoring System of End Milling Operations." Key Engineering Materials 450 (November 2010): 267–70. http://dx.doi.org/10.4028/www.scientific.net/kem.450.267.
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This paper describes the utilisation of multi sensor fusion model using force, vibration, acoustic emission, strain and sound sensors for monitoring tool wear in end milling operations. The paper applies the ASPS approach (Automated Sensor and Signal Processing Selection) method for signal processing and sensor selection [1]. The sensory signals were processed using different signal processing methods to create a wide range of Sensory Characteristic Features (SCFs). The sensitivity of these SCFs to tool wear is investigated. The results indicate that the sensor fusion system is capable of detecting machining faults in comparison to a single sensor using the suggested approach.
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Chodorek, Agnieszka, Robert Ryszard Chodorek, and Paweł Sitek. "Response Time and Intrinsic Information Quality as Criteria for the Selection of Low-Cost Sensors for Use in Mobile Weather Stations." Electronics 11, no. 15 (2022): 2448. http://dx.doi.org/10.3390/electronics11152448.
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Smart-city management systems use information about the environment, including the current values of weather factors. The specificity of the urban sites requires a high density of weather measurement points, which forces the use of low-cost sensors. A typical problem of devices using low-cost sensors is the lack of legalization of the sensors and the resulting inaccuracy and uncertainty of measurement, which one can attempt to solve by additional sensor calibration. In this paper, we propose a different approach to this problem, i.e., the two-stage selection of sensors, carried out on the basis of both the literature (pre-selection) and experiments (actual selection). We formulated the criteria of the sensor selection for the needs of the sources of weather information: the major one, which is the fast response time of a sensor in a cyber-physical subsystem and two minor ones, which are based on the intrinsic information quality dimensions related to measurement information. These criteria were tested by using a set of twelve weather sensors from different manufacturers. Results show that the two-stage sensor selection allows us to choose the least energy consuming (due to the major criterion) and the most accurate (due to the minor criteria) set of weather sensors, and is able to replace some methods of sensor selection reported in the literature. The proposed method is, however, more versatile and can be used to select any sensors with a response time comparable to electric ones, and for the application of low-cost sensors that are not related to weather stations.
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Su, Shen, Yanbin Sun, Xiangsong Gao, Jing Qiu, and Zhihong Tian. "A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection." Applied Sciences 9, no. 3 (2019): 437. http://dx.doi.org/10.3390/app9030437.
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Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.
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Guan, Fei, Wei-Wei Cui, Lian-Feng Li, and Jie Wu. "A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering." Sensors 20, no. 6 (2020): 1710. http://dx.doi.org/10.3390/s20061710.
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Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.
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Liang, Shuang, Yun Zhu, Hao Li, and Junkun Yan. "Evolutionary Computational Intelligence-Based Multi-Objective Sensor Management for Multi-Target Tracking." Remote Sensing 14, no. 15 (2022): 3624. http://dx.doi.org/10.3390/rs14153624.
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In multi-sensor systems (MSSs), sensor selection is a critical technique for obtaining high-quality sensing data. However, when the number of sensors to be selected is unknown in advance, sensor selection is essentially non-deterministic polynomial-hard (NP-hard), and finding the optimal solution is computationally unacceptable. To alleviate these issues, we propose a novel sensor selection approach based on evolutionary computational intelligence for tracking multiple targets in the MSSs. The sensor selection problem is formulated in a partially observed Markov decision process framework by modeling multi-target states as labeled multi-Bernoulli random finite sets. Two conflicting task-driven objectives are considered: minimization of the uncertainty in posterior cardinality estimates and minimization of the number of selected sensors. By modeling sensor selection as a multi-objective optimization problem, we develop a binary constrained evolutionary multi-objective algorithm based on non-dominating sorting and dynamically select a subset of sensors at each time step. Numerical studies are used to evaluate the performance of the proposed approach, where the MSS tracks multiple moving targets with nonlinear/linear dynamic models and nonlinear measurements. The results show that our method not only significantly reduces the number of selected sensors but also provides superior tracking accuracy compared to generic sensor selection methods.
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Hausmann, M., L. Häfner, and E. Kirchner. "A Procedure Model for the Systematic Sensor Selection and Integration into Technical Systems." Proceedings of the Design Society 2 (May 2022): 445–54. http://dx.doi.org/10.1017/pds.2022.46.
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AbstractNew sensor solutions are under development in the context of digitalization in order to integrate sensory functions into systems. When integrating sensors, the three domains of mechanical, electrical and information engineering must be considered. This results in complex development processes that require suitable procedure models. However, specific procedure models for sensor selection and integration are missing. This contribution proposes a procedure model for sensor selection and integration on the basis of the Munich Procedure Model (MPM) and gives an outlook on open research questions.
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Wu, Lei, Muneesh Maheshwari, Yaowen Yang, and Wensheng Xiao. "Selection and Characterization of Packaged FBG Sensors for Offshore Applications." Sensors 18, no. 11 (2018): 3963. http://dx.doi.org/10.3390/s18113963.
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With the development in the exploitation of maritime resources, the structural health monitoring (SHM) of offshore structures becomes necessary. This study focuses on addressing the practical issues of application of fiber Bragg grating (FBG) sensors for the SHM of offshore structures, in particular an FPSO (floating, production, storage, and offloading unit) vessel. Due to the harsh marine environment and tough working conditions, the FBG sensors must have sufficient protection and good repeatability for long-term monitoring. Thorough research has been conducted to identify the most suitable, commercially available protection packaging for FBG sensors for offshore applications. Further, the performance of the selected FBG sensor packaging is tested under conditions of strong sunlight, heavy rain, and salty water in order to emulate the marine environment. Moreover, the installation method of the packaged FBG sensors is equally important, as it ensures the repeatability and durability of the sensors for their long-term performance. It is shown that the packaged FBG sensors can be installed using resin-based epoxy to maintain the repeatability of the sensor over the long-term. Further, the packaged FBG sensors are installed and tested on a simple FPSO model. The experimental results under full load and ballast draft conditions show that the proposed FBG sensors are competent for the SHM of offshore structures.
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Wang, Qiang, Tao Cheng, Yijun Lu, Haichuan Liu, Runhua Zhang, and Jiandong Huang. "Underground Mine Safety and Health: A Hybrid MEREC–CoCoSo System for the Selection of Best Sensor." Sensors 24, no. 4 (2024): 1285. http://dx.doi.org/10.3390/s24041285.
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This research addresses the paramount issue of enhancing safety and health conditions in underground mines through the selection of optimal sensor technologies. A novel hybrid MEREC-CoCoSo system is proposed, integrating the strengths of the MEREC (Method for Eliciting Relative Weights) and Combined Compromise Solution (CoCoSo) methods. The study involves a three-stage framework: criteria and sensor discernment, criteria weight determination using MEREC, and sensor prioritization through the MEREC-CoCoSo framework. Fifteen criteria and ten sensors were identified, and a comprehensive analysis, including MEREC-based weight determination, led to the prioritization of “Ease of Installation” as the most critical criterion. Proximity sensors were identified as the optimal choice, followed by biometric sensors, gas sensors, and temperature and humidity sensors. To validate the effectiveness of the proposed MEREC-CoCoSo model, a rigorous comparison was conducted with established methods, including VIKOR, TOPSIS, TODIM, ELECTRE, COPRAS, EDAS, and TRUST. The comparison encompassed relevant metrics such as accuracy, sensitivity, and specificity, providing a comprehensive understanding of the proposed model’s performance in relation to other established methodologies. The outcomes of this comparative analysis consistently demonstrated the superiority of the MEREC-CoCoSo model in accurately selecting the best sensor for ensuring safety and health in underground mining. Notably, the proposed model exhibited higher accuracy rates, increased sensitivity, and improved specificity compared to alternative methods. These results affirm the robustness and reliability of the MEREC-CoCoSo model, establishing it as a state-of-the-art decision-making framework for sensor selection in underground mine safety. The inclusion of these actual results enhances the clarity and credibility of our research, providing valuable insights into the superior performance of the proposed model compared to existing methodologies. The main objective of this research is to develop a robust decision-making framework for optimal sensor selection in underground mines, with a focus on enhancing safety and health conditions. The study seeks to identify and prioritize critical criteria for sensor selection in the context of underground mine safety. The research strives to contribute to the mining industry by offering a structured and effective approach to sensor selection, prioritizing safety and health in underground mining operations.
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Duan, Mu, Yunbo Zhang, Ran Liu, et al. "Observation Capability Evaluation Model for Flood-Observation-Oriented Satellite Sensor Selection." Applied Sciences 13, no. 22 (2023): 12482. http://dx.doi.org/10.3390/app132212482.
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Satellite sensors are one of the most important means of collecting real-time geospatial information. Due to their characteristics such as large spatial coverage and strong capability for dynamic monitoring, they are widely used in the observation of real-time flood situation information for flood situational awareness and response. Selecting the optimum sensor is vital when multiple sensors exist. Presently, sensor selection predominantly hinges on human experience and various quantitative and qualitative evaluation methods. Yet, these methods lack optimization considering the flood’s spatiotemporal characteristics, such as different flood phases and geographical environmental factors. Consequently, they may inaccurately evaluate and select the inappropriate sensor. To address this issue, an innovative observation capability evaluation model (OCEM) is proposed to quantitatively pre-evaluate the performance of flood-water-observation-oriented satellite sensors. The OCEM selects and formulates various flood-water-observation-related capability factors and supports dynamic weight assignment considering the spatiotemporal characteristics of the flood event. An experiment involving three consecutive flood phase observation tasks was conducted. The results demonstrated the flexibility and effectiveness of the OCEM in pre-evaluating the observation capability of various satellite sensors across those tasks, accounting for the spatiotemporal characteristics of different flood phases. Additionally, qualitative and quantitative comparisons with related methods further affirmed the superiority of the OCEM. In general, the OCEM has provided a “measuring table” to optimize the selection and planning of sensors in flood management departments for acquiring real-time flood information.
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Spoorthi, K., Saha Snehanshu, and Mathur Archana. "Discrete Path Selection and Entropy Based Sensor Node Failure Detection in Wireless Sensor Networks." Cybernetics and Information Technologies 16, no. 3 (2016): 137–53. http://dx.doi.org/10.1515/cait-2016-0039.
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Abstract Exertion of wireless sensor networks has been increasing in recent years, and it imprints in almost all the technologies such as machine industry, medical, military and civil applications. Due to rapid growth in electronic fabrication technology, low cost, efficient, multifunctional and accurate sensors can be produced and thus engineers tend to incorporate many sensors in the area of deployment. As the number of sensors in the field increases, the probability of failure committed by these sensors also increases. Hence, efficient algorithms to detect and recover the failure of sensors are paramount. The current work concentrates mainly on mechanisms to detect sensor node failures on the basis of the delay incurred in propagation and also the energy associated with sensors in the field of deployment. The simulation shows that the algorithm plays in the best possible way to detect the failure in sensors. Finally, the Boolean sensing model is considered to calculate the network coverage of the wireless sensor network for various numbers of nodes in the network.
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Shieh, J., J. E. Huber, N. A. Fleck, and M. F. Ashby. "The selection of sensors." Progress in Materials Science 46, no. 3-4 (2001): 461–504. http://dx.doi.org/10.1016/s0079-6425(00)00011-6.
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Zhou, Chuandi, Yibing Liu, Ji Wu, and Chao Zhou. "Optimal Sensor Placement and Minimum Number Selection of Sensors for Health Monitoring of Transmission Towers." Shock and Vibration 2020 (December 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/2375947.
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Transmission towers are structurally complex, which makes it challenging to choose the right place and number of sensors for health monitoring. In this paper, optimal sensor placement of a cat-head-type transmission tower is conducted by using the Effective Independent Method (EIM) and a method is proposed for calculating the minimum number of sensors for structural health monitoring by combining EIM and Modal Assurance Criterion (MAC). The method for calculating the number of sensors prescribed in this paper derives a curve that shows the relationship between MAC value and the number of sensors. It is found that the MAC value decreases with increase in the number of sensors. When the number of sensors reaches a certain threshold, the curve tends to stabilize. Then, the number of sensors corresponding to the minimum MAC is proposed as the minimum number of sensors. Through calculation, the minimum number of sensors of the cat-head-type transmission tower is obtained. Also, the optimal sensor placement results show that the position of a large number of sensors includes the position of a smaller number of sensors.
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Aktaş, Başak, Talha Şahin, Ersin Toptaş, Aydın Güllü, Ahmet Feyzioğlu, and Sezgin Ersoy. "Material selection in sensor design for additive manufacturing." Journal of Mechatronics and Artificial Intelligence in Engineering 4, no. 2 (2023): 122–32. http://dx.doi.org/10.21595/jmai.2023.23794.
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Surface acoustic sensor technology plays a crucial role in numerous mechatronic systems as it enables the detection of physical interactions with the environment. These sensors, operating at micro scales, can be seamlessly integrated into various industrial applications. To harness their full potential, it is essential to establish a systematic approach for the design and manufacturing of these sensors to meet the demands of cutting-edge applications. This study focuses on creating a finite element analysis-based model, aiming to identify the most suitable Interdigital Transducer (IDT) material for the production of surface acoustic wave sensors using additive manufacturing techniques. By leveraging statistical methods, the research seeks to optimize material selection. The structural design parameters of the chosen material will then be utilized to evaluate the performance of the surface acoustic wave sensor. The study also delves into the prospective applications of this technology in diverse fields, shedding light on its promising future.
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Wulandari, Sari Ayu, Sutikno Madnasri, Ratih Pramitasari, and Susilo Susilo. "Feature Selection Method to Improve the Accuracy of Diabetes Mellitus Detection Instrument." IJID (International Journal on Informatics for Development) 9, no. 2 (2020): 72–79. http://dx.doi.org/10.14421/ijid.2020.09203.
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The need for aroma recognition devices or often known as enose (electronic nose), is increasing. In the health field, enose can detect early diabetes mellitus (DM) type 2 from the aroma of urine. Enose is an aroma recognition tool that uses a pattern recognition algorithm to recognize the urine aroma of diabetics based on input signals from an array of gas sensors. The need for portable enose devices is increasing due to the increasing need for real-time needs. Enose devices have an enormous impact on the choice of the gas sensor Array in the enose. This article discusses the effect of the number of sensor arrays used on the recognition results. Enose uses a maximum of 4 sensors, with a maximum feature matrix. After that, the feature matrix enters the PCA (Principal Component Analysis) feature extraction and clustering using the FCM (Fuzzy C Means) method. The number of sensors indicates the number of features. Enose using method for feature selection, it’s a variation from 4 sensors, where experiment 1 uses 4 sensors, experiment 2 uses a variation of 3 sensors and experiment 3 uses a variation of 2 sensors. Especially for sensors 3 and 4 using feature extraction method, PCA (Principal Component Analysis), to reduce features to only 2 best features. As for the variation of 2 sensors use primer feature matrix. After feature selection, the number of features is 2 out of 11 variations. Next, do the grouping using the FCM (Fuzzy C Means) method. The results show that using two sensors has a high accuracy rate of 92.5%.
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Wang, Litao, Elijah Kannatey-Asibu,, and Mostafa G. Mehrabi. "A Method for Sensor Selection in Reconfigurable Process Monitoring." Journal of Manufacturing Science and Engineering 125, no. 1 (2003): 95–99. http://dx.doi.org/10.1115/1.1531145.
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Sensor selection is important for process monitoring. Each time a machining system is reconfigured, the corresponding monitoring system needs to be re-designed for effective detection of the faults of interest. To accomplish this, a sensor selection methodology is proposed in this paper. Fuzzy theory is utilized for the multi-criteria decision-making process. Evaluation criteria are selected based on a thorough study of fault characteristics. Under subjective criteria, the ratings of different sensors are evaluated using linguistic terms, which are converted to trapezoidal fuzzy numbers. Under objective criteria, the fuzzy performance of each sensor is obtained directly from its specifications. The suitable sensor or sensors can be selected by aggregating and ranking the fuzzy numbers. Sensor selection in the turning process is used to illustrate the proposed methodology.
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Kour, Kanwalpreet, Deepali Gupta, Kamali Gupta, et al. "Monitoring Ambient Parameters in the IoT Precision Agriculture Scenario: An Approach to Sensor Selection and Hydroponic Saffron Cultivation." Sensors 22, no. 22 (2022): 8905. http://dx.doi.org/10.3390/s22228905.
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The world population is on the rise, which demands higher food production. The reduction in the amount of land under cultivation due to urbanization makes this more challenging. The solution to this problem lies in the artificial cultivation of crops. IoT and sensors play an important role in optimizing the artificial cultivation of crops. The selection of sensors is important in order to ensure a better quality and yield in an automated artificial environment. There are many challenges involved in selecting sensors due to the highly competitive market. This paper provides a novel approach to sensor selection for saffron cultivation in an IoT-based environment. The crop used in this study is saffron due to the reason that much less research has been conducted on its hydroponic cultivation using sensors and its huge economic impact. A detailed hardware-based framework, the growth cycle of the crop, along with all the sensors, and the block layout used for saffron cultivation in a hydroponic medium are provided. The important parameters for a hydroponic medium, such as the concentration of nutrients and flow rate required, are discussed in detail. This paper is the first of its kind to explain the sensor configurations, performance metrics, and sensor-based saffron cultivation model. The paper discusses different metrics related to the selection, use and role of sensors in different IoT-based saffron cultivation practices. A smart hydroponic setup for saffron cultivation is proposed. The results of the model are evaluated using the AquaCrop simulator. The simulator is used to evaluate the value of performance metrics such as the yield, harvest index, water productivity, and biomass. The values obtained provide better results as compared to natural cultivation.
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Lata, Suman, and Harish Kumar Verma. "Selection of Number and Locations of Multi-Sensor Nodes Inside Greenhouse." Pertanika Journal of Science and Technology 30, no. 2 (2022): 933–48. http://dx.doi.org/10.47836/pjst.30.2.05.
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One of the possible solutions for meeting the rising food demands is to opt for wireless sensor networks (WSN) monitored intelligent greenhouses. Such greenhouses require wireless sensor nodes rather than individual sensors to monitor and control the various parameters responsible for the growth of the plants. The appropriate selection of the number of wireless sensor nodes and their placement is crucial for optimizing the cost of the wireless sensor network by minimizing the number of sensor nodes as well as the measurement error. This paper extends the two techniques, namely, equal step (ES) and equal segment area (ESA) techniques, reported earlier for the selection of the number and locations of sensors to suit multi-sensor nodes inside a greenhouse. It also compares these techniques with the equal-spacing approach. The multi-sensor nodes considered here have temperature and luminosity sensors. Initial locations of the multi-sensor nodes have been fixed on the basis of temperature profile on the premise that temperature is the most important parameter for the growth of the plants. Evaluation of these techniques has been done on the basis of the root of the sum of square errors (RSSE) of the individual parameters. The ESA technique has been found to be better than the ES technique for the assumed temperature and luminosity profiles. In the future, this work may be extended to other situations where other than temperature is the most important parameter. The other direction in which the work can be extended may be considering the 2D or even 3D distribution of sensors.
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Heck, L. P., J. A. Olkin, and K. Naghshineh. "Transducer Placement for Broadband Active Vibration Control Using a Novel Multidimensional QR Factorization." Journal of Vibration and Acoustics 120, no. 3 (1998): 663–70. http://dx.doi.org/10.1115/1.2893881.
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This paper advances the state of the art in the selection of minimal configurations of sensors and actuators for active vibration control with smart structures. The method extends previous transducer selection work by (1) presenting a unified treatment of the selection and placement of large numbers of both sensors and actuators in a smart structure, (2) developing computationally efficient techniques to select the best sensor-actuator pairs for multiple unknown force disturbances exciting the structure, (3) selecting the best sensors and actuators over multiple frequencies, and (4) providing bounds on the performance of the transducer selection algorithms. The approach is based on a novel, multidimensional extension of the Householder QR factorization algorithm applied to the frequency response matrices that define the vibration control problem. The key features of the algorithm are its very low computational complexity, and a computable bound that can be used to predict whether the transducer selection algorithm will yield an optimal configuration before completing the search. Optimal configurations will result from the selection method when the bound is tight, which is the case for many practical vibration control problems. This paper presents the development of the method, as well as its application in active vibration control of a plate.
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Zhou, Jingmeng. "A Review of LiDAR sensor Technologies for Perception in Automated Driving." Academic Journal of Science and Technology 3, no. 3 (2022): 255–61. http://dx.doi.org/10.54097/ajst.v3i3.2993.
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After more than 20 years of research, ADAS is common in modern vehicles on the market. Automated driving systems have gradually moved from the research stage to public roads for commercial testing. These systems rely on information provided by onboard sensors that describe the state of the vehicle, its environment, and other participants. The selection and placement of sensing sensors are key factors in system design. In order to better understand the principles and functional implementation of sensing sensors, this paper reviews the existing and latest sensing sensor technologies and presents a detailed analysis of the principles, advantages, and disadvantages, as well as common types and performance of LiDAR sensor technologies, and then introduces two proposed solutions to the problem of how to improve the recognition accuracy of LiDAR sensors under the influence of different weather factors by selecting. Finally, this paper briefly introduces several latest LiDAR sensors under research and proposes an innovative multi-sensor fusion solution based on the existing research, and analyzes the feasibility.
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Sousan, Sinan, Alyson Gray, Christopher Zuidema, et al. "Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network." Sensors 18, no. 9 (2018): 3008. http://dx.doi.org/10.3390/s18093008.
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Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field.
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Suhaila, Suhaila, Danang Lelono, and Yunita Sari Sari. "Seleksi Fitur dengan Artificial Bee Colony untuk Optimasi Klasifikasi Data Teh menggunakan Support Vector Machine." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 12, no. 1 (2022): 81. http://dx.doi.org/10.22146/ijeis.63902.
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Tea quality can be recognized through the aroma it produces. Tea classification using e-nose generally only detects aroma using a general gas sensor. However, redundancy of sensor features can cause a decreasing in the system performance. Therefore we need a system that can select features so the classification performance becomes optimal. A software system of feature selection was built to optimize classification performance. Input data for the system is e-nose sensor response to 3 black tea qualities. The features are sensors on the e-nose instrument. Feature selection is implemented using wrapper approach, ABC algorithm is used for feature selection, then the selected features are evaluated by SVM classification. The results of the ABC-SVM system are then compared with the SVM only system. The results showed that from 12 e-nose sensors, sensors that most characterized black tea quality were TGS 2600, TGS 813, TGS 825, TGS 2602, TGS 2611, TGS 832, TGS 2612, TGS 2620 and TGS 822. Meanwhile, MQ-7, TGS 826 and TGS 2610 sensors are redundant in the system because the gas detected by the 3 sensors can be represented by other sensors. With the reduction in features to 9, the classification accuracy performance increased by 16.7%.
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Rahmantyo, Wikan Haryo, and Danang Lelono. "Analisis Respons Sensor Electroni Tongue terhadap Sampel Ganja menggunakan Support Vector Machine." IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) 9, no. 2 (2019): 141. http://dx.doi.org/10.22146/ijeis.49173.
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Electronic tongue sensors consisting of 16 sensor array made of TOMA and OA lipids that have been used to classify samples of pure cannabis, cannabis mixed with tea and cannabis mixed with tobacco does not involve the feature selection technique so that a lot of duplicated data is generated from data sampling. Feature selection is performed using PCA. Data analysis resulted in loading values shows the contribution of each sensor, and the similarity in sensor performance in characterizing samples, then analyzed using the correlation test so that the sensors that produce redundant information are known. Validation is performed using the SVM method and the classification performance is compared to the original sensor.The sensor optimization produces a subset of features with 6 sensors (Sensor 7, Sensor 10, Sensor 12, Sensors 13, Sensor 14 and Sensor 15) in the cannabis-tea sample test and a feature subset with 3 sensors (Sensor 3, Sensor 7 and Sensor 14) in the cannabis-tobacco sample test. Sensor optimization that has been done produced classification accuracy by 100% and shorten the running time by a difference of 0.578 microseconds in the test of cannabis-tea samples and a difference of 1.696 microseconds in the test of cannabis-tobacco samples.
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Wang, Xingwang, Debing Wei, Xiaohui Wei, Junhong Cui, and Miao Pan. "HAS4: A Heuristic Adaptive Sink Sensor Set Selection for Underwater AUV-Aid Data Gathering Algorithm." Sensors 18, no. 12 (2018): 4110. http://dx.doi.org/10.3390/s18124110.
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In this paper, we target solving the data gathering problem in underwater wireless sensor networks. In many underwater applications, it is not quick to retrieve sensed data, which gives us the opportunity to leverage mobile autonomous underwater vehicles (AUV) as data mules to periodically collect it. For each round of data gathering, the AUV visits part of the sensors, and the communication between AUV and sensor nodes is a novel high-speed magnetic-induction communication system. The rest of the sensors acoustically transmit their sensed data to the AUV-visit sensors. This paper deploys the HAS 4 (Heuristic Adaptive Sink Sensor Set Selection) algorithm to select the AUV-visited sensors for the purpose of energy saving, AUV cost reduction and network lifetime prolonging. By comparing HAS 4 with two benchmark selection methods, experiment results demonstrate that our algorithm can achieve a better performance.
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LORENZ, MARKUS. "SENSOR SELECTION." New Electronics 55, no. 5 (2022): 16–17. http://dx.doi.org/10.12968/s0047-9624(22)60169-x.
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Chan, Tung Jung, Ching Mu Chen, and Tsair Rong Chen. "A Forwarding Station Integrated with Optimal Cluster Number Selection in Wireless Sensor Networks." Applied Mechanics and Materials 201-202 (October 2012): 745–48. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.745.
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In wireless sensor networks, power consumption is the most important issue. That is wireless sensors are normally deployed into unattended places where power of sensors is hard to be charged. Indeed, the network lifetime of wireless sensor networks equipped with city power or deployed into attended place is much longer than those wireless sensors equipped with batteries. In general, wireless sensor nodes are connected together and become a network after deployed into certain places. With the certain range places that wireless senor nodes deployed into, finding the optimal clusters can increase the entire network lifetime. Also, adding the forwarding station extends the network lifetime. Therefore, this paper proposes the integration of both the forwarding station and optimal clusters in ad-hoc wireless sensor networks. Simulation results show that the entire network lifetime proposed is extended in this paper compared to both optimal cluster number selection and normal forwarding station.
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Kain, Raslan, and Hazem Hajj. "An Optimization Approach to Multi-Sensor Operation for Multi-Context Recognition." Sensors 21, no. 20 (2021): 6862. http://dx.doi.org/10.3390/s21206862.
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Mobile devices and sensors have limited battery lifespans, limiting their feasibility for context recognition applications. As a result, there is a need to provide mechanisms for energy-efficient operation of sensors in settings where multiple contexts are monitored simultaneously. Past methods for efficient sensing operation have been hierarchical by first selecting the sensors with the least energy consumption, and then devising individual sensing schedules that trade-off energy and delays. The main limitation of the hierarchical approach is that it does not consider the combined impact of sensor scheduling and sensor selection. We aimed at addressing this limitation by considering the problem holistically and devising an optimization formulation that can simultaneously select the group of sensors while also considering the impact of their triggering schedule. The optimization solution is framed as a Viterbi algorithm that includes mathematical representations for multi-sensor reward functions and modeling of user behavior. Experiment results showed an average improvement of 31% compared to a hierarchical approach.
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Ferreira, Pedro M., Miguel A. Machado, Marta S. Carvalho, and Catarina Vidal. "Embedded Sensors for Structural Health Monitoring: Methodologies and Applications Review." Sensors 22, no. 21 (2022): 8320. http://dx.doi.org/10.3390/s22218320.
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Sensing Technology (ST) plays a key role in Structural Health-Monitoring (SHM) systems. ST focuses on developing sensors, sensory systems, or smart materials that monitor a wide variety of materials’ properties aiming to create smart structures and smart materials, using Embedded Sensors (ESs), and enabling continuous and permanent measurements of their structural integrity. The integration of ESs is limited to the processing technology used to embed the sensor due to its high-temperature sensitivity and the possibility of damage during its insertion into the structure. In addition, the technological process selection is dependent on the base material’s composition, which comprises either metallic or composite parts. The selection of smart sensors or the technology underlying them is fundamental to the monitoring mode. This paper presents a critical review of the fundaments and applications of sensing technologies for SHM systems employing ESs, focusing on their actual developments and innovation, as well as analysing the challenges that these technologies present, in order to build a path that allows for a connected world through distributed measurement systems.
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Cherqui, F., R. James, P. Poelsma, et al. "A platform and protocol to standardise the test and selection low-cost sensors for water level monitoring." H2Open Journal 3, no. 1 (2020): 437–56. http://dx.doi.org/10.2166/h2oj.2020.050.
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Abstract Water infrastructure in cities is complex and requires proactive management to optimise function. The scale and distribution of assets across municipalities requires affordable systems which can trigger alerts. Systems underpinned by low-cost sensors could meet increasing monitoring needs: more assets, more often, and at a better resolution. However, low-cost sensors require appropriate testing to assess their performance and optimise their use. Here, we focus on low-cost water level sensors, often considered as the main monitoring parameters for water-related infrastructures. We developed a platform and testing protocol to assess the suitability of low-cost sensors. We assessed the performance of three widely used low-cost sensors: laser-ranging, ultrasonic-ranging, and pressure. Our main results showed that the ultrasonic sensor offers the best price to accuracy ratio, and the pressure sensor provides the highest accuracy while still at a very low cost. Our platform and protocol provide a standardised testing and calibration method which can be applied to any sensor. The platform can be used to gather and share results, to enhance community knowledge and encourage the use of new (low-cost or not) sensors. The development of low-cost sensors is an important step toward the wider use monitoring systems for water infrastructure.
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Norris, G. A., and R. E. Skelton. "Selection of Dynamic Sensors and Actuators in the Control of Linear Systems." Journal of Dynamic Systems, Measurement, and Control 111, no. 3 (1989): 389–97. http://dx.doi.org/10.1115/1.3153066.
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This paper selects sensors and actuators (location, type, and number) from an admissible set. We seek an approximate solution to this integer programming problem. Given the optimal use of the entire admissible set of sensors and actuators, it is possible to decompose the quadratic cost function into contributions from each stochastic input and each weighted output. In the past, these suboptimal cost decomposition methods of sensor and actuator selection have been used to locate perfect (infinite bandwidth) sensors and actuators on large scale systems. This paper extends these ideas to the more practical case of imperfect actuators and sensors with dynamics of their own. Secondly, the old cost decomposition methods are discarded for improved formulas for sensor and actuator deletion (from the admissible set). These results show that there exists an optimal number of actuators (it is possible to use too few and too many). Preliminary attempts to solve this new research question are described. It is also shown that there exists optimal dynamics of the actuators. NASA’s SCOLE example demonstrates the concepts.
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Тulebekova, А. S., Ye B. Utepov, and Sh Zh Zharasov. "APPLICATION FEATURES OF CONCRETE STRENGTH MONITORING SENSORS." Bulletin of Kazakh Leading Academy of Architecture and Construction 81, no. 3 (2021): 146–56. http://dx.doi.org/10.51488/1680-080x/2021.3-18.
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The paper presents an algorithm of application of concrete strength monitoring sensors taking into account such features as a selection of sensor type, selection of concrete mixture calibration method according to regulated requirements, consideration of concrete maturity sensor location, degree of influence of hardening temperature on strength gain based on isotherms construction. This algorithm was reflected in practice, as the wireless sensor for concrete strength monitoring developed within the project was applied according to the selected scheme in real-time.
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Santos-Ruiz, Ildeberto, Francisco-Ronay López-Estrada, Vicenç Puig, Guillermo Valencia-Palomo, and Héctor-Ricardo Hernández. "Pressure Sensor Placement for Leak Localization in Water Distribution Networks Using Information Theory." Sensors 22, no. 2 (2022): 443. http://dx.doi.org/10.3390/s22020443.
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This paper presents a method for optimal pressure sensor placement in water distribution networks using information theory. The criterion for selecting the network nodes where to place the pressure sensors was that they provide the most useful information for locating leaks in the network. Considering that the node pressures measured by the sensors can be correlated (mutual information), a subset of sensor nodes in the network was chosen. The relevance of information was maximized, and information redundancy was minimized simultaneously. The selection of the nodes where to place the sensors was performed on datasets of pressure changes caused by multiple leak scenarios, which were synthetically generated by simulation using the EPANET software application. In order to select the optimal subset of nodes, the candidate nodes were ranked using a heuristic algorithm with quadratic computational cost, which made it time-efficient compared to other sensor placement algorithms. The sensor placement algorithm was implemented in MATLAB and tested on the Hanoi network. It was verified by exhaustive analysis that the selected nodes were the best combination to place the sensors and detect leaks.
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Grattan, K. T. V. "Pressure sensors—Selection and application." Optics & Laser Technology 26, no. 2 (1994): 139–40. http://dx.doi.org/10.1016/0030-3992(94)90099-x.
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Westphal, R. "Sensors, Medical Image and Signal Processing." Yearbook of Medical Informatics 16, no. 01 (2007): 70–71. http://dx.doi.org/10.1055/s-0038-1638528.
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SummaryTo summarize current excellent research in the field of sensor, signal and imaging informatics.Synopsis of the articles selected for the IMIA Yearbook 2007.The selection process for this yearbook section “Sensor, signal and imaging informatics” results in five excellent articles, representing research in four different nations. Papers from the fields of brain machine interfaces, sound surveillance in telemonitoring, soft tissue modeling, and body sensors have been selected.The selection for this yearbook section can only reflect a small portion of the worldwide copious work in the field of sensors, signal and image processing with applications in medical informatics. However, the selected papers demonstrate, how advances in this field may positively affect future patient care.
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Worsey, Espinosa, Shepherd, and Thiel. "A Systematic Review of Performance Analysis in Rowing Using Inertial Sensors." Electronics 8, no. 11 (2019): 1304. http://dx.doi.org/10.3390/electronics8111304.
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Sporting organizations such as professional clubs and national sport institutions are constantly seeking novel training methodologies in an attempt to give their athletes a cutting edge. The advent of microelectromechanical systems (MEMS) has facilitated the integration of small, unobtrusive wearable inertial sensors into many coaches’ training regimes. There is an emerging trend to use inertial sensors for performance monitoring in rowing; however, the use and selection of the sensor used has not been appropriately reviewed. Previous literature assessed the sampling frequency, position, and fixing of the sensor; however, properties such as the sensor operating ranges, data processing algorithms, and validation technology are left unevaluated. To address this gap, a systematic literature review on rowing performance monitoring using inertial-magnetic sensors was conducted. A total of 36 records were included for review, demonstrating that inertial measurements were predominantly used for measuring stroke quality and the sensors were used to instrument equipment rather than the athlete. The methodology for both selecting and implementing technology appeared ad hoc, with no guidelines for appropriate analysis of the results. This review summarizes a framework of best practice for selecting and implementing inertial sensor technology for monitoring rowing performance. It is envisaged that this review will act as a guide for future research into applying technology to rowing.
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Rao, D. Raghunatha, T. Jayachandra Prasad, and M. N. Giri Prasad. "Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 10 (2022): 65–77. http://dx.doi.org/10.17762/ijritcc.v10i10.5737.
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The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature.
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Lee, JeeEun, and Sun K. Yoo. "Radar-Based Detection of Respiration Rate with Adaptive Harmonic Quefrency Selection." Sensors 20, no. 6 (2020): 1607. http://dx.doi.org/10.3390/s20061607.
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Continuous respiration monitoring is important for predicting a potential disease. Due to respiration measurements using contact sensors, it is difficult to achieve continuous measurement because the sensors are inconvenient to attach. In this study, a radar sensor was used for non-contact respiration measurements. The radar sensor had a high precision and could even be used in the dark. It could also be used continuously regardless of time and place. The radar sensor relied on the periodicity of respiration to detect the respiration rate. A respiration adaptive interval was set and the respiration rate was detected through harmonic quefrency selection. As a result, it was confirmed that there was no difference between the respiratory rate measured using a respiration belt and the respiratory rate detected using a radar sensor. Furthermore, case studies on changes in the radar position and about measurement for long periods confirmed that the radar sensor could detect respiration rate continuously regardless of the position and measurement duration.
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Dora, Sidhartha Sankar, and Prasanta Kumar Swain. "Feature Selection and Energy Management in Wireless Sensor Networks using Deep Learning." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 628–33. http://dx.doi.org/10.17762/ijritcc.v11i9s.7476.
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In wireless sensor networks, when the available energy sources and battery capacity are extremely constrained, energy efficiency is a major issue to be adressed. One of the main goals in the design of wireless sensor networks (WSNs) is to maximize longevity of battery life. Designers can benefit from the use of intelligent power utilization models to accomplish this goal. These models seek to decrease the number of chosen sensors used to record environmental measures in order to minimize power utilization while retaining the acceptable level of measurement accuracy. In order to simulate wireless sensor networks, we looked at real world datasets. Our simulation findings demonstrate that the suggested strategy can be used to accomplish significant goals by using the right number of sensors using deep learning, extend the lifespan of the wireless sensor networks.
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Wei, Yu, Libin Jiao, Jie Sha, Jixin Ma, Anton Umek, and Anton Kos. "Sensor selection scheme in activity recognition based on hierarchical feature reduction." International Journal of Distributed Sensor Networks 14, no. 8 (2018): 155014771879380. http://dx.doi.org/10.1177/1550147718793801.
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To better understand the activity state of human, we might need multiple sensors on different parts of the body. According to different types of activities, the number and slot of required sensors would also be different. Therefore, how to determine the number and slot of necessary sensors regarding to wearers’ experience and processing efficiency is a meaningful study in actual practice. In this work, we propose a novel sensor selection scheme that is based on the improvement of the feature reduction process of the recognition. This scheme applies a hierarchical feature reduction method based on mutual information with max relevance and low-dimensional embedding strategy. It divides the process of feature reduction into two stages: first, redundant sensors are removed with one-order sequential forward selection based on mutual information; second, feature selection strategy that maximizing class-relevance is integrated with low-dimensional mapping so that the set of features will be further compressed. To verify the feasibility and superiority of the scheme, we design a complete solution for real practice of human activity recognition. According to the results of the experiments, we are able to recognize human activities accurately and efficiently with as few sensors as possible.
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Brunello, Andrea, Andrea Urgolo, Federico Pittino, András Montvay, and Angelo Montanari. "Virtual Sensing and Sensors Selection for Efficient Temperature Monitoring in Indoor Environments." Sensors 21, no. 8 (2021): 2728. http://dx.doi.org/10.3390/s21082728.
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Real-time estimation of temperatures in indoor environments is critical for several reasons, including the upkeep of comfort levels, the fulfillment of legal requirements, and energy efficiency. Unfortunately, setting an adequate number of sensors at the desired locations to ensure a uniform monitoring of the temperature in a given premise may be troublesome. Virtual sensing is a set of techniques to replace a subset of physical sensors by virtual ones, allowing the monitoring of unreachable locations, reducing the sensors deployment costs, and providing a fallback solution for sensor failures. In this paper, we deal with temperature monitoring in an open space office, where a set of physical sensors is deployed at uneven locations. Our main goal is to develop a black-box virtual sensing framework, completely independent of the physical characteristics of the considered scenario, that, in principle, can be adapted to any indoor environment. We first perform a systematic analysis of various distance metrics that can be used to determine the best sensors on which to base temperature monitoring. Then, following a genetic programming approach, we design a novel metric that combines and summarizes information brought by the considered distance metrics, outperforming their effectiveness. Thereafter, we propose a general and automatic approach to the problem of determining the best subset of sensors that are worth keeping in a given room. Leveraging the selected sensors, we then conduct a comprehensive assessment of different strategies for the prediction of temperatures observed by physical sensors based on other sensors’ data, also evaluating the reliability of the generated outputs. The results show that, at least in the given scenario, the proposed black-box approach is capable of automatically selecting a subset of sensors and of deriving a virtual sensing model for an accurate and efficient monitoring of the environment.
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Zhang, Bin, Kai Zheng, Qingqing Huang, Song Feng, Shangqi Zhou, and Yi Zhang. "Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis." Sensors 20, no. 3 (2020): 920. http://dx.doi.org/10.3390/s20030920.
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Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.
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Wang, Chung-Ying, Chien-Yao Huang, and Yen-Han Chiang. "Solutions of Feature and Hyperparameter Model Selection in the Intelligent Manufacturing." Processes 10, no. 5 (2022): 862. http://dx.doi.org/10.3390/pr10050862.
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In the era of Industry 4.0, numerous AI technologies have been widely applied. However, implementation of the AI technology requires observation, analysis, and pre-processing of the obtained data, which takes up 60–90% of total time after data collection. Next, sensors and features are selected. Finally, the AI algorithms are used for clustering or classification. Despite the completion of data pre-processing, the subsequent feature selection and hyperparameter tuning in the AI model affect the sensitivity, accuracy, and robustness of the system. In this study, two novel approaches of sensor and feature selecting system, and hyperparameter tuning mechanisms are proposed. In the sensor and feature selecting system, the Shapley Additive ExPlanations model is used to calculate the contribution of individual features or sensors and to make the black-box AI model transparent, whereas, in the hyperparameter tuning mechanism, Hyperopt is used for tuning to improve model performance. Implementation of these two new systems is expected to reduce the problems in the processes of selection of the most sensitive features in the pre-processing stage, and tuning of hyperparameters, which are the most frequently occurring problems. Meanwhile, these methods are also applicable to the field of tool wear monitoring systems in intelligent manufacturing.
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Balovsyak, Serhiy, Vitaly Lacusta, and Khrystyna Odaiska. "Reading of Sensor Signals with Automatic Selection of Sampling Frequency." Security of Infocommunication Systems and Internet of Things, no. 1 (June 30, 2023): 01010. http://dx.doi.org/10.31861/sisiot2023.1.01010.
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The correct selection of sampling frequency when reading signals from sensors ensures high quality of digitized data and saves memory when storing such data. The complexity of automatic selection of the sampling frequency is explained by the fact that this frequency depends on the frequencies of the useful signal, which are not always known. Therefore, in the work the computer system for reading signals from temperature, humidity, and lighting sensors with automatic selection of the sampling frequency based on the Fourier spectrum analysis of the signals was developed. Signals from digital sensors (DHT22) are transmitted directly to the Raspberry Pi3 microcomputer. Signals from analog sensors (LM335M, light sensor) are fed to the Arduino Uno device. An algorithm for the analysis of Fourier spectra of one-dimensional signals has been developed, which is designed to determine the optimal sampling frequency and decimation coefficient of signals read from sensors. Based on the initial signals, their Fourier spectra are calculated, and by analyzing the spectra, the maximum frequency of the useful signal and the optimal sampling frequency are determined. Specified sampling frequency according to the sampling theorem is calculated as a double value of the maximum frequency of the useful signal. Decimation (thinning) of the signal is performed with a coefficient determined by the ratio of the initial and specified sampling frequencies. To assess the quality of the signal after decimation, the decimated values were interpolated by splines. The root mean square error of interpolation was calculated. Experimental testing of the developed tools for reading and analyzing signals from temperature, humidity and lighting sensors was carried out. In all considered cases, the sampling frequency is determined correctly. The resulting sampling rates can be used for decimation of signals or for subsequent reading of signals from sensors.
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Gupta, Sneha. "Enhancing Mood Based Music Selection through Physiological Sensing Technology." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 71–74. http://dx.doi.org/10.22214/ijraset.2024.59658.
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Abstract: This research paper presents the development of an emotional sensor system that combines computer vision techniques with physiological sensors to detect and analyse facial expressions. The system utilizes a webcam for capturing facial images and integrates Arduino-based hardware components such as a pulse sensor, temperature sensor, galvanic skin sensor, and heart rate sensor for comprehensive emotional analysis. The proposed system aims to contribute to various fields such as human-computer interaction, psychology, and healthcare by providing a non-invasive and real-time method for emotion detection and monitoring.
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Aksüt, Güler, and Tamer Eren. "SELECTION OF WEARABLE SENSORS FOR HEALTH AND SAFETY USE IN THE CONSTRUCTION INDUSTRY." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 29, no. 7 (2023): 577–86. http://dx.doi.org/10.3846/jcem.2023.19175.
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Construction industry workers; are exposed to serious safety and health risks, hazardous work environments, and intense physical work. This situation causes fatal and non-fatal accidents, reduces productivity, and causes a loss of money and time. Construction safety management can use wearable sensors to improve safety performance. Since there are many types of sensors and not all sensors can be used in construction applications, it is necessary to identify suitable and reliable sensors. This requirement causes a sensor selection problem. The study aims to determine the priority order of physiological and kinematic sensors in preventing risks in the construction industry. Within the scope of this purpose, five criteria and seven alternatives were determined in line with the literature research and expert opinions. The criteria weights were calculated with the AHP method, and the alternatives were ranked with PROMETHEE and AHP. Providing a proactive approach to the use of sensors in the construction industry will provide safer working conditions, identify workers at risk, and help identify and predict potential health and safety risks. It will contribute to the literature on improving construction health and safety management.