Journal articles on the topic 'Deployment error estimation'
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1
Gutierrez, Edgar Andres, Ivan Fernando Mondragon, Julian D. Colorado, and Diego Mendez Ch. "Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations." Plants 11, no. 13 (June 21, 2022): 1636. http://dx.doi.org/10.3390/plants11131636.
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This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.
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Lin, Feilong, Wenbai Li, and Liyong Yuan. "Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks." Sensors 18, no. 10 (October 6, 2018): 3338. http://dx.doi.org/10.3390/s18103338.
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Process parameter estimation, to a large extent, determines the industrial production quality. However, limited sensors can be deployed in a traditional wired manner, which results in poor process parameter estimation in hostile environments. Industrial wireless sensor networks (IWSNs) are techniques that enrich sampling points by flexible sensor deployment and then purify the target by collaborative signal denoising. In this paper, the process industry scenario is concerned, where the workpiece is transferred on the belt and the parameter estimate is required before entering into the next process stage. To this end, a consensus-based sequential estimation (CSE) framework is proposed which utilizes the co-design of IWSN and parameter state estimation. First, a group-based network deployment strategy, together with a TDMA (Time division multiple access)-based scheduling scheme is provided to track and sample the moving workpiece. Then, by matching to the tailored IWSN, the sequential estimation algorithm, which is based on the consensus-based Kalman estimation, is developed, and the optimal estimator that minimizes the mean-square error (MSE) is derived under the uncertain wireless communications. Finally, a case study on temperature estimation during the hot milling process is provided. The results show that the estimation error can be reduced to less than 3 ∘ C within a limited time period, although the measurement error can be more than 100 ∘ C in existing systems with a single-point temperature sensor.
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Rolling, Craig A., Yuhong Yang, and Dagmar Velez. "COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS." Econometric Theory 35, no. 6 (November 6, 2018): 1089–110. http://dx.doi.org/10.1017/s0266466618000397.
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Estimating a treatment’s effect on an outcome conditional on covariates is a primary goal of many empirical investigations. Accurate estimation of the treatment effect given covariates can enable the optimal treatment to be applied to each unit or guide the deployment of limited treatment resources for maximum program benefit. Applications of conditional treatment effect estimation are found in direct marketing, economic policy, and personalized medicine. When estimating conditional treatment effects, the typical practice is to select a statistical model or procedure based on sample data. However, combining estimates from the candidate procedures often provides a more accurate estimate than the selection of a single procedure. This article proposes a method of model combination that targets accurate estimation of the treatment effect conditional on covariates. We provide a risk bound for the resulting estimator under squared error loss and illustrate the method using data from a labor skills training program.
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Hsiao, Chiu-Han, Frank Yeong-Sung Lin, Hao-Jyun Yang, Yennun Huang, Yu-Fang Chen, Ching-Wen Tu, and Si-Yao Zhang. "Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors." Sensors 21, no. 21 (October 27, 2021): 7121. http://dx.doi.org/10.3390/s21217121.
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As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below a given threshold. Moreover, the results significantly ensure the accuracy of the gathered information while minimizing the cost of deployment and maximizing the lifetime of the WSN. Furthermore, the proposed solution can be readily applied to sensor distribution problems in various fields.
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Park, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho, and Kangdae Lee. "Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction." Canadian Journal of Civil Engineering 36, no. 4 (April 2009): 580–91. http://dx.doi.org/10.1139/l08-129.
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With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.
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Madsen, Tatiana, Hans-Peter Schwefel, Lars Mikkelsen, and Annelore Burggraf. "Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data." Sensors 22, no. 11 (May 28, 2022): 4111. http://dx.doi.org/10.3390/s22114111.
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Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccuracy with the WLAN estimator comes from a need to filter out mobile devices outside the bus and to map the number of detected devices to a number of people. The comparison is performed based on data collected from a real-life deployment in a medium sized German city. The comparison shows the trade-off in selecting either of the two methods. Furthermore, a novel approach for fusion of the light sensor and WLAN estimators is proposed which has a big potential in improving accuracy of both estimators. A fusion approach is proposed that utilizes the different error characteristics for error compensation by calculating compensation terms. The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated.
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Rathore, Kapil Singh, Sricharan Vijayarangan, Preejith SP, and Mohanasankar Sivaprakasam. "A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation." Sensors 23, no. 3 (February 1, 2023): 1599. http://dx.doi.org/10.3390/s23031599.
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Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model’s parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.
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McLoughlin, Benjamin, Harry Pointon, John McLoughlin, Andy Shaw, and Frederic Bezombes. "Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments." Sensors 18, no. 7 (July 13, 2018): 2274. http://dx.doi.org/10.3390/s18072274.
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Recent developments in localisation systems for autonomous robotic technology have been a driving factor in the deployment of robots in a wide variety of environments. Estimating sensor measurement noise is an essential factor when producing uncertainty models for state-of-the-art robotic positioning systems. In this paper, a surveying grade optical instrument in the form of a Trimble S7 Robotic Total Station is utilised to dynamically characterise the error of positioning sensors of a ground based unmanned robot. The error characteristics are used as inputs into the construction of a Localisation Extended Kalman Filter which fuses Pozyx Ultra-wideband range measurements with odometry to obtain an optimal position estimation, all whilst using the path generated from the remote tracking feature of the Robotic Total Station as a ground truth metric. Experiments show that the proposed method yields an improved positional estimation compared to the Pozyx systems’ native firmware algorithm as well as producing a smoother trajectory.
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Ukani, Neema Amish, and Saurabh S. Chakole. "Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012026. http://dx.doi.org/10.1088/1742-6596/2327/1/012026.
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Abstract Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.
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Aljohani, Nader, Tierui Zou, Arturo S. Bretas, and Newton G. Bretas. "Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers." Applied Sciences 11, no. 10 (May 13, 2021): 4419. http://dx.doi.org/10.3390/app11104419.
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In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions consider a weighted norm of residual measurement model, which might hinder masked gross errors contained in the null-space of the Jacobian matrix. Towards the solution of this, a distributed innovation-based model is presented. Measurement innovation is used towards error composition. The measurement error is an independent random variable, where the residual is not. Thus, the masked component is recovered through measurement innovation. Model solution is obtained through an Alternating Direction Method of Multipliers (ADMM), which requires minimal information communication. The presented framework is validated using the IEEE 14 and IEEE 118 bus systems. Easy-to-implement model, build-on the classical weighted norm of the residual solution, and without hard-to-design parameters highlight potential aspects towards real-life implementation.
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He, Peng, Quan Zhou, Libing Bai, Songlin Xie, and Weijing Zhang. "A Current Sharing State Estimation Method of Redundant Switched-Mode Power Supply Based on LSTM Neural Network." Applied Sciences 12, no. 7 (March 24, 2022): 3303. http://dx.doi.org/10.3390/app12073303.
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Redundant Switched-mode Power supplies (SMPSs) are commonly used to improve electronic systems’ reliability, and accurate estimation of the current sharing state is significant for evaluating the system’s health. Currently, the current sharing state estimation is mainly realized by using current sensors to detect each branch’s current, and the deployment and maintenance costs are high. In this paper, a method for power supply current sharing state estimation based on LSTM recurrent neural network is proposed. By taking advantage of subtle differences in the inherent spectral characteristics of SMPSs, this method only needs to detect the voltage ripple at the switching frequency of the load terminal to estimate the output current of each power supply branch. The verification experiment on the three-power redundant experimental platform shows that the estimation error is less than 10%. The method has the characteristics of simple structure, non-invasion, convenient deployment and maintenance, so it has high application and promotion value.
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Le, Nam Tuan, and Yeong Min Jang. "Photography Trilateration Indoor Localization with Image Sensor Communication." Sensors 19, no. 15 (July 26, 2019): 3290. http://dx.doi.org/10.3390/s19153290.
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Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the achievement of a low error rate and its controllability by the integration of deployed products. In this study, we propose and analyze a new distance estimation technique employing photography and image sensor communications, also named optical camera communications (OCC). It represents one of the most important steps in the implemented trilateration localization scheme with real architectures and conditions of deployment which is the second our contribution for this article. With the advantages of the image sensor hardware integration in smart mobile devices, this technology has great potential in localization-based optical wireless communication
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Wu, Yufei, Xiaofei Ruan, Yu Zhang, Huang Zhou, Shengyu Du, and Gang Wu. "Lightweight Architecture for Real-Time Hand Pose Estimation with Deep Supervision." Symmetry 11, no. 4 (April 23, 2019): 585. http://dx.doi.org/10.3390/sym11040585.
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The high demand for computational resources severely hinders the deployment of deep learning applications in resource-limited devices. In this work, we investigate the under-studied but practically important network efficiency problem and present a new, lightweight architecture for hand pose estimation. Our architecture is essentially a deeply-supervised pruned network in which less important layers and branches are removed to achieve a higher real-time inference target on resource-constrained devices without much accuracy compromise. We further make deployment optimization to facilitate the parallel execution capability of central processing units (CPUs). We conduct experiments on NYU and ICVL datasets and develop a demo1 using the RealSense camera. Experimental results show our lightweight network achieves an average running time of 32 ms (31.3 FPS, the original is 22.7 FPS) before deployment optimization. Meanwhile, the model is only about half parameters size of the original one with 11.9 mm mean joint error. After the further optimization with OpenVINO, the optimized model can run at 56 FPS on CPUs in contrast to 44 FPS running on a graphics processing unit (GPU) (Tensorflow) and it can achieve the real-time goal.
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Ai, Xiaofeng, Yuqing Zheng, Zhiming Xu, and Feng Zhao. "Parameter Estimation for Uniformly Accelerating Moving Target in the Forward Scatter Radar Network." Remote Sensing 14, no. 4 (February 18, 2022): 1006. http://dx.doi.org/10.3390/rs14041006.
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Passive radar based on the global navigation satellite positioning system (GNSS) has become the focus of attention in the field of radar. A parameter estimation method is proposed in the forward scatter radar (FSR) network based on GNSS to extend the application scenarios. For uniformly accelerating moving targets, only the instant times when the target crosses the individual baselines are used to retrieve the target motion parameters. The target position, velocity, and acceleration information can be obtained. Firstly, the minimum network configuration is derived theoretically. Then, the effects of crossing time error, station location error, transmitting/receiving station deployment, and target height on the accuracy are analyzed through Monte Carlo simulations. Finally, the simulation results indicate that the target position estimation error is in the order of 100 m. This paper provides the fundamental theory of aerial target positioning with a GNSS-based FSR network.
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Mallary, C., C. J. Berg, John R. Buck, and Amit Tandon. "Detection and estimation of rainfall from broadband acoustic signals." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A97. http://dx.doi.org/10.1121/10.0018293.
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Jeff Nystuen contributed major advances in our understanding of the physical acoustics of rain falling on the ocean, culminating in his pioneering work quantifying rainfall from the shape of the acoustic spectrum. Ma and Nystuen [JAOT (2005)] exploited Vagle’s wind spectrum to self-calibrate hydrophones for long deployments as acoustic rain gauges. Their algorithm predominantly relied on 3 narrowband frequencies to detect rainfall, and correlated the rainfall amount with the power spectral density (PSD) at 5kHz. Recent research at UMass Dartmouth built upon the foundational work of Nystuen, Ma and others to examine how much additional information can be gleaned from broadband acoustic spectra. These algorithms exploit Principal Component Analysis and Linear Discriminant Analysis for rainfall detection, coupled with Error-Correcting Output Codes (ECOC) for quantizing rainfall estimation. Testing on 5 months of 3-minute PSDs from a noisy cove found a detection probability of 78 ± 5% with a 1.1 ± 0.3% false alarm rate. Moreover, ECOC-based hourly rainfall estimates achieved 0.97 ± 0.01 correlation with rainfall measurements at a co-located meteorological station. This talk plans to present results from a recent 6 week coastal deployment in deeper water. [Work supported by ONR/UMassD MUST program]
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Vidal-Valladares, Matías G., and Marcos A. Díaz. "A Femto-Satellite Localization Method Based on TDOA and AOA Using Two CubeSats." Remote Sensing 14, no. 5 (February 24, 2022): 1101. http://dx.doi.org/10.3390/rs14051101.
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This article presents a feasibility analysis to remotely estimate the geo-location of a femto-satellite only using two station-CubeSats and the communication link between the femto-satellite and each CubeSat. The presented approach combines the Time Difference Of Arrival (TDOA) and Angle Of Arrival (AOA) methods. We present the motivation, the envisioned solution together with the constraints for reaching it, and the best potential sensitivity of the location precision for different (1) deployment scenarios of the femto-satellite, (2) precisions in the location of the CubeSats, and (3) precisions in each CubeSat’s Attitude Determination and Control Systems (ADCS). We implemented a simulation tool to evaluate the average performance for different random scenarios in space. For the evaluated cases, we found that the Cramér-Rao Bound (CRB) for Gaussian noise over the small error region of the solution is highly dependent on the deployment direction, with differences in the location precision close to three orders of magnitude between the best and worst deployment directions. For the best deployment case, we also studied the best location estimation that might be achieved with the current Global Navigation Satellite System (GNSS) and ADCS commercially available for CubeSats. We found that the mean-square error (MSE) matrix of the proposed solution under the small error condition can attain the CRB for the simulated time, achieving a precision below 30 m when the femto-satellite is separated by around 800 m from the mother-CubeSat.
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Thimmaiah, Sudha H., and Mahadevan G. "A Radio Signal Strength Based Localization Error Optimization Technique for Wireless Sensor Network." Indonesian Journal of Electrical Engineering and Computer Science 11, no. 3 (September 1, 2018): 839. http://dx.doi.org/10.11591/ijeecs.v11.i3.pp839-847.
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Wireless Sensor Networks (WSN) is useful in collecting data from various sensor devices that are distributed over a network which is generally positioned in a stationary manner. Wireless sensor based communication system is an ever growing sector in the industry of communication. Wireless infrastructure is a network that enables correspondence between various devices associated through an infrastructure protocol. Finding the position or location of sensor node (Localization) is an important factor in sensor network for proving efficient service to end user. The existing technique proposed so for adopt AOA (Angle of Arrival), TOA (Time of Arrival) etc… suffers in estimating the likelihood of localization error and induces high cost of deployment. To cater this in this work the author proposes a cost effective RSS (Received signal strength) based localization technique and also proposes an adaptive information estimation to reduce or approximate the localization error in wireless sensor network. The author compares our proposed localization model with existing protocol and analyse its efficiency.
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Nakutis, Žilvinas, and Paulius Kaškonas. "A Contemplation on Electricity Meters In-Service Surveillance Assisted by Remote Error Monitoring." Energies 13, no. 20 (October 9, 2020): 5245. http://dx.doi.org/10.3390/en13205245.
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In this paper, remote error monitoring techniques for electricity meters are overviewed suggesting their utilization for in-service surveillance assistance. It is discussed that in-service error observation could provide valuable input, contributing to the timely detection of batches of meters reaching nonconformance status. The payback period analysis of the deployment of a remote error monitoring solution is considered. However, it is pointed out that such an analysis lacks input information describing the relationship between the remote monitoring system’s performance and its ability to detect nonconformance of the batch. It is also noticed that there is no published methodology for grading the status of an entire batch of meters referring to error estimates of a subset of the meters, when the uncertainty of estimation is rather high.
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Onakoya, Adegbemi Babatunde, and Hassan Akolade Alayande. "Macroeconomic Variables, the Oil, and the Agricultural Sectors in Nigeria." Asian Social Science 16, no. 1 (December 31, 2019): 69. http://dx.doi.org/10.5539/ass.v16n1p69.
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The present study examined the impact of the macroeconomic variables and the oil sector on the performance of the agricultural sector between 1981and 2017 in Nigeria. The study adopted a three-stage estimation approach. The initial step in this estimation was the conduct of descriptive statistics and stationarity tests of the variables. Some of the series were stationary at level and some others at the first difference which informed the deployment of the Auto regressive distributed lag (ARDL) technique for model estimation. The third stage was the post-estimation of the model in order ascertain its robustness for predictability and policy formulation. These were the Cumulative Sum Control Chart (CUSUM) stability, Vector Error Correction (VEC) Residual Heteroscedasticity, Breusch-Godfrey Serial Correlation LM, Vector Error Correction Residual Normality, and Vector Error Correction (VEC) Residual Heteroscedasticity tests. The results indicated that contrary to the Dutch disease postulation the oil sector positively impacted the output of the agricultural sector. The influence of exchange rate was also positive. Interest and unemployment rates on the other hand, had negative effects. The rate of inflation and the national output had no impact. The study recommended that the Nigerian government should channel resources towards the agricultural sector to ensure increase in foreign earnings and sufficient domestic production.
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Hidayah, Entin, Gusfan Halik, and Minarni Nur Trilita. "Rain Station Network Analysis in the Sampean Watershed: Comparison of Variations in Data Aggregation." Geosfera Indonesia 7, no. 1 (April 27, 2022): 96. http://dx.doi.org/10.19184/geosi.v7i1.29160.
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The lack of rainfall-runoff accuracy is important for some applications. The choice of data aggregation that affects the estimation results is important at the level of accuracy. Some commonly used aggregations are daily, ten days, and monthly rainfall. This study aimed to compare the results of the estimation of the effect of data aggregation and to analyze the density of the rain gauge network in the Sampean watershed. The evaluation of the rain station network is carried out through the Kagan calculation. Rainfall data are from the rainfall data records for 20 years at 33 rain gauge stations. Measurement of the performance of aggregation variations using the relationship between the correlation value of rainfall with the distance between station locations. Station network positioning is assessed from alignment errors and interpolation errors. The results showed differences in the correlation and estimation values in the variation of data aggregation.The greater interval can increase the effectiveness of deployment with minimum error. Based on Kagan's analysis, there is an uneven distribution of gauge stations in the Sampean watershed eventhough the average and interpolation error in the monthly rainfall is less than 5%. It is this inequality that causes gauge stations to be inefficient.
Keywords : Rain gauge network; correlation; Kagan; data aggregation
Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember
This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
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Hammouda, Marwan, Anna Maria Vegni, and Valeria Loscrí. "On the Noise Effect of Fingerprinting-Based Positioning Error in Underwater Visible Light Networks." Sensors 21, no. 16 (August 10, 2021): 5398. http://dx.doi.org/10.3390/s21165398.
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This paper assesses the performance of a localization technique for underwater visible light networks. The proposed approach is based on a fingerprinting technique, collecting the channel impulse responses from different wireless optical signals in the visible range. A local database related to the power level distribution within a maritime environment is built and exploited to estimate user position, e.g., a diver moving in a given space for underwater fish monitoring. In this paper, we investigate on the noise effect on the localization accuracy in underwater scenarios and for different water turbidity coefficient and we demonstrate that the estimation error suffers on variable channel impulse responses. Different configuration parameters and environmental scenarios have been considered, showing that the LED transmitter deployment can be effective in the localization estimation. A comparison of the proposed localization approach to the traditional triangulation method has been finally carried out, showing the effectiveness of the fingerprinting-based solution for a lower number of LED transmitters.
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Zhang, Zhe, ChenLu Shi, Xiao Lv, and ZiHong Ling. "Active control of interior road noise using the remote microphone technique." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 7 (February 1, 2023): 916–20. http://dx.doi.org/10.3397/in_2022_0130.
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A multichannel feedforward headrest system for the active control of interior road noise in a vehicle cabin is built. The remote microphone technique is applied, which enables the estimation of the sound pressure responses at the passenger's ear positions without direct deployment of error microphones there. The optimal observation filter for the remote microphone technique is formulated in a so-called training stage using signals measured at two error microphones on the passenger's ears and an array of four to five monitoring microphones on the headrest, passenger seat and vehicle ceiling. The estimation accuracy of the observation filter is investigated through simulations and road test. Regarding the causality error encountered in a certain test case where the passenger leans forward, thus making the noise signals arrive at the monitoring microphones prior to the error ones, a delay factor is added into the original remote microphone technique to correctly compensate for the time delay. The noise attenuation performance of the active headrest system is then experimentally and subjectively determined, indicating a larger noise abatement in a wider spatial environment by applying the remote microphone technique.
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Zhao, Licheng, Wei Guo, Jian Wang, Haozhou Wang, Yulin Duan, Cong Wang, Wenbin Wu, and Yun Shi. "An Efficient Method for Estimating Wheat Heading Dates Using UAV Images." Remote Sensing 13, no. 16 (August 4, 2021): 3067. http://dx.doi.org/10.3390/rs13163067.
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Convenient, efficient, and high-throughput estimation of wheat heading dates is of great significance in plant sciences and agricultural research. However, documenting heading dates is time-consuming, labor-intensive, and subjective on a large-scale field. To overcome these challenges, model- and image-based approaches are used to estimate heading dates. Phenology models usually require complicated parameters calibrations, making it difficult to model other varieties and different locations, while in situ field-image recognition usually requires the deployment of a large amount of observational equipment, which is expensive. Therefore, in this study, we proposed a growth curve-based method for estimating wheat heading dates. The method first generates a height-based continuous growth curve based on five time-series unmanned aerial vehicle (UAV) images captured over the entire wheat growth cycle (>200 d). Then estimate the heading date by generated growth curve. As a result, the proposed method had a mean absolute error of 2.81 d and a root mean square error of 3.49 d for 72 wheat plots composed of different varieties and densities sown on different dates. Thus, the proposed method is straightforward, efficient, and affordable and meets the high-throughput estimation requirements of large-scale fields and underdeveloped areas.
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T P, Mohankumar. "Enhancing Location Accuracy by Minimizing RMS Using RSS-AMLE in WSN." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 5, no. 1 (2024): 1–8. http://dx.doi.org/10.54060/jieee.2024.99.
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Over the past decade, there has been significant growth in wireless sensor networks, particularly in the context of industrial applications. Mobile sensor networks have garnered research interest due to their ability to facilitate communication between various devices. Still, the mobility of these nodes gives rise to challenges such as network coverage and connectivity issues. Addressing these challenges necessitates accurate estimation of sensor node locations, a critical factor in network performance. Numerous methods, such as Angle of Arrival (AOA) and Time of Arrival (TOA), have been proposed for node localization. Still, these methods are plagued by localization errors and high implementation costs. To overcome these localization errors in wireless sensor networks, we present an adaptive approach based on the Received Signal Strength (RSS) model. This model views localization as a non-convex problem and employs an adaptive maximum likelihood estimation to minimize localization errors. An extensive simulation study is carried out to measure the performance of the intended approach in minimizing the localization error. The results unequivocally demonstrate that our localization scheme achieves higher accuracy in locating sensor nodes while reducing deployment costs. Comparative analysis against existing methods further underscores the significance of our approach.
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Hervis Santana, Yosvany, Rodney Martinez Alonso, Glauco Guillen Nieto, Luc Martens, Wout Joseph, and David Plets. "Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation." Applied Sciences 12, no. 8 (April 13, 2022): 3923. http://dx.doi.org/10.3390/app12083923.
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Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.
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Li, Y., L. Wang, J. Liu, P. Zhang, and Y. Lu. "ACCURACY EVALUATION OF MULTI-GNSS DOPPLER VELOCITY ESTIMATION USING ANDROID SMARTPHONES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 89–96. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-89-2022.
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Abstract. The number of smartphone users has increased dramatically as smartphones have become more widely available. The mass-market urgently requires the deployment of low-cost GNSS chips in smartphones to achieve continuous, high-precision velocity and location services. Currently, most GNSS velocity determination research relies on traditional geodetic receivers and focuses on comparing velocity determination methods and analyzing velocity determination influencing factors, while research into the direction of multi-GNSS velocity determination for smartphones is insufficient. To solve this problem, we study the differences in velocity determination accuracy of Android smartphones with single-constellation, dual-constellation and multi-constellation focusing on BDS-2 and BDS-3. The results show that the accuracy of velocity determination differs significantly between different mobile phone models. The velocity determination accuracy of BDS is the highest in all single constellations, and the accuracy of BDS-2, BDS-3 and GPS are not much different. The velocity determination accuracy of dual-constellation and multi-constellation is better than single-constellation. In static velocity determination, the Huawei nova5 quad-constellation velocity determination error is in the cm/s level, while the Redmi K40 error is in the dm/s level. In kinematic velocity determination, the Huawei nova5’s quad-constellation velocity determination error in horizontal and vertical directions is 1.06 dm/s and 1.77 dm/s respectively; The Redmi’s quad-constellation velocity determination error in horizontal and vertical directions is 3.76 dm/s and 4.59 dm/s respectively.
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Alghanem, Hussah, and Alastair Buckley. "Global Benchmarking and Modelling of Installed Solar Photovoltaic Capacity by Country." Energies 17, no. 8 (April 10, 2024): 1812. http://dx.doi.org/10.3390/en17081812.
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Setting solar photovoltaic capacity targets and implementing supportive policies is a widespread strategy among nations aiming to achieve decarbonisation goals. However, policy implementation without a thorough understanding of the intricate relationship between social, economic, and land-use factors and solar photovoltaic deployment can lead to unintended consequences, including over- or underdeployment and failure to reach targets. To address this challenge, an investigation was conducted into the relationship between 36 factors and solar photovoltaic deployment across 143 countries from 2001 to 2020 using correlation analysis and principal component analysis. From these factors, five key variables were identified that collectively explain 79% of the year-to-year variation in photovoltaic capacity. Using these variables, a neural network model was constructed, enabling the estimation of capacity additions by country with an error of less than 10%. Additionally, a solar photovoltaic deployment index was developed, serving as a benchmark for comparing a country’s actual historical photovoltaic deployment to similar nations. Furthermore, the model’s utility in evaluating the impact of solar photovoltaic policies was explored. Through three distinct use cases—forecasting solar photovoltaic capacity additions, developing a solar photovoltaic deployment index, and assessing the impact of solar photovoltaic policies—the model emerges as a potentially powerful tool for governments and policy makers to assess solar photovoltaic deployment effectively and formulate strategies to promote sustainable solar energy growth.
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Ortega, Andres, and Velio Tralli. "QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks." Sensors 22, no. 12 (June 16, 2022): 4545. http://dx.doi.org/10.3390/s22124545.
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The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.
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Jiang, Ye, Shuyan Xiao, Jian Liu, Bo Chen, Bangbang Zhang, Hongzhi Zhao, and Zhaoneng Jiang. "A Deterministic Sensor Deployment Method for Target Coverage." Journal of Sensors 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/2343891.
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In order to monitor the gas leakage, the gas sensors are deployed conventionally in chemical industry park, with little considerations given to the gas characteristics and weather conditions, which give rise to the problems of coverage hole and coverage repetition. To solve the problems, this paper proposes a deterministic sensor deployment method with the gas diffusion models which takes into account wind speed and direction and then studies the influence of wind speed and direction on the monitoring error of gas sensors. Then, we research the deterministic deployment method of gas sensors in condition of the main wind speed and direction somewhere. Firstly, we use the CFD theory to simulate the gas diffusion situation so as to obtain the concentration value of the relevant points. Secondly, we put forward a new optimization criterion, namely, the more alarm concentration points covered by gas sensors, the coverage performance is better, and the deployment method is better. Accordingly, a new objection function is built. Thirdly, we obtain the weight values of the function using entropy estimation method. Finally, we deploy the gas sensors determinately using particle swarm optimization (PSO) algorithm. The simulation results show that the proposed method can improve the monitoring efficiency and the coverage performance of gas sensor network.
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Yu, Yun-Shuai, and Yeong-Sheng Chen. "A Measurement-Based Frame-Level Error Model for Evaluation of Industrial Wireless Sensor Networks." Sensors 20, no. 14 (July 17, 2020): 3978. http://dx.doi.org/10.3390/s20143978.
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Industrial wireless sensor networks (IWSNs) are a key technology for smart manufacturing. To identify the performance bottlenecks in an IWSN before its real-world deployment, the IWSN must first be evaluated through simulations using an error model which accurately characterizes the wireless links in the industrial scenario within which it will be deployed. However, the traditional error models used in most IWSN simulators are not derived from the real traces observed in industrial environments. Accordingly, this study first measured the transmission quality of IEEE 802.15.4 in a one-day experiment in a manufacturing factory and then used the measurement records to construct a second-order Markov frame-level error model for simulating the performance of an IWSN. The proposed model was incorporated into the simulator of OpenWSN, which is an industrial WSN implementing the related IEEE and IETF standards. The simulation results showed that the proposed error model improved the accuracy of the estimated transmission reliability by up to 12% compared to the original error model. Moreover, the estimation accuracy improved with increasing burst losses.
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Deng, Zhian, Jianxiang Feng, Shengao Wang, Zhiyu Qu, Jiahao Zhang, and Weijian Si. "An accurate and easy deployment array gain-phase error calibration method for DoA estimation in Wi-Fi network." Ad Hoc Networks 112 (March 2021): 102355. http://dx.doi.org/10.1016/j.adhoc.2020.102355.
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Narata, Ana Paula, Jordi Blasco, Luis San Roman, Juan Miguel Macho, Hector Fernandez, Raquel Kale Moyano, Renaud Winzenrieth, and Ignacio Larrabide. "Early Results in Flow Diverter Sizing by Computational Simulation: Quantification of Size Change and Simulation Error Assessment." Operative Neurosurgery 15, no. 5 (January 17, 2018): 557–66. http://dx.doi.org/10.1093/ons/opx288.
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Abstract BACKGROUND Sizing of flow diverters (FDs) stent in the treatment of intracranial aneurysms is a challenging task due to the change of stent length after implantation. OBJECTIVE To quantify the size change and assess the error in length prediction in 82 simulated FD deployments. METHODS Eighty-two consecutive patients treated with FDs were retrospectively analyzed. Implanted FD length was measured from angiographic images and compared to the nominal sizes of the implanted device. Length change was obtained by subtracting the nominal length from the real length and dividing by the nominal length. Implanted devices were simulated on 3-dimensional models of each patient. Simulation error was obtained by subtracting real length from simulated length and dividing by the real length of the FD. Subanalysis was done using ANOVA. Statistical significance was set to P < .05, and bootstrap resampling was used. RESULTS When assessing the length change of the FD after implantation, changes of 30% in average and up to 80% with reference to the nominal length of the device were observed. The simulation results showed a lower error of 3.52% in average with a maximum of 30%. Paired t-test showed nonsignificant differences between measured and real length (P = .07, with the mean of differences at 0.45 mm, 95% confidence interval [−0.950 0.038]). CONCLUSION Nominal length is not an accurate sizing metric when choosing the size of an FD irrespective of the brand and manufacturer. Good estimation of the final length of the stent after deployment as expressed by an error of 3.5% in average.
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Chia, Harmon Lee Bruce. "Quantum computing and its revolutionary potential." Advances in Engineering Innovation 4, no. 1 (November 22, 2023): 26–32. http://dx.doi.org/10.54254/2977-3903/4/2023022.
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The rapid emergence of quantum computing offers the potential to revolutionize numerous domains, promising computational advantages over classical counterparts. This study aimed to evaluate the performance, efficiency, and robustness of selected quantum algorithmsQuantum Variational Eigensolver (VQE), Quantum Fourier Transform (QFT), and Quantum Phase Estimation (QPE)on near-term quantum devices. Our benchmarking revealed that, despite promising theoretical benefits, the practical deployment of these algorithms remains challenged by noise, error rates, and hardware limitations. The VQE showed promise in molecular modeling, while the utility of QFT and QPE in cryptography and optimization became evident. Nevertheless, their practical efficiency is contingent upon specific quantum hardware and employed error mitigation techniques. The findings underscore the transformative potential of quantum computing, but also emphasize the ongoing challenges that need addressing to make quantum computing practically advantageous.
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Monkman, Graham G., Kieran Hyder, Michel J. Kaiser, and Franck P. Vidal. "Accurate estimation of fish length in single camera photogrammetry with a fiducial marker." ICES Journal of Marine Science 77, no. 6 (March 14, 2019): 2245–54. http://dx.doi.org/10.1093/icesjms/fsz030.
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Abstract Videogrammetry and photogrammetry are increasingly being used in marine science for unsupervised data collection. The camera systems employed are complex, in contrast to “consumer” digital cameras and smartphones carried by potential citizen scientists. However, using consumer cameras in photogrammetry will introduce unknown length estimation errors through both the image acquisition process and lens distortion. This study presents a methodology to achieve accurate 2-dimensional (2-D) total length (TL) estimates of fish without specialist equipment or proprietary software. Photographs of fish were captured with an action camera using a background fiducial marker, a foreground fiducial marker and a laser marker. The geometric properties of the lens were modelled with OpenCV to correct image distortion. TL estimates were corrected for parallax effects using an algorithm requiring only the initial length estimate and known fish morphometric relationships. Correcting image distortion decreased RMSE by 96% and the percentage mean bias error (%MBE) by 50%. Correcting for parallax effects achieved a %MBE of −0.6%. This study demonstrates that the morphometric measurement of different species can be accurately estimated without the need for complex camera equipment, making it particularly suitable for deployment in citizen science and other volunteer-based data collection endeavours.
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Xia, Di, Yeqing Zhu, and Heng Zhang. "Faster Deep Inertial Pose Estimation with Six Inertial Sensors." Sensors 22, no. 19 (September 21, 2022): 7144. http://dx.doi.org/10.3390/s22197144.
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We propose a novel pose estimation method that can predict the full-body pose from six inertial sensors worn by the user. This method solves problems encountered in vision, such as occlusion or expensive deployment. We address several complex challenges. First, we use the SRU network structure instead of the bidirectional RNN structure used in previous work to reduce the computational effort of the model without losing its accuracy. Second, our model does not require joint position supervision to achieve the best results of the previous work. Finally, since sensor data tend to be noisy, we use SmoothLoss to reduce the impact of inertial sensors on pose estimation. The faster deep inertial poser model proposed in this paper can perform online inference at 90 FPS on the CPU. We reduce the impact of each error by more than 10% and increased the inference speed by 250% compared to the previous state of the art.
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Galati, Rocco, Giacomo Mantriota, and Giulio Reina. "RoboNav: An Affordable Yet Highly Accurate Navigation System for Autonomous Agricultural Robots." Robotics 11, no. 5 (September 21, 2022): 99. http://dx.doi.org/10.3390/robotics11050099.
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The paper presents RoboNav, a cost-effective and accurate decimeter-grade navigation system that can be used for deployment in the field of autonomous agricultural robots. The novelty of the system is the reliance on a dual GPS configuration based on two u-blox modules that work in conjunction with three low-cost inertial sensors within a Gaussian Sum Filter able to combine multiple Extended Kalman filters dealing with IMU bias and GPS signal loss. The system provides estimation of both position and heading with high precision and robustness, at a significantly lower cost than existing equivalent navigation systems. RoboNav is validated in a commercial vineyard by performing experimental tests using an all-terrain tracked robot commanded to follow a series of GPS waypoints, trying to minimize the crosstrack error and showing average errors on the order of 0.2 m and 0.2∘ for the measurement of position and yaw angle, respectively.
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Kakirde, Shubham, Shubham Jain, Swaraj Kaondal, Reena Sonkusare, and Rita Das. "Automated Dimension Measurement System." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 163–69. http://dx.doi.org/10.35940/ijeat.d2399.0610521.
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In this fast-paced world, it is inevitable that the manual labor employed in industries will be replaced by their automated counterparts. There are a number of existing solutions which deal with object dimensions estimation but only a few of them are suitable for deployment in the industry. The reason being the trade-off between the cost, time for processing, accuracy and system complexity. The proposed system aims to automate the mentioned tasks with the help of a single camera and a line laser module for each conveyor belt setup using laser triangulation method to measure the height and edge detection algorithm for measuring the length and breadth of the object. The minimal use of equipment makes the system simple, power and time efficient. The proposed system has an average error of around 3% in the dimension estimation.
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Sahlaoui, Zahra, and Soumia Mordane. "Radar Rainfall Estimation in Morocco: Quality Control and Gauge Adjustment." Hydrology 6, no. 2 (May 23, 2019): 41. http://dx.doi.org/10.3390/hydrology6020041.
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This study focused on investigating the impact of gauge adjustment on the rainfall estimate from a Moroccan C-band weather radar located in Khouribga City. The radar reflectivity underwent a quality check before deployment to retrieve the rainfall amount. The process consisted of clutter identification and the correction of signal attenuation. Thereafter, the radar reflectivity was converted into rainfall depth over a period of 24 h. An assessment of the accuracy of the radar rainfall estimate over the study area showed an overall underestimation when compared to the rain gauges (bias = −6.4 mm and root mean square error [RMSE] = 8.9 mm). The adjustment model was applied, and a validation of the adjusted rainfall versus the rain gauges showed a positive impact (bias = −0.96 mm and RMSE = 6.7 mm). The case study conducted on December 16, 2016 revealed substantial improvements in the precipitation structure and intensity with reference to African Rainfall Climatology version 2 (ARC2) precipitations.
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Bernini, Martina, Rudolf Hellmuth, Craig Dunlop, William Ronan, and Ted J. Vaughan. "Recommendations for finite element modelling of nickel-titanium stents—Verification and validation activities." PLOS ONE 18, no. 8 (August 9, 2023): e0283492. http://dx.doi.org/10.1371/journal.pone.0283492.
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The objective of this study is to present a credibility assessment of finite element modelling of self-expanding nickel-titanium (Ni-Ti) stents through verification and validation (VV) activities, as set out in the ASME VV-40 standard. As part of the study, the role of calculation verification, model input sensitivity, and model validation is examined across three different application contexts (radial compression, stent deployment in a vessel, fatigue estimation). A commercially available self-expanding Ni-Ti stent was modelled, and calculation verification activities addressed the effects of mesh density, element integration and stable time increment on different quantities of interests, for each context of use considered. Sensitivity analysis of the geometrical and material input parameters and validation of deployment configuration with in vitro comparators were investigated. Results showed similar trends for global and local outputs across the contexts of use in response to the selection of discretization parameters, although with varying sensitivities. Mesh discretisation showed substantial variability for less than 4 × 4 element density across the strut cross-section in radial compression and deployment cases, while a finer grid was deemed necessary in fatigue estimation for reliable predictions of strain/stress. Element formulation also led to substantial variation depending on the chosen integration options. Furthermore, for explicit analyses, model results were highly sensitive to the chosen target time increment (e.g., mass scaling parameters), irrespective of whether quasistatic conditions were ensured (ratios of kinetic and internal energies below 5%). The higher variability was found for fatigue life simulation, with the estimation of fatigue safety factor varying up to an order of magnitude depending on the selection of discretization parameters. Model input sensitivity analysis highlighted that the predictions of outputs such as radial force and stresses showed relatively low sensitivity to Ni-Ti material parameters, which suggests that the calibration approaches used in the literature to date appear reasonable, but a higher sensitivity to stent geometry, namely strut thickness and width, was found. In contrast, the prediction of vessel diameter following deployment was least sensitive to numerical parameters, and its validation with in vitro comparators offered a simple and accurate (error ~ 1–2%) method when predicting diameter gain, and lumen area, provided that the material of the vessel is appropriately characterized and modelled.
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Li, Wenyu, Yanlin He, Peng Geng, and Yi Yang. "Research on Posture Sensing and Error Elimination for Soft Manipulator Using FBG Sensors." Electronics 12, no. 6 (March 21, 2023): 1476. http://dx.doi.org/10.3390/electronics12061476.
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Fiber-optic sensors are highly promising within soft robot sensing applications, but sensing methods based on geometry-based reconstruction limit the sensing capability and range. In this study, a fiber-optic sensor with a different deployment strategy for indirect sensing to monitor the outside posture of a soft manipulator is presented. The internal support structure’s curvature was measured using the FBG sensor, and its mapping to the external pose was then modelled using a modified LSTM network. The error was assumed to follow the Gaussian distribution in the LSTM neural network and was rectified by maximum likelihood estimation to address the issue of noise generated during the deformation transfer and curvature sensing of the soft structure. For the soft manipulator, the network model’s sensing performance was demonstrated. The proposed method’s average absolute error for posture sensing was 63.3% lower than the error before optimization, and the root mean square error was 56.9% lower than the error before optimization. The comparison results between the experiment and the simulation demonstrate the viability of the indirect measurement of the soft structure posture using FBG sensors based on the data-driven method, as well as the significant impact of the error optimization method based on the Gaussian distribution assumption.
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Limberg, Christian, Heiko Wersing, and Helge Ritter. "Beyond Cross-Validation—Accuracy Estimation for Incremental and Active Learning Models." Machine Learning and Knowledge Extraction 2, no. 3 (September 1, 2020): 327–46. http://dx.doi.org/10.3390/make2030018.
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For incremental machine-learning applications it is often important to robustly estimate the system accuracy during training, especially if humans perform the supervised teaching. Cross-validation and interleaved test/train error are here the standard supervised approaches. We propose a novel semi-supervised accuracy estimation approach that clearly outperforms these two methods. We introduce the Configram Estimation (CGEM) approach to predict the accuracy of any classifier that delivers confidences. By calculating classification confidences for unseen samples, it is possible to train an offline regression model, capable of predicting the classifier’s accuracy on novel data in a semi-supervised fashion. We evaluate our method with several diverse classifiers and on analytical and real-world benchmark data sets for both incremental and active learning. The results show that our novel method improves accuracy estimation over standard methods and requires less supervised training data after deployment of the model. We demonstrate the application of our approach to a challenging robot object recognition task, where the human teacher can use our method to judge sufficient training.
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Harbour, Eric, Michael Lasshofer, Matteo Genitrini, and Hermann Schwameder. "Enhanced Breathing Pattern Detection during Running Using Wearable Sensors." Sensors 21, no. 16 (August 20, 2021): 5606. http://dx.doi.org/10.3390/s21165606.
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Breathing pattern (BP) is related to key psychophysiological and performance variables during exercise. Modern wearable sensors and data analysis techniques facilitate BP analysis during running but are lacking crucial validation steps in their deployment. Thus, we sought to evaluate a wearable garment with respiratory inductance plethysmography (RIP) sensors in combination with a custom-built algorithm versus a reference spirometry system to determine its concurrent validity in detecting flow reversals (FR) and BP. Twelve runners completed an incremental running protocol to exhaustion with synchronized spirometry and RIP sensors. An algorithm was developed to filter, segment, and enrich the RIP data for FR and BP estimation. The algorithm successfully identified over 99% of FR with an average time lag of 0.018 s (−0.067,0.104) after the reference system. Breathing rate (BR) estimation had low mean absolute percent error (MAPE = 2.74 [0.00,5.99]), but other BP components had variable accuracy. The proposed system is valid and practically useful for applications of BP assessment in the field, especially when measuring abrupt changes in BR. More studies are needed to improve BP timing estimation and utilize abdominal RIP during running.
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Khoptar, Alina, and Stepan Savchuk. "Estimation of Ionospheric Delay Influence on the Efficiency of Precise Positioning of Multi-GNSS Observations." Baltic Surveying 12 (June 29, 2020): 14–18. http://dx.doi.org/10.22616/j.balticsurveying.2020.002.
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Currently, Global Navigation Satellite Systems (GNSS) are developing at a fairly rapid pace. Over the last years US GPS and Russian GLONASS were modernizing, whilst new systems like European Galileo and Chinese BDS are launched. The modernizations of the existing and the deployment of new GNSS made a whole range of new signals available to the users, and create a new concept multi-GNSS. Ionospheric delay is one of the major error sources in multi-GNSS observations. At present, GNSS users usually eliminate the influence of ionospheric delay of the first order items by dual-frequency ionosphere-free combinations. But there is still residual ionospheric delay error of higher orders. In this paper we present four different processing scenarios to exclude the higher orders ionospheric delay effects on multi-GNSS Precise Point Positioning (PPP) performance, including: “only GPS” and “GPS+GLONASS+Galileo+BDS” – without/with eliminating ionospheric delay error of higher orders. Dataset collected from one GNSS station BOR1 (Borowiec, Poland) over almost two years provided by multi-GNSS experiment (MGEX) were used for dual-frequency PPP tests with one- and quadconstellation signals. For the second pair of scenarios were used a IONosphere map EXchange format (IONEX) that supports the exchange of 2- and 3-dimensional TEC maps given in a geographic grid. Numeric experiments show that, the results of different pairs of scenarios differ at the submillimeter level. The results also show that the multi-GNSS processing are better than those based on “only GPS”.
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Xie, Wupeng, Guanghong Liu, Xiaoxiao Xiang, Kang Xing, and Xiaojuan Zhang. "Time Difference of Arrival Estimation of Stationary Targets Utilizing Deep Neural Network." Journal of Physics: Conference Series 2414, no. 1 (December 1, 2022): 012021. http://dx.doi.org/10.1088/1742-6596/2414/1/012021.
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Abstract Time difference of arrival (TDOA) is widely used in the field of passive location because of its flexible base station deployment and simple principle. However, when solving the solution of stationary targets position, the traditional method, such as the two-stage weighted least squares (TSWLS) algorithm, is easily affected by noise, and cannot achieve good localization results in practical situations. To solve this problem, we propose a deep neural network (DNN) for TDOA estimation of stationary targets. First, a large number of simulation samples are generated according to the TDOA model. Each sample contains the time difference from each secondary base station to the main base station, the error of time difference, and the real three-dimensional (3-D) coordinates of the targets. Next, a suitable DNN architecture is designed to solve the solution of the stationary target position. The simulation results prove that the method proposed herein outperforms TSWLS for multiple base stations based on the TDOA model.
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Fadeev, Vladimir A., Shaikhrozy V. Zaidullin, and Adel F. Nadeev. "Investigation of the Bayesian and non-Bayesian time series forecasting frameworks in application to OSS systems of the LTE/LTE-A and 5G mobile networks." T-Comm 16, no. 4 (2022): 52–60. http://dx.doi.org/10.36724/2072-8735-2022-16-4-52-60.
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Applicability of some conventional Time-Series prediction models for the temporal dynamics of the EPS Radio Bearer Setup Failure Rate is examinated in this paper. Two main problems of the proactive network management have been considered: the prediction of regular part of time series and the outliers prediction. For the regular part prediction Holt-Winters Exponential Smoothing, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Python Dynamic Linear Model (PyDLM) and Seasonal Auto Regressive Integrated Moving Average (SARIMAX) have been used. The error performance has been analyzed using Median Absolute Error, Mean Absolute Error, Mean Square Error and Root Mean Square Error. A two-step approach has been proposed for the outliers prediction. Deployment of such approach is justified by the fact, which the time moments of the anomalies, are preferable for practical purposes. On the first step, the values of time-series are predicted using one of the above mentioned models. On the second step the resulting values from the step one are classified using discrete state Hidden Markov Model. For the error performance estimation purpose True Positive, False Positive and False negative rates have been calculated for the respective models. Finally, several proposals for the usage of the considered algorithms in a proactive offline network management in Operation Support System (OSS) or Network Data Analytics Function (NWDAF) have been made.
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Shafiei, Sajjad, Meead Saberi, and Hai L. Vu. "Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (June 18, 2020): 972–81. http://dx.doi.org/10.1177/0361198120933267.
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Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.
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Xu, Pan, Shijie Xu, Kequan Shi, Mingyu Ou, Hongna Zhu, Guojun Xu, Dongbao Gao, Guangming Li, and Yun Zhao. "Prediction of Water Temperature Based on Graph Neural Network in a Small-Scale Observation via Coastal Acoustic Tomography." Remote Sensing 16, no. 4 (February 9, 2024): 646. http://dx.doi.org/10.3390/rs16040646.
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Coastal acoustic tomography (CAT) is a remote sensing technique that utilizes acoustic methodologies to measure the dynamic characteristics of the ocean in expansive marine domains. This approach leverages the speed of sound propagation to derive vital ocean parameters such as temperature and salinity by inversely estimating the acoustic ray speed during its traversal through the aquatic medium. Concurrently, analyzing the speed of different acoustic waves in their round-trip propagation enables the inverse estimation of dynamic hydrographic features, including flow velocity and directional attributes. An accurate forecasting of inversion answers in CAT rapidly contributes to a comprehensive analysis of the evolving ocean environment and its inherent characteristics. Graph neural network (GNN) is a new network architecture with strong spatial modeling and extraordinary generalization. We proposed a novel method: employing GraphSAGE to predict inversion answers in OAT, using experimental datasets collected at the Huangcai Reservoir for prediction. The results show an average error 0.01% for sound speed prediction and 0.29% for temperature predictions along each station pairwise. This adequately fulfills the real-time and exigent requirements for practical deployment.
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Wang, Jinyu, Caiping Zhang, Xiangfeng Meng, Linjing Zhang, Xu Li, and Weige Zhang. "A Novel Feature Engineering-Based SOH Estimation Method for Lithium-Ion Battery with Downgraded Laboratory Data." Batteries 10, no. 4 (April 19, 2024): 139. http://dx.doi.org/10.3390/batteries10040139.
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Accurate estimation of lithium-ion battery state of health (SOH) can effectively improve the operational safety of electric vehicles and optimize the battery operation strategy. However, previous SOH estimation algorithms developed based on high-precision laboratory data have ignored the discrepancies between field and laboratory data, leading to difficulties in field application. Therefore, aiming to bridge the gap between the lab-developed models and the field operational data, this paper presents a feature engineering-based SOH estimation method with downgraded laboratory battery data, applicable to real vehicles under different operating conditions. Firstly, a data processing pipeline is proposed to downgrade laboratory data to operational fleet-level data. The six key features are extracted on the partial ranges to capture the battery’s aging state. Finally, three machine learning (ML) algorithms for easy online deployment are employed for SOH assessment. The results show that the hybrid feature set performs well and has high accuracy in SOH estimation for downgraded data, with a minimum root mean square error (RMSE) of 0.36%. Only three mechanism features derived from the incremental capacity curve can still provide a proper assessment, with a minimum RMSE of 0.44%. Voltage-based features can assist in evaluating battery state, improving accuracy by up to 20%.
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Pattanaik, Sambit, Agbotiname Lucky Imoize, Chun-Ta Li, Sharmila Anand John Francis, Cheng-Chi Lee, and Diptendu Sinha Roy. "Data-Driven Diffraction Loss Estimation for Future Intelligent Transportation Systems in 6G Networks." Mathematics 11, no. 13 (July 6, 2023): 3004. http://dx.doi.org/10.3390/math11133004.
Full textAbstract:
The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users’ locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems.
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Reimann, Daniel, Christine Blech, and Robert Gaschler. "Visual Model Fit Estimation in Scatterplots and Distribution of Attention." Experimental Psychology 67, no. 5 (September 2020): 292–302. http://dx.doi.org/10.1027/1618-3169/a000499.
Full textAbstract:
Abstract. Scatterplots are ubiquitous data graphs and can be used to depict how well data fit to a quantitative theory. We investigated which information is used for such estimates. In Experiment 1 ( N = 25), we tested the influence of slope and noise on perceived fit between a linear model and data points. Additionally, eye tracking was used to analyze the deployment of attention. Visual fit estimation might mimic one or the other statistical estimate: If participants were influenced by noise only, this would suggest that their subjective judgment was similar to root mean square error. If slope was relevant, subjective estimation would mimic variance explained. While the influence of noise on estimated fit was stronger, we also found an influence of slope. As most of the fixations fell into the center of the scatterplot, in Experiment 2 ( N = 51), we tested whether location of noise affects judgment. Indeed, high noise influenced the judgment of fit more strongly if it was located in the middle of the scatterplot. Visual fit estimates seem to be driven by the center of the scatterplot and to mimic variance explained.