Unknown Point Research Articles

Identification of multiple unknown point sources occurring in the 2D transport equation: application to groundwater pollution source identification

Identification of multiple unknown point sources occurring in the 2D transport equation: application to groundwater pollution source identification

Deep neural network‐based Wi‐Fi/pedestrian dead reckoning indoor positioning system using adaptive robust factor graph model

A deep neural network (DNN)-based Wi-Fi/pedestrian dead reckoning (PDR) indoor positioning system using an adaptive robust factor-graph model is proposed in this study for the indoor positioning of smartphones. In Wi-Fi positioning, the authors use a DNN to extract robust features from fluctuant Wi-Fi signals in the off-line phase, and obtain more accurate positioning results by computing posterior probabilities in online positioning. Acceleration, gyroscope, and magnetometer data are used to calculate attitude angle, step frequency, and step length, respectively. Received Wi-Fi signal strength is susceptible in complex indoor environments, and PDR errors accumulate over time. A factor-graph model with adaptive robust adjustment is proposed to fuse the positioning results of Wi-Fi and PDR, and it overcomes such shortcomings as slow update frequency and gross errors of Wi-Fi and PDR errors accumulated over time, respectively. When the absence of PDR occurs, hidden Markov model is introduced to smooth multiple DNN-based Wi-Fi positioning estimates at the unknown point to obtain the optimal solution. Experimental results show that the proposed system is more robust and has better accuracy under different motion gestures (held-in-hand, dangling, and calling).

Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Currently, indoor locations based on the received signal strength (RSS) of Wi-Fi are attracting more and more attention thanks to the technology’s low cost, low power consumption and wide availability in mobile devices. However, the accuracy of Wi-Fi positioning is limited, due to the signal fluctuation and indoor multipath interference. In order to overcome this problem, this paper proposes a robust and accurate Wi-Fi fingerprint location recognition method based on a deep neural network (DNN). A stacked denoising auto-encoder (SDAE) is used to extract robust features from noisy RSS to construct a feature-weighted fingerprint database offline. We use the combination of the weights of posteriori probability and geometric relationship of fingerprint points to calculate the coordinates of unknown points online. In addition, we use constrained Kalman filtering and hidden Markov models (HMM) to smooth and optimize positioning results and overcome the influence of gross error on positioning results, combined with characteristics of user movement in buildings, both dynamic and static. The experiment shows that the DNN is feasible for position recognition, and the method proposed in this paper is more accurate and stable than the commonly used Wi-Fi positioning methods in different scenes.

Avaliação de modelo probabilístico de propagação de incêndios florestais utilizando autômatos celulares aplicado a pequenas áreas

O Parque Municipal da Serra de São Domingos, localizado em Poços de Caldas, Minas Gerais, Brasil, apresenta ocorrências de incêndios anuais, que promovem perdas ambientais e socioeconômicas. A utilização de um modelo de propagação de incêndio que possa auxiliar no planejamento do combate, e até na prevenção desses incêndios, seria extremamente benéfico para o município e para Corpo de Bombeiros. Os objetivos deste trabalho são a apresentação dos estudos de incêndios na área e a parametrização e avaliação de um modelo probabilístico de propagação de incêndio, baseado na metodologia dos autômatos celulares, similar a modelos utilizados na região do bioma Cerrado. A caracterização dá-se a partir de utilização de ferramentas de sensoriamento remoto e dados meteorológicos, e a parametrização do modelo utilizou dados de um incêndio com área afetada bem mapeada e ponto de ignição bem conhecido, procedendo-se o ajuste do modelo aplicando-se o algoritmo meta-heurístico de enxame de vagalumes. A eficácia do modelo foi posteriormente testada com duas ocorrências de incêndio distintas, ambas com área atingida bem mapeada, porém, com ponto de ignição desconhecido. O modelo mostrou-se capaz de representar até 57% da área real afetada pelo incêndio, e alguns parâmetros foram considerados satisfatórios, como as probabilidades elementares de sustentabilidade e ignição do fogo. Entretanto, os estudos indicam que há a necessidade de maior refinamento nos dados utilizados no modelo, principalmente, dados de umidade e ponto de ignição dos incêndios.

Inverse Optimal Control for Multiphase Cost Functions

In this paper, we consider a dynamical system whose trajectory is a result of minimizing a multiphase cost function. The multiphase cost function is assumed to be a weighted sum of specified features (or basis functions) with phase-dependent weights that switch at some unknown phase transition points. A new inverse optimal control approach for recovering the cost weights of each phase and estimating the phase transition points is proposed. The key idea is to use a length-adapted window moving along the observed trajectory, where the window length is determined by finding the minimal observation length that suffices for a successful cost weight recovery. The effectiveness of the proposed method is first evaluated on a simulated robot arm, and then, demonstrated on a dataset of human participants performing a series of squatting tasks. The results demonstrate that the proposed method reliably retrieves the cost function of each phase and segments each phase of motion from the trajectory with a segmentation accuracy above 90%.

CAM-ICU may not be the optimal screening tool for early delirium screening in older emergency department patients: a prospective cohort study.

Delirium is a frequent problem among older patients in the emergency department (ED) and early detection is important to prevent its associated adverse outcomes. Several screening tools for delirium have been proposed for the ED, such as the 6-Item Cognitive Impairment Test (6-CIT) and the Confusion Assessment Method-ICU (CAM-ICU). Previous validation of the CAM-ICU for use in the ED showed varying results, possibly because it was administered at different or unknown time points. The aim was to study the prevalence of delirium in older ( ≥ 70 years) ED patients using the CAM-ICU and 6-CIT. A prospective cohort study was carried out in one tertiary care and one secondary care hospital in the Netherlands. Patients aged 70 years and older attending the ED were included. Delirium screening was performed within 1 h after ED registration using the CAM-ICU. The 6-CIT was determined for comparison using a cut-off point of at least 14 points indicating possible delirium. A total of 997 patients were included in the study, with a median age of 78 years (interquartile range 74-84). Delirium as assessed with CAM-ICU was positive in only 13 (1.3%, 95% confidence interval: 0.8-2.2) patients. Ninety-five (9.5% 95% confidence interval: 7.9-11.5) patients had 6-CIT more than or equal to 14. We found a delirium prevalence of 1.3% using the CAM-ICU, which was much lower than the expected prevalence of around 10% as being frequently reported in the literature and what we found when using the 6-CIT. On the basis of these results, caution is warranted to use the CAM-ICU for early screening in the ED.

Adaptive Sliding Mode Impedance Control of Single-Link Flexible Manipulators interacting with the Environment at an Unknown Intermediate Point

SUMMARYThis paper proposes an adaptive robust impedance control for a single-link flexible arm when it encounters an environment at an unknown intermediate point. First, the intermediate collision point is estimated using a collision detection algorithm. The controller, then, switches from free to constrained motion mode. In the unconstrained motion mode, the exerted force to environment is nearly zero. Thus, the reference trajectory is a prescribed desired trajectory in position control. In the constrained motion mode, the reference trajectory is determined by the desired target dynamic impedance. The simulation results demonstrate the efficiency of proposed control scheme.

A New Methodology for Automatic Cluster-Based Kriging Using K-Nearest Neighbor and Genetic Algorithms

Kriging is a geostatistical interpolation technique that performs the prediction of observations in unknown locations through previously collected data. The modelling of the variogram is an essential step of the kriging process because it drives the accuracy of the interpolation model. The conventional method of variogram modelling consists of using specialized knowledge and in-depth study to determine which parameters are suitable for the theoretical variogram. However, this situation is not always possible, and, in this case, it becomes interesting to use an automatic process. Thus, this work aims to propose a new methodology to automate the estimation of theoretical variogram parameters of the kriging process. The proposed methodology is based on preprocessing techniques, data clustering, genetic algorithms, and the K-Nearest Neighbor classifier (KNN). The performance of the methodology was evaluated using two databases, and it was compared to other optimization techniques widely used in the literature. The impacts of the clustering step on the stationary hypothesis were also investigated with and without trend removal techniques. The results showed that, in this automated proposal, the clustering process increases the accuracy of the kriging prediction. However, it generates groups that might not be stationary. Genetic algorithms are easily configurable with the proposed heuristic when setting the variable ranges in comparison to other optimization techniques, and the KNN method is satisfactory in solving some problems caused by the clustering task and allocating unknown points into previously determined clusters.

BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching.

Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms.

Detection and Measurement of the Unknown Moment and Magnitude of the Gaussian Random Process Energy Parameter Abrupt Change

Synthesis, analysis, and simulation of the algorithms for detecting and measuring the abrupt change in the unknown mathematical expectation and dispersion of a fast fluctuating Gaussian random process at an unknown point in time are carried out in this paper. These algorithms can be technically implemented in a simpler way than the ones obtained by means of the common approaches. New asymptotic expressions are proposed for the logarithm of the functional of the likelihood ratio and its adaptive versions obtained after maximization by the unknown regular parameters under various hypotheses. By applying the joint application of the small parameter method and the local Markov approximation method, the closed asymptotically exact expressions are found for the characteristics of the performance of the synthesized detector and measurer. Both the procedure for determining the distribution law and the error moments of the estimate of the discontinuous parameter (abrupt change point) are illustrated, with an allowance of anomalous effects. By means of the statistical computer simulation, a satisfactory agreement is established between the obtained theoretical results and the corresponding experimental data.

Three-dimensional reconstruction with single-shot structured light dot pattern and analytic solutions

Structured light three-dimensional reconstruction is one of the most important techniques in computer vision. However, most existing methods require multiple projections of the designed patterns to achieve the closed form solution, which makes them fail to measure the dynamic objects. In this paper, we propose a two-view based single-shot structured light 3D reconstruction approach that calculates the 3D coordinates of the object with analytical solutions. The designed structured light dot pattern is projected onto the object, distorted and imaged in two cameras. For each point in the structured light dot pattern, there is a ray of light that goes through the optical center and the physically imaged point on the camera’s image plane. Two rays could be determined by two cameras and their intersection solves the unknown point on the object in the 3D world coordinate system. To segment the dot pattern robustly and efficiently, an iterative dot segmentation method is proposed. Experimental results verified the effectiveness of the proposed approach in reconstructing objects without bright colors, rich textures, large discontinuities and obvious occlusions.

Comparing crossing hazard rate functions by joint modelling of survival and longitudinal data

It is one of the important issues in survival analysis to compare two hazard rate functions to evaluate treatment effect. It is quite common that the two hazard rate functions cross each other at one or more unknown time points, representing temporal changes of the treatment effect. In certain applications, besides survival data, we also have related longitudinal data available regarding some time-dependent covariates. In such cases, a joint model that accommodates both types of data can allow us to infer the association between the survival and longitudinal data and to assess the treatment effect better. In this paper, we propose a modelling approach for comparing two crossing hazard rate functions by joint modelling survival and longitudinal data. Maximum likelihood estimation is used in estimating the parameters of the proposed joint model using the EM algorithm. Asymptotic properties of the maximum likelihood estimators are studied. To illustrate the virtues of the proposed method, we compare the performance of the proposed method with several existing methods in a simulation study. Our proposed method is also demonstrated using a real dataset obtained from an HIV clinical trial.

An optimization for reducing the size of an existing urban-like monitoring network for retrieving an unknown point source emission

Abstract. This study presents an optimization methodology for reducing the size of an existing monitoring network of the sensors measuring polluting substances in an urban-like environment in order to estimate an unknown emission source. The methodology is presented by coupling the simulated annealing (SA) algorithm with the renormalization inversion technique and the computational fluid dynamics (CFD) modeling approach. This study presents an application of the renormalization data-assimilation theory for optimally reducing the size of an existing monitoring network in an urban-like environment. The performance of the obtained reduced optimal sensor networks is analyzed by reconstructing the unknown continuous point emission using the concentration measurements from the sensors in that optimized network. This approach is successfully applied and validated with 20 trials of the Mock Urban Setting Test (MUST) tracer field experiment in an urban-like environment. The main results consist of reducing the size of a fixed network of 40 sensors deployed in the MUST experiment. The optimal networks in the MUST urban region are determined, which makes it possible to reduce the size of the original network (40 sensors) to ∼1/3 (13 sensors) and 1∕4 (10 sensors). Using measurements from the reduced optimal networks of 10 and 13 sensors, the averaged location errors are obtained as 19.20 and 17.42 m, respectively, which are comparable to the 14.62 m obtained from the original 40-sensor network. In 80 % of the trials with networks of 10 and 13 sensors, the emission rates are estimated within a factor of 2 of the actual release rates. These are also comparable to the performance of the original network, whereby in 75 % of the trials the releases were estimated within a factor of 2 of the actual emission rates.

A piecewise model for two-phase growth phenomena

In this paper, we introduce a nonlinear model which can be used as a regression model for modeling phenomena requiring a two-phase growth curves. The proposed model is defined as a continuous piecewise combination of two exponential functions with an unknown break point. Numerical results on real and simulated data sets are presented to illustrate the applicability and the utility of the proposed model on growth data.

The Mask object in psychotherapy: Presentation and Representation

The idea of bringing together contributions from different disciplines stems from the fact that many of the failures of psychotherapy arise from the lack of comprehension about unknown points of view, from inhomogeneous readings, from unavailability to change methods. In other words, approaches that are not sufficiently oriented to get out of an ultraspecialized perspective. The advantage of a methodology based on interdisciplinarity allowed us to put different contributions into a dialogue. This connection led to the emergence of original ideas on the considered topics. Moreover, by responding positively to the challenges of transdisciplinarity, the same contributions also turned out to be compared to the considerations that would have been posed opening up to dialogue with the multiple fields of knowledge.

Machine Learning for Haptics: Inferring Multi-Contact Stimulation From Sparse Sensor Configuration.

Robust haptic sensation systems are essential for obtaining dexterous robots. Currently, we have solutions for small surface areas, such as fingers, but affordable and robust techniques for covering large areas of an arbitrary 3D surface are still missing. Here, we introduce a general machine learning framework to infer multi-contact haptic forces on a 3D robot's limb surface from internal deformation measured by only a few physical sensors. The general idea of this framework is to predict first the whole surface deformation pattern from the sparsely placed sensors and then to infer number, locations, and force magnitudes of unknown contact points. We show how this can be done even if training data can only be obtained for single-contact points using transfer learning at the example of a modified limb of the Poppy robot. With only 10 strain-gauge sensors we obtain a high accuracy also for multiple-contact points. The method can be applied to arbitrarily shaped surfaces and physical sensor types, as long as training data can be obtained.

Inversion of Lake Bathymetry through Integrating Multi-Temporal Landsat and ICESat Imagery.

Lake bathymetry provides valuable information for lake basin planning and treatment, lake watershed erosion and siltation management, water resource planning, and environmental protection. Lake bathymetry has been surveyed with sounding techniques, including single-beam and multi-beam sonar sounding, and unmanned ship sounding. Although these techniques have high accuracy, most of them require long survey cycles and entail a high degree of difficulty. On the contrary, optical remote sensing inversion methods are easy to implement, but tend to provide less accurate bathymetry measures, especially when applied to turbid waters. The present study, therefore, aims to improve the accuracy of bathymetry measurements through integrating Landsat Thematic Mapper imagery, the Ice, Cloud, and Land Elevation Satellite’s Geoscience Laser Altimeter System (ICESat/GLAS) data, and water level data measured at hydrological stations. First, the boundaries of a lake at multiple dates were derived using water extraction, initial boundary extraction, and Landsat Thematic Mapper/Enhanced Thematic Mapper (TM/ETM+) strip removal processing techniques. Second, ICESat/GLAS data were introduced to obtain additional topographic information of a lake. The striped topography of a lake’s area was then obtained through eliminating and correcting erroneous points and interpolating the values of unknown points. Third, the entire bathymetry of the lake was obtained through interpolating water level values of lake boundary points in various dates. Experiments show that accurate bathymetry (±1 m) can be successfully derived.

Estimation in the single change-point hazard function for interval-censored data with a cure fraction

ABSTRACTIn reliability or survival analysis, the hazard function plays a significant part for it can display the instantaneous failure rate at any time point. In practice, the abrupt change in hazard function at an unknown time point may occur after a maintenance activity or major operation. Under these circumstances, identifying the change point and estimating the size of the change are meaningful. In this paper, we assume that the hazard function is piecewise constant with a single jump at an unknown time. We propose the single change-point model for interval-censored survival data with a cure fraction. Estimation methods for the proposed model are investigated, and large-sample properties of the estimators are established. Simulation studies are carried out to evaluate the performance of the estimating method. The liver cancer data and breast cancer data are analyzed as the applications.

Simulation of extreme temperatures using a new method: TIN‐copula

AbstractIn climatology, there is a clear need for more reliable data, especially in regions where no meteorological stations exist. Different statistical methods as well as regional climate models are usually used for covering areas with limited data. However, important biases between real and simulated climate parameters are observed, especially with respect to extremes. The present study introduces a new statistical method that combines triangular irregular networks and copulas for the simulation of extreme maximum and minimum temperatures. According to the new method, a studied region can be divided into triangles, and a data series can be simulated for every unknown x‐point in each triangle. The simulation of new data series is based on both the distances between the unknown point and the triangle vertices and the observational data available at each vertex. The statistical evaluation of this new method was successful and further demonstrated that the size of the triangle as well as the climatic characteristics of the stations at the triangle vertices can significantly affect the final results. The present investigation proposes the triangular irregular network‐copula method for the simulation of extreme temperatures, as the projections provided by this method are shown to approach the observed extremes values for individual meteorological stations.

MET amplification and exon 14 skipping in treatment naïve patients in lung cancer.

e14543 Background: Somatic mutations in MET are diverse and include MET amplification, exon 14 skipping and also some functional point mutations (H1094Y, Y1230H, D1228N, etc). Generally, amplification and ex14 skipping are the two main mutation types. Methods: MET mutation profiles were analyzed using hybridization capture based next-generation sequencing (NGS), which enables the simultaneous detection of multi-mutation types of at least 59 genes (range 59-1021 genes). Results: Screening tens of thousands treatment naive patients, we found 319 patients with MET mutations, 56 patients (18%) with MET amplification, 55 patients (17%) with MET ex14 skipping, and 214 patients with other unknown functional point mutations. 39 cases with MET amplification and 52 cases with MET ex14 skipping in lung cancer. Patients with MET amplification or ex14 skipping had fewer mutations compared with patients with other MET mutations ( P < 0.0001), while there were no differences in ctDNA abundance between two groups ( P = 0.58). Further compared the MET amplification and ex14 skipping groups, we found mutation numbers and ctDNA abundance were both significant lower in ex14 skipping group (mutation numbers: P = 0.0134; ctDNA abundance: P < 0.0001). MET amplification range from 2 to 7 copies in these 39 patients, and patients with more than 3 copies had a higher ctDNA abundance than = < 3 copies patients ( P < 0.0005). Six patients with both amplification and ex 14 skipping, all of them with advanced age ( > 70 years). Of the MET amplification group, 11 patients (28%) developed EGFR driver mutation concurrently, four patients with L858R, three with ex19 del, one with G719A, one with both L858R and EGFR amplification, one with both ex19 del and amplification and one with only amplification, while no patients were found to have any EGFR driver mutation in MET ex14 skipping group. Conclusions: The cfDNA from patients with EGFR amplification or ex14 skipping was only 18% or 17% in MET mutations. Both of these two mutation types were mainly found in lung cancer. Furthermore, MET amplification group patients had fewer mutations and lower ctDNA abundance. In treatment naïve patients, MET amplification concurrent with other EGFR drive mutations, rather than ex14 skipping.