Maximum Probability Estimation Research Articles

Asymptotic estimation for statistical models of continuous-time discrete martingales

The paper deals with statistical experiments of the continuous-time discrete local martingales, including models of all types of point processes. The process of local density of the discrete local martingales is expressed by a stochastic exponent of the stochastic integral according to the compensated point measure. General conditions convenient for testing are determined to assure continuous validity of the maximum probability and Bayesian estimators, as well as the continuous asymptotic normality and the asymptotic minimaxity in each compact of the parametric set. The research applies the optimum Fréchet differentiation of random and parametric functions based on the probability in normed spaces in terms of a continuous compensator. It is demonstrated how the basic conditions become much simpler in the case of the renewal process with the continuous compensator.

Hybrid cumulative approach for localization of nodes with adaptive threshold gradient feature on energy minimization using federated learning

PurposeLocalization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.Design/methodology/approachUsing a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.FindingsImprovisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.Originality/valueLocalization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

A petrophysically driven seismic inversion method for the total organic carbon content of source rocks

Organic carbon content is the main index for evaluating organic matter abundance of source rocks, and it is still difficult to quantitatively predict total organic carbon (TOC) in source rocks based on seismic data. The study of seismic fluid identification driven by petrophysics can help to understand the fluid characteristics and distribution patterns of subsurface oil and gas reservoirs. First, this paper clarifies the sweet spot parameters (parameters characterizing hydrocarbon enrichment) and sensitive elastic parameters (a parameter characterizing the nature of an ideal elastic body) of source rocks through theoretical petrophysical modeling. Next, the paper establishes the relationship between sensitive elastic parameters and sweet spot parameters TOC and constructs a statistical petrophysical model that can characterize the relationship between the two on this basis. And then we construct the joint distribution of TOC and elastic impedance through the Bayesian theoretical framework to obtain the maximum posterior probability estimate as the final TOC inversion results of source rocks. Our method successfully predicts the spreading of high-quality source rocks in the Wen4 Section of Lufeng 13 Subsag, and the inversion results are within an uncertainty range of ±14 m for well data, which proves the reliability of the method. The prediction results show that the organic matter abundance of source rocks in the Wen4 Section is high, and the organic carbon content is generally higher than 2%, which provides a reliable basis for the further implementation of the resource scale of the depression and the clarification of the hydrocarbon-rich area, which provides technical support for the evaluation of the source rocks of the new depression in the new area.

On the optimality of DIA-estimators: theory and applications

In this contribution, we introduce, in analogy to penalized ambiguity resolution, the concept of penalized misclosure space partitioning, with the goal of directing the performance of the DIA-estimator towards its application-dependent tolerable risk objectives. We assign penalty functions to each of the decision regions in misclosure space and use the distribution of the misclosure vector to determine the optimal partitioning by minimizing the mean penalty. As each minimum mean penalty partitioning depends on the given penalty functions, different choices can be made, in dependence of the application. For the DIA-estimator, we introduce a special set of penalty functions that penalize its unwanted outcomes. It is shown how this set allows one to construct the optimal DIA-estimator, being the estimator that within its class has the largest probability of lying inside a user specified tolerance region. Further elaboration shows how these penalty functions are driven by the influential biases of the different hypotheses and how they can be used operationally. Hereby the option is included of extending the misclosure partitioning with an additional undecided region to accommodate situations when it will be hard to discriminate between some of the hypotheses or when identification is unconvincing. By extending the analogy with integer ambiguity resolution to that of integer-equivariant ambiguity resolution, we also introduce the maximum probability estimator within the similar larger class.

АНАЛІЗ МОЖЛИВИХ ПІДХОДІВ ДО ВИЗНАЧЕННЯ НАЙБІЛЬШ ДОСТОВІРНОГО МЕТОДУ ПОБУДОВИ ЕНЕРГОБАЛАНСУ

In the process of analyzing the methods, numerous results were obtained. On the basis of which it can be concluded that the results of calculations carried out using the method of maximum likelihood became the closest to the actual values. However, it is important to note that these results turned out to be very close to the results obtained when determining the average value of the data set. The main advantage of this definition is ease of implementation. Since the experiments were conducted on the basis of a relatively small amount of initial data, it is impossible to consider that the determination of the average value is the most appropriate for its implementation. The conclusion is suggested that there is a need to conduct an analysis based on a larger amount of initial data. And also, using other methods of finding the most reliable energy balances. The possibility of constructing maximum likelihood graphs when assessing the quality of energy balance indicators and directions of possible use of this method in tasks were considered. References 4, figure, tables 5. Key words: energy efficiency, energy balance, fuel-energy balance, maximum probability estimation method, maximum likelihood method.

Adversarial pre-optimized graph representation learning with double-order sampling for cross-modal retrieval

Cross-modal retrieval aims at retrieving relevant information across different media data, such as images and text. However, inconsistent distribution of different modal data causes a heterogeneity gap, which becomes an intractable issue. Previous researches aim at finding a semantic share space for various modalities, in which the similarity of them can be calculated by a uniform measurement. Graph representation learning has shown excellent performance in many tasks, like information retrieval. However, it is difficult to construct the most proper cross-modal graph which can better present the semantics of multi-modal data. Besides, the projected representations of various modal data optimized by the supervised class labels and unsupervised graph context learning are insufficiently aligned. To address these issues, this paper presents an Adversarial Pre-optimized Graph Representation Learning (AP-GRL) for cross-modal retrieval. Similar to mainstream methods, AP-GRL aims to learn a shared representation for various modalities. We design an adaptive pre-optimization mechanism to optimize the cross-modal data and their relationships, which is different from previously fixed graph. The raw constructed cross-modal graph is optimized by maximum posterior probability estimation. To fully exploit the graph, we proposed a double-order sampling strategy to take the width and depth into representation learning. Besides, we adopt adversarial learning with Wasserstein distance to reduce domain discrepancy in the domain-invariant representation learning. Extensive experiments on five widely used datasets demonstrate that AP-GRL outperforms state-of-the-art cross-modal retrieval methods.

The mediating role of academic self-efficacy between the answer-copying tendency and the fear of negative evaluation

The aim of this research study was to analyse the relationship among answer-copying tendency, academic self-efficacy, and fear of negative evaluation. To this end, we formed a structural equivalence model, and we evaluated the mediating role of academic self-efficacy between answer-copying tendency and fear of negative evaluation. A total of 562 university students participated in the study. We used the following as data collection tools: The Brief Fear of Negative Evaluation Scale, Academic Self-Efficacy Scale, and Answer-Copying Tendency Scale. For the analysis of the data, first the measurement model was tested, then the Structural Equation Model was established and estimations were made with Maximum Probability Estimation. According to the results, academic self-efficacy plays the role of a mediatory variable between fear of negative evaluation and answer-copying tendency. As can be seen from the impact of fear of negative evaluation on answer-copying tendency, there is a meaningful, positive correlation between the two variables. When we included academic self-efficacy in the model as a mediatory variable, we observed that the relationship between fear of negative evaluation and answer-copying tendency weakened and became less noteworthy. In the light of these observations, we can assert that the tendency of individuals with high academic self-efficacy to cheat in academic contexts is lower even if they have a fear of negative evaluation.

An Hybrid Integration Method-Based Track-before-Detect for High-Speed and High-Maneuvering Targets in Ubiquitous Radar

Due to the limited transmission gain of ubiquitous radar systems, it has become necessary to use a long-time coherent integration method for range-Doppler (RD) analysis. However, when the target exhibits high-speed and high-maneuver capabilities, it introduces challenges, such as range migration (RM), Doppler frequency migration (DFM), and velocity ambiguity (VA) in the RD domain, thus posing significant difficulties in target detection and tracking. Moreover, the presence of VA further complicates the problem. To address these complexities while maintaining integration efficiency, this study proposes a hybrid integration approach. First, methods called Keystone-transform (KT) and matched filtering processing (MFP) are proposed for compensating for range migration (RM) and velocity ambiguity (VA) in Radar Detection (RD) images. The KT approach is employed to compensate for RM, followed by the generation of matched filters with varying ambiguity numbers. Subsequently, MFP enables the production of multiple RD images covering different but contiguous Doppler frequency ranges. These RD images can be compiled into an extended RD (ERD) image that exhibits an expanded Doppler frequency range. Second, an improved particle-filter (IPF) algorithm is raised to perform incoherent integration among ERD images and to achieve track-before-detect (TBD) for a target. In the IPF, the target state vector is augmented with ambiguous numbers, which are estimated via maximum posterior probability estimation. Then, to compensate for the DFM, a line spread model (LSM) is proposed instead of the point spread model (PSM) used in traditional PF. To evaluate the efficacy of the proposed method, a radar simulator is devised, encompassing comprehensive radar signal processing. The findings demonstrate that the proposed approach achieves a harmonious equilibrium between integration efficiency and computational complexity when it comes to detecting and tracking high-speed and high-maneuvering targets with intricate maneuvers. Furthermore, the algorithm’s effectiveness is authenticated by exploiting ubiquitous radar data.

Le Cam–Stratonovich–Boole theory for Itô diffusions

Abstract We connect the theory of local asymptotic normality (LAN) of Le Cam to Boole’s approximation of the Stratonovich stochastic integral by estimating the parameter in the nonlinear drift coefficient of an ergodic diffusion process satisfying a homogeneous Itô stochastic differential equation based on discretely spaced dense observations of the process. The asymptotic normality and local asymptotic minimaxity (in the Hajek–Le Cam sense) of approximate maximum likelihood estimators, approximate maximum probability estimators and approximate Bayes estimators based on Itô and Boole’s approximations of the continuous likelihood are obtained under an almost slowly increasing experimental design (ASIED) condition ( T n 6 / 7 → 0 {\frac{T}{n^{6/7}}\to 0} as T → ∞ {T\to\infty} and n → ∞ {n\to\infty} , where T is the length of the observation time and n is the number of observations) through the weak convergence of the approximate likelihood ratio random fields. Among other things, the Bernstein–von Mises type theorems concerning the convergence of suitably normalized and centered approximate posterior distributions to normal distribution under the same design condition are proved. Asymptotic normality and asymptotic efficiency of the conditional least squares estimator under the same design condition are obtained as a by-product. The log-likelihood derivatives based on Itô approximations are martingales, but the log-likelihood derivatives based on Boole’s approximations are not martingales but weighted averages of forward and backward martingales. These new approximations have faster rate of convergence than the martingale approximations. The methods would have advantages over Euler and Milstein approximations for Monte Carlo simulations.

Channel Estimation in Visible Light Communication Systems: The Effect of Input Signal-Dependent Noise

Visible light communication (VLC) has been proposed as a promising way for next generation wireless communication networks to mitigate the scarcity of the radio frequency (RF) spectrum, and has consequently attracted much attention. This paper introduces a single-input single-output (SISO) VLC system under the joint effects of statistical random channel and signal-dependent shot noise (SDSN). Moreover, it estimates the channel of the considered system using maximum likelihood (ML), least square (LS), linear minimum mean square error (LMMSE), maximum posteriori probability (MAP) and minimum mean square error (MMSE) estimators. Furthermore, a Bayesian Cramér-Rao lower bound (BCRLB) is derived for the proposed system and it is compared to the mean square error (MSE) of the proposed estimators. The problem of unknown SDSN factor, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\zeta ^{2}$</tex-math></inline-formula> , at the receiver side is discussed and two solutions are investigated. The receiver of a VLC system under SDSN and random channel gain <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h$</tex-math></inline-formula> is designed and its BER is studied. Finally, Monte Carlo simulation results of the proposed estimators, which show the dramatic effect of the SDSN on the considered system, are provided. In particular, the presence of noise variance, as well as the SDSN factor, causes an increase in the MSE of the system, while increasing the power reinforces the system performance.

Resilient Filtering of Nonlinear Complex Dynamical Networks Under Randomly Occurring Faults and Hybrid Cyber-Attacks

This paper investigates the problem of recursive resilient filtering for discrete-time nonlinear dynamical networks with randomly switching topologies under randomly occurring faults and hybrid cyber-attacks. The attacks considered are composed of DoS attacks describing by binary random sequences and deception attacks, the amplitude of which obeys the Gaussian mixing distribution. A recursive resilient filter dependent on DoS attack sequences is designed to simultaneously estimate system states and occurred faults, where the desired gain is parameterized via the matrix iterative formula of filtering error covariance. Furthermore, the maximum posterior probability estimation is calculated to identify DoS attack sequences with the help of the classical Bayesian rule. In particular, a modified recursive filtering algorithm is established by replacing the corresponding parameters in the previously derived formulas about filter gains and covariance matrices. Finally, the effectiveness of the proposed recursive filtering algorithm is demonstrated by a simulation example.

A unified probabilistic framework of robust and efficient color consistency correction for multiple images

The task of color consistency correction for multiple images mainly arises from applications like orthoimage producing, panoramic image stitching and 3D reconstruction. In these applications, images usually have been geometrically aligned. So correspondences can be easily extracted and used to solve color correction models. Almost all previous methods assume that the color residuals of correspondences follow Gaussian distribution and solve color models based on least squares. However, correspondences often contain unreliable ones due to altered areas and misalignments, which results in unusual large color residuals, namely, outliers. Imposing color consistency constaints on unreliable correspondences significantly affects the performance of color correction since Gaussian is highly sensitive to outliers. In this paper, to solve this problem theoretically, we first propose a unified probabilistic framework that formulates global color correction as a maximum posteriori probability (MAP) estimation. It is flexible enough to allow for any assumptions of residual distribution. And most color correction methods can be explained in this unified framework. Then, to robust against outliers, we use t-distribution with heavier tails than Gaussian to fit the color residuals. It is more robust because higher probabilities can be assigned to outliers. We show that the MAP formulation based on t-distribution actually leads to weighted least squares, which downweights outliers adaptively. Besides, our framework requires no user-defined robustness parameter. Because all parameters of color models and t-distribution are optimized jointly. In addition, to decrease the huge computational cost of large scale dataset, we extend the proposed framework to a parallel vesion which can achieve efficiency and global optimal at the same time. In the experiments, we compare our approach with the state-of-the-art approaches of Shen et al., Xia et al., etc. on several challenging datasets with outliers. The results demonstrate that our approach achieves the best robustness (average color consistency scores CD=5.4, DeltaE2000=5.7 and PSNR=24.0) and the best efficiency (given 100 images, non-parallel/parallel runs more than 5/50 times faster than others). The implementation is available at https://github.com/yinxuanLi/ColorConsistencyCorrectionForMultipleImages.

Assessing the susceptibility to permethrin and deltamethrin of two laboratory strains of Phlebotomus perniciosus from Madrid region, Spain

Leishmania infantum is a protozoan causing cutaneous and visceral leishmaniasis in several regions of the world, including the Mediterranean basin. Phlebotomus perniciosus is one of the most important vectors of leishmaniasis in the countries of the western Mediterranean basin. Sand fly vector control by insecticides remains a useful tool in the framework of leishmaniasis control programs. Pyrethroids are the most widely used class of insecticides for sand fly control. There is currently a lack of information on the insecticide susceptibility and discriminating concentrations (DCs) of P. perniciosus. The aim of this study was to determine lethal concentrations (LC50, LC95, and LC99) and DCs of deltamethrin and permethrin against two strains of P. perniciosus from Madrid region (Spain). According to WHO tube bioassay protocol 24-h mortality obtained after 1-h exposure to deltamethrin (0.0003%, 0.001%, 0.003%, 0.01%, 0.03%, and 0.1%) and permethrin (0.003%, 0.01%, 0.03%, 0.1%, 0.3%, and 1%) was recorded. The LC50, LC95, and LC99 as well as their respective 95% confidence intervals values were calculated from the baseline data using maximum probability estimates of parameters and binary logistic regression analysis (QCal software). The 100% mortality was recorded from 0.01% of deltamethrin for both P. perniciosus strains and from 0.1% and 0.3% permethrin for Fuenlabrada and Boadilla strains, respectively. Final DCs of deltamethrin and permethrin of each P. perniciosus strain were determined based on setting this parameter at twice the minimum concentration of insecticide that kills 99% (LC99) at the following percentages: Fuenlabrada strain (0.0582% deltamethrin and 0.2648% permethrin) and Boadilla strain (0.0406% deltamethrin and 0.2446% permethrin). The results indicate that both P. perniciosus strains are susceptible to deltamethrin and permethrin and can be used in susceptibility tests, although Boadilla strain offers more consistent results. Due to the scarce existing literature on insecticide DCs for sand flies and the different current procedures to determine their susceptibility to insecticides it is a priority to multiply efforts in order to develop standards for monitoring insecticide resistance in sand flies.

Estimation of parameter for the Pareto distribution based on right censoring

In this paper, we found the estimation of the unknown parameter $delta$ when $vartheta$ is a known parameter in the Pareto distribution. First, we get the maximum probability estimators(MLEs) for unknown parameters. We have obtained the Bayes Estimators of unknown parameter $delta$ using Lindley's approximation. A Monte Carlo simulation is performed and used a programming language R to compare the performance of the method used, and the data set was analyzed for illustration purposes.

Determination of Effectiveness and Efficiency of Production Factors in Sweet Potato Farming in Lamongan, Indonesia

Food crops have a strategic role in agricultural development, covering food security growth, opening up employment opportunities, and income sources in regional and national economies. This research aims to determine the relationship patterns of sweet potato farming production factors. This research utilized a quantitative approach with the survey method. The data were obtained through questionnaires, and the sampling method used a census of 348 respondent farmers in six sweet potato center villages. The Maximum Probability Estimation frontier 4.1 method was employed to calculate the technical efficiency of sweet potato farming. The results showed that the production factors significant at the trust level of 99 percent, 90 percent, and positive value for sweet potato production in Lamongan Regency, Indonesia, were land area production, Urea fertilizer, Phonska fertilizer, ZA fertilizer, and SP36 fertilizer. The technical efficiency attainment level of sweet potato farming was very high, indicating that sweet potato farming in the research was efficient, with an average technical efficiency of 0.90 percent. The achievement level of allocative efficiency on sweet potato farming was relatively low, implying that sweet potato farming was inefficient, with an average localized efficiency of 0.50 percent. The economic efficiency of sweet potato farming was very low, depicting inefficient sweet potato farming, with an average economic efficiency of 0.48 percent. Therefore, sweet potato farmers must optimize land use, ZA, Urea, SP36, and Phonska to produce sweet potatoes optimally to support food security.

Distributed resilient filtering for a class of nonlinear dynamical systems subject to hybrid cyber attacks

AbstractIn this paper, the distributed resilient filtering issue is studied for a class of nonlinear dynamical systems subject to hybrid cyber attacks and gain disturbances. The gain perturbation could come from data rounding errors due to the fixed word length and is modeled by multiplicative noises. The addressed hybrid cyber attacks consist of both denial‐of‐service (DoS) attacks and deception attacks, in which the injected signal obeys a Gaussian mixture distribution. With the help of Kalman filtering theory, an optimally distributed filtering scheme is developed and the desired filter gain is derived by minimizing the filtering error covariance at each time instant. Furthermore, when the statistical information on DoS attacks is not available, the states of DoS attacks are identified in real‐time by resorting to a maximum posterior probability estimation approach combined with the classical Bayesian rule. Based on the developed formulas and the identified state, a suboptimal distributed filtering algorithm is established to realize the state estimation. Finally, a simulation example shows the effectiveness of the proposed distributed resilient filtering algorithm.

Stochastic Wave Distribution Function Method

AbstractThe characteristics of plasma waves propagating in the Earth's plasmasphere and magnetosphere are important in terms of understanding the plasma environment in which these waves are excited and propagate. The propagation direction is a critical in propagation analysis of ELF/VLF band waves. The Means, eigenvector, amplitude, and SVD methods have been proposed previously to estimate the direction of arrival of a single wave, and the wave distribution function (WDF) method, which solves the ill‐posed problem, has been proposed to estimate the propagation direction of multiple arriving waves. In this paper, we propose a stochastically extended WDF method. Conventional WDF methods are based on maximum posterior probability estimation; however, the proposed method uses a Markov chain Monte Carlo method. In the proposed method, the estimated solution is obtained based on the sampling approximation by obtaining multiple samples following the posterior distribution. In addition, the WDF is discretized and quantized to improve calculation stability and efficiency. From the sampled WDF, the mean value of the WDF and 95% credible interval can be calculated without arbitrary parameters. The proposed method is more objective than conventional methods and can be used effectively as a basis for performance evaluation of the WDF method.

Simulation of land use land cover change in Melbourne metropolitan area from 2014 to 2030: using multilayer perceptron neural networks and Markov chain model

ABSTRACT The explosive growth of the Melbourne metropolitan area (MMA) population in the last two decades and the spatial expansion of the city to the periphery have necessitated the analysis of land use land cover (LULC) changes. To this end, Landsat 8 Operational Land Imager (OLI) and Multilayer Perceptron (MLP) neural networks, Markov chain model, GIS and TerrSet software package were used. Initially, nine driving variables affecting the future development of the city were identified. Then, using the maximum probability estimation model, land uses were classified into six categories in the MMA with an area of 8819.6 km2 for 2014, 2017 and 2020. During 2014–2020, two land uses have experienced positive changes (residential and cultivated land uses with 16.48% and 11.56% respectively) and four of them (forest cover with −5.74%, grass and green space with −9.72%, barren lands with −11.14% and water body with −1.45%) have experienced a decrease in area. To validate the prediction of land use changes until 2030, the kappa coefficient (0.617) was used. The result of using Cramer's V statistics (above 0.15) to measure the effect of driving variables on land use forecast was acceptable. LULC change was predicted by MLP and Markov chain model for 2030. The results showed that two land uses (residential and industrial land use with 26.3% and cultivated land with 6.99%) will have positive change and four of them (forest coverage with −17.11%, barren land with −9 29%, grass and green space with −5.33% and water bodies with −1.38%) should have negative growth. Spatial changes of land use will occur mostly in the northern and north-western regions on barren land, and on the east and southeast on forest and green spaces. Findings are useful for planning authorities in MMA where it is highly affected by population growth and LULC change.

Global Vision Localization of Indoor Service Robot Based on Improved Iterative Extended Kalman Particle Filter Algorithm

Localization is the primary problem of mobile robot navigation. Monte Carlo localization based on particle filter has better accuracy and is easier to implement, but there is also the problem of particle degradation. In this paper, the iterative extended Kalman filter is optimized by the Levenberg‐Marquardt optimization method. An improved particle filter algorithm based on the upon optimized iterative Kalman filter is proposed, and the importance probability density function of the particle filter is generated by the maximum posterior probability estimation of the improved iterative Kalman filter. Simulation results of the improved particle filter algorithm show that the algorithm can approximate the state posterior probability distribution more closely with fewer sampled particles under the premise of ensuring sufficient state estimation accuracy. Meanwhile, the computation is reduced and the real‐time performance is enhanced. Finally, the algorithm is validated on the indoor mobile service robot. The experimental results show that the localization algorithm’s accuracy meets requirement for real‐time localizing of the restaurant service robot.

Tryptophan-like fluorescence as a fingerprint of dry-weather misconnections into storm drainage system

BackgroundInappropriate dry-weather misconnections into storm drainage system are a demanding environmental problem worldwide, which leads to unexpected dry-weather discharge into surface waters. It often costs a large amount of manpower and resources to identify the source of misconnections and estimate its contributions. In this study, we evaluated the possibility of quantifying proportional source contribution in a storm drainage system with dry-weather misconnections from domestic sewage and river water inflow, using rapid and low-cost fluorescence spectroscopy methods. For this purpose, samples of both misconnection sources and outflows of storm drainage system were collected and analyzed in a downtown catchment of Shanghai, China.ResultsResults showed that fluorescent peak intensity of tryptophan-like T1 in domestic sewage (802 ± 126 a.u.) was significantly higher than that in urban river water (57 ± 12 a.u.), while fluorescent peak intensities of tryptophan-like T2 in urban river water (732 ± 304 a.u.) was much higher than that in domestic sewage (261 ± 64 a.u.) due to increased algal activity in the local river and upstream inflow chemistry. However, only peak T2 passed the conservative behavior test in the incubation experiments, which could be used as a fingerprint for quantitatively identifying the misconnections. We further developed a Bayesian fluorescence mass balance model (FMBM) to infer the percentage of dry-weather misconnections into the storm drainage system as a function of fluorescence intensities of peak T2 in the samples of sources and outflow. It was found that the maximum posteriori probability estimate of the percentage of river water intrusion into the storm drains was up to 20.8% in this site, which was validated by the results of on-site investigation.ConclusionOur findings implied that in situ fluorescent sensors and Bayesian FMBM for the fingerprint fluorescence peak could be applied to fast track inappropriate dry-weather misconnections into storm drainage system qualitatively and quantitatively with low costs.