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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2022年 3卷 464-495页

作者： Braca, Paolo Millefiori, Leonardo M. Aubry, Augusto Marano, Stefano De Maio, Antonio Willett, Peter Ctr Maritime Res & Experimentat Res Dept I-19126 La Spezia SP Italy Univ Naples Federico II DIETI I-80125 Naples NA Italy Univ Salerno DIEM I-84084 Fisciano SA Italy Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

We study the performance of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say exp(-n I), where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and the related error rate I depend on the given training set. The conditions for the exponential convergence can be verified and tested numerically exploiting the available dataset or a synthetic dataset generated according to the underlying statistical model. Coherently with the large deviations theory, we can also establish the convergence of the normalized D3F statistic to a Gaussian distribution. Furthermore, approximate error probability curves zeta(n) exp(-n I) are provided, thanks to the refined asymptotic derivation, where zeta n represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an accurate analytical approximation of the classification performance for both the regimes of small and large values of n. Theoretical findings are corroborated by extensive numerical simulations and by the use of real-world data, acquired by an X-band maritime radar system for surveillance.

关键词： Error probability Training Artificial intelligence Convergence Error analysis Surveillance Signal processing Machine learning deep learning large deviations principle exact asymptotics statistical hypothesis testing Fenchel-Legendre transform extended target detection radar sonar detection X-band maritime radar

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Sound and relatively complete belief Hoare logic for statistical hypothesis testing programs

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ARTIFICIAL INTELLIGENCE 2024年 326卷

作者： Kawamoto, Yusuke Sato, Tetsuya Suenaga, Kohei AIST Tokyo Japan JST PRESTO Kawaguchi Japan Tokyo Inst Technol Tokyo Japan Kyoto Univ Kyoto Japan

We propose a new approach to formally describing the requirement for statistical inference and checking whether a program uses the statistical method appropriately. Specifically, we define belief Hoare logic (BHL) for formalizing and reasoning about the statistical beliefs acquired via hypothesis testing. This program logic is sound and relatively complete with respect to a Kripke model for hypothesis tests. We demonstrate by examples that BHL is useful for reasoning about practical issues in hypothesis testing. In our framework, we clarify the importance of prior beliefs in acquiring statistical beliefs through hypothesis testing, and discuss the whole picture of the justification of statistical inference inside and outside the program logic.

关键词： Knowledge representation Epistemic logic Program logic Kripke model statistical hypothesis testing

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statistical hypothesis testing of Controller Implementations Under Timing Uncertainties 28

Statistical Hypothesis Testing of Controller Implementations...

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28th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

作者： Ghosh, Bineet Hobbs, Clara Xu, Shengjie Duggirala, Parasara Sridhar Anderson, James H. Thiagarajan, P. S. Chakraborty, Samarjit Univ N Carolina Chapel Hill NC 27515 USA Chennai Math Inst Chennai Tamil Nadu India

ISBN: (数字)9781665453448

ISBN: (纸本)9781665453448

Software in autonomous systems, owing to performance requirements, is deployed on heterogeneous hardware comprising task specific accelerators, graphical processing units, and multicore processors. But performing timing analysis for safety critical control software tasks with such heterogeneous hardware is becoming increasingly challenging. Consequently. a number of recent papers have addressed the problem of stability analysis of feedback control loops in the presence of timing uncertainties (cf., deadline misses). In this paper, we address a different class of safety properties, viz., whether the system trajectory deviates too much from the nominal trajectory, with the latter computed for the ideal timing behavior. Verifying such quantitative safety properties involves performing a reachability analysis that is computationally intractable, or is too conservative. To alleviate these problems we propose to provide statistical guarantees over behavior of control systems with timing uncertainties. More specifically, we present a Bayesian hypothesis testing method based on Jeffreys's Bayes factor test that estimates deviations from a nominal or ideal behavior. We show that our analysis can provide, with high confidence, tighter estimates of the deviation from nominal behavior than using known reachability based methods. We also illustrate the scalability of our techniques by obtaining bounds in cases where reachability analysis fails to converge, thereby establishing the former's practicality.

关键词： Control reachability real-time systems safety weakly-hard systems statistical hypothesis testing

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A Note on statistical hypothesis testing: Probabilifying Modus Tollens Invalidates Its Force? Not True!

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EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 2023年第6期83卷 1160-1172页

作者： Widaman, Keith F. Univ Calif Riverside Riverside CA 92521 USA Univ Calif Riverside Sch Educ 900 Univ Ave Riverside CA 92521 USA

The import or force of the result of a statistical test has long been portrayed as consistent with deductive reasoning. The simplest form of deductive argument has a first premise with conditional form, such as p -> q, which means that "if p is true, then q must be true." Given the first premise, one can either affirm or deny the antecedent clause (p) or affirm or deny the consequent claim (q). This leads to four forms of deductive argument, two of which are valid forms of reasoning and two of which are invalid. The typical conclusion is that only a single form of argument-denying the consequent, also known as modus tollens-is a reasonable analog of decisions based on statistical hypothesis testing. Now, statistical evidence is never certain, but is associated with a probability (i.e., a p-level). Some have argued that modus tollens, when probabilified, loses its force and leads to ridiculous, nonsensical conclusions. Their argument is based on specious problem setup. This note is intended to correct this error and restore the position of modus tollens as a valid form of deductive inference in statistical matters, even when it is probabilified.

关键词： statistical hypothesis testing modus tollens

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Adaptive Resilience in Navigation: Multi-Spoofing Attacks Defence with statistical hypothesis testing and Directional Receivers 27

Adaptive Resilience in Navigation: Multi-Spoofing Attacks De...

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27th International Conference on Information Fusion (FUSION)

作者： Venturino, Antonello D'Afflisiot, Enrica Forti, Nicola Braca, Paolo Willett, Peter Wins, Moe Z. Univ Calabria DIMES Arcavacata Di Rende Italy NATO STO Ctr Maritime Res & Expt La Spezia Italy Univ Firenze DINFO Florence Italy Univ Connecticut Dept Elect & Comp Engn Storrs CT USA MIT Lab Informat & Decis Syst 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9798350371420;9781737749769

This paper explores filtering methods to protect range-based localization systems from spoofing attacks on vehicles with directional receivers. It focuses on scenarios where multiple spoofers, potentially from unmanned vehicles, disrupt vehicle localization by strategically positioning themselves between the target and the transmitter. The paper introduces an Adaptive Resilience Navigation Filter (ARNF) that detects ongoing attacks, identifies compromised signals, and mitigates their effects using statistical hypothesis testing. Simulations demonstrate the ARNF's effectiveness under realistic Global Navigation Satellite System conditions, comparing it with the 2-Stage Extended Kalman Filter and an ideal Clairvoyant Extended Kalman Filter.

关键词： Localization Adaptive Filtering statistical hypothesis testing Spoofing Attack Mitigation Global Navigation Satellite System

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Dynamic Radio Map Using statistical hypothesis testing

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2021年第3期7卷 752-766页

作者： Katagiri, Keita Sato, Koya Inage, Kei Fujii, Takeo Univ Electrocommun Adv Wireless & Commun Res Ctr Tokyo 1828585 Japan Tokyo Univ Sci Dept Elect Engn Tokyo 1258585 Japan Tokyo Metropolitan Coll Ind Technol Elect & Elect Engn Program Tokyo 1400011 Japan

Private fifth-generation mobile communication networks (5G) have garnered considerable attention for providing various communication services. In private 5G systems, such as those employed in smart factories, it is necessary to appropriately manage the interference power among diverse systems because each nonpublic operator deploys a gNodeB in the local area and utilizes a similar allocated frequency. Several researchers have discussed the use of a radio map, which stores the spatial distribution of received signal power values as a tool for managing intra-system interference. Radio maps are typically applied to television white space, where the location of the base station (BS) is fixed and the average value of the received signal power exhibits time-invariant characteristics. However, in private 5G networks, the estimation accuracy using a conventional radio map is lowered because the location of the BS may be changed when the factory is rearranged. In this study, a database server is used to infer a significant difference in the average values of the received signal power by means of Welch's t-test;the server then updates the radio map based on the test results. The simulation results indicate that the proposed method can detect changes in the BS location and update the radio map accurately.

关键词： 5G mobile communication Databases Servers testing Shadow mapping Interference Real-time systems Crowdsourcing radio map radio propagation statistical hypothesis testing

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Identifying the presence of structural damage: A statistical hypothesis testing approach combined with residual strain energy

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年 140卷 106655-000页

作者： Wang, Shuqing Wang, Haoyu Xu, Mingqiang Guo, Jian Ocean Univ China Shandong Prov Key Lab Ocean Engn Qingdao 266100 Peoples R China

A statistical hypothesis testing scheme is presented for damage detection considering environmental variations, in which the presence of damage is identified via Welch's t-test by comparing the means of two groups of residual strain energy (RSE) samples from healthy and inspected structures. The RSE, as a damage indication feature, is determined from the mode shape residual (MSR), and the corresponding effects of environmental variations are eliminated via the PCA method. To further improve the damage sensitivity of RSE samples, an MSR selection strategy is proposed to examine the sensitivity of the available MSR matrices to damage and select the most effective MSR matrix. The sensitivity formula of the test statistic with respect to damage is derived and defined as the sensitivity index. Moreover, the mode shape expansion process is not required because an MSR that only includes x- or y-translational degrees of freedom (DoFs) at the measured points is sufficient for detecting damage. The effects of the location and extent of damage, incomplete measurements, and the critical value of the t-test on the performance of the proposed scheme are discussed by numerically analyzing an offshore platform. The results indicate that the scheme can simultaneously limit the probability of committing Type I and Type II errors under temperature variations and noise contamination. An analysis of field test data for a real-world wind turbine also indicates that this approach can avoid Type I error in a real marine environment. (C) 2020 Elsevier Ltd. All rights reserved. A statistical hypothesis testing scheme is presented for damage detection considering environmental variations, in which the presence of damage is identified via Welch's t-test by comparing the means of two groups of residual strain energy (RSE) samples from healthy and inspected structures. The RSE, as a damage indication feature, is determined from the mode shape residual (MSR), and the corresponding effects of envi

关键词： Damage detection Environmental variation statistical hypothesis testing Residual strain energy Sensitivity analysis Degree of freedom

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Detecting and Assuaging against Interest flooding Attack using statistical hypothesis testing in Next Generation ICN 2

Detecting and Assuaging against Interest flooding Attack usi...

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2nd International Conference on Computer, Communication, and Signal Processing (ICCCSP) - Special Focus on Technology and Innovation for Smart Environment

作者： Rani, P. Vimala Ravi, Nagarathna Shalinie, S. Mercy Pariventhan, P. Thiagarajar Coll Engn Comp Sci & Engn Madurai Tamil Nadu India

ISBN: (纸本)9781538611418

Information Centric Network (ICN) is a promising Internet Architecture designed for reliable content distribution which is built with named data objects. The content disseminating capability is decreased due to service poisoning attack like DDoS attack even when well-cache distribution policy is achieved. In order to improve the content delivery ratio and to reduce the processing delay we must be aware of Interest Flooding Attack (IFA) in ICN. Although myriad of solutions are introduced, none of them is helpful to prevent and mitigate the problem of flooding attack owing to lack of detection. The objective of the paper is to develop a secure routing policy which is congestion-free,scalable and consumes minimal resource. In order to achieve this, we propose optimal Neyman Pearson detection method to reduce the workload of router and enhance the router performance. The detection is a preliminary part of IFA and it is based on statistical testing *** intrinsically reduces the false alarm and missed detection probability. The experimental results of the proposed system prove the effectiveness in terms of reduced false alarm rate and missed detection *** processing delay is reduced and content delivery ratio is increased.

关键词： ICN Content Distribution Content Delivery ratio statistical hypothesis testing

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Wind Turbine Fault Detection through Principal Component Analysis and statistical hypothesis testing

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ENERGIES 2016年第1期9卷 3-3页

作者： Pozo, Francesc Vidal, Yolanda Univ Politecn Cataluna Dept Matemat Escola Univ Engn Tecn Ind Barcelona Control Dynam & Applicat CoDAlab Comte Urgell 187 Barcelona 08036 Spain

This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of datathe baseline and the data from the current wind turbineare compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.

关键词： wind turbine fault detection principal component analysis statistical hypothesis testing FAST (Fatigue Aerodynamics Structures and Turbulence)

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statistical hypothesis testing for Chemical Detection in Changing Environments

Statistical Hypothesis Testing for Chemical Detection in Cha...

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IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

作者： Ladi, Anna Timmis, Jon Tyrrell, Andy M. Hickey, Peter J. Univ York Dept Elect Heslington YO10 5DD N Yorkshire England DSTL Ft Halstead Sevenoaks TN14 7BP Kent England

ISBN: (纸本)9781479945153

This paper addresses the problem of adaptive chemical detection, using the Receptor Density Algorithm (RDA), an immune inspired anomaly detection algorithm. Our approach is to first detect when and if something has changed in the environment and then adapt the RDA to this change. statistical hypothesis testing is used to determine whether there has been concept drift in consecutive time windows of the data. Five different statistical methods are tested on mass spectrometry data, enhanced with artificial events that signify a changing environment. The results show that, while no one method is universally best, statistical hypothesis testing performs reasonably well on the context of chemical sensing and it can differentiate between anomalies and concept drift.

关键词： chemical sensors mass spectra statistical testing RDA chemical detection chemical sensing data time window immune inspired anomaly detection algorithm mass spectrometry data receptor density algorithm statistical hypothesis testing Accuracy Chemicals Kernel Robots Sensors testing Vegetation Geochemical surveys hypothesis testing statistical tests CHEMICAL SENSOR remote database access Robot chemical compounds mass spectra Time windows Concept Drift Changing environments Sensors vegetation Mass spectroscopy Kernel

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