Error Probability Research Articles

The influence of sample size and sampling design on estimating population-level intra specific trait variation (ITV) along environmental gradients.

Understanding the relationship between intraspecific trait variability (ITV) and its biotic and abiotic drivers is crucial for advancing population and community ecology. Despite its importance, there is a lack of guidance on how to effectively sample ITV and reduce bias in the resulting inferences. In this study, we explored how sample size affects the estimation of population-level ITV, and how the distribution of sample sizes along an environmental gradient (i.e., sampling design) impacts the probabilities of committing Type I and II errors. We investigated Type I and II error probabilities using four simulated scenarios which varied sampling design and the strength of the ITV-environment relationships. We also applied simulation scenarios to empirical data on populations of the small mammal, Peromyscus maniculatus across gradients of latitude and temperature at sites in the National Ecological Observatory Network (NEON) in the continental United States. We found that larger sample sizes reduce error rates in the estimation of population-level ITV for both in silico and Peromyscus maniculatus populations. Furthermore, the influence of sample size on detecting ITV-environment relationships depends on how sample sizes and population-level ITV are distributed along environmental gradients. High correlations between sample size and the environment result in greater Type I error, while weak ITV-environmental gradient relationships showed high Type II error probabilities. Therefore, having large sample sizes that are even across populations is the most robust sampling design for studying ITV-environment relationships. These findings shed light on the complex interplay among sample size, sampling design, ITV, and environmental gradients.

Error Probability Optimization for Non-Orthogonal Multiple Access in DBMC Networks

Error Probability Optimization for Non-Orthogonal Multiple Access in DBMC Networks

Quantum subspace expansion in the presence of hardware noise

Finding ground state energies on current quantum processing units (QPUs) using algorithms such as the variational quantum eigensolver (VQE) continues to pose challenges. Hardware noise severely affects both the expressivity and trainability of parameterized quantum circuits, limiting them to shallow depths in practice. Here, we demonstrate that both issues can be addressed by synergistically integrating VQE with a quantum subspace expansion, allowing for an optimal balance between quantum and classical computing capabilities and costs. We perform a systematic benchmark analysis of the iterative quantum-assisted eigensolver in the presence of hardware noise. We determine ground state energies of 1D and 2D mixed-field Ising spin models on noisy simulators and the IBM QPUs ibmq_quito (5 qubits) and ibmq_guadalupe (16 qubits). To maximize accuracy, we propose a suitable criterion to select the subspace basis vectors according to the trace of the noisy overlap matrix. Finally, we show how to systematically approach the exact solution by performing controlled quantum error mitigation based on probabilistic error reduction on the noisy backend fake_guadalupe.

Probabilistic Prediction on Seismic Responses of a Long-Span High-Speed Railway Bridge Using BPINN

Efficient probabilistic prediction of seismic responses is crucial for assessing the seismic-resistant capability of long-span continuous girder high-speed railway bridges. Thus, a Bayesian physics-informed neural network (BPINN) is adopted to rapidly and effectively predict the probabilistic seismic responses of such bridges. The BPINN model combines deep learning and physics to improve the accuracy and consistency of predictions, while also quantifying the uncertainties using Bayesian inference methods. Various seismic excitations, including pulse, far-field and near-field types, are employed to probabilistically predict the seismic responses of the top of bridge pier. Evaluation metrics, including mean squared error and prediction interval coverage probability, are used to assess the deterministic and probabilistic estimates of BPINN. Results demonstrate that BPINN performs better in deterministic results for far-/near-field ground motions compared to pulse-like earthquakes, with most cases exhibiting a close approximation to the 95% confidence interval. The flexibility and adaptability of BPINN in handling different types of ground motions can provide valuable insights for assessing the seismic performance of such structures.

Dynamic risk assessment of Uncertain Random System considering operator's simple emergency-stop action in short time window

Human-machine systems can be considered as typical Uncertain Random Systems (URS). In human-machine systems, operator's simple emergency-stop actions act as a vital soft barrier. Conventional Human Reliability Analyses face challenges in evaluating operator's simple emergency-stop action due to their static characteristics, incompatibility with short time windows, and problems posed by small samples. This paper provides a novel solution to risk assessment of human-machine systems involving operator's simple emergency-stop mechanism. Specifically, an evaluation method of operator's simple emergency-stop action is established, where human cognition is decomposed according to its temporal and logical dimensions, key indicators of human sub-cognition are evaluated, and uncertainty theory is used to integrate these indicators. The output of proposed method is uncertainty of operator's behavior (UOB), which can offer more information than traditional Human Error Probability (HEP) and can be readily converted into uncertainty of human error. Further, a precise simulation method for dynamic risk assessment of Uncertain Random Systems is developed, in which the integration of the UOB is achieved. A case study demonstrates the effectiveness of the proposed methods, and its results show that the proposed methods can clearly reflect the differences in system risk under short time window conditions that have minor distinctions.

Hardware requirements for trapped-ion-based verifiable blind quantum computing with a measurement-only client

Abstract In blind quantum computing (BQC), a user with a simple client device can perform a quantum computation on a remote quantum server such that the server cannot gain knowledge about the computation. Here, we numerically investigate hardware requirements for verifiable BQC using an ion trap as server and a distant measurement-only client. While the client has no direct access to quantum-computing resources, it can remotely execute quantum programs on the server by measuring photons emitted by the trapped ion. We introduce a numerical model for trapped-ion quantum devices in NetSquid, a discrete-event simulator for quantum networks. Using this, we determine the minimal hardware requirements on a per-parameter basis to perform the verifiable BQC protocol. We benchmark these for a five-qubit linear graph state, with which any single-qubit rotation can be performed, where client and server are separated by 50 km. Current state-of-the-art ion traps satisfy the minimal requirements on a per-parameter basis, but all current imperfections combined make it impossible to perform the blind computation securely over 50 km using existing technology. Using a genetic algorithm, we determine the set of hardware parameters that minimises the total improvements required, finding directions along which to improve hardware to reach our threshold error probability that would enable experimental demonstration. In this way, we lay a path for the near-term experimental progress required to realise the implementation of verifiable BQC over a 50 km distance.

Quantum error cancellation in photonic systems: Undoing photon losses

Real photonic devices are subject to photon losses that can decohere quantum information encoded in the system. In the absence of full fault tolerance, quantum error mitigation techniques have been introduced to help manage errors in noisy quantum devices. In this paper, we introduce an error mitigation protocol inspired by probabilistic error cancellation (a popular error mitigation technique in discrete variable systems) for continuous variable systems. We show that our quantum error cancellation protocol can undo photon losses in expectation value estimation tasks. To do this, we analytically derive the (nonphysical) inverse photon loss channel and decompose it into a sum over physically realizable channels with potentially negative coefficients. The bias of our ideal expectation value estimator can be made arbitrarily small at the cost of increasing the sampling overhead. The protocol requires a noiseless amplification followed by a series of photon subtractions. While these operations can be implemented probabilistically, for certain classes of initial state one can avoid the burden of carrying out the amplification and photon subtractions by leveraging Monte Carlo methods to give an unbiased estimate of the ideal expectation value. We validate our proposed mitigation protocol by simulating the scheme on squeezed vacuum states, cat states, and entangled coherent states. Published by the American Physical Society 2024

Assessment of Human Errors in the Operation of the Water Treatment Plant

The water supply system (WSS) is an anthropotechnical system whose reliability depends on proper human activity. Research indicates that 75% of WSS failures are due to human errors. The water treatment plant (WTP) is a key element of the WSS. The water treatment process requires human control as the operator. His task is to maintain an appropriate level of reliability and safety for the system by controlling the technological objects. The aim of the work was to assess the reliability of the WTP operator. The paper presents a Human Reliability Assessment (HRA) of the operator of the WTP using the Fuzzy-Bayes CREAM method. The values of the Human Error Probability (HEP) for operators were determined, which are key to carrying out further analyses of the human impact on the reliability and operational safety of anthropotechnical critical infrastructure systems. The HEP value of the water treatment plant operator varies in the range of 0.0005–0.0746 (depending on the technological process). Identification of new threats related to the impact of the human factor on the WSS’s functioning and taking them into account in reliability calculations will allow for a better representation of actual operating conditions.

Beta Distribution Function for Cooperative Spectrum Sensing against Byzantine Attack in Cognitive Wireless Sensor Networks

In order to explore more spectrum resources to support sensors and their related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PU). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensor paradigms is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attacks from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attacks, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on the sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. The numerical simulation results demonstrate that, compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability and average number of samples under various attack ratios and probabilities.

Probability errors in children's judgements about the likelihood of social characteristics.

The studies reported here investigated mechanisms underlying children's tendency to commit the conjunction fallacy (judging that a conjunction of two events is more likely than one of the events in isolation) when judging people's characteristics. Study 1 investigated these errors in 4- and 5-year-olds (N = 58) using a newly developed social judgement task in which children judged whether a conjunction or one of its elements would apply to a protagonist. Children made conjunction fallacy errors at chance level. Study 2 (N = 71) replicated these findings using an adapted version of the task, in which children separately judged the likelihood of the conjunction and each of its events. Study 3 investigated age-related changes in conjunction fallacy errors in a sample of 148 children aged 4 to 11 years old and 130 adults. This study also investigated how providing background information on the protagonist influenced error rate. Unlike younger children, 10- and 11-year-olds committed the conjunction fallacy at chance level in the absence of background information, but providing information consistent with the likely component of the conjunction significantly increased their error rate. Adults' error rate also significantly increased after the introduction of background information. Across all three studies, conjunction fallacy errors were unrelated to cognitive and social-cognitive abilities, such as verbal ability, theory of mind, and inhibitory control (Studies 1 and 2), and prejudice and hindsight bias (Study 3). These findings suggest that it is only in the second decade of life that children use social information to inform their judgements about people and that social decision-making errors are not determined by core aspects of cognitive and social-cognitive development. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Probability Errors in Children’s Judgements About the Likelihood of Social Characteristics

Supplemental Material for Probability Errors in Children’s Judgements About the Likelihood of Social Characteristics

In simple but challenging search tasks, most errors are stochastic.

In visual search tasks in the lab and in the real world, people routinely miss targets that are clearly visible: so-called look but fail to see (LBFTS) errors. If search displays are shown to the same observer twice, we can ask about the probability of joint errors, where the target is missed both times. If errors are "deterministic," then the probability of a second error on the same display-given that the target was missed the first time-should be high. If errors are "stochastic," the probability of joint errors should be the product of the error rate for first and second appearances. Here, we report on two versions of a T among Ls search with somewhat degraded letters to make search more difficult. In Experiment 1, Ts could either appear amidst crowded "clumps" of Ls or more in isolation. Observers made more errors when the T was in a clump, but these errors were mainly stochastic. In Experiment 2, the task was made harder by making Ts and Ls more similar. Again, errors were predominantly stochastic. If other, socially important errors are also stochastic, this would suggest that "double reading," where two observers (human or otherwise) look at each stimulus, could reduce overall error rates.

Evaluation of Scikit-Learn Machine Learning Algorithms for Improving CMA-WSP v2.0 Solar Radiation Prediction

This article aims to evaluate the performance of solar radiation forecasts produced by CMA-WSP v2.0 (version 2 of the China Meteorological Administration Wind and Solar Energy Prediction System) and to explore the application of machine learning algorithms from the scikit-learn Python library to improve the solar radiation prediction made by the CMA-WSP v2.0. It is found that the performance of the solar radiation forecasting from the CMA-WSP v2.0 is closely related to the weather conditions, with notable diurnal fluctuations. The mean absolute percentage error (MAPE) produced by the CMA-WSP v2.0 is approximately 74% between 11:00 and 13:00. However, the MAPE ranges from 193% to 242% at 07:00–08:00 and 17:00–18:00, which is greater than that observed at other daytime periods. The MAPE is relatively low (high) for both sunny and cloudy (overcast and rainy) conditions, with a high probability of an absolute percentage error below 25% (above 100%). The forecasts tend to underestimate (overestimate) the observed solar radiation in sunny and cloudy (overcast and rainy) conditions. By applying machine learning models (such as linear regression, decision trees, K-nearest neighbors, random forests regression, adaptive boosting, and gradient boosting regression) to revise the solar radiation forecasts, the MAPE produced by the CMA-WSP v2.0 is significantly reduced. The reduction in the MAPE is closely connected to the weather conditions. The models of K-nearest neighbors, random forests regression, and decision trees can reduce the MAPE in all weather conditions. The K-nearest neighbor model exhibits the most optimal performance among these models, particularly in rainy conditions. The random forest regression model demonstrates the second-best performance compared to that of the K-nearest neighbor model. The gradient boosting regression model has been observed to reduce the MAPE of the CMA-WSP v2.0 in all weather conditions except rainy. In contrast, the adaptive boosting (linear regression) model exhibited a diminished capacity to improve the CMA-WSP v2.0 solar radiation prediction, with a slight reduction in MAPE observed only in sunny (sunny and cloudy) conditions. In addition, the input feature selection has a considerable influence on the performance of the machine learning model. The incorporation of the time series data associated with the diurnal variation of solar radiation as an input feature can further improve the model’s performance.

Optimasi Human Error pada Proses Produksi Air Minum Dalam Kemasan di PT. Telaga Tanjung Pomosda Nganjuk

PT. Telaga Tanjung Pomosda, a bottled mineral water manufacturer in Nganjuk, East Java, faces challenges in maintaining high production standards and addressing operational issues such as product rejection and production process errors, impacting overall company efficiency and reputation. This study identifies operational error factors and evaluates the effectiveness of improvement methods. Research procedures utilize HTA, SHERPA, and Poka-Yoke to identify and reduce human errors in bottled mineral water production at PT. Telaga Tanjung Pomosda Nganjuk, focusing on error analysis, probability assessment, and Poka-Yoke strategy implementation. The bottled water packaging process involves critical stages such as cup packaging, pressing, and packing. Errors in each stage can result in delays, decreased water volume, imperfectly pressed lids, and packing mistakes, hindering the entire production process. Research at PT. Telaga Tanjung Pomosda identifies operator issues in machine settings, water measurement, lid positioning, task interruptions, packaging, and cup positioning, with solutions including routine checks and training to enhance product quality

Analyzing ASEP with imperfect‐phase error and various modulation schemes on IRS‐aided wireless network over κ–μ fading channel

AbstractNext‐generation wireless systems constitute a promising technology, and intelligent reflecting surface (IRS)‐aided mobile communications can be used to design and simulate a smart and flexible radio environment. We examine the average symbol error probability (ASEP) on an IRS‐aided wireless network over a κ–μ fading channel with imperfect phase error. In addition, the ASEP for non‐coherent M‐ary frequency shift keying, M‐ary phase shift keying, and M‐ary differential phase shift keying on an IRS‐aided wireless network over a κ–μ fading channel are analyzed. Furthermore, exact closed‐form mathematical expressions for ASEP across different modulation schemes including ASEP with imperfect phase error are derived. The accuracy of the derived expressions is validated through simulations. The system effectiveness is comprehensively examined, uncovering valuable insights into channel fading values and the number of reflecting components.

A conceptual risk modelling for cargo tank fire/explosion in chemical tanker by using Evidential Reasoning -SLIM and Bayesian belief network approach

The transportation of hazardous cargoes, particularly in chemical tankers, represents significant risks such as fire and explosion, which can have catastrophic consequences for both human life, commodity and maritime environment. This article presents a conceptual risk modelling for cargo tank fire/explosion incidents in chemical tanker ships under the Evidential Reasoning (ER) - Success likelihood index method (SLIM) approach with the Bayesian belief network (BBN) to provide a practical tool for assessing and managing the associated risks. Whilst the ER-SLIM approach offers a systematic human error probability (HEP) prediction and handling uncertainty in risk, allowing for the incorporation of expert judgments and data-driven information, the BBN provides a probabilistic graphical model to represent the causal relationships among various risk factors. The result shows that the risk of cargo tank fire/explosion during the cargo tank cleaning operation is calculated as 2.83E-02. The findings of the research including consequences analysis provide valuable insights for decision-makers, safety managers, superintendents, ship masters and officers in the maritime industry to prioritize risk management strategies and enhance the safety of chemical tanker operations.

Error Credits: Resourceful Reasoning about Error Bounds for Higher-Order Probabilistic Programs

Probabilistic programs often trade accuracy for efficiency, and thus may, with a small probability, return an incorrect result. It is important to obtain precise bounds for the probability of these errors, but existing verification approaches have limitations that lead to error probability bounds that are excessively coarse, or only apply to first-order programs. In this paper we present Eris, a higher-order separation logic for proving error probability bounds for probabilistic programs written in an expressive higher-order language. Our key novelty is the introduction of error credits, a separation logic resource that tracks an upper bound on the probability that a program returns an erroneous result. By representing error bounds as a resource, we recover the benefits of separation logic, including compositionality, modularity, and dependency between errors and program terms, allowing for more precise specifications. Moreover, we enable novel reasoning principles such as expectation-preserving error composition, amortized error reasoning, and error induction. We illustrate the advantages of our approach by proving amortized error bounds on a range of examples, including collision probabilities in hash functions, which allow us to write more modular specifications for data structures that use them as clients. We also use our logic to prove correctness and almost-sure termination of rejection sampling algorithms. All of our results have been mechanized in the Coq proof assistant using the Iris separation logic framework and the Coquelicot real analysis library.

Study of noise in virtual distillation circuits for quantum error mitigation

Virtual distillation has been proposed as an error mitigation protocol for estimating the expectation values of observables in quantum algorithms. It proceeds by creating a cyclic permutation of M noisy copies of a quantum state using a sequence of controlled-swap gates. If the noise does not shift the dominant eigenvector of the density operator away from the ideal state, then the error in expectation-value estimation can be exponentially reduced with M. In practice, subsequent error mitigation techniques are required to suppress the effect of noise in the cyclic permutation circuit itself, leading to increased experimental complexity. Here, we perform a careful analysis of the effect of uncorrelated, identical noise in the cyclic permutation circuit and find that the estimation of expectation value of observables are robust against dephasing noise. We support the analytical result with numerical simulations and find that 67% of errors are reduced for M=2, with physical dephasing error probabilities as high as 10%. Our results imply that a broad class of quantum algorithms can be implemented with higher accuracy in the near-term with qubit platforms where non-dephasing errors are suppressed, such as superconducting bosonic qubits and Rydberg atoms.

On the integral and derivative identities of bivariate Fox H-function and applications in performance analysis of wireless communications under generalized Gaussian noise

In conjunction with an algebraic, exponential, complementary error function, and a generalized Q-function, this paper provides analytical solutions for the integral of the bivariate Fox H-function (BFHF). The paper also includes derivative identities related to function arguments. The proposed formulations are then applied for analyzing the performance of point-to-point wireless communication subjected to fading, the characterization of which involves the bivariate Fox H-function, and additive white generalized Gaussian noise. Novel expressions are presented for evaluating the average symbol error probability performance considering different noise distributions, such as Gamma, Gaussian, and Laplacian. An asymptotic analysis is also conducted to determine the system’s potential diversity order. Finally, the accuracy of the analytical findings is confirmed via comparisons of numerical results with Monte-Carlo simulations.

Occupational safety management and human reliability testing during the operation of a plastic injection molding machine

Work safety is a key element in the design and maintenance of industrial workplaces and processes. This article examines occupational safety and the reliability of the human factor in the role of a plastic injection molding machine operator. Analyzing the work process and the dangerous, harmful, and burdensome factors present in the environment and workplace allowed for the identification of occupational hazards. Additionally, factors such as the frequency of threats, the likelihood of events, the possibility of avoiding and limiting damage, and the consequences of events were considered to conduct an occupational risk assessment according to the JSA (Job Safety Analysis) method. Following this, human reliability was assessed by determining the probability of operator errors using the TESEO and HEART methods. Finally, the possibility of using selected immersion techniques on the results of the research was discussed. The research methods and tools used in this study included a literature review and analysis, observation, interviews, and inference.