Description Of Uncertainty Research Articles

Composite anti-disturbance predictive control of unmanned systems with time-delay using multi-dimensional Taylor network

A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network (MTN) is presented for unmanned systems subject to time-delay and multi-source disturbances. First, the multi-source disturbances are addressed according to their specific characteristics as follows: (A) an MTN data-driven model, which is used for uncertainty description, is designed accompanied with the mechanism model to represent the unmanned systems; (B) an adaptive MTN filter is used to remove the influence of the internal disturbance; (C) an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance; (D) the Extended Kalman Filter (EKF) algorithm is utilized as the learning mechanism for MTNs. Second, to address the time-delay effect, a recursive τ−step-ahead MTN predictive model is designed utilizing recursive technology, aiming to mitigate the impact of time-delay, and the EKF algorithm is employed as its learning mechanism. Then, the MTN predictive control law is designed based on the quadratic performance index. By implementing the proposed composite controller to unmanned systems, simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted. Finally, the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem. Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.

A novel set-induced robust optimization model under correlated uncertainty: a comparative theoretical and computational study

In practical scenarios, optimization problems frequently involve uncertain parameters that exhibit correlated fluctuations. The widely adopted set-induced robust optimization approach is employed to address correlated uncertainty in environments with limited data. This approach focuses on deriving a deterministic reformulation of the original uncertain optimization problem, ensuring that the constructed solution remains feasible for all potential realizations of input parameters within a predefined uncertainty set. However, this approach often yields overly conservative solutions. In this study, an innovative uncertainty set is developed to mitigate the over-conservatism associated with the set-induced robust optimization approach under correlated uncertainty. Moreover, from the proposed description of data uncertainty, a robust linear reformulation of the original uncertain optimization problem is established to strike a balance between conservativeness and optimality. Computational studies exploring production planning optimization programs demonstrate the superior performance of the proposed robust model over other alternatives, considering various levels of correlation among uncertain parameters.

Multi-sensor possibility PHD filter for space situational awareness

Tracking multiple space objects using multiple surveillance sensors is a critical approach in many Space Situation Awareness (SSA) applications. In this process, the uncertainties of targets, dynamics, and observations are usually represented by the probability distributions. However, precise characterization of uncertainty becomes challenging due to imperfect knowledge about some key aspects, such as birth targets and sensor detection profiles. To overcome this challenge, this paper proposes a multi-sensor possibility PHD filter based on the theory of outer probability measures. An effective compensation method is introduced to tackle variations in the fields of view of SSA sensors or instances of missed detections, aiming to mitigate the inconsistency in localized information. The proposed method is adapted to centralized and distributed sensor networks, offering effective solutions for multi-sensor multi-target tracking. The major innovation of the proposed method compared with typical methods is the proper description of epistemic uncertainty, which yields more robust performance in the scenarios of lacking some information about the system. The effectiveness of the multi-sensor possibility PHD filter is demonstrated by a comparison with conventional methods in two simulated scenarios.

Planck data revisited: Low-noise synchrotron polarization maps from the WMAP and Planck space missions

Observations of cosmic microwave background (CMB) polarization, essential for probing a potential phase of inflation in the early Universe, suffer from contamination by polarized emission from the Galactic interstellar medium. This work combines existing observations from the WMAP and space missions to make a low-noise map of polarized synchrotron emission that can be used to clean forthcoming CMB observations. We combine DR5 K, Ka and Q WMAP maps with PR4 30 GHz and 44 GHz LFI maps and WMAP K, Ka and Q maps with LFI 30 GHz and 44 GHz maps, using weights that near-optimally combine the observations as a function of sky direction, angular scale, and polarization orientation. We publish well-characterized maps of synchrotron $Q$ and $U$ Stokes parameters at $ = 30$ GHz and an angular resolution of 1 degree. A statistical description of uncertainties is provided with Monte-Carlo simulations of additive and multiplicative errors. Our maps are the most sensitive full-sky maps of synchrotron polarization to date, and are made available to the scientific community on a dedicated website.

Monitoring an Emergent Pathogen at Low Incidence in Wastewater Using qPCR: Mpox in Switzerland.

Wastewater-based epidemiology offers a complementary approach to clinical case-based surveillance of emergent diseases and can help identify regions with infected people to prioritize clinical surveillance strategies. However, tracking emergent diseases in wastewater requires reliance on novel testing assays with uncertain sensitivity and specificity. Limited pathogen shedding may cause detection to be below the limit of quantification or bordering the limit of detection. Here, we investigated how the definition of limit of detection for quantitative polymerase chain reaction (qPCR) impacts epidemiological insights during an mpox outbreak in Switzerland. 365 wastewater samples from three wastewater treatment plants in Switzerland from 9 March through 31 October 2022 were analyzed for mpox DNA using qPCR. We detected mpox DNA in 22% (79 of 365) wastewater samples based on a liberal definition of qPCR detection as any exponentially increasing fluorescence above the threshold. Based on a more restrictive definition as the lowest concentration at which there is 95% likelihood of detection, detection was 1% (5 of 365). The liberal definition shows high specificity (90%) and accuracy (78%), but moderate sensitivity (64%) when benchmarked against available clinical case reporting, which contrasts with higher specificity (98%) but lower sensitivity (10%) and accuracy (56%) of the 95% likelihood definition. Wastewater-based epidemiology applied to an emergent pathogen will require optimizing public health trade-offs between reporting data with high degrees of uncertainty and delaying communication and associated action. Information sharing with relevant public health stakeholders could couple early results with clear descriptions of uncertainty.Impact Statement: When a novel pathogen threatens to enter a community, wastewater-based epidemiology offers an opportunity to track its emergence and spread. However, rapid deployment of methods for to detect a novel pathogen may rely on assays with uncertain sensitivity and specificity. Benchmarking the detection of mpox DNA in Swiss wastewaters with reported clinical cases in 2022, we demonstrate how definitions of detection of a qPCR assay influence epidemiological insights from wastewater. The results highlight the need for information sharing between public health stakeholders that couple early insights from wastewater with descriptions of methodological uncertainty to optimize public health actions.

Three-way graph convolutional network for multi-label classification in multi-label information system

The Graph Convolutional Neural Networks (GCNs) have demonstrated a powerful capacity for relation processing. To improve the representation learning capability of GCNs, numerous studies have begun to excavate latent topological graphs based on node features, aiming to obtain richer representations of nodes. However, these graph mining methods based on global metrics, such as Euclidean distance, lack descriptions of uncertainty and fine granularity, leading to the loss of structural information, which affects the final representation of nodes. Therefore, this paper proposes the Three-way Graph Convolutional Neural Network (TW-GCN), which is based on multi-granularity and three-way decision. First, from the perspective of multi-granularity, each attribute is analyzed as a granule, and we establish relations of advantage, disadvantage, and uncertainty for objects under each attribute granule. Three-way relation captures the complex connections between nodes from multiple perspectives, improves the comprehensive understanding of graph data, and avoids the information loss caused by traditional two-way processing. Second, a three-way graph convolutional layer is constructed to capture uncertainty and ambiguity by performing graph convolution on different topological graphs. The final node representation is obtained through message passing on the node’s neighbor graph, which considers not only the global structure, but also effectively captures local structural features. Finally, we employ the TW-GCN for multi-label classification within information systems to validate the model’s validity based on multiple indicators.

A Review of Robust Machine Scheduling

Robust optimization (RO) has been recognized as an effective means to deal with unanticipated events in highly uncertain and risky environments. This paper systematically reviews two types of emerging RO machine scheduling approaches—robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) methods—which usually offer tractable formulations and analytical results for machine scheduling problems under uncertainty. First, after highlighting the advantages of RO methods over the stochastic approach in terms of tractability and robustness, we use the bibliometric method to analyze the literature related to R-MS/DR-MS problems and classify them from the following aspects: (1) uncertain factors, (2) uncertainty descriptions, (3) robustness criteria, (4) machine environments and (5) solution methods. Second, we discuss the uncertainty descriptions, and the robust feasibility and robust optimality criteria. We further provide a state-of-the-art review of R-MS/DR-MS models in different machine environments and discuss the performance of the R-MS/DR-MS models. Third, we review and discuss the existing exact, approximation, online, and heuristic solution methods for solving R-MS/DR-MS models. Finally, we present future research opportunities in two promising areas: green machine scheduling problems and machine learning-enabled algorithms. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Machine scheduling plays an essential role in industrial and service systems, such as manufacturing, power generation, transportation and medical systems. However, in practice, scheduling systems usually operate in highly uncertain environments due to noisy measurements, prediction errors, and implementation deviations. To ensure robust feasibility and robust optimality, robust machine scheduling (R-MS) and distributionally R-MS (DR-MS) approaches have been recently proposed to hedge against the uncertainties related to processing time, release time, due date, machine breakdown, etc. This paper provides a comprehensive review of the R-MS/DR-MS models and algorithms in different machine environments from the aspects of uncertainty descriptions, robustness criteria and solution methods. This paper further highlights the challenges of R-MS problems and provides promising and valuable research opportunities in terms of problem formulations and algorithm designs.

Quantitative assessment method of new energy output uncertainty based on the prediction error

With a high percentage of distributed new energy sources connected to the power system, the power grid needs to reserve a larger margin to deal with the uncertainty of renewable energy outputs, leading to an increase in the cost of controlling the margins for the safe operation of the power grid. In order to reduce costs and increase efficiency, a quantitative assessment of new energy output uncertainty is needed. In this paper, a quantitative assessment method of new energy output uncertainty based on the prediction error is proposed, which makes use of a graph database to efficiently obtain massive new energy historical data, uses the clustering in quest (CLIQUE) algorithm to cluster the new energy historical data, and calculates the renewable energy real power confidence interval based on a given new energy power prediction, taking account of the impact of prediction errors caused by the new energy uncertainty and realizing the quantitative description of new energy output uncertainty. Finally, the method is calculated and analyzed together with the actual example data to verify the practical effect of the method.

Stochastic emulation with enhanced partial‐ and no‐replication strategies for seismic response distribution estimation

AbstractModern performance earthquake engineering practices frequently require a large number of time‐consuming non‐linear time‐history simulations to appropriately address excitation and structural uncertainties when estimating engineering demand parameter (EDP) distributions. Surrogate modeling techniques have emerged as an attractive tool for alleviating such high computational burden in similar engineering problems. A key challenge for the application of surrogate models in earthquake engineering context relates to the aleatoric variability associated with the seismic hazard. This variability is typically expressed as high‐dimensional or non‐parametric uncertainty, and so cannot be easily incorporated within standard surrogate modeling frameworks. Rather, a surrogate modeling approach that can directly approximate the full distribution of the response output is warranted for this application. This approach needs to additionally address the fact that the response variability may change as input parameter changes, yielding a heteroscedastic behavior. Stochastic emulation techniques have emerged as a viable solution to accurately capture aleatoric uncertainties in similar contexts, and recent work by the second author has established a framework to accommodate this for earthquake engineering applications, using Gaussian Process (GP) regression to predict the EDP response distribution. The established formulation requires for a portion of the training samples the replication of simulations for different descriptions of the aleatoric uncertainty. In particular, the replicated samples are used to build a secondary GP model to predict the heteroscedastic characteristics, and these predictions are then used to formulate the primary GP that produces the full EDP distribution. This practice, however, has two downsides: it always requires minimum replications when training the secondary GP, and the information from the non‐replicated samples is utilized only for the primary GP. This research adopts an alternative stochastic GP formulation that can address both limitations. To this end, the secondary GP is trained by measuring the square of sample deviations from the mean instead of the crude sample variances. To establish the primitive mean estimates, another auxiliary GP is introduced. This way, information from all replicated and non‐replicated samples is fully leveraged for estimating both the EDP distribution and the underlying heteroscedastic behavior, while formulation accommodates an implementation using no replications. The case study examples using three different stochastic ground motion models demonstrate that the proposed approach can address both aforementioned challenges.

Bayesian System Identification of a River

Hydrological systems like rivers and lakes are a vital part of any community. Mathematical models of such systems underpin management, decision making and control of rivers. There is always uncertainty associated with the models, and in this paper we consider Bayesian system identification of a river. Bayesian system identification delivers an a-posteriori probability distribution of the unknown parameters. This uncertainty description is useful for solving chance-constrained problems encountered in the control and management of rivers. The Bayesian system identification approach is demonstrated by applying it to the upper part of the Murray River in Australia using real water level and flow measurements. The obtained models show good simulation performance capturing the observed water levels well. Moreover, posterior distributions of the parameters are delivered which are useful for control of rivers.

НЕВИЗНАЧЕНІСТЬ В КОНТЕКСТІ ФОРМУВАННЯ ФІНАНСОВОЇ СТРАТЕГІЇ

The financial development of business entities and the strengthening of the influence of external factors on it actualize the renewal and further development of the uncertainty theory. Therefore, the purpose of the study is to develop conceptual approaches to the generation of uncertainty management methodology to build a theoretical basis for the development of a financial strategy taking into account the uncertainty factor. Groups of factors influencing uncertainty on the formation and implementation of the financial strategy of business entities were analyzed. The essence and content of strategic uncertainty are determined from the point of view of economic theory, adaptive approach, informational approach. The classification features of uncertainty are summarized: time horizon, focus, level of impact, predictability, information requirements, difficulty of forecasting,scope, the degree of impact on business,ability to manage. On the basis of the conducted content analysis, the characteristics of uncertainty and strategic uncertainty are distinguished. A comparative description of tactical and strategic uncertainty in the plane of financial and economic activity is given. It is noted that the theory of uncertainty has a complementary nature and contains theories, concepts of management, financial management, economic theory, risk management, strategic management, praxeology, and applied mathematics. The system of basic concepts on which the uncertainty theory is based is summarized: theory of "black swan", theory of games and economic behavior, theory of chaos, theory of risk management, theory of risk management, theory of complex adaptive systems, theory of asymmetric information, theory of stochastic control, theory of options, dynamic capabilities, the open system theory. The components of the methodology of strategic uncertainty management are systematized and substantiated. The methodical provision of strategic uncertainty management is substantiated. The main components of strategic uncertainty management are determined, which allows building the gradient of financial development of the business entity.

MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS

In data assimilation, the description of the model error uncertainty is of the utmost importance because, when incorrectly defined, it may lead to information loss about the real state of the system. In this work, we proposed a novel approach that finds the optimal distribution for describing the model error uncertainty within a particle filtering framework. The method was applied to nonlinear waves in compressible flows. We investigated the influence of observation noise statistics, resolution of the numerical model, smoothness of the solutions, and sensor location. The results showed that in almost all situations the Pearson Type I is preferred, but with different curve-shape characteristics, namely, skewed, nearly symmetric, &amp;cap;-, &amp;cup;-, and J-shaped. The distributions became, in most cases, &amp;cup;-shaped when the sensors were located near the discontinuities.

Climatic analysis based on interval-valued complex Pythagorean fuzzy graph structure

The Interval-valued Complex Pythagorean Fuzzy Set (IVCPFS), an extension of the Pythagorean Fuzzy Set (PFS), offers a more accurate description of uncertainty than traditional fuzzy sets. It has numerous uses in fuzzy control. In this research study, we show the concept of Interval-Valued Complex Pythagorean Fuzzy Graph Structure (IVCPFGS), as well as the results of some regular IVCPFGS and totally regular IVCPFGS, along with the degree of vertex present. We also discussed the subdivision of an IVCPFGS, the complement IVCPFGS, and the strength of a Path in an IVCPFGS. Finally, we discuss a realworld example based on temperature variation and climatic data analysis in an environment with an interval-valued complex Pythagorean fuzzy framework to demonstrate the applicability of the generated results.

STUDY OF A DISTRICT HEATING SYSTEM WATER BOILER AS A OBJECT OF CONTROL

&#x0D; &#x0D; The analysis of the operating conditions of a typical district heating system, in particular, a hot water gas boiler, was carried out. It is shown that the boiler, as the main control object, operates under conditions of constant change in external heat load, which, due to their random nature, leads to a number of uncertainties. The expediency of the mathematical description of uncertainties using the stochastic method as the most tested in practical conditions is substantiated. According to the results of the passive experiment on the hot water gas boiler KVG6.5-150 of the district heating system of one of the districts of Kharkov, an array of hourly experimental data was obtained, reflecting the main performance indicators of the hot water boiler. As a result of data processing by the least squares method, a mathematical model of the boiler was obtained in the form of a linear regression equation, reflecting the relationship between the temperature of the coolant at the boiler outlet and the ambient air temperature, the temperature of the coolant at the inlet to the boiler and with the flow rates of natural gas and coolant to the boiler. The obtained regression equation was verified by Student's t-test, which confirmed the significance of all coefficients of the regression model. The practical significance of the multiple regression equation was assessed using the coefficient of determination. The quality of the multiple regression equation as a whole was assessed using Fisher's F-test. Since parallel surveys were not conducted, instead of checking the adequacy, the quality of the approximation of the experimental points by the accepted regression equation was assessed, that is, it was checked whether this equation makes sense. Such a test was carried out by comparing the residual variance and the variance relative to the mean. The calculation results showed that the value of the coefficient of determination significantly exceeds the allowable value, and the actual value of the Fisher criterion significantly exceeds the table value. The obtained indicators led to the conclusion that the relationship between the variables in the regression model is significant, and the proposed stochastic method and the multiple linear regression equation can be used to make decisions in the process of synthesizing the technical structure of a computer-integrated control system for objects of a district heating system.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Robust linear algebra

We propose a robust optimization (RO) framework that immunizes some of the central linear algebra problems in the presence of data uncertainty. Namely, we formulate linear systems, matrix inversion, eigenvalues–eigenvectors and matrix factorization under uncertainty, as robust optimization problems using appropriate descriptions of uncertainty. The resulting optimization problems are computationally tractable and scalable. We show in theory that RO improves the relative error of the linear system by reducing the condition number of the underlying matrix. Moreover, we provide empirical evidence showing that the proposed approach outperforms state of the art methods for linear systems and matrix inversion, when applied on ill-conditioned matrices. We show that computing eigenvalues–eigenvectors under RO, corresponds to solving linear systems that are better conditioned than the nominal and illustrate with numerical experiments that the proposed approach is more accurate than the nominal, when perturbing ill-conditioned matrices. Finally, we demonstrate empirically the benefit of the robust Cholesky factorization over the nominal.

Quantum Computing with Dartboards.

We present a physically appealing and elegant picture for quantum computing, using rules constructed for a game of darts. A dartboard is used to represent the state space in quantum mechanics, and the act of throwing the dart is shown to have close similarities to the concept of measurement or collapse of the wave function in quantum mechanics. The analogy is constructed in arbitrary dimensional spaces, that is, using arbitrary dimensional dartboards, and for such arbitrary spaces this also provides us a "visual" description of uncertainty. Finally, connections between qubits and quantum computing algorithms are also made, opening the possibility to construct analogies between quantum algorithms and coupled dart throws.

The 14C dose assessment model chain – 14C source term definition and uncertainty quantification

Abstract. Carbon-14 (14C) is an important, dose-determining contributing radionuclide in a deep geological repository for low and intermediate level radioactive waste (L/ILW). 14C is relevant for safety assessments as it can be present both as dissolved and gaseous species in the disposal facility and the host rock, and 14C-organic compounds migrate in the near field and the geosphere due to weak interaction with mineral surfaces in alkaline to near-neutral conditions. Thus, the chemical form of the 14C-bearing carbon species dictates the routes of 14C migration in the engineered barrier system of a deep geological repository and the surrounding host rock and therefore determines the long-term contribution of 14C to dose release from a repository for radioactive waste. Furthermore, 14C released from a deep repository would be subject to a certain accumulation in the biosphere, given the role of carbon as main building block of any life form on earth. The contribution will illustrate Nagra's recent progress within the 14C dose assessment model chain for a L/ILW deep geological repository to address the quantification of uncertainties. The systematic approach began by incorporating a realistic best-estimate plus uncertainty description of 14C activities within the Swiss model inventory of radioactive materials assembled specifically for the purpose of the general licence application. In this context state-of-the-art activation calculations for expected reactor and decommissioning wastes as well as extensive inquiries into origin and use of 14C within medicine, research, and industry played a key role. Uncertainties were screened for relevance and main contributing factors taken into account, differentiating between the characterisation methods of the relevant waste streams. Based on detailed knowledge of the expected activity and material inventory, release rates for the release pathways through congruent and instantaneous release processes as well as resulting chemical species could be defined with confidence. Key findings and characteristics of the 14C source term will be highlighted and discussed.

N-Map: High-resolution groundwater N-retention mapping and modelling by integration of geophysical, geological, geochemical, and hydrological data

A key aspect of protecting aquatic ecosystems from agricultural nitrogen (N) is to locate (i) farmlands where nitrate leaches from the bottom of the root zone and (ii) denitrifying zones in the aquifers where nitrate is removed before entering the surface water (N-retention). N-retention affects the choice of field mitigation measures to reduce delivered N to surface water. Farmland parcels associated with high N-retention gives the lowest impact of the targeted field measures and vice versa. In Denmark, a targeted N-regulation approach is currently implemented on small catchment scale (approx. 15 km2). Although this regulatory scale is much more detailed than what has been used previously, it is still so large that regulation for most individual fields will be either over- or under-regulated due to large spatial variation in the N-retention. The potential cost reduction for farmers is of up to 20–30% from detailed retention mapping at the field scale compared to the current small catchment scale.In this study, we present a mapping framework (N-Map) for differentiating farmland according to their N-retention, which can be used for improving the effectiveness of targeted N-regulation. The framework currently only includes N-retention in the groundwater. The framework benefits from the incorporation of innovative geophysics in hydrogeological and geochemical mapping and modelling. To capture and describe relevant uncertainties a large number of equally probable realizations are created through Multiple Point Statistical (MPS) methods. This allows relevant descriptions of uncertainties of parts of the model structure and includes other relevant uncertainty measures that affects the obtained N-retention. The output is data-driven high-resolution groundwater N-retention maps, to be used by the individual farmers to manage their cropping systems due to the given regulatory boundary conditions.The detailed mapping allows farmers to use this information in the farm planning in order to optimize the use of field measures to reduce delivered agricultural N to the surface water and thereby lower the costs of the field measures. From farmer interviews, however, it is clear that not all farms will have an economic gain from the detailed mapping as the mapping costs will exceed the potential economic gains for the farmers. The costs of N-Map is here estimated to 5–7 €/ha/year plus implementation costs at the farm. At the society level, the N-retention maps allow authorities to point out opportunities for a more targeted implementation of field measures to efficiently reduce the delivered N-load to surface waters.

Extended Fuzzy-Based Models of Production Data Analysis within AI-Based Industry 4.0 Paradigm

Fast, accurate, and efficient analysis of production data is a key element of the Industry 4.0 paradigm. This applies not only to newly built solutions but also to the digitalization, automation, and robotization of existing factories and production or repair lines. In particular, technologists’ extensive experience and know-how are necessary to design correct technological processes to minimize losses during production and product costs. That is why the proper selection of tools, machine tools, and production parameters during the manufacturing process is so important. Properly developed technology affects the entire production process. This paper presents an attempt to develop a post-hoc model of already existing manufacturing processes with the increased requirements and expectations resulting from the introduction of the Industry 4.0 paradigm. In particular, we relied on fuzzy logic to support the description of uncertainties, incomplete data, and discontinuities in the manufacturing process. This translates into better controls compared to conventional systems. An analysis of the proposed solution’s limitations and proposals for further development constitute the novelty and contribution of the article.

Quasi-site-specific prediction of shear wave velocity from CPTu

A rapid and reasonable estimation of the local site shear wave velocity (Vs) profile is indispensable for site assessment. For soil deposits, researchers have developed numerous empirical correlations between Vs and piezocone penetration test (CPTu) based on a global/regional (generic) database. However, these transformations are usually biased for local sites because the generic database contains all available data without regard to site-specific differences, and the transformation uncertainty is usually non-negligible due to the complicated geology factors and measurement errors. Moreover, it is challenging to construct a site-specific correlation due to the sparse in-situ measurements. To this end, this study develops a quasi-site-specific CPTu-Vs transformation model that algorithmically combines both the generic database and site-specific measurements to provide reliable Vs prediction with uncertainty description. A generic database containing 3116 cases of 6 parameters is collected from different countries, including corrected cone tip resistance, sleeve frictional resistance, depth, soil behavior index, pore pressure ratio, and Vs. As the significant impact of soil type on CPTu-Vs correlation, independent Johnson transformation (JS) for different soil types is first adopted to normalize the generic database as a whole, ensuring the constructed model is applicable to any soil type site. Three state-of-art quasi-site-specific models are investigated for CPTu-Vs transformation: probabilistic multiple regression (PMR), the hybridization method (HYD), and the hierarchical Bayesian model (HBM). Empirical correlations and the recently-developed data-driven model (XGBoost) are also considered to illustrate the novelty of the quasi-site-specific model. The applications on three sites with different soil types show that considering soil type in JS improves model accuracy and reduces the transformation uncertainty. The HBM with multiple basic CPTu parameters performs best regarding accuracy, uncertainty, and robustness.