Probabilistic Hybrid Research Articles

A deterministic and probabilistic hybrid model for wind power forecasting based improved feature screening and optimal Gaussian mixed kernel function

Forecasting wind power is crucial to ensuring the electricity system runs steadily. However, wind power forecasting is hampered by the randomness and volatility of wind power data. In this paper, a deterministic and probabilistic decomposition ensemble model based on improved feature screening and optimal Gaussian mixed kernel function is proposed, which mainly includes feature screening, feature learning, intelligent weighted integration and interval prediction modules. First, an improved feature screening (IFS) is proposed to capture the optimal features of the original wind power data, eliminating the interference of redundant features. Then an optimized deep neural network is suggested to learn the outcomes of IFS and further explore the temporal correlation of these features. Then, considering the difference of the contribution of each feature component, the final result is obtained by integrating the preliminary forecast results using intelligent weighted integration (IWI). Finally, an optimal mixed kernel-based Gaussian process regression model is developed for interval prediction, which improves the adaptability of the model. The data sets of two wind farms in Inner Mongolia were used for empirical testing. On the two datasets, the model's root mean square error (RMSE) is 1.1845 and 1.7354, respectively, both of which are lower than the comparison model. The model has the lowest coverage width criterion (CWC) of 0.1499 and 0.1460 on the two datasets, respectively.

Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study

Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.

Estimation of building project completion duration using a natural gradient boosting ensemble model and legal and institutional variables

Estimation of building project completion duration using a natural gradient boosting ensemble model and legal and institutional variables

Probabilistic sizing and scheduling co-optimisation of hybrid battery/super-capacitor energy storage systems in micro-grids

While established deterministic capacity planning models for single-component energy storage systems exist, little attention has been given to probabilistic sizing of hybrid energy storage systems (ESSs) using swarm-based meta-heuristic algorithms. This highlights two key research opportunities, namely: (1) studying the impact of preserving model-inherent characteristics and optimising daily system dispatch on narrowing reality gaps in hybrid ESS designs, and (2) the optimal integration of hybrid ESSs into grid-connected micro-grids based on their applications, with potentially significant financial implications for model designs. In response, this paper introduces a novel probabilistic hybrid ESS capacity planning optimisation model based on a state-of-the-art meta-heuristic algorithm. To demonstrate the effectiveness of the model within a community micro-grid scheme, a case study of an eco-village in Aotearoa New Zealand is presented. The simulation results indicate a ∼4 % and ∼36 % premium above the deterministic results respectively in the most likely case and worst-case probabilistic scenarios. On the other hand, the best-case stochastic estimate of the life-cycle cost of the hybrid ESS is found to be ∼39 % lower than that of the deterministic modelling. Additionally, the economics of temporal energy arbitrage using the battery bank is investigated, indicating that at the current capital cost of stationary LiFePO4 batteries and the present fixed feed-in-tariff (NZ$0.08/kWh), it is not economically viable to cycle the storage for arbitrage reasons alone. In conclusion, this paper highlights the critical need to incorporate probabilistic optimisation techniques and emphasises the importance of sizing and scheduling co-optimisation when designing hybrid ESSs for integration into grid-connected micro-grids.

Probabilistic robotic logic programming with hybrid Boolean and Bayesian inference

AbstractBayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic.

A Novel GDMD-PROMETHEE Algorithm Based on the Maximizing Deviation Method and Social Media Data Mining for Large Group Decision Making

Multi-attribute group decision making is widely used in the real world, and many scholars have done a lot of research on it. The public’s focus on emergencies can provide an important reference for emergency handling decision making in the social media big data environment. Due to the complexity of emergency handling decision making, the asymmetry of user evaluation information is easy to cause the loss of important information. It is very important to mine valuable information for decision making through online reviews. Then, a generalized extended hybrid distance measure method between the probabilistic linguistic term sets is proposed. Based on this, an extended GDMD-PROMETHEE large-scale multi-attribute group decision-making method is proposed as well, which can be used to decision making under symmetric information and asymmetric information. Firstly, web crawler technology is used to explore the topics of public concern of emergency handling on social media platforms, and use k-means cluster analysis to classify the crawling variables, then the attributes and subjective weights of emergency handling plans are obtained by TF-IDF and Word2vec technology. Secondly, in order to better retain the linguistic evaluation information from decision-makers, a new generalized probabilistic hybrid distance measure method based on Hamming distance is proposed. Considering the difference of decision makers’ evaluation, the objective weight of decision makers is calculated by combining the maximum deviation method with the new extended hybrid Euclidean distance. On this basis, the comprehensive weights of the attributes are calculated by combining subjective and objective factors. Meanwhile, this paper realizes the distance measures and information fusion of probabilistic linguistic term sets under cumulative prospect theory, and the ranking results of the emergency handling plans based on the extended GDMD-PROMETHEE algorithm are given. Finally, the feasibility and effectiveness of the extended GDMD-PROMETHEE algorithm are verified by the case study of the explosion accident handling decision making of Shanghai “6.18” Petrochemical, and the comparative analyses between the several traditional algorithms demonstrate the extended GDMD-PROMETHEE algorithm is more scientific and superior in this paper.

Reliability Evaluation of Smart Substation Based on Time-Varying Probabilistic Hybrid Attack Graph

A substation is the portion of a power grid that forms a link between the cyber system and the physical system. Reliability evaluation of smart substations based on a time-varying probabilistic hybrid attack graph (TVPHAG) is studied in this paper. First, the topology network of the smart substation is established, whose attributes are represented by probability. Then, in order to solve the problem of asynchrony in the cyber-physical system and the hybrid caused by heterogeneity, time-varying state equation in topology and cuts in algebra are introduced to TVPHAG. Based on TVPHAG, the evaluation of the reliability of cyber-physical systems with multiple equipment and multiple timescales is established. On this basis, the influences of physical conditions, cyberattacks, physical attacks, and cyber-physical attacks on substations are analyzed, respectively. Finally, the simulation shows that the method is effective in evaluating the reliability of smart substations, providing a new method for the evaluation of reliability.

Strategies for reducing healthcare supply chain inventory costs

PurposeThis study aims to identify empirically proven strategies for reducing healthcare supply chain inventory costs.Design/methodology/approachThe author conducted in-depth interviews in 80 hospitals covering different supply chains. The author treated the healthcare firm as the unit of analysis and examined Vrat's taxonomy of inventory models based on the static and dynamic complexity theories of inventory models to identify an appropriate approach. The author addressed 33 highly priced and moderately priced stock-keeping units from 1,432 items and test several inventory policies. Next, the author applied combinations of inventory models, testing probabilistic hybrid inventory models.FindingsThe study finds that medical supplies, equipment, and medications are indispensable for a quality healthcare system. Hence, healthcare supply chain management (SCM) professionals must adopt basic inventory cost-reduction strategies, implementing inventory software functionalities effectively and efficiently. This study shows that probabilistic hybrid inventory techniques in healthcare SCM effectively determine an optimal stocking level, significantly reducing costs.Research limitations/implicationsThis study analyzes data from primary care and (to some extent) secondary care institutions. Although tertiary and quaternary care systems do not represent a large portion of the healthcare system, future research should also address these highly specialized organizations' needs.Practical implicationsThis study proposes practical strategies to help continuously improve supply chain operations in healthcare organizations worldwide.Originality/valueThis study suggests probabilistic hybrid inventory models as empirically proven solutions for evaluating stock-keeping units in the healthcare sector. In doing so, the study provides a new healthcare supply chain approach, proposing a modified taxonomy of inventory models.

Formal Modeling and Performance Evaluation for Hybrid Systems: A Probabilistic Hybrid Process Algebra-Based Approach

Probabilistic behavior is omnipresent in computer-controlled systems, in particular, so-called safety-critical hybrid systems, due to various reasons, like uncertain environments or fundamental properties of nature. In this paper, we extend the existing hybrid process algebra ACP[Formula: see text] with probability without sacrificing the nondeterministic choice operator. The existing approximate probabilistic bisimulation relation is fragile and not robust in the sense of being dependent on the deviation range of the transition probability. To overcome this defect, a novel approximate probabilistic bisimulation is proposed which is inspired by the idea of Probably Approximately Correct (PAC) by relaxing the constraints of transition probability deviation range. Traditional temporal logics, even probabilistic temporal logics, are expressive enough, but they are limited to producing only true or false responses, as they are still logics and not suitable for performance evaluation. To settle this problem, we present a new performance evaluation language that expands quantitative analysis from the value range of [Formula: see text] to real number to reason over probabilistic systems. After that, the corresponding algorithms for performance evaluation are given. Finally, an industrial example is given to demonstrate the effectiveness of our method.

Online quantitative safety monitoring approach for unattended train operation system considering stochastic factors

Online safety monitoring is the key technology to the realize unattended train operation (UTO). So, online quantitative safety monitoring method is proposed to solve the state space explosion problem of the traditional model checking method. The quantitative safety level is defined to quantitatively describe the safety level of the operational state of UTO. To begin with, the composite transition graph of the linear hybrid automata (LHA) of train tracking control and the probabilistic hybrid automata (PHA) model of moving block control principles is constructed based on the composition rules between hybrid automata. Then, the reachable probability distribution of dangerous states can be obtained by verifying the established transition graph with abundant simulation results. Furthermore, the safety constrained boundary of the selected stochastic parameters in bounded time can be achieved for the corresponding quantitative safety level by using the proposed Safety Constraint Computation Algorithm. Finally, based on the performances of stochastic events evaluated by hybrid automata online, the safety status of UTO can be quantitatively monitored in real-time.

Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments

A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or overconservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.

Multimodal Hybrid Pedestrian: A Hybrid Automaton Model of Urban Pedestrian Behavior for Automated Driving Applications

For automated vehicles (AVs) to navigate safely, they must be able to anticipate and predict the behavior of pedestrians. This is particularly critical in urban driving environments where risks of collisions are high. However, a major challenge is that pedestrian behavior is inherently multimodal in nature, i.e., pedestrians can plausibly take multiple paths. This is because, in large part, pedestrian behaviors are driven by unique intentions and decisions made by each pedestrian walking along a particular sidewalk or crosswalk. As described in this paper, we developed a hybrid automaton model of multimodal pedestrian behavior called Multimodal Hybrid Pedestrian (MHP) . We account for multimodal pedestrian behavior by identifying pedestrian decision-making points and developing decision-making models to predict pedestrian behaviors in a probabilistic hybrid automaton framework. The resulting MHP model is more likely to predict the ground truth trajectory compared to two baseline models—a baseline hybrid automaton model and a constant velocity model. The MHP model is applicable to a wide variety of urban scenarios—midblocks, intersections, one-way, and two-way streets, etc., and the probabilistic predictions from the model can be utilized for AV motion planning.

Probabilistic Path Prioritization for Hybrid Fuzzing

Hybrid fuzzing that combines fuzzing and concolic execution has become an advanced technique for software vulnerability detection. Based on the observation that fuzzing and concolic execution are complementary in nature, state-of-the-art hybrid fuzzing systems deploy “optimal concolic testing” and “demand launch” strategies. Although these ideas sound intriguing, we point out several fundamental limitations in them, due to unrealistic or oversimplified assumptions. Further, we propose a novel “discriminative dispatch” strategy and design a probabilistic hybrid fuzzing system to better utilize the capability of concolic execution. Specifically, we design a Monte Carlo-based probabilistic path prioritization model to quantify each path’s difficulty, and then prioritize them for concolic execution. Our model assigns the most difficult paths to concolic execution. We implement a prototype named <inline-formula><tex-math notation="LaTeX">${\sf DigFuzz}$</tex-math></inline-formula> and evaluate our system with two representative datasets and real-world programs. Results show that the concolic execution in <inline-formula><tex-math notation="LaTeX">${\sf DigFuzz}$</tex-math></inline-formula> outperforms than those in state-of-the-art hybrid fuzzing systems in every major aspect. In particular, the concolic execution in <inline-formula><tex-math notation="LaTeX">${\sf DigFuzz}$</tex-math></inline-formula> contributes to discovering more vulnerabilities (12 versus 5) and producing more code coverage (18.9 versus 3.8 percent) on the CQE dataset than the concolic execution in Driller.

Probabilistic Hybrid Linguistic Approaches for Multiple Attribute Group Decision Making with Decision Hesitancy and the Prioritization of Attribute Relationships

Focusing on ill-structured multiple attribute decision-making (MADM) problems, including decision hesitancy and attribute prioritization relationships, this paper investigates appropriate approaches for decision making. Firstly, we introduce the probabilistic hybrid linguistic term set (P-HLTS) for capturing probabilistic preferences about possible linguistic labels belonging to a wide range of hesitant linguistic term sets. Entropy and distance measurements for P-HLTS are developed without arbitrary complementing operations. To facilitate decision making with attribute prioritization relationships, we present a probabilistic uncertain balanced linguistic-prioritized weighted average (PUBL-PWA) operator and the probabilistic uncertain balanced linguistic-induced prioritized ordered weighted average (PUBL-IPOWA) operator. In terms of the strength of the above tools, we further construct two multiple attribute group decision-making (MAGDM) approaches under P-HLTS environments, namely, an approach for decision-making situations where attribute prioritization relationships are known in advance and the relative importance of decision makers (DMs) or decision-making units (DMUs) is not required for consideration, and second approach for decision-making situations where both attribute prioritization relationships and the weighted vectors of DMs or DMUs are explicitly unknown. In general, our proposed approaches are more flexible and practical when considering heterogeneous opinions, avoiding information distortion brought about by complementing operation-based distance measures. Furthermore, illustrative application studies are conducted to verify our developed approaches.

Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

There is an increasing desire for autonomous systems to have high levels of robustness and safety, attained through continuously planning and self-repairing online. Underlying this is the need to accurately estimate the system state and diagnose subtle failures. Estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing these estimates. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete, consistency based state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts learn inconsistencies from constraint violation, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method using this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.

Counterexample Guided Abstraction Refinement for Polyhedral Probabilistic Hybrid Systems

We consider the problem of safety analysis of probabilistic hybrid systems, which capture discrete, continuous and probabilistic behaviors. We present a novel counterexample guided abstraction refinement (CEGAR) algorithm for a subclass of probabilistic hybrid systems, called polyhedral probabilistic hybrid systems (PHS), where the continuous dynamics is specified using a polyhedral set within which the derivatives of the continuous executions lie. Developing a CEGAR algorithm for PHS is complex owing to the branching behavior due to the probabilistic transitions, and the infinite state space due to the real-valued variables. We present a practical algorithm by choosing a succinct representation for counterexamples, an efficient validation algorithm and a constructive method for refinement that ensures progress towards the elimination of a spurious abstract counterexample. The technical details for refinement are non-trivial since there are no clear disjoint sets for separation. We have implemented our algorithm in a Python toolbox called Procegar; our experimental analysis demonstrates the benefits of our method in terms of successful verification results, as well as bug finding.

A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

Verification and control for probabilistic hybrid automata with finite bisimulations

A hybrid automaton is a formal model for a system characterised by a combination of discrete and continuous components. Probabilistic hybrid automata generalise hybrid automata with the possibility of representing random behaviour of the discrete components of the system, in addition to nondeterministic choice regarding aspects such as time durations between mode switches and gradients of continuous flows. Two standard problems for probabilistic hybrid automata are verification and control: verification concerns the existence of a resolution of nondeterminism such that the resulting probability of an ω-regular property exceeds some bound; control concerns the existence of a resolution of the controllable nondeterminism, however the uncontrollable nondeterminism of the environment of the system is resolved, such that the probability of an ω-regular property exceeds some bound. While simple verification and control problems for (probabilistic) hybrid automata are in general undecidable, previous work has defined various subclasses for which the problems are decidable. In this paper, we generalise previous results by showing how bisimulation-based finite abstractions of non-probabilistic hybrid automata can be lifted to the setting of probabilistic hybrid automata. We apply these results to the subclass of probabilistic rectangular hybrid automata in a semantics in which discrete control transitions can occur only at integer points in time. These results allow us to show that, for this class of probabilistic hybrid automaton, the verification problems and control problems are decidable.

A method for estimating watershed restoration feasibility under different treatment levels

The restoration of watershed health can be influenced by ecological, technical and socio-economic factors. The paper presents a conceptual framework and typology to assess watershed ecological restoration based on the properties and processes of sustainable watershed development. According to multiple life stages, habitat properties and existing legal frameworks and applicable valuation approaches, the bio-indicator that integrates natural, political and socio-economic dimensions is proposed. With existing assessment results and official web-pages as references, evaluation systems concerning human impacts on aquatic systems are set forth. Suitable aquatic bio-indicators can standardize the monitoring methodology with respect to water quality, organic pollutants and pesticides, generation time, migration ability, saprobic status, taxonomic composition and diversity. A large number of fish-based indexes have been developed to monitor and manage river ecosystems. Biophysical and statistical models are being used to identify influential stream variables that correlate with macroinvertebrate indices. A probabilistic fuzzy hybrid model to assess river water quality is proposed. The method and process of ecological risk assessment are provided based on adaptive management principles. The environmental sustainability index (ESI) is used to estimate the degree of environmental restoration sustainability with the emergy triangle as a reference.

Probabilistic Approach for Long-Run Price Projections: Case Study of Concrete and Asphalt

AbstractPractitioners increasingly use pavement management systems for determining the allocation of resources for multidecade investments. One important and uncertain input that will affect decisions in these frameworks is future changes in cost for rehabilitation and reconstruction actions. Despite this, existing paradigms overlook its consideration largely because little research to date has evaluated the performance of forecasting over extended time horizons. Therefore, the contribution of this study is the demonstration (via a case study) of the long-term fidelity of probabilistic price projections relative to current practice. Two paving materials, asphalt and concrete, are projected through a probabilistic hybrid forecasting model that convolves conventional forecasts for underlying constituent prices and a long-term price equilibrium relationship between commodities. Out-of-sample forecasts are conducted to test the performance of the proposed model in estimating future prices in terms of their (1...