Imprecise Information Research Articles

Target Search in Dynamic Environments With Multiple Solar-Powered UAVs

This article focuses on target search in dynamic environments using multiple solar-powered unmanned aerial vehicles (SUAVs) with the probability map method. First, the energy model of the SUAV and an external solar environment model are established. Second, the Gaussian mixture model (GMM) is used to extract the prior probability distribution of the target to obtain multiple search areas composed of two-dimensional Gaussian distributions. Then, dynamic task allocation of the SUAVs based on the environment is performed utilizing the GMM information. Next, the SUAVs search for the target according to the assignment and plan a path with the energy model and probability established before, and the probability map is updated in real time according to the detection result of the SUAVs and the velocity vector field of the environment. In this study, two probability map update methods, namely the equivalent area iteration (EAI) and probability estimated diffusion (PED) methods, are proposed for the situations of precise and imprecise environmental information, respectively. Finally, comparative simulations are carried out for these two different situations to prove the improvement obtained using SUAVs.

Cosine similarity measures between q-rung orthopair linguistic sets and their application to group decision making problems

The q-rung orthopair linguistic set (q-ROLS), a combined version of linguistic term sets and q-rung orthopair fuzzy set, is an efficient mathematical tool to accomplish the imprecise information while solving the decision-making problems. Under this environment, we propose additional operations and relations to deal with the decision information, and some properties are well proved. Furthermore, we propound some cosine similarity measures and weighted cosine similarity measures for q-ROLSs based on the traditional cosine similarity measures with a brief study of related properties. In the proposed similarity measures, various linguistic scale functions are utilized in order to take into account the semantics of linguistic terms. Besides this, we employ the stated q-rung orthopair linguistic similarity measures to multi-criteria group decision making problems, in which the weights of DMs are delineated by the projection of individual decisions on the ideal decision results. At last, a numerical example is used to demonstrate the practicality of the suggested technique, and its efficacy is validated by comparison with prevailing techniques. The suggested method’s sensitivity and stability analyses are also provided.

Convex rough sets on finite domains

This paper addresses a foundational aspect of imprecision in information and knowledge. It makes a convincing case that convexity can take part in the progress of rough set theory in finite settings. To this purpose we resort to convex geometries, which constitute a special type of coverings that abstract many combinatorial features of convexity. We define convex geometry (cg) approximation spaces on a grand set, and we produce novel cg-upper and cg-lower approximation operators. Their basic properties are presented. Then we show that the model that arises has connections with well-established models in the rough set literature, both from relation and covering-based approaches. We identificate three types of subsets of the grand set that have different behaviors with respect to their cg-approximations, and we refine this classification in some benchmark cases. Finally, we produce a canonical convex geometry approximation space from any covering on a set. Examples illustrate our constructions and main results.

Rough set generalization models based information retrieval

World Wide Web has become the largest pool of knowledge in terms of volume, but web users still continue to have difficulty in finding the relevant information. Two issues influence the output of information retrieval (IR) systems: first, information overload and second, the vagueness and imprecision in document representations as well as user information need. Rough set theory (RST) proved successful in tackling ambiguous, uncertain, imprecise, or incomplete information. RST is being used in a range of artificial intelligence applications as a result of this. RST has several uses, one of which is IR. This survey focused on Rough Sets and their generalization models in IR. Some previous studies attempted to comprehend rough set-based IR models, but their scope was limited. This research examines various rough set-based IR models, their basic methodologies, features, strengths, and limitations systematically. For critical analysis, a comparison of the existing frameworks is also presented.

On Some Extension of Intuitionistic Fuzzy Synthetic Measures for Two Reference Points and Entropy Weights.

In this paper, a novel Double Intuitionistic Fuzzy Synthetic Measure (DIFSM), based on intuitionistic fuzzy values for handling multi-criteria decision-making problems used to rank alternatives, is presented. In the studies, intuitionistic fuzzy sets (IFSs) represented uncertain, imprecise information or human judgment. The intuitionistic fuzzy sets can also reflect the approval, rejection, and hesitation of decision-makers. The degrees of satisfiability and non-satisfiability and uncertainty of each alternative with respect to a set of criteria are described by membership functions, non-membership functions, and hesitancy indexes, respectively. The aggregation algorithm DIFSM is inspired by Hellwig’s method based on two reference points: ideal point (pattern) and anti-ideal point (anti-pattern), measuring distances between the alternative and ideal point and distance between the ideal and anti-ideal point. The proposed methods take into consideration the entropy-based weights of criteria. An illustrative example is given to demonstrate the practicality and effectiveness of the proposed approach. Additionally, the comparative analysis results, using the DIFSM and the Intuitionistic Fuzzy TOPSIS-based framework, are presented.

Extended EDAS method for multiple attribute decision making in mixture z-number environment based on CRITIC method

In multiple attribute decision making (MADM) issues, the ambiguity, imprecision, and imperfection of assessment information may lead to inadequate decision-making results. However, the Z-number suggested by Zadeh in 2011 could somehow prevent this problem. For MADM issues with unknown attributes weights, an extended Distance from the Average Solution (EDAS) method is proposed under a mixture Z-number environment. In addition, the Criteria Importance Through Inter-criteria Correlation (CRITIC) method is used to estimate the weights of the criterion, which is easy to calculate and avoids subjective forecasts. A novel illustrative example is provided to demonstrate the feasibility, validity, and practicability of the presented method, and is compared with existing decision methods. The outcome indicates that the suggested method can solve complicated decision-making problems.

Single-valued neutrosophic Schweizer-Sklar Hamy mean aggregation operators and their application in multi-attribute decision making

Single-valued neutrosophic sets can efficiently depict a great deal of imprecise, uncertain and discordant information. Hamy mean operator can consider the interrelationships among multiple integrated arguments and Schweizer-Sklar operations express great flexibility in the process of information aggregation. To give full consideration to these advantages, we merge the Hamy mean operator with the Schweizer-Sklar operations in single-valued neutrosophic environment, proposing a single-valued neutrosophic Schweizer-Sklar Hamy mean operator and a single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator. Besides, we illustrate some specific cases and attributes of the two operators. Moreover, based on the entropy weight method and the single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator, this paper presents a single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method to tackle multi-attribute decision making problems. At last, the method and other three existing methods are applied to solve a practical multi-attribute decision making problem, which validates the credibility and validity of the single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method by comparing the differences among them.

A Dynamic Neural Field Model of Multimodal Merging: Application to the Ventriloquist Effect.

Multimodal merging encompasses the ability to localize stimuli based on imprecise information sampled through individual senses such as sight and hearing. Merging decisions are standardly described using Bayesian models that fit behaviors over many trials, encapsulated in a probability distribution. We introduce a novel computational model based on dynamic neural fields able to simulate decision dynamics and generate localization decisions, trial by trial, adapting to varying degrees of discrepancy between audio and visual stimulations. Neural fields are commonly used to model neural processes at a mesoscopic scale-for instance, neurophysiological activity in the superior colliculus. Our model is fit to human psychophysical data of the ventriloquist effect, additionally testing the influence of retinotopic projection onto the superior colliculus and providing a quantitative performance comparison to the Bayesian reference model. While models perform equally on average, a qualitative analysis of free parameters in our model allows insights into the dynamics of the decision and the individual variations in perception caused by noise. We finally show that the increase in the number of free parameters does not result in overfitting and that the parameter space may be either reduced to fit specific criteria or exploited to perform well on more demanding tasks in the future. Indeed, beyond decision or localization tasks, our model opens the door to the simulation of behavioral dynamics, as well as saccade generation driven by multimodal stimulation.

A q-rung orthopair fuzzy decision-making model with new score function and best-worst method for manufacturer selection

Selecting the appropriate manufacturer is important in customized product development because a poor selection may delay the delivery schedule, increase costs, and even affect product quality. This selection task inherently involves ambiguous and imprecise information from decision-makers. The q-rung orthopair fuzzy (q-ROF) sets theory has been proven as a valuable instrument to model human uncertain expressions. However, the existing q-ROF score functions, an essential tool to rank q-ROF values, have some deficiencies, such as generating counterintuitive solutions and antilogarithm or division by zero problems. Furthermore, little research presented the modified best-worst methods (BWM) under q-ROF environments. Nevertheless, these models only generate crisp weights rather than q-ROF weights, which is against the original intention of the q-ROF BWM. To address these problems, this paper first introduces a novel q-ROF score function for measuring q-ROF values. A new q-ROF-BWM is then presented based on the q-ROF preference relations to determine the fuzzy criteria weights. Subsequently, an improved weighted aggregated sum product assessment (WASPAS) with q-ROF settings is presented. Based on them, an integrated q-ROF-BWM-WASPAS model is introduced to rank manufacturers. Additionally, a case study, sensitivity analysis, and several comparisons are conducted to illustrate and validate the usefulness of the developed model.

SHACL-Based Report Quality Evaluation for Health IT-Induced Medication Errors.

Patient safety event (PSE) reports are an important source of information for analyzing risks in healthcare processes. However, the reports' quality is often low due to missing or imprecise information. We work towards an automatic analysis of reports and quality evaluation. To leverage a suitable data representation of health IT-induced medication error reports, we apply the Shapes Constraint Language (SHACL). We define an ontology representing these reports and construct a corresponding SHACL graph. Three authors manually annotate and transform 20 textual reports to the SHACL representation. Furthermore, we use this representation to compute a quality score for each report. The results indicate the suitability of SHACL as a representation of health IT-induced medication error reports, which paves a path of automatically extracting information from PSE reports using text mining and transform them to SHACL for quality evaluation.

Integrated three-dimensional visualization and soft-sensing system for underground paste backfilling

Cemented paste backfilling (CPB) has attracted worldwide attention because of its advantages in the processing of waste tailings and underground goafs. A problem of the underground CPB procedure that has received little attention is the shortage of devices or techniques for monitoring the real-time backfilling process accurately due to the harsh underground environment. Imprecise information of the backfilling progress will mislead the operators to make improper decisions, such as not pausing the transport system promptly or tuning the ratio of cement to tailings incorrectly. This study proposes a 3D visualization and soft-sensing system for the cemented paste backfilling process. By employing a GeoSLAM ZEB-HORIZON laser scanner to scan the 3D model of the underground goaf, the system estimates the backfilling height in real-time according to the accumulated volume of filled paste and simulates the backfilling of the underground goaf on web pages. In a real Copper Mine, we examined the developed system and soft-sensing technique in goafs and found that the relative error of estimated backfilling height is under 10%, which is acceptable in real production. The results indicate that the developed backfilling visualization and the soft-sensing techniques provide significant guidance for production management in underground backfilling.

Decision-making framework for supplier selection using an integrated MCDM approach in a lean-agile-resilient-green environment: evidence from Indian automotive sector

PurposeToday, proficient practices are required to stimulate along various boundaries of the supply chain (SC) to exploit manufacturing resources economically, effectually and gracefully for retaining operational excellence. Accordingly, varieties of paramount practices, i.e. Lean, Agile, Resilient and Green practices, are integrated in present study with the objective to develop a Decision Support Framework (DSF) to select robust supplier under the extent of Lean-Agile-Resilient-Green (LARG) practices for a manufacturing firm. The framework is developed and validated in the Indian automotive sector, where the primary data is collected based on perceptions of the respondents working in an automotive company.Design/methodology/approachLARG metrics can ponder ecological balance, customer satisfaction, associations, effectiveness and sustainability and thus, the study consolidated LARG practices in one umbrella to develop a DSF. The analytical approach under DSF is developed by the integration AHP, DEMATEL, ANP, Extended MOORA and SAW techniques in present study to evaluate a robust supplier under the aegis of LARG practices in SC. DSF is developed by scrutinizing and categorizing LARG characteristics, where the selected LARG characteristics are handled by fuzzy sets theory to deal with the impreciseness and uncertainty in decision making.FindingsThe study has identified 63 measures (15 for Lean, 15 for Agile, 14 for resilient and 19 for Green) to support the robust supplier selection process for manufacturing firms. The findings of study explicate “Internal communication agility”, “Interchangeability to personnel resources”, “Manufacturing flexibility”, “degree of online solution”, “Quickness to resource up-gradation”, “Manageability to demand and supply change”, “Overstocking inventory practices” as significant metrics in ranking order. Additionally, “Transparency to share information”, “Internal communication agility”, “Manufacturing Flexibility”, “Green product (outgoing)” are found as influential metrics under LARG practices respectively.Practical implicationsA technical DSF to utilize by the managers is developed, which is connected with knowledge-based theory and a case of an automobile manufacturing firm is presented to illustrate its implementation. The companies can utilize presented DSF to impose service excellence, societal performance, agility and green surroundings in SC for achieving sustainable outcomes to be welcomed by the legislations, society and rivals. The framework represents an important decision support tool to enable managers to overcome imprecise SC information sources.Originality/valueThe study presented a proficient platform to review the most significant LARG alternative in the SC. The study suggested a cluster of LARG metrics to support operational improvement in manufacturing firms for shifting gear toward sustainable SC practices. The present study embraces its existence in enrolling a high extent of collaboration amongst clients, project teams and LARG practices to virtually eradicate the likelihood of absolute project failure.

Investigating the association of psychological impacts of delay on the perceived importance of real-time information: three-stage least square instrumental regression

This study was conducted to evaluate the association between psychological impacts of delay on commuters and three indictors of the rail transport real-time information. The case study experienced sporadic long delay and simultaneously imprecise real-time information. Three dependent variables related to the passenger information system (PIS) were modeled simultaneously, including perceived waiting time, satisfaction, and the quality of rail transport. Given the likely correlation between the considered models error terms, the relationship between dependent and the independent variables was modeled with the three-stage least square (3SLS) model. We found, for instance, that there are significant association between the impacts of the real-time information and commuters’ gender, purpose of travel, frequency of travel, and various psychological feelings that commuters experienced. However, interaction results highlighted that the associations between experienced negative emotions and dependent variables are not stable but vary based on other predictors. For instance, the association between purpose of travel and increased satisfaction due to accurate real-time information varies based on the frequency of using the transport by commuters. Also, the association between feeling frustrated and increased perceived time due to imprecise real-time information differs based on the amount of anger the commuters experience due to delay. In summary, we found that commuters who experience a higher level of negative feelings are expected to assign a higher importance to the accuracy of real-time information, but those effect are varied based on their purpose of travels or other demographic characteristics.

Fuzzy-Logic-Based Recommendation System for Processing in Condition Monitoring.

The development of a machine’s condition monitoring system is often a challenging task. This process requires the collection of a sufficiently large dataset on signals from machine operation, context information related to the operation conditions, and the diagnosis experience. The two referred problems are today relatively easy to solve. The hardest to describe is the diagnosis experience because it is based on imprecise and non-numerical information. However, it is essential to process acquired data to develop a robust monitoring system. This article presents a framework for a system dedicated to recommending processing algorithms for condition monitoring. It includes a database and fuzzy-logic-based modules composed within the system. Based on the contextual knowledge provided by the user, the procedure suggests processing algorithms. This paper presents the evaluation of the proposed agent on two different parallel gearboxes. The results of the system are processing algorithms with assigned model types. The obtained results show that the algorithms recommended by the system achieve a higher accuracy than those selected arbitrarily. The results obtained allow for an average of 5 to 14.5% higher accuracy.

PF‐ITS: Intelligent traffic service recommendation based on DeepAFM model

Due to the progressive complexity of traffic networks, the traffic pressure increases sharply and traffic accidents occur frequently, which is largely posed by imprecise traffic information service provided in existing Intelligent traffic service systems (ITSSs). To relieve traffic information trek encountered in complex traffic networks, the personalized traffic information recommendation based on click through rate (CTR) prediction has attracted extensive attention. However, the data sparsity and cold start problems in traditional recommendations hinder their real-time applications in ITSSs. In this paper, the multisource data with different types of context information are utilized to construct a personalized fine-grained recommendation method for intelligent traffic services (PF-ITS), which includes three components: a road condition optimization strategy (RCOS) to capture users' behavior preferences and traffic patterns, an encoder–decoder long short-term memory (LSTM) model to address the data sparsity and cold start problems with rich context information, and an improved DeepFM model based on attention mechanism (DeepAFM) to exert personalized fine-grained traffic information recommendation with embeddings of multisource data. More specifically, the RCOS is proposed to perform coarse-grained route recommendation based on path planning theory, in which the traffic data and drivers' preferences are fully utilized for comprehensive modeling. The encoder–decoder LSTM model is employed for representation learning, in which the traffic sequences and driving behaviors are mapped into dense distributed representations with rich semantic information. The DeepAFM is utilized to achieve effective driving safety guarantee according to an individual's requirements, in which the weighted low-order feature combinations and high-order feature interactions are incorporated for personalized fine-grained recommendation. We also conduct extensive experiments on public data sets and in real-world scenarios. The experimental results demonstrate that the PF-ITS method based on RCOS and DeepAFM outperforms the state-of-the-art baseline models in effectiveness and efficiency.

Hierarchical Bayesian Modeling for Improved High-Resolution Mapping of the Completeness Magnitude of Earthquake Catalogs

Abstract Assessing the completeness magnitude Mc is essential for most seismicity studies. However, when studying the spatial variation of Mc in a region, the conventional methods that compute Mc based on the frequency–magnitude distribution (FMD) tend to give gaps and large uncertainties of Mc in subregions of low seismicity, thus rendering high-resolution Mc mapping infeasible. To address the limitations of the FMD-based methods, the Bayesian magnitude of completeness (BMC) method was proposed a decade ago to incorporate a priori information about Mc derived from its empirical relationship to the seismic network spatial configuration Mc=f(d), with d being the distance to the kth (typically k = 4 or 5) nearest seismic station at each node in space. Although widely used, the BMC method has several critical shortcomings that have long been neglected. In this study, we propose a hierarchical Bayesian model that inherently overcomes these shortcomings of the BMC method for high-resolution Mc mapping coined hierarchical Bayesian magnitude of completeness (H-BMC), which provides a unified and more appropriate approach to the integration of a priori information and local observations concerning Mc. We use an earthquake catalog from the Taiwan region to demonstrate that, compared with the FMD-based methods based solely on observed magnitudes, the proposed H-BMC method effectively utilizes a priori information via prior distributions and thereby gives complete and more reliable high-resolution Mc mapping in terms of gap filling and uncertainty reduction. We also highlight that the H-BMC method for Mc mapping serves as a generic and flexible modeling framework for logically combining imprecise information about Mc from different sources.

Construction of Hyperbolic Fuzzy Set and its Applications in Diverse COVID-19 Associated Problems

This paper’s core objective is to introduce a novel notion called hyperbolic fuzzy set (HFS) where, the grades follow the stipulation that the product of optimistic and pessimistic degree must be less than or equal to one (1), rather than their sum not exceeding one (1) as in case of IFSs. The concept of HFS originates from a hyperbola, which provides extreme flexibility to the decision makers in the representation of vague and imprecise information. It is observed that IFSs, Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (Q-ROFSs) often failed to express the uncertain information properly under some specific situations, while HFS tends to overcome such limitations by being applicable under those perplexed situations too. In this paper, we first define some basic operational laws and few desirable properties of HFSs. Second, we define a novel score function, accuracy function, and also establish some of their properties. Third, a novel similarity and distance measure is proposed for HFSs that are capable of distinguishing between different physical objects or alternatives based on the grounds of “similitude degree” and “farness coefficient”, respectively. Later, the advantages of all of these newly defined measures have been showcased by performing a meticulous comparative analysis. Finally, these measures have been successfully applied in various COVID-19 associated problems such as medical decision-making, antivirus face-mask selection, efficient sanitizer selections, and effective medicine selection for COVID-19. The final results obtained with our newly defined measures comply with several other existing methods that we considered and the decision strategy adopted is simple, logical, and efficient. The significant findings of this study are certain to aid the healthcare department and other frontline workers to take necessary measures to reduce the intensity of the coronavirus transmission, so that we can hopefully progress toward the end of this ruthless pandemic.

Plausible sealing for gradual parametricity

Graduality and parametricity have proven to be extremely challenging notions to bring together. Intuitively, enforcing parametricity gradually requires possibly sealing values in order to detect violations of uniform behavior. Toro et al. (2019) argue that the two notions are incompatible in the context of System F, where sealing is transparently driven by potentially imprecise type information, while New et al. (2020) reconcile both properties at the cost of abandoning the syntax of System F and requiring user-provided sealing annotations that are not subject to graduality guarantees. Furthermore, all current proposals rely on a global form of dynamic sealing in order to enforce parametric behavior at runtime, which weakens parametric reasoning and breaks equivalences in the static language. Based on the observation that the tension between graduality and parametricity comes from the early commitment to seal values based on type information, we propose plausible sealing as a new intermediate language mechanism that allows postponing such decisions to runtime. We propose an intermediate language for gradual parametricity, Funky, which supports plausible sealing in a simplified setting where polymorphism is restricted to instantiations with base and variable types. We prove that Funky satisfies both parametricity and graduality, mechanizing key lemmas in Agda. Additionally, we avoid global dynamic sealing and instead propose a novel lexically-scoped form of sealing realized using a representation of evidence inspired by the category of spans. As a consequence, Funky satisfies a standard formulation of parametricity that does not break System F equivalences. In order to show the practicality of plausible sealing, we describe a translation from Funk, a source language without explicit sealing, to Funky, that takes care of inserting plausible sealing forms. We establish graduality of Funk, subject to a restriction on type applications, and explain the source-level parametric reasoning it supports. Finally, we provide an interactive prototype along with illustrative examples both novel and from the literature.

Balanced wireless crowd charging with mobility prediction and social awareness

The advancements in peer-to-peer wireless power transfer (P2P-WPT) have empowered the portable and mobile devices to wirelessly replenish their battery by directly interacting with other nearby devices. The existing works unrealistically assume the users to exchange energy with any of the users and at every such opportunity. However, due to the users’ mobility, the inter-node meetings in such opportunistic mobile networks vary, and P2P energy exchange in such scenarios remains uncertain. Additionally, the social interests and interactions of the users influence their mobility as well as the energy exchange between them. The existing P2P-WPT methods did not consider the joint problem for energy exchange due to user’s inevitable mobility, and the influence of sociality on the latter. As a result of computing with imprecise information, the energy balance achieved by these works at a slower rate as well as impaired by energy loss for the crowd. Motivated by this problem scenario, in this work, we present a wireless crowd charging method, namely MoSaBa, which leverages mobility prediction and social information for improved energy balancing. MoSaBa incorporates two dimensions of social information, namely social context and social relationships, as additional features for predicting contact opportunities. In this method, we explore the different pairs of peers such that the energy balancing is achieved at a faster rate as well as the energy balance quality improves in terms of maintaining low energy loss for the crowd. We justify the peer selection method in MoSaBa by detailed performance evaluation. Compared to the existing state-of-the-art, the proposed method achieves better performance trade-offs between energy-efficiency, energy balance quality and convergence time.

Indiscernibility and Discernibility Relations Attribute Reduction with Variable Precision

Attribute reduction is a popular topic in research on rough sets. In the classical model, much progress has been made in the study of the attribute reduction of indiscernibility and discernibility relations. To enhance the fault tolerance of the model, concepts of both indiscernibility and discernibility relations involving uncertain or imprecise information are proposed in this paper. The attribute reductions of the relative β -indiscernibility relation and relative β -discernibility relation and their algorithms are proposed. When the precision satisfies certain conditions, the reduction of two relation concepts can be converted into a positive region reduction. Therefore, the discernibility matrix is used to construct the reductions of the two relation concepts and the positive region. Furthermore, the corresponding algorithm of the relative β -indiscernibility (discernibility) relation reduction can be optimized when the precision is greater than 0.5, and this is used to develop an optimization algorithm that constructs the discernibility matrix more efficiently. Experiments show the feasibility of the two relation reduction algorithms. More importantly, the reduction algorithms of the two relations and the optimization algorithm are compared to demonstrate the feasibility of the optimization algorithm proposed in this paper.