Crisp Set Research Articles

Automobile Instrument Detection Using Prior Information and Fuzzy Sets

Since there are many kinds of automobile instruments and the industrial environment is usually complex, automatic instrument detection during automobile production test becomes a challenging task. This article presents an automobile pointer instrument detection method based on prior information and fuzzy sets. The proposed method consists of two frameworks built around a pointer meter prior information model (PMPIM). The first one targets PMPIM construction to obtain the required prior information. With this purpose, a pointer-free template is obtained from a template generation algorithm and pointer positions are mapped into an energy function for optimization, using an energy function-based pointer positioning algorithm. The energy function is defined based on the distance between the crisp and fuzzy sets. The second framework targets PMPIM utilization to detect pointer meters during production test. A fuzzy-based image enhancement method is proposed to enhance test images and the template simultaneously. A prior information and energy function-based pointer positioning algorithm is also proposed to locate pointers in test images. Finally, the indicator value (the value the pointer points to) is calculated according to the positions of the pointer and scale marks. Experimental results show that the proposed method achieves better generalization and robustness than existing state-of-the-art methods.

DISCUS AQUARIUM WATER QUALITY ANALYSIS USING THE METHOD FUZZY MAMDANI

Aquarium is one of the media that is often used as a habitat for various types of animals that live both on land and in water. Many beautiful fish in the scenery are placed in aquariums filled with water as decorations. However, to place fish whose natural habitat is in vast water into an aquarium must require knowledge of the water quality that is suitable for these fish. Clear water is not necessarily suitable for the living conditions of fish. Fish that live in an open water environment grow well because not only is there sufficient food but there is an exchange of matter and energy such as oxygen, carbon dioxide and salts. Whereas in an aquarium environment, as a closed system, sudden and drastic changes to water parameters often occur (such as temperature, ph and ammonia content), which will cause fish stress and often lead to death. Therefore, the knowledge and ability to determine good fish aquarium water quality is an important element for fish keepers, especially hobbyists of ornamental fish such as discus. Classifying quality with a crisp set is a common practice. The number of variables involved in the aquarium (in this case, the object of research is discus) causes determination with a firm (crisp) set to be less appropriate. Therefore, the fuzzy mamdani method will produce outputs that are more suitable for analyzing the quality of aquarium discus. The input variables are density, temperature, and acidity. The outputs used are "Good", "Medium", "Bad".
 

A new Bayesian network model for risk assessment based on cloud model, interval type-2 fuzzy sets and improved D-S evidence theory

Traditional Bayesian network (BN) model is established by crisp sets and probabilities, and its effectiveness and applicability are restricted. In order to solve this problem, a new BN model for risk assessment based on cloud model (CM), interval type-2 fuzzy sets (IT2FSs) and the improved Dempster-Shafer (D-S) evidence theory was developed in the following route. Firstly, CM and IT2FSs were used to define a new solution for the digital characteristics, and the standard and index cloud membership functions for the IT2FSs-CM were established. Then, the index cloud membership function for the IT2FSs-CM was defined as the basic probability assignment (BPA) function, a new evidence fusion rule algorithm was defined, and the improved D-S evidence theory was proposed. Furthermore, the improved D-S evidence theory was used to transform the BPA function into the IT2FSs based prior probability, and a new BN model for risk assessment was developed. Finally, the BN model was applied to a nuclear power plant (NPP) construction project, its risk was assessed at level I (very low risk), and the results by IT2FSs-CM and Bayesian reasoning were compared with those by common methods. The findings show that the new BN model is effective and applicable in risk assessment.

Optimization of system reliability for multi-level RAPs in intuitionistic fuzzy atmosphere using genetic algorithm

Modular redundancy plays a significant role for designing a reliable system. This study examines the optimization of system reliability in a modular redundancy allocation problem in crisp and intuitionistic fuzzy atmospheres with the goal of maximizing total system reliability while adhering to resource restrictions. On applying modular technique on a redundancy allocation problem, one can increase the fault tolerance to the optimum design of the system, making it very effective in terms of component redundancy. So, modular redundancy should be seen as a proper replacement for the old technique of component level redundancy for improving the reliability, efficiency, and maintainability of a working system. The multi-level redundancy allocation problem is being addressed and solved comprehensively in this study using an advanced genetic algorithm (GA) and a penalty function approach in both crisp and intuitionistic fuzzy settings Finally, numerical examples are solved and sensitivity studies are carried out visually to evaluate the consequences of changing key parameters involved in GA.

Anarchist and Anarchistic Anti-Systemic Movements in World-Systems Perspective

While world-systems anti-systemic movement scholarship has briefly acknowledged the existence of anti-state “cultural” movements—namely, autonomous indigenous movements in the periphery and anarchist worker movements in the core and semi-periphery—it relegates them to secondary importance to statist “political” movements. In this paper, we provide an intervention in the world-systems anti-systemic movements literature by centering anti-state movements in our analysis. In order to investigate the mechanisms essential for anti-state, anti-systemic movements over the longue durée of the world-system, we operationalize a qualitative comparative analysis (QCA) using nine cases of non-state spaces from different geographies and historical time periods throughout the world-system. We use a Boolean crisp set, or binary approach, denoting the presence, or absence of factors to determine the pathways that lead to the variation between explicitly anarchist and implicitly anarchistic movements as well as short-term or long-term non-state spaces established by anti-state movements. We find that the core and semi-periphery classification of anarchist movements is false. We also find that non-state spaces succeed when they are not repressed by statist anti-systemic movements or core imperial nation-states. In effect, the anti-systemic political actor replicates the logic of the core nation-state it claims to be opposed to when it comes to its repression of non-state spaces and movements. Prior to the “liberal geoculture” (1848–1968), even core states had difficulty repressing non-state spaces, and after the liberal geoculture semi-periphery and periphery states have had difficulty repressing non-state spaces.

COVID-19-related innovations: A study on underlying motivations and inter-organizational collaboration

While many COVID-related innovations have been profitable, others were not even conceived to bear revenues but were driven by other motivations. To what extent “traditional” profit-oriented motivations coexisted with other motivations (such as corporate social responsibility and marketing) remains largely under-investigated. Similarly, while many studies emphasized that inter-organizational collaborations enabled COVID-related innovation projects, how these collaborations intertwined with the motivations to innovate is unknown. This article fills the literature gaps by exploring the motivations underlying COVID-related innovations, the role of inter-organizational collaboration, and their relationship with innovation novelty. We studied 18 Italian COVID-19-related innovations developed during the initial pandemic phase. We considered two industrial motivations based on the exploration-exploitation dichotomy and two institutional motivations (corporate social responsibility and marketing). Using the crisp set Qualitative Comparative Analysis, we found that institutional motivations have driven most radical and incremental innovation projects. However, they were not sufficient conditions for them. We observed that radical innovations were supported by either transversal alliances, involving horizontal collaboration and R&D institutions, or by vertical alliances, where a supplier-customer collaboration aimed to explore new business opportunities while benefiting from the favorable contingent marketing effects. Incremental innovations often occurred without industrial motivations, supported by either vertical or horizontal collaboration.

On a Solution Set to a Mathematical Programming Problem With a Fuzzy Set of Constraint Indices

The present paper investigates a mathematical programming problem with a fuzzy set of constraint indices. To formalize the set of feasible alternatives, the concept of intersection of crisp sets with a fuzzy set of operands is used. The result of this operation is a type-2 fuzzy set (T2FS) (a fuzzy set whose membership function takes a fuzzy set of values). Furthermore, the corresponding membership function is given. It is shown that the T2FS of feasible alternatives can be represented in the form of the collection of T2FSs with constant secondary grades. The notion of the solution T2FS to the mathematical programming problem with a fuzzy set of constraint indices is proposed. The basic properties of the solution T2FS are investigated. The notion of a compromise maximizing solution is given. Several results are proved that make it possible to simplify its construction. Illustrative examples are given.

Fuzzy Closed Filters in Bounded BE-Algebras

ABSTRACT In this paper we use the method of fuzzification of crisp set to fuzzy set as extension of crisp set to fuzzy set. In particular the concepts of fuzzy closed filters of a bounded BE-algebra are introduced; and also some related properties are investigated. As a result a new concept of fuzzy closed filters of a bounded BE-algebras are discussed. Using the idea of fuzzy closed filters of a bounded BE-algebras, some related properties are investigated. In general characterization theorems that related fuzzy closed filters and fuzzy filter are proved. Furthermore, characterization theorem that relate closed filter and fuzzy closed filter in abounded BE-algebra is investigated.

Innovative standard degree-day indicator (SDI) concept and application for monthly energy consumption control

In practice, monthly temperature records play a major role calculating the heating and cooling degree-days of the air conditioner management in buildings. Classically, heating and cooling degree calculations are based on the comparison of outdoor temperature measurements with a standard level called the base temperature. Truncating the temperature series at a constant base temperature causes heating (deficits) and cooling (surpluses) degrees. Since outdoor temperature records are of random character, future predictions with probabilistic and statistical methods are convenient means for calculations. All previous methodologies in the literature are based on crisp logic with simple mathematical calculations. This paper proposes a new methodological approach by converting the actual temperature record probability distribution function (PDF) into a probabilistic standardized degree indicator (SDI) in normal (Gaussian) PDF form with zero mean and unit standard deviation. Furthermore, as a new evaluation methodology fuzzy logic inference calculations are provided. SDI is divided into seven classes, three for cooling, heating, and a single class for uncooled-unheated states. Heating and cooling degree changes according to SDI values are presented with crisp and fuzzy sets. The application of the methodology is made for Kadıköy urban area on the Asian side of Istanbul, Turkey. In the case of fuzzy set SDI air conditioned operation, it has been determined that there is cooling energy saving of approximately 25% compared to the crisp set. The same is true for the heating degree-day side.

Dual-Dominating Numbers in Neutrosophic Setting and Crisp Setting Obtained From Classes of Neutrosophic Graphs

Dual-Dominating Numbers in Neutrosophic Setting and Crisp Setting Obtained From Classes of Neutrosophic Graphs

Fuzzy closure systems: Motivation, definition and properties

The aim of this paper is to extend closure systems from being crisp sets with certain fuzzy properties to proper fuzzy sets. The presentation of the paper shows a thorough discussion on the different alternatives that could be taken to define the desired fuzzy closure systems. These plausible alternatives are discarded if they are proven impossible to be in a bijective correspondence with closure operators. Finally, a definition of fuzzy closure system is established and a one-to-one relation with closure operators is proved.

Fully Bipolar Single-Valued Neutrosophic Transportation Problems

Transportation problem (TP) has its uses in real life because it has versatile applications. Real-life problems are often uncertain due to which it is difficult to find the accurate cost. The fuzzy set and intuitionistic fuzzy set are useful for handling the uncertainty, but these also have some limitations. For that reason, in this study, we worked on another set of values called bipolar single-valued neutrosophic set (BSNS) which is the generalization of crisp sets, fuzzy sets, and intuitionistic fuzzy sets to handle the uncertain, unpredictable, and insufficient information in real-life problems. In this study, we develop a new technique for solving transportation problems based on bipolar single-valued neutrosophic sets having nonnegative triangular bipolar single-valued neutrosophic numbers (TBSNNs). A score function is used to transform bipolar single-valued neutrosophic numbers (BSNNs) into crisp numbers. We compare our proposed model with fuzzy transportation and intuitionistic fuzzy transportation models and proved that bipolar single-valued neutrosophic transportation model is more admirable than the existing models. Furthermore, we apply the proposed technique to fully solve the bipolar single-valued neutrosophic transportation (FBSNT) model.

Soft Sets with Atoms

The theory of finitely supported structures is used for dealing with very large sets having a certain degree of symmetry. This framework generalizes the classical set theory of Zermelo-Fraenkel by allowing infinitely many basic elements with no internal structure (atoms) and by equipping classical sets with group actions of the permutation group over these basic elements. On the other hand, soft sets represent a generalization of the fuzzy sets to deal with uncertainty in a parametric manner. In this paper, we study the soft sets in the new framework of finitely supported structures, associating to any crisp set a family of atoms describing it. We prove some finiteness properties for infinite soft sets, some order properties and Tarski-like fixed point results for mappings between soft sets with atoms.

The individual characteristics, organizational characteristics and research productivity of early career LIS researchers in China’s mainland: A crisp set qualitative comparative analysis (csQCA)

There existed discrimination, including gender discrimination, first degree discrimination, etc., when assessing the future research productivity of PhD graduates in recruitment in China’s mainland. Were PhD students who did not possess certain conditions (e.g. first degree receiving from a non-key university) unable to achieve high research productivity after graduation? Previous studies focused on the “net effects” of individual and organizational characteristics on research productivity by using quantitative methods (e.g. regression analysis). However, researchers’ research productivity might be due to the interactions of multiple factors rather than a single factor. This study aimed to analyze the effects of the combined conditions (interactions) of individual and organizational characteristics on the research productivity of early career library and information science (LIS) researchers under the context of employment discrimination in the academic job market of China’s mainland. Early career LIS researchers who graduated from China’s mainland universities/institutions between 2011 and 2015 were selected as the sample ( n = 62). csQCA was employed to analyze the data. The results revealed that the effects of a single condition did not directly contribute to the occurrence of high research productivity. There were two combinations of conditions that could contribute to the high research productivity of early career LIS researchers. The first combination that contributed to the high research productivity of an early career LIS researcher was receiving his or her bachelor’s degree from a key university, publishing higher than the median number of articles indexed by Web of Science core collections (WOS) during their PhD and working in a key university after PhD graduation. The second combination was being male, publishing more than the median number of articles indexed by the WOS and the local core journals index during their PhD, and working at a key university after PhD graduation.

Robust Design Optimization of Mechatronics Systems: Parallel Electric Drivetrain Application

AbstractThis paper addresses the problem of finding a robust optimal design when uncertain parameters in the form of crisp or interval sets are present in the optimization. Furthermore, in order to make the approach as general as possible, direct search methods with the help of sensitivity analysis techniques are employed to optimize the design. Consequently, the presented approach is suitable for black box models for which no, or very little, information of the equations governing the model is available. The design of an electric drivetrain is used to illustrate the benefits of the proposed method.

Post-symptomatic detection of COVID-2019 grade based mediative fuzzy projection

The concept of fuzzy set, intuitionistic set, and mediative fuzzy set as a generalization of a crisp set have been introduced in many real-life applications. The concept of crisp relation between elements of sets can be extended to fuzzy relations. Extended relations will be considered as relations on fuzzy sets. In this work, we developed the concept of mediative fuzzy relation and meditative fuzzy projection in the context of fuzzy relation and fuzzy projection. We extended the basic operations of fuzzy projection into intuitionistic fuzzy projection and then in the mediative fuzzy projection. We have shown the credibility and impact of mediative index factor involves in the mediative fuzzy projection in context of prediction work in relation to the proposed model. Further, we applied the mediative fuzzy projection in the medical diagnosis in post-COVID-19 patients. The obtained results have also been discussed with their geometrical representation.

(How) do advanced data and analyses enable HR analytics success? A neo-configurational analysis

PurposeEnabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.Design/methodology/approachTheoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.FindingsThe authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.Research limitations/implicationsMajor limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.Originality/valueThe authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success.

Experiential and Stochastic Learning Algorithms Based on the Probability of a Fuzzy Event and Modified Fuzzy Metric Distance in Intelligent Robotic Part Micro-Assembly

Experiential and stochastic learning algorithms for improving the time consumed in robot’s part micro-assembly procedures are presented. A comprehensive comparison of the results with other recent algorithms is described. The experiential learning algorithm unites the neighboring sections in a manner that is very similar to the larger united section based on the specified criterion. If the experiential learning algorithm is consecutively repeated, the micro-assembly dynamics analysis chart will be not only minimized in the number of sections, but also formed such that each united section will have one selected optimal plan with the lowest modified fuzzy metric distance within its own section. Thus, if the new input appertaining to the section is merely identified, the final selected plan within the section will execute the related task quickly and accurately thus resulting to a significant improvement in the time spent in the part insertion. Through the stochastic learning algorithm extended from the crisp set domain to the fuzzy set domain, based on the probability of a fuzzy event and the modified fuzzy metric distance, to deal well with the uncertainties arising during the micro-assembly procedure, if one specific plan among the feasible plans is successively selected to solve its confronting micro-assembly problems, the probability that the selected plan will be continuously chosen will increase as a consequence of compensation, namely, a higher value of membership degree is continuously allocated to the selected plan. As a result, the selected control plan with the highest probability of success and the lowest degree of uncertainty acquired by the stochastic learning facilitates the stabilization of the execution phase of the part insertion task. The degree of uncertainty, which is measured by a modified fuzzy metric distance and related to the task execution of the micro-assembly, is used as a criterion to select the most valid plan. The results demonstrate that the micro-assembly stuck problems can be successfully resolved by the experiential and stochastic learning algorithms.

Newly Proposed Matrix Reduction Technique Under Mean Ranking Method for Solving Trapezoidal Fuzzy Transportation Problems Under Fuzzy Environment

In this paper, improved matrix Reduction Method is proposed for the solution of fuzzy transportation problem in which all inputs are taken as fuzzy numbers. Since ranking fuzzy number is important tool in decision making, Fuzzy trapezoidal number is converting in to crisp set by using Mean techniques and solved by proposed method for fuzzy transportation problem. We give suitable numerical example for unbalanced and compare the optimal value with other techniques. The Result shows that the optimum profit of transportation problem using proposed technique under robust ranking method is better than the other method. Novelty: The numerical illustration demonstrates that the new projected method for managing the transportation problems on fuzzy algorithms.

Synergising lean objectives through BIM to enhance productivity and performance

PurposeThe construction industry worldwide is looking for overall efficiency in the entire project life cycle. Lean construction and Building Information Modeling (BIM) are together, key constituents in this quest. This study looks to reinforce the contribution of BIM to deliver applied lean principles to enhance productivity and performance.Design/methodology/approachThe study adopts mixed methodologies of Ethnographic Action Research (EAR) and crisp set Qualitative Comparative Analysis (csQCA). The mosaic of case studies provides a descriptive and instrumental approach. While a descriptive approach investigates the interventions and phenomenon relating BIM to Lean, an instrumental approach constitutes exemplars of the interrelated phenomenon of BIM and Lean.FindingsThe themes of Elimination of Waste, Kaizen, Respecting the Human Elements, Poke-a-Yoke and Jidoka are contributed to the maximum by BIM. All the case studies discussed evidence of the contribution to Lean, as they enhance end-customer value.Research limitations/implicationsThe contribution of BIM towards Lean could encourage collaborations amongst stakeholders for mutual benefits. BIM could enhance decision making, predictive simulation, risk analysis, etc. with many other exciting possibilities of delivering productivity and performance. The study is limited by the insights offered by the individual cases.Originality/valueThe study provides insights into potential developments and research. It is expected to encourage reflective professional practice triggered by innovative thinking for the gainful utilization of BIM to deliver Lean.