8 results on '"z-distance"'
Search Results
2. Toward Ordering of n-Tuples of Z-Numbers
- Author
-
Alizadeh, Akif V., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Aliev, Rafik Aziz, editor, Yusupbekov, Nodirbek Rustambekovich, editor, Pedrycz, Witold, editor, and Sadikoglu, Fahreddin M., editor
- Published
- 2021
- Full Text
- View/download PDF
3. A Decade of the Z-Numbers.
- Author
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Banerjee, Romi, Pal, Sankar K., and Pal, Jayanta Kumar
- Subjects
EXPERT systems ,MULTISENSOR data fusion ,INITIAL value problems ,ROUGH sets ,GRANULAR computing ,SPACE sciences ,SPACE exploration - Abstract
In this article, we present a study on the development in the theory and application of the Z-numbers since its inception in 2011. The review covers the formalization of Z-number-based mathematical operators, the role of Z-numbers in computing with words, decision-making, and trust modeling, application of Z-numbers in real-world problems such as multisensor data fusion, dynamic controller design, safety analytics, and natural language understanding, a brief comparison with conceptually similar paradigms, and some potential areas of future investigation. The paradigm currently has at least four extensions to its definition: multidimensional Z-numbers, parametric Z-numbers, hesitant-uncertain linguistic Z-numbers, and Z*-numbers. The Z-numbers have also been used in conjunction with rough sets and granular computing for enhanced uncertainty handling. While this decade has seen a plethora of theoretical initiatives toward its growth, there remains a major work scope in the direction of practical realization of the paradigm. Some challenges yet unresolved are automated translation of (imprecise, sarcastic, and metaphorical) linguistic expressions to their Z-number forms, discernment of probability–possibility distributions to map real-world situations under consideration, analysis of linguistic equivalents of Z-operator results to intuitive human responses, the endogenous arousal of belief in intelligent agents, and analysis of biases embedded in expert-belief values that are primary inputs to Z-number-based expert systems. After a decade of the Z-numbers, the paradigm has proved to be of use in expert-input-based decision-making systems and initial value problems. Its applicability in high-risk, high-precision areas, such as deep-sea exploration and space science, remains unexplored. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Influence of Simple and Double-Weave Structures on the Adhesive Properties of 3D Printed Fabrics
- Author
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Marjeta Čuk, Matejka Bizjak, and Tanja Nuša Kočevar
- Subjects
3D printing ,adhesion ,simple weave ,double weave ,morphology ,z-distance ,Organic chemistry ,QD241-441 - Abstract
The double-weave structure of a fabric allows for the use of different materials and weave structures for the upper and lower layer, which can be advantageous in the functionalization of 3D printed textiles. Therefore, the aim of this research was to investigate the influence of simple and double-weave structures on the adhesion of 3D printed fabrics. From this perspective, we investigated the influence of different twill derivates and weft densities on the adhesion force. We produced fabrics specifically for this study and printed them with a polylactic acid filament using Fused Deposition Modeling technology. The T-peel test was performed to measure the adhesion, and the results were statistically analyzed. A morphological study of the surfaces and cross-sections of the 3D printed fabrics helped us interpret the results. We found that adhesion was higher for double fabrics when printed with a smaller z-distance, where the molten polymer reached the lower layer of the fabric and adhered to it. The opposite was confirmed when printing with a larger z-distance, where adhesion was higher for simple fabrics. Both weave and density had a significant effect on adhesion in all cases. Surprisingly, different twill derivatives generally had a greater influence on adhesion than density.
- Published
- 2022
- Full Text
- View/download PDF
5. Influence of simple and double-weave structures on the adhesive properties of 3D printed fabrics
- Author
-
Matejka Bizjak, Marjeta Čuk, and Tanja Nuša Kočevar
- Subjects
3D tiskanje ,Polymers and Plastics ,z-distance ,technology, industry, and agriculture ,General Chemistry ,3D printing ,udc:677 ,adhesion ,simple weave ,double weave ,morphology ,tkanje ,parasitic diseases - Abstract
The double-weave structure of a fabric allows for the use of different materials and weave structures for the upper and lower layer, which can be advantageous in the functionalization of 3D printed textiles. Therefore, the aim of this research was to investigate the influence of simple and double-weave structures on the adhesion of 3D printed fabrics. From this perspective, we investigated the influence of different twill derivates and weft densities on the adhesion force. We produced fabrics specifically for this study and printed them with a polylactic acid filament using Fused Deposition Modeling technology. The T-peel test was performed to measure the adhesion, and the results were statistically analyzed. A morphological study of the surfaces and cross-sections of the 3D printed fabrics helped us interpret the results. We found that adhesion was higher for double fabrics when printed with a smaller z-distance, where the molten polymer reached the lower layer of the fabric and adhered to it. The opposite was confirmed when printing with a larger z-distance, where adhesion was higher for simple fabrics. Both weave and density had a significant effect on adhesion in all cases. Surprisingly, different twill derivatives generally had a greater influence on adhesion than density.
- Published
- 2022
6. IFZ-Graph and Extracting Shortest Path by IFZ-Dijkstra's Algorithm.
- Author
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Biswas, Siddhartha Sankar
- Subjects
GRAPH theory ,INTUITIONISTIC mathematics ,ALGORITHMS ,FUZZY logic ,FUZZY systems - Abstract
In this paper the author introduces the notion of IFZ-graph in Graph Theory. The classical Dijkstra's algorithm to find the shortest path in graphs is not applicable to IFZ-graphs. Consequently the author proposes a new algorithm called by IFZ-Dijkstra's Algorithm to solve the Shortest Path Problem (SPP) in an IFZ-graph. [ABSTRACT FROM AUTHOR]
- Published
- 2017
7. Influence of Simple and Double-Weave Structures on the Adhesive Properties of 3D Printed Fabrics.
- Author
-
Čuk, Marjeta, Bizjak, Matejka, and Kočevar, Tanja Nuša
- Subjects
- *
FUSED deposition modeling , *ADHESIVES , *WEAVING patterns , *POLYLACTIC acid , *TEXTILES - Abstract
The double-weave structure of a fabric allows for the use of different materials and weave structures for the upper and lower layer, which can be advantageous in the functionalization of 3D printed textiles. Therefore, the aim of this research was to investigate the influence of simple and double-weave structures on the adhesion of 3D printed fabrics. From this perspective, we investigated the influence of different twill derivates and weft densities on the adhesion force. We produced fabrics specifically for this study and printed them with a polylactic acid filament using Fused Deposition Modeling technology. The T-peel test was performed to measure the adhesion, and the results were statistically analyzed. A morphological study of the surfaces and cross-sections of the 3D printed fabrics helped us interpret the results. We found that adhesion was higher for double fabrics when printed with a smaller z-distance, where the molten polymer reached the lower layer of the fabric and adhered to it. The opposite was confirmed when printing with a larger z-distance, where adhesion was higher for simple fabrics. Both weave and density had a significant effect on adhesion in all cases. Surprisingly, different twill derivatives generally had a greater influence on adhesion than density. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
8. Weighted z-Distance-Based Clustering and Its Application to Time-Series Data
- Author
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Chen-Yu Wu, Yan-Ting Lin, Zhao-Yu Wang, and Shie-Jue Lee
- Subjects
z-distance ,Computer science ,quadratic programming ,02 engineering and technology ,Set (abstract data type) ,Dimension (vector space) ,Similarity (network science) ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,Cluster (physics) ,General Materials Science ,Cluster analysis ,similarity ,Instrumentation ,Fluid Flow and Transfer Processes ,business.industry ,Process Chemistry and Technology ,General Engineering ,Process (computing) ,Pattern recognition ,Computer Science Applications ,data clustering ,Benchmark (computing) ,training cycle ,Unsupervised learning ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
Clustering is the practice of dividing given data into similar groups and is one of the most widely used methods for unsupervised learning. Lee and Ouyang proposed a self-constructing clustering (SCC) method in which the similarity threshold, instead of the number of clusters, is specified in advance by the user. For a given set of instances, SCC performs only one training cycle on those instances. Once an instance has been assigned to a cluster, the assignment will not be changed afterwards. The clusters produced may depend on the order in which the instances are considered, and assignment errors are more likely to occur. Also, all dimensions are equally weighted, which may not be suitable in certain applications, e.g., time-series clustering. In this paper, improvements are proposed. Two or more training cycles on the instances are performed. An instance can be re-assigned to another cluster in each cycle. In this way, the clusters produced are less likely to be affected by the feeding order of the instances. Also, each dimension of the input can be weighted differently in the clustering process. The values of the weights are adaptively learned from the data. A number of experiments with real-world benchmark datasets are conducted and the results are shown to demonstrate the effectiveness of the proposed ideas.
- Published
- 2019
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