The proposed Per-C-RPN model allows these three sources of risks from each FMEA user to be considered and combined in terms of IT2FSs. A case study related to edible bird nest farming in Borneo Island is reported. The results indicate the effectiveness of the proposed model. In summary, this paper contributes to a new Per-C-RPN model that utilizes imprecise assessment grades pertaining to group decision making in FMEA.
a b s t r a c tIn this paper, a new fuzzy peer assessment methodology that considers vagueness and imprecision of words used throughout the evaluation process in a cooperative learning environment is proposed. Instead of numerals, words are used in the evaluation process, in order to provide greater flexibility. The proposed methodology is a synthesis of perceptual computing (Per-C) and a fuzzy ranking algorithm. Per-C is adopted because it allows uncertainties of words to be considered in the evaluation process. Meanwhile, the fuzzy ranking algorithm is deployed to obtain appropriate performance indices that reflect a student's contribution in a group, and subsequently rank the student accordingly. A case study to demonstrate the effectiveness of the proposed methodology is described. Implications of the results are analyzed and discussed. The outcomes clearly demonstrate that the proposed fuzzy peer assessment methodology can be deployed as an effective evaluation tool for cooperative learning of students.
A new approach that adopts the use of fuzzy adaptive resonance theory (ART) neural network in estimating high-performance concrete (HPC) mix proportion from experimental data is devised. The proposed model receives a set of desired concrete performances, searches for a set of mix proportions that is near to the desired concrete performances, classifies the mix proportions into clusters, measures the similarity between performances of deduced clusters with desired performances, and deduces a mix proportion. The proposed model was used to estimate the mix proportions of five batches of concrete based on the performance criteria of 7th and 28th day compressive strengths. The generated mix proportions were used in an experimental work and the errors were within 13% for 7th compressive strength; and 7% for the 28th day compressive strength, signifying the reliability of the fuzzy ART-based model in estimating the mix proportion of HPC. This article contributes to an alternative method of mix proportion estimation of HPC by avoiding the use of complicated function approximation techniques.
NOTATIONSThe following symbols are used in this article:
FDesired concrete performances ε Tolerance range q m
The preservation of plant specimens in herbaria has been carried out for centuries in efforts to study and confirm plant taxa. With the increasing collection of herbaria made available digitally, it is practical to use herbarium specimens for the automation of plant identification. They are also substantially more accessible and less expensive to obtain compared to field images. In fact, in remote and inaccessible habitats, field images of rare plant species are still immensely lacking. As a result, rare plant species identification is challenging due to the deficiency of training data. To address this problem, we investigate a cross-domain adaptation approach that allows knowledge transfer from a model learned from herbarium specimens to field images. We propose a model called Herbarium–Field Triplet Loss Network (HFTL network) to learn the mapping between herbarium and field domains. Specifically, the model is trained to maximize the embedding distance of different plant species and minimize the embedding distance of the same plant species given herbarium–field pairs. This paper presents the implementation and performance of the HFTL network to assess the herbarium–field similarity of plants. It corresponds to the cross-domain plant identification challenge in PlantCLEF 2020 and PlantCLEF 2021. Despite the lack of field images, our results show that the network can generalize and identify rare species. Our proposed HFTL network achieved a mean reciprocal rank score of 0.108 and 0.158 on the test set related to the species with few training field photographs in PlantCLEF 2020 and PlantCLEF 2021, respectively.
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