查看更多>>摘要：? 2022 Elsevier Inc.The small sample problem is an open challenge in data-driven industrial fault diagnosis, which leads to low accuracy and weak generalization for modeling. Domain adaptation (DA) attempts to transfer samples of the source domain to the target domain for small sample enhancement. However, existing approaches cannot be directly applied to distant domains, due to insufficient exploration of domains with large discrepancies and negative transfer. To tackle the above issues, this paper proposes a transitive distant domain adaptation network (TDDA-Net), in which the sample features are decomposed into three orthogonalized dimensions to reliably express the sample information. Then, distant domain samples are explored and negative transfer is alleviated in different feature dimensions. In particular, distant domain samples are transitively explored to obtain abundant types of samples, wherein useful information from intermediate domains can be employed. Thus, the modeling generalization is improved. Additionally, the marginal and conditional distributions of the samples are adaptively matched to correct distribution drift, such that the negative transfer is alleviated. Thus, the modeling accuracy is improved. Benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed network. All the results demonstrate that our TDDA-Net performs favorably against the state-of-the-art methods.
查看更多>>摘要：? 2022 Elsevier Inc.In this work, we propose the notion of interval-valued pre-(quasi-)overlap functions, called interval-valued R-(quasi-)overlap functions. The increasingness is replaced with interval directional increasingness. Subsequently, we analyze relative properties of such functions as well as its relationship by related r-(quasi-)overlap functions. Besides, we present several methods for constructing interval-valued pre-(quasi-)overlap functions from certain binary interval-valued functions, interval multiplicative generator pairs and interval-valued 0￣,1￣-aggregation functions. In the second part of it, we argue that interval multiplicative generator pairs and additive ones can be converted to each other.
查看更多>>摘要：? 2022 Elsevier Inc.Identifying DNA N4-methylcytosine (4mC) sites is an essential step to study the biological functional mechanism. Feature representation is the primary step to identify 4mC sites due to its influencing the performance of the downstream 4mC site predictive model. Extracting numerical features having strong categorical information from DNA sequences is the key issue to build a 4mC predictive model having good performance. Therefore, a feature representation algorithm referred to as PSP-PJMI is proposed in this paper. It first proposes Pointwise Joint Mutual Information (PJMI), then the bidirectional k-nucleotide Position-Specific Propensities (PSP), so that the PSP-PJMI feature representation algorithm is developed. The parameter ξ is used to indicate the interval from the current nucleotide to the forward or backward dinucleotide in the bidirectional trinucleotide PSP, so that the position information of nucleotides is extracted from a DNA sequence as far as possible. The features corresponding to various ξ are concatenated to comprise the high dimensional feature vector having rich categorical information. The 4mC-BiNP model for identifying DNA 4mC sites is constructed using SVM and the extracted features. The experimental results of 10-fold cross validation test, cross-species validation test, and independent test on 6 species datasets show that the proposed PSP-PJMI algorithm can extract features having richer categorical information than the available feature representation algorithms can do. The 4mC-BiNP model is superior to the state-of-the-art predictive models for identifying DNA 4mC sites. Furthermore, the PSP-PJMI algorithm can be used to extract features for identifying other DNA methylation sites, and also be used for RNA sequences to predict RNA methylation sites.
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