This paper proposes a new outlier detection approach that measures the degree of outlierness for each instance in a given dataset. The proposed model utilizes a mass-based dissimilarity measure to address the weaknesses of neighbor-based outlier models while detecting local outliers in the dataset within a variety of data point densities. In particular, it first applies a hierarchical partitioning technique to generate a set of tree-like nested structure partitions for the input dataset, and then a mass-based dissimilarity measure is defined to quantify the dissimilarity between two data instances given the generated hierarchical partition structure. After that, for each data instance, a context set is obtained by gathering the neighbors around it with the k lowest mass dissimilarities, and based on those context sets, a mass-based local outlier score model is introduced to compute the outlierness for each individual instance. The proposed approach fundamentally changes the perspective of the outlier model by using the mass-based measurement instead of the distance-based functions used in most neighbor-based methods. A comprehensive experiment conducted on both synthetic and real-world datasets demonstrates that the proposed approach is not only competitive with the existing state-of-the-art outlier detection models but is also an efficient and effective alternative for local outlier detection methods.
As-supplied SUS420 stainless steel was annealed at 880 °C for1 h and quenched (1040 °c, 30 min), followed by tempering at 530 °c for 1 h. The sample surface after tempering was polished with different roughness levels by mechanical grinding. Samples in the states of as-annealed, as-quenched and as-tempered samples as well as ground tempered ones with different surface roughness were gas nitrided with NH3 gas at 520 °c for 5 h. The microstructure and mechanical properties of SUS420 steel before and after the heat and surface treatments were investigated by optical microscopy, scanning electron microscopy, X-ray diffraction, and micro-hardness testing. The results show that if the natural oxide layer on the surface of the SUS420 samples was not removed, the nitriding process was very difficult or even imposible. The annealed steel gave the highest nitriding depth but low surface hardness. The samples after quenching and/or tempering had lower nitriding depth but higher hardness. The surface hardness of the as-supplied steel (333 HV) decreased with annealing (181 HV). After quenching, tempering, and gas nitriding, the values were 632, 560 and > 1000 HV, respectively. The samples after nitriding showed the appearance of fine CrN phase in the nitrided layer. The highest nitriding depth (125 pm) was obtained for the annealed samples, and subsequently decreased for the quenched samples and tempered samples. The lower the roughness of the sample, the higher is the hardness. The nitrided layer thickness tended to increase as the roughness decreased.
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