REDUCED ATTRIBUTE ORIENTED HANDLING OF INCONSISTENCY IN DECISION GENERATION

Abstract Due to the discarded attributes, the effectual condition classes of the decision rules are highly different. To provide a unified evaluative measure, the derivation of each rule is depicted by the reduced attributes with a layered manner. Therefore, the inconsistency is divided into two primary categories in terms of the reduced attributes. We introduce the notion of joint membership function wrt. the effectual joint attributes, and a classification method extended from the default decision generation framework is proposed to handle the inconsistency. Keywords: reduced attributes, reduced layer, joint membership function, rough set Introduction Classification in rough set theory [1] is mainly composed of two components: feature extraction and decision synthesis. Many researches focus on the construction of classification algorithm, such as probabilistic method [2], decision trees[3] and parameterized rule inducing method This paper, based on the default rule extracting framewor

Probabilistic approximation under incomplete information systems,”

Abstract By applying the probability estimation of the unavailable attributes derived from the available attributes to the neighborhood system, the suited degree of each neighbor to a given object is depicted. Therefore, the neighborhood space with guaranteed suited precision is obtained. We show how to shrink the rule search space via VPRS model for this space, and also, we will prove the incredibility degree of decision class is guaranteed by the two-layer thresholds

Novel Asymmetric Pyramid Aggregation Network for Infrared Dim and Small Target Detection

Robust and efficient detection of small infrared target is a critical and challenging task in infrared search and tracking applications. The size of the small infrared targets is relatively tiny compared to the ordinary targets, and the sizes and appearances of the these targets in different scenarios are quite different. Besides, these targets are easily submerged in various background noise. To tackle the aforementioned challenges, a novel asymmetric pyramid aggregation network (APANet) is proposed. Specifically, a pyramid structure integrating dual attention and dense connection is firstly constructed, which can not only generate attention-refined multi-scale features in different layers, but also preserve the primitive features of infrared small targets among multi-scale features. Then, the adjacent cross-scale features in these multi-scale information are sequentially modulated through pair-wise asymmetric combination. This mutual dynamic modulation can continuously exchange heterogeneous cross-scale information along the layer-wise aggregation path until an inverted pyramid is generated. In this way, the semantic features of lower-level network are enriched by incorporating local focus from higher-level network while the detail features of high-level network are refined by embedding point-wise focus from lower-level network, which can highlight small target features and suppress background interference. Subsequently, recursive asymmetric fusion is designed to further dynamically modulate and aggregate high resolution features of different layers in the inverted pyramid, which can also enhance the local high response of small target. Finally, a series of comparative experiments are conducted on two public datasets, and the experimental results show that the APANet can more accurately detect small targets compared to some state-of-the-art methods

A Novel Method to Evaluate the Effect of Slope Blasting under Impact Loading

In this paper, the quantitative evaluation of the explosion effect based on the fuzzy comprehensive evaluation method is proposed to describe the qualitative evaluation results. The selected state characteristic parameters are expressed by two kinds of membership functions, fuzzy normal and triangular distribution membership functions, and preliminary evaluation results are obtained. The validity index of the maximum membership principle is used to assess the accuracy of the evaluation results of two algorithms, and a relevant approaching degree is chosen to optimize the results. The entire evaluation process selects eleven indicators to form an evaluation set, including the boulder yield, root rate, flying distance of flyrock, explosive consumption, postcracking distance, detonator unit consumption, vibration velocity, loose coefficient, cast distance, throw rate, and blasted volume per meter of hole. Part of the indicator parameters are derived from field test monitoring, and another part of the indicator parameters are derived from numerical simulation. The simulation process uses the user-defined material interface function provided by LS-DYNA. And the numerical model of slope blasting is established by embedding the evolution relationship of tensile and compressive damage into the elastoplastic constitutive material. The evaluation method proposed in this paper is used to evaluate the postexplosion effect of Zijin Mountain gold-copper mine slope cast blasting. The results demonstrate that the fuzzy normal distribution membership function can correlate the state characteristic information and evaluation index effectively, and the working condition after explosion can be reflected accurately. Additionally, the influencing factors can be ranked by the importance degrees according to the calculated value of the evaluation index

K-shell photoabsorption edge of strongly coupled aluminum driven by laser-converted radiation

The first observation of the K-shell photoabsorption edge of strongly coupled aluminum generated by intense x-ray radiation-driven shocks is reported. By using a “dog bone” gold hohlraum as an x-ray converter, colliding shocks compression and preheating shielding are achieved to generate an unexplored state with a density of 5.5 g/cm3 and temperature of 0.43 eV (the ion-ion coupling parameter $\Gamma_{ii}$ is around 240). The time-resolved K-shell photoabsorption edges are measured with a crystal spectrometer using a short x-ray backlighter. The broadenings and redshifts of the edges are studied by using the slope fitting of the edge and quantum molecular dynamics calculations. This work shows that the K-edge of aluminum driven by laser-converted radiation provides a novel capability to probe WDM at extended conditions