Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learning is time-consuming. (2) Dependence on object and parts annotations for discriminative localization learning leads to heavily labor-consuming labeling. It is highly challenging to address these two important limitations simultaneously. Existing methods only focus on one of them. Therefore, this paper proposes the discriminative localization approach via saliency-guided Faster R-CNN to address the above two limitations at the same time, and our main novelties and advantages are: (1) End-to-end network based on Faster R-CNN is designed to simultaneously localize discriminative regions and encode discriminative features, which accelerates classification speed. (2) Saliency-guided localization learning is proposed to localize the discriminative region automatically, avoiding labor-consuming labeling. Both are jointly employed to simultaneously accelerate classification speed and eliminate dependence on object and parts annotations. Comparing with the state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach achieves both the best classification accuracy and efficiency.Comment: 9 pages, to appear in ACM MM 201

The Finite Element Analysis of the Deflection of the Crankshaft of Rotary Compressor

The deflection of the crankshaft which transfers the power of motor to the pump of the compressor directly affects the vibration, noises and wear problems in the rotary compressor, therefore, with the requirement of higher reliability, it is important to obtain it exactly in compressor design. Various forces that the crankshaft suffers were calculated by theoretical analysis in the operation process of the compressor. And based on the finite element method (FEM), the deflection of the crankshaft was obtained by simulation in the rotary compressor. And then the measurements were performed concerning the orbit of the top dead centre of the crankshaft with non-contacting displacement sensors in the compressor. In comparison with the tests, the validity of the calculation method was verified. It was found that the results of calculation were good agreement with the tests’. In addition, several factors which affect the deflection of the crankshaft were analyzed with the FEM, and the influences of flange height, shaft diameter, mechanical air gap in the motor, rotor weight on the deflection were found distinctly, which as a primary theoretical basis is provided for the compressor design

A Novel Structure of High Efficiency Rotary Compressor

In recent years, various frequency compressors are developed rapidly and successfully in household air conditioner area. However, it is difficult to make advance progress on compressor performance, noise and reliability. The innovation of structure and technique are indispensable impetus to make a breakthrough. This paper presents a novel structure of high efficiency rotary compressor, which focuses on the connection mode between roller and vane of a compressor. On the one hand, the leakage gap between roller wall and vane tip is eliminated, which upgrades the capacity of compressor. On the other hand, through changing movement type of compressor parts, the mechanical state is meliorated and frictional loss is decreased. Several analysis are studied to validate the rationality of the above amelioration, which include strength and deformation simulation, frictional loss and leakage loss calculation. By comparison with conventional compressor, the performance of the novel compressor is improved obviously. In the end, the results of reliability and durability experiments reveal that they satisfy the national standard

Study on the Performance of CO2 Two-stage Rotary Compressor in Freezing and Cold Storage Conditions

This paper describes a new type CO2 two-stage rotary compressor for cold storage and freezing of food. A two-stage compression form with an upper cylinder (first-stage) and a lower cylinder (second-stage), unique oil road structures and technical parameters have been used in the rotary compressor to increase the performance. The results indicating that the optimized CO2 two-stage rotary compressor has a significant performance advantage, which the coefficient of performance (COP) increases by 4.4% ~ 6.7%

Estimating Large Language Model Capabilities without Labeled Test Data

Large Language Models (LLMs) have exhibited an impressive ability to perform in-context learning (ICL) from only a few examples, but the success of ICL varies widely from task to task. Thus, it is important to quickly determine whether ICL is applicable to a new task, but directly evaluating ICL accuracy can be expensive in situations where test data is expensive to annotate -- the exact situations where ICL is most appealing. In this paper, we propose the task of ICL accuracy estimation, in which we predict the accuracy of an LLM when doing in-context learning on a new task given only unlabeled data for that task. To perform ICL accuracy estimation, we propose a method that trains a meta-model using LLM confidence scores as features. We compare our method to several strong accuracy estimation baselines on a new benchmark that covers 4 LLMs and 3 task collections. On average, the meta-model improves over all baselines and achieves the same estimation performance as directly evaluating on 40 labeled test examples per task, across the total 12 settings. We encourage future work to improve on our methods and evaluate on our ICL accuracy estimation benchmark to deepen our understanding of when ICL works.Comment: 14 pages, 4 figure