泼点冷水如何？

泼点冷水如何？刘国文近日，从山东电视台的节目上看到，山东省一些游乐宫因无游人而变成了仓库。看后，心中很是沉重。最近几年，全国各地这＂热＂、那＂热＂，一哄而起：搞开发，省里搞，市里搞，县乡搞，村里的也搞，结果是＂开发开发，开而不发＂，空卷一片肥田沃土任...

关于铝电解电容器短路项目的研究与整改

电解电容短路问题一直是行业内的整改顽疾,因其本身的结构及制造工艺限制,短路问题经常存在不稳定性,在实际生产应用过程中常因该问题导致批量的封存返包,而根据分析情况,电容内部的铝箔首卷位置毛刺短路(铝箔裁切面)是常见的短路问题之一。从该位置引起电容短路的失效原理、可行性的整改方案研究以及效果评估方面等展开一系列的分析验证,提出了电容"空卷"概念,实现了对首卷位置毛刺短路的杜绝性整改。

Tongue image segmentation and tongue color classification based on deep learning

Objective To propose two novel methods based on deep learning for computer-aided tongue diagnosis,including tongue image segmentation and tongue color classification,improving their diagnostic accuracy.Methods LabelMe was used to label the tongue mask and Snake model to optimize the labeling results.A new dataset was constructed for tongue image segmentation.Tongue color was marked to build a classified dataset for network training.In this research,the Inception+Atrous Spatial Pyramid Pooling(ASPP)+UNet(IAUNet)method was proposed for tongue image segmentation,based on the existing UNet,Inception,and atrous convolution.Moreover,the Tongue Color Classification Net(TCCNet)was constructed with reference to ResNet,Inception,and Triple-Loss.Several important measurement indexes were selected to evaluate and compare the effects of the novel and existing methods for tongue segmentation and tongue color classification.IAUNet was compared with existing mainstream methods such as UNet and DeepLabV3+for tongue segmentation.TCCNet for tongue color classification was compared with VGG16 and GoogLeNet.Results IAUNet can accurately segment the tongue from original images.The results showed that the Mean Intersection over Union(MIoU)of IAUNet reached 96.30%,and its Mean Pixel Accuracy(MPA),mean Average Precision(mAP),F1-Score,G-Score,and Area Under Curve(AUC)reached 97.86%,99.18%,96.71%,96.82%,and 99.71%,respectively,suggesting IAUNet produced better segmentation than other methods,with fewer parameters.Triplet-Loss was applied in the proposed TCCNet to separate different embedded colors.The experiment yielded ideal results,with F1-Score and mAP of the TCCNet reached 88.86% and 93.49%,respectively.Conclusion IAUNet based on deep learning for tongue segmentation is better than traditional ones.IAUNet can not only produce ideal tongue segmentation,but have better effects than those of PSPNet,SegNet,UNet,and DeepLabV3+,the traditional networks.As for tongue color classification,the proposed network,TCCNet,had better F1-Score and mAP values as compared with other neural networks such as VGG16 and GoogLeNet.

A numerical investigation in buoyancy effects on micro jet diffusion flame

The buoyancy effect on micro hydrogen jet flames in still air was numerially studied.The results show that when the jet velocity is relatively large(V≥0.2 m/s),the flame height,width and temperature decrease,whereas the peak OH mass fraction increases significantly under normal gravity(g=9.8 m/s^2).For a very low jet velocity(e.g.,V=0.1 m/s),both the peak OH mass fraction and flame temperature under g=9.8 m/s^2 are lower than the counterparts under g=0 m/s^2.Analysis reveals that when V≥0.2 m/s,fuel/air mixing will be promoted and combustion will be intensified due to radial flow caused by the buoyancy effect.However,the flame temperature will be slightly decreased owing to the large amount of entrainment of cold air into the reaction zone.For V=0.1 m/s,since the heat release rate is very low,the entrainment of cold air and fuel leakage from the rim of tube exit lead to a significant drop of flame temperature.Meanwhile,the heat loss rate from fuel to inner tube wall is larger under g=9.8 m/s^2 compared to that under g=0 m/s^2.Therefore,the buoyancy effect is overall negative at very low jet velocities.

SA-FRCNN:An Improved Object Detection Method for Airport Apron Scenes

The airport apron scene contains rich contextual information about the spatial position relationship.Traditional object detectors only considered visual appearance and ignored the contextual information.In addition,the detection accuracy of some categories in the apron dataset was low.Therefore,an improved object detection method using spatial-aware features in apron scenes called SA-FRCNN is presented.The method uses graph convolutional networks to capture the relative spatial relationship between objects in the apron scene,incorporating this spatial context into feature learning.Moreover,an attention mechanism is introduced into the feature extraction process,with the goal to focus on the spatial position and key features,and distance-IoU loss is used to achieve a more accurate regression.The experimental results show that the mean average precision of the apron object detection based on SAFRCNN can reach 95.75%,and the detection effect of some hard-to-detect categories has been significantly improved.The proposed method effectively improves the detection accuracy on the apron dataset,which has a leading advantage over other methods.

Questionnaire Research on Indoor Environment Quality i n China

With 1 185 pi eces of questionnaire, it is found that in China, people take fresh air, odor, e tc., as well as indoor air temperature, humidity, as the most important indoor a ir parameters. It is also found that there is a significant sensitivity differen ce in indoor environment between southerners and northerners in China. People fr om different regions have different demands for their working and living environ ment. Therefore, as a good design of air conditioning system, it is strongly rec ommended that the different demands of people from different regions should be t aken into consideration.

Deeplearning method for single image dehazing based on HSI colour space

The traditional single image dehazing algorithm is susceptible to the prior knowledge of hazy image and colour distortion.A new method of deep learning multi-scale convolution neural network based on HSI colour space for single image dehazing is proposed in this paper,which directly learns the mapping relationship between hazy image and corresponding clear image in colour,saturation and brightness by the designed structure of deep learning network to achieve haze removal.Firstly,the hazy image is transformed from RGB colour space to HSI colour space.Secondly,an end-to-end multi-scale full convolution neural network model is designed.The multi-scale extraction is realized by three different dehazing sub-networks:hue H,saturation S and intensity I,and the mapping relationship between hazy image and clear image is obtained by deep learning.Finally,the model was trained and tested with hazy data set.The experimental results show that this method can achieve good dehazing effect for both synthetic hazy images and real hazy images,and is superior to other contrast algorithms in subjective and objective evaluations.

Boundedness of Hausdorff operators on Lebesgue spaces and Hardy spaces

In this paper, we study the boundedness of the Hausdorff operator H_? on the real line R. First, we start with an easy case by establishing the boundedness of the Hausdorff operator on the Lebesgue space L^p(R)and the Hardy space H^1(R). The key idea is to reformulate H_? as a Calder′on-Zygmund convolution operator,from which its boundedness is proved by verifying the Hrmander condition of the convolution kernel. Secondly,to prove the boundedness on the Hardy space H^p(R) with 0 < p < 1, we rewrite the Hausdorff operator as a singular integral operator with the non-convolution kernel. This novel reformulation, in combination with the Taibleson-Weiss molecular characterization of H^p(R) spaces, enables us to obtain the desired results. Those results significantly extend the known boundedness of the Hausdorff operator on H^1(R).

Convolutions, Tensor Products and Multipliers of the Orlicz-Lorentz Spaces

In this paper, the authors first give the properties of the convolutions of Orlicz- Lorentz spaces Aφ1,w and Aφ2,w on the locally compact abelian group. Secondly, the authors obtain the concrete representation as function spaces for the tensor products of Orlicz-Lorentz spaces Aφ1,w and Aφ2,w, and get the space of multipliers from the space Aφ1,w to the space Mφ2.w. Finally, the authors discuss the homogeneous properties for the Orlicz-Lorentz space Aφ,w^p,q.

Transference on Some Non-convolution Operators from Euclidean Spaces to Torus

The authors prove the certain de Leeuw type theorems on some non-convolution operators,and give some applications on certain known results.

A CLOSEDNESS CRITERION FOR THEDIFFERENCE OF TWO CLOSED CONVEXSETS IN GENERAL BANACH SPACES

The authors give some sufficient conditions for the difference of two closed convex sets to be closed in general Banach spaces, not necessarily reflexive.