-The great difference in density between steam and liquid during wet steam injection always results in steam override, that is, steam gathers on the top of the pay zone. In this article, the equation for steam override coefficient was firstly established based on van Lookeren's steam override theory and then radius of steam zone and hot fluid zone were derived according to a more realistic temperature distribution and an energy balance in the pay zone. On this basis, the equation for the reservoir heat efficiency with the consideration of steam override was developed. Next, predicted results of the new model were compared with these of another analytical model and CMG STARS (a mature commercial reservoir numerical simulator) to verify the accuracy of the new mathematical model. Finally, based on the validated model, we analyzed the effects of injection rate, steam quality and reservoir thickness on the reservoir heat efficiency. The results show that the new model can be simplified to the classic model (Marx-Langenheim model) under the condition of the steam override being not taken into account, which means the Marx-Langenheim model is corresponding to a special case of this new model. The new model is much closer to the actual situation compared to the Marx-Langenheim model because of considering steam override. Moreover, with the help of the new model, it is found that the reservoir heat efficiency is not much affected by injection rate and steam quality but significantly influenced by reservoir thickness, and to ensure that the reservoir can be heated effectively, the reservoir thickness should not be too small.Résumé -Un modèle amélioré d'injection de vapeur prenant en compte la surcharge de vapeur -La différence de densité entre la vapeur et le liquide lors de l'injection de vapeur humide conduit toujours à un débordement de vapeur, en d'autres termes, la vapeur s'accumule sur le dessus de la zone de production. Dans cet article, l'équation pour le coefficient de surcharge de vapeur a d'abord été établie sur la base de la théorie de la surcharge de vapeur de van Lookeren, puis le rayon de la zone de vapeur et la zone de fluide chaud ont été dérivés selon une distribution de température plus réaliste et un bilan énergétique dans la zone de production. Sur cette base, l'équation d'efficacité thermique du réservoir en tenant compte de la surpression de la vapeur d'eau a été développée. Par la suite, les résultats prévus par le nouveau modèle ont été comparés à ceux d'un autre modèle analytique et à CMG STARS (un simulateur numérique à réservoir commercial reconnu) pour vérifier la précision du nouveau modèle mathématique. Enfin, sur la base du modèle validé, nous avons analysé les effets du taux d'injection, de la qualité de la vapeur et de la densité du réservoir sur l'efficacité thermique du réservoir. Les résultats montrent que le nouveau modèle peut être simplifié par rapport au modèle classique (modèle de Marx-Langenheim) à condition que la 2017 DOI: 10.2516 This is an Open Access article dist...

During the last two or three decades where innovations in technology have been dominant, especially the rapid development of electronic information technology, various types of electronic devices have been developed for different application areas. It is this technological-assisted equipment that has drastic effects on the lifestyle of every creature in general and human beings in particular. In addition to the other activities or services, technology has enabled human beings to write on electronic devices, which is due to the fact that these devices will generate electronic signature handwriting that is extremely useful for human beings. They may effectively cope with the electronization of traditional signature handwriting and ease the difficulties of authenticating the identification information of the signatory of electronic documents when used in conjunction with electronic documents. This method is frequently utilized in e-government, e-commerce, banking and insurance, medical care, and other industries. This study uses the current mainstream computer vision technology to compare and analyze the handwriting dynamic characteristics of electronic signature and conventional signature. It uses the electronic signature God and software to collect and extract the original characteristic data of user’s electronic signature and then extracts the characteristics of average writing speed, duration, and average pressure on the basis of these data for analysis. Among these techniques, the writing time of electronic signature is longer than that of conventional signature, and the average speed of conventional signature notes is higher than that of electronic signature, and when analyzing the average pressure characteristics, the conventional signature pressure is greater than the electronic signature pressure.

Due to the imaging mechanism of synthetic aperture radar (SAR), it is difficult and costly to acquire abundant labeled SAR images. Moreover, a typical matched filtering (MF) based image faces the problems of serious noise, sidelobes, and clutters, which will bring down the accuracy of SAR target classification. Different from the MF-based result, a sparse image shows better quality with less noise and higher image signal-to-noise ratio (SNR). Therefore, theoretically using it for target classification will achieve better performance. In this paper, a novel contrastive domain adaptation (CDA) based sparse SAR target classification method is proposed to solve the problem of insufficient samples. In the proposed method, we firstly construct a sparse SAR image dataset by using the complex image based iterative soft thresholding (BiIST) algorithm. Then, the simulated and real SAR datasets are simultaneously sent into an unsupervised domain adaptation framework to reduce the distribution difference and obtain the reconstructed simulated SAR images for subsequent target classification. Finally, the reconstructed simulated images are manually labeled and fed into a shallow convolutional neural network (CNN) for target classification along with a small number of real sparse SAR images. Since the current definition of the number of small samples is still vague and inconsistent, this paper defines few-shot as less than 20 per class. Experimental results based on MSTAR under standard operating conditions (SOC) and extended operating conditions (EOC) show that the reconstructed simulated SAR dataset makes up for the insufficient information from limited real data. Compared with other typical deep learning methods based on limited samples, our method is able to achieve higher accuracy especially under the conditions of few shots.

Because of its large specific surface area, small particle size, high surface energy, and unique nanoeffect, the morphological characteristics of nanoparticles are the key factors affecting the properties of materials. How to detect and evaluate the morphological characteristics of nanoparticles is the first problem to be solved in the preparation and application of nanomaterials. The main purpose of this paper is to use TEM to recognize the image features of nanoparticles and introduce the transmission electron microscope and image edge segmentation method and random forest algorithm. A method integrating the in situ characterization of modern electron microscopy and the measurement of the electrical properties of nanomonomers was developed. In this paper, a multielectrode TEM in situ electrical measurement platform is prepared, which improves the contact during the integration of nanomaterials and improves the electrical measurement accuracy of the TEM in situ electrical method. In this paper, based on the random forest algorithm, a multirandom forest algorithm is proposed. Due to the different gray levels of images referenced by the multirandom forest algorithm, the segmentation results are processed by FCM clustering algorithm. Experimental results show that in terms of image segmentation accuracy, the minimum Jaccard coefficient obtained by multiple random forest algorithm is 89% and 95%, respectively, which is obviously better than watershed segmentation method and maximum entropy threshold segmentation. In the aspect of automatic image segmentation of nanoparticles, the image segmentation accuracy is the highest when the sample block size and the number of sample blocks selected in the multiple random forest algorithm are 5 ∗ 5 , 7500, and 35, respectively. Therefore, the multirandom forest algorithm has achieved high accuracy in image segmentation of nanoparticles, which provides valuable information for the preparation and application of nanomaterials. A new type of TEM dark-field imaging diaphragm was prepared, which greatly improved the imaging quality of weak-phase bulk materials represented by graphene and nonspiral biological samples represented by intracellular polyvesicles.