PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

Due to the complex and non linear character, wastewater treatment process is difficult to be controlled. The demand for removing the pollutant, especially for nitrogen (N) and phosphorus (P), as well as reducing the cost of wastewater treatment plant is an important research theme recently. Thus, in this paper, the benchmark proposed default control strategy and 10 additional control strategies are applied on the combined biological P and N removal Benchmark Simulation Model No.1 (BSM1-P). In addition, according to the results of applying PI controllers, as usual, we also chose the group with the better performance, as well as the default control strategy, to replace the PI controllers with fuzzy controllers. In this way, it can be seen that in all cases the quality of effluent of the controlled process could be improved in some degree; and the fuzzy controllers get a better phosphorus removal

ADD: An Automatic Desensitization Fisheye Dataset for Autonomous Driving

Autonomous driving systems require many images for analyzing the surrounding environment. However, there is fewer data protection for private information among these captured images, such as pedestrian faces or vehicle license plates, which has become a significant issue. In this paper, in response to the call for data security laws and regulations and based on the advantages of large Field of View(FoV) of the fisheye camera, we build the first Autopilot Desensitization Dataset, called ADD, and formulate the first deep-learning-based image desensitization framework, to promote the study of image desensitization in autonomous driving scenarios. The compiled dataset consists of 650K images, including different face and vehicle license plate information captured by the surround-view fisheye camera. It covers various autonomous driving scenarios, including diverse facial characteristics and license plate colors. Then, we propose an efficient multitask desensitization network called DesCenterNet as a benchmark on the ADD dataset, which can perform face and vehicle license plate detection and desensitization tasks. Based on ADD, we further provide an evaluation criterion for desensitization performance, and extensive comparison experiments have verified the effectiveness and superiority of our method on image desensitization

PEGylated graphene oxide for tumor-targeted delivery of paclitaxel.

AIM: The graphene oxide (GO) sheet has been considered one of the most promising carbon derivatives in the field of material science for the past few years and has shown excellent tumor-targeting ability, biocompatibility and low toxicity. We have endeavored to conjugate paclitaxel (PTX) to GO molecule and investigate its anticancer efficacy. MATERIALS & METHODS: We conjugated the anticancer drug PTX to aminated PEG chains on GO sheets through covalent bonds to get GO-PEG-PTX complexes. The tissue distribution and anticancer efficacy of GO-PEG-PTX were then investigated using a B16 melanoma cancer-bearing C57 mice model. RESULTS: The GO-PEG-PTX complexes exhibited excellent water solubility and biocompatibility. Compared with the traditional formulation of PTX (Taxol®), GO-PEG-PTX has shown prolonged blood circulation time as well as high tumor-targeting and -suppressing efficacy. CONCLUSION: PEGylated graphene oxide is an excellent nanocarrier for paclitaxel for cancer targeting

Research on Method of Dynamic Stability Analysis for Slopes of Earth and Rockfill Dam Basing on the P-Z Model

According to the problems in dynamic stability analysis for slopes of earth and rockfill dam, the P-Z constitutive model, which is a kind of the multi-mechanism plastic model based on generalized plasticity, is introduced in the paper. Strength reduction factors of P-Z model are derived and verified, and based on them a new kind of method of dam slopes dynamic stability is put forward. For the method, the dynamic stability of dam slopes is judged by dynamic displacement time history and post-earthquake permanent displacement. The results show that local instability of dam slopes and variation features of dynamic response are obtained by the method, which is more reasonable

Video-driven Neural Physically-based Facial Asset for Production

Production-level workflows for producing convincing 3D dynamic human faces have long relied on an assortment of labor-intensive tools for geometry and texture generation, motion capture and rigging, and expression synthesis. Recent neural approaches automate individual components but the corresponding latent representations cannot provide artists with explicit controls as in conventional tools. In this paper, we present a new learning-based, video-driven approach for generating dynamic facial geometries with high-quality physically-based assets. For data collection, we construct a hybrid multiview-photometric capture stage, coupling with ultra-fast video cameras to obtain raw 3D facial assets. We then set out to model the facial expression, geometry and physically-based textures using separate VAEs where we impose a global MLP based expression mapping across the latent spaces of respective networks, to preserve characteristics across respective attributes. We also model the delta information as wrinkle maps for the physically-based textures, achieving high-quality 4K dynamic textures. We demonstrate our approach in high-fidelity performer-specific facial capture and cross-identity facial motion retargeting. In addition, our multi-VAE-based neural asset, along with the fast adaptation schemes, can also be deployed to handle in-the-wild videos. Besides, we motivate the utility of our explicit facial disentangling strategy by providing various promising physically-based editing results with high realism. Comprehensive experiments show that our technique provides higher accuracy and visual fidelity than previous video-driven facial reconstruction and animation methods.Comment: For project page, see https://sites.google.com/view/npfa/ Notice: You may not copy, reproduce, distribute, publish, display, perform, modify, create derivative works, transmit, or in any way exploit any such content, nor may you distribute any part of this content over any network, including a local area network, sell or offer it for sale, or use such content to construct any kind of databas