Development of Multivariate Statistical Process Control for an industrial prototype wastewater bio-treatment plant

This research analyzes the feasibility of developing a Multivariate Statistical Process Control (MSPC) framework for monitoring and diagnosing a biological wastewater treatment plant. MSPC makes use of historical database of past successful operations as a reference to judge the normality of future operations. The projection method, Principal Component Analysis (PCA), is utilized not only to compress the originally correlated data but also to extract statistically meaningful information, by projecting the multivariate trajectory data onto a lower dimensional space, spanned by the Principal Components (PC s) retained. From the established \u27normal\u27 operation domain, departure of new operating points from that of \u27normal\u27 domain can be detected by the use of several MSPC monitoring plots. The proposed methodology generates monitoring charts by analyzing the process variables gathered in a reference database; new observations are analyzed by contrasting their projections onto the reference PC s space against that of normal, using a variety of monitoring charts. Possible root causes can sometimes be identified when abnormal deviations have been detected. The capability of such MSPC scheme in monitoring and assessing the behavior of new wastewater treatment operations against the reference is illustrated through simulations of the bio-wastewater treatment plant under a variety of operating conditions. The research first reviews the concepts and techniques of MSPC and the Activated Sludge Model No. 1. It then utilizes these techniques in creating the monitoring and diagnosis framework for a wastewater bio-treatment plant using the activated sludge model No. 1 description as the process model. Simulation is carried out using the Matlab (version 4.2c) and Simulink ^ as the programming platform. The MSPC framework is able to detect abnormal process deviations by comparing the projection of new observations onto the principal component subspace to the \u27normal operation\u27 region established from base case data. If current operating points fall inside this region, it implies that the current operation is \u27normal\u27; If they fall or show a trend of migrating toward outside of the region, it implies emergence of abnormal operations. Usually, it is possible to trace back from the abnormal behavior to their assignable causes by analyzing contribution plots. In this study, a reference database is generated based on the simulation of a large number of variations in the process operating conditions in the neighborhood of a nominal operating condition. These variations include: -21% to +21% changes in the influent nitrate concentration, [NO3 ], in the maximum growth rate of the heterotrophic biomass, pm, h, in the half-saturation constant of COD, Kg, [cod] and - 15% to +15% changes in the influent ammonia concentration, [NH4\u27^]. These deviations are defined as \u27normal operation\u27 deviations. Monitoring charts are obtained based on this simulated database. Acceptable regions are identified in these charts as the standards for monitoring all future processes. Three abnormal cases are simulated to validate the established base case PGA model. They represent 1) bigger than normal amount of changes in the operating conditions not affecting the biological model; 2) bigger than normal amount of changes in the bioprocess parameters altering the process model; 3) new biological event causing plant/model mismatch. Analysis results show that the indication of the migration, over time, toward a state of abnormality is clear and direct. Diagnosis is carried out by analyzing the contribution plot for each of the three abnormal cases. Results show that the PCA method can also identify the possible root causes for the observed abnormality. In addition, the interpretation of the principal components provides more insights to the behavior of the process variables. However, important implementation issues remain that must be addressed before it can proved to be effective when brought on line

High-Q exterior whispering gallery modes in a metal-coated microresonator

We propose a kind of plasmonic whispering gallery modes highly localized on the exterior surface of a metal-coated microresonator. This exterior (EX) surface mode possesses high quality factors at room temperature, and can be efficiently excited by a tapered fiber. The EX mode can couple to an interior (IN) mode and this coupling produces a strong anti-crossing behavior, which not only allows conversion of IN to EX modes, but also forms a long-lived anti-symmetric mode. As a potential application, the EX mode could be used for a biosensor with a sensitivity high up to 500 nm per refraction index unit, a large figure of merit, and a wide detection range

Beyond Narrative Description: Generating Poetry from Images by Multi-Adversarial Training

Automatic generation of natural language from images has attracted extensive attention. In this paper, we take one step further to investigate generation of poetic language (with multiple lines) to an image for automatic poetry creation. This task involves multiple challenges, including discovering poetic clues from the image (e.g., hope from green), and generating poems to satisfy both relevance to the image and poeticness in language level. To solve the above challenges, we formulate the task of poem generation into two correlated sub-tasks by multi-adversarial training via policy gradient, through which the cross-modal relevance and poetic language style can be ensured. To extract poetic clues from images, we propose to learn a deep coupled visual-poetic embedding, in which the poetic representation from objects, sentiments and scenes in an image can be jointly learned. Two discriminative networks are further introduced to guide the poem generation, including a multi-modal discriminator and a poem-style discriminator. To facilitate the research, we have released two poem datasets by human annotators with two distinct properties: 1) the first human annotated image-to-poem pair dataset (with 8,292 pairs in total), and 2) to-date the largest public English poem corpus dataset (with 92,265 different poems in total). Extensive experiments are conducted with 8K images, among which 1.5K image are randomly picked for evaluation. Both objective and subjective evaluations show the superior performances against the state-of-the-art methods for poem generation from images. Turing test carried out with over 500 human subjects, among which 30 evaluators are poetry experts, demonstrates the effectiveness of our approach

AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal Generation

AI illustrator aims to automatically design visually appealing images for books to provoke rich thoughts and emotions. To achieve this goal, we propose a framework for translating raw descriptions with complex semantics into semantically corresponding images. The main challenge lies in the complexity of the semantics of raw descriptions, which may be hard to be visualized (e.g., "gloomy" or "Asian"). It usually poses challenges for existing methods to handle such descriptions. To address this issue, we propose a Prompt-based Cross-Modal Generation Framework (PCM-Frame) to leverage two powerful pre-trained models, including CLIP and StyleGAN. Our framework consists of two components: a projection module from Text Embeddings to Image Embeddings based on prompts, and an adapted image generation module built on StyleGAN which takes Image Embeddings as inputs and is trained by combined semantic consistency losses. To bridge the gap between realistic images and illustration designs, we further adopt a stylization model as post-processing in our framework for better visual effects. Benefiting from the pre-trained models, our method can handle complex descriptions and does not require external paired data for training. Furthermore, we have built a benchmark that consists of 200 raw descriptions. We conduct a user study to demonstrate our superiority over the competing methods with complicated texts. We release our code at https://github.com/researchmm/AI_Illustrator

WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection

We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations. Predominant works integrate region proposal mechanisms with convolutional neural networks (CNN). Although CNN is proficient in extracting discriminative local features, grand challenges still exist to measure the likelihood of a bounding box containing a complete object (i.e., "objectness"). In this paper, we propose a novel WSOD framework with Objectness Distillation (i.e., WSOD^2) by designing a tailored training mechanism for weakly-supervised object detection. Multiple regression targets are specifically determined by jointly considering bottom-up (BU) and top-down (TD) objectness from low-level measurement and CNN confidences with an adaptive linear combination. As bounding box regression can facilitate a region proposal learning to approach its regression target with high objectness during training, deep objectness representation learned from bottom-up evidences can be gradually distilled into CNN by optimization. We explore different adaptive training curves for BU/TD objectness, and show that the proposed WSOD^2 can achieve state-of-the-art results.Comment: Accepted as a ICCV 2019 poster pape