207 research outputs found
Predictive PDF control in shaping of molecular weight distribution based-on a new modelling Algorithm
The aims of this work are to develop an efficient modeling method for establishing dynamic output probability density function (PDF) models using measurement data and to investigate predictive control strategies for controlling the full shape of output PDF rather than the key moments. Using the rational square-root (RSR) B-spline approximation, a new modeling algorithm is proposed in which the actual weights are used instead of the pseudo weights in the weights dynamic model. This replacement can reduce computational load effectively in data-based modeling of a high-dimensional output PDF model. The use of the actual weights in modeling and control has been verified by stability analysis. A predictive PDF model is then constructed, based on which predictive control algorithms are established with the purpose to drive the output PDF towards the desired target PDF over the control process. An analytical solution is obtained for the non-constrained predictive PDF control. For the constrained predictive control, the optimal solution is achieved via solving a constrained nonlinear optimization problem. The integrated method of data-based modeling and predictive PDF control is applied to closed-loop control of molecular weight distribution (MWD) in an exemplar styrene polymerization process, through which the modeling efficiency and the merits of predictive control over standard PDF control are demonstrated and discussed
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book
Heat transfer across a nanoscale pressurized air gap and its application in magnetic recording
In this study, we investigated how a thermally actuated air bearing slider heats up a fast-spinning storage disk through a highly pressurized nanoscale air gap in a magnetic recording system. A Euleriandescription- based computational approach is developed considering heat conduction through a pressurized air film and near-field radiation across the gap. A set of field equations that govern the air bearing dynamics, slider thermo-mechanics and disk heat dissipation are solved simultaneously through an iterative approach. A temperature field on the same order as the hot slider surface itself is found to be established in the disk. The effective local heat transfer coefficient is found to vary substantially with disk materials and linear speeds. This approach quantifies the magnitude of different thermal transport schemes and the accuracy is verified by an excellent agreement with our experiment, which measures the local slider temperature rise with a resistance temperature sensor. It also demonstrates an effective computational approach to treat transient thermal processes in a system of components with fast relative speed and different length scales. Finally, the investigated thermal transport mechanism leads to a substantial spacing change that has a significant impact on the spacing margin of today’s magnetic storage systems
Data-Driven Fault Detection and Reasoning for Industrial Monitoring
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book
Integrated time sampling design and measurement set selection for biochemical systems
The optimal experimental design (OED) for observation strategy is investigated in this paper to collect the most informative experimental data for parameter estimation. The aim is to determine the best sampling time points and also select the most valuable measurement state variables through OED. The two design objectives are integrated together as a single-objective optimisation problem in which the variables and their sampling times are weighted in an expanded time sampling framework. Three optimisation methods, i.e., the Powell’s method, the sequen- tial selection method, and the sequential quadratic programming method, are employed to solve the optimisation problem. Their computation efficiencies are compared using a biodiesel case study system simulation. Simulation results demonstrate the effectiveness of the proposed method in reducing parameter estimation uncertainties as well as reducing parameter correlations. It can also be observed that the integrated OED doesn’t cost extra computation efforts when variable selection is considered together with the time sampling task
Soft-bound interval control system and its robust fault-tolerant controller design
A soft-bound interval control problem is proposed for general non-Gaussian systems with the aim to control the output variable within a bounded region at a specified probability level. To find a feasible solution to this challenging task, the initial soft-bound interval control problem has been transformed into an output probability density function (PDF) tracking control problem with constrained tracking errors, thereby the controller can be designed under the established framework of stochastic distribution control. Fault tolerant control (FTC) is investigated for soft-bound interval control systems in presence of faults. Three fault detection methods are proposed based on criteria extracted from the initial soft-bound control problem and the recast PDF tracking problem. An integrated design for fault estimation and FTC is proposed based on a double proportional integral structure. This integrated FTC design is developed through linear matrix inequality. Extensive simulation studies have been conducted to examine the key design factors, the implementation issues and the effectiveness of the proposed approach
VarietySound: Timbre-Controllable Video to Sound Generation via Unsupervised Information Disentanglement
Video to sound generation aims to generate realistic and natural sound given
a video input. However, previous video-to-sound generation methods can only
generate a random or average timbre without any controls or specializations of
the generated sound timbre, leading to the problem that people cannot obtain
the desired timbre under these methods sometimes. In this paper, we pose the
task of generating sound with a specific timbre given a video input and a
reference audio sample. To solve this task, we disentangle each target sound
audio into three components: temporal information, acoustic information, and
background information. We first use three encoders to encode these components
respectively: 1) a temporal encoder to encode temporal information, which is
fed with video frames since the input video shares the same temporal
information as the original audio; 2) an acoustic encoder to encode timbre
information, which takes the original audio as input and discards its temporal
information by a temporal-corrupting operation; and 3) a background encoder to
encode the residual or background sound, which uses the background part of the
original audio as input. To make the generated result achieve better quality
and temporal alignment, we also adopt a mel discriminator and a temporal
discriminator for the adversarial training. Our experimental results on the VAS
dataset demonstrate that our method can generate high-quality audio samples
with good synchronization with events in video and high timbre similarity with
the reference audio
Learning Vertex Representations for Bipartite Networks
Recent years have witnessed a widespread increase of interest in network
representation learning (NRL). By far most research efforts have focused on NRL
for homogeneous networks like social networks where vertices are of the same
type, or heterogeneous networks like knowledge graphs where vertices (and/or
edges) are of different types. There has been relatively little research
dedicated to NRL for bipartite networks. Arguably, generic network embedding
methods like node2vec and LINE can also be applied to learn vertex embeddings
for bipartite networks by ignoring the vertex type information. However, these
methods are suboptimal in doing so, since real-world bipartite networks concern
the relationship between two types of entities, which usually exhibit different
properties and patterns from other types of network data. For example,
E-Commerce recommender systems need to capture the collaborative filtering
patterns between customers and products, and search engines need to consider
the matching signals between queries and webpages
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