Stepwise Model Reconstruction of Robotic Manipulator Based on Data-Driven Method

Research on dynamics of robotic manipulators provides promising support for model-based control. In general, rigorous first-principles-based dynamics modeling and accurate identification of mechanism parameters are critical to achieving high precision in model-based control, while data-driven model reconstruction provides alternative approaches of the above process. Taking the level of activation of data as an indicator, this paper classifies the collected robotic manipulator data by means of K-means clustering algorithm. With the fundamental prior knowledge, we find the corresponding dynamical properties behind the classified data separately. Afterwards, the sparse identification of nonlinear dynamics (SINDy) method is used to reconstruct the dynamics model of the robotic manipulator step by step according to the activation level of the classified data. The simulation results show that the proposed method not only reduces the complexity of the basis function library, enabling the application of SINDy method to multi-degree-of-freedom robotic manipulators, but also decreases the influence of data noise on the regression results. Finally, the dynamic control based on the reconfigured model is deployed on the experimental platform, and the experimental results prove the effectiveness of the proposed method.Comment: 8 pages, 11 figure

Connectionist Temporal Modeling for Weakly Supervised Action Labeling

We propose a weakly-supervised framework for action labeling in video, where only the order of occurring actions is required during training time. The key challenge is that the per-frame alignments between the input (video) and label (action) sequences are unknown during training. We address this by introducing the Extended Connectionist Temporal Classification (ECTC) framework to efficiently evaluate all possible alignments via dynamic programming and explicitly enforce their consistency with frame-to-frame visual similarities. This protects the model from distractions of visually inconsistent or degenerated alignments without the need of temporal supervision. We further extend our framework to the semi-supervised case when a few frames are sparsely annotated in a video. With less than 1% of labeled frames per video, our method is able to outperform existing semi-supervised approaches and achieve comparable performance to that of fully supervised approaches.Comment: To appear in ECCV 201

An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system based approach to estimate prices for residential properties. The data set consists of historic sales of homes in a market in Midwest USA and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

Locating and quantifying gas emission sources using remotely obtained concentration data

We describe a method for detecting, locating and quantifying sources of gas emissions to the atmosphere using remotely obtained gas concentration data; the method is applicable to gases of environmental concern. We demonstrate its performance using methane data collected from aircraft. Atmospheric point concentration measurements are modelled as the sum of a spatially and temporally smooth atmospheric background concentration, augmented by concentrations due to local sources. We model source emission rates with a Gaussian mixture model and use a Markov random field to represent the atmospheric background concentration component of the measurements. A Gaussian plume atmospheric eddy dispersion model represents gas dispersion between sources and measurement locations. Initial point estimates of background concentrations and source emission rates are obtained using mixed L2-L1 optimisation over a discretised grid of potential source locations. Subsequent reversible jump Markov chain Monte Carlo inference provides estimated values and uncertainties for the number, emission rates and locations of sources unconstrained by a grid. Source area, atmospheric background concentrations and other model parameters are also estimated. We investigate the performance of the approach first using a synthetic problem, then apply the method to real data collected from an aircraft flying over: a 1600 km^2 area containing two landfills, then a 225 km^2 area containing a gas flare stack

Computational Intelligence Techniques for Control and Optimization of Wastewater Treatment Plants

The development of novel, practice-oriented and reliable instrumentation and control strategies for wastewater treatment plants in order to improve energy efficiency, while guaranteeing process stability and maintenance of high cleaning capacity, has become a priority for WWTP operators due to increasing treatment costs. To achieve these ambitious and even contradictory objectives, this thesis investigates a combination of online measurement systems, computational intelligence and machine learning methods as well as dynamic simulation models. Introducing the state-of-the-art in the fields of WWTP operation, process monitoring and control, three novel computational intelligence enabled instrumentation, control and automation (ICA) methods are developed and presented. Furthermore, their potential for practical implementation is assessed. The methods are, on the one hand, the automated calibration of a simulation model for the Rospe WWTP that provides a basis for the development and evaluation of the subsequent methods, and on the other hand, the development of soft sensors for the WWTP inflow which estimate the crucial process variables COD and NH4-N, and the estimation of WWTP operating states using Self- Organising Maps (SOM) that are used to determine the optimal control parameters for each state. These collectively, provide the basis for achieving comprehensive WWTP optimization. Results show that energy consumption and cleaning capacity can be improved by more than 50%

Development of Real-time Optimal Control Strategy of Hybrid Transit Bus Based on Predicted Driving Pattern

The control strategy of a hybrid electric vehicle (HEV) has been an active research area in the past decades. The main goal of the optimal control strategy is to maximize the fuel economy and minimize exhaust emissions while also satisfying the expected vehicle performance. Dynamic programming (DP) is an algorithm capable of finding the global optimal solution of HEV operation. However, DP cannot be used as a real-time control approach as it requires pre-known driving information. The equivalent consumption minimization strategy (ECMS) is a real-time control approach, but it always results in local optima due to the non-convex cost function. In my research, a ECMS with DP combined model (ECMSwDP) was proposed, which was a compromise between optimality and real-time capability. In this approach, the optimal equivalent factor (lambda) for a real-time ECMS controller can be derived using ECMSwDP for a given driving condition. The optimal lambda obtained using ECMSwDP was further processed to derive the lambda map, which was a function of vehicle operation and driving information. Six lambda maps were generated corresponding to the developed representative driving patterns. At each distance segment of a drive cycle, the suitable lambda value is available from one of the six lambda maps based on the identified driving pattern and current vehicle operation.;An adaptive ECMS (A-ECMS) model with a driving pattern identification model is developed to achieve the real-time optimal control for a HEV. A-ECMS was capable of controlling the ratio of power provided by the ICE and battery of a hybrid vehicle by selecting the lambda based on the identified lambda map. The effect on fuel consumption of the control strategies developed using the rule-based controller, ECMSwDP, A-ECMS, and DP was simulated using the parallel hybrid bus model developed in this research. The control strategies developed using A-ECMS are able to significantly improve the fuel economy while maintaining the battery charge sustainability. The corrected fuel economy observed with A-ECMS with a penalty function and the average lambda of RDPs was 5.55%, 13.67%, and 19.19% gap to that observed with DP when operated over the Beijing cycle, WVU-CSI cycle, and the actual transit bus route, respectively. The corrected fuel economy observed with A-ECMS with lambda maps of the RDPs was 4.83%, 10.61%, and 14.33% gap to that observed with DP when operated on the Beijing cycle, WVU-CSI cycle, and actual transit bus route, respectively. The simulation results demonstrated that the proposed A-ECMS approaches have the capability to achieve real time suboptimal control of a HEV while maintaining the charge sustainability of the battery

Computational Investigations of Biomolecular Mechanisms in Genomic Replication, Repair and Transcription

High fidelity maintenance of the genome is imperative to ensuring stability and proliferation of cells. The genetic material (DNA) of a cell faces a constant barrage of metabolic and environmental assaults throughout the its lifetime, ultimately leading to DNA damage. Left unchecked, DNA damage can result in genomic instability, inviting a cascade of mutations that initiate cancer and other aging disorders. Thus, a large area of focus has been dedicated to understanding how DNA is damaged, repaired, expressed and replicated. At the heart of these processes lie complex macromolecular dynamics coupled with intricate protein-DNA interactions. Through advanced computational techniques it has become possible to probe these mechanisms at the atomic level, providing a physical basis to describe biomolecular phenomena. To this end, we have performed studies aimed at elucidating the dynamics and interactions intrinsic to the functionality of biomolecules critical to maintaining genomic integrity: modeling the DNA editing mechanism of DNA polymerase III, uncovering the DNA damage recognition/repair mechanism of thymine DNA glycosylase and linking genetic disease to the functional dynamics of the pre-initiation complex transcription machinery. Collectively, our results elucidate the dynamic interplay between proteins and DNA, further broadening our understanding of these complex processes involved with genomic maintenance