Neuromorphic Learning Systems for Supervised and Unsupervised Applications

The advancements in high performance computing (HPC) have enabled the large-scale implementation of neuromorphic learning models and pushed the research on computational intelligence into a new era. Those bio-inspired models are constructed on top of unified building blocks, i.e. neurons, and have revealed potentials for learning of complex information. Two major challenges remain in neuromorphic computing. Firstly, sophisticated structuring methods are needed to determine the connectivity of the neurons in order to model various problems accurately. Secondly, the models need to adapt to non-traditional architectures for improved computation speed and energy efficiency. In this thesis, we address these two problems and apply our techniques to different cognitive applications. This thesis first presents the self-structured confabulation network for anomaly detection. Among the machine learning applications, unsupervised detection of the anomalous streams is especially challenging because it requires both detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research need. We present AnRAD (Anomaly Recognition And Detection), a bio-inspired detection framework that performs probabilistic inferences. We leverage the mutual information between the features and develop a self-structuring procedure that learns a succinct confabulation network from the unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base from the data streams. Compared to several existing anomaly detection methods, the proposed approach provides competitive detection accuracy as well as the insight to reason the decision making. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementation of the recall algorithms on the graphic processing unit (GPU) and the Xeon Phi co-processor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor (GPP). The implementation enables real-time service to concurrent data streams with diversified contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle abnormal behavior detection, the framework is able to monitor up to 16000 vehicles and their interactions in real-time with a single commodity co-processor, and uses less than 0.2ms for each testing subject. While adapting our streaming anomaly detection model to mobile devices or unmanned systems, the key challenge is to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of neural models. As a follow-up to the AnRAD framework, we proposed to port the detection network to the TrueNorth architecture. Implementing inference based anomaly detection on a neurosynaptic processor is not straightforward due to hardware limitations. A design flow and the supporting component library are developed to flexibly map the learned detection networks to the neurosynaptic cores. Instead of the popular rate code, burst code is adopted in the design, which represents numerical value using the phase of a burst of spike trains. This does not only reduce the hardware complexity, but also increases the result\u27s accuracy. A Corelet library, NeoInfer-TN, is implemented for basic operations in burst code and two-phase pipelines are constructed based on the library components. The design can be configured for different tradeoffs between detection accuracy, hardware resource consumptions, throughput and energy. We evaluate the system using network intrusion detection data streams. The results show higher detection rate than some conventional approaches and real-time performance, with only 50mW power consumption. Overall, it achieves 10^8 operations per Joule. In addition to the modeling and implementation of unsupervised anomaly detection, we also investigate a supervised learning model based on neural networks and deep fragment embedding and apply it to text-image retrieval. The study aims at bridging the gap between image and natural language. It continues to improve the bidirectional retrieval performance across the modalities. Unlike existing works that target at single sentence densely describing the image objects, we elevate the topic to associating deep image representations with noisy texts that are only loosely correlated. Based on text-image fragment embedding, our model employs a sequential configuration, connects two embedding stages together. The first stage learns the relevancy of the text fragments, and the second stage uses the filtered output from the first one to improve the matching results. The model also integrates multiple convolutional neural networks (CNN) to construct the image fragments, in which rich context information such as human faces can be extracted to increase the alignment accuracy. The proposed method is evaluated with both synthetic dataset and real-world dataset collected from picture news website. The results show up to 50% ranking performance improvement over the comparison models

Improving the resilience of post-disaster water distribution systems using a dynamic optimization framework

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step towards sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events and in an organized manner, to prioritize the use of available resources to restore service rapidly whilst minimizing the negative impacts. Many methods have been developed to evaluate the WDS resilience, but few efforts are made so far to improve resilience of a post-disaster WDS through identifying optimal sequencing of recovery actions. To address this gap, a new dynamic optimization framework is proposed here where the resilience of a post-disaster WDS is evaluated using six different metrics. A tailored Genetic Algorithm is developed to solve the complex optimization problem driven by these metrics. The proposed framework is demonstrated using a real-world WDS with 6,064 pipes. Results obtained show that the proposed framework successfully identifies near-optimal sequencing of recovery actions for this complex WDS. The gained insights, conditional on the specific attributes of the case study, include: (i) the near-optimal sequencing of recovery strategy heavily depends on the damage properties of the WDS, (ii) replacements of damaged elements tend to be scheduled at the intermediate-late stages of the recovery process due to their long operation time, and (iii) interventions to damaged pipe elements near critical facilities (e.g., hospitals) should not be necessarily the first priority to recover due to complex hydraulic interactions within the WDS

Application Of Real-Time Control Strategy To Improve Nitrogen Removal In Wastewater Treatment

Biological nitrogen removal is an important task in the wastewater treatment. However, the actual removal of total nitrogen (TN) in the wastewater treatment plant (WWTP) is often unsatisfactory due to several causes, one of which is the insufficient availability of carbon source. One possible approach to improve the nitrogen removal therefore is addition of external carbon source, while the amount of which is directly related to operation cost of a WWTP. It is obviously necessary to determine the accurate amount of addition of external carbon source according to the demand depending on the influent wastewater quality. This study focused on the real-time control of external carbon source addition based on the on-line monitoring of influent wastewater quality. The relationship between the influent wastewater quality (specifically the concentration of COD and ammonia) and the demand of carbon source was investigated through experiments on a pilot-scale A/O reactor (1m3) at the Nanjing WWTP, China. The minimum doses of carbon source addition at different situations of influent wastewater quality were determined to ensure the effluent wastewater quality meets the discharge standard. The obtained relationship is expected to be applied in the full-scale WWTPs.

Response Of River Hydrological And Habitat Features To Water Supplement By Upstream Dam In Lijiang River, China

River development has impact on river hydrology instantly, and then produces far-reaching influence on river ecology gradually. Research on hydrological and ecological response to river environmental variation has caught much attention for river sustainable exploitation. This research, takingLijiangRiveras a case, developed a river habitat model integrated with water environmental model, which used to analyze the relations of hydrological and habitat features to flow regime variation due to upstream reservoir operation. This paper analyzed the hydraulic and water quality variation under two different flow schemes, and assessed habitat evolution before and after implementation of water supplement project. Finally, the optimum objective of water supplement was determined by comprehensive consideration of hydrodynamic and river habitat

A method for assessing the feasibility of air-bubble screens to reduce morphological gradients in open-channel bends

Within the fluvial network, confluences are particular areas characterized by great ecological value where flow dynamics and bed morphology are much influenced by local patterns. The aim of this article is to describe the influence of the convergence angle on the morphology and hydrodynamics at river channel confluences, where the tributary bed level is higher than the main channel bed (discordant bed). For that purpose, experiments were carried out in a laboratory flume running three discharge ratio scenarios for two different convergence angles (70 and 90 degrees). The tests were run until equilibrium was reached, i.e. when the outgoing solid discharge was equal or larger than 90% of the incoming. Once the bed topography remained stable, bed and water level surfaces were measured. As a result of these tests, and based on the performed measurements, the convergence angle is identified as an important parameter that influences the main-channel bed morphology features and the water level by modifying the shape and position of the main morphological and flow features. Also, the influence of the discharge ratio (Qr = Qt / Qm) on these modifications is observed and evaluated

Hydropower reservoirs on the upper Mekong river modify nutrient bioavailability downstream

Hydropower development is the key strategy in many developing countries for energy supply, climate-change mitigation and economic development. However, it is commonly assumed that river dams retain nutrients and therefore reduce downstream primary productivity and fishery catches, compromising food security and causing trans-boundary disputes. Contrary to expectation, here we found that a cascade of reservoirs along the upper Mekong River increased downstream bioavailability of nitrogen and phosphorus. The dams caused phytoplankton density to increase with hydraulic residence time and stratification of the stagnant reservoirs caused hypoxia at depth. This allowed the release of bioavailable phosphorus from the sediment and an increase in dissolved inorganic nitrogen as well as a shift in nitrogen species from nitrate to ammonium, which were transported downstream by the discharge of water from the base of the dam. Our findings provide a new perspective on the environmental impacts of river dams on nutrient cycling and ecosystem functioning, with potential implications for sustainable development of hydropower worldwide

A New Spatial Interpolation Method Based On Cross-Sections Sampling

The spatial interpolation results of the channel topography by the different methods have a very important effect on the topographic distribution of river channel. The study shows that the conventional interpolation methods such as TIN, Kriging and IDW methods can give good results with reasonable parameters when the sampling data is dense enough. However, the spatial distribution of source data sampled by the classical cross-section method in hydrological measurement may have large and small spaces along the longitudinal and transverse directions of river channel respectively, and then these interpolation methods above may give the unreasonable interpolation results. In this case, a new interpolation method named Linear Interpolation on the Fitted Curvilinear Grid of the river channel (LIFCG) is proposed, in which the river regime is considered by a set of curvilinear grids. The topography along the rows of grid dots which have same position with cross sections where the topography data is measured is firstly checked out and calculated by using linear interpolation method, then the topography at other grid nodes are interpolated along the longitudinal lines by using linear interpolation method. The application shows that the new method can give the more reasonable results than TIN, IDW and Kriging methods. In further, based on the new method, the river thalweg is firstly calculated and used to regenerate the channel fitted non-orthogonal curvilinear grid and then applied the grid to interpolate when there are complicated distribution and large spatial variation of channel topography and plane shape. The applications in the curved and braided natural channel’s interpolation show the interpolation by the new method with coupling of the river thalweg is more reasonable at some degree than the method without coupling of the river thalweg and other classical method when sparse cross-section measured sampling data is used

Reduction of bend scour with an air-bubble screen - morphology and flow patterns

The interplay between streamwise flow, curvature-induced secondary flow, sediment transport and bed morphology leads to the formation of a typical bar-pool bed morphology in open-channel bends. The associated scour at the outer bank and deposition at the inner bank may endanger the outer bank's stability or reduce the navigable width of the channel. Previous preliminary laboratory experiments in a sharply curved flume with a fixed horizontal bed have shown that a bubble screen located near the outer bank can generate an additional secondary flow located between the outer bank and the curvature-induced secondary flow and with a sense of rotation opposite to the latter. This bubble-induced secondary flow redistributes velocities and bed shear stresses. The reported study investigates the implications of a bubble screen on the flow and the morphology in configurations with mobile bed. Velocity measurements show that the bubble-induced secondary flow shifts the curvature-induced secondary flow in inwards direction and reduces its strength. The bubble screen considerably reduces morphological gradients. Maximum bend scour is reduced by about 50% and occurs further away from the outer bank where it does not endanger the bank stability anymore. The location of maximum scour coincides with the junction of the curvature-induced and bubble-induced secondary flows. At this same location, the maximum streamwise velocities and maximum vertical velocities impinging on the bed also occur, which indicates their importance with respect to the formation of bend scour. The bubble screen also substantially reduced deposition at the inner bank. These preliminary experiments show the potential of a bubble screen to influence and modify the bed morphology