Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy

The Reasonable Effectiveness of Randomness in Scalable and Integrative Gene Regulatory Network Inference and Beyond

Gene regulation is orchestrated by a vast number of molecules, including transcription factors and co-factors, chromatin regulators, as well as epigenetic mechanisms, and it has been shown that transcriptional misregulation, e.g., caused by mutations in regulatory sequences, is responsible for a plethora of diseases, including cancer, developmental or neurological disorders. As a consequence, decoding the architecture of gene regulatory networks has become one of the most important tasks in modern (computational) biology. However, to advance our understanding of the mechanisms involved in the transcriptional apparatus, we need scalable approaches that can deal with the increasing number of large-scale, high-resolution, biological datasets. In particular, such approaches need to be capable of efficiently integrating and exploiting the biological and technological heterogeneity of such datasets in order to best infer the underlying, highly dynamic regulatory networks, often in the absence of sufficient ground truth data for model training or testing. With respect to scalability, randomized approaches have proven to be a promising alternative to deterministic methods in computational biology. As an example, one of the top performing algorithms in a community challenge on gene regulatory network inference from transcriptomic data is based on a random forest regression model. In this concise survey, we aim to highlight how randomized methods may serve as a highly valuable tool, in particular, with increasing amounts of large-scale, biological experiments and datasets being collected. Given the complexity and interdisciplinary nature of the gene regulatory network inference problem, we hope our survey maybe helpful to both computational and biological scientists. It is our aim to provide a starting point for a dialogue about the concepts, benefits, and caveats of the toolbox of randomized methods, since unravelling the intricate web of highly dynamic, regulatory events will be one fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases

An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

Evaluation of Suspension Seats Under Multi-Axis Vibration Excitations - A Neural Net Model Approach to Seat Selection

Whole-body vibration describes vibrations that are transferred from a supporting surface to the human body. Low back injury is a major health issue amongst heavy machine operators and seat selection is important for reducing vibration exposure. Modeling the vibration attenuation properties of seats is one approach for predicting the performance of seats in different vibration environments. An efficient neural network (NN) algorithm identified the vibration attenuation properties of five suspension seats that are commonly used in the Northern Ontario mining sector. Each of the NN seat models strongly predicted vertical seatpan r.m.s. accelerations from the chassis accelerations and a measure of driver anthropometrics. We implemented the developed NN models to evaluate the performance of industrial seats for a variety of skidders from the forestry sector and load-haul-dump vehicles from the underground mining environment. Our results demonstrated that seat selection is not universal. The performance and rank orders of industrial seats varied between vibration environments based on the calculated equivalent daily exposure (A(8)) values. We performed a sensitivity analysis to evaluate the influence of specific vibration frequency components on the predicted daily exposure values. This analysis revealed that each of the industrial seats responded differently to specific vibration frequencies and explained why the seat selection algorithm matched particular seats to specific vibration environments. We also evaluated the performance of the new No-JoltTM air-inflated cushion with multi-axis vibration exposures and vertical jolt exposures. The vibration attenuation properties were assessed for two seat suspensions (with relatively good and poor initial performance) when their foam cushions were replaced with the air-inflated cushion. The air cushion only improved the vibration attenuation properties of the seat that initially had good performance. We also observed that operator’s anthropometrics and sex influenced the performance of the air-inflated cushion in certain cases when vibration environment includes jolt exposures. All of our findings emphasize the importance of matching the specific seat/cushion to the particular vibration environment in order to reduce heavy machine operators’ vibration exposure and minimize their health risks

Human Emotion Recognition Approach Based on Facial Expression, Ethnicity and Gender Using Backpropagation Artificial Neural Network

The emotions of the human are create by God almighty since the creation of humankind. Human emotions are mostly represents based on the psychological situation of humans through facial expressions, speech, or through the movement of the body. Overall, the interactions between humans are using several factors including the knowledge of these emotions. Although people differ in their veins, races and languages, the language of emotions is almost general and comprehensive which makes it easy to understand. There are six basic emotions which usually researchers consider, these are happy, sad, fear, disgust, anger and shame. In this thesis, we study the impact of both race and gender on the accuracy of the recognition of emotion through facial expressions. We claim that knowing the gender and race would increase the accuracy of the emotion recognition. This is due to the difference between the face appearances of various races and gender. To test our claim, we developed an approach based on Artificial Neural Networks (ANN) using backpropgation algorithm to recognize the human emotion. The proposed model consists of five stages: the first stage is inputting the image. Second stage is for image preprocessing. The third is to identifying points of the face, which will help in defining the face features. The fourth stage is to extract the features and the last stage is the emotion recognition. These stages are divides into two sections: the first section consists of the first four stages and the second section consists of only the fifth stage. We have built a program to implement the first section and we used Matlab to implement the other section. Our model has been test by using MSDEF dataset, and we found that there is a positive effect on the accuracy of the recognition of emotion if we use both the ethnic group and gender as inputs to the system. Although this effect is not significant, but considerable (Improvement rate reached 8%). In addition, we found that females have a more accurate emotion expression recognition than males. In additional, regardless of the used dataset, our approach obtained better results than some researches on emotion recognition. This could be due to various reasons such as the type of the selected features and consideration of race and gender

NengoFPGA: an FPGA Backend for the Nengo Neural Simulator

Low-power, high-speed neural networks are critical for providing deployable embedded AI applications at the edge. We describe a Xilinx FPGA implementation of Neural Engineering Framework (NEF) networks with online learning that outperforms mobile Nvidia GPU implementations by an order of magnitude or more. Specifically, we provide an embedded Python-capable PYNQ FPGA implementation supported with a Xilinx Vivado High-Level Synthesis (HLS) workflow that allows sub-millisecond implementation of adaptive neural networks with low-latency, direct I/O access to the physical world. The outcome of this work is NengoFPGA, a seamless and user-friendly extension to the neural compiler Python package Nengo. To reduce memory requirements and improve performance we tune the precision of the different intermediate variables in the code to achieve competitive absolute accuracy against slower and larger floating-point reference designs. The online learning component of the neural network exploits immediate feedback to adjust the network weights to best support a given arithmetic precision. As the space of possible design configurations of such quantized networks is vast and is subject to a target accuracy constraint, we use the Hyperopt hyper-parameter tuning tool instead of manual search to find Pareto optimal designs. Specifically, we are able to generate the optimized designs in under 500 short iterations of Vivado HLS C synthesis before running the complete Vivado place-and-route phase on that subset, a much longer process not conducive to rapid exploration. For neural network populations of 64–4096 neurons and 1–8 representational dimensions our optimized FPGA implementation generated by Hyperopt has a speedup of 10–484× over a competing cuBLAS implementation on the Jetson TX1 GPU while using 2.4–9.5× less power. Our speedups are a result of HLS-specific reformulation (15× improvement), precision adaptation (3× improvement), and low-latency direct I/O access (1000× improvement)