1,508 research outputs found

    Neuromorphic Learning Systems for Supervised and Unsupervised Applications

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    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

    Pruning GHSOM to create an explainable intrusion detection system

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    Intrusion Detection Systems (IDS) that provide high detection rates but are black boxes leadto models that make predictions a security analyst cannot understand. Self-Organizing Maps(SOMs) have been used to predict intrusion to a network, while also explaining predictions throughvisualization and identifying significant features. However, they have not been able to compete withthe detection rates of black box models. Growing Hierarchical Self-Organizing Maps (GHSOMs)have been used to obtain high detection rates on the NSL-KDD and CIC-IDS-2017 network trafficdatasets, but they neglect creating explanations or visualizations, which results in another blackbox model.This paper offers a high accuracy, Explainable Artificial Intelligence (XAI) based on GHSOMs.One obstacle to creating a white box hierarchical model is the model growing too large and complexto understand. Another contribution this paper makes is a pruning method used to cut down onthe size of the GHSOM, which provides a model that can provide insights and explanation whilemaintaining a high detection rate

    A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions

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    Anomaly detection modeled as a one-class classification is an essential task for tool condition monitoring (TCM) when only the normal data are available. To confront with the real-world settings, it is crucial to take the different operating conditions, e.g., rotation speed, into account when approaching TCM solutions. This work mainly addresses issues related to multi-operating-condition TCM models, namely the varying discriminability of sensory features with different operating conditions; the overlap between normal and anomalous data; and the complex structure of input data. A feature selection scheme is proposed in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is presented as a tool to aid the multi-objective selection of sensory features. In addition, four anomaly detection approaches based on Self-Organizing Map (SOM) are studied. To examine the stability of the four approaches, they are applied on different single-operating-condition models. Further, to examine their robustness when dealing with complex data structures, they are applied on multi-operating-condition models. The experimental results using the NASA Milling Data Set showed that all the studied anomaly detection approaches achieved a higher assessment accuracy with our feature selection scheme as compared to the Principal Component Analysis (PCA), Laplacian Score (LS), and extended LS in which we added a final step to the original LS method in order to eliminate redundant features

    A Neural Network and Principal Component Analysis Approach to Develop a Real-Time Driving Cycle in an Urban Environment: The Case of Addis Ababa, Ethiopia

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    This study aimed to develop the Addis Ababa Driving Cycle (DC) using real-time data from passenger vehicles in Addis Ababa based on a neural network (NN) and principal component analysis (PCA) approach. Addis Ababa has no local DC for automobile emissions tests and standard DCs do not reflect the current scenario. During the DC's development, the researchers determined the DC duration based on their experience and the literature. A k-means clustering method was also applied to cluster the dimensionally reduced data without identifying the best clustering method. First, a shape-preserving cubic interpolation technique was applied to remove outliers, followed by the Bayes wavelet signal denoising technique to smooth the data. Rules were then set for the extraction of trips and trip indicators before PCA was applied, and the machine learning classification was applied to identify the best clustering method. Finally, after training the NN using Bayesian regularization with a back propagation, the velocity for each route section was predicted and its performance had an overall R-value of 0.99. Compared with target data, the DCs developed by the NN and micro trip methods have a relative difference of 0.056 and 0.111, respectively, and resolve the issue of the DC duration decision in the micro trip method

    LUNG PATTERN CLASSIFICATION VIA DCNN

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    Interstitial lung disease (ILD) causes pulmonary fibrosis. The correct classification of ILD plays a crucial role in the diagnosis and treatment process. In this research work, we disclose a lung nodules recognition method based on a deep convolutional neural network (DCNN) and global features, which can be used for computer-aided diagnosis (CAD) of global features of lung nodules. Firstly, a DCNN is constructed based on the characteristics and complexity of lung computerized tomography (CT) images. Then discussed the effects of different iterations on the recognition results and influence of different model structures on the global features of lung nodules. We also improved the convolution kernel size, feature dimension, and network depth. Finally, the effects of different pooling methods, activation functions and training algorithms on the performance of DCNN were analyzed from the network optimization dimension. The experimental results verify the feasibility of the proposed DCNN for CAD of global features of lung nodules. Selecting appropriate model parameters and model structure and using the elastic momentum training method can achieve good recognition results

    Process Monitoring and Data Mining with Chemical Process Historical Databases

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    Modern chemical plants have distributed control systems (DCS) that handle normal operations and quality control. However, the DCS cannot compensate for fault events such as fouling or equipment failures. When faults occur, human operators must rapidly assess the situation, determine causes, and take corrective action, a challenging task further complicated by the sheer number of sensors. This information overload as well as measurement noise can hide information critical to diagnosing and fixing faults. Process monitoring algorithms can highlight key trends in data and detect faults faster, reducing or even preventing the damage that faults can cause. This research improves tools for process monitoring on different chemical processes. Previously successful monitoring methods based on statistics can fail on non-linear processes and processes with multiple operating states. To address these challenges, we develop a process monitoring technique based on multiple self-organizing maps (MSOM) and apply it in industrial case studies including a simulated plant and a batch reactor. We also use standard SOM to detect a novel event in a separation tower and produce contribution plots which help isolate the causes of the event. Another key challenge to any engineer designing a process monitoring system is that implementing most algorithms requires data organized into “normal” and “faulty”; however, data from faulty operations can be difficult to locate in databases storing months or years of operations. To assist in identifying faulty data, we apply data mining algorithms from computer science and compare how they cluster chemical process data from normal and faulty conditions. We identify several techniques which successfully duplicated normal and faulty labels from expert knowledge and introduce a process data mining software tool to make analysis simpler for practitioners. The research in this dissertation enhances chemical process monitoring tasks. MSOM-based process monitoring improves upon standard process monitoring algorithms in fault identification and diagnosis tasks. The data mining research reduces a crucial barrier to the implementation of monitoring algorithms. The enhanced monitoring introduced can help engineers develop effective and scalable process monitoring systems to improve plant safety and reduce losses from fault events

    Unsupervised machine learning clustering and data exploration of radio-astronomical images

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    In this thesis, I demonstrate a novel and efficient unsupervised clustering and data exploration method with the combination of a Self-Organising Map (SOM) and a Convolutional Autoencoder, applied to radio-astronomical images from the Radio Galaxy Zoo (RGZ) dataset. The rapidly increasing volume and complexity of radio-astronomical data have ushered in a new era of big-data astronomy which has increased the demand for Machine Learning (ML) solutions. In this era, the sheer amount of image data produced with modern instruments and has resulted in a significant data deluge. Furthermore, the morphologies of objects captured in these radio-astronomical images are highly complex and challenging to classify conclusively due to their intricate and indiscrete nature. Additionally, major radio-astronomical discoveries are unplanned and found in the unexpected, making unsupervised ML highly desirable by operating with few assumptions and without labelled training data. In this thesis, I developed a novel unsupervised ML approach as a practical solution to these astronomy challenges. Using this system, I demonstrated the use of convolutional autoencoders and SOM’s as a dimensionality reduction method to delineate the complexity and volume of astronomical data. My optimised system shows that the coupling of these methods is a powerful method of data exploration and unsupervised clustering of radio-astronomical images. The results of this thesis show this approach is capable of accurately separating features by complexity on a SOM manifold and unified distance matrix with neighbourhood similarity and hierarchical clustering of the mapped astronomical features. This method provides an effective means to explore the high-level topological relationships of image features and morphology in large datasets automatically with minimal processing time and computational resources. I achieved these capabilities with a new and innovative method of SOM training using the autoencoder compressed latent feature vector representations of radio-astronomical data, rather than raw images. Using this system, I successfully investigated SOM affine transformation invariance and analysed the true nature of rotational effects on this manifold using autoencoder random rotation training augmentations. Throughout this thesis, I present my method as a powerful new approach to data exploration technique and contribution to the field. The speed and effectiveness of this method indicates excellent scalability and holds implications for use on large future surveys, large-scale instruments such as the Square Kilometre Array and in other big-data and complexity analysis applications

    Explainable Intrusion Detection Systems using white box techniques

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    Artificial Intelligence (AI) has found increasing application in various domains, revolutionizing problem-solving and data analysis. However, in decision-sensitive areas like Intrusion Detection Systems (IDS), trust and reliability are vital, posing challenges for traditional black box AI systems. These black box IDS, while accurate, lack transparency, making it difficult to understand the reasons behind their decisions. This dissertation explores the concept of eXplainable Intrusion Detection Systems (X-IDS), addressing the issue of trust in X-IDS. It explores the limitations of common black box IDS and the complexities of explainability methods, leading to the fundamental question of trusting explanations generated by black box explainer modules. To address these challenges, this dissertation presents the concept of white box explanations, which are innately explainable. While white box algorithms are typically simpler and more interpretable, they often sacrifice accuracy. However, this work utilized white box Competitive Learning (CL), which can achieve competitive accuracy in comparison to black box IDS. We introduce Rule Extraction (RE) as another white box technique that can be applied to explain black box IDS. It involves training decision trees on the inputs, weights, and outputs of black box models, resulting in human-readable rulesets that serve as global model explanations. These white box techniques offer the benefits of accuracy and trustworthiness, which are challenging to achieve simultaneously. This work aims to address gaps in the existing literature, including the need for highly accurate white box IDS, a methodology for understanding explanations, small testing datasets, and comparisons between white box and black box models. To achieve these goals, the study employs CL and eclectic RE algorithms. CL models offer innate explainability and high accuracy in IDS applications, while eclectic RE enhances trustworthiness. The contributions of this dissertation include a novel X-IDS architecture featuring Self-Organizing Map (SOM) models that adhere to DARPA’s guidelines for explainable systems, an extended X-IDS architecture incorporating three CL-based algorithms, and a hybrid X-IDS architecture combining a Deep Neural Network (DNN) predictor with a white box eclectic RE explainer. These architectures create more explainable, trustworthy, and accurate X-IDS systems, paving the way for enhanced AI solutions in decision-sensitive domains
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