NPTSN:RL-Based Network Planning with Guaranteed Reliability for In-Vehicle TSSDN

To achieve strict reliability goals with lower redundancy cost, Time-Sensitive Software-Defined Networking (TSSDN) enables run-time recovery for future in-vehicle networks. While the recovery mechanisms rely on network planning to establish reliability guarantees, existing network planning solutions are not suitable for TSSDN due to its domain-specific scheduling and reliability concerns. The sparse solution space and expensive reliability verification further complicate the problem. We propose NPTSN, a TSSDN planning solution based on deep Reinforcement Learning (RL). It represents the domain-specific concerns with the RL environment and constructs solutions with an intelligent network generator. The network generator iteratively proposes TSSDN solutions based on a failure analysis and trains a decision-making neural network using a modified actor-critic algorithm. Extensive performance evaluations show that NPTSN guarantees reliability for more test cases and shortens the decision trajectory compared to state-of-the-art solutions. It reduces the network cost by up to 6.8x in the performed experiments

Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures

Power distribution grids are typically installed outdoors and are exposed to environmental conditions. When contamination accumulates in the structures of the network, there may be shutdowns caused by electrical arcs. To improve the reliability of the network, visual inspections of the electrical power system can be carried out; these inspections can be automated using computer vision techniques based on deep neural networks. Based on this need, this paper proposes the Semi-ProtoPNet deep learning model to classify defective structures in the power distribution networks. The Semi-ProtoPNet deep neural network does not perform convex optimization of its last dense layer to maintain the impact of the negative reasoning process on image classification. The negative reasoning process rejects the incorrect classes of an input image; for this reason, it is possible to carry out an analysis with a low number of images that have different backgrounds, which is one of the challenges of this type of analysis. Semi-ProtoPNet achieves an accuracy of 97.22%, being superior to VGG-13, VGG-16, VGG-19, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-201, and also models of the same class such as ProtoPNet, NP-ProtoPNet, Gen-ProtoPNet, and Ps-ProtoPNet

A System-level Perspective Towards Efficient, Reliable and Secure Neural Network Computing

The Digital Era is now evolving into the Intelligence Era, driven overwhelmingly by the revolution of Deep Neural Network (DNN), which opens the door for intelligent data interpretation, turning the data and information into actions that create new capabilities, richer experiences, and unprecedented economic opportunities, achieving game-changing outcomes spanning from image recognition, natural language processing, self-driving cars to biomedical analysis. Moreover, the emergence of deep learning accelerators and neuromorphic computing further pushes DNN computation from cloud to the edge devices for the low-latency scalable on-device neural network computing. However, such promising embedded neural network computing systems are subject to various technical challenges. First, performing high-accurate inference for complex DNNs requires massive amounts of computation and memory resources, causing very limited energy efficiency for existing computing platforms. Even the brain-inspired spiking neuromorphic computing architecture which originates from the more bio-plausible spiking neural network (SNN) and relies on the occurrence frequency of a large number of electrical spikes to represent the data and perform the computation, is subject to significant limitations on both energy efficiency and processing speed. Second, although many memristor-based DNN accelerators and emerging neuromorphic accelerators have been proposed to improve the performance-per-watt of embedded DNN computing with the highly parallelizable Processing-in-Memory (PIM) architecture, one critical challenge faced by these memristor-based designs is their poor reliability. A DNN weight, which is represented as the memristance of a memristor cell, can be easily distorted by the inherent physical limitations of memristor devices, resulting in significant accuracy degradation. Third, DNN computing systems are also subject to ever-increasing security concerns. Attackers can easily fool a normally trained DNN model by exploiting the algorithmic vulnerabilities of DNN classifiers through adversary examples to mislead the inference results. Moreover, system vulnerabilities in open-sourced DNN computing frameworks such as heap overflow are increasingly exploited to either distort the inference accuracy or corrupt the learning environment. This dissertation focuses on designing efficient, reliable, and secured neural network computing systems. An architecture and algorithm co-design approach is presented to address the aforementioned design pillars from a system-level perspective, namely efficiency, reliability and security. Three case study examples centered around each design pillar, including Single-spike Neuromorphic Accelerator, Fault-tolerant DNN Accelerator, and Mal-DNN: Malicious DNN-powered Stegomalware, are discussed in this dissertation, offering the community an alternative thinking about developing more efficient, reliable and secure deep learning systems

Neural Network Model-Based Control for Manipulator: An Autoencoder Perspective

Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as a powerful tool to achieve deep learning and has gained success in recent years. However, the performance of existing autoencoder approaches for manipulator control may be still largely dependent on the quality of data, and for extreme cases with noisy data it may even fail. How to incorporate the model knowledge into the autoencoder controller design with an aim to increase the robustness and reliability remains a challenging problem. In this work, a sparse autoencoder controller for kinematic control of manipulators with weights obtained directly from the robot model rather than training data is proposed for the first time. By encoding and decoding the control target though a new dynamic recurrent neural network architecture, the control input can be solved through a new sparse optimization formulation. In this work, input saturation, which holds for almost all practical systems but usually is ignored for analysis simplicity, is also considered in the controller construction. Theoretical analysis and extensive simulations demonstrate that the proposed sparse autoencoder controller with input saturation can make the end-effector of the manipulator system track the desired path efficiently. Further performance comparison and evaluation against the additive noise and parameter uncertainty substantiate robustness of the proposed sparse autoencoder manipulator controller

Deep Learning Methods for Detection and Tracking of Particles in Fluorescence Microscopy Images

Studying the dynamics of sub-cellular structures such as receptors, filaments, and vesicles is a prerequisite for investigating cellular processes at the molecular level. In addition, it is important to characterize the dynamic behavior of virus structures to gain a better understanding of infection mechanisms and to develop novel drugs. To investigate the dynamics of fluorescently labeled sub-cellular and viral structures, time-lapse fluorescence microscopy is the most often used imaging technique. Due to the limited spatial resolution of microscopes caused by diffraction, these very small structures appear as bright, blurred spots, denoted as particles, in microscopy images. To draw statistically meaningful biological conclusions, a large number of such particles need to be analyzed. However, since manual analysis of fluorescent particles is very time consuming, fully automated computer-based methods are indispensable. We introduce novel deep learning methods for detection and tracking of multiple particles in fluorescence microscopy images. We propose a particle detection method based on a convolutional neural network which performs image-to-image mapping by density map regression and uses the adaptive wing loss. For particle tracking, we present a recurrent neural network that exploits past and future information in both forward and backward direction. Assignment probabilities across multiple detections as well as the probabilities for missing detections are computed jointly. To resolve tracking ambiguities using future information, several track hypotheses are propagated to later time points. In addition, we developed a novel probabilistic deep learning method for particle tracking, which is based on a recurrent neural network mimicking classical Bayesian filtering. The method includes both aleatoric and epistemic uncertainty, and provides valuable information about the reliability of the computed trajectories. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. Moreover, we developed a convolutional Long Short-Term Memory neural network for combined particle tracking and colocalization analysis in two-channel microscopy image sequences. The network determines colocalization probabilities, and colocalization information is exploited to improve tracking. Short and long-term temporal dependencies of object motion as well as image intensities are taken into account to compute assignment probabilities jointly across multiple detections. We also introduce a deep learning method for probabilistic particle detection and tracking. For particle detection, temporal information is integrated to regress a density map and determine sub-pixel particle positions. For tracking, a fully Bayesian neural network is presented that mimics classical Bayesian filtering and takes into account both aleatoric and epistemic uncertainty. Uncertainty information of individual particle detections is considered. Network training for the developed deep learning-based particle tracking methods relies only on synthetic data, avoiding the need of time-consuming manual annotation. We performed an extensive evaluation of our methods based on image data of the Particle Tracking Challenge as well as on fluorescence microscopy images displaying virus proteins of HCV and HIV, chromatin structures, and cell-surface receptors. It turned out that the methods outperform previous methods