Event-driven continuous STDP learning with deep structure for visual pattern recognition

Human beings can achieve reliable and fast visual pattern recognition with limited time and learning samples. Underlying this capability, ventral stream plays an important role in object representation and form recognition. Modeling the ventral steam may shed light on further understanding the visual brain in humans and building artificial vision systems for pattern recognition. The current methods to model the mechanism of ventral stream are far from exhibiting fast, continuous and event-driven learning like the human brain. To create a visual system similar to ventral stream in human with fast learning capability, in this study, we propose a new spiking neural system with an event-driven continuous spike timing dependent plasticity (STDP) learning method using specific spiking timing sequences. Two novel continuous input mechanisms have been used to obtain the continuous input spiking pattern sequence. With the event-driven STDP learning rule, the proposed learning procedure will be activated if the neuron receive one pre- or post-synaptic spike event. The experimental results on MNIST database show that the proposed method outperforms all other methods in fast learning scenarios and most of the current models in exhaustive learning experiments

Event-driven control in theory and practice : trade-offs in software and control performance

Feedback control algorithms are indispensable for the proper functioning of many industrial high-tech systems like copiers, wafer steppers and so on. Most research in digital feedback control considers periodic or time-driven control systems, where continuous-time signals are represented by their sampled values at a fixed frequency. In most applications, these digital control algorithms are implemented in a real-time embedded software environment. As a consequence of the time-driven nature of controllers, control engineers pose strong, non-negotiable requirements on the real-time implementations of their algorithms as the required control performance can be guaranteed in this manner. This might lead to non-optimal solutions if the design problem is considered from a broader multi-disciplinary system perspective. As an example, time-driven controllers perform control calculations all the time at a fixed rate, so also when nothing significant has happened in the process. This is clearly an unnecessary waste of resources like processor load and communication bus load and thus not optimal if these aspects are considered as well. To reduce the severe real-time constraints imposed by the control engineer and the accompanying disadvantages, this thesis proposes to drop the strict requirement of equidistant sampling. This enables the designers to make better balanced multidisciplinary trade-offs resulting in a better overall system performance and reduced cost price. By not requiring equidistant sampling, one could for instance vary the sample frequency over time and dynamically schedule the control algorithms in order to optimize over processor load. Another option is to perform a control update when new measurement data arrives. In this manner quantization effects and latencies are reduced considerably, which can reduce the required sensor resolution and thus the system cost price. As it is now an event (e.g. the arrival of a new measurement), rather than the elapse of time, that triggers the controller to perform an update, this type of feedback controllers is called event-driven control. In this thesis, we present two different event-driven control structures. The first one is sensor-based event-driven control in the sense that the control update is triggered by the arrival of new sensor data. In particular, this control structure is applied to accurately control a motor, based on an (extremely) low resolution encoder. The control design is based on transforming the system equations from the time domain to the angular position (spatial) domain. As controller updates are synchronous with respect to the angular position of the motor, we can apply variations on classical control theory to design and tune the controller. As a result of the transformation, the typical control measures that we obtain from analysis, are formulated in the spatial domain. For instance, the bandwidth of the controller is not expressed in Hertz (s¡1) anymore, but in rad¡1 and settling time is replaced by settling distance. For many high-tech systems these spatial measures directly relate to the real performance requirements. Moreover, disturbances are often more easily formulated in terms of angular position than in terms of time, which has clear advantages from a modeling point of view. To validate the theory, the controller is implemented on a high speed document printing system, to accurately control a motor based on an encoder resolution of only 1 pulse per revolution. By means of analysis, simulation and measurements we show that the control performance is similar to the initially proposed industrial controller that is based on a much higher encoder resolution. Moreover, we show that the proposed event-driven controller involves a significant lower processor load and hence outperforms the time-driven controller from a system perspective. The aim of the second type of event-driven controllers is to reduce the resource utilization for the controller tasks, such as processor load and communication bus load. The main idea is to only update the controller when it is necessary from a control performance point of view. For instance, we propose event-driven controllers that do not update the control value when the tracking/stabilization error is below a certain threshold. By choosing this threshold, a trade-off can be made between control performance and processor load. To get insight in this trade-off, theory is presented to analyze the control performance of these event-driven control loops in terms of ultimate bounds on the tracking/stabilization error. The theory is based on inferring properties (like robust positive invariance, ultimate boundedness and convergence indices) for the event-driven controlled system from discrete-time linear systems and piecewise linear systems. Next to the theoretical analysis, simulations and experiments are carried out on a printer paper path test-setup. It is shown that for the particular application the average processing time, needed to execute the controller tasks, was reduced by a factor 2 without significant degradation of the control performance in comparison to a timedriven implementation. Moreover, we developed a method to accurately predict the processor load for different processing platforms. This method is based on simulation models and micro measurements on the processing platform, such that the processor load can be estimated prior to implementing the controller on the platform. Next to these contributions in the field of event-driven control, a system engineering technique called "threads of reasoning" is extended and applied to the printer case study to achieve a focus on the right issues and trade-offs in a design. In summary, two types of event-driven controllers are theoretically analyzed and experimentally validated on a prototype document printing system. The results clearly indicate the potential benefits of event-driven control with respect to the overall system performance and in making trade-offs between control performance, software performance and cost price

Concurrent Design of Embedded Control Software

Embedded software design for mechatronic systems is becoming an increasingly time-consuming and error-prone task. In order to cope with the heterogeneity and complexity, a systematic model-driven design approach is needed, where several parts of the system can be designed concurrently. There is however a trade-off between concurrency efficiency and integration efficiency. In this paper, we present a case study on the development of the embedded control software for a real-world mechatronic system in order to evaluate how we can integrate concurrent and largely independent designed embedded system software parts in an efficient way. The case study was executed using our embedded control system design methodology which employs a concurrent systematic model-based design approach that ensures a concurrent design process, while it still allows a fast integration phase by using automatic code synthesis. The result was a predictable concurrently designed embedded software realization with a short integration time

Development of a Novel Method for Biochemical Systems Simulation: Incorporation of Stochasticity in a Deterministic Framework

Heart disease, cancer, diabetes and other complex diseases account for more than half of human mortality in the United States. Other diseases such as AIDS, asthma, Parkinson’s disease, Alzheimer’s disease and cerebrovascular ailments such as stroke not only augment this mortality but also severely deteriorate the quality of human life experience. In spite of enormous financial support and global scientific effort over an extended period of time to combat the challenges posed by these ailments, we find ourselves short of sighting a cure or vaccine. It is widely believed that a major reason for this failure is the traditional reductionist approach adopted by the scientific community in the past. In recent times, however, the systems biology based research paradigm has gained significant favor in the research community especially in the field of complex diseases. One of the critical components of such a paradigm is computational systems biology which is largely driven by mathematical modeling and simulation of biochemical systems. The most common methods for simulating a biochemical system are either: a) continuous deterministic methods or b) discrete event stochastic methods. Although highly popular, none of them are suitable for simulating multi-scale models of biological systems that are ubiquitous in systems biology based research. In this work a novel method for simulating biochemical systems based on a deterministic solution is presented with a modification that also permits the incorporation of stochastic effects. This new method, through extensive validation, has been proven to possess the efficiency of a deterministic framework combined with the accuracy of a stochastic method. The new crossover method can not only handle the concentration and spatial gradients of multi-scale modeling but it does so in a computationally efficient manner. The development of such a method will undoubtedly aid the systems biology researchers by providing them with a tool to simulate multi-scale models of complex diseases

Conflict-driven Hybrid Observer-based Anomaly Detection

This paper presents an anomaly detection method using a hybrid observer -- which consists of a discrete state observer and a continuous state observer. We focus our attention on anomalies caused by intelligent attacks, which may bypass existing anomaly detection methods because neither the event sequence nor the observed residuals appear to be anomalous. Based on the relation between the continuous and discrete variables, we define three conflict types and give the conditions under which the detection of the anomalies is guaranteed. We call this method conflict-driven anomaly detection. The effectiveness of this method is demonstrated mathematically and illustrated on a Train-Gate (TG) system

Event-Driven Network Model for Space Mission Optimization with High-Thrust and Low-Thrust Spacecraft

Numerous high-thrust and low-thrust space propulsion technologies have been developed in the recent years with the goal of expanding space exploration capabilities; however, designing and optimizing a multi-mission campaign with both high-thrust and low-thrust propulsion options are challenging due to the coupling between logistics mission design and trajectory evaluation. Specifically, this computational burden arises because the deliverable mass fraction (i.e., final-to-initial mass ratio) and time of flight for low-thrust trajectories can can vary with the payload mass; thus, these trajectory metrics cannot be evaluated separately from the campaign-level mission design. To tackle this challenge, this paper develops a novel event-driven space logistics network optimization approach using mixed-integer linear programming for space campaign design. An example case of optimally designing a cislunar propellant supply chain to support multiple lunar surface access missions is used to demonstrate this new space logistics framework. The results are compared with an existing stochastic combinatorial formulation developed for incorporating low-thrust propulsion into space logistics design; our new approach provides superior results in terms of cost as well as utilization of the vehicle fleet. The event-driven space logistics network optimization method developed in this paper can trade off cost, time, and technology in an automated manner to optimally design space mission campaigns.Comment: 38 pages; 11 figures; Journal of Spacecraft and Rockets (Accepted); previous version presented at the AAS/AIAA Astrodynamics Specialist Conference, 201

Issues in modelling and identification of Discrete Event Systems

2011 - 2012Discrete Event System (DES) are systems whose behavior is governed by discrete events occurring asynchronously over time and solely responsible for generating state transitions. DESs are particularly used in the field of the manufactured systems, handling systems and transportation systems: even if such system are being studying for long time, because of their complexity, they still present many issues that attract research interest. In particular this dissertation focuses about handling system modeling and DES identification. Obtaining a good model of a system (both time-driven and event-driven) allows to more easily execute operations as performance analysis, control, monitoring of system evolution. However, in some cases modeling of a system is not simple because of several complications due to the behavior of the system or of the context it belongs to. As example, sometimes, especially in the context of material handling and transportation, systems present both an event-driven and a time-driven behavior. In all that cases a very high accuracy is not requested it is usual neglect the latter and “looking” at the system as a DES (as example modeling a handling system it is possible to be interested in knowing if a vehicle is or not in a zone of the path while it is not important to know its exactly position). When the time-driven behavior plays a fundamental role in the obtaining the overall system performance, such dynamics cannot be neglected and they have to be explicitly modeled. This is the case, as example, of the automated warehouse systems, where the handling subsystem, as will be shown in the rest of this dissertation, presents time-driven dynamics that greatly influence the warehouse’s performance. Consequently a new way to model the system behavior has to be used. However, there are situation in which the difficult issue is not choosing the right formalism to model the system but it is the modeling itself. This is typical in many practical contexts, where it can occur that one has to work with unknown ready-made systems and no documentation about their behavior is available, or the model of a very complex system is needed. In these and other cases modeling becomes hard and another way to obtain the model of the system is needed: automated identification can be the solution. In the modeling environment, contribution of this thesis consists in presenting a new methodology to obtain a model oriented to the control and performance analysis of complex material handling systems that is highly modular, compact and made of parameterized modules. First a discrete event model is presented and then a new formalism that merges the concepts of Hybrid Petri Nets and Colored Petri Nets is introduced: the Colored Modified Hybrid Petri Nets (CMHPNs). Hence a new CMHPN model is proposed: it allows to model both the event nature and the continuous nature of the system. As more, to allow the monitoring of system evolutions, a freeware simulation tool for the CMHPNs is presented. Finally it is shown how the CMHPN model can be used to execute analysis and performance evaluation. Liveness analysis is performed by means of a hybrid automaton obtained from the net model. A deadlock prevention policy is synthesized working on an aggregated model. To prove the effectiveness of this new formalism an existing large automated warehouse system is presented as case study: its CMHPNs model is used to simulate the system behavior and to analyze the warehouse’s performance. In the identification environments, the guidelines of a new “active” approach to identify the model of a preexisting system is described. The proposed preliminary algorithm identifies a free labeled PN model on the basis of the observed output sequences and of the modifiable input consisting of the enabled controllable transitions set. The main idea is to use the knowledge of the set of enabled controllable transitions together with additional information on the conflicting transitions to accelerate the net identification with respect to the passive identification approaches. In particular, the system assumes that the maximum time that must elapse from the enabling of a transition until it fires is known and that it is possible to detect if the system is entered in a cyclic behavior. Using this additional information, it is possible to determine set of constraints to represent sequences that are not accepted by the system. Such constraints can be used to improve the net identification. [edited by author]XI n.s