320 research outputs found
Research on Standardization of Train Dispatcher Operations
Based on the actual operation of daily train reception and departure at stations, the author conducted research on the standardized
operation evaluation of train dispatchers. Through the research of the project, a 1:1 station simulation training system is simulated, using
technologies such as eye movement recognition, limb motion recognition, and speech recognition to record and analyze the “eye, finger, and
mouth call” operation information of train dispatchers. Combined with the operation analysis of the CTC system, the standardization of train
dispatchers’ work is achieved
Evolving developmental, recurrent and convolutional neural networks for deliberate motion planning in sparse reward tasks
Motion planning algorithms have seen a diverse set of approaches in a variety of disciplines. In the domain of artificial evolutionary systems, motion planning has been included in models to achieve sophisticated deliberate behaviours. These algorithms rely on fixed rules or little evolutionary influence which compels behaviours to conform within those specific policies, rather than allowing the model to establish its own specialised behaviour. In order to further these models, the constraints imposed by planning algorithms must be removed to grant greater evolutionary control over behaviours. That is the focus of this thesis.
An examination of prevailing neuroevolution methods led to the use of two distinct approaches, NEAT and HyperNEAT. Both were used to gain an understanding of the components necessary to create neuroevolution planning. The findings accumulated in the formation of a novel convolutional neural network architecture with a recurrent convolution process. The architecture’s goal was to iteratively disperse local activations to greater regions of the feature space. Experimentation showed significantly improved robustness over contemporary neuroevolution techniques as well as an efficiency increase over a static rule set. Greater evolutionary responsibility is given to the model with multiple network combinations; all of which continually demonstrated the necessary behaviours. In comparison, these behaviours were shown to be difficult to achieve in a state-of-the-art deep convolutional network.
Finally, the unique use of recurrent convolution is relocated to a larger convolutional architecture on an established benchmarking platform. Performance improvements are seen on a number of domains which illustrates that this recurrent mechanism can be exploited in alternative areas outside of planning. By presenting a viable neuroevolution method for motion planning a potential emerges for further systems to adopt and examine the capability of this work in prospective domains, as well as further avenues of experimentation in convolutional architectures
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning
In e-commerce markets, on time delivery is of great importance to customer
satisfaction. In this paper, we present a Deep Reinforcement Learning (DRL)
approach for deciding how and when orders should be batched and picked in a
warehouse to minimize the number of tardy orders. In particular, the technique
facilitates making decisions on whether an order should be picked individually
(pick-by-order) or picked in a batch with other orders (pick-by-batch), and if
so with which other orders. We approach the problem by formulating it as a
semi-Markov decision process and develop a vector-based state representation
that includes the characteristics of the warehouse system. This allows us to
create a deep reinforcement learning solution that learns a strategy by
interacting with the environment and solve the problem with a proximal policy
optimization algorithm. We evaluate the performance of the proposed DRL
approach by comparing it with several batching and sequencing heuristics in
different problem settings. The results show that the DRL approach is able to
develop a strategy that produces consistent, good solutions and performs better
than the proposed heuristics.Comment: Preprin
A literature review of Artificial Intelligence applications in railway systems
Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges
Evolving Robust, Deliberate Motion Planning With a Shallow Convolutional Neural Network
Deep Convolutional Neural Networks (ConvNets) have seen great success on machine learning tasks in recent years but have shown difficulty with tasks that require long-term deliberative planning. Whereas, purpose-built hybrid network architectures have been able to solve increasingly challenging deliberate tasks in two-dimensional and three-dimensional artificial worlds. Starting from a purpose-built network and transitioning to a general architecture, like a deep ConvNet, may retain long-term deliberative planning while allowing greater flexibility in the task domain. This paper employs a standard genetic algorithm (GA) to train the weights of a ConvNet with a recurrent 3x3 filter to produce robust and deliberative motion planning. This technique resulted in an average of 98.97% completion over 10,000 runs in the most difficult deliberate task. This demonstrates that a shallow ConvNet with recurrent connections is capable of producing deliberate and robust motion planning. Further, the evolved ConvNet exhibits superior motion planning in the most challenging environments, when compared to the previous taskspecific motion-planning network
A Search For Principles of Basal Ganglia Function
The basal ganglia are a group of subcortical nuclei that contain about 100
million neurons in humans. Different modes of basal ganglia dysfunction lead to
Parkinson's disease and Huntington's disease, which have debilitating motor and
cognitive symptoms. However, despite intensive study, both the internal computational
mechanisms of the basal ganglia, and their contribution to normal brain
function, have been elusive. The goal of this thesis is to identify basic principles that
underlie basal ganglia function, with a focus on signal representation, computation,
dynamics, and plasticity.
This process begins with a review of two current hypotheses of normal basal
ganglia function, one being that they automatically select actions on the basis of
past reinforcement, and the other that they compress cortical signals that tend to
occur in conjunction with reinforcement. It is argued that a wide range of experimental
data are consistent with these mechanisms operating in series, and that in
this configuration, compression makes selection practical in natural environments.
Although experimental work is outside the present scope, an experimental means
of testing this proposal in the future is suggested.
The remainder of the thesis builds on Eliasmith & Anderson's Neural Engineering
Framework (NEF), which provides an integrated theoretical account of computation,
representation, and dynamics in large neural circuits. The NEF provides
considerable insight into basal ganglia function, but its explanatory power is potentially
limited by two assumptions that the basal ganglia violate. First, like most
large-network models, the NEF assumes that neurons integrate multiple synaptic
inputs in a linear manner. However, synaptic integration in the basal ganglia is
nonlinear in several respects. Three modes of nonlinearity are examined, including
nonlinear interactions between dendritic branches, nonlinear integration within terminal
branches, and nonlinear conductance-current relationships. The first mode
is shown to affect neuron tuning. The other two modes are shown to enable alternative
computational mechanisms that facilitate learning, and make computation
more flexible, respectively.
Secondly, while the NEF assumes that the feedforward dynamics of individual
neurons are dominated by the dynamics of post-synaptic current, many basal
ganglia neurons also exhibit prominent spike-generation dynamics, including adaptation,
bursting, and hysterses. Of these, it is shown that the NEF theory of
network dynamics applies fairly directly to certain cases of firing-rate adaptation.
However, more complex dynamics, including nonlinear dynamics that are diverse
across a population, can be described using the NEF equations for representation.
In particular, a neuron's response can be characterized in terms of a more complex
function that extends over both present and past inputs. It is therefore straightforward
to apply NEF methods to interpret the effects of complex cell dynamics at
the network level.
The role of spike timing in basal ganglia function is also examined. Although
the basal ganglia have been interpreted in the past to perform computations on
the basis of mean firing rates (over windows of tens or hundreds of milliseconds)
it has recently become clear that patterns of spikes on finer timescales are also
functionally relevant. Past work has shown that precise spike times in sensory
systems contain stimulus-related information, but there has been little study of how post-synaptic neurons might use this information. It is shown that essentially any neuron can use this information to perform flexible computations, and that these
computations do not require spike timing that is very precise. As a consequence,
irregular and highly-variable firing patterns can drive behaviour with which they
have no detectable correlation.
Most of the projection neurons in the basal ganglia are inhibitory, and the effect
of one nucleus on another is classically interpreted as subtractive or divisive. Theoretically, very flexible computations can be performed within a projection if each
presynaptic neuron can both excite and inhibit its targets, but this is hardly ever
the case physiologically. However, it is shown here that equivalent computational flexibility is supported by inhibitory projections in the basal ganglia, as a simple consequence of inhibitory collaterals in the target nuclei.
Finally, the relationship between population coding and synaptic plasticity is
discussed. It is shown that Hebbian plasticity, in conjunction with lateral connections, determines both the dimension of the population code and the tuning of
neuron responses within the coded space. These results permit a straightforward
interpretation of the effects of synaptic plasticity on information processing at the
network level.
Together with the NEF, these new results provide a rich set of theoretical principles
through which the dominant physiological factors that affect basal ganglia
function can be more clearly understood
- …