796 research outputs found
Investigation of automated task learning, decomposition and scheduling
The details and results of research conducted in the application of neural networks to task planning and decomposition are presented. Task planning and decomposition are operations that humans perform in a reasonably efficient manner. Without the use of good heuristics and usually much human interaction, automatic planners and decomposers generally do not perform well due to the intractable nature of the problems under consideration. The human-like performance of neural networks has shown promise for generating acceptable solutions to intractable problems such as planning and decomposition. This was the primary reasoning behind attempting the study. The basis for the work is the use of state machines to model tasks. State machine models provide a useful means for examining the structure of tasks since many formal techniques have been developed for their analysis and synthesis. It is the approach to integrate the strong algebraic foundations of state machines with the heretofore trial-and-error approach to neural network synthesis
The design of a neural network compiler
Computer simulation is a flexible and economical way for
rapid prototyping and concept evaluation with Neural
Network (NN) models. Increasing research on NNs has led
to the development of several simulation programs. Not
all simulations have the same scope. Some simulations
allow only a fixed network model and some are more
general. Designing a simulation program for general
purpose NN models has become a current trend nowadays
because of its flexibility and efficiency. A proper
programming language specifically for NN models is
preferred since the existing high-level languages such as
C are for NN designers from a strong computer background.
The program translations for NN languages come from
combinations which are either interpreter and/or
compiler. There are also various styles of programming
languages such as a procedural, functional, descriptive
and object-oriented.
The main focus of this thesis is to study the
feasibility of using a compiler method for the
development of a general-purpose simulator - NEUCOMP that
compiles the program written as a list of mathematical
specifications of the particular NN model and translates
it into a chosen target program. The language supported
by NEUCOMP is based on a procedural style. Information
regarding the list of mathematical statements required by
the NN models are written in the program. The
mathematical statements used are represented by scalar,
vector and matrix assignments. NEUCOMP translates these
expressions into actual program loops.
NEUCOMP enables compilation of a simulation program
written in the NEUCOMP language for any NN model,
contains graphical facilities such as portraying the NN
architecture and displaying a graph of the result during
training and finally to have a program that can run on a
parallel shared memory multi-processor system
Automated observation scheduling for the VLT
It is becoming increasingly evident that, in order to optimize the observing efficiency of large telescopes, some changes will be required in the way observations are planned and executed. Not all observing programs require the presence of the astronomer at the telescope: for those programs which permit service observing it is possible to better match planned observations to conditions at the telescope. This concept of flexible scheduling has been proposed for the VLT: based on current and predicted environmental and instrumental observations which make the most efficient possible use of valuable time. A similar kind of observation scheduling is already necessary for some space observatories, such as Hubble Space Telescope (HST). Space Telescope Science Institute is presently developing scheduling tools for HST, based on the use of artificial intelligence software development techniques. These tools could be readily adapted for ground-based telescope scheduling since they address many of the same issues. The concept are described on which the HST tools are based, their implementation, and what would be required to adapt them for use with the VLT and other ground-based observatories
Hierarchical workflow management system for life science applications
In modern laboratories, an increasing number of automated stations and instruments are applied as standalone automated systems such as biological high throughput screening systems, chemical parallel reactors etc. At the same time, the mobile robot transportation solution becomes popular with the development of robotic technologies. In this dissertation, a new superordinate control system, called hierarchical workflow management system (HWMS) is presented to manage and to handle both, automated laboratory systems and logistics systems.In modernen Labors werden immer mehr automatisierte Stationen und Instrumente als eigenständige automatisierte Systeme eingesetzt, wie beispielsweise biologische High-Throughput-Screening-Systeme und chemische Parallelreaktoren. Mit der Entwicklung der Robotertechnologien wird gleichzeitig die mobile Robotertransportlösung populär. In der vorliegenden Arbeit wurde ein hierarchisches Verwaltungssystem für Abeitsablauf, welches auch als HWMS bekannt ist, entwickelt. Das neue übergeordnete Kontrollsystem kann sowohl automatisierte Laborsysteme als auch Logistiksysteme verwalten und behandeln
PCLIPS
CLIPS is an expert system, created specifically to allow rapid implementation of an expert system. CLIPS is written in C, and thus needs a very small amount of memory to run. Parallel CLIPS (PCLIPS) is an extension to CLIPS which is intended to be used in situations where a group of expert systems are expected to run simultaneously and occasionally communicate with each other on an integrated network. PCLIPS is a coarse-grained data distribution system. Its main goal is to take information in one knowledge base and distribute it to other knowledge bases so that all the executing expert systems are able to use that knowledge to solve their disparate problems
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications,
including image classification, game playing, protein structure analysis,
language translation, and content generation. Despite these powerful
applications, there are still many tasks in our daily life that are rather
simple to humans but pose great challenges to AI. These include image and
language understanding, few-shot learning, abstract concepts, and low-energy
cost computing. Thus, learning from the brain is still a promising way that can
shed light on the development of next-generation AI. The brain is arguably the
only known intelligent machine in the universe, which is the product of
evolution for animals surviving in the natural environment. At the behavior
level, psychology and cognitive sciences have demonstrated that human and
animal brains can execute very intelligent high-level cognitive functions. At
the structure level, cognitive and computational neurosciences have unveiled
that the brain has extremely complicated but elegant network forms to support
its functions. Over years, people are gathering knowledge about the structure
and functions of the brain, and this process is accelerating recently along
with the initiation of giant brain projects worldwide. Here, we argue that the
general principles of brain functions are the most valuable things to inspire
the development of AI. These general principles are the standard rules of the
brain extracting, representing, manipulating, and retrieving information, and
here we call them the first principles of the brain. This paper collects six
such first principles. They are attractor network, criticality, random network,
sparse coding, relational memory, and perceptual learning. On each topic, we
review its biological background, fundamental property, potential application
to AI, and future development.Comment: 59 pages, 5 figures, review articl
Path planning for general mazes
Path planning is used in, but not limited to robotics, telemetry, aerospace, and medical applications. The goal of the path planning is to identify a route from an origination point to a destination point while avoiding obstacles. This path might not always be the shortest in distance as time, terrain, speed limits, and many other factors can affect the optimality of the path. However, in this thesis, the length, computational time, and the smoothness of the path are the only constraints that will be considered with the length of the path being the most important. There are a variety of algorithms that can be used for path planning but Ant Colony Optimization (ACO), Neural Network, and A* will be the only algorithms explored in this thesis. The problem of solving general mazes has been greatly researched, but the contributions of this thesis extended Ant Colony Optimization to path planning for mazes, created a new landscape for the Neural Network to use, and added a bird\u27s eye view to the A* Algorithm. The Ant Colony Optimization that was used in this thesis was able to discover a path to the goal, but it was jagged and required a larger computational time compared to the Neural Network and A* algorithm discussed in this thesis. The Hopfield-type neural network used in this thesis propagated energy to create a landscape and used gradient decent to find the shortest path in terms of distance, but this thesis modified how the landscape was created to prevent the neural network from getting trapped in local minimas. The last contribution was applying a bird\u27s eye view to the A* algorithm to learn more about the environment which helped to create shorter and smoother paths
Internet of things (IoT) based adaptive energy management system for smart homes
PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the
development of advanced wireless sensors and communication networks on the smart grid
infrastructure would be essential for energy efficiency systems. It makes deployment of a
smart home concept easy and realistic. The smart home concept allows residents to control,
monitor and manage their energy consumption with minimal wastage. The scheduling of
energy usage enables forecasting techniques to be essential for smart homes. This thesis
presents a self-learning home management system based on machine learning techniques
and energy management system for smart homes.
Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed
self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and
smart energy theft system to enhance the capabilities of the self-learning home management
system. These functions were developed and implemented through the use of computational
and machine learning technologies. In order to validate the proposed system, real-time power
consumption data were collected from a Singapore smart home and a realistic experimental
case study was carried out. The case study had proven that the developed system performing
well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to
traditional smart home models.
Forecasting systems for the electricity market generation have become one of the foremost
research topics in the power industry. It is essential to have a forecasting system that can
accurately predict electricity generation for planning and operation in the electricity market.
This thesis also proposed a novel system called multi prediction system and it is developed
based on long short term memory and gated recurrent unit models. This proposed system is
able to predict the electricity market generation with high accuracy.
Multi Prediction System is based on four stages which include a data collecting and
pre-processing module, a multi-input feature model, multi forecast model and mean absolute
percentage error. The data collecting and pre-processing module preprocess the real-time
data using a window method. Multi-input feature model uses single input feeding method,
double input feeding method and multiple feeding method for features input to the multi
forecast model. Multi forecast model integrates long short term memory and gated recurrent
unit variations such as regression model, regression with time steps model, memory between
batches model and stacked model to predict the future generation of electricity. The mean
absolute percentage error calculation was utilized to evaluate the accuracy of the prediction.
The proposed system achieved high accuracy results to demonstrate its performance
- …