2,615 research outputs found
Vertical transportation in buildings
Nowadays, the building industry and its associated technologies are
experiencing a period of rapid growth, which requires an equivalent growth regarding
technologies in the field of vertical transportation. Therefore, the installation of
synchronised elevator groups in modern buildings is a common practice in order to
govern the dispatching, allocation and movement of the cars shaping the group. So,
elevator control and management has become a major field of application for Artificial
Intelligence approaches. Methodologies such as fuzzy logic, artificial neural networks,
genetic algorithms, ant colonies, or multiagent systems are being successfully proposed
in the scientific literature, and are being adopted by the leading elevator companies as
elements that differentiate them from their competitors. In this sense, the most relevant
companies are adopting strategies based on the protection of their discoveries and
inventions as registered patents in different countries throughout the world. This paper
presents a comprehensive state of the art of the most relevant recent patents on
computer science applied to vertical transportationConsejería de Innovación, Ciencia y Empresa, Junta de Andalucía P07-TEP-02832, Spain
Hierarchical D ∗ algorithm with materialization of costs for robot path planning
In this paper a new hierarchical extension of the D
∗ algorithm for robot path planning is introduced. The hierarchical D
∗
algorithm uses a down-top strategy and a set of precalculated paths (materialization of path costs) in order to improve performance.
This on-line path planning algorithm allows optimality and specially lower computational time. H-Graphs (hierarchical graphs)
are modified and adapted to support on-line path planning with materialization of costs and multiple hierarchical levels. Traditional
on-line robot path planning focused in horizontal spaces is also extended to vertical and interbuilding spaces. Some experimental
results are showed and compared to other path planning algorithms
Multi crteria decision making and its applications : a literature review
This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
A Hierarchical Extension of the D ∗ Algorithm
In this paper a contribution to the practice of path planning using a new hierarchical
extension of the D
∗ algorithm is introduced. A hierarchical graph is stratified into several abstraction
levels and used to model environments for path planning. The hierarchical D∗ algorithm uses a downtop
strategy and a set of pre-calculated trajectories in order to improve performance. This allows
optimality and specially lower computational time. It is experimentally proved how hierarchical
search algorithms and on-line path planning algorithms based on topological abstractions can be
combined successfully
The Role of Machine Learning in Improved Functionality of Lower Limb Prostheses
Lower-limb amputations can cause a plethora of obstacles that lead to a lower quality of life. Implementing machine learning techniques means advanced prosthetics can contribute to facilitating the lives of those that live with lower-limb amputations. Using the publicly available HuGaDB data set, the current study investigates several classification models (random forest, neural network, and Vowpal Wabbit) to predict the locomotive intentions of individuals using lower-limb prostheses. The results of this study show that the neural network model yielded the highest accuracy, comparable precision, and recall scores to the other models. However, the Vowpal Wabbit model\u27s advantage in speed may allow for other, more practical implementations in practice. These findings provide insight into the advantages of specific classification models over others in predicting the intentions of specific movements during locomotive transitions. These findings present direct comparisons of several machine learning methods, identifying the strengths and weaknesses of each classification model tested
Environmental Technology Applications in the Retrofitting of Residential Buildings
The impact of buildings on the environment is nothing short of devastating. In recent years, much attention has been given to creating an environmentally friendly built environment. Nonetheless, it has been levied on new buildings. Residential buildings make up at least 80% of the built environment, most of which were built before any energy efficiency guidelines or regulations were introduced. Retrofitting existing residential buildings is a key yet neglected priority in effecting the transition to an environmentally friendly, sustainable built environment. It is pivotal to reducing a building’s energy consumption while simultaneously improving indoor environmental quality and minimizing harmful emissions. This Special Issue showcases studies investigating applications of environmental technology that is tailored to enhance the sustainable performance of existing residential buildings. It helps to better understand the innovations that have been taking place in retrofitting residential buildings, as well as highlighting many opportunities for future research in this field
Chaining Test Cases for Reactive System Testing (extended version)
Testing of synchronous reactive systems is challenging because long input
sequences are often needed to drive them into a state at which a desired
feature can be tested. This is particularly problematic in on-target testing,
where a system is tested in its real-life application environment and the time
required for resetting is high. This paper presents an approach to discovering
a test case chain---a single software execution that covers a group of test
goals and minimises overall test execution time. Our technique targets the
scenario in which test goals for the requirements are given as safety
properties. We give conditions for the existence and minimality of a single
test case chain and minimise the number of test chains if a single test chain
is infeasible. We report experimental results with a prototype tool for C code
generated from Simulink models and compare it to state-of-the-art test suite
generators.Comment: extended version of paper published at ICTSS'1
Experimenting with Constraint Programming Techniques in Artificial Intelligence: Automated System Design and Verification of Neural Networks
This thesis focuses on the application of Constraint Satisfaction and Optimization techniques
in two Artificial Intelligence (AI) domains: automated design of elevator systems and
verification of Neural Networks (NNs). The three main areas of interest for my work
are (i) the languages for defining the constraints for the systems, (ii) the algorithms and
encodings that enable solving the problems considered and (iii) the tools that implement
such algorithms.
Given the expressivity of the domain description languages and the availability of effective
tools, several problems in diverse application fields have been solved successfully using
constraint satisfaction techniques. The two case studies herewith presented are no exception,
even if they entail different challenges in the adoption of such techniques. Automated design
of elevator systems not only requires encoding of feasibility (hard) constraints, but should
also take into account design preferences, which can be expressed in terms of cost functions
whose optimal or near-optimal value characterizes “good” design choices versus “poor” ones.
Verification of NNs (and other machine-learned implements) requires solving large-scale
constraint problems which may become the main bottlenecks in the overall verification
procedure.
This thesis proposes some ideas for tackling such challenges, including encoding techniques
for automated design problems and new algorithms for handling the optimization
problems arising from verification of NNs. The proposed algorithms and techniques are evaluated
experimentally by developing tools that are made available to the research community
for further evaluation and improvement
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