Search results for "artificial intelligence"
showing 10 items of 6122 documents
A hybrid genetic algorithm for the resource-constrained project scheduling problem
2008
Abstract In this paper we propose a Hybrid Genetic Algorithm (HGA) for the Resource-Constrained Project Scheduling Problem (RCPSP). HGA introduces several changes in the GA paradigm: a crossover operator specific for the RCPSP; a local improvement operator that is applied to all generated schedules; a new way to select the parents to be combined; and a two-phase strategy by which the second phase re-starts the evolution from a neighbour’s population of the best schedule found in the first phase. The computational results show that HGA is a fast and high quality algorithm that outperforms all state-of-the-art algorithms for the RCPSP known by the authors of this paper for the instance sets j…
Adaptive control of a class of strict-feedback time-varying nonlinear systems with unknown control coefficients
2018
Abstract In this paper, robust adaptive control of a class of strict-feedback nonlinear systems with unknown control directions is investigated. A novel Nussbaum-type function is developed and a key theorem is drawn which involves quantifying the addition of multiple Nussbaum functions with different control directions in a single inequality. Global stability of the closed-loop system and asymptotic stabilization of system output are proved. A simulation example is given to illustrate the effectiveness of the proposed control scheme.
Management of uncertain pairwise comparisons in AHP through probabilistic concepts
2019
Abstract Fast and judicious decision-making is paramount for the success of many activities and processes. However, various degrees of difficulty may affect the achievement of effective and optimal solutions. Decisions should ideally meet the best trade-off among as many of the involved factors as possible, especially in the case of complex problems. Substantial cognitive and technical skills are indispensable, while not always sufficient, to carry out optimal evaluations. One of the most common causes of wrong decisions derives from uncertainty and vagueness in making forecasts or attributing judgments. The literature shows numerous efforts towards the optimization and modeling of uncertai…
Simulation of parallel mechanisms for motion cueing generation in vehicle simulators using AM-FM bi-modulated signals
2018
Abstract The use of robotic motion platforms in vehicle simulators is relatively common. However, the process of testing and tuning the so-called washout algorithms, used for motion cueing generation in motion-based vehicle simulators, is complex. This process can be reduced in cost, simplified, improved, shortened and performed safer if virtual motion platforms are used instead of real devices. This paper deals with identifying a method to perform a fast but reliable simulation of parallel mechanisms to be used for motion cueing generation. The method relies on the use of Laplacian polynomial transfer function models by means of using AM-FM bi-modulated signals as reference inputs to achie…
Motion Cueing Algorithms: A Review
2017
Robotic motion platforms are commonly used in motion-based vehicle simulation. However, the reproduction of realistic accelerations within a reduced workspace is a major challenge. Thus, high-level control strategies commonly referred to as motion cueing algorithms (MCA) are required to convert the simulated vehicle physical state into actual motion for the motion platform. This paper reviews the most important strategies for the generation of motion cues in simulators, listing the advantages and drawbacks of the different solutions. The motion cueing problem, a general scheme and the four most common approaches – classical washout, adaptive washout, optimal control and model predictive con…
A Learning Automata Based Solution to Service Selection in Stochastic Environments
2010
Published version of a paper published in the book: Trends in Applied Intelligent Systems. Also available on SpringerLink: http://dx.doi.org/10.1007/978-3-642-13033-5_22 With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based …
UCS: Ultimate course search
2016
International audience; In this system prototype demonstration we present, Ultimate Course Search (UCS), a learning tool developed to provide students ways to efficiently search electronic educational materials. UCS integrates slides, lecture videos and textbooks into a single platform. The keywords extracted from the textbooks and the slides are the basis of the indexing scheme. For the videos, UCS relies on slide transitions and metadata to establish the correspondence between slides and video segments. The video segmentation is based on the slides being presented using the meta-data provided by the video recording software and image processing techniques.
A real-time webcam based Eye Ball Tracking System using MATLAB
2015
Eye Ball Tracking System is a device which is intended to assist patients that cannot perform any voluntary tasks related to daily life. Patients who only can control their eyes can still communicate with the real-world using the assistive devices like one proposed in this paper. This device provides a human computer interface in order to take decisions based on their eye movement. A real time data stream is captured via webcam that transfers data serially to MATLAB. Then a sequential image processing scheme segments the iris of the eye and calculates the centroid, thereby generating control signal with the help of a reference axis. The control signals are then used to manipulate the positi…
On Obtaining Classification Confidence, Ranked Predictions and AUC with Tsetlin Machines
2020
Tsetlin machines (TMs) are a promising approach to machine learning that uses Tsetlin Automata to produce patterns in propositional logic, leading to binary (hard) classifications. In many applications, however, one needs to know the confidence of classifications, e.g. to facilitate risk management. In this paper, we propose a novel scheme for measuring TM confidence based on the logistic function, calculated from the propositional logic patterns that match the input. We then use this scheme to trade off precision against recall, producing area under receiver operating characteristic curves (AUC) for TMs. Empirically, using four real-world datasets, we show that AUC is a more sensitive meas…
Use of balanced detection in single-pixel imaging
2016
We introduce balanced detection in combination with simultaneous complementary illumination in a single-pixel architecture. With this novel detection scheme we are able to recover a real-time video stream in presence of ambient light.