7,832 research outputs found
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
A Review on the Application of Natural Computing in Environmental Informatics
Natural computing offers new opportunities to understand, model and analyze
the complexity of the physical and human-created environment. This paper
examines the application of natural computing in environmental informatics, by
investigating related work in this research field. Various nature-inspired
techniques are presented, which have been employed to solve different relevant
problems. Advantages and disadvantages of these techniques are discussed,
together with analysis of how natural computing is generally used in
environmental research.Comment: Proc. of EnviroInfo 201
Identifying Modes of Intent from Driver Behaviors in Dynamic Environments
In light of growing attention of intelligent vehicle systems, we propose
developing a driver model that uses a hybrid system formulation to capture the
intent of the driver. This model hopes to capture human driving behavior in a
way that can be utilized by semi- and fully autonomous systems in heterogeneous
environments. We consider a discrete set of high level goals or intent modes,
that is designed to encompass the decision making process of the human. A
driver model is derived using a dataset of lane changes collected in a
realistic driving simulator, in which the driver actively labels data to give
us insight into her intent. By building the labeled dataset, we are able to
utilize classification tools to build the driver model using features of based
on her perception of the environment, and achieve high accuracy in identifying
driver intent. Multiple algorithms are presented and compared on the dataset,
and a comparison of the varying behaviors between drivers is drawn. Using this
modeling methodology, we present a model that can be used to assess driver
behaviors and to develop human-inspired safety metrics that can be utilized in
intelligent vehicular systems.Comment: Submitted to ITSC 201
Soft computing techniques applied to finance
Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad
Reliable and fast human body tracking under information deficiency
Session MA-4 System Identification and Pattern Recognition IHuman body tracking is useful in applications like medical diagnostic, human computer interface, visual surveillance etc. In most cases, only rough position of the target is needed, and blob tracking can be used. The blob region is located within a searching window, which is shifted and resized in each frame based on previous observations. The observations are the locations of the blob in the frames, and are fed into an estimator for predicting the position and the size of the searching window. However, a blob region is regarded as a noisy observation, and the information provided by the blob observation is deficient for most estimators to work well. In this paper, a reliable and efficient estimation algorithm using wavelet is proposed to track human body under information deficiency. The human body is located roughly within a small searching window using color and motion as heuristics. The location and the size of the searching window are estimated using the proposed wavelet estimation scheme. Experimental results show that human body can be tracked accurately and efficiently using the proposed method. The tracker works well in various conditions like clutter background, and background with distractors.published_or_final_versionThe 2003 IEEE Intelligent Automation Conference (2003å¹´ä¸å›½æ™ºèƒ½è‡ªåŠ¨åŒ–会议), Hong Kong, 15-17 December 2003. In Proceedings of the IEEE Intelligent Automation Conference, 2003, p. 491-49
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