3,972 research outputs found
Forecasting bus passenger flows by using a clustering-based support vector regression approach
As a significant component of the intelligent transportation system, forecasting bus passenger
flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains
challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to
varied destinations and departure times. For this reason, a novel forecasting model named as affinity
propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear
simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based
intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each
cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally,
the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model
is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate
that the proposed model performs better than other peer models in terms of absolute percentage error and
mean absolute percentage error. It is recommended that the deterministic clustering technique with stable
cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF
The future evolution of technological systems dedicated to improve energy
efficiency will strongly depend on effective and reliable Energy Storage
Systems, as key components for Smart Grids, microgrids and electric mobility.
Besides possible improvements in chemical materials and cells design, the
Battery Management System is the most important electronic device that improves
the reliability of a battery pack. In fact, a precise State of Charge (SoC)
estimation allows the energy flows controller to exploit better the full
capacity of each cell. In this paper, we propose an alternative definition for
the SoC, explaining the rationales by a mechanical analogy. We introduce a
novel cell model, conceived as a series of three electric dipoles, together
with a procedure for parameters estimation relying only on voltage measures and
a given current profile. The three dipoles represent the quasi-stationary, the
dynamics and the istantaneous components of voltage measures. An Extended
Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we
propose a multi-cell EKF system based on a round-robin approach to allow the
same processing block to keep track of many cells at the same time. Performance
tests with a prototype battery pack composed by 18 A123 cells connected in
series show encouraging results.Comment: 8 page, 12 figures, 1 tabl
Hybrid model using logit and nonparametric methods for predicting micro-entity failure
Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper
by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to
detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods
(Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as
either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and
Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method
implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic
variables complement financial ratios for bankruptcy prediction
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Literature Review on Big Data Analytics Methods
Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed
A theoretical framework for supervised learning from regions
Supervised learning is investigated, when the data are represented not only by labeled points but also labeled regions of the input space. In the limit case, such
regions degenerate to single points and the proposed approach changes back to the classical learning context. The adopted framework entails the minimization
of a functional obtained by introducing a loss function that involves such regions. An additive regularization term is expressed via differential operators that model
the smoothness properties of the desired input/output relationship. Representer
theorems are given, proving that the optimization problem associated to learning
from labeled regions has a unique solution, which takes on the form of a linear
combination of kernel functions determined by the differential operators together
with the regions themselves. As a relevant situation, the case of regions given
by multi-dimensional intervals (i.e., “boxes”) is investigated, which models prior
knowledge expressed by logical propositions
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