Journal: Expert Systems with Applications

Abbreviation

Expert syst. appl.

Publisher

Elsevier

Journal Volumes

ISSN

0957-4174

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Publications 1 - 10 of 25
  • Sensor placement determination for range-difference positioning using evolutionary multi-objective optimization
    Item type: Journal Article
    Domingo-Perez, Francisco; Lazaro-Galilea, José L.; Wieser, Andreas; et al. (2016)
    Expert Systems with Applications
  • Machine learning for predicting used car resale prices using granular vehicle equipment information
    Item type: Journal Article
    Bergmann, Svenja; Feuerriegel, Stefan (2025)
    Expert Systems with Applications
    Millions of used cars are sold every year, and, hence, accurate estimates of resale values are needed. One reason is that under- and overestimating the value of used cars at the end of their leasing period is directly related to the financial return of car retailers. However, in previous literature, granular vehicle equipment information (e.g., alloy rims, park assistance systems) as a predictor has been largely overlooked. In order to address this research gap, we assess the predictive power of granular information about vehicle equipment when forecasting the resale value of used cars. To achieve this, we first preprocess 50,000 equipment options through a tailored, end-to-end automated procedure. Subsequently, we employ machine learning using a comprehensive real-world dataset comprising 92,239 sales where each vehicle is characterized by a unique equipment configuration. We find that including equipment information improves the prediction performance (i.e., mean absolute error) by 3.27% and at a statistically significant level. Altogether, car retailers can use information about the specific vehicle configuration to more accurately predict prices of used vehicles, and, as an implication for businesses, this may eventually increase returns.
  • Ensemble of surrogates in black-box-type engineering optimization: Recent advances and applications
    Item type: Review Article
    Chen, Hao; Zhang, Zhilang; Li, Weikun; et al. (2024)
    Expert Systems with Applications
    Due to its high efficiency, surrogate models have been extensively used in black -box -type engineering optimization problems. However, due to the nature of black -box functions, it is difficult to decide which surrogate model is the best for a given problem. In addition, it is difficult to accurately estimate the prediction accuracy of a surrogate on an expensive black -box function in advance. Because we cannot use many real expensive samples to test the prediction accuracy of different surrogates due to the limited computational resources. Ensembles of surrogates fully exploit the advantages of each surrogate model in predicting a given implicit function and have been shown to be effective in improving the robustness of surrogates. The use of ensembles of surrogates to the engineering design optimization has recently attracted huge attention. This work reviews the state-of-the-art ensembles of surrogates and their applications in black -box -type engineering optimization problems. We first familiarize readers with ensembles of surrogates, and give a description of different types of ensembles of surrogates. Further, the recent advances in using ensembles of surrogates for black -box -type engineering optimization are reviewed, and the applications in various optimization needs are highlighted. Finally, we identify the research gaps and most important trends. This review aims to serve as a comprehensive guide that enhances the accessibility of ensembles of surrogates, which can act as an insurance policy in predicting expensive black -box functions and are increasingly used in the field of black -box -type engineering optimization.
  • Smart rebar progress monitoring using 3D point cloud model
    Item type: Journal Article
    Qureshi, Abdul Hannan; Alaloul, Wesam Salah; Murtiyoso, Arnadi; et al. (2024)
    Expert Systems with Applications
    Current rebar inspection practices in construction projects are manual in nature; hence, they are time-consuming. Rebar drawings are technical; therefore, the accuracy of inspection outcomes depends on the inspector's (supervisor or engineer) experience. In contrast, with the emergence of the fourth industrial revolution, the construction industry has also adopted a few technological solutions for rebar progress monitoring. However, most of the available solutions are detecting qualitative aspects, and quantitative aspects are not much covered. Moreover, available studies have focused on specific rebar monitoring parameters and have not given a complete solution. This study aims to develop an automated smart rebar evaluation model (SREM) giving a complete solution for evaluating on-site rebar quality (rebar spacing, rebar diameter) and quantity (number of rebars, rebar length). The devised system will be able to interpret the 3D point cloud model generated via photogrammetry for rebar quality and quantity parameters easily accessible by project stakeholders. The SREM has the capability to evaluate on-site rebar with the help of images with an accuracy of more than 99% for rebar length and the number of rebars, 97% for rebar spacing, and an accuracy of more than 90% for rebar diameter. The SREM offers an economical, effective, and efficient solution with minimum human involvement. It provides site safety, especially for high-rise buildings, remote progress monitoring to far projects, and minimizes CO2 emission by controlling unnecessary site visits. Most importantly, it is an IoT-supported model.
  • Robust enhanced index tracking problem with mixture of distributions
    Item type: Journal Article
    Kang, Zhilin; Yao, Haixiang; Li, Xingyi; et al. (2022)
    Expert Systems with Applications
    Enhanced index tracking (EIT) is a popular form of investment strategy, which seeks to create a portfolio to generate excess return relative to a given benchmark index without purchasing all the index components. In this paper we develop an EIT model with mixture distribution under a lower partial moment (LPM) framework. Furthermore, we formulate the EIT problem as a robust and tractable model by integrating uncertain information on the proportions of Gaussian mixture distribution specified by the ϕ-divergence. By applying Lagrange duality theory, we demonstrate that the EIT problem on the basis of the worst-case LPMs of degree 1 and 2 can be transformed into a mathematically tractable optimization problem. Out-of-sample experiments using the FTSE100 and S&P500 data sets show that the portfolios based on our proposed model exhibit better performance than those from the benchmark index in most cases.
  • Communicating delays and adjusted disposition timetables
    Item type: Journal Article
    Leng, Nuannuan; Corman, Francesco (2022)
    Expert Systems with Applications
    When delays occur in public transport systems, operating companies update the planned timetable to the actual disturbed conditions. Passengers face an adjusted service; the corrective actions (a disposition timetable) are disseminated by the operating companies as service (or route) information. Different from the common assumption of complete, correct and immediate information, we consider the case when passengers have incomplete information, and study the resulting effects on passengers’ route choice and delays in their beliefs and in reality. We model information and passengers’ route choices on a public transport network based on a Belief-Desire-Intention model. On an illustrative realistic test case, we evaluate different types of information in their completeness (perfect or on-route) and correctness (passengers extrapolating future unknown operation times by schedule-extension or delay-propagation). We analyse the feasibility (a promised transfer not taking place) and optimality (i.e. other choices would have led to a shorter travel time in reality). In case of perfect information, the delays are the least, but the effects of passengers’ expectations about future operations the highest, compared to on-route information. Schedule-extension causes fewer passengers’ delays when information is available only for a short time ahead; otherwise, delay-propagation is better.
  • Modeling spatial layout for scene image understanding via a novel multiscale sum-product network
    Item type: Journal Article
    Yuan, Zehuan; Wang, Hao; Wang, Limin; et al. (2016)
    Expert Systems with Applications
  • Modeling train timetables as images
    Item type: Journal Article
    Huang, Ping; Li, Zhongcan; Wen, Chao; et al. (2021)
    Expert Systems with Applications
    As a vital component of train operational control, train delay propagation pattern discovery is critically important for both railway controllers and passengers. In this study, we present a carefully designed deep learning model, called FCF-Net, that comprises fully connected neural networks (FCNN) and convolutional neural networks (CNN) for train delay propagation pattern recognition in railway systems. FCF-Net first uses a CNN component that handles train timetables as images to capture interactions of train events and an FCNN component to capture the influence of non-operational features separately; then it uses another FCNN component to combinedly learn the dependencies between operational and non-operational features. In addition, considering the imbalance of train delay data, a cost-sensitive technique that assigns different misclassification costs for different class was used to better deal with the imbalanced data. The main goal of the FCF-Net is to realize efficient and accurate train delay propagation pattern recognition by mining potential knowledge from train operation data. The predictive and computational performance of the model was tested and evaluated on data from two high-speed railway lines with different operational features in China. The results show that FCF-Net, once trained with sufficient data, outperforms conventional deep learning with common loss and other state-of-the-art deep learning models for train delay propagation pattern recognition, indicating its capability in knowledge discovery from train operation data. In addition, the computational results show that FCF-Net exhibits more efficient training process than existing state-of-the-art deep learning models.
  • BF-QC: Belief functions on quantum circuits
    Item type: Journal Article
    Zhou, Qianli; Tian, Guojing; Deng, Yong (2023)
    Expert Systems with Applications
    Dempster–Shafer Theory (DST) of belief function is a basic theory of artificial intelligence, which can represent the underlying knowledge more reasonably than Probability Theory (ProbT). Because of the computation complexity exploding exponentially with the increasing number of elements, the practical application scenarios of DST are limited. In this paper, we encode Basic Belief Assignments (BBA) into quantum superposition states and propose the implementation and operation methods of BBA on quantum circuits. We decrease the computation complexity of the Matrix Evolution on BBA (MEoB) on quantum circuits. Based on the MEoB, we realize the quantum belief functions’ implementation, the similarity measurements of BBAs, evidence Combination Rules (CR), and probability transformation on quantum circuits.
  • A Time-varying Shockwave Speed Model for Reconstructing Trajectories on Freeways using Lagrangian and Eulerian Observations
    Item type: Journal Article
    Zhang, Yifan; Kouvelas, Anastasios; Makridis, Michail (2024)
    Expert Systems with Applications
    Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.
Publications 1 - 10 of 25