Deep learning powers automotive innovations like self-driving cars.
problem Improving vehicle safety and functionality.
method Deployment of deep learning models in ADAS systems.
result Deep learning enhances performance and functionality of ADAS solutions.
The Brazilian automotive market is concentrated but evolving towards a less monopolistic structure.
problem Understanding the evolution of industrial concentration in the Brazilian automotive market.
method Cluster analysis using k-means technique and estimation of industrial concentration indices (CR ratios, HHI, B, CCI).
result The Brazilian automotive market is currently moderately concentrated and evolving towards a less monopolistic structure.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
problem Supply chain disruptions and volatile demand pose challenges to the UK automotive industry.
method Integrates Bayesian inference with inventory optimization for a two-echelon system subject to stochastic demand and disruptions.
result The integrated approach achieves significant cost reductions and improved resilience during disruptions.
Language models learn automotive complaints, improving defect detection.
problem Detecting technical quality issues in customer feedback.
method Continual pre-training on ODI Complaints data set.
result Models achieve high precision in identifying technical terms.
Study forecasts supply chain disruptions in automotive industry.
problem Operational disruptions in automotive supply chain cause financial losses.
method Constructed dataset of multivariate time series, used Attention Sequence to Sequence Deep Learning architecture.
result Model achieved 0.85 precision and 0.8 recall in QA phase across five plants.
Machine learning predicts and generates appealing car designs.
problem Improving automotive aesthetics to boost sales.
method Combines VAE and GAN with supervised learning; trained on 203 SUVs and 180,000 images.
result Predicts aesthetic scores with 43.5% improvement over baseline; generates appealing designs.
New approach detects small defects on car surfaces with high accuracy.
problem Automated detection of small defects on specular car surfaces.
method Spline smoothing for feature extraction and k-nearest neighbour classifier.
result Near zero misclassification error rate achieved with standard learning classifiers.
The use of machine learning (ML) is on the rise in many sectors of software development, and automotive software development is no different. In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role. In automotive development, safety is…
The paper develops a faster surrogate model for simulators using hybrid methods.
problem The need for faster validation of automotive technologies using simulators.
method Testing classical methods and building hybrid models combining them.
result A hybrid surrogate model outperforms classical methods in multivariate time series prediction.
The overall equipment effectiveness (OEE) is a performance measurement metric widely used. Its calculation provides to the managers the possibility to identify the main losses that reduce the machine effectiveness and then take the necessary decisions in order to improve the situation. However, this calculation is done…
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatica…
SAAD enhances anomaly detection in automotive systems with high accuracy.
problem Anomaly detection in automotive systems.
method Integrates statistical techniques with machine learning.
result SAAD achieves 88.3% accuracy and 0.921 F1 score.
A new method uses active learning to monitor industrial processes more accurately.
problem Classifying process states (IC, OC) with limited labeled data.
method Stream-based active learning for partially hidden Markov models.
result Improved dynamic recognition of process states, especially unseen classes.
Efficient model for foggy scene understanding in vehicles.
problem Challenging scene understanding and segmentation under foggy conditions.
method Domain adaptation and illumination-invariant image transformation.
result Outperforms state-of-the-art models in foggy scene understanding.
Build-to-order (BTO) supply chains have become common-place in industries such as electronics, automotive and fashion. They enable building products based on individual requirements with a short lead time and minimum inventory and production costs. Due to their nature, they differ significantly from traditional supply …
Risk hedging can reduce operational costs by adjusting prices and production levels in response to asset price movements.
problem How risk hedging impacts operational decisions in response to asset price movements.
method Developed and solved a risk-management model integrating risk hedging into a price-setting newsvendor problem.
result Hedging generally reduces optimal price and VPQ, but may increase VPQ under certain conditions.
New framework for 3D spatial topology enumeration and identification.
problem Efficient navigation through complex engineering system topologies.
method Mathematical spatial graph theory to represent, enumerate, and identify unique topological classes.
result Identification of distinctive 3D topological classes for engineering systems.
This research tackles unsupervised topic extraction in noisy social media data.
problem Capturing customer insights from social media data is challenging due to noise and heterogeneity.
method The research presents three nonparametric approaches based on the Variational Autoencoder framework: Embedded Dirichlet Process, Embedded Hierarchical Dirichlet Process, and time-aware Dynamic Embedded Dirichlet Process.
result The models achieve equal to better performance than state-of-the-art methods in topic extraction from noisy social media data.
Fisheye cameras are commonly employed for obtaining a large field of view in surveillance, augmented reality and in particular automotive applications. In spite of their prevalence, there are few public datasets for detailed evaluation of computer vision algorithms on fisheye images. We release the first extensive fish…
Proposes a framework to incorporate global sensitivity into local surrogate models.
problem Narrowing focus to local scale in surrogate modeling leads to re-learning global trends.
method Integrates global sensitivity analysis into local surrogate models through input warping.
result Local models become equally sensitive to all input directions, focusing on local dynamics.
Annotating automotive radar data is a difficult task. This article presents an automated way of acquiring data labels which uses a highly accurate and portable global navigation satellite system (GNSS). The proposed system is discussed besides a revision of other label acquisitions techniques and a problem description …
Graded Transformers embed algebraic structure in neural networks through graded transformations.
problem Efficiently modeling hierarchical and structured data in neural networks.
method Introduces Linearly Graded Transformer (LGT) and Exponentially Graded Transformer (EGT) with graded scaling operators.
result Establishes rigorous guarantees and improved efficiency for structured data.
DeepHybrid uses radar data to classify objects accurately.
problem Challenges in classifying objects using automotive radar sensors.
method Combines radar signal processing and Deep Learning; uses neural architecture search.
result Improves object classification performance compared to models using only spectra.
Enhanced multi-fidelity models improve digital twin accuracy and uncertainty quantification.
problem Lack of detailed application-specific data and inaccurate sensor data hinder surrogate model learning for digital twins.
method Proposes a multi-fidelity surrogate model framework integrating PCFE and GP, and deep-HPCFE with auto-regression schemes.
result Demonstrates improved accuracy and uncertainty quantification in digital twin systems.
Surrogate models enhance digital twin technology for dynamic systems.
problem Lack of clarity and effective methods for digital twin applications.
method Exploration of Gaussian process (GP) emulators within digital twin framework.
result Surrogate models like GP emulators are effective for digital twin development.
This work uses GANs to generate realistic vehicle test inputs.
problem Lack of realistic input sequences for automotive testing.
method Applying GANs to learn from unlabeled in-vehicle signals and generate synthetic inputs.
result Improved virtual test coverage and reduced need for expensive field tests.
As automotive electronics continue to advance, cars are becoming more and more reliant on sensors to perform everyday driving operations. These sensors are omnipresent and help the car navigate, reduce accidents, and provide comfortable rides. However, they can also be used to learn about the drivers themselves. In thi…
Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like …
A neural network, IHT-Net, improves DOA estimation with sparse arrays.
problem Single-snapshot DOA estimation with sparse arrays in dynamic settings.
method IHT-inspired neural network with recurrent neural network and autoencoders.
result IHT-Net achieves faster convergence and higher accuracy in DOA estimation.
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
Improves industry classification for diversified companies.
problem Traditional industry classification struggles with multi-sector conglomerates.
method Bayesian Non-Parametrics, Markov Updating, and hierarchical modeling.
result MIS-2 provides a measurable improvement over GICS in predicting future correlations.
A digital twin for multi-scale systems uses physics-based and machine learning models.
problem Lack of application-specific details in digital twin technology.
method Strategically separates into physics-based and data-driven models; uses mixture of experts with Gaussian Process.
result Robust and accurate predictions at future time-steps for multi-scale systems.
Develops MIS, a probabilistic model for multi-industry classification.
problem GICS's limitation of assigning each firm to exactly one industry, especially for diversified firms.
method Topic modeling to probabilistically assign firms to multiple industries based on business descriptions.
result Demonstrates MIS's ability to flexibly assign firms to multiple industries with relevance probabilities.
Study finds environmental liability insurance reduces industrial carbon emissions.
problem Reduction of industrial carbon emissions.
method Two-way fixed effect model using provincial (city) level panel data from 2010 to 2020.
result Environmental liability insurance reduces industrial carbon emissions at both direct and indirect levels, with varying effects.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
problem Understanding the dynamics of drug pipelines in global pharmaceutical industry.
method Multilayer network analysis of drug pipeline, global supply chain, and ownership data.
result Proven similarities in knowledge flows between pharmaceutical and AI industries.
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
The paper reviews machine learning safety techniques for autonomous vehicles.
problem Challenges in machine learning safety for autonomous vehicles.
method Organizes practical safety techniques to complement engineering safety.
result Enhances dependability and safety of machine learning algorithms in autonomous vehicles.
New online learning algorithms improve cyberattack detection in industrial control systems.
problem Detecting cyberattacks in industrial control systems with limited resources.
method Online learning algorithms to process continuous data streams and address class imbalance.
result Improved detection rate of cyberattacks in industrial control systems.
We introduce a technique that can automatically tune the parameters of a rule-based computer vision system comprised of thresholds, combinational logic, and time constants. This lets us retain the flexibility and perspicacity of a conventionally structured system while allowing us to perform approximate gradient descen…
We provide complete source code for building a fundamental industry classification based on publically available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssr…
The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication…
We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Ret…
Quantum computing offers financial industry new optimization and risk management tools.
problem Traditional computing limits financial industry's problem-solving capabilities.
method Structured review of quantum computing platforms, algorithms, and use cases.
result Quantum computing can enhance financial industry applications like optimization and risk management.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
problem Understanding and predicting environmental impacts of industrial activities.
method Comparative analysis of ML and statistical models on time series data.
result ML models outperform statistical models in predicting environmental impacts.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
problem Impact of oil price volatility on Tehran stock and industry indices.
method Feed-forward neural networks analysis of two periods: sanctions and post-sanctions.
result Neural networks predict stock and industry indices well, showing significant oil price volatility impact.
New dataset for industrial machine sounds to aid maintenance.
problem Lack of public datasets for industrial machine sounds.
method Recorded normal and anomalous sounds of industrial machines.
result Assists in automated facility maintenance development.
Tool converts industrial systems to RL environments for optimization.
problem Lack of RL methods for optimizing industrial systems.
method Develops a tool to convert FSM models to OpenAI Gym wrappers.
result Traditional RL methods can be applied to industrial systems.