Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Real-time control of an SI engine using ion current based algorithms

Reducing emissions and improving fuel efficiency in automobiles are today important issues. New sensor techniques are developed to extract detailed combustion information to enable closed loop engine control. This thesis is about a virtual sensor; measuring an ion current inside the cylinder by using the already existing spark plug, followed by signal processing for estimation of combustion parameters.First, the thesis aims to show that the ion current signal can be used for closed loop control of Exhaust Gas Recirculation (EGR). Use of EGR is very common in modern automobiles because of the potential reduction of NOx emissions and fuel consumption, but using too much EGR can have the reverse effect (e.g. increased fuel consumption and driveability problems). Algorithms for estimating combustion variability are proposed and a closed loop scheme for controlling an EGR valve is demonstrated for driving on the highway in a SAAB 9000.Estimation of the pressure peak position is treated for closed loop control of ignition timing. Such estimation can be performed with the ion current but may not work if a fuel additive is used. Different methods are compared and it is shown that using a fuel additive may even improve the estimation accuracy of the pressure peak position with about 25%. An algorithm is also proposed to estimate the pressure peak position even in presence of EGR.Strategies for transient control of the air-fuel ratio are also compared. Air-fuel ratio control is important because even small deviations from the stoichiometric value can result in significantly increased emissions. It is found that a neural network based controller had the best performance with approximately 23% lower RMS error than the adapted standard control module

Real-time control of an SI engine using ion current based algorithms

Reducing emissions and improving fuel efficiency in automobiles are today important issues. New sensor techniques are developed to extract detailed combustion information to enable closed loop engine control. This thesis is about a virtual sensor; measuring an ion current inside the cylinder by using the already existing spark plug, followed by signal processing for estimation of combustion parameters.First, the thesis aims to show that the ion current signal can be used for closed loop control of Exhaust Gas Recirculation (EGR). Use of EGR is very common in modern automobiles because of the potential reduction of NOx emissions and fuel consumption, but using too much EGR can have the reverse effect (e.g. increased fuel consumption and driveability problems). Algorithms for estimating combustion variability are proposed and a closed loop scheme for controlling an EGR valve is demonstrated for driving on the highway in a SAAB 9000.Estimation of the pressure peak position is treated for closed loop control of ignition timing. Such estimation can be performed with the ion current but may not work if a fuel additive is used. Different methods are compared and it is shown that using a fuel additive may even improve the estimation accuracy of the pressure peak position with about 25%. An algorithm is also proposed to estimate the pressure peak position even in presence of EGR.Strategies for transient control of the air-fuel ratio are also compared. Air-fuel ratio control is important because even small deviations from the stoichiometric value can result in significantly increased emissions. It is found that a neural network based controller had the best performance with approximately 23% lower RMS error than the adapted standard control module

Interview with Peter Pyclik, President of The Faxon Company

This paper investigates the use of the ionization current to estimate the Coefficient of Variation for the Indicated Mean Effective Pressure, COV(IMEP), which is a common variable for combustion stability in a spark ignited engine. Stable combustion in this definition implies that the variance of the produced work, measured over a number of consecutive combustion cycles, is small compared to the mean of the produced work. The COV(IMEP) is varied experimentally either by increasing EGR flow or by changing the air-fuel ratio, in both a laboratory setting (engine in dynamometer) and in an on-road setting. The experiments show a positive correlation between COV(Ion integral), the Coefficient of Variation for the integrated Ion Current, and COV(IMEP), when measured under low load on an engine in a dynamometer, but not under high load conditions. On-road experiments show a positive correlation, but only in the EGR and the lean burn case. An approach based on individual cycle classification for real time estimation of combustion stability is discussed

Closed-loop control of EGR using ion currents

Two virtual sensors are proposed that use the spark-plug based ion current sensor for combustion engine control. The first sensor estimates combustion variability for the purpose of controlling exhaust gas recirculation (EGR) and the second sensor estimates the pressure peak position for control of ignition timing. Use of EGR in engines is important because the technique can reduce fuel consumption and NOx emissions, but recirculating too much can have the adverse effect with e.g. increased fuel consumption and poor driveability of the vehicle. Since EGR also affects the phasing of the combustion (because of the diluted gas mixture with slower combustion) it is also necessary to control ignition timing otherwise efficiency will be lost. The combustion variability sensor is demonstrated in a closed-loop control experiment of EGR on the highway and the pressure peak sensor is shown to handle both normal and an EGR condition.Sponsors, the International Association of Science and Technology for Development (IASTED), Technical Committee on Modelling and Simulation, Technical Committee on Control, World Modelling and Simulation Forum (WMSF)</p

Ideas for Fault Detection Using Relation Discovery

Predictive maintenance is becoming more and more important in many industries, especially taking into account the increasing focus on offering uptime guarantees to the customers. However, in automotive industry, there is a limitation on the engineering effort and sensor capabilities available for that purpose. Luckily, it has recently become feasible to analyse large amounts of data on-board vehicles in a timely manner. This allows approaches based on data mining and pattern recognition techniques to augment existing, hand crafted algorithms. Automated deviation detection offers both broader applicability, by virtue of detecting unexpected faults and cross-analysing data from different subsystems, as well as higher sensitivity, due to its ability to take into account specifics of a selected, small set of vehicles used in a particular way under similar conditions. In a project called Redi2Service we work towards developing methods for autonomous and unsupervised relationship discovery, algorithms for detecting deviations within those relationships (both considering different moments in time, and different vehicles in a fleet), as well as ways to correlate those deviations to known and unknown faults. In this paper we present the type of data we are working with, justify why we believe relationships between signals are a good knowledge representation, and show results of early experiments where supervised learning was used to evaluate discovered relations.Redi2Servic

Self-organizing maps for automatic fault detection in a vehicle cooling system

A telematic based system for enabling automatic fault detection of a population of vehicles is proposed. To avoid sending huge amounts of data over the telematics gateway, the idea is to use low-dimensional representations of sensor values in sub-systems in a vehicle. These low-dimensional representations are then compared between similar systems in a fleet. If a representation in a vehicle is found to deviate from the group of systems in the fleet, then the vehicle is labeled for diagnostics for that subsystem. The idea is demonstrated on the engine coolant system and it is shown how this self-organizing approach can detect varying levels of clogged radiator.©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.</p

Ideas for Fault Detection Using Relation Discovery

Predictive maintenance is becoming more and more important in many industries, especially taking into account the increasing focus on offering uptime guarantees to the customers. However, in automotive industry, there is a limitation on the engineering effort and sensor capabilities available for that purpose. Luckily, it has recently become feasible to analyse large amounts of data on-board vehicles in a timely manner. This allows approaches based on data mining and pattern recognition techniques to augment existing, hand crafted algorithms. Automated deviation detection offers both broader applicability, by virtue of detecting unexpected faults and cross-analysing data from different subsystems, as well as higher sensitivity, due to its ability to take into account specifics of a selected, small set of vehicles used in a particular way under similar conditions. In a project called Redi2Service we work towards developing methods for autonomous and unsupervised relationship discovery, algorithms for detecting deviations within those relationships (both considering different moments in time, and different vehicles in a fleet), as well as ways to correlate those deviations to known and unknown faults. In this paper we present the type of data we are working with, justify why we believe relationships between signals are a good knowledge representation, and show results of early experiments where supervised learning was used to evaluate discovered relations.Redi2Servic