Energy Comparison between a Load Sensing System and Electro-Hydraulic Solutions Applied to a 9-Ton Excavator

With the increasingly stringent regulations on air quality and the consequent emission limits for internal combustion engines, researchers are concentrating on studying new solutions for improving efficiency and energy saving even in off-road mobile machines. To achieve this task, pump-controlled or displacement-controlled systems have inspired interest for applications in offroad working machines. Generally, these systems are derived from the union of a hydraulic machine coupled to an electric one to create compact components that could be installed near the actuator. The object of study of this work is a 9-ton excavator, whose hydraulic circuit is grounded on load sensing logic. The validated mathematical model, created previously in the Simcenter Amesim© environment, represents the starting point for developing electro-hydraulic solutions. Electric components have been inserted to create different architectures, both with open-and closed-circuit layouts, in order to compare the energy efficiency of the different configurations with respect to the traditional load sensing system. The simulations of a typical working cycle show the energy benefits of electrohydraulic solutions that allow for drastically reducing the mechanical energy required by the diesel engine and, consequently, the fuel consumption. This is mainly possible because of the elimination of directional valves and pressure compensators, which are necessary in a load sensing circuit, but are also a source of great energy dissipations. The results show that closed-circuit solutions produce the greatest benefits, with higher energy efficiencies than the open-circuit solution. Furthermore, closed-circuit configurations require fewer components, allowing for more compact and lighter solutions, as well as being cheaper

Instantaneous flowrate measurements in high-pressure liquid flows

A new flowmeter suitable for high-pressure flows and with a prompt dynamic response is presented. It is constituted by two pressure transducers installed on the monitored high-pressure pipe at a fixed distance one from the other, together with a low-pressure flowmeter that provides a time-averaged flowrate. The measurement algorithm consists of an ordinary differential equation obtained by combining the mass conservation partial differential equation and the momentum balance one applied to the considered piece of pipe comprised between the two pressure transducers. Due to the absence of a master instrument that can be employed as reference to verify the consistency of the measured flowrate, the flowmeter accuracy has been demonstrated by means of numerical models of various hydraulic components, rigorously validated through pressure measurements. The flow ripple of gear pumps has been measured, as well as the flowrate entering a Common Rail injector. For all these cases, the measured flowrate and the one obtained by means of the numerical model are in very good agreement, leading to a robust validation of the presented measurement device

Downsizing the electric machines of energy-efficient electro-hydraulic drives for mobile hydraulics

The poor energy efficiency of state-of-the-art mobile hydraulics affects the carbon dioxide released into the atmosphere and the operating costs. These crucial factors require urgent improvements that can be addressed by the electrification of fluid power. This approach has already generated electro-hydraulic drives that remove flow throttling and enable energy recovery. However, the entire power managed by the actuators of conventional systems must pass through the electric machines. This characteristic is unfeasible for medium-to-high power applications since they need electric motors and electronics with high power ratings and large onboard generation of electricity. Thus, this paper applies to a hydraulic excavator’s boom the idea of splitting the power being transferred to/from the actuator between the hydraulic and electric domains (i.e., a centralized hydraulic power supply is involved). The objective is downsizing the power rating of the boom’s electric components while maintaining the highpower output of the hydraulic actuator. The results show the expected behavior of the hybrid excavator in terms of motion control, but only 57% of the boom’s peak power is now exchanged electrically. The resulting electric machine with 61% downsizing favors the system’s cost and compactness supporting the electrification process that is aligned with the low-carbon economy

Energy analysis of a hybrid electro-hydraulic system for efficient mobile hydraulics

Energy efficiency plays a significant role in mobile hydraulics due to the high amount of carbon dioxide and pollutants being released into the atmosphere. Efficiency improvements are urgently needed, so the electrification of mobile hydraulics represents a fantastic opportunity in this regard. This approach leads to electro-hydraulic systems that remove functional flow throttling in control valves and enable energy recovery. Fuels savings were already demonstrated in simulation, but the literature does not offer entire energy analyses of these electro-hydraulic solutions. This limitation prevents complete system-level comprehension and does not give enough insight to pinpoint areas for further efficiency improvements. Thus, this paper focuses on a hybrid system for excavators based on electro-hydraulic drives that is compared against the original valve-controlled layout. The objective is to quantify the energy flows insight the excavator during relevant operations and highlight the resulting energy losses. The outcomes confirm that electro-hydraulic solutions are suitable for a low-carbon economy. They indicate hydraulic actuators, speed-controlled pumps, and electric motors as the critical components for further energy efficiency enhancement excluding the combustion engine

GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis

Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent slices, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has shown how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1 loss---trained on 3 healthy slices to reconstruct the next 3 ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late stage much more accurately with AUC 0.917; since our method is fully unsupervised, it should also discover and alert any anomalies including rare disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics (LNBI) as a volume in the Springer serie

A multimodal retina-iris biometric system using the levenshtein distance for spatial feature comparison

The recent developments of information technologies, and the consequent need for access to distributed services and resources, require robust and reliable authentication systems. Biometric systems can guarantee high levels of security and multimodal techniques, which combine two or more biometric traits, warranting constraints that are more stringent during the access phases. This work proposes a novel multimodal biometric system based on iris and retina combination in the spatial domain. The proposed solution follows the alignment and recognition approach commonly adopted in computational linguistics and bioinformatics; in particular, features are extracted separately for iris and retina, and the fusion is obtained relying upon the comparison score via the Levenshtein distance. We evaluated our approach by testing several combinations of publicly available biometric databases, namely one for retina images and three for iris images. To provide comprehensive results, detection error trade-off-based metrics, as well as statistical analyses for assessing the authentication performance, were considered. The best achieved False Acceptation Rate and False Rejection Rate indices were and 3.33%, respectively, for the multimodal retina-iris biometric approach that overall outperformed the unimodal systems. These results draw the potential of the proposed approach as a multimodal authentication framework using multiple static biometric traits

Robustness Analysis of DCE-MRI-Derived Radiomic Features in Breast Masses: Assessing Quantization Levels and Segmentation Agreement

Featured Application The use of highly robust radiomic features is fundamental to reduce intrinsic dependencies and to provide reliable predictive models. This work presents a study on breast tumor DCE-MRI considering the radiomic feature robustness against the quantization settings and segmentation methods. Machine learning models based on radiomic features allow us to obtain biomarkers that are capable of modeling the disease and that are able to support the clinical routine. Recent studies have shown that it is fundamental that the computed features are robust and reproducible. Although several initiatives to standardize the definition and extraction process of biomarkers are ongoing, there is a lack of comprehensive guidelines. Therefore, no standardized procedures are available for ROI selection, feature extraction, and processing, with the risk of undermining the effective use of radiomic models in clinical routine. In this study, we aim to assess the impact that the different segmentation methods and the quantization level (defined by means of the number of bins used in the feature-extraction phase) may have on the robustness of the radiomic features. In particular, the robustness of texture features extracted by PyRadiomics, and belonging to five categories-GLCM, GLRLM, GLSZM, GLDM, and NGTDM-was evaluated using the intra-class correlation coefficient (ICC) and mean differences between segmentation raters. In addition to the robustness of each single feature, an overall index for each feature category was quantified. The analysis showed that the level of quantization (i.e., the 'bincount' parameter) plays a key role in defining robust features: in fact, in our study focused on a dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) dataset of 111 breast masses, sets with cardinality varying between 34 and 43 robust features were obtained with 'binCount' values equal to 256 and 32, respectively. Moreover, both manual segmentation methods demonstrated good reliability and agreement, while automated segmentation achieved lower ICC values. Considering the dependence on the quantization level, taking into account only the intersection subset among all the values of 'binCount' could be the best selection strategy. Among radiomic feature categories, GLCM, GLRLM, and GLDM showed the best overall robustness with varying segmentation methods

Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation

The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice. Performance is often the only metric used to evaluate segmentations produced by deep neural networks, and calibration is often neglected. However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual information to model predictions for interpretation by scientists and clinicians. In this study, we provide a simple yet effective extension of the DSC loss, named the DSC++ loss, that selectively modulates the penalty associated with overconfident, incorrect predictions. As a standalone loss function, the DSC++ loss achieves significantly improved calibration over the conventional DSC loss across six well-validated open-source biomedical imaging datasets, including both 2D binary and 3D multi-class segmentation tasks. Similarly, we observe significantly improved calibration when integrating the DSC++ loss into four DSC-based loss functions. Finally, we use softmax thresholding to illustrate that well calibrated outputs enable tailoring of recall-precision bias, which is an important post-processing technique to adapt the model predictions to suit the biomedical or clinical task. The DSC++ loss overcomes the major limitation of the DSC loss, providing a suitable loss function for training deep learning segmentation models for use in biomedical and clinical practice. Source code is available at https://github.com/mlyg/DicePlusPlus

Long-term retention of radium in female former dial workers

The results of measurements of radium contents in 13 women made over 20 years or more were used to measure radium retention. The women were all employed at different studios of the same Illinois plant as luminous dial workers for up to 4 years. The data for each woman were fitted to an exponential function of time, yielding a biological half-life of approximately 30 to 60 years. There was a strong negative correlation between elimination rate and body content, suggesting an effect of radiation of bone resorption or remodelling. A strong negative correlation was observed between the elimination rate and the reduced x-ray score, a measure of bone damage observed radiographically in the same subjects. Age at first exposure and duration of exposure were not significantly correlated with the elimination rate. The concentration of radium in soft tissues from eight other subjects, expressed as a percentage of the terminal radium content, decreased with increasing body content, providing further evidence that radiation was affecting the elimination rate. An effect of radiation on the late retention of radium has been demonstrated. 10 references, 4 figures, 2 tables