Semi-Supervised Learning for Diagnosing Faults in Electromechanical Systems

Safe and reliable operation of the systems relies on the use of online condition monitoring and diagnostic systems that aim to take immediate actions upon the occurrence of a fault. Machine learning techniques are widely used for designing data-driven diagnostic models. The training procedure of a data-driven model usually requires a large amount of labeled data, which may not be always practical. This problem can be untangled by resorting to semi-supervised learning approaches, which enables the decision making procedure using only a few numbers of labeled samples coupled with a large number of unlabeled samples. Thus, it is crucial to conduct a critical study on the use of semi-supervised learning for the purpose of fault diagnosis. Another issue of concern is fault diagnosis in non-stationary environments, where data streams evolve over time, and as a result, model-based and most of the data-driven models are impractical. In this work, this has been addressed by means of an adaptive data-driven diagnostic model

Weak-value amplification and optimal parameter estimation in the presence of correlated noise

We analytically and numerically investigate the performance of weak-value amplification (WVA) and related parameter estimation methods in the presence of temporally correlated noise. WVA is a special instance of a general measurement strategy that involves sorting data into separate subsets based on the outcome of a second "partitioning" measurement. Using a simplified noise model that can be analyzed exactly together with optimal statistical estimators, we compare WVA to a conventional measurement method. We find that introducing WVA indeed yields a much lower variance of the parameter of interest than does the conventional technique, optimized in the absence of any partitioning measurements. In contrast, a statistically optimal analysis that employs partitioning measurements, incorporating all partitioned results and their known correlations, is found to yield an improvement -- typically slight -- over the noise reduction achieved by WVA. This is because the simple WVA technique is not tailored to a given noise environment and therefore does not make use of correlations between the different partitions. We also compare WVA to traditional background subtraction, a familiar technique where measurement outcomes are partitioned to eliminate unknown offsets or errors in calibration. Surprisingly, in our model background subtraction turns out to be a special case of the optimal partitioning approach in the balanced case, possessing a similar typically slight advantage over WVA. These results give deeper insight into the role of partitioning measurements, with or without post-selection, in enhancing measurement precision, which some have found puzzling. We finish by presenting numerical results to model a more realistic laboratory situation of time-decaying correlations, showing our conclusions hold for a wide range of statistical models.Comment: Revisions incorporate feedback from reviewer

Brain Tumor as a Late Outcome of a Child with Nephrotic Syndrome - Is There Any Association with Immunosuppression?

The association of idiopathic nephrotic syndrome with some malignancies has been reported. We hereunder report a child with focal segmental sclerosis who presented with brain tumor eleven years after renal presentation. A 16- year-old boy presented with nephrotic syndrome since was 5 years old. He was a steroid responder at first but became steroid dependent after subsequent relapses. He received cyclosporine for two years and then mycophenolate mofetil was added for three years. After that, he received losartan and enalapril. Four years later, he developed glioblastoma multiforme.  He passed away two years after surgical resection and chemo-radiotherapy. The occurrence of brain tumor after immunosuppressive therapy in this child might be a late sequel or a coincidence. This might be an alarm for using immunosuppressive agents more cautiously.Keywords: Glioblastoma; Immunosuppression; Mycophenolate Mofetil; Cyclosporine; Losartan; Enalapri; Nephrotic Syndrome.

Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.Comment: Accepted for publication in edited book titled "Federated and Transfer Learning", Springer, Cha

Observing the Onset of Effective Mass

The response of a particle in a periodic potential to an applied force is commonly described by an effective mass which accounts for the detailed interaction between the particle and the surrounding potential. Using a Bose-Einstein condensate of 87-Rb atoms initially in the ground band of an optical lattice, we experimentally show that the initial response of a particle to an applied force is in fact characterized by the bare mass. Subsequently, the particle response undergoes rapid oscillations and only over timescales long compared to that of the interband dynamics is the effective mass observed to be an appropriate description

Adversarial Learning on Incomplete and Imbalanced Medical Data for Robust Survival Prediction of Liver Transplant Patients

The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients. Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model. This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process. To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients\u27 survival chance. Secondly, a hybrid framework is designed that contains three main modules for missing data imputation, class imbalance learning, and classification, each of which employing multiple advanced techniques for the given task. Furthermore, these two approaches are compared and evaluated using a real clinical case study. The experimental results indicate the robust and superior performance of the proposed deep learning method in terms of F-measure and area under the receiver operating characteristic curve (AUC)

3D Numerical Investigation of Ground Settlements Induced by Construction of Istanbul Twin Metro Tunnels with Special Focus on Tunnel Spacing

One of the most important considerations of tunneling in urban areas is controlling the amount of surface settlement that occurs during construction stages. The goal of this paper is to investigate the effect of spacing of Istanbul Twin Metro Tunnels on the surface settlement excavated by NATM method in YENIKAPI-UNKAPANI metro line. For this purpose, the focus has been placed on the effect of longitudinal and transversal spacing between tunnels supported by an umbrella arch protecting method. (FLAC3D) was implemented to simulate the excavation sequence. According to the analysis, the amount of settlement by numerical approach was about 23.5 mm which was in good agreement with the field monitoring results that was 26.5 mm. Moreover, the interaction between twin tunnels by the increase in spacing between twin tunnels in the direction perpendicular to tunnel axis decreases and becomes less effective at the location about 3 times of the tunnel diameter. Similarly, the interaction between twin tunnels in the direction parallel to tunnel axis decreases as the spacing increases. In other words, by increasing the distance between tunnel faces in longitudinal direction at a distance about 3 times of the tunnel diameter, there is still interaction between tunnels and it doesn’t disappear completely. Therefore, it is recommended to keep this distance at about more than 2.5 times of tunnel diameter so that settlement can stay within acceptable range