Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms

Reconstruction of tokamak plasma safety factor profile using deep learning

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep learning-based surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system

Topological optimization of a variable cross-section cantilever-based piezoelectric wind energy harvester

Wind energy is a typical foreseeable renewable energy source. This study constructs and optimizes a variable cross-section cantilever-based piezoelectric energy harvester for low-speed wind energy harvesting. The Galerkin approach is usually used to discretize the continuum model and then get the ordinary differential equations. However, this method is more suitable for calculating uniformity than the variable cross-sectional beam model. To solve this problem, we proposed an improved piecewise Galerkin approach for discretizing the continuum model with a variable cross section. By modifying the boundary expressions and modal functions between segments, it can improve both computation speed and accuracy. COMSOL simulations demonstrate that natural frequencies calculated via the improved method are more accurate than those of the traditional Galerkin method. The method of multiple scales is applied to determine the output power and critical wind velocity. A distinctive numerical approach is presented for shape optimization by combining the analytical calculation method with the particle swarm optimization (PSO) technique for low-speed wind energy harvesting. Additionally, the logic function is chosen to produce the optimal shape’s fitting expression for engineering applications. With all the improvements, the output power of a variable cross-section beam-based harvester reaches as much as 3.668 times that of a uniform beam model, demonstrating the importance of structural optimization for this type of energy harvesters. Finally, experiments are set up to verify the optimization procedure. Actually, it builds an analytical framework for the adaptive selection of variable-section piezoelectric cantilever wind-induced vibration energy harvesters

Numerical study on complex conductivity characteristics of hydrate-bearing porous media

The complex conductivity method is frequently used in hydro-/petro-/environmental geophysics, and considered to be a promising tool for characterizing and quantifying the properties of subsurface rocks, sediments and soils. We report a study on the complex conductivity characteristics of porous media containing gas hydrates through numerical modelling. The effects of the hydrate saturation, pore-water salinity and micro-distribution mode were studied, and hydrate-saturation evaluation correlations based on complex conductivity parameters were developed. A pore-scale numerical approach for developing the finite-element based models for hydrate-bearing porous media is proposed and a two-dimensional (2D) model is built to compute the complex conductivity responses of porous media under various conditions. We demonstrate that the simple 2D model can capture the dominant characteristics of the complex conductivity of hydrate-bearing porous media within the frequency range related to the induced polarization. The in-phase conductivity, quadrature conductivity and effective dielectric constant can be correlated with the saturation based on a power law in the log-log space, by which the hydrate-saturation evaluation models can be derived. A constant saturation exponent of the power-law correlation between the hydrate saturation and quadrature conductivity can be obtained when the pore-water conductivity exceeds 1.0 S/m. This is highly desirable in the hydrate-saturation models due to the variations of the pore-water conductivity in the processes of hydrate formation and dissociation. Within the framework of the complex conductivity analysis, the micro-distribution modes of hydrates in porous media can be categorized into two types. These are the fluid-suspending mode and grain-attaching mode. The in-phase conductivity exhibits significant variations under the same saturation and salinity but different micro-distribution modes, which can be attributed to the change in the tortuosity of the electrical conduction paths in the void space of porous media

The effect of poly-β-hydroxyalkanoates degradation rate on nitrous oxide production in a denitrifying phosphorus removal system

Poly-beta-hydroxyalkanoates (PHAs) and free nitrous acid (FNA) have been revealed as significant factors causing nitrous oxide (N2O) production in denitrifying phosphorus removal systems. In this study, the effect of PHA degradation rate on N2O production was studied at low FNA levels. N2O production always maintained at approximately 40% of the amount of nitrite reduced independent of the PHA degradation rate. The electrons distributed to nitrite reduction were 1.6 times that to N2O reduction. This indicated that electron competition between these two steps was not affected by the PHA degradation rate. Continuous feed of nitrate was proposed, and demonstrated to reduce N2O accumulation by 75%. While being kept low, a possible compounding effect of a low-level FNA could not be ruled out. The sludge used likely contained both polyphosphate- and glycogen-accumulating organisms, and the results could not be simply attributed to either group of organisms. (C) 2014 Elsevier Ltd. All rights reserved

Chromatin remodeling enzyme Brg1 is required for mouse lens fiber cell terminal differentiation and its denucleation

These studies demonstrate a cell-autonomous role for Brg1 in lens fiber cell terminal differentiation and identified DNase IIβ as a potential direct target of SWI/SNF complexes. Brg1 is directly or indirectly involved in processes that degrade lens fiber cell chromatin. The presence of nuclei and other organelles generates scattered light incompatible with the optical requirements for the lens

UpWB: An Uncoupled Architecture Design for White-box Cryptography Using Vectorized Montgomery Multiplication

White-box cryptography (WBC) seeks to protect secret keys even if the attacker has full control over the execution environment. One of the techniques to hide the key is space hardness approach, which conceals the key into a large lookup table generated from a reliable small block cipher. Despite its provable security, space-hard WBC also suffers from heavy performance overhead when executed on general purpose hardware platform, hundreds of magnitude slower than conventional block ciphers. Specifically, recent studies adopt nested substitution permutation network (NSPN) to construct dedicated white-box block cipher [BIT16], whose performance is limited by a massive number of rounds, nested loop dependency and high-dimension dynamic maximal distance separable (MDS) matrices. To address these limitations, we put forward UpWB, an uncoupled and efficient accelerator for NSPN-structure WBC. We propose holistic optimization techniques across timing schedule, algorithms and operators. For the high-level timing schedule, we propose a fine-grained task partition (FTP) mechanism to decouple the parameteroriented nested loop with different trip counts. The FTP mechanism narrows down the idle time for synchronization and avoids the extra usage of FIFO, which efficiently increases the computation throughput. For the optimization of arithmetic operators, we devise a flexible and vectorized modular multiplier (VMM) based on the complexity-reduced Montgomery algorithm, which can process multi-precision variable data, multi-size matrix-vector multiplication and different irreducible polynomials. Then, a configurable matrix-vector multiplication (MVM) architecture with diagonal-major dataflow is presented to handle the dynamic MDS matrix. The multi-scale (Inv)Mixcolumns are also unified in a compact manner by intensively sharing the common sub-operations and customizing the constant multiplier. To verify the proposed methodology, we showcase the unified design implementation for three recent families of WBCs, including SPNbox-8/16/24/32, Yoroi-16/32 and WARX-16. Evaluated on FPGA platform, UpWB outperforms the optimized software counterpart (executed on 3.2 GHz Intel CPU with AES-NI and AVX2 instructions) by 7x to 30x in terms of computation throughput. Synthesized under TSMC 28nm technology, 36x to 164x improvement of computation throughput is achieved when UpWB operates at the maximum frequency of 1.3 GHz and consumes a modest area 0.14 mm2. Besides, the proposed VMM also offers about 30% improvement of area efficiency without pulling flexibility down when compared to state-of-the-art work

A murine preclinical syngeneic transplantation model for breast cancer precision medicine

We previously demonstrated that altered activity of lysophosphatidic acid in murine mammary glands promotes tumorigenesis. We have now established and characterized a heterogeneous collection of mouse-derived syngeneic transplants (MDSTs) as preclinical platforms for the assessment of personalized pharmacological therapies. Detailed molecular and phenotypic analyses revealed that MDSTs are the most heterogeneous group of genetically engineered mouse models (GEMMs) of breast cancer yet observed. Response of MDSTs to trametinib, a mitogen-activated protein kinase (MAPK) kinase inhibitor, correlated with RAS/MAPK signaling activity, as expected from studies in xenografts and clinical trials providing validation of the utility of the model. Sensitivity of MDSTs to talazoparib, a poly(adenosine 5′-diphosphate–ribose) polymerase (PARP) inhibitor, was predicted by PARP1 protein levels and by a new PARP sensitivity predictor (PSP) score developed from integrated analysis of drug sensitivity data of human cell lines. PSP score–based classification of The Cancer Genome Atlas breast cancer suggested that a subset of patients with limited therapeutic options would be expected to benefit from PARP-targeted drugs. These results indicate that MDSTs are useful models for studies of targeted therapies, and propose novel potential biomarkers for identification of breast cancer patients likely to benefit from personalized pharmacological treatments

Menstrual irregularity and bone mass in premenopausal women: Cross-sectional associations with testosterone and SHBG

Background. There have been few studies examining the associations between menstrual irregularity, androgens and bone mass in population-based samples of premenopausal women. This study aimed to describe the associations between menstrual pattern, testosterone, sex hormone binding globulin (SHBG) and bone mass in a population-based sample of premenopausal women. Methods. Cross-sectional study (N = 382, mean age 31.5 years). Menstrual pattern was assessed by questionnaire, bone mass measured by quantitative ultrasound (QUS) and androgen status was assessed by levels of serum testosterone, SHBG and the free androgen index (FAI). Results. Women with irregular cycles (n = 41, 11%) had higher free androgen index (FAI, P = 0.01) and higher QUS measurements including speed of sound (SOS, 1%, P < 0.05), quantitative ultrasound index (QUI, 7%, p < 0.05), and broadband ultrasound attenuation (BUA, 7%, p = 0.10). These associations persisted after adjustment for age and body mass index (BMI). After further adjustment for hormonal factors (either testosterone, SHBG or FAI), the strength of the associations was moderately attenuated, however, women with irregular cycles still had a 6% increase in mean QUS. Total testosterone, FAI and SHBG were also associated with QUS measures (testosterone and FAI, r +0.11 to +0.21, all p < 0.05; SHBG r -0.14 to -0.16, all p < 0.05) and the associations remained significant after adjustment. Conclusion. Irregular menstrual cycles were associated with higher bone mass in this population-based sample of premenopausal women suggesting menstrual disturbance should continue to be evaluated but may be less harmful for bone mass. The association between menstrual irregularity and bone mass was partially mediated by markers of androgen status especially free testosterone