Multimodal Classification of Urban Micro-Events

In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect only a small group of citizens. Because of their scale these are difficult to identify in most data sources. However, by using citizen sensing to gather data, detecting them becomes feasible. The data gathered by citizen sensing is often multimodal and, as a consequence, the information required to detect urban micro-events is distributed over multiple modalities. This makes it essential to have a classifier capable of combining them. In this paper we explore several methods of creating such a classifier, including early, late, hybrid fusion and representation learning using multimodal graphs. We evaluate performance on a real world dataset obtained from a live citizen reporting system. We show that a multimodal approach yields higher performance than unimodal alternatives. Furthermore, we demonstrate that our hybrid combination of early and late fusion with multimodal embeddings performs best in classification of urban micro-events

A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

Fault diagnosis of photovoltaic (PV) arrays plays a significant role in safe and reliable operation of PV systems. In this paper, the distribution of the PV systems’ daily operating data under different operating conditions is analyzed. The results show that the data distribution features significant nonspherical clustering, the cluster center has a relatively large distance from any points with a higher local density, and the cluster number cannot be predetermined. Based on these features, a density peak-based clustering approach is then proposed to automatically cluster the PV data. And then, a set of labeled data with various conditions are employed to compute the minimum distance vector between each cluster and the reference data. According to the distance vector, the clusters can be identified and categorized into various conditions and/or faults. Simulation results demonstrate the feasibility of the proposed method in the diagnosis of certain faults occurring in a PV array. Moreover, a 1.8 kW grid-connected PV system with 6×3 PV array is established and experimentally tested to investigate the performance of the developed method

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

Enhanced 3D visualization of human fallopian tube morphology using a miniature optical coherence tomography catheter

We demonstrate the use of our miniature optical coherence tomography catheter to acquire three-dimensional human fallopian tube images. Images of the fallopian tube\u27s tissue morphology, vasculature, and tissue heterogeneity distribution are enhanced by adaptive thresholding, masking, and intensity inverting, making it easier to differentiate malignant tissue from normal tissue. The results show that normal fallopian tubes tend to have rich vasculature accompanied by a patterned tissue scattering background, features that do not appear in malignant cases. This finding suggests that miniature OCT catheters may have great potential for fast optical biopsy of the fallopian tube

A switched reluctance motor torque ripple reduction strategy with deadbeat current control and active thermal management

This paper presents a switched reluctance motor (SRM) torque ripple reduction strategy with deadbeat current control and active thermal management. In this method, the SRM torque is indirectly controlled by the phase current. A deadbeat current control method is used to improve the SRM phase current control accuracy, so that SRM torque control error can be reduced significantly. According to the online measurement of the power switching device temperature, the switching frequency will be reduced to prevent the SRM power converter from being damaged by over-temperature. The feasibility and effectiveness of the proposed strategy have been verified in both simulation and experimental studies

Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition-based learning

Accurate and efficient parameter extraction of PV models from I-V characteristic curves is significant for modeling, evaluation and fault diagnosis of PV modules/arrays. Recently, a large number of algorithms are proposed for this problem, but there are still some issues like premature convergence, low accurate and instability. In this paper, a new improved shuffled complex evolution algorithm enhanced by the opposition-based learning strategy (ESCE-OBL) is proposed. The proposed algorithm improves the quality of the candidate solution by the opposition-based learning strategy. Moreover, the basic SCE algorithm evolves with the traditional competition complex evolution (CCE) strategy, but it converges slowly and is prone to be trapped in local optima. In order to improve the exploration capability, the complex in the basic SCE is evolved by a new enhanced CCE. The ESCE-OBL algorithm is compared with some state-of-the-art algorithms on the single diode model (SDM) and double diode model (DDM) using benchmark I-V curves data. The comparison results demonstrate that the proposed ESCE-OBL algorithm can achieve faster convergence, stronger robustness and higher efficiency

Cruciferous vegetable feeding alters UGT1A1 activity: diet- and genotype-dependent changes in serum bilirubin in a controlled feeding trial.

Chemoprevention by isothiocyanates from cruciferous vegetables occurs partly through up-regulation of phase-II conjugating enzymes, such as UDP-glucuronosyl-transferases (UGT). UGT1A1 glucuronidates bilirubin, estrogens, and several dietary carcinogens. The UGT1A1*28 polymorphism reduces transcription compared to the wild-type, resulting in decreased enzyme activity. Isothiocyanates are metabolized by glutathione-S-transferases (GST); variants may alter isothiocyanate clearance, such that response to crucifers may vary by genotype. We evaluated, in a randomized, controlled, cross-over feeding trial in humans (n=70), 3 test diets, (single- and double-“dose” cruciferous and cruciferous plus apiaceous) compared to a fruit-and-vegetable-free basal diet. We measured serum bilirubin concentrations on days 0, 7, 11 and 14 of each 2-week feeding period to monitor UGT1A1 activity, and determined effects of UGT1A1*28 and GSTM1/GSTT1-null variants on response. Aggregate bilirubin response to all vegetable-containing diets was statistically significantly lower compared to the basal diet (p<0.03 for all). Within each UGT1A1 genotype, lower bilirubin concentrations were seen in: *1/*1 in both single and double-dose cruciferous diets compared to basal (p<0.03 for both); *1/*28 in double-dose cruciferous and cruciferous plus apiaceous compared to basal, and cruciferous plus apiaceous compared to single-dose cruciferous (p<0.02 for all); and *28/*28 in all vegetable-containing diets compared to basal (p<0.02 for all). Evaluation of the effects of diet stratified by GST genotype revealed some statistically significant genotypic differences however, the magnitude was similar and not statistically significant between genotypes. These results may have implications for altering carcinogen metabolism through dietary intervention, particularly among UGT1A1*28/*28 individuals

An intelligent fault diagnosis method for PV arrays based on an improved rotation forest algorithm

With the exponential growth of global photovoltaic (PV) power capacity, it is essential to monitor, detect and diagnose the faults in PV arrays for optimal operation. This paper presents an improved rotation forest (RoF) algorithm classifiers ensemble hybridized with extreme learning machine (ELM) for fault diagnosis of PV arrays, which mainly consists of feature selection and classification. In the feature selection step, all the attributes are ranked by the ReliefF algorithm and the top-ranked attributes are chosen to create the new training data subset. In the classification step, the base classifier decision tree of the RoF is replaced by the extreme learning machine to form a new hybrid RoF-ELM ensemble classifier. In the RoF-ELM algorithm, the feature space is first split into several subspaces and the best number of feature subsets is found through the traversal search method. Then, the bootstrap algorithm is employed to carry out bootstrap resampling for each feature subspace, and the principal component analysis (PCA) is then used to transform the resampled samples. Finally, the ELM base classifier is exploited to build each classification model and the final decision is determined by the simple voting approach. By combining the RoF ensemble method with the ELM classifier, the proposed RoF-ELM algorithm not only overcomes the overfitting problem of the basic RoF algorithm, but also improves the generalization ability of the basic ELM. In order to experimentally verify the proposed approach, different types and levels of faults have been created in a laboratory small scale grid-connected PV power system to obtain the fault data samples. Experimental results demonstrate that the RoF-ELM can achieve higher diagnosis accuracy and reliability compared to the basic RoF and ELM algorithms