Power Capability Estimation Accounting for Thermal and Electrical Constraints of Lithium-Ion Batteries.

Lithium-ion (Li-ion) batteries have become one of the most critical components in vehicle electrification due to their high specific power and energy density. The performance and longevity of these batteries rely on constraining their operation such that voltage and temperature are regulated within prescribed intervals. Enforcement of constraints on the power capability is a viable solution to protect Li-ion batteries from overheating as well as over-charge/discharge. Moreover, the ability to estimate power capability is vital in formulating power management strategies that account for battery performance limitations while minimizing fuel consumption and emissions. To estimate power capability accounting for thermal and electrical constraints, the characterization of thermal and electrical system behavior is required. In the course of addressing this problem, first, a computationally efficient thermal model for a cylindrical battery is developed. The solution of the convective heat transfer problem is approximated by polynomials with identifiable parameters that have physical meaning. The parameterized thermal model is shown to accurately predict the measured core and surface temperatures. The model-based thermal estimation methodology is augmented for cases of unknown cooling conditions. The proposed method is shown with experimental data to accurately provide estimates of the core temperature even under faults in the cooling system. To jointly account for the thermal and electrical constraints, we utilize time scale separation, and propose a real-time implementable method to predict power capability of a Li-ion battery. The parameterized battery thermal model and estimation algorithms are integrated into a power management system for a series hybrid electric vehicle. An algorithm for sequential estimation of coupled model parameters and states is developed using sensitivity-based parameter grouping. The fully integrated co-simulation of the battery electro-thermal behavior and the on-line adaptive estimators reveal that the power management system can effectively determine power flow among hybrid powertrain components without violating operational constraints.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107128/1/youngki_1.pd

Human mesangial cell production of monocyte chemoattractant protein-1: Modulation by lovastatin

Human mesangial cell production of monocyte chemoattractant protein-1: Modulation by lovastatin. Macrophages play a critical role in the progression of clinical and experimental glomerular injury. Serum-stimulated human fetal mesangial cells in culture produce a chemotactic factor that is monocyte-selective. This chemotactic factor is most likely monocyte chemoattractant protein-1 (MCP-1) as a monoclonal antibody directed against MCP-1, but not an irrelevant antibody, suppressed the mesangial cell-derived chemotactic activity. Inhibition of 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase by lovastatin resulted in a reduction of the mesangial cell-derived chemotactic activity as well as MCP-1 mRNA expression. The inhibitory effects of lovastatin in the presence of exogenous cholesterol were reversed by mevalonate, suggesting a role for isoprenoid intermediates of the mevalonate pathway and/or isoprenylated proteins in mesangial cell MCP-1 regulation. These findings suggest an additional mechanism by which HMG-CoA reductase inhibition in vivo may reduce glomerular injury

High-resolution embedding extractor for speaker diarisation

Speaker embedding extractors significantly influence the performance of clustering-based speaker diarisation systems. Conventionally, only one embedding is extracted from each speech segment. However, because of the sliding window approach, a segment easily includes two or more speakers owing to speaker change points. This study proposes a novel embedding extractor architecture, referred to as a high-resolution embedding extractor (HEE), which extracts multiple high-resolution embeddings from each speech segment. Hee consists of a feature-map extractor and an enhancer, where the enhancer with the self-attention mechanism is the key to success. The enhancer of HEE replaces the aggregation process; instead of a global pooling layer, the enhancer combines relative information to each frame via attention leveraging the global context. Extracted dense frame-level embeddings can each represent a speaker. Thus, multiple speakers can be represented by different frame-level features in each segment. We also propose an artificially generating mixture data training framework to train the proposed HEE. Through experiments on five evaluation sets, including four public datasets, the proposed HEE demonstrates at least 10% improvement on each evaluation set, except for one dataset, which we analyse that rapid speaker changes less exist.Comment: 5pages, 2 figure, 3 tables, submitted to ICASS

Absolute decision corrupts absolutely: conservative online speaker diarisation

Our focus lies in developing an online speaker diarisation framework which demonstrates robust performance across diverse domains. In online speaker diarisation, outputs generated in real-time are irreversible, and a few misjudgements in the early phase of an input session can lead to catastrophic results. We hypothesise that cautiously increasing the number of estimated speakers is of paramount importance among many other factors. Thus, our proposed framework includes decreasing the number of speakers by one when the system judges that an increase in the past was faulty. We also adopt dual buffers, checkpoints and centroids, where checkpoints are combined with silhouette coefficients to estimate the number of speakers and centroids represent speakers. Again, we believe that more than one centroid can be generated from one speaker. Thus we design a clustering-based label matching technique to assign labels in real-time. The resulting system is lightweight yet surprisingly effective. The system demonstrates state-of-the-art performance on DIHARD 2 and 3 datasets, where it is also competitive in AMI and VoxConverse test sets.Comment: 5pages, 2 figure, 4 tables, submitted to ICASS

PADA: Power-aware development assistant for mobile sensing applications

� 2016 ACM. We propose PADA, a new power evaluation tool to measure and optimize power use of mobile sensing applications. Our motivational study with 53 professional developers shows they face huge challenges in meeting power requirements. The key challenges are from the significant time and effort for repetitive power measurements since the power use of sensing applications needs to be evaluated under various real-world usage scenarios and sensing parameters. PADA enables developers to obtain enriched power information under diverse usage scenarios in development environments without deploying and testing applications on real phones in real-life situations. We conducted two user studies with 19 developers to evaluate the usability of PADA. We show that developers benefit from using PADA in the implementation and power tuning of mobile sensing applications.N

Glomerular distribution of type IV collagen in diabetes by high resolution quantitative immunochemistry

Glomerular distribution of type IV collagen in diabetes by high resolution quantitative immunochemistry. We examined type IV collagen distribution and density in human diabetic kidneys by quantitative immunogold electron microscopy. We studied normal kidney transplant donors and “slow-track” and “fast-track” insulin dependent diabetic (IDDM) patients. The “slow-track” patients had IDDM for ≥ 20 years and mesangial volume fraction (VvMes/glom) of ≤ 0.32. The “fast-track” patients had IDDM for ≤ 20 years and VvMes/glom ≥ 0.37. Renal biopsies were embedded in Lowicryl, reacted with polyclonal anti-type IV collagen (in the distribution of the classical α1(IV) and α2(IV) collagen chains) and monoclonal anti-α4(IV) collagen chain antibody followed by gold conjugated secondary antibody. We found, by morphometric techniques, a decrease in the immunogold densities of anti-type IV collagen in the subendothelial zone of the GBM in the “fast-track” IDDM patients. There was a trend towards a decrease in mesangial matrix (MM) particle density in the “fast-track” (P = 0.07) but not in the “slow-track” patients. However, because of the marked increase in MM in the “fast-track” patients, the per glomerulus estimated quantity of these antigens in MM was increased. In contrast, the density of α4(IV) collagen chain was increased in the epithelial zone of the GBM in the “fast-track” IDDM patients. It is not known whether these changes in glomerular type IV collagen represent markers of advanced diabetic lesions or whether these changes might be detected earlier in diabetic patients destined for the later development of serious lesions

Disentangled dimensionality reduction for noise-robust speaker diarisation

The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation. Speaker embeddings play a crucial role in the performance of diarisation systems, but they often capture spurious information such as noise and reverberation, adversely affecting performance. Our previous work has proposed an auto-encoder-based dimensionality reduction module to help remove the redundant information. However, they do not explicitly separate such information and have also been found to be sensitive to hyper-parameter values. To this end, we propose two contributions to overcome these issues: (i) a novel dimensionality reduction framework that can disentangle spurious information from the speaker embeddings; (ii) the use of a speech/non-speech indicator to prevent the speaker code from representing the background noise. Through a range of experiments conducted on four different datasets, our approach consistently demonstrates the state-of-the-art performance among models without system fusion.Comment: This paper was submitted to Interspeech202