Development of battery management system for hybrid electric propulsion system.

Because of the high overall efficiency and low emissions, Hybrid Electric Propulsion System (HEPS) have become an attractive research area. In this research, a parallel HEPS architecture is adopted and a Hardware test platform is constructed. As a relative new power source in powertrains, battery system plays an important role in HEPS. Hence, a Battery Management System (BMS) is investigated in this research. Battery pack State of Charge (SOC) is a key feedback value in HEPS control. In order to estimate SOC, firstly, an operation-classification adaptive battery model is proposed for Li-Po batteries. Considering the fact that model parameter accuracy is of importance in model-based system state estimation method, an event triggered Adaptive Genetic Algorithm (AGA) is applied for online parameter identification. Secondly, the Extended Kalman Filter (EKF) is applied for single battery cell SOC estimation. Finally, a fuzzy estimator is proposed for battery pack SOC estimation based on maximum/minimum cell voltages and SOC values. Experimental results show that the proposed AGA can effectively track battery parameter variation and SOC estimation error for single cell as well as for the battery pack are both less than 1%. Moreover, considering the Li-Po battery characteristics, a converter based battery cell balancing method is proposed. Simulation result shows that proposed balancing method can be effective in balancing battery cells. In addition, in relation to safety and reliability concerns, a Discrete Wavelet Transform (DWT) based battery circuit detection method is proposed and simulation results showing its feasibility are presented.PhD in Aerospac

Capacity Planning with Financial and Operational Hedging in Low‐Cost Countries

The authors of this paper outline a capacity planning problem in which a risk-averse firm reserves capacities with potential suppliers that are located in multiple low-cost countries. While demand is uncertain, the firm also faces multi-country foreign currency exposures. This study develops a mean-variance model that maximizes the firm’s optimal utility and derives optimal utility and optimal decisions in capacity and financial hedging size. The authors show that when demand and exchange rate risks are perfectly correlated, a risk- averse firm, by using financial hedging, will achieve the same optimal utility as a risk-neutral firm. In this paper as well, a special case is examined regarding two suppliers in China and Vietnam. The results show that if a single supplier is contracted, financial hedging most benefits the highly risk-averse firm when the demand and exchange rate are highly negatively related. When only one hedge is used, financial hedging dominates operational hedging only when the firm is very risk averse and the correlation between the two exchange rates have become positive. With both theoretical and numerical results, this paper concludes that the two hedges are strategic tools and interact each other to maximize the optimal utility

Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data

As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to generate safe and efficient actions, AVs need to not only predict the future behaviors of other traffic participants, but also be aware of the uncertainties associated with such behavior prediction. In this paper, we propose an uncertain-aware integrated prediction and planning (UAPP) framework. It allows the AVs to infer the characteristics of other road users online and generate behaviors optimizing not only their own rewards, but also their courtesy to others, and their confidence regarding the prediction uncertainties. We first propose the definitions for courtesy and confidence. Based on that, their influences on the behaviors of AVs in interactive driving scenarios are explored. Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior against ground truth. Results show that the online inference can significantly improve the human-likeness of the generated behaviors. Furthermore, we find that human drivers show great courtesy to others, even for those without right-of-way. We also find that such driving preferences vary significantly in different cultures.Comment: Accepted by IEEE Robotics and Automation Letters. January 202

Answer is All You Need: Instruction-following Text Embedding via Answering the Question

This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. Despite its tremendous potential to deploy user-oriented embeddings, none of previous approaches provides a concrete solution for it. This paper offers a new viewpoint, which treats the instruction as a question about the input text and encodes the expected answers to obtain the representation accordingly. Intuitively, texts with the same (implicit) semantics would share similar answers following the instruction, thus leading to more similar embeddings. Specifically, we propose InBedder that instantiates this embed-via-answering idea by only fine-tuning language models on abstractive question answering tasks. InBedder demonstrates significantly improved instruction-following capabilities according to our proposed instruction awareness tests and instruction robustness tests, when applied to both large language models (LLMs) (e.g., llama-2-7b) and smaller encoder-based LMs (e.g., roberta-large). Additionally, our qualitative analysis of clustering outcomes, achieved by applying different instructions to the same corpus, demonstrates a high degree of interpretability

Genetic Evolution and Molecular Selection of the HE Gene of Influenza C Virus

Influenza C virus (ICV) was first identified in humans and swine, but recently also in cattle, indicating a wider host range and potential threat to both the livestock industry and public health than was originally anticipated. The ICV hemagglutinin-esterase (HE) glycoprotein has multiple functions in the viral replication cycle and is the major determinant of antigenicity. Here, we developed a comparative approach integrating genetics, molecular selection analysis, and structural biology to identify the codon usage and adaptive evolution of ICV. We show that ICV can be classified into six lineages, consistent with previous studies. The HE gene has a low codon usage bias, which may facilitate ICV replication by reducing competition during evolution. Natural selection, dinucleotide composition, and mutation pressure shape the codon usage patterns of the ICV HE gene, with natural selection being the most important factor. Codon adaptation index (CAI) and relative codon deoptimization index (RCDI) analysis revealed that the greatest adaption of ICV was to humans, followed by cattle and swine. Additionally, similarity index (SiD) analysis revealed that swine exerted a stronger evolutionary pressure on ICV than humans, which is considered the primary reservoir. Furthermore, a similar tendency was also observed in the M gene. Of note, we found HE residues 176, 194, and 198 to be under positive selection, which may be the result of escape from antibody responses. Our study provides useful information on the genetic evolution of ICV from a new perspective that can help devise prevention and control strategies

Quantum Dueling: an Efficient Solution for Combinatorial Optimization

In this paper, we present a new algorithm for generic combinatorial optimization, which we term quantum dueling. Traditionally, potential solutions to the given optimization problems were encoded in a ``register'' of qubits. Various techniques are used to increase the probability of finding the best solution upon measurement. Quantum dueling innovates by integrating an additional qubit register, effectively creating a ``dueling'' scenario where two sets of solutions compete. This dual-register setup allows for a dynamic amplification process: in each iteration, one register is designated as the 'opponent', against which the other register's more favorable solutions are enhanced through a controlled quantum search. This iterative process gradually steers the quantum state within both registers toward the optimal solution. With a quantitative contraction for the evolution of the state vector, classical simulation under a broad range of scenarios and hyper-parameter selection schemes shows that a quadratic speedup is achieved, which is further tested in more real-world situations. In addition, quantum dueling can be generalized to incorporate arbitrary quantum search techniques and as a quantum subroutine within a higher-level algorithm. Our work demonstrates that increasing the number of qubits allows the development of previously unthought-of algorithms, paving the way for advancement of efficient quantum algorithm design.Comment: 18 pages, 10 figure

Diversity Synthesis of Complex Pyridines Yields a Probe of a Neurotrophic Signaling Pathway

Recognizing the value of including complex pyridines in small-molecule screening collections, we developed a previously unexplored [2 + 2 + 2]-cycloaddition of silyl-tethered diynes with nitriles. The tether provides high regioselectivity, while the solvent THF allows catalytic CpCo(CO)2 to be used without exogenous irradiation. One of the resulting bicyclic and monocyclic (desilylated) pyridines was identified as an inhibitor of neuregulin-induced neurite outgrowth (EC50 = 0.30 µM) in a screen that probes a pathway likely to be involved in breast cancers and schizophrenia.Chemistry and Chemical Biolog