Target Tracking System Constructed by ELM-AE and Transfer Representation Learning

In the target tracking algorithm, the feature model’s ability to quickly learn image features and the ability to adapt to changes in target features during tracking has always been one of the main research directions of target tracking algorithms. Especially for discriminative target trackers based on image block learning, these two points have become decisive factors affecting the efficiency and robustness of the tracker. However, the performance of most existing similar algorithms on these two abilities cannot achieve satisfactory results. To solve this problem, an efficient and robust feature model is proposed. The feature model first uses extreme learning machine autoencoder (ELM-AE) to quickly perform random feature mapping on complex image features of the target and background image blocks, and then uses the transfer learning ability of transfer representation learning (TRL) to improve the adaptability of random feature space. The feature model is named transfer representation learning with ELM-AE (TRL-ELM-AE). Compared with original complex image features, this model can provide the classifier with more compact and expressive shared features, so that the classifier can learn and classify more quickly and efficiently. In addition, in the target tracking process, the target and background usually change continuously over time. Although the feature migration capability of TRL can already adapt to this, in order to further improve the robustness of the tracker, a strategy of dynamically updating training samples is adopted. Through a large number of experimental and analysis results on the 11 target tracking challenge scenarios proposed by OTB, it is proven that the proposed target tracker has significant advantages over the existing target tracker

Layer-wise Representation Fusion for Compositional Generalization

Despite successes across a broad range of applications, sequence-to-sequence models' construct of solutions are argued to be less compositional than human-like generalization. There is mounting evidence that one of the reasons hindering compositional generalization is representations of the encoder and decoder uppermost layer are entangled. In other words, the syntactic and semantic representations of sequences are twisted inappropriately. However, most previous studies mainly concentrate on enhancing token-level semantic information to alleviate the representations entanglement problem, rather than composing and using the syntactic and semantic representations of sequences appropriately as humans do. In addition, we explain why the entanglement problem exists from the perspective of recent studies about training deeper Transformer, mainly owing to the ``shallow'' residual connections and its simple, one-step operations, which fails to fuse previous layers' information effectively. Starting from this finding and inspired by humans' strategies, we propose \textsc{FuSion} (\textbf{Fu}sing \textbf{S}yntactic and Semant\textbf{i}c Representati\textbf{on}s), an extension to sequence-to-sequence models to learn to fuse previous layers' information back into the encoding and decoding process appropriately through introducing a \emph{fuse-attention module} at each encoder and decoder layer. \textsc{FuSion} achieves competitive and even \textbf{state-of-the-art} results on two realistic benchmarks, which empirically demonstrates the effectiveness of our proposal.Comment: work in progress. arXiv admin note: substantial text overlap with arXiv:2305.1216

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Application of Fuzzy Control in a Photovoltaic Grid-Connected Inverter

To realize the maximum power output of a grid-connected inverter, the MPPT (maximum power point tracking) control method is needed. The perturbation and observation (P&O) method can cause the inverter operating point to oscillate near the maximum power. In this paper, the fuzzy control P&O method is proposed, and the fuzzy control algorithm is applied to the disturbance observation method. The simulation results of the P&O method with fuzzy control and the traditional P&O method prove that not only can the new method reduce the power loss caused by inverter oscillation during maximum power point tracking, but also it has the advantage of speed. Inductive loads in the post-grid-connected stage cause grid-connected current distortion. A fuzzy control algorithm is added to the traditional deadbeat grid-connected control method to improve the quality of the system’s grid-connected operation. The fuzzy deadbeat control method is verified by experiments, and the harmonic current of the grid-connected current is less than 3%

Predicting Equivalent Static Density of Fuzzy Ball Drilling Fluid by BP Artificial Neutral Network

A back-propagation artificial neutral network model is built based on 220 groups of PVT experimental data to predict the equivalent static density versus depth for fuzzy ball drilling fluid which is a kind of gas-liquid two-phase material. The model is applied in the Mo80-C well located in Sichuan Province of China; the maximum relative error between calculated results and measured data is less than 2%. By comparing with the multiple regression model, the present model has a higher precision and flexibility. The equivalent static density of fuzzy ball drilling fluid from ground to the depth of 6000 m is predicted by the present model, and the results show that the equivalent static density of fuzzy ball drilling fluid will decrease slowly with the growth of depth, which indicates that the gas cores of the fuzzy balls still can exist as deep as 6000 m

Lithium Titanate Battery Management System Based on MPPT and Four-Stage Charging Control for Photovoltaic Energy Storage

To overcome the unstable photovoltaic input and high randomness in the conventional three-stage battery charging method, this paper proposes a charging control strategy based on a combination of maximum power point tracking (MPPT), and an enhanced four-stage charging algorithm for a photovoltaic power generation energy storage system. This control algorithm ensures that the charging process is not affected by fluctuations in the photovoltaic power. The discharge bus waveform, push&#8315;pull discharge load switching waveform, push&#8315;pull circuit efficiency, and voltage and current regulation accuracies of the system were investigated. The experimental results show that the charging process is consistent with the designed four-stage charging control algorithm, the voltage and current regulation accuracies satisfy the charging requirements, the busbar remained stable during the battery charging and discharging switch, and the battery balancing effect was good

Power-efficient generation of two-octave mid-IR frequency combs in a germanium microresonator

Octave-spanning frequency comb generation in the deep mid-infrared (>5.5 μm) typically requires a high pump power, which is challenging because of the limited power of narrow linewidth lasers at long wavelengths. We propose twofold dispersion engineering for a Ge-on-Si microcavity to enable both dispersion flattening and dispersion hybridization over a wide band from 3.5 to 10 μm. A two-octave mode-locked Kerr frequency comb can be generated from 2.3 to 10.2 μm, with a pump power as low as 180 mW. It has been shown that dispersion flattening greatly enhances the spectral broadening of the generated comb, whereas dispersion hybridization improves its spectral flatness