Dynamic process fault isolation by partial DPCA

Principal component analysis (PCA) is a popular tool in fault detecting of the complex plant, but offers little support on fault isolation. Partial PCA (PPCA) is well developed for its capability of fault isolation utilizing a structured residual. In this paper, partial dynamic PCA(PDPCA) is proposed to enhance the isolation ability of dynamic process, which is a method combining PPCA and dynamic PCA. Simulation of PDPCA on a CSTR shows the effectiveness of the proposed method

The Optimization of Finishing Train Based on Improved Genetic Algorithm

The central issue of finishing train is that we should distribute the thickness of each exit with reason and determine the rolling force and relative convexity. The optimization methods currently used are empirical distribution method and the load curve method, but they both have drawbacks. To solve those problems we established a mathematical model of the finishing train and introduced an improved Genetic Algorithm. In this algorithm we used real number encoding, selection operator of a roulette and elitist selection and then improved crossover and mutation operators. The results show that the model and algorithm is feasible and could ensure the optimal effect and convergence speed. The products meet the production requirements. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.389

Solution to a class of multistate Landau-Zener model beyond integrability conditions

We study a class of multistate Landau-Zener model which cannot be solved by integrability conditions or other standard techniques. By analyzing analytical constraints on its scattering matrix and performing fitting to results from numerical simulations of the Schr\"{o}dinger equation, we find nearly exact analytical expressions of all its transition probabilities for specific parameter choices. We also determine the transition probabilities up to leading orders of series expansions in terms of the inverse sweep rate (namely, in the diabatic limit) for general parameter choices. We further show that this model can describe a Su-Schrieffer-Heeger chain with couplings changing linearly in time. Our work presents a new route, i.e., analytical constraint plus fitting, to analyze those multistate Landau-Zener models which are beyond the applicability of conventional solving methods.Comment: Version accepted by Physica Script

Multi-modal preference alignment remedies regression of visual instruction tuning on language model

In production, multi-modal large language models (MLLMs) are expected to support multi-turn queries of interchanging image and text modalities. However, the current MLLMs trained with visual-question-answering (VQA) datasets could suffer from degradation, as VQA datasets lack the diversity and complexity of the original text instruction datasets which the underlying language model had been trained with. To address this challenging degradation, we first collect a lightweight (6k entries) VQA preference dataset where answers were annotated by Gemini for 5 quality metrics in a granular fashion, and investigate standard Supervised Fine-tuning, rejection sampling, Direct Preference Optimization (DPO), and SteerLM. Our findings indicate that the with DPO we are able to surpass instruction-following capabilities of the language model, achieving a 6.73 score on MT-Bench, compared to Vicuna's 6.57 and LLaVA's 5.99 despite small data scale. This enhancement in textual instruction proficiency correlates with boosted visual instruction performance (+4.9\% on MM-Vet, +6\% on LLaVA-Bench), with minimal alignment tax on visual knowledge benchmarks compared to previous RLHF approach. In conclusion, we propose a distillation-based multi-modal alignment model with fine-grained annotations on a small dataset that reconciles the textual and visual performance of MLLMs, restoring and boosting language capability after visual instruction tuning

Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data

Federated learning (FL) offers a privacy-centric distributed learning framework, enabling model training on individual clients and central aggregation without necessitating data exchange. Nonetheless, FL implementations often suffer from non-i.i.d. and long-tailed class distributions across mobile applications, e.g., autonomous vehicles, which leads models to overfitting as local training may converge to sub-optimal. In our study, we explore the impact of data heterogeneity on model bias and introduce an innovative personalized FL framework, Multi-level Personalized Federated Learning (MuPFL), which leverages the hierarchical architecture of FL to fully harness computational resources at various levels. This framework integrates three pivotal modules: Biased Activation Value Dropout (BAVD) to mitigate overfitting and accelerate training; Adaptive Cluster-based Model Update (ACMU) to refine local models ensuring coherent global aggregation; and Prior Knowledge-assisted Classifier Fine-tuning (PKCF) to bolster classification and personalize models in accord with skewed local data with shared knowledge. Extensive experiments on diverse real-world datasets for image classification and semantic segmentation validate that MuPFL consistently outperforms state-of-the-art baselines, even under extreme non-i.i.d. and long-tail conditions, which enhances accuracy by as much as 7.39% and accelerates training by up to 80% at most, marking significant advancements in both efficiency and effectiveness.Comment: 14 pages, 10 figure

Visualization of the entire process of rice spikelet infection by Ustilaginoidea virens through nondestructive inoculation

IntroductionRice false smut caused by Ustilaginoidea virens, is a destructive fungal disease encountered in many rice-producing areas worldwide. To determine the process by which U. virens infects rice spikelets in the field.MethodsThe green fluorescent protein-labeled U. virens was used as an inoculum to conduct artificial inoculation on rice at the booting stage via non-destructive panicle sheath instillation inoculation.ResultsThe results showed that the conidia of U. virens germinated on the surface of rice glumes and produced hyphae, which clustered at the mouth of rice glumes and entered the glumes through the gap between the palea and lemma. The conidia of U. virens colonized in rice floral organs, which led to pollen abortion of rice. U. virens wrapped the whole rice floral organ, and the floral organ-hyphae complex gradually expanded to open the glumes to form a rice false smut ball, which was two to three times larger than that observed in normal rice.DiscussionPanicle sheath instillation inoculation was shown to be a non-destructive inoculation method that could simulate the natural infection of U. virens in the field. The entire infection process of U. virens was visualized, providing a theoretical reference for formulating strategies to control rice false smut in the field

A liquid-infiltrated Al 2 O 3 framework electrolyte enables aqueous zinc batteries †

Aqueous zinc-ion battery anodes face the twin challenges of dendrite growth and severe side reactions. Here a liquid-infiltrated Al2O3 framework electrolyte (LIAFE) is developed to address these issues and enables stable long-life Zn anodes for over 4000 hours. The LIAFE shows a uniform morphology and exhibits enhanced performance