Iterative Learning Approach Research Articles

Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors

Iterative Deep Learning Approach to Active Portfolio Management with Sentiment Factors

FoodAtlas: Automated knowledge extraction of food and chemicals from literature

Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to construct a large-scale knowledge graph using large language models in an active learning setting. We apply our pipeline to the association of raw food, ingredients, and chemicals, a domain that lacks such knowledge resources. By using an iterative active learning approach of 4120 manually curated premise-hypothesis pairs as training data for ten consecutive cycles, the entailment model extracted 230,848 food-chemical composition relationships from 155,260 scientific papers, with 106,082 (46.0 %) of them never been reported in any published database. To augment the knowledge incorporated in the knowledge graph, we further incorporated information from 5 external databases and ontology sources. We then applied a link prediction model to identify putative food-chemical relationships that were not part of the constructed knowledge graph. Validation of the 443 hypotheses generated by the link prediction model resulted in 355 new food-chemical relationships, while results show that the model score correlates well (R2 = 0.70) with the probability of a novel finding. This work demonstrates how automated learning from literature at scale can accelerate discovery and support practical applications through reproducible, evidence-based capture of latent interactions of diverse entities, such as food and chemicals.

Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach

Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach

Iterative learning for efficient additive mass production

Material extrusion could enable on-demand production of complex and personalized parts but is limited by low reliability, particularly in higher-volume production. Machine learning-based methods may enhance reliability, but are often themselves insufficiently reliable for use in production. Foundation artificial intelligence models have enabled significant improvements in performance across many tasks. Here, a vision-based control system is reported, coupling active learning and uncertainty awareness with a foundation model to continually learn to build a specific part better. The resulting framework is called Iterative Learning, as it improves performance by learning from its own errors during repeated build cycles of the same part. The iterative learning approach is shown to enable robust error detection and correction while being more space, time and computationally efficient compared to a naive fine-tuning approach. This provides a path showing how foundation models may be adapted to enhance reliability across a wider range of additive manufacturing processes.

Iterative-learning-based tracking control of a two-wheeled mobile robot with model uncertainties and unknown periodic disturbances

In this paper, we develop an adaptive iterative learning approach to investigate the trajectory tracking control issue for a class of two-wheeled mobile robots subject to model uncertainties and unknown disturbances. First, we derive the nonlinear velocity error dynamics. Then a parameterization-based adaptive iterative learning control scheme is adopted to achieve precise tracking, along with logic-based update law for the estimated period and bound of the disturbance. Moreover, the boundness of all the closed-loop signals is rigorously analyzed based on the Lyapunov stability theory to provide the theoretical foundation for the proposed method. The experimental results show the efficacy and viability of our results.

Controlling the weld penetration depth of laser beam micro welding by using an iterative learning approach

AbstractIn order to meet the increasing demand for high‐speed joining processes, laser beam micro welding is used to reproducibly weld metallic joining partners such as steel. Long weld seams or high cycle speeds, however, remain a challenge to achieve a constant welding depth throughout the entire welding process without major fluctuations. This paper shows the development and evaluation of a weld penetration depth control system based on interferometric measurement of the depth of the vapor capillary. High path speeds and small weld depths are challenging for real‐time depth control. Therefore, the offline data of the interferometric measurements are used to implement an iterative learning control of the weld penetration depth. The quality of the control is verified by welding with and without spatial power modulation on 1.4301 steel while following a linear and sinusoidal trajectory.

Toward Value-Oriented Renewable Energy Forecasting: An Iterative Learning Approach

Toward Value-Oriented Renewable Energy Forecasting: An Iterative Learning Approach

Global Bipartite Exact Consensus of Unknown Nonlinear Multi-Agent Systems With Switching Topologies: Iterative Learning Approach

Global Bipartite Exact Consensus of Unknown Nonlinear Multi-Agent Systems With Switching Topologies: Iterative Learning Approach

ESO-Based Model-Free Adaptive Iterative Learning Energy-Efficient Control for Subway Train With Disturbances and Over-Speed Protection

An extended state observer based model-free adaptive iterative learning energy-efficient control (ESO-based MFAILEEC) scheme for subway train speed tracking with external disturbances and over-speed protection under the constraint on traction/braking force is proposed. Firstly, the continuous-time train motion dynamics is formulated into a discrete-time data model with consideration of external disturbances by applying the iterative dynamic linearization. Meanwhile, the external disturbances and the unknown nonlinear uncertainties of the train are transformed into a new state, which is estimated by an ESO designed in the iteration domain. Then, the ESO-based energy-efficient controller with learning ability is designed, which enables the train to achieve the purpose of energy efficiency by reducing the input force. Further, over-speed protection with trigger mechanism is developed to ensure the train operates within safe speed range. All the control strategies are designed under the constraint on traction/braking force by considering the practical limitation of the train system. No model information is involved in the whole design processes and it is a pure data-driven iterative learning approach. Rigorous mathematical analysis proves the feasibility and the robustness of the proposed method, which can guarantee the train operates safely and reliably. Finally, the simulation results further demonstrate the effectiveness of the proposed algorithm.

Adaptive Fault-Tolerant Control for a 2-Body Point Absorber Wave Energy Converter Against Actuator Faults: An Iterative Learning Control Approach

In this paper, the design issue of adaptive fault-tolerant control (FTC) is investigated for a class of continuous-time 2-body point absorber wave energy converter (WEC) systems against actuator faults based on the iterative learning approach. The actuator faults considered in this paper contain both the lock-in-place and the loss of effectiveness faults, simultaneously. The WEC dynamic equations, including two moving parts (i.e., the float and the spar), are firstly transformed into a state-space model. Then, a group of novel iterative learning based adaptive multiple controllers are developed to decrease the tracking error between the measurement output and the desired output, and two novel adaptive laws are designed to cope with two types of actuator faults. Based on the theories mentioned above, a novel algorithm is provided to present the operation flows of both adaptive laws and iterative learning. Furthermore, a sufficient condition is obtained with the aid of proper Lyapunov function, such that the related closed-loop faulty-WEC system is asymptotically stable with a guaranteed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance index. Finally, an example with a set of physical parameters of a WEC dynamic model is worked out to verify the applicability and effectiveness of the proposed iterative learning based adaptive FTC strategy.

A Data-Driven Iterative Learning Approach for Optimizing the Train Control Strategy

The energy-efficient train control (EETC) problem is investigated in this paper. And a soft actor-critic-based method is proposed to optimize the train driving strategy. Firstly, EETC problem is converted to the inverse problem, i.e., minimizing the trip time of the journey with constant energy consumption. Based on the conversion, the EETC problem is reformulated as a finite Markov decision process which can be solved by deep reinforcement learning algorithms. Secondly, an optimization method based on the soft actor-critic (SAC) method is designed to calculate the optimal driving strategy of the train with introducing the reservoir sampling method. Finally, some case studies are conducted to verify the effectiveness and performance of the proposed method. Simulation results demonstrate that a good energy-saving performance can be achieved. In single interval, the SAC-based method can reduce about 1.65&#x0025; of the energy consumption compared with numerical method. And the energy consumption reduction can be extended to be 6.49&#x0025; when the proposed approach is applied in multiple intervals.

Semantics-Aware Dynamic Localization and Refinement for Referring Image Segmentation

Referring image segmentation segments an image from a language expression. With the aim of producing high-quality masks, existing methods often adopt iterative learning approaches that rely on RNNs or stacked attention layers to refine vision-language features. Despite their complexity, RNN-based methods are subject to specific encoder choices, while attention-based methods offer limited gains. In this work, we introduce a simple yet effective alternative for progressively learning discriminative multi-modal features. The core idea of our approach is to leverage a continuously updated query as the representation of the target object and at each iteration, strengthen multi-modal features strongly correlated to the query while weakening less related ones. As the query is initialized by language features and successively updated by object features, our algorithm gradually shifts from being localization-centric to segmentation-centric. This strategy enables the incremental recovery of missing object parts and/or removal of extraneous parts through iteration. Compared to its counterparts, our method is more versatile—it can be plugged into prior arts straightforwardly and consistently bring improvements. Experimental results on the challenging datasets of RefCOCO, RefCOCO+, and G-Ref demonstrate its advantage with respect to the state-of-the-art methods.

Iterative transfer learning for automatic collective motion tuning on multiple robot platforms.

This paper proposes an iterative transfer learning approach to achieve swarming collective motion in groups of mobile robots. By applying transfer learning, a deep learner capable of recognizing swarming collective motion can use its knowledge to tune stable collective motion behaviors across multiple robot platforms. The transfer learner requires only a small set of initial training data from each robot platform, and this data can be collected from random movements. The transfer learner then progressively updates its own knowledge base with an iterative approach. This transfer learning eliminates the cost of extensive training data collection and the risk of trial-and-error learning on robot hardware. We test this approach on two robot platforms: simulated Pioneer 3DX robots and real Sphero BOLT robots. The transfer learning approach enables both platforms to automatically tune stable collective behaviors. Using the knowledge-base library the tuning procedure is fast and accurate. We demonstrate that these tuned behaviors can be used for typical multi-robot tasks such as coverage, even though they are not specifically designed for coverage tasks.

Accelerating development of engineered T cell therapies in the EU: current regulatory framework for studying multiple product versions and T2EVOLVE recommendations.

To accelerate the development of Advanced Therapy Medicinal Products (ATMPs) for patients suffering from life-threatening cancer with limited therapeutic options, regulatory approaches need to be constantly reviewed, evaluated and adjusted, as necessary. This includes utilizing science and risk-based approaches to mitigate and balance potential risks associated with early clinical research and a more flexible manufacturing paradigm. In this paper, T2EVOLVE an Innovative Medicine Initiative (IMI) consortium explores opportunities to expedite the development of CAR and TCR engineered T cell therapies in the EU by leveraging tools within the existing EU regulatory framework to facilitate an iterative and adaptive learning approach across different product versions with similar design elements or based on the same platform technology. As understanding of the linkage between product quality attributes, manufacturing processes, clinical efficacy and safety evolves through development and post licensure, opportunities are emerging to streamline regulatory submissions, optimize clinical studies and extrapolate data across product versions reducing the need to perform duplicative studies. It is worth noting that this paper is focusing on CAR- and TCR-engineered T cell therapies but the concepts may be applied more broadly to engineered cell therapy products (e.g., CAR NK cell therapy products).

Advanced prompting as a catalyst: Empowering large language models in the management of gastrointestinal cancers

&lt;p&gt;Large Language Models' (LLMs) performance in healthcare can be significantly impacted by prompt engineering. However, the area of study remains relatively uncharted in gastrointestinal oncology until now. Our research delves into this unexplored territory, investigating the efficacy of varied prompting strategies, including simple prompts, templated prompts, in-context learning (ICL), and multi-round iterative questioning, for optimizing the performance of LLMs within a medical setting. We develop a comprehensive evaluation system to assess the performance of LLMs across multiple dimensions. This robust evaluation system ensures a thorough assessment of the LLMs' capabilities in the field of medicine. Our findings suggest a positive relationship between the comprehensiveness of the prompts and the LLMs' performance. Notably, the multi-round strategy, which is characterized by iterative question-and-answer rounds, consistently yields the best results. ICL, a strategy that capitalizes on interrelated contextual learning, also displays significant promise, surpassing the outcomes achieved with simpler prompts. The research underscores the potential of advanced prompt engineering and iterative learning approaches for boosting the applicability of LLMs in healthcare. We recommend that additional research be conducted to refine these strategies and investigate their potential integration, to truly harness the full potential of LLMs in medical applications.&lt;/p&gt;

Iterative learning approach for consensus tracking of partial difference multi-agent systems with control delay under switching topology

In this paper, the consensus tracking problem for the linear and nonlinear partial difference multi-agent systems with switching communication topology and control delay is investigated. Based on relative local measurements of neighboring followers, while considering spatio-temporal discretization and initial state deviation, a discrete distributed consensus protocol with initial value learning is designed for each agent via D-type iterative learning approach. Through rigorous mathematical theoretical analysis, the necessary and sufficient conditions are obtained. Under the switching of the communication topology, these conditions ensure that the consensus tracking control of the MASs can be solved. After applying the designed protocol, in the sense of the L2 norm and along the positive direction of the iteration axis, the consensus tracking error between any two agents can converge to zero. Finally, some simulation examples are used to demonstrate the validity of the protocol and theoretical results.

Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples.

To evaluate an iterative learning approach for enhanced performance of robust artificial-neural-networks for k-space interpolation (RAKI), when only a limited amount of training data (auto-calibration signals [ACS]) are available for accelerated standard 2D imaging. In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI [iRAKI]), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in vivo datasets from the fastMRI neuro database with different contrast settings. For limited training data (18 and 22 ACS lines for R=4 and R=5, respectively), iRAKI outperforms standard RAKI by reducing residual artifacts and yields better noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. Additionally, iRAKI shows better performance than both GRAPPA and standard RAKI in case of pre-scan calibration with varying contrast between training- and undersampled data. RAKI benefits from the iterative learning approach, which preserves the noise suppression feature, but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.

An iterative learning approach to formation control of discrete‐time multi‐agent systems With varying trial lengths

AbstractIn this article, an iterative learning approach is proposed for the formation control of discrete‐time multi‐agent systems, where the trial length of each learning iteration is randomly varying. In particular, a modified state error related to the prescribed formation is defined by taking into account the nonuniform actual trial length that could be different from the desired one. Then, a P‐type iterative learning protocol is established for switching networks of agents subject to nonuniform trial lengths, and the convergence analyses are given for the fixed and the iteration‐varying initial conditions respectively. It shows that through iterative learning, the given formation will be maintained among multiple agents in the entire time interval of one trial. In the end, simulations are done to demonstrate the correctness of the obtained theoretical results.

An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook

The snow depth or snow water equivalent affects water, carbon, and energy cycles as well as surface–atmosphere interactions. Therefore, the global monitoring of spatiotemporal changes in snow water equivalent is a crucial issue, which is performed by characterizing the macrophysical, microstructural, optical, and thermal characteristics of the snowpack. This paper is a review of the retrieval methods of snow water equivalent in three main categories, including in situ measurements, reconstruction approaches, and space-borne measurements, along with their basic concepts, advantages, and uncertainties. Since satellite observations are the most important tool used to detect snow properties, the paper focuses on inversion models and techniques using microwave remote sensing. The inversion models, based on various theoretical foundations, are classified into empirical, statistical, and physical (emission) models, and the techniques are described in four groups: iterative methods, lookup table, machine learning, and data assimilation approaches. At the end, the available global and regional gridded products providing the spatiotemporal maps of snow water equivalent with different resolutions are presented, as well as approaches for improving the snow data.

APNet: Adversarial Learning Assistance and Perceived Importance Fusion Network for All-Day RGB-T Salient Object Detection

To improve the performance of salient object detection (SOD) in scenes with low-light conditions (e.g., nighttime) and cluttered backgrounds, infrared thermal images are used to supplement RGB images to achieve good all-day imaging as infrared images are insensitive to light source changes. Therefore, we built an adversarial learning assistance and perceived importance fusion network (APNet) for all-day RGB-thermal (RGB-T) SOD. First, an iterative adversarial learning approach was used to establish a generator and three discriminators. The generator provides salient maps that are eventually accepted by the discriminators and are used to determine their similarities with the labels. Second, a progressively guided optimization structure with high-level features is used to refine low-level features across multiple scales gradually. To further improve the detection results, a perceived importance fusion module (PIFM) is used to weigh and fuse different modalities in cases where the presence of noise may degrade sensor fusion. Extensive experiments on existing RGB-T datasets demonstrate that the proposed APNet notably outperforms state-of-the-art models.