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A Zero-Crossing Detection-Based adaptive Stabilization Method for Cascaded DC Systems

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A Zero-Crossing Detection-Based Adaptive Stabilization Metho...

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Bi’An Zhao Youlin Fan Tonglu Wang Yufei Li Yue Wang Xinyang Su State Key Laboratory of Electrical Insulation & Power Equipment Xi’an Jiaotong University Xi’an China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

The virtual impedance (VI) method can solve the instability problem caused by constant power load (CPL) in dc systems. In order to make the VI method adapt to various frequency-point changes in external system parameters, traditional peak valley detection (PVD) method has been proposed, but their applicability is limited due to the unreasonable assumption. Therefore, this article proposes an adaptive stabilization algorithm based on zero-crossing detection (ZCD), which has a wide range of application, such as multi-frequency oscillation. In addition, a detailed parameter design method is provided. The simulation results verify the advantages of the proposed ZCD over the PVD. Finally, a 500W, 48-60V cascaded system is built to validate the advantages of the ZCD method in terms of the frequency extraction accuracy and the oscillation suppression.

关键词： adaptive systems Accuracy Simulation Design methodology adaptive algorithms Energy conversion Frequency conversion Impedance Oscillators Frequency control

adaptive Nuclear Norm Regularization Model for Prediction of Potential Small Molecule–miRNA Associations 2nd

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Adaptive Nuclear Norm Regularization Model for Prediction of...

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Tao, Ran Wu, HongJie Gu, Cen Chen, Jing Suzhou University of Science and Technology Jiangsu Suzhou China

ISBN: (纸本)9789819619061

A large number of studies have proved the importance of using microRNAs (miRNAs) as targets for small molecule (SM) drug therapy. But just exploring new SM-miRNA associations through experiments in biology is very expensive, so there are many current models to predict SM-miRNA associations, and most of the currently existed models have some drawbacks, in this paper, we use an adaptive bounded nuclear norm regularization model to predict SM-miRNA associations. Which constructs a heterogeneous SM-miRNA network using similarities and predicts associations by minimizing nuclear norms. It uses an adaptive algorithm for dynamic parameter adjustment and learning rate optimization, ensuring matrix elements are practical. The model introduces regularization to handle noise. Validation is done using two datasets and cross-validation methods. Based on dataset 1, the AUC (Area Under the Receiver Operating Characteristic Curve) values for global Leave-One-Out Cross-Validation (LOOCV), miRNA-fixed LOOCV, and 5-fold CV are 0.9815, 0.9779, and 0.9760, respectively. Furthermore, many predicted associations have been validated by experimental literature. All these results confirm that Our Model is a reliable tool for inferring potential SM-miRNA associations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： adaptive algorithms

Using Gamification to Reduce Youth Depression: A Step-by-Step Method

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Using Gamification to Reduce Youth Depression: A Step-by-Ste...

Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Soumyadeb Bakshi Sourashis Bhaumik Subhra Debdas Saptak Das Deepak Jain Bijay Kishor Shishir Sekhar Pattanaik School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India School of Business Shri Mata Vaishno Devi University Katra Jammu and Kashmir School of Computer Engineering Gita Autonomous College Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Youth depression is a major global health concern due to its alarmingly high occurrence and harmful impact on mental health. Traditional therapy methods frequently struggle with involvement, accessibility, and stigma. This research addresses depression in young people by proposing a novel solution based on gamification concepts. Our method is based on creating a game module integrated with a ML model that is especially designed for young people with depression. It provides a fun and active way to reduce symptoms and postpone thoughts of suicide. The module includes a number of levels and activities that are meant to be enjoyable while also supporting mental and emotional health. Crucially, the game has an adaptable system that is connected to players’ depression levels, modifying job complexity in response to continuous evaluation to guarantee maximum effectiveness and engagement. The key component of our invention is the incorporation of a depression assessment question into the game interface, which allows users’ psychological states to be assessed in real time. The game's algorithm is informed by this data, which constantly adjusts the difficulty and intensity of the challenges to suit each player's demands. Our goal is to improve users’ feeling of agency and accomplishment by personalizing their gaming experience, which will have a beneficial effect on their mental health. Our work emphasizes the value of customized approaches in tackling the complex problems associated with depression and adds to the expanding corpus of research that examines the possibility of gamification in mental health therapies.

关键词： Technological innovation Medical treatment adaptive algorithms Mental health Games Machine learning Depression Real-time systems Complexity theory

Fixed-Budget Change Point Identification in Piecewise Constant Bandits

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arXiv 2025年

作者： Lazzaro, Joseph Pike-Burke, Ciara Department of Mathematics Imperial College London United Kingdom

We study the piecewise constant bandit problem where the expected reward is a piecewise constant function with one change point (discontinuity) across the action space [0, 1] and the learner's aim is to locate the change point. Under the assumption of a fixed exploration budget, we provide the first non-asymptotic analysis of policies designed to locate abrupt changes in the mean reward function under bandit feedback. We study the problem under a large and small budget regime, and for both settings establish lower bounds on the error probability and provide algorithms with near matching upper bounds. Interestingly, our results show a separation in the complexity of the two regimes. We then propose a regime adaptive algorithm which is near optimal for both small and large budgets simultaneously. We complement our theoretical analysis with experimental results in simulated environments to support our findings. © 2025, CC BY-NC-SA.

关键词： adaptive algorithms

Clustering with Non-adaptive Subset Queries

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arXiv 2024年

作者： Black, Hadley Lee, Euiwoong Mazumdar, Arya Saha, Barna UC San Diego United States University of Michigan United States

Recovering the underlying clustering of a set U of n points by asking pair-wise same-cluster queries has garnered significant interest in the last decade. Given a query S ⊂ U, |S| = 2, the oracle returns yes if the points are in the same cluster and no otherwise. For adaptive algorithms with pair-wise queries, the number of required queries is known to be Θ(nk), where k is the number of clusters. However, non-adaptive schemes allow for the querying process to be parallelized, which is a highly desirable property. It turns out that non-adaptive pair-wise query algorithms are extremely limited for the above problem: even for k = 3, such algorithms require Ω(n2) queries, which matches the trivial O(n2) upper bound attained by querying every pair of points. To break the quadratic barrier for non-adaptive queries, we study a natural generalization of this problem to subset queries for |S| > 2, where the oracle returns the number of clusters intersecting S. Our aim is to determine the minimum number of queries needed for exactly recovering an arbitrary k-clustering. Allowing for subset queries of unbounded size, O(n) queries is possible with an adaptive scheme (Chakrabarty-Liao, 2024). However, the realm of non-adaptive algorithms is completely unknown. In this paper, we give the first non-adaptive algorithms for clustering with subset queries. Our main result is a non-adaptive algorithm making O(n log k·(log k+log log n)2) queries, which improves to O(n log log n) when k is a constant. In addition to non-adaptivity, we take into account other practical considerations, such as enforcing a bound, s, on the query size. In this setting we prove that Ω(max(n2/s2, n)) queries are necessary and obtain algorithms making Oe(n2k/s2) queries for any s ≤ √n and Oe(n2/s) queries for any s ≤ n. In particular, our first algorithm is optimal up to log n factors when k is constant. We also consider the natural special case when the clusters are balanced, obtaining non-adaptive algorithms

关键词： adaptive algorithms

Efficient adaptive Optimization via Subset-Norm and Subspace-Momentum: Fast, Memory-Reduced Training with Convergence Guarantees

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arXiv 2024年

作者： Nguyen, Thien Hang Le Nguyen, Huy Northeastern University Boston United States

We introduce two complementary techniques for efficient adaptive optimization that reduce memory requirements while accelerating training of large-scale neural networks. The first technique, Subset-Norm adaptive step size, generalizes AdaGrad-Norm and AdaGrad(-Coordinate) by reducing the second moment term’s memory footprint from O(d) to O(√d) through step-size sharing, where d is the model size. For non-convex smooth objectives under coordinate-wise sub-gaussian gradient noise, we prove a noise-adapted high-probability convergence guarantee showing improved dimensional dependence over existing methods. Our second technique, Subspace Momentum, reduces the momentum state’s memory footprint by operating in a low-dimensional subspace while applying standard SGD in the orthogonal complement. We establish high-probability convergence rates under similar relaxed assumptions. Empirical evaluation on LLaMA models from 60M to 1B parameters demonstrates the effectiveness of our methods, where combining subset-norm with subspace-momentum achieves Adam’s validation perplexity in approximately half the training tokens (6.8B vs 13.1B) while using only 20% of the Adam’s optimizer-states memory footprint and requiring minimal additional hyperparameter tuning. © 2024, CC BY.

关键词： adaptive algorithms

FedCAda: adaptive Client-Side Optimization for Accelerated and Stable Federated Learning

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arXiv 2024年

作者： Zhou, Liuzhi He, Yu Zhai, Kun Liu, Xiang Liu, Sen Ma, Xingjun Ye, Guangnan Jiang, Yu-Gang Chai, Hongfeng School of Computer Science Fudan University Shanghai China Tandon School of Engineering New York University New York United States

Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients while preserving data privacy. However, the quest to balance acceleration and stability becomes a significant challenge in FL, especially on the client-side. In this paper, we introduce Fed- CAda, an innovative federated client adaptive algorithm designed to tackle this challenge. FedCAda leverages the Adam algorithm to adjust the correction process of the first moment estimate m and the second moment estimate v on the client-side and aggregate adaptive algorithm parameters on the server-side, aiming to accelerate convergence speed and communication efficiency while ensuring stability and performance. Additionally, we investigate several algorithms incorporating different adjustment functions. This comparative analysis revealed that due to the limited information contained within client models from other clients during the initial stages of federated learning, more substantial constraints need to be imposed on the parameters of the adaptive algorithm. As federated learning progresses and clients gather more global information, FedCAda gradually diminishes the impact on adaptive parameters. These findings provide insights for enhancing the robustness and efficiency of algorithmic improvements. Through extensive experiments on computer vision (CV) and natural language processing (NLP) datasets, we demonstrate that FedCAda outperforms the state-of-the-art methods in terms of adaptability, convergence, stability, and overall performance. This work contributes to adaptive algorithms for federated learning, encouraging further exploration. Copyright © 2024, The Authors. All rights reserved.

关键词： adaptive algorithms

A Conformalized Empirical Bayes Method for Multiple Testing with Side Information

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arXiv 2025年

作者： Zhao, Zinan Sun, Wenguang Center for Data Science School of Mathematical Sciences Zhejiang University China Center for Data Science School of Management Zhejiang University China

This article presents a Conformalized Locally adaptive Weighting (CLAW) approach to multiple testing with side information. The proposed method employs innovative data-driven strategies to construct pairwise exchangeable scores, which are integrated into a generic algorithm that leverages a mirror process for controlling the false discovery rate (FDR). By combining principles from empirical Bayes with powerful techniques in conformal inference, CLAW provides a valid and efficient framework for incorporating structural information from both test data and auxiliary covariates. Unlike existing empirical Bayes FDR methods that primarily offer asymptotic validity, often under strong regularity conditions, CLAW controls the FDR in finite samples under weaker conditions. Extensive numerical studies using both simulated and real data demonstrate that CLAW exhibits superior performance compared to existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： adaptive algorithms

Development of Automotive Exhaust Emission Detection System Based on Fuzzy PID adaptive Algorithm

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Development of Automotive Exhaust Emission Detection System ...

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Huang Hao He Jinglin School of Foreign Studies and Trade Hubei University of Automotive Technology China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Based on the theory of fuzzy PID control, a brand-new and efficient automobile exhaust emission detection system is successfully developed through constant speed, constant torque and constant power control. The system is designed with a signal conditioning circuit to achieve monitoring of automobile exhaust emissions. This paper elaborates the technical route of the development of the automobile exhaust emission detection system, introduces the detection methods, procedures and criteria for automobile exhaust emissions, analyzes the overall structure of the automobile exhaust emission detection system, discusses the design of the system, and carries out system tests and result analysis to improve the level of automobile exhaust emission detection.

关键词： Torque PI control Power control adaptive algorithms Software Recording Automobiles PD control Protection Testing