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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Tao Zhang Zhenhua Tan Software College of Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Existing studies on multi-modal emotion recognition in conversation (MMERC) mainly focus on multi-modal fusion and context modeling for emotion representation, facing limitations in uncovering the intrinsic structure of emotion-related data. The existing geometric consistency regularization (GCR) technique aims to build meaningful latent feature structures in multiple modalities and has been validated for non-conversational data, therefore we further explore its application to the MMERC task, which involves conversational data. However, we find that the geometric consistency in conversational data varies among different speaker and conversation relations (intra-speaker, inter-speaker, and inter-conversation relations), probably due to differences in speaker expressions and conversation topics. This makes the direct application of GCR less effective. To address this issue, we propose a Multi-Relational Geometric Regularization Framework for MMERC (R 4 -MMERC). Our framework constructs geometric structures of conversational data and performs dynamically balanced consistency regularization based on multiple speaker and conversation relations (intra-speaker, interspeaker, and inter-conversation relations). We tested our framework by integrating it with three typical MMERC models on the IEMOCAP benchmark dataset. The results show significant performance improvements, demonstrating the effectiveness of our approach 1 .

关键词： Emotion recognition Computational modeling Oral communication Speech recognition Signal processing Benchmark testing Acoustics Proposals Speech processing Context modeling

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Quantum controlling and the topological properties of the magnon photo-transport in two-dimensional collinear ferromagnet

arXiv

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arXiv 2025年

作者： Li, Jun-Cen Du, An College of Sciences Northeastern University Shenyang110004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China

In our work, we study magnon transport induced by light through Aharonov-Casher (AC) effect, including magnon spin photocurrent (MSPC) and magnon energy photocurrent (MEPC). Firstly, we regard the effect of the electric field on the magnon through the AC effect as a perturbation. Then we derived the expressions of MSPC and MEPC in two-dimensional collinear ferromagnetic system. And we apply our theory to the two-dimension ferromagnetic Hexagonal and Kagome lattice. We find that the optical frequency and the relaxation time of the material can be used to control the photo-transport of magnons. In addition, under the condition of low light frequncy and infinite relaxation time, the longitudinal magnon photo-transport is related to the topological property of the magnon system. Copyright © 2025, The Authors. All rights reserved.

关键词： Ferromagnetic materials

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A Novel Black Box Process Quality optimization Approach based on Hit Rate

arXiv

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arXiv 2023年

作者： Yang, Yang Wu, Jian Song, Xiangman Wu, Derun Su, Lijie Tang, Lixin Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China Manufacturing Department of Shanghai Meishan Iron & Steel Co. LTD. Jiangsu Nanjing210039 China Frontier Science Center for Industrial Intelligence and Systems Optimization

Hit rate is a key performance metric in predicting process product quality in integrated industrial processes. It represents the percentage of products accepted by downstream processes within a controlled range of quality. However, optimizing hit rate is a non-convex and challenging problem. To address this issue, we propose a data-driven quasi-convex approach that combines factorial hidden Markov models, multitask elastic net, and quasi-convex optimization. Our approach converts the original non-convex problem into a set of convex feasible problems, achieving an optimal hit rate. We verify the convex optimization property and quasi-convex frontier through Monte Carlo simulations and real-world experiments in steel production. Results demonstrate that our approach outperforms classical models, improving hit rates by at least 41.11% and 31.01% on two real datasets. Furthermore, the quasi-convex frontier provides a reference explanation and visualization for the deterioration of solutions obtained by conventional models. Copyright © 2023, The Authors. All rights reserved.

关键词： Convex optimization

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PARE: A Plane-Assisted Autonomous Robot Exploration Framework in Unknown and Uneven Terrain

PARE: A Plane-Assisted Autonomous Robot Exploration Framewor...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Pu Xu Zhaoqiang Bai Haoming Liu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Identifying traversable areas is a critical task for unmanned vehicles exploring safely through unstructured environments. In practice, the ambiguity in perceiving terrain traversability usually brings great challenges for autonomous exploration in unknown and uneven terrain, which often leads to conservative strategies or potential risk of vehicle damage, resulting in many unexplored areas in the environment. To that end, this paper proposes a plane-assisted autonomous robot exploration framework (PARE) to achieve maximum volume and safe autonomous exploration. The process is carried out by a three-step dual-layer framework: constructing a local tree using Plane-Assisted RRT* (PA-RRT*), calculating exploration gain based on terrain information, and maintaining a global search graph. Firstly, the planar feature metrics (flatness, sparsity, elevation variation, slope and slope variation) are introduced to determine the terrain traversability. Secondly, to completely explore the rugged environment, we propose a dual-layer exploration framework comprising local and global strategies. A local planner based on PA-RRT* is proposed to find the best path by evaluating the planar information and the volumetric gain within the local exploration tree. Meanwhile, a global planner constructed by graph is proposed to record unexplored nodes with high exploration gain from the local tree to ensure a high level of exploration volume. Extensive simulation and real-world experiments demonstrate that our method significantly outperforms existing frameworks, with an average improvement of more than 12% in exploration volume.

关键词： Measurement Intelligent robots Autonomous vehicles Autonomous robots

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A Novel Correlation-optimized Deep Learning Method for Wind Speed Forecast

arXiv

引用

arXiv 2023年

作者： Yang, Yang Lang, Jin Wu, Jian Zhang, Yanyan Zhao, Xiang Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education 110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Northeastern University Shenyang110819 China Institute of Industrial & Systems Engineering Northeastern University Shenyang110819 China

The increasing installation rate of wind power poses great challenges to the global power system. In order to ensure the reliable operation of the power system, it is necessary to accurately forecast the wind speed and power of the wind turbines. At present, deep learning is progressively applied to the wind speed prediction. Nevertheless, the recent deep learning methods still reflect the embarrassment for practical applications due to model interpretability and hardware limitation. To this end, a novel deep knowledge-based learning method is proposed in this paper. The proposed method hybridizes pre-training method and auto-encoder structure to improve data representation and modeling of the deep knowledge-based learning framework. In order to form knowledge and corresponding absorbers, the original data is preprocessed by an optimization model based on correlation to construct multi-layer networks (knowledge) which are absorbed by sequence to sequence (Seq2Seq) models. Specifically, new cognition and memory units (CMU) are designed to reinforce traditional deep learning framework. Finally, the effectiveness of the proposed method is verified by three wind prediction cases from a wind farm in Liaoning, China. Experimental results show that the proposed method increases the stability and training efficiency compared to the traditional LSTM method and LSTM/GRU-based Seq2Seq method for applications of wind speed forecasting. Copyright © 2023, The Authors. All rights reserved.

关键词： Wind power

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A Thousand-Hand Bodhisattva: Emergent Abilities of Artificial General Intelligence Via Single- Objective to Multi-Objective optimization

SSRN

引用

SSRN 2024年

作者： Xu, Wendi Ming, Zhang Shengchun, Zheng National Frontier Sciences Center for Industrial Intelligence and Systems Optimization Ministry of Education China College of Information Science and Engineering Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China Xinjiang Astronomical Observatory Chinese Academy of Sciences China Key Laboratory for Radio Astronomy Chinese Academy of Sciences Nanjing China University of Chinese Academy of Sciences China Fujian Coastal Environmental Monitoring Station China

Towards artificial general intelligence, emergent abilities of large language models (LLMs) are observed wildly especially for well-known GPTs, which are due to scaling up primarily along three factors: training computation, model parameters, and dataset size. Scaling up makes emergence. Inspired by the insights for LLMs case, we scale up the number of auxiliary tasks (from 2 to many) to boost the core task for the model of single-objective to multi-objective optimization (SMO) in evolutionary transfer optimization (ETO), therefore achieve a new version of SMO in the open benchmarks of vehicle routing problems, permutation flow shop scheduling problem and travel salesman problem. We name the new SMO armed with transfer core as a "Thousand-Hand Bodhisattva" with many arms or hands and analyze its emergent abilities. © 2024, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Construction of a Neural Network Potential for Fe-Cr-Ni Alloys and its Application in Uniaxial Tension Simulations

SSRN

引用

SSRN 2025年

作者： Wang, Kun Ren, Lin Liu, Zhongqiu Wang, Chun Zhang, Hao National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education China School of Metallurgy Northeastern University Shenyang110819 China

Fe-Cr-Ni alloys are widely used in industrial applications and serve as crucial high-temperature materials, for which accurate interatomic potential functions are essential for understanding their microstructure and properties. In this study, we developed a machine learning potential (Deep Potential, DP) with high precision for Fe-Cr-Ni alloys using the Deep Potential for Molecular Dynamics (DeePMD) framework combined with an extensive density functional theory (DFT) database. First, eight different crystal structures of the Fe-Cr-Ni system were systematically sampled through high-throughput first-principles calculations. We compared the accuracy of potentials trained on two distinct datasets using the DeepMD model. Subsequently, molecular dynamics simulations were employed to verify the performance of these potentials in predicting crystal structures, thermodynamic properties, and mechanical properties. The results demonstrate that the model trained with heating-process data exhibits superior accuracy in energy and force predictions, achieving root mean square errors (RMSE) of 3.27 meV/atom for atomic energy and 72.4 meV/Å for forces across the eight Fe-Cr-Ni systems. These predictions show excellent agreement with DFT-calculated energies. In large-scale molecular dynamics simulations, the DP demonstrates mechanical accuracy comparable to experimental values. Specifically, the elastic constant of Fe60Cr25Ni15 alloy calculated using the DP is 101.321 GPa, which is within a 1% error compared to the DFT-calculated value of 102.221 GPa. © 2025, The Authors. All rights reserved.

关键词： Crystal atomic structure

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Rapid Screening and Experimental Investigation of Oxygen Carriers for Chemical Looping Oxidative Dehydrogenation of Ethane

SSRN

引用

SSRN 2024年

作者： Wang, Kun Lin, Shuai Wu, Haoyang Liu, Guoyuan National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China School of Metallurgy State Environmental Protection Key Laboratory of Eco-Industry Northeastern University Shenyang110819 China

Chemical looping oxidative dehydrogenation of ethane (CLODHE) is a promising alternative to ethylene production. The key of this technology is the development of oxygen carriers. Based on machine learning and thermodynamics, a novel rapid screening strategy of perovskite oxygen carriers is proposed in this paper. Dozens of thermodynamically feasible oxygen carriers are screened for CLODHE process and three of them are selected for further experimental verification. In addition, the effects of temperature and weight hourly space velocity are further explored. Finally, the cycle stability is investigated. To obtain Gibbs free energy changes of perovskite oxidizing ethane to ethylene, different machine learning models are established to predict the Gibbs free energy changes of oxygen release reaction under different oxygen vacancy ranges and the Adaboost models have excellent performance. Among selected oxygen carriers, LaCuO3 has the best performance. Ethane conversion and ethylene selectivity of LaCuO3 are 51.3% and 93.93% over 10 cycles at the optimal conditions of 750°C and 18000 ml·h-1·g-1. The XPS patterns show that the composition and content of oxygen are almost unaltered before and after cycles, which may be also one of reasons for its stability. Moreover, the SEM-EDS patterns show that oxygen carriers change from bulk to loosen after cycles, which increase contact area between ethane and particles. © 2024, The Authors. All rights reserved.

关键词： Ethane

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NSMD-NAS: Retinal Image Segmentation with Neural Architecture Search and Non-Subsampled Multiscale Decomposition

NSMD-NAS: Retinal Image Segmentation with Neural Architectur...

引用

Congress on Evolutionary Computation

作者： Hanyu Zhang Lixin Tang Xiangman Song Te Xu National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shen Yang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shen Yang China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Accurate segmentation of the microvascular component of the retinal fundus image is of great significance for the clinical diagnosis. Currently, deep learning-based methods are often employed for retinal fundus image segmentation. However, most deep learning models manually designed by experts are not suitable for task-specific applications. Therefore, we propose a novel retinal image segmentation method that integrates a neural architecture search framework with a non-subsampled multiscale decomposition (NSMD-NAS) technique. Specifically, the feature extraction layer includes the convolutional layer and the transformer layer. In addition, the non-subsampling filter bank is embedded into the convolutional and the transformer modules respectively to generate a new network module for exploring multiscale, multi-frequency, and multi-directional information. Unlike previous wavelet-based network modules, non-subsampling network modules have translation invariance. Subsequently, the components of the network are specified by the elaborated encoding strategy, and then the network with the highest adaptation is searched by a genetic evolutionary algorithm (GA) to finally obtain the best network structure. Experimental results demonstrate the effectiveness of each component in the proposed model on the public benchmark datasets.

关键词： Convolutional codes Wavelet transforms Image segmentation Filter banks Computer architecture Evolutionary computation Retina

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Prospects for measuring neutrino mass with 21-cm forest

arXiv

引用

arXiv 2025年

作者： Shao, Yue Du, Guo-Hong Li, Tian-Nuo Zhang, Xin Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China

Both particle physics experiments and cosmological observations have been used to explore neutrino properties. Cosmological researches of neutrinos often rely on the early-universe cosmic microwave background observations or other late-universe probes, which mostly focus on large-scale structures. We introduce a distinct probe, the 21-cm forest, that differs from other probes in both time and scale. Actually, the 21-cm forest is a unique tool for studying small-scale structures in the early universe. Below the free-streaming scale, massive neutrinos suppress the matter power spectrum, influencing small-scale fluctuations in the distribution of matter. The one-dimensional (1D) power spectrum of the 21-cm forest can track these fluctuations across different scales, similar to the matter power spectrum, providing an effective method to constrain neutrino mass. Although heating effects in the early universe can also impact the 1D power spectrum of the 21-cm forest, we assess the potential of the 21-cm forest as a tool for measuring neutrino mass, given that the temperature of the intergalactic medium can be constrained using other methods within a certain range. In the ideal scenario, the 21-cm forest observation will have the ability to constrain the total neutrino mass to around 0.1 eV. With the accumulation of observational data and advancements in observational technology, the 21-cm forest holds great promise as an emerging and potent tool for measuring neutrino mass. Copyright © 2025, The Authors. All rights reserved.

关键词： Probes

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