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arXiv 2022年

作者： Zhao, Yue Zhang, Lingming Liu, Yang Meng, Deyu Cui, Zhiming Gao, Chenqiang Gao, Xinbo Lian, Chunfeng Shen, Dinggang Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China The Chongqing Key Laboratory of Signal and Information Processing Chongqing400065 China Department of Orthodontics Stomatological Hospital of Chongqing Medical University Chongqing401147 China Chongqing Key Laboratory for Oral Diseases and Biomedical Sciences Chongqing401147 China Faculty of Information Technology Macau University of Science and Technology China School of Mathematics and Statistics Xi'an Jiaotong University Xian710049 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai200030 China The School of Computer Science The University of Hong Kong 999077 Hong Kong School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods often simply concatenate the raw geometric attributes (i.e., coordinates and normal vectors) of mesh cells to train a single-stream network for automatic intra-oral scanner image segmentation. However, since different raw attributes reveal completely different geometric information, the naive concatenation of different raw attributes at the (low-level) input stage may bring unnecessary confusion in describing and differentiating between mesh cells, thus hampering the learning of high-level geometric representations for the segmentation task. To address this issue, we design a two-stream graph convolutional network (i.e., TSGCN), which can effectively handle inter-view confusion between different raw attributes to more effectively fuse their complementary information and learn discriminative multi-view geometric representations. Specifically, our TSGCN adopts two input-specific graph-learning streams to extract complementary high-level geometric representations from coordinates and normal vectors, respectively. Then, these single-view representations are further fused by a self-attention module to adaptively balance the contributions of different views in learning more discriminative multi-view representations for accurate and fully automatic tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of dental (mesh) models acquired by 3D intraoral scanners. Experimental results show that our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation. Github: https://***/ZhangLingMing1/TSGCNet. © 2022, CC BY.

关键词： Scanning

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Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

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PLoS Computational Biology 2021年第9期17卷 e1009345-e1009345页

作者： Zhao, Zhengqiao Woloszynek, Stephen Agbavor, Felix Mell, Joshua Chang Sokhansanj, Bahrad A. Rosen, Gail L. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University PhiladelphiaPA United States Beth Israel Deaconess Medical Center BostonMA United States Harvard Medical School BostonMA United States School of Biomedical Engineering Science and Health Systems Drexel University PhiladelphiaPA United States College of Medicine Drexel University PhiladelphiaPA United States

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/ sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention- based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://***/EESI/sequence_attention (a python package) and https://***/ EESI/seq2att (a command line tool). Copyright: © 2021 Zhao et

关键词： Classification (of information)

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New simplified energy flow management algorithms for a DC bus-coupled photovoltaic-grid hybrid system

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Energy Systems 2025年

作者： Mbende, Ernest Titi Pesdjock, Mathieu Jean Pierre Muluh, Fombu Andrew Kenne, Godpromesse Department of Electrical and Power engineering Research Unit of Automation and Applied Computer Science IUT-FV Bandjoun University of Dschang P.O. Box 134 west Bandjoun Cameroon Department of Physics Research Unit of Condensed matter electronics and signal processing Faculty of Science University of Dschang P.O. Box 67 west Dschang Cameroon Department of Electrical and Electronic Engineering National Higher Polytechnic Institute University of Bamenda P.O. Box 39 North West Bambili Bamenda Cameroon

This paper presents a new simplified linear self-adjusting algorithm and an improved artificial ant colony self adjusting algorithm, for optimal energy flow management of a photovoltaic-grid hybrid energy system coupled through a DC link. The two proposed algorithms rely on the voltage error in the stabilization of the AC link to self determine a compensation current and hence power deficit, which is automatically extracted from the back-up storage to maintain an uninterrupted service and ensure the balance between power production and consumption. Numerical simulation results in Matlab/simulink under different scenarios show the effectiveness of the algorithms in mitigating the challenges posed by the intermittent nature of the photovoltaic source and also in facilitating the rapid and flexible integration of the renewable energy source. The algorithms also contribute effectively in regulating the AC bus voltage and in maintaining the frequency and total harmonic distortion within acceptable standards. The specificity of the two proposed algorithms is the fast response speed of the system in balancing power output and consumption. A comparative study of the proposed algorithms with a proportional integral (PI) controller under different operating modes clearly show the superiority of the new linear self-adjusting algorithm in terms of system stability, response time, error reduction and total harmonic distortion mitigation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Power management (telecommunication)

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Difficulty adjustable and scalable constrained multiobjective test problem toolkit

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Evolutionary Computation 2019年第3期28卷 339-378页

作者： Fan, Zhun Li, Wenji Cai, Xinye Li, Hui Wei, Caimin Zhang, Qingfu Deb, Kalyanmoy Goodman, Erik Department of Electronic Engineering Shantou University Guangdong515063 China Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu210016 China School of Mathematics and Statistics Xi’an Jiaotong University Shan’xi710049 China Department of Computer Science City University of Hong Kong Hong Kong BEACON Center for the Study of Evolution in Action Michigan State University East LansingMI United States

Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, butmost of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrainedmany-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving conv

关键词： Hardness

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EEG-Based Preference Classification for Neuromarketing Application

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Injamamul Haque Sourov Faiyaz Alvi Ahmed Md. Tawhid Islam Opu Aunnoy K. Mutasim M. Raihanul Bashar Rayhan Sardar Tipu Md. Ashraful Amin Md. Kafiul Islam Biomedical Instrumentation and Signal Processing Lab Department of Electrical and Electronic Engineering Independent University Bangladesh Plot 16 Block B Bashundhara R/A Dhaka 1229 Bangladeshiub.edu.bd Agency Lab Department of Computer Science and Engineering Independent University Bangladesh Plot 16 Block B Bashundhara R/A Dhaka 1229 Bangladeshiub.edu.bd

Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.

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Noise can speed Markov chain Monte Carlo estimation and quantum annealing

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Physical Review E 2019年第5期100卷 053309-053309页

作者： Brandon Franzke Bart Kosko Center for Quantum Information Science and Technology Signal and Image Processing Institute Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA

Carefully injected noise can speed the average convergence of Markov chain Monte Carlo (MCMC) estimates and simulated annealing optimization. This includes quantum annealing and the MCMC special case of the Metropolis-Hastings algorithm. MCMC seeks the solution to a computational problem as the equilibrium probability density of a reversible Markov chain. The algorithm must cycle through a long burn-in phase until it reaches equilibrium because the Markov samples are statistically correlated. The special injected noise reduces this burn-in period in MCMC. A related theorem shows that it reduces the cooling time in simulated annealing. Simulations showed that optimal noise gave a 76% speed-up in finding the global minimum in the Schwefel optimization benchmark. The noise-boosted simulations found the global minimum in 99.8% of trials compared with only 95.4% of trials in noiseless simulated annealing. Simulations also showed that the noise boost is robust to accelerated cooling schedules and that noise decreased convergence times by more than 32% under aggressive geometric cooling. Molecular dynamics simulations showed that optimal noise gave a 42% speed-up in finding the minimum potential energy configuration of an eight-argon-atom gas system with a Lennard-Jones 12-6 potential. The annealing speed-up also extends to quantum Monte Carlo implementations of quantum annealing. Noise improved ground-state energy estimates in a 1024-spin simulated quantum annealing simulation by 25.6%. The quantum noise flips spins along a Trotter ring. The noisy MCMC algorithm brings each Markov step closer on average to equilibrium if an inequality holds between two expectations. Gaussian or Cauchy jump probabilities reduce the noise-benefit inequality to a simple quadratic inequality. Simulations show that noise-boosted simulated annealing is more likely than noiseless annealing to sample high probability regions of the search space and to accept solutions that increase the search bre

关键词： Fluctuations & noise Noise

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Evo-RL: Evolutionary-Driven Reinforcement Learning

arXiv

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arXiv 2020年

作者： Hallawa, Ahmed Born, Thorsten Schmeink, Anke Dartmann, Guido Peine, Arne Martin, Lukas Iacca, Giovanni Eiben, A.E. Ascheid, Gerd Integrated Signal Processing Systems RWTH Aachen University Department of Intensive Care and Intermediate Care University Hospital RWTH Aachen Research Area Information Theory and Systematic Design of Communication Systems RWTH Aachen University Research Area Distributed Systems Trier University of Applied Sciences Department of Information Engineering and Computer Science University of Trento Computer Science Department Vrije Universiteit Amsterdam Netherlands

In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (Evo-RL), embeds the reinforcement learning algorithm in an evolutionary cycle, where we distinctly differentiate between purely evolvable (instinctive) behaviour versus purely learnable behaviour. Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing Evo-RL to be adaptive to different environments. In addition, Evo-RL facilitates learning on environments with rewardless states, which makes it more suited for real-world problems with incomplete information. To show that Evo-RL leads to state-of-the-art performance, we present the performance of different state-of-the-art reinforcement learning algorithms when operating within Evo-RL and compare it with the case when these same algorithms are executed independently. Results show that reinforcement learning algorithms embedded within our Evo-RL approach significantly outperform the stand-alone versions of the same RL algorithms on OpenAI Gym control problems with rewardless states constrained by the same computational budget. Copyright © 2020, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Comments on "A new computing approach for power signal modeling using fractional adaptive algorithms"

arXiv

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arXiv 2020年

作者： Wahab, Abdul Khan, Shujaat Khan, Farrukh Zeeshan Sector H-12 Islamabad44000 Pakistan Bio-Imaging Signal Processing and Learning Lab. Department of Bio and Brain Engineering Korea Advanced Institute of Science and Technology 291 Daehak-ro Yuseong-gu Daejeon34141 Korea Republic of Department of Computer Science University of Engineering and Technology Taxila Taxila47080 Pakistan

In this paper, some points to the convergence analysis performed in the paper [A new computing approach for power signal modeling using fractional adaptive algorithms, ISA Transactions 68 (2017) 189-202] are presented. It is highlighted that the way the authors prove convergence, suffers lack of correct and valid mathematical justifications. Copyright © 2020, The Authors. All rights reserved.

关键词： Adaptive algorithms

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An Interpretable Galaxy Morphology Classification Approach Using Modified SqueezeNet and Local Interpretable Model-agnostic Explanation

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Research in Astronomy and Astrophysics 2025年第6期25卷 065018-065018页

作者： Kam Meng Goh Derrick Hiang Yaol Lim Zhen Dong Sham Kolla Bhanu Prakash Centre for Multimodal Signal Processing Faculty of Engineering and Technology Tunku Abdul Rahman University of Management and Technology 53300 Kuala Lumpur Malaysia Starlight Education Group London W1W 5PF UK Faculty of Institute for Advanced Studies UM Power Energy Dedicated Advanced Centre Universiti Malaya 59990 Kuala Lumpur Malaysia Department of Computer Science & Engineering DST—FIST Sponsored Department Koneru Lakshmaiah Education Foundation Andhra Pradesh 522502 India

The recent surge in computer vision and deep learning has attracted significant attention within the galaxy morphology community. Various models have been implemented for galaxy morphology prediction with near-perfect accuracy for certain classes. However, many studies treat deep learning models as black-box entities, lacking interpretability of their predictions. To address these limitations while ensuring good performance, we introduced an Improved SqueezeNet (I-SqueezeNet) by incorporating unique residual connections to improve the prediction performance, and we utilize Local Interpretable Model-Agnostic Explanations (LIME) to understand the interpretability. We evaluated the simplified SqueezeNet and I-SqueezeNet, with both channel and vertical concatenation, and compared their performances with those of some exiting methods such as Dieleman's CNN, VGG13, DenseNet121, ResNet50, ResNext50, ResNext101, DSCNN and customized CNN in classifying galaxy objects using a dataset from the publicly available Galaxy Zoo Data Challenge Project. Our experiments showed that I-SqueezeNet with vertical concatenation achieved the highest average accuracy of 94.08% compared to other methods. Beyond achieving high accuracy, the application of LIME for model interpretation sheds light on the machine learning features and reasoning processes behind the model's predictions. This information provides valuable insight into the galaxy morphology decision-making process, paving the way for further functional enhancements.

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Personalized automatic sleep staging with single-night data: A pilot study with kl-divergence regularization

arXiv

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arXiv 2020年

作者： Phan, Huy Mikkelsen, Kaare Chén, Oliver Y. Koch, Philipp Mertins, Alfred Kidmose, Preben de Vos, Maarten School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Engineering Aarhus University Denmark Institute of Biomedical Engineering University of Oxford United Kingdom Institute for Signal Processing University of Lübeck Germany Department of Electrical Engineering KU Leuven Belgium

Brain waves vary between people. An obvious way to improve automatic sleep staging for longitudinal sleep monitoring is personalization of algorithms based on individual characteristics extracted from the first night of data. As a single night is a very small amount of data to train a sleep staging model, we propose a Kullback-Leibler (KL) divergence regularized transfer learning approach to address this problem. We employ the pretrained SeqSleepNet (i.e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model. This is done by adding the KL divergence between the output of the subject independent model and the output of the personalized model to the loss function during finetuning. In effect, KL-divergence regularization prevents the personalized model from overfitting to the single-night data and straying too far away from the subject independent model. Experimental results on the Sleep-EDF Expanded database with 75 subjects show that sleep staging personalization with a single-night data is possible with help of the proposed KL-divergence regularization. On average, we achieve a personalized sleep staging accuracy of 79.6%, a Cohen’s kappa of 0.706, a macro F1-score of 73.0%, a sensitivity of 71.8%, and a specificity of 94.2%. We find both that the approach is robust against overfitting and that it improves the accuracy by 4.5 percentage points compared to non-personalization and 2.2 percentage points compared to personalization without regularization. Copyright © 2020, The Authors. All rights reserved.

关键词： Sleep research

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