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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhao, Kai Liu, Yanmin Dian, Longyang Sun, Shiwei Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Shandong University State Key Laboratory of Microbial Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)9798350386226

Mass spectrometry serves as a pivotal tool for the analysis of small molecules through an examination of their mass-to-charge ratios. Recent advancements in deep learning have markedly enhanced the analysis of mass spectrometric data, facilitating the prediction of novel small molecule structures without the necessity of extensive databases. Nonetheless, the paucity of annotated datasets impedes the efficacious training of molecular generation models predicated on MS2 spectra. To mitigate this limitation, we introduce ctMSNovelist, an avant-garde method that amalgamates pre-training, fine-tuning, and co-training techniques to construct a more precise model for the generation of molecular structures from tandem mass spectrometry data. This novel approach augments both the training regimen and the predictive accuracy of the MSNovelist model, thereby surmounting the obstacle of limited data. The methodology commences with the pretraining of a Variational Autoencoder (VAE) to generate molecular fingerprints derived from SMILES strings. Subsequently, it undergoes fine-tuning to emulate noisy fingerprints originating from mass spectrometry (MS) data. Concurrently, MSNovelist is co-trained utilizing these simulated fingerprints, inclusive of the highly noisy variants produced in the early stages of VAE training. The incorporation of a substantial volume of noisy data serves to enhance model accuracy and avert overfitting. We evaluated ctMSNovelist using the GNPS dataset and attained a SMILES prediction accuracy of 48.8%, representing a 4.1% enhancement over MSNovelist. It is pertinent to note that the sole distinction between ctMSNovelist and MSNovelist in this experiment was the training process. The code and models are publicly available at https://***/xfcui/ctMSNovelist. © 2024 IEEE.

关键词： Neural networks

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Improved beluga whale optimization algorithm based cluster routing in wireless sensor networks

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Mathematical Biosciences and Engineering 2024年第3期21卷 4587-4625页

作者： Yuan, Hao Chen, Qiang Li, Hongbing Zeng, Die Wu, Tianwen Wang, Yuning Zhang, Wei Chongqing Key Laboratory of Geological Environmental Monitoring and Disaster Early Warning in the Three Gorges Reservoir Area Chongqing Three Gorges University Chongqing404120 China Internet of Things and Intelligent Control Technology Chongqing Engineering Research Center Chongqing Three Gorges University Chongqing404120 China Chongqing Municipal Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing404120 China

Cluster routing is a critical routing approach in wireless sensor networks (WSNs). However, the uneven distribution of selected cluster head nodes and impractical data transmission paths can result in uneven depletion of network energy. For this purpose, we introduce a new routing strategy for clustered wireless sensor networks that utilizes an improved beluga whale optimization algorithm, called tCBWO-DPR. In the selection process of cluster heads, we introduce a new excitation function to evaluate and select more suitable candidate cluster heads by establishing the correlation between the energy of node and the positional relationship of nodes. In addition, the beluga whale optimization (BWO) algorithm has been improved by incorporating the cosine factor and t-distribution to enhance its local and global search capabilities, as well as to improve its convergence speed and ability. For the data transmission path, we use Prim’s algorithm to construct a spanning tree and introduce DPR for determining the optimal route between cluster heads based on the correlation distances of cluster heads. This effectively shortens the data transmission path and enhances network stability. Simulation results show that the improved beluga whale optimization based algorithm can effectively improve the survival cycle and reduce the average energy consumption of the network. © 2024 the Author(s).

关键词： Heuristic algorithms

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Short-term power load forecasting based on TCN-LSTM model 3

Short-term power load forecasting based on TCN-LSTM model

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3rd International Conference on Energy, Power and Electrical technology, ICEPET 2024

作者： Xu, Xinyu Zhou, Zhenliu Zhao, Huaxin Electrical Engineering College of Shenyang Institute of Engineering Shenyang China Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology Shenyang China Information College of Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9798350352658

In order to improve the accuracy of short-term power load forecasting and provide a stronger guarantee for the stable operation of the power system, this paper proposes a short-term power load forecasting method TCN-LSTM, which combines time convolution network ( TCN ) and long short-term memory ( LSTM ). Firstly, the time series features in the original load data are extracted by using the characteristics of TCN. Then, the extracted temporal features and non-temporal features are input into the LSTM model to train the model;finally, the trained model is used to predict the short-term power load data. Through the simulation experiment of the load data of a certain area in the south, the TCN-LSTM combined model is compared with the single model. It is found that the prediction accuracy of the TCN-LSTM model is higher, indicating that the model has better short-term power load forecasting *** experimental simulation comparison shows that the prediction accuracy of the combined model proposed in this paper is 0.66 % higher than that of the basic model. © 2024 IEEE.

关键词： Prediction models

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Short-term wind power load forecasting based on ISSA-CNN-BiLSTM 3

Short-term wind power load forecasting based on ISSA-CNN-BiL...

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3rd International Conference on Energy, Power and Electrical technology, ICEPET 2024

作者： Liu, Shaoxiong Zhou, Zhenliu Zhao, Huaxin Electrical Engineering College of Shenyang Institute of Engineering Shenyang China Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology Shenyang China Information College of Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9798350352658

Accurate wind power prediction is of great significance in the balance of electricity supply and demand. Aiming at the problems of poor convergence and easy to fall into local extremes of the power prediction model using the traditional sparrow algorithm (SSA) to optimise the parameters of the bidirectional long- and short-term memory neural network (CNN-BiLSTM), this paper adopts the refractive inverse learning strategy, the positive cosine strategy and the Cauchy variation strategy to improve the sparrow search algorithm, and a better convergence is obtained. An improved sparrow search algorithm (ISSA) with stronger convergence is obtained. The ISSA algorithm is used to optimise the bi-directional long and short-term memory neural network, and the ISSA-CNN-BiLSTM neural network prediction model is established. This paper takes the data of a wind farm in Inner Mongolia as the research object, and uses SSA-CNN-BiLSTM algorithm,CNN-BiLSTM algorithm and ISSA-CNN-BiLSTM algorithm to simulate and test the measured historical data of this wind farm, respectively. The experimental simulation comparison shows that the combined model proposed in this paper has a 7.6% improvement in prediction accuracy over its base model. © 2024 IEEE.

关键词： Wind forecasting

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Multiband Kick Drum Separation with Genre-Guided Optimization 10th

Multiband Kick Drum Separation with Genre-Guided Optimizati...

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10th Conference on Sound and Music technology, CSMT 2023

作者： Li, Si Jiang, Yuanfeng Wang, Zhaowen Qian, Jiale Li, Wei School of Computer Science and Technology Fudan University Shanghai200438 China Department of Music AI and Information Technology Central Conservatory of Music Beijing100091 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai200433 China

ISBN: (纸本)9789819779611

Kick drum plays an important role in electronic music. When hearing an excellent kick drum sound from a reference music track, music production beginners always feel confused about how it will sound if we solo the kick track and why it sounds perfect in a specific song. Reproducing the kick of a music track is not easy even for professional producers and arrangers. To investigate the ability of artificial intelligence in separating kick-drum sounds, in this paper we propose an end-to-end system which is capable of separating multi-genre high-quality electronic kick drum clips from mixed song tracks. Unlike most of the previous music source separation tasks, we only focus on the electronic kick drum rather than the whole percussion track. First, we design a multiband data representation approach for non-pitched percussions such as kick drums, then adopt a multi-discriminator module trained on a multi-genre kick drum corpus to further optimize the separated kick sound. To the authors’ knowledge, this study marks the first attempt at kick drum separation with stylistic optimization. Objective and subjective evaluations demonstrate that joint multiband representation and genre-guided optimisation can form a high-performance framework, showing a promising solution to the kick drum separation task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Source separation

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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

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Computers, Materials & Continua 2023年第8期76卷 1947-1976页

作者： Asif Rahim Yanru Zhong Tariq Ahmad Sadique Ahmad Mohammed A.ElAffendi School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of *** Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of *** this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship *** conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 *** compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN *** results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like *** findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this ***,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic *** findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

关键词： Convolutional Neural Network(CNN) hyper-tuning authorship verification digital forensics

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Exploring the Cation Regulation Mechanism for Interfacial Water Involved in the Hydrogen Evolution Reaction by In Situ Raman Spectroscopy

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Nano-Micro Letters 2024年第3期16卷 303-312页

作者： Xueqiu You Dongao Zhang Xia‑Guang Zhang Xiangyu Li Jing‑Hua Tian Yao‑Hui Wang Jian‑Feng Li School of Ocean Information Engineering Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent ProcessingJimei UniversityXiamen 361021People’s Republic of China State Key Laboratory of Physical Chemistry of Solid Surfaces MOE Key Laboratory of Spectrochemical Analysis and InstrumentationiChEMCollege of Chemistry and Chemical EngineeringCollege of MaterialsCollege of EnergyXiamen UniversityXiamen 361005People’s Republic of China Key Laboratory of Green Chemical Media and Reactions Ministry of EducationCollaborative Innovation Center of Henan Province for Green Manufacturing of Fine ChemicalsCollege of Chemistry and Chemical EngineeringHenan Normal UniversityXinxiang 453007People’s Republic of China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005People’s Republic of China

Interfacial water molecules are the most important participants in the hydrogen evolution reaction(HER).Hence,understanding the behavior and role that interfacial water plays will ultimately reveal the HER ***,investigating interfacial water is extremely challenging owing to the interference caused by bulk water molecules and complexity of the interfacial ***,the behaviors of interfacial water in different cationic electrolytes on Pd surfaces were investigated by the electrochemistry,in situ core-shell nanostructure enhanced Raman spectroscopy and theoretical simulation *** spectral evidence reveals a red shift in the frequency and a decrease in the intensity of interfacial water as the potential is shifted in the positively *** comparing the different cation electrolyte systems at a given potential,the frequency of the interfacial water peak increases in the specified order:Li+

关键词： In situ Raman Interfacial water Hydrogen evolution reaction Cations

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Multi-Scale and Improved Skip Connections Network for Skin Lesion Segmentation 9

Multi-Scale and Improved Skip Connections Network for Skin L...

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9th International Conference on intelligent Computing and Signal processing, ICSP 2024

作者： Zhang, Botao Chen, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9798350376548

To solve the problem that the current skin lesion segmentation networks have a poor segmentation accuracy when lesion areas are of different shapes and sizes and low contrast at the edge areas, based on U-Net, we present a skin lesion segmentation network based on multi-scale and improved skip connections. Firstly, a multi-scale feature enhancement module is designed to replace the U-Net's 3× 3 convolution blocks, which can improve the feature extraction ability of the network about lesion areas of different shapes and sizes. Secondly, a lesion attention module is introduced into the skip connection to improve the semantic expression of low-level features and the location attention of high-level features, which can improve the fine-grained information transmission efficiency at the skip connection. Finally, a new hybrid loss function is designed to calculate the label and the edge loss of lesion areas, which improves the ability of the network to segment lesion areas and edges. Experiments are conducted on the ISIC2017 and ISIC2018 datasets, and the results prove that our method have a higher segmentation accuracy in skin lesion segmentation. © 2024 IEEE.

关键词： Problem solving

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Scattering Characteristics-guided Self-supervised Learning for Target Classification in SAR Images 24

Scattering Characteristics-guided Self-supervised Learning f...

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7th International Conference on Image and Graphics processing, ICIGP 2024

作者： Zhong, Honghu Li, Jianhao Shi, Hao Fang, Zhonghao Chen, Liang Institute of Radar Technology Beijing Institute of Technology Beijing China National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing China Innovation Center of Beijing Institute of Technology Chongqing Chongqing401120 China

ISBN: (纸本)9798400716720

With the advancement of science and technology, the spatial resolution of spaceborne Synthetic Aperture Radar (SAR) images has achieved sub-meter precision, enabling the classification and identification of targets. Notably, the rapid and accurate classification of aircraft targets using SAR images has become a significant application requirement. Nevertheless, challenges persist in the classification of targets within aircraft remote sensing images. This paper introduces a novel method aimed at enhancing the scattering characteristics in SAR images to address issues associated with discrete imaging pixels, strong scattering characteristics, and the consequent difficulty in distinguishing aircraft features. The proposed method systematically correlates the discrete SAR image scattering characteristics, thereby facilitating the extraction of characteristic information pertaining to the edge contours of aircraft targets. Recognizing the limitations of traditional supervised classification methods, which often demand substantial manually labeled information, this study integrates self-supervised contrastive learning to mitigate labeling costs. Additionally, acknowledging the imbalanced distribution of samples across different categories and the prevalent "long tail"effect, a weighted loss function is introduced to rectify the imbalance and enhance the network's focus on the learning of underrepresented samples. The efficacy of the proposed method is evaluated using a self-established dataset. The results demonstrate a 1.48% increase in accuracy compared to the original self-supervised method, indicating an improvement in the classification performance for categories characterized by an imbalanced sample distribution. © 2024 ACM.

关键词： Image classification

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RGB-guided hyperspectral image super-resolution with deep progressive learning

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CAAI Transactions on Intelligence technology 2024年第3期9卷 679-694页

作者： Tao Zhang Ying Fu Liwei Huang Siyuan Li Shaodi You Chenggang Yan School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Satellite Information Intelligent Processing and Application Research Laboratory Beijing Institute of Remote SensingBeijingChina Key Laboratory of Spectral Imaging Technology Xi'an Institute of Optics and Precision Mechanics of Chinese Academy of SciencesXi'anChina Institute of Informatics University of AmsterdamAmsterdamNetherlands School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina

Due to hardware limitations,existing hyperspectral(HS)camera often suffer from low spatial/temporal ***,it has been prevalent to super-resolve a low reso-lution(LR)HS image into a high resolution(HR)HS image with a HR RGB(or mul-tispectral)image *** approaches for this guided super-resolution task often model the intrinsic characteristic of the desired HR HS image using hand-crafted ***,researchers pay more attention to deep learning methods with direct supervised or unsupervised learning,which exploit deep prior only from training dataset or testing *** this article,an efficient convolutional neural network-based method is presented to progressively super-resolve HS image with RGB image ***-ically,a progressive HS image super-resolution network is proposed,which progressively super-resolve the LR HS image with pixel shuffled HR RGB image ***,the super-resolution network is progressively trained with supervised pre-training and un-supervised adaption,where supervised pre-training learns the general prior on training data and unsupervised adaptation generalises the general prior to specific prior for variant testing *** proposed method can effectively exploit prior from training dataset and testing HS and RGB images with spectral-spatial *** has a good general-isation capability,especially for blind HS image *** experimental results show that the proposed deep progressive learning method out-performs the existing state-of-the-art methods for HS image super-resolution in non-blind and blind cases.

关键词： computer vision deep neural networks image processing image resolution unsupervised learning

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