Hong Peng

I&C Journal 2026 Journal Article

Spiking Neural P Systems Incorporating Winner-take-all Mechanism

• Tingting Bao
• Bifan Wei
• Bo Li
• Lingling Zhang
• Hong Peng
• Xiaoqing Zhang
• Jun Liu

Spiking neural P systems (SNP systems), a class of parallel distributed computational models inspired by biological neurons, have become an important research direction in biocomputing in recent years due to their biological interpretability and low-power computing. There are many studies on the expressive power of SNP system variants, but their computational efficiency is not high in terms of resource overhead. Inspired by the biological winner-take-all (WTA) computation mechanism, this study proposes spiking neural P systems incorporating WTA mechanism (WTASNP systems). Competing neuron nodes are introduced into the WTASNP systems to realize the competitive selection and inhibition control mechanisms of the WTA computation. After competition, only the winner neuron is allowed to emit spikes, while the loser neurons are inhibited. It is proved that the WTASNP systems have Turing universality. Furthermore, it reduces the computational resource requirements for solving the NP-complete SAT problem with SNP systems from O(n 2) or O(2 n ) complexity levels down to linearly solvable O(n). The WTASNP systems not only effectively preserve the preamble spike information, but also inhibits the loser neuron spike issuance by the WTA computational mechanism of competing neurons, reduces redundant computation to avoid neuron over-excitation, and improves the computational efficiency and expressive power.

EAAI Journal 2025 Journal Article

An image segmentation method for solid-liquid separation on shale shaker based on an improved U2Net

• Wenbin Wang
• Yongjun Hou
• Rui Jiang
• Pan Fang
• Hong Peng
• Qing Li
• Huachuan Li

In the actual production process of shale shakers, detecting the solid-liquid separation state of the screen surface faces numerous challenges, such as difficulty in recognizing the mud boundary, insufficient anti-interference ability, and misjudgment caused by background interference. To address these issues, this paper proposes a screen surface mud image segmentation method based on U2Net, namely CBAM-U2Net. By introducing the Convolutional Block Attention Module (CBAM) and combining it with Multi-layer Recursive Residual Blocks (RSU), a network structure is designed that can efficiently fuse global and local features, significantly improving segmentation accuracy and robustness. The network includes encoder and decoder parts, employing convolution, batch normalization, ReLU activation, and multi-scale feature fusion strategies. Experimental results show that the CBAM-U2Net method demonstrates excellent segmentation performance under various working conditions, achieving outstanding results with mIoU, F1-score, Precision, and Recall at 83. 38%, 89. 75%, 89. 38%, and 92. 64%, respectively, with significantly enhanced anti-interference capability. The CBAM-U2Net method provides an efficient and reliable solution for the intelligent monitoring of the solid-liquid separation state in shale shakers, offering significant practical application value.

EAAI Journal 2025 Journal Article

Dynamic mask network based on spiking neural convolutional model for missing modality brain tumor segmentation

• Junjie Li
• Rui Cai
• Bing Li
• Hong Peng

Brain tumor segmentation is a medical image processing task aimed at accurately locating and isolating tumor regions from brain scan images (e. g. , Magnetic Resonance Imaging, MRI) in order to help doctors in diagnosis, treatment planning and surgical navigation. Automatic brain tumor segmentation is extremely challenging due to incomplete feature representation in the case of missing modalities and insufficient inter-modal information interaction. To this end, this paper proposes a novel dynamic threshold mask Transformer network for the missing modality brain tumor segmentation task, which is designed based on the nonlinear spiking neural convolutional model. The network consists of four independent encoders and a shared decoder to extract the features of each modality and perform shared representation learning. Among them, the dynamic threshold mask Transformer introduces learnable embedding vectors, generates dynamic masks on top of static masks to achieve fine-grained feature filtering, and enhances the ability of inter-modal information interaction. The adaptive gating weighting module and the channel cross spiking neural P attention module fuse modal features layer by layer in both spatial and channel dimensions to strengthen the modeling capability of local and global features. We conducted extensive comparative experiments on different missing modal cases in the BraTS2020 and BraTS2018 datasets. The experimental results show that the method effectively improves the robustness of missing modalities and the performance of brain tumor segmentation while maintaining the computational efficiency, and has good generalization ability and practicality.

IJCAI Conference 2025 Conference Paper

GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection

• Xiong Zhang
• Hong Peng
• Zhenli He
• Cheng Xie
• Xin Jin
• Hua Jiang

Anomalies often occur in real-world information networks/graphs, such as malevolent users, malicious comments, banned users, and fake news in social graphs. The latest graph anomaly detection methods use a novel mechanism called truncated affinity maximization (TAM) to detect anomaly nodes without using any label information and achieve impressive results. TAM maximizes the affinities among the normal nodes while truncating the affinities of the anomalous nodes to identify the anomalies. However, existing TAM-based methods truncate suspicious nodes according to a rigid threshold that ignores the specificity and high-order affinities of different nodes. This inevitably causes inefficient truncations from both normal and anomalous nodes, limiting the effectiveness of anomaly detection. To this end, this paper proposes a novel truncation model combining contextual and global affinity to truncate the anomalous nodes. The core idea of the work is to use contextual truncation to decrease the affinity of anomalous nodes, while global truncation increases the affinity of normal nodes. Extensive experiments on massive real-world datasets show that our method surpasses peer methods in most graph anomaly detection tasks. In highlights, compared with previous state-of-the-art methods, the proposed method has +15% ~ +20% improvements in two famous real-world datasets, Amazon and YelpChi. Notably, our method works well in large datasets, Amazin-all and YelpChi-all, and achieves the best results, while most previous models cannot complete the tasks.

EAAI Journal 2025 Journal Article

Multi-stages attention breast cancer classification based on nonlinear spiking neural P neurons with autapses

• Yaorui Tang
• Bo Yang
• Hong Peng
• Xiaohui Luo

Breast cancer (BC) is a prevalent type of malignant tumor in women. Early diagnosis and treatment are vital for enhancing the patients’ survival rate. A challenge is that downsampling may lead to loss of information in deep networks. So we propose a multi-stages attention architecture based on Nonlinear spiking neural P (NSNP) systems neurons with autapses (NSNP-AU) to compensate for the detail and edge information and allow convolutional neural networks to pay more attention to seek the lesion region. Unlike the single-scale attention acquisition methods of existing methods, we set up spatial attention acquisition at each feature map scale of the convolutional network to obtain a fusion global of information on attention guidance. Besides, we introduce a new type of NSNP variant called NSNP-AU neurons. Specifically, NSNP systems are modularized as a feature dimensionality reduction tool, recoding the features extracted from convolutional neural networks as well as the fusion of attention information and preserving the key characteristic elements in feature maps. This ensures the retention of valuable data while gradually transforming high-dimensional complicated info into low-dimensional ones. The proposed method is evaluated on the public dataset BreakHis at various magnifications and classification tasks. It achieves a classification accuracy of 96. 32% at all magnification cases, outperforming state-of-the-art methods. Ablation studies are also performed, verifying the proposed model’s efficacy.

EAAI Journal 2024 Journal Article

Revisiting the loss functions in sequential recommendation

• Fangyu Li
• Hong Peng
• Shenbao Yu
• Feng Zeng
• Fei Chen
• Fan Yang

There has been a growing interest in benchmarking sequential recommendation models and reproducing/improving existing models. However, the exploration of loss functions in this context has been relatively limited. To address this gap, we investigate the application of diverse loss functions in sequential recommendation, focusing on Cross-Entropy (CE), Binary Cross-Entropy (BCE), and Bayesian Personalized Ranking (BPR) losses. Our objective is to enhance model performance through refining these loss functions. Existing loss functions in sequential recommendation are discussed and analyzed, summarizing their pros and cons. Following this, we identify the two crucial characteristics that an efficient loss function in sequential recommendation should embody, namely Time-Awareness and Efficiency. Subsequently, we introduce a straightforward yet impactful approach that seamlessly integrates time-awareness and efficiency into the computation of the loss function. Further, by utilizing meta-loss learning, we train time-aware parameters and integrate them into the loss function, enabling the model to adaptively capture significant user interactions with items. Our methodology is extensively validated through comprehensive experiments involving various classical and advanced sequential recommendation models on multiple publicly available datasets. Impressively, our approach not only leads to substantial performance improvements but also enables baseline models to surpass state-of-the-art models.

EAAI Journal 2024 Journal Article

Time series classification models based on nonlinear spiking neural P systems

• Xin Xiong
• Min Wu
• Juan He
• Hong Peng
• Jun Wang
• Xianzhong Long
• Qian Yang

Reservoir computing (RC) is a novel class of recurrent neural networks (RNN) models. Nonlinear spiking neural P (NSNP) systems are neural-like computing models with nonlinear spiking mechanisms. By introducing NSNP systems as the reservoir, we propose a new RC model for time series classification task, termed TSC-NSNP model. However, due to the high-dimensional nature of the reservoir state space, the TSC-NSNP model, like existing RC models, will encounter some challenges. To address the challenges. we utilize the reservoir model space representation and dimensionality reduction method to propose two improved models, termed TSC-DR-NSNP model and TSC-RMS-NSNP model. The three RC models can be easily realized and learnt in the RC framework. The proposed three RC models are evaluated on 21 benchmark time series classification data sets, and are compared with 20 classification models. The comparisons demonstrate the effectiveness of the presented three RC models for time series classification tasks.

TCS Journal 2023 Journal Article

Nonlinear spiking neural P systems with multiple channels

• Qian Yang
• Xin Xiong
• Hong Peng
• Jun Wang
• Xiaoxiao Song

This paper investigates a new variant of spiking neural P systems (SN P systems), called nonlinear spiking neural P systems with multiple channels (in short, NSN P-MC systems). In this variant, we consider two features: (i) each neuron can use its multiple channels to connect one or more different successor neurons; (ii) nonlinear spiking rules are introduced to control the spiking of neurons. The computational power of NSN P-MC systems is discussed. Turing universality of NSN P-MC systems as number generating/accepting devices is proven. In addition, a small universal NSN P-MC system with 54 neurons is constructed to compute any Turing computable function, and a small universal NSN P-MC system with 63 neurons is constructed to generate number.

TCS Journal 2022 Journal Article

Computational completeness of spiking neural P systems with inhibitory rules for generating string languages

• Nan Zhou
• Hong Peng
• Jun Wang
• Qian Yang
• Xiaohui Luo

Spiking neural P systems with inhibitory rules (in short, SNP-IR systems) are a distributed parallel computing model, abstracted by the spiking and inhibitory mechanisms of biological neurons. Computational completeness of SNP-IR systems as number generating/accepting and function computing devices has been studied recently. However, computational completeness of SNP-IR systems as language generating devices still has not been investigated. We discuss the relationship of languages generated by SNP-IR systems with regular languages. Moreover, we prove that SNP-IR systems can generate recursively enumerable languages by means of a projection of inverse-morphic image.

TCS Journal 2022 Journal Article

Dynamic threshold P systems with delay on synapses for shortest path problems

• Silu Yang
• Dong Li
• Hong Peng
• Wenjie Zhou
• Xiaohui Luo
• Qian Yang
• Jun Wang
• Xiaoxiao Song

Dynamic threshold neural P (DTNP) systems are recently developed neural-like membrane computing models. By introducing the delay mechanism on synapses, we propose a variant, called dynamic threshold neural P systems with delay on synapses or termed as DTNP-DS systems. DTNP-DS systems possess three recognizable features: spiking mechanism, dynamic threshold mechanism, and delay mechanism on synapses. Based on the three mechanisms, we develop a novel algorithm based on DTNP-DS systems for solving shortest path problems. In principle, the three mechanisms provide a parallel search way for the shortest path in a given network, and they can effectively control the speed and direction of the search. Simulation results demonstrate that DTNP-DS systems can provide an effective solution for the shortest path problems.

I&C Journal 2021 Journal Article

Computational completeness of sequential spiking neural P systems with inhibitory rules

• Tingting Bao
• Nan Zhou
• Hong Peng
• Qian Yang
• Jun Wang

Spiking neural P systems with inhibitory rules (in short, IR-SN P systems) are a kind of bio-inspired computing systems, which are abstracted by the inhibitory synaptic mechanism of biological neurons. IR-SN P systems work in synchronous mode. This paper investigates their sequential version, sequential IR-SN P systems (in short, IR-SSN P systems). In sequential mode, not only the rules in each neuron are applied sequentially, but also the neurons fire in a sequential manner. The maximum-spike-number strategy is considered in sequential mode, and two sub-modes are further distinguished: max-sequentiality strategy and max-pseudo-sequentiality strategy. Computational completeness of IR-SSN P systems as number generating/accepting devices and function computing devices are discussed. It is proven that IR-SSN P systems are Turing universal number generating/accepting devices. Moreover, a small universal IR-SSN P system for computing functions is established in max-sequential strategy.

TCS Journal 2021 Journal Article

Computational power of dynamic threshold neural P systems for generating string languages

• Yue Huang
• Wenmei Yi
• Hong Peng
• Jun Wang
• Xiaohui Luo
• Qian Yang

Inspired from spiking and dynamic mechanisms of neurons, dynamic threshold neural P systems (DTNP systems) have been developed and their computational completeness as number-generating/accepting devices and function computing devices has been investigated. However, a universality result of DTNP systems as language generators has not been established so far. This paper discusses computational power of DTNP systems as language generators. We first discuss the relationship between the languages generated by DTNP systems and finite languages, and then prove that regular languages can be generated by finite DTNP systems. Moreover, we prove that recursively enumerable languages can be characterized by projections of inverse-morphic images of the languages generated by DTNP systems.

TCS Journal 2021 Journal Article

Computational power of sequential dendrite P systems

• Tingting Bao
• Qian Yang
• Hong Peng
• Xiaohui Luo
• Jun Wang
• Xiaoxiao Song

Dendrite P (DeP) systems are a new variant of neural-like P systems, abstracted by the information processing and feedback mechanisms of dendrites. In the variant, a global block is assumed to synchronize all of neurons, hence, DeP systems work in synchronous mode. This paper investigates sequential version of the variant, that is, sequential dendrite P (SDeP) systems. Based on maximum number of spikes in neurons, two sequential modes are distinguished: max-sequentiality and max-pseudo-sequentiality strategies. SDeP systems have two interesting and recognizable features: (i) it behaves as a firing-storing process; (ii) cooperative firing mechanism. The computational completeness of SDeP systems is discussed. We prove that SDeP systems can be used as Turing universal number generating/accepting devices for max-sequentiality and max-pseudo-sequentiality strategies. We also establish a small universal function computing device of SDeP systems with 91 neurons in max-sequentiality strategy.

I&C Journal 2021 Journal Article

Nonlinear neural P systems for generating string languages

• Nan Zhou
• Qian Yang
• Hong Peng
• Jun Wang
• Xiaohui Luo

Nonlinear spiking neural P (NSNP) system is a distributed parallel computing model inspired from the mechanisms of spiking neurons. Computational completeness of NSNP systems as number generating/accepting devices and function computing devices has been already discussed. However, universality result of NSNP systems as language generating devices has not been established so far. This paper investigates computational power of NSNP systems as language generating devices. The relationships of languages generated by NSNP systems with regular languages are investigated. Moreover, we prove that recursively enumerable languages can be characterized by the projection of inverse-morphic images of languages generated by NSNP systems.

EAAI Journal 2020 Journal Article

A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies

• Tao Wang
• Xiaoguang Wei
• Jun Wang
• Tao Huang
• Hong Peng
• Xiaoxiao Song
• Luis Valencia Cabrera
• Mario J. Pérez-Jiménez

This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FD-WCFRSNPS. In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrix-based reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to topological changes in power systems and a Temporal Order Information Processing Method based on Cause–Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective.

TCS Journal 2020 Journal Article

kNN-P: A kNN classifier optimized by P systems

• Juan Hu
• Hong Peng
• Jun Wang
• Wenping Yu

In this paper we propose a k-nearest neighbors (kNN) classifier optimized by P systems, called kNN-P, which can improve the performance of the original kNN classifier. A P system consisting of multiple cells is considered as its computational framework. Under the control of both evolution rules and communication rules, each cell determines the optimal set of k-nearest neighbors for a test sample. The proposed algorithm is evaluated on eighteen benchmark datasets and compared with classical kNN algorithm and eight recently developed improved algorithms. Experimental results demonstrate the availability and effectiveness of the proposed algorithm.

TCS Journal 2020 Journal Article

Spiking neural P systems with structural plasticity and anti-spikes

• Qian Yang
• Bo Li
• Yue Huang
• Hong Peng
• Jun Wang

Spiking neural P systems (in short, SNP systems) are a class of distributed parallel computing devices, abstracted from the way neurons communicate by means of spikes. This paper discusses spiking neural P systems with structural plasticity and anti-spikes (in short, SNP-SPA systems), a new variant of SNP systems with two interesting features: structural plasticity and anti-spike. By means of plasticity rules in neurons, SNP-SPA systems can provide a dynamic directed graph structure. Turing universality of SNP-SPA systems is discussed. It is proven that SNP-SPA systems as number generating/accepting devices are Turing universal, and a small example with 56 neurons that computes a universal function is constructed. The introduction of anti-spikes allows to reduce the modules in the proof of universality.

EAAI Journal 2019 Journal Article

Interval-valued fuzzy spiking neural P systems for fault diagnosis of power transmission networks

• Jun Wang
• Hong Peng
• Wenping Yu
• Jun Ming
• Mario J. Pérez-Jiménez
• Chengyu Tao
• Xiangnian Huang

It is a challenge problem how to deal with the uncertainty in fault diagnosis of power systems. To solve the challenge problem, this paper introduces an interval-valued fuzzy spiking neural P system (IVFSNP system), where the interval-valued fuzzy logic is integrated into spiking neural P systems to characterize the uncertainty. Based on the IVFSNP system, a fuzzy reasoning algorithm is presented, and the corresponding fault diagnosis model is developed. IVFSNP system is capable of describing the incomplete and uncertain fault signals from a supervisory control and data acquisition system equipped together with electric power systems. In order to evaluate the availability and effectiveness of the proposed fault diagnosis model, two case studies of fault diagnosis of a transmission network are discussed and analyzed, including complex and multiple fault situations with the incomplete and uncertain status signals. The results of the case studies demonstrate that IVFSNP system can be used to diagnose the faulty sections in power transmission networks accurately and effectively.

TCS Journal 2019 Journal Article

Numerical P systems with Boolean condition

• Liucheng Liu
• Wenmei Yi
• Qian Yang
• Hong Peng
• Jun Wang

Numerical P systems (NP systems, in short) are a class of distributed and parallel computation systems abstracted from the structure of biological cells. By introducing some control mechanisms to manipulate the application of programs, a number of NP systems have been proposed, including enzymatic NP systems and NP systems with thresholds/production thresholds. In order to control the application of programs more effectively, this paper introduces a control condition with Boolean form to propose a variant of NP systems, NP systems with Boolean condition (for short, BNP systems). Compared to the existing variants, BNP systems can provide flexible and more powerful program control ability. The computational power of the new variant is investigated. In particular, we prove that BNP systems as number generating/accepting devices and function computing devices, respectively, under three working modes (all-parallel, one-parallel and sequential), are Turing universal.

JBHI Journal 2013 Journal Article

Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications

• Hong Peng
• Bin Hu
• Qiuxia Shi
• Martyn Ratcliffe
• Qinglin Zhao
• Yanbing Qi
• Guoping Gao

A new model to remove ocular artifacts (OA) from electroencephalograms (EEGs) is presented. The model is based on discrete wavelet transformation (DWT) and adaptive noise cancellation (ANC). Using simulated and measured data, the accuracy of the model is compared with the accuracy of other existing methods based on stationary wavelet transforms and our previous work based on wavelet packet transform and independent component analysis. A particularly novel feature of the new model is the use of DWTs to construct an OA reference signal, using the three lowest frequency wavelet coefficients of the EEGs. The results show that the new model demonstrates an improved performance with respect to the recovery of true EEG signals and also has a better tracking performance. Because the new model requires only single channel sources, it is well suited for use in portable environments where constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices. The model is also applied and evaluated against data recorded within the EUFP 7 Project-Online Predictive Tools for Intervention in Mental Illness (OPTIMI). The results show that the proposed model is effective in removing OAs and meets the requirements of portable systems used for patient monitoring as typified by the OPTIMI project.

IS Journal 2011 Journal Article

EEG-Based Cognitive Interfaces for Ubiquitous Applications: Developments and Challenges

• Bin Hu
• Dennis Majoe
• Martyn Ratcliffe
• Yanbing Qi
• Qinglin Zhao
• Hong Peng
• Dangping Fan
• Fang Zheng

Technical advances in the neuroelectric recordings and in the computational tools for the analysis of the brain activity and connectivity make it now possible to follow and to quantify, in real time, the interactive brain activity in a group of subjects engaged in social interactions. The degree of interaction between persons can then be assessed by "reading" their neuroelectric activities. Imaging the social brain can thus open a new area of study in neuroscience.

IJCAI Conference 2009 Conference Paper

• Wei Huang
• Zhonghua Wen
• YUNFEI JIANG
• Hong Peng

In many real world planning domains, some observation information is optional and useless to the execution of a plan; on the other hand, information acquisition may require some kind of cost. The problem of observation reduction for strong plans has been addressed in the literature. However, observation reduction for plans with contexts (which are more general and useful than strong plans in robotics) is still a open problem. In this paper, we present an attempt to solve the problem. Our first contribution is the definition of structured plans, which can encode sequential, conditional and iterative behaviors, and is expressive enough for dealing with incomplete observation information and internal states of the agent. A second contribution is an observation reduction algorithm for plans with contexts, which can transform a plan with contexts into a structured plan that only branches on necessary observation information.

IS Journal 2006 Journal Article

Knowledge Acquisition Based on Rough Set Theory and Principal Component Analysis

• A. Zeng
• D. Pan
• Qi-Lun Zheng
• Hong Peng

In this paper, we've developed a novel approach to knowledge acquisition based on rough set theory and principal component analysis. A PCA-based quantitative index measures the relative importance of different condition attributes among the state space constructed by all condition attributes. The index strengthens the attribute and attribute-value reductions while maintaining the decision table's discernibility relations. Our KA-RSPCA algorithm outperformed four other RS algorithms on two test data sets.