Knowledge-Based Systems最新文献_第7页

Adaptive generic prototype network with geodesic distance for cross-domain few-shot fault diagnosis 采用大地距离的自适应通用原型网络，用于跨域少量故障诊断

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-10 DOI: 10.1016/j.knosys.2024.112726

Yi Qin , Qijun Wen , Lv Wang , Yongfang Mao

{"title":"Adaptive generic prototype network with geodesic distance for cross-domain few-shot fault diagnosis","authors":"Yi Qin , Qijun Wen , Lv Wang , Yongfang Mao","doi":"10.1016/j.knosys.2024.112726","DOIUrl":"10.1016/j.knosys.2024.112726","url":null,"abstract":"<div><div>Since the fault samples of equipment are limited and the working condition is often variable, it is valuable for researching the cross-domain few-shot fault diagnosis method to assure the safe operation of various machines. As the well-known few-shot classification approaches, the traditional prototype networks are difficult to process the complex sample distributions caused by variable operating condition data and the substantial distributional discrepancies between different machines, which seriously affects the accuracy of cross-domain fault diagnosis. To address these issues, this study proposes a new adaptive geodesic prototype network (AGPN), which can extract the category prototypes with enhanced adaptability and generalization capabilities. Firstly, a geodesic distance-driven learning strategy is developed to better measure the distance between complex samples in the embedding space. Secondly, an adaptive area prototype with a dynamic expansion coefficient is proposed, which allows for more flexible representation of different data categories. Furthermore, an adaptive momentum prototype method is put forward via a model-agnostic adaptive momentum factor, which can reduce the prototype oscillation during training and maximize the learning ability of model. The proposed AGPN is successfully applied to fault diagnosis across bearings with different operating conditions. Compared with the existing few-shot diagnosis methods, the proposed method possesses higher diagnostic accuracy and training stability, thus it is more suitable for cross-domain few-shot fault diagnosis.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112726"},"PeriodicalIF":7.2,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Granular intents learning via mutual information maximization for knowledge-aware recommendation 通过互信息最大化学习细粒度意图，实现知识感知推荐

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-09 DOI: 10.1016/j.knosys.2024.112705

Hyeongjun Yang , Yerim Lee , Gayeon Park , TaeYoung Kim , Heesun Kim , Kyong-Ho Lee , Byungkook Oh

{"title":"Granular intents learning via mutual information maximization for knowledge-aware recommendation","authors":"Hyeongjun Yang , Yerim Lee , Gayeon Park , TaeYoung Kim , Heesun Kim , Kyong-Ho Lee , Byungkook Oh","doi":"10.1016/j.knosys.2024.112705","DOIUrl":"10.1016/j.knosys.2024.112705","url":null,"abstract":"<div><div>Knowledge-aware recommender systems, which utilize knowledge graphs (KGs) to enrich item information, have been shown to improve the accuracy and explainability of recommendations. Besides, KGs are further explored to determine the intent of choosing items (i.e., the reason why users select items of interest). Conventional methods represent intents either as sets of relations in a KG or as KG entities. However, such approaches fail to fully leverage the combined information provided by both entities and relations. To address this issue, we propose a new KG-based user Intent Extraction Framework (KIEF) to capture user intents at a more fine-grained level for recommendation. Specifically, we propose a novel intent representation constructed with relation-aware entity representation, encouraging finer granularity for user intents. Furthermore, since a KG may contain noisy information that impairs the quality of user intent, it is compulsory to consider which factors in a KG are important to represent a user’s intent. Thus, we introduce global intent which are comprehensive features for the entire interactions of all users and local intent, which are empirical features of individual users from personal history. By maximizing mutual information between global and local intents, KIEF captures user preference for items. Through extensive experiments on four real-world benchmark datasets, we prove the superior performance of KIEF over the state-of-the-art and analyze interpretable explanations for understanding user intents.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112705"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hyperparameter recommendation via automated meta-feature selection embedded with kernel group Lasso learning 通过嵌入核群拉索学习的自动元特征选择推荐超参数

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-09 DOI: 10.1016/j.knosys.2024.112706

Liping Deng , MingQing Xiao

{"title":"Hyperparameter recommendation via automated meta-feature selection embedded with kernel group Lasso learning","authors":"Liping Deng , MingQing Xiao","doi":"10.1016/j.knosys.2024.112706","DOIUrl":"10.1016/j.knosys.2024.112706","url":null,"abstract":"<div><div>Hyperparameter recommendation via meta-learning relies on the characterization and quality of meta-features. These meta-features provide critical information about the underlying datasets but are often selected manually based on the practitioner’s experience and preference, which can be inefficient and ineffective in many applications. In this paper, we propose a novel hyperparameter recommendation approach that integrates with a Lasso-based multivariate kernel group (KGLasso) model. The developed KGLasso model automatically identifies primary meta-features through model training. By selecting the most explanatory meta-features for a specific meta-learning task, the recommendation performance becomes much more effective. Our KGLasso model builds on a group-wise generalized multivariate Lasso approach. Within this framework, we establish a minimization algorithm using a corresponding auxiliary function, which is mathematically proven to be convergent and robust. As an application, we develop a hyperparameter recommendation system using our built KGLasso model on 120 UCI datasets for the well-known support vector machine (SVM) algorithm. This system efficiently provides competent hyperparameter recommendations for new tasks. Extensive experiments, including comparisons with popular meta-learning baselines and search algorithms, demonstrate the superiority of our proposed approach. Our results highlight the benefits of integrating model learning and feature selection to construct an automated meta-learner for hyperparameter recommendation in meta-learning.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112706"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TGPO-WRHNN: Two-stage Grad-CAM-guided PMRS Optimization and weighted-residual hypergraph neural network for pneumonia detection TGPO-WRHNN：两阶段 Grad-CAM 引导的 PMRS 优化和加权残差超图神经网络用于肺炎检测

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-09 DOI: 10.1016/j.knosys.2024.112708

Chaosheng Tang , Xinke Zhi , Junding Sun , Shuihua Wang , Yudong Zhang

{"title":"TGPO-WRHNN: Two-stage Grad-CAM-guided PMRS Optimization and weighted-residual hypergraph neural network for pneumonia detection","authors":"Chaosheng Tang , Xinke Zhi , Junding Sun , Shuihua Wang , Yudong Zhang","doi":"10.1016/j.knosys.2024.112708","DOIUrl":"10.1016/j.knosys.2024.112708","url":null,"abstract":"<div><div>Recent studies based on chest X-ray images have shown that pneumonia can be effectively detected using deep convolutional neural network methods. However, these methods tend to introduce additional noise and extract only local feature information, making it difficult to express the relationship between data objects. This study proposes a Two-stage Grad-CAM-guided pre-trained model and removal scheme (PMRS) Optimization and weighted-residual hypergraph neural network model (TGPO-WRHNN). First, our model extracts high-dimensional features using the TGPO module to capture both global and local information from an image. Second, we propose a new distance-based hypergraph construction method (DBHC) to amplify the difference between distances and better distinguish the relation between nearby and distant neighbors. Finally, we introduce a weighted-residual hypergraph convolution module (WRHC) to ensure the model maintains excellent performance, even at deeper levels. Our model was tested on a dataset of chest X-ray images of pediatric patients aged 1 to 5 years at the Guangzhou Women and Children’s Medical Centre by 10-fold cross-validation. The results showed that the method achieved a maximum accuracy of 98.97%, precision of 98.86%, recall of 98.43%, F1 score of 98.64%, and AUC of 99.78%. Compared to other existing models, our model demonstrated improvements of 0.87%, 0.86%, 0.16%, and 0.38% in terms of accuracy, precision, F1 score, and AUC, respectively.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112708"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seeking optimal and explainable deep learning models for inertial-based posture recognition 为基于惯性的姿势识别寻找最佳和可解释的深度学习模型

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-09 DOI: 10.1016/j.knosys.2024.112700

Diogo R. Martins , Sara M. Cerqueira , Cristina P. Santos

{"title":"Seeking optimal and explainable deep learning models for inertial-based posture recognition","authors":"Diogo R. Martins , Sara M. Cerqueira , Cristina P. Santos","doi":"10.1016/j.knosys.2024.112700","DOIUrl":"10.1016/j.knosys.2024.112700","url":null,"abstract":"<div><div>Deep Learning (DL) models, widely used in several domains, are often applied for posture recognition. This work researches five DL architectures for posture recognition: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, hybrid CNN-LSTM, and hybrid CNN-Transformer. Agriculture and construction working postures were addressed as use cases, by acquiring an inertial dataset during the simulation of their typical tasks in circuits. Since model performance greatly depends on the choice of the hyperparameters, a grid search was conducted to find the optimal hyperparameters. An extensive analysis of the hyperparameter combinations’ effects is presented, identifying some general tendencies. Moreover, to unveil the black-box DL models, we applied the Gradient-weighted Class Activation Mapping (Grad-CAM) explainability method on CNN’s outputs to better understand the model’s decision-making, in terms of the most important sensors and time steps for each window output. Innovative hybrid architectures combining CNN and LSTM or Transformer encoder were implemented, by using the convolution feature maps as LSTM’s or Transformer’s inputs and fusing both subnetworks’ outputs with weights learned during the training. All architectures successfully recognized the eight posture classes, with the best model of each architecture exceeding 91.5% F1-score in the test. A top F1-score of 94.33%, with an inference time of just 0.29 ms (in a regular laptop), was achieved by a hybrid CNN-Transformer.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112700"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework 通过 CAWAL 框架在大型门户网站中进行预测建模和异常检测

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-09 DOI: 10.1016/j.knosys.2024.112710

Özkan Canay , Ümit Kocabıçak

{"title":"Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework","authors":"Özkan Canay , Ümit Kocabıçak","doi":"10.1016/j.knosys.2024.112710","DOIUrl":"10.1016/j.knosys.2024.112710","url":null,"abstract":"<div><div>This study presents an approach that uses session and page view data collected through the CAWAL framework, enriched through specialized processes, for advanced predictive modeling and anomaly detection in web usage mining (WUM) applications. Traditional WUM methods often rely on web server logs, which limit data diversity and quality. Integrating application logs with web analytics, the CAWAL framework creates comprehensive session and page view datasets, providing a more detailed view of user interactions and effectively addressing these limitations. This integration enhances data diversity and quality while eliminating the preprocessing stage required in conventional WUM, leading to greater process efficiency. The enriched datasets, created by cross-integrating session and page view data, were applied to advanced machine learning models, such as Gradient Boosting and Random Forest, which are known for their effectiveness in capturing complex patterns and modeling non-linear relationships. These models achieved over 92% accuracy in predicting user behavior and significantly improved anomaly detection capabilities. The results show that this approach offers detailed insights into user behavior and system performance metrics, making it a reliable solution for improving large-scale web portals’ efficiency, reliability, and scalability.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112710"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ODDF-Net: Multi-object segmentation in 3D retinal OCTA using optical density and disease features ODDF-Net：利用光密度和疾病特征在三维视网膜 OCTA 中进行多目标分割

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-09 DOI: 10.1016/j.knosys.2024.112704

Chaozhi Yang , Jiayue Fan , Yun Bai , Yachuan Li , Qian Xiao , Zongmin Li , Hongyi Li , Hua Li

{"title":"ODDF-Net: Multi-object segmentation in 3D retinal OCTA using optical density and disease features","authors":"Chaozhi Yang , Jiayue Fan , Yun Bai , Yachuan Li , Qian Xiao , Zongmin Li , Hongyi Li , Hua Li","doi":"10.1016/j.knosys.2024.112704","DOIUrl":"10.1016/j.knosys.2024.112704","url":null,"abstract":"<div><div>Automatic extraction of retinal structures, including Retinal Capillaries (RC), Retinal Arteries (RA), Retinal Veins (RV), and the Foveal Avascular Zone (FAZ), is crucial for the diagnosis and treatment of ocular diseases. This paper presents ODDF-Net, a segmentation network leveraging optical density and disease features, for the simultaneous 2D segmentation of RC, RA, RV, and FAZ in 3D Optical Coherence Tomography Angiography (OCTA). We introduce the concept of optical density to generate additional input images, enhancing the specificity for distinguishing arteries and veins. Our network employs a decoupled segmentation head to separate independent features of each object from shared features by focusing on object boundaries. Given the impact of ocular diseases on the morphology of retinal objects, we designed an auxiliary classification head and a cross-dimensional feature fusion module to model the relationship between various diseases and changes in retinal structures. Extensive experiments on two subsets of the OCTA-500 dataset demonstrate that ODDF-Net outperforms state-of-the-art methods, achieving mean intersection over union ratios of 88.17% and 82.80%. The source code is available at <span><span>https://github.com/y8421036/ODDF-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112704"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A safety realignment framework via subspace-oriented model fusion for large language models 通过面向子空间的模型融合实现大型语言模型的安全调整框架

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-09 DOI: 10.1016/j.knosys.2024.112701

Xin Yi , Shunfan Zheng , Linlin Wang , Xiaoling Wang , Liang He

{"title":"A safety realignment framework via subspace-oriented model fusion for large language models","authors":"Xin Yi , Shunfan Zheng , Linlin Wang , Xiaoling Wang , Liang He","doi":"10.1016/j.knosys.2024.112701","DOIUrl":"10.1016/j.knosys.2024.112701","url":null,"abstract":"<div><div>To improve the performance of large language models (LLMs) on specific tasks, task-specific instruction fine-tuning is essential. However, this process can easily compromise the safety of a task-specific model, making it susceptible to obeying malicious instructions and generating harmful content. Current methods against fine-tuning attack usually interfere with the original fine-tuning objectives or require substantial amounts of data to realign the compromised model. To address these two major challenges, we propose reusing the initial aligned model and realigning task-specific model in the safety subspace. In this paper, we introduce a safety realignment framework through subspace-oriented model fusion (SOMF), aiming to transfer the safeguard capabilities of an initially aligned model into the current task-specific model. Our approach begins by disentangling all task vectors from the parameters of each task-specific model. We then identify safety-critical regions within these vectors by subspace masking techniques. Finally, we fuse the initial safely aligned LLM with all task vectors based on the identified safety subspace to restore the model’s safety properties. Our experiments confirm that our safety realignment framework satisfies the safety requirements of an independent task-specific model as well as traditional multitask models during their fusion. Our findings confirm that SOMF preserves safety without notably compromising performance on specific tasks while exhibiting higher data efficiency. The code is publicly available at <span><span>https://github.com/xinykou/safety_realignment</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112701"},"PeriodicalIF":7.2,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TBicomR: Event Prediction in Temporal Knowledge Graphs with Bicomplex Rotation TBicomR：时态知识图谱中的事件预测与二元旋转

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-08 DOI: 10.1016/j.knosys.2024.112711

Ngoc-Trung Nguyen , Chi Tran , Thanh Le

{"title":"TBicomR: Event Prediction in Temporal Knowledge Graphs with Bicomplex Rotation","authors":"Ngoc-Trung Nguyen , Chi Tran , Thanh Le","doi":"10.1016/j.knosys.2024.112711","DOIUrl":"10.1016/j.knosys.2024.112711","url":null,"abstract":"<div><div>Temporal knowledge graphs (TKGs) capture relationships and entities evolving over time, making event prediction a challenging task due to the complex temporal and relational dynamics. In this work, we propose BiCoTime, a novel model using bicomplex embeddings to represent entities, relations, and time. While quaternions capture asymmetric relations through non-commutativity, bicomplex numbers provide a commutative algebraic structure, ideal for modeling both symmetric and asymmetric relations. Unlike quaternions, bicomplex embeddings maintain interpretability in symmetric relations while preserving key algebraic properties like distributivity. Temporal rotations further enhance BiCoTime's ability to model the interaction between relations and time, capturing how entities and relationships evolve. This combination of bicomplex embeddings and temporal rotations ensures a more interpretable and accurate modeling of TKGs. Our experiments show that TBiComR achieved a 21% improvement in Mean Reciprocal Rank (MRR) on the ICEWS14 dataset, which emphasizes time points, and a 15% improvement on the YAGO11k dataset, which focuses on time spans. The choice of bicomplex numbers balances computational complexity and expressive power, offering efficient training and better predictive performance compared to models using quaternions or octonions.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112711"},"PeriodicalIF":7.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A lightweight Future Skeleton Generation Network(FSGN) based on spatio-temporal encoding and decoding 基于时空编码和解码的轻量级未来骨架生成网络（FSGN）

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-11-08 DOI: 10.1016/j.knosys.2024.112717

Tingyu Liu , Chenyi Weng , Jun Huang , Zhonghua Ni

{"title":"A lightweight Future Skeleton Generation Network(FSGN) based on spatio-temporal encoding and decoding","authors":"Tingyu Liu , Chenyi Weng , Jun Huang , Zhonghua Ni","doi":"10.1016/j.knosys.2024.112717","DOIUrl":"10.1016/j.knosys.2024.112717","url":null,"abstract":"<div><div>Since early warning in industrial applications is far more valuable than post-event analysis, human activity prediction based on partially observed skeleton sequences has become a popular research area. Recent studies focus on building complex deep learning networks to generate accurate future skeleton data, but overlook the requirement for timeliness. Different from such frame-by-frame generation methods, we propose a Future Skeleton Generation Network (FSGN) based on spatio-temporal encoding and decoding framework. Firstly, we design a dynamically regulated input module to ensure equal-length input of partially observed data, and set modules like discrete cosine transform(DCT) and low-pass filtering(LPF) to filter important information. Then, we employ an improved multi-layer perceptron(MLP) structure as the basic computational unit for the encoding and decoding framework to extract spatio-temporal information, and propose using multi-dimensional motion error of human skeleton to form the loss function. Finally, we use an output module symmetrical to the input module to achieve the generation of future activity data. Results show that the proposed FSGN achieves fewer parameters(0.12 M) and higher generation accuracy, which can effectively provide future information for human activity prediction tasks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"306 ","pages":"Article 112717"},"PeriodicalIF":7.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0