Knowledge-Based Systems最新文献_第10页

Semiconcept and concept representations 半概念和概念表征

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-05 DOI: 10.1016/j.knosys.2024.112579

{"title":"Semiconcept and concept representations","authors":"","doi":"10.1016/j.knosys.2024.112579","DOIUrl":"10.1016/j.knosys.2024.112579","url":null,"abstract":"<div><div>In FCA, we often deal with a formal context <span><math><mrow><mi>K</mi><mo>=</mo><mrow><mo>(</mo><mi>G</mi><mo>,</mo><mi>M</mi><mo>,</mo><mi>I</mi><mo>)</mo></mrow></mrow></math></span> that is only partially known, i.e. only the attributes that belong to an observable set <span><math><mrow><mi>N</mi><mo>⊂</mo><mi>M</mi></mrow></math></span> are known. There must also exist a part <span><math><mi>H</mi></math></span> of the object set <span><math><mi>G</mi></math></span> – called a training set – that consists of elements with all attributes known. The concepts of <span><math><mi>K</mi></math></span> have to be determined using the subcontexts corresponding to the training object set <span><math><mi>H</mi></math></span> and to the observable attribute set <span><math><mi>N</mi></math></span>. In our paper, this problem is examined within the extended framework of the semiconcepts of the original context, which are generalizations of its concepts. Each semiconcept of the original context induces a semiconcept in both subcontexts. In this way, each semiconcept of the context is represented by an induced pair of semiconcepts, which can also be considered its approximations — as in the case of rough sets. We describe the properties of the mapping defined by this representation and prove that the poset formed by these semiconcept pairs is a union of two complete lattices. We show that these induced semiconcept pairs can be generated by using a simplified representation of them. As the number of semiconcepts grows exponentially with the size of the training set and the observable attribute set, an algorithm that selects the representation pairs for which their support and relevance reach a certain threshold is also presented.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446718","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

A novel interpretable semi-supervised graph learning model for intelligent fault diagnosis of hydraulic pumps 用于液压泵智能故障诊断的新型可解释半监督图学习模型

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-05 DOI: 10.1016/j.knosys.2024.112598

{"title":"A novel interpretable semi-supervised graph learning model for intelligent fault diagnosis of hydraulic pumps","authors":"","doi":"10.1016/j.knosys.2024.112598","DOIUrl":"10.1016/j.knosys.2024.112598","url":null,"abstract":"<div><div>Although deep learning has gained popularity in the field of fault diagnosis, its limitations are also equally apparent, including: (1) heavy reliance on a substantial volume of labeled samples; (2) a lack of interpretability. To confront these issues, this article proposes a novel interpretable semi-supervised graph learning model for intelligent fault diagnosis of hydraulic pumps. A comparison between the raw data and the model's hidden layer representations is conducted to minimize feature loss. The model commences by preliminarily learning fault information that is intermixed with noise, leveraging a substantial corpus of unlabeled data. In response to the intricacy of downstream tasks, an interpretable feature reconstruction module is introduced. This module employs a nonlinear surrogate model to fit and elucidate the learned features, embedding the explanation scores into the features to reconstruct the samples, a process utilized for model fine-tuning. The feature reconstruction module capitalizes on the explanatory power of the surrogate model, guiding the model to concentrate more on features with significant impact. This method not only provides interpretability during model training but also expedites the convergence speed of the model. Finally, two hydraulic pump experiment cases are used to verify the effectiveness of the model, and the results show that our method has obvious advantages in reducing label dependence and increasing model reliability for decision making.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433945","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

Improving Bayesian optimization via hierarchical variation modeling for combinatorial experiments given limited runs guided by process knowledge 通过分层变异建模改进贝叶斯优化，用于在过程知识指导下进行有限运行的组合实验

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-05 DOI: 10.1016/j.knosys.2024.112596

{"title":"Improving Bayesian optimization via hierarchical variation modeling for combinatorial experiments given limited runs guided by process knowledge","authors":"","doi":"10.1016/j.knosys.2024.112596","DOIUrl":"10.1016/j.knosys.2024.112596","url":null,"abstract":"<div><div>Active learning based on Bayesian optimization (BO) is a popular black-box combinatorial search method, particularly effective for autonomous experimentation. However, existing BO methods did not consider the joint variation caused by the process degradation over time and input-dependent variation. The challenge is more significant when the affordable experimental runs are very limited. State-of-the-art approaches did not address allocating limited experimental runs that can jointly cover (1) representative inputs over large search space for identifying the best combination, (2) replicates reflecting the true input-dependent testing variation, and (3) process variations that increase over time due to process degradation. This paper proposed Empirical Bayesian Hierarchical Variation Modeling in Bayesian Optimization (EHVBO) guided by the process knowledge to maximize the exploration of potential combinations in sequential experiments given limited experimental runs. The method first mitigates the process degradation effect through generalized linear modeling of grouped variations, guided by the knowledge of the re-calibration cycle of process conditions. Then, EHVBO introduces an empirical Bayesian hierarchical model to reduce the replicates for learning the input-dependent variation, leveraging the process knowledge of the common structure shared across different testing combinations. This way can reduce the necessary replicates for each input condition. Furthermore, the paper developed a heuristics-based strategy incorporated in EHVBO to improve search efficiency by selectively refining the search space over pivotal regions and excluding less-promising regions. A case study based on real experimental data demonstrates that the proposed method outperforms testing results from various optimization models.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433835","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

Cascaded Cross-modal Alignment for Visible-Infrared Person Re-Identification 用于可见光-红外线人员再识别的级联跨模态比对

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-04 DOI: 10.1016/j.knosys.2024.112585

{"title":"Cascaded Cross-modal Alignment for Visible-Infrared Person Re-Identification","authors":"","doi":"10.1016/j.knosys.2024.112585","DOIUrl":"10.1016/j.knosys.2024.112585","url":null,"abstract":"<div><div>Visible-Infrared Person Re-Identification faces significant challenges due to cross-modal and intra-modal variations. Although existing methods explore semantic alignment from various angles, severe distribution shifts in heterogeneous data limit the effectiveness of single-level alignment approaches. To address this issue, we propose a Cascaded Cross-modal Alignment (CCA) framework that gradually eliminates distribution discrepancies and aligns semantic features from three complementary perspectives in a cascaded manner. First, at the input-level, we propose a Channel-Spatial Recombination (CSR) strategy that strategically reorganizes and preserves crucial details from channel and spatial dimensions to diminish visual discrepancies between modalities, thereby narrowing the modality gap in input images. Second, at the frequency-level, we introduce a Low Frequency Masking (LFM) module to emphasize global details that CSR might overlook by randomly masking low-frequency information, thus driving comprehensive alignment of identity semantics. Third, at the part-level, we design a Prototype-based Semantic Refinement (PSR) module to refine fine-grained features and mitigate the impact of irrelevant areas in LFM. It accurately aligns body parts and enhances semantic consistency guided by global discriminative clues from LFM and flipped views with pose variations. Comprehensive experimental results on the SYSU-MM01 and RegDB datasets demonstrate the superiority of our proposed CCA.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433831","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

Novel comprehensive unified classification system toward smart standardized built environment knowledge and low-risk international collaborations: UniCCC 面向智能标准化建筑环境知识和低风险国际合作的新颖综合统一分类系统：UniCCC

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-04 DOI: 10.1016/j.knosys.2024.112533

{"title":"Novel comprehensive unified classification system toward smart standardized built environment knowledge and low-risk international collaborations: UniCCC","authors":"","doi":"10.1016/j.knosys.2024.112533","DOIUrl":"10.1016/j.knosys.2024.112533","url":null,"abstract":"<div><div>The Construction Industry (CI) data is acknowledged to still be unstructured and high-dimensional. Efforts were undertaken to tackle this problem using regional Unified Classification Systems (UCSs). The study emphasizes the necessity for a comprehensive international UCS to manage the growing trend of international collaborations within the CI and associated data risks. Existing UCSs exhibit significant inconsistencies, such as regional omissions, terminological disparities, and diverse data structuring, underscoring the need for a unified approach. Accordingly, using a 3-stage mixed method, the novel UCS ‘<em>Unified Classification of Construction Components_ UniCCC</em>’ was developed, comprising 4 phyla, 9 divisions, and 54 classes, and was shown to have good adaptability/scalability, structure/breakdown, clarity, codifiability, and inclusivity of the 11 key CI aspects. By addressing inconsistencies and smartly structuring CI knowledge from North America, Europe, Africa, and the Middle East, using a faceted classification scheme aligned with ISO 12,006, and being developed using a conventional modeling language, UniCCC represents a significant advancement in UCSs, with promising implications for global CI practices and research, paving the way for new avenues in CI performance, innovation, and sustainability. It demonstrated efficiency in various CI aspects, including risk reduction, automation, and sustainability enhancement. As evidence of its performance, using UniCCC for scheduling might decrease up to 58.17%, 66.85%, and 31.74% of the related time, request for information, and omissions, respectively. The study suggests future research perspectives to explore UniCCC's applicability in different regions, its integration with emerging technologies, its proficiency metrics for different purposes, and strategies for organizational implementation.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529369","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 novel incremental ensemble learning for real-time explainable forecasting of electricity price 用于电价实时可解释预测的新型增量集合学习

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-04 DOI: 10.1016/j.knosys.2024.112574

引用次数: 0

Improved User Identification through Calibrated Monte-Carlo Dropout 通过校准蒙特卡洛剔除改进用户识别

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-04 DOI: 10.1016/j.knosys.2024.112581

{"title":"Improved User Identification through Calibrated Monte-Carlo Dropout","authors":"","doi":"10.1016/j.knosys.2024.112581","DOIUrl":"10.1016/j.knosys.2024.112581","url":null,"abstract":"<div><div>This paper presents an enhanced approach to user identification using smartphone and wearable sensor data. Our methodology involves segmenting input data and independently analyzing subsequences with CNNs. During testing, we apply calibrated Monte-Carlo Dropout to measure prediction uncertainty. By leveraging the weights obtained from uncertainty quantification, we integrate the results through weighted averaging, thereby improving overall identification accuracy. The main motivation behind this paper is the need to calibrate the CNN for improved weighted averaging. It has been observed that incorrect predictions often receive high confidence, while correct predictions are assigned lower confidence. To tackle this issue, we have implemented the Ensemble of Near Isotonic Regression (ENIR) as an advanced calibration technique. This ensures that certainty scores more accurately reflect the true likelihood of correctness. Furthermore, our experiment shows that calibrating CNN reduces the need for Monte Carlo samples in uncertainty quantification, thereby reducing computational costs. Our thorough evaluation and comparison of different calibration methods have shown improved accuracy in user identification across multiple datasets. Our results showed notable performance improvements when compared to the latest models available. In particular, our approach achieved better results than DB2 by 1.12% and HAR by 0.3% in accuracy.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433834","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

Bi-Interfusion: A bidirectional cross-fusion framework with semantic-guided transformers in LiDAR-camera fusion 双向融合：在激光雷达与相机融合中使用语义引导转换器的双向交叉融合框架

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-03 DOI: 10.1016/j.knosys.2024.112577

{"title":"Bi-Interfusion: A bidirectional cross-fusion framework with semantic-guided transformers in LiDAR-camera fusion","authors":"","doi":"10.1016/j.knosys.2024.112577","DOIUrl":"10.1016/j.knosys.2024.112577","url":null,"abstract":"<div><div>Multi-sensor modal fusion has shown significant advantages in 3D object detection tasks. However, existing methods for fusing multi-modal features into the bird’s eye view (BEV) space often encounter challenges such as feature misalignment, underutilization of semantic information, and inaccurate depth estimation on the Z-axis, resulting in suboptimal performance. To address these issues, we propose Bi-Interfusion, a novel multi-modal fusion framework based on transformers. Bi-Interfusion incorporates a bidirectional fusion architecture, including components such as Pixel-wise Semantic Painting, Gaussian Depth Prior Distribution module, and Semantic Guidance Align module, to overcome the limitations of traditional fusion methods. Specifically, Bi-Interfusion employs a bidirectional cross-fusion strategy to merge image and LiDAR features, enabling the generation of multi-sensor BEV features. This approach leverages a refined Gaussian Depth Prior Distribution generated from LiDAR points, thereby improving the precision of view transformation. Additionally, we apply a pixel-wise semantic painting technique to embed image semantic information into LiDAR point clouds, facilitating a more comprehensive scene understanding. Furthermore, a transformer-based model is utilized to establish soft correspondences among multi-sensor BEV features, capturing positional dependencies and fully exploiting semantic information for alignment. Through experiments on nuScenes benchmark dataset, Bi-Interfusion demonstrates notable performance improvements, achieving a competitive performance of 72.6% mAP and 75.4% NDS in the 3D object detection task.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142446720","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

Parallel–serial architecture with instance correlation label-specific features for multi-label learning 采用实例相关标签特定特征的并行串行架构，用于多标签学习

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-03 DOI: 10.1016/j.knosys.2024.112568

{"title":"Parallel–serial architecture with instance correlation label-specific features for multi-label learning","authors":"","doi":"10.1016/j.knosys.2024.112568","DOIUrl":"10.1016/j.knosys.2024.112568","url":null,"abstract":"<div><div>Feature extraction plays a crucial role in capturing data correlations, thereby improving the performance of multi-label learning models. Popular approaches mainly include feature space manipulation techniques, such as recursive feature elimination, and feature alternative techniques, such as label-specific feature extraction. However, the former does not utilize label information, while the latter does not consider correlation among instances. In this study, we propose a label-specific feature extraction approach embedding instance correlation by a joint loss function under a parallel–serial architecture (LSIC-PS). Our approach incorporates three main techniques. First, we employ a parallel isomorphic network to extract label-specific features, which are directly integrated into a serial network to enhance label correlation. Second, we introduce instance correlation to guide feature extraction in parallel networks, leveraging label information from other instances to improve generalization. Third, we design a parameter-setting strategy to control a new joint loss function, adapting its instance correlation proportion to different datasets. We conduct experiments on sixteen widely used datasets and compare the results of our approach with those of twelve popular algorithms. Across eight evaluation metrics, LSIC-PS demonstrates state-of-art performance in multi-label learning. The source code is available at <span><span>github.com/fansmale/lsic-ps</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428276","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

FNNGM: A neural-driven fractional-derivative multivariate fusion model for interpretable real-time CPI forecasts FNNGM：用于可解释的实时 CPI 预测的神经驱动分数派生多元融合模型

IF 7.2 1区计算机科学

Knowledge-Based Systems Pub Date : 2024-10-02 DOI: 10.1016/j.knosys.2024.112591

{"title":"FNNGM: A neural-driven fractional-derivative multivariate fusion model for interpretable real-time CPI forecasts","authors":"","doi":"10.1016/j.knosys.2024.112591","DOIUrl":"10.1016/j.knosys.2024.112591","url":null,"abstract":"<div><div>Integrating models from diverse sources has attracted substantial interest in developing advanced time series forecasting technologies. However, current research lacks a comprehensive and deep fusion model to integrate multiple forecasting methodologies. To this end, this paper proposes a neural-driven fractional-derivative multivariate fusion model (<em>FNNGM (p, n)</em>) to assimilate the fractional-derivative dynamical system, the driving factor in grey multivariate models, and the neural network into a cohesive framework. Consequently, this fusion architecture can benefit from the synergy of the target system's dynamics, extensive exogenous information, and non-linear transformation. Additionally, <em>FNNGM (p, n)</em> fosters extra functionalities through its inherent memory layer and sequence decomposition, bolstering model interpretability with the visible memory mechanism and understandable model workflows. To showcase the utility of <em>FNNGM (p, n)</em>, this paper conducts real-time monthly consumer price index (CPI) forecasts that span ten years (from 2013:08 to 2023:07), analyzing the interpretable results from <em>FNNGM (p, n)</em> and contrasting it against many prevailing benchmark models. The comparison results reveal <em>FNNGM (p, n)</em>’s highly concentrated error distributions and the minimum mean absolute percentage forecasting error (<em>APFE</em>), squared forecasting error (<em>SFE)</em>, and absolute forecasting error (<em>AFE</em>) values of 0.22 %, 0.59, and 0.56, respectively. Furthermore, the ablation experiments are performed to explore the specific effects and compatibilities of the fusion components, validating the effectiveness of the proposed fusion approach.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428303","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