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AS-Index： A Structure for String Search Using n-Grams and Algebraic Signatures

Journal of Computer Science & Technology 2016年第1期31卷 147-166页

作者： Camelia Constantin Cedric du Mouza witold litwin Philippe Rigaux Thomas Schwarz LIP6 Laboratory University Pierre et Marie Curie Paris 75005 France CEDRIC Laboratory Conservatoire National des Arts et Metiers Paris 75003 France LAMSADE Laboratory University Paris-Dauphine Paris 75775 France DICC Laboratory Universidad Catolica del Uruguay Montevideo 11200 Uruguay

We present the AS-Index, a new index structure for exact string search in disk resident databases. AS-Index relies on a classical inverted file structure, whose main innovation is a probabilistic search based on the properties of algebraic signatures used for both n-grams hashing and pattern search. Specifically, the properties of our signatures allow to carry out a search by inspecting only two of the posting lists. The algorithm thus enjoys the unique feature of requiring a constant number of disk accesses, independently from both the pattern size and the database size. We conduct extensive experiments on large datasets to evaluate our index behavior. They confirm that it steadily provides a search performance proportional to the two disk accesses necessary to obtain the posting lists. This makes our structure a choice of interest for the class of applications that require very fast lookups in large textual databases. We describe the index structure, our use of algebraic signatures, and the search algorithm. We discuss the operational trade-offs based on the parameters that affect the behavior of our structure, and present the theoretical and experimental performance analysis. We next compare the AS-Index with the state-of-the-art alternatives and show that 1） its construction time matches that of its competitors, due to the similarity of structures, 2） as for search time, it constantly outperforms the standard approach, thanks to the economical access to data complemented by signature calculations, which is at the core of our search method.

关键词： full text indexing large-scale indexing algebraic signature

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Alternative Method of Constructing Granular Neural Networks

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Computers, Materials & Continua 2024年第4期79卷 623-650页

作者： Yushan Yin witold Pedrycz Zhiwu Li School of Electro-Mechanical Engineering Xidian UniversityXi’an710071China Department of Electrical and Computer Engineering University of AlbertaEdmontonABT6R 2V4Canada

Utilizing granular computing to enhance artificial neural network architecture, a newtype of network emerges—thegranular neural network (GNN). GNNs offer distinct advantages over their traditional counterparts: The ability toprocess both numerical and granular data, leading to improved interpretability. This paper proposes a novel designmethod for constructing GNNs, drawing inspiration from existing interval-valued neural networks built uponNNNs. However, unlike the proposed algorithm in this work, which employs interval values or triangular fuzzynumbers for connections, existing methods rely on a pre-defined numerical network. This new method utilizesa uniform distribution of information granularity to granulate connections with unknown parameters, resultingin independent GNN structures. To quantify the granularity output of the network, the product of two commonperformance indices is adopted: The coverage of numerical data and the specificity of information *** this combined performance index helps determine the optimal parameters for the network. Finally,the paper presents the complete model construction and validates its feasibility through experiments on datasetsfrom the UCIMachine Learning Repository. The results demonstrate the proposed algorithm’s effectiveness andpromising performance.

关键词： Granular neural network granular connection interval analysis triangular fuzzy numbers particle swarm optimization(PSO)

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ witold HIROTA Kaoru School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta The Tokyo Institute of Technology

In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer(LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then an LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is 1 r/min, and the overshoot is reduced to 5.8%. This greatly improves the stability and security, which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Multi-granularity feature enhancement network for maritime ship detection

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CAAI Transactions on Intelligence Technology 2024年第3期9卷 649-664页

作者： Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang witold Pedrycz Department of Computer Science and Technology Tongji UniversityShanghaiChina Project Management Office of China National Scientific Seafloor Observatory Tongji UniversityShanghaiChina Department of Electrical and Computer Engineering AlbertaEdmontonCanada System Research Institute Polish Academy of SciencesWarsawPoland

Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship ***,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection *** address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is *** 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image ***,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from *** results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关键词： object classification object recognition rough sets rough set theory

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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Computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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基于冷却壁温度信息粒度分析的高炉炉况智能预测方法

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冶金自动化 2024年第2期48卷 41-49页

作者：黄元峰杜胜胡杰吴敏 Pedrycz witold 中国地质大学(武汉)自动化学院复杂系统先进控制与智能自动化湖北省重点实验室地球探测智能化技术教育部工程研究中心阿尔伯塔大学电子与计算机工程系

稳定顺行的高炉炉况对提高铁水的产量和质量有着至关重要的作用，渣皮稳定性是表征炉况的重要指标，而冷却壁温度常用于衡量渣皮稳定性。为了利用冷却壁温度的动态特征预测炉况，本文提出了一种基于冷却壁温度信息粒度分析的炉况智能预... 详细信息

稳定顺行的高炉炉况对提高铁水的产量和质量有着至关重要的作用，渣皮稳定性是表征炉况的重要指标，而冷却壁温度常用于衡量渣皮稳定性。为了利用冷却壁温度的动态特征预测炉况，本文提出了一种基于冷却壁温度信息粒度分析的炉况智能预测方法。首先，采用斯皮尔曼相关性分析方法，选择与冷却壁温度波动相关性较大的参数。然后，针对选取的参数，利用信息粒化方法进行动态特征提取，构成信息粒。再次，利用相应参数的信息粒作为输入，建立基于支持向量回归的冷却壁温度信息粒预测模型，预测冷却壁温度信息粒。最后，结合炉况预测方法，分析预测的冷却壁温度信息粒完成炉况预测。利用实际钢铁企业数据进行的实验表明，所提方法能有效预测冷却壁温度信息粒与高炉炉况，为操作人员制定合理的布料策略提供了有力指导。

关键词：高炉炉况预测冷却壁温度信息粒时间序列

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Granular Computing for Data Analytics:A Manifesto of Human-Centric Computing

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1025-1034页

作者： witold Pedrycz Department of Electrical&Computer Engineering University of Alberta EdmontonCanadaand also with the Systems Research InstitutePolish Academy of SciencesWarsawPoland

In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling,humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the *** advocate that the level of abstraction,which can be flexibly adjusted,is conveniently realized through Granular *** Computing is concerned with the development and processing information granules–formal entities which facilitate a way of organizing knowledge about the available data and relationships existing *** study identifies the principles of Granular Computing,shows how information granules are constructed and subsequently used in describing relationships present among the data.

关键词： Aggregation clustering design of information granules fuzzy sets granular computing information granules,principle of justifiable granularity.

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Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 75-89页

作者： Aijuan Song Guohua Wu witold Pedrycz Ling Wang School of Traffic and Transportation Engineering Central South UniversityChangsha 410075China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 2V4Canada Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Systems Research Institute Polish Academy of SciencesWarsaw 01447Poland Department of Automation Tsinghua UniversityBeijing 100084China

Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple *** algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one ***,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used *** contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve *** VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation *** enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the *** test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,*** results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared ***,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

关键词： Evolutionary algorithm(EA) nonlinear equations systems(ENSs) problem domain knowledge variable reduction strategy(VRS)

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Residual-driven Fuzzy C-Means Clustering for Image Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第4期8卷 876-889页

作者： Cong Wang witold Pedrycz ZhiWu Li MengChu Zhou Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China the Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071and also with the Institute of Systems EngineeringMacao University of Science and TechnologyMacaoChina Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

In this paper,we elaborate on residual-driven Fuzzy C-Means(FCM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in *** propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of *** on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown ***,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image *** experiments on synthetic,medical,and real-world images are *** results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.

关键词： Fuzzy C-Means image segmentation mixed or unknown noise residual-driven weighted regularization

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Constrained tolerance rough set in incomplete information systems

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CAAI Transactions on Intelligence Technology 2021年第4期6卷 440-449页

作者： Renxia Wan Duoqian Miao witold Pedrycz School of Mathematics and Inforrmation Scicncc North Minzu UniversityYinchuanNingxiaChina Hongyang Institute for Big Data in Health FuzhouFujianChina Department of Computer Science and Technology Tongi UniversityShanghaiChina Department of Electrical and Computer Engineering University of AlbertaEdmontonCanada

The tolerance rough set is developed as one of the outstanding extensions of the Pawlak's rough set model under incomplete information,and the limited tolerance relation is developed to overcome the problem that objects leniently satisfy the tolerance ***,the classification based on the limited tolerance relationship cannot reflect the matching degree of uncertain information of *** this article,we explore the influence of null values in an incomplete system,and propose the constrained tolerance relation based on the matching degree of uncertain information of *** proposed rough set based on the constrained tolerance relation can provide a more detailed structure of an object class through *** and example analyses further show the rationality and superiority of the proposed model.

关键词： incomplete matching constrained

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