Article
Engineering, Mechanical
Junchang Zhai, Huanqing Wang, Jiaqing Tao, Zuowei He
Summary: This paper proposes an observer-based adaptive fuzzy finite time control scheme for non-strict feedback uncertain nonlinear systems with unmodeled dynamics and input delay. A fuzzy state observer is used to estimate unmeasurable states, and unknown nonlinearities are identified using fuzzy logic systems. The design challenges caused by unmodeled dynamics and input delay are addressed with a dynamic signal and a compensation signal. The proposed method achieves finite time stability performance constraints in the presence of input delay, unmodeled dynamics, and unmeasurable states.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Wei Wu, Shaocheng Tong
Summary: This paper presents a novel fuzzy adaptive control design scheme for a class of non-strict feedback switched nonlinear systems with unknown nonlinearities, unmeasured states, and arbitrary switching. By combining adaptive backstepping with dynamic surface control technique, the proposed control scheme overcomes the 'explosion of complexity' problem. It is shown that all signals of the closed-loop system can be bounded by the common Lyapunov function theory.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Mathematics, Interdisciplinary Applications
Fan Zhang, Xiongfeng Deng, Lisheng Wei
Summary: This paper proposes an adaptive dynamic surface control law for a type of strict-feedback fractional-order nonlinear system, which deals with input quantization and unknown external disturbances. The control law utilizes a dynamic surface control approach and a nonlinear compensating term with the estimation of unknown bounded parameters to overcome the effects of external disturbances and surface errors. Adaptive laws for relevant parameters and an improved fractional-order nonlinear filter are also designed. The proposed control law ensures the convergence of tracking error and the stability is verified using fractional Lyapunov stability theory. Simulation examples demonstrate the effectiveness of the proposed control law.
FRACTAL AND FRACTIONAL
(2022)
Article
Computer Science, Information Systems
Jipeng Zhao, Shaocheng Tong, Yongming Li
Summary: This work investigates fuzzy adaptive robust output feedback control for SISO strict-feedback nonlinear systems, addressing unknown control gain functions, unmodeled dynamics, and immeasurable states. By approximating unknown nonlinear functions with fuzzy logic systems and estimating immeasurable states with a new fuzzy state observer, a Logarithm Lyapunov function approach is used to propose a new fuzzy adaptive robust output feedback control scheme. The designed algorithm ensures semi-globally uniformly ultimately boundedness of the closed-loop system and robustness to unmodeled dynamics.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Tianping Zhang, Penghao Chen
Summary: This article discusses the issue of stochastic adaptive dynamic surface control for stochastic nonstrict-feedback constrained nonlinear systems, utilizing linearly parametrized neural networks and MT-filters to estimate unknown functions and observe system states, respectively. By developing adaptive neural control technology and applying the Lyapunov synthesis approach, the theoretical findings were verified through two numerical examples.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Tianping Zhang, Hailin Tang, Xiaonan Xia, Yang Yi
Summary: In this article, a new decentralized adaptive neural output feedback control scheme based on first-order command filter is proposed for stochastic nonstrict-feedback interconnected systems with prescribed performance, input quantization, actuator failures, and unmodeled dynamics. The proposed method effectively eliminates unknown smooth functions, estimates immeasurable states, processes unmodeled dynamics, and compensates for the effects of quantization and actuator failures. The effectiveness of the proposed method is verified through numerical and example simulations, demonstrating its ability to ensure system stability and time-varying constraint satisfaction.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Mathematics, Applied
Meizhen Xia, Zhucheng Liu, Tianping Zhang
Summary: This study focuses on the distributed adaptive cooperative tracking control problem of non-strict feedback multi-agent systems, including various challenges such as input unmodeled dynamics, prescribed performance, sensor faults, and unknown control directions. An adaptive neural control method is proposed, which overcomes these challenges using command filtered backstepping technique, Gaussian function property, and finite-time performance function design. Simulation results demonstrate the effectiveness of the proposed control method.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Xiongfeng Deng, Chen Zhang, Yuan Ge
Summary: This paper investigates the tracking control problem of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault. An adaptive neural network dynamic surface control law is designed using the neural network control approach and dynamic surface control technique.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2022)
Article
Mathematics
Shuyan Qi, Jun Zhao, Li Tang
Summary: This paper investigates the adaptive output feedback control problem for switched uncertain nonlinear systems with input quantization, unmeasured system states, and state constraints. Fuzzy logic systems are introduced to identify system uncertainties, and a fuzzy-based observer is constructed to estimate unavailable states. By combining the backstepping technique and the barrier Lyapunov function method, an adaptive fuzzy output feedback control law is designed to guarantee bounded signals, system output tracking, and satisfaction of state constraint conditions. Simulation results demonstrate the effectiveness of the proposed control scheme.
Article
Automation & Control Systems
Hang Su, Qiang Zhang
Summary: This paper investigates the problem of tracking control for a class of nonstrict-feedback nonlinear systems with input quantization, asymmetric fuzzy dead zones, and unknown control directions. A useful coordinate transformation is proposed to tackle the issue caused by the unknown control coefficients, followed by transforming the researched system into an equivalent one. By combining the nonlinear decomposition of an asymmetric hysteresis quantizer and a simplified asymmetric dead zone model, a feasible connection between system input and control signal is established. Fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions, and a novel adaptive fuzzy control scheme is proposed via backstepping technique, ensuring bounded signals and minimal tracking error. Simulation results demonstrate the effectiveness of the proposed algorithm.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Jipeng Zhao, Xiaomei Li, Shaocheng Tong
Summary: The study focuses on fuzzy adaptive control for SISO uncertain nonlinear systems, using fuzzy state observer and fuzzy logic systems to estimate states and approximate functions, introducing dynamic surface control method and Logarithm Lyapunov functions to address complexity issues in control design.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xiaona Song, Peng Sun, Shuai Song, Vladimir Stojanovic
Summary: In this article, the issue of neural adaptive decentralized finite-time prescribed performance (FTPP) control is investigated for interconnected nonlinear time-delay systems. An integrated approach utilizing hyperbolic tangent function and radial basis function neural networks is proposed to handle the unknown nonlinear items. The developed adaptive FTPP control strategy applies an improved fractional-order filter and an adaptive self-triggered control law considering bandwidth limitation, ensuring the system's states are semi-globally uniformly ultimately bounded and the output is confined to a small area in finite time. Two simulation examples validate the effectiveness and superiority of the proposed method.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Bo Xu, Yuan-Xin Li, Choon Ki Ahn
Summary: This article presents a new stabilizing control scheme for a class of interconnected nonlinear systems subjected to unmodeled dynamics and immeasurable states. Fuzzy logic systems are used to approximate unknown functions and a fuzzy-based state observer is constructed. The overall system interconnection is fully compensated using the cyclic-small-gain condition theorem, and the small-gain theorem is introduced to overcome the unmodeled dynamics in each subsystem. Assumptions from prior literature are relaxed and the computing burden is reduced by designing less adaptive laws. The article proves that the designed control scheme ensures input-to-state practical stability of the closed-loop systems and guarantees semiglobally uniformly ultimately bounded signals. The simulation section illustrates the effectiveness of the proposed approach through an example derived from a practical system model.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Jianhao Song, Yong Chen, Yuezhi Liu, Longjie Zhang
Summary: This article investigates the problem of fixed-time fuzzy adaptive fault-tolerant tracking control for strict-feedback nonlinear systems (SFNSs) suffering from actuator fault and input delay. A fixed-time fuzzy adaptive fault-tolerant control (FTC) algorithm is proposed to rapidly deal with these two problems. The controller utilizing fixed-time stability has better control and fault tolerance performance, thus verifying the superiority of the proposed method.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Engineering, Mechanical
Xin Zhou, Chuang Gao, Zhi-gang Li, Xin-yu Ouyang, Li-bing Wu
Summary: This paper presents a novel control scheme for finite-time tracking control of nonlinear systems using fuzzy logic systems and fuzzy state observer, and converts non-smooth input dead-zone and saturated nonlinearity to affine form via the mean value theorem.
NONLINEAR DYNAMICS
(2021)
Article
Automation & Control Systems
Wen Bai, Peter Xiaoping Liu, Huanqing Wang
Summary: This paper addresses the output tracking problem of a class of nonlinear systems and designs a robust adaptive fault-tolerant controller for two types of actuator faults. By applying classical backstepping algorithm and fixed-time control theory, the tracking error is guaranteed to converge to a small neighborhood around the origin within a fixed time.
INTERNATIONAL JOURNAL OF CONTROL
(2023)
Article
Automation & Control Systems
Pengwen Xiong, Junjie Liao, MengChu Zhou, Aiguo Song, Peter X. Liu
Summary: This article proposes a deeply supervised subspace learning method to help robots understand and perceive an object's properties during noncontact robot-object interaction. It extracts contactless feature information from noncontact sensors to retrieve cross-modal information, estimating and inferring material properties of known and unknown objects. Experimental results show the effectiveness of this approach compared to other advanced methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Guanglong Du, Yijia Li, Kang Su, Chunquan Li, Peter X. Liu
Summary: This article proposes a mobile natural human-robot interaction interface for virtual Chinese acupuncture teaching, which addresses the limitation of existing methods in allowing natural interaction in a large area. With an automatic hand tracking method and an acupuncture interaction method combining vision and force feedback, operators can perform acupuncture in a large area and receive visual feedback and force feedback, enhancing the naturalness and authenticity of the operation.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Shijia Kang, Peter Xiaoping Liu, Huanqing Wang
Summary: In this article, a finite-time adaptive fuzzy output-feedback control strategy is proposed for MIMO nonlinear systems with multiple actuator constraints and unmeasured states. Fuzzy logic systems and a fuzzy adaptive state observer are used for uncertainty estimation and state estimation, respectively. The 'explosion of complexity' problem is tackled by command filter technology, and the corresponding error compensation mechanism is utilized. The presented approach is shown to be practical through numerical and practical examples.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2023)
Article
Engineering, Mechanical
Shanglin Li, Yangzhou Chen, Peter Xiaoping Liu
Summary: This paper addresses the leader-following consensus and fault detection problem for a class of multi-agent systems with Lipschitz nonlinear dynamics. An efficient network framework with a double periodic event-triggered mechanism is proposed to reduce redundant information and avoid continuous checking of triggering conditions. Improved fault detection observer and consensus controller are designed based on this framework. The original problem is then transformed into a set of stability problems with constraints and solved using a bilinear matrix inequalities (BMIs) formulation derived from Lyapunov-Krasovskii theorem and free-weighting matrix technique. Two iterative algorithms based on linear matrix inequalities (LMIs) are developed to eliminate the nonlinear terms of BMIs and achieve optimal performance. Simulation examples are provided to demonstrate the practicality and validity of the proposed approach.
NONLINEAR DYNAMICS
(2023)
Article
Computer Science, Artificial Intelligence
Hui Chen, Tingting Xie, Man Liang, Wanquan Liu, Peter Xiaoping Liu
Summary: This paper proposes an effective measure for planar segmentation based on the clustering method. It characterizes the relationship between data by using the distance from a point to the local plane as a metric, enabling high similarity between coplanar data points to distinguish each plane. By evaluating the dissimilarity matrix of the input point cloud and using multidimensional scaling analysis, the correlation information between data points in the 3D Euclidean space is reconstructed. The obtained reconstructed point cloud shows the separation between different planes. An adaptive DBSCAN clustering method based on density stratification is developed for cluster segmentation on the reconstructed point cloud. Experimental results demonstrate the effectiveness of the proposed method in solving the over-segmentation problem and providing high segmentation accuracy.
PATTERN RECOGNITION
(2023)
Article
Chemistry, Analytical
Yongqing Zhu, Peter Xiaoping Liu, Jinfeng Gao
Summary: This paper presents a novel wearable upper arm tactile display device that can provide three types of tactile stimuli simultaneously. Squeezing and stretching stimulation are generated by two motors driving the nylon belt in opposite and same directions respectively. Additionally, vibration motors are fixed around the user's arm. Psychophysical experiments reveal that different tactile stimuli interfere with the user's perception, and squeezing has a significant impact on the stretch just noticeable difference (JND) values.
Article
Mathematics, Applied
Ke Xu, Huanqing Wang, Peter Xiaoping Liu
Summary: This paper proposes an adaptive fuzzy finite-time tracking control method for uncertain non-linear systems with unmodeled dynamics using the backstepping technique. The traditional dynamic signal structure is improved to be suitable for universal controller design and to dominate the unmodeled dynamics. Nonlinear damping terms are used to compensate for dynamic disturbances, while fuzzy logic systems are employed to handle the unknown nonlinearities. The developed FLS-based adaptive tracking control strategy ensures boundedness of all closed-loop signals and convergence of tracking error using appropriate Lyapunov functions and the finite-time stability theorem.
APPLIED MATHEMATICS AND COMPUTATION
(2023)
Article
Engineering, Mechanical
Wen Bai, Peter Xiaoping Liu, Huanqing Wang
Summary: This study investigates an adaptive fixed-time tracking problem of nonlinear interconnected high-order systems with unknown control direction and stochastic disturbances. The Nussbaum gain technique is utilized to propose an adaptive fixed-time controller to overcome the difficulties associated with unknown control directions. A modified fixed-time control scheme is presented to deal with the positive odd integer terms from the interconnected high-order system by adding a power integrator method. The designed control strategy guarantees convergence of tracking error within a fixed settling time and fixed-time stability of all signals in the closed-loop system. Simulation results validate the designed control approach.
NONLINEAR DYNAMICS
(2023)
Article
Automation & Control Systems
Xinwei Yan, Peter X. Liu
Summary: Neurosurgeons face challenges such as poor geometric accuracy, unpredictable factors, and visual obstacles. A neurosurgery simulator allows for preoperative simulation and planning, reducing the risk of surgery. A haptic device with six degrees of freedom is designed based on the general requirements of neurosurgery, and its kinematics is analyzed. The feasibility of the design is confirmed through simulation and data analysis.
INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION
(2023)
Article
Engineering, Electrical & Electronic
Zengshuai Wang, Minhua Zheng, Peter Xiaoping Liu
Summary: This article presents improvements for imbalanced problems, including both data and algorithm level adjustments. At the data level, a data-space balanced partition method based on undersampling is proposed, recursively dividing the imbalanced data into relatively balanced subspaces. At the algorithm level, a cost-sensitive stacking approach is introduced to enhance attention to minority classes. A new classification method, Data-Space balanced Partition based on Cost-sensitive Stacking learning (DPCStacking), is proposed based on these improvements. Experimental results demonstrate the effectiveness of the proposed method, outperforming other six algorithms designed for imbalanced problems in terms of average accuracy and true positive rate.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Civil
Guangyu Zhu, Ranran Sun, Jiaxin Fan, Furong Li, Yuhong Hou, Hui Yu, Peter Xiaoping Liu
Summary: This paper investigates the coupling effects and chain evolution of emergency events in urban rail transit through the construction of a GERTS evolution network and a CML-based coupled map model. Numerical simulations are conducted on the fire chain example to analyze the impact of different coupling effects and influencing factors on the evolution of emergency events. The results show that OR-coupling and CO-coupling can expand the impact scope of emergency events compared to AND-coupling, and the evolution speed of emergency events can be controlled by increasing the coupling action time and improving the URT repair ability. Therefore, analyzing the coupling effect and accompanying chain evolution can help managers make scientific judgments on the development trend of emergency events and take targeted emergency defense measures.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Hongyu Liang, Yongxuan Wang, Honghui Li, Yuying Wang, Peter Xiaoping Liu, Rong Liu
Summary: While current EEG wearables can be miniaturized to fit into in-ear devices, the existing ear-EEG techniques have limitations such as customization, time-consuming fabrication, and short life cycle. To overcome these drawbacks, a new method of designing a sensor with one-channel dry electrode embedded in a generic substrate is proposed. Electrical characterizations of the sensor were evaluated using 15 subjects and five EEG paradigms, and compared to conventional scalp EEG recordings. The results show that the designed ear-EEG sensor is feasible and has the potential to achieve sustained, cost-effective, and off-the-shelf recording use.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Artificial Intelligence
Guanglong Du, Wenpei Zhou, Chunquan Li, Di Li, Peter X. Liu
Summary: This article proposes a hybrid neural network learning framework called CSFFN to detect a player's emotional states in real-time during a gaming process based on electroencephalogram (EEG) signals. CSFFN combines a convolutional neural network (CNN), a fuzzy neural network (FNN), and a recurrent neural network (RNN) to improve the accuracy and noise resistance in game emotion recognition. Experimental results show that CSFFN outperforms other methods in recognizing four emotional states (happiness, sadness, superiority, and anger).
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Shanglin Li, Yangzhou Chen, Peter Xiaoping Liu
Summary: This paper addresses the problem of fault estimation and fault-tolerant consensus tracking control for Lipschitz nonlinear multi-agent systems subject to external disturbance. Two periodic event-triggered mechanisms are developed to improve communication efficiency between sensor to observer and observer to controller. An event-triggered fault observer is developed to estimate existing faults and ensure system security. Model gains of the observer and the controller can be obtained by solving a series of bilinear matrix inequalities (BMIs) using Lyapunov-Krasovskii theorem and the free-weighting matrix technique. Two iterative algorithms based on linear matrix inequalities (LMIs) are developed to address the difficulty associated with BMIs. A simulation example of satellite vehicles is provided to illustrate the effectiveness of the obtained theoretical results.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS
(2023)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
