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Learning Error Refinement in Stochastic Gradient Descent-Based Latent Factor Analysis via Diversified PID Controllers
IEEE Transactions on Emerging Topics in Computational Intelligence
Year: 2025 | Early Access Article |
In Big Data-based applications, high-dimensional and incomplete (HDI) data are frequently used to represent the complicated interactions among numerous nodes. A stochastic gradient descent (SGD)-based latent factor analysis (LFA) model can process such data efficiently. Unfortunately, a standard SGD algorithm trains a single latent factor relying on the stochastic gradient related to the current l...Show More
An Adaptive p-Norms-Based Kinematic Calibration Model for Industrial Robot Positioning Accuracy Promotion
Industrial robots inevitably incur kinematic errors in the advanced manufacturing and assembly processes, resulting in the severe reduction of the absolute positioning accuracy (APA). Kinematic calibration (KC) is well-known as a vital technique in APA-promoting tasks. However, existing KC models generally adopt a single distance-oriented Loss, e.g., an $L_{2}$ norm-oriented one that neglects th...Show More
Fourth-Order Dimension Preserved Tensor Completion With Temporal Constraint for Missing Traffic Data Imputation
IEEE Transactions on Intelligent Transportation Systems
Year: 2025 | Early Access Article |
In the intelligent transportation system, the collected traffic data are usually incomplete. The low-rank completion models are effective in processing missing traffic data (MTD) imputation. However, the existing low-rank completion models encounter the following challenges: 1) Incorporating spatio-temporal information of traffic data often leads to significant time overhead; 2) To reduce the time...Show More
Graph Linear Convolution Pooling for Learning in Incomplete High-Dimensional Data
High-dimensional and incomplete (HDI) data are frequently encountered in diverse real-world applications involving complex interactions among numerous nodes. Approaches based on latent feature analysis (LFA) have proven effective in performing representation learning in HDI data. Nevertheless, they cannot handle the high-order connectivity among nodes in HDI data well, resulting in severe accuracy...Show More
Latent Factor Analysis Model With Temporal Regularized Constraint for Road Traffic Data Imputation
Intelligent Transportation Systems (ITSs) are designed to alleviate traffic congestion and provide convenience for travelers or decision-makers. However, the challenge lies in obtaining complete and accurate traffic data because of various factors. Therefore, many models have been studied for traffic data imputation. Most studies overlook the extreme case where the missing rate of observed traffic...Show More
A 3D Convolution-Incorporated Dimension Preserved Decomposition Model for Traffic Data Prediction
Traffic data prediction is a crucial component of Intelligent Transport Systems (ITS) as it contributes significantly to real-time navigation and congestion management. However, due to incomplete deployment of sensor and instability of data transmission, the traffic data that we collect is usually a high-dimensional and incomplete (HDI) matrix or tensor. Currently, there are two challenges with tr...Show More
An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data
Di Wu;Yuanpeng Hu;Kechen Liu;Jing Li;Xianmin Wang;Song Deng;Nenggan Zheng;Xin Luo
High-dimensional and incomplete (HDI) data commonly arise in various Big Data-related applications, e.g., recommender systems and bioinformatics. Representation is a learning paradigm to map HDI data into low-dimensional latent space for attracting valuable knowledge and patterns. Currently, deep neural network (DNN) is one of the most popular and successful approaches to represent HDI data due to...Show More
A Calibrator Fuzzy Ensemble for Highly-Accurate Robot Arm Calibration
The absolute positioning accuracy of an industrial robot arm is vital for advancing manufacturing-related applications like automatic assembly, which can be improved via the data-driven approaches to robot arm calibration. Existing data-driven calibrators have illustrated their efficiency in addressing the issue of robot arm calibration. However, they mostly are single learning models that can be ...Show More
Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery
Wireless sensor network (WSN) is an emerging and promising developing area in the intelligent sensing field. Due to various factors like sudden sensors breakdown or saving energy by deliberately shutting down partial nodes, there are always massive missing entries in the collected sensing data from WSNs. Low-rank matrix approximation (LRMA) is a typical and effective approach for pattern analysis ...Show More
A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High-Dimensional and Incomplete Data
High-dimensional and incomplete (HDI) data are frequently encountered in big date-related applications for describing restricted observed interactions among large node sets. How to perform accurate and efficient representation learning on such HDI data is a hot yet thorny issue. A latent factor (LF) model has proven to be efficient in addressing it. However, the objective function of an LF model i...Show More
Vibration Control using A Robust Input Shaper via Extended Kalman Filter-Incorporated Residual Neural Network
2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2024 | Conference Paper |
With the rapid development of industry, there is a growing concern over the vibration control challenges associated with flexible structures and underactuated systems. Input shaping technology enables stable performance for high-speed motion in industrial motion systems. However, existing input shapers commonly suffer from the ineffective control performance caused by the ignorance of observation ...Show More
A Fine-Grained Regularization Scheme for Non-negative Latent Factorization of High-Dimensional and Incomplete Tensors
A Dynamically Weighted Directed Network (DWDN) fundamentally illustrates the complex interactions among massive nodes from a big-data-oriented application, like the dynamic interactions among numerous terminals in a metropolitan network management system (MNMS). A High-Dimensional and Incomplete (HDI) tensor is able to flexibly quantize it, where lots of entries are missing primarily due to the im...Show More
A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data
Jiufang Chen;Kechen Liu;Xin Luo;Ye Yuan;Khaled Sedraoui;Yusuf Al-Turki;MengChu Zhou
High-dimensional and incomplete (HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis (LFA) model is capable of conducting efficient representation learning to an HDI matrix, whose hyper-parameter adaptation can be implemented through a particle swarm optimizer (PSO) to meet scalable requirements. However, conventional PSO is limit...Show More
An Efficient Industrial Robot Calibrator With Multiplaner Constraints
Calibration technology has become an essential part of the task of improving robot precision. However, existing calibration schemes suffer from accuracy loss in actual working conditions due to the ignorance of the target robot's constrained workspace. In response to this challenge, this study proposes a novel robot calibrator with three-fold ideas: 1) optimizing the measurement configuration via ...Show More
With the ongoing advancements in sensor networks and data acquisition technologies across various systems like manufacturing, aviation, and healthcare, the data driven vibration control (DDVC) has attracted broad interests from both the industrial and academic communities. Input shaping (IS), as a simple and effective feedforward method, is greatly demanded in DDVC methods. It convolves the desire...Show More
Alternating-Direction-Method of Multipliers-Based Adaptive Nonnegative Latent Factor Analysis
Large scale interaction data are frequently found in industrial applications related with Big Data. Due to the fact that few interactions commonly happen among numerous nodes in real scenes, such data can be quantified into a High-Dimensional and Incomplete (HDI) matrix where most entries are unknown. An alternating-direction-method-based nonnegative latent factor model can perform efficient and a...Show More
Iterative Role Negotiation via the Bilevel GRA++ With Decision Tolerance
Qian Jiang;Dongning Liu;Haibin Zhu;Shijue Wu;Naiqi Wu;Xin Luo;Yan Qiao
Role negotiation (RN) is situated at the initial stage of the role-based collaboration (RBC) methodology and is independent of the subsequent agent evaluation and role assignment (RA) processes. RN is to determine the roles and the resource requirements for each role. In existing RBC-related research, RN is assumed to be static. This means that the roles and the resource requirements for each role...Show More
A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor Analysis
IEEE Transactions on Fuzzy Systems
Cited by: Papers (9)
A stochastic gradient descent (SGD) based latent factor analysis (LFA) model can obtain superior performance when performing representation to a high-dimensional and incomplete (HDI) matrix, which is encountered in various big data-related applications cause of the great demand for describing the highly complex interactions among tremendous nodes. However, an SGD-based LFA model is often stacked b...Show More
A Generalized Nesterov's Accelerated Gradient-Incorporated Non-Negative Latent-Factorization-of-Tensors Model for Efficient Representation to Dynamic QoS Data
Dynamic Quality-of-Service (QoS) data can be efficiently represented by a Non-negative Latent-factorization-of-tensors model, which relies on a Non-negative and Multiplicative Update on Incomplete Tensors (NMU-IT) algorithm. Nevertheless, NMU-IT frequently encounters slow convergence and inefficient hyper-parameters selection. Targeting at overcome these critical defects, this paper proposed to im...Show More
A Robust and Efficient Ensemble of Diversified Evolutionary Computing Algorithms for Accurate Robot Calibration
Industrial robots are regarded as essential instruments for advanced industry upgrading. The kinematic parameters of an industrial robot should be calibrated precisely to guarantee its absolute positioning accuracy, which can be implemented via an evolutionary computing (EC) algorithm; however, existing calibrators are mostly based on an EC algorithm with a homogeneous learning scheme, which may l...Show More
A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme
Unmanned aerial vehicles (UAVs) have gained significant attention in practical applications, especially the low-altitude aerial (LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network (TDKD-Net) is proposed, where the TT-format TD (tensor decomposi...Show More
Adaptive Divergence-Based Non-Negative Latent Factor Analysis of High-Dimensional and Incomplete Matrices From Industrial Applications
High-Dimensional and Incomplete (HDI) data are commonly seen in various big-data-related applications concerning the inherent non-negativity interactions among numerous nodes. A Non-negative Latent Factor Analysis (NLFA) model performs efficient representation learning to such HDI data. However, existing NLFA models all adopt a static divergence metric like Euclidean distance or α-β divergence to ...Show More
Pseudo Gradient-Adjusted Particle Swarm Optimization for Accurate Adaptive Latent Factor Analysis
A latent factor analysis (LFA) model can be efficiently built via the stochastic gradient descent (SGD) algorithm to address high-dimensional and incomplete (HDI) data generated by various big data-related applications. However, its performance depends hugely on its hyperparameters, which is conventionally decided through the grid-search that yields extremely high-computational costs. Particle swa...Show More
Fuzzy k-Winner-Take-All Network for Competitive Coordination in Multirobot Systems
IEEE Transactions on Fuzzy Systems
Cited by: Papers (7)
This study focuses on exploring the solution to coordination in multirobot systems faced with competitive states, where the competition behavior can be described as a $k$-winner-take-all ($k$WTA) operation. To address the $k$WTA problem, a fuzzy $k$WTA (F-$k$WTA) network is proposed, which incorporates a fuzzy mechanism into the network that adapts the convergence rate of the F-$k$WTA network base...Show More
A Novel Machine Learning System for Industrial Robot Arm Calibration
The application of industrial robot arms in intelligent manufacturing is highly vigorous. Generally, robot arms have high repetitive positioning accuracy. However, they frequently suffer from large absolute positioning error, which can not be directly adopted in high-precision production activities, like chip and cell phone manufacturing. To address this critical issue, we first propose a novel cu...Show More
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