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CCO: A Cluster Core-Based Oversampling Technique for Improved Class-Imbalanced Learning

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第2期9卷 1153-1165页

作者： Mondal, Priyobrata Ansari, Faizanuddin Das, Swagatam Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

Supervised classification problems from the real world typically face a challenge characterized by the scarcity of samples in one or more target classes compared to the rest of the majority classes. In response to such class imbalance, we propose an oversampling technique based on clustering, aiming to populate the minority class with synthetic samples. This approach capitalizes on the notion of "Cluster Cores," representing locally dense regions within clusters. These Cluster Cores act as central, densely crowded areas that capture intricate topological properties of the corresponding clusters, especially in complex datasets with a non-convex spatial orientation in the feature space. By concentrating on these high-density regions, our clustering-based oversampling technique generates synthetic samples within the convex hull region of minority class instances in the formed clusters. This strategy ensures the creation of points that align with the data space and considers each minority instance within a specific cluster, thereby averting the problems encountered due to the generation of artificial samples by mere linear combination of the minority class data points, as is encountered in SMOTE (Synthetic Minority Oversampling Technique)-based algorithms. To assess the efficacy of our proposal, we conducted experimental comparisons against several cutting-edge algorithms, considering an array of evaluation metrics on well-known datasets used in the literature for both binary and multi-class classification. Additionally, we undertook a detailed ablation study, scrutinized existing algorithms in our context, delineated their strengths and limitations, and contemplated potential research directions in this domain.

关键词： Clustering algorithms Noise measurement Interpolation Noise Computational intelligence classification algorithms Task analysis classification imbalanced data oversampling synthetic minority oversampling technique

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Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection

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IEEE ACCESS 2025年 13卷 618-632页

作者： Guo, Shuangle Li, Yongxia Zhang, Jianguang Liu, Yue Tian, Tian Guo, Mengchen Shandong Univ Aeronaut Sch Informat Engn Binzhou 256600 Shandong Peoples R China Hengshui Univ Off Acad Affairs Hengshui 053000 Peoples R China Hengshui Univ Dept Math & Comp Sci Hengshui 053000 Peoples R China Hengshui Univ Off Human Resources Hengshui 053000 Peoples R China Univ Calif Davis Dept Appl Math Davis CA 95616 USA Xiamen Univ Malaysia Dept Elect & Elect Engn Sepang 43900 Malaysia

Feature selection is an important preprocessing step in machine learning to remove irrelevant and redundant features. Due to its ability to effectively maintain the discriminability of extracted features, Trace Ratio Linear Discriminant Analysis (TR-LDA) has become the foundation for many feature selection algorithms. As is known, TR-LDA is a challenging problem to solve because of its trace-ratio form, and it also faces the scale invariance problem. These two drawbacks of TR-LDA significantly reduce the performance of feature selection algorithms based on it. To overcome these drawbacks, this paper proposes the sparse LDA with constant between-class distance (SLDA-CBD) model to select relavant features. This model first transforms TR-LDA into a non-trace ratio problem with a constant between-class distance constraint, and then imposes row constraints on the projection matrix to implement feature selection. Since the SLDA-CBD model is rooted in TR-LDA, it ensures the discriminative performance of the selected features. The constant between-class distance constraint successfully avoids the scale invariance problem. Additionally, due to the non-trace ratio form of the SLDA-CBD model, it is easily solvable. The experimental results show that the proposed method has better performance compared to the baseline and six state-of-the-art relative methods, with improvements of over 1% on image datasets and over 2% on video datasets in most cases, while also demonstrating high stability, proving its effectiveness and advantage in practical applications.

关键词： Feature extraction classification algorithms Training Filtering algorithms Computational modeling Machine learning algorithms Sparse matrices Mathematical models Vectors Predictive models Feature selection TR-LDA sparse regression

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Viewpoint Generation Algorithm for Gimbal-Mounted Robots for Visual Inspection of Civil Engineering Structures

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IEEE ACCESS 2025年 13卷 40813-40823页

作者： Haneda, Masaya Wu, Weitong Funabora, Yuki Doki, Shinji Doki, Kae Nagoya Univ Grad Sch Engn Dept Elect Engn & Comp Sci Nagoya Aichi 4648603 Japan Aichi Inst Technol Dept Elect & Elect Engn Toyota Aichi 4700392 Japan

We propose a viewpoint generation algorithm for the visual inspection of civil engineering structures using robots equipped with a gimbal-mounted camera. This algorithm enables robots to completely inspect target surfaces at the required spatial resolution while reducing movement by effectively utilizing the gimbal. Previous studies generally assume that the camera is always oriented orthogonally to the target surface, and they focus on the inspection position optimization problem, resulting in the underutilization of gimbals and heavy movement of robots. In this study, firstly we have formulated the conditions under which the collected data's quality does not deteriorate in terms of damage detection, and we have relaxed the orthogonal measuring restrictions. Based on the conditions, we propose a viewpoint generation algorithm that can carry out inspection work by changing measurement orientations instead of moving the robot. In simulation experiments, our method reduces inspection time by 47.7% compared to the benchmark method for the same tasks of inspecting a pier surface with the required spatial resolution. In addition, the viewpoint generation time was reduced by 96.8% compared to the benchmark method. We also conducted experiments in real environments using pan-tilt heads and confirmed that our algorithm can collect data qualified for the damage detection of visual inspections.

关键词： Inspection Cameras Spatial resolution Visualization Autonomous aerial vehicles Planning Data collection Robot vision systems Clustering algorithms classification algorithms Surface inspection coverage path planning (CPP) autonomous robots remote sensing condition monitoring data collection

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IAR 2.0: An Algorithm for Refining Inconsistent Annotations for Time-Series Data Using Discriminative Classifiers

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IEEE ACCESS 2025年 13卷 19979-19995页

作者： Vaaras, Einari Airaksinen, Manu Rasanen, Okko Tampere Univ Signal Proc Res Ctr Tampere 33720 Finland Univ Helsinki BABA Ctr Helsinki 00029 Finland

The performance of discriminative machine-learning classifiers, such as neural networks, is limited by training label inconsistencies. Even expert-based annotations can suffer from label inconsistencies, especially in the case of ambiguous phenomena-to-annotate. To address this, we propose a novel algorithm, iterative annotation refinement (IAR) 2.0, for refining inconsistent annotations for time-series data. IAR 2.0 uses a procedure that utilizes discriminative classifiers to iteratively combine original annotations with increasingly accurate posterior estimates of classes present in the data. Unlike most existing label refinement approaches, IAR 2.0 offers a simpler yet effective solution for resolving ambiguities in training labels, working with real label noise on time-series data instead of synthetic label noise on image data. We demonstrate the effectiveness of our algorithm through five distinct classification tasks on two highly distinct data modalities. As a result, we show that the labels produced by IAR 2.0 systematically improve classifier performance compared to using the original labels or a previous state-of-the-art method for label refinement. We also conduct a set of controlled simulations to systematically investigate when IAR 2.0 fails to improve on the original training labels. The simulation results demonstrate that IAR 2.0 improves performance in nearly all tested conditions. We also find that the decrease in performance when IAR 2.0 fails is small compared to the average performance gain when IAR 2.0 succeeds, encouraging the use of IAR 2.0 even when the nature of data is unknown. The code is freely available at https://***/SPEECHCOG/IAR_2.

关键词： Training Annotations Noise measurement Data models Predictive models classification algorithms Prediction algorithms Computational modeling Neural networks Standards Annotation refinement daylong audio recordings discriminative classifiers human activity recognition inconsistent labels label refinement movement sensors multi-sensor inertial measurement unit speech emotion recognition time-series data

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Nondestructive Evaluation of Aging Failure in Insulation Coatings by Ultrasonic Guided Wave Based on Signal Processing and Machine Learning

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COATINGS 2025年第3期15卷 347-347页

作者： Qiu, Mengmeng Ge, Xin Anhui Tech Coll Mech & Elect Engn Sch Elect Engn Wuhu 241002 Peoples R China Wuhu Inst Technol Sch Elect & Automat Wuhu 241006 Peoples R China

In the field of nondestructive evaluation (NDE) using ultrasonic guided waves, accurately assessing the aging failure of insulation coatings remains a challenging and prominent research topic. While the application of ultrasonic guided waves in material testing has been extensively explored in the existing literature, there is still a significant gap in quantitatively evaluating the aging failure of insulation coatings. This study innovatively proposes an NDE method for assessing insulation coating aging failure by integrating signal processing and machine learning technologies, thereby effectively addressing both theoretical and practical gaps in this domain. The proposed method not only enhances the accuracy of detecting insulation coating aging failure but also introduces new approaches to non-destructive testing technology in related fields. To achieve this, an accelerated aging experiment was conducted to construct a cable database encompassing various degrees of damage. The effects of aging time, temperature, mechanical stress, and preset defects on coating degradation were systematically investigated. Experimental results indicate that aging time exhibits a three-stage nonlinear evolution pattern, with 50 days marking the critical inflection point for damage accumulation. Temperature significantly influences coating damage, with 130 degrees C identified as the critical threshold for performance mutation. Aging at 160 degrees C for 100 days conforms to the time-temperature superposition principle. Additionally, mechanical stress concentration accelerates coating failure when the bending angle is >= 90 degrees. Among preset defects, cut defects were most destructive, increasing crack density by 5.8 times compared to defect-free samples and reducing cable life to 40% of its original value. This study employs Hilbert-Huang Transform (HHT) for noise reduction in ultrasonic guided wave signals. Compared to Fast Fourier Transform (FFT), HHT demonstrates superior per

关键词： aviation cable insulation coating accelerated aging experiments HHT noise reduction classification algorithms hybrid machine learning prediction models

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Dynamic Window Approach Using GOA Search Algorithm for Collision Avoidance Based on Obstacle Trajectory Prediction

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IEEE ACCESS 2025年 13卷 44169-44183页

作者： Liu, Yuan Zhang, Lingyu Liu, Jiasheng She, Yingying Zhang, Birui Min, Feiyan Wuhan Second Ship Design & Res Inst Wuhan 430030 Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 519070 Peoples R China

Mobile robots not only need to navigate autonomously in complex environments, but also need to avoid collisions with dynamic obstacles, especially pedestrians. This paper realizes dynamic obstacles detection and state estimation algorithm with LiDAR data, and proposes the path planning and obstacle avoidance algorithm based on dynamic obstacle trajectory prediction. The current planning methods of mobile robots are mostly applicable to static scenes and lack adaptability to pedestrian dynamics. The core idea of the proposed algorithm is to guide the mobile robot towards the back of the pedestrian to reduce the psychological pressure on the pedestrian as well as to avoid interfering with the pedestrian's movement and to achieve dynamic obstacle avoidance, which is in line with the idea of going around from the back in the Convention on the International Regulations for the Avoidance of Collisions at Sea (COLREGS). Firstly, the pedestrian detection method is studied on the basis of Lidar data, and the pedestrian motion state is estimated based on Kalman filter to avoid error detection. Secondly, the dynamic window approach (DWA) method is improved and pedestrian dynamic is added with carefully designed algorithm for path planning and obstacle avoidance. Furthermore, to improve the computational efficiency and flexibility, the GOA(Grasshopper Optimization Algorithm) is used to improve the DWA search schema. Finally, the effectiveness of the proposed method is proved by comparing the existing methods with simulation and experimental tests. The research of this paper can improve the safety of the robot in the human-machine crowd scene.

关键词： Heuristic algorithms Pedestrians Collision avoidance Laser radar Prediction algorithms Trajectory Legged locomotion Planning classification algorithms Vehicle dynamics dynamic window approach trajectory prediction GOA algorithm

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Dengue-Infected Mosquito Detection with Uncertainty Evaluation based on Monte Carlo Dropout

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IEEE LATIN AMERICA TRANSACTIONS 2025年第2期23卷 135-143页

作者： Torres, Israel Nakano, Mariko Cime-Castillo, Jorge Escamilla-Hernandez, Enrique Lopez-Garcia, Osvaldo Lanz-Mendoza, Humberto Inst Politecn Nacl Culhuacan Coyoacan Mexico Inst Nacl Salud Publ Cuernavaca Morelos Mexico

Considering Aedes mosquitoes are a principal vector of the Dengue virus causing, in the worst case, the death of infected people, accurate detection of infected Aedes mosquitoes is very important to prevent the further spread of the virus. In this paper, we propose a detection algorithm for infected Aedes aegypti mosquitoes by Dengue Virus-2 using their wingbeat signals. The proposed algorithm uses Long Short-Term Memory (LSTM) as a classifier of the input wingbeat signal into healthy mosquitoes and infected mosquitoes. All living beings, even if they are of the same species, have different characteristics depending on the season in which they are born, temperature, humidity, food, etc. This individual difference perhaps influences the level of infection, although it is fed by the same infected blood. Considering these differences between individuals, we introduce an uncertainty measure based on Monte-Carlo dropout. The proposed algorithm detects approximately 5% of uncertainty data from all input wingbeat signals in the test set and provides a classification accuracy of 94.87% without any uncertainty.

关键词： Uncertainty Long short term memory Vectors Viruses (medical) Monte Carlo methods Logic gates Training Accuracy classification algorithms Approximation algorithms dengue fever infected mosquitoes long short-term memory Monte Carlo dropout uncertainty

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Probabilistic Decision-Making for Virtually Coupled Trains Under Uncertainty

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 8602-8611页

作者： Wu, Yiling Yan, Fei Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China

The virtually coupled trains present challenges due to the risks associated with small separation. The following train must make appropriate decisions in uncertain and high-risk situations to ensure safety and efficiency. We address uncertainty from sensor noise and unknown preceding train behaviors, establishing a probabilistic decision-making model using a partially observable Markov decision process (POMDP). Collision and risk envelopes are defined using the emergency and full-service braking distance of the following train, considering the "worst-case separation" scenarios, leading to a novel chance-constrained POMDP (CC-POMDP) for the virtually coupled trains. We propose an online planning algorithm, Partially Observable Monte Carlo Planning based on Double Shielding and Progressive Widening (POMCP-DS-PW). The results demonstrate that even without a pre-planned recommended speed curve, the following train can optimize acceleration based on incomplete observations and unknown behaviors of the preceding train, thereby ensuring safety while achieving operational efficiency.

关键词： Decision making Safety Uncertainty Planning Resistance Probabilistic logic classification algorithms Vehicle dynamics Optimization Heuristic algorithms Virtual coupling POMDP uncertainty chance constraint

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CP-YOLO: An Algorithm for Cigarette Pack Defects Detection Based on CCD Images

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IEEE ACCESS 2025年 13卷 57354-57368页

作者： Dong, Peng Feng, Weihua Wang, Rui Zhang, Mingyan Hong, Qunye Wang, Yongsheng Wang, Di Zong, Guohao China Natl Tobacco Corp CNTC Zhengzhou Tobacco Res Zhengzhou 450001 Peoples R China Zhengzhou Univ Henan Inst Adv Technol Zhengzhou 450001 Peoples R China China Tobacco Henan Ind Co Ltd Golden Leaf Prod & Mfg Ctr Zhengzhou 450001 Peoples R China

In cigarette production, detecting cigarette pack defects is crucial for ensuring that products meet quality standards. The failure to detect defective packs promptly may affect production efficiency and material consumption. Hence, in this study, we used Charge-Coupled Device (CCD) cameras to collect many defect images from real industrial production lines. A relatively comprehensive cigarette pack defect dataset, called CigPack, was established based on complex defect features of different sizes and structures. Consequently, this paper proposes an improved CP-YOLO algorithm based on YOLOv5 to address the characteristics of cigarette pack defects including large size variations and complex foreground-background information. The algorithm integrates multi-scale aggregate convolution into the BottleneckCSP architecture to form the C3MSAC module. This module extracts and fuses grouped features at multiple scales to enhance the multi-scale representation of input feature maps. Additionally, a balanced-domain loss function, which introduces normalized Wasserstein distance to optimize the guidance of the network for bounding boxes with different geometric characteristics, is proposed. Experimental results demonstrate that CP-YOLO achieved average accuracy improvements of 2.9% and 2.5% on the CigPack dataset, without increasing computational complexity. The model also demonstrated excellent detection accuracy and robustness on steel surface defect dataset and printed circuit board defect dataset.

关键词： Cameras Feature extraction YOLO Accuracy Prediction algorithms Deep learning Computational modeling classification algorithms Computational complexity YOLOv5 cigarette pack defect detection multi-scale feature extraction Wasserstein distance Wasserstein distance

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Differentially Private Distributed Optimization Over Time-Varying Unbalanced Networks With Linear Convergence Rates

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2025年 73卷 1138-1152页

作者： Yang, Zhen He, Wangli Yang, Shaofu East China Univ Sci & Technol State Key Lab Ind Control Technol Shanghai 200237 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Smart Mfg Energy Chem Proc Shanghai 200237 Peoples R China Huzhou Inst Ind Control Technol Huzhou 313099 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China

This paper addresses the distributed optimization problem with privacy concerns over time-varying unbalanced networks, where agents collaborate to optimize the average of local objective functions while preserving the privacy of sensitive information encoded in local functions. To tackle the problem, the paper proposes a differentially private algorithm by exploiting decaying Laplace noise without requiring bounded gradients. The proposed algorithm is demonstrated to achieve linear convergence to the sub-optimal solution determined by the noise injected to gradient estimations in mean square and ensure $\epsilon$-differential privacy (DP) of local functions under carefully designed noise parameters. The inherent privacy-accuracy trade-off is revealed through both theoretical insights and simulation results. Furthermore, the image classification and deblurring problems are effectively solved with sensitive data being strictly protected through the deployment of the proposed algorithm, demonstrating the convergence and privacy-preserving performance of the algorithm.

关键词： Convergence Optimization Privacy Signal processing algorithms Noise Differential privacy Vectors Linear programming classification algorithms Training Distributed optimization differential privacy time-varying unbalanced networks

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