Adaptive weights learning in CNN feature fusion for crime scene investigation image classification by Liu Ying, Zhang Qian Nan, Wang Fu Ping, Chiew Tuan Kiang, Lim Keng Pang, Zhang Heng Chang, Chao Lu, Lu Guo Jun, Ling Nam

“…To solve this problem, this paper proposes an adaptive feature fusion method based on an auto-encoder to improve classification accuracy. …”
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Magnetic Flux Density Feature Analysis and CT Saturation Region Recognition and Reconstruction Technology by Feng WANG, Jia ZHU, Shaolin JIAO, Yiquan LI, Hua XIE, Qingchun ZHAO

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WEAK FAULT FEATURE EXTRACTION OF ROLLING BEARING BASED ON PARAMETER OPTIMIZED MOMEDA AND CEEMDAN by HAN XueFei, SHI Zhan, HUA YunSong

“…Aiming at the problem that the fault feature information of rolling bearing is weak under the strong background noise environment,and the single use of the complete ensemble empirical mode decomposition with adaptive noise（ CEEMDAN）method is not effective in extracting the fault feature,a method based on parameter optimized multi-point optimal minimum entropy deconvolution adjusted（ POMOMEDA） and CEEMDAN was proposed. …”
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Color Visualization of the Artistic Image as a Feature of G. R. Derzhavin’s Creative Method by K. A. Potashova

“…The relevance of the problem is associated with the need to clarify the features of the creative method of G. …”
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Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation by Xiaoyun Gong, Weiye Zhang, Yongjie Jing, Wenliao Du

“…The results show that bearing faults are usually submerged under coupled faults， and it exhibits weak fault characteristics compared to unbalanced faults. Aiming at the problem that the bearing fault feature is difficult to extract in the rotor unbalance bearing coupling fault， using the sparse representation method based on Gabor atom to match the periodic vibration component in the rotor unbalance bearing coupled fault vibration signal， and the spectral kurtosis algorithm is used to find the frequency band where the impact signal is located for bearing fault feature extraction. …”
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Feature Consistency-Based Style Transfer for Landscape Images Using Dual-Channel Attention by Qiang Zhang, Shuai Wang, Dong Cui

“…To solve this problem, we propose a feature-consistent landscape image style transfer model based on two-channel attention. …”
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An efficient non-parametric feature calibration method for few-shot plant disease classification by Jiqing Li, Zhendong Yin, Dasen Li, Hongjun Zhang, Mingdong Xu

“…Few-shot learning has emerged as an effective solution to this problem. This paper proposes a method based on the Feature Adaptation Score (FAS) metric, which calculates the FAS for each feature layer in the Swin-TransformerV2 structure. …”
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Enhanced Driver Posture Feature Extraction Method Based on the Spatial Prior Probabilistic Model by WANG Han; SUN Yu; YIN Jie

“…Aiming at the problem of driving posture recognition difference, a enhanced driver′ s posture feature extraction based on the skin-color histogram of spatial prior probabilistic model is proposed. …”
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MRSNet: Multi-Resolution Scale Feature Fusion-Based Universal Density Counting Network by Yi Zhang, Wei Song, Mingyue Shao, Xiangchun Liu

“…This study focuses on the problem of dense object counting. In dense scenes, variations in object scales and uneven distributions greatly hinder counting accuracy. …”
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Multipath Suppression and High-precision Angle Measurement Method Based on Feature Game Preprocessing by Houhong XIANG, Yongliang WANG, Yuxi LI, Yufeng CHEN, Fengyu WANG, Xiaolu ZENG

“…This method, based on a signal-level feature game approach, incorporates two interconnected components working together. …”
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A Unified Deep-Domain Adaptation Framework: Advancing Feature Separability and Local Alignment by Pranav Kumar, Jimson Mathew, Rakesh Kumar Sanodiya, Avinash Kumar Chouhan, Rahul Reddy Bukkasamudram, Chandra Sai Teja Adhikarla

“…To address this issue of domain shift, we introduce a novel method, called “A Unified Deep-Domain Adaptation Framework: Advancing Feature Separability and Local Alignment” (DDASLA) that incorporates an attention mechanism into the ResNet18 model to improve its feature extraction capability. …”
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A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles by Qiao Meng, Huansheng Song, Gang Li, Yu’an Zhang, Xiangqing Zhang

“…Nowadays, automatic multi-objective detection remains a challenging problem for autonomous vehicle technologies. In the past decades, deep learning has been demonstrated successful for multi-objective detection, such as the Single Shot Multibox Detector (SSD) model. …”
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Remote Sensing Image Change Detection Based on Multi-Level Diversity Feature Fusion by Honggang Xie, Wanjie Ma

“…Furthermore, to effectively address the problem of boundary misjudgment in change areas caused by fixed thresholds, an Adaptive Threshold Module is devised to adaptively learn the texture features of change and unchanged regions, enabling the generation of more accurate thresholds for boundary determination, thereby improving the robustness of the algorithm model and alleviating false alarms. …”
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Double Filter and Double Wrapper Feature Selection Algorithm for High-Dimensional Data Analysis by Hong Chen, Yuefeng Zheng

“…Subsequently, the algorithm enters the double wrapper stage, where it uses the Random Walk Whale Optimization Algorithm (RWWOA) and the improved Adaptive Differential Evolution (ADE) to jointly optimize and obtain the optimal feature subset. Among them, in order to overcome the problem of single algorithm falling into the local optimum, the Algorithm Iteration Mechanism is proposed, which selectively runs two wrapper algorithms to make the algorithm jump out of local optimum and explore a broader optimization space. …”
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Enhancing Adversarial Transferability With Intermediate Layer Feature Attack on Synthetic Aperture Radar Images by Xuanshen Wan, Wei Liu, Chaoyang Niu, Wanjie Lu, Yuanli Li

“…Existing SAR adversarial attack algorithms require access to the network structure, parameters, and training data, which are often inaccessible in real-world scenarios. To address this problem, this study proposes an intermediate layer feature attack algorithm that does not rely on training data for the adversary model. …”
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Bearing Fault Diagnosis Method Based on Self-attentional Time-frequency Feature Fusion by LIU Jing, ZHANG Minghui, WU Jian, CHEN Erlong, JI Haipeng

“…Aiming at the problem that the single time and frequency domain signal contains incomplete features in bearing fault diagnosis , which leads to the poor generalization ability of traditional fault diagnosis models, a bearing fault diagnosis method based on self-attention time-frequency feature fusion was proposed. …”
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A new verification approach for nowcasting based on intensity and spatial-temporal feature correction by Jun Liu, Tong Zhang, Yuanzhao Chen, Rui Wang, Mingjie Wang, Shuxin Wang, Ting Xu, Chunyang Zhao, Xunlai Chen

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Learning Distinctive Feature Representation for Descriptors on Multimodal Images by Incorporating Multiple Negative Samples by Bohan Li, Yixuan Li, Yong Li, Guohan Zhang

“…This article proposes a method composed of a loss function and a feature extractor structure, to learn the distinctive feature representation (DIFR) for descriptors on multimodal images. …”
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Imbalanced feature generation based on bootstrap power spectral curve for estimating respiratory rate by Soojeong Lee, Gyanendra Prasad Joshi, Gangseong Lee

“…Hence, we use the parametric bootstrap model generated by artificial feature curves to estimate RR and solve this problem. …”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

“…Abstract To address the challenge of accurately capturing tool wear states in small sample scenarios, this paper proposes a tool wear prediction method that combines XGBoost feature selection with a PSO-BP network. In order to solve the problem of input feature selection and parameter selection in BP neural network, a double-layer programming model of input feature and parameter selection is established, which is solved by XGBoost and PSO. …”
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