Article
Geochemistry & Geophysics
Carlos Claudino, Wagner Moreira Lupinacci
Summary: Preconditioning is a practice that improves the resolution of seismic data, but the challenge of extracting all frequency contents remains. A new method called spectral stacking is proposed, which extends frequency content while preserving the distribution of seismic events. The method includes the creation of a sharpening filter that resolves layers under Ricker's criterion and preserves the amplitude using a variable-window correction. Synthetic and real tests confirm the effectiveness of the method, showing increased resolution and sharper seismic images.
GEOPHYSICAL PROSPECTING
(2023)
Article
Nuclear Science & Technology
Zhan Li, Jie Huang, Ming Ding
Summary: This study compared and analyzed ten different selection strategies in nuclear fuel reloading optimization. It was found that exponential sorting was the best selection strategy for single-objective optimization problems, whereas hybrid truncation exponential sorting was the best selection strategy for multi-objective optimization problems.
NUCLEAR ENGINEERING AND DESIGN
(2021)
Article
Engineering, Electrical & Electronic
R. Krishnamoorthy, I. D. Soubache, Ali Farmani
Summary: A low loss wavelength-selective filter in terahertz frequencies is proposed and demonstrated. The filter exhibits high quality resonance and has high transmission efficiency and quality factor.
OPTICAL AND QUANTUM ELECTRONICS
(2022)
Article
Acoustics
Abdullah Secgin, Murat Kara, Neil Ferguson
Summary: This article enhances the discrete singular convolution method for free vibration analysis of non-uniform thin beams with variability in their geometrical and material properties. The method accurately predicts natural frequencies and establishes probability distribution functions using polynomial chaos expansion. Monte Carlo simulations validate the accuracy and efficiency of the proposed algorithm.
JOURNAL OF VIBRATION AND CONTROL
(2022)
Article
Energy & Fuels
Dongdong Chen, Long Xiao, Wenduan Yan, Yan Li, Yinbiao Guo
Summary: This paper presents a new harmonic detection method based on the triangle orthogonal principle algorithm, which has the advantages of simple structure, low calculation, fast execution time, lower complexity for implementation, and less microcontroller resource consumption, making it practical for low-cost microcontroller implementation. The proposed method is analyzed in detail and its great performance is verified through experimental results.
Article
Computer Science, Artificial Intelligence
G. Sakthi Priya, N. Padmapriya
Summary: Deep learning models have better performance for image classification, but they require extensive memory usage and computational power. This paper introduces a lightweight deep learning architecture, PT-CNN, for multi-class texture classification, which is well suited for analyzing texture images.
NEURAL PROCESSING LETTERS
(2023)
Article
Engineering, Civil
Qiang Zhou, Zhong Qu, Shi-Yan Wang, Kang-Hua Bao
Summary: In this study, a network model combining enhanced convolution and dynamic feature fusion was proposed to improve crack detection performance. By using enhanced convolution and dynamic feature fusion strategy, the model achieved good performance in experiments on different scale crack image datasets.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Hui Lv, Pengfei Shan, Hongfang Shi, Li Zhao
Summary: In this paper, an improved adaptive bilateral filter method is proposed for enhancing the details of infrared images and suppressing noise by combining edge detection operator with bilateral filtering.
SIGNAL IMAGE AND VIDEO PROCESSING
(2022)
Article
Mathematics, Applied
Li-Bin Liu, Yige Liao, Guangqing Long
Summary: This study investigates a novel parameter-uniform finite difference scheme for a singularly perturbed Volterra integro-differential equation on a Shishkin-type mesh. The problem is discretized using the variable two-step backward differentiation formula (BDF2) for the first-order derivative term and the trapezoidal formula for the integral term. The stability of the proposed numerical method is analyzed, and the convergence analysis shows that the presented method is almost second-order uniformly convergent with respect to the perturbation parameter ? in the discrete maximum norm. Numerical results are provided to support the theoretical findings.
COMPUTATIONAL & APPLIED MATHEMATICS
(2023)
Article
Chemistry, Analytical
Hong Men, Mei Liu, Yan Shi, Xiuxin Xia, Tianzuo Wang, Jingjing Liu, Qingjun Liu
Summary: The combination of multi-sensor data fusion can comprehensively show the overall attributes of the sample, but it may bring redundant information and reduce recognition accuracy. In this study, a new deep learning model called interleaved attention convolutional compression network (IACCN) is proposed to identify rice quality in six storage periods under different storage conditions. The results demonstrate that IACCN achieves better classification performance and good stability compared to other deep learning methods.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Geochemistry & Geophysics
Xu Li, Zhenxin Zhang, Yong Li, Mingmin Huang, Jiaxin Zhang
Summary: In recent years, deep learning has achieved impressive results in point cloud semantic segmentation, but there are still issues regarding the efficiency, speed, and parameter size of point cloud processing. This study proposes an efficient and lightweight deep neural network called SFL-Net, which uses slight filter convolution (SFConv) and hourglass block (HB) to accelerate semantic segmentation for large-scale point clouds. The performance of SFL-Net surpasses state-of-the-art approaches on public datasets and its model parameters are reduced significantly compared to other methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Chemistry, Multidisciplinary
Jianxian Cai, Li Wang, Jiangshan Zheng, Zhijun Duan, Ling Li, Ning Chen
Summary: In this study, a seismic co-band denoising model called ARDU is proposed, which combines a U-shaped convolutional neural network (U-Net), atrous convolution, and residual dense blocks. Experimental results demonstrate that the ARDU model effectively removes seismic co-band noise, improves the signal-to-noise ratio (SNR) of seismic signals, and enhances their quality.
APPLIED SCIENCES-BASEL
(2023)
Article
Environmental Sciences
Shiqi Huang, Ouya Zhang, Qilong Chen
Summary: Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR images are affected by speckle noise, which makes ship target detection challenging. To overcome this, a new method called block thumbnail particle swarm optimization clustering (BTPSOC) is proposed, which improves the accuracy and robustness of ship target detection using block thumbnails and particle swarm optimization.
Article
Engineering, Electrical & Electronic
Prabhat Chandra Shrivastava, Prashant Kumar, Manish Tiwari, Amit Dhawan
Summary: This study presents a high-performance block reconfigurable finite impulse response filter structure, which achieves lower ADP and EPO by optimizing multipliers. The structure utilizes a partial-product-based approach, along with pipelining and parallelism to improve design throughput.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Environmental Sciences
Bulat Tuyakov, Mateusz Kruszewski, Lidia Glinka, Oksana Klonowska, Michal Borys, Pawel Piwowarczyk, Dariusz Onichimowski
Summary: This study aimed to assess the incidence of catheter dislocation with different continuous peripheral nerve block methods after total knee arthroplasty. The results showed that the combination of CFTB and SMC had lower catheter dislocation rates and provided adequate analgesic effect.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)