A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence by Zheng Zhou, Yusong Qiao, Xusheng Lin, Purui Li, Nan Wu, Dong Yu

“…This approach significantly reduces the computational complexity of the model while maintaining high diagnostic accuracy, making it well suited for edge nodes in industrial IoT scenarios. …”
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Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey by Milan Ganai, Sicun Gao, Sylvia L. Herbert

“…Previously, HJ reachability was restricted to verifying low-dimensional dynamical systems primarily because the computational complexity of the dynamic programming approach it relied on grows exponentially with the number of system states. …”
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Probe Selection and Power Weighting in Multiprobe OTA Testing: A Neural Network-Based Approach by Yong Li, Hao Sun, Xingyu Chen, Lijian Xin, Xiang Zhang

“…., the well-known multishot algorithm, the proposed NN-based approach can yield similar channel emulation accuracy with significantly reduced computational complexity.…”
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Control of Magnetic Manipulator Using Reinforcement Learning Based on Incrementally Adapted Local Linear Models by Martin Brablc, Jan Žegklitz, Robert Grepl, Robert Babuška

“…While all of the approximation methods were successful, MGGP achieved the best results at the cost of higher computational complexity. Index Terms–AI-based methods, local linear regression, nonlinear systems, magnetic manipulation, model learning for control, optimal control, reinforcement learning, symbolic regression.…”
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Abnormal link detection algorithm based on semi-local structure by Haoran SHI, Lixin JI, Shuxin LIU, Gengrun WANG

“…With the research in network science, real networks involved are becoming more and more extensive.Redundant error relationships in complex systems, or behaviors that occur deliberately for unusual purposes, such as wrong clicks on webpages, telecommunication network spying calls, have a significant impact on the analysis work based on network structure.As an important branch of graph anomaly detection, anomalous edge recognition in complex networks aims to identify abnormal edges in network structures caused by human fabrication or data collection errors.Existing methods mainly start from the perspective of structural similarity, and use the connected structure between nodes to evaluate the abnormal degree of edge connection, which easily leads to the decomposition of the network structure, and the detection accuracy is greatly affected by the network type.In response to this problem, a CNSCL algorithm was proposed, which calculated the node importance at the semi-local structure scale, analyzed different types of local structures, and quantified the contribution of edges to the overall network connectivity according to the semi-local centrality in different structures, and quantified the reliability of the edge connection by combining with the difference of node structure similarity.Since the connected edges need to be removed in the calculation process to measure the impact on the overall connectivity of the network, there was a problem that the importance of nodes needed to be repeatedly calculated.Therefore, in the calculation process, the proposed algorithm also designs a dynamic update method to reduce the computational complexity of the algorithm, so that it could be applied to large-scale networks.Compared with the existing methods on 7 real networks with different structural tightness, the experimental results show that the method has higher detection accuracy than the benchmark method under the AUC measure, and under the condition of network sparse or missing, It can still maintain a relatively stable recognition accuracy.…”
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A Robust k-Means Clustering Algorithm Based on Observation Point Mechanism by Xiaoliang Zhang, Yulin He, Yi Jin, Honglian Qin, Muhammad Azhar, Joshua Zhexue Huang

“…The theoretical analysis and experimental results show that the proposed clustering algorithm has the lower computational complexity and better robustness in comparison with k-means clustering algorithm, thus demonstrating the feasibility and effectiveness of our proposed clustering algorithm.…”
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Three-Dimensional Microwave Imaging for Concealed Weapon Detection Using Range Stacking Technique by Weixian Tan, Pingping Huang, Zengshu Huang, Yaolong Qi, Wenqin Wang

“…For the 3D image reconstruction under two-dimensional (2D) planar aperture condition, most of current imaging algorithms focus on decomposing the 3D free space Green function by exploiting the stationary phase and, consequently, the accuracy of the final imagery is obtained at a sacrifice of computational complexity due to the need of interpolation. …”
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Reactive power optimization via deep transfer reinforcement learning for efficient adaptation to multiple scenarios by Congbo Bi, Di Liu, Lipeng Zhu, Chao Lu, Shiyang Li, Yingqi Tang

“…Existing reinforcement learning algorithms alleviate the computational complexity in optimization but suffer from the inefficiency of model retraining for different operating scenarios. …”
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LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection by Anjun Yu, Hongrui Fan, Yonghua Xiong, Longsheng Wei, Jinhua She

“…Finally, a bidirectional feature pyramid network (BiFPN) is introduced in the neck to effectively reduce the parameters and computational complexity and improve the overall performance of rockfall detection. …”
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Power Converter Fault Detection Using MLCA–SpikingShuffleNet by Li Wang, Feiyang Zhu, Fengfan Jiang, Yuwei Yang

“…This paper first designs an efficient SpikingShuffle Unit that integrates grouped convolutions and channel shuffle techniques, effectively reducing the model’s computational complexity by optimizing feature extraction and channel interaction. …”
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Identifying vital spreaders in large-scale networks based on neighbor multilayer contributions by Weiwei Zhu, Xuchen Meng, Jiaye Sheng, Dayong Zhang

“…These tests demonstrated the effectiveness of our proposed algorithm in identifying influential spreaders accurately.DiscussionFurthermore, computational complexity analysis indicates that our algorithm consumes less time compared to existing methods, suggesting it can be efficiently applied to large-scale networks.…”
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RWA-BFT: Reputation-Weighted Asynchronous BFT for Large-Scale IoT by Guanwei Jia, Zhaoyu Shen, Hongye Sun, Jingbo Xin, Dongyu Wang

“…To address these limitations, RWA-BFT adopts a two-layer blockchain architecture; the first layer leverages reputation-based filtering to reduce computational complexity by excluding low-reputation nodes, while the second layer employs an asynchronous consensus mechanism to ensure efficient and secure communication among high-reputation nodes, even under network delays. …”
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PilotCareTrans Net: an EEG data-driven transformer for pilot health monitoring by Kun Zhao, Xueying Guo

“…PilotCareTrans Net was evaluated on multiple public EEG datasets, including MODA, STEW, SJTUEmotion EEG, and Sleep-EDF, where it outperformed state-of-the-art models in key metrics.Results and discussionThe experimental results demonstrate the model's ability to not only enhance prediction accuracy but also reduce computational complexity, making it suitable for real-time applications in resource-constrained settings. …”
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Compressive Sensing Based Bayesian Sparse Channel Estimation for OFDM Communication Systems: High Performance and Low Complexity by Guan Gui, Li Xu, Lin Shan, Fumiyuki Adachi

“…To improve the estimation performance, we proposed a compressive sensing based Bayesian sparse channel estimation (BSCE) method which cannot only exploit the channel sparsity but also mitigate the unexpected channel uncertainty without scarifying any computational complexity. The proposed method can reveal potential ambiguity among multiple channel estimators that are ambiguous due to observation noise or correlation interference among columns in the training matrix. …”
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Graphon Neural Networks-Based Detection of False Data Injection Attacks in Dynamic Spatio-Temporal Power Systems by Rachad Atat, Abdulrahman Takiddin, Muhammad Ismail, Erchin Serpedin

“…This allows to leverage the learning by transference on the graphs to address the computational complexity and environmental concerns of training on large-scale systems, and the dynamicity resulting from the spatio-temporal evolution of power systems. …”
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Rapid Fluid Velocity Field Prediction in Microfluidic Mixers via Nine Grid Network Model by Qian Li, Yuwei Chen, Taotao Sun, Junchao Wang

“…Traditionally, the simulation of these mixers relies on the finite element method (FEM), which, although effective, presents challenges due to its computational complexity and time-consuming nature. To address this, we propose a nine-grid network (NGN) model theory with a centrally symmetric structure.The NGN uses a symmetric structure similar to a 3 × 3 grid to partition the fluid space to be predicted. …”
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Novel and Efficient Randomized Algorithms for Feature Selection by Zigeng Wang, Xia Xiao, Sanguthevar Rajasekaran

“…We conduct theoretical computational complexity analysis and further explain our algorithms’ generic parallelizability. …”
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Outage analysis and power optimization in uplink and downlink NOMA systems with Rician fading by Dipinkrishnan R, Vinoth Babu Kumaravelu

“…By approximating the Rician distribution with a Gamma distribution, the closed-form expressions for outage probability are derived, reducing computational complexity. A novel low-complexity power allocation (PA) strategy is introduced to maximize the sum spectral efficiency (SSE) in NOMA systems even in the presence of iSIC. …”
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A high speed processor for elliptic curve cryptography over NIST prime field by Xianghong Hu, Xueming Li, Xin Zheng, Yuan Liu, Xiaoming Xiong

“…It is challenging to implement a scalar multiplication (SM) operation which has the highest computational complexity in ECC. In this study, we propose a hardware processor which achieves high speed and high security for ECC. …”
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Multi-Centroid Extraction Method for High-Dynamic Star Sensors Based on Projection Distribution of Star Trail by Xingyu Tang, Qipeng Cao, Zongqiang Fu, Tingting Xu, Rui Duan, Xiubin Yang

“…First, the mapping relationship between attitude information and star trails is constructed based on a geometric imaging model, and an endpoint centroid group extraction strategy is designed from the perspectives of time synchronization and computational complexity. Then, the endpoint position parameters are determined by fitting the star trail grayscale projection using a line spread function, and accurate centroid localization is achieved through principal axis analysis and inter-frame correlation. …”
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