Research on an internal model control algorithm and parameter tuning for metro traction PMSMs by SI Junmin, YUE Xuelei, TIAN Di, XU Kelei

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Information hiding algorithm based on mapping and structure data of 3D model by Shuai REN, Zhen WANG, Dongxu SU, Tao ZHANG, Dejun MU

“…The existing 3D information hiding schemes are not robust enough against the joint attacks,as a result the secret information will be vulnerable and cannot be extracted correctly.In order to solve the above problem,an information hiding algorithm based on mapping and structure data of 3D models was proposed.First,several texture maps of the original 3D models in .stl format were picked from the standard model library,so the backup secret data after twice two-dimension discrete Daubechies transform can be embedded using dbl function just as the watermark.Secondly,the original 3D model in .stl format was operated by frame sampling in wavelet domain to obtain the coefficient in transform domain,thus the secret data was embedded into the corresponding transform coefficient.Finally,the .obj documents with the secret information were generated by multiplying the 2D texture map data and the 3D .stl data matrix based on orthogonal projection.Texture maps and coordinate space of 3D model were both used to embed the secret information repeatedly in order to enhance the robustness.The experiment analysis indicated that the imperceptibility,robustness and resistance against analysis are improved and information transmission safety in complex environment can be achieved based on the redundancy space of multi-type carriers.…”
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Information hiding algorithm based on mapping and structure data of 3D model by Shuai REN, Zhen WANG, Dongxu SU, Tao ZHANG, Dejun MU

“…The existing 3D information hiding schemes are not robust enough against the joint attacks,as a result the secret information will be vulnerable and cannot be extracted correctly.In order to solve the above problem,an information hiding algorithm based on mapping and structure data of 3D models was proposed.First,several texture maps of the original 3D models in .stl format were picked from the standard model library,so the backup secret data after twice two-dimension discrete Daubechies transform can be embedded using dbl function just as the watermark.Secondly,the original 3D model in .stl format was operated by frame sampling in wavelet domain to obtain the coefficient in transform domain,thus the secret data was embedded into the corresponding transform coefficient.Finally,the .obj documents with the secret information were generated by multiplying the 2D texture map data and the 3D .stl data matrix based on orthogonal projection.Texture maps and coordinate space of 3D model were both used to embed the secret information repeatedly in order to enhance the robustness.The experiment analysis indicated that the imperceptibility,robustness and resistance against analysis are improved and information transmission safety in complex environment can be achieved based on the redundancy space of multi-type carriers.…”
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A Computationally Efficient Iterative Algorithm for Estimating the Parameter of Chirp Signal Model by Jiawen Bian, Jing Xing, Zhihui Liu, Lihua Fu, Hongwei Li

“…A novel iterative algorithm is proposed to estimate the frequency rate of the considered model by constructing the iterative statistics with one-lag and multilag differential signals. …”
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Construction of prognostic scoring model for ovarian cancer based on deep learning algorithm by Xiaolin Zhong, Hongyang Xiao, Weihong Lu, Jiayuan Chen, Fan Chao, Ruiqin Tu

“…Accurate prognostic prediction and timely treatment are critical for improving patient outcomes. The aim of this study was to develop a prognostic prediction model for ovarian cancer based on pathological images. 158 In-house and 105 TCGA-OV pathological slides were processed with Macenko’s algorithm for stain normalization and patch extraction (256 × 256 pixels). …”
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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

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Modeling and Optimization of Concrete Mixtures Using Machine Learning Estimators and Genetic Algorithms by Ana I. Oviedo, Jorge M. Londoño, John F. Vargas, Carolina Zuluaga, Ana Gómez

“…This study presents a methodology to optimize concrete mixtures by integrating machine learning (ML) and genetic algorithms. ML models are used to predict compressive strength, while genetic algorithms optimize the mixture cost under quality constraints. …”
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Fine-Pruning: A biologically inspired algorithm for personalization of machine learning models by Joseph Bingham, Saman Zonouz, Dvir Aran

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Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model by HONG Jingke, DU Wei, SHAO Jin*, LAO Huimin

“…First, the variable mode decomposition method is used to decompose the historical data of carbon emissions in Zhejiang Province, enabling an analysis of its cyclicality fluctuations. Second, the LASSO algorithm is employed to identify the key influencing factors of carbon emissions. …”
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Long tunnel group driving fatigue detection model based on XGBoost algorithm by Huazhi Yuan, Kun Zhao, Ying Yan, Li Wan, Zhending Tian, Xinqiang Chen

“…In addition, a significant correlation exists between the driving duration index and driving fatigue, which can provide a reference for improving the tunnel safety. Using the mean value of blink duration and driving duration as the characteristic indexes, the accuracy of the driving fatigue detection model based on the XGBoost algorithm reaches 98%. …”
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CKRT coagulation risk prediction and nursing feedback model based on intelligent algorithms by Xianrong Xu, Mou Chen, Lvjing Chen, Kaixing Huang, Shiqi Cao, Wenwen Gao, Kang Liu, Buyun Wu, Huijuan Mao

“…Conclusion The intelligent Continuous Kidney Replacement Therapy nursing feedback model improves prediction accuracy while reducing redundant information. …”
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Short-term load estimation based on improved DBN-LSTM by Nan Dong, Yuwen Wu, Buyun Su, Zhanzhi Liu

“…The pruning algorithm is used to optimize the redundant structure of the model, reduce the complexity and training time of the model, and maintain or improve the forecasting accuracy. …”
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EVALUATION OF POSSIBILITIES TO IMPROVE ACCURACY CHARACTERISTICS FOR ALGORITHMS OF REFINEMENT ANGULAR VELOCITY OF BALLISTIC AND SPACE OBJECTS IN EARLY WARNING RADARS by Sergei V. Kovbasjuk, Felix M. Andreev, Andrei V. Statkus

“…Therefore, the article deals with the methodology of comparative evaluation accuracy characteristics of algorithms of refinement angular velocity ballistic and space objects and assessing the level of their improvement through the use of different variants of construction algorithm of increasing the accuracy of range higher order derivatives, including information on the third derivative of distance. …”
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Improving Segmentation of MRI Images of Brain Glial Cells Using Fuzzy C-Means Algorithm and Combination of PET Images by Wei Wang, Wu Beibei

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Short-Term Electricity Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Improved Sparrow Search Algorithm–Convolutional Neural Network–Bidirectional Long Short-Term Memory Model by Han Qiu, Rong Hu, Jiaqing Chen, Zihao Yuan

“…Accurate power load forecasting plays an important role in smart grid analysis. To improve the accuracy of forecasting through the three-level “decomposition–optimization–prediction” innovation, this study proposes a prediction model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved sparrow search algorithm (ISSA), a convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM). …”
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BRA-YOLOv7: improvements on large leaf disease object detection using FasterNet and dual-level routing attention in YOLOv7 by Rong Ye, Rong Ye, Quan Gao, Quan Gao, Tong Li, Tong Li

“…The experimental results show that the improved algorithm achieved a 4.8% improvement in recognition accuracy, a 5.3% improvement in recall rate, a 5% improvement in balance score, and a 2.6% improvement in mAP compared to the traditional YOLOv7 algorithm. …”
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Novel hybrid model to improve the monthly streamflow prediction: Integrating ANN and PSO by Baydaa Abdul Kareem, Salah L. Zubaidi

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A Recommendation Algorithm Based on Restricted Boltzmann Machine by WANG Weibing, ZHANG Lichao, XU Qian

“…In the case where the amount of data is too large, the recommended results output by the RBM model will be broader Besides, many collaborative filtering algorithms currently do not handle large data sets better So, we try to use the deep learning technology to strengthen the personalized recommendation model We propose a hybrid recommendation model combining the bound Boltzmann model and the hidden factor model First, we use the RBM algorithm to generate candidate sets, and score the sparse matrix of the candidate set Then we use the LFM model to sort the candidate results and select the optimal solution for recommendation The hybrid model is validated using used large public datasets It can be seen from the verification that compared with the traditional recommendation model, the proposed method can improve the accuracy of the score prediction…”
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Weakly supervised semantic segmentation and optimization algorithm based on multi-scale feature model by Changzhen XIONG, Hui ZHI

“…In order to improve the accuracy of weakly-supervised semantic segmentation method,a segmentation and optimization algorithm that combines multi-scale feature was proposed.The new algorithm firstly constructs a multi-scale feature model based on transfer learning algorithm.In addition,a new classifier was introduced for category prediction to reduce the failure of segmentation due to the prediction of target class information errors.Then the designed multi-scale model was fused with the original transfer learning model by different weights to enhance the generalization performance of the model.Finally,the predictions class credibility was added to adjust the credibility of the corresponding class of pixels in the segmentation map,avoiding false positive segmentation regions.The proposed algorithm was tested on the challenging VOC 2012 dataset,the mean intersection-over-union is 58.8% on validation dataset and 57.5% on test dataset.It outperforms the original transfer-learning algorithm by 12.9% and 12.3%.And it performs favorably against other segmentation methods using weakly-supervised information based on category labels as well.…”
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Blending physical and artificial intelligence models to improve satellite-derived bathymetry mapping by Daniel García-Díaz, Sandra Paola Viaña-Borja, Mar Roca, Gabriel Navarro, Isabel Caballero

“…In this study, we evaluated the performance of two different algorithms for estimating SDB in two areas of the Western Mediterranean: a physics-driven model and an Artificial Neural Network (ANN). …”
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