Recent Patents on Engineering - Volume 19, Issue 3, 2025

To resist the time-variant underwater acoustic (UWA) channel, large amounts of channel estimation algorithms for the UWA orthogonal frequency division multiplexing (OFDM) are presented. An updated review of the recent UWA OFDM channel estimators is suggested in this article.

The goal of this patent is to review and conclude the development of different types of channel estimators. The possible perspectives about the future UWA channel estimator design are also discussed.

The principles and performances of the linear channel estimators, the compressed sensing (CS)-based channel estimators, and the neural network (NN)-based channel estimators are reviewed and discussed. Simulations are conducted to compare the typical implementations of the different methods.

To take more channel state characteristics into account, the data-driven methods have been applied in the channel estimator design. Compared with the linear and CS-based methods, the NN-based channel estimator shows the higher performance, robustness and lower complexity, which is promising to be applied with the proper structure and training sets.

The seafloor is an essential ocean boundary, and the detection of seafloor information is necessary basis for seafloor scientific research. The classification and identification of seafloor geological types is necessary for researchers to conduct seafloor research, military activities, and marine platform construction.

The purpose of this patent paper is to summarize the progress of seafloor substrate classification research based on backscattering and to seek a new development direction for seafloor substrate classification research.

The literature on various types of submarine sediment attenuation geoacoustic models, backscatter intensity calculations, and submarine substrate classification is summarized, and the progress of theoretical research required for the positive and negative problems of submarine substrate classification is described that include the geoacoustic parameter models based on fluid theory, elastomer theory and poroelastic theory and submarine acoustic scattering models, including the small roughness perturbation approximation model, the Kirchhoff approximation model, the Kirchhoff approximation model and the Kirchhoff approximation model.

The development of the Kirchhoff approximation model, the slight slope approximation model, the volume scattering model, and the inversion methods for seafloor substrate classification are summarized, and breakthroughs in seafloor substrate classification are sought by summarizing previous studies.

The classification of seafloor substrate based on backscattering intensity needs the support of a perfect geoacoustic model and scattering model, and the current research of low and medium-frequency scattering models and multi-layer seafloor scattering models are the further development direction in the future. Currently, the better performance of the prediction model, geo-acoustic parameter inversion results are more than 90% accuracy, sound velocity ratio and other parameters in the high-frequency band inversion accuracy of 98%, are able to better meet the measured data. Finally, some patented technologies are also reported.

Intramuscular hemangioma (IMH) is a diffuse growth hemangioma located in the striated muscle, which is often overlooked due to its rarity.

This patent pertains to the integration of electroacupuncture with electrothermotherapy. By introducing electrical signals into the electroacupuncture system, electromagnetic heat is produced. This heat leads to the electrolysis and thermal destruction of tumor cells, enabling targeted and precise cancer treatment. Furthermore, the patent offers a theoretical foundation for exploring the distribution of electrical signals and the associated heat in arm muscles, ensuring accurate treatment.

To enable subsequent experimental validation, this patent integrates human anatomy and histological structure theory. The arm's geometric structure was derived from segmentation, reconstruction, and substantiation based on a digital human image dataset. Using the COMSOL Multiphysics 5.5 software, a semi-detailed finite element model was developed for the numerical simulation of electrothermotherapy. Within a time domain setting, a carrier signal of 1 MHz and 22 V was introduced to assess the distribution of electrical signals and the associated heat in the arm muscle.

Electrical signals, electromagnetic heat, and tissue necrosis primarily concentrate in a spherical region within 10 mm of the exposed electroacupuncture tip, with the maximum coupling temperature reaching 250°C at the tip. Time domain analysis revealed that the coupling temperature can rise within 1 min, sufficiently to cause complete tissue damage, with the tissue necrosis ratio reaching 100% in the same timeframe. While the coupling temperature continues to rise over time, the increment is modest. After 5 min, there is negligible temperature change, and once the tissue necrosis ratio reaches 100%, it remains consistent.

In the precision tumor treatment system utilizing electrothermotherapy, factors, such as the magnitude of the injected electrical signal, placement of the electroacupuncture tip, and treatment duration play a crucial role in the treatment's accuracy. This model delves into the treatment of intramuscular hemangiomas using electroacupuncture electrothermotherapy from two perspectives: Spatial and temporal domains. It provides a theoretical foundation for precise electrothermotherapy in cancer treatment.

With the depletion of fossil energy and the increasingly serious environmental pollution, the task of developing renewable energy is imminent. As a green and pollution-free renewable energy, the penetration of wind energy in the power grid continues to rise.

In order to reduce the volatility and randomness of wind power series and increase the accuracy of wind power prediction, a wind power combination model based on the ICEEMDAN (improved adaptive noise full set empirical mode decomposition) method is proposed.

First, the complex original wind power data have been decomposed into several relatively simple subsequences using the ICEEMDAN method. Aiming at the different lengths of coarse grain time series and data loss in traditional multi-scale entropy, a fine composite multi-scale dispersion entropy is proposed to calculate the entropy value of each decomposition component, and divide the high- and low-frequency modal components to predict the modal components of different frequencies; secondly, differential moving autoregressive model (ARIMA) and short-term memory neural network (LSTM) are used to establish the prediction models of high- and low-frequency components, respectively.

Finally, the prediction results of each component have been superimposed and reconstructed to obtain the final prediction results. The effectiveness of the combined model is verified by the actual operation data of a European wind farm.

As the effectiveness of the combined model is verified by the actual operation data of a European wind farm, the results have shown that compared to the other four single and combined forecasting models, the combined model in this patent paper has higher forecasting accuracy. Therefore, the model proposed in this article can be used for predicting wind power with significant fluctuations, which will help to provide support for optimized scheduling and energy storage configuration of wind farms, thereby reducing costs and increasing income for the power grid and wind farms.

Spraying, as an important part of industrial production, is a labor-intensive industry. Manual spraying is greatly influenced by the personal factors of the operators. Furthermore, there are some problems in this method, such as substandard environmental protection and unstable spraying quality. However, intelligent spraying robots can effectively improve the efficiency of spraying, safety, environmental protection, flexible production, and high-quality spraying and reduce labor requirements. Therefore, the development trend of intelligent spraying robots has received increasing attention.

In order to solve the problem that manual spraying is harmful to workers and is inefficient, the joint-type and non-joint-type intelligent spraying robots are constantly improving.

The patents of intelligent spraying robots are summarized. The structural characteristics, advantages, and disadvantages of various intelligent spraying robots are introduced.

By investigating the patents of intelligent spraying robots, the main problems, such as poor manufacturing ability of high-end parts and insufficient software research and development, are summarized. In addition, the development trend of intelligent spraying robots is also discussed.

The analysis shows that the intelligent spraying robot is an efficient and safe pioneering work. Compared with manual spraying, it has a very broad application prospect in various industrial spraying and is helpful to the development of the manufacturing industry.

Rolling bearings are being widely used in various applications because of their unique functions, but at the same time, bearing failures are becoming more and more prominent. Among them, the bearing failure caused by cage failure also accounted for 25% of the total proportion. With the progress of intelligent manufacturing and “Industry 4.0”, the operating environment of rolling bearings is becoming increasingly complex and variable, and higher demands are made on the dynamic characteristics of cages. In this regard, the research on the nonlinear dynamics of the cell under different influencing factors can help to explain better the mechanism of dynamic, unstable motion of the cage under various influencing factors and solve the bearing life and reliability problems caused by uneven rolling bearing cage from the perspective of design and experimental research.

By summarizing the influencing factors of cage dynamics characteristics, the design of new structures, test methods, and testing devices of dynamics attributes in recent years, some valuable conclusions were obtained, and the future development direction of rolling bearing cage dynamics characteristics was proposed to provide a reference for researchers in related fields.

This paper reviews the patents and documents related to the dynamic characteristics of rolling bearing cages, as well as analyzes the different influencing factors on the operational stability of the bearings, and introduces the improved design of cage structures, as well as the test methods and testing devices for the dynamic characteristics of cages.

Through the analysis of relevant patents and papers on rolling bearing cage dynamics, the development status and problems of rolling bearing cage dynamics were discussed, future research on rolling bearing cage dynamics was prospected, and design ideas and research directions were proposed.

Through the discussion of the factors influencing the cage dynamics, the optimization of the rolling bearing cage structure design, and the study of test methods and testing devices, it was found that the research direction of the rolling bearing cage dynamics needs to be further expanded so that a better research method and testing devices could be researched, and a new structure with more stable operation and higher cage life and reliability could be designed.

In recent years, trade on credit has become increasingly common around the world, exposing companies in the supply chain to significantly increased financial risk due to extended billing periods. As an innovative financing model, supply chain finance (SCF) has received a lot of attention.

The goal of this work is to examine the impact of supply chain finance on the performance of the automobile industry in the post-COVID-19 era.

After an in-depth understanding of the relevant theoretical literature, two models of inquiry are established in this patent paper, and the relevant data are collected from the CSMAR database for a sample of some enterprises in the automotive industry in the listed market, followed by an empirical analysis using the Stata 16.0. Then, the fixed effects model (FEM) and difference-in-difference model (DID) are used to test the hypothesis.

The results show a significant impact of supply chain finance on the performance of automobile firms. It is effective in improving the flow of funds and contributes to the performance of enterprises in the automotive industry. Besides, we have registered a software patent named “Cross-border e-commerce big data supply chain information management system” to aid our study.

In the context of the pandemic, supply chain finance can effectively help enterprises reduce the risk of bankruptcy due to capital rupture and provide a guarantee for the sustainable development of automobile industry enterprises.

Visual tracking is a crucial component of computer vision systems.

To deal with the problems of occlusion, pose variation, and illumination in long-time tracking, we propose a new kernel-based multiple instances learning tracker.

The tracker captures five positive bags, including the occlusion bag, pose bag, illumination bag, scale bag, and object bag, to deal with the appearance changes of an object in a complex environment. A Gaussian kernel function is used to compute the inner product for selecting the powerful weak classifiers, which further improves the efficiency of the tracker. Moreover, the tracking situation is determined by using these five classifiers, and the correlating classifiers are updated.

The experimental results show that the proposed algorithm is robust in terms of occlusion and various appearance changes.

The proposed algorithm preforms well in complex situations. The patented technology will be applied in the future.

This study aims to utilize the Machine Learning (ML) model to produce high-precision maps of urban ground subsidence susceptibility, providing a scientific basis for disaster prevention and mitigation efforts in the Kunming Basin.

In this patent study, remote sensing interpretation of Kunming City was conducted using SBAS-InSAR technology to acquire subsidence data. Based on the frequency ratio method, ten evaluative factors with strong correlations were selected to establish an evaluation index system for the subsidence susceptibility of the Kunming Basin. Five models, including CNN, Back Propagation Neural Network (BPNN), Genetic Algorithm optimized BPNN (GA-BPNN), Particle Swarm Optimization optimized BPNN (PSO-BPNN), and Radial Basis Function Neural Network (RBFNN), were employed. The frequency ratio method and the ROC curve were used to compare the effectiveness and precision of these models.

The frequency ratio method indicated that the CNN model had the highest values in the very high and high susceptibility areas, reaching 4.10, which was the highest among all models; in the very low and low susceptibility areas, its value was 0.34, which was the lowest among the models. The ROC curve demonstrated that the CNN model, based on deep learning (AUC = 0.952), was more precise than the machine learning-based models such as BPNN (AUC = 0.896), RBFNN (AUC = 0.917), GA-BPNN (AUC = 0.890), and PSO-BPNN (AUC = 0.906).

The CNN model has predicted that 81.06% of the ground subsidence grid cells fall into the very high and high susceptibility categories, demonstrating good predictive performance. According to the established evaluation index system for ground subsidence susceptibility, the fundamental causes of ground subsidence in the Kunming Basin are identified as poor soil mechanical properties and low bearing capacity, while construction activities have exacerbated the development of ground subsidence.