Recent Patents on Engineering - Volume 18, Issue 3, 2024

As a clean energy source, magnetic energy is a significant power source of mechanical equipment, which affects the direction of control mode and mechanical structure of mechanical equipment. Magnetic machinery is widely used in industrial power systems and automatic control systems as the power source of mechanical equipment transmission parts or actuators. Magnetic machinery is essential to make mechanical equipment easy to control, energy saving and environmental protection, simple structure, and so on. In order to innovate the control mode of mechanical equipment and optimize the types of traditional mechanical systems, magnetic machinery developed magnetic coupling, magnetic gear, magnetic spring, and other magnetic machinery. The characteristics of magnetic machines with different functions are analyzed based on the types of magnetic machines. Therefore, this aspect's development and application are getting more attention in recent years. To innovate the control mode of mechanical equipment and optimize the types of traditional mechanical structures, magnetic types of machinery, such as magnetic coupling, magnetic gear, and magnetic spring, are making continuous progress. New magnetic machines are also being developed. This paper summarizes the typical patents on three kinds of magnetic machinery: magnetic coupling, magnetic gear and magnetic spring. In addition, the advantages of existing magnetic machinery are summarized, the current situation of magnetic machinery is analyzed, and the development of magnetic machinery is summarized. Through the analysis of enormous patents of magnetic coupling, magnetic gear, and magnetic spring, it is concluded that although there are many kinds of magnetic machinery, there are no systematic and mature design theories and methods. The development trend of magnetic machinery is also discussed. The research on magnetic machinery helps innovate mechanical equipment's control mode and optimize the types of traditional mechanical structures. As a result, more patents will be invented in the future.

The size of the medical information system is growing gradually. Due to this, traditional data analysis for extracting helpful information for any disease has become inefficient in providing accurate real-time valid information. Traditional data mining and statistical learning techniques, which lack sufficient domain knowledge on a complicatedly colossal amount of data, need to stop adopting new advances in deep learning technologies. Thus, the use of sophisticated machines with in-depth understanding and analysis capability is vital to provide real-time information in detecting and diagnosing diseases in the healthcare system. In this paper, we study recent deep learning approaches which are capable of working on high-dimensional and multi-dimensional data. These approaches have been deployed to identify the root cause of various diseases like Cancer, Lung Diseases, Heart Diseases, Diabetes, Hepatitis, Alzheimer’s, Dengue, Parkinson’s, etc. In this study, our key contributions are: 1. A decent overview of the deep learning techniques. 2. Several modalities in the diagnosis of various diseases. 3. Set out recent trends in various applications of deep learning in healthcare, some of which are analysis and diagnosis of medical images, precision medicine, drug discovery, predictive analysis to support clinical decisions, and sustainable public health. 4. Several deep learning approaches with their performance are described in detail. 5. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially the availability of the patient’s data for training deep learning models on a particular disease is comparatively much less than required.

Background: With the rapid consumption of non-renewable energy such as coal, oil and natural gas, the growing demand for environmental protection, the re-utilization of low-grade waste heat energy has become an important approach to improve energy utilization efficiency. As a new technology, organic Rankine cycle (ORC) power generation technology can make full use of and convert heat waste. Objective: Both the suction and discharge pressures of the scroll expander have a certain influence on the output and motion characteristics of the orbiting scroll. By studying the position and arrangement of the suction and discharge ports of the expander, a theoretical basis can be provided for the design of these ports. Methods: For the scroll expander using working fluid R134a, establishing the geometrical and three-dimensional models of the suction and discharge ports of the scroll expander with different positions and structures, based on the Computational Fluid Dynamics (CFD) method. Results/Discussion: Through comprehensive comparison, it was found that the structure of the original suction pipe outperformed any of the other structures; the fluid flow in the original discharge pipe was more complicated, and the simplified model of the commonly used scroll mechanical discharge pipe had the optimal performance. Conclusion: Compared with the original prototype SEI2, the suction port area is increased, and the suction port pulsation intensity coefficient and the suction pressure loss coefficient of the prototype SEI4 are reduced by 34.833% and 5.264% respectively, which can make the suction process of the expander more stable. Since the unilateral discharge ports Outlet3 and Outlet5 are located in the moving and static regions, respectively, there is a difference in the perturbation of the outlet fluid by the movable scroll, so that the gas pulsation intensity at Outlet3 is nearly double that of Outlet5.

The electro-hydraulic load simulator is a semi-physical simulation system. It is used to simulate different loads on the bearing capacity object (steering gear system) under working conditions and further test the dynamic and static characteristics of the bearing capacity object. Therefore, it is widely used in important applications such as the simulation of various loads and the development of high-performance steering gear systems. By analyzing and discussing the electro-hydraulic load simulator equipment and its improvement methods, some useful conclusions have been drawn to predict the development of the electro-hydraulic load simulator and its key technologies. The current status of research on electro-hydraulic load simulator prototypes is discussed in depth. The electro-hydraulic load simulators are classified according to the loading system applying force (torque). The improvement methods of the electro-hydraulic load simulator, including the method of suppressing the extraneous force, the compensation method of nonlinear factors, and the method of establishing the mathematical model, are analyzed and summarized. By summarizing the literature and patents on electro-hydraulic load simulation technology, the principle, and characteristics of different equipment and improvement methods are analyzed and compared, and future development of electro-hydraulic load simulation equipment and technology is discussed. The research on the characteristics of the prototype, the hardware means and control strategy to suppress the extraneous force, the compensation of nonlinear factors, and the methods of establishing the mathematical model are beneficial to improve the loading accuracy of the electro-hydraulic load simulator.

The current scenario reveals a growing demand for foldable and flexible materials that are well-suited for curved screen surfaces in wearable devices and acclimatize with the environment. The development of eco-designed electronic devices for energy storage, optoelectronics and more applications employing biopolymers or carbon-based substrates was preferred on account of being economical and readily available. The present study explores the recent trends in flexible, transient and green electronics engaging biopolymers like cellulose, chitosan, silk, paper and others to substitute silicon-based electronics. Nanoparticles blended with eco-friendly substrates, nanofibers and nano-sized natural polymers are observed to receive attention in soft electronics and robotics. The study mentions recent patents in this sector and the analysis based on the SCOPUS database highlights the leading authors, countries and keywords which are widely used in this research.

Background: MANET is a self-organized wireless network with no infrastructure. Especially data transfer from one system to another system needs to be done in a secure way. In order to provide data integrity, authentication plays an important role in data communication. RSA and ECC are widely used algorithms in the real world, but authentication using these algorithms is time-consuming. Towards this, various algorithms came into existence with different security primitives. However, it is important to design an effective key agreement process with reduced computational cost among these security mechanisms. We have designed an effective mechanism to mitigate packet dropping attacks to secure end-to-end communication in MANET. Objective: The proposed light weight authentication method is based on chaotic maps that uses Chebyshev polynomials as primitive operation. Methods: The methodology includes semi group property of Chebyshev polynomials and also the discrete logarithmic problem based chaotic maps that takes less time than these existing algorithms and evaluates with respect to attack Resilience, packet deliverya fraction, delay, throughput, and overhead. Results: Simulation produces the result of the proposed mechanism and give better performance in terms of packet delivery ratio, overhead and computational cost. Conclusion: Authentication based on developed mechanism consumes less time as shown in statistical analysis and mitigates packet dropping attacks effectively than that of traditional methods like RSA and ECC with respect to key generation mechanism.

Background: Even the technology has advanced a lot in these modern times, women's safety is still a massive issue. Women are not safe anywhere. Their safety is an essential and decisive issue in today’s world. They face extreme troubles when they are alone, travelling on lonely roads and at workplace. Education is essential for everyone and especially for women. It is a powerful weapon to end poverty. Government and NGOs have made many rules and regulations but the crime against women has not ended. There are many instances of harassment, abuse, eve-teasing, kidnapping, domestic violence and rape. Women are working equally as men but still their safety is a big challenge. With the boom in the IT and corporate sector, many women are now working and even during nightshifts. Objectives: A Real Time Intelligent System for Women Safety has been proposed to overcome these limitations. Methods: In this paper, many women's safety systems that were proposed or developed have been discussed. Major drawbacks in the current system, devices or applications have been highlighted. Latest technologies like machine learning, artificial intelligence or data science have been ignored. Results: The proposed system will be highly sophisticated with the latest techniques and women will feel safe in all situations. It will predict the safe route while moving alone. Conclusion: Moreover, it will record the evidence and will also suggest the rights of women.

Purpose: Recent advancements in artificial intelligence in agriculture have had excellent results. This paper aims to discuss the patent perspective of tomato plant disease using artificial intelligence and discover the global research scenario. Methodology: Patent documents from Lens.org were extracted for this study. A total of 4793 patent documents were analyzed manually. It also provides state-of-the-art patent documents on the readymix of researchers involved in tomato plant disease. The application used for patent landscape is MS Excel, PatCite, and Origin. Results: The study analyzed the research activities worldwide, top players in the field, and patent landscape analysis. This article focuses on the different facets of the competitive landscape process of intellectual property related to tomato plant disease. Conclusion: This review familiarizes tomato plant diseases with new and changing trends and convinces them of the importance of applying for patents to protect their innovation and the industry’s use of those innovations to promote commercialization.

Background: LoRaWAN technology enables IoT devices with ubiquitous connectivity, which further empowers a broad spectrum of applications, including civil engineering, agriculture, and smart home. Although LoRaWAN has low-power energy consumption and long-range communication capability, it has many features that should be carefully managed to improve its performance. Objective: This paper aimed to review recent patents on improving the performance of LoRaWAN. Methods: The most related patents on improving the performance of LoRaWAN were discussed and analyzed. Results: A total of 71 patents were reviewed. This paper proposed a 2D taxonomy to categorize the existing patents. These patents aimed at focusing on the main components of LoRaWAN, i.e., end node, gateway, and server, and improving the performance of LoRaWAN from six aspects, i.e., energy efficiency, communication range, transmission efficiency, collision management, network security, and interoperability. The methodologies used in the current patent development were also discussed. Conclusion: Tremendous efforts have been devoted to improving the performance of LoRaWAN in recent years. To continue the growth of LoRaWAN technology, future patent development may focus on scalability, cost-effectiveness, and ease of implementation.

Background: Methods that are used to obtain oil and gas resources by penetrating deep strata are gradually being developed. The complex lithology of deep strata causes difficulties for traditional drill bits to cut into the rock effectively, which affects the drilling efficiency. Objective: The goal of this work is to optimize the bit structure and design a patented drill bit for more efficient rock breaking. Methods: Through a series of tests, the rock mechanics parameters of deep strata in a block of Jilin Oilfield were measured, and the drill bit model XFXR5195 was selected to best match the lithology of the formation by using the Virtual Strength Index and the Technique for Order Preference by Similarity to an Ideal Solution. Simulation of the rock breaking process of the conical PDC cutter and cylindrical PDC cutter with the finite element method. Results: The simulation results show that the pre-crushing of the conical cutter can effectively reduce the difficulty of the subsequent cylindrical cutter in breaking and cutting rocks under 100 MPa compressive strength rocks; under 50 MPa compressive strength rocks, the combination of the conical cutter and cylindrical cutter are not effective with regards to drilling. Conclusion: The conical PDC cutter has a more uniform force range and more concentrated stress on the rock than the cylindrical PDC cutter. The rock-breaking ability of the conical PDC cutter is higher than that of the cylindrical PDC cutter under different compressive strength rocks and especially in high compressive strength rocks.

Objective: The paper aims to analyze the hydrogeological conditions of a proposed hazardous waste landfill and the migration characteristics of lead, zinc, and nickel in fractured aquifers and porous aquifers under accident conditions and provide a reference for the influence of the proposed landfill on groundwater. Methods: In this patent study, based on a 1:50000 regional hydrogeological survey and 1:2000 site hydrogeological mapping, the hydrogeological conceptual model was established. Finite difference software GMS was used to analyze the migration characteristics. Results: The study demonstrated that when the pollutants in the hazardous waste landfill leaked, they migrated from northeast to southwest along the gully. The pollutants in the porous aquifer migrated quickly, and the polluted area expanded rapidly from point to surface. The pollutants migration in fractured aquifers was slow, and the groundwater quality was deteriorating continuously. During the simulation period, the pollutants of lead, zinc and nickel all polluted the aquifer. Among them, the lead pollution range w reported to be the largest, with an exceeding distance of 216.7 m; the zinc pollution range was the smallest, with an exceeding distance of 33.3 m, and the exceeding distance of nickel was 165.1 m. Conclusion: In order to ensure the safety of the groundwater environment in the simulated area, the impervious treatment must be carried out according to the requirements of the proposed hazardous waste landfill. Meantime, an emergency plan should be formulated.

Background: At present, the traditional manual whitewashing method is used in tree whitewashing in China. The quality of tree whitening can only be judged by the naked eye. Objective: Up to now, the quality of tree whitewashing is still judged manually. In order to improve work efficiency, an automatic evaluation method of tree whitewashing quality based on multi-level feature fusion is proposed. Methods: The images extract texture features from white-washed trees (gray-level co-occurrence matrix) and shape features (gradient direction histogram) from pixel-level fusion to obtain a global characteristic matrix. Using a support vector machine (SVM), random forests, and clustering algorithm (KNN), three classifiers were selected to identify the integration characteristics of the training test. In order to reduce the correlation of feature information and improve the execution efficiency of the classifier, the global feature matrix was optimized by combining principal component analysis (PCA) with pixel-level fusion and feature-level fusion. Results: The experiment results showed that the classification accuracy of the support vector machine is 94.00%, which is higher than that of the random forest classifier (92.67) and KNN classifier (92.67%). Meanwhile, the support vector machine is superior to the other two classification algorithms in recall rate, accuracy rate, and algorithm execution efficiency. The results showed that the execution efficiency of each classification algorithm was improved after the optimization of the feature data. The support vector machine classification algorithm is more stable than the other two algorithms. Conclusion: The feature fusion method combined with PCA can improve the execution efficiency and recognition precision of classifiers to a certain extent. For the feature matrix obtained by different data processing methods, the SVM classifier performs more stably and reliably than the random forest classifier and KNN in the inspection of tree whitening quality.