Recent Patents on Engineering - Volume 16, Issue 3, 2022

As a kind of sliding bearing with liquid as a lubricant, Hydrostatic journal bearing can be shown from tribology, mechanics, optimization method and structural design in engineering practice. Hydrostatic bearing characterized by high precision, high rigidity, and long life has drawn people's attention and been widely adopted. As progress has been made in science and technology, people will have a better understanding of the principles and characteristics of the hydrostatic journal bearing. The development of hydrostatic bearing in recent years is studied and analyzed. More than 70 recently published patents and journal and conference papers related to the developments in air traffic flow management are reviewed, and the outcomes in temperature field, pressure field, deformation, lubrication, bearing capacity and friction are studied. Through these research results, the current research status of hydrostatic bearings is summarized, and the three aspects of flow field characteristics, lubrication characteristics and tribology are explained, and solutions and feasibility measures for existing outstanding problems in each aspect are mentioned. Based on this article, the design and optimization of hydrostatic bearings are of guiding significance. Finally, the current problems and challenges of hydrostatic bearings are analyzed, and future research directions and research priorities are prospected.

With the vast development of Internet technology 2.0, millions of people share their opinions on different social networking sites. To obtain the necessary information from the large volume of user-generated data, the attention on sentiment analysis among the research community is growing. The growth and prominence of sentiment analysis are synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of usergenerated content such as noisy, irrelevant information and fake reviews, there is a significant demand for a practical methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies. It highlights the challenges and performance factors of various sentiment analysis approaches, including text preprocessing, opinion spam detection, and aspect level sentiment analysis. Users use social media as a medium for their activities and are passionate about their posts on social networking platforms on various issues, topics, and events. Sentiment analysis plays a significant role in online e-commerce servicing sites in which users share their views and rating on products and services. With the help of sentiment analysis, companies identify customer dissatisfaction and enhance the quality of the products and services. This study seeks diverse methods and performance measures on various application domains in sentiment analysis. The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis, namely text preprocessing, opinion spam detection, and aspect level sentiment analysis based on machine learning and deep learning. User-generated content is growing worldwide, and people more eagerly express their views on social media towards various aspects. The opinionated text is challenging to interpret and arrive at a conclusion based on the feedback gathered from reviews on various sites. Hence, the significance of sentiment analysis is growing to analyze the user-generated data. This will be useful to researchers who focus on the challenges very specifically and identify the most common challenges to work forward for a new solution.

Fluid pressure, an important parameter of fluid flow, affects the control accuracy and stability of fluid transmission. The control devices of fluid pressure are widely applied to the mechanical transmission control, which is the main means to change the fluid pressure and improve control accuracy and stability in the mechanical transmission system. To control the accuracy and stability of the fluid transmission, the pressure control device is divided into pressure reduction devices, pressurization devices, and stabilizing pressure devices. The characteristics of pressure control devices with different functions are analyzed from the structure of the device. Thus, more and more attention has been paid to the control devices of fluid pressure. To meet the increasing requirement of control accuracy and stability of fluid transmission, the control devices of fluid pressure, such as pressure reduction devices, pressurization devices, and stabilizing pressure devices, are being improved continuously. This paper reviews various current representative patents related to pressure reduction devices, pressurization devices, and stabilizing pressure devices of fluid pressure. By investigating various patents on the control devices of fluid pressure, the main current existing problems of the pressure control devices, such as low control accuracy and poor stability, are summarized and analyzed. Furthermore, the development trend of the control devices of fluid pressure has also been discussed. The optimization of the structure and function of the control devices of fluid pressure is conducive to improve the control accuracy and stability of transmission of the fluid. More relevant patents will be invented in the future.

Precision medicine has emerged with the development of science and technology and the rise of big data. This study first defines and presents the advantages of precision medicine and then introduces the development of three technologies: gene sequencing, cellular immunotherapy, and gene editing. The clinical applications of precision medicine in lung cancer, cervical cancer, breast cancer, and prostate cancer are thus analyzed. Lastly, the existing problems and future development directions of precision medicine are identified. The introduction of gene sequencing, bioanalytical techniques, and big data analysis tools has propelled medicine into the era of precision medicine. Key technologies in precision medicine form the foundation of its development. Therefore, this study elaborates on the development of key technologies in precision medicine, the current status of its clinical application, and the main problems that currently exist. This study also suggests solutions to the problems. To systematically explain the development and principle of three core technologies in precision medicine and to predict the main research trends of precision medicine. Research in gene sequencing, cell immunotherapy, and gene editing technology has shown significant progress, and accurate medical treatment has achieved remarkable results, effectively prolonging the survival time and improving the quality of life of patients. Precision medicine has made significant achievements, but problems remain. Ensuring safety and efficiency in precision medicine should be the focus of future research.

Background: One of the critical factors affecting the life of bearings is the bearing temperature during operation, so the temperature is an important parameter to detect the running state of the bearing. The abnormal increase in bearing temperature can reflect problems such as bearing damage, lack of lubricating oil, and installation errors. With the continuous improvement of rotating machinery equipment operating speed, load level and equipment processing accuracy requirements, there is an urgent need to detect and analyze the temperature rise of bearings. Objective: By analyzing recent representative patents for bearing temperature detection, we summarize the characteristics and problems of current bearing temperature detection devices and provide a reference for the future development of bearing temperature detection devices. Methods: This article traces the recent representative patents related to bearing temperature detection method and device structure. Results: Through the investigation of a large number of patents of bearing temperature detection devices, the main existing problems in the system and structure of bearing temperature detection are concluded and analyzed, and the development of bearing temperature detection is discussed in the future. Conclusion: The bearing temperature measuring device is significant for analyzing bearing heating and detecting the running state of the bearing. The current research results need further development and improvement. With the development of automated detection technology, bearing temperature measurement is also evolving towards intelligence and dynamics. It is foreseeable that more related patents on bearing temperature detection will be invented.

Background: Superhydrophobic surfaces have unique wettability and have shown broad application prospects in many fields, such as self-cleaning and lubrication drag reduction. However, the superhydrophobic surfaces are severely restricted by their poor mechanical stability in practical application, and the mechanical stability of the superhydrophobic surface is always affected by different preparation methods. Therefore, people pay more attention to the preparation methods of superhydrophobic surfaces. Objective: The study aims to improve the mechanical stability of the superhydrophobic surface and expand the application fields of the superhydrophobic surfaces; preparation methods of the superhydrophobic surface have been continuously improved. Methods: This paper reviews various representative patents and papers on preparation methods of the superhydrophobic surface at home and abroad. Results: In this review, the basic methods of preparing superhydrophobic surfaces were introduced, and three typical methods were summarized, such as etching method, coating method, and sol-gel method, then their advantages and disadvantages were discussed. In combination with the latest research progress, it is proposed that the use of environmentally friendly low surface energy modifiers and the use of the one-step method to prepare superhydrophobic surfaces are the future development trends. Aiming at the problem of the mechanical stability of superhydrophobic surfaces, the instability mechanism and stability evaluation methods of superhydrophobic surfaces under mechanical action are reviewed, and three basic methods to improve the mechanical stability of superhydrophobic surfaces are proposed. Conclusion: The optimization of the preparation method of the superhydrophobic surface is beneficial to improve the mechanical stability of the superhydrophobic surface and expand the application prospect of the superhydrophobic surfaces in various fields. More patents and papers on the superhydrophobic surface will be invented later.

Background: The single state enumeration method cannot meet the requirement of accuracy and high efficiency in the reliability assessment of complex power systems because of many uncertain factors and the large scale of the power grid. Methods: A new method of generating system reliability assessment based on Self-Organizing Map (SOM) neural network and state enumeration is presented. First, the input parameters of the state enumeration method are optimized by using the feature of the SOM neural network algorithm that can automatically, quickly, and accurately classify the sample parameters in this method. Second, combining with Markov Model, the optimized system state samples are divided into fault state and normal state, and then the reliability indexes are enumerated. Finally, this method is used to calculate the reliability indexes of IEEE-RTS single-stage power units under different operation conditions. Results: The results show that this method is superior to the single state enumeration method in calculating time; it can be used to evaluate the reliability of modern complex power systems. Conclusion: The optimized state enumeration method is more suitable to evaluate the reliability of the system with a large network scale, and its reliability index is more accurate; while retaining the higher calculation accuracy of the state enumeration method, it can promote the safe, reliable, and economical operation of the power system.

Background: Enabling industrial environment with automation is growing trend due to the recent developments as industry 4.0 centric production. The industrial wireless sensor network environments have a number of constraints, including densely deployed nodes, delay constraint for mechanical operation, and access constraints due to node position within instruments. The related literature have applied existing models of wireless sensor network in industrial environment without appropriate updating in the different layers of communication, which results in performance degradation in realistic industrial scenario. Method: This paper presents a framework for Energy Oriented Cross Layer Data Dissemination Path (E-CLD²P) towards enabling green computing in industrial wireless sensor network environments. It is a cross-layer design approach considering deployment of sensors at the physical layer up to data dissemination at the network layer and smart services at application layer. In particular, an energy centric virtual circular deployment visualization model is presented focusing on physical layer signal transmission characteristics in industrial WSNs scenario. A delay centric angular striping is designed for cluster based angular transmission to support deadline constrained industrial operation in the WSNs environments. Algorithms for energy centric delivery path formulation and node’s role transfer are developed to support green computing in restricted access industrial WSNs scenario. Results: The green computing framework is implemented to evaluate the performance in a realistic industrial WSNs environment. Conclusion: The performance evaluation attests the benefits in terms of number of metrics in realistic industrial constrained environments.

Background: The study on facemask detection is of great significance because facemask detection is difficult, and the workload is heavy in places with a large number of people during the COVID-19 outbreak. Objective: The study aims to explore new deep learning networks that can accurately detect facemasks and improve the network's ability to extract multi-level features and contextual information. In addition, the proposed network effectively avoids the interference of objects like masks. The new network could eventually detect masks wearers in the crowd. Methods: A Multi-stage Feature Fusion Block (MFFB) and a Detector Cascade Block (DCB) are proposed and connected to the deep learning network for facemask detection. The network's ability to obtain information improves. The network proposed in the study is Double Convolutional Neural Networks (CNN) called DCNN, which can fuse mask features and face position information. During facemask detection, the network extracts the featural information of the object and then inputs it into the data fusion layer. Results: The experiment results show that the proposed network can detect masks and faces in a complex environment and dense crowd. The detection accuracy of the network improves effectively. At the same time, the real-time performance of the detection model is excellent. Conclusion: The two branch networks of the DCNN can effectively obtain the feature and position information of facemasks. The network overcomes the disadvantage that a single CNN is susceptible to the interference of the suspected mask objects. The verification shows that the MFFB and the DCB can improve the network's ability to obtain object information, and the proposed DCNN can achieve excellent detection performance.

Background: Coronavirus (COVID-19) has appeared first time in Wuhan, China, as an acute respiratory syndrome and spread rapidly. It has been declared a pandemic by the WHO. Thus, there is an urgent need to develop an accurate computer-aided method to assist clinicians in identifying COVID-19-infected patients by computed tomography CT images. The contribution of this paper is that it proposes a pre-processing technique that increases the recognition rate compared to the techniques existing in the literature. Methods: The proposed pre-processing technique, which consists of both contrast enhancement and open-morphology filter, is highly effective in decreasing the diagnosis error rate. After carrying out pre-processing, CT images are fed to a 15-layer convolution neural network (CNN) as deep-learning for the training and testing operations. The dataset used in this research has been publically published, in which CT images were collected from hospitals in Sao Paulo, Brazil. This dataset is composed of 2482 CT scans images, which include 1252 CT scans of SARS-CoV-2 infected patients and 1230 CT scans of non-infected SARS-CoV-2 patients. Results: The proposed detection method achieves up to 97.8% accuracy, which outperforms the reported accuracy of the dataset by 97.3%. Conclusion: The performance in terms of accuracy has been improved up to 0.5% by the proposed methodology over the published dataset and its method.