Recent Patents on Engineering - Volume 15, Issue 6, 2021

Currently, blockchain technology has evolved into a reliable and secure platform to provide security for the data. This technology's main adoption areas are agriculture, supply chain, food sector, energy industries, Health care, Real estate, Voting system, and education sector. This paper reviewed existing related works and applications using blockchain technology. Forgery or fraud on medical documents is a significant challenge in the health care sector. Hence this paper proposed a Blockchain-based framework using Ehterum that is a public blockchain. Users required medical documents generated and issued by officials authentically in the way of a unique blockchain-based ID. Users can submit this Blockchain-based ID anywhere instead of a physical paper. Attacks or forgery on the medical document can reduce using advanced featured technology, Blockchain. Blockchain technology has the features such as immutable, distributive, secure, reliable, and transparent. Remix Etherum IDE, solidity programming, and Metamask Wallet were used to implement the proposed application. The main strength of this work is designing and deploying results and comparisons with the existing applications.

Background: Electrical Discharge Machining (EDM) has been widely applied in manufacturing high strength and high hardness of mental material due to no mechanical forces in the EDM process. The discharge state, an important process parameter of EDM, directly affects the machining quality and machining efficiency. So, it is necessary to detect the real-time discharge state of EDM. Therefore, discharge state detection of EDM at present has been paid more and more attention. Objective: To meet the increasing requirement of machining quality and machining efficiency in the EDM process, the methods for discharge state detection of EDM are being improved continuously. Methods: This paper reviews various representative patents related to the methods for discharge state detection of EDM. Results: By summarizing numerous patents about the methods for discharge state detection of EDM, the main problems such as low detection accuracy and long delay time of discharge state detection of EDM are summarized and analyzed. In addition, the further development of discharge state detection method of EDM is discussed. Conclusion: The optimization of methods for discharge state detection is conducive to improving machining quality and machining efficiency in the EDM process. Moreover, more relevant patents will be invented in the future.

Background: Ball bearings are widely used in industry. They are core components of mechanical equipment. The research on the structural improvement of ball bearings is beneficial to enhancing the performance of bearing. Comparing with traditional structure, novel ball bearing has brought a lot of advantages and solved many problems in the working. Objective: The study aims to report the latest research on the structure of ball bearings and provides a reference for scholars and engineers. Methods: This paper reviews various representative patents related to ball bearings in principal aspects, such as lubrication, sealing, temperature, vibration, noise, and intelligence. Results: Through retracing the characteristics of different types of ball bearings, the main existing problems in the current situation are summarized and analyzed. The future development of patents on the structure of ball bearing is discussed. Conclusion: The structural improvement of ball bearing is conducive to the development of bearing technology. Ball bearings with characteristics of simple structure, intelligence and integration will have a good prospect in the future.

Background: People suffering from hearing and speaking disabilities have a few ways of communicating with other people. One of these is to communicate through the use of sign language. Objective: Developing a system for sign language recognition becomes essential for the deaf as well as a mute person. The recognition system acts as a translator between a disabled and an able person. This eliminates the hindrances in the exchange of ideas. Most of the existing systems are very poorly designed with limited support for the needs of their day to day facilities. Methods: The proposed system embedded with gesture recognition capability has been introduced here, which extracts signs from a video sequence and displays them on screen. On the other hand, a speech to text, as well as text to speech system, is also introduced to further facilitate the grieved people. To get the best out of the human computer relationship, the proposed solution consists of various cutting-edge technologies and Machine Learning based sign recognition models, which have been trained by using Tensor Flow and Keras library. Results: The proposed architecture works better than several gesture recognition techniques like background elimination and conversion to HSV because of the sharply defined image provided to the model for classification. The results of testing indicate reliable recognition systems with high accuracy that includes most of the essential and necessary features for any deaf and dumb person in his/her day to day tasks. Conclusion: It is the need of current technological advances to develop reliable solutions which can be deployed to assist deaf and dumb people to adjust to normal life. Instead of focusing on a standalone technology, a plethora of them have been introduced in this proposed work. The proposed Sign Recognition System is based on feature extraction and classification. The trained model helps in the identification of different gestures.

Background: Polycystic ovary syndrome is commonly known as PCOS and it is surprising that it affects up to 18% of women of reproductive age. PCOS is the most usually occurring hormonerelated disorder. Some of the symptoms of PCOS are irregular periods, increased facial and body hair growth, attain more weight, darkening of skin, diabetes and trouble conceiving (infertility). It also came to light that patients suffering from PCOS also possess a range of metabolic abnormalities. Due to metabolic abnormalities, some disorders may occur, which increased the risk of insulin resistance, type 2 diabetes, and impaired glucose tolerance (a sign of prediabetes). Family members of women suffering from PCOS are also at a higher level for developing the same metabolic abnormalities. Obesity and overweight status contribute to insulin resistance in PCOS. Objective: In the modern era, there are several new technologies available to diagnose PCOS, and one of them is Machine learning algorithms because they are exposed to new data. These algorithms learn from past experiences to produce reliable and repeatable decisions. In this article, Machine learning algorithms are used to identify the important features to diagnose PCOS. Methods: Several classification algorithms like Support vector machine (SVM), Logistic Regression, Gradient Boosting, Random Forest, Decision Tree and K-Nearest Neighbor (KNN) used wellorganized test datasets to classify huge records. Initially, a dataset of 541 instances and 41 attributes has been taken to apply the prediction models, and a manual feature selection is made over it. Results: After the feature selection, a set of 12 attributes has been identified, which plays a crucial role in diagnosing PCOS. Conclusion: There are several types of research progressing in the direction of diagnosing PCOS, but till now, the relevant features are not identified for the same.

Background: Breast cancer causes millions of deaths all over the world every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increase the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi-class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. Methods: The current paper presents an ensemble Convolutional neural network for multi-class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from the publicly available BreakHis dataset and classified into 8 classes. Results: The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification. Conclusion: In this paper, an approach for multi-class classification on the breast images for cancer detection is proposed. The proposed architecture can be a viable option for the classification of histopathology images.

Objective: The paper aims to solve the problem of determining the sliding surface of soil landslide, and to provide the basis for landslide stability analysis and prevention engineering. Methods: In this paper, the sliding surface is determined by field exploration and comprehensively determined by the FLAC3D numerical simulation method. At the same time, Sweden Arc Method and FLAC3D numerical simulation are used to make a comprehensive comparative analysis on the deformation and stability of the landslide. Results: The position of the sliding surface of dawanjiang landslide is determined by the FLAC3D numerical simulation method, which is located in the eluvium, and the maximum depth is about 7m, which is basically consistent with the speculated position. It provides a theoretical basis for landslide stability calculation and control engineering. Conclusion: In the determination of the sliding surface and stability calculation of soil landslide, the comprehensive analysis combined with numerical simulation is the more reliable method.

Background: The influence of the second air-gap length on the performance of permanent magnet synchronous motor is usually ignored. A comparative analysis is made of line-start high-voltage permanent magnet synchronous motor in this paper. Objective: The purpose of this paper is to analyze the influence of the second air-gap length on the motor performance. Methods: A 10kV 1000kW permanent magnet synchronous motor is taken as an example and the model is established based on the finite element calculation method when the second air-gap length is different. The influence of the second air-gap length on the phasor diagram, no-load back electrodynamic force, starting performance and steady-state operation performance are studied. Based on the finite element calculation method, the torque angle, current, power factor, external power factor and d/q-axis current of the motor are analyzed, and the change of the motor phasor diagram is obtained. The influence of the second air-gap length on the no-load back electrodynamic force is studied. Results and Conclusion: The relationship between the no-load back electrodynamic force and the no-load air-gap fundamental magnetic field is determined, and the influence mechanism of the noload air-gap fundamental magnetic field on no-load back electrodynamic force is revealed. Furthermore, the influence of the no-load back electrodynamic force on the starting performance is studied. Finally, the influence of the second air-gap length on the 10kV 1000kW permanent magnet synchronous motor steady-state performance is obtained. Based on the above analysis, some references are given for the design of motor.