Vector Method Research Articles

Theoretical characterization of the dynamics of a new planetary gearbox

Based on the newly designed planetary gear gearbox with reciprocating linear motion of its output shaft, research on its dynamic characteristics was conducted. The “kinematic mechanism method” was used to calculate the system’s transmission ratio, revealing its characteristic of having a relatively large transmission ratio. Combined with the calculation of transmission efficiency, the characteristic of high transmission efficiency was verified. The “velocity vector method” was used to calculate the relationship between the angular velocities of various components of the gearbox. By conducting a stress analysis on the planetary gear transmission system, the forces and torques acting on each component, as well as the meshing forces of the gears were calculated. The “central parameter method” was adopted to simulate the meshing contact between the gears in the planetary gear transmission system as undamped springs. Considering the two translational vibrations along the radial direction and the rotational twisting along the circumferential direction of each component, a translational-torsional coupled dynamic theoretical model of the system was constructed. Dynamic differential equations of the planetary gear transmission system were derived and solved to obtain the support forces and torques of each component at the bearing support positions, enhancing the research on the dynamic characteristics of the planetary gear transmission system.

Optical flow vector of elastic waves in transversely isotropic media

The propagation direction of the wavefield is particularly important for migration imaging in the reverse-time migration (RTM) of elastic waves in transversely isotropic (TI) media. However, due to the problem of the computational instability of the Poynting vector, the wave field propagation direction estimated based on the Poynting vector method has errors and cannot accurately indicate the real propagation direction of the elastic wavefield. To solve this problem, a method for calculating the optical flow vector of elastic waves in TI media is developed to obtain the propagation direction. The optical flow vector of elastic waves in TI media is determined by applying the spatial and temporal derivatives of the wavefield at each time step under the assumption that the wavefields are almost the same at subsequent time steps and are smooth in the spatial direction. As the additional smoothing item is added and the multiple iterative algorithm is introduced in calculating the optical flow vector, the direction is calculated more accurately than the Poynting vector. Based on the optical flow vectors, we can separate the source wavefield and receiver wavefield into four directions: up-going, down-going, left-going, and right-going wavefields, respectively, and finally perform elastic reverse-time migration (ERTM) imaging based on the optical flow vector traveling-wave separation. We use a layered model and the BP model to test our method. The testing results demonstrate that the optical flow vector can overcome the Poynting vector limitations and obtain more accurate and reliable information regarding the direction of elastic wave propagation in TI media, as well as precisely separate the wavefields. The separated wavefields for migration effectively improve the quality of the ERTM.

Comparison of SERS spectra of intact and inactivated viruses via machine learning algorithms for the viral disease’s diagnosis application

In this work, Surface-enhanced Raman spectroscopy (SERS) along with machine learning algorithms (MLA) were used to detect and classify the viral particles to assess the possibility of using the spectra of inactivated influenza A viruses for MLA training and spectra database compilation for further study and diagnosis of intact forms of the virus. Viral particles inactivation was performed by formalin, ultraviolet and beta-propiolactone. Support vector method and principal component analysis allowed to classify intact and inactivated viral particles spectra with an accuracy of 80.0–96.7 %. The results obtained suggest that it is not advisable to create a spectral database and train machine learning algorithms for their further application in SERS diagnostics of intact viruses based on the spectra of the inactivated virus particles.

Evaluation of the efficiency and accuracy of the system for collecting and processing EMG signals obtained using a bracelet

The object of research is a bracelet that uses the electromyography (EMG) method to control a bionic prosthesis. In the conditions of the development of modern biomedical technologies and robotics, such a system becomes key to improving the quality of life of people with disabilities, providing efficient and accurate control of prostheses. The problem addressed in the research is the development and analysis of a bionic prosthesis control system using a bracelet using the EMG method. The main focus is on the optimization of data collection and processing processes, as well as the development of machine learning algorithms for gesture recognition in order to improve the accuracy and efficiency of prosthetic control. The essence of the obtained results is the development and testing of a new bionic prosthesis control system that uses EMG signals obtained with the help of a bracelet. The study showed that the classifier based on the support vector method outperforms other algorithms such as neural networks and decision trees, achieving an average accuracy of 90 %. The obtained data were successfully filtered and subjected to feature extraction, which allowed to create effective gesture recognition algorithms. The system was tested in real time, which confirmed its high accuracy and efficiency. The proposed system includes an innovative bracelet for collecting EMG data, which are then processed and analyzed using modern machine learning algorithms. The innovativeness of the proposed approach lies not only in the high accuracy of gesture recognition, but also in the possibility of adapting the system to different types of bionic prostheses and operating conditions. This is achieved by using a classifier based on the support vector method, which demonstrates significantly higher accuracy compared to other algorithms such as neural networks and decision trees. The test results show an average accuracy of 92.5 %, which confirms the high efficiency of the system. The use of this system involves the intensive use of EMG sensors, which allows more accurate determination of the user's intentions regarding the control of the prosthesis. This, in turn, contributes to the improvement of the quality of life of users, providing them with greater functionality and convenience in the use of bionic prostheses.

Dynamic topology optimization of two-phase viscoelastic composite structures considering frequency- and temperature-dependent properties under random excitation

This article proposes an efficient optimum design method of constrained layer damping (CLD) plates characterized by non-proportional damping subjected to random excitation. Considering frequency and temperature dependence, a two-phase viscoelastic composite material composed of stiffness-phase and damping-phase materials is used as the damping layer. The pseudo excitation and hybrid expansion methods are adopted to compute the random responses at the specified frequency intervals as the objective functions. The sensitivity analysis is performed through the adjoint vector method. The similarity index is defined to distinguish the various optimal layouts quantitatively. Several numerical examples clearly demonstrate the robustness and effectiveness of the proposed method, highlighting a remarkable improvement of over 60% in computational efficiency. Furthermore, it is proved that, when subjected to broadband random excitation, the damping materials' temperature- and frequency-dependent properties are critical factors in obtaining accurate vibration responses and desirable optimal layouts. The effects of the layer thicknesses and volume fractions of composite structures on topology optimization are also discussed. The results indicate that both layer thickness and volume fraction can change the mechanical properties of the composite structure and further affect the distribution characteristics of the damping material.

Determining the most useful renewable energy alternative for Turkey by combining MAUT and TAOV methods

This study focuses on various Multi-Criteria Decision Making (MCDM) methods developed in the academic literature to facilitate the decision-making process. These methods are primarily aimed at establishing a ranking of alternatives based on specific criteria, with the ultimate goal of determining the most optimal alternative. In this work, we propose a novel integrated MCDM approach that combines the Total Area Based on Orthogonal Vectors (TAOV) method with the Multi-Attribute Utility Theory (MAUT) method. Within this framework, criteria weights are determined using the MAUT method, while the TAOV method is employed to ascertain the ranking of alternatives. The primary objective of this approach is to identify the most suitable renewable energy resource for the context of Turkey. Based on a comprehensive review of relevant literature regarding the evaluation of renewable energy sources in Turkey, a set of critical criteria influencing the decision-making process is delineated. These criteria encompass a range of factors including efficiency, construction duration, cost, government incentives, economic lifespan, external dependence, employment opportunities, social acceptance, spatial requirements, and environmental considerations such as greenhouse gas emissions. The outcomes of this study reveal that, according to the proposed approach, the most beneficial renewable energy resources for Turkey are ranked as follows: hydroelectric, wind, solar, biomass, and geothermal energy sources, respectively.

Retraction Note: Automatic epilepsy detection using hybrid decomposition with multi class support vector method

Retraction Note: Automatic epilepsy detection using hybrid decomposition with multi class support vector method

CИЛОВОЙ АНАЛИЗ ПЛОСКИХ РЫЧАЖНЫХ МЕХАНИЗМОВ ВЕКТОРНЫМ МЕТОДОМ

An analytical method is presented for force analysis of plain lever mechanisms with one degree of freedom, based on the vector method. Using the example of a two-drive structural group, a method is outlined for finding reaction vectors in all its kinematic pairs, while maintaining the clarity and consistency of the solution inherent in the graphic-analytical method of diagrams of component forces. An original analytical description is given for finding the vectors of tangential and normal components of reactions in kinematic pairs. Application of the proposed method of force analysis makes it possisble to find reaction vectors in kinematic pairs without compiling and solving complex systems of equilibrium equations or graphical construction. Adaptation of the proposed force analysis in modern mathematical packages allows for the study of plain lever mechanisms with one degree of freedom in a short time and with high accuracy.

Enhancing Tumor Localization in Brain MRI Images Using a Scalable Systematic Vector Algorithm for Result-Oriented Accuracy

This paper introduces a novel foundational architecture incorporating two primary algorithms. The Partially Enhanced Linear Mean Analysis Algorithm for significant vectorization and the Vertically Enhanced Tensor-Load Interface Algorithm for structural vectorization. These algorithms are meticulously designed to optimize the extraction of valuable information from complex data sets, enabling efficient and insightful historical data analysis. The performance of these algorithms has been rigorously compared with existing significant vectorization and structural methods, demonstrating superior accuracy in mean value estimation. Simulated results provide robust evidence of the high-dimensional capability and throughput of the proposed architecture. Incorporating tools such as Microsoft Excel for data organization, MATLAB for advanced computational analysis, and Python for algorithmic implementation and automation further enhances the framework’s efficiency and precision. Imminent work on this architecture could expand its applicability into multi-functional systems, potentially integrating a multi-role tape framework. Such an expansion would be particularly beneficial for identifying brain tumors with greater accuracy and precision, contributing significantly to medical diagnostics. The proposed system represents a promising advancement in applying computational tools for complex data analysis and structural vectorization.

Classifying possible hate speech from text with deep learning and ensemble on embedding method

Hate speech can be defined as the use of language to express hatred towards another party. Twitter is one of the most widely used social media platforms in the community. In addition to submitting user-generated content, other users can provide feedback through comments. There are several users who intentionally or unintentionally provide negative comments. Even though there are regulations regarding the prohibition of hate speech, there are still those who make negative comments. Using the deep learning method with the long short-term memory (LSTM) model, a classifier of possible hate speech from messages on Twitter is carried out. With the ensemble method, term frequency times inverse document frequency (TF-IDF) and global vector (GloVe) get 86% accuracy, better than the stand-alone word to vector (Word2Vec) method, which only gets 80%. From these results, it can be concluded that the ensemble method can improve accuracy compared to only using the stand-alone method. Ensemble methods can also improve the performance of deep learning systems and produce better results than using only one method.

АНАЛІЗ МЕТОДІВ ЗНАХОДЖЕННЯ АНОМАЛІЙ У ЗОБРАЖЕННЯХ

Abstract. Automatic anomaly detection is of great importance in industry, remote sensing, and medicine. It is important to be able to automatically process large amounts of data to detect, for example, chemical objects in multispectral and hyperspectral satellite images, sea mines in side-scan sonar images, or defects in industrial monitoring applications. Automatic detection of anomalous structures on arbitrary images refers to the task of finding inappropriate patterns relative to the normal state of the image. This is a difficult task in computer vision, since there is no clear and straightforward definition of what is normal or not normal for a given arbitrary image. The practical importance is manifested in the development of algorithms and models that can automatically detect unusual or anomalous patterns in images. An analysis of methods for finding anomalies in images from the point of view of the possibility of application to arbitrary images has been carried out. The classification of anomaly detection methods according to the criteria of the involved approaches and models used for modeling the background is presented. Methods that use machine learning, such as one-class support vector method and variational autoencoder, nearest neighbor-based anomaly detection, clustering-based anomaly detection, statistical anomaly detection, spectral anomaly detection, anomaly detection using information theory are discussed. The main attention is paid to the methods classified according to the background modeling approach. Five categories of background modeling methods based on probability density function, global and local homogeneity, sparsity, and self-similarity are considered. For anomaly detection applications, it is recommended to use methods in which the background model best describes the expected anomaly-free background, as this generally results in the best performance. On the basis of research, it was established that an effective universal model for detecting anomalies in arbitrary images should: use only a self-similar or sparse background model; process the residual image as a stochastic process to detect anomalies, such as anomalies in color noise; preprocess the residual image before detecting the anomaly.

Core Technology Topic Identification and Evolution Analysis Based on Patent Text Mining—A Case Study of Unmanned Ship

Accurate identification and evolutionary analysis of core technology topics within patent texts play a crucial role in enabling enterprises to discern the development trajectory of core technologies, optimize research and development (R&D) strategies, and foster technological innovation. Based on the perspective of time series dynamic analysis, this study uses the Latent Dirichlet Allocation (LDA) topic modeling and TF-IDF text vectorization methods to comprehensively mine and identify patent technology topics in the field of unmanned ships. This study deeply analyzes the dynamic evolution of unmanned ship technology topics from two aspects: the evolution of technology theme intensity and the evolution of technology theme content. We refine the development characteristics and future development directions of unmanned ship technology. The findings reveal two hot technologies, six growth technologies, and six declining technologies in unmanned ship technology. Furthermore, the analysis of technical topic evolution illustrates a pattern of fragmentation, inheritance, and integration. This study advances the methodologies used for identifying and analyzing patent technology topics and helps to grasp the development rules and evolutionary trends of core technologies. In addition, this paper has reference value for the research and practice of core technology topic identification and evolution analysis methods based on patent text mining.

A comparative study of 2D numerical simulations using vector and potential methods for extending 3D modeling of planetary evolution

A comparative study of 2D numerical simulations using vector and potential methods for extending 3D modeling of planetary evolution

MOVIE SIMILARITY FROM PLOT SUMMARIES

This project focuses on developing a Python application to analyze and measure the similarity between movie plot summaries. The goal is to provide a tool that can assist in identifying similarities between movies based on their storyline, enabling users to discover related movies or recommend similar ones.The project utilizes natural language processing (NLP) techniques, particularly text preprocessing, vectorization, and similarity metrics, to achieve its objectives. First, it preprocesses the plot summaries by removing stop words, punctuation, and performing stemming or lemmatization to normalize the text data. Next, it employs vectorization methods such as TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings like Word2Vec or GloVe to convert the text into numerical representations suitable for similarity calculations.

Automating modern code review processes with code similarity measurement

Context:Modern code review is a critical component in software development processes, as it ensures security, detects errors early and improves code quality. However, manual reviews can be time-consuming and unreliable. Automated code review can address these issues. Although deep-learning methods have been used to recommend code review comments, they are expensive to train and employ. Instead, information retrieval (IR)-based methods for automatic code review are showing promising results in efficiency, effectiveness, and flexibility. Objective:Our main objective is to determine the optimal combination of the vectorization method and similarity to measure what gives the best results in an automatic code review, thereby improving the performance of IR-based methods. Method:Specifically, we investigate different vectorization methods (Word2Vec, Doc2Vec, Code2Vec, and Transformer) that differ from previous research (TF-IDF and Bag-of-Words), and similarity measures (Cosine, Euclidean, and Manhattan) to capture the semantic similarities between code texts. We evaluate the performance of these methods using standard metrics, such as Blue, Meteor, and Rouge-L, and include the run-time of the models in our results. Results:Our results demonstrate that the Transformer model outperforms the state-of-the-art method in all standard metrics and similarity measurements, achieving a 19.1% improvement in providing exact matches and a 6.2% improvement in recommending reviews closer to human reviews. Conclusion:Our findings suggest that the Transformer model is a highly effective and efficient approach for recommending code review comments that closely resemble those written by humans, providing valuable insight for developing more efficient and effective automated code review systems.

Design and Development of a Hybrid Cooperative- Based Routing Protocol avoiding the void nodes in WSNs using the Concepts of Minimum Energy Consumption with good Data Security features

This paper presents a novel cooperative-based routing protocol designed for underwater Wireless Sensor Network Cloud Applications (uWSNCA). The protocol aims to minimize energy consumption by avoiding void nodes, which are areas without sensor coverage. The proposed approach consists of two main steps: first, avoiding void nodes using an adaptive hop-by-hop vector method, and second, applying a cooperative rule-based adaptive hop-by-hop vector method to create a hybrid algorithm that is more efficient than existing approaches, such as the watch-dog protocol. The research focuses on developing an energy-efficient distributed cooperative routing protocol for wireless sensor networks, specifically tailored for cooperation-based uWSNCA. This is achieved by preventing data packets from being forwarded through void nodes. The proposed routing protocol utilizes cooperation-based adaptive hop-by-hop vector-based forwarding concepts, where sensor nodes forward data packets in a multi-hop fashion within a virtual pipeline. In the developed algorithm for the underwater WSN cloud application, data packets are forwarded in a multi-hop manner using a virtual pipelining mechanism.Nodes outside the virtual pipeline do not forward data packets, thereby reducing flooding in the cooperative WSN. To prevent data packets from being forwarded towards void areas in the WSN, 2-hop information is used at each hop. This approach improves the performance of the wireless sensor network in terms of various performance criteria compared to existing routing protocols. The simulation tool NS2 is used to observe the simulation results of the energy packets being transferred from the source to the sink. The simulation results demonstrate the effectiveness of the proposed methodology. Overall, this paper contributes to the field of underwater WSNs by introducing a novel routing protocol that improves energy efficiency and performance for cloud applications.

The optimal metric for viral genome space

Understanding the structural similarity between genomes is pivotal in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families in biological taxonomy. However, determining an optimal metric that combines different elements in natural vectors remains challenging due to the absence of a rigorous theoretical framework for weighting different k-mers and orders. In this study, we address this challenge by transforming the determination of optimal weights into an optimization problem and resolving it through gradient-based techniques. Our experimental results underscore the substantial improvement in classification accuracy achieved by employing these optimal weights, reaching an impressive 92.73% on the testing set, surpassing other alignment-free methods. On one hand, our method offers an outstanding metric for virus classification, and on the other hand, it provides valuable insights into feature integration within alignment-free methods.

The use of the multi-sequential LSTM in electrical tomography for masonry wall moisture detection

The study describes an innovative approach to reconstructing moisture distribution within walls using EIT, extending beyond the conventional single measurement vector methods. The research introduces a unique strategy involving a series of five measurement vectors, each deliberately disrupted by noise, to mirror real-life conditions more accurately. The aim was to compare the efficacy of this sequential approach with traditional single vector techniques in terms of accuracy and robustness in mapping moisture in masonry structures. The effectiveness of these reconstructions was evaluated based on various quality indicators. A multi-branch LSTM network was employed to model the moisture distribution reconstruction. The validation process included a comparative analysis using results from dielectric and gravimetric methods, enhanced with thermal imaging. The findings indicate that the multi-sequential reconstructions provide more detailed and accurate EIT representations than single-vector methods, demonstrating the increased accuracy and reliability of EIT reconstructions using this innovative technique.

Vibration and underwater noise simulation of a river-crossing tunnel based on a 2.5D structural-acoustic finite element method

To protect the underwater ecosystem and species, the vehicle-induced vibration and underwater radiated noise of river-crossing tunnels need to be calculated efficiently and accurately. For this purpose, this study proposed a 2.5-dimensional (2.5D) structural-acoustic finite element method which combines 2.5D finite element vibration algorithm and 2.5D infinite element acoustic algorithm. The proposed method utilizes the spatial wave number transformation theory and the acoustic transfer vector (ATV) method. Based on a river-crossing tunnel, a 2.5D numerical simulation model was established in this study to analyze the vehicle-induced tunnel-soil vibration as well as the underwater radiated noise during operation. The results showed that the tunnel vibration and the associated radiated noise mainly distributed in the range of 10 to 40 Hz, with two peaks at 15.6 Hz and 31.5 Hz, respectively. The total sound pressure level (SPL) of the predicted noise at each field point reached a maximum value of 126 dB. The 2.5D algorithm proposed in this study achieves higher computational efficiency compared to traditional 3D vibration and noise analysis methods, which can provide theoretical and technical supports for the protection of underwater ecological diversity.

Drug Recommendation System Based on Sentiment Analysis of Drug Reviews using Machine Learning

People are self-medicating more during the COVID-19 pandemic because they can't get to good medical tools. This makes their health situations worse. This study suggests a drug suggestion system that uses machine learning and emotion analysis of patient reviews to make the job of healthcare workers easier. We use different vectorization methods, like Bag of Words (BoW), TF-IDF, Word2Vec, and Manual Feature Analysis, to guess how people feel about certain diseases and suggest the best drugs for them. We use classification methods, such as LinearSVC, to rate emotions based on their accuracy, F1-score, precision, and AUC score. The results show that LinearSVC with TF-IDF vectorization works well, as it achieved 93% accuracy, which was better than other models. By making drug suggestions, this system aims to make it easier for people to get medical care when they need it most, especially during emergencies.