Learning Vector Quantization Research Articles

Detecting loss of excitation condition of synchronous generator in the presence of unified power flow controller based on data mining method

Loss-of-excitation (LOE) conditions may cause devastating effects on both the generator and power system. If LOE is not meticulously detected, two dangerous events may emerge: 1. unnecessary trip signal, 2. not detecting this abnormal condition or detecting it with time delay. Both mentioned flaws have considerable harmful consequences on the generator and power system. Conventional techniques for detecting LOE conditions are based on impedance measurement. The presence of flexible alternating current transmission (FACT) system devices can considerably affect the impedance characteristic of the system and as a result, the operation of impedance-based relays. This paper simulates LOE and stable power swing (SPS) conditions causing failures and errors in the LOE protection in the presence or absence of a unified power flow compensator (UPFC), and parameters with different behaviors are determined. Then, the most sensitive parameters are combined using an intelligent method. The single parameter, achieved from a combination of features in the previous process, is applied to a learning vector quantization (LVQ) neural network. The LVQ is a type of artificial neural network that is based on a prototype supervised learning classification algorithm and trained its network through a competitive learning algorithm. The simulation results show that, in a single machine infinitive bus (SMIB) test system, R-X directional scheme has 2 mal-operations and the proposed algorithm has no mal-operation without UPFC in the system. With UPFC in the system, the R-X directional scheme has four mal-operations and the proposed algorithm has no mal-operation. In the IEEE 9-bus test system, the R-X directional scheme has five mal-operations and the proposed algorithm has no mal-operation. Moreover, applying the proposed algorithm, LOE is detected very quickly in the presence or absence of the UPFC in the system and this approach prevents electrical and mechanical stresses enforced to the machine and power system. © 2017 Elsevier Inc. All rights reserved.

Implementasi Algoritma Learning Vector Quantization Dalam Aplikasi Pengenalan Pola

Along with the development of technology, a lot of technology has emerged and aims to make computers intelligent so that they can mimic the daily work of humans. One of the jobs that is often done is typing documents from a book or article. If the typed document is not too long, it will not cause problems. However, if there are quite a lot of documents to be typed, the operator will need a relatively long time to complete the job. This work can be streamlined by utilizing Artificial Intelligence, namely the Learning Vector Quantization (LVQ) algorithm. LVQ is used because it can learn, calculate distances and separate clusters from the patterns it has learned. This application is implemented using the Microsoft Visual Basic 6 programming language. The result is that the application can segment image characters, and carry out the training process as well as the process of recognizing patterns or shapes of characters, which in turn can recognize each character in a sentence. As well as displaying the steps in the training process and the recognition process, so that it can help learning the LVQ algorithm, especially in character recognition.

Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out

Universities, as providers of academic education services, are required to provide an optimal educational process for students to produce a generation of quality human beings. Student learning success is seen as the success of universities in implementing the higher education process. One of the problems universities face in maintaining the quality of education is student dropout. The high dropout rate in universities can impact accreditation assessments. As a result, it will affect the level of public trust. The number of dropouts in higher education can be minimized from an early age by analyzing the factors that cause student dropouts using data on students who graduated and those who dropped out. This data can be used to determine student dropout patterns by classifying them using the artificial neural network learning vector quantization (LVQ) approach. The data used in this research was 4053, consisting of 3840 graduate student data and 213 dropout student data. This data is considered unbalanced, an unbalanced dataset can cause errors because the model tends to classify the majority class with a high classification and pays less attention to the minority class. So, it is necessary to apply oversampling techniques to overcome this problem. The research results show that the application of the LVQ method to unbalanced data produces an accuracy value of 95.53%, a precision value of 100%, a recall value of 15.02% and an f1-score of 0.26, while the application of the LVQ method to data that has undergone resampling resulting in an accuracy value of 94.66%, a precision value of 92.22%, a recall value of 97.55%, and an f1-score value of 0.95. The LVQ method can be used to classify dropout students with excellent results.

Weighted distance classification method based on data intelligence

AbstractIn today's world, data is essential for enhancing an organization's development and decision‐making processes. Implementing artificial intelligence is necessary to analyse data and make meaningful recommendations. Machine learning distance classification methods are used to classify observations in various algorithms, such as K‐nearest neighbours (KNN), learning vector quantization and support vector machines, and are commonly used in academia and industry. However, this procedure faces a significant challenge in finding optimal parameters (i.e., distance metrics and the desired number of neighbours) in multidimensional datasets. This study presents a novel variation of a general method for classifying new observations. The method defines a new measure called closeness, which represents the proximity between an observation and the distribution. The advantages of this method are the use of both parametric and non‐parametric distance metrics and the ability to classify observations in cases where the simple method does not provide a clear answer. This method was demonstrated using KNN over three datasets and was observed to succeed in providing correct classifications, while the simple KNN method did not. The results showed that the proposed method increased the accuracy score to 40.7% in two of the three cases and that the closeness values were well defined by the proximity between the new observation and the given distribution. In addition, the F1 score increased up to 47.97%. The innovative method introduced here may be examined and used in various distance classification algorithms.

The nexus between quality of customer relationship management systems and customers' satisfaction: Evidence from online customers’ reviews

Customer Relationship Management (CRM) is a method of management that aims to establish, develop, and improve relationships with targeted customers in order to maximize corporate profitability and customer value. There have been many CRM systems in the market. These systems are developed based on the combination of business requirements, customer needs, and industry best practices. The impact of CRM systems on the customers' satisfaction and competitive advantages as well as tangible and intangible benefits are widely investigated in the previous studies. However, there is a lack of studies to assess the quality dimensions of these systems to meet an organization's CRM strategy. This study aims to investigate customers' satisfaction with CRM systems through online reviews. We collected 5172 online customers' reviews from 8 CRM systems in the Google play store platform. The satisfaction factors were extracted using Latent Dirichlet Allocation (LDA) and grouped into three dimensions; information quality, system quality, and service quality. Data segmentation is performed using Learning Vector Quantization (LVQ). In addition, feature selection is performed by the entropy-weight approach. We then used the Adaptive Neuro Fuzzy Inference System (ANFIS), the hybrid of fuzzy logic and neural networks, to assess the relationship between these dimensions and customer satisfaction. The results are discussed and research implications are provided.

An energy active regulation management strategy based on driving mode recognition for electro-hydraulic hybrid vehicles

Electro-hydraulic hybrid vehicles(EHHVs) have shown astonishing advantages in energy utilization and recovery. However, the key factor affecting the energy efficiency lies in the selection and switching conditions of driving modes. This paper introduces the structure and working principle of an electro-hydraulic hybrid vehicle that combines planetary gear mechanism. And an energy active regulation management strategy combined with driving mode recognition(DMR) and fuzzy control strategy is proposed. Firstly, a driving mode feature parameter evaluation is presented based on the collected speed and the principal component analysis(PCA). Secondly, K-means clustering algorithm is applied to identify offline driving modes through the extraction of driving short cycles. Then, combined with the PCA results, a Learning Vector Quantization neural network(LVQ) is constructed to recognize driving modes. Simultaneously, a fuzzy controller is added to allocate torque ratio. Finally, the DMR and fuzzy controller are combined to realize the energy active regulation of motors and hydraulic pump/motor. Experimental results indicate that energy management strategies based on DMR and fuzzy controller can realize optimal switching between modes according to condition recognition. It improves the utilization rate of the accumulator, and reduces battery energy consumption compared with traditional strategies.

Multi-Label Radar Compound Jamming Signal Recognition Using Complex-Valued CNN with Jamming Class Representation Fusion

In the complex battlefield electromagnetic environment, multiple jamming signals can enter the radar receiver simultaneously due to the development of jammers and modulation technology. The received compound jamming signals aggravate the difficulty of recognition and subsequent counter-countermeasure. In the face of strong overlapping signals and unseen jamming signal combinations, the performance of existing recognition methods usually seriously degrades. In this paper, an end-to-end multi-label classification framework combining a complex-valued convolutional neural network (CV-CNN) and jamming class representations is proposed to automatically recognize the jamming signal components of compound jamming signals. A basic multi-label CV-CNN (ML-CV-CNN) is first designed to directly process time–domain complex signals and fully retain jamming signal information. Then, the jamming class representations are generated using prototype clustering implemented by learning vector quantization, and they are fused with the ML-CV-CNN using class decoupling implemented by the attention mechanism to construct a multi-label class representation CV-CNN (ML-CR-CV-CNN), which can better learn the class-related features required for recognition. Finally, an adaptive threshold calibration is adopted to obtain optimal recognition results by multi-threshold discrimination. Simulation results verify that the proposed method has superior recognition performance, which is reflected in the strong robustness to the varying jamming-to-noise ratio (JNR) and power ratio, faster convergence speed with high JNRs, and better generalization for unseen jamming signal combinations.

A transient stability enhancement framework based on rapid fault-type identification for virtual synchronous generators

The virtual synchronous generator (VSG) control, widely used for voltage source inverters, is prone to the loss of synchronization (LOS) under large disturbances such as grid voltage sags. Despite the significant amount of research that has been conducted on the transient stability of VSGs, the existing conclusions on the influence of virtual inertia under different fault conditions remain controversial. In this paper, the conflicting effect of virtual inertia on VSG transient stability under different severities of grid voltage sag is comprehensively investigated. Faults, like grid voltage sags, are classified into two types in this paper: transient tolerable faults and transient intolerable faults. Moreover, a unified control framework for rapid fault-type identification and transient stability improvement is proposed. The Learning vector quantization (LVQ) neural networks are used to distinguish fault types within 5 ms. Based on the fault-type identification results, the proposed control strategies are applied to enhance the transient stability of VSG. Finally, simulations and control-hardware-in-loop experimental validations are carried out to demonstrate the effectiveness of the proposed framework.

Application of Learning Vector Quantization and Trajectory Planning On a 4-DoF Robotic Arm to Move the Object

The robotic arm is a type of statistical robot with a limited range of movement. Robotic arms are generally within the scope of Cartesian coordinates, according to the specified link length. The development of robot technology leads us to continue to upgrade soft computing. Intelligent systems in robots can improve good navigation detection systems or carry out the operator's tasks. On the other hand, using a camera is an important part of finding clear information about objects or capturing the environment around the robot. In this research, we implemented an intelligent system and computer-based camera on a 4-DoF robotic arm system. This robotic arm consists of a computer as the main processor, a microcontroller to adjust the joint angle, additional electronics, and a camera to detect objects and classify them by color. The colors used are red, green, and blue. The learning process uses these colors using Learning Vector Quantization (LVQ). The implementation of LVQ also carries out pre-processing, training, and testing stages. In the experiments that have been carried out, the robotic arm successfully navigates toward the target object and moves the object using the Trajectory Planning method. This computing process is done on a computer and connected to the robot arm's microcontroller. The experiment was carried out 60 times, and the success rate was 95%. Overall, the robot successfully picked up objects and grouped them by color.

Measuring the Maturity Level of Oil Palm Fruit For CPO Production Based on Color With Using the LVQ Method

Palm oil is a very important commodity besides oil and gas which also has a fairly good export value. The palm oil produced must be supported by the quality standards set by SNI. The level of maturity when harvesting oil palm fruit greatly influences the quality of Crude Palm Oil (CPO) production, which is crude palm oil that has a reddish color obtained from extraction or from the pressing process of oil palm fruit flesh. In fact, in the field of oil palm fruit harvest, there are still many oil palm fruit that are not ripe enough and can even be said to be still raw, entering the CPO production process. Determination of the maturity level of oil palm fruit is generally determined based on the amount of loose fruit and color, so handling the harvest of oil palm fruit is an important activity in improving the quality of CPO. It is necessary to build a system capable of managing and processing palm fruit images to measure the maturity level of the palm fruit to be produced. To obtain the right level of accuracy, this research uses the Learning Vector Quantization (LVQ) method. LVQ is a method for conducting supervised competitive layer learning. From the results of trials conducted, it is proven that the system can measure very ripe oil palm fruit with HSV values (0.052209; 0.896021; 0.791114).

UCAV situation assessment method based on C-LSHADE-Means and SAE-LVQ

The unmanned combat aerial vehicle (UCAV) is a research hot issue in the world, and the situation assessment is an important part of it. To overcome shortcomings of the existing situation assessment methods, such as low accuracy and strong dependence on prior knowledge, a data-driven situation assessment method is proposed. The clustering and classification are combined, the former is used to mine situational knowledge, and the latter is used to realize rapid assessment. Angle evaluation factor and distance evaluation factor are proposed to transform multi-dimensional air combat information into two-dimensional features. A convolution success-history based adaptive differential evolution with linear population size reduction-means (C-LSHADE-Means) algorithm is proposed. The convolutional pooling layer is used to compress the size of data and preserve the distribution characteristics. The LSHADE algorithm is used to initialize the center of the mean clustering, which overcomes the defect of initialization sensitivity. Comparing experiment with the seven clustering algorithms is done on the UCI data set, through four clustering indexes, and it proves that the method proposed in this paper has better clustering performance. A situation assessment model based on stacked autoencoder and learning vector quantization (SAE-LVQ) network is constructed, and it uses SAE to reconstruct air combat data features, and uses the self-competition layer of the LVQ to achieve efficient classification. Compared with the five kinds of assessments models, the SAE-LVQ model has the highest accuracy. Finally, three kinds of confrontation processes from air combat maneuvering instrumentation (ACMI) are selected, and the model in this paper is used for situation assessment. The assessment results are in line with the actual situation.

Toward improving the performance of learning by joining feature selection and ensemble classification techniques: an application for cancer diagnosis.

Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method. The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages. The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE-LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance. ADE-LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE-LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE-LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE-LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method. Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment.

PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP

The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.

OSNN: An Online Semisupervised Neural Network for Nonstationary Data Streams.

Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of target functions and data distributions over time (concept drifts). Most existing work relies solely on labeled data to adapt to concept drifts in classification problems. However, labeling all instances in a potentially life-long data stream is frequently prohibitively expensive, hindering such approaches. Therefore, we propose a novel algorithm to exploit unlabeled instances, which are typically plentiful and easily obtained. The algorithm is an online semisupervised radial basis function neural network (OSNN) with manifold-based training to exploit unlabeled data while tackling concept drifts in classification problems. OSNN employs a novel semisupervised learning vector quantization (SLVQ) to train network centers and learn meaningful data representations that change over time. It uses manifold learning on dynamic graphs to adjust the network weights. Our experiments confirm that OSNN can effectively use unlabeled data to elucidate underlying structures of data streams while its dynamic topology learning provides robustness to concept drifts.

An active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database

Heart disease is a significant global cause of mortality, and predicting it through clinical data analysis poses challenges. Machine learning (ML) has emerged as a valuable tool for diagnosing and predicting heart disease by analyzing healthcare data. Previous studies have extensively employed ML techniques in medical research for heart disease prediction. In this study, eight ML classifiers were utilized to identify crucial features that enhance the accuracy of heart disease prediction. Various combinations of features and well-known classification algorithms were employed to develop the prediction model. Neural network models, such as Naïve Bayes and Radial Basis Functions, were implemented, achieving accuracies of 94.78% and 90.78% respectively in heart disease prediction. Among the state-of-the-art methods for cardiovascular problem prediction, Learning Vector Quantization exhibited the highest accuracy rate of 98.7%. The motivation behind predicting Cardiovascular Heart Disease lies in its potential to save lives, improves health outcomes, and allocates healthcare resources efficiently. The key contributions encompass early intervention, personalized medicine, technological advancements, the impact on public health, and ongoing research, all of which collectively work toward reducing the burden of CHD on both individual patients and society as a whole.

Generalized Learning Vector Quantization With Log-Euclidean Metric Learning on Symmetric Positive-Definite Manifold.

In many classification scenarios, the data to be analyzed can be naturally represented as points living on the curved Riemannian manifold of symmetric positive-definite (SPD) matrices. Due to its non-Euclidean geometry, usual Euclidean learning algorithms may deliver poor performance on such data. We propose a principled reformulation of the successful Euclidean generalized learning vector quantization (GLVQ) methodology to deal with such data, accounting for the nonlinear Riemannian geometry of the manifold through log-Euclidean metric (LEM). We first generalize GLVQ to the manifold of SPD matrices by exploiting the LEM-induced geodesic distance (GLVQ-LEM). We then extend GLVQ-LEM with metric learning. In particular, we study both 1) a more straightforward implementation of the metric learning idea by adapting metric in the space of vectorized log-transformed SPD matrices and 2) the full formulation of metric learning without matrix vectorization, thus preserving the second-order tensor structure. To obtain the distance metric in the full LEM learning (LEML) approaches, two algorithms are proposed. One method is to restrict the distance metric to be full rank, treating the distance metric tensor as an SPD matrix, and readily use the LEM framework (GLVQ-LEML-LEM). The other method is to cast no such restriction, treating the distance metric tensor as a fixed rank positive semidefinite matrix living on a quotient manifold with total space equipped with flat geometry (GLVQ-LEML-FM). Experiments on multiple datasets of different natures demonstrate the good performance of the proposed methods.

Multi-proximity based embedding scheme for learning vector quantization-based classification of biochemical structured data

In this paper, we propose a data embedding technique for structured data that allows for the direct application of standard vector-based machine learning models without the need for explicit feature extraction. Our approach relies on multiple notions of data proximity, making it suitable for handling mixed data types or incorporating domain knowledge. Our method also reduces the computational costs of pairwise proximity calculations, resulting in improved efficiency and scalability. We demonstrate the effectiveness of our technique on graph and sequence datasets from the biochemical domain. In particular, we show that the method can efficiently be combined with interpretable machine learning approaches like relevance-based learning vector quantization for sophisticated classification learning.

Classification Appropriateness Recipient Help Non-Cash Food Using Learning Vector Quantization (LVQ) Method

Help Non-Cash Food is a program from the Government that is used to overcome poverty. The program is not functioning as well as it could because the procedure of receiving aid is not uniform, and individuals responsible for making choices are having trouble determining which families are qualified to receive the assistance. To overcome this problem, a classification system is needed to classify the eligibility of Non-Cash Food Assistance recipients so that the results are more efficient and accurate. This research uses the Learning Vector Quantization (LVQ) method with Python. This research aims to implement the LVQ method for the eligibility classification of non-cash food assistance recipients. System design is a stage that contains the process from start to finish of running this system which is described in the form of a flowchart, including system requirements that support this research, both software and hardware. In the process of analyzing the results and tests that are used as evaluation material in the process of finding a solution to a problem and making decisions in the process of planning activities, it is necessary to assess whether or not the LVQ approach is practicable to apply based on the findings of the research. In this study, 200 datasets were used with three epoch values and a learning rate of 0.1. The data set was randomly divided into a training portion of 80% and a testing portion of 20%. So that the results of this research using the LVQ method on the eligibility classification of recipients of Non-Cash Food Assistance obtain an accuracy of 97.5%.

Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="388"><p>The problem often occurs in chili leaves is organisms that interfere with chili plants which can reduce chili production. There are chili plant diseases that are difficult for farmers to recognize by using their eyes and without using tools. The purpose of this study was to produce a model capable of identifying chili leaf diseases based on leaf colour in order to make it easier for farmers to identify chili leaf diseases, especially Phytophthora, Anthracnose, and Cercospora diseases, using the Learning Vector Quantization (LVQ) classification algorithm. Data was collected in the form of digital images of 30 chili leaves which were processed by resizing and transforming RGB to HSV which then proceeded to Canny Edge detection process with the aim of getting patterns from images of chili leaves. The result of testing LVQ algorithm using a confusion matrix get an accuracy of 80%, the precision value of 80%, recall value of 82%, and f-1 score of 81%.</p><p> </p></td></tr></tbody></table>

Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology

One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.