APPLIED MACHINE LEARNING IN LOAD BALANCING

A common way to maintain the quality of service on systems that are growing rapidly is by increasing server specifications or by adding servers. The utility of servers can be balanced with the presence of a load balancer to manage server loads. In this paper, we propose a machine learning algorithm that utilizes server resources CPU and memory to forecast the future of resources server loads. We identify the timespan of forecasting should be long enough to avoid dispatcher's lack of information server distribution at runtime. Additionally, server profile pulling, forecasting server resources, and dispatching should be asynchronous with the request listener of the load balancer to minimize response delay. For production use, we recommend that the load balancer should have friendly user interface to make it easier to be configured, such as adding resources of servers as parameter criteria. We also recommended from beginning to start to save the log data server resources because the more data to process, the more accurate prediction of server load will be

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

© 2018 In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand

Iterative variational mode decomposition and extreme learning machine for gearbox diagnosis based on vibration signals

Vibration-based monitoring and diagnosis provide an excellent and reliable monitoring strategies for maintaining and sustaining a million dollars of industrial assets. The signal processing method is one of the key elements in gearbox fault diagnosis for extracting most useful information from raw vibration signals. Variational mode decomposition (VMD) is one of the recent signal processing methods that helps to solve many limitations in traditional signal processing method. However, pre-determine the input parameters especially the mode number become a challenging task for using this method. Then, this study aims to propose an iterative approach for selecting the mode number for the VMD method by using the normalized mean value (NMV) plot. The NMV value is calculates based on the ratio of a summation of VMD modes and the input signals. The result shows that the proposed iterative VMD approach can select an accurate mode number for the VMD method. Then, the vibration signals decomposed into different VMD modes and used for gearbox fault diagnosis. Statistical features have been extracted from the selected VMD modes and pass into extreme learning machine (ELM) for fault classification. Iterative VMD-ELM provide significance improvement of about 20% higher accuracy in classification result as compared with EMD-ELM. Hence, this research study offers a new mean for gearbox diagnosis strategy

Predictive Trading Strategy for Physical Electricity Futures

This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading

Customer active power consumption prediction for the next day based on historical profile

Energy consumption prediction application is one of the most important fieldsthat is artificially controlled with Artificial Intelligence technologies to maintainaccuracy for electricity market costs reduction. This work presents a way to buildand apply a model to each costumer in residential buildings. This model is built by using Long Short Term Memory (LSTM) networks to address a demonstration of time-series prediction problem and Deep Learning to take into consideration the historical consumption of customers and hourly load profiles in order to predict future consumption. Using this model, the most probable sequence of a certain industrial customer’s consumption levels for a coming day is predicted. In the case of residential customers, determining the particular period of the prediction in terms of either a year or a month would be helpful and more accurate due to changes in consumption according to the changes in temperature and weather conditions in general. Both of them are used together in this research work to make a wide or narrow prediction window.A test data set for a set of customers is used. Consumption readings for anycustomer in the test data set applying LSTM model are varying between minimum and maximum values of active power consumption. These values are always alternating during the day according to customer consumption behavior. This consumption variation leads to leveling all readings to be determined in a finite set and deterministic values. These levels could be then used in building the prediction model. Levels of consumption’s are modeling states in the transition matrix. Twenty five readings are recorded per day on each hour and cover leap years extra ones. Emission matrix is built using twenty five values numbered from one to twenty five and represent the observations. Calculating probabilities of being in each level (node) is also covered. Logistic Regression Algorithm is used to determine the most probable nodes for the next 25 hours in case of residential or industrial customers.Index Terms—Smart Grids, Load Forecasting, Consumption Prediction, Long Short Term Memory (LSTM), Logistic Regression Algorithm, Load Profile, Electrical Consumption.</p

Hybrid meta-heuristic algorithm based parameter optimization for extreme learning machines classification

Most classification algorithms suffer from manual parameter tuning and it affects the training computational time and accuracy performance. Extreme Learning Machines (ELM) emerged as a fast training machine learning algorithm that eliminates parameter tuning by randomly assigning the input weights and biases, and analytically determining the output weights using Moore Penrose generalized inverse method. However, the randomness assignment, does not guarantee an optimal set of input weights and biases of the hidden neurons. This will lead to ELM instability and local minimum solution. ELM performance also is affected by the network structure especially the number of hidden nodes. Too many hidden neurons will increase the network structure complexity and computational time. While too few hidden neuron numbers will affect the ELM generalization ability and reduce the accuracy. In this study, a heuristic-based ELM (HELM) scheme was designed to secure an optimal ELM structure. The results of HELM were validated with five rule-based hidden neuron selection schemes. Then HELM performance was compared with Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Classification and Regression Tree (CART) to investigate its relative competitiveness. Secondly, to improve the stability of ELM, the Moth-Flame Optimization algorithm is hybridized with ELM as MFO-ELM. MFO generates moths and optimizes their positions in the search space with a logarithm spiral model to obtain the optimal values of input weights and biases. The optimal weights and biases from the search space were passed into the ELM input space. However, it did not completely solve the problem of been stuck in the local extremum since MFO could not ensure a good balance between the exploration and exploitation of the search space. Thirdly, a co-evolutionary hybrid algorithm of the Cross-Entropy Moth-Flame Optimization Extreme Learning Machines (CEMFO-ELM) scheme was proposed. The hybrid of CE and MFO metaheuristic algorithms ensured a balance of exploration and exploitation in the search space and reduced the possibility of been trapped in the local minima. The performances of these schemes were evaluated on some selected medical datasets from the University of California, Irvine (UCI) machine learning repository, and compared with standard ELM, PSO-ELM, and CSO-ELM. The hybrid MFO-ELM algorithm enhanced the selection of optimal weights and biases for ELM, therefore improved its classification accuracy in a range of 0.4914 - 6.0762%, and up to 8.9390% with the other comparative ELM optimized meta-heuristic algorithms. The convergence curves plot show that the proposed hybrid CEMFO meta-heuristic algorithm ensured a balance between the exploration and exploitation in the search space, thereby improved the stability up to 53.75%. The overall findings showed that the proposed CEMFO-ELM provided better generalization performance on the classification of medical datasets. Thus, CEMFO-ELM is a suitable tool to be used not only in solving medical classification problems but potentially be used in other real-world problems

Analisis Keunikan Fitur Cwt Sinyal Eeg Untuk Pembuatan Lima Indikator Pengendalian Kursi Roda BCI

Penelitian ini dilakukan dengan tujuan untuk membuat lima indikator pengendalian kursi roda BCI berdasarkan fitur yang diekstraksi dari sinyal elektroensefalogram (EEG). Sinyal EEG didekomposisi menggunakan metode continuous wavelet transform (CWT). Nilai rata-rata absolut dan standar deviasi dari sinyal yang telah didekomposisi tersebut digunakan sebagai fitur. Fitur hasil ekstraksi kemudian dianalisis keunikannya menggunakan metode Friedman. Untuk mendekati sifat alami fitur sinyal EEG yang nonlinier, metode support vector machine (SVM) dengan kernel radial basis function (RBF) digunakan untuk membuat indikator pengendalian kursi roda BCI berdasarkan fitur sinyal EEG yang paling unik. Hasil penelitian ini menunjukkan bahwa metode yang diusulkan dapat mengukur tingkat keunikan fitur CWT sinyal EEG. Dari penelitian penentuan keunikan fitur CWT dapat diperoleh lima indikator pengendalian untuk kursi roda BCI yang didasarkan pada sinyal EEG dari Neurosky MW001. Akan tetapi, akurasi kelima indikator tersebut belum dapat digunakan sebagai indikator kontrol untuk aktuator kursi roda BCI. Hal ini disebabkan oleh tingkat kepercayaan rata-rata indikator tersebut masih di bawah 60%, sedangkan untuk indikator yang berpasangan masih di bawah 70%