Abstract.The induction motor has in the industry . More attention has been a focus to develop and design of induction motor drive. With the method of vector control novelty prove the efficiency of induction motor over their entire speed range. In this paper desirable to design a loss minimization controller which can improve the efficiency. Also, this research described Modeling of an induction motor with core loss included. Realization of methods vector control for an induction motor drive with loss element included. The case of the loss minimization condition. The procedure was successful to calculate the gains of a PI controller. Though the problem of obtaining a robust and sensorless induction motor drive is by no means completely solved, the results obtained as part of this work point in a promising direction. Introduction.IM are critical components in industrial processes. A motor failure may yield an unexpected interruption at the industrial plant, with consequences in costs, product quality, commonly used in adjustable speed drive systems. Induction motors have been widely employed in various industries as actuators or drivers to produce mechanical motions and forces. Since it is estimated that more than 50% of the world electric energy is generated and consumed by electric machines, to improve an efficiency of electric drives are important [1.2]. Induction motors require both full operating range of speed and fast torque response in operational conditions, regardless of load variations. Namely, induction motors have a high efficiency at rated speed and torque.Its efficient control requires a suitable model with accurate parameters, the minimization of the objective function is carried out using the Particle Swarm Optimization. Particle Swarm Optimization (PSO) is an evolutionary algorithm inspired by social interaction. PSO is an evolutionary once technique (a search method based on a natural system) developed by Kennedy and Eberhart. The basic concept of the PSO technique 'lies in accelerating each particle towards its p best and g best locations, with a random weighted acceleration at each time step. PSO has many parameters, and these are described as follow: V max is the maximum allowable velocity of the particles (i.e. in the case where the velocity of the particle exceeds Vmax, then it is limited to Vmax). Particle swarm optimization (PSO) is one of the modern heuristic algorithms [16], [17]. PSO has attracted great attention due to its features of easy implementation, robustness to control parameters and computation efficiency compared with other existing heuristic algorithms, and has been successful. Particle swarm
Text-based input becomes a common channel for humans in sharing their opinions/emotions to the product or service through online social media, shopping platform etc. Humans are easy to make errors in interpreting emotions, especially the emotion that derived from text based. The main aim of this study is to develop text-based emotion recognition and prediction system. Several market challenges facing in the advancement of emotion analysis with accuracy being the main issue. Therefore, four supervised machine learning classification algorithms such as Multinomial Naïve Bayes, Support Vector Machine, Decision Trees, and k-Nearest Neighbors were investigated. The model was developed based on Ekman’s six basic emotions which are anger, fear, disgust, joy, guilt and sadness. Data pre-processing techniques such as stemming, stop-words, digits and punctuation marks removal, spelling correction, and tokenization were implemented. A benchmark of ISEAR (International Survey on Emotion Antecedents and Reactions) dataset was used to test all models. Multinomial Naïve Bayes classifier resulted the best performance with an average accuracy of 64.08%. Finally, the best model was integrated to graphical user interface using Python Tkinter library to complete the whole system development. Besides, the detailed performance of the best model such as tf-idf and count vectorizer, confusion matrix, precision-recall rate, as well as ROC (Receiver Operating Characteristic) score were also discussed. Text-based emotion prediction system to interpret and understand human emotions was successfully developed.
New normal diterapkan oleh pemerintah untuk mengembalikan masyarakat beraktivitas normal ditengah pandemi covid-19 dengan protokol kesehatan. Penerapan new normal menuai beragam komentar dari masyarakat dan masuk kedalam topik terpopuler di media sosial twitter. Analisis sentimen untuk memprediksi komentar ataupun opini masyarakat yang kecenderungan beropini positif maupun negatif. Preprocessing data menggunakan cleaning, case folding, normalisasi, stemming, filtering, dan tokenizing. Pada normalisasi kata bertujuan memperbaiki kesalahan penulisan kata (typo) berdasarkan KBBI dan TF-IDF sebagai metode pembobotan kata. Data yang digunakan terdiri dari 1000 tweet. Metode klasifikasi opini menggunakan metode K-Nearest Neighbor dan melakukan pengujian agar mendapatkan hasil akurasi yang paling terbaik serta mengevaluasi menggunakan confusion matrix. Hasil dari pelabelan untuk sentimen positif berjumlah 811 dan 189 untuk sentimen negatif. Klasifikasi K-NN dengan nilai k = 1 menghasilkan pengujian use training set dengan accuracy sebesar 100%, 92,60% untuk 10-fold cross-validation dan 94,50% untuk 80% percentage split.
Noise in the image caused a decrease in image quality, so that the image will look dirty and spots appear on the resulting image. Noise also results in reduced information on the resulting image so that noise limits valuable information when image analysis is performed. Filtering technique is one way to overcome noise. The filtering technique used in this study is using the Contraharmonic Mean Filter algorithm and the Arithmetic Mean Filter algorithm with the type of noise used to reduce the Exponential Noise. The results of the two algorithms show that the Arithmetic Mean Filter algorithm is a better algorithm to reduce the Exponential Noise compared to the Contraharmonic Mean Filter algorithm which is proven based on the value of MSE (Mean Square Error) and PSNR (Peak Signal-to-Noise Ratio).
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