scholarly journals Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization

2019 ◽  
Vol 9 (12) ◽  
pp. 2414 ◽  
Author(s):  
Wafa Njima ◽  
Rafik Zayani ◽  
Iness Ahriz ◽  
Michel Terre ◽  
Ridha Bouallegue
Keyword(s):  
Gradient Descent ◽  
Indoor Localization ◽  
Matrix Completion ◽  
Stochastic Gradient Descent ◽  
Mathematical Concepts ◽  
Localization Accuracy ◽  
Moment Estimation ◽  
Localization Scheme ◽  
Accelerated Gradient ◽  
The Internet Of Things

In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity.

scholarly journals Optimization of Intrusion Detection Systems Determined by Ameliorated HNADAM-SGD Algorithm

2021 ◽  
Author(s):  
Shyla Shyla ◽  
Vishal Bhatnagar ◽  
Vikram Bali ◽  
Shivani Bali
Keyword(s):  
Intrusion Detection ◽  
Gradient Descent ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Intrusion Detection Systems ◽  
Stochastic Gradient Descent ◽  
Data Traffic ◽  
Detection Systems ◽  
Moment Estimation ◽  
Classi Fication

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques

scholarly journals An Improvement of Stochastic Gradient Descent Approach for Mean-Variance Portfolio Optimization Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Stephanie S. W. Su ◽  
Sie Long Kek
Keyword(s):  
Portfolio Optimization ◽  
Standard Error ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Stochastic Gradient Descent ◽  
Moment Estimation ◽  
The Mean ◽  
Portfolio Optimization Problem ◽  
Mean Variance Portfolio ◽  
Mean Variance

In this paper, the current variant technique of the stochastic gradient descent (SGD) approach, namely, the adaptive moment estimation (Adam) approach, is improved by adding the standard error in the updating rule. The aim is to fasten the convergence rate of the Adam algorithm. This improvement is termed as Adam with standard error (AdamSE) algorithm. On the other hand, the mean-variance portfolio optimization model is formulated from the historical data of the rate of return of the S&P 500 stock, 10-year Treasury bond, and money market. The application of SGD, Adam, adaptive moment estimation with maximum (AdaMax), Nesterov-accelerated adaptive moment estimation (Nadam), AMSGrad, and AdamSE algorithms to solve the mean-variance portfolio optimization problem is further investigated. During the calculation procedure, the iterative solution converges to the optimal portfolio solution. It is noticed that the AdamSE algorithm has the smallest iteration number. The results show that the rate of convergence of the Adam algorithm is significantly enhanced by using the AdamSE algorithm. In conclusion, the efficiency of the improved Adam algorithm using the standard error has been expressed. Furthermore, the applicability of SGD, Adam, AdaMax, Nadam, AMSGrad, and AdamSE algorithms in solving the mean-variance portfolio optimization problem is validated.

scholarly journals Hyperparameter-free optimizer of stochastic gradient descent that incorporates unit correction and moment estimation

2018 ◽  
Author(s):  
Kazunori D Yamada
Keyword(s):  
Deep Learning ◽  
Gradient Descent ◽  
Mathematical Optimization ◽  
Descent Method ◽  
Learning Rate ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Gradient Descent Method ◽  
Moment Estimation ◽  
Estimation System

ABSTRACTIn the deep learning era, stochastic gradient descent is the most common method used for optimizing neural network parameters. Among the various mathematical optimization methods, the gradient descent method is the most naive. Adjustment of learning rate is necessary for quick convergence, which is normally done manually with gradient descent. Many optimizers have been developed to control the learning rate and increase convergence speed. Generally, these optimizers adjust the learning rate automatically in response to learning status. These optimizers were gradually improved by incorporating the effective aspects of earlier methods. In this study, we developed a new optimizer: YamAdam. Our optimizer is based on Adam, which utilizes the first and second moments of previous gradients. In addition to the moment estimation system, we incorporated an advantageous part of AdaDelta, namely a unit correction system, into YamAdam. According to benchmark tests on some common datasets, our optimizer showed similar or faster convergent performance compared to the existing methods. YamAdam is an option as an alternative optimizer for deep learning.

scholarly journals Photography Trilateration Indoor Localization with Image Sensor Communication

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3290 ◽  
Author(s):  
Nam Tuan Le ◽  
Yeong Min Jang
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Channel Modeling ◽  
Image Sensor ◽  
Error Rates ◽  
Localization Scheme ◽  
Wide Range ◽  
Successful Approach ◽  
Smart Mobile ◽  
The Internet Of Things

Localization has become an important aspect in a wide range of mobile services with the integration of the Internet of things and service on demand. Numerous mechanisms have been proposed for localization, most of which are based on the estimation of distances. Depending on the channel modeling, each mechanism has its advantages and limitations on deployment, exhibiting different performances in terms of error rates and implementation. With the development of technology, these limitations are rapidly overcome with hybrid systems and enhancement schemes. The successful approach depends on the achievement of a low error rate and its controllability by the integration of deployed products. In this study, we propose and analyze a new distance estimation technique employing photography and image sensor communications, also named optical camera communications (OCC). It represents one of the most important steps in the implemented trilateration localization scheme with real architectures and conditions of deployment which is the second our contribution for this article. With the advantages of the image sensor hardware integration in smart mobile devices, this technology has great potential in localization-based optical wireless communication

scholarly journals Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent

2020 ◽  
Author(s):  
Bowen Weng ◽  
Huaqing Xiong ◽  
Yingbin Liang ◽  
Wei Zhang
Keyword(s):  
Convergence Rate ◽  
Gradient Descent ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Linear Quadratic Regulator ◽  
Stochastic Gradient Descent ◽  
Learning Method ◽  
Linear Quadratic ◽  
Q Learning ◽  
Moment Estimation

Existing convergence analyses of Q-learning mostly focus on the vanilla stochastic gradient descent (SGD) type of updates. Despite the Adaptive Moment Estimation (Adam) has been commonly used for practical Q-learning algorithms, there has not been any convergence guarantee provided for Q-learning with such type of updates. In this paper, we first characterize the convergence rate for Q-AMSGrad, which is the Q-learning algorithm with AMSGrad update (a commonly adopted alternative of Adam for theoretical analysis). To further improve the performance, we propose to incorporate the momentum restart scheme to Q-AMSGrad, resulting in the so-called Q-AMSGradR algorithm. The convergence rate of Q-AMSGradR is also established. Our experiments on a linear quadratic regulator problem demonstrate that the two proposed Q-learning algorithms outperform the vanilla Q-learning with SGD updates. The two algorithms also exhibit significantly better performance than the DQN learning method over a batch of Atari 2600 games.

scholarly journals Using Acoustic Signal and Image to Achieve Accurate Indoor Localization

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2566 ◽  
Author(s):  
Rui Xi ◽  
Daibo Liu ◽  
Mengshu Hou ◽  
Yujun Li ◽  
Jun Li
Keyword(s):  
Pervasive Computing ◽  
Indoor Localization ◽  
Practical Application ◽  
Location Based Service ◽  
Localization Accuracy ◽  
Indoor Location ◽  
Location Service ◽  
Localization Scheme ◽  
True Location ◽  
Acoustic Sources

Location information plays a key role in pervasive computing and application, especially indoor location-based service, even though a mass of systems have been proposed, an accurate and practical indoor localization system remains unsettled. To tackle this issue, in this paper, we present a new localization scheme, SITE, combining acoustic Signals and Images to achieve accurate and robust indoor locaTion sErvice. Relying on a pre-deployed platform of acoustic sources with different frequencies, using proactively generated Doppler effect signals, SITE could track relative directions between the phone and the sources. Given m (m≥5) relative directions, SITE can use the angle differences to compute a set of locations corresponding to different subsets of sources. Then, based on a key observation—while the simultaneously estimated locations using different sets of acoustic anchors are within a small circle, the results converge to a point near the true location—SITE proposes a decision scheme that confirms whether these locations satisfy the demand of localization accuracy and can be used to search the user’s location. If not, SITE utilizes VSFM(Visual Structure from Motion) technique to achieve a set of relative locations using some images captured by the phone’s camera. By exploiting the synergy between the set of relative locations and the set of initial locations computed by relative directions, an optimal transformation relationship is obtained and applied to refine the initial calculated results. The refined result will be regarded as the user’s location. In the evaluation, we implemented a prototype and deployed a real platform of acoustic sources in different scenarios. Experimental results show that SITE has excellent performance of localization accuracy, robustness and feasibility in practical application.

Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition

2019 ◽  
Vol 34 (02) ◽  
pp. 2051003 ◽  
Author(s):  
Seda Postalcıoğlu
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Gradient Descent ◽  
Good Accuracy ◽  
Threshold Value ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Image Size ◽  
Moment Estimation ◽  
Validation Set

Deep learning refers to Convolutional Neural Network (CNN). CNN is used for image recognition for this study. The dataset is named Fruits-360 and it is obtained from the Kaggle dataset. Seventy percent of the pictures are selected as training data and the rest of the images are used for testing. In this study, an image size is [Formula: see text]. Training is realized using Stochastic Gradient Descent with Momentum (sgdm), Adaptive Moment Estimation (adam) and Root Mean Square Propogation (rmsprop) techniques. The threshold value is determined as 98% for the training. When the accuracy reaches more than 98%, training is stopped. Calculation of the final validation accuracy is done using trained network. In this study, more than 98% of the predicted labels match the true labels of the validation set. Accuracies are calculated using test data for sgdm, adam and rmsprop techniques. The results are 98.08%, 98.85%, 98.88%, respectively. It is clear that fruits are recognized with good accuracy.

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