This study focused on the development of a system based on evolutionary Algorithms to obtain the optimum parameters of the fuzzy controller to increase the convergence speed and accuracy of the controller. The aim of the study is to design fuzzy controller without expert’s knowledge by using evolutionary genetic algorithms and carry out on a DC motor. The design is based on optimization of rule bases of fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated the termination criteria. The proposed fuzzy controller is performed on the dc motor from a PC program using a interface circuit. Note : This article has been accepted for publication in a future issue of ELECTRICA journal, it is now in the early view. Citation information: M. Bulut, “Optimal Adjustment of Evolutionary Algorithm-based Fuzzy Controller for Driving Electric Motor with Computer Interface”, Electrica, August 5, 2021. DOI: 10.5152/electrica.2021.21033.

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The Stone-Age Method of Communication that is ever practical than ever in a quick approach to a form of communication. This paper describes a communication setup made of can-threadline-can in a head-mouth voice transmission in a physical free environment. The basic physical quantity of the stage setup is also looked into.

Purpose Respiratory motion during positron emission tomography (PET) scans can be a major detriment to image quality in oncological imaging, leading to loss of quantification accuracy and false negative findings. The impact of motion on lesion quantification and detectability can be assessed using anthropomorphic phantoms with realistic anatomy representation and motion modelling. In this work we design and build such a phantom, with careful consideration of system requirements and detailed force analysis. Methods: We start from a previously-developed anatomically-accurate shell of a human torso and add elastic lungs with a highly controllable actuation mechanism which replicates the physics of breathing. The space outside the lungs is filled with a radioactive water solution. To maintain anatomical accuracy in the torso and realistic gamma ray attenuation, all motion mechanisms and actuators are positioned outside of the phantom compartment. The actuation mechanism can produce a plethora of custom respiratory waveforms with breathing rates up to 25 breaths per minute and tidal volumes up to 1200mL. Results: Several tests were performed to validate the performance of the phantom assembly, in which the phantom was filled with water and given respiratory waveforms to execute. All parts demonstrated nominal performance. Force requirements were not exceeded and no leaks were detected, although continued use of the phantom is required to evaluate wear. The respiratory motion was determined to be within a reasonable realistic range. Conclusions: The full mechanical design is described in this paper, as well as a software application with graphical user interface which was developed to plan and visualize respiratory patterns. Both are available open source and linked in this paper. The developed phantom will facilitate future work in evaluating the impact of respiratory motion on lesion quantification and detectability.

Power flow is an integral part of distribution system planning, monitoring, operation, and analysis. This two-part paper proposes a sensitivity-based three-phase weather-dependent power flow approach for accurately simulating distribution networks with local voltage controllers (LVC). This part II, firstly, presents simulation results of the proposed approach in an 8-Bus and 7-Bus network, which are validated using dynamic simulation. Secondly, simulation results for the IEEE 8500-node network are also presented. An extensive comparison is conducted between the proposed sensitivity-based approach and the other existing power flow approaches with respect to result accuracy and convergence speed. Moreover, the influence of weather and magnetic effects on the power flow results and the LVC states is also investigated. Simulation results confirm that the proposed sensitivity-based approach produces more accurate results than the existing approaches since it considers the actual switching sequence of LVCs as well as the weather and magnetic effects on the network. Moreover, the proposed algorithm exhibits accelerated convergence due to the usage of the sensitivity parameters, which makes it an important tool for distribution system analysis.

Deep Neural Networks (DNNs) are increasingly being used in a variety of applications. However, DNNs have huge computational and memory requirements. One way to reduce these requirements is to sparsify DNNs by using smoothed LASSO (Least Absolute Shrinkage and Selection Operator) functions. In this paper, we show that for the same maximum error with respect to the LASSO function, the sparsity values obtained using various smoothed LASSO functions are similar. We also propose a layer-wise DNN pruning algorithm, where the layers are pruned based on their individual allocated accuracy loss budget determined by estimates of the reduction in number of multiply-accumulate operations (in convolutional layers) and weights (in fully connected layers). Further, the structured LASSO variants in both convolutional and fully connected layers are explored within the smoothed LASSO framework and the tradeoffs involved are discussed. The efficacy of proposed algorithm in enhancing the sparsity within the allowed degradation in DNN accuracy and results obtained on structured LASSO variants are shown on MNIST, SVHN, CIFAR-10, and Imagenette datasets.

As theoretically predicted by Prof. Chua, the input signal frequency has a major impact on the electrical behavior of memristors. According with one of the so-called fingerprints of such devices, the resistive window, i.e. the difference between the low and high resistance states, shrinks as the frequency increases for a given input signal amplitude. Physically, this effect stems from the incapability of ions/vacancies to follow the external electrical stimulus. In terms of the electrical behavior, the collapse of the resistive window can be ascribed to the shift of the set/reset voltages toward higher values. Moreover, for a given frequency, the resistance window increases with the signal amplitude. In this letter, we show that both phenomena are the two sides of the same coin and that can be consistently explained after considering the snapback effect and a balance model equation for the device memory state.

Learning by ignoring, which identifies less important things and excludes them from the learning process, is broadly practiced in human learning and has shown ubiquitous effectiveness. There has been psychological studies showing that learning to ignore certain things is a powerful tool for helping people focus. In this paper, we explore whether this useful human learning methodology can be borrowed to improve machine learning. We propose a novel machine learning framework referred to as learning by ignoring (LBI). Our framework automatically identifies pretraining data examples that have large domain shift from the target distribution by learning an ignoring variable for each example and excludes them from the pretraining process. We formulate LBI as a three-level optimization framework where three learning stages are involved: pretraining by minimizing the losses weighed by ignoring variables; finetuning; updating the ignoring variables by minimizing the validation loss. A gradient-based algorithm is developed to efficiently solve the three-level optimization problem in LBI. Experiments on various datasets demonstrate the effectiveness of our framework.

The Split-Radix Fast Fourier Transform has the same low arithmetic complexity as the related Conjugate Pair Fast Fourier Transform. Both transforms have an irregular datapath structure which is straightforwardly expressed only in recursive forms. Furthermore, the conjugate pair variant has a complicated input indexing pattern which requires existing iterative implementations to rely on precomputed tables. It however allows optimization of the memory bandwidth as it requires a single twiddle factor load per radix-4 butterfly. In existing algorithms, this comes at the cost of using additional precomputed tables or performing recursive function calls. In this paper we present two novel approaches that handle both the butterfly scheduling and the input index generation of the Conjugate Pair Fast Fourier Transform. The proposed algorithm is cache-friendly because it is depth-first, non-recursive and does not rely on precomputed index tables. In order to achieve this, we relate the butterfly execution pattern of the Split-Radix and Conjugate Pair FFTs to the binary carry sequence. Based on this finding, we describe how common integer arithmetic and bitwise operations can be used to perform input reordering and depth-first traversal of the transform datapath with O(1) space complexity.

This paper proposes a new approach to perform unmixing of hyperspectral image with the help of spectral library. The work introduces the concept of virtual dimensionality to unmixing

Learning by teaching is a broadly used methodology in human learning and shows great effectiveness in improving learning outcome: a learner deepens his/her understanding of a topic by teaching this topic to others. We are interested in investigating whether this powerful learning technique can be borrowed from humans to improve the learning abilities of machines. We propose a novel machine learning approach called learning by teaching (LBT). In our approach, the teacher creates a pseudo-labeled dataset and uses it to train a student model. Based on how the student performs on the validation dataset, the teacher re-learns its model and re-teaches the student until the student achieves great validation performance. We propose a multi-level optimization framework to formulate LBT which involves three learning stages: teacher learns; teacher teaches student; teacher and student validate themselves. We develop an efficient algorithm to solve the LBT problem. We apply our approach to neural architecture search on CIFAR-100, CIFAR-10, and ImageNet, where the results demonstrate the effectiveness of our method.

The analysis of power system dynamics is usually conducted using traditional models based on the standard nonlinear differential algebraic equations (DAEs). In general, solutions to these equations can be obtained using numerical methods such as the Monte Carlo simulations. The use of methods based on the Stochastic Hybrid System (SHS) framework for power systems subject to stochastic behavior is relatively new. These methods have been successfully applied to power systems subjected to stochastic inputs. This study discusses a class of SHSs referred to as Markov Jump Linear Systems (MJLSs), in which the entire dynamic system is jumping between distinct operating points, with different local small-signal dynamics. The numerical application is based on the analysis of the IEEE 37-bus power system switching between grid-tied and standalone operating modes. The Ordinary Differential Equations (ODEs) representing the evolution of the conditional moments are derived and a matrix representation of the system is developed. Results are compared to the averaged Monte Carlo simulation. The MJLS approach was found to have a key advantage of being far less computational expensive.

A document by Zeeshan Mumtaz . Click on the document to view its contents.

The paper belongs to the field of ultrafast optics. Acousto-optic pulse shaping has been studied experimentally for binary intensity modulation in a chirped pulse amplification laser system. Direct time-domain measurement of pulse front duration with a picosecond streak camera was performed for a Ti:sapphire regenerative amplifier. It was discovered that the sign of the second order dispersion produced by the acousto-optic dispersion delay line affects the modulation rise/fall time. Two algorithms for synthesis of ultrasonic waveforms for feeding the delay line, dispersive Fourier synthesis and the Gerchberg-Saxton algorithm, were compared. Minimum pulse front duration of 3.6 ps for 3 mJ pulses with the linear chirp of 6.2 ps/nm at the wavelength of 795 nm was obtained.

We derive an analytical formulation of the Raman-induced frequency shift experienced by a fundamental soliton. By including propagation losses, self-steepening, and dispersion slope, the resulting formulation is a high-order (HO) extension of the well-known Gordon’s formula for soliton self-frequency shift (SSFS). The HO-SSFS formula closely agrees with numerical results of the generalized nonlinear Schrödinger equation, but without the computational complexity and required computation time. The HO-SSFS formula is a useful tool for the design and validation of wavelength conversion systems and supercontinuum generation systems.

Analysis of human posture has many applications in the field of sports and medical science including patient monitoring, lifestyle analysis, elderly care etc. Many of the works in this area have been based on computer vision techniques. These are limited in providing real-time solution. Thus, Internet of Things (IoT) based solution are being planned and used for the human posture recognition and detection. The data collected from sensors is then passed to machine learning or deep learning algorithms to find different patterns. In this chapter an introduction to IoT based posture detection is provided with an introduction to underlying sensor technology, which can help in selection for appropriate sensors for the posture detection.

Analysis of Machine Learning for Ransomware Detection - A Systematic Literature Review

This paper discusses the interest of explicit software processes of design and architecture for (1) architectural view understanding and communication, (2) design reuse and principles of Jxta-based game software development. The considered architecture includes hierarchical and logical views which are modeled in the Java-based Maiar API and achieved by Jxta message exchanges. The topology of the world map of the game software can be viewed as undirected graph in which vertices (nodes) represent the rooms and the edges represent the rooms and the edges represent the playable movements between nodes.