Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
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A Meandered Line Patch Antenna at Low Frequency Range for Early Stage Breast Cancer Detection
Every year a concerning number of women are affected by breast cancer which is one of the deadliest and common types of cancers. Breast cancer is curable at early stages. For detecting breast cancer, there are several methods such as MRI, Mammography, Tomography, Ultrasound, and biopsy are available in medical technology. Still, none of them are as easy and efficient as a microwave imaging technique, in this method, the antenna plays an important role. Therefore, this paper focuses on developing an antenna at a low-frequency range for microwave imaging techniques to detect cancerous tissue inside the breast. For this, the antenna parameters, i.e., return loss, VSWR, directivity, current density, and specific absorption rate were studied, by setting the antenna over without tumor and with tumor breast as up-side-down, to ensure the compatibility of the antenna for the technique as well as for the patient’s body. A 5mm radius cancerous tumor was created inside the breast with dielectric conductivity of 4 and relative permittivity of 50. Cancerous cells were detected by reading the antenna parameters’ comparison between the healthy breast and the affected breast. The whole study was conducted by using CST MICROWAVE STUDIO SUITE 2020.
Improved DC-Link Voltage Controller for Photo Voltaic ON-Grid Systems
The DC-link voltage is an essential intermediate stage between the DC/DC and DC/AC converters in solar energy system. Due to the voltage fluctuations and current disturbances, a controller should be suitably designed to maintain the DC-link voltage well stabilized. In this paper, a variant of an adaptive DC-link voltage controller is proposed, namely PI-Fuzzy controller. The proposed controller has a fixed integral gain, while the proportional gain is determined based on a set of Fuzzy logic inference rules. Moreover, an anti-windup mechanism is added to the proposed controller to overcome integrator windup when the actuator saturates. Simulation results show that the proposed controller provides better rejection of current disturbances and fastest rise-time comparing to recently proposed PI-controllers for the DC-link voltage control
Design of Power-Efficient Structures of the CAM Cell using a New Approach in QCA Nanoelectronics Technology
Quantum-dot Cellular Automata (QCA) is a new emerging nano-electronic technology. Owing to its many fa-vorable features such as low energy requirements, high speed, and small size, QCA is being actively suggested as a future CMOS replacement by researchers. Many digital circuits have been introduced in QCA technology, most of them aiming to reach the function with optimum construction in terms of area, cell count and power consumption. The memory circuit is the main building block in the digital system therefore the researchers paid attention to design the memory cells with minimum requirements. In this paper, a new methodology is intro-duced to design two forms of CAM cell. The proposed designs required two 2:1 multiplexers, one OR gate and one inverter. The first proposed design reduces the power consumption by 53.3%, 35% and 25.9% at (0.5 Ek, 1 Ek, and 1.5 Ek) while the second design by 53.2%, 31.9% and 20.5% (0.5 Ek, 1 Ek, and 1.5 Ek) respectively
Three-Dimensional Modeling of Wave Propagation over Different Types of Terrains and Environments Using the Parabolic Equation Solved by Higher Order Approximation of the Finite Difference Method
Designing communications and radar systems depends on accurate modeling of ground waves in three-dimensional environment. Propagation of ground waves in the VHF and UHF bands affected by the characteristics of the terrain and the troposphere. Although some three-dimensional modeling of ground waves was found in the literature based on solving the parabolic equation, they were limited to a specific terrain and/or environment. Also, a lot of important factors such as the refractive index of the troposphere were ignored. In this paper, a computational model was developed for predicting the electromagnetic wave propagation over different types of terrains and environments under three-dimensional conditions. The model is based on solving the parabolic equation using higher order approximation of the finite difference method. The model allows specifications of an antenna and the electrical characteristics of the ground. Moreover, the model treats flat and non-flat terrains, mixed path with different electrical characteristics, and forest environment. Furthermore, the model enables calculations to be performed under standard and non-standard refractive conditions of the troposphere that varies in height, width, and range. The results were compared with two-dimensional parabolic equation solved by Fourier split-step and showed excellent agreement
Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset
The growth in internet traffic volume presents a new issue in anomaly detection, one of which is the high data dimension. The feature selection technique has been proven to be able to solve the problem of high data dimension by producing relevant features. On the other hand, high-class imbalance is a problem in feature selection. In this study, two feature selection approaches are proposed that are able to produce the most ideal features in the high-class imbalanced dataset. CICIDS-2017 is a reliable dataset that has a problem in high-class imbalance, therefore it is used in this study. Furthermore, this study performs experiments in Information Gain feature selection technique on the imbalance class datasaet. For validation, the Random Forest classification algorithm is used, because of its ability to handle multi-class data. The experimental results show that the proposed approaches have a very surprising performance, and surpass the state-of-the-art methods
A Novel Approach for the Power Ramping Metrics
One of the biggest concerns associated with incorporating a large amount of renewable energy into power systems is the need to cope with significant ramps in renewable power output. Power system operators need to have statistical information on the power ramping features of renewable generation, load, and net-load that can be used to mitigate ramping events in the case of a large forecast error to ensure the power system's flexibility and reliability; on the other hand, for economic considerations. So far, there is no consensus on a precise definition for the ramp event and so far there are hardly any metrics describing the ramping features of a power system. The paper introduces new metrics describing the power ramping features in a power system. The new metrics are ramp regularity factor (RRF), ramp intensity factor (RIF), and maximum ramp ratio (MRR). In addition, the coefficient of variation (CV) is used to characterize the average value of power ramps. The new ramp metrics are applied to the output power of Belgium's aggregated wind farms in 2017 and 2018. The results obtained by comparing the two years demonstrate that the two years have the same ramping behavior, although the average installed wind capacity has been increased. The new metrics can also be applied to other renewable sources (PV, tidal power, etc.), load, and net-load at any stage of operation
Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems
Nowadays, it became difficult to ensure data security because of the rapid development of information technology according to the Vs of Big Data. To secure a network against malicious activities and to ensure data protection, an intrusion detection system played a very important role. The main objective was to obtain a high-performance solution capable of detecting different types of attacks around the system. The main aim of this paper is to study the lacks of traditional and open source Intrusion Detection Systems and the Machine Learning techniques commonly used to overcome these lacks. A comparison of some existing works by Intrusion Detection System type, detection method, algorithm and accuracy was provided
A Low-complexity Complex-valued Activation Function for Fast and Accurate Spectral Domain Convolutional Neural Network
Conventional Convolutional Neural Networks (CNNs), which are realized in spatial domain, exhibit high computational complexity. This results in high resource utilization and memory usage and makes them unsuitable for implementation in resource and energy-constrained embedded systems. A promising approach for low-complexity and high-speed solution is to apply CNN modeled in the spectral domain. One of the main challenges in this approach is the design of activation functions. Some of the proposed solutions perform activation functions in spatial domain, necessitating multiple and computationally expensive spatial-spectral domain switching. On the other hand, recent work on spectral activation functions resulted in very computationally intensive solutions. This paper proposes a complex-valued activation function for spectral domain CNNs that only transmits input values that have positive-valued real or imaginary component. This activation function is computationally inexpensive in both forward and backward propagation and provides sufficient nonlinearity that ensures high classification accuracy. We apply this complex-valued activation function in a LeNet-5 architecture and achieve an accuracy gain of up to 7% for MNIST and 6% for Fashion MNIST dataset, while providing up to 79% and 85% faster inference times, respectively, over state-of-the-art activation functions for spectral domain
A Context-Aware Mobile-Based System for Crime Prevention and Emergencies
Crime is a global issue that arises as a consequence of social problems in society such as poverty and densely population due to urbanization. In large cities, governments have applied technology to support crime prevention. In this paper, a mobile-based system is proposed to increase knowledge and public awareness which may reduce the risk of crimes. A context-aware system, namely, geo-fence, is used to enable virtual fences around crime hotspots. Crime hotspots are determined using crime histories and crowd-sourced data provided by citizens. As citizens enter crime hotspots, they would be alerted and provided information. Meanwhile, if they find or experience crime, they are able to report and label the location of the crime
An Improved Current-Sensorless Method for Induction Motor Drives Applying Hysteresis Current Controller
A novel strategy based on the feed-forward field-oriented control (FOC) method is proposed for the Hysteresis Current technique to control the induction motor (IM) drive without current sensors (CSs). A control scheme is proposed to estimate stator currents from reference rotor flux, rotor flux angle, and state variables as a replacement for the feedback-signal of CSs used in the hysteresis current controller (HCC). Here the rotor flux angle component is extracted from the feed-forward FOC loop. MATLAB/Simulink is applied to implement the simulations under many different operating conditions. The simulation results demonstrated the feasibility of the proposed method to obtain high performance in controlling the IM drives without the current sensors