AEEE. 2023, vol. 21

Permanent URI for this collectionhttp://hdl.handle.net/10084/151418

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Now showing 1 - 20 out of 32 results

Tomato Plant Disease Classification Using Local Patterns
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Agarwal, Megha
Agricultural sector has significant impact on the people health and on the economy of the world. Cli- mate variation is important reason in causing plant dis- eases hence, affecting the estimated crop production. Prior detection of plant diseases is utmost important for improving the quality and quantity of production within the due course of time. In this paper, this chal- lenge is addressed by automatically detecting tomato diseases from the hand-crafted features extracted from the plant leaves and machine learning classifiers. Dif- ferent frequency bands are extracted using Gaussian fil- ters and local statistics of leaves are captured using pat- terns to design frequency decomposed local ternary pat- tern (FDLTP). It provides a fast and accurate solution to avoid uncertainty in the farm production. Bench- marked dataset of Taiwan tomato leaves is used to verify the results. Performance of machine learning classifiers as well as deep learning solutions are com- pared, and 95.6% accuracy is obtained using proposed feature along with k-nearest neighbor classifier. It is a quick and easy to deploy method for real time applica- tion.
D2D Communication Network with the Assistance of Power Beacon under the Impact of Co-channel Interferences and Eavesdropper: Performance Analysis
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Minh, Bui Vu; Minh, Tran Hoang Quang; Phan, Van Duc; Nguyen, Hieu T.
In this paper, we study and demonstrate the performance analysis of a device-to-device (D2D) com- munication network. Specifically, a source node trans- mits data to the destination node using the power bea- con’s harvested energy in order to overcome the limited energy budget. Besides, an eavesdropper located in the proximal region of a source is trying to overhear secure information. Notably, both eavesdropper and destina- tion are affected by co-channel interferences from other sources when they utilize the same frequency. By con- sidering the above discussions, we derived the closed- form expressions for outage probability (OP), intercept probability (IP), and secrecy outage probability (SOP) in connection with using the system model. The derived analytical expressions are then verified by utilizing both simulation and numerical results. Finally, the inten- sive parameters’ influences on the OP, IP, and SOP are also investigated.
Multiple-Input Single-Output Voltage-Mode Multifunction Filter Based On Vdddas
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Huahongthong, Pintira; Suwanjan, Peerawut; Siripongdee, Surapong; Jaikla, Winai; Chaichana, Amornchai
n recent years, the voltage differencing dif- ferential difference amplifier (VDDDA) has been used in various analog signal processing circuit designs. A second-order multifunction filter with multiple-inputs and single-output (MISO) voltage mode using VDDDA as active elements is proposed in this paper. The struc- ture of the proposed filter comprises two VDDDAs, two grounded capacitors, and two resisters. The proposed filter has a cascadability feature in a voltage-mode sys- tem, producing voltage input and voltage output at high and low impedance ports, respectively. It can offer re- sponses for all-pass (AP), band-reject (BR), band-pass (BP), low-pass (LP), and high-pass (HP) filters with- out additional inverting and double gain amplifiers, as well as the matching conditions. Choosing the appro- priate input signals provides these five filter responses in the same circuit topology. With two VDDDAs, the bias currents can be utilized to electronically tune the natural frequency (ω0) independently from the quality factor (Q). Experimental results using available com- mercial ICs have supported the theoretical expectations and confirmed the practical operation of the proposed multifunction biquad filter.
A Low-Cost Real-Time Simulator Of Fuzzy Logic Control For Brushed Dc Motor Drives
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Nguyen, Son Thanh; Hoang, Anh; Pham, Tu Minh; Pham, Tuan Van
This article describes the development of a low-cost, real-time simulator of several control strate- gies for brushed DC motor drives. An educational brushed DC motor drive test bench and an Arduino Mega 2560 microcontroller board were used to build the simulation system. The system can serve as a platform for laboratory studies in which various control strate- gies helpful for instruction and research purposes. The interaction between MATLAB Simulink and the Ar- duino board can be managed through the exploration of the MATLAB Support Package for Arduino. Dif- ferent speed control algorithms for brushed DC motors can be easily deployed, monitored, and analysed using the Simulink multiform library. Three different types of controllers have been used in this study: a standard PI controller, a fuzzy logic controller, and a fuzzy PI controller. Finally, the system is particularly helpful in comparing various control approaches for many other controlled objects in addition to drives for brushed DC motors.
Experimental Verification of a Regenerative Braking System with an SOC Based Energy Management System for an E-Rickshaw Motor
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Abraham, Peter Kodathu; Mary, Dolly; Madasseri, Jayan
E-rickshaws are relatively new additions to India’s public road transportation system, gaining pop- ularity as a convenient and cost-effective means of com- muting for fellow travelers. However, they do not come equipped with a regenerative braking system. This paper proposes a simple and cost-effective regenerative brak- ing system for e-rickshaw motors, incorporating an en- ergy management system based on the state of charge of the battery. The proposed system can function ef- fectively even when the battery is fully or nearly fully charged. Additionally, it eliminates the need for any supplementary current or voltage sensors, significantly reducing the circuit’s complexity and cost. To evalu- ate the system’s performance under various traction conditions, simulations, and tests are conducted us- ing the MATLAB/Simulink model. The results confirm the high capabilities of the proposed system. The func- tionality and effectiveness of the proposed regenerative braking system are validated through laboratory exper- iments conducted under various conditions, including different speeds and levels of braking force, on a proto- type equipped with an e-rickshaw motor. The results of the experiments demonstrate that the proposed regen- erative braking system is successful in achieving its in- tended purpose, even with the fully charged battery, and without the need for any additional current sensors or voltage sensors. The proposed regenerative braking sys- tem not only enhances the efficiency and sustainability of e-rickshaws but also contributes to reducing overall energy consumption and environmental impact. As a result, this innovative solution holds great potential for widespread adoption in India’s growing e-rickshaw in- dustry.
Stability Prediction Of Quadruped Robot Movement Using Classification Methods And Principal Component Analysis
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Divandari, Mohammad; Ghabi, Delaram; Kalteh, Abdol Aziz
This paper introduces a novel technique for predicting the stability of quadruped robot locomo- tion using a central pattern generator (CPG). The proposed method utilizes classification methods and principal component analysis (PCA) to predict sta- bility. The objective of this study is to anticipate the stability or instability of robot movement by mod- ifying controlling parameters, referred to as features. The simulations of robot locomotion are conducted in MATLAB/SIMULINK R©, generating a dataset of 82 observations with different parameters. Machine learn- ing (ML) techniques are then applied, using classi- fication methods and PCA, to determine the stabil- ity condition. Six classification methods, including K-nearest neighbors (KNN), support vector classifier (SVC), Gaussian Naïve Bayes (GaussianNB), logistic regression (LR), decision tree (DT), and random for- est (RF) are implemented using Scikit-learn, an open- source ML library in Python. The performance of these classifiers is evaluated using four metrics: precision, recall, accuracy, and F1-score. The results indicate that KNN and SVC exhibit higher metric values com- pared to the other classifiers, making them more effec- tive for stability prediction.
Identification of Open-Circuit Faults in T-Type Inverters Using Fuzzy Logic Approach
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Mimouni, Amina; Laribi, Souad; Allaoui, Tayeb; Sebaa, Morsli; Bengharbi, Abdelkader Azzeddin
With the increasing adoption of solar Pho- tovoltaic (PV) systems in several applications, reliabil- ity and service continuity are important challenges that need to be addressed. Power converters are vital com- ponents in solar PV systems and inverters tend to be the most likely devices of equipment to experience faults which usually occur in the switching devices. It is there- fore critical to assess the functioning of the inverters and identify these faults in order to lower risks and the resulting financial losses. Open-circuit faults (OCF) are among the most common. This paper suggests a fuzzy-based fault detection approach for the T-Type in- verter in grid connected PV system based on the diag- nosis variables which are calculated using the average values of positive and the negative parts of the normal- ized output currents data. After that, these variables are analyzed using a fuzzy logic technique, The single, multiple power switch open circuit faults may all be de- tected and diagnosed utilizing this fuzzy-based fault di- agnostic technique. The results of the simulation in MATLAB show that the proposed method can accu- rately identify and locate OCF the inverter switches.
An Analysis Of Energy Demand In Iot Integrated Smart Grid Based On Time And Sector Using Machine Learning
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Managre, Jitendra; Gupta, Namit
Smart Grids (SG) encompass the utiliza- tion of large-scale data, advanced communication in- frastructure, and enhanced efficiency in the manage- ment of electricity demand, distribution, and produc- tivity through the application of machine learning tech- niques. The utilization of machine learning facilitates the creation and implementation of proactive and au- tomated decision-making methods for smart grids. In this paper, we provide an experimental study to un- derstand the power demands of consumers (domestic and commercial) in SGs. The power demand source is considered a smart plug reading dataset. This dataset is large dataset and consists of more than 850 user plug readings. From the dataset, we have extracted two different user data. Additionally, their hourly, daily, weekly, and monthly power demand is analysed individ- ually. Next, these power demand patterns are utilized as a time series problem and the data is transformed into 5 neighbour problems to predict the next hour, day, week, and month power demand. To learn from the transformed data, Artificial Neural Network (ANN) and Linear Regression (LR) ML algorithms are used. According to the conducted experiments, we found that ANN provides more accurate prediction than LR Addi- tionally, we observe that the prediction of hourly de- mand is more accurate than the prediction of daily, weekly, and monthly demand. Additionally, the pre- diction of each kind of pattern needs an individually refined model for performing with better accuracy.
Design Of Deep Learning Model Applied For Smart Parking System
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Vo, Van-An; Phan, Van Duc; Bui, Vu Minh; Do, Tri Nhut
This article proposes and introduces a smart parking system using RFID technology incorporating a Deep Learning model to identify license plates. It tries to simulate the ability of the brain to recognize, differ- entiate and learn patterns from data. The employed algorithms are mainly based on neural network mod- els where neurons are organized in stacked layers. The system is designed to manage incoming and outgoing vehicles by collecting and processing images and data on passenger information to update parking status with the news of empty lots. Another function of the park- ing system also designed is a fully automatic method of paying the parking fee by the user. The deep learning model for the smart parking system is implemented us- ing the Raspberry PI 3 embedded system and sensors. Experimental results with the plate identification rate in the worst condition, up to 80%, have proven the re- liability of the proposed smart parking system. In terms of quantity, the percentage of the worst plate identifi- cation down to 10% has established the stability of the proposed smart parking system.
Despeckling Of Synthetic Aperture Radar Images Using Shearlet Transform
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Garg, Amit; Goel, Anshika
Synthetic Aperture Radar (SAR) is widely used for producing high quality imaging of Earth sur- face due to its capability of image acquisition in all- weather conditions. However, one limitation of SAR image is that image textures and fine details are usually contaminated with multiplicative granular noise named as speckle noise. This paper presents a speckle reduc- tion technique for SAR images based on statistical mod- elling of detail band shearlet coefficients (SC) in ho- momorphic environment. Modelling of SC correspond- ing to noiseless SAR image are carried out as Nor- mal Inverse Gaussian (NIG) distribution while speckle noise SC are modelled as Gaussian distribution. These SC are segmented as heterogeneous, strongly hetero- geneous and homogeneous regions depending upon the local statistics of images. Then maximum a posteri- ori (MAP) estimation is employed over SC that belong to homogenous and heterogenous region category. The performance of proposed method is compared with seven other methods based on objective and subjective quality measures. PSNR and SSIM metrics are used for objec- tive assessment of synthetic images and ENL metric is used for real SAR images. Subjective assessment is carried out by visualizing denoised images obtained from various methods. The comparative result analy- sis shows that for the proposed method, higher values of PSNR i.e. 26.08 dB, 25.39 dB and 23.82 dB and SSIM i.e. 0.81, 0.69 and 0.61 are obtained for Barbara im- age at noise variances 0.04, 0.1 and 0.15, respectively as compared to other methods. For other images also results obtained for proposed method are at higher side. Also, ENL for real SAR images show highest average value of 125.91 79.05. Hence, the proposed method sig- nifies its potential in comparison to other seven existing image denoising methods in terms of speckle denoising and edge preservation.
Designing And Analysing Of Operational Load Of The Presence Service In IMS
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Baroňák, Ivan; Drienovský, Martin; Demeterová, Lucia
The presence in contrast to instant messag- ing (IM) is a dynamic user profile that is visible to others and shares information about itself. It can be represented through statuses that are perceived by other users of the service. Status includes personal infor- mation, information about the device, location, type of terminal device or preferred way of contacting the user (messages / video / call). This information is al- ways linked to the person concerned. The presence ser- vice uses the SIP (Session Initiation Protocol) protocol, which manages presence information and determines how it is used (for example, who can see the presence information and to what extent) and is designed for a wide range of applications. The article describes a new way of analysing operational load in the presence service based on mathematical expressions. The whole concept is derived from the Markov chain. The traffic load simulations described in this paper have shown that NOTIFY messages have a higher quantity than other system messages and increase over time. This can lead to server overload and data loss [1] [2].
Mathematical Modeling Of Multi-Units Pumping Station With Asynchronous Electric Drive Of Centrifugal Pumps
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Lysiak, Vladyslav
Based on the system approach and the principle of electrohydrodynamic analogy, a formal- ized mathematical model of a generalized system of asynchronous centrifugal pumping units of a multi-unit pumping station with an asynchronous electric drive of centrifugal pumps was created. This model is easy to adapt to the specific configuration of the pumping sta- tion. A feature of the model is the use of such pa- rameters of the centrifugal pump, which are calculated directly through the geometric dimensions of its inter- nal elements, taking into account the dependence of these parameters on the physical properties of the work- ing fluid. Unlike existing models, this makes it pos- sible to take into account the influence of operational or emergency changes in pump parameters and opera- tional changes in the physical properties of the working fluid both on its pumping regimes and on the operation regimes of the electric drive directly during the simu- lation. The scope of use and ways of improving the developed model, as well as the direction of further re- search, are proposed.
Understanding Frequency Response of Induction Motor Winding through Electromagnetic Wave Equations
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Amrolia, Hormaz; Vora, Santosh C.; Badgujar, Ketan P.
Frequency response analysis offers an in- sight about the integrity of machine windings, when employed as a tool for condition monitoring. To en- sure that, an electromagnetic wave is injected from one terminal of winding, and the power of the wave at the receiving terminal is measured. The power at the termi- nals is measured in terms of either voltage or current. This difference in power at the two terminals can be at- tributed to the medium’s permittivity, permeability and conductivity, through which the signal is being trans- mitted. This paper offers an explanation for the behav- ior of the voltage gain frequency response of induction motor winding and propagating medium parameters by employing the fundamental electromagnetic wave equa- tions. Their explanation illustrates how these param- eters can affect the response. The correlation estab- lished using Maxwell’s equation and these parameters with frequency response analysis is evident while iden- tifying open winding fault and issue with machine core inductance. The results are analyzed and interpreted with the new correlation.
SC-FDMA Uplink System In Heavily Faded Areas With Low Signal-To-Noise Ratio
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Mishra, Subhra Surochita; Roy, Jibendu Sekhar
These Single carrier frequency division mul- tiple access (SC-FDMA) has very low power consump- tion at the sender’s side, and it is the access scheme used for the uplink in long-term evolution (LTE). The objective of this work is to explore the error proba- bility of SC-FDMA system under sub-carrier mapping (SM) in heavily faded areas where the signal-to-noise ratios (SNRs) are very low. Wireless environment with heavily faded areas includes military radio systems; di- rect sequence spread spectrum system (DS-SS), global positioning system (GPS) etc. The localized FDMA (LFDMA) and distributed FDMA (DFDMA) are used to compare the performances of SC-FDMA in heavily faded areas. In heavily faded area with negative signal- to-noise ratio (SNR), the SC-FDMA system is imple- mented using modulation and encoding methods to re- ceive a very weak signal. Here, binary phase shift key- ing (BPSK), quadrature phase shift keying (QPSK), 16-PSK, quadrature amplitude modulation (QAM) and 16-QAM modulation techniques are used to calculate the bit error rate (BER) performances. The results show the BER performances of SC-FDMA using map- ping schemes for different channels, like, AWGN chan- nel, Rayleigh channel, COST207TU, and COST207RA channel models for heavily faded areas. In AWGN channel, BER at -15dB is about 10 times more than BER at 15dB. The COST207 model shows that the BER is less in typical urban (TU) area compared to the rural area (RA).The performance of BPSK modu- lation in SC-FDMA system is better in heavily faded areas than other modulation schemes.
Influence Of Cuts In The Housing And Armature Of The Forced Electromagnet Of The Fuel Injection System On Its Speed
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Baida, Yevhen I.; Pantelyat, Michael G.; Vyrovets, Serhii V.
Electromagnetic mechanisms are widely used in various systems due to the simplicity of their design and reliability in operation. One such system is the fuel injection system for an internal combustion engine. The electromagnets installed in such systems should have a number of special properties: they should have small dimensions, high speed, which is ensured by forcing, a small mass of moving elements, an increased residual air gap, a small armature stroke and minimal eddy currents in the magnetic core. The reduction of eddy currents is carried out by the use of steels with in- creased resistivity and special cuts in the housing and armature, which significantly complicates the design of the electromagnet, but is an effective approach for re- ducing losses in DC electromagnetic systems. To as- sess the influence of the design of an electromagnet on its speed, the article present a comparative analysis of the solution of the problem of dynamics in a 3D for- mulation for a forced armored DC electromagnet and an improved electromagnet with reduced eddy current losses due to special structural cuts in the housing and armature.
Effects Of Unbalanced Magnetic Force And Torque Ripple On The Performance Of A Double Stator Permanent Magnet Machine
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Chijioke Awah, Chukwuemeka; Ejiofor Oti, Stephen; Nnabuenyi, Ifeanyi
he effect of unbalanced magnetic force and torque ripple of on the output performance of a double stator switched-flux permanent magnet machine would be investigated and compared, with particular reference to the machine’s varying rotor pole number. The finite element analysis (FEA) predicted machine parameters are: unbalanced magnetic force (UMF), total harmonic distortion (THD), torque ripple (Tr), cogging torque and static torque. The considered machine types hav- ing 10-, 11-, 13- and 14-rotor pole numbers are desig- nated as: 6S/10P, 6S/11P, 6S/13P and 6S/14P, re- spectively; 6S represents six (6) stator slot or teeth number. It is revealed that the machine types that have odd number of rotor poles would have lower machine output characteristics such as: low cogging torque, low torque ripple and low total harmonic distortion of the voltage, compared to the ones that have even number of rotor poles; though, with higher amount of unbalanced magnetic force on the rotor. Further, the sensitivity of the machine’s output performances due to electric loadings is higher than its corresponding response as a result of machine’s varying rotational speed. More so, the machine types having odd number of rotor poles would have higher air gap flux density magnitudes; and would also exhibit better average torque per magnet us- age, which is desirable for reduced cost implications.
Coot Bird Behavior-Based Optimization Algorithm For Optimal Placement Of Thyristor Controlled Series Compensator Devices In Transmission Power Networks
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Nguyen, Anh Tuan; Le, Chi Kien; Phan, Minh Tan; Nguyen, Trung Thang
This study presents the new application of Coot bird behavior-based optimization algorithm (COOTBA) for optimal placement of Thyristor Con- trolled Series Compensator (TCSC) devices in an IEEE 30-node transmission power network with three single objectives, including fuel cost, power loss, and voltage deviation. COOTBA is implemented for the system with one case without TCSC devices and three others with TCSC. COOTBA can reach smaller cost and loss than previous algorithms by from 0.04% to 3.78%, and from 6.7% to 40.3% in the first case with- out TCSC. In the second case with TCSC, COOTBA can reach smaller cost than others by from 0.008% to 0.66%. In addition, the comparisons of results from COOTBA in the three cases with TCSC indicate that TCSC should be optimized for both location and reac- tance, and the limitation of TCSC devices should be high enough. Thus, COOTBA is an effective algorithm for optimizing TCSC devices on transmission power systems.
Control Approach of a grid connected DFIG based wind turbine using MPPT and PI controller
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Aedi Yonis, Samatar; Yusupov, Ziyodulla; Habbal, Adib; Toipov, Olimjon
double-fed induction generator (DFIG) has been frequently utilized in wind turbines due to its ability to handle variable-speed operations. This study investigates the real parameters of the Mitsubishi MWT 92/2.4 MW wind turbine model. It performs and implements grid-connected variable-speed turbines to control the active and reactive powers. Moreover, it presents a vector control strategy for DFIG for con- trolling the generated stator power. The unique fea- ture of the approach proposed in the study is the com- parison between two control techniques - the Maxi- mum Power Point Tracking (MPPT) algorithm and the Proportional-Integral (PI) controller - for regulat- ing DFIG based wind turbine systems. Thus, the result demonstrates that the performance of the MPPT tech- nique provides strong robustness and reaches steady- state much faster than the PI controller with variable parameters. To the contrary, a typical PI controller gives a fast response when tracking the references of DFIG magnitudes. The effectiveness of the overall sys- tem is tested by MATLAB simulation.
Analysis of mutual inductance between transmitter and receiver coils in Wireless Power Transfer System of Electric Vehicle
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Zheng, Junlong; Jettanasen, Chaiyan
n the electric vehicle wireless power trans- fer (WPT) system, the mutual inductance (M) between the transmitting and receiving coils is an important fac- tor influencing overall system efficiency. The M is af- fected by various factors such as the physical structure of the coils diagram, the distance and relative position between the transmitting and receiving coils, and so on. Our work here has two outstanding contributions. First, the detailed mathematical model of the M was developed. Second, the three-dimensional spatial distri- bution diagram of the M was drawn using Python soft- ware, the maximum value of the M and its correspond- ing position coordinates were calculated. Then, the the- oretical analysis of the M distribution was proven cor- rect through experiments. The theoretical analysis and experimental verification of the M distribution provided a theoretical reference for the positioning requirements between the transmitting and receiving coils.in the elec- tric vehicle WPT system.
Formulation of Pattern Recognition Framework - Analysis and Detection of Tyre Cracks Utilizing Integrated Texture Features and Ensemble Learning Methods
(Vysoká škola báňská - Technická univerzita Ostrava, 2023) Mahesh, Vijayalakshmi Gopasandra Venkateshappa; Joseph Raj, Alex Noel
For a safe drive with a vehicle and better tyre life, it is important to regularly monitor the tyre damages to diagnose its condition and chose appropri- ate solution. This paper proposes a framework based on pattern recognition utilizing the strength of texture attributes and ensemble learning to detect the damages on the tyre surfaces. In this paper, a concatenation of the statistical and edge response based texture features derived from Gray Level Co-occurrence Matrix and Local directional pattern are proposed to describe and represent the tyre surface characteristics and their variations due to any damages. The derived fea- tures are provided to train machine learning algorithms using ensemble learning methods for a better under- standing to discriminate the tyre surfaces into normal or damaged. The experiments of tyre surface classifica- tion were conducted on the tyre surface images acquired from Kaggle tyre dataset. The results demonstrated the ability of the combined texture features and ensemble learning methods in effectively analysing the tyre sur- faces and discriminate them with better performance provided by adaboost and histogram gradient boosting methods.

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