TELKOMNIKA (Telecommunication Computing Electronics and Control)
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Design and analysis of a new scheme of the FOSTA for DFIG based wind turbine
Full text linkAn super-twisting algorithm (STA)-based controller was designed and implemented in this study to achieve precise control over the stator active and reactive power of a doubly fed induction generator (DFIG)-equipped wind turbine device. The fractional calculus theory (FCT) allowed the STA to maximize its effectiveness and performance. A distinct form is sent to the FCT-based STA controller. The stator flux orientation technique uses control that is independent of stator active and reactive powers. In order to achieve a quick system with sufficient precision and a robust control strategy, the hybrid method control is based on the fractional-order super twisting algorithm (FOSTA) and FCT. To demonstrate the performance, efficacy, and resilience of the stated nonlinear approach, a number of simulations are provided
Advanced crop yield prediction using machine learning and deep learning: a comprehensive review
Full text linkThe advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present
A dual-band rectangular shape incorporated into circular patch antenna for 2.4/5 GHz wireless local area network applications
Full text linkThis research exhibits a design of a dual-band patch antenna (DBPA) implemented by a rectangular shape incorporated into a circular patch together with a pair of half-wavelength inverted U-slots (HWIUSs) with the single feed for 2.4/5 GHz wireless local area network (WLAN) applications. For this work, the HWIUSs are key in designing a dual-band antenna. The DBPA is fed by a 50-Ohm microstrip line, printed on a copper layer overlaid on an FR4 substrate with a relative permittivity of 4.3 and height of 1.6 mm, while the bottom layer is backed by a partial ground plane. An antenna prototype with a dimension of 0.384λ_L×0.304λ_L×0.013λ_L was contrived and admeasured to verify the simulation. The measurement provides a nearly omnidirectional pattern with a 2.55 and 3.3 dBi peak gain covering a dual-band 10 dB return loss bandwidth of 15% (2.4–2.8 GHz) and 20% (4.96–5.86 GHz), respectively. Noticeably, simulated |S11| and radiation patterns are reasonable following experimental results showing its potential in 2.4/5 GHz WLAN services
Comparison of word embedding features using deep learning in sentiment analysis
Full text linkIn this research, we use several deep learning methods with the word embedding feature to see their effect on increasing the evaluation value of classification performance from processing sentiment analysis data. The deep learning methods used are conditional random field (CRF), bidirectional long short term memory (BLSTM) and convolutional neural network (CNN). Our test uses social media data from Netflix application user comments. Through experimentation on different iterations of various deep learning techniques alongside multiple word embedding characteristics, the BLSTM algorithm achieved the most notable accuracy rate of 79.5% prior to integrating word embedding features. On the other hand, the highest accuracy value results when using the word embedding feature can be seen in the BLSTM algorithm which uses the word to vector (Word2Vec) feature with a value of 87.1%. Meanwhile, a very significant change in value increase was obtained from the FastText feature in the CNN algorithm. After all the evaluation processes were carried out, the best classification evaluation results were obtained, namely the BLSTM algorithm with stable values on all word embedding features
Design of the automation system for the chemical water treatment plant of the oil refinery in Santiago de Cuba
Full text linkProduction processes in modern industry demand higher levels of quality and efficiency in their products. The “Hermanos Díaz” Oil Refinery Company of Santiago de Cuba, a fundamental pillar in the economic and social development of the eastern part of the country, has a chemical water treatment plant responsible for supplying processed water to the industry’s boilers. The current state of this plant supports the lack of optimal physical-chemical conditions in the water it delivers and, therefore, the gradual deterioration of the boilers. This work conceives an automation solution for the dosing, precipitation, and clarification processes of the chemical water treatment plant. Control systems were designed based on instrumentation proposals, enabling reliable measurements and practical actions. In addition, an algorithm of supervision and automatic control using a programmable programmable logic controller (PLC) is presented, making the plant capable of delivering a product in optimal conditions. Images were designed for local and remote process control using a human-machine interface (HMI) panel and a supervisory control and data acquisition (SCADA) system. Finally, an automation architecture with a decentralized periphery is proposed to ensure safety and accuracy in the system’s decision-making through communication protocols
Homogeneous transformation matrix for force-torque sensor orientation compensation in rotatable control handle
Full text linkThe high inertia ceiling suspended systems with multiple degrees of freedom uses power assist technologies to reduce operator’s burden to operate the machine. Such systems are popularly used in medical diagnostic systems, construction machines, material handling, automotive, and aerospace assembly lines. These systems commonly use multi-axis force-torque (FT) sensor to sense the forces applied by user on rotatable control handle. These sensed forces are utilized by power assist algorithm to drive system in required direction with the help of electrical motor drives. The rotatable control handle used to control the machine poses a significant obstacle for maintaining alignment between FT sensor co-ordinate frame and the system’s base frame. This research paper focuses on the development of homogeneous transformation matrix to compensate for any change in FT sensor orientation caused by rotation of control handle. The homogeneous transformation matrix developed in this research paper, transforms the force and torque values measured by FT sensor with respect to system base frame. This adaptive technique provided seamless control of the power assist ceiling suspended system from different directions during handling and movement. This helped to enhance control and flexibility of power assist ceiling suspended system
Developing a robust data integrity verification framework for cloud storage utilizing Boneh-Lynn-Shacham signatures
Full text linkCloud computing (CC) is a service that allows both data storage and data accessibility on the cloud through any on-demand service and application, without incurring the costs of local data storage and upkeep expenses. On the other hand, CC providers have to make sure good security among the consumers to inspire them to use the resources on the cloud as if it were their local storage, without bothering about the data quality. Therefore, data checking in the cloud gains much importance mostly at the demand of the customers for the data integrity that a third-party auditor (TPA) can be introduced to verify the data soundness. A present investigation introduced a secure and efficient auditing solution for cloud data under the Boneh-Lynn-Shacham (BLS) public identities to keep the information secure and at the same time perform auditing securely. this study restructured the public key equation with the signature equation in our proposed system so that there is an increment in complexity while quality execution speed is maintained, and the proposed system allows dynamic data processing in terms of insert, delete and modification. From the analysis of the system, which includes security and performance, the proposed system is very effective and safe
A novel-shaped THz MIMO antenna with high bandwidth for advanced 6G wireless application
Full text linkThis article presents an industrial and innovation highly efficient drone shaped slotted graphene-based multiple input multiple output (MIMO) antenna with improved isolation, designed for high-speed short-range communication, video rate imaging, medical imaging, and explosive detection in the THz band. The proposed antenna is constructed on an 88×244 μm2 polyimide substrate. Key performance parameters such as reflection coefficient, gain, directivity, radiation pattern, and antenna efficiency are evaluated at the resonating frequencies of 1.7 THz, 3.35 THz, and 5.31 THz, covering a wide bandwidth of 4.88 THz with a reflection coefficient of less than -10 dB. The antenna achieves a maximum gain of 13.92 dB and a radiation efficiency of 95.77% within the resonating band. The MIMO design parameters include an envelope correlation coefficient (ECC) of 0.00015, a diversity gain (DG) of 9.9992, and an isolation of less than -31.55 dB between its elements across the entire bandwidth. The outcomes from CST simulations were verified by designing and simulating a similar resistance-inductance-capacitance (RLC) circuit in advanced design system (ADS), with both simulators producing comparable reflection coefficients. These features underscore the potential of the proposed antenna, utilizing simulations and an equivalent RLC circuit model, as a robust candidate for THz band applications in 6G wireless communication
Optimized human detection in NLOS scenarios using hybrid dimensionality reduction and SVM with UWB signals
Full text linkTrapped victim localization in search and rescue (SAR) operations is especially difficult in non-line-of-sight (NLOS) conditions, where traditional techniques fail due to debris and signal distortion. Ultra-wideband (UWB) NLOS signal datasets offer a promising alternative but are often high-dimensional and noisy. This study proposes an optimized dimensionality reduction framework combining an adaptive human presence detector (AHPD) with genetic algorithms (GA) and independent component analysis (ICA), followed by support vector machine (SVM) classification. The approach is tested on a public NLOS dataset comprising 23,522 dynamic instances, each with 256 signal samples per attribute, simulating complex SAR scenarios including rubble and dynamic obstacles. The results indicate that the AHPD+GA+SVM model reached an accuracy of 85.78%, sensitivity of 80.00%, and specificity of 96.46%, which is better than the AHPD+ICA +SVM model that had an accuracy of 79.20%, sensitivity of 73.07%, and specificity of 81.05%. These findings demonstrate the framework’s robustness and scalability, making it a strong candidate for real-time human detection in disaster recovery missions
Prediction of land suitability for food crop types using classification algorithms
Full text linkDecision-making in the selection of crop types is often conducted using conventional approaches. It is relying on limited experience and knowledge without considering the latest data or information. This approach has the loss of opportunities to use crop types. The crop types are more suited to environmental conditions and market demand, and it inhibits the application of innovation in agriculture. Therefore, the use of information technology becomes crucial to enhance accuracy in determining land suitability and crop selection. This study recommends the Random Forest algorithms and AdaBoost due to their excellent performance across all metrics (AUC, CA, F1, Precision, Recall) on various dataset sizes with scores above 0.9, so it is the solution to predict land suitability for specific crop types. Furthermore, it enables farmers to maximize land potential and achieve optimal yields