Journal of Applied Research and Technology

Controlling the Furuta pendulum: Proof of concept through virtual prototyping

L. R. C. Moura — 2024-06-28

Furuta pendulum is a mechanism with two rotating arms. One arm rotates in the horizontal plane, while the other rotates freely in the vertical plane. The arm rotating in the vertical plane acts as an inverted pendulum. Controlling the Furuta pendulum is challenging because the underlying mechanism is highly nonlinear, unstable, and underactuated. How to control the Furuta pendulum effectively motivates this study. The main contribution of this paper is to revisit a linear control strategy that seems to stabilize the Furuta pendulum. This paper revisits the Euler-Lagrange formulation and shows how to use this formulation to represent the Furuta pendulum's nonlinear dynamics. Data from simulating the Furuta pendulum through equations and virtual prototyping suggest the effectiveness of the linear control.

TEDDEEC: Threshold enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks

G. M. Tamilselvan — 2024-06-28

Wireless Sensor Networks (WSNs) comprise of vast number of arbitrarily conveyed energy required sensor nodes. Sensor nodes have capacity to sense and send detected information to Base Station (BS). Detecting and in addition transmitting information towards BS require more energy. In WSNs, sparing energy and developing network lifetime are awesome difficulties. Clustering is a key method used to enhance energy utilization in WSNs. Among two types of network; Homogeneous and Heterogeneous, the later has demonstrated to be significantly more essential in upgrading the network lifetime and making the network a great deal more energy adjusted with fitting probabilistic cluster head choice. In this paper, a novel clustering based routing protocol called Threshold Enhanced Developed Distributed Energy Eﬃcient Clustering scheme (TEDDEEC) for heterogeneous WSNs is proposed. This method depends on more efficient selection of Cluster Heads (CH) for the rounds. Simulation is carried out for different proportion of normal and advanced nodes. Simulation results demonstrate that this proposed scheme accomplishes longer lifetime, stability period and more eﬀective messages to BS than Enhanced Developed Distributed Energy Eﬃcient Clustering scheme (EDDEEC) in heterogeneous situations.

Real driving cycle based SoC and battery temperature prediction for electric vehicle using AI models

C. Nainika — 2024-06-28

The increase in electric vehicles has surpassed expectations leading to the eventual replacement of traditional IC (internal combustion) engine vehicles. However, to achieve this, it is crucial to research and develop more efficient and reliable electric batteries to create a sustainable transportation system. The performance of the battery directly impacts the power and range of the vehicle making battery management research imperative. Accurate estimation of battery state of charge (SoC) and temperature is vital for the overall performance, drivability and safety of the vehicle. This paper proposes a comprehensive approach to create an AI-based model to estimate the battery SoC and temperature that matches the performance of conventional vehicles. Various regression models are used as prediction models and the results are presented. These insights offer valuable understandings of battery thermal behavior, aiding in the design of an effective battery management system.

Improvement of an AC microgrid system’s stability by using a fuzzy logic-based PSS controller

A. Arora — 2024-06-28

This research article presents the low-frequency oscillation damping control of an AC Microgrid. It is integrated with four distributed generation units in which two DGs are renewable energy source based solar PV and wind energy using DFIG and the other two DGs are conventional synchronous generator-based hydro and diesel generators. There is insufficient generation through renewable-based DG then a diesel generator is employed as an emergency unit to fulfil the requirements and maintain and control the deviation of the frequency of the system. The use of renewable-based DG affected the system's dynamic stability and the characteristics of an integrated microgrid differ from conventional sources of generation. This investigation explores the impression of the deployment of multiple forms of the power oscillation damping (POD) controllers to distributed generation sources of grid forming AC Microgrid. The main various types of POD controllers are conventional lead-lag PSSs, MBPSS-4B, and proposed robust fuzzy logic PSS (FLPSS) controllers are implemented on distributed generation units of AC Microgrid for the elimination of low-frequency oscillation. Low oscillation frequencies between 0.1 and 2 Hz are the focus of this work. The system's damping ratios and stability margins because of MG integration are observed using eigenvalues analysis. The results of the simulation test demonstrate the accomplishment of the robust fuzzy logic PSS controller regarding conventional lead-lag PSSs and the MBPSS-4B controller during circumstances of fault.

A novel quadriphase ZCZ sequences for QS-CDMA systems

B. Fassi — 2024-06-28

In this article, a novel method for constructing quadriphase Zero Correlation Zone (ZCZ) sequence sets based on the mutually orthogonal complementary sets (MOCS) matrix and polyphase perfect sequence is presented. Based on the obtained MATLAB results in terms of correlation functions, it can be mentioned that the resultant ZCZ set is suitable for quasi-synchronous Code Division Multiple Access (QS-CDMA) systems. The obtained sequence sets are near-optimal concerning mathematical bound, and their construction is more flexible than other polyphase ZCZ constructions. Furthermore, their parameters can be chosen flexibly.

Development of a ChatBot model for health telecare: Integration of LangChain, embeddings with OpenAI, and Pinecone using the question answering technique

O. Cárdenas — 2024-06-28

This study was dedicated to the development of an advanced model for a medical ChatBot, aimed at optimizing Telecare in healthcare. Cutting-edge technologies such as LangChain, Embeddings with OpenAI, and Pinecone were incorporated, applying Question Answering techniques to enhance user-Bot interaction. Google Colab was essential, serving as the main environment for both running the code and the ChatBot, in addition to facilitating intensive data processing. The methodology involved a manual and rigorous data collection from official medical sources to ensure the accuracy and reliability of the information integrated into the ChatBot. The results showed a notable variability in response times between GPT-3.5-turbo and GPT-4, with a comprehensive cost evaluation to analyze the profitability of both models in specific medical contexts. In conclusion, the study provided significant insights into the implementation and efficacy of advanced medical ChatBot models, revealing crucial considerations about optimizing response times and cost evaluations in the development of innovative Telecare solutions in healthcare.

Core neutronic design of small modular molten salt reactor for submarine propulsion

M. H. Hasibuan — 2024-06-28

Molten salt reactor (MSR) shows great promise as a Generation IV nuclear reactor concept with high thermal-electric conversion efficiency, inherent safety features, and online reprocessing capability. During operation, the molten salt reactor MSR does not require a long refueling shutdown owing to online fuel reprocessing, making it suitable for watercraft propulsion. This paper presents the neutronic design of the 250 MWt iso-breeder MSR concept for submarine propulsion. The reactor is fueled with LiF-BeF2-ThF4-233UF4 salt with a virtual one-and-a-half fluid configuration. The MSR core is moderated with beryllium oxide and equipped with a high-density graphite reflector to improve neutronic. All calculations on the reactor core design were performed using MCNP6.2 code with ENDF/B-VII.0 neutron cross-section library. As a part of the neutronic analysis, the calculated parameters were the effective multiplication factor (), temperature coefficient of reactivity (TCR), and breeding ratio (BR). The value of ± 1.005 was obtained using Th : U ratio of 98.42% : 1.58%. The TCR value was obtained at -2.56 pcm/K, while the BR value was 1.057. These initial values indicate that the marine propulsion MSR can achieve iso-breeding with inherent safety characteristics. By using MSR design, it is hoped that the submarines will be able to reach significantly longer operational range than its Diesel-powered counterpart..

Electrochemical performance of YSZ coupled Ba_(0.5)Sr_(0.5)Fe_(0.9)Cu_(0.1)O_(3-δ) electrode at intermediate temperature

S. Setiyono — 2024-06-28

Abstract. Synthesis of electrolyte 8% Y₂O₃ Stabilized Zirconia (YSZ) and characterization of electrical conductivity with BSFC Ba_0.5Sr_0.5Fe_0.9Cu_0.1O_3-δ (BSFC) electrode have been conducted. YSZ and BSFC were synthesized using sol-gel self-combustion. YSZ powder was calcined at 700℃ for 2 hours, then pressed into a disk and sintered at 1550 ⁰C for 6 hours. While BSFC powder was calcined at 850 ℃ for 6 hours and sintered at 1100 ⁰C for 6 hours. X-ray diffraction (XRD) analysis shows that calcined powder of both YSZ and BSFC has cubic structure possessing space group (Fm-3m) and (Pm-3m), respectively. The lattice parameter of the YSZ a = 5.17 Å and the crystallite size Φ ≈ 11 nm were evaluated using the Debye-Scherer equation. A scanning electron microscope (SEM) shows that BSFC can adhere well to the surface of YSZ. The electrical behavior of the YSZ and half-cell BSFC||YSZ||BSFC measured at 150- 850 ⁰C shows the resistivity decreased with increasing temperature. The resistivity of BSFC||YSZ||BSFC is lower than YSZ due to the influence of BSFC. Cathode material BSFC is suitable for YSZ electrolytes.

Improving image quality via Deep learning: Aspects of speckle noise reduction applying the optimal weighted filter

L. S. Mezher — 2024-06-28

This article explores the dynamic field of image denoising, with a specific emphasis on the novel Adaptive Euclidean Weighted Filters (AEWF) and their utilization in mitigating speckle noise. The inadequacy of conventional filters to handle the intricacies of contemporary applications has prompted researchers to investigate the potential benefits of integrating deep learning and optimization algorithms. By combining Convolutional Neural Networks (CNNs) and the Bat Optimization Algorithm (BOA), we develop an adaptive dynamic AEWF that prioritizes the reduction of speckle noise while accommodating various image attributes. By emulating the echolocation behavior of bats, BOA optimizes denoising parameters to guarantee outputs of superior quality. Convolutional and ReLU layers of CNNs are crucial during the denoising phase, while pooling layers are utilized to reduce the size of the image. Our comprehensive investigation contrasts the suggested methodology with traditional filters, evaluating its efficacy in mitigating speckle noise. The benchmarking metrics, namely the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), have respective mean values of 32.05 and 0.98. Through an exhaustive evaluation of feature preservation, noise reduction, and overall image quality, this study underscores the integrated AEWF's effectiveness in tackling current obstacles in the field of image processing.

A comparison of the physicochemical properties of microalgae biodiesel with other oilseed feedstocks for sustainable energy production: A meta-analysis

D. Ariyanti — 2024-06-28

Biodiesel has emerged as an alternative diesel fuel, offering environmental advantages such as biodegradability, non-toxicity, and low emissions. This study conducted a meta-analysis to compare microalgae biodiesel with other oilseed feedstocks for sustainable energy production. The analysis involved compiling a database of published articles on biodiesel derived from microalgae and plant seeds. The results demonstrated that microalgae are one of the most promising feedstocks among various oilseeds. The cetane number and fuel density of biodiesel from microalgae were significantly higher than those from plant seeds (p<0.05). In conclusion, microalgae are a suitable feedstock for biodiesel production, surpassing traditional oil and crop sources, and can potentially mitigate the expansion of conventional agricultural practices. Moreover, microalgae can be cultivated year-round and in various climates, allowing for extended production seasons and site flexibility. Additionally, their utilization of waste streams as nutrient sources and non-requirement of arable land can contribute to reducing the environmental impact on other industries.

CT image quality and dose optimization by combining tube voltage, dose reduction setting and ASIR-V (explorative study using VCT phantom)

S. N. Dewi — 2024-06-28

Utilization of ASIR-V software to improve CT scan images with lower exposure factor settings has enormous potential to produce CT scan images with minimal dose and optimal image quality. Otherwise, the excessive use of iterative reconstruction may lead to poor image quality. The purpose of this study was to determine the differences in radiation dose and image quality in the combination of tube voltage, dose reduction and ASIR-V settings and giving recommendation out of it. This type of research is quasi-experimental with a pretest posttest design. VCT Quality Assurance phantom scanned with abdominal CT parameters with the combination of tube voltage variations and dose reduction settings then reconstructed using ASIR-V. Image quality data including noise, LCD and HCR obtained were analyzed by using the paired t-test statistical test. The lowest dose (2.01 mGy) resulted from the combination of tube voltage setting 80 kV, 60% dose reduction, and the dose reduction in the treatment group was in the range of 49% - 87%. The lowest noise was produced by a combination of tube voltage settings of 80 kV, 60% dose reduction, and 100% ASIR-V and noise reduction in the treatment group in the range of 15% -68%. There are significant differences in low contrast detectability of image obtained from 15 combination of tube voltage settings, and dose reduction setting after ASIR-V reconstruction on CT scans. Meanwhile, there are significant differences of extreme contrast spatial resolution in 13 combinations of the tube voltage and dose reduction setting after ASIR-V reconstruction showed. Decreasing the tube voltage and increasing the dose reduction value results in a lesser radiation dose accompanied by a decrease in image quality, while the application of ASIR-V is proven to improve the quality of CT scan images including noise and low contrast detectability, in the other hand the excessive use of ASIR-V may also decrease high contrast spatial resolution of the image. The combination of image quality that is closest to the image quality of the control group is a combination of tube voltage settings of 120 kV, 60% dose reduction and 60% ASIR-V which produces a radiation dose of 6.37 mGy.

Cross-modal learning representation using new margin combination for speech recognition task

D. Karim — 2024-06-28

Cross-modal retrieval aims to elucidate the fusion of information, mimic human learning, and advance the field. The main challenge in cross-modal matching is to build a shared subspace reflecting semantic proximity. Previous works fail to capture asymmetric relevance by adopting symmetric similarity computations. To overcome these shortcomings, an efficient approach called quaternion representation learning (QRL) is introduced for better cross-modal matching. Thus, a better representation of the shared semantics is offered by virtue of its richer representation capacity of the quaternionic space and its strong expressive power. Transfer learning is a crucial aspect in this context. By leveraging pre-trained models, the knowledge gained from one task or domain can be effectively transferred to another, allowing for improved performance and generalization. In this study, transfer learning is employed to enhance the cross-modal retrieval system. Specifically, a pre-trained ResNet-512 model is utilized in conjunction with the proposed total margin (TM) loss function, which combines the QRL approach with the novel adaptive mean margin (AMM) methodology. The TM loss function, coupled with the pre-trained ResNet-512 model, is evaluated on the Audio-Visual Arabic Speech Database (AVAS) and the Arabic Visual Speech Database (AVSD), along with other audio-visual datasets. Experimental results demonstrate the effectiveness of the TM loss function in consistently improving performance on both databases. The recall scores (R@k) and mean average precision (mAP) values achieved on the AVAS Database are as follows: R@1: 42.1±0.7, R@2: 70.2±0.1, R@5: 78.5±1.0, and mAP: 53.0±1.1. Similarly, on the AVSD Database, the results are R@1: 41.7±0.3, R@2: 69.2±1.1, R@5: 78.0±0.3, and mAP: 52.7±0.5. By incorporating transfer learning and the TM loss function into the cross-modal retrieval framework, this study demonstrates the potential for improving clustering efficiency and enhancing visual and speech understanding. The combination of pre-trained models and the TM loss function offers a promising avenue for advancing crossmodal matching techniques and achieving state-of-the-art performance.