Search results for: transformer oil

Authors: Zhiyan Meng, Dan Liu, Jintao Meng

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Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters.

Keywords: sharpness-aware optimization, encrypted traffic classification, squeeze-and-excitation block, pretrained model

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Authors: Toktam Khatibi

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Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

Procedia PDF Downloads 78

Authors: M. Ananthakrishnan, Sunil K Patil, Koti Naveen, Inuganti Hemanth Kumar

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State estimation output of EMS forms the base case for all other advanced applications used in real time by a power system operator. Ensuring tuning of state estimator is a repeated process and cannot be left once a good solution is obtained. This paper attempts to demonstrate methods to improve state estimator solution by identifying incorrect modelling and telemetry inputs to the application. In this work, identification of database topology modelling error by plotting static network using node-to-node connection details is demonstrated with examples. Analytical methods to identify wrong transmission parameters, incorrect limits and mistakes in pseudo load and generator modelling are explained with various cases observed. Further, methods used for active and reactive power tuning using bus summation display, reactive power absorption summary, and transformer tap correction are also described. In a large power system, verifying all network static data and modelling parameter on regular basis is difficult .The proposed tuning methods can be easily used by operators to quickly identify errors to obtain the best possible state estimation performance. This, in turn, can lead to improved decision-support capabilities, ultimately enhancing the safety and reliability of the power grid.

Keywords: active power tuning, database modelling, reactive power, state estimator

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Authors: Salah Al-Enezi, Rashed Al-Zufairi, Naseer Ahmad

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A Thermo-mechanical technique was developed to determine softening point temperature/glass transition temperature (Tg) of polystyrene exposed to high pressures. The design utilizes the ability of carbon dioxide to lower the glass transition temperature of polymers and acts as plasticizer. In this apparatus, the sorption of carbon dioxide to induce softening of polymers as a function of temperature/pressure is performed and the extent of softening is measured in three-point-flexural-bending mode. The polymer strip was placed in the cell in contact with the linear variable differential transformer (LVDT). CO₂ was pumped into the cell from a supply cylinder to reach high pressure. The results clearly showed that full softening point of the samples, accompanied by a large deformation on the polymer strip. The deflection curves are initially relatively flat and then undergo a dramatic increase as the temperature is elevated. It was found that increasing the pressure of CO₂ causes the temperature curves to shift from higher to lower by increment of about 45 K, over the pressure range of 0-120 bars. The obtained experimental Tg values were validated with the values reported in the literature. Finally, it is concluded that the defection model fits consistently to the generated experimental results, which attempts to describe in more detail how the central deflection of a thin polymer strip affected by the CO₂ diffusions in the polymeric samples.

Keywords: softening, high-pressure, polystyrene, CO₂ diffusions

Procedia PDF Downloads 127

Authors: Jalal M. Abdallah

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There is a great problem in testing and investigations the reliability of different type of transformers insulation materials. It summarized in how to create and simulate the real conditions of working transformer and testing its insulation materials for Partial Discharge PD, typically as in the working mode. A lot of tests may give untrue results as the physical behavior of the insulation material differs under tests from its working condition. In this work, the real working conditions were simulated, and a large number of specimens have been tested. The investigations first stage, begin with choosing samples of different types of insulation materials (papers, pressboards, etc.). The second stage, the samples were dried in ovens at 105 C⁰and 0.01bar for 48 hours, and then impregnated with dried and gasless oil (the water content less than 6 ppm.) at 105 C⁰and 0.01bar for 48 hours, after so specimen cooling at room pressure and temperature for 24 hours. The third stage is investigating PD for the samples using ICM PD measuring device. After that, a continuous test on oil-impregnated insulation materials (paper, pressboards) was developed, and the phase resolved partial discharge pattern of PD signals was measured. The important of this work in providing the industrial sector with trusted high accurate measuring results based on real simulated working conditions. All the PD patterns (results) associated with a discharge produced in well-controlled laboratory condition. They compared with other previous and other laboratory results. In addition, the influence of different temperatures condition on the partial discharge activities was studied.

Keywords: transformers, insulation materials, voids, partial discharge

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Authors: Atena Tazikeh Lemeski, Ismail Ozdamar

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This study presents a detailed analysis of harmonic distortion in a grid-connected photovoltaic (PV) system using real-time data captured from a solar power plant. Harmonics introduced by inverters in PV systems can degrade power quality and lead to increased Total Harmonic Distortion (THD), which poses challenges such as transformer overheating, increased power losses, and potential grid instability. This research addresses these issues by applying Fast Fourier Transform (FFT) to identify significant harmonic components and employing notch filters to target specific frequencies, particularly the 3rd harmonic (150 Hz), which was identified as the largest contributor to THD. Initial analysis of the unfiltered voltage signal revealed a THD of 21.15%, with prominent harmonic peaks at 150 Hz, 250 Hz and 350 Hz, corresponding to the 3rd, 5th, and 7th harmonics, respectively. After implementing the notch filters, the THD was reduced to 5.72%, demonstrating the effectiveness of this approach in mitigating harmonic distortion without affecting the fundamental frequency. This paper provides practical insights into the application of real-time filtering techniques in PV systems and their role in improving overall grid stability and power quality. The results indicate that targeted harmonic mitigation is crucial for the sustainable integration of renewable energy sources into modern electrical grids.

Keywords: grid-connected photovoltaic systems, fast Fourier transform, harmonic filtering, inverter-induced harmonics

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Authors: Zeenat Parveen, Ashiq Hussain

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This review paper consists of applications of PDMS (polydimethylsiloxane) materials for enhanced performance, optical fiber sensors in acousto-ultrasonic, mechanical measurements, current applications, sensing, measurements and interferometric optical fiber sensors. We will discuss the basic working principle of fiber optic sensing technology, various types of fiber optic and the PDMS as a coating material to increase the performance. Optical fiber sensing methods for detecting dynamic strain signals, including general sound and acoustic signals, high frequency signals i.e. ultrasonic/ultrasound, and other signals such as acoustic emission and impact induced dynamic strain. Optical fiber sensors have Industrial and civil engineering applications in mechanical measurements. Sometimes it requires different configurations and parameters of sensors. Optical fiber current sensors are based on Faraday Effect due to which we obtain better performance as compared to the conventional current transformer. Recent advancement and cost reduction has simulated interest in optical fiber sensing. Optical techniques are also implemented in material measurement. Fiber optic interferometers are used to sense various physical parameters including temperature, pressure and refractive index. There are four types of interferometers i.e. Fabry–perot, Mach-Zehnder, Michelson, and Sagnac. This paper also describes the future work of fiber optic sensors.

Keywords: fiber optic sensing, PDMS materials, acoustic, ultrasound, current sensor, mechanical measurements

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Authors: Aderonke Oluseun Akinwumi, Olusola A. Komolaf

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This paper gives information about the wide array of challenges facing both the electric utilities and consumers in the distribution system in developing countries, using Obafemi Awolowo University, Ile-Ife Nigeria as a case study. It also proffers cost-effective solution through remote monitoring, diagnostic and operation of distribution networks without compromising the system reliability. As utilities move from manned and unintelligent networks to completely unmanned smart grids, switching activities at substations and feeders will be managed and controlled remotely by dedicated systems hence this design. The Substation Remote Diagnostic and Operation Solution (sRDOs) would remotely monitor the load on Medium Voltage (MV) and Low Voltage (LV) feeders as well as distribution transformers and allow the utility disconnect non-paying customers with absolutely no extra resource deployment and without interrupting supply to paying customers. The aftermath of the implementation of this design improved the lifetime of key distribution infrastructure by automatically isolating feeders during overload conditions and more importantly erring consumers. This increased the ratio of revenue generated on electricity bills to total network load.

Keywords: electric utility, consumers, remote monitoring, diagnostic, system reliability, manned and unintelligent networks, unmanned smart grids, switching activities, medium voltage, low voltage, distribution transformer

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Authors: Khairul Zahreen Mohd Arof, Rahimah Muhamad

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Oil palm fiber (OPF) is a fibrous material produced from the waste of palm oil industry which is suitable to be used in construction industry. The applications of OPF in concrete can reduce the material costs and enhance concrete behavior. Dog-bone test provides significant results for investigating the behavior of fiber reinforced concrete under tensile loading. It is able to provide stress-strain profile, modulus of elasticity, stress at cracking point and total crack width. In this research, dog-bone tests have been conducted to analyze total crack width, stress-strain profile, and modulus of elasticity of OPFC. Specimens are in a dog-bone shape with a long notch in the middle as compared to the end, to ensure cracks occur only within the notch. Tests were instrumented using a universal testing machine Shimadzu 300kN, a linear variable differential transformer and two strain gauges. A total of nine specimens with different fibers at fiber volume fractions of 0.75%, 1.00%, and 1.25% have been tested to analyze the behavior under tensile loading. Also, three specimens of plain concrete fiber have been tested as control specimens. The tensile test of all specimens have been carried out for concrete age exceed 28 days. It shows that OPFC able to reduce total crack width. In addition, OPFC has higher cracking stress than plain concrete. The study shows plain concrete can be improved with the addition of OPF.

Keywords: cracks, crack width, dog-bone test, oil palm fiber concrete

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Authors: Himanshu J. Bahirat, Apoorva S. Janawlekar

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Plasma actuators are essential for aerodynamic flow separation control due to their lack of mechanical parts, lightweight, and high response frequency, which have numerous applications in hypersonic or supersonic aircraft. The working of these actuators is based on the formation of a low-temperature plasma between a pair of parallel electrodes by the application of a high-voltage AC signal across the electrodes, after which air molecules from the air surrounding the electrodes are ionized and accelerated through the electric field. The high-frequency operation is required in dielectric discharge barriers to ensure plasma stability. To carry out flow separation control in a hypersonic flow, the optimal design and construction of a power supply to generate dielectric barrier discharges is carried out in this paper. In this paper, it is aspired to construct a simplified circuit topology to emulate the dielectric barrier discharge and study its various frequency responses. The power supply can generate high voltage pulses up to 20kV at the repetitive frequency range of 20-50kHz with an input power of 500W. The power supply has been designed to be short circuit proof and can endure variable plasma load conditions. Its general outline is to charge a capacitor through a half-bridge converter and then later discharge it through a step-up transformer at a high frequency in order to generate high voltage pulses. After simulating the circuit, the PCB design and, eventually, lab tests are carried out to study its effectiveness in controlling flow separation.

Keywords: aircraft propulsion, dielectric barrier discharge, flow separation control, power source

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Authors: Amit Barde, Dipali Khatave, Vaishali Savale, Atharva Chavan, Sapna Wagaj, Aditya Jilla

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AgriInnoConnect Pro is an advanced agricultural automation system designed to enhance irrigation efficiency and overall farm management through IoT technology. Using MIT App Inventor, Telegram, Arduino IDE, and Firebase Console, it provides a user-friendly interface for farmers. Key hardware includes soil moisture sensors, DHT11 sensors, a 12V motor, a solenoid valve, a stepdown transformer, Smart Fencing, and AC switches. The system operates in automatic and manual modes. In automatic mode, the ESP32 microcontroller monitors soil moisture and autonomously controls irrigation to optimize water usage. In manual mode, users can control the irrigation motor via a mobile app. Telegram bots enable remote operation of the solenoid valve and electric fencing, enhancing farm security. Additionally, the system upgrades conventional devices to smart ones using AC switches, broadening automation capabilities. AgriInnoConnect Pro aims to improve farm productivity and resource management, addressing the critical need for sustainable water conservation and providing a comprehensive solution for modern farm management. The integration of smart technologies in AgriInnoConnect Pro ensures precision farming practices, promoting efficient resource allocation and sustainable agricultural development.

Keywords: agricultural automation, IoT, soil moisture sensor, ESP32, MIT app inventor, telegram bot, smart farming, remote control, firebase console

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Authors: Abdul Muqsit Abbasi, Ibrahim Chhipa, Asad Anwer, Saad Farooq, Hassan Berry, Sonu Kumar, Sundar Ali, Muhammad Owais Mahmood, Areeb Ur Rehman, Bahram Baloch

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Accurate translation between structurally diverse languages, such as Arabic and English, presents a critical challenge in natural language processing due to significant linguistic and cultural differences. This paper investigates the effectiveness of Facebook’s mBART model, fine-tuned specifically for sequence-tosequence (seq2seq) translation tasks between Arabic and English, and enhanced through advanced refinement techniques. Our approach leverages the Alef Dataset, a meticulously curated parallel corpus spanning various domains to capture the linguistic richness, nuances, and contextual accuracy essential for high-quality translation. We further refine the model’s output using advanced language models such as GPT-3.5 and GPT-4, which improve fluency, coherence, and correct grammatical errors in translated texts. The fine-tuned model demonstrates substantial improvements, achieving a BLEU score of 38.97, METEOR score of 58.11, and TER score of 56.33, surpassing widely used systems such as Google Translate. These results underscore the potential of mBART, combined with refinement strategies, to bridge the translation gap between Arabic and English, providing a reliable, context-aware machine translation solution that is robust across diverse linguistic contexts.

Keywords: natural language processing, machine translation, fine-tuning, Arabic-English translation, transformer models, seq2seq translation, translation evaluation metrics, cross-linguistic communication

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Patience Muchini, Electdom Matandiroya, Emmanuel Mashonjowa

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A ground-end manifestation of space weather phenomena is known as geomagnetically induced currents (GICs). GICs flow along the electric power transmission cables connecting the transformers and between the grounding points of power transformers during significant geomagnetic storms. Geomagnetically induced currents have been studied in other regions and have been noted to affect the power grid network. In Zimbabwe, grid failures have been experienced, but it is yet to be proven if these failures have been due to GICs. The purpose of this paper is to characterize geomagnetically induced currents with a power grid network. This paper analyses data collected, which is geomagnetic data, which includes the Kp index, DST index, and the G-Scale from geomagnetic storms and also analyses power grid data, which includes reactive power, relay tripping, and alarms from high voltage substations and then correlates the data. This research analysis was first theoretically analyzed by studying geomagnetic parameters and then experimented upon. To correlate, MATLAB was used as the basic software to analyze the data. Latitudes of the substations were also brought into scrutiny to note if they were an impact due to the location as low latitudes areas like most parts of Zimbabwe, there are less severe geomagnetic variations. Based on theoretical and graphical analysis, it has been proven that there is a slight relationship between power system failures and GICs. Further analyses can be done by implementing measuring instruments to measure any currents in the grounding of high-voltage transformers when geomagnetic storms occur. Mitigation measures can then be developed to minimize the susceptibility of the power network to GICs.

Keywords: adverse space weather, DST index, geomagnetically induced currents, KP index, reactive power

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Authors: Vaishakh Medikeri

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Energy in this Universe is the most basic characteristic of every particle. Since the birth of life on this planet, there has been a quest undertaken by the living beings to analyze, understand and harness the precious natural facts of the nature. In this quest, one of the greatest undertaken is the process of harnessing the naturally available energy. Scientists around the globe have discovered many ways to harness the freely available energy. But even today we speak of “Power Crisis”. Nikola Tesla once said “Nature has stored up in this universe infinite energy”. Energy is everywhere around us in unlimited quantities; all of it waiting to be harnessed by us. Here in this paper a method has been proposed to harness earth's electric field and transmit the stored electric energy using strong magnetic fields and electric fields. In this paper a new technique has been proposed to harness earth's electric field which is everywhere around the world in infinite quantities. Near the surface of the earth there is an electric field of about 120V/m. This electric field is used to charge a capacitor with high capacitance. Later the energy stored is allowed to pass through a device which converts the DC stored into AC. The AC so produced is then passed through a step down transformer to magnify the incoming current. Later the current passes through the RLC circuit. Later the current can be transmitted wirelessly using the principle of resonant inductive coupling. The proposed apparatus can be placed in most of the required places and any circuit tuned to the frequency of the transmitted current can receive the energy. The new source of renewable energy is of great importance if implemented since the apparatus is not costly and can be situated in most of the required places. And also the receiver which receives the transmitted energy is just an RLC circuit tuned to the resonant frequency of the transmitted energy. By using the proposed apparatus the energy losses can be reduced to a very large extent.

Keywords: capacitor, inductive resonant coupling, RLC circuit, transmission of electricity

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Mubarak Suliman, Mohamed Hassan

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Detection of downed conductors occurring with high fault resistance (reaching kilo-ohms) has always been a challenge, especially in countries like Saudi Arabia, on which earth resistivity is very high in general (reaching more than 1000 Ω-meter). The new approaches for the detection of resistive and high impedance faults are based on the analysis of the fault current waveform. These methods are still under research and development, and they are currently lacking security and dependability. The other approach is communication-based solutions which depends on voltage measurement at the end of overhead line branches and communicate the measured signals to substation feeder relay or a central control center. However, such a detection method is costly and depends on the availability of communication medium and infrastructure. The main objective of this research is to utilize the available standard protection schemes to increase the probability of detection of downed conductors occurring with a low magnitude of fault currents and at the same time avoiding unwanted tripping in healthy conditions and feeders. By specifying the operating region of the faulty feeder, use of tripping curve for discrimination between faulty and healthy feeders, and with proper selection of core balance current transformer (CBCT) and voltage transformers with fewer measurement errors, it is possible to set the pick-up of sensitive earth fault current to minimum values of few amps (i.e., Pick-up Settings = 3 A or 4 A, …) for the detection of earth faults with fault resistance more than (1 - 2 kΩ) for 13.8kV overhead network and more than (3-4) kΩ fault resistance in 33kV overhead network. By implementation of the outcomes of this study, the probability of detection of downed conductors is increased by the utilization of existing schemes (i.e., Directional Sensitive Earth Fault Protection).

Keywords: sensitive earth fault, zero sequence current, grounded system, resistive fault detection, healthy feeder

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Authors: Daniel O. Johnson, Abiodun A. Ogunseye, Aaron Aransiola, Majors Samuel

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Power Inverter is a very important device in renewable energy use particularly for solar photovoltaic power application because it is the effective interface between the DC power generator and the load or the grid. Transformerless inverter is getting more and more preferred to the power converter with galvanic isolation transformer and may eventually supplant it. Transformerless inverter offers advantages of improved DC to AC conversion and power delivery efficiency; and reduced system cost, weight and complexity. This work presents thorough analysis of the design and prototyping of 3KVA grid-tie transformerless inverter. The inverter employs electronic switching method with minimised heat generation in the system and operates based on the principle of pulse-width modulation (PWM). The design is such that it can take two inputs, one from PV arrays and the other from Battery Energy Storage BES and addresses the safety challenge of leakage current. The inverter system was designed around microcontroller system, modeled with Proteus® software for simulation and testing of the viability of the designed inverter circuit. The firmware governing the operation of the grid-tied inverter is written in C language and was developed using MicroC software by Mikroelectronica® for writing sine wave signal code for synchronization to the grid. The simulation results show that the designed inverter circuit performs excellently with very high efficiency, good quality sinusoidal output waveform, negligible harmonics and gives very stable performance under voltage variation from 36VDC to 60VDC input. The prototype confirmed the simulated results and was successfully synchronized with the utility supply. The comprehensive analyses of the circuit design, the prototype and explanation on overall performance will be presented.

Keywords: grid-tied inverter, leakage current, photovoltaic system, power electronic, transformerless inverter

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Authors: Sohal Latif

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Solar power is in the form of photovoltaic, also known as PV, which is a form of renewable energy that applies solar panels in producing electricity from the sun. It has a vital role in fulfilling the present need for clean and renewable energy to get rid of conventional and non-renewable energy sources that emit high levels of greenhouse gases. Solar energy is embraced because of its availability, easy accessibility, and effectiveness in the provision of power, chiefly in country areas. In solar charging, device charge entails a change of light power into electricity using photovoltaic or PV panels, which supply direct current electric power or DC. Here, the solar charge controller has a very crucial role to play regarding the voltages and the currents coming from the solar panels to take up the changing needs of a battery without overcharging the same. Certain devices, such as inverters, are required to transform the DC power produced by the solar panels into an AC to serve the normal electrical appliances and the current power network. This project was initiated for a project of a solar charge controller and power converter with the MULTISIM. The formation of this project begins with a literature survey to obtain basic knowledge about power converters, charge controllers, and photovoltaic systems. Fundamentals of the operation of solar panels include the process by which light is converted into electricity and a comparison of PWM and MPPT chargers with controllers. Knowledge of rectifiers is built to help achieve AC-to-DC and DC-AC change. Choosing a resistor, capacitance, MOSFET, and OP-AMP is done by the need of the system. The circuit diagrams of converters and charge controllers are designed using the Multisim program. Pulse width modulation, Bubba oscillator circuit, and inverter circuits are modeled and simulated. In the subsequent steps, the analysis of the simulation outcomes indicates the efficiency of the intended converter systems. The various outputs from the different configurations, with the transformer incorporated as well as without it, are then monitored for effective power conversion as well as power regulation.

Keywords: solar charge controller, MULTISIM, converter, inverter

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Authors: Mariem Saied, Jens Gustedt, Gilles Muller

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We present Dido, a source-to-source auto-generation and optimization framework for multi-dimensional stencil computations. It enables a large programmer community to easily and safely implement stencil codes on distributed-memory parallel architectures with Ordered Read-Write Locks (ORWL) as an execution and communication back-end. ORWL provides inter-task synchronization for data-oriented parallel and distributed computations. It has been proven to guarantee equity, liveness, and efficiency for a wide range of applications, particularly for iterative computations. Dido consists mainly of an implicitly parallel domain-specific language (DSL) implemented as a source-level transformer. It captures domain semantics at a high level of abstraction and generates parallel stencil code that leverages all ORWL features. The generated code is well-structured and lends itself to different possible optimizations. In this paper, we enhance Dido to handle both Jacobi and Gauss-Seidel grid traversals. We integrate temporal blocking to the Dido code generator in order to reduce the communication overhead and minimize data transfers. To increase data locality and improve intra-node data reuse, we coupled the code generation technique with the polyhedral parallelizer Pluto. The accuracy and portability of the generated code are guaranteed thanks to a parametrized solution. The combination of ORWL features, the code generation pattern and the suggested optimizations, make of Dido a powerful code generation framework for stencil computations in general, and for distributed-memory architectures in particular. We present a wide range of experiments over a number of stencil benchmarks.

Keywords: stencil computations, ordered read-write locks, domain-specific language, polyhedral model, experiments

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Authors: Rishabh Nagpal, Riya Uchagaonkar, Venkata Naga Narasimha Ashish Mernedi, Ahmed Hambaba

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The rapid evolution of technology in the communication sector continually seeks to bridge the gap between different communities, notably between the deaf community and the hearing world. This project develops a comprehensive American Sign Language (ASL) recognition system, leveraging the advanced capabilities of convolutional neural networks (CNNs) and vision transformers (ViTs) to interpret and translate ASL in real-time. The primary objective of this system is to provide an effective communication tool that enables seamless interaction through accurate sign language interpretation. The architecture of the proposed system integrates dual networks -VGG16 for precise spatial feature extraction and vision transformers for contextual understanding of the sign language gestures. The system processes live input, extracting critical features through these sophisticated neural network models, and combines them to enhance gesture recognition accuracy. This integration facilitates a robust understanding of ASL by capturing detailed nuances and broader gesture dynamics. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing diverse ASL signs, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced ASL recognition system and lays the groundwork for future innovations in assistive communication technologies.

Keywords: sign language, computer vision, vision transformer, VGG16, CNN

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Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Bartosz Kedra, Robert Malkowski

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This paper provides description and presentation of laboratory test unit built basing on 150 kVA power frequency converter and Simulink RealTime platform. Assumptions, based on criteria which load and generator types may be simulated using discussed device, are presented, as well as control algorithm structure. As laboratory setup contains transformer with thyristor controlled tap changer, a wider scope of setup capabilities is presented. Information about used communication interface, data maintenance, and storage solution as well as used Simulink real-time features is presented. List and description of all measurements are provided. Potential of laboratory setup modifications is evaluated. For purposes of Rapid Control Prototyping, a dedicated environment was used Simulink RealTime. Therefore, load model Functional Unit Controller is based on a PC computer with I/O cards and Simulink RealTime software. Simulink RealTime was used to create real-time applications directly from Simulink models. In the next step, applications were loaded on a target computer connected to physical devices that provided opportunity to perform Hardware in the Loop (HIL) tests, as well as the mentioned Rapid Control Prototyping process. With Simulink RealTime, Simulink models were extended with I/O cards driver blocks that made automatic generation of real-time applications and performing interactive or automated runs on a dedicated target computer equipped with a real-time kernel, multicore CPU, and I/O cards possible. Results of performed laboratory tests are presented. Different load configurations are described and experimental results are presented. This includes simulation of under frequency load shedding, frequency and voltage dependent characteristics of groups of load units, time characteristics of group of different load units in a chosen area and arbitrary active and reactive power regulation basing on defined schedule.

Keywords: MATLAB, power converter, Simulink Real-Time, thyristor-controlled tap changer

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Authors: Sang Wook Park, Jun Su Park, Byung Kwan Oh, Yousok Kim, Hyo Seon Park

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This study suggests the estimation method of stress distribution for the beam structures based on TLS (Terrestrial Laser Scanning). The main components of method are the creation of the lattices of raw data from TLS to satisfy the suitable condition and application of CSSI (Cubic Smoothing Spline Interpolation) for estimating stress distribution. Estimation of stress distribution for the structural member or the whole structure is one of the important factors for safety evaluation of the structure. Existing sensors which include ESG (Electric strain gauge) and LVDT (Linear Variable Differential Transformer) can be categorized as contact type sensor which should be installed on the structural members and also there are various limitations such as the need of separate space where the network cables are installed and the difficulty of access for sensor installation in real buildings. To overcome these problems inherent in the contact type sensors, TLS system of LiDAR (light detection and ranging), which can measure the displacement of a target in a long range without the influence of surrounding environment and also get the whole shape of the structure, has been applied to the field of structural health monitoring. The important characteristic of TLS measuring is a formation of point clouds which has many points including the local coordinate. Point clouds is not linear distribution but dispersed shape. Thus, to analyze point clouds, the interpolation is needed vitally. Through formation of averaged lattices and CSSI for the raw data, the method which can estimate the displacement of simple beam was developed. Also, the developed method can be extended to calculate the strain and finally applicable to estimate a stress distribution of a structural member. To verify the validity of the method, the loading test on a simple beam was conducted and TLS measured it. Through a comparison of the estimated stress and reference stress, the validity of the method is confirmed.

Keywords: structural healthcare monitoring, terrestrial laser scanning, estimation of stress distribution, coordinate transformation, cubic smoothing spline interpolation

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Authors: Sheelagh Heugh, Michael Upton, Kriya Kalidas, Stephen Breen

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This article aims to create awareness of the opportunities and issues the artificial intelligence (AI) tool GPT-3 (Generative Pre-trained Transformer-3) brings to higher education. Technological disruptors have featured in higher education (HE) since Konrad Klaus developed the first functional programmable automatic digital computer. The flurry of technological advances, such as personal computers, smartphones, the world wide web, search engines, and artificial intelligence (AI), have regularly caused disruption and discourse across the educational landscape around harnessing the change for the good. Accepting AI influences are inevitable; we took mixed methods through participatory action research and evaluation approach. Joining HE communities, reviewing the literature, and conducting our own research around Chat GPT-3, we reviewed our institutional approach to changing our current practices and developing policy linked to assessments and the use of Chat GPT-3. We review the impact of GPT-3, a high-powered natural language processing (NLP) system first seen in 2020 on HE. Historically HE has flexed and adapted with each technological advancement, and the latest debates for educationalists are focusing on the issues around this version of AI which creates natural human language text from prompts and other forms that can generate code and images. This paper explores how Chat GPT-3 affects the current educational landscape: we debate current views around plagiarism, research misconduct, and the credibility of assessment and determine the tool's value in developing skills for the workplace and enhancing critical analysis skills. These questions led us to review our institutional policy and explore the effects on our current assessments and the development of new assessments. Conclusions: After exploring the pros and cons of Chat GTP-3, it is evident that this form of AI cannot be un-invented. Technology needs to be harnessed for positive outcomes in higher education. We have observed that materials developed through AI and potential effects on our development of future assessments and teaching methods. Materials developed through Chat GPT-3 can still aid student learning but lead to redeveloping our institutional policy around plagiarism and academic integrity.

Keywords: artificial intelligence, Chat GPT-3, intellectual property, plagiarism, research misconduct

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Authors: Asli Tiktas, Huseyin Gunerhan, Arif Hepbasli

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Solar energy, with its abundant and clean features, is one of the prominent renewable energy sources in multigeneration energy systems where various outputs, especially power generation, are produced together. In the literature, concentrated solar energy systems, which are an expensive technology, are mostly used in solar power plants where medium-high capacity production outputs are achieved. In addition, although different methods have been developed and proposed for solar energy-supported integrated power generation systems by different investigators, absorption technology, which is one of the key points of the present study, has been used extensively in cooling systems in these studies. Unlike these common uses mentioned in the literature, this study designs a system in which a flat plate solar collector (FPSC), Rankine cycle, absorption heat transformer (AHT), and cooling systems (ACS) are integrated. The system proposed within the scope of this study aims to produce medium-high-capacity electricity, heating, and cooling outputs using a technique different from the literature, with lower production costs than existing systems. With the proposed integrated system design, the average production costs based on electricity, heating, and cooling load production for similar scale systems are 5-10% of the average production costs of 0.685 USD/kWh, 0.247 USD/kWh, and 0.342 USD/kWh. In the proposed integrated system design, this will be achieved by increasing the outlet temperature of the AHT and FPSC system first, expanding the high-temperature steam coming out of the absorber of the AHT system in the turbine up to the condenser temperature of the ACS system, and next directly integrating it into the evaporator of this system and then completing the AHT cycle. Through this proposed system, heating and cooling will be carried out by completing the AHT and ACS cycles, respectively, while power generation will be provided because of the expansion of the turbine. Using only a single generator in the production of these three outputs together, the costs of additional boilers and the need for a heat source are also saved. In order to demonstrate that the system proposed in this study offers a more optimum solution, the techno-economic parameters obtained based on energy, exergy, economic, and environmental analysis were compared with the parameters of similar scale systems in the literature. The design parameters of the proposed system were determined through a parametric optimization study to exceed the maximum efficiency and effectiveness and reduce the production cost rate values of the compared systems.

Keywords: solar energy, absorption technology, Rankine cycle, multigeneration energy system

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Authors: Giovanni Duran Polo

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Demonstrate the importance of the stages such as Information, Diagnosis, Management, and Intervention is fundamental to have a historical, live, and quality inhabited center. One of the major actions to take is to promote the concept of the management of a historic center with harmonious development. For that, concerned actors should strengthen the concept that said historic center may be the neighborhood of all and for all. The centers of historical cities, presented as any other urban area, social, environmental issues etc; yet they get added value that have no other city neighborhoods. The equity component, either by the urban plan, or environmental quality offered properties of architectural, landscape or some land uses are the differentiating element, while the tool that makes them attractive face pressure exerted by new housing developments or shopping centers. That's why through the experience of working in historical centers, they are declared the actions in heritage areas. This paper will show how the encounter with each of these places are trying to take the phases of information, to gather all the data needed to be closer to the territory with specific data, diagnosis; which allowed the actors to see what state they were, felt how the heart is related to the rest of the city, show what problems affected the situation and what potential it had to compete in a global market. Also, to discuss the importance of the organization, as it is legal and normative basis for it have an order and a concept, when you know what can and what cannot, in an area where the citizen has many myth or history, when he wanted to intervene in protected buildings. It is also appropriate to show how it could develop the intervention phase, where the shares on the tangible elements and intervention for the protection of the heritage property are executed. The management is the final phase which will carry out all that was raised on paper, it's time to orient, explain, persuade, promote, and encourage citizens to take care of the heritage. It is profitable and also an obligation and it is not an insurmountable burden. It has to be said this is the time to pull all the cards to make the historical center and heritage becoming more alive today. It is the moment to make it more inhabited and to transformer it into a quality place, so citizens will cherish and understand the importance of such a place. Inhabited historical centers, endowments and equipment required, with trade quality, with constant cultural offer, with well-preserved buildings and tidy, modern and safe public spaces are always attractive for tourism, but first of all, the place should be conceived for citizens, otherwise everything will be doomed to failure.

Keywords: development, diagnosis, heritage historic center, intervention, management, patrimony

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Authors: Nkwachukwu Chukwuchekwa, Joy Ulumma Chukwuchekwa

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Magnetic characteristics of grain-oriented electrical steel (GOES) are usually measured at high flux densities suitable for its typical applications in power transformers. There are limited magnetic data at low flux densities which are relevant for the characterization of GOES for applications in metering instrument transformers and low frequency magnetic shielding in magnetic resonance imaging medical scanners. Magnetic properties such as coercivity, B-H loop, AC relative permeability and specific power loss of conventional grain oriented (CGO) and high permeability grain oriented (HGO) electrical steels were measured and compared at high and low flux densities at power magnetising frequency. 40 strips comprising 20 CGO and 20 HGO, 305 mm x 30 mm x 0.27 mm from a supplier were tested. The HGO and CGO strips had average grain sizes of 9 mm and 4 mm respectively. Each strip was singly magnetised under sinusoidal peak flux density from 8.0 mT to 1.5 T at a magnetising frequency of 50 Hz. The novel single sheet tester comprises a personal computer in which LabVIEW version 8.5 from National Instruments (NI) was installed, a NI 4461 data acquisition (DAQ) card, an impedance matching transformer, to match the 600  minimum load impedance of the DAQ card with the 5 to 20  low impedance of the magnetising circuit, and a 4.7 Ω shunt resistor. A double vertical yoke made of GOES which is 290 mm long and 32 mm wide is used. A 500-turn secondary winding, about 80 mm in length, was wound around a plastic former, 270 mm x 40 mm, housing the sample, while a 100-turn primary winding, covering the entire length of the plastic former was wound over the secondary winding. A standard Epstein strip to be tested is placed between the yokes. The magnetising voltage was generated by the LabVIEW program through a voltage output from the DAQ card. The voltage drop across the shunt resistor and the secondary voltage were acquired by the card for calculation of magnetic field strength and flux density respectively. A feedback control system implemented in LabVIEW was used to control the flux density and to make the induced secondary voltage waveforms sinusoidal to have repeatable and comparable measurements. The low noise NI4461 card with 24 bit resolution and a sampling rate of 204.8 KHz and 92 KHz bandwidth were chosen to take the measurements to minimize the influence of thermal noise. In order to reduce environmental noise, the yokes, sample and search coil carrier were placed in a noise shielding chamber. HGO was found to have better magnetic properties at both high and low magnetisation regimes. This is because of the higher grain size of HGO and higher grain-grain misorientation of CGO. HGO is better CGO in both low and high magnetic field applications.

Keywords: flux density, electrical steel, LabVIEW, magnetization

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Authors: Rowan P. Martnishn

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During the execution of a National Endowment of the Arts grant, approximately 55 interviews were collected from professionals across various fields. These interviews were used to create deliverables – historical connections for creations that began as art and evolved entirely into computing technology. With dozens of hours’ worth of transcripts to be analyzed by qualitative coders, a quantitative methodology was created to sift through the documents. The initial step was to both clean and format all the data. First, a basic spelling and grammar check was applied, as well as a Python script for normalized formatting which used an open-source grammatical formatter to make the data as coherent as possible. 10 documents were randomly selected to manually review, where words often incorrectly translated during the transcription were recorded and replaced throughout all other documents. Then, to remove all banter and side comments, the transcripts were spliced into paragraphs (separated by change in speaker) and all paragraphs with less than 300 characters were removed. Secondly, a keyword extractor, a form of natural language processing where significant words in a document are selected, was run on each paragraph for all interviews. Every proper noun was put into a data structure corresponding to that respective interview. From there, a Bidirectional and Auto-Regressive Transformer (B.A.R.T.) summary model was then applied to each paragraph that included any of the proper nouns selected from the interview. At this stage the information to review had been sent from about 60 hours’ worth of data to 20. The data was further processed through light, manual observation – any summaries which proved to fit the criteria of the proposed deliverable were selected, as well their locations within the document. This narrowed that data down to about 5 hours’ worth of processing. The qualitative researchers were then able to find 8 more connections in addition to our previous 4, exceeding our minimum quota of 3 to satisfy the grant. Major findings of the study and subsequent curation of this methodology raised a conceptual finding crucial to working with qualitative data of this magnitude. In the use of artificial intelligence there is a general trade off in a model between breadth of knowledge and specificity. If the model has too much knowledge, the user risks leaving out important data (too general). If the tool is too specific, it has not seen enough data to be useful. Thus, this methodology proposes a solution to this tradeoff. The data is never altered outside of grammatical and spelling checks. Instead, the important information is marked, creating an indicator of where the significant data is without compromising the purity of it. Secondly, the data is chunked into smaller paragraphs, giving specificity, and then cross-referenced with the keywords (allowing generalization over the whole document). This way, no data is harmed, and qualitative experts can go over the raw data instead of using highly manipulated results. Given the success in deliverable creation as well as the circumvention of this tradeoff, this methodology should stand as a model for synthesizing qualitative data while maintaining its original form.

Keywords: B.A.R.T.model, keyword extractor, natural language processing, qualitative coding

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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