Proceedings of the International Conference on Advanced Technologies
NEW SUBMISSION The Proceedings of the International Conference on Advanced Technologies" is a journal that will publish all the proceedings of the ICAT conferences. The main objective of the ICAT conferences is to provide a platform for scientists working in the fields of Computer Science, Electrical and Electronics, Energy Technologies, Manufacturing Technologies, Mechatronics, and Biomedical Technologies to present the latest research and results, exchange new ideas and application experiences, establish business or research relations, and find global partners for future collaboration. All paper submissions to "The Proceedings of the International Conference on Advanced Technologies" will undergo a double-blind peer-review process and will be evaluated based on originality, technical and/or research content/depth, correctness, relevance to the conference, contributions, and readability. The Proceedings of the International Conference on Advanced Technologies will be a valuable resource for researchers, scholars, and practitioners interested in the latest advances in these fields. ISSN : 2980-3853 DOI Prefix: 10.58190/icat
Deep Learning-Based Classification of Black Gram Plant Leaf Diseases: A Comparative Study
The escalating incidence of plant diseases presents considerable obstacles to the agricultural domain, resulting in substantial reductions in crop yield and posing a threat to food security. To address the pressing concern of Black Gram Plant Leaf Diseases (BPLD), this research endeavors to tackle disease classification through the application of a deep learning methodology. The approach leverages a comprehensive dataset that encompasses Anthracnose, Leaf Crinkle, Powdery Mildew, and Yellow Mosaic diseases, all of which affect the black gram crop. By employing this advanced technique, we aim to contribute valuable insights to combat BPLD effectively. Our research applies deep learning models, including Darknet-53, ResNet-101, GoogLeNet, and EfficientNet-B0, to classify plant diseases. Darknet-53 achieved 98.51% accuracy, followed by ResNet-101 (97.51%), GoogLeNet (96.52%), and EfficientNet-B0 (77.61%). These findings demonstrate the potential of deep learning for accurate disease identification, benefiting agriculture. The study provides a comparative analysis of deep learning models for Black Gram Plant Leaf Disease (BPLD) classification, revealing Darknet-53 and ResNet-101 as superior performers. Implementing these models in real-world agricultural scenarios holds promise for early disease detection and intervention, reducing potential crop losses. The high accuracy achieved signifies significant progress in automating disease recognition, benefiting the agricultural sector.
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Design and optimisation of tubular linear motor (TLM) for oxygen concentrator device
Patients with chronic respiratory conditions such as Chronic Obstructive Pulmonary Disease (COPD) receive long- term oxygen therapy (USOT) to sustain their lives [1],[2]. With the development of oxygen concentrator (OC) devices that can produce the concentrated oxygen required for USOT, COPD patients are required to use these devices for more than 12 hours daily depending on the prescription [3],[4]. OC are medical devices that separate oxygen from the atmosphere using physical means to produce concentrated gas for medical purposes [5],[6]. The use of conventional motors based on the permanent magnetic rotary motor operating principle in OC devices increases the mass of the device and the operating noise disturbs the patients [3],[4],[7]. In this study, with the advances in magnet material, a tubular linear motor (TLM) structure with a strong, fixed coil moving permanent magnet, which is stronger than the linear motor used in many fields such as medical electronics, nanotechnology, defence industry, maglev trains, is designed and proposed for use in OK devices. It is difficult to optimise the TLM due to multiple design parameters and each parameter has a non- linear relationship with the static electromagnetic force. In this study, the thrust of the TLM is optimised by the finite element method (FEM) using the magnetic magnetostatic and transient solvers in Ansys Maxwell3D. Optimisation method based on FEM 3D model was used to optimise the design parameters. Comparing the pre- and post-optimisation of the TLM designed for use in the OC device, the thrust force was increased from 567.91 fN to 5.82 nN at the same working stroke distance.
The Effectiveness of Deep Learning Methods on Groundnut Disease Detection
Early detection of plant diseases in the agricultural sector is considered an important goal to increase productivity and minimize damage. This study deals with the use of deep learning methods to realize the automatic detection of leaf diseases in peanut plants and the explicability of the model with heatmap visualizations formed during the detection of diseases. In the study, a dataset containing 3058 images with 5 classes enriched with diseased and healthy samples of peanut leaves was used. The explainability property has also been studied to understand why the models detect a particular disease. The decision processes of deep learning models, which are usually described as the "magic box", were visualized with the heatmap method in this study. By highlighting the pixels that are effective in detecting diseased leaves with heatmap visualization, the decision-making process of the model has been tried to be made understandable. The results show that deep learning models have high performance in detecting peanut leaf diseases, and the explainability obtained by heatmap visualization is a reliable tool for agricultural specialists and producers. Thanks to the visual explanations provided by the model, the level of confidence in the detection of diseases has been increased and confidence in the decision processes of the model has been provided. This study constitutes an important step towards increasing efficiency in agricultural applications and providing a more efficient approach to disease management by investigating the impact and explicability of deep learning methods in the field of disease detection in peanut plants.
Detection of Fungal Infections from Microscopic Fungal Images Using Deep Learning Techniques
Fungal infections, due to their diverse manifestations and varying characteristics, present significant challenges in medical diagnosis. This study delves into applying deep-learning techniques for detecting fungal infections from microscopic fungal images. By harnessing the power of Convolutional Neural Networks (CNNs), we propose an approach that employs transfer learning to accurately classify different fungal species. The dataset comprises microscopic images of various fungal types, and to enhance model performance, we utilize data augmentation techniques. Furthermore, we aim to boost performance by fine-tuning the model's layers. Initially starting at 84.38% accuracy, our experimental results progressively reached high values of 95.35% and 97.19%. These results underscore the effectiveness of our deep learning approach in precisely identifying and classifying fungal infections. This success holds promising potential to aid medical professionals in timely and accurate diagnoses. The findings presented in this study contribute to ongoing research in medical image analysis and drive advancements in the field of automated disease detection.
Prediction of Sleep Health Status, Visualization and Analysis of Data
Sleep, as an indispensable element of human life, is accepted as one of the main sources of health, vitality and productivity. There are many factors that affect sleep health. Stress level, irregularity of sleep patterns and excessive use of technological devices can be given as examples. Sleep health can be determined by analyzing various variables about sleep. Sleep health can be determined by using these variables with machine learning methods. For this purpose, a dataset containing 374 rows of data and 13 features was used in this study. Sleep disorder conditions can be classified as None, Sleep Apnea, and Insomnia using 12 features. Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR) and k Nearest Neighbor (kNN) methods were used for classification. Classification success was 91.66% from the RF model, 90.27% from the SVM model, 90.27% from the LR model and 87.50% from the kNN model. In order to analyze which feature is more effective in classification processes, box plot and correlation analysis methods were used. As a result of the analyzes, it was determined that the body mass index has the greatest effect on the determination of sleep disorder.
A System Architecture Based on The RNN Classifier for Heart Disease Detection
Diagnosing heart disease is a challenging process for physicians. Insufficient number of experts, late diagnosis and misdiagnosis are the difficulties in this process. To overcome these difficulties, systems based on artificial intelligence are used today. Appropriate system selection and obtaining sufficient data sets are a challenge for researchers. In this study, a high-performance CAD architecture was proposed for the detection of heart disease. The proposed architecture has shown a higher performance than the studies carried out using the UCI dataset in the literature
Detection of Credit Card Fraud with Artificial Neural Networks
Along with the Internet, digital technologies are frequently used in every moment of our lives. Many transactions that we carry out in monetary terms such as shopping in our daily life are now done digitally. With the developing digitalization in the world, people's lives become easier and people can access different products in a short time. In particular, people can spend and shop quickly and easily without carrying cash in their pockets with a credit card. However, with the increase in the use of credit cards, there are also some security vulnerabilities. Fraudsters can gain unfair advantage by obtaining certain credit card information such as passwords. They can shop with someone else's credit card without permission. These transactions cause substantial financial damage to individuals and institutions. With the increase in the use of credit cards with the developing technology, such credit card fraud is also increasing rapidly. Taking precautions against credit card fraud is a very important issue in order to ensure the safety of people. For this reason, in order to ensure the security of both banks and financial institutions that provide credit card services, it is necessary to prevent credit card fraud and to detect fraud that may occur in credit cards within the scope of combating fraud. In our study, Artificial Neural Networks were used to detect credit card fraud transactions. A prediction model has been developed to detect fraud in credit card transactions with ANN. Using the Credit Card data set obtained from the Kaggle database, modeling was done with the Feed Forward Artificial Neural Network method. The aim of this study is to automatically detect abnormal behaviors made with credit cards. 98.44% success was achieved with feedforward artificial neural network.
Smart Waste Management System: A Novel Approach to Waste Collection in Twenty-First Century Smart City
It has been observed that domestic household bins are still being manually collected by the municipality. This old method of trash removal has flaws. It is labour-intensive. In this paper, we design and implement a novel innovative domestic waste management system. To achieve this aim, specific objectives had to be achieved. These were to design and implement a motor driver controller (MDC), obstacle detection system (ODS), email notification system, trash status monitoring, internet time-based trigger (ITT), and finally, integrating all the systems together. The project was divided into two phases: the design phase and the integration phase. The finished prototype was tested and demonstrated to function according to the design specifications. When the bin is empty, the system remains at the origin. Only when the bin is full that the system moves to the disposal point. When an obstacle is detected, it stops and sends a push notification via email to the user. Once the obstacle is removed, the system continues its path until it reaches its destination. The design objectives were achieved.
Detection of Tuberculosis Disease with Convolutional Neural Networks
Tuberculosis is an infectious disease caused by bacteria called Mycobacterium tuberculosis. Tuberculosis is still an important public health problem worldwide and is common especially in developing countries. This respiratory disease can cause serious symptoms, especially affecting the lungs. Symptoms of tuberculosis include prolonged coughing, shortness of breath, chest pain, weakness, fever, night sweats, and malaise. The diagnosis of the disease is made by clinical signs as well as biomedical imaging methods and laboratory tests. These imaging modalities include techniques such as x-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Early diagnosis of tuberculosis disease is of great importance in terms of treatment and prevention of the spread of the disease. The use of deep learning methods to classify biomedical images of tuberculosis disease can accelerate the diagnosis process, increase accuracy and guide treatment more effectively. In this study, it aims to be an important step in the classification of tuberculosis disease with deep learning. The generated CNN network, parameter values, layers used, complexity matrices obtained for verification data, accuracy and loss graphs are shown in detail. In our study, the success rate was increased by using a different network structure than the neural networks used in the literature. Approximately 98% success was achieved with the proposed CNN model.
Defining The Decisive Factors on Purchase and Comparing Feature Importance Methods
Online retail companies focus on two activities for getting revenue in their businesses and to survive in the market. First activity is increasing traffic of the online shopping platform and second activity is converting this traffic to revenue for the company. Marketing facilities try to attract customers to the online shopping platforms at great costs. Because of the costs of getting traffic, it is crucial to make customers order. Online shopping platforms need to understand which factors are decisive on customer purchase decision. In this study, which factors are decisive on consumer purchase decisions will be studied on an e-commerce retail platform from Turkey, Hepsiburada. Which of these factors are most decisive try to be defined: traffic source type (google, campaign, direct etc.) of the customer, customer persona or segment, which types of page or page components has been seen, product position on the page, does the customer benefited from campaign or discount, product review scores and counts, has the product recommended or not. In this study, data will be gathered from Hepsiburada transactions stored in google's big query environment. Performance problems will be solved via SQL optimization and other methods. Data quality issues will be fixed to get consistent results. Then statistical methods, supervised machine learning and deep learning methods will be applied to data for getting feature importances. Importance value of the features will show which factor decisive on customer purchase decision. Feature importance values will be compared and evaluated according to method, model results. Hyperparameter tunings is applied to the methods. Also, the model performances will be compared and evaluated. This study uses and compares 7 methods and there is no comprehensive study in literature in terms of method variety.
MPPT String Arrangement and Production Improvements in String Inverters
Today, as the demand for electrical energy increases, the demand for renewable energy, which is a clean energy source, continues to increase. Solar inverters used in solar power plants are devices that convert DC power to AC. Solar inverters are divided into three groups as central inverter, micro inverter and string inverter. String inverter model is used in this study. It has been determined that as a result of incorrectly connecting the string cables during installation and workmanship in the string inverter, the solar inverter does not work at full capacity and full efficiency cannot be obtained at its output. A problem experienced during the connection and installation phase causes inefficiency in the system and inability to get full efficiency from the inverter. For this reason, in this study, MPPT string cable arrangement, which is connected incorrectly in a string inverter, has been carried out and the production has been increased to 96.72 kW.
Utilizing Transfer Learning on Landscape Image Classification Using the VGG16 Model
In recent times, the need for the use of image classification techniques of machine learning to solve worldly problems in various areas such as agriculture, the health sector, and tourism is rocketing up day by day. Traditionally, one of the most used techniques in image classification is the use of deep neural networks called convolution neural networks (CNN). To come up with a good network model, one needs to have an enormous quantity of data in the form of images and design a network model from scratch in a trial-and-error way. This not only takes a lot of time but also requires very powerful computation equipment such as graphical processing units (GPU). To overcome such barriers, a machine learning technique called transfer learning enables the use of already trained network models in the form of fine-tuning them to solve related issues. In this work, the 2014 ImageNet winner model called Vgg16 was adopted to classify landscape images in the Intel dataset. The dataset contains 5 categories of images namely buildings, forest, glacier, mountain, sea, and street. The performance of Vgg16 was compared to that of a 7-layer ordinary convolution neural network and the results showed that transfer learning with Vgg16 outperformed the ordinary network by 90.1% for Vgg16 compared to 62.5% for the ordinary convolutional neural network model.
The Double Skin Façade Mechanical Systems as Ad-anced Building Technologies
The double-wall façades (or “double skin façades”) are determined as building mechanical and engineered systems realized by the cavity between the inner curtain and the outer screen, for thermal and acoustic insulation, for ventilation and for the insertion of functional devices (such as sunscreens) and, also, plant ducts. The cavity between the two enclosures constitutes a ventilated cavity that can be used according to certain modes of functioning (passive or active type) aimed at controlling external climatic and environmental stresses in order to regulate the conditions of the interior spaces. The double envelope system is realized as an apparatus of mediation and reaction towards environmental loads, according to the needs of well-being and reduction of energy consumption. In general, the system offers the functioning in the form of a passive solar system, assuming the use and accumulation of solar radiation for the regulation of indoor thermal comfort conditions, and the functioning for the capture and input of air flows. The calibration of solar radiation is integrated with the use of shading devices in order to achieve diffuse lighting conditions in interior spaces. In addition, the application of the double glass surface reduces thermal losses from the interior spaces by reducing the speed of the airflow in contact with the inner curtain, increasing thermal insulation. The system foresees that the ventilated cavity performs various integrated functions (for the definition of complex mechanisms of dynamic interaction with the external climatic conditions), both permanent (e.g. for the increase of thermal inertia and acoustic insulation relative to the internal curtain) and temporary (e.g. for the cooling of the same spaces during periods of high temperature).
Prediction of fatigue life of mistuned steam turbine blades subjected to variations in blade geometry
There is a large number of power stations suffering from fatigue failures of the steam turbine blades. The steam turbine blades are also subjected to steam flow bending, centrifugal loading, vibration response, and structural mistuning. These mentioned factors significantly contribute to the fatigue failure of the steam turbine blades. Low-Pressure (LP) steam turbines experience premature blade and disk failures due to the stress concentrations at the blade root area of its bladed disk. Driven by the problems encountered by the steam power plant electricity generating utilities with regards to steam turbine blades fatigue failure, this study of the mistuned steam turbine blades subjected to variation in blade geometry will be of great significance to the electricity generation industry. A simplified, scaled-down mistuned steam turbine bladed disk model was developed using ABAQUS finite element analysis (FEA) software. Acquisition of the vibration characteristics and steady-state stress response of the disk models was performed through FEA. Thereafter, numerical stress distributions were acquired, and the model was subsequently exported to Fe-Safe software for fatigue life calculations based on centrifugal and harmonic sinusoidal pressure loading. The vibration characteristics and the response of the variation steam turbine geometric blade was conducted. The FEA natural frequencies compared well with published literature of the real steam turbines indicating reliability of the developed FEA model. The study found that the fatigue life is most sensitive to changes in blade length, followed by the width, and then the thickness, in this order. The analytical life cycles and Fe-Safe software shows the percentage difference of less than 4.86%. This concludes that the developed numerical methodology can be used for real-life mistuned steam turbine blades subjected to variations in blade geometry.
Development of Resolver Circuit with Long Short Term Memory and Reinforcement Learning Algorithms
In our age, the usage areas of artificial intelligence have increased considerably. These areas were particularly concerned with the correct predictability of future data using available data. It has become necessary to work on various machine learning algorithms to be used in the calculations of the resolver circuit, which is a feedback element used for tracking the position and position information of the electric motor unit used in various vehicles. The use of machine learning algorithms in the design and implementation of the resolver circuit, which is one of the most important elements of electric motor designs, will shed light on future studies. In this study, it is focused on the use of machine learning algorithms in the calculation of the resolver circuit, position and position information and the performance differences between each other. In this study, LSTM (Long Short Term Memory) and Reinforcement Learning (RL) algorithms were compared. While comparing these algorithms, the types of LSTM and RL algorithms were also studied and compared. As a result of the results obtained, it was aimed that the motor designs would be less costly, and the results obtained in terms of more reliable motor position and position information to be used were promising. In addition, with this study, a basis was created for working on machine learning algorithms in the calculation of different parameters. With this study, a great way has been achieved in integrating algorithms used in electric vehicles, which are quite obsolete today, into AI-based algorithms.
This paper studies the electromechanical model of a jigsaw mechanism. The jigsaw is powered by a battery, which drives a DC motor. The rotational motion is converted into linear motion through a Scotch Yoke mechanism. The electromechanical equations are based on energy approach using the Lagrange-equation. The Lagrange function contains the energies of the model, i.e., the kinetic co-energy of the kinematic chain and the magnetic co-energy of the inductance. As regard the non-conservative elements their virtual works are written. The formulated equations of the jigsaw model allow us to examine this force-energy relationship.
Antioxidant Activity of Green Synthesized Platinum Nanoparticles by Using Tornabea scutellifera Extract
First time in this study Tornabea scutellifera extract were used for synthesis of Platinium nanoparticles (Pt NPs). The DPPH scavenging activity of T. scutellifera-based Pt NPs was determined and its usability as antioxidant activity was evaluated. With characterization tests, it was observed that Pt NPs were in spherical structure and had an average diameter of 88.7 nm. Functional groups that play a role in the synthesis were determined by FT-IR analysis with the peaks determined at 1623 cm-1, 1146 cm-1, 1042 cm-1, 987 cm-1, 625 cm-1 ve 558 cm-1. Elemental structure (presence of Pt) was revealed by EDX analysis. It was determined that T. scutellifera-based Pt NPs exhibited anti-oxidant activity against DPPH (184.06 µg/ml, R2=0.8727). It is thought that the study can be used in nanotechnology-related multidisciplinary studies.
Using a Soccer team as a test bed for multi-agent systems simulation
The STMAS system is intended to imitate a soccer team and its behavior; we believe it can be used effectively as a test bed for multi-agent systems. It is constructed utilizing distributed agents that interact, communicate, and negotiate with each other to achieve the team objectives. It is based on the Jade simulation platform. The system is tested and compared to a pure soccer team using multiple MAS techniques. The results demonstrated that applying MAS techniques of negotiation and task distribution improves team performance, and STMAS is offered as an efficient test bed for new and distinct MAS techniques with varied scenario experiments. In addition, a mathematical model is created to compare the simulation results. Overall, STMAS provides a versatile and efficient MAS simulation and evaluation test bed. It is an excellent platform for comparing and evaluating various MAS approaches.
Utilization of Peltier Chipsets in Electric Vehicles to Charge Li-Ion Batteries
Using the Peltier effect for power generation is a relatively new technology that has been gaining attention in recent years. Using Peltier chips for power generation in EVs is an interesting approach that has the potential to provide a renewable and sustainable source of energy. By using the heat generated by the car's components during operation, the Peltier chips can generate electricity, which can be used to charge the battery. This approach has several benefits, including reducing the reliance on fossil fuels, improving the efficiency of the vehicle, and reducing the carbon footprint of the EV. The Peltier effect is a thermoelectric phenomenon that converts temperature differences into electrical energy to generate enough power to recharge an electric vehicle battery, several Peltier chips can be connected in series, and a converter can be used to convert the generated voltage into a sufficient voltage and can charge the battery. In this paper, an in-depth exploration will be conducted to evaluate the overall effectiveness and efficiency of Pelter chips, with a particular focus on simulating the utilization of these chips through the utilization of Proteus software.
A Framework for Detecting AI-Generated Text in Research Publications
The use of generative artificial intelligence is becoming increasingly prevalent in creating content in various formats such as text, video, and image. However, there is a need to distinguish between content that has been generated by humans and content that has been generated by AI as misuse of these technologies can raise scientific and social challenges. Moreover, there are concerns about the reliability and comprehensiveness of the content generated by AI without human validation. This paper presents a framework for AI-generated text. The prototype implementation of the proposed approach is to train a model using predefined datasets and deploy this model on a cloud-based service to predict whether a text was created by a human or AI. This approach is specifically focused on assessing the accuracy of scientific writings and research papers rather than general text. The proposed framework is compared with recently developed tools such as OpenAI Text Classifier, ZeroGPT, and Turnitin. The results show that training a text classifier can be highly useful in detecting whether a text is written by a human or AI. The source code and dataset are made open source so others can experiment with the prototype implementation and use it for future research.
Analysis of Thermal Performance of Double Flow Finned Absorber Solar Air Heater
This paper investigated the thermal performance of a solar air collector with attached rectangular fins both theoretically and experimentally. The effects of mass flow rate (0.015 kg/s to 0.06 kg/s) and fin spacing on thermal performance and temperature rise were investigated using varying solar radiation intensity (500, 600, 700, and 800 W/m2). The results show that, when using a finned absorber instead of a plane absorber, the maximum thermal efficiency of 1.264 times was achieved. Additionally, at a lower mass flow rate of 0.015 kg/s, a maximum increase in temperature rise has been observed to be 1.243 times greater than that of a plane absorber.
TOWARD TO VIBRATION ANALYSIS BY KNOT THEORY
Vibration analysis is one of the most important aspects in the design of structures and mechanical systems, among others, subject to dynamic loads. As well as for the analysis of failures caused by vibratory aspects. A good performance of an industrial system is often associated with the availability of mathematical models of the dynamic behaviour of the system. In some situations, the complexity of the processes makes it difficult to have models that help us to analyse these processes. This paper proposes the use of knot theory, which is a topological tool, for vibration analysis. This topological tool, in this case, associates a topological invariant when there is a drastic change in vibrations. The present work is based on the fact that it is well known that the equations representing harmonic motion generate Lissajous figures. In knot theory, there are several classifications of knots, one of these classifications is known as Lissajous knots. The use of this tool is shown in the supposition that we have a system represented by three equations of the form f(t) = Acos(Bt + C) , where with the indicated parameters it generates a knot (being its nominal value). Making a change in the phase, which represents a fault, generates a different knot than the nominal knot. One of the advantages of this proposed method is that it is not necessary to have the model, and one of the disadvantages by nature of this method is that three signals are required to use this topological tool.
Investigation of T-history Method Application for Sub-zero Phase Change Material Thermal Properties
The T-history method, first proposed by Zhang & Jiang, is a simple method used to determine the latent heat, specific heat, thermal conductivity, melting point, and degree of supercooling of a phase-change material (PCM). The method is based on the measurement of the temperature of the material over time. It assumes that the PCM sample and the reference material exchange thermal energy with the environment in a similar manner, because it is based on the lumped capacitance method, and the temperature distribution inside the sample is uniform. There are multiple advantages that the T history method has over conventional methods; however, the T history method also has some downfalls, which have been improved over the years by different authors. This study experimentally investigates the reliability of this technique for PCM with a phase change temperature below 0 ° C. The technique is used to first determine the properties of denoised water with a phase change temperature of 0 ° C, using ethylene glycol with a mixture ratio of 50/50 as reference material. Second, by determining the properties of KCl with a concentration of 19.5% salt to water for a phase change temperature of -10 ° C. Two analytical approaches were used, one by immersing the samples in antifreeze during the charging process and the other by exposing the samples to ambient air during the discharging phase. The experimental results were further validated using the known literature to determine the latent heat of KCl. This study found that the latent heat of KCl measured using the technique has a 6.2% difference from the known results determined by the DSC. This study recommends the use of the T history method when designing latent heat thermal energy storage systems (LHTESS), because the sample size is an important characteristic for accurate thermophysical properties during the design process.
Low-Cost Potassium Chloride Saltwater Phase Change Material System for a Household Refrigerator
Thermal energy storage systems (TESS) are receiving attention because there are constant power cuts, major changes in electrical rate structures, increased maximum power demands, and incentive programmes sponsored by utilities. Household refrigerators were one of the main consumers of residential electricity, as they consume up to 26% and have an additional 17% greenhouse gas emissions. By incorporating TESS, this figure can be reduced. This study presents a low-cost potassium chloride saltwater phase change material (PCM) system to maintain a household refrigerator compartment below 5 ° C to preserve food and pharmaceutical products. After determining the 5-liter volume of PCM required, the experiments were carried out on a KIC KBF 525/1 ME refrigerator with an average daily power consumption of 0.567 kWh. With a heat transfer rate of 5 W and a total of 80 kJ of energy, the PCM took 2.5 days to fully solidify. It was then able to maintain the frigerated compartment at a temperature below 5 ° C for close to 25 hours, resulting in a 8W output power and a total of 90 kJ of energy being released. For power consumption analysis, 1.4175 kWh was used during the charging phase and 0.567 kWh was saved during the discharging phase. Heat transfer during the charging phase needs to be improved to better optimise the TESS.
Evaluation Framework for the Use of Privacy Preserving Technologies for Production Data
To prevent unplanned machine downtime in production, machine conditions can be monitored and even predicted using condition and failure models based on current machine and process data. As most of these models are data-intensive, machine users often do not have enough data to develop these models themselves and want to collaborate with other companies. Since these models often require critical and classified machine and process data, which could be extracted from the models using attacks such as model inversion, sharing existing models between companies is not an option as it leaves one party vulnerable. Privacy preserving technologies such as homomorphic encryption, differential privacy, federated learning and secure multi-party computation can help overcome this problem. With the help of these approaches, there is no need to transmit sensitive data unencrypted to third parties in order to cooperate and take advantage of high-performance models. The aim of this paper is to first summarize the current state of research on privacy-preserving technologies in production, and then to provide a simple to use evaluation method and criteria. The focus is on enabling production workers to make informed decisions and exploit the full potential of existing data without the need for prior knowledge of privacy-preserving technologies. Finally, the evaluation method is validated using two example use cases in a production environment and the results are discussed.
Review of Digitalization using IoT Maturity Models: The Case of American Automotive SMEs
This study aims to review studies related to IoT maturity models in manufacturing systematically. Digitalization is vital for small and medium enterprises (SMEs) to retain a competitive advantage, reduce their operational expenses, and compete with larger firms in their respective market. Globally IoT-induced transformation in manufacturing has been significant. Due to the lack of resources, American Automotive SMEs have not been farsighted with regards to digitalization but benefits like improved workflows, efficiencies, reduced overheads, and value creation make it beneficial and invaluable to customers. This paper reviews the state of IoT applications in American Automotive SMEs using the assessment provided by the IoT maturity model.
Formal Methods for an Agile Scrum Software Development Methodology
Efficient, high-quality software systems embodying dependable methods are in high demand, which has led to a wide range of competitive market solutions. One effective technique that arguably has excelled above others is the Agile Software Development Methodology (ASDM). Agile approaches’ capacity to produce software in a way that is flexible to changes is the main factor that makes them preferable. Scrum, a recommended Agile methodology, prioritises feature coverage and project structure. Because iterative methodologies encourage engagement from cross-functional teams, including consumers, Agile provides flexibility in responding to change. However, achieving methodological efficiency is insufficient while developing software; high-quality software should be achieved with equal consideration. Formal Methods (FMs), which are mathematically based techniques, can offer highly dependable software but suffer from a steep learning curve in mastering the underlying discrete mathematics and logic. This research investigates the extent to which FMs may be embedded in traditional Agile as embodied by Scrum. Future work in this area would be the development of a framework for embedding FMs in Scrum, followed by a survey among software practitioners to establish the feasibility of our technique.
Toward Competent City Management using Multi-agent System (MAS)
multi-agent system plays a crucial role in many aspects of life. This refers to its ability to provide scalable and flexible solutions to complex problems. One of these problems is the management of resources in different cities around the world. In this study, we have used the multi-agent system concepts to build competent city resources management. Because of the time and software complexity that arises from implementing the whole system, we chose to implement one city entity, the energy entity. Petri net is used to model the system mathematically. At the same time, we used CPN tools software to simulate the system behaviour in real life. The mathematical model and simulation results proved that the designed energy MAS would achieve its goals when applied in real life. In the future, we intend to complete the construction of the whole system for better resource management of all entities in the city
Classification of Unmanned Aerial Vehicle and Bird Images Using Deep Transfer Learning Methods
The increasing accessibility and affordability of unmanned aerial vehicles (UAVs), commonly known as drones, have led to the emergence of malicious users. In precaution to this perceived threat, various anti-UAV systems are being developed, including electro-optical systems utilizing cameras. It is possible to detect UAVs from images using various machine learning methods. However, the similarity between UAVs and birds poses a challenge, as birds can be mistakenly identified as UAVs, leading to false alarms in a security system. In order to avoid this problem, this study provided the classification of birds and unmanned aerial vehicles over images using deep learning methods. In this study, a data set consisting of 400 birds and 428 UAV images was used. The data were divided into 70% for training, 30% for testing and validation purposes. Three different deep learning models, based on DenseNet, VGG16, and VGG19 architectures, were trained using transfer learning techniques, and their performances were compared. Experimental results on the test data showed an accuracy of 94.64% with the DenseNet model, 89.67% with the VGG16 model, and 90.67% with the VGG19 model.
Thermo-mechanical properties of fired clay brick incorporating industrial by-product materials cork waste
The quantity of solid wastes (domestic, agricultural or industrial) throughout the world is increasing and their elimination becomes more complex. However, recycling industrial by-product materials waste has become an attractive topic of materials research in civil engineering. These industrial by-product materials waste must be managed responsibly to insure a clean environment. The use of waste in fired clay brick production may also save clay from avoidable depletion and reduce the environmental contamination by waste, contributing to sustainability. The aim of this research is to study the influence of Ground Cork Waste (GCW) on the thermo-mechanical properties of fired clay brick. For this purpose, increasing amounts of Cork Waste (0, 5, 10 and 15% of weight) with a grain size under 1.00 mm were mixed with a clay to produce clay bricks by pressing, drying and then firing at 900°C. The results obtained demonstrate that an increase in the content of CW leads to a significant increase in apparent porosity of fired clay brick. The compressive strength and thermal conductivity of the samples decreased with the increase in content of (GCW).
The Effects of Data Standardization and Normalization Techniques in Click Through Rate Prediction
Accurate ranking is critical for the user experience as well as applications such as information retrieval, recommender systems, and decision-making. To transform data into a common scale or distribution, standardization and normalization techniques are used. The purpose of this paper is to look into the effects of various data standardization and normalization techniques on ranking performance in order to improve performance or reduce computational complexity. It examines methods such as z-score standardization, min-max scaling, and robust scaling in existing literature and experimental studies. The paper assesses their impact on various ranking algorithms and models using benchmark datasets and discusses the benefits, limitations, and trade-offs associated with each technique, taking into account factors such as data distribution characteristics, outliers, and interpretability. The results can aid in the selection of the best normalization and standardization techniques for ranking tasks, particularly in recommender systems.
Analysis of HRV and Blood Pressure Variables of Obese Male Subjects After Aquatic Therapies
This paper describes and analyzes blood pressure variables and 2 Heart Rate Variability (HRV) variables, namely RMSSD and Mean RR, after treatment with either immersion in water or WATSU® therapies of 26 obese young males. The objective of this paper is to show that subjects have shown a near random relation between systolic and diastolic blood pressures, before aquatic therapy of 26 obese subjects, and that the relation was corrected after aquatic therapy, in the sense that it demonstrated a near linear correlation and that is what is shown in medical literature. Also, the paper shows a correction of the relation between RMSSD and Mean RR HRV variables. These corrections highlight the significance of aquatic therapies for obese subjects as demonstrated by the results and analysis.
Dissimilarity Metric Score Estimation for Time Series with Missing Values
Missing values in time series data pose significant challenges for data modeling and further analyses. Interpolation methods are often used to fill in the missing values in the data, however, they may cause extra computational complexities and may make the analysis not suitable for real-time operations. Hereby, considering this, this paper focuses on the problem of estimating the dissimilarity metric score for time series data with missing values without interpolating the data. Hereby, we propose an approach to estimate the dissimilarity metric scores without utilizing the imputation methods. Our pro- posed algorithm utilizes a basic, but effective statistical model composed of statistical moments of a time series window to estimate the dissimilarity score of the respective window without applying the interpolation methods. Correlation between the proposed approach scores and the Euclidean dissimilarity metric scores on a benchmark dataset is computed for the most commonly used interpolation methods. To observe the dissimilarity values, several different missing value rates were selected to randomly erase the samples with that ratio from the data. The experimental results show that our proposed method provides comparable correlation results with some dissimilarity measures especially with spline interpolation by creating a correlation coefficient value of 0.819. Hence, the application of such a basic approach to estimating the dissimilarity values without applying interpolation or dissimilarity calculations to observe the time-varying data behavior can be used to reduce the computational complexity in real-time applications.
An Investigation of the Usage of Dynamic Time Warping in String Similarity Estimation
String similarity estimation is important in many fields, including natural language processing, information retrieval, and data mining. Dynamic Time Warping (DTW) has emerged as a widely used technique for measuring sequence similarity, effectively accommodating variations in length and temporal distortions. This paper presents an examination of the use of DTW in string similarity estimation. We delve into the adoption of DTW in string similarity estimation in various contexts, such as approximate string matching and spelling correction. We investigate DTW’s strengths and limitations through empirical analysis, particularly in capturing complex patterns and variations within strings, while taking into account factors such as adaptability and robustness. Furthermore, we discuss the impact of various traditional similarity metrics in comparison with DTW based on their evaluation on two different experimental settings. The findings of this study provide important insights into the effectiveness and challenges of using DTW in string similarity estimation. This work may open up alternative ways for the development of more adaptive string similarity estimation techniques through the use of DTW.