Museums are responsible for maintaining stable and appropriate indoor climate conditions for their artifacts and visitors. However, the microclimate of museums is often adjusted for visitors' comfort, disregarding the guidelines and standards suggested in literature for the proactive conservation of exhibits. Temperature and humidity imbalances caused by heating, air conditioning, ventilating systems, air exchange with outdoors, building structures, and high visitor traffic pose imminent risk factors. To address this issue, museums must adopt innovative, non-invasive, and cost-effective technologies to monitor environmental parameters and minimize negative impacts. This work presents a wireless sensor network solution for monitoring the temperature and relative humidity affecting the microclimate of the Tsitsanis Museum in Trikala, Greece, dedicated to the great Greek composer Vassilis Tsitsanis. The methodology applied for the monitoring process takes into account the location, building structure, and exhibits. The small-sized sensing nodes perform measurements with a specified time step and the sensory data are uploaded via a Wi-Fi connection to a cloud platform. The acquired data are accessible continuously and in real-time for closely analyzing changes in these parameters and avoiding unfavorable environmental conditions that can cause deterioration and damage to exhibits. The evaluation results indicate that the solution performs satisfactorily in terms of functionality, computational performance, consistency, and efficiency.
Nowadays, the 3-phase inverters for the induction motors drives are provided with many additional features that allow drive's operation real-time monitoring. One may use the observations of both electrical and mechanical variables to estimating the changes of the load's parameters (the time constant and the instantaneous value of the reduced axial moment at the shaft). In the positioning applications with uncertainties, the information provided from the drive, allow real-time optimization of the control law for the inverter and thus improving the quality of the control. The challenge of this endeavor is splitting the acquired time-series from the process into sequences defining the different stages of the transients and the steady-state operation. A data set generated by means of the 3-phase inverter that supplies the induction motor was used. The trained network was implemented onto real-time, noisy, acquired data from the drive. The artificial intelligence algorithm made the clustering of the data and the data sequences could be classified. The experiment was made on a 3-phase induction motor supplied from an ACPM 750 - 3-phase IGBT power bridge (Technosoft) with different values of the inertial load at the shaft.
Using hydrogen as a substitute for traditional fossil fuels has gained considerable attention as it can be used in various sectors, such as transportation, heating, and electricity production. This paper thoroughly examines the significance of hydrogen in addressing climate change. It will emphasize the association between renewable energy, power engineering, and hydrogen production and utilization. Hydrogen can be produced from renewable and non-renewable sources, like natural gas, solar, and wind power. This versatility positions hydrogen to play an essential role in transitioning to a low-carbon economy and mitigating the effects of climate change. The paper will highlight the advantages of hydrogen over traditional fossil fuels, such as its lower carbon emissions and the potential to reduce reliance on imported oil. Moreover, it will explore various industries where hydrogen can be utilized, like transportation, heating, and electricity production. Additionally, the paper will investigate the potential barriers to the widespread use of hydrogen, including technological, economic, policy, and regulatory challenges. The ultimate goal of this paper is to offer a comprehensive overview of hydrogen's potential to combat climate change, its relationship with renewable energy and power engineering, and the potential roadblocks in its implementation.
Alzheimer's disease is a severe neurological disorder, with sub-optimal symptomatic pharmacological treatment. Various sound and music-based therapeutic interventions are currently being used as alternative solutions to alleviate symptoms and enhance the quality of life of those impacted. This paper presents the integration of these music-based therapeutical methods into a single, unitary software application specifically designed to supplement traditional music therapy sessions and be easily employed in real-life scenarios. The app is free, modular, customizable, and developed in a software environment capable of deploying on all major platforms, for the advantage of easy accessibility to therapy professionals, caregivers and patients suffering from Alzheimer's disease-related symptoms.
This paper presents a fuzzy model of a physical deposition monitoring system using AI (Artificial Intelligence) elements. The studied process for thin film deposition is thermal evaporation method. The fuzzy model is proved by both a thermocouple for temperature monitoring and by a digital camera with AI algorithms that can measure the dimensions of the melted material that is evaporated.
There are numerous systems with objects in motion where data retrieval from sensors can only be done via wireless communication. This paper presents the possibility of transmitting the data from the sensors located on the spindle of a stepper motor to a LabView application using Bluetooth Low Energy. Controlling the movement of the stepper motor is done from the app via the Arduino. The sensor used is the MPU9250, nine-axis MEMS motion tracking device and is part of a system that uses the TI CC2650 circuit for BLE communication with a BLED112 USB dongle. The data taken from the accelerometer, gyroscope and magnetometer are used to study the sensor movement at various rotational frequencies and operating regimes of the stepper motor.
This paper studies the design, fabrication and simulation of a robotic arm that possesses the ability to use information that receives from its environment through machine vision. The main objective of this work is to design, construct and simulate a robotic system able to recognize and identify various target objects located in the robot's workspace using a camera. The image processing is implemented using a clever convolutional neural network (CNN). After studying the kinematics of the 5-dof robotic manipulator, a robotic arm is designed through a 3D design program. The mechanical parts of the robot are constructed through a 3D printer and are assembled with the servo motors. Next, the desired signals are being sent to the servo motors through a Matlab Simulink program and the Arduino Microcontroller with the intention to rotate the robot joints.
The shape of the wavefront is important for the most realistic reproduction of the acoustic wave. In acoustics, the wave front (or wave surface) is defined as the totality of points in the space of the propagating medium that have the same instantaneous elongation amplitude. In the ideal case, in a homogeneous and isotropic propagation medium without other disturbances, the wave has a spherical symmetry, i.e. the shape of the wavefront is circular and at large distances from the wave source, due to dispersion, the shape of the wavefront can be considered plan. In practice, however, due to the multiple component harmonics, reflections and possible summations of the primary acoustic wave, the elongation has a non-sinusoidal shape, and the wavefront may have an irregular random shape. Moreover, if the source of the wave or bodies from which it is reflected are in motion, the shape of the wavefront may even be variable in time. The paper presents a method for determining the shape of the wave front of a non-sinusoidal periodic plane acoustic wave, resulting from the addition of several harmonic components, reflected waves, etc. It uses an array of omnidirectional capacitive microphones arranged in line, at equal distances between them. At each of the microphones, the acoustic wave is captured out of phase depending on the position of the microphone in space and the shape of the wave front. These phase shifts placed next to each other give an image of the shape of the wavefront captured by the microphone system. The method presented in this paper reproduces sounds very realistically and can be used in Active Noise Cancel systems, in open spaces, with one or more mobile noise sources. One of the benefits is the increase in hearing comfort in noisy environments. Another is to record sounds with high fidelity.
Inthis paper many improvements in dealing with energy quality issues that could arise in electrical drive systems using asynchronous machines are presented. A series active power filter is used in electrical drive systems with induction motors to reduce harmonic current and voltage. We also propose a variety of active power filters with hysteresis current value control. Lastly, some experimental results for an electrical drive system utilizing an induction motor and a PWM converter are provided. These findings are followed by a discussion of the system's energy quality issues.
The development of automated solutions is poten-tially crucial to enable self-healing in smart grids. Software developers will be overwhelmed with the rise of Metaverse and game industry. Therefore, it is essential to fully automate the entire software development process with the help of cutting-edge artificial intelligence (AI) technologies and advanced data analytics approaches. In this paper, we propose a new framework for automated solution development for smart grids that makes use of AI and advanced data analytics to address this challenge. Recent developments in AI technologies are used by our proposed framework to facilitate automated solution development. Our modular approach simplifies and expedites several phases of the software development process by building on the shortcomings of the current frameworks. To generate revenue, our suggested framework offers a number of business models from its adoption. We expect that our idea will inspire others to make unique contributions to the development of automated solution in smart grids to improve its operations and boost our economy.
This research evaluates an electromagnetic damper prototype model that could be integrated in the vehicle suspension and the capability to recover energy during its operation when the mechanical energy received produce the movement of the permanent magnets thus inducing voltages in the conductive material. In the study we determined the derivative of the magnetic flux, observed how the magnetic field varies, calculated and measured the induced voltages.
The paper describes a mechanical multimotion jaw gripper used in industrial robots. Designed and realized practically in a first form, we observed the existence of a high level of vibration that could endanger the gripping safety of the handled parts. For the precise analysis of the vibration level, vibration sensors were mounted on the jaw support elements of the gripping modules and graphs for the variation of the vibration level during the maximum possible stroke of the jaw support elements were drawn up. Analyzing the construction elements used, we concluded that the source of these high amplitude vibrations is the actuation solution of each gripper module mechanism, directly of the first element of the actuation mechanism by the stepper motor used. As a result, it was proposed that this actuation solution be replaced by driving the first element of the actuation mechanism via a helical gear. The new shape of the gripper was practically obtained and the vibration level was analyzed again and a significant decrease in the vibration level was found, evidenced by the corresponding graphs shown, vibration level below the threshold that would endanger the safety of the handled objects gripping. The paper is useful in the practical example of constructive optimization of a product, in this case a multimobile mechanical gripper, by carefully analyzing the vibration level and identifying its cause.
This paper presents the control system of a mobile robot, capable of detecting, tracking and following an object of a certain color. The mobile robot was created for educational purposes, in the CIM laboratory of the University of Oradea. It is based on a crawler robot, controlled with a Raspberry Pi development system. This system controls the movements necessary for tracking the object: moving forward, backward, left turn or right turn. For detecting and locating the object, the robot is also equipped with a video camera connected to Raspberry Pi. The acquisition and processing of images in real time, provided by the camera, are performed with programming functions provided by the OpenCV (Open Source Computer Vision Library). These functions are implemented in a control program, written in Python language. In order to facilitate the tracking of the images obtained and processed during the movements, the robot has also been provided with a graphic display connected to the Raspberry Pi board. This paper also presents the implementation of a numerical control algorithm used for tracking the objects.
Gears are an important component in the structure of mechanical power transmission machines. It consists of two gears, which by means of the gear teeth transmit torsional motion and torque between the shafts on which the gears are mounted. Generally speaking, gears are the link between the driving machine and the working machine. The main advantages of gears are constant transmission ratio, operational safety, high efficiency, small size, etc. The main disadvantages are high cost due to high precision of execution and assembly. An important aspect is gear damage due to the complex stresses to which the teeth are subjected during operation. This paper presents a study of the design factors of gears in terms of tooth contact resistance and tooth bending strength, with a view to reducing the amount of material required for gear construction.
While the main focus of research for time series forecasting models has been on improving the accuracy of the forecasts by increasingly complex models and parameter tuning, the increasingly large data sets being used require constantly stronger hardware and computational resources. Typically, the accuracy of models during training is evaluated by looking at in-sample errors, which does not always translate into better out-of-sample forecasts. In this paper, we will investigate whether possibly combining weaker models can result in acceptable results especially when compared to more resource-intensive parameter optimizations. We then take a look at the trade-off between this race for finding the best model and balancing the resource cost from a monetary perspective.
The emergence of social networks has facilitated the development of new ways of communication and social engagement. The dynamics of the conversation on social networks on a given topic help determine how users who post messages are affected. Since people's opinions are important factors influencing human behavior, we were concerned with extracting relevant information from social networks on one of the big global concerns, namely the energy crisis. In this context, the aim of our study was to identify and analyze the most important Twitter posts from April to September 2022 regarding the energy crisis using text mining methods. We focused on determining the most frequent words found in the extracted tweets with API V2 method and we analyzed the posts in relation to the identified opinion/sentiment using VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm. The paper presents the results we obtained. Determining the impact of the sentiment expressed in the Twitter posts on the “Energy Crisis” and correlating it with the analysis of the pandemic Covid-19's effects on people can help decision-makers develop policies that would also take public opinion into account.
The paper presents an investigation on electric arc models in order to find a good agreement between simulation and experimental results. Based on the representative models of electrical arc, a simulation in Matlab was performed in order to evaluate the time variation of current and voltage in a circuit affected by an arc fault. An experimental setup was used to visualize the behavior of current and voltage in the series arc within a resistive or inductive circuit. The paper also describes the principle of the newly arc fault detection device and its applicability in low voltage electrical installations.
In contemporary society, marked by unprecedented technological development in the history of humanity and globalization, the dialogue between technical sciences and the humanities is necessary, especially when technical discoveries and applications advance the level of public understanding or when these discoveries are used outside of norms and clear and assumed conventions, social disturbances occur with serious consequences. On the other hand, engineering serves the progress of society. Facial reconstruction is needed and used in various fields of activity, from medicine, art, to forensics and archaeology. In our study, we highlight the evolution of different facial reconstruction methods used: two-dimensional reconstruction, the classic three-dimensional manual technique (e.g. Manchester combined method, computerized facial reconstruction). The case study presents a stage of our research activity through which we want to use different technical applications forthe facial reconstruction of a skull from the 10th century - artifactdiscovered in the archaeological excavations in Timis County (Romania) by researchers from Faculty of History of West University of Timisoara. With the instruments made available by our Faculty (scanner, 3D printer), at this stage we only realized part of the facial remodeling: recreating the temporal muscle, using a mixed method, manual and computerized, of recreating the surfaces through points. Computerized remodeling of the missing individual is significantly easier compared to the manual method and also helpsin simplifying the training of the practitioner. Our initiative represents a first in the Timisoara university center, which we want to develop through interdisciplinary methodological collaborations, between our field and archaeology.
Enterprise applications must be able nowadays to handle complex business processes efficiently and effectively. The use of workflow engines enables the automation of business processes and provide a mechanism to define, manage and execute workflows, enabling enterprises to adapt faster to changing business requirements and facilitating the modification of business processes without requiring significant changes to the underlying software code, which can help enterprises to stay competitive in a constantly evolving business landscape. In this paper, an analysis of the impact of using workflow engines on business process management in enterprise applications is presented. The research investigates several benefits which are brought to enterprise applications, such as improved productivity by reducing the dependency on manual intervention, increased efficiency and flexibility. The analysis is done on a case-study application that uses Camunda open-source workflow engine in comparison with Java driven workflow; based on this, the present study also addresses both the advantages and the limitations of Camunda with respect to the possibility of creating flexible development and execution of models and processes in enterprise applications.
The Internet of Things (IoT) is a cutting-edge paradigm that involves the interconnection of multiple advanced sensors and peripheral devices to enable the automation and orchestration of various complex systems. In the agricultural context, smart devices are utilized to acquire and transmit data from a diverse range of sensors that monitor intricate environmental parameters crucial to ensuring optimal plant growth. These factors encompass but are not limited to humidity, temperature, soil moisture, and water pH. Continuous monitoring and control of these environmental aspects are essential for maximizing plant growth and yield. Manual disease monitoring is labor-intensive and requires specialized expertise in plant pathology. To overcome these challenges, convolutional neural network models employing deep learning techniques have been developed to detect and diagnose diseases in plants. Our models utilize images of diseased plant leaves to accurately classify the health status of plants. The dataset includes healthy plants, as well as those affected by Bacterial Blight, Anthracnose Bacteria, and Thrips insects. The proposed IoT framework can significantly enhance crop monitoring, enabling farmers to manage their crops effectively and increase productivity through state-of-the-art technologies.
