bluuebunny/crossref_metadata_2025 huggingface dataset:
ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset
Authors: Śmigiel Sandra , Pałczyński Krzysztof , Ledziński Damian
Published: August 2021

The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons.
Computation of Kullback–Leibler Divergence in Bayesian Networks
Authors: Moral Serafín , Cano Andrés , Gómez-Olmedo Manuel
Published: August 2021

Kullback–Leibler divergence KL(p,q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q. Its efficient computation is essential in many tasks, as in approximate computation or as a measure of error when learning a probability. In high dimensional probabilities, as the ones associated with Bayesian networks, a direct computation can be unfeasible. This paper considers the case of efficiently computing the Kullback–Leibler divergence of two probability distributions, each one of them coming from a different Bayesian network, which might have different structures. The paper is based on an auxiliary deletion algorithm to compute the necessary marginal distributions, but using a cache of operations with potentials in order to reuse past computations whenever they are necessary. The algorithms are tested with Bayesian networks from the bnlearn repository. Computer code in Python is provided taking as basis pgmpy, a library for working with probabilistic graphical models.
Overview of Machine Learning Process Modelling
Authors: Brumen Boštjan , Černezel Aleš , Bošnjak Leon
Published: August 2021

Much research has been conducted in the area of machine learning algorithms; however, the question of a general description of an artificial learner’s (empirical) performance has mainly remained unanswered. A general, restrictions-free theory on its performance has not been developed yet. In this study, we investigate which function most appropriately describes learning curves produced by several machine learning algorithms, and how well these curves can predict the future performance of an algorithm. Decision trees, neural networks, Naïve Bayes, and Support Vector Machines were applied to 130 datasets from publicly available repositories. Three different functions (power, logarithmic, and exponential) were fit to the measured outputs. Using rigorous statistical methods and two measures for the goodness-of-fit, the power law model proved to be the most appropriate model for describing the learning curve produced by the algorithms in terms of goodness-of-fit and prediction capabilities. The presented study, first of its kind in scale and rigour, provides results (and methods) that can be used to assess the performance of novel or existing artificial learners and forecast their ‘capacity to learn’ based on the amount of available or desired data.
State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing
Authors: Ali Wasiq , Li Yaan , Raja Muhammad Asif Zahoor , Khan Wasim Ullah , He Yigang
Published: August 2021

In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.
Effects of Cardiac Resynchronization Therapy on Cardio-Respiratory Coupling
Authors: Radovanović Nikola N. , Pavlović Siniša U. , Milašinović Goran , Platiša Mirjana M.
Published: August 2021

In this study, the effect of cardiac resynchronization therapy (CRT) on the relationship between the cardiovascular and respiratory systems in heart failure subjects was examined for the first time. We hypothesized that alterations in cardio-respiratory interactions, after CRT implantation, quantified by signal complexity, could be a marker of a favorable CRT response. Sample entropy and scaling exponents were calculated from synchronously recorded cardiac and respiratory signals 20 min in duration, collected in 47 heart failure patients at rest, before and 9 months after CRT implantation. Further, cross-sample entropy between these signals was calculated. After CRT, all patients had lower heart rate and CRT responders had reduced breathing frequency. Results revealed that higher cardiac rhythm complexity in CRT non-responders was associated with weak correlations of cardiac rhythm at baseline measurement over long scales and over short scales at follow-up recording. Unlike CRT responders, in non-responders, a significant difference in respiratory rhythm complexity between measurements could be consequence of divergent changes in correlation properties of the respiratory signal over short and long scales. Asynchrony between cardiac and respiratory rhythm increased significantly in CRT non-responders during follow-up. Quantification of complexity and synchrony between cardiac and respiratory signals shows significant associations between CRT success and stability of cardio-respiratory coupling.
Intelligent Fault Diagnosis of Rolling-Element Bearings Using a Self-Adaptive Hierarchical Multiscale Fuzzy Entropy
Authors: Yan Xiaoan , Xu Yadong , Jia Minping
Published: August 2021

The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different levels or scales, but do not consider bearing fault information on both multiple layers and multiple scales at the same time, thus easily resulting in incomplete fault information extraction and low-rise identification accuracy. Besides, the key parameters of most existing entropy-based complexity metric methods are selected based on specialist experience, which indicates that they lack self-adaptation. To address these problems, this paper proposes a new intelligent bearing fault diagnosis method based on self-adaptive hierarchical multiscale fuzzy entropy. On the one hand, by integrating the merits of HFE and MFE, a novel complexity metric method, named hierarchical multiscale fuzzy entropy (HMFE), is presented to extract a multidimensional feature matrix of the original bearing vibration signal, where the important parameters of HMFE are automatically determined by using the bird swarm algorithm (BSA). On the other hand, a nonlinear feature matrix classifier with strong robustness, known as support matrix machine (SMM), is introduced for learning the discriminant fault information directly from the extracted multidimensional feature matrix and automatically identifying different bearing health conditions. Two experimental results on bearing fault diagnosis show that the proposed method can obtain average identification accuracies of 99.92% and 99.83%, respectively, which are higher those of several representative entropies reported by this paper. Moreover, in the two experiments, the standard deviations of identification accuracy of the proposed method were, respectively, 0.1687 and 0.2705, which are also greater than those of the comparison methods mentioned in this paper. The effectiveness and superiority of the proposed method are verified by the experimental results.
Stereo Video Quality Metric Based on Multi-Dimensional Analysis
Authors: He Zhouyan , Xu Haiyong , Luo Ting , Liu Yi , Song Yang
Published: August 2021

Stereo video has been widely applied in various video systems in recent years. Therefore, objective stereo video quality metric (SVQM) is highly necessary for improving the watching experience. However, due to the high dimensional data in stereo video, existing metrics have some defects in accuracy and robustness. Based on the characteristics of stereo video, this paper considers the coexistence and interaction of multi-dimensional information in stereo video and proposes an SVQM based on multi-dimensional analysis (MDA-SVQM). Specifically, a temporal-view joint decomposition (TVJD) model is established by analyzing and comparing correlation in different dimensions and adaptively decomposes stereo group of frames (sGoF) into different subbands. Then, according to the generation mechanism and physical meaning of each subband, histogram-based and LOID-based features are extracted for high and low frequency subband, respectively, and sGoF quality is obtained by regression. Finally, the weight of each sGoF is calculated by spatial-temporal energy weighting (STEW) model, and final stereo video quality is obtained by weighted summation of all sGoF qualities. Experiments on two stereo video databases demonstrate that TVJD and STEW adopted in MDA-SVQM are convincible, and the overall performance of MDA-SVQM is better than several existing SVQMs.
Digital Suppression of EMI-Induced Errors in a Baseband Acquisition Front-End including Off-the-Shelf, EMI-Sensitive Operational Amplifiers
Authors: Crovetti Paolo , Musolino Francesco
Published: August 2021

In this paper, the susceptibility to Electromagnetic Interference (EMI) of an analog signal acquisition front-end (AFE) due to EMI distortion in opamp-based pre-conditioning amplifiers is addressed. More specifically, the possibility to correct EMI-induced errors in the digital domain by post-processing the acquired digital waveforms is discussed and experimentally demonstrated for the first time with reference to an AFE based on EMI-sensitive, off-the-shelf operational amplifiers mounted on a specific EMI test PCB. Extensive experimental characterization in the presence of continuous wave and amplitude modulated EMI reveals the superior immunity to EMI of the proposed AFE and the robustness of the approach.
Secure Mobile Edge Server Placement Using Multi-Agent Reinforcement Learning
Authors: Kasi Mumraiz Khan , Abu Ghazalah Sarah , Akram Raja Naeem , Sauveron Damien
Published: August 2021

Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.
A Novel OFDM Format and a Machine Learning Based Dimming Control for LiFi
Authors: Nowrin Itisha , Mondal M. Rubaiyat Hossain , Islam Rashed , Kamruzzaman Joarder
Published: August 2021

This paper proposes a new hybrid orthogonal frequency division multiplexing (OFDM) form termed as DC-biased pulse amplitude modulated optical OFDM (DPO-OFDM) by combining the ideas of the existing DC-biased optical OFDM (DCO-OFDM) and pulse amplitude modulated discrete multitone (PAM-DMT). The analysis indicates that the required DC-bias for DPO-OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO-OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO-OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO-OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the individual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO-OFDM component of DPO-OFDM for a given constellation size of PAM-DMT. With the component switching and the machine learning algorithms, DPO-OFDM-based LiFi is power efficient at a wide dimming range.
Output Voltage and Resistance Assessment of Load-Independent-Voltage-Output Frequency Operating Inductive Wireless Power Transfer Link Utilizing Input DC-Side Measurements Only
Authors: Trachtenberg Or , Kuperman Alon
Published: August 2021

The paper puts forward a method for predicting output voltage and resistance of a series-series (SS) compensated inductive wireless power transfer (IWPT) link operating at load-independent-voltage-output (LIVO) frequency. The link is a part of the static system (reported by the authors in earlier works), wirelessly delivering power into an enclosed compartment without any secondary-to-primary feedback. The proposed algorithm employs input DC-side quantities (which are slow-varying and nearly noise-free, thus measured utilizing low-cost, low-bandwidth sensors) only to monitor output DC-side quantities, required for protection and/or control. It is shown that high estimation accuracy is retained as long as system parameter values are known and the phasor-domain equivalent circuit is valid (i.e., upon continuous-conduction mode (CCM) of the diode rectifier, where the proposed methodology utilizes the recently revealed modified diode rectifier equivalent model for enhanced accuracy). Under light loading (i.e., in discontinuous conduction mode (DCM)), a nonlinear correction is combined with the proposed technique to retain accuracy. The proposed methodology is well-verified by application to a 400 V to 400 V, 1 kW static IWPT link by simulations and experiments.
High-Temperature Chloride-Carbonate Phase Change Material: Thermal Performances and Modelling of a Packed Bed Storage System for Concentrating Solar Power Plants
Authors: Sau Giovanni Salvatore , Tripi Valerio , Tizzoni Anna Chiara , Liberatore Raffaele , Mansi Emiliana , Spadoni Annarita , Corsaro Natale , Capocelli Mauro , Delise Tiziano , Della Libera Anna
Published: August 2021

Molten salts eutectics are promising candidates as phase change materials (PCMs) for thermal storage applications, especially considering the possibility to store and release heat at high temperatures. Although many compounds have been proposed for this purpose in the scientific literature, very few data are available regarding actual applications. In particular, there is a lack of information concerning thermal storage at temperatures around 600 °C, necessary for the coupling with a highly efficient Rankine cycle powered by concentrated solar power (CSP) plants. In this contest, the present work deals with a thermophysical behavior investigation of a storage heat exchanger containing a cost-effective and safe ternary eutectic, consisting of sodium chloride, potassium chloride, and sodium carbonate. This material was preliminarily and properly selected and characterized to comply with the necessary melting temperature and latent enthalpy. Then, an indirect heat exchanger was considered for the simulation, assuming aluminum capsules to confine the PCM, thus obtaining the maximum possible heat exchange surface and air at 5 bar as heat transfer fluid (HTF). The modelling was carried out setting the inlet and outlet air temperatures at, respectively, 290 °C and 550 °C, obtaining a realistic storage efficiency of around 0.6. Finally, a conservative investment cost was estimated for the storage system, demonstrating a real possible economic benefit in using these types of materials and heat exchange geometries, with the results varying, according to possible manufacturing prices, in a range from 25 to 40 EUR/kWh.
Permanent Gas Emission from the Seyakha Crater of Gas Blowout, Yamal Peninsula, Russian Arctic
Authors: Bogoyavlensky Vasily , Bogoyavlensky Igor , Nikonov Roman , Yakushev Vladimir , Sevastyanov Viacheslav
Published: August 2021

The article is devoted to the four-year (2017–2020) monitoring of gas emissions from the bottom of the Seyakha Crater, located in the central part of the Yamal Peninsula (north of Western Siberia). The crater was formed on 28 June 2017 due to a powerful blowout, self-ignition and explosion of gas (mainly methane) at the site of a heaving mound in the river channel. On the basis of a comprehensive analysis of expeditionary geological and geophysical data (a set of geophysical equipment, including echo sounders and GPR was used) and remote sensing data (from space and with the use of UAVs), the continuing nature of the gas emissions from the bottom of the crater was proven. It was revealed that the area of gas seeps in 2019 and 2020 increased by about 10 times compared to 2017 and 2018. Gas in the cryolithosphere of the Arctic exists in free and hydrated states, has a predominantly methane composition, whereas this methane is of a biochemical, thermogenic and/or mixed type. It was concluded that the cryolithosphere of Yamal has a high level of gas saturation and is an almost inexhaustible unconventional source of energy resources for the serving of local needs.
Impact of PV System Tracking on Energy Production and Climate Change
Authors: Ahmed Waqas , Sheikh Jamil , Mahmud M.
Published: August 2021

Green energy by PV systems reduces the dependence on fossil fuel-based power plants. Maximizing green energy to meet the demand reduces the burden on conventional power plants, hence lesser burning and greenhouse gases (GHG) emissions. For this purpose, this study draws a relationship between tracking schemes of the PV systems to GHG mitigation potential. The best fit location for detailed analyses is selected among the 15 most populous cities of Australia. The solar radiation potential is increased to 7.78 kWh/m2/d through dual axes tracking compared to 7.54, 6.82, 5.94, 5.73 kWh/m2/d through the one axis, azimuth based, fixed-tilted, and fixed-horizontal surface schemes, respectively. Through the dual axes tracking scheme, a 1 MW PV system per annum energy output avoids the burning of 796,065.3 L of gasoline, 4308.7 barrels of crude oil which is equal to the mitigation of 1852.7 tCO2 equivalent GHGs. Concisely, the PV system, through its green energy output, can avoid the release of greenhouse gases from fossil-fuel plants to tackle climate change more effectively.
Environmental Life Cycle Assessment of Refrigerator Modelled with Application of Various Electricity Mixes and Technologies
Authors: Lewandowska Anna , Kurczewski Przemysław , Joachimiak-Lechman Katarzyna , Zabłocki Marek
Published: August 2021

Improving national electricity mixes and increasing a share of renewable energy covered by credible and reliable tracking systems are vital topics, also in a context of life cycle assessment. There are many publications devoted to the relevance of energy in the life cycle of products, but only few LCA examples applying residual mixes have been found in the literature. The paper presents the results of an LCA study for a refrigerator calculated with using different electricity mixes and technologies. The life cycle was divided into eight stages and the electricity consumption was modelled as renewable energy, national residual mix, or national supplier mix. Electricity mixes for three different countries were selected and used. The study aimed to answer the following questions: “what are the most relevant elements in the life cycle of the analysed refrigerator?”, “do the elements change if various electricity mixes are applied?”, and “what differences are there in the environmental impact of electricity generation modelled as residual and supplier mixes?”. From the life cycle perspective, not only may differences in national electricity systems between countries turn out to be important, but equally significant may be the choice between different types of mixes for a certain country.
Parental Attitudes and the Attitude to Energy Saving among Young People with ASD
Authors: Gagat-Matuła Anna , Frączek Bożena , Dewalska-Opitek Anna
Published: August 2021

The aim of the study was to determine the association between parental attitudes and saving energy in the perception of young people with autism spectrum (13–17 years old). The character of studies was pilot. The diagnostic survey method was used, along with The Scale of Parental Attitudes by M. Plopa and an authored survey questionnaire. Deliberate random sampling was applied. Statistical analysis showed that an attitude towards autonomy manifested by mothers, as well as an attitude of acceptance in mothers and fathers, turned out to be significant in adolescents with ASD showing a positive attitude towards energy saving, and these correlations were positive, strong and statistically significant. An overly protective attitude of mothers turned out to be significant in shaping a negative attitude towards energy saving among adolescents with ASD with a significant, positive moderate correlation. Regression analysis showed that the perception of the father’s parental attitude as accepting was a statistically significant predictor of a positive attitude towards energy saving, and that the perception of the mother’s parental attitude as demanding and overly protective was a statistically significant predictor of a negative attitude to energy saving. The achieved results acquire an additional aspect, i.e., a financial dimension in the household budget.
Experimental Study on the Initiation and Propagation of Multi-Cluster Hydraulic Fractures within One Stage in Horizontal Wells
Authors: Bi Zhenhui , Wang Lei , Yang Hanzhi , Guo Yintong , Chang Xin , Zhou Jun
Published: August 2021

Competitive propagation of fractures initiated from multiple perforation clusters is universal in hydraulic fracturing of unconventional reservoirs, which largely influences stimulation. However, the propagation mechanism of multi-fractures has not been fully revealed for the lack of a targeted laboratory observation. In this study, a physical simulation experiment system was developed for investigating the initiation and propagation of multi-cluster hydraulic fractures. Different from the traditional hydro-fracking test system, the new one was equipped with a multi-channel shunting module and a strain monitoring system, which could guarantee the full fracture extension at each perforation clusters and measure the internal deformation of specimens, respectively. Several groups of true tri-axial fracturing tests were performed, considering the factors of in situ stress, cluster spacing, pumping rate, and bedding structures. The results showed that initiation of multi-cluster hydraulic fractures within one stage could be simultaneous or successive according to the difference of the breakdown pressure and fracturing fluid injection. For simultaneous initiation, the breakdown pressure of the subsequent fracture was lower than or equal to the value of the previous fracture. Multiple fractures tended to attract and merge. For successive initiation, the breakdown pressures of fractures were gradually increasing. The subsequent fracture tended to intersect with or deviated from the previous fracture. Multiple fractures interaction was aggravated by the decrease of horizontal stress difference, bedding number and cluster spacing, and weakened by the increase of pump rate. The propagation area of multiple fractures increased with the pump rate, decreased with the cluster spacing. The strain response characteristics corresponded with the initiation and propagation of fracture, which was conducive to understanding the process of the fracturing. The test results provide a basis for optimum design of hydraulic fracturing.
Techno-Economic Analysis of the Oxy-Fuel Combustion Power Cycles with Near-Zero Emissions
Authors: Kindra Vladimir , Rogalev Andrey , Lisin Evgeny , Osipov Sergey , Zlyvko Olga
Published: August 2021

This paper is devoted to improvement of environmental safety in hydrocarbon-firing TPPs. Despite the development of renewable power sources, the number of traditional power production facilities continues its growth. The toxic emission mitigation in traditional TPPs has been deeply investigated, but the problem of greenhouse gas atmospheric emissions is of topical interest. Oxy-fuel technology reduces CO2 emissions and is highly efficient and environmentally safe. Also, it requires relatively low capital investments. Thermal efficiency analysis shows that the Allam cycle facilities have the best efficiency. Their thermodynamic parameters can be optimized with minimal primary costs and capital investments. This newly developed analysis was used to compare the investment efficiency of projects for the buildup of oxy-fuel and combined cycle facilities. Without emission quote payments, the NPV of combined cycle projects is 16% higher, as well as having a lower DPP. The electricity production primary costs in oxy-fuel and combined cycle facilities are similar, which reflects the technologies’ similarity and similar fuel costs. Implementation of carbon dioxide emission quote marketing makes oxy-fuel facilities more investment-attractive. Parametric studies show that when Russia implements CO2 emission quotes compatible with the current EU level, an oxy-fuel facility erection project will be financially reasonable. Thus, it can be concluded that the construction of oxy-fuel power plants is one of the most promising and investment-attractive solutions to reduce CO2 emissions in the energy sector for large industrialized countries. The managerial consequences of their implementation will include the stabilization of greenhouse gas emissions while ensuring the financial stability of the energy industry.
Sensorless Speed Control of Brushed DC Motor Based at New Current Ripple Component Signal Processing
Authors: Vidlak Michal , Gorel Lukas , Makys Pavol , Stano Michal
Published: August 2021

Signal processing of the brushed DC motor current was developed in this paper to obtain information about a rotor speed from a measured motor current. The brushed DC motor current contains a signal with a frequency proportional to the rotor speed. This signal is the outcome of a commutation process occurring in the brushed DC motor, and it is called a ripple component. Since the number of ripples in the measured motor current per one rotation is constant, the rotor speed can be estimated. A discrete bandpass filter with a floating bandwidth was developed as the main part of signal processing. This new interpretation of the bandpass filter was used to extract a frequency of the ripple component from the measured motor current. This frequency was used to acquire information about the estimated rotor speed. The estimated speed was set as a feedback value to a cascade control structure to provide sensorless speed control. The advantages and limitations of this approach are presented in this paper. Based on simulations and experimental results, it was confirmed that the proposed sensorless speed control is robust, accurate, and works precisely in a wide range of speeds.
Crude Oil–Brine–Rock Interactions in Tight Chalk Reservoirs: An Experimental Study
Authors: Mohammadkhani Samira , Anabaraonye Benaiah U. , Afrough Armin , Mokhtari Rasoul , Feilberg Karen Louise
Published: August 2021

We present a systematic study of crude oil–brine–rock interactions in tight chalk cores at reservoir conditions. Flooding experiments are performed on outcrops (Stevns Klint) as well as on reservoir core plugs from Dan field, the Ekofisk and Tor formations. These studies are carried out in core plugs with reduced pore volumes, i.e., short core samples and aged with a dynamic ageing method. The method was evaluated by three different oil compositions. A series of synthetic multicomponent brines and designed fluid injection scenarios are investigated; injection flow rates are optimized to ensure that a capillary-dominant regime is maintained. Changes in brine compositions and fluid distribution in the core plugs are characterized using ion chromatography and X-ray computed tomography, respectively. First, we show that polar components in the oil phase play a major role in wettability alteration during ageing; this controls the oil production behavior. We also show that, compared to seawater, both formation water and ten-times-diluted seawater are better candidates for enhanced oil recovery in the Dan field. Finally, we show that the modified flow zone indicator, a measure of rock quality, is likely the main variable responsible for the higher oil recoveries observed in Tor core samples.
Healthy and Faulty Experimental Performance of a Typical HVAC System under Italian Climatic Conditions: Artificial Neural Network-Based Model and Fault Impact Assessment
Authors: Rosato Antonio , Guarino Francesco , Sibilio Sergio , Entchev Evgueniy , Masullo Massimiliano , Maffei Luigi
Published: August 2021

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized by labeled normal and faulty data covering a wide range of operating and climatic conditions; (ii) providing an accurate simulation tool able to generate operation data for assisting further research in fault detection and diagnosis of HVAC units; and (iii) evaluating the impact of selected faults on occupant indoor thermo-hygrometric comfort, temporal trends of key operating system parameters, and electric energy consumptions.
The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review
Authors: Táczi István , Sinkovics Bálint , Vokony István , Hartmann Bálint
Published: August 2021

Global trends such as the growing share of renewable energy sources in the generation mix, electrification, e-mobility, and the increasing number of prosumers reshape the electricity value chain, and distribution systems are necessarily affected. These systems were planned, developed, and operated as a passive structure for decades with low level of observability. Due to the increasing number of system states, real time operation planning and flexibility services are the key in transition to an active grid management. In this pathway, distribution system state estimation (DSSE) has a great potential, but the real demonstration of this technique is in an early stage, especially on low-voltage level. This paper focuses on the gap between theory and practice and summarizes the limits of low-voltage DSSE implementation. The literature and the main findings follow the general structure of a state estimation process (meter placement, bad data detection, observability, etc.) giving a more essential and traceable overview structure. Moreover, the paper provides a comprehensive mapping of the possible use-cases state estimation and evaluates 27 different experimental sites to conclude on the practical applicability aspects.
IoT Solution for AI-Enabled PRIVACY-PREServing with Big Data Transferring: An Application for Healthcare Using Blockchain
Authors: Elhoseny Mohamed , Haseeb Khalid , Shah Asghar Ali , Ahmad Irshad , Jan Zahoor , Alghamdi Mohammed. I.
Published: August 2021

Internet of Things (IoT) performs a vital role in providing connectivity between computing devices, processes, and things. It significantly increases the communication facilities and giving up-to-date information to distributed networks. On the other hand, the techniques of artificial intelligence offer numerous and valuable services in emerging fields. An IoT-based healthcare solution facilitates patients, hospitals, and professionals to observe real-time and critical data. In the literature, most of the solution suffers from data intermission, high ethical standards, and trustworthiness communication. Moreover, network interruption with recurrent expose of sensitive and personal health data decreases the reliance on network systems. Therefore, this paper intends to propose an IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain. Firstly, the proposed algorithm uses a graph-modeling to develop a scalable and reliable system for gathering and transmitting data. In addition, it extracts the subset of nodes using the artificial intelligence approach and achieves efficient services for the healthcare system. Secondly, symmetric-based digital certificates are utilized to offer authentic and confidential transmission with communication resources using blockchain. The proposed algorithm is explored with existing solutions through multiple simulations and proved improvement in terms of realistic parameters.
The Role of Universities in Sustainable Development and Circular Economy Strategies
Authors: Sukiennik Marta , Zybała Karolina , Fuksa Dariusz , Kęsek Marek
Published: August 2021

In this article, the conditions and determinants of the introduction and development of the circular economy (CE) in Poland were presented against the background of the widely discussed essence of circular economy, with particular emphasis on the Polish mining sector. In addition, the European Green Deal—an action plan for a sustainable economy for the entire European Union, was discussed. Particular attention was paid to the role of human resources in the area of supporting research and stimulating innovation, and in the area of activating education and training. These issues are further discussed in the next chapter by presenting the forms, ways and scope of education for sustainable development in the international context. Another chapter describes the EIT Raw Materials KAVA 5 RIS competition in Poland (in Kraków) at AGH-UST from 2019 to 2022, an international project in a consortium of four universities whose main objective is to strengthen entrepreneurship in the V4 countries in line with the recommendations of the “Green action plan for SMEs” in a way to meet the requirements of circular economy. The initiatives and trainings carried out within the framework of this project were discussed, and the results of the student survey on the level of knowledge achieved regarding entrepreneurship in the area of the raw materials industry were analysed. The validity and necessity of continuous education in eco-responsible citizenship has thus been confirmed.
Evaluation of Dairy Wastewater Treatment Systems Using Carbon Footprint Analysis
Authors: Karolinczak Beata , Dąbrowski Wojciech , Żyłka Radosław
Published: August 2021

Modernisation of municipal and industrial wastewater treatment plants (WWTPs) should be carried out, taking into account its impact on global warming, e.g., through carbon footprint (CF) analysis. An important industrial sector in Poland is the dairy industry. In dairy WWTPs, the aerobic sewage sludge stabilisation applied thus far is being replaced by the anaerobic process. This change is positive due to the possibility of energy production, but it is unclear how it affects greenhouse gases (GHG) emissions. The aim of the research was to perform CF analysis for two scenarios of dairy WWTP operation. The analysis was based on the real operating data of the current system (current scenario) and project of its modernisation (alternative scenario). The current scenario consists of mechanical and dissolved air flotation (DAF) treatment, biological treatment in sequence batch reactors (SBRs), aerobic sewage sludge stabilisation and its final farmland usage. The alternative scenario assumes replacing aerobic stabilisation with anaerobic stabilisation and a combined heat and power (CHP) system. The CF calculations were based on empirical models, taking into account different emission input parameters, expressed in CO2 equivalents (CO2e). The total CF of the current scenario was 22 kg CO2e PE−1 year−1, while the alternative was 45 kg CO2e PE−1 year−1. The largest share in the current scenario belongs to emissions from WWTPs and energy use, while in the alternative, there is the addition of emissions from biogas use.
Development and Experimental Validation of an Adaptive, Piston-Damage-Based Combustion Control System for SI Engines: Part 1—Evaluating Open-Loop Chain Performance
Authors: Brusa Alessandro , Cavina Nicolò , Rojo Nahuel , Mecagni Jacopo , Corti Enrico , Ravaglioli Vittorio , Cucchi Matteo , Silvestri Nicola
Published: August 2021

This work is focused on the development and validation of a spark advance controller, based on a piston “damage” model and a predictive knock model. The algorithm represents an integrated and innovative way to manage both the knock intensity and combustion phase. It is characterized by a model-based open-loop algorithm with the capability of calculating with high accuracy the spark timing that achieves the desired piston damage in a certain period, for knock-limited engine operating conditions. Otherwise, it targets the maximum efficiency combustion phase. Such controller is primarily thought to be utilized under conditions in which feedback is not needed. In this paper, the main models and the structure of the open-loop controller are described and validated. The controller is implemented in a rapid control prototyping device and validated reproducing real driving maneuvers at the engine test bench. Results of the online validation process are presented at the end of the paper.
Concept of an Innovative Autonomous Unmanned System for Bathymetric Monitoring of Shallow Waterbodies (INNOBAT System)
Authors: Specht Mariusz , Stateczny Andrzej , Specht Cezary , Widźgowski Szymon , Lewicka Oktawia , Wiśniewska Marta
Published: August 2021

Bathymetry is a subset of hydrography, aimed at measuring the depth of waterbodies and waterways. Measurements are taken inter alia to detect natural obstacles or other navigational obstacles that endanger the safety of navigation, to examine the navigability conditions, anchorages, waterways and other commercial waterbodies, and to determine the parameters of the safe depth of waterbodies in the vicinity of ports, etc. Therefore, it is necessary to produce precise and reliable seabed maps, so that any hazards that may occur, particularly in shallow waterbodies, can be prevented, including the high dynamics of hydromorphological changes. This publication is aimed at developing a concept of an innovative autonomous unmanned system for bathymetric monitoring of shallow waterbodies. A bathymetric and topographic system will use autonomous unmanned aerial and surface vehicles to study the seabed relief in the littoral zone (even at depths of less than 1 m), in line with the requirements set out for the most stringent International Hydrographic Organization (IHO) order—exclusive. Unlike other existing solutions, the INNOBAT system will enable the coverage of the entire surveyed area with measurements, which will allow a comprehensive assessment of the hydrographic and navigation situation in the waterbody to be conducted.
Simulation and Performance Analysis of Air-Type PVT Collector with Interspaced Baffle-PV Cell Design
Authors: Ahn Jong-Gwon , Yu Ji-Suk , Boafo Fred Edmond , Kim Jin-Hee , Kim Jun-Tae
Published: August 2021

A Photovoltaic Thermal (PVT) collector produces heat and electricity simultaneously. Air-type PVT collector uses air as a transfer medium to take heat from PV back side surface. The performance of the air-type PVT collector is affected by design elements such as PV types, inside structures in heat collecting space (baffle or fins), the shape of the air pathway, etc. In this study, an advanced air-type PVT collector was designed with curved baffles (absorber) to improve thermal performance. Within the air-type PVT collector, PV cells were arranged in an interspaced design, and the curved baffles were located in the collecting space to increase heat efficiently. The absorber received solar radiation directly and was utilized as baffles for improving thermal performance. The air-type PVT collector was fabricated and tested in an outdoor environment considering the climatic conditions of Daejeon, Republic of Korea. In addition, based on experiment parameters and data, the annual thermal and electrical performances of the system were analyzed by simulation modeling using the TRNSYS program. Thermal and electrical efficiencies were 37.1% and 6.4% (according to module area) for outdoor test conditions, respectively. Numerical and experimental results were in good agreement with an error of 4% and 0.24% for thermal and electrical efficiencies, respectively. Annual heat gain was 644 kWh th/year, and generated power was 118 kWh el/year.
Special Issue: “Energy Data Analytics for Smart Meter Data”
Authors: Reinhardt Andreas , Pereira Lucas
Published: August 2021

Smart electricity meters are a cornerstone for the realization of next-generation electrical power grids [...]
Optimal Sizing of Hybrid Wind-Solar Power Systems to Suppress Output Fluctuation
Authors: Al-Shereiqi Abdullah , Al-Hinai Amer , Albadi Mohammed , Al-Abri Rashid
Published: August 2021

Harnessing wind energy is one of the fastest-growing areas in the energy industry. However, wind power still faces challenges, such as output intermittency due to its nature and output reduction as a result of the wake effect. Moreover, the current practice uses the available renewable energy resources as a fuel-saver simply to reduce fossil-fuel consumption. This is related mainly to the inherently variable and non-dispatchable nature of renewable energy resources, which poses a threat to power system reliability and requires utilities to maintain power-balancing reserves to match the supply from renewable energy resources with the real-time demand levels. Thus, further efforts are needed to mitigate the risk that comes with integrating renewable resources into the electricity grid. Hence, an integrated strategy is being created to determine the optimal size of the hybrid wind-solar photovoltaic power systems (HWSPS) using heuristic optimization with a numerical iterative algorithm such that the output fluctuation is minimized. The research focuses on sizing the HWSPS to reduce the impact of renewable energy resource intermittency and generate the maximum output power to the grid at a constant level periodically based on the availability of the renewable energy resources. The process of determining HWSPS capacity is divided into two major steps. A genetic algorithm is used in the initial stage to identify the optimum wind farm. A numerical iterative algorithm is used in the second stage to determine the optimal combination of photovoltaic plant and battery sizes in the search space, based on the reference wind power generated by the moving average, Savitzky–Golay, Gaussian and locally weighted linear regression techniques. The proposed approach has been tested on an existing wind power project site in the southern part of the Sultanate of Oman using a real weather data. The considered land area dimensions are 2 × 2 km. The integrated tool resulted in 39 MW of wind farm, 5.305 MW of PV system, and 0.5219 MWh of BESS. Accordingly, the estimated cost of energy based on the HWSPS is 0.0165 EUR/kWh.
Centralized Energy Prediction in Wireless Sensor Networks Leveraged by Software-Defined Networking
Authors: Nunez Segura Gustavo A. , Borges Margi Cintia
Published: August 2021

Resource Constraints in Wireless Sensor Networks are a key factor in protocols and application design. Furthermore, energy consumption plays an important role in protocols decisions, such as routing metrics. In Software-Defined Networking (SDN)-based networks, the controller is in charge of all control and routing decisions. Using energy as a metric requires such information from the nodes, which would increase packets traffic, impacting the network performance. Previous works have used energy prediction techniques to reduce the number of packets exchanged in traditional distributed routing protocols. We applied this technique in Software-Defined Wireless Sensor Networks (SDWSN). For this, we implemented an energy prediction algorithm for SDWSN using Markov chain. We evaluated its performance executing the prediction on every node and on the SDN controller. Then, we compared their results with the case without prediction. Our results showed that by running the Markov chain on the controller we obtain better prediction and network performance than when running the predictions on every node. Furthermore, we reduced the energy consumption for topologies up to 49 nodes for the case without prediction.