bluuebunny/crossref_metadata_2025 huggingface dataset:
Fractal Modeling of Polymer Plasma Laser Ablation, Plasma Plume Tsallis Entropy and Its q-Statistics Interpretation, Part I: Theory
Authors: Paun Maria-Alexandra , Paun Vladimir-Alexandru , Paun Viorel-Puiu
Published: February 2022

Polymer plasma produced by laser ablation is investigated in a theoretical manner. In relation to the fact that the charge carrier circulation is assumed to take place on fractal curves, the so-called fractality type, electrical charge transport can be resolved by an extended scale relativity method. In addition, an elegant mathematical model, utilizing a conjecture of fractal space-time, is elaborated. The complete solution and its graphical representation for temperature distribution in two-dimensional and three-dimensional cases are successfully introduced. The discrete physical behavior and irrevocable transformation of nanoscale microdomain substructures by laser ablation are realistically examined. Further, benefiting from the interpretation of the fractal analysis, each of the experimental results can be fairly explained. On top of that, this paper presents a proof of Tsallis nonextensive q-statistics, especially for the plasma plume studied. Tsallis entropy in direct connection with fractal dynamics and chaotic-type mechanics of the plasma plume and time-series representation of plasma temperature is introduced for the first time in the present publication, and the q-statistics of the plume plasma temperature are also studied, among others.
Dissipation-Induced Information Scrambling in a Collision Model
Authors: Li Yan , Li Xingli , Jin Jiasen
Published: February 2022

In this paper, we present a collision model to stroboscopically simulate the dynamics of information in dissipative systems. In particular, an all-optical scheme is proposed to investigate the information scrambling of bosonic systems with Gaussian environmental states. Varying the states of environments, in the presence of dissipation, transient tripartite mutual information of system modes may show negative value, signaling the appearance of information scrambling. We also find that dynamical indivisibility based non-Markovianity plays dual roles in affecting the dynamics of information.
What Should I Notice? Using Algorithmic Information Theory to Evaluate the Memorability of Events in Smart Homes
Authors: Houzé Étienne , Dessalles Jean-Louis , Diaconescu Ada , Menga David
Published: February 2022

With the increasing number of connected devices, complex systems such as smart homes record a multitude of events of various types, magnitude and characteristics. Current systems struggle to identify which events can be considered more memorable than others. In contrast, humans are able to quickly categorize some events as being more “memorable” than others. They do so without relying on knowledge of the system’s inner working or large previous datasets. Having this ability would allow the system to: (i) identify and summarize a situation to the user by presenting only memorable events; (ii) suggest the most memorable events as possible hypotheses in an abductive inference process. Our proposal is to use Algorithmic Information Theory to define a “memorability” score by retrieving events using predicative filters. We use smart-home examples to illustrate how our theoretical approach can be implemented in practice.
Executive Functions, Motor Development, and Digital Games Applied to Elementary School Children: A Systematic Mapping Study
Authors: Moron Victória Branca , Barbosa Débora Nice Ferrari , Sanfelice Gustavo Roese , Barbosa Jorge Luis Victória , Leithardt Daiana R. F. , Leithardt Valderi Reis Quietinho
Published: February 2022

Studies show that executive functions and motor development are associated with each other and with learning ability. A more technological lifestyle combined with digital culture should be considered a viable alternative to stimulate children’s development. Therefore, this study aimed to present a systematic mapping of the literature involving executive functions, motor development, and the use of digital games in intervention programs for elementary school children from 6 to 11 years old. Four databases were researched: PubMed, Scielo, Science Direct, and SCOPUS, including publications between 2012 and March 2021. The initial results indicated 4881 records. After the selection process, 15 manuscripts that presented the central theme of the study were selected. The main results indicated that intervention strategies are rather heterogeneous. Most of the studies demonstrated efficient results after intervention protocols, many of them were conducted in Europe, and 46% occurred in a school environment. No research was identified involving technological solutions using executive functions, motor development, and digital games in an integrated manner. Hence, this constitutes a field of future scientific research.
Gain and Phase Calibration of Uniform Rectangular Arrays Based on Convex Optimization and Neural Networks
Authors: Jie Xiran , Zheng Bolun , Gu Boxuan
Published: February 2022

A calibration method based on convex optimization (CVX) and neural networks is proposed for the large planar arrays of phased array three-dimensional imaging sonar systems. The method only needs an acoustic calibration source at an unknown position in the far field, and the direction of arrival (DOA) and gain and phase error are jointly estimated. The method uses a CVX algorithm to solve an optimization problem and initially estimates the DOA of the calibration source robustly. Subsequently, according to the estimation results, a neural network is used for fitting to obtain off-grid DOA estimation of the calibration source. Thereafter, spatial matched filtering is performed to obtain the gain and phase residual estimations. The root mean square error (RMSE) of the beam pattern calibrated by the method for uniform planar arrays can reach a value of 4.9542 × 10−5. The experimental results demonstrate the efficiency of the proposed method for gain and phase calibration.
Embedded Real-Time Simulator for Sensorless Control of Modular Multi-Level Converters
Authors: Tormo Daniel , Vidal-Albalate Ricardo , Idkhajine Lahoucine , Monmasson Eric , Blasco-Gimenez Ramon
Published: February 2022

This paper suggests the application of an embedded real-time simulator (eRTS) in the context of voltage–sensorless control of a modular multilevel power converter (MMC). This eRTS acts as an observer and ensures digital redundancy in the case of any fault occurring among the capacitor voltage sensors of the MMC submodules. Hence, in such a faulty situation, the MMC controller switches from the measured voltages to their estimated counterparts. As for the digital implementation, to ensure a high level of integration of the overall control system, the Xilinx Zynq-7020 system-on-chip field programmable gate array (SoC-FPGA) device was used. The controller was implemented in the hardwired ARM Cortex-A9 processor, with a 100 µs time step. Regarding the time-sensitive blocks (PWM, eRTS and measurements filtering), a full hardware implementation was privileged, using the FPGA fabric. The execution time of these blocks was 710 ns with a 100 MHz system clock, and the synchronization with the analog to digital acquisition chain was made with a 5 µs time resolution. The whole proof-of-concept system was experimentally tested, including the time/area evaluation of the implemented designs and the experimental validation of the eRTS estimations in both healthy and faulty scenarios.
Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks
Authors: Shahwar Tayyaba , Zafar Junaid , Almogren Ahmad , Zafar Haroon , Rehman Ateeq , Shafiq Muhammad , Hamam Habib
Published: February 2022

Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML). In this study, we present a hybrid classical–quantum machine learning model for the detection of Alzheimer’s using 6400 labeled MRI scans with two classes. Hybrid classical–quantum transfer learning is used, which makes it possible to optimally pre-process complex and high-dimensional data. Classical neural networks extract high-dimensional features and embed informative feature vectors into a quantum processor. We use resnet34 to extract features from the image and feed a 512-feature vector to our quantum variational circuit (QVC) to generate a four-feature vector for precise decision boundaries. Adam optimizer is used to exploit the adaptive learning rate corresponding to each parameter based on first- and second-order gradients. Furthermore, to validate the model, different quantum simulators (PennyLane, qiskit.aer and qiskit.basicaer) are used for the detection of the demented and non-demented images. The learning rate is set to 10−4 for and optimized quantum depth of six layers, resulting in a training accuracy of 99.1% and a classification accuracy of 97.2% for 20 epochs. The hybrid classical–quantum network significantly outperformed the classical network, as the classification accuracy achieved by the classical transfer learning model was 92%. Thus, a hybrid transfer-learning model is used for binary detection, in which a quantum circuit improves the performance of a pre-trained ResNet34 architecture. Therefore, this work offers a method for selecting an optimal approach for detecting Alzheimer’s disease. The proposed model not only allows for the automated detection of Alzheimer’s but would also speed up the process significantly in clinical settings.
HMM-Based Dynamic Mapping with Gaussian Random Fields
Authors: Li Hongjun , Barão Miguel , Rato Luís , Wen Shengjun
Published: February 2022

This paper focuses on the mapping problem for mobile robots in dynamic environments where the state of every point in space may change, over time, between free or occupied. The dynamical behaviour of a single point is modelled by a Markov chain, which has to be learned from the data collected by the robot. Spatial correlation is based on Gaussian random fields (GRFs), which correlate the Markov chain parameters according to their physical distance. Using this strategy, one point can be learned from its surroundings, and unobserved space can also be learned from nearby observed space. The map is a field of Markov matrices that describe not only the occupancy probabilities (the stationary distribution) as well as the dynamics in every point. The estimation of transition probabilities of the whole space is factorised into two steps: The parameter estimation for training points and the parameter prediction for test points. The parameter estimation in the first step is solved by the expectation maximisation (EM) algorithm. Based on the estimated parameters of training points, the parameters of test points are obtained by the predictive equation in Gaussian processes with noise-free observations. Finally, this method is validated in experimental environments.
An Accurate Bandgap Voltage Reference Ready-Indicator
Authors: Malatesta Rafailia , Souliotis George , Plessas Fotis , Vlassis Spyridon
Published: February 2022

An accurate indication that the bandgap voltage reference (BGR) circuit is settled on its nominal value is essential in analog or mixed signal systems. In this paper, a generic method for accurate bandgap voltage reference (BGR) Ready-Indication (RI) is proposed. A RI signal shows that the BGR operates correctly but also is used to enable PoR circuits when those employ the bandgap voltage to generate the required thresholds. In many low power applications, several systems are periodically switched on and off to save power and to increase battery life. In such systems, most circuits must remain off, while BGR is not in ready mode, saving battery energy. In other critical applications, the Ready-Indicator can ensure that critical systems can be set on and the reference voltage is within the operating range. In this paper, the introduced methodology is applied to different reference voltage generators. Initially, a BGR with Ready-Indication is presented extensively, including post-layout simulations results in 22 nm, while thereafter, the study of this method is extended in other BGR topologies. The universality of the proposed method is proved by verifying the operation in all the BGR topologies under study covering designs in 22 nm and 65 nm and with supply voltages 1.8 V, 1 V, 0.55 V.
Relation between Student Engagement and Demographic Characteristics in Distance Learning Using Association Rules
Authors: Jawthari Moohanad , Stoffa Veronika
Published: February 2022

Distance learning has made learning possible for those who cannot attend traditional courses, especially in pandemic periods. This type of learning, however, faces a challenge in keeping students engaged and interested. Furthermore, it is important to identify students who are in need of help to ensure that their progress does not deteriorate. First, the research identifies students’ engagement based on their behaviors in Virtual Learning Environment (VLE) and their performances in assessments. This research goal is to investigate the association/relationship between demographic characteristics and engagement level. It identifies less engaged students by using an unsupervised clustering model based on VLE interactions and assessments of submission-derived features. According to results, the two-level clustering model outperforms other models in regard to cluster separation using silhouette coefficient. Apriori algorithm is utilized to obtain a set of rules that connect demographic features to student engagement. Results show gender, highest education, studied credits, and number of previous attempts have positive correlation with engagement level in distance-based learning.
A Novel Fixed-Time Trajectory Tracking Strategy of Unmanned Surface Vessel Based on the Fractional Sliding Mode Control Method
Authors: Chen Dong , Zhang Jundong , Li Zhongkun
Published: February 2022

A novel sliding mode control method is proposed to achieve the trajectory tracking of the Unmanned Surface Vessel (USV) and effectively deal with the unmodeled dynamics and external unknown disturbances. First, a fixed-time fractional-order sliding mode control (FTFOSMC) strategy is proposed, combined with the fixed-time control theory and fractional-order control theory based on the sliding mode control method. The FTFOSMC strategy can improve the convergence velocity of the system, and effectively track the desired path, weakening the “chattering” effect in sliding mode control systems. Second, a fixed-time fractional-order sliding mode control strategy combined with the radial basis function neural network (RBF-FTFOSMC) was designed, which can effectively estimate the lumped uncertainties, such as the disturbance of external wind, wave, and current, and the unmodeled dynamics of the USV model. Then, the stability and effectiveness of the designed control strategy are guaranteed by the Lyapunov theory and the corresponding lemmas. Finally, a rigorous simulation experiment is designed to validate the effectiveness and stability of the proposed control strategy. The simulation results show that the control strategy can effectively achieve trajectory tracking of the USV, reduce the “chattering” phenomenon of sliding mode, and effectively estimate the lumped uncertainties.
Enhancement of the HILOMOT Algorithm with Modified EM and Modified PSO Algorithms for Nonlinear Systems Identification
Authors: Mahfuz Asif , Mannan Mohammad Abdul , Muyeen S. M.
Published: February 2022

Developing a mathematical model has become an inevitable need in studies of all disciplines. With advancements in technology, there is an emerging need to develop complex mathematical models. System identification is a popular way of constructing mathematical models of highly complex processes when an analytical model is not feasible. One of the many model architectures of system identification is to utilize a Local Model Network (LMN). Hierarchical Local Model Tree (HILOMOT) is an iterative LMN training algorithm that uses the axis-oblique split method to divide the input space hierarchically. The split positions of the local models directly influence the accuracy of the entire model. However, finding the best split positions of the local models presents a nonlinear optimization problem. This paper presents an optimized HILOMOT algorithm with enhanced Expectation–Maximization (EM) and Particle Swarm Optimization (PSO) algorithms which includes the normalization parameter and utilizes the reduced-parameter vector. Finally, the performance of the improved HILOMOT algorithm is compared with the existing algorithm by modeling the NOx emission model of a gas turbine and multiple nonlinear test functions of different orders and structures.
LMI-Based Model Predictive Current Control for an LCL-Filtered Grid-Connected Inverter under Unexpected Grid and System Uncertainties
Authors: Kim Yubin , Tran Thuy Vi , Kim Kyeong-Hwa
Published: February 2022

To guarantee a system stability and reliable operation of an inductor-capacitor-inductor (LCL)-filtered grid-connected inverter (GCI) under unexpected grid and system uncertainties, a linear matrix inequality (LMI)-based model predictive control (MPC) is presented in this paper. Even though the conventional MPC scheme is constructed by a simple concept, it is difficult to determine an optimized weighting matrix of the MPC cost function against parameter discrepancies. To overcome this problem, the MPC scheme is combined with LMI-based optimization. The system states are estimated by the LMI-based current-type observer in the stationary reference frame to implement the proposed scheme. Additionally, the MPC scheme is combined with the disturbance observer to eliminate offset error, which improves the reference tracking performance. In comparison with the other studies, the proposed control method ensures high robust control performance under grid voltage imbalance, parameter uncertainty, and frequency variation. In addition, the proposed approach achieves a robust active damping even for the grid impedance variation without the need of considering further damping method. The control design step is systematic and straightforward. Furthermore, unlike the conventional schemes, the proposed controller does not require an integral term and the 2nd harmonic compensation term to obtain a good reference tracking performance under grid imbalanced condition, which contributes to the reduction of the controller complexity by decreasing the order of the controller model. To verify the effectiveness of the proposed LMI-based MPC control scheme, the simulation and experiments are carried out by using prototype three-phase GCI. The comprehensive simulation and experimental results clearly demonstrate the robustness of the proposed current controller under various adverse test conditions with unexpected grid and system uncertainties.
A Novel Feature-Selection Method for Human Activity Recognition in Videos
Authors: Tweit Nadia , Obaidat Muath A. , Rawashdeh Majdi , Bsoul Abdalraoof K. , Al Zamil Mohammed GH.
Published: February 2022

Human Activity Recognition (HAR) is the process of identifying human actions in a specific environment. Recognizing human activities from video streams is a challenging task due to problems such as background noise, partial occlusion, changes in scale, orientation, lighting, and the unstable capturing process. Such multi-dimensional and none-linear process increases the complexity, making traditional solutions inefficient in terms of several performance indicators such as accuracy, time, and memory. This paper proposes a technique to select a set of representative features that can accurately recognize human activities from video streams, while minimizing the recognition time and memory. The extracted features are projected on a canvas, which keeps the synchronization property of the spatiotemporal information. The proposed technique is developed to select the features that refer only to progression of changes. The original RGB frames are preprocessed using background subtraction to extract the subject. Then the activity pattern is extracted through the proposed Growth method. Three experiments were conducted; the first experiment was a baseline to compare the classification task using the original RGB features. The second experiment relied on classifying activities using the proposed feature-selection method. Finally, the third experiment provided a sensitivity analysis that compares between the effect of both techniques on time and memory resources. The results indicated that the proposed method outperformed original RBG feature-selection method in terms of accuracy, time, and memory requirements.
New High-Gain Transformerless DC/DC Boost Converter System
Authors: Ahmed Hassan Yousif , Abdel-Rahim Omar , Ali Ziad M.
Published: February 2022

This article proposes a new high-gain transformerless dc/dc boost converter. Although they possess the ability to boost voltage at higher voltage levels, converter switching devices are under low voltage stress. The voltage stress on active switching devices is lower than the output voltage. Therefore, low-rated components are used to implement the converter. The proposed converter can be considered as a promising candidate for PV microconverter applications, where high voltage-gain is required. The principle of operation and the steady-state analysis of the converter in the continuous conduction mode are presented. A hardware prototype for the converter is implemented in the laboratory to prove the concept of operation.
Chipless RFID Sensing System for Precise Ethanol Determination in Alcoholic Solutions
Authors: Mulloni Viviana , Marchi Giada , Lorenzelli Leandro , Donelli Massimo
Published: February 2022

The paper presents a new methodology to assess the ethanol content in water solutions. The system exploits a microwave resonator embedded in a polycarbonate sampling cell. The resonator frequency depends on the ethanol percentage and is monitored in real-time through a non-contacting probe. The method shows a precision better than 1% in ethanol percentage in the range of 1–30%, but can be applied also outside this range. Moreover, the methodology can be further improved or adapted for the analysis of other types of aqueous solutions or liquid mixtures upon evaluation of their dielectric properties.
Palmprint Translation Network for Cross-Spectral Palmprint Recognition
Authors: Ma Ye , Guo Zhenhua
Published: February 2022

Nowadays, palmprint recognition has been well developed since plenty of promising algorithms have emerged. Palmprints have also been applied under various authentication scenarios. However, these approaches are designed and tested only when the registration images and probe images are taken under the same illumination condition; thus, a cross-spectral performance degradation is speculated. Therefore, we test the cross-spectral performance of extended binary orientation co-occurrence vector (E-BOCV), which is unsatisfactory, illustrating the necessity of a specific algorithm. Trying to achieve the cross-spectral palmprint recognition with image-to-image translation, we have made efforts in the following two aspects. First, we introduce a scheme to evaluate the images of different spectra, which is a reliable basis for translation direction determination. Second, in this paper, we propose a palmprint translation convolutional neural network (PT-net) and the performance of translation from NIR to blue is tested on the PolyU multispectral dataset, which achieves a 91% decrease in Top-1 error using E-BOCV as the recognition framework.
SUKRY: Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi for Classifying IoT Botnet Attacks
Authors: Syamsuddin Irfan , Barukab Omar Mohammed
Published: February 2022

The focus of this research is the application of the k-Nearest Neighbor algorithm in terms of classifying botnet attacks in the IoT environment. The kNN algorithm has several advantages in classification tasks, such as simplicity, effectiveness, and robustness. However, it does not perform well in handling large datasets such as the Bot-IoT dataset, which represents a huge amount of data about botnet attacks on IoT networks. Therefore, improving the kNN performance in classifying IoT botnet attacks is the main concern in this study by applying several feature selection techniques. The whole research process was conducted in the Rapidminer environment using three prebuilt feature selection techniques, namely, Information Gain, Forward Selection, and Backward Elimination. After comparing accuracy, precision, recall, F1 score and processing time, the combination of the kNN algorithm and the Forward Selection technique (kNN-FS) achieves the best results among others, with the highest level of accuracy and the fastest execution time among others. Finally, kNN-FS is used in developing SUKRY, which stands for Suricata IDS with Enhanced kNN Algorithm on Raspberry Pi.
Modified Yolov3 for Ship Detection with Visible and Infrared Images
Authors: Chang Lena , Chen Yi-Ting , Wang Jung-Hua , Chang Yang-Lang
Published: February 2022

As the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the processing efficiency and detection accuracy at the same time, the study presented an improved deep-learning network based on You only look once version 3 (Yolov3) for all-day ship detection with visible and infrared images. Yolov3 network can simultaneously improve the recognition ability of large and small objects through multiscale feature-extraction architecture. Considering reducing computational time and network complexity with relatively competitive detection accuracy, the study modified the architecture of Yolov3 by choosing an appropriate input image size, fewer convolution filters, and detection scales. In addition, the reduced Yolov3 was further modified with the spatial pyramid pooling (SPP) module to improve the network performance in feature extraction. Therefore, the proposed modified network can achieve the purpose of multi-scale, multi-type, and multi-resolution ship detection. In the study, a common self-built data set was introduced, aiming to conduct all-day and real-time ship detection. The data set included a total of 5557 infrared and visible light images from six common ship types in northern Taiwan ports. The experimental results on the data set showed that the proposed modified network architecture achieved acceptable performance in ship detection, with the mean average precision (mAP) of 93.2%, processing 104 frames per second (FPS), and 29.2 billion floating point operations (BFLOPs). Compared with the original Yolov3, the proposed method can increase mAP and FPS by about 5.8% and 8%, respectively, while reducing BFLOPs by about 47.5%. Furthermore, the computational efficiency and detection performance of the proposed approach have been verified in the comparative experiments with some existing convolutional neural networks (CNNs). In conclusion, the proposed method can achieve high detection accuracy with lower computational costs compared to other networks.
Virtual Prototyping of the Cement Transporter Including Strength Criterion Based on Geotechnical Boundary Conditions
Authors: Nieśpiałowski Krzysztof , Tokarczyk Jarosław , Bartoszek Sławomir , Kanty Piotr , Kurek Norbert , Dymarek Andrzej , Dzitkowski Tomasz
Published: February 2022

Regarding the vehicles used in civil engineering, some are manufactured in large quantities and some (especially innovative ones) are still prototypes manufactured for tests or rare use. The latter describes the case of equipment for mass mixing (mass stabilization) technology which is not widely used compared to other types of geotechnical equipment. The purpose of this paper is to present a research and development project on an innovative cement transporter designed for mass mixing. Three-dimensional models and advanced finite element method (FEM) calculations are used to validate the design of the most important part of the cement transporter—the frame connecting the undercarriage and the upper carriage. The results presented in the paper confirm that the design based on the strength criterion and boundary conditions from geotechnical safety requirements can be used for designing the parts of the prototype vehicle. It is concluded that in innovative vehicle design for geotechnical purposes, the analyses may extend beyond the standard static analyses.
Multi-Step Short-Term Building Energy Consumption Forecasting Based on Singular Spectrum Analysis and Hybrid Neural Network
Authors: Wei Shangfu , Bai Xiaoqing
Published: February 2022

Short-term building energy consumption forecasting is vital for energy conservation and emission reduction. However, it is challenging to achieve accurate short-term forecasting of building energy consumption due to its nonlinear and non-stationary characteristics. This paper proposes a novel hybrid short-term building energy consumption forecasting model, SSA-CNNBiGRU, which is the integration of SSA (singular spectrum analysis), a CNN (convolutional neural network), and a BiGRU (bidirectional gated recurrent unit) neural network. In the proposed SSA-CNNBiGRU model, SSA is used to decompose trend and periodic components from the original building energy consumption data to reconstruct subsequences, the CNN is used to extract deep characteristic information from each subsequence, and the BiGRU network is used to model the dynamic features extracted by the CNN for time series forecasting. The subsequence forecasting results are superimposed to obtain the predicted building energy consumption results. Real-world electricity and natural gas consumption datasets of office buildings in the UK were studied, and the multi-step ahead forecasting was carried out under three different scenarios. The simulation results indicate that the proposed model can improve building energy consumption forecasting accuracy and stability.
Mining Technologies Innovative Development: Industrial, Environmental and Economic Perspectives
Authors: Zhironkin Sergey , Szurgacz Dawid
Published: February 2022

This Special Issue of Energies contains the successful invited submissions [...]
A Production Performance Model of the Cyclic Steam Stimulation Process in Multilayer Heavy Oil Reservoirs
Authors: Fan Tingen , Xu Wenjiang , Zheng Wei , Jiang Weidong , Jiang Xiuchao , Wang Taichao , Dong Xiaohu
Published: February 2022

Cyclic steam stimulation (CSS) is a typical enhanced oil recovery method for heavy oil reservoirs. In this paper, a new model for the productivity of a CSS well in multilayer heavy oil reservoirs is proposed. First, for the steam volume of each formation layer, it is proposed that the total steam injection volume will be split by the formation factor (Kh) for the commingled steam injection mode. Then, based on the equivalent flow resistance principle, the productivity model can be derived. In this model, the heavy oil reservoir is composed of a cold zone, a hot water zone, and a steam zone. Next, using the energy conservation law, the equivalent heating radius can be calculated with the consideration of the steam overlay. Simultaneously, a correlation between the threshold pressure gradient (TPG) and oil mobility is also applied for the productivity formula in the cold zone and the hot water zone. Afterward, this model is validated by comparing the simulation results with the results of an actual CNOOC CSS well. A good agreement is observed, and the relative error of the cumulative oil production is about 2.20%. The sensitivity analysis results indicate that the effect of the bottom hole pressure is the most significant, followed by the TPG, and the effect of the steam overlay is relatively slight. The formation factor can affect the splitting of the steam volume in each layer; thus, the oil production rate will be impacted. The proposed mathematical model in this paper provides an effective method for the prediction of preliminary productivity of a CSS well in a multilayer heavy oil reservoir.
Regression Models for Performance Prediction of Internally-Cooled Liquid Desiccant Dehumidifiers
Authors: Pakari Ali , Ghani Saud
Published: February 2022

In this study, using response surface methodology and central composite design, regression models were developed relating 12 input factors to the supply air outlet humidity ratio and temperature of 4-fluid internally-cooled liquid desiccant dehumidifiers. The selected factors are supply air inlet temperature, supply air inlet humidity ratio, exhaust air inlet temperature, exhaust air inlet humidity ratio, liquid desiccant inlet temperature, liquid desiccant concentration, liquid desiccant flow rate, supply air mass flow rate, the ratio of exhaust to supply air mass flow rate, the thickness of the channel, the channel length, and the channel width of the dehumidifier. The designed experiments were performed using a numerical two-dimensional heat and mass transfer model of the liquid desiccant dehumidifier. The numerical model predicted the measured values of the supply air outlet humidity ratio within 6.7%. The regression model’s predictions of the supply air outlet humidity ratio matched the numerical model’s predictions and measured values within 4.5% and 7.9%, respectively. The results showed that the input factors with the most significant effect on the dehumidifying process in order of significance from high to low are as follows: supply air inlet humidity ratio, liquid desiccant concertation, length of channels, and width of channels. The developed regression models provide a straightforward means for performance prediction and optimization of internally-cooled liquid desiccant dehumidifiers.
Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network
Authors: Rao Jinjun , Li Bo , Zhang Zhen , Chen Dongdong , Giernacki Wojciech
Published: February 2022

In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.
An Adaptive Energy Optimization Method of Hybrid Battery-Supercapacitor Storage System for Uncertain Demand
Authors: Wu Xiaomin , Li Shaoyi , Gan Shengfeng , Hou Changhui
Published: February 2022

To address the problem of DC bus voltage surge caused by load demand fluctuation in an off-grid microgrid, here, an adaptive energy optimization method based on a hybrid energy-storage system to maintain the stability of DC bus voltage is presented. The adaptive energy optimization method consists of three parts: the average filtering algorithm, extracting fluctuating power in demand load; the supercapacitor terminal voltage control, keeping the terminal voltage of the supercapacitor near reference; and the battery pack balance control, adjusting the charge/discharge to balance the state of charge for battery packs. In this proposed method, after extracting the fluctuating power by the low-pass filter when the demand load fluctuates, the battery packs release the power to offset the low-frequency fluctuation load and the supercapacitor to instantaneously compensate the high-frequency fluctuation power, to prolong the service life of batteries and maintain the stability of DC bus voltage. The effectiveness of the proposed adaptive energy optimization method is validated and is confirmed to maintain the stable operation of the off-grid microgrid, extend the cycle life of batteries in off-grid microgrid simulations and experiments.
Technologies of Wind Turbine Blade Repair: Practical Comparison
Authors: Mishnaevsky Leon , Frost-Jensen Johansen Nicolai , Fraisse Anthony , Fæster Søren , Jensen Thomas , Bendixen Brian
Published: February 2022

Maintenance and repair of wind turbines contribute to the higher costs of wind energy. In this paper, various technologies of structural repair of damaged and broken wind turbine blades are compared. The composite plates, mimicking damaged blade parts, were damaged and repaired, using various available curing and bonding technologies. Technologies of repair with hand layup lamination, vacuum repair with hand layup and infusion, ultraviolet repair and high temperature thermal curing were compared. The repaired samples were tested under tensile static and fatigue tests, and subject to microscopic X-ray investigations. It was observed that both the strength of the repaired structures and the porosity depend on the repair technology used. Vacuum-based technologies lead to relatively stiff and lower-strength repaired plates, while ultraviolet-curing technologies lead to average stiffness and high strength. High-temperature vacuum curing leads to the highest maximum stress. Hand layup (both vacuum and without vacuum) leads to high post-repair porosity in the adhesive and scarf, while vacuum infusion leads to low porosity. Fatigue lifetime generally follows the trend of porosity. There exist risks of micro-damaging the parent laminate and the formation of residual stresses in the repaired structure.
CO2 Emissions and Economy of Co-Firing Carbonized Wood Pellets at Coal-Fired Power Plants: The Case of Overseas Production of Pellets and Use in Japan
Authors: Ashizawa Masami , Otaka Maromu , Yamamoto Hiromi , Akisawa Atsushi
Published: February 2022

CO2 emissions reduction from coal-fired power plants is an urgent issue in Japan, as well as around the world. The purpose of this study is to estimate the CO2 emissions and economy of using carbonized wood pellets produced overseas and co-fired at coal-fired power plants in Japan. We examined carbonized wood pellets produced in Canada and Vietnam, since those countries are major exporters of wood pellets for Japan. The results obtained are as follows: (1) The CO2 emissions and calculated cost per calorific value of carbonized wood pellets (CP25), which have a fixed carbon content of 25 wt.%, are lower than those of wood pellets at the port of import in Japan. When the fixed carbon of carbonized biomass is controlled at 25 wt.% or more via a carbonizer, sufficient pyrolysis gas (the heat source used for drying and carbonization without auxiliary fuel) can be obtained. (2) Carbonized wood pellets manufactured in Vietnam are more economical than those manufactured in Canada, since the resource of wood is less expensive and the transportation distance is shorter from Vietnam compared to Canada. (3) When carbonized wood pellets at CP25 are co-fired in coal-fired power plants, they do not affect the cost of the electricity generated, even if the carbonized pellets are blended at a high ratio.
Automatic Generation Control in Modern Power Systems with Wind Power and Electric Vehicles
Authors: Ullah Kaleem , Basit Abdul , Ullah Zahid , Albogamy Fahad R. , Hafeez Ghulam
Published: February 2022

The modern power system is characterized by the massive integration of renewables, especially wind power. The intermittent nature of wind poses serious concerns for the system operator owing to the inaccuracies in wind power forecasting. Forecasting errors require more balancing power for maintaining frequency within the nominal range. These services are now offered through conventional power plants that not only increase the operational cost but also adversely affect the environment. The modern power system emphasizes the massive penetration of wind power that will replace conventional power plants and thereby impact the provision of system services from conventional power plants. Therefore, there is an emergent need to find new control and balancing solutions, such as regulation reserves from wind power plants and electric vehicles, without trading off their natural behaviors. This work proposes real-time optimized dispatch strategies for automatic generation control (AGC) to utilize wind power and the storage capacity of electric vehicles for the active power balancing services of the grid. The proposed dispatch strategies enable the AGC to appropriately allocate the regulating reserves from wind power plants and electric vehicles, considering their operational constraints. Simulations are performed in DIgSILENT software by developing a power system AGC model integrating the generating units and an EVA model. The inputs for generating units are considered by selecting a particular day of the year 2020, when wind power plants are generating high power. Different coordinated dispatch strategies are proposed for the AGC model to incorporate the reserve power from wind power plants and EVs. The performance of the proposed dispatch strategies is accessed and discussed by obtaining responses of the generating units and EVs during the AGC operation to counter the initial power imbalances in the network. The results reveal that integration of wind power and electric vehicles alongside thermal power plants can effectively reduce real-time power imbalances acquainted in power systems due to massive penetration of wind power that subsequently improves the power system security. Moreover, the proposed dispatch strategy reduces the operational cost of the system by allowing the conventional power plant to operate at their lower limits and therefore utilizes minimum reserves for the active power balancing services.
A Vision-Based Detection and Spatial Localization Scheme for Forest Fire Inspection from UAV
Authors: Lu Kangjie , Xu Renjie , Li Junhui , Lv Yuhao , Lin Haifeng , Liu Yunfei
Published: February 2022

Forest fires have the characteristics of strong unpredictability and extreme destruction. Hence, it is difficult to carry out effective prevention and control. Once the fire spreads, devastating damage will be caused to natural resources and the ecological environment. In order to detect early forest fires in real-time and provide firefighting assistance, we propose a vision-based detection and spatial localization scheme and develop a system carried on the unmanned aerial vehicle (UAV) with an OAK-D camera. During the high incidence of forest fires, UAVs equipped with our system are deployed to patrol the forest. Our scheme includes two key aspects. First, the lightweight model, NanoDet, is applied as a detector to identify and locate fires in the vision field. Techniques such as the cosine learning rate strategy and data augmentations are employed to further enhance mean average precision (mAP). After capturing 2D images with fires from the detector, the binocular stereo vision is applied to calculate the depth map, where the HSV-Mask filter and non-zero mean method are proposed to eliminate the interference values when calculating the depth of the fire area. Second, to get the latitude, longitude, and altitude (LLA) coordinates of the fire area, coordinate frame conversion is used along with data from the GPS module and inertial measurement unit (IMU) module. As a result, we experiment with simulated fire in a forest area to test the effectiveness of this system. The results show that 89.34% of the suspicious frames with flame targets are detected and the localization error of latitude and longitude is in the order of 10−5 degrees; this demonstrates that the system meets our precision requirements and is sufficient for forest fire inspection.
Evaluating Potential Respiratory Benefits of Forest-Based Experiences: A Regional Scale Approach
Authors: Droli Maurizio , Sigura Maurizia , Vassallo Fabio Giuseppe , Droli Giovanni , Iseppi Luca
Published: February 2022

Background: Several studies have suggested the possibility of obtaining specific respiratory benefits by experiencing forests and other natural resources. Despite this, forests have never been considered according to such potential. This study aims to compare municipalities by considering the absence/presence of tree species generating ‘above threshold’ potential respiratory benefits. Methods: The autonomous Region of Friuli Venezia Giulia in Italy has been assumed as a research area. The natural resource based view (NRBV), postulating the strategic role played by natural resources in achieving both above-average (thus ‘valuable’) and ‘concentrated’ (thus ‘rare’ among competitors) performance, has been adopted. The literature reviews dealing with potential respiratory benefits of biogenic organic compounds (BVOCs) emitted by trees, published within the ‘forest therapy’ research field, have been adopted. Three analysis models rating tree species by their potential respiratory benefits in ‘holistic-general’ (P1), ‘particular’ (P2), and ‘dynamic” terms (P3) have been outlined. The resulting overall potentials of tree species have been assessed by adopting the well-rooted Hollerith distance (HD) model. Tree species have been rated “1” when they satisfy one or more of 58 potential respiratory benefits. Municipalities have been ranked by considering the surface area covered by forest types whose dominant tree species achieve above-average potential respiratory benefits. QGIS software has been adopted to geographically reference the results obtained. Results: (P1) Valuable municipalities include those covered by both coniferous and deciduous forests; (P2–3) Municipalities achieving the highest potential respiratory benefits, in both particular and dynamic terms, have been mapped. Discussion: Forest-based initiatives that are running in the preselected municipalities can be both further improved and diversified in a targeted way. Conclusions: Despite some limitations mostly embedded in the concept of ‘model’, this study allows scholars to reduce uncertainties when locating municipalities in which to conduct local-scale experiments.
Drought Sensitivity and Resilience of Oak–Hickory Stands in the Eastern United States
Authors: Au Tsun Fung , Maxwell Justin T.
Published: February 2022

Forest composition in the eastern United States (US) has been shifting from an oak–hickory to maple–beech assemblage, but whether there are species-specific differences within these oak–hickory stands in their responses and recovery from drought remains unclear. Here, we examined drought responses and resilience derived from radial growth of 485 co-occurring Carya ovata and Quercus alba individual trees at 15 forests in the eastern US. Water availability over the growing season (May to August) of the current year controls growth variability of both C. ovata and Q. alba. Drought that occurred in June caused the greatest growth reduction for both species while interspecific differences inof drought-induced growth reduction was found in July, where Q. alba experienced stronger reduction than C. ovata. Both species are resilient to early growing season drought, but late growing season drought caused larger drought legacy effects for Q. alba. The increasing drought frequency and intensity will have a more prominent impact in oak–hickory stands in the eastern US. The species composition of a forest along with species-specific responses and recovery is likely to be a critical control on forest productivity and species abundance.