bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
Authors: Teng Rui , Han Hongwei , Chen Jilin
Published: June 2022

This paper proposes a novel method to efficiently construct the aerocapture attainability set based on convex optimization. Using dimensionality reduction, constructing the attainability set is equivalent to solving a set of discrete points on its boundary. As solving each of the boundary points is a typical nonlinear optimal control problem, a sequential convex programming method is adopted. The efficiency and accuracy of the proposed algorithm is demonstrated by high-fidelity numerical results. This is the first time that the configuration of the aerocapture attainability set is precisely described by the state variables at atmospheric exit. Since the quantification of the set is significant for assessing the feasibility of performing an aerocapture maneuver, the proposed method can be used as a reliable tool for systematic design for aerocapture mission.
Meshless Electrophysiological Modeling of Cardiac Resynchronization Therapy—Benchmark Analysis with Finite-Element Methods in Experimental Data
Authors: Albors Carlos , Lluch Èric , Gomez Juan Francisco , Cedilnik Nicolas , Mountris Konstantinos A. , Mansi Tommaso , Khamzin Svyatoslav , Dokuchaev Arsenii , Solovyova Olga , Pueyo Esther , Sermesant Maxime , Sebastian Rafael , Morales Hernán G. , Camara Oscar
Published: June 2022

Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution).
Photoinduced Reorientation and Polarized Fluorescence of a Photoalignable Liquid Crystalline Polymer
Authors: Furukawa Hiroki , Yamaguchi Hinano , Kondo Mizuho , Sakamoto Moritsugu , Sasaki Tomoyuki , Ono Hiroshi , Kawatsuki Nobuhiro
Published: June 2022

Thermally stimulated photoinduced reorientation of liquid crystalline (LC) polymethacrylate composed of a phenyl benzoate mesogen connected with N-benzylideneaniline (NBA2) end moiety exhibits a significant molecular reorientation (D > 0.7) when the film is exposed to linearly polarized 313 nm light and subsequently annealed in the LC temperature range of the material. Hydrolysis of the NBA2 end moieties yields an oriented mesogen with phenylamine moieties without distorting the oriented structure. In situ condensation of 2-hydroxybenzaldehyde derivatives and phenylamine moieties yields oriented N-salicylideneaniline side groups. The resultant film displays a polarized fluorescence with a polarization ratio up to 3.4.
Lightweight Hybrid Deep Learning Architecture and Model for Security in IIOT
Authors: Ankita  , Rani Shalli , Singh Aman , Elkamchouchi Dalia H. , Noya Irene Delgado
Published: June 2022

Remarkable progress in the Internet of Things (IoT) and the requirements in the Industrial era have raised new constraints of industrial data where huge data are gathered by heterogeneous devices. Recently, Industry 4.0 has attracted attention in various fields of industries such as medicines, automobiles, logistics, etc. However, every field is suffering from some threats and vulnerabilities. In this paper, a new model is proposed for detecting different types of attacks and it is analyzed with a deep learning technique, i.e., classifier-Convolution Neural Network and Long Short-Term Memory. The UNSW NB 15 dataset is used for the classification of various attacks in the field of Industry 4.0 for providing security and protection to the different types of sensors used for heterogeneous data. The proposed model achieves the results using Cortex processors, a 1.2 GHz processor, and four gigabytes of RAM. The attack detection model is written in Python 3.8.8 and Keras. Keras constructs the model using layers of Convolutional, Max Pooling, and Dense Layers. The model is trained using 250 batch size, 60 epochs, 10 classes. For this model, the activation functions are Relu and softmax pooling.
Automatic Evaluation of the Robotic Production Process for an Aircraft Jet Engine Casing
Authors: Uliasz Marek , Ornat Artur , Burghardt Andrzej , Muszyńska Magdalena , Szybicki Dariusz , Kurc Krzysztof
Published: June 2022

This paper investigated the design of a system to monitor the status of the robotic welding process of thin-walled components for an aircraft jet engine. Opportunities to measure and log processing parameters, such as welding speed, current, and voltage, on the existing production cell were taken. The acquired data were processed using elements of descriptive statistics. Obtained indicators were matched with physical inspection results of the weld’s quality. The adopted methodology was used to identify an essential parameter determining the presence of defined weld defects. The developed solution was implemented in the production cell.
Development of an Electronic Smart Safe Box Using Private Blockchain Technology
Authors: Alothman Basil , Joumaa Chibli , Alotaibi Abdulrahman , Alotaibi Bandar , Almutairi Bashayer , Aldhafairi Asmaa , Khan Murad
Published: June 2022

Effective security has become a vital part of our daily routine, for example securing cash, jewelry, documents, and other valuables. One way is to protect these items by keeping them in a safe box. However, current safe boxes are vulnerable to a number of security attacks such as physical, repudiation, anonymously modifying, etc. In this regard, this paper presents a robust smart safe box using the blockchain solution to ensure each transaction and secure it from unauthorized access. Also, it traces back the local safe items by reading back to blockchain data transactions and protecting them from thieves by tracking the burglar with a real-time tracking location Global Positioning System (GPS) tracker and capturing a picture and video recording through the external camera. Similarly, with more security enhancement using the TensorFlow framework to analyze optical detection and identify things inside the box using an internal camera. At the same time, each transaction is stored by using Write Once and Read Many (WORM) patterns saved in local private blockchain data block logs. Finally, it uses three-way authentication mechanisms to unlock the safe box that authenticates the users, (1) face recognition, (2) fingerprint and (3) the keypad password. The performance of the proposed smart safe box is tested by a number of hardware and blockchain tests. The results reveal that the proposed smart safe box ensures safe and authenticated access to the box and records each transaction using blockchain technology. Further, the performance evaluation of the private blockchain significantly improved the integrity and safeness of the proposed smart safe box.
Comparison of Presowing Wheat Treatments by Low-Temperature Plasma, Electric Field, Cold Hardening, and Action of Tebuconazole-Based Disinfectant
Authors: Lazukin Alexander , Pinchuk Mikhail , Korsukova Anna , Nikiforov Anton , Romanov Gennadij , Stepanova Olga , Grabelnych Olga
Published: June 2022

This work compares the presowing treatment of winter wheat seeds with a low-temperature plasma, a constant high-voltage electric field, a plant protection disinfectant, and cold hardening on the resistance of seedlings to freezing and their morphophysiological characteristics at the initial stage of germination. Various treatment combinations were considered, including the effect of the disinfectant jointly with low-temperature plasma treatment. The greatest stimulating effect from the point of view of seedlings’ morphophysiological characteristics was achieved when seeds were cold-hardened. The action of low-temperature plasma is noticeable up to the third day of germination. The treatment with the low-temperature plasma of seeds pretreated and not-pretreated with the disinfectant had a similar effect on the morphophysiological characteristics of seedlings. The plasma treatment and the electric field were combined with each other, i.e., the plasma treatment effects were added to the electric field effects. Resistance to low temperatures was increased with the hardening of seeds treated with the electric field and the disinfectant. Resistance to low temperatures was reduced when treated with the electric field and/or low-temperature plasma after being treated with the disinfectant.
Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
Authors: Mabrouk Alhassan , Díaz Redondo Rebeca P. , Dahou Abdelghani , Abd Elaziz Mohamed , Kayed Mohammed
Published: June 2022

Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models, which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database. These models are trained on the chest X-ray data set using fine-tuning. Finally, the results are obtained by combining the extracted features from these three models during the experimental phase. The proposed EL approach outperforms other existing state-of-the-art methods and obtains an accuracy of 93.91% and a F1-score of 93.88% on the testing phase.
Investigation of an Inline Inspection Method for the Examination of Cylinder-like Specular Surfaces Using Deflectometry
Authors: Gielinger Sebastian , Bohn Gunther , Deinzer Frank , Linke Andreas
Published: June 2022

An optical measuring method is presented, with which it is possible to measure and evaluate reflective cylindrical surfaces using a combination of deflectometry and subsequent reconstruction of the surface. The system is set up and tested on rolling elements of cylindrical roller bearings. However, it is not limited to this use case and can be applied to other cylindrical specular surfaces. The system distinguishes itself from existing test methods through the combination of high-resolution three-dimensional defect measurement with a very short recording time, and offers the possibility of introducing tolerance limits in the production of cylindrical specular surfaces. With this method, it is possible to record a defect with the dimensions of 1.3mm by 1.8mm within 5 s and to reconstruct the absolute depth. The resolution of the system is below 10 μm in both X- and Y-direction, and is therefore sufficiently accurate to detect typical surface defects such as scratches, dents, or deformations. To validate the measured values of the system, the results of an artificially generated 10.35 μm deep defect location were compared with those of a highly accurate mechanical stylus measurement.
Analysis of Healthcare Push and Pull Task via JACK: Predicted Joint Accuracy during Full-Body Simulation
Authors: Ji Xiaoxu , Piovesan Davide , Arenas Maria , Liu He
Published: June 2022

The posture accuracy of full-body dynamic simulation has been successfully evaluated in JACK Siemens software via analyzing two common push and pull tasks. The difference in joint angles between the actual and predicted human movement directly results in the strength of force exposed on the lumbar spine. In this study, the individual factors, such as body height, body weight, trunk and hip flexion, shoulder movement, and muscle strength between genders, have shown a strong association with the adopted postures and exposed spinal forces during task performance. To provide robust ergonomics analysis, these individual variables should be adequately considered in software design for the long-term goal of injury prevention in diverse occupational workplaces.
Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
Authors: Sauka Kudzai , Shin Gun-Yoo , Kim Dong-Wook , Han Myung-Mook
Published: June 2022

The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is due to deep neural networks (DNN) complexity and efficiency in making anomaly detection activities more accurate. However, the complexity of these models makes them black-box models, as they lack explainability and interpretability. Not only is the DNN perceived as a black-box model, but recent research evidence has also shown that they are vulnerable to adversarial attacks. This paper developed an adversarial robust and explainable network intrusion detection system based on deep learning by applying adversarial training and implementing explainable AI techniques. In our experiments with the NSL-KDD dataset, the PGD adversarial-trained model was a more robust model than DeepFool adversarial-trained and FGSM adversarial-trained models, with a ROC-AUC of 0.87. The FGSM attack did not affect the PGD adversarial-trained model’s ROC-AUC, while the DeepFool attack caused a minimal 9.20% reduction in PGD adversarial-trained model’s ROC-AUC. PGD attack caused a 15.12% reduction in the DeepFool adversarial-trained model’s ROC-AUC and a 12.79% reduction in FGSM trained model’s ROC-AUC.
Impact of Heat Treatment of Spruce Wood on Its Fire-Technical Characteristics Based on Density and the Side Exposed to Fire
Authors: Mitrenga Patrik , Vandlíčková Miroslava , Konárik Milan , Košútová Katarína
Published: June 2022

The paper assessed the impact of the heat treatment of spruce wood, the (radial and tangential) side of the specimens exposed to fire, and the type of material (prism—higher density, floor—lower density) on the combustion process and the rate of fire spread. Five groups of specimens were used—untreated spruce wood specimens, two groups of heat-treated spruce wood specimens from the prism (higher density specimens), and two groups of heat-treated spruce wood specimens from the floor (lower density specimens). In one group, the flame was applied to the radial side, and in the other group to the tangential side of the specimens. The effect on the combustion process was assessed based on the parameters of mass loss and mass loss rate over time. The effect on the rate of fire spread across the specimens was assessed by the parameter fire spread rate. These parameters were determined using a simple test method where the specimens were exposed to a direct flame at an angle of 45°. To complement the results and to assess the processes involved, the temperatures at the specimen surfaces were also measured during the experiment. The main achieved results of the study are the findings on how the heat treatment, the density, and the side of the wood along which the fire spreads affect the burning process of the wood. The results indicated a significant effect of the density of the spruce thermowood on its combustion process. The higher density radial specimens exhibited a higher mass loss rate, and the overall average mass loss of the higher density samples was 27% of the original mass higher than that of the lower density samples. Additionally, the results suggested that the heat treatment of lower-density spruce wood (floor) does not significantly affect the mass loss and the mass loss rate. The difference in the overall average mass loss of the thermowood of floor and untreated wood samples was less than 2%, which is statistically insignificant. It was also found that for thermowood, fire will spread faster on the tangential side, where the fire spread rate is 29% higher compared with the radial side (for the floor samples). Based on the findings of other authors in a similar field, the results confirm that heat-treated spruce wood is more easily ignitable than untreated wood, which was proven by the spontaneous combustion of most of the thermowood samples during the experiment compared with the untreated wood samples.
Inline Defective Laser Weld Identification by Processing Thermal Image Sequences with Machine and Deep Learning Techniques
Authors: Buongiorno Domenico , Prunella Michela , Grossi Stefano , Hussain Sardar Mehboob , Rennola Alessandro , Longo Nicola , Di Stefano Giovanni , Bevilacqua Vitoantonio , Brunetti Antonio
Published: June 2022

The non-destructive testing methods offer great benefit in detecting and classifying the weld defects. Among these, infrared (IR) thermography stands out in the inspection, characterization, and analysis of the defects from the camera image sequences, particularly with the recent advent of deep learning. However, in IR, the defect classification becomes a cumbersome task because of the exposure to the inconsistent and unbalanced heat source, which requires additional supervision. In light of this, authors present a fully automated system capable of detecting defective welds according to the electrical resistance properties in the inline mode. The welding process is captured by an IR camera that generates a video sequence. A set of features extracted by such video feeds supervised machine learning and deep learning algorithms in order to build an industrial diagnostic framework for weld defect detection. The experimental study validates the aptitude of a customized convolutional neural network architecture to classify the malfunctioning weld joints with mean accuracy of 99% and median f1 score of 73% across five-fold cross validation on our locally acquired real world dataset. The outcome encourages the integration of thermographic-based quality control frameworks in all applications where fast and accurate recognition and safety assurance are crucial industrial requirements across the production line.
Energy-Efficient Hybrid Flow-Shop Scheduling under Time-of-Use and Ladder Electricity Tariffs
Authors: Chen Weidong , Wang Junnan , Yu Guanyi , Hu Yumeng
Published: June 2022

With the increasing influence of climate change, green development has become an important trend. Since manufacturing represents approximately one-half of total energy consumption, reducing the amount of energy consumed within this industry is imperative. This study provides a hybrid flow shop scheduling issue under a time-of-use and ladder electricity price system to reduce total energy consumption without compromising maximum completion time. An improved non-dominated sorting genetic algorithm II with some optimization strategies is proposed to solve the problem. First, an enhanced constructive heuristic algorithm is used to improve the quantity of initial solution in the initialization. Besides, an adaptive genetic operation is introduced, aiming to avoid the emergence of locally optimal solutions. In addition, the right-shift approach is developed to reduce the total energy consumption without affecting completion time. By maintaining the production efficiency and reducing the energy consumption cost by 4.33%. A trade-off proposal is made between productivity and sustainability in view of the calculation results.
Prediction of a Grid-Connected Photovoltaic Park’s Output with Artificial Neural Networks Trained by Actual Performance Data
Authors: Roumpakias Elias , Stamatelos Tassos
Published: June 2022

Increased penetration of grid-connected PV systems in modern electricity networks induces uncertainty factors to be considered from several different viewpoints, including the system’s protection and management. Accurate short-term prediction of a grid-connected PV park’s output is essential for optimal grid control and grid resilience. Out of the numerous types of models employed to this end during the last decade, artificial neural networks, (ANNs) have proven capable of handling the uncertainty issues of solar radiation. Insolation and ambient, or panel temperature, are most commonly employed as the independent variables, and the system’s output power is successfully predicted within 3 to 5% error. In this paper, we apply a common type of ANN for the long-term prediction of a 100 kWp grid-connected PV park’s output, by exploiting experimental data from the last 8 years of operation. Solar radiation and backsheet temperature were utilized for the ANN training stage. The performance metrics of this model, along with a standard linear regression model, are compared against the actual performance data. The capabilities of the ANN model are exploited in the effort to decouple the fluctuating effect of PV panel soiling which interferes with the efficiency degradation process. The proposed methodology aimed to quantify degradation effects and is additionally employed as a fault diagnosis tool in long-term analysis.
Energy-to-Mass Ratio—A Novel Selection Criterion of Pneumatic Motors Used for the Actuation of Wearable Assistive Devices
Authors: Deaconescu Andrea , Deaconescu Tudor
Published: June 2022

The requirements to be met by a wearable assistive device are compactness, lightweight and energy efficiency. While the literature discusses the construction and performance of such devices, no information is provided as to the criteria to be applied in selecting such an actuator, capable of satisfying the mentioned conditions. Ensuring the high autonomy of a wearable assistive device requires actuators that can store a large quantity of energy in a small as possible volume, for example, actuators with a high energy density. This paper presents a comparative study of the performance of two types of pneumatic actuators: single-acting cylinders and pneumatic muscles, respectively, and offers information that will enable users to select an optimum solution. The quality indicators considered in conducting the comparative study are size, mass, the developed force and the energy-to-mass ratio. A method is proposed to determine the energy developed by the motors over the entire stroke; based on that, the energy-to-mass ratio is subsequently calculated. This indicator is a valuable tool made available to designers of wearable assistive devices. The conclusion yielded by the study asserts that while pneumatic muscles have larger radial and axial dimensions, they present benefits as to the developed forces and the energy-to-mass ratios.
Development of Technology for Hydromechanical Breakdown of Mud Plugs and Improvement of Well Cleaning by Controlled Buckling of the Drill String
Authors: Kadochnikov Viacheslav G. , Dvoynikov Mikhail V.
Published: June 2022

The article provides brief information about a non-standard experimental setup developed in the laboratory of the St. Petersburg Mining University Well Drilling Department. The developed technique presented makes it possible to simulate well cleaning process of cuttings by incorporating the variation of the parameters (the zenith angle of the well, the volume flow and rheological properties of the cleaning agent, the rotation frequency, the number and length of the drill string half-waves) that cause buckling. For the first time, the positive side of the drill string (DS) buckling phenomenon is considered. A positive hydro-mechanical effect on mud plugs and improved well cleaning were revealed. The results of the experimental study confirm an intense difficulty in transporting cuttings to the surface at a critical zenith angle of the well of 55°. Regularities have been established making it possible to determine the effect of DS buckling on the cutting-carrying capacity when drilling deviated and extended reach wells. It is proposed to use hydromechanical impact on the accumulated cuttings by artificially controlling the resulting DS buckling in order to destroy the mud plug and increase the efficiency of well cleaning without the use of specialized devices. A conceptual solution aimed at implementing a method for hydromechanical destruction of mud plugs—the use of drill pipes equipped with a quasi-distributed differential measuring system of strain gauges based on a fiber-optic Bragg grating—is presented.
Dosimetric Effect of Injection Ports in Tissue Expanders on Post-Mastectomy Volumetric Modulated Arc Therapy (VMAT) Planning for Left-Sided Breast Cancer
Authors: Hwang Na-Hyun , Kim Myungsoo , Lee Nam Kwon , Lee Suk , Hwang Jinho
Published: June 2022

This study aimed to compare the dosimetric effect of traditional metallic ports and radio frequency identification (RFID) ports (Motiva Flora®) on post-mastectomy volumetric modulated arc therapy (VMAT) planning for left-sided breast cancer. Computed tomography (CT) simulation was performed on an anthropomorphic torso phantom by attaching two types of tissue expander on the left chest wall. For the comparison of CT artifacts, five points of interest were selected and compared: point A = central chest wall, B = medial chest wall, point C = lateral chest wall, point D = axilla, and point E = left anterior descending artery. VMAT planning using two partial arcs with a single isocenter was generated, and dosimetric parameters were investigated. Compared to metallic ports, RFID ports tremendously decreased distortion on CT images, with the exception of point D. The V5Gy, V10Gy, V15Gy, V20Gy, V30Gy, and Dmean values of the heart in RFID ports were lower than those in metallic ports. The V5Gy, V15Gy, V20Gy, V30Gy, and Dmean values of the ipsilateral lung in RFID ports were also lower than those in metallic ports. RFID ports showed superior dosimetric results for doses to the heart and lungs as compared to that in metallic ports.
Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation
Authors: Cândido Paulo Gustavo Lopes , Silva Jonathan Andrade , Faria Elaine Ribeiro , Naldi Murilo Coelho
Published: June 2022

The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of clusters, and their shapes. The present work aims to improve the accuracy of sequential clustering batches of data streams for scenarios in which clusters evolve dynamically and continuously, automatically estimating their number. In order to achieve this goal, three evolutionary algorithms are presented, along with three novel algorithms designed to deal with clusters of normal distribution based on goodness-of-fit tests in the context of scalable batch stream clustering with automatic estimation of the number of clusters. All of them are developed on top of MapReduce, Discretized-Stream models, and the most recent MPC frameworks to provide scalability, reliability, resilience, and flexibility. The proposed algorithms are experimentally compared with state-of-the-art methods and present the best results for accuracy for normally distributed data sets, reaching their goal.
A Method of Pruning and Random Replacing of Known Values for Comparing Missing Data Imputation Models for Incomplete Air Quality Time Series
Authors: Menéndez García Luis Alfonso , Menéndez Fernández Marta , Sokoła-Szewioła Violetta , Álvarez de Prado Laura , Ortiz Marqués Almudena , Fernández López David , Bernardo Sánchez Antonio
Published: June 2022

The data obtained from air quality monitoring stations, which are used to carry out studies using data mining techniques, present the problem of missing values. This paper describes a research work on missing data imputation. Among the most common methods, the method that best imputes values to the available data set is analysed. It uses an algorithm that randomly replaces all known values in a dataset once with imputed values and compares them with the actual known values, forming several subsets. Data from seven stations in the Silesian region (Poland) were analyzed for hourly concentrations of four pollutants: nitrogen dioxide (NO2), nitrogen oxides (NOx), particles of 10 μm or less (PM10) and sulphur dioxide (SO2) for five years. Imputations were performed using linear imputation (LI), predictive mean matching (PMM), random forest (RF), k-nearest neighbours (k-NN) and imputation by Kalman smoothing on structural time series (Kalman) methods and performance evaluations were performed. Once the comparison method was validated, it was determine that, in general, Kalman structural smoothing and the linear imputation methods best fitted the imputed values to the data pattern. It was observed that each imputation method behaves in an analogous way for the different stations The variables with the best results are NO2 and SO2. The UMI method is the worst imputer for missing values in the data sets.
The Impact of Distributed Energy Storage on Distribution and Transmission Networks’ Power Quality
Authors: Adewumi Olurotimi Babatunde , Fotis Georgios , Vita Vasiliki , Nankoo Daniel , Ekonomou Lambros
Published: June 2022

This study investigates the effect of distributed Energy Storage Systems (ESSs) on the power quality of distribution and transmission networks. More specifically, this project aims to assess the impact of distributed ESS integration on power quality improvement in certain network topologies compared to typical centralized ESS architecture. Furthermore, an assessment is made to see if the network topology in which an ESS position supports its ability to restore node voltage magnitude within acceptable ranges. The power quality of a benchmark interconnected distribution and transmission network was determined using NEPLAN software. Following that, twelve variants of the benchmark were modeled, each with a different ESS integration architecture and (or) topology. Their power quality performance was compared to that of a benchmark network in addition to several cross analyses to determine the relative impact on power quality within the context of their respective ESS integration methodologies. The findings of this study buttress the understanding that the distributed ESS integration architecture within the distribution network topology, where the majority of consumer loads are connected, provides the strongest case for voltage magnitude power quality compensation, as required by the UK Electrical System Grid Code’s 5% rated node voltage compliance processes regulation.
Prediction of Minimum Night Flow for Enhancing Leakage Detection Capabilities in Water Distribution Networks
Authors: Lee Sang Soo , Lee Ho-Hyun , Lee Yun-Jung
Published: June 2022

In South Korea, a water supply enhancement project is being carried out to preemptively respond to drought and water loss by reducing pipeline leakages and supplying stable tap water through the maintenance of an aging water supply network. In order to reduce water leakage, a District Metered Area (DMA) was established to monitor and predict the minimum night flow based on flow data collected from IoT sensors. In this study, a model based on Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) was constructed to predict the MNF (minimum night flow) of County Y. The prediction of MNF results was compared with the MLP networks and the LSTM model. The outcome showed that the LSTM-MNF model proposed in this study performed better than the MLP-MNF model. Therefore, the research methods of this study can contribute to technical support for leakage reductions by preemptively responding to the expected increase in leakage through the prediction of the minimum flow at night.
An Efficient Semi-Analytical Scheme for Determining the Reflection of Lamb Waves in a Semi-Infinite Elastic Waveguide
Authors: Davey Robert C. , Assier Raphaël C. , Abrahams I. David
Published: June 2022

The classical problem of reflection of Lamb waves from a free edge perpendicular to the centre line of an elastodynamic plate is studied. It is known that Lamb wave expansions for the displacement and stress fields poorly represent the irregular behaviour near corners, leading to the slow convergence of a series of such waves. The form of the irregularity for an elastodynamic corner is derived asymptotically, and a new solution method, which incorporates this corner behaviour analytically, is then implemented. Results are presented showing that this new approach represents the near-field and far-field behaviour very accurately, requiring very modest numbers of Lamb wave and corner modes. Further, it is revealed that the method can recover the trapped-mode phenomenon encountered in this configuration at the Lamé frequency and a specific Poisson’s ratio that we find to be approximately 0.224798.
Very Short-Term Photoplethysmography-Based Heart Rate Variability for Continuous Autoregulation Assessment
Authors: Huang Po-Hsun , Hsiao Tzu-Chien
Published: June 2022

Background: Heart rate variability (HRV) has been widely applied for disease diagnosis. However, the 5 min signal length for HRV analysis is needed. Method: A signal processing procedure for very short-term photoplethysmography (PPG) signal for fever detection and autoregulation assessment was proposed. The Time-Shift Multiscale Entropy Analysis (TSME) was applied to instantaneous pulse rate time series (iPR) and normalized by the cumulative distribution function (CDF) of all scales to calculate novel indices. A total of 33 subjects were recruited for the study. Fifteen participants whose body temperatures were higher than 37.9 °C were served as the fever group. Others were served as the non-fever group. The total 15 s PPG signal with 200 sampling rates was used for iPR calculation. Result: The CDF value of entropy on the scale k = 19 (CDF(E(k = 19))) of iPR had the lowest p-value calculated by the Weltch t-test between two groups (p < 0.001). The Spearman correlation r between CDF(E(k = 19)) and body temperature is −0.757, 0.287, and −0.830 in all subjects, the non-fever group and the Fever group, respectively. The area under the curve, calculated from the receiver operating characteristic of CDF(E(k = 19)) of iPR is 0.915. Conclusion: The entropy of iPR is useful for detecting fever. Moreover, a short-term PPG signal is suitable to develop real-time applications, and multiscale entropy provides different scales of information for daily healthcare.
A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
Authors: He Zhengxiang , Ma Shaowei , Wang Liguan , Peng Pingan
Published: June 2022

Wavelet transform is a widespread and effective method in seismic waveform analysis and processing. Choosing a suitable wavelet has also aroused many scholars’ research interest and produced many effective strategies. However, with the convenience of seismic data acquisition, the existing wavelet selection methods are unsuitable for the big dataset. Therefore, we proposed a novel wavelet selection method considering the big dataset for seismic signal intelligent processing. The relevance r is calculated using the seismic waveform’s correlation coefficient and variance contribution rate. Then values of r are calculated from all seismic signals in the dataset to form a set. Furthermore, with a mean value μ and variance value σ2 of that set, we define the decomposition stability w as μ/σ2. Then, the wavelet that maximizes w for this dataset is considered to be the optimal wavelet. We applied this method in automatic mining-induced seismic signal classification and automatic seismic P arrival picking. In classification experiments, the mean accuracy is 93.13% using the selected wavelet, 2.22% more accurate than other wavelets generated. Additionally, in the picking experiments, the mean picking error is 0.59 s using the selected wavelet, but is 0.71 s using others. Moreover, the wavelet packet decomposition level does not affect the selection of wavelets. These results indicate that our method can really enhance the intelligent processing of seismic signals.
A Kinematic Analysis of the Basketball Shot Performed with Different Ball Sizes
Authors: França Cíntia , Gouveia Élvio R. , Coelho-e-Silva Manuel J. , Gomes Beatriz B.
Published: June 2022

In youth basketball, the ball’s size is adapted to fit the participants’ physical capacities and improve the development of manipulative skills. The current study compared the kinematic parameters of the basketball shot (BS) performed with two different ball sizes. Twenty-seven adolescent females aged 12.1 ± 0.9 years (height: 153.3 ± 8.0 cm; body mass: 48.8 ± 12.8 kg) completed 10 BS trials from a frontal position at 5.75 m from the basket with two ball sizes: a smaller and lighter ball (size five, 480 g) and the standard ball size for their age (size six, 566 g). No statistically significant differences were observed for ball release variables or efficacy levels. Significantly greater shoulder flexion was detected at release while shooting with a size six ball (F = 2.982, p ≤ 0.01). The shoulder’s angular velocity at release was significantly lower while performing with a size six ball (F = 3.089, p ≤ 0.01). No significant differences were found for the elbow or knee angles or angular velocities. Stature and upper-body strength were significantly correlated with selected kinematic parameters. A change in ball size may be a helpful strategy for coaches seeking to promote optimal shooting patterns, enhancing effectiveness and enjoyment, particularly among young people and more inexperienced players.
Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
Authors: Ye Zhonglin , Luo Haixiu , Gong Yunchao , Li Gege , Zhou Lin , Renqing Zhuome
Published: June 2022

Disease spreading in multi-layer complex networks often spreads among multiple populations with the help of media. In order to reveal the cross-layer spreading characteristics of diseases and control the cross-layer spreading of diseases by analyzing the inter-layer coupling mechanism in multi-layer complex networks, this paper proposes and establishes three kinds of three-layer disease spreading models based on SIS spreading mechanism, and analyzes the influence of spreading conditions, spreading speed, and diffusion range on disease spreading and corresponding immunization strategies. It is deduced theoretically that the spreading threshold of global network is relatively lower than that of a single-layer network. As the intermediary layer of the global network bridge, the strong coupling is more likely to cause the network to show multi-peak characteristic, and the influence of multi-peak transformation on the spreading speed is higher than that of the network generation mechanism. The immune strategy of isolating middle-layer nodes can effectively inhibit the spread of diseases. The research results of this paper are helpful to understand the spreading characteristics of various information in the real world.
Smart Attacks Learning Machine Advisor System for Protecting Smart Cities from Smart Threats
Authors: Ali Hussein , Elzeki Omar M. , Elmougy Samir
Published: June 2022

The extensive use of Internet of Things (IoT) technology has recently enabled the development of smart cities. Smart cities operate in real-time to improve metropolitan areas’ comfort and efficiency. Sensors in these IoT devices are immediately linked to enormous servers, creating smart city traffic flow. This flow is rapidly increasing and is creating new cybersecurity concerns. Malicious attackers increasingly target essential infrastructure such as electricity transmission and other vital infrastructures. Software-Defined Networking (SDN) is a resilient connectivity technology utilized to address security concerns more efficiently. The controller, which oversees the flows of each appropriate forwarding unit in the SDN architecture, is the most critical component. The controller’s flow statistics are thought to provide relevant information for building an Intrusion Detection System (IDS). As a result, we propose a five-level classification approach based on SDN’s flow statistics to develop a Smart Attacks Learning Machine Advisor (SALMA) system for detecting intrusions and for protecting smart cities from smart threats. We use the Extreme Learning Machine (ELM) technique at all levels. The proposed system was implemented on the NSL-KDD and KDDCUP99 benchmark datasets, and achieved 95% and 99.2%, respectively. As a result, our approach provides an effective method for detecting intrusions in SDNs.
SLedge: Scheduling and Load Balancing for a Stream Processing EDGE Architecture
Authors: Hidalgo Nicolas , Rosas Erika , Saavedra Teodoro , Morales Jefferson
Published: June 2022

Natural disasters have a significant impact on human welfare. In recent years, disasters are more violent and frequent due to climate change, so their impact may be higher if no preemptive measures are taken. In this context, real-time data processing and analysis have shown great potential to support decision-making, rescue, and recovery after a disaster. However, disaster scenarios are challenging due to their highly dynamic nature. In particular, we focus on data traffic and available processing resources. In this work, we propose SLedge—an edge-based processing model that enables mobile devices to support stream processing systems’ tasks under post-disaster scenarios. SLedge relies on a two-level control loop that automatically schedules SPS’s tasks over mobile devices to increase the system’s resilience, reduce latency, and provide accurate outputs. Our results show that SLedge can outperform a cloud-based infrastructure in terms of latency while keeping a low overhead. SLedge processes data up to five times faster than a cloud-based architecture while improving load balancing among processing resources, dealing better with traffic spikes, and reducing data loss and battery drain.
pH-Sensitive Sensors at Work on Poultry Meat Degradation Detection: From the Laboratory to the Supermarket Shelf
Authors: Magnaghi Lisa Rita , Zanoni Camilla , Bancalari Elena , Hadj Saadoun Jasmine , Alberti Giancarla , Quadrelli Paolo , Biesuz Raffaela
Published: June 2022

In the last twenty years, the number of publications presenting generalized pH-sensitive devices proposed for food freshness monitoring has been steadily growing, but to date, none of them have succeeded in exiting the laboratory and reaching the supermarket shelf. To reach this scope, we developed a large-scale applicable pH-sensitive sensor array to monitor perishable foods’ degradation. We ensured freshness monitoring in domestic conditions, using sales packages and during chilled storage, by simple naked-eye readout and multivariate imaging analysis, and we fully corroborated the device by (i) projection of unknown independent samples in the PCA model, (ii) TVB-N quantification and (iii) microbiological assay. The choice of commercial and cheap dye and polymeric support already employed in food packaging ensures the low-cost and scalability of the device and the promising results obtained make this device an eligible candidate for large-scale implementation.
The Dichotomy of Neural Networks and Cryptography: War and Peace
Authors: Zolfaghari Behrouz , Koshiba Takeshi
Published: June 2022

In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.
Mapping Thunderstorm Electrical Structure in the Troposphere in Warm Season with VLF/LF Total Lightning Monitoring Data over the Pearl River Delta Region, China
Authors: Wang Jianguo , Cheng Si , Cai Li , Fan Yadong , Zhou Mi , Li Quanxin , Huang Yijun
Published: June 2022

Mapping the thunderstorm electrical structure could provide an effective way for lightning-sensitive facilities protection, such as aircraft and maritime assets. However, the weather radar that is normally used to forecast storms and rainfall mainly detects precipitation in the atmosphere and indicates the existence of liquid raindrops and ice particles by reflectivity. Here, we use intra-cloud events of eight thunderstorm days in the warm season, which are detected by VLF/LF Total Lightning monitoring system, to reveal the thunderstorm electrical structures in the 300 × 300 km area of the Pearl River Delta (PRD) region. The differences in height range in four types of time intervals and three types of intro-cloud events proportions are compared on 16 May. With the proportion between 20% and 80% in the time interval of 15 min, the height distribution and the electrical structure of eight thunderstorm days are clearly exhibited. The positive IC events lie in the average height between 7.5 and 12.4 km, while the negative IC events are located between 5.3 and 11.7 km. The electrical structures show the variations during the evolution process, with a dipole structure in most circumstances, while temporary reversions are identified in the initial and the dissipating stage of thunderstorms, presenting the inverted dipole and the tripole structures.
Investigation and Evaluation of Flue Gas Pollutants Emission in Waste-to-Energy Plant with Flue Gas Recirculation
Authors: Wang Weishu , Tian Shujian , Long Jisheng , Liu Jun , Ma Qinhui , Xu Kai , Zhang Zhen
Published: June 2022

The emissions of pollutants by waste-to-energy power plants, which contain more toxic substances owing to the complicated composition of municipal solid waste (MSW), such as NOx, SO2, HCl, HF, particulate matter, and heavy metals, has attracted increasing attention worldwide. To effectively control the pollutants, a flue gas cleaning system is indispensable in the operation of MSW incineration power plants. In this study, the flue gas cleaning system in a waste-to-energy power plant with flue gas recirculation (FGR) was evaluated. The concentrations of various pollutants were measured and compared with the standards at home and abroad. The results indicated that NOx emission can be effectively reduced by FGR, and that the emission concentration of NOx may meet the national emission standards only by adopting FGR. However, the emission levels of HCl and PM exceeded the limits in legislative standards; therefore, operation optimization or retrofit of a deacidification system and bag filter were proposed to comply with the international standards and near-zero-emissions goal.