bluuebunny/crossref_metadata_2025 huggingface dataset:
Diffusion-Weighted Imaging Prior to Percutaneous Sclerotherapy of Venous Malformations—Proof of Concept Study for Prediction of Clinical Outcome
Authors: Gerwing Mirjam , Schindler Philipp , Schneider Kristian Nikolaus , Sundermann Benedikt , Köhler Michael , Stamm Anna-Christina , Schmidt Vanessa Franziska , Perkowski Sybille , Deventer Niklas , Heindel Walter L. , Wildgruber Moritz , Masthoff Max
Published: June 2022

Prediction of response to percutaneous sclerotherapy in patients with venous malformations (VM) is currently not possible with baseline clinical or imaging characteristics. This prospective single-center study aimed to predict treatment outcome of percutaneous sclerotherapy as measured by quality of life (QoL) by using radiomic analysis of diffusion-weighted (dw) magnetic resonance imaging (MRI) before and after first percutaneous sclerotherapy. In all patients (n = 16) pre-interventional (PRE-) and delta (DELTA-) radiomic features (RF) were extracted from dw-MRI before and after first percutaneous sclerotherapy with ethanol gel or polidocanol foam, while QoL was assessed using the Toronto Extremity Salvage Score (TESS) and the 36-Item Short Form Survey (SF-36) health questionnaire. For selecting features that allow differentiation of clinical response, a stepwise dimension reduction was performed. Logistic regression models were fitted and selected PRE-/DELTA-RF were tested for their predictive value. QoL improved significantly after percutaneous sclerotherapy. While no common baseline patient characteristics were able to predict response to percutaneous sclerotherapy, the radiomics signature of VMs (independent PRE/DELTA-RF) revealed high potential for the prediction of clinical response after percutaneous sclerotherapy. This proof-of-concept study provides first evidence on the potential predictive value of (delta) radiomic analysis from diffusion-weighted MRI for Quality-of-Life outcome after percutaneous sclerotherapy in patients with venous malformations.
Our Experience in Using the Endovascular Therapy in the Management of Hemorrhages in Obstetrics and Gynecology
Authors: Munteanu Octavian , Secara Diana , Neamtu Maria Narcisa , Baros Alexandru , Dimitriade Adela , Dorobat Bogdan , Carp Alexandra , Gheoca Mutu Daniela Elena , Cirstoiu Monica Mihaela
Published: June 2022

(1) Background: A quarter of maternal deaths are caused by post-partum hemorrhage; hence obstetric bleeding is a significant cause of morbidity and mortality among women. Pelvic artery embolization (PAE) represents a minimally invasive interventional procedure which plays an important role in conservative management of significant bleeding in Obstetrics and Gynecology. The aim of this study was to evaluate the effect and the complications of PAE in patients with significant vaginal bleeding with different obstetrical and gynecological pathologies. (2) Methods: We conducted an observational, retrospective study on 1135 patients who presented to the University Emergency Hospital of Bucharest with vaginal bleeding of various etiology treated with endovascular therapy. All the patients included in the study presented vaginal hemorrhage that was caused by: uterine leiomyomas, genital tract malignancies, ectopic pregnancy, arterio-venous mal-formations and other obstetrical causes. We excluded patients with uncontrolled high blood pressure, severe hepatic impairment, congestive heart failure, renal failure or ventricular arrhythmias. (3) Results: Bleeding was caused in 88.19% of cases by uterine leiomyomas (n = 1001), 7.84% (n = 89) by cervical cancer, 2.29% by ectopic pregnancy (n = 26), 1.23% by arteriovenous malformation (n = 14) and 0.52% by major hemorrhage of obstetrical causes. Endovascular procedures were used in all the cases. In patients with uterine leiomyomas, supra-selective uterine arteries embolization was used. In 97% (n = 1101) of patients, bleeding was stopped after the first attempt of PAE. 3% (n = 34) needed a second embolization. In 12 of 14 cases of AVM, PAE was successful, two other cases needed reintervention; (4) Conclusions: Endovascular procedures represent a major therapy method for both acute and chronic hemorrhage in Obstetrics and Gynecology. It can be used in post-partum or post-traumatic causes of vaginal bleeding, but also in patients with chronic hemorrhage from uterine leiomyomas or inoperable genital malignancies or even as a preoperative adjuvant in cases of voluminous uterine fibroids or invasive malignant tumors, aiming to reduce intraoperative hemorrhage.
Accuracy of Mini-Implant Placement Using a Computer-Aided Designed Surgical Guide, with Information of Intraoral Scan and the Use of a Cone-Beam CT
Authors: Vasoglou Georgios , Stefanidaki Ioanna , Apostolopoulos Konstantinos , Fotakidou Evmorfia , Vasoglou Michail
Published: June 2022

Background: The purpose of the study was to investigate the accuracy of mini-implant placement with the use of a computer designed surgical guide derived by intraoral scanning alongside Cone-Beam Computed Tomography (CBCT) or the use of a 2D radiograph. Methods: Thirty-five mini-implants (Aarhus® System: n = 20, Ø: 1.5 mm and AbsoAnchor®: n = 15, Ø: 1.3–1.4 mm) were placed in the maxilla and mandible of 15 orthodontic patients for anchorage purposes in cases where a CBCT was needed due to impacted teeth or for safety reasons. All were placed with the help of a computer designed surgical guide. One implant became loose and was excluded from the study. For 18 mini-implants (study group), CBCT and intraoral scanning were used for guide design, while for 16 (control group) only intraoral scanning and panoramic imaging information were used. Mini-implant position was recorded by angular and linear measurements on digital models created by combining Digital Imaging and Communications in Medicine (DICOM) and stereolithography (.stl) files. Accuracy in positioning was determined by comparing corresponding measurements for virtual and real positioned mini-implants on digital models before and after operation. The results were statistically analyzed with t-tests and the Mann-Whitney test. Results: No significant statistical differences were found for pre- and post-operational angular measurements in the study group, while significant statistical differences occurred on the same measurements for the control group (coronal angle 13.6° pre-op and 22.7° post-op, p-value = 0.002, axial angle 13.4° pre-op and 15.9° post-op, p-value = 0.034). Linear measurements pre- and post-operational for either group presented no significant statistical differences. Conclusions: A 3D designed and manufactured surgical guide with information concerning CBCT and intraoral scanning ensures accuracy on mini-implant placement while design of the guide without the use of a CBCT is less accurate, especially on inclination of the implant.
Optimizing the Scale of Observation for Intertidal Habitat Classification through Multiscale Analysis
Authors: Espriella Michael C. , Lecours Vincent
Published: June 2022

Monitoring intertidal habitats, such as oyster reefs, salt marshes, and mudflats, is logistically challenging and often cost- and time-intensive. Remote sensing platforms, such as unoccupied aircraft systems (UASs), present an alternative to traditional approaches that can quickly and inexpensively monitor coastal areas. Despite the advantages offered by remote sensing systems, challenges remain concerning the best practices to collect imagery to study these ecosystems. One such challenge is the range of spatial resolutions for imagery that is best suited for intertidal habitat monitoring. Very fine imagery requires more collection and processing times. However, coarser imagery may not capture the fine-scale patterns necessary to understand relevant ecological processes. This study took UAS imagery captured along the Gulf of Mexico coastline in Florida, USA, and resampled the derived orthomosaic and digital surface model to resolutions ranging from 3 to 31 cm, which correspond to the spatial resolutions achievable by other means (e.g., aerial photography and certain commercial satellites). A geographic object-based image analysis (GEOBIA) workflow was then applied to datasets at each resolution to classify mudflats, salt marshes, oyster reefs, and water. The GEOBIA process was conducted within R, making the workflow open-source. Classification accuracies were largely consistent across the resolutions, with overall accuracies ranging from 78% to 82%. The results indicate that for habitat mapping applications, very fine resolutions may not provide information that increases the discriminative power of the classification algorithm. Multiscale classifications were also conducted and produced higher accuracies than single-scale workflows, as well as a measure of uncertainty between classifications.
Comparison of Lumped Oscillator Model and Energy Participation Ratio Methods in Designing Two-Dimensional Superconducting Quantum Chips
Authors: Yuan Benzheng , Wang Weilong , Liu Fudong , He Haoran , Shan Zheng
Published: June 2022

Over the past two decades, superconducting quantum circuits have become one of the essential platforms for realizing quantum computers. The Hamiltonian of a superconducting quantum circuit system is the key to describing the dynamic evolution of the system. For this reason, various methods for analyzing the Hamiltonian of a superconducting quantum circuit system have been proposed, among which the LOM (Lumped Oscillator Model) and the EPR (Energy Participation Ratio) methods are the most popular ones. To analyze and improve the design methods of superconducting quantum chips, this paper compares the similarities and differences of the LOM and the EPR quantification methods. We verify the applicability of these two theoretical approaches to the design of 2D transmon quantum chips. By comparing the theoretically simulated results and the experimentally measured data at extremely low temperature, the errors between the theoretical calculation and observed measurement values of the two methods were summarized. Results show that the LOM method has more parameter outputs in data diversity and the qubit frequency calculation in LOM is more accurate. The reason is that in LOM more coupling between different systems are taken into consideration. These analyses would have reference significance for the design of superconducting quantum chips.
Medical Image Authentication Method Based on the Wavelet Packet and Energy Entropy
Authors: Sun Tiankai , Wang Xingyuan , Zhang Kejun , Jiang Daihong , Lin Da , Jv Xunguang , Ding Bin , Zhu Weidong
Published: June 2022

The transmission of digital medical information is affected by data compression, noise, scaling, labeling, and other factors. At the same time, medical data may be illegally copied and maliciously tampered with without authorization. Therefore, the copyright protection and integrity authentication of medical information are worthy of attention. In this paper, based on the wavelet packet and energy entropy, a new method of medical image authentication is designed. The proposed method uses the sliding window to measure the energy of the detail information. In the time–frequency data distribution, the local details of the data are mined. The complexity of energy is quantitatively described to highlight the valuable information. Based on the energy weight, the local energy entropy is constructed and normalized. The adjusted entropy value is used as the feature vector of the authentication information. A series of experiments show that the authentication method has good robustness against shearing attacks, median filtering, contrast enhancement, brightness enhancement, salt-and-pepper noise, Gaussian noise, multiplicative noise, image rotation, scaling attacks, sharpening, JPEG compression, and other attacks.
Image Encryption Using a Spectrally Efficient Halton Logistics Tent (HaLT) Map and DNA Encoding for Secured Image Communication
Authors: Patel Sakshi , Veeramalai Thanikaiselvan
Published: June 2022

With the advancement of technology worldwide, security is essential for online information and data. This research work proposes a novel image encryption method based on combined chaotic maps, Halton sequence, five-dimension (5D) Hyper-Chaotic System and Deoxyribonucleic Acid (DNA) encoding. Halton sequence is a known low-discrepancy sequence having uniform distribution in space for application in numerical methods. In the proposed work, we derived a new chaotic map (HaLT map) by combining chaotic maps and Halton sequence to scramble images for cryptography applications. First level scrambling was done by using the HaLT map along with a modified quantization unit. In addition, the scrambled image underwent inter- and intra-bit scrambling for enhanced security. Hash values of the original and scrambled image were used for initial conditions to generate a 5D hyper-chaotic map. Since a 5D chaotic map has complex dynamic behavior, it could be used to generate random sequences for image diffusion. Further, DNA level permutation and pixel diffusion was applied. Seven DNA operators, i.e., ADD, SUB, MUL, XOR, XNOR, Right-Shift and Left-Shift, were used for pixel diffusion. The simulation results showed that the proposed image encryption method was fast and provided better encryption compared to ‘state of the art’ techniques. Furthermore, it resisted various attacks.
What Do the Relationships between Pre-Service Biology Teachers’ Personality and Professional Knowledge Reveal about Their Innovativeness?—An Exploratory Study Using Canonical Correlation Analysis
Authors: Welter Virginia Deborah Elaine , Emmerichs Lars , Schlüter Kirsten
Published: June 2022

Already in 2016, the German educational policy adopted the Education in the Digital World strategy, recommending that all teachers should become experts in using media. However, despite this initiative regarding the promotion of innovative forms of teaching using digital media, most teachers did not feel optimally prepared to successfully cope with the demands of implementing e-learning during the COVID-19 pandemic. Most empirical studies on potential barriers to innovation pertain to comparatively easy, changeable environmental factors, whereas only a few studies have focused on teachers as an individual factor so far. Since several organizational psychological studies on the relationships between innovativeness and personality traits in professional contexts identified the personality trait of openness to experience to be particularly influential on the innovative behaviors of employees’, our study aimed to explore whether comparable results can also be found in the educational context. Therefore, we conducted a cross-sectional survey (n = 201) to analyze to what extent the Big Five personality traits are related to pre-service teachers’ self-concept of professional knowledge and, in particular, its digitalization-related domains. The results of our canonical correlation analysis show that the two personality traits of openness to experience and conscientiousness appear to be significantly related to the overall professional knowledge of our sample. Furthermore, a dominant affinity for technology seems to be associable with the risk of lower values on personality traits that are regarded to be pedagogically relevant. However, we found that our canonical model could also get along with fewer variables since the actual digitalization-related teaching skills were not sufficiently reflected by the canonical solution but were rather caught up in the domain of pedagogical content knowledge. Interpretations of these findings as well as practical implications are discussed.
Hybrid AC/DC Provisional Microgrid Planning Model Considering Converter Aging
Authors: Rezaei Omid , Mirzapour Omid , Panahazari Mohammad , Gholami Hassan
Published: June 2022

Renewable energy deployment through distributed energy resources is among the central goals of future power systems. Microgrids have proven to be an economically viable solution for distributed energy resources’ integration into the power system and benefits customers with uninterrupted power supply. In this context, provisional microgrids have been introduced with the main goal of rapid renewable energy resource deployment. Since a considerable portion of renewable energy resources, as well as residential loads, are DC, and this portion is expected to grow even further, hybrid AC/DC design of provisional microgrids can improve both efficiency and economic benefit through an optimal arrangement of AC/DC feeders and converters. This paper presents a planning model for hybrid provisional microgrids considering the long-term influence of energy storage and the aging process of converters on economic revenues. Due to several intrinsic uncertainties involved in provisional microgrid operation, robust optimization is applied to the problem to ensure optimality under worst-case conditions. Results show the economic benefits of a hybrid provisional microgrid compared to a provisional microgrid and independent microgrid. Several sensitivity analyses are carried out to identify critical factors in planning.
A Novel SIW Leaky-Wave Antenna for Continuous Beam Scanning from Backward to Forward
Authors: Kamalzadeh Saeed , Soleimani Mohammad
Published: June 2022

A novel, periodic, leaky-wave array antenna using substrate-integrated waveguide (SIW) technology is proposed for continuous beam scanning applications. For this purpose, a periodic structure with the ability to radiate from backward to forward is proposed. The unit cell of this periodic structure includes a longitudinal slot and an H-plane discontinuity. The H-plane step discontinuity is suggested to suppress the open stopband (OSB) and enable continuous beam scanning from backward to forward through the broadside. The impedance matching technique is used to suppress the open stopband. In contrast to phased array antennas, this form of antenna is distinguished by its ability to scan without requiring a complex feeding network. These antennas are used for different factors such as scanning the beam, determining the direction of arrival, avoiding collisions, indoor communications, etc. A prototype of the proposed antenna was fabricated for experimental characterization. The overall physical dimensions of the fabricated antenna are 7.9 mm × 128 mm. The results demonstrate that an adequate level of agreement between measurement and simulation is satisfactory. The results indicate that the suggested antenna can scan continuously in the frequency range of 14.5 to 22.5 GHz between −60 and +57.5 degrees through broadside with a maximum gain of 16 dBi and radiation efficiency of 71%.
Non-Cooperative UAV Detection with Semi-Adaptive Sampling of Control Signal and SNR Estimation
Authors: Wang Changce , Zhang Fangpei , Ouyang Wenjiang , Jing Xiaojun , Mu Junsheng
Published: June 2022

This paper proposes a non-cooperative unmanned aerial vehicle (UAV) signal detection strategy based on a multichannel control signal with an energy detector (ED), wherein the sampling point of the control signal on each subchannel is adjusted with environmental signal-to-noise (SNR) in a semi-adaptive manner. In order to estimate the SNR in the environment, not only is a convolutional neural network (CNN) applied in the proposed signal detection strategy, but a long shor-term memory network (LSTM) network is also included; in terms of features, it combines deep features and time-dimension features. The numbers of layers of the CNN and LSTM impact the performance of the algorithm. The decision on the presence or absence of a control signal is made at the fusion center (FC) based on the majority voting rule. This paper shows that the network with a two-layer CNN and a two-layer LSTM can achieve high estimation accuracy of environmental SNR. Simultaneously, the detection accuracy is improved by about 1 dB compared with the classical multichannel detection schemes.
An Ontology-Based and Deep Learning-Driven Method for Extracting Legal Facts from Chinese Legal Texts
Authors: Ren Yong , Han Jinfeng , Lin Yingcheng , Mei Xiujiu , Zhang Ling
Published: June 2022

The construction of smart courts promotes the in-deep integration of internet, big data, cloud computing and artificial intelligence with judicial trial work, which can both improve trials and ensure judicial justice with more efficiency. High-quality structured legal facts, obtained by extracting information from unstructured legal texts, are the foundation for the construction of smart courts. Based on the strong normative characteristics of Chinese legal text content and structure composition and the strong text feature learning ability of deep learning, this paper proposes an ontology-based and deep learning-driven method for extracting legal facts from Chinese legal texts. The proposed method utilizes rules and patterns generated in the process of knowledge modeling to extract simple entities, and then extracts complex entities hidden in legal text details with deep learning methods. Finally, the extracted entities are mapped into structured legal facts with clear logical relationships by the Chinese Legal Text Ontology. In the information extraction test of judicial datasets composed of Chinese legal texts on theft, the proposed method effectively extracts up to 38 categories of legal facts from legal texts and the number of categories extracted increases significantly. Among them, the rule-based extractor obtains an F1-score of 99.70%, and the deep learning-driven extractor obtains an F1-score of 91.43%. Compared with existing methods, the proposed method has great advantages in extracting the completeness and accuracy of legal facts.
ConAs-GRNs: Sentiment Classification with Construction-Assisted Multi-Scale Graph Reasoning Networks
Authors: Chen Bo , Peng Weiming , Song Jihua
Published: June 2022

Traditional neural networks have limited capabilities in modeling the refined global and contextual semantics of emotional texts and usually ignore the dependencies between different emotional words. To address this limitation, this paper proposes a construction-assisted multi-scale graph reasoning network (ConAs-GRNs), which explores the details of the contextual semantics as well as the emotional dependencies between emotional texts from multiple aspects by focusing on the salient emotional information. In this network, an emotional construction-based multi-scale topological graph is used to describe multiple aspects of emotional dependency, and a sentence dependency tree is utilized to construct a relationship graph based on emotional words and texts. Then, the transfer learning and pooling learning on the topology map is performed. In our case, a weighted edge reduction strategy is used to aggregate the adjacency information which enables the internal transfer of semantic information in a single graph. Moreover, to implement the inter-graph transfer of semantic information, we rely on the construction structure to coordinate the heterogeneous graph information. The extensive experiments conducted on two baseline datasets, SemEval 2014 and ACL-14, demonstrate that the proposed ConAs-GRNs can effectively coordinate and integrate the heterogeneous information from within constructions.
A Combined Model Incorporating Improved SSA and LSTM Algorithms for Short-Term Load Forecasting
Authors: Han Mingchong , Zhong Jianwei , Sang Pu , Liao Honghua , Tan Aiguo
Published: June 2022

To address the current difficulties and problems of short-term load forecasting (STLF), this paper proposes a combined forecasting method based on the improved sparrow search algorithm (ISSA), with fused Cauchy mutation and opposition-based learning (OBL), to optimize the hyperparameters of the long- and short-term-memory (LSTM) network. For the sparrow-search algorithm (SSA), a Sin-chaotic-initialization population, with an infinite number of mapping folds, is first used to lay the foundation for global search. Secondly, the previous-generation global-optimal solution is introduced in the discoverer-location update way, to improve the adequacy of the global search, while adaptive weights are added to reconcile the ability of the local exploitation and global search of the algorithm as well as to hasten the speed of convergence. Then, fusing the Cauchy mutation arithmetic and the OBL strategy, a perturbation mutation is performed at the optimal solution position to generate a new solution, which, in turn, strengthens the ability of the algorithm to get rid of the local space. After that, the ISSA-LSTM forecasting model is constructed, and the example is verified based on the power load data of a region, while the experimental comparison with various algorithms is conducted, and the results confirm the superiority of the ISSA-LSTM model.
SLAM, Path Planning Algorithm and Application Research of an Indoor Substation Wheeled Robot Navigation System
Authors: Ren Jianxin , Wu Tao , Zhou Xiaohua , Yang Congcong , Sun Jiahui , Li Mingshuo , Jiang Huayang , Zhang Anfeng
Published: June 2022

Staff safety is not assured due to the indoor substation’s high environmental risk factor. The Chinese State Grid Corporation has been engaged in the intelligentization of substations and the employment of robots for inspection tasks. The autonomous navigation and positioning system of the mobile chassis is the most important feature of this type of robot, as it allows the robot to perceive the surrounding environment information at the initial position using its own sensors and find a suitable path to move to the target point to complete the task. Automatic navigation is the basis for the intelligentization of indoor substation robots, which is of great significance to the efficient and safe inspection of indoor substations. Based on this, this paper formulates a new navigation system, and builds a chassis simulation environment in the Robot Operating System (ROS). To begin with, we develop a novel hardware and sensor-based chassis navigation system experimental platform. Secondly, to conduct the fusion of the odometer and inertial navigation data, the Extended Kalman Filter (EKF) is used. The map’s creation approach determines how the environmental map is created. The global path is scheduled with the A* algorithm, whereas the local path is scheduled with the Dynamic Window Method (DWA). Finally, the created robot navigation system is applied to an auxiliary operation robot chassis suited for power distribution cabinet switch and the navigation system’s experimental analysis is conducted using this platform, demonstrating the system’s efficacy and practicability.
Implementation of Voice-Based Report Generator Application for Visually Impaired
Authors: Choi Jungyoon , Song Yoojeong , Lee Jongwoo
Published: June 2022

The college entrance rate of the disabled is gradually increasing, and each university is trying to provide equal rights and opportunities for college students with disabilities. However, students with disabilities still have difficulty adapting to college life due to limitations in the range of experience and diversity, restrictions in walking ability, and restrictions on interaction with the environment. Visually impaired students cannot perform tasks given by universities independently without the help of others, but universities do not have a system that is helpful except for supporting helpers. Therefore, in this paper, we aimed to develop independent report generation software, VTR4VI (Voice to Report program for the Visually Impaired) for students with visual impairment by using mobile devices that are always in possession. Since the existing speech recognition document editing software is designed for non-visually impaired people, it is difficult for the visually impaired to use. Accordingly, the requirements of a report generator for blind students were identified so blind students could freely perform assignments or write reports without helpers, just like non-visually impaired students. This software can be easily used by clicking on the Bluetooth remote control instead of touching the phone screen, and the operation is simple. As a result of our usability evaluation, our VTR4VI will surely help the visually impaired to study and make a written report.
Deciphering the Interplay between Binders and Electrolytes on the Performance of Li4Ti5O12 Electrodes for Li-Ion Batteries
Authors: De Giorgio Francesca , Gaboardi Mattia , Gigli Lara , Brutti Sergio , Arbizzani Catia
Published: June 2022

Lithium titanium oxide (Li4Ti5O12, LTO) is an attractive negative electrode for the development of safe—next-generation—lithium-ion batteries (LIBs). LTO can find specific applications complementary to existing alternatives for LIBs thanks to its good rate capability at high C-rates, fast lithium intercalation, and high cycling stability. Furthermore, LIBs featuring LTO electrodes are inherently safer owing to the LTO’s operating potential of 1.55 V vs. Li+/Li where the commonly used organic-based electrolytes are thermodynamically stable. Herein, we report the combined use of water-soluble sodium alginate (SA) binder and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI)-tetraglyme (1m-T) electrolyte and we demonstrate the improvement of the electrochemical performance of LTO-based electrodes with respect to those operating in conventional electrolyte 1M LiPF6-ethylene carbonate: dimethyl carbonate (LP30). We also tackle the analysis of the impact of combining the binder/electrolyte on the long-term cycling performance of LTO electrodes featuring SA or conventional polyvinylidene fluoride (PVdF) as binders. Therefore, to assess the impact of the combination of binder/electrolyte on performance, we performed post-mortem characterization by ex situ synchrotron diffraction experiments of LTO electrodes after cycling in LP30 and 1m-T electrolytes.
A Comprehensive Electric Vehicle Model for Vehicle-to-Grid Strategy Development
Authors: Rücker Fabian , Schoeneberger Ilka , Wilmschen Till , Chahbaz Ahmed , Dechent Philipp , Hildenbrand Felix , Barbers Elias , Kuipers Matthias , Figgener Jan , Sauer Dirk Uwe
Published: June 2022

A comprehensive electric vehicle model is developed to characterize the behavior of the Smart e.d. (2013) while driving, charging and providing vehicle-to-grid services. To facilitate vehicle-to-grid strategy development, the EV model is completed with the measurement of the on-board charger efficiency and the charging control behavior upon external set-point request via IEC 61851-1. The battery model is an electro-thermal model with a dual polarization equivalent circuit electrical model coupled with a lumped thermal model with active liquid cooling. The aging trend of the EV’s 50 Ah large format pouch cell with NMC chemistry is evaluated via accelerated aging tests in the laboratory. Performance of the model is validated using laboratory pack tests, charging and driving field data. The RMSE of the cell voltage was between 18.49 mV and 67.17 mV per cell for the validation profiles. Cells stored at 100% SOC and 40 °C reached end-of-life (80% of initial capacity) after 431–589 days. The end-of-life for a cell cycled with 80% DOD around an SOC of 50% is reached after 3634 equivalent full cycles which equates to a driving distance of over 420,000 km. The full parameter set of the model is provided to serve as a resource for vehicle-to-grid strategy development.
PCViT: A Pre-Convolutional ViT Coal Gangue Identification Method
Authors: Yang Jianjian , Chang Boshen , Zhang Yuzeng , Zhang Yucheng , Luo Wenjie
Published: June 2022

For the study of coal and gangue identification using near-infrared reflection spectroscopy, samples of anthracite coal and gangue with similar appearances were collected, and different dust concentrations (200 ug/m3, 500 ug/m3 and 800 ug/m3), detection distances (1.2 m, 1.5 m and 1.8 m) and mixing gangue rates (one-third coal, two-thirds coal, full coal) were collected in the laboratory by the reflection spectroscopy acquisition device and the gangue reflection spectral data. The spectral data were pre-processed using three methods, first-order differentiation, second-order differentiation and standard normal variable transformation, in order to enhance the absorption characteristics of the reflectance spectra and to eliminate the effects of changes in the experimental environment. The PCViT gangue identification model is established, and the disadvantages of the violent patch embedding of the ViT model are improved by using the stepwise convolution operation to extract features. Then, the interdependence of the features of the hyperspectral data is modeled by the self-attention module, and the learned features are optimized adaptively. The results of gangue recognition under nine working conditions show that the proposed recognition model can significantly improve the recognition accuracy, and this study can provide a reference value for gangue recognition using the near-infrared reflection spectra of gangue.
Impact of Separator Thickness on Temperature Distribution in Single Cell of Polymer Electrolyte Fuel Cell Operated at Higher Temperature of 90 °C and 100 °C
Authors: Nishimura Akira , Kono Nozomu , Toyoda Kyohei , Mishima Daiki , Kolhe Mohan
Published: June 2022

The New Energy and Industry Technology Development Organization (NEDO) road map (Japan, 2017) has proposed that a polymer electrolyte fuel cell (PEFC) system, which operates at a temperature of 90 °C and 100 °C, be applied for stationary and mobility usage, respectively. This study suggests using a thin polymer electrolyte membrane (PEM) and a thin gas diffusion layer (GDL), at the same time, to achieve better power-generation performance, at a higher temperature than usual. The focus of this paper is to clarify the effect of separator thickness on the distribution of temperature at the reaction surface (Treact), with the relative humidity (RH) of the supply gasses and initial operation temperature (Tini), quantitatively. In this study, separator thickness is investigated in a system using a thin PEM and a thin GDL. Moreover, this study investigates the difference between the maximum temperature and the minimum temperature obtained from the distribution of Treact as well as the relation between the standard deviation of Treact − Tini and total voltage, to clarify the effect of separator thickness. The impact of the flow rates of the supply gases on the distribution of Treact is not large, among the investigated conditions. It is noticed that the temperature distribution is wider when a separator thickness of 2.0 mm is selected. On the other hand, it is observed that the temperature increases along with the gas flow through the gas channel, by approximately 2 °C, when using a separator thickness between 1.5 mm and 1.0 mm. The impact of the RH on the distributions of Treact − Tini is larger at Tini = 100 °C, when a separator thickness of 1.0 mm is selected. It is revealed that the wider temperature distribution provides a reduction in power-generation performance. This study proposes that the thin separators, i.e., with a thickness of 1.5 mm and 1.0 mm, are not suitable for higher temperature operation than usual.
Optimization of Voltage Security with Placement of FACTS Device Using Modified Newton–Raphson Approach: A Case Study of Nigerian Transmission Network
Authors: Okampo Ewaoche John , Nwulu Nnamdi , Bokoro Pitshou N.
Published: June 2022

Power flow reliability, voltage security and transmission congestion management are paramount operational issues in a power system. Flexible AC transmission system (FACTS) controllers are suitable technologies that can provide compensation and dynamic control of power system transmission parameters to enhance effective performance and reliability. The interline power flow controller (IPFC), if optimally placed, can regulate the impedance of multiple lines to improve active power transfer capacity and voltage profile. This study examines the performance of IPFCs for voltage enhancement by suppressing fluctuation. A modified Newton–Raphson load flow problem with an incorporated IPFC variable has been formulated with the objective to improve voltage stability and maintain active power flow. The effectiveness of the proposed method was tested on the Nigerian 41 bus transmission network. The obtained result of the system with an IPFC placed at the weakest bus of the network was compared with Newton–Raphson load flow analysis of the same network without an IPFC. The results of load flow analysis for Case 1 (the system without an IPFC) showed that the transmission network without an IPFC had a real power loss of 4.699488 p.u., and reactive power loss of 4.467413 p.u., whereas the integration of an IPFC to the power flow formation in Case 2 resulted in the reduction in the transmission network’s overall losses to 0.55297 p.u. and −38.3329 p.u. The modified method proves effective as the power system network with an IPFC returns a more stable voltage profile and improves active power flow. In addition, this method, similar to all other mathematical optimization approaches, returns a strong accurate result but may be a drawback in terms of longer computational time compared with metaheuristic methods which are preferred for a larger network system.
Prediction-Driven Sequential Optimization for Refined Oil Production-Sales-Stock Decision-Making
Authors: Zhang Jindai , Zhao Jinlou
Published: June 2022

This paper proposes a prediction-driven sequential optimization methodology for joint decision-making problems of production-sales-stock in refined oil enterprises. In the proposed prediction-driven sequential optimization methodology, three dynamic nonlinear programming models are first constructed to model the production-sales-stock decision-making problems in refined oil enterprises. Then, the analytical solutions to sequential optimization for production-sales-stock decision-making issues are presented by using the inverse inference method in dynamic programming. Finally, the impact of price and demand prediction of refined oil products on sequential optimization for production-sales-stock decision-making are analyzed using a numerical analysis method. Numerical results demonstrated the significant impact of forecasting results of price and demand of refined oil products on sequential optimization decision-making, indicating that the prediction-driven sequential optimization methodology can be used as an effective tool for joint decision-making of production-sales-stock.
Techno-Economic Feasibility of Off-Grid Renewable Energy Electrification Schemes: A Case Study of an Informal Settlement in Namibia
Authors: Amupolo Aili , Nambundunga Sofia , Chowdhury Daniel S. P. , Grün Gunnar
Published: June 2022

This paper examines different off-grid renewable energy-based electrification schemes for an informal settlement in Windhoek, Namibia. It presents a techno-economic comparison between the deployment of solar home systems to each residence and the supplying power from either a centralized roof-mounted or ground-mounted hybrid microgrid. The objective is to find a feasible energy system that satisfies technical and user constraints at a minimum levelized cost of energy (LCOE) and net present cost (NPC). Sensitivity analyses are performed on the ground-mounted microgrid to evaluate the impact of varying diesel fuel price, load demand, and solar photovoltaic module cost on system costs. HOMER Pro software is used for system sizing and optimization. The results show that a hybrid system comprising a solar photovoltaic, a diesel generator, and batteries offers the lowest NPC and LCOE for both electrification schemes. The LCOE for the smallest residential load of 1.7 kWh/day and the largest microgrid load of 5.5 MWh/day is USD 0.443/kWh and USD 0.380/kWh, respectively. Respective NPCs are USD 4738 and USD 90.8 million. A sensitivity analysis reveals that variation in the fuel price and load demand changes linearly with system costs and capacities. However, reducing the PV module price in an energy system that includes wind and diesel power sources does not offer significant benefits. Furthermore, deploying an energy system that relies on fossil fuels to each residence in an informal settlement is not environmentally responsible. Unintended negative environmental impacts may result from the mass and simultaneous use of diesel generators. Therefore, a microgrid is recommended for its ability to control the dispatch of diesel generation, and its scalability, reliability of supply, and property security. A roof-mounted microgrid can be considered for piloting due to its lower initial investment. The electricity tariff also needs to be subsidized to make it affordable to end-users. Equally, government and community involvement should be prioritized to achieve long-term economic sustainability of the microgrid.
An Improved U-Net Segmentation Model That Integrates a Dual Attention Mechanism and a Residual Network for Transformer Oil Leakage Detection
Authors: Li Xuxu , Liu Xiaojiang , Xiao Yun , Zhang Yao , Yang Xiaomei , Zhang Wenhai
Published: June 2022

Accurately detecting oil leakage from a power transformer is important to maintain its normal operation. Deep learning (DL) methods have achieved satisfactory performance in automatic oil detection, but challenges remain due to the small amount of training data and oil targets with large variations in position, shape, and scale. To manage these issues, we propose a dual attention residual U-net (DAttRes-Unet) within a U-net architecture that extensively uses a residual network as well as spatial and channel-wise attention modules. To overcome the vanishing gradient problem due to deeper layers and a small amount of training data, a residual module from ResNet18 is used to construct the encoder path in the U-net framework. Meanwhile, to overcome the issue of training difficulty for the network, inspired by the advantage of transfer learning, initial network parameters in the encoder are obtained from the pre-trained ResNet18 on the ImageNet dataset. Further, in the decoder path, spatial attention and channel attention are integrated to highlight oil-stained regions while suppressing the background or irrelevant parts/channels. To facilitate the acquisition of the fluorescence images of the transformer, we designed a portable acquisition device integrating an ultraviolet light source and a digital camera. The proposed network is trained on the amount of fluorescence images after data augmentation is used and tested on actual fluorescence images. The experiment results show that the proposed DAttRes-Unet network can recognize oil-stained regions with a high accuracy of 98.49% for various shapes and scales of oil leakage.
Immobilization of Alcohol Dehydrogenase, Acetaldehyde Lyase, and NADH Oxidase for Cascade Enzymatic Conversion of Ethanol to Acetoin
Authors: Li Xue-Yong , Huang Jia-Ying , Zhou Qiang , Xu Yuan-Yuan , Prabhu Ponnandy , Zhang Ye-Wang
Published: June 2022

Acetoin, a four-carbon hydroxyl-keto compound, is used in the food, pharmaceutical, and chemical industries. The cascade enzymatic production is considered a promising and efficient method to produce acetoin. However, the stability and compatibility of the enzymes under the same catalytic conditions are challenges that need to be resolved. In this work, alcohol dehydrogenase, acetaldehyde lyase, and NADH oxidase were selected to work at the same conditions to efficiently convert ethanol into acetoin. These three enzymes were immobilized on epoxy-modified magnetic nanomaterials to obtain highly stable biocatalysts. The stability and the immobilization conditions, including temperature, pH, enzyme–carrier ratio, and immobilization time, were optimized to obtain the immobilized enzymes with a high catalytic activity. The cascade reactions catalyzed by the immobilized enzymes yielded a high conversion of 90%, suggesting that the use of immobilized enzymes is a promising way to produce acetoin.
Modified Master–Slave Controller for Stable Power Supply of Energy Storage Based Microgrid
Authors: Jeong Byoung-chang
Published: June 2022

This paper presents a method for supplying stable electricity using renewable energy sources and energy storage systems (ESSs) in a small-scale microgrid (MG) such as an island. Traditional control methods, such as master–slave control and droop control, have focused on equalizing power sharing among a small number of generators and do not deal well with emergencies such as unplanned generator failures. This paper proposes a control method that can stably maintain the frequency of the MG in various situations by combining the advantages of master–slave control and droop control and complementing the disadvantages. Simulations were performed under various conditions to verify the proposed control method.
Diagnosing Improper Membrane Water Content in Proton Exchange Membrane Fuel Cell Using Two-Dimensional Convolutional Neural Network
Authors: Zhang Heng , Liu Zhongyong , Liu Weilai , Mao Lei
Published: June 2022

In existing proton exchange membrane fuel cell (PEMFC) applications, improper membrane water management will cause PEMFC performance decay, which restricts the reliability and durability of PEMFC systems. Therefore, diagnosing improper water content in the PEMFC membrane is the key to taking appropriate mitigations to guarantee its operating safety. This paper proposes a novel approach for diagnosing improper PEMFC water content using a two-dimensional convolutional neural network (2D-CNN). In the analysis, the collected PEMFC voltage signal is transformed into 2D image data, which is then used to train the 2D-CNN. Data enhancement and pre-processing techniques are applied to PEMFC voltage data before the training. Results demonstrate that with the trained model, the diagnostic accuracy for PEMFC membrane improper water content can reach 97.5%. Moreover, by comparing it with a one-dimensional convolutional neural network (1D-CNN), the noise robustness of the proposed method can be better highlighted. Furthermore, t-distributed Stochastic Neighbor Embedding (t-SNE) is used to visualize the feature separability with different methods. With the findings, the effectiveness of using 2D-CNN for diagnosing PEMFC membrane improper water content is explored.
Research on Directional Controllability of Cracking in Hydraulic Fracturing of Hard Overburden Based on Local Stress Field Intervention
Authors: Chen Dingchao , Wang Xiangyu , Zhang Feiteng , Li Menglong , Zhao Xiangqian , Li Guanjun , Yu Yang , Wang Guanghui , Zhao Jiaxin , Wang Xiangdong
Published: June 2022

As a widely-used method of digging roadways in China, gob-side entry driving features specific advantages, such as a high recovery rate and good isolation effects. However, under the condition of hard overburden, the excessive bearing pressure of small coal pillars will easily cause serious internal damage in the coal and the run-through of the plastic zone, leading to harmful gas leakage in the goaf. Therefore, based on the engineering background of small coal pillars in the 18506 working face of Xiqu Coal Mine, this paper comprehensively adopts theoretical analysis, numerical simulation, industrial tests, and other methods, analyzes the evolution mechanism of isolated bearing and plastic fracture areas of small coal pillar under hard overburden, studies the influence law of hard overburden cutting parameters on the isolation and stability of small coal pillars, and puts forward the technology of actively cutting the top to weaken the stress concentration of coal pillars under hard overburden. With the reasonable cutting parameters determined, the controllable mechanism of hydraulic fracturing cutting under hard overburden further revealed, and the hydraulic fracturing cutting technology with “controllable cutting orientation of hydraulic fracturing with local stress field intervention” formed as the basic core, the stress situation on the roof is improved, realizing the stability control of the coal pillars for the roadway protection, and avoiding gas leakage and other disasters caused by small coal pillar destruction.