bluuebunny/crossref_metadata_2025 huggingface dataset:
Scattering of Light from the Systemic Circulatory System
Authors: Batool Sidra , Nisar Mehwish , Mangini Fabio , Frezza Fabrizio , Fazio Eugenio
Published: November 2020

There are many factors of methodological origin that influence the measurement of optical properties of the entire circulatory system which consists of blood as the basic component. The basic idea of this review article is to provide the optical properties of the circulatory system with all those factors of influence that have been employed in biomedical optics for different applications. We begin with the available optical properties, i.e., absorption, scattering and, reduced scattering coefficient, in general for any tissue inside the human body and prominent scattering theories (e.g., light, X-rays, neutrons) that are helpful in this regard. We have reviewed and compiled already available formulas and their respective available data for different human tissues for these optical properties. Then we have descended to the blood composition and to different scattering techniques available in the literature to study scattering and light propagation inside blood. We have reviewed both computational and theoretical scattering techniques.
Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing
Authors: Kou Ziming , Yang Fen , Wu Juan , Li Tengyu
Published: November 2020

The mine hoist sheave bearing is a large heavy-duty bearing, located in a derrick of tens of meters. Aiming at the difficulty of sheave bearing fault diagnosis, a combined fault-diagnosis method based on the improved complete ensemble EMD (ICEEMDAN) energy entropy and support vector machine (SVM) optimized by artificial fish swarm algorithm (AFSA) was proposed. Different location of the bearing defect will result in different frequency components and different amplitude energy of the frequency. According to this feature, the position of the bearing defect can be determined by calculating the ICEEMDAN energy entropy of different vibration signals. In view of the difficulty in selecting the penalty factor and radial basis kernel parameter in the SVM model, the AFSA was used to optimize them. The experimental results show that the accuracy rate of the optimized fault-diagnosis model is improved by 10% and the diagnostic accuracy rate is 97.5%.
Transitional Channel Flow: A Minimal Stochastic Model
Authors: Manneville Paul , Shimizu Masaki
Published: November 2020

In line with Pomeau’s conjecture about the relevance of directed percolation (DP) to turbulence onset/decay in wall-bounded flows, we propose a minimal stochastic model dedicated to the interpretation of the spatially intermittent regimes observed in channel flow before its return to laminar flow. Numerical simulations show that a regime with bands obliquely drifting in two stream-wise symmetrical directions bifurcates into an asymmetrical regime, before ultimately decaying to laminar flow. The model is expressed in terms of a probabilistic cellular automaton of evolving von Neumann neighborhoods with probabilities educed from a close examination of simulation results. It implements band propagation and the two main local processes: longitudinal splitting involving bands with the same orientation, and transversal splitting giving birth to a daughter band with an orientation opposite to that of its mother. The ultimate decay stage observed to display one-dimensional DP properties in a two-dimensional geometry is interpreted as resulting from the irrelevance of lateral spreading in the single-orientation regime. The model also reproduces the bifurcation restoring the symmetry upon variation of the probability attached to transversal splitting, which opens the way to a study of the critical properties of that bifurcation, in analogy with thermodynamic phase transitions.
Application of Dynamic Fragmentation Methods in Multimedia Databases: A Review
Authors: Castro-Medina Felipe , Rodríguez-Mazahua Lisbeth , López-Chau Asdrúbal , Cervantes Jair , Alor-Hernández Giner , Machorro-Cano Isaac
Published: November 2020

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented.
Modified Gerchberg–Saxton (G-S) Algorithm and Its Application
Authors: Zhao Tieyu , Chi Yingying
Published: November 2020

The Gerchberg–Saxton (G-S) algorithm is a phase retrieval algorithm that is widely used in beam shaping and optical information processing. However, the G-S algorithm has difficulty obtaining the exact solution after iterating, and an approximate solution is often obtained. In this paper, we propose a series of modified G-S algorithms based on the Fresnel transform domain, including the single-phase retrieval (SPR) algorithm, the double-phase retrieval (DPR) algorithm, and the multiple-phase retrieval (MPR) algorithm. The analysis results show that the convergence of the SPR algorithm is better than that of the G-S algorithm, but the exact solution is not obtained. The DPR and MPR algorithms have good convergence and can obtain exact solutions; that is, the information is recovered losslessly. We discuss the security advantages and verification reliability of the proposed algorithms in image encryption. A multiple-image encryption scheme is proposed, in which n plaintexts can be recovered from n ciphertexts, which greatly improves the efficiency of the system. Finally, the proposed algorithms are compared with the current phase retrieval algorithms, and future applications are discussed. We hope that our research can provide new ideas for the application of the G-S algorithm.
Looking for Special Education in the Swedish After-School Leisure Program Construction and Testing of an Analysis Model
Authors: Karlsudd Peter
Published: November 2020

How the Swedish after-school leisure program pedagogy relates to special education is rarely the subject of research. The problematization of the special education concept in the after-school leisure centers will be the starting point of this analysis model. This has been constructed with the aim of investigating how actors in the Swedish after-school leisure activities define how special education is being actualized in after school programs. The premises for the study regard the after-school leisure program mission; namely, to complement, compensate, and teach. In order to validate the analysis model, an exploratory pilot study was conducted through interviews with two teacher educators and two teachers in the after-school leisure program. The results show that the models developed for this investigation can be used in further studies. The analysis model provided important key words for further investigation and discussion of the program. These results can in no way be generalized, but they clearly show that the constructed and tested analysis model may form the basis for valuable discussions and pedagogical approaches in teacher education and in the program that the education prepares students for. Therefore, the pilot study comprises the foundation for a more comprehensive future study.
Exploring Pedagogical Content Knowledge (PCK) of Physics Teachers in a Colombian Secondary School
Authors: Melo Lina , Cañada-Cañada Florentina , González-Gómez David , Jeong Jin Su
Published: November 2020

The purpose of this paper is to address the most significant contributions of pedagogical content knowledge (PCK) involved in teaching physics. Most studies have observed the characterizations of PCK via specific content in the lower secondary and upper secondary curricula. Here, we present a number of studies that show evidence for the development of some PCK components, including those that present PCK as an articulating axis for physics teacher training models. The present work is a descriptive study that analyzes, by means of a case study, the changes in PCK through a physics teacher training intervention program. This program is based on reflections about teaching, concerning the electric field in physics education. The results show that categories, such as knowledge about the curriculum and teaching strategies, evolved after the intervention program, in contrast with knowledge about evaluation and pupils. This suggests that an approach involving a teacher’s reflection on what he/she designs allows for progression towards a teaching and learning process that is more focused on innovative tendencies.
The Case for Evidence-Based Outdoor Recreation Interventions for Girls: Helping Girls “Find Their Voice” in the Outdoors
Authors: Evans Kate , Walters Kellie , Anderson Denise
Published: November 2020

Females’ participation in outdoor recreation is often limited for a variety of reasons including social gender norms, a lack of exposure, and fear. Research has uncovered a wide range of positive outcomes for those females who do participate ranging from enhanced self-esteem and confidence to improved body image, indicating the importance of opening the outdoors as a welcoming place for all to experience. Finding Your Voice is a recreation intervention created with the focus of introducing middle school girls to outdoor recreation to increase the participants’ self-efficacy and self-empowerment. Empirical research focusing on participant experiences has demonstrated promising results and the best practices from Finding Your Voice and the broader research on female empowerment in the outdoors are presented.
Secure Exchange of Medical Data Using a Novel Real-Time Biometric-Based Protection and Recognition Method
Authors: Ud Din Shams , Jan Zahoor , Sajjad Muhammad , Hussain Maqbool , Ali Rahman , Ali Asmat , Lee Sungyoung
Published: November 2020

Security and privacy are essential requirements, and their fulfillment is considered one of the most challenging tasks for healthcare organizations to manage patient data using electronic health records. Electronic health records (clinical notes, images, and documents) become more vulnerable to breaching patients’ privacy when shared with an external organization in the current arena of the internet of medical things (IoMT). Various watermarking techniques were introduced in the medical field to secure patients’ data. Most of the existing techniques focus on an image or document’s imperceptibility without considering the watermark(logo). In this research, a novel technique of watermarking is introduced, which supersedes the shortcomings of existing approaches. It guarantees the imperceptibility of the image/document and takes care of watermark(biometric), which is further passed through a process of recognition for claiming ownership. It extracts suitable frequencies from the transform domain using specialized filters to increase the robustness level. The extracted frequencies are modified by adding the biomedical information while considering the strength factor according to the human visual system. The watermarked frequencies are further decomposed through a singular value decomposition technique to increase payload capacity up to (256 × 256). Experimental results over a variety of medical and official images demonstrate the average peak signal-to-noise ratio (PSNR 54.43), and the normal correlation (N.C.) value is 1. PSNR and N.C. of the watermark were calculated after attacks. The proposed technique is working in real-time for embedding, extraction, and recognition of biometrics over the internet, and its uses can be realized in various platforms of IoMT technologies.
Intelligent Fog-Enabled Smart Healthcare System for Wearable Physiological Parameter Detection
Authors: Ijaz Muhammad , Li Gang , Wang Huiquan , El-Sherbeeny Ahmed M. , Moro Awelisah Yussif , Lin Ling , Koubaa Anis , Noor Alam
Published: November 2020

Wearable technology plays a key role in smart healthcare applications. Detection and analysis of the physiological data from wearable devices is an essential process in smart healthcare. Physiological data analysis is performed in fog computing to abridge the excess latency introduced by cloud computing. However, the latency for the emergency health status and overloading in fog environment becomes key challenges for smart healthcare. This paper resolves these problems by presenting a novel tri-fog health architecture for physiological parameter detection. The overall system is built upon three layers as wearable layer, intelligent fog layer, and cloud layer. In the first layer, data from the wearable of patients are subjected to fault detection at personal data assistant (PDA). To eliminate fault data, we present the rapid kernel principal component analysis (RK-PCA) algorithm. Then, the faultless data is validated, whether it is duplicate or not, by the data on-looker node in the second layer. To remove data redundancy, we propose a new fuzzy assisted objective optimization by ratio analysis (FaMOORA) algorithm. To timely predict the user’s health status, we enable the two-level health hidden Markov model (2L-2HMM) that finds the user’s health status from temporal variations in data collected from wearable devices. Finally, the user’s health status is detected in the fog layer with the assist of a hybrid machine learning algorithm, namely SpikQ-Net, based on the three major categories of attributes such as behavioral, biomedical, and environment. Upon the user’s health status, the immediate action is taken by both cloud and fog layers. To ensure lower response time and timely service, we also present an optimal health off procedure with the aid of the multi-objective spotted hyena optimization (MoSHO) algorithm. The health off method allows offloading between overloaded and underloaded fog nodes. The proposed tri-fog health model is validated by a thorough simulation performed in the iFogSim tool. It shows better achievements in latency (reduced up to 3 ms), execution time (reduced up to 1.7 ms), detection accuracy (improved up to 97%), and system stability (improved up to 96%).
Employing Subjective Tests and Deep Learning for Discovering the Relationship between Personality Types and Preferred Music Genres
Authors: Dorochowicz Aleksandra , Kurowski Adam , Kostek Bożena
Published: November 2020

The purpose of this research is two-fold: (a) to explore the relationship between the listeners’ personality trait, i.e., extraverts and introverts and their preferred music genres, and (b) to predict the personality trait of potential listeners on the basis of a musical excerpt by employing several classification algorithms. We assume that this may help match songs according to the listener’s personality in social music networks. First, an Internet survey was built, in which the respondents identify themselves as extraverts or introverts according to the given definitions. Their task was to listen to music excerpts that belong to several music genres and choose the ones they like. Next, music samples were parameterized. Two parametrization schemes were employed for that purpose, i.e., low-level MIRtoolbox parameters (MIRTbx) and variational autoencoder neural network-based, which automatically extract parameters of musical excerpts. The prediction of a personality type was performed employing four baseline algorithms, i.e., support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF), and naïve Bayes (NB). The best results were obtained by the SVM classifier. The results of these analyses led to the conclusion that musical excerpt features derived from the autoencoder were, in general, more likely to carry useful information associated with the personality of the listeners than the low-level parameters derived from the signal analysis. We also found that training of the autoencoders on sets of musical pieces which contain genres other than ones employed in the subjective tests did not affect the accuracy of the classifiers predicting the personalities of the survey participants.
FPGA-Based Solution for On-Board Verification of Hardware Modules Using HLS
Authors: Caba Julián , Rincón Fernando , Barba Jesús , de la Torre José Antonio , López Juan Carlos
Published: November 2020

High-Level Synthesis (HLS) tools provide facilities for the development of specialized hardware accelerators (HWacc). However, the verification stage is still the longest phase in the development life-cycle. Unlike in the software industry, HLS tools lack testing frameworks that could cover the whole design flow, especially the on-board verification stage of the generated RTL. This work introduces a framework for on-board verification of HLS-based modules by using reconfigurable systems and Docker containers with the aim to automate the verification process and preserve a clean testing environment, making the testbed reusable across different stages of the design flow. Moreover, our solution features a mechanism to check timing requirements of the HWacc. We have applied our solution to the C-kernels of the CHStone Benchmark on a Zedboard, in which the on-board verification process has been accelerated up to four times.
Thermal Stress Reduction of Power MOSFET with Dynamic Gate Voltage Control and Circulation Current Injection in Electric Drive Application
Authors: Wang Lie , Vermulst Bas , Duarte Jorge , Huisman Henk
Published: November 2020

While operating an electric drive under different load conditions, power switch devices experience thermal stress which provokes wear-out failures and compromises lifetime. In this paper, a model-based dynamic gate voltage control strategy is proposed to reduce the thermal stress by shaping the profile of conduction losses. Thermal stability criteria are investigated, which limit the gate voltage operating range; thus, current focalization and associated local heat up are avoided. After that, simulations and lifetime estimation are conducted for performance evaluation in two different operation scenarios, which show promising results at high speed operation conditions. Furthermore, a current injection method is applied for low-speed operating conditions to improve the compensation effort. This method is experimentally verified by using a custom proof-of-concept gate driver that supplies an adjustable three-level gate voltage. A three-phase electric drive is prototyped, on which power cycling tests are conducted. The junction temperature is measured and the results confirm the thermal control method.
Slime Mold Inspired Distribution Network Initial Solution
Authors: Püvi Verner , Millar Robert J. , Saarijärvi Eero , Hayami Ken , Arbelot Tahitoa , Lehtonen Matti
Published: November 2020

Electricity distribution network optimisation has attracted attention in recent years due to the widespread penetration of distributed generation. A considerable portion of network optimisation algorithms rely on an initial solution that is supposed to bypass the time-consuming steps of optimisation routines. The aim of this paper is to present a nature inspired algorithm for initial network generation. Based on slime mold behaviour, the algorithm can generate a large-scale network in a reasonable computation time. A mathematical formulation and parameter exploration of the slime mold algorithm are presented. Slime mold networks resemble a relaxed minimum spanning tree with better balance between the investment and loss costs of a distribution network. Results indicate lower total costs for suburban and urban networks.
Research on the Starting Acceleration Characteristics of a New Mechanical–Electric–Hydraulic Power Coupling Electric Vehicle
Authors: Yang Jian , Zhang Tiezhu , Zhang Hongxin , Hong Jichao , Meng Zewen
Published: November 2020

To simplify the layout of a purely electric vehicle transmission system and improve the acceleration performance of the vehicle, this paper utilizes the characteristics of the large torque of a hydraulic transmission system and proposes a new mechanical–electric–hydraulic dynamic coupling drive system (MEH-DCDS). It integrates the traditional motor and the swashplate hydraulic pump/motor into one, which can realize the mutual conversion between the mechanical energy, electrical energy, and hydraulic energy. This article explains its working principle and structural characteristics. At the same time, the mathematical model for the key components is established and the operation mode is divided into various types. Based on AMESim software, the article studies the dynamic characteristics of the MEH-DCDS, and finally proposes a method that combines real-time feedback of the accumulator output torque with PID control to complete the system simulation. The results show that the MEH-DCDS vehicle has a starting time of 4.52 s at ignition, and the starting performance is improved by 40.37% compared to that of a pure motor drive system vehicle; after a PID adjustment, the MEH-DCDS vehicle’s starting time is shortened by 1.04 s, and the acceleration performance is improved by 23.01%. The results indicated the feasibility of the system and the power performance was substantially improved. Finally, the system is integrated into the vehicle and the dynamic performance of the MEH-DCDS under cycle conditions is verified by joint simulation. The results show that the vehicle is able to follow the control speed well when the MEH-DCDS is loaded on the vehicle. The state-of-charge (SOC) consumption rate is reduced by 20.33% compared to an electric vehicle, while the MEH-DCDS has an increased range of 45.7 m compared to the EV. This improves the energy efficiency and increases the driving range.
Gate-Opening Criterion for Generating Dam-Break Flow in Non-Rectangular Wet Bed Channels
Authors: Yang Sha , Wang Bo , Guo Yakun , Zhang Jianmin , Chen Yunliang
Published: November 2020

A sudden dam failure is usually simulated by the rapid removal of a gate in laboratory tests and numerical simulations. The gate-opening time is often determined according to the Lauber and Hager instantaneous collapse criterion (referred to as the Lauber–Hager criterion), which is established for a rectangular open channel with a dry bed. However, this criterion is not suitable for non-rectangular channels or initial wet-bed conditions. In this study, the effect of the gate-opening time on the wave evolution is investigated by using the large eddy simulation (LES) model. The instantaneous dam-break, namely, a dam-break without a gate, is simulated for comparison. A gate-opening criterion for generating dam-break flow in a non-rectangular wet-bed channel is proposed in this study, which can be used as an extension of the Lauber–Hager criterion and provides a more comprehensive and reasonable estimate of the gate opening time.
Prediction of Wind Speed Using Hybrid Techniques
Authors: Lopez Luis , Oliveros Ingrid , Torres Luis , Ripoll Lacides , Soto Jose , Salazar Giovanny , Cantillo Santiago
Published: November 2020

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).
Offshore Wind Farm Black Start Service Integration: Review and Outlook of Ongoing Research
Authors: Pagnani Daniela , Blaabjerg Frede , Bak Claus Leth , Faria da Silva Filipe Miguel , Kocewiak Łukasz H. , Hjerrild Jesper
Published: November 2020

A review of the ongoing research on black start (BS) service integrated with offshore wind farms (OWFs) is presented in this paper. The overall goal is to firstly gain a better understanding of the BS capabilities required by modern power systems. Subsequently, the challenges faced by OWFs as novel BS service providers as well as an outlook on the ongoing research which may provide solutions to these are presented. OWFs have the potential to be a fast and environmentally friendly technology to provide BS services for power system restoration and, therefore, to ensure resiliency after blackouts. As a power electronic-based system, OWFs can be equipped with a self-starter in the system in order to perform BS. The self-start unit could be a synchronous generator (SG) or a power electronic unit such as a grid-forming (GFM) converter. Preliminary BS studies performed in PSCAD/EMTDC are presented in a simplified OWF system via an SG as the self-start unit. Consequently, technical challenges during the BS procedure in an OWF benchmark system are outlined via theoretical discussions and simulations results. This is useful to understand the threats to power electronics during BS. Finally, the most relevant GFM strategies in the state-of-the-art literature are presented and their application to OWF BS is discussed.
Experimental and Numerical Study on Proppant Transport in a Complex Fracture System
Authors: Zhang Zhaopeng , Zhang Shicheng , Ma Xinfang , Guo Tiankui , Zhang Wenzhe , Zou Yushi
Published: November 2020

Slickwater fracturing can create complex fracture networks in shale. A uniform proppant distribution in the network is preferred. However, proppant transport mechanism in the fracture network is still uncertain, which restricts the optimization of sand addition schemes. In this study, slot flow experiments are conducted to analyze the proppant placement in the complex fracture system. Dense discrete phase method is used to track the particle trajectories to study the transport mechanism into the branch. The effects of the pumping rate, sand ratio, sand size, and branch angle and location are discussed in detail. Results demonstrate that: (1) under a low pumping rate or coarse proppant conditions, the dune development in the branch depends on the dune geometry in the primary fracture, and a high proportion of sand can transport into the branch; (2) using a high pumping rate or fine proppants is beneficial to the uniform placement in the fracture system; (3) sand ratio dominates the proppant placement in the branch and passing-intersection fraction of a primary fracture; (4) more proppants may settle in the near-inlet and large-angle branch due to the size limit. Decreasing the pumping rate can contribute to a uniform proppant distribution in the secondary fracture. This study provides some guidance for the optimization of proppant addition scheme in the slickwater fracturing in unconventional resources.
Bioethanol Production from Biomass of Selected Sorghum Varieties Cultivated as Main and Second Crop
Authors: Batog Jolanta , Frankowski Jakub , Wawro Aleksandra , Łacka Agnieszka
Published: November 2020

In recent years, there has been a dynamic development of alternative energy sources and the use of plant biomass for the production of bioenergy is one of the possibilities of improving the energy mix. Therefore, it is worth reaching for new, less popular and perspective solutions, which certainly include sorghum, a drought-resistant plant with a high yielding potential and various applications in the bioeconomy. The aim of the research was to determine the amount of bioethanol obtained from the biomass of three sorghum varieties (Rona 1, Santos, Sucrosorgo 506) grown in the main and second crop for three years in the temperate climate typical of Central and Eastern Europe. The yields of sorghum cultivars grown as main and second crops, chemical components of sorghum biomass (cellulose, hemicellulose, lignin) and the amount of ethanol per a ton of dry matter of straw and ethanol yield per hectare were evaluated. The experiments and research carried out show, especially in the second year, that the Sucrosorgo 506 variety can be recommended for the cultivation of biomass and its use for the production of lignocellulosic ethanol is effective, both in main and second crop cultivation. The discussed results were confirmed by detailed statistical analysis, incl. principal component analysis (PCA) and cluster analysis. To sum up, the production of bioethanol from sorghum biomass is possible in temperate climate and it does not compete with the production of food due to the possibility of growing sorghum after rye.
A Policy Effect Analysis of China’s Energy Storage Development Based on a Multi-Agent Evolutionary Game Model
Authors: Zhang Ting , Cao Shuaishuai , Pan Lingying , Zhou Chenyu
Published: November 2020

Energy storage technology plays a significant role in the pursuit of the high-quality development of the electricity market. Many regions in China have issued policies and regulations of different intensities for promoting the popularization of the energy storage industry. Based on a variety of initial conditions of different regions, this paper explores the evolutionary process of electricity market players considering energy storage technology. The trilateral evolutionary game model is adopted to analyze the strategies of the power plant, the power grid, and the government. After assigning the model according to an actual situation, each equilibrium point corresponds to a real electricity market situation. The results indicate the following: (1) In the process of stabilizing, the role of “Advanced Imitators” leading the strategy of building energy storage changes between the power plant and the power grid. (2) In Eastern, Middle, and Southern China, the power plants and power grids on a greater-than-medium scale will choose to build energy storage without governmental regulations, due to the abundant net profit. (3) In the northeast of China, power plants with a medium-or-lower scale will choose not to build energy storage because of the relatively low on-grid price, and small power grids can make enough profits by operating energy storage facilities. (4) In Northern China, the large power plants and the medium power grids will choose to build energy storage due to the high electricity sale price and the resulting high profit. (5) In Western China, the small power plants and power grids cannot afford to build energy storage due to the low electricity price. The results lead to valuable policy suggestions for the local governments of China in promoting energy storage in the future. To meet the goal of energy storage popularization, regional electricity market plans need relevant policies based on its existing conditions, offering suitable external conditions for adding energy storage.
Design and Implementation of a Control Method for GaN-Based Totem-Pole Boost-Type PFC Rectifier in Energy Storage Systems
Authors: Do Nguyen-Nghia , Huang Bing-Siang , Phan Nhat-Truong , Nguyen Tan-Tung , Wu Jian-Hong , Liu Yu-Chen , Chiu Huang-Jen
Published: November 2020

With the unceasing advancement of wide-bandgap (WBG) semiconductor technology, the minimal reverse-recovery charge Qrr and other more powerful natures of WBG transistors enable totem-pole bridgeless power factor correction to become a dominant solution for energy storage systems (ESS). This paper focuses on the design and implementation of a control structure for a totem-pole boost PFC with newfangled enhancement-mode gallium nitride field-effect transistors (eGaN FETs), not only to simplify the control implementation but also to achieve high power quality and efficiency. The converter is designed to convert a 90–264-VAC input to a 385-VDC output for a 2.6-kW output power. Lastly, to validate the methodology, an experimental prototype is characterized and fabricated. The uttermost efficiency at 230 VAC reaches 99.14%. The lowest total harmonic distortion in the current (ITHD) at high line condition (230 V) attains 1.52% while the power factor gains 0.9985.
Mathematical Modelling of Active Magnetic Regenerator Refrigeration System for Design Considerations
Authors: Effatpisheh Aref , Vadiee Amir , Monfared Behzad A.
Published: November 2020

A magnetic refrigeration system has the potential to alternate the compression system with respect to environmental compatibility. Refrigeration systems currently operate on the basis of the expansion and compression processes, while active magnetic refrigeration systems operate based on the magnetocaloric effect. In this study, a single layer of Gd was used as the magnetocaloric material for six-packed-sphere regenerators. A one-dimensional numerical model was utilized to simulate the magnetic refrigeration system and determine the optimum parameters. The optimum mass flow rate and maximum cooling capacity at frequency of 4 Hz are 3 L·min−1 and 580 W, respectively. The results show that the maximum pressure drop increased by 1400 W at a frequency of 4 Hz and mass flow rate of 5 L·min−1. In this study, we consider the refrigeration system in terms of the design considerations, conduct a parametric study, and determine the effect of various parameters on the performance of the system.
Regional Spatial Analysis of the Offshore Wind Potential in Japan
Authors: Bardenhagen Yannek , Nakata Toshihiko
Published: November 2020

This study presents an approach for estimating the offshore wind potential of Japan. Bathymetry data (1 km mesh) and near shore wind speed data of the year 2018 were used to assess the potential. A turbine with a peak power of 10.6 MW was employed for the analysis. The potential was calculated for multiple regions. These regions are based on the service areas of the major electricity supply companies in Japan. Overall, the results show that Japan has the potential to produce up to 32,028 PJ electricity per year. The electricity demand of 2018 amounts to 3231 PJ. The potential is therefore large enough to cover Japan’s electricity needs ten-times over. The capacity that could theoretically be installed amounts to 2720 GW, which is a multiple of the current worldwide installed capacity of 29.1 GW (2019). In addition to the huge potential, the regional assessment shows that the regions vary greatly in their potential; of all the considered regions, Hokkaido and Kyushu have the highest overall potential.
Coordinated Control of a Hybrid Energy Storage System for Improving the Capability of Frequency Regulation and State-of-Charge Management
Authors: Nguyen-Huu Thien-An , Nguyen Van Thang , Hur Kyeon , Shim Jae Woong
Published: November 2020

The paper proposes a coordinated operation method of two independent storages for managing state-of-charge (SOC) and for providing ancillary service concerning frequency regulation (FR); furthermore, this article also introduces the power allocation scheme between two storages in consideration of the coverage of the frequency band for each storage along with the SOC management scheme of the supercapacitor and battery. We also provide a guideline for the storage sizing on the basis of the smoothing time constant. Additionally, we verify the advantage of the HESS in extending the lifetime of the battery, which is estimated by a real-time state-of-health (SOH) calculation method. The Bode plot of the proposed method is analyzed to observe the power spectrum coverage in the frequency domain through the case studies using PSCAD/EMTDC and MATLAB.
Evaluation of the Summer Overheating Phenomenon in Reinforced Concrete and Cross Laminated Timber Residential Buildings in the Cold and Severe Cold Regions of China
Authors: Guo Haibo , Huang Lu , Song Wenjie , Wang Xinyue , Wang Hongnan , Zhao Xinning
Published: November 2020

As the climate changed in recent years, an increase in summer indoor temperatures in severe cold and cold regions of China has started to affect thermal comfort. However, the local design standard for energy efficiency does not recognize this phenomenon. This paper reports the potential overheating phenomenon in residential buildings and examines the rationale for the current thermal designs adopted in severe cold and cold regions of China. In this study, the two most commonly used building materials, reinforced concrete (RC) and cross laminated timber (CLT), are used separately in the design of an 18-story residential building envelope located in six different cities in the severe cold and cold regions. The energy consumption and indoor operative temperatures during the operation of these buildings are simulated using Integrated Environmental Solutions Virtual Environment (IES VE). The results demonstrate that both the RC and the CLT buildings experience varying degrees of overheating in any climate subregion. The CLT buildings have longer overheating hours compared to the RC buildings, especially in the cold regions. The results also indicate that for apartments on higher stories, the cooling energy consumption and indoor temperature also increase gradually. The research results suggest that the local design standard for energy efficiency needs to be adjusted by adding thermal design methods for summer to reduce the periods of overheating.
Predicting the Surveillance Data in a Low-Permeability Carbonate Reservoir with the Machine-Learning Tree Boosting Method and the Time-Segmented Feature Extraction
Authors: Wang Cong , Zhao Lisha , Wu Shuhong , Song Xinmin
Published: November 2020

Predictive analysis of the reservoir surveillance data is crucial for the high-efficiency management of oil and gas reservoirs. Here we introduce a new approach to reservoir surveillance that uses the machine learning tree boosting method to forecast production data. In this method, the prediction target is the decline rate of oil production at a given time for one well in the low-permeability carbonate reservoir. The input data to train the model includes reservoir production data (e.g., oil rate, water cut, gas oil ratio (GOR)) and reservoir operation data (e.g., history of choke size and shut-down activity) of 91 producers in this reservoir for the last 20 years. The tree boosting algorithm aims to quantitatively uncover the complicated hidden patterns between the target prediction parameter and other monitored data of a high variety, through state-of-the-art automatic classification and multiple linear regression algorithms. We also introduce a segmentation technique that divides the multivariate time-series production and operation data into a sequence of discrete segments. This feature extraction technique can transfer key features, based on expert knowledge derived from the in-reservoir surveillance, into a data form that is suitable for the machine learning algorithm. Compared with traditional methods, the approach proposed in this article can handle surveillance data in a multivariate time-series form with different strengths of internal correlation. It also provides capabilities for data obtained in multiple wells, measured from multiple sources, as well as of multiple attributes. Our application results indicate that this approach is quite promising in capturing the complicated patterns between the target variable and several other explanatory variables, and thus in predicting the daily oil production rate.
Effect of Rheological Properties of Aqueous Solution of Na-CMC on Spray Angle for Conical Pressure-Swirl Atomizers
Authors: Czernek Krystian , Ochowiak Marek , Włodarczak Sylwia
Published: November 2020

Aerosol is a multiphase system, created as a result of the dispersion of a liquid in a gaseous medium. The atomized liquids are most often water and fuel; however, they can be any other substance. Even a small addition of a substance that changes the rheological properties (i.e., the nature of the flow) can change the properties of the resulting aerosol. The most important parameters that characterize the aerosol are the outflow rate, the droplet diameter, the spray spectrum, and the spray angle. The latter is important when selecting atomizers, especially those working in groups on the sprayer boom. The spray angle is an important parameter of the atomization process, providing a great deal of information about the quality of the spray. This study presents the results of rheological tests and the atomization of aqueous solutions with varying concentrations of sodium carboxymethylcellulose (Na-CMC). We found that the spray angle decreased with increasing Na-CMC concentration in the solution, which is attributable to an increase in shear viscosity. The design of the atomizer is also important. The largest spray angles were obtained for an atomizer with a diameter of 0.02 m and with the inlet port being placed at an angle to the atomizer axis. Based on the experimental results for various liquids and atomizer designs, a correlation equation describing the spray angle is proposed.
Mechanical Cutting Effect of Electrical Steel on the Performance of Induction Motors
Authors: Seo Un-Jae , Kim Dong-Jun , Chun Yon-Do , Han Pil-Wan
Published: November 2020

This paper investigates the mechanical cutting effect on the performance of induction motors. Numerical modeling of cutting effect is described in this paper. The approach inverts the degradation of the permeability model for inclusion of it into magnetic vector potential formula by Newton method. The effect of cutting on iron losses is implemented in finite element simulation. The simulation results are compared with experimental results of prototype IE4 efficiency induction motors rated at 2.2 kW. One of them was manufactured with annealed electrical steel lamination to highlight the cutting effect on the performance of the motor. The notable effect of cutting was measured in increased stator current; however, negligible differences were found in measured iron losses. The presented model in this paper follows the measurements.
Development of a Reduced Order Model of Solar Heat Gains Prediction
Authors: Tamm Meril , Macià Cid Jordi , Capdevila Paramio Roser , Farnós Baulenas Joan , Thalfeldt Martin , Kurnitski Jarek
Published: November 2020

The aim of this study was to elaborate and validate a reduced order model able to forecast solar heat gains as a function of the architectural parameters that determine the solar heat gains. The study focused on office buildings in Catalonia and Spain and their physical values were taken from the Spanish Building Technical Code and European Union Directive 2018/844. A reduced order model with three direct variables (solar heat gain coefficient, shade factor, window to wall ratio) and one indirect design variable (building orientation) was obtained and validated in respect to the International Performance Measurement and Verification Protocol. Building envelope properties were fixed and the values were taken from the national standards of Spain. This work validates solar heat gain coefficient as a primary variable in determining the annual solar heat gains in a building. Further work of developed model could result in building energy need quick evaluation tool in terms of solar heat gains for architects in building early stage as it has an advantage over detailed building simulation programs in terms of instant calculation and the limited need for predefined input data.
Method of Improving Lateral Stability by Using Additional Yaw Moment of Semi-Trailer
Authors: Bai Zhenyuan , Lu Yufeng , Li Yunxia
Published: November 2020

The lateral stability control of tractor semi-trailer plays a vital role for enhancing its driving safety, and the distributed electric drive structure of a hub motor creates opportunities and challenges for realising the lateral stability accurately. Based on the dynamics simulation software TruckSim, a nonlinear dynamic tractor semi-trailer model is established, and a MATLAB/Simulink linear three-degree-of-freedom monorail reference model is established. The upper controller adopts fuzzy proportional–integral–derivative control to export active yaw torque values of the tractor and semi-trailer. The lower controller outputs the driving/braking torque of each wheel according to the target wheel driving/braking rules and torque distribution rules. The tractor produce an active yaw torque through conventional differential braking the hub motor is installed on both sides of the semi-trailer, and the active yaw torque is produced by the coordinated control of the driving/braking torque of the hub motor and the differential braking of the mechanical braking system. To prevent wheel locking, the slip rate of each wheel is controlled. Finally, based on the TruckSim–MATLAB/Simulink cosimulation platform, cosimulation is performed under typical working conditions. The simulation results show that the control strategy proposed in this report is superior to the conventional differential braking control (ESP). It can not only improve the lateral stability of the vehicle more effectively, but also improve the roll stability.
Short Term Wind Power Prediction Based on Data Regression and Enhanced Support Vector Machine
Authors: Tu Chia-Sheng , Hong Chih-Ming , Huang Hsi-Shan , Chen Chiung-Hsing
Published: November 2020

This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data into clusters using the data regression (DR) algorithm to get meaningful training data, so as to reduce the number of modeling data and improve the efficiency of computing. The regression model was constructed based on the principle of the least squares support vector machine (LSSVM). We carried out wind speed forecasting for one hour and one day and used the correlation between marine wind speed and the corresponding wind power regression model to realize an indirect wind power forecasting model. Proper parameter settings for LSSVM are important to ensure its efficiency and accuracy. In this paper, we used an enhanced bee swarm optimization (EBSO) to perform the parameter optimization for LSSVM, which not only improved the forecast model availability, but also improved the forecasting accuracy.