bluuebunny/crossref_metadata_2025 huggingface dataset:
A Reinforcement Learning-Based Routing for Real-Time Multimedia Traffic Transmission over Software-Defined Networking
Authors: Al Jameel Mohammed , Kanakis Triantafyllos , Turner Scott , Al-Sherbaz Ali , Bhaya Wesam S.
Published: August 2022

Recently, video streaming services consumption has grown massively and is foreseen to increase even more in the future. The tremendous traffic usage has negatively impacted the network’s quality of service due to network congestion and end-to-end customers’ satisfaction represented by the quality of experience, especially during evening peak hours. This paper introduces an intelligent multimedia framework that aims to optimise the network’s quality of service and users’ quality of experience by taking into account the integration of Software-Defined Networking and Reinforcement Learning, which enables exploring, learning, and exploiting potential paths for video streaming flows. Moreover, an objective study was conducted to assess video streaming for various realistic network environments and under low and high traffic loads to obtain two quality of experience metrics; video multimethod assessment fusion and structural similarity index measure. The experimental results validate the effectiveness of the proposed solution strategy, which demonstrated better viewing quality by achieving better customers’ quality of experience, higher throughput and lower data loss compared with the currently existing solutions.
Study of Breakdown Voltage Stability of Gas-Filled Surge Arresters in the Presence of Gamma Radiation
Authors: Živanović Emilija , Živković Marija , Veljković Sandra
Published: August 2022

The results presented in this article relate to the study of the impact of gamma radiation on the breakdown voltage of gas-filled surge arrester manufactured by CITEL, Littelfuse and EPCOS at an operating voltage of 230 V. Radium was considered as a source of gamma radiation in this research. The stability of breakdown voltage as well as the reliability of gas-filled surge arresters of different manufacturers were investigated using different statistical methods. This gas component operation was based on processes that lead to electrical breakdown and discharge in gas. The breakdown voltage has a stochastic nature, and it is a subject of certain distribution. One thousand voltage measurements of breakdown voltage were carried out for each value of the voltage increase rate, from 1 V/s up to 10 V/s, with and without the presence of additional gamma radiation. The detailed statistical analysis of the obtained experimental data was performed for both cases for all three GFSA types. Moreover, the cumulative distribution functions of breakdown voltage were presented with the applied Weibull distribution fit. The coefficient of correlation as well as Pearson χ2 test showed the strength of the relationship between the experimental distribution functions and the Weibull distribution fits. The values of the Weibull distribution coefficients for all voltage increase rates and for all components were also analyzed with and without gamma radiation.
Task-Space Cooperative Tracking Control for Networked Uncalibrated Multiple Euler–Lagrange Systems
Authors: Zhao Zhuoqun , Wang Jiang , Zhao Hui
Published: August 2022

Task-space cooperative tracking control of the networked multiple Euler–Lagrange systems is studied in this paper. On the basis of establishing kinematic and dynamic modeling of a Euler–Lagrange system, an innovative task-space coordination controller is designed to deal with the time-varying communicating delays and uncertainties. First, in order to weaken the influence of the uncertainty of kinematic and dynamic parameters on the control error of the system, the product of the Jacobian matrix and the generalized spatial velocity are linearly parameterized; thus, the unknown parameters are separated from known parameters. The online estimation of uncertain parameters is realized by designing parameters and by proposing new adaptive laws for the dynamic and kinematic parameters. Furthermore, to describe the transmission of time-varying delay errors among networked agents, a new error term is introduced, obtained by adding the observation error and tracking error, and the coefficient of the network mutual coupling term related to the time-varying delay rate is added with reference to the generalized space velocity and task-space velocity of the Lagrange systems. In the end, the influence of the time-varying delay on the cooperative tracking control error of the networked multiple Euler–Lagrange systems is eliminated. With the help of Lyapunov stability theory, the tracking errors and synchronization errors of this system are calculated by introducing the Lyapunov–Krasovskii functional; the asymptotic convergence results rigorously prove the stability of the adaptive cooperative control systems. The simulation results verify the excellent performance of the controller.
Compiler Optimization Parameter Selection Method Based on Ensemble Learning
Authors: Liu Hui , Xu Jinlong , Chen Sen , Guo Te
Published: August 2022

Iterative compilation based on machine learning can effectively predict a program’s compiler optimization parameters. Although having some limits, such as the low efficiency of optimization parameter search and prediction accuracy, machine learning-based solutions have been a frontier research field in the field of iterative compilation and have gained increasing attention. The research challenges are focused on learning algorithm selection, optimal parameter search, and program feature representation. For the existing problems, we propose an ensemble learning-based optimization parameter selection (ELOPS) method for the compiler. First, in order to further improve the optimization parameter search efficiency and accuracy, we proposed a multi-objective particle swarm optimization (PSO) algorithm to determine the optimal compiler parameters of the program. Second, we extracted the mixed features of the program through the feature-class relevance method, rather than using static or dynamic features alone. Finally, as the existing research usually uses a separate machine learning algorithm to build prediction models, an ensemble learning model using program features and optimization parameters was constructed to effectively predict compiler optimization parameters of the new program. Using standard performance evaluation corporation 2006 (SPEC2006) and NAS parallel benchmark (NPB) benchmarks as well as some typical scientific computing programs, we compared ELOPS with the existing methods. The experimental results showed that we can respectively achieve 1.29× and 1.26× speedup when using our method on two platforms, which are better results than those of existing methods.
Textile Bandwidth-Enhanced Coupled-Mode Substrate-Integrated Cavity Antenna with Slot
Authors: Cui Jie , Liu Feng-Xue , Shen Xiaopeng , Zhao Lei , Yin Hongsheng
Published: August 2022

A textile bandwidth-enhanced coupled-mode substrate-integrated cavity antenna with a slot is presented. The original coupled-mode substrate-integrated cavity antenna is of two close resonances for the odd and even coupled modes, and a rectangular slot is added on the top layer to introduce a third resonance. Parameters are optimized to merge the bands of the three resonances to realize a widened −10 dB impedance band to cover the Medical Body Area Network band, 2.45 GHz Industrial Scientific Medical band and Long-Term Evolution Band7. The proposed antenna can operate in a −10 dB impedance band of 2.32–2.69 GHz with a 14.9% fractional bandwidth according to the measurements on a fabricated prototype. Simulation and measurement results illustrate the robustness of the proposed textile antenna in the vicinity of the human body and cylindrical bending conditions. In addition, the simulated specific absorption rate of the antenna radiation in the human body is lower than the IEEE and EN limits.
Classification of Atrial Fibrillation and Congestive Heart Failure Using Convolutional Neural Network with Electrocardiogram
Authors: Fu’adah Yunendah Nur , Lim Ki Moo
Published: August 2022

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.
Multi-Method Diagnosis of CT Images for Rapid Detection of Intracranial Hemorrhages Based on Deep and Hybrid Learning
Authors: Mohammed Badiea Abdulkarem , Senan Ebrahim Mohammed , Al-Mekhlafi Zeyad Ghaleb , Rassem Taha H. , Makbol Nasrin M. , Alanazi Adwan Alownie , Almurayziq Tariq S. , Ghaleb Fuad A. , Sallam Amer A.
Published: August 2022

Intracranial hemorrhaging is considered a type of disease that affects the brain and is very dangerous, with high-mortality cases if there is no rapid diagnosis and prompt treatment. CT images are one of the most important methods of diagnosing intracranial hemorrhages. CT images contain huge amounts of information, requiring a lot of experience and taking a long time for proper analysis and diagnosis. Thus, artificial intelligence techniques provide an automatic mechanism for evaluating CT images to make a diagnosis with high accuracy and help radiologists make their diagnostic decisions. In this study, CT images for rapid detection of intracranial hemorrhages are diagnosed by three proposed systems with various methodologies and materials, where each system contains more than one network. The first system is proposed by three pretrained deep learning models, which are GoogLeNet, ResNet-50 and AlexNet. The second proposed system using a hybrid technology consists of two parts: the first part is the GoogLeNet, ResNet-50 and AlexNet models for extracting feature maps, while the second part is the SVM algorithm for classifying feature maps. The third proposed system uses artificial neural networks (ANNs) based on the features of the GoogLeNet, ResNet-50 and AlexNet models, whose dimensions are reduced by a principal component analysis (PCA) algorithm, and then the low-dimensional features are combined with the features of the GLCM and LBP algorithms. All the proposed systems achieved promising results in the diagnosis of CT images for the rapid detection of intracranial hemorrhages. The ANN network based on fusion of the deep feature of AlexNet with the features of GLCM and LBP reached an accuracy of 99.3%, precision of 99.36%, sensitivity of 99.5%, specificity of 99.57% and AUC of 99.84%.
Performance Metric Evaluation of Error-Tolerant Adders for 2D Image Blending
Authors: Mendez Tanya , Nayak Subramanya G. , Kumar P. Vasanth , S. R. Vijay , Kedlaya K. Vishnumurthy
Published: August 2022

The hardware implementation of error-tolerant adders using the paradigm of approximate computing has considerably influenced the performance metrics, especially in applications that can compromise accuracy. The foundation for approximate processing is the inclusion of errors in the design to enhance the effectiveness and reduce the complexity. This work presents three base adders using the novel concept of error tolerance in digital VLSI design. The research is extended to construct nine variants of power and delay-efficient 16 and 32-bit error-tolerant carry select adders (CSLA). To attain optimization in power and delay, conventional CSLA is refined by substituting ripple carry adders (RCA) with the newly proposed selector unit to minimize the switching activity. The research work includes the power, area, and delay estimates of the design from synthesis using the gpdk-90 nm and gpdk-45 nm standard cell libraries. The proposed adders exhibit reduced delay, power dissipation, area, power delay product (PDP), energy delay product (EDP), and area delay product (ADP) compared to the existing approximate adders. The proposed adder is used in an image blending application. There is a significant improvement in the peak-signal-to-noise ratio (PSNR) in the blended image compared to the standard designs.
A Comparative Study on Wind Energy Assessment Distribution Models: A Case Study on Weibull Distribution
Authors: Teimourian Hanifa , Abubakar Mahmoud , Yildiz Melih , Teimourian Amir
Published: August 2022

Wind power generation highly depends on the determination of wind power potential, which drives the design and feasibility of the wind energy production investment. This gives an important role to wind power estimation, which creates the need for an accurate wind data analysis and wind energy potential assessments for a given location. Such assessments require the implementation of an accurate and suitable wind distribution model. Therefore, in the quest for a well-fitted model, eight methods for estimating the Weibull parameters are investigated in this paper. The methods were then investigated by employing statistical tools, and their performances have been discussed in terms of various error indicators such as root mean squared error (RMSE), regression error (R2), chi-square (X2), and mean absolute error (MAE). Meteorological data for diverse terrain from 14 provinces with 30 sites scattered across Iran were employed to examine the performance of the investigated methods. The results demonstrated that the empirical method has superiority over the investigated technique in terms of errors.
Tram System as a Challenge for Smart and Sustainable Urban Public Transport: Effects of Applying Bi-Directional Trams
Authors: Pietrzak Krystian , Pietrzak Oliwia
Published: August 2022

Smart and sustainable urban public transport is a considerable challenge for contemporary cities. Society’s ever-increasing transport needs require the search for solutions to increase the attractiveness of public transport. In view of the above, the main objective of this article was to determine what effects can ensue from applying bi-directional trams in the context of the smart and sustainable city concept. To attain the said objective, the research process involved desk research as well as primary research using the Delphi method, a case study, and the participant observation method. The research area covered by the study was the city of Szczecin, Poland. The completed research made it possible to identify the limitations of tram systems and the effects of applying bi-directional trams in cities, as well as to develop some practical applications for the city in question. The research study showed that application of bi-directional trams may contribute to improved functionality of a tram system, which is particularly important from the perspective of the smart and sustainable city concept. The results of this research study have both theoretical and practical implications.
Cable-Partial-Discharge Recognition Based on a Data-Driven Approach with Optical-Fiber Vibration-Monitoring Signals
Authors: Pan Wenxia , Chen Xingchi , Zhao Kun
Published: August 2022

The effective pattern recognition of cable partial discharges (PDs) enables prompt corresponding measures to ensure cable safety. Traditional PD monitoring methods have limitations in online monitoring and accurate positioning, and the feature extraction of the monitored electrical signals requires significant prior knowledge. Therefore, this paper reports the performance of the distributed optical-fiber vibration-sensing monitoring of PDs on a cable with different insulation defects, and proposes a data-driven recognition approach based on the monitoring signals. The time series of the backscattered Rayleigh light intensity (BRLI) changes at the PD position were collected as the sample data. The coefficients of the time series’ autoregressive moving average (ARMA) models were extracted as features. Next, a classification model trained by the random forest (RF) algorithm was established. After the model’s validation with the experimental data and a comparative analysis with previously published methods, the PD recognition model was simply optimized based on the RF principle. The results showed that the proposed method achieved a high recognition accuracy, of about 98%, indicating that the data-driven approach—combining the ARMA model and the RF—is effective for cable-PD pattern recognition in distributed optical-fiber vibration-sensing systems.
Short-Term Field Evaluation of Low-Cost Sensors Operated by the “AirSensEUR” Platform
Authors: Pichlhöfer Alexander , Korjenic Azra
Published: August 2022

Electrochemical low-cost sensors, suitable for the monitoring of different air quality parameters such as carbon monoxide or nitrogen dioxide levels, are viable tools for creating affordable handheld devices for short-term or dense air quality monitoring networks for long-term measurements and IoT applications. However, most devices that utilize such sensors are based on proprietary hardware and software and, therefore, do not offer users the ability to replace sensors or interact with the hardware, software, and data in a meaningful way. Initiatives that focus on an open framework for air quality monitoring, such as the AirSensEUR project, offer competitive open source alternatives. In this study, we examined the feasibility of the application of such devices. Five AirSensEUR units equipped with chemical sensors were placed next to a reference air quality measuring station in Vienna, Austria. During co-location, concentrations of 0.20 ± 0.06 ppm, 7.14 ± 8.66 ppb, and 17.58 ± 9.90 ppb were measured for CO, NO, and NO2, respectively. The process of evaluating the performance of the low-cost sensors was carried out and compared to similar studies. Data analysis was carried out with the help of the basic functions in MS Excel. We investigated the linear correlation between the sensor and reference data and thus calculated the coefficient of determination, the average and maximum residuals, and the correlation coefficient. Furthermore, we discuss sensor properties in regard to selectivity and long-term stability.
Multidisciplinary Geophysical Investigations over Deep Coal-Bearing Strata: A Case Study in Yangjiazhangzi, Northeast China
Authors: Wang Kun , Ge Xinbo , Ning Jianguo , Li Jing , Zhao Xueyu
Published: August 2022

With the majority of coal mines in uncovered and semi-covered coal strata now explored and developed, most of the undiscovered coal-bearing strata are concealed. Compared with expensive drilling, deep targets such as concealed coal-bearing strata can be more efficiently and cost-effectively discovered through geophysical methods. We designed an integrated geophysical exploration approach to detect coal-bearing strata in the Yangjiazhangzi (YJZZ) area. Large-scale magnetotellurics (MT) is used to describe the geological structure along with its profile through the YJZZ area. An aeromagnetic survey was used to delineate the spatial distribution characteristics of the YJZZ syncline, a coal-bearing prospect tectonic unit. Localized exploration with controlled-source audio magnetotellurics (CSAMT) and MT reveals coal-bearing targets for drilling. Drilling results verified the targets identified through the integrated geophysical approach. Coal-bearing strata in the Benxi formation, the Taiyuan formation, and the Shanxi formation of the Permo-Carboniferous age are found between 630 and 770 m. This case study demonstrates that the multidisciplinary geophysical strategy can provide reliable results and credible data interpretation for deep coal seam resources exploration. The findings of this study can provide reference for explorers to carry out their specific exploration cases.
Novel Magnetic Suspension Platform with Three Types of Magnetic Bearings for Mass Transfer
Authors: Cao Shinan , Niu Pingjuan , Wang Wei , Zhao Tiantian , Liu Qiang , Bai Jie , Sheng Sha
Published: August 2022

For ultra-precision, large stroke, and high start/stop acceleration, a novel magnetic suspension platform with three types of magnetic bearings is proposed. The structure and working principle of the novel platform are introduced. The passive magnetic bearings are used to compensate for the weight of the actuator. The repulsive force of the passive magnetic bearing model is established and analyzed. The Lorentz force-type magnetic bearings are used to provide driving force and rotational torque in the XY-plane. The driving force model and rotational torque model are established. The electromagnetic suspension bearing is used to provide driving force in the Z-axis and rotational torque along the X-axis and Y-axis. A novel Halbach magnetic array is designed to improve the magnetic flux density in the air gap. The finite element method is used to validate the force model, torque model, and magnetic flux density in the air gap. The results show that the maximum force of the passive magnetic bearing is 79 N, and the rotational torque stiffness is 35 N/A in the XY-plane and 78 N/A along the Z-axis. The driving force stiffness is 91 N/A in the XY-plane and 45 N/A along Z-axis.
Correction: Kim et al. Optimal Operation Strategy of ESS for EV Charging Infrastructure for Voltage Stabilization in a Secondary Feeder of a Distribution System. Energies 2020, 13, 179
Authors: Kim Dae-Jin , Ryu Kyung-Sang , Ko Hee-Sang , Kim Byungki
Published: August 2022

The authors wish to make the following correction to their paper [...]
A Novel Measurement-Based Method for Assessing Global Warming Mitigation via High-Albedo Solutions
Authors: Rossi Federico , Filipponi Mirko , Castellani Beatrice , Bonafoni Stefania , Ghenai Chaouki
Published: August 2022

Global warming mitigation via terrestrial albedo increase has been widely investigated in literature; the proposed methodologies relate CO2 compensation to albedo increase generally via the concept of Radiative Forcing (RF). However, literature methods calculate RF by averaged input data, without considering RF variation due to many local and temporal phenomena. For instance, an average value of compensated effect of albedo change (Δα = 0.01) is 3 kg CO2eq/m2, which has been introduced no matter the position and climatic condition of the site. In our study, we propose a novel procedure to measure RF continuous time history by means of ground measurements, astronomical equations, and satellite calibration. The procedure is called RF-meter. In this way, a more accurate assessment of compensated CO2 may be achieved. A test facility is also designed and proposed to double check the procedure, and preliminary results are reported in order to show and test the calibration procedure. It is expected that albedo-increased surfaces as well as cool roofs and/or other technical solutions will be eligible to obtain Emission Credits (EC). The proposed procedure will aid in the assignment of EC to High-Albedo Solutions (HAS), as it could represent an objective and accurate method to relate the albedo increase to a corresponding CO2 offset.
Optimisation of Induced Steam Residual Moisture Content in a Clothing Conditioner Based on a Genetic Algorithm
Authors: Saleem Arslan , Saeed Muhammad , Kim Man-Hoe
Published: August 2022

This paper presents the modelling of heat and moisture transfer in a clothes-conditioning unit with the aim of improving the moisture content distribution to the clothes. A multicomponent, non-reacting, two-phase Eulerian–Eulerian model was utilised to solve the computational model. The clothes inside the conditioning unit were modeled as retangular towels (porous medium) of uniform thickness. Mass flow distribution of air and steam through the clothes was studied by systematically varying the steam nozzle angle (30° to 75°) and air inflow grill angle (45° to 105°). The simulation results were studied to identify the impact of design parameters on the mass flow distribution inside the clothes-conditioning unit. The mass flow of steam and the air–steam mixture were calculated through each towel in the forward and reverse direction. Response surface analysis was conducted to correlate the total mass flow rate and steam mass flow rate through each towel with the design variables. Moreover, a multiobjective genetic algorithm was employed to optimise the mass flow through the clothes and ascertain the optimal design configuration. The geometric configuration with a steam nozzle angle of 45° and air grill angle of 105° resulted in optimal steam and mixture distribution.
New Model of Granite Buried-Hill Reservoir in PL Oilfield, Bohai Sea, China
Authors: Jia Daji , Zhu Xiaomin , Song Laiming , Liang Xu , Li Li , Li Haichen , Li Zhandong
Published: August 2022

The geological estimated hydrocarbon reserves of the Mesozoic granite buried-hill reservoir in the PL oilfield exceed 200 million tons. During the exploration stage, it was commonly considered that the PL buried-hill reservoir should demonstrate a “layered” edge water reservoir mode, because of weathering and tectonics. However, during a later stage, the formation of water was found at structurally high locations, which does not fit the intuition. Therefore, a suitable model is still lacking. Based on the comprehensive information of thin-section, core plug, well logging, and seismic data, etc., the buried-hill of the PL oilfield was re-divided into three northeast–southwest-trending zones. In this model, the reservoir is layered, with each layer having the characteristics of bottom water and two kinds of horizontal and vertical seepage. This new model fits the field-drilling test very well. These insights can effectively guide the exploration and development practice for this kind of buried-hill.
Fault Diagnosis Using Bond Graphs in an Expert System
Authors: Zhou Zhuoran , Ma Zhanguo , Jiang Yingying , Peng Minjun
Published: August 2022

A fault diagnosis method using bond graphs in an expert system is proposed for a reactor coolant system. Firstly, the time causality graph and the variable relationship graph are derived from the bond graph. Secondly, the fault signature matrix is obtained by combining the change relationship of fault parameters. Finally, the fault signature matrix is used as the rule of the inference engine design in the expert system for fault diagnosis. In this paper, the key equipment of the reactor coolant system is used to verify the fault diagnosis method of the bond graph expert system, and the path reasoning relationship between alarms is obtained, which can accurately obtain the deep knowledge required by the operators. A new idea for fault diagnosis in a nuclear power plant’s expert system is provided by this method.
Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems
Authors: Dubey Salil Madhav , Dubey Hari Mohan , Salkuti Surender Reddy
Published: August 2022

This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems.
Rate Transient Behavior of Wells Intercepting Non-Uniform Fractures in a Layered Tight Gas Reservoir
Authors: Zhang Chengwei , Cheng Shiqing , Wang Yang , Chen Gang , Yan Ke , Ma Yongda
Published: August 2022

RTA (Rate Transient Analysis) is a valuable method for obtaining reservoir parameters and well performance, but current RTA models hardly consider the MLVF (Multi-Layer Vertical Fractured) well in a layered tight gas reservoir. To capture the production response caused by the fracture with non-uniform length and conductivity, a novel RTA model for an MLVF well in a layered tight reservoir was presented. In this paper, we present a novel tight gas reservoir RTA model, an extended MLVF well with non-uniform fracture length and conductivity to investigate the production decline feature by the combined RTA type curves. After that, the proposed RTA model is verified to ensure calculation accuracy. Sensitivity analysis is conducted on the crucial parameters, including the formation transmissibility, formation storability, fracture length, fracture conductivity, and fracture extension. Research results show that there are three rate decline stages caused by a multi fracture with non-uniform conductivity. The wellbore storage and formation skin can be ignored in the rate transient analysis work. The formation transmissibility affects the rate transient response more than the formation storability. The increase in fracture length, fracture conductivity, and the extension of a high conductivity fracture will improve the well’s production rate in a tight gas reservoir’s early production stage. Therefore, it is significant to incorporate how the effects of the MLVF well intercepting with non-uniform length fractures change conductivity. The RTA model proposed in this paper enables us to better evaluate well performance and capture the formation of complex fracture characteristics in a layered tight gas reservoir based on rate transient data.
Towards Active Buildings: Stakeholder Perceptions of the Next Generation of Buildings
Authors: Nikolaidou Elli , Walker Ian , Coley David , Allen Stephen , Fosas Daniel , Roberts Matthew
Published: August 2022

Several regulations and standards have been developed to reduce the carbon footprint of buildings, but these have failed to provide a clear pathway to a net zero future. Hence, we recently introduced the Active Building Code (ABCode). This provides guidance on reducing the environmental impact of the next generation of buildings, termed Active Buildings (ABs), through their synergy with the grid. This paper aims to illuminate the regulatory landscape, justify our initial proposal for the ABCode, and reveal opportunities and challenges to the popularisation of ABs. Twelve online focus group discussions were conducted, with thirty stakeholders in total, all selected on the basis of their expertise. A grounded theory approach identified five core themes in such discussions. These strongly overlap with what is incorporated in the ABCode, suggesting the code successfully captures issues important to experts. Stakeholders defined ABs as responsive buildings and proposed both energy and carbon are considered in their assessment. They hence aligned with the definition and evaluation framework proposed by the ABCode. Finally, stakeholders considered people’s tendency to prioritise capital cost as the greatest challenge to the popularisation of ABs, and the increasing demand for healthy environments as its greatest opportunity.
A Review on Experimental Studies Investigating the Effect of Hydrogen Supplementation in CI Diesel Engines—The Case of HYMAR
Authors: Frantzis Charalambos , Zannis Theodoros , Savva Petros G. , Yfantis Elias Ar.
Published: August 2022

Hydrogen supplementation in diesel Compression Ignition (CI) engines is gaining more attention, since it is considered as a feasible solution to tackle the challenges that are related to the emission regulations that will be applied in the forthcoming years. Such a solution is very attractive because it requires only limited modifications to the existing technology of internal combustion CI engines. To this end, numerous work on the investigation of an engine’s performance and the effects of emissions when hydrogen is supplied in the engine’s cylinders has been completed by researchers. However, contradictory results were found among these studies regarding the efficiency of the engine and the emission characteristics achieved compared to the diesel-only operation. The different conclusions might be attributed to the different characteristics and technology level of the engines that were utilized, as well as on the chosen operational parameters. This paper aims to present an overview of the experimental studies that have examined the effects of hydrogen addition in CI four-stroke diesel engines, reporting the characteristics of the utilized engines, the quantities of hydrogen tested, the method of hydrogen induction used, as well as the operational conditions tested, in order to help interested researchers to easily identify relevant and appropriate studies to perform comparisons or validations by repeating certain cases. The presented data do not include any results or conclusions from these studies. Furthermore, an experimental configuration along with the appropriate modifications on a heavy-duty auxiliary generator-set engine that was recently developed by the authors for the purposes of the HYMAR project is presented.
Thermal, Microstructural and Electrochemical Hydriding Performance of a Mg65Ni20Cu5Y10 Metallic Glass Catalyzed by CNT and Processed by High-Pressure Torsion
Authors: Révész Ádám , Gajdics Marcell , Alifah Miratul , Kovács Kis Viktória , Schafler Erhard , Varga Lajos Károly , Todorova Stanislava , Spassov Tony , Baricco Marcello
Published: August 2022

A Mg65Ni20Cu5Y10 metallic glass was produced by melt spinning and was mixed with a 5 wt.% multiwall carbon nanotube additive in a high-energy ball mill. Subsequently, the composite mixture was exposed to high-pressure torsion deformation with different torsion numbers. Complimentary XRD and DSC experiments confirmed the exceptional structural and thermal stability of the amorphous phase against severe plastic deformation. Combined high-resolution transmission electron microscopy observations and fast Fourier transform analysis revealed deformation-induced Mg2Ni nanocrystals, together with the structural and morphological stability of the nanotubes. The electrochemical hydrogen discharge capacity of the severely deformed pure metallic glass was substantially lower than that of samples with the nanotube additive for several cycles. It was also established that the most deformed sample containing nanotubes exhibited a drastic breakdown in the electrochemical capacity after eight cycles.
Optimization of Load Sharing in Compressor Station Based on Improved Salp Swarm Algorithm
Authors: Zhang Jiawei , Li Lin , Zhang Qizhi , Wu Yanbin
Published: August 2022

In long-distance gas transmission pipelines, there are many booster compressor stations consisting of parallel compressors that provide pressure for the delivery of natural gas. So, it is economically important to optimize the operation of the booster compressor station. The booster compressor station optimization problem is a typical mixed integer nonlinear programming (MINLP) problem, and solving it accurately and stably is a challenge. In this paper, we propose an improved salp swarm algorithm based on good point set, adaptive population division and adaptive inertia weight (GASSA) to solve this problem. In GASSA, three improvement strategies are utilized to enhance the global search capability of the algorithm and help the algorithm jump out of the local optimum. We also propose a constraint handling approach. By using semi-continuous variables, we directly describe the on or off state of the compressor instead of using auxiliary binary variables to reduce the number of variables and the difficulty of solving. The effectiveness of GASSA is firstly verified using eight standard benchmark functions, and the results show that GASSA has better performance than other selected algorithms. Then, GASSA is applied to optimize the booster compressor station load distribution model and compared with some well-known meta-heuristic algorithms. The results show that GASSA outperforms other algorithms in terms of accuracy and reliability.
Development and Performance Verification of Frequency Control Algorithm and Hardware Controller Using Real-Time Cyber Physical System Simulator
Authors: Jin Tae-Hwan , Shin Ki-Yeol , Chung Mo , Lim Geon-Pyo
Published: August 2022

Frequency stability is a critical factor in maintaining the quality of the power grid system. A battery energy storage system (BESS) with quick response and flexibility has recently been used as a primary frequency control (PFC) resource, and many studies on its control algorithms have been conducted. The cyber physic system (CPS) simulator, which can perform virtual physical modelling and verification of many hardware systems connected to the network, is an optimal solution for the performance verification of control algorithms and hardware systems. This study introduces a large-scale real-time dynamic simulator that includes the national power system. This simulator comprises a power grid model, an energy management system (EMS) model, a BESS system model, and a communication model. It performs the control algorithm performance evaluation and the hardware controller’s response performance evaluation. The performance of the control algorithm was evaluated by tracking the power system’s characteristic trajectory in the transient state based on the physical response delay time between the output instruction of the frequency regulation controller (FRC), a hardware controller, and the output response of the BESS. Based on this, we examined the response performance evaluation results by linking them to the optimally designed actual FRC. As a result, we present an analysis of the BESS’s characteristic trajectories in the transient state, such as frequency, power system inertia, and power grid constant, and provide FRC response performance evaluation results at a level of 163 ms, by connecting the BESS installed at the actual site with the CPS simulator.
Study of Downhole Lateral Force Measurement Modelling and Devices in Petroleum Exploration
Authors: Deng Kui , Hu Wei , Ge Liang , Hu Ze , Yang Qing , Xiao Xiaoting
Published: August 2022

Measurement while drilling (MWD) technology is important for obtaining downhole parameters. As an essential parameter in drilling engineering, lateral force provides a powerful reference basis for judging the downhole drilling direction. Although stress measurement technology has matured, research on downhole lateral forces, especially near-bit lateral forces during the drilling process in petroleum exploration, is lacking. Based on the force analysis of a short measurement circuit, the lateral force measurement value of a drill bit and centralizer was converted to a drill string’s radial-bending-force measurement. Two perpendicular lateral force components were measured using strain gauge technology, a lateral force measurement theory model was established, and a set of MWD systems was designed according to the model. The systems’ function was verified through laboratory and field tests, and the field-test data were successfully obtained in the field application. The test results showed that the tested MWD system had acceptable accuracy, stability, and reliability and had the application potential to measure lateral force in the drilling industry. This article provides a new idea to study lateral force while drilling, which is of great significance to oil-drilling exploration and development.
Coordinated Path Following Control of 4WID-EV Based on Backstepping and Model Predictive Control
Authors: Wang Chenning , He Ren , Jing Zhecheng , Chen Shijun
Published: August 2022

A path following control strategy for a four-wheel-independent-drive electrical vehicle (4WID-EV) based on backstepping and model predictive control is presented, which can ensure the accuracy of path following and maintain vehicle stability simultaneously. Firstly, a 2-DOF vehicle dynamic model and a path following error model are built and the desired yaw rate is obtained through backstepping. Then, a model predictive controller is adopted to track the desired yaw rate and obtain the optimal front wheel steering and external yaw moment. Meanwhile, an optimal torque distribution algorithm is carried out to allocate it to each tire. Finally, the effectiveness and superiority of the strategy is validated via CarSim–Simulink joint simulation. Results show that the strategy has higher following accuracy, smaller sideslip angle, and better yaw rate tracking.
Solid Rotor Core vs. Lamination Rotor Core in Fractional-Slot PMSM Motor with High Power Density
Authors: Wolnik Tomasz , Jarek Tomasz
Published: August 2022

Fractional-slot PMSM motors allow for obtaining high values of power density factors, but at the same time, they are characterized by high values of rotor losses—in the rotor core and permanent magnets. The main causes of rotor losses in this type of motor are subharmonics and a high content of higher harmonics in the distribution of the magnetomotive force MMF. The use of a solid rotor core simplifies the construction and technology of the rotor but eddy current losses in the core account for a significant percentage of the total rotor losses. It is well known that a laminated core reduces eddy currents, while for motors with an outer rotor, it complicates the construction and increases weight. Thus, the question arises about the necessity to use a laminated core in a high power density motor and the real benefits of this. The article presents a comparison of the motors with a solid rotor core and a laminated rotor core, considering the value of rotor losses, power density factor, efficiency and the range of rotational speed and range of current load. The analysis was carried out for various types of sheets for laminated core and solid steel and SMC (Soft Magnetic Composite) material for solid rotor core. FEM models were used in the analysis, and the results were partially verified with the results of laboratory tests of motor models. The object of the analysis is a fractional-slot PMSM motor with an external rotor with surface permanent magnets (SPM). Motor weight is about 10 kg, and the maximum power is 50 kW at 4800 rpm.
Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities
Authors: Chen Ping , Gao Jiawei , Ji Zheng , Liang Han , Peng Yu
Published: August 2022

A growing number of countries worldwide have committed to achieving net zero emissions targets by around mid-century since the Paris Agreement. As the world’s greatest carbon emitter and the largest developing economy, China has also set clear targets for carbon peaking by 2030 and carbon neutrality by 2060. Carbon-reduction AI applications promote the green economy. However, there is no comprehensive explanation of how AI affects carbon emissions. Based on panel data for 270 Chinese cities from 2011 to 2017, this study uses the Bartik method to quantify data on manufacturing firms and robots in China and demonstrates the effect of AI on carbon emissions. The results of the study indicate that (1) artificial intelligence has a significant inhibitory effect on carbon emission intensity; (2) the carbon emission reduction effect of AI is more significant in super- and megacities, large cities, and cities with better infrastructure and advanced technology, whereas it is not significant in small and medium cities, and cities with poor infrastructure and low technology level; (3) artificial intelligence reduces carbon emissions through optimizing industrial structure, enhancing information infrastructure, and improving green technology innovation. In order to achieve carbon peaking and carbon neutrality as quickly as possible during economic development, China should make greater efforts to apply AI in production and life, infrastructure construction, energy conservation, and emission reduction, particularly in developed cities.
Study on Heat Storage Performance of Phase Change Reservoir in Underground Protection Engineering
Authors: Zhang Hongyu , Gan Fei , Huang Guangqin , Zhuang Chunlong , Shen Xiaodong , Li Shengbo , Cheng Lei , Hou Shanshan , Xu Ningge , Sang Zhenqun
Published: August 2022

In view of the main problems of the condensing heat discharge modes of the existing underground air-conditioning system, the technical scheme of using phase change heat storage modules to improve the heat storage capacity of the reservoir is proposed. By establishing a 3D flow and transient heat transfer model of the phase change reservoir, the effects of thermal property parameters, package size and arrangement of the phase change heat storage modules on the heat storage performance of the phase change reservoir were quantitatively analyzed based on three indexes: heat storage capacity per volume Δq, guaranteed efficiency coefficient η and slope of temperature rise per unit load ε. The results show that when the phase change temperature is 29 °C (23 °C increased to 33 °C) and the latent heat value is 250 kJ/kg (100 kJ/kg increased to 250 kJ/kg), Δq (110.92 MJ/m3, 112.83 MJ/m3) and η (1.22, 1.24) under both conditions are at their most, respectively, indicating that the phase change temperature should be less than 4 °C at the outlet temperature of the reservoir, and phase change materials with a high latent heat should be selected in engineering design whenever possible. When the size of the phase change module is 150 mm × 20 mm and the phase change reservoir adopts four intakes, ε (0.259, 0.244) under both conditions is the smallest, indicating that increasing the area of the phase change heat storage module and the fluid and increasing the inlet disturbance of the reservoir can enhance its heat storage capacity.