bluuebunny/crossref_metadata_2025 huggingface dataset:
Bearing Fault Diagnosis Method Based on RCMFDE-SPLR and Ocean Predator Algorithm Optimizing Support Vector Machine
Authors: Yi Mingxiu , Zhou Chengjiang , Yang Limiao , Yang Jintao , Tang Tong , Jia Yunhua , Yuan Xuyi
Published: November 2022

For the problem that rolling bearing fault characteristics are difficult to extract accurately and the fault diagnosis accuracy is not high, an unsupervised characteristic selection method of refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) combined with self-paced learning and low-redundant regularization (SPLR) is proposed, for which the fault diagnosis is carried out by support vector machine (SVM) optimized by the marine predator algorithm (MPA). First, we extract the entropy characteristics of the bearings under different fault states by RCMFDE and the introduction of the fine composite multiscale coarse-grained method and fluctuation strategy improves the stability and estimation accuracy of the bearing characteristics; then, a novel dimensionality-reduction method, SPLR, is used to select better entropy characteristics, and the local flow structure of the fault characteristics is preserved and the redundancy is constrained by two regularization terms; finally, using the MPA-optimized SVM classifier by combining Levy motion and Eddy motion strategies, the preferred RCMFDE is fed into the MPA–SVM model for fault diagnosis, for which the obtained bearing fault diagnosis accuracy is 97.67%. The results show that the RCMFDE can effectively improve the stability and accuracy of the bearing characteristics, the SPLR-based low-dimensional characteristics can suppress the redundancy characteristics and improve the effectiveness of the characteristics, and the MPA-based adaptive SVM model solves the parameter randomness problem and, therefore, the proposed method has outstanding superiority.
Correction: Kudo, K. Localization Detection Based on Quantum Dynamics. Entropy 2022, 24, 1085
Authors: Kudo Kazue
Published: November 2022

In the original publication [...]
Effect of Biochar on Rhizosphere Soil Microbial Diversity and Metabolism in Tobacco-Growing Soil
Authors: Wang Huanhuan , Zhang Rui , Zhao Yuanyuan , Shi Hongzhi , Liu Guoshun
Published: November 2022

In this study, four different biochar application rates and a control were set up using indoor potted tobacco, to study the effects of biochar on the microbial diversity and metabolism of tobacco-growing soil. The five treatments were as follows: control—0% biochar (w/w) + 26 g fertilizer/pot; biochar treatments—1% biochar (w/w) + 26 g fertilizer/pot, 2% biochar (w/w) + 26 g fertilizer/pot, 3% biochar (w/w) + 26 g fertilizer/pot, and 4% biochar (w/w) + 26 g fertilizer/pot. We found that biochar increases the microbial diversity of soils and simultaneously changes the microbial community structure. Under the influence of biochar, soil urease activity increased by 18%, invertase activity increased by 23.40%, polyphenol oxidase activity increased by 59.50%, and catalase activity increased by 30.92%. Biochar also significantly increased the microbial biomass carbon and nitrogen content of the soil. Soil microbial biomass nitrogen had a positive correlation on bacterial diversity, with the highest coefficient, while soil microbial biomass carbon had a positive correlation on fungal diversity, with the highest coefficient. The microbial diversity and metabolic capacity of soil are improved under the influence of biochar, and soil enzyme activity and microbial biomass carbon and nitrogen have positive impacts on soil microbial diversity.
The Power of Compensation System (CS) on Employee Satisfaction (ES): The Mediating Role of Employee Motivation (EM)
Authors: Zayed Nurul Mohammad , Rashid Md. Mamunur , Darwish Saad , Faisal-E-Alam Md. , Nitsenko Vitalii , Islam K. M. Anwarul
Published: November 2022

The compensation system, employee motivation, and employee satisfaction have received much attention from academics for many years. Existing research, however, does not yet detail the mediation effect of employee motivation on the relationship between the entire compensation system and employee satisfaction. The study explores the influence of the compensation structure on employee satisfaction using employee motivation as mediation. This research embraced a quantitative design, positivism paradigm, deductive approach, and explanatory research. Cross-sectional data from 100 employees were drawn with a random sampling technique using a self-administered survey questionnaire. First, in sequence of analysis, descriptive statistics were conducted. After that, a reliability test was used to test internal consistency. Finally, a correlation test, direct effect, indirect effect, and total effect were used to test the hypotheses at the 0.05 level while analyzing the data. The findings show that the compensation system has a favorable impact on employee satisfaction by partially mediating motivation. Concurrently, this study establishes awareness intending to revise a robust compensation strategy so that employee morale, engagement will increase and turnover will reduce. The study outcomes will assist policymakers in improving the situation of the existing workforce in insurance companies and other financial companies in Bangladesh.
Gamification Tools in Higher Education: Creation and Implementation of an Escape Room Methodology in the Pharmacy Classroom
Authors: Fraguas-Sánchez Ana Isabel , Serrano Dolores R. , González-Burgos Elena
Published: November 2022

Educational escape rooms have emerged as an excellent active learning tool to improve student learning, motivation, and engagement. In this work, a methodology to design and develop escape rooms in the classroom has been established and implemented within the general pharmacology, biopharmacy and pharmacokinetics, and pharmaceutical technology disciplines for pharmacy students. Each escape room consisted of three sequential challenges that the students had to solve, and we divided the students into groups of 3–6 participants to complete a mission containing educational questions related to the curriculum of each module. The escape rooms were successfully implemented in all these disciplines, and the activity was positively evaluated by the students (>95% satisfaction). They allowed the students to apply the theoretical learning outcomes of each subject. Moreover, escape rooms promoted teamwork and improved the problem-solving skills of the students. For an escape room to be successful and meet the established learning outcomes, challenges must be adapted to the target students, the time should be precisely set, the tasks of the game master should be well-defined, and final feedback should be included in the session.
A Series of Happenstances: How the Pandemic Created Opportunities with Technology
Authors: Kiekel Jean , Courduff Jennifer , Hessling Peter
Published: November 2022

The Happenstance Learning Theory (HLT) has primarily been used in career counseling fields, predominantly in helping clients transition through different happenstances in life to find a career path. However, the COVID-19 pandemic showed that our nation’s teachers also developed the skills that are considered the pillars of HLT, as they made the transition from teaching traditionally one day to remote learning the next. This study explored how 46 special educators adapted their teaching methods to best address the needs of their students despite challenging circumstances. This study also suggests the application of the Happenstance Learning Theory in understanding and explaining how teachers seemingly make changes “in the moment”.
The Challenge of an Interactive Audiovisual-Supported Lesson Plan: Information and Communications Technologies (ICTs) in Adult Education
Authors: Nicolaou Constantinos , Matsiola Maria , Kalliris George
Published: November 2022

The rapid development in the fields of science, and information and communications technologies (ICTs) in recent years, as well as the COVID-19 pandemic, have brought about and continue to generate transformations in education, especially in adult education. In the present enhanced research article, an interactive audiovisual-supported lesson plan template and its non-verbal role in our psychological and mental health are presented in-depth. Applying a multi-methodological approach, this interactive communication-themed adult lesson plan was executed and researched in the framework of an interactive seminar in Greece and Cyprus. The research sample consisted of adult educators as adult learners and involved empirical research where technology-enhanced research methods were applied as qualitative action research with quasi-experiments. Specifically, the attitudes and views of an adult educators’ group regarding the research interactive seminar that they participated in were re-investigated through secondary analysis. Similarly, the suitability of specific ICTs as well as whether they help or change the physical or psychological and mental health of the participants at the end of a teaching–learning procedure as a pilot case study were explored. The research results, effects and findings confirm the current debate on the employment of contemporary ICTs within the framework of the educational process of technology-enhanced learning in education (including adult education) as derived both by the literature, and by the research results, effects and findings of various other studies and research papers. Finally, this study can be used as a basis for creating and/or developing an audiovisual-supported lesson plan aimed at adult learners as an alternative approach.
Analytics Framework for Comparing National Performance Achievements in International Mathematical Olympiads
Authors: Canellidis Vassilios , Sofianopoulou Stella
Published: November 2022

This study measures and analyzes the performances of the participating nations at the International Mathematical Olympiad (IMO). A country’s success at the IMO is evaluated by the number of gold, silver, and bronze medals won. To measure and compare the performances of the countries, the data envelopment analysis approach is employed and all countries that have won at least one medal have been included in our dataset. As measures for the inputs, two macro-level determinants that can support the effort made by a country are considered: population size and gross domestic product (GDP) per capita. Our findings indicate a mean level of technical efficiency for the nations analyzed. The results suggest that, on average, the inefficient countries in the sample could achieve improved results by increasing the number of outputs produced while maintaining the same level of effort spent.
Predictor Variables of Mental Health in Spanish University Students
Authors: Rodríguez-Rodríguez Amalia , Guerrero-Barona Eloísa , Chambel Maria José , Guerrero-Molina Mónica , González-Rico Pablo
Published: November 2022

There is currently great concern over the increase in the cases of university students with mental health problems as the numbers have doubled over recent years. Nevertheless, there is also total agreement concerning the relationship between positive psychological variables and mental health. The objective of this study was to know the relationship between the risk of mental health disorders and the variables of gender, affects, academic engagement, satisfaction with life, health perception and the perception of happiness, as well as predict the risk of suffering mental health disorders. The sample comprised 461 students from the Science Faculty of the University of Extremadura (Spain), of whom 50.3% were women. We used the General Health Questionnaire (GHQ12) to assess mental health, The results indicate that 65.6% of the students showed a risk of suffering from mental health disorders. A greater number of negative affect is related to a greater risk of suffering mental health disorders; while the perception of both health and happiness are related to a lower risk. Similarly, it should be said that the results demonstrate that being a woman increases the risk of suffering mental health disorders by 63.7%.
Design of a Dual-Polarization Dipole Antenna for a Cylindrical Phased Array in Ku-Band
Authors: Zhang Ning , Xue Zhenghui , Zheng Pei , Gao Lu , Liu Jia Qi
Published: November 2022

This paper proposes a dual-polarization dipole antenna for a cylindrical phased array working in Ku-band. The dipole antenna is double-layer structured and is composed of two orthogonal butterfly shaped dipole radiators, two ground co-planar waveguide (GCPW) feeding structures and vias. Each dipole is in the shape of a butterfly. The dipole patch is grooved triangularly and one side of it is bent into an N shape, which effectively expands the working frequency band of the antenna. The double-layer structure improves the isolation between the antenna ports. The antenna works between 15 GHz to 16.2 GHz and the isolation between the antenna’s two feeding ports in this band is better than 20 dB. The proposed dipole antenna is applied in a 32-element cylinder array. The simulation and measured results show that the array can scan between −60° to +60° in the azimuth plane with a gain fluctuation less than 2.5 dB. Therefore, the proposed design is an attractive candidate for conformal devices at Ku-band frequencies, and it also has a great potential for application in larger antenna arrays.
Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID-19 Early Diagnostics from X-ray Images
Authors: Zivkovic Miodrag , Bacanin Nebojsa , Antonijevic Milos , Nikolic Bosko , Kvascev Goran , Marjanovic Marina , Savanovic Nikola
Published: November 2022

Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic since its emergence. One of the most important ways to control the spread of this disease begins with early detection, which allows that isolation and treatment could perhaps be started. According to recent results, chest X-ray scans provide important information about the onset of the infection, and this information may be evaluated so that diagnosis and treatment can begin sooner. This is where artificial intelligence collides with skilled clinicians’ diagnostic abilities. The suggested study’s goal is to make a contribution to battling the worldwide epidemic by using a simple convolutional neural network (CNN) model to construct an automated image analysis framework for recognizing COVID-19 afflicted chest X-ray data. To improve classification accuracy, fully connected layers of simple CNN were replaced by the efficient extreme gradient boosting (XGBoost) classifier, which is used to categorize extracted features by the convolutional layers. Additionally, a hybrid version of the arithmetic optimization algorithm (AOA), which is also developed to facilitate proposed research, is used to tune XGBoost hyperparameters for COVID-19 chest X-ray images. Reported experimental data showed that this approach outperforms other state-of-the-art methods, including other cutting-edge metaheuristics algorithms, that were tested in the same framework. For validation purposes, a balanced X-ray images dataset with 12,000 observations, belonging to normal, COVID-19 and viral pneumonia classes, was used. The proposed method, where XGBoost was tuned by introduced hybrid AOA, showed superior performance, achieving a classification accuracy of approximately 99.39% and weighted average precision, recall and F1-score of 0.993889, 0.993887 and 0.993887, respectively.
An Adaptive Offset Activation Function for CNN Image Classification Tasks
Authors: Jiang Yuanyuan , Xie Jinyang , Zhang Dong
Published: November 2022

The performance of the activation function in convolutional neural networks is directly related to the model’s image classification accuracy. The rectified linear unit (ReLU) activation function has been extensively used in image classification models but has significant shortcomings, including low classification accuracy. The performance of a series of parametric activation functions has made parameter addition a popular research avenue for improving the performance of activation functions in recent years, and excellent progress has been achieved. Existing parametric activation functions often focus on assigning a different slope to the negative part of the activation function and still involve the negative value alone in the activation function calculation, without considering the impact of linking the negative value to the positive value on the performance of the activation function. As a result, this work proposes a novel parametric right-shift activation function, the adaptive offset activation function (AOAF). By inserting an adaptive parameter (the mean value of the input feature tensor) and two custom ReLU parameters, the negative parameters previously driven to zero by ReLU can be turned into positive parameters with lower weight and participate in CNN feature extraction. We compared the performance of the suggested activation function to the performance of a selection of typical activation functions using four distinct public datasets. Compared with ReLU, the average classification accuracy of our proposed activation function improved by 3.82%, 0.6%, 1.02%, and 4.8% for the four datasets, respectively.
MM-LMF: A Low-Rank Multimodal Fusion Dangerous Driving Behavior Recognition Method Based on FMCW Signals
Authors: Hao Zhanjun , Li Zepei , Dang Xiaochao , Ma Zhongyu , Liu Gaoyuan
Published: November 2022

Multimodal research is an emerging field of artificial intelligence, and the analysis of dangerous driving behavior is one of the main application scenarios in the field of multimodal fusion. Aiming at the problem of data heterogeneity in the process of behavior classification by multimodal fusion, this paper proposes a low-rank multimodal data fusion method, which utilizes the complementarity between data modalities of different dimensions in order to classify and identify dangerous driving behaviors. This method uses tensor difference matrix data to force low-rank fusion representation, improves the verification efficiency of dangerous driving behaviors through multi-level abstract tensor representation, and solves the problem of output data complexity. A recurrent network based on the attention mechanism, AR-GRU, updates the network input parameter state and learns the weight parameters through its gated structure. This model improves the dynamic connection between modalities on heterogeneous threads and reduces computational complexity. Under low-rank conditions, it can quickly and accurately classify and identify dangerous driving behaviors and give early warnings. Through a large number of experiments, the accuracy of this method is improved by an average of 1.76% compared with the BiLSTM method and the BiGRU-IAAN method in the training and verification of the self-built dataset.
Improved Metallic Enclosure Electromagnetic Imaging Using Ferrite Loaded Antennas
Authors: Mohamadi Cena T. , Asefi Mohammad , Thakur Sandeep , Paliwal Jitendra , Gilmore Colin
Published: November 2022

Three-dimensional electromagnetic imaging can be used to monitor grain within metallic grain bins. Data acquisition requires multiple antennas surrounding the imaging space, which are used to transmit and receive the electromagnetic energy inside the bin. Due to their presence inside a metallic enclosure and due to very large mechanical forces these antennas are required to be low profile. In addition, since they are part of the imaging domain, they should be simple to model in the imaging software (i.e., using a point source). Existing half-loop magnetic field antennas meet these design criteria, but can be improved, particularly with better radiation efficiency. Herein, we present an enhanced antenna design: a ferrite-loaded shielded half-loop antenna designed to measure only the tangential component of the magnetic field against the metal enclosure wall, while rejecting the normal component of the electric field. Experimental results in two bins show that the enhanced design improves the signal level over existing probes by 6–18 dB inside a small-scale enclosure and around 20 dB inside a larger 28 m3 (800 bushel) bin. Full 3D imaging results of a high-moisture target within a low-moisture grain background inside the test enclosure show that the enhanced antennas improve the quality of the reconstructed results in the smaller bin, particularly where the antenna performance improvements are prominent.
Feasibility Analysis and Implementation of Adaptive Dynamic Reconfiguration of CNN Accelerators
Authors: Han Ke , Luo Yingqi
Published: November 2022

In multi-tasking scenarios with dynamically changing loads, the parallel computing of convolutional neural networks (CNNs) causes high energy and resource consumption in the system. Another critical problem is that previous neural network hardware accelerators are often limited to fixed scenarios and lack the function of adaptive adjustment. To solve these problems, a reconfiguration adaptive system based on the prediction of algorithm workload is proposed in this paper. Deep Learning Processor Unit (DPU) from Xilinx has excellent performance in accelerating network computing. After summarizing the characteristics of hardware accelerators and gaining an in-depth understanding of the DPU structure, we propose a regression model for CNNs runtime prediction and a guidance scheme for adaptive reconfiguration combined with the characteristics of Deep Learning Processor Unit. For different DPU sizes, the accuracy of the proposed prediction model achieves 90.7%. With the dynamic reconfiguration technology, the proposed strategy can enable accurate and fast reconfiguration. In the load change scenario, the proposed system can significantly reduce power consumption.
A 177 ppm RMS Error-Integrated Interface for Time-Based Impedance Spectroscopy of Sensors
Authors: Radogna Antonio Vincenzo , Capone Simonetta , Francioso Luca , Siciliano Pietro Aleardo , D’Amico Stefano
Published: November 2022

This paper presents an integrated circuit for time-based electrical impedance spectroscopy (EIS) of sensors. The circuit exploits maximum-length sequences (MLS) in order to perform a broadband excitation of the sensors under test. Therefore, the measured time-domain EIS is obtained by cross-correlating the input with the output of the analog front end (AFE). Unlike the conventional digital approach, the cross-correlation operation is performed in the analog domain. This leads to a lower RMS error in the measured time-domain EIS since the signal processing is not affected by the quantization noise of the analog-to-digital converter (ADC). It also relaxes the sampling frequency of the ADC leading, along with the lack of random access memory (RAM) usage, to a reduced circuit complexity. Theoretical concepts about the circuit’s design and operation are presented, with an emphasis on the thermal noise phenomenon. The simulated performances are shown by testing a sensor’s equivalent model composed of a 50 kΩ resistor in parallel with a 100 pF capacitor. A time-based EIS output of 255 points was obtained with a maximum tested frequency of 500 kHz and a simulated RMS error of 0.0177% (or 177 ppm).
Artificial Intelligence Model for the Identification of the Personality of Twitter Users through the Analysis of Their Behavior in the Social Network
Authors: Villegas-Ch. William , Erazo Daniel Mauricio , Ortiz-Garces Iván , Gaibor-Naranjo Walter , Palacios-Pacheco Xavier
Published: November 2022

Currently, social networks have become one of the most used channels by society to share their ideas, their status, generate trends, etc. By applying artificial intelligence techniques and sentiment analysis to the large volume of data found in social networks, it is possible to predict the personality of people. In this work, the development of a data analysis model with machine learning algorithms with the ability to predict the personality of a user based on their activity on Twitter is proposed. To do this, a data collection and transformation process is carried out to be analyzed with sentiment analysis techniques and the linguistic analysis of tweets. Very successful results were obtained by developing a training process for the machine learning algorithm. By generating comparisons of this model, with the related literature, it is shown that social networks today house a large volume of data that contains significant value if your approach is appropriate. Through the analysis of tweets, retweets, and other factors, there is the possibility of creating a virtual profile on the Internet for each person; the uses can vary, from creating marketing campaigns to optimizing recruitment processes.
Knowledge Distillation for Image Signal Processing Using Only the Generator Portion of a GAN
Authors: Heo Youngjun , Lee Sunggu
Published: November 2022

Knowledge distillation, in which the parameter values learned in a large teacher network are transferred to a smaller student network, is a popular and effective network compression method. Recently, researchers have proposed methods to improve the performance of a student network by using a Generative Adverserial Network (GAN). However, because a GAN is an architecture that is ideally used to create realistic synthetic images, a pure GAN architecture may not be ideally suited for knowledge distillation. In knowledge distillation for image signal processing, synthetic images do not need to be realistic, but instead should include features that help the training of the student network. In the proposed Generative Image Processing (GIP) method, this is accomplished by using only the generator portion of a GAN and utilizing special techniques to capture the distinguishing feature capability of the teacher network. Experimental results show that the GIP method outperforms knowledge distillation using GANs as well as training using only knowledge distillation.
A Generic Preprocessing Architecture for Multi-Modal IoT Sensor Data in Artificial General Intelligence
Authors: Dmytryk Nicholas , Leivadeas Aris
Published: November 2022

A main barrier for autonomous and general learning systems is their inability to understand and adapt to new environments—that is, to apply previously learned abstract solutions to new problems. Supervised learning system functions such as classification require data labeling from an external source and do not have the ability to learn feature representation autonomously. This research details an unsupervised learning method for multi-modal feature detection and evaluation to be used for preprocessing in general learning systems. The learning method details a clustering algorithm that can be applied to any generic IoT sensor data, and a seeded stimulus labeling algorithm impacted and evolved by cross-modal input. The method is implemented and tested in two agents consuming audio and image data, each with varying innate stimulus criteria. Their run-time stimulus changes over time depending on their experiences, while newly experienced features become meaningful without preprogrammed labeling of distinct attributes. The architecture provides interfaces for higher-order cognitive processes to be built on top of the unsupervised preprocessor. This method is unsupervised and modular, in contrast to the highly constrained and pretrained learning systems that exist, making it extendable and well-disposed for use in artificial general intelligence.
Robust Phase Bias Estimation Method for Azimuth Multi-Channel HRWS SAR System Based on Maximum Modified Kurtosis
Authors: Pan Xingbo , Zhang Hanqing , Shu Gaofeng
Published: November 2022

The azimuth multi-channel synthetic aperture radar (MC-SAR) systems can simultaneously realize high-resolution and wide-swath (HRWS) earth observations. However, channel phase bias inevitably exists in the practical work of the azimuth MC-SAR system, which is the main factor for the “virtual target” in SAR images. To accurately estimate the phase bias, a channel phase bias estimation approach based on modified kurtosis maximization (MMK) is proposed in this paper. By analyzing the echo characteristics of multi-channel SAR, the proposed approach constructs the objective optimization function of MMK of the reconstructed Doppler spectrum (RDS), and the channel phase bias can be accurately estimated. Simulation experiments and real raw data processing verify the effectiveness and robustness of the proposed approach, which is not limited by the scene and has a good estimation performance at a low signal-to-noise ratio (SNR).
Simultaneous Beam Forming and Focusing Using a Checkerboard Anisotropic Surface
Authors: Park Jeong-Hyun , Lee Jae-Gon
Published: November 2022

A novel design method of simultaneous beam forming and focusing using a checkerboard anisotropic surface is proposed and verified in this paper. The proposed multibeam control regardless of far and near regions can easily be achieved through a rearrangement of the checkerboard structure. The unit cell of the utilized anisotropic surface consists of two identical metallic structures divided by a dielectric material. When the EM wave with a circular polarization (CP) is incident on the unit cell, the maximum transmission phase variation of the unit cell is 360 degrees by half rotation of the unit cell. A microstrip patch antenna with trimmed corners is used to launch the CP wave and the distance between the microstrip patch antenna and anisotropic surface is about 2 wavelengths considering the optimized spillover and taper efficiencies. After designing each anisotropic surface for beam forming and focusing, the unit cells of the surface are rearranged in the form of a checkerboard. The feasibility of the proposed method is confirmed by full-wave simulation and measurement for anisotropic surface with a beam forming angle of 30 degrees and beam focusing point 60 mm away from center at 5.8 GHz. The forming angle and focal length are simulated and measured to be 28 degrees and about 65 mm, respectively.
Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types
Authors: Shaqour Ayas , Hagishima Aya
Published: November 2022

Owing to the high energy demand of buildings, which accounted for 36% of the global share in 2020, they are one of the core targets for energy-efficiency research and regulations. Hence, coupled with the increasing complexity of decentralized power grids and high renewable energy penetration, the inception of smart buildings is becoming increasingly urgent. Data-driven building energy management systems (BEMS) based on deep reinforcement learning (DRL) have attracted significant research interest, particularly in recent years, primarily owing to their ability to overcome many of the challenges faced by conventional control methods related to real-time building modelling, multi-objective optimization, and the generalization of BEMS for efficient wide deployment. A PRISMA-based systematic assessment of a large database of 470 papers was conducted to review recent advancements in DRL-based BEMS for different building types, their research directions, and knowledge gaps. Five building types were identified: residential, offices, educational, data centres, and other commercial buildings. Their comparative analysis was conducted based on the types of appliances and systems controlled by the BEMS, renewable energy integration, DR, and unique system objectives other than energy, such as cost, and comfort. Moreover, it is worth considering that only approximately 11% of the recent research considers real system implementations.
Experimental and Numerical Study on Fan-Supplied Condenser Deterioration under Built-In Condition and Its Corresponding Refrigerator Performance
Authors: Sun Yu , Zhao Rijing , Yang Yikun , Huang Dong
Published: November 2022

The built-in refrigerator has been popular in China in recent years due to users’ high requirements for the integration of home appliances and furnishings. However, the built-in configuration will cause a significant performance deterioration, which has been less quantitively studied. The condenser and its refrigerator performance are compared experimentally and numerically between built-in and free-standing configurations. By contrast with the free-standing condition, the built-in condenser has poor performance attributed to two reasons: 28.6% lower condenser air flowrate and 10.72 °C higher condenser inlet air temperature caused by the hot short-circuited airflow. This heat dissipation deterioration increases the condensing temperature and discharge temperature, resulting in a refrigerator cooling capacity loss. Correspondingly, the compressor increases the rotating speed and power to compensate for the loss. The compressor ON-time ratio reduces by 8% but the average power during the compressor-on period increases by 56.9%, finally increasing the energy consumption by 43.4%. This study also provides some guidance for further heat dissipation and airflow field optimization of built-in refrigerators.
Pendulum Energy Harvesters: A Review
Authors: Chen Jiatong , Bao Bin , Liu Jinlong , Wu Yufei , Wang Quan
Published: November 2022

In recent years, energy harvesters using pendulum systems have often been applied in ultra-low-frequency environments, such as ocean waves, human motion, and structural vibration. To illustrate the research progress in pendulum-type energy harvesting, a comprehensive review is provided in the present study. Specifically, single- and double-pendulum energy harvesters based on different energy-conversion mechanisms are separately grouped. In addition, different improvement techniques and design schemes used in studies on pendulum energy harvesters are summarized. Theoretical studies have explored the dynamic characteristics of single and double pendulums. Various key aspects, including the fundamental mechanisms, optimization methods, core structures, and applications, to improve the performance of single- and double-pendulum energy harvesters are discussed. Finally, several potential research directions and applications are proposed.
Liquefied Natural Gas and Hydrogen Regasification Terminal Design through Neural Network Estimated Demand for the Canary Islands
Authors: García-Lajara José Ignacio , Reyes-Belmonte Miguel Ángel
Published: November 2022

This publication explores how the existing synergies between conventional liquefied natural gas regasification and hydrogen hydrogenation and dehydrogenation processes can be exploited. Liquid Organic Hydrogen Carrier methodology has been analyzed for hydrogen processes from a thermodynamic point of view to propose an energy integration system to improve energy efficiency during hybridization periods. The proposed neural network can acceptably predict power demand using daily average temperature as a single predictor, with a mean relative error of 0.25%, while simulation results based on the estimated natural gas peak demand show that high-pressure compression is the most energy-demanding process in conventional liquefied natural gas regasification processes (with more than 98% of the total energy consumption). In such a scenario, exceeding energy from liquid organic hydrogen carrier processes have been used as a Rankine’s cycle input to produce both power for the high-pressure compressors and the liquefied natural gas heat exchangers, generating energy savings up to 77%. The designed terminal can securely process up to 158,036 kg/h of liquefied natural gas and 11,829 kg/h of hydrogen.
Optimization of Pitch Control Parameters for a Wind Turbine Based on Tower Active Damping Control
Authors: Liu Yingming , Zhang Shuyuan , Wang Xiaodong , Xie Hongfang , Cao Tian
Published: November 2022

Given the difficulty of accurately setting multiple control parameters in wind turbines, a design method for a pitch controller considering tower load reduction is proposed, which enhances the control performance and reduces both the tower vibration and load. Firstly, the pitch-speed system and the tower fore-aft active damping control are built. In addition, the explicit equation of the tower fore-aft active damping gain is deduced to calculate its initial value. Secondly, the pitch-speed system is identified as an inertial time-delay system using the least squares method. Subsequently, the pitch PI control parameters are set using the Chien–Hrones–Reswick method. Thirdly, the pitch PI control parameters and the tower fore-aft active damping gains are optimized based on the kindred-protected genetic algorithm, which improves the accuracy of the control parameters. Meanwhile, the Pareto method is used to coordinate the control objectives by allocating the weight. Furthermore, the adaptive control is built by fitting the parameters with the wind speed points using the least squares method to enhance the control performance. Finally, the effectiveness of the proposed design method is verified by comparing the control performance with the tower vibration and load.
Prediction Method for Office Building Energy Consumption Based on an Agent-Based Model Considering Occupant–Equipment Interaction Behavior
Authors: Ding Yan , Pan Xiao , Chen Wanyue , Tian Zhe , Wang Zhiyao , He Qing
Published: November 2022

Traditional building energy consumption prediction methods lack the description of occupant behaviors. The interactions between occupants and equipment have great influence on building energy consumption, which cause a large deviation between the predicted results and the actual situation. To address this problem, a two-part prediction model, consisting of a basic part related to the building area and a variable part related to stochastic occupant behaviors, is proposed in this study. The wavelet decomposition and reconstruction method is firstly used to split the energy consumption. A relationship between the low frequency energy consumption data and the building area is discovered, and an area-based index is used to fit the basic part of the prediction model. With a quantitative description of the occupant–equipment interaction by classifying the equipment into environmentally relevant and environmentally irrelevant equipment, an agent-based model is established in the variable part. According to the validation given by two case office buildings, the prediction error can be controlled to 2.8% and 10.1%, respectively, for the total and the hourly building energy consumption. Compared to the prediction method which does not consider occupant–equipment interactions, the proposed model can improve prediction accuracy by 55.8%.
Analysis of the Exhaust Emissions of Hybrid Vehicles for the Current and Future RDE Driving Cycle
Authors: Skobiej Kinga , Pielecha Jacek
Published: November 2022

Hybrid vehicles account for the largest share of new motor vehicle sales in Europe. These are vehicles that are expected to bridge the technological gap between vehicles with internal combustion engines and electric vehicles. Such a solution also makes it possible to meet the limits of motor vehicle emissions, at a time when it is particularly important to test them under actual traffic conditions. This article analyzes the impact of the length of the test routes in relation to current, but also future regulations of approval standards. Three routes of post-phase composition (urban, rural, motorway) with lengths of about 30, 16 and 8 km were selected for the study. Measurements of the main emission components were made using portable emission measurement systems (PEMS), and exhaust emissions were determined using the moving average window (MAW) method. Analysis of the obtained results led to the conclusion that the current requirements for the RDE test (in particular, the duration of the test) enforce a length of each part of 32 km. Reducing the test to 60–90 min causes the individual phases to last 16 km, and the main advantage of such a solution is the very strong influence of the cold start phase on the emission results in the urban phase. Future declarations by lawmakers to drastically reduce the length of the test phases to 8 km will force hybrid vehicles to be tested largely using the internal combustion engine. This will be the right thing to do, especially in the urban phase, as now in addition to a significant reduction in the engine warm-up phase, manufacturers will have to take into account that such an engine thermal condition can also occur in the rural phase.
Artificial Neural Network for Fault Diagnosis of Solar Photovoltaic Systems: A Survey
Authors: Yuan Zixia , Xiong Guojiang , Fu Xiaofan
Published: November 2022

Solar energy is one of the most important renewable energy sources. Photovoltaic (PV) systems, as the most crucial conversion medium for solar energy, have been widely used in recent decades. For PV systems, faults that occur during operation need to be diagnosed and dealt with in a timely manner to ensure the reliability and efficiency of energy conversion. Therefore, an effective fault diagnosis method is essential. Artificial neural networks, a pivotal technique of artificial intelligence, have been developed and applied in many fields including the fault diagnosis of PV systems, due to their strong self-learning ability, good generalization performance, and high fault tolerance. This study reviews the recent research progress of ANN in PV system fault diagnosis. Different widely used ANN models, including MLP, PNN, RBF, CNN, and SAE, are discussed. Moreover, the input attributes of ANN models, the types of faults, and the diagnostic performance of ANN models are surveyed. Finally, the main challenges and development trends of ANN applied to the fault diagnosis of PV systems are outlined. This work can be used as a reference to study the application of ANN in the field of PV system fault diagnosis.
Prediction of Mixing Uniformity of Hydrogen Injection inNatural Gas Pipeline Based on a Deep Learning Model
Authors: Su Yue , Li Jingfa , Guo Wangyi , Zhao Yanlin , Li Jianli , Zhao Jie , Wang Yusheng
Published: November 2022

It is economical and efficient to use existing natural gas pipelines to transport hydrogen. The fast and accurate prediction of mixing uniformity of hydrogen injection in natural gas pipelines is important for the safety of pipeline transportation and downstream end users. In this study, the computational fluid dynamics (CFD) method was used to investigate the hydrogen injection process in a T-junction natural gas pipeline. The coefficient of variation (COV) of a hydrogen concentration on a pipeline cross section was used to quantitatively characterize the mixing uniformity of hydrogen and natural gas. To quickly and accurately predict the COV, a deep neural network (DNN) model was constructed based on CFD simulation data, and the main influencing factors of the COV including flow velocity, hydrogen blending ratio, gas temperature, flow distance, and pipeline diameter ratio were taken as input nodes of the DNN model. In the model training process, the effects of various parameters on the prediction accuracy of the DNN model were studied, and an accurate DNN architecture was constructed with an average error of 4.53% for predicting the COV. The computational efficiency of the established DNN model was also at least two orders of magnitude faster than that of the CFD simulations for predicting the COV.
Reservoir Characteristics and Resource Potential of Marine Shale in South China: A Review
Authors: Zhang Zhiyao , Xu Shang , Gou Qiyang , Li Qiqi
Published: November 2022

Many sets of Paleozoic marine organic-rich shale strata have developed in South China. However, the exploration and development results of these shale formations are quite different. Based on the data of core experiment analysis, drilling, fracturing test of typical wells, the reservoir differences and controlling factors of four sets of typical marine organic-rich shale in southern China are investigated. The four sets of shale have obvious differences in reservoir characteristics. Ordovician–Silurian shale mainly develops siliceous shale, mixed shale and argillaceous shale, with large pore diameter, high porosity, moderate thermal maturity, large pore volume and specific surface area. Cambrian shale mainly develops siliceous shale and mixed shale, with small pore diameter, low porosity, high thermal maturity and smaller pore volume and specific surface area than Ordovician–Silurian shale. Devonian–Carboniferous shale has similar mineral composition to Ordovician–Silurian shale, with small pore diameter, low porosity, moderate thermal maturity and similar pore volume and specific surface area to that of Cambrian shale. Permian shale has very complex mineral composition, with large pore diameter, low to medium thermal maturity and small specific surface area. Mineral composition, thermal maturity and tectonic preservation conditions are the main factors controlling shale reservoir development. Siliceous minerals in Cambrian shale and Ordovician–Silurian shale are mainly of biological origin, which make the support capacity better than Devonian–Carboniferous shale and Permian shale (siliceous minerals are mainly of terrigenous origin and biological origin). Thermal maturity of Ordovician–Silurian shale and Devonian–Carboniferous shale is moderate, with a large number of organic pores developed. Thermal maturity of Cambrian shale and Permian shale is respectively too high and too low, the development of organic pores is significantly weaker than the two sets of shale above. There are obvious differences in tectonic preservation conditions inside and outside the Sichuan Basin. Shale reservoirs inside the Sichuan Basin are characterized by overpressure due to stable tectonic activities, while shale reservoirs outside the Sichuan Basin are generally normal–pressure. Four sets of marine shale in South China all have certain resource potentials, but the exploration and development of shale gas is still constrained by complicated geological conditions, single economic shale formation, high exploration and development costs and other aspects. It is necessary for further research on shale gas accumulation theory, exploration and development technology and related policies to promote the development of China’s shale gas industry.
Fuzzy-Based Simultaneous Optimal Placement of Electric Vehicle Charging Stations, Distributed Generators, and DSTATCOM in a Distribution System
Authors: Mohanty Ajit Kumar , Babu Perli Suresh , Salkuti Surender Reddy
Published: November 2022

Electric vehicles (EVs) are becoming increasingly popular due to their inexpensive maintenance, performance improvements, and zero carbon footprint. The electric vehicle’s load impacts the distribution system’s performance as the electric vehicle’s adoption rises. As a result, the distribution system’s dependability depends on the precise location of the electric vehicle charging station (EVCS). The main challenge is the deteriorating impact of the distribution system caused by the incorrect placement of the charging station. The distribution system is integrated with the charging station in conjunction with the distribution static compensator (DSTATCOM) and distributed generation (DG) to reduce the impact of the EVCS. This paper presents a fuzzy classified method for optimal sizings and placements of EVCSs, DGs, and DSTATCOMs for 69-bus radial distribution systems using the RAO-3 algorithm. The characteristic curves of Li-ion batteries were utilized for the load flow analysis to develop models for EV battery charging loads. The prime objective of the proposed method is to (1) Reduce real power loss; (2) Enhance the substation (SS) power factor (pf); (3) Enhance the distribution network’s voltage profile; and (4) Allocate the optimum number of vehicles at the charging stations. The proposed fuzzified RAO-3 algorithm improves the substation pf in the distribution system. The fuzzy multi-objective function is utilized for the two stages and simultaneous placements of the EVCS, DG, and DSTATCOM. The simulation results reveal that the simultaneous placement method performs better, due to the significant reduction in real power loss, improved voltage profile, and the optimum number of EVs. Moreover, the existing system performances for increased EV and distribution system loads are presented.