bluuebunny/crossref_metadata_2025 huggingface dataset:
Intergenerational Learning and Its Impact on the Improvement of Educational Processes
Authors: Trujillo-Torres Juan Manuel , Aznar-Díaz Inmaculada , Cáceres-Reche María Pilar , Mentado-Labao Trinidad , Barrera-Corominas Aleix
Published: October 2023

The promotion of intergenerational dialogue is postulated as one of the most frequently used strategies to promote meaningful learning. The objective is to analyze the impact of intergenerational dynamics on the learning process. To this end, this work is based on the methodology of a systematic review following the PRISMA method through the establishment of inclusion and exclusion criteria and analysis of their suitability. The priority indicator was to collect empirical experiences in the WOS and Scopus databases that implemented strategies on intergenerational learning and promoted improvements in learning, obtaining a total of 14 documents to be analyzed. The results of the review reaffirm the effectiveness of promoting dynamics of this type, observing positive outcomes in different sets of samples in six main areas: (1) attitudes, well-being, and happiness; (2) integration of vulnerable groups; (3) improvement of family relationships; (4) promotion of social and human values; (5) prevention of diseases and increased knowledge about health; and (6) combating the generational digital divide. There are also weaknesses associated with these experiences, such as the strong involvement required by the participants or the initial prejudices that exist between one generation and another.
The Methodology of Virtualizing Sculptures and Drawings: A Case Study of the Virtual Depot of the Gallery of Matica Srpska
Authors: Obradović Miloš , Mišić Snežana , Vasiljević Ivana , Ivetić Dragan , Obradović Ratko
Published: October 2023

The aim of this paper is to introduce the public to the virtual depot of the Gallery of Matica Srpska (GMS), which showcases a collection of sculptures that have been digitized using a Structure-from-Motion photogrammetry and presented by the use of virtual reality and paintings, and drawings were presented through a digital flipbook application. Through the application of cutting-edge methods, highly precise digital replicas of these significant cultural artifacts have been created with details that are difficult to see without a magnifying glass when observing the physical artifact. Additionally, it explores the profound significance and advantages of the virtual depot, such as facilitating remote access, augmenting preservation endeavors, and fostering interdisciplinary collaborations, academic research, educational purposes, and public engagement. The implementation of the virtual depot offers a novel approach to showcasing and studying cultural heritage, opening up new possibilities for the exploration and appreciation of these artifacts in a digital environment. Today, the collection of the GMS encompasses more than 10,000 art objects, which means that one visitor would need about 53 years to access each individual artifact. Virtual depot enables each visitor to do this much faster, but more importantly, in any occasion and setting that they find suitable.
A Wideband and Low Reference Spur PLL with Clock Feedthrough Suppressed and Low Current Mismatch Charge Pump and Symmetrical CML Divider
Authors: Wang Yingxi , Liu Yueyue , Xu Haotang , Li Zhongmao , Li Zhiqiang
Published: October 2023

This paper presents the design and performance analysis of a wideband charge-pump phase-locked loop (CPPLL) characterized by low reference spur and low phase noise. The proposed CPPLL, operating as a wideband phase-locked loop (PLL) with a reference frequency of 100 MHz, achieves a wide tuning range of 40% from 2.0 GHz to 3.0 GHz. A clock feedthrough suppressed charge pump with additional bias current branches is used to reduce the PLL’s loop reference spur. The 4-stage current mode logic (CML) divide-by-2/3 circuit is utilized in the frequency divider to achieve high-speed frequency division. The circuit of an AND gate and latch in the 2/3 divider adopts a full differential symmetric structure to minimize the phase error of high-frequency differential signals. The voltage-controlled oscillator (VCO) is designed to provide a wide tuning range while optimizing the trade-off between the phase noise and power consumption. The fabricated PLL is implemented using a 0.13 µm CMOS process. Experimental measurements reveal a reference spur of −74.39 dBc at an oscillation frequency of 2.4 GHz. Moreover, the CPPLL achieves phase noise of −102.55 dBc/Hz@100 kHz and −127.15 dBc/Hz@1 MHz, while consuming 33.6 mW under a 1.2 V supply voltage. The integrated root-mean-square (rms) jitter, measured from 10 kHz to 10 MHz, is 340.99 fs, and the figure-of-merit (FoM) is −234.08 dB at a carrier frequency of 2.4 GHz, highlighting the potential of the proposed PLL for integrated circuit applications.
Economic Dispatch of Integrated Electricity–Heat–Hydrogen System Considering Hydrogen Production by Water Electrolysis
Authors: Wang Jinhao , Pan Zhaoguang , Ge Huaichang , Zhao Haotian , Xia Tian , Wang Bin
Published: October 2023

Water electrolysis is a clean, non-polluting way of producing hydrogen that has seen rapid development in recent years. It offers the possibility of resolving the issue of excessive carbon emissions in conventional hydrogen production methods. In addition, waste heat recovery in hydrogen fuel cells can significantly increase the efficiency of energy use. Thus, to combine the electric power system, the hydrogen energy system, and the district heating system, this research suggests a novel optimal multi-energy complementary electricity–hydrogen–heat model. Rooftop photovoltaics, energy storage batteries, electric boilers, and hydrogen energy systems made up of hydrogen generation, hydrogen storage, and hydrogen fuel cells are all included in the suggested model. Furthermore, the electricity–hydrogen–heat system can be connected successfully using waste heat recovery in hydrogen fuel cells to create a coordinated supply of heat and power. In this work, the waste heat of hydrogen fuel cells is taken into account to increase the efficiency of energy use. To show the effectiveness of the suggested optimal multi-energy complementary model, many case studies have been conducted.
Road Scene Instance Segmentation Based on Improved SOLOv2
Authors: Yang Qing , Peng Jiansheng , Chen Dunhua , Zhang Hongyu
Published: October 2023

Road instance segmentation is vital for autonomous driving, yet the current algorithms struggle in complex city environments, with issues like poor small object segmentation, low-quality mask edge contours, slow processing, and limited model adaptability. This paper introduces an enhanced instance segmentation method based on SOLOv2. It integrates the Bottleneck Transformer (BoT) module into VoVNetV2, replacing the standard convolutions with ghost convolutions. Additionally, it replaces ResNet with an improved VoVNetV2 backbone to enhance the feature extraction and segmentation speed. Furthermore, the algorithm employs Feature Pyramid Grids (FPGs) instead of Feature Pyramid Networks (FPNs) to introduce multi-directional lateral connections for better feature fusion. Lastly, it incorporates a convolutional Block Attention Module (CBAM) into the detection head for refined features by considering the attention weight coefficients in both the channel and spatial dimensions. The experimental results demonstrate the algorithm’s effectiveness, achieving a 27.6% mAP on Cityscapes, a 4.2% improvement over SOLOv2. It also attains a segmentation speed of 8.9 FPS, a 1.7 FPS increase over SOLOv2, confirming its practicality for real-world engineering applications.
A Network Intrusion Detection Model Based on BiLSTM with Multi-Head Attention Mechanism
Authors: Zhang Jingqi , Zhang Xin , Liu Zhaojun , Fu Fa , Jiao Yihan , Xu Fei
Published: October 2023

A network intrusion detection tool can identify and detect potential malicious activities or attacks by monitoring network traffic and system logs. The data within intrusion detection networks possesses characteristics that include a high degree of feature dimension and an unbalanced distribution across categories. Currently, the actual detection accuracy of some detection models is relatively low. To solve these problems, we propose a network intrusion detection model based on multi-head attention and BiLSTM (Bidirectional Long Short-Term Memory), which can introduce different attention weights for each vector in the feature vector that strengthen the relationship between some vectors and the detection attack type. The model also utilizes the advantage that BiLSTM can capture long-distance dependency relationships to obtain a higher detection accuracy. This model combined the advantages of the two models, adding a dropout layer between the two models to improve the detection accuracy while preventing training overfitting. Through experimental analysis, the network intrusion detection model that utilizes multi-head attention and BilSTM achieved an accuracy of 98.29%, 95.19%, and 99.08% on the KDDCUP99, NSLKDD, and CICIDS2017 datasets, respectively.
CLICK: Integrating Causal Inference and Commonsense Knowledge Incorporation for Counterfactual Story Generation
Authors: Li Dandan , Guo Ziyu , Liu Qing , Jin Li , Zhang Zequn , Wei Kaiwen , Li Feng
Published: October 2023

Counterfactual reasoning explores what could have happened if the circumstances were different from what actually occurred. As a crucial subtask, counterfactual story generation integrates counterfactual reasoning into the generative narrative chain, which requires the model to preserve minimal edits and ensure narrative consistency. Previous work prioritizes conflict detection as a first step, and then replaces conflicting content with appropriate words. However, these methods mainly face two challenging issues: (a) the causal relationship between story event sequences is not fully utilized in the conflict detection stage, leading to inaccurate conflict detection, and (b) the absence of proper planning in the content rewriting stage results in a lack of narrative consistency in the generated story ending. In this paper, we propose a novel counterfactual generation framework called CLICK based on causal inference in event sequences and commonsense knowledge incorporation. To address the first issue, we utilize the correlation between adjacent events in the story ending to iteratively calculate the contents from the original ending affected by the condition. The content with the original condition is then effectively prevented from carrying over into the new story ending, thereby avoiding causal conflict with the counterfactual conditions. Considering the second issue, we incorporate structural commonsense knowledge about counterfactual conditions, equipping the framework with comprehensive background information on the potential occurrence of counterfactual conditional events. Through leveraging a rich hierarchical data structure, CLICK gains the ability to establish a more coherent and plausible narrative trajectory for subsequent storytelling. Experimental results show that our model outperforms previous unsupervised state-of-the-art methods and achieves gains of 2.65 in BLEU, 4.42 in ENTScore, and 3.84 in HMean on the TIMETRAVEL dataset.
Novel Synchronization Criteria for Non-Dissipative Coupled Networks with Bounded Disturbances and Time-Varying Delays of Unidentified Bounds via Impulsive Sampling Control
Authors: Fan Hongguang , Shi Kaibo , Xu Yanan , Zhang Rui , Zhou Shuai , Wen Hui
Published: October 2023

The μ−synchronization issues of non-dissipative coupled networks with bounded disturbances and mixed delays are studied in this article. Different from existing works, three kinds of time delays, including internal delays, coupling delays, and impulsive sampling delays, have unidentified bounds and even evolve towards infinity over time, making the concerned network more practical. Considering μ−stability theory and impulse inequality techniques, a hybrid non-delayed and time-delayed impulsive controller including both current and historical state information is designed, and several novel sufficient conditions are derived to make nonlinear complex networks achieve μ−synchronization. Moreover, not only can the constriction of dissipative coupling conditions on network topology be relaxed, but also the restriction of various time delays on impulsive intervals can be weakened, which makes the theoretical achievements in this article more general than the previous achievements. Ultimately, numerical simulations confirm the effectiveness of our results.
A Reinforcement Learning Method of Solving Markov Decision Processes: An Adaptive Exploration Model Based on Temporal Difference Error
Authors: Wang Xianjia , Yang Zhipeng , Chen Guici , Liu Yanli
Published: October 2023

Traditional backward recursion methods face a fundamental challenge in solving Markov Decision Processes (MDP), where there exists a contradiction between the need for knowledge of optimal expected payoffs and the inability to acquire such knowledge during the decision-making process. To address this challenge and strike a reasonable balance between exploration and exploitation in the decision process, this paper proposes a novel model known as Temporal Error-based Adaptive Exploration (TEAE). Leveraging reinforcement learning techniques, TEAE overcomes the limitations of traditional MDP solving methods. TEAE exhibits dynamic adjustment of exploration probabilities based on the agent’s performance, on the one hand. On the other hand, TEAE approximates the optimal expected payoff function for subprocesses after specific states and times by integrating deep convolutional neural networks to minimize the temporal difference error between the dual networks. Furthermore, the paper extends TEAE to DQN-PER and DDQN-PER methods, resulting in DQN-PER-TEAE and DDQN-PER-TEAE variants, which not only demonstrate the generality and compatibility of the TEAE model with existing reinforcement learning techniques but also validate the practicality and applicability of the proposed approach in a broader MDP reinforcement learning context. To further validate the effectiveness of TEAE, the paper conducts a comprehensive evaluation using multiple metrics, compares its performance with other MDP reinforcement learning methods, and conducts case studies. Ultimately, simulation results and case analyses consistently indicate that TEAE exhibits higher efficiency, highlighting its potential in driving advancements in the field.
Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort
Authors: Márquez-Sánchez Sergio , Calvo-Gallego Jaime , Erbad Aiman , Ibrar Muhammad , Hernandez Fernandez Javier , Houchati Mahdi , Corchado Juan Manuel
Published: October 2023

Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment.
Survey on Application of Trusted Computing in Industrial Control Systems
Authors: Bai Jing , Zhang Xiao , Qi Longyun , Liu Wei , Zhou Xianfei , Liu Yin , Lv Xiaoliang , Sun Boyan , Duan Binbin , Zhang Siyuan , Che Xin
Published: October 2023

The Fourth Industrial Revolution, also known as Industrial 4.0, has greatly accelerated inter-connectivity and smart automation in industrial control systems (ICSs), which has introduced new challenges to their security. With the fast growth of the Internet of Things and the advent of 5G/6G, the collaboration of Artificial Intelligence (Al) and the Internet of Things (loT) in ICSs has also introduced lots of security issues as it highly relies on advanced communication and networking techniques. Frequent ICS security incidents have demonstrated that attackers have the ability to stealthily breach the current system defenses and cause catastrophic effects to ICSs. Thankfully, trusted computing technology, which has been a popular research topic in the field of information security in recent years, offers distinct advantages when applied to ICSs. In this paper, we first analyze the vulnerabilities of ICSs and the limitations of existing protection technologies. Then, we introduce the concept of trusted computing and present a security framework for ICSs based on Trusted Computing 3.0. Finally, we discuss potential future research directions.
Exploring Deep Learning for Adaptive Energy Detection Threshold Determination: A Multistage Approach
Authors: Bedir Oguz , Ekti Ali Riza , Ozdemir Mehmet Kemal
Published: October 2023

The concept of spectrum sensing has emerged as a fundamental solution to address the growing demand for accessing the limited resources of wireless communications networks. This paper introduces a straightforward yet efficient approach that incorporates multiple stages that are based on deep learning (DL) techniques to mitigate Radio Frequency (RF) impairments and estimate the transmitted signal using the time domain representation of received signal samples. The proposed method involves calculating the energies of the estimated transmitted signal samples and received signal samples and estimating the energy of the noise using these estimates. Subsequently, the received signal energy and the estimated noise energy, adjusted by a correction factor (k), are employed in binary hypothesis testing to determine the occupancy of the wireless channel under investigation. The proposed system demonstrates encouraging outcomes by effectively mitigating RF impairments, such as carrier frequency offset (CFO), phase offset, and additive white Gaussian noise (AWGN), to a considerable degree. As a result, it enables accurate estimation of the transmitted signal from the received signal, with 3.85% false alarm and 3.06% missed detection rates, underscoring the system’s capability to adaptively determine a decision threshold for energy detection.
Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System
Authors: Jankowska Kamila , Dybkowski Mateusz
Published: October 2023

This article presents the classification of optoelectronics encoder faults in a permanent magnet synchronous motor (PMSM) drive system. This paper proposes the deep neural networks (DNNs) speed sensor faults classification application in the vector-controlled PMSM drive. This approach to the issue has not been discussed in the literature before. This work presents a solution based on early detection with the use of the model reference adaptive system (MRAS) estimator and fault classification based on artificial intelligence. The innovative nature of this work is also due to the simulation of speed sensor damage using the developed optoelectronics encoder model in the Matlab/Simulink environment. This work is focused on simulation studies, which have been supported by experimental results obtained on the MicroLabBox platform. This article compares two structures of deep neural networks in fault detection. The results were also compared with previous experimental studies on the classification of speed sensor failures using shallow neural networks.
Analysis of the Output Characteristics of a Vertical Photovoltaic System Based on Operational Data: A Case Study in Republic of Korea
Authors: Lee Seung-Min , Lee Eui-Chan , Lee Jung-Hun , Yu Sun-Ho , Heo Jae-Sil , Lee Woo-Young , Kim Bong-Suck
Published: October 2023

The proliferation of renewable energy sources to achieve carbon neutrality has rapidly increased the adoption of photovoltaic (PV) systems. Consequently, specialized solar PV systems have emerged for various installation purposes. This study focuses on grid connecting vertically installed bifacial PV modules facing east and west by establishing a test bed within Republic of Korea. Based on weather and generation data collected in Republic of Korea, located in the middle of latitude 34.98° N, from January to July 2023, we analyzed and compared the generation patterns, peak generation, peak hours, and total generation of conventional and vertical PV systems. Moreover, PVsyst was used to model the solar PV generation and analyze the consistency and viability of vertical PV generation by comparing actual operational data with simulation results. The vertical PV system demonstrated a peak power generation of 89.1% compared with the conventional PV system with bifacial modules. Based on operational data from January to July, the power generation output of the vertical PV system decreased to 65.7% compared with that of the conventional system with bifacial modules. This corresponded to 78.8% to 80.2% based on the PVsyst simulation results. In particular, the investigations related to the peak generation levels and occurrence times of vertical PV systems provide insights into the practicality of vertical solar PV systems and their potential for improving the PV hosting capacity.
Detailed Energy Analysis of a Sheet-Metal-Forming Press from Electrical Measurements
Authors: Carrillo Camilo , Díaz Dorado Eloy , Cidrás Pidre José , Garrido Campos Julio , San Facundo López Diego , Lisboa Cardoso Luiz A. , Martínez Castañeda Cristina I. , Sánchez Rúa José F.
Published: October 2023

This paper presents a methodology that allows for the detection of the state of a sheet-metal-forming press, the parts being produced, their cadence, and the energy demand for each unit produced. For this purpose, only electrical measurements are used. The proposed analysis is conducted at the level of the press subsystems: main motor, transfer module, cushion, and auxiliary systems, and is intended to count, classify, and monitor the production of pressed parts. The power data are collected every 20 ms and show cyclic behavior, which is the basis for the presented methodology. A neural network (NN) based on heuristic rules is developed to estimate the press states. Then, the production period is determined from the power data using a least squares method to obtain normalized harmonic coefficients. These are the basis for a second NN dedicated to identifying the parts in production. The global error in estimating the parts being produced is under 1%. The resulting information could be handy in determining relevant information regarding the press behavior, such as energy per part, which is necessary in order to evaluate the energy performance of the press under different production conditions.
Estimation of 3D Permeability from Pore Network Models Constructed Using 2D Thin-Section Images in Sandstone Reservoirs
Authors: Luo Chengfei , Wan Huan , Chen Jinding , Huang Xiangsheng , Cui Shuheng , Qin Jungan , Yan Zhuoyu , Qiao Dan , Shi Zhiqiang
Published: October 2023

Using thin-section images to estimate core permeability is an economical and less time-consuming method for reservoir evaluation, which is a goal that many petroleum developers aspire to achieve. Although three-dimensional (3D) pore volumes have been successfully applied to train permeability models, it is very expensive to carry out. In this regard, deriving permeability from two-dimensional (2D) images presents a novel approach in which data are fitted directly on the basis of pore-throat characteristics extracted from more cost-effective thin sections. This work proposes a Fluid–MLP workflow for estimating 3D permeability models. We employed DIA technology combined with artificial lithology and pore classification to calculate up to 110 characteristic parameters of the pore-throat structure on the basis of 2D rock cast thin sections. The MLP network was adopted to train the permeability prediction model, utilizing these 110 parameters as input. However, the accuracy of the conventional MLP network only reached 90%. We propose data preprocessing using fluid flow simulations to improve the training accuracy of the MLP network. The fluid flow simulations involve generating a pore network model based on the 2D pore size distribution, followed by employing the lattice Boltzmann method to estimate permeability. Subsequently, six key structural parameters, including permeability calculated by LBM, pore type, lithology, two-dimensional porosity, average pore–throat ratio, and average throat diameter, were fed into the MLP network for training to form a new Fluid–MLP workflow. Comparing the results predicted using this new Fluid–MLP workflow with those of the original MLP network, we found that the Fluid–MLP network exhibited superior predictive performance.
Industry 4.0 Driven Green Supply Chain Management in Renewable Energy Sector: A Critical Systematic Literature Review
Authors: Labaran Muhammad Jameel , Masood Tariq
Published: October 2023

This paper is aimed at systematically reviewing the literature on the interface of Green Supply Chain Management (GSCM), Industry 4.0 and renewable energy. The aims of this review are to ascertain current state of the art in GSCM practices, to critically examine the role of industry 4.0 technologies in GSCM adoption, to examine sustainability and supply chain issues within the renewable energy sector, and to critically discuss tackling these issues using industry 4.0 driven GSCM. A total of 215 review and empirical papers from 2004 to 2023 gathered from three databases (ScienceDirect, Scopus and Web of Science) were reviewed. This research found several GSCM practices and succinctly categorized those based on their respective positions along any given value chain. The practices were categorized as related to: procurement and sourcing, product design, corporate relationship, organizational structure/strategy, logistics and transportation, waste and pollution management and manufacturing. We have found that the ten most mentioned GSCM practices are: Green purchasing, Eco-design, Cooperation with customers, Internal Environmental Management, Reverse logistics, Investment recovery, Green manufacturing, Green design, Collaboration with suppliers and Remanufacturing. Various Industry 4.0 technologies including blockchain, Internet of Things, Big Data and Artificial Intelligence were identified and their role in successful GSCM implementation is highlighted. Sustainability and supply chain related challenges within the renewable energy supply chain were found that include material sourcing and end-of-life management challenges. Finally, a perspective is given on the role of Industry 4.0 driven GSCM in tackling the renewable energy supply chain and sustainability challenges. This article concludes with a presentation of managerial implications and future research directions for managers within different sectors, especially the renewable energy industry.
Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast
Authors: Villegas Mier Oscar , Dittmann Anna , Herzberg Wiebke , Ruf Holger , Lorenz Elke , Schmidt Michael , Gasper Rainer
Published: October 2023

Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.
Thermoelectric-Based Radiant Cooling Systems: An Experimental and Numerical Investigation of Thermal Comfort
Authors: Kubwimana Benjamin , Seyednezhad Mohadeseh , Najafi Hamidreza
Published: October 2023

Researching novel cooling and heating technologies as alternatives to conventional vapor-compression refrigeration cycles has received growing attention in recent years. Thermoelectric (TE) systems rank among promising emerging technologies within this category. This paper presents a comprehensive investigation, utilizing numerical modeling and analysis via COMSOL Multiphysics along with experimental validation, to evaluate the performance of a radiant cooling ceiling panel working on thermoelectric principles. Performance metrics are based on thermal comfort levels within the designed test chamber. The system comprises a rectangular test chamber (~1.2 m × 1.2 m × 1.5 m) with a centrally positioned ceiling panel (dimensions: 0.6 m × 0.6 m × 0.002 m). Four TE modules are attached on top of the ceiling panel, facilitating effective cooling to regulate the ceiling temperature to the desired setpoint. The resultant lower ceiling temperature enables heat exchange within the chamber environment via radiation and convection mechanisms. This study examines the time-dependent variations in mean radiant temperature and operative temperature under natural convection conditions, with comfort level assessment carried out using the PMV method according to ASHRAE Standard 55. An experimental chamber is built to validate the numerical model by performing experiments at various ceiling temperatures. Design challenges are discussed in detail. The results of this investigation offer valuable insights into the anticipated thermal comfort achievable through TE-based radiant cooling systems across various operating conditions.
Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization
Authors: Tao Xinghua , Mo Nan , Qin Jianbo , Yang Xiaozhe , Yin Linfei , Hu Likun
Published: October 2023

This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability.
Sliding Mode Control of Buck DC–DC Converter with LC Input Filter
Authors: Tatar Karol , Chudzik Piotr , Leśniewski Piotr
Published: October 2023

The employment of input filters in modern DC–DC converters is mandatory in order to ensure EMC (Electro-Magnetic Compatibility), provide power electronics with decent voltage and minimize the converter influence on the power grid. The LC type input filters bring also a possibility of voltage and current oscillation that may occur in the system. This oscillation may rise to a certain level that affects system stability. A wide range of methods are employed in order to attenuate these oscillations and allow appropriate system transient response. In this paper, the sliding mode control (SMC) strategy is proposed to reduce the input LC filter voltage and current oscillation and allow DC (Direct Current) output voltage control for both resistive and constant power load. The proposed control algorithm is intended for use in railway, tram and trolleybus DC/DC converters. For the proposed scheme of control simulation, a model is developed using Simulink software. Furthermore, laboratory stand experiments are carried out to verify simulation results.
Photovoltaic Thermal Heat Pump Assessment for Power and Domestic Hot Water Generation
Authors: Zhou Chao , Riaz Ahmad , Wang Jingjing , Zhang Jili , Xu Lin
Published: October 2023

The efficient utilization of solar energy significantly contributes to energy efficiency in buildings. Solar photovoltaic thermal (PVT) heat pumps, a hybrid of photovoltaic and solar-assisted heat pumps, have demonstrated a significant development trend due to their multi-generational capacity for heating, power, and cooling with reliable operational performance. This research work presents and investigates a single-stage compression PVT heat pump system, along with the operation principle of the system’s heating and power co-generation throughout the winter and transitional season. The construction of the testing facility, data reduction, error analysis, and performance evaluation indices of the system are all explained theoretically. A continuous experiment research project focusing on system heating and power performance was carried out in Dalian during the transition season (November in this study) and winter season (December in this study) as part of our investigation into the potential uses for space heating, residential hot water, and power supply in northern China. The findings of the experimental research demonstrate that the proposed system can generate electricity and heat at high efficiency during the winter and transitional seasons, with long-term stable performance. The system’s average heating COPt is 5 during the transitional season and 4.4 during the winter season. Meanwhile, the average photovoltaic power efficiency under both weather conditions is 11.9% and 10.2%, with a peak value of 15.7% and 12.0%, respectively. Additionally, the system compression ratio’s variation range is 2 to 3.88, which is lower than the standard heat pump system. As a result, the entire system heating operating process remains constant.
The Effects of Cracking Ratio on Ammonia/Air Non-Premixed Flames under High-Pressure Conditions Using Large Eddy Simulations
Authors: Wang Chengming , Wang Haiou , Luo Kun , Fan Jianren
Published: October 2023

Ammonia is a promising carbon-free fuel. However, one of the main challenges for ammonia combustion is the high level of NO emissions. In this study, simulations were conducted for ammonia/air laminar counterflow flames and turbulent non-premixed jet flames in the KAUST high-pressure combustion duct (HPCD) at a pressure of 5 bar, with two ammonia cracking ratios of 14% and 28%. The influence of ammonia cracking ratio on the flame structure and NO formation mechanism were examined. The laminar counterflow flame results showed that HNO is one of the most critical species related to NO formation and NO is mainly generated through the path of NH2→NH→HNO→NO. For the turbulent flames, the flamelet/progress variable (FPV) approach was employed in the context of large eddy simulations (LES) for high-fidelity simulations. The simulation results were compared with the measured data with promising agreements, which proves the accuracy of the FPV method for the present flames. It was shown that with increasing cracking ratio, not only the flame reactivity is enhanced, but also the generation of NO is increased. The correlation between NO and HNO is weaker when compared to that between NO and radicals such as O, H and OH in the entire flame. Through the distribution of NO source terms, it was found that the NO source term has a higher absolute value in the upstream region and the absolute value rapidly decreases with increasing streamwise distance. The total NO source term is positive in the fuel-lean zone and shows negative values in the fuel-rich zone.
Bypass Configurations of Membrane Humidifiers for Water Management in PEM Fuel Cells
Authors: Vu Hoang Nghia , Trinh Dinh Hoang , Truong Le Tri Dat , Yu Sangseok
Published: October 2023

Water management is an important criterion in the operation of proton-exchange membrane fuel cells to maintain the high performance and reliability of the system. The water content in the cathode air that is supplied to the cathode channel contributes to the membrane humidification and the transport of protons inside the membrane structure. In automotive applications, the supply air is typically driven through an external membrane humidifier to absorb more moisture from the recirculated cathode exhaust. In the literature, humidifiers and fuel cell stacks have been separately investigated without considering whole-system configurations for water management. This study investigates changes in the cathode air characteristics through a membrane humidifier and compares two configurations using a humidifier bypass of the supply flow and exhaust flow to adjust the cathode inlet air relative humidity. Each component in the system was modeled using mathematical relations and converted into blocks of inputs and outputs in MATLAB/Simulink for simulation. The bypass valve was demonstrated to effectively reduce the relative humidity of the supply air from the saturation rate to above 60%, with a bypass fraction of up to 0.6 in both configurations. These adjustments provide system flexibility to accommodate load changes and prevent flooding in the stack channels. Bypassing the supply air through the humidifier effectively maintained consistent cathode inlet humidity across a wide operational range. A 0.4 bypass fraction on the supply side sustained a relative humidity of around 80% for the whole range of operating flow rates. In contrast, the exhaust-side bypass had a smaller impact, and the relative humidity of the cathode air was reduced when the flow rate and bypass fraction increased. This study further supports the control system design to regulate the bypass fraction according to load transients.
A Two-Terminal Directional Protection Method for HVDC Transmission Lines of Current Fault Component Based on Improved VMD-Hilbert Transform
Authors: Liu Shuhao , Han Kunlun , Li Hongzheng , Zhang Tengyue , Chen Fengyuan
Published: October 2023

The traveling wave protection of high voltage direct current (HVDC) transmission lines is susceptible to the influence of transition resistance. As a backup protection, current differential protection has absolute selectivity, but usually requires an increase in delay to avoid misoperation caused by distributed capacitance on the line, resulting in a longer action time. Based on this, a two-terminal directional protection method for HVDC transmission lines is proposed based on Sparrow Search Algorithm (SSA)-Variational Mode Decomposition (VMD) and Hilbert phase difference. On the basis of analyzing the directional characteristics of the current fault component at both ends of the rectifier and inverter sides under different faults, SSA is first used to optimize the parameters of VMD. The residual components representing the direction of the current fault component at both ends are extracted through VMD, and then the Hilbert phase difference of the residual components at both ends is calculated to identify faults inside and outside the line area. In addition, fault pole selection can be achieved based on the ratio of the sum of multi-band Hilbert energy of single-terminal voltage fault components at the positive and negative poles. Simulation experiments have shown that the proposed protection scheme can quickly and effectively identify fault and has good tolerance to transition resistance and noise interference.
Energy and Exergy Analysis of Cascade Mixed Refrigerant Joule–Thomson System with the Application of a Precooler
Authors: Yoon Ji-Hoon , Yoon Jung-In , Son Chang-Hyo , Seol Sung-Hoon
Published: October 2023

This study proposes the application of a precooler to the cascade mixed refrigerant Joule–Thomson (CMR J–T) cycle, herein referred to as the precooled CMR J–T (PCMR J–T) system. The purpose of the precooler is to utilize the temperature gradient characteristics within the two-phase region exhibited by the non-azeotropic mixed refrigerant. The precooler reduces the temperature of the high-temperature gas exiting the compressor by using cooling water from the condenser, thereby decreasing the capacity requirements of the high-temperature cycle (HTC). The working fluid comprises a nonflammable mixed refrigerant (R218, R23, R14, and Ar), and simulations were conducted by varying the HTC evaporation temperature and cooling water temperature for energy and exergy analysis. Under the analysis conditions, the capacity of each component in the HTC can be reduced by over 45%, leading to a maximum increase of 21.6% in the system’s coefficient of performance. Furthermore, the exergy destruction in the PCMR J–T system decreases along with the reduction in component capacity, with the most significant reduction occurring at the HTC expansion valve. The exergy efficiency of the system increases by up to 47.4%.
Measuring Eco-Efficiency of the Global Shipping Sector Based on an Energy and Environmental Approach: A Dynamic Slack-Based Measure Non-Oriented Model
Authors: Parris Dimitrios , Spinthiropoulos Konstantinos , Ragazou Konstantina , Kanavas Vasileios , Tsanaktsidis Constantinos
Published: October 2023

The compatibility of shipping with environmental protection is a subject that the international community is becoming increasingly concerned about, considering the threat of climate change. The current study aims to assess the worldwide shipping sector’s eco-efficiency performance for the first time using the Dynamic Slack-Based assess non-oriented Data Envelopment Analysis methodology, while the visualization of the results has been made with the geographic information system of ArcGIS. The findings show that the most vibrant shipping sectors in the world, such as the Marshall Islands, present the lowest eco-efficiency levels due to the nations’ roles as tax havens for shipowners. Furthermore, traditional maritime economies such as the Chinese one show a great growth in the eco-efficiency score due to the strategies of the shipping companies that are headquartered in the region to invest and adopt Environmental, Social, and Governance principles, which help them to achieve high scores in eco-efficiency. Finally, nations with small fleets have the greatest eco-efficiency score, as local governments have engaged in sustainable activities and initiatives over the previous four to five years to enable their marine industry to thrive and dominate the market.
Performance Signature of the Best Candidate-Graded Bandgap Materials for Solar Cells with Steady-State Conversion Efficiency
Authors: El-Hageen Hazem M. , Zaki Rashed Ahmed Nabih , Albalawi Hani , Alhartomi Mohammed A. , Alfaifi Yousef H. , Alsubaie Madhi Tarikham , Mead Mohamed A.
Published: October 2023

This is a comprehensive research endeavor focused on enhancing the efficiency of the proposed solar cell design. The integration of the simulation techniques, judicious material selection, and meticulous performance metrics showcase a methodical approach toward creating a solar cell capable of achieving high efficiency across a wide spectrum of light in the AM 1.5 G1 sun solar cell illumination spectrum. Having said this, many researchers are still working on the efficiency potential—based on external radiative efficiency (ERE), open-circuit voltage loss, and fill factor loss—of high-efficiency solar cells. The solar cell is built on aluminum-doped zinc oxide (ZnO) as a transparent conductive oxide layer; aluminum nitride (AlN) as the window layer (emitter); an SWCNT layer as the absorber layer; gallium phosphide (GaP) as the contact layer; and silicon as the substrate. The proposed solar cell transmission, reflection, and absorption relative to the variations in wavelength band spectrum are studied. The conduction and valence band energy diagrams of the solar cell design structure are simulated against the layer thickness variations for the suggested solar cell structure. Short-circuit current density and maximum power variations are clarified versus the bias voltage. Light current density is simulated versus the bias voltage (J/V characteristics curve) of the suggested solar cell design structure. The carrier generation–recombination rate is also simulated by the COMSOL simulation program versus the layer thickness of the suggested solar cell structure. The solar cell circuit design has a fill factor (FF) value of 74.31% and a power conversion efficiency value of 29.91%.
The Production of Bioethanol from Lignocellulosic Biomass: Pretreatment Methods, Fermentation, and Downstream Processing
Authors: Beluhan Sunčica , Mihajlovski Katarina , Šantek Božidar , Ivančić Šantek Mirela
Published: October 2023

Bioethanol is the most widely used alternative transportation fuel to petrol. Bioethanol is considered a clean, renewable, and environmentally friendly fuel that can contribute to climate change mitigation, decreased environmental pollution, and enhanced energy security. Commercial bioethanol production is based on traditional agricultural crops such as corn, sugarcane, and sugarbeet, primarily used as food and feed. In order to meet the growing demand for this fuel and decrease competition in the food and biofuel sectors for the same feedstock, other raw materials and process technologies have been intensively studied. Lignocellulosic biomass is one of the most abundant renewable resources, with it being rich in compounds that could be processed into energy, transportation fuels, various chemical compounds, and diverse materials. Bioethanol production from lignocellulosic biomass has received substantial attention in recent decades. This review gives an overview of bioethanol production steps from lignocellulosic biomass and challenges in the production process. The following aspects of bioethanol production are covered here, including pretreatment methods, process strategies, strain development, ethanol isolation and purification, and technical hurdles.