bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on MLD Modeling and Predictive Control of Magnetically Coupled Resonant Bidirectional WPT System
Authors: Li Xin , Zhao Tianyang , Xu Jiqiang
Published: March 2024

The recent studies on magnetically coupled resonant bidirectional wireless power transfer (MCR-BWPT) systems disregard the challenges posed by nonlinearity, discrete switching action, and hybrid properties within the system. This research focuses on the D-LCL resonant compensation topology MCR-BWPT system. The switch tube’s switching state dictates the division of various working modes and the determination of the switching conditions between them. The coupling relationship between the continuous dynamic characteristics of the system and discrete events and the constrained conditions of the system are derived. The Hybrid System Description Language (HYSDEL) is used to build the Mixed Logic Dynamic (MLD) model of the system. The MLD model is employed as the prediction model, and the hybrid model predictive controller of the MCR-BWPT system is constructed according to the quadratic performance index. Finally, to verify the accuracy of the MLD model and the feasibility of the control strategy, the simulation model of the MLD model is established in MATLAB/Simulink. The study’s findings show that, in terms of response time at system startup and power fluctuation suppression, the approach put forward in this research performs better than both the conventional bilateral dual-phase-shift control strategy and the PQ-based bilateral power control strategy. The MCR-BWPT system can operate more steadily now that PQ’s bidirectional power control technique is in place. The system’s forward and reverse transmission efficiency is increased by 0.29% and 0.32% compared to the conventional bilateral dual-phase-shift control approach; the increases are 0.28% and 0.09%, each compared to the bilateral power control strategy based on PQ.
Blasthole Location Detection Using Support Vector Machine and Convolutional Neural Networks on UAV Images and Photogrammetry Models
Authors: Valencia Jorge , Emami Ebrahim , Battulwar Rushikesh , Jha Ankit , Gomez Jose A. , Moniri-Morad Amin , Sattarvand Javad
Published: March 2024

Identifying the as-drilled location of blastholes is crucial for achieving optimal blasting results. This research proposes a novel integrated methodology to control drilling accuracy in open-pit mines. This approach is developed by combining aerial drone images with machine learning techniques. The study investigates the viability of photogrammetry combined with machine learning techniques, particularly Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), for automatically detecting blastholes in photogrammetry representations of blast patterns. To verify the hypothesis that machine learning can detect blastholes in images as effectively as humans, various datasets (drone images) were obtained from different mine sites in Nevada, USA. The images were processed to create photogrammetry mapping of the drill patterns. In this process, thousands of patches were extracted and augmented from the photogrammetry representations. Those patches were then used to train and test different CNN architectures optimized to locate blastholes. After reaching an acceptable level of accuracy during the training process, the model was tested using a piece of completely unknown data (testing dataset). The high recall, precision, and percentage of detected blastholes prove that the combination of SVM, CNN, and photogrammetry (PHG) is an effective methodology for detecting blastholes on photogrammetry maps.
Characteristics of Surface Charge Accumulation on Spacers and Its Influencing Factors
Authors: Lai Yundong , Jiang Hui , Han Yufei , Tang Jinyu
Published: March 2024

Charge accumulation usually happens on the surface of spacers under DC operation, which is susceptible to inducing surface flashover. In order to explore the surface charge accumulation mechanisms and the influences of dielectric conductivity, gas ion mobility, and temperature field on the surface charges, a time-varying charge density model at the gas–solid interface of spacers was established. The results of the simulation show that the discontinuity of the current density between the spacer bulk side and the gas ion flow is the fundamental reason for the charge accumulation on the spacer surface. Additionally, the value of current density fluxes at the interface continues to decrease with the change of the electric field, and the progress of charge transfer gradually stabilizes. Moreover, the dielectric conductivity directly affects the charge accumulation process, and there is a critical conductivity in which the effect of charge conduction in dielectrics counteracts that of gas-phase charge deposition, theoretically. When the conductivity is higher than the critical conductivity, the solid-side charge conduction is the main source of the surface charge accumulation, while the gas-phase charge deposition on the gas side plays a dominant role when the conductivity is lower than the critical conductivity. The charge accumulation is not significantly affected by gas ion mobility when the temperature is evenly distributed. However, under the temperature field with gradient distribution, the current density fluxes at the interface change, causing the polarity reverse of the accumulated charge. In the high-temperature region, the volume current density surges simultaneously with the conductivity, leading to a higher density of surface charge accumulation. Lastly, the design of spacers needs to keep the current densities on both sides of the interface as similar as possible in order to avoid excessive charge gathering in localized areas.
A Highly Flexible Passive/Active Discrete-Time Delta-Sigma Receiver
Authors: Nguyen Minh Tien , Jabbour Chadi , Ben Kalaia Karim , Le Hanh-Phuc , Nguyen Ngoc , Nguyen Van-Tam
Published: March 2024

This paper presents a fourth-order discrete-time direct RF-to-digital Delta-Sigma receiver architecture for flexible receivers with a wide frequency range. The use of a current-driven passive mixer with RF feedback enables high-Q bandpass filtering and relaxes the linearity requirement of the RF amplifier. In addition, the reconfigurable passive/active loop filter offers a good compromise between power consumption, linearity, and dynamic range. The other important feature of the proposed architecture is the use of a sampling frequency that is a divisor of the LO frequency. This solves several problems such as the upmixing of quantization noise, the need to reconfigure the Delta-Sigma loop when changing the LO frequency, and the use of two independent clocks for the LO and the sampling frequency. The circuit was implemented using 65 nm CMOS technology. The I/Q Direct Delta-Sigma receiver has an RF bandwidth of 20 MHz and a sampling frequency of 400 MHz. Measurement results show a very high dynamic range of up to 80 dB with a peak SNDR of 46 dB for a power consumption of 46 mW at 800 MHz.
Exploring the Mediating Role of Different Aspects of Learning Motivation between Metaverse Learning Experiences and Gamification
Authors: Yang Wei , Fang Meichen , Xu Junping , Zhang Xiao , Pan Younghwan
Published: March 2024

In recent years, research and applications related to the metaverse have garnered widespread attention in the field of online education. However, enhancing user experiences in metaverse learning remains a challenging issue. This study aims to explore how gamification enhances the metaverse learning experience by boosting learning motivation. In the first phase of the research, the relationship between gamification, learning motivation, and user learning satisfaction was examined. The results indicated higher user satisfaction with gamified metaverse learning experiences, with intrinsic and external regulations serving as mediating factors between gamification and learning satisfaction. In the second phase of the study, the five elements of gamification (challenge, reward, feedback, PBL, social interaction) were further validated for their role in enhancing learning motivation and, consequently, improving learning satisfaction. Notably, the rewarding element emerged as the most significant factor. These research findings hold practical significance for providers of metaverse learning experiences and the application of gamification in metaverse learning. They provide valuable insights for future research and practical implementation in this evolving field.
Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection
Authors: Choi Su-Hwan , Buu Seok-Jun
Published: March 2024

Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereum’s transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security.
Overcoming Challenges in OLED Technology for Lighting Solutions
Authors: Liguori Rosalba , Nunziata Fiorita , Aprano Salvatore , Maglione Maria Grazia
Published: March 2024

In academic research, OLEDs have exhibited rapid evolution thanks to the development of innovative materials, new device architectures, and optimized fabrication methods, achieving high performance in recent years. The numerous advantages that increasingly distinguish them from traditional light sources, such as a large and customizable emission area, color tunability, flexibility, and transparency, have positioned them as a promising candidate for various applications in the lighting market, including the residential, automotive, industrial, and agricultural sectors. However, despite these promising attributes, the widespread industrial production of OLEDs encounters significant challenges. Key considerations center around efficiency and lifetime. In the present review, after introducing the theoretical basis of OLEDs and summarizing the main performance developments in the industrial field, three crucial aspects enabling OLEDs to establish a competitive advantage in terms of performance and versatility are critically discussed: the quality and stability of the emitted light, with a specific focus on white light and its tunability; the transparency of both electrodes for the development of fully transparent and integrable devices; and the uniformity of emission over a large area.
The Condition Number Perspective in Modeling and Designing an Electronic IDBIC Converter
Authors: Salcu Sorin Ionut , Suciu Vasile Mihai , Teodosescu Petre Dorel , Mathe Zsolt
Published: March 2024

This paper proposes a novel method of evaluating the Independent Double-Boost Interleaved Converter’s performance, which can offer an overview of its behavior and can enhance the design stage of the converter. The concept of “condition number” is utilized and applied to the converter’s model. Correlations between the condition number variation and the converter parameters lead to a qualitative assessment of the converter’s behavior and offer the possibility to anticipate aspects like the stability and response of the converter. An in-depth analysis is conducted, starting from the state matrix and continuing with a series of presumptions regarding the converter and its operating mode, obtaining a series of expressions that define the condition number for the converter.
A Low-Reflection Tuning Strategy for Three-Stub Waveguides
Authors: Liu Rufan , Lai Shimiao , Hong Tao , Zhang Zihao , Dong Lu , Zhu Huacheng , Yang Yang
Published: March 2024

In high-power microwave applications, the reflection of energy can be effectively reduced by adjusting the three-stub waveguide. However, most of the existing tuning algorithms do not make an arrangement for the adjustment sequence of stubs, and accurately calculating the depth of the stubs requires a great deal of time via electromagnetic (EM) simulations, which may cause large reflection in the matching process. To solve these problems, we first propose an improved calculation method that can accurately calculate the input impedance of a three-stub waveguide. Then, an impedance matching algorithm is designed based on the equivalent circuit model that can quickly and accurately calculate the optimal depth of the stubs. Finally, we present a low-reflection tuning strategy to suppress the large reflection during the adjustment of the stub process. An experimental system is built to verify the calculation method and the tuning strategy. The results show that the strategy can avoid large reflection in the case of load mutation and can maintain low reflection when the load changes continuously. The algorithm meets the needs of the industry and can be used for automatic and real-time adjustment of three-stub waveguides of different specifications.
LCSNet: Light-Weighted Convolution-Based Segmentation Method with Separable Multi-Directional Convolution Module for Concrete Crack Segmentation in Drones
Authors: Zhang Xiaohu , Huang Haifeng
Published: March 2024

Concrete cracks pose significant safety hazards to buildings, and semantic segmentation models based on deep learning have achieved state-of-the-art results in concrete crack detection. However, these models usually have a large model size which is impossible to use in drones. To solve this problem, we propose a Light-Weighted Convolution-Based Segmentation Method with a Separable Multi-Directional Convolution Module (LCSNet). In our proposed method, light-weighted convolution is used to substitute all traditional convolutions. In addition, a light-weighted structure named a Separable Multi-Directional Convolution Module (SMDCM) is used to substitute traditional parallel structures or attention modules to learn contextual or detail features. Thus, the ability to extract the contextual feature information of the model can be retained while the computational complexity is largely reduced. Through these two improvements, the model size of the proposed model can have a lower computational complexity. The experimental results show that our proposed LCSNet can achieve accuracies of 94.2%, 83.6%, 99.2%, and 83.3% on the Cracktree200, CRACK500, CFD, and RECrack datasets, respectively, which are higher than those of traditional models. However, the model size of our LCSNet is only 2M.
Energy Efficient CLB Design Based on Adiabatic Logic for IoT Applications
Authors: Yang Wu , Tanavardi Nasab Milad , Thapliyal Himanshu
Published: March 2024

Many IoT applications require high computational performance and flexibility, and FPGA is a promising candidate. However, increased computation power results in higher energy dissipation, and energy efficiency is one of the key concerns for IoT applications. In this paper, we explore adiabatic logic for designing an energy efficient configurable logic block (CLB) and compare it to the CMOS counterpart. The simulation results show that the proposed adiabatic-logic-based look-up table (LUT) has significant energy savings for the frequency range of 1 MHz to 40 MHz, and the least energy savings is at 40 MHz, which is 92.94% energy reduction compared to its CMOS counterpart. Further, the three proposed adiabatic-logic-based memory cells are 14T, 16T, and 12T designs with at least 88.2%, 84.2%, and 87.2% energy savings. Also, we evaluated the performance of the proposed CLBs using an adiabatic-logic-based LUT (AL-LUT) interfacing with adiabatic-logic-based memory cells. The proposed design shows significant energy reduction compared to a CMOS LUT interface with SRAM cells for different frequencies; the energy savings are at least 91.6% for AL-LUT 14T, 89.7% for AL-LUT 16T, and 91.3% AL-LUT 12T.
Temporal Convolutional Network-Enhanced Real-Time Implicit Emotion Recognition with an Innovative Wearable fNIRS-EEG Dual-Modal System
Authors: Chen Jiafa , Yu Kaiwei , Wang Fei , Zhou Zhengxian , Bi Yifei , Zhuang Songlin , Zhang Dawei
Published: March 2024

Emotion recognition remains an intricate task at the crossroads of psychology and artificial intelligence, necessitating real-time, accurate discernment of implicit emotional states. Here, we introduce a pioneering wearable dual-modal device, synergizing functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to meet this demand. The first-of-its-kind fNIRS-EEG ensemble exploits a temporal convolutional network (TC-ResNet) that takes 24 fNIRS and 16 EEG channels as input for the extraction and recognition of emotional features. Our system has many advantages including its portability, battery efficiency, wireless capabilities, and scalable architecture. It offers a real-time visual interface for the observation of cerebral electrical and hemodynamic changes, tailored for a variety of real-world scenarios. Our approach is a comprehensive emotional detection strategy, with new designs in system architecture and deployment and improvement in signal processing and interpretation. We examine the interplay of emotions and physiological responses to elucidate the cognitive processes of emotion regulation. An extensive evaluation of 30 subjects under four emotion induction protocols demonstrates our bimodal system’s excellence in detecting emotions, with an impressive classification accuracy of 99.81% and its ability to reveal the interconnection between fNIRS and EEG signals. Compared with the latest unimodal identification methods, our bimodal approach shows significant accuracy gains of 0.24% for EEG and 8.37% for fNIRS. Moreover, our proposed TC-ResNet-driven temporal convolutional fusion technique outperforms conventional EEG-fNIRS fusion methods, improving the recognition accuracy from 0.7% to 32.98%. This research presents a groundbreaking advancement in affective computing that combines biological engineering and artificial intelligence. Our integrated solution facilitates nuanced and responsive affective intelligence in practical applications, with far-reaching impacts on personalized healthcare, education, and human–computer interaction paradigms.
Predicting Cybersickness Using Machine Learning and Demographic Data in Virtual Reality
Authors: Ramaseri-Chandra Ananth N. , Reza Hassan
Published: March 2024

The widespread adoption of virtual reality (VR) technologies is significantly hindered by the prevalence of cybersickness, a disruptive experience causing symptoms like nausea, dizziness, and disorientation. Traditional methodologies for predicting cybersickness predominantly depend on biomedical data. While effective, these methods often require invasive data collection techniques, which can be impractical and pose privacy concerns. Furthermore, existing research integrating demographic information typically does so in conjunction with biomedical or behavioral data, not as a standalone predictive tool. Addressing this gap, we investigated machine learning techniques that exclusively use demographic data to classify and predict the likelihood of cybersickness and its severity in VR environments. This method relies on noninvasive, easily accessible demographic information like age, gender, and previous VR exposure. It offers a more user-friendly and ethically sound approach to predicting cybersickness. The study explores the potential of demographic variables as standalone predictors through comprehensive data analysis, challenging the traditional reliance on biomedical metrics. We comprehensively presented the input data and statistical analysis and later carefully selected the widely used machine learning models from different classes, including k-nearest neighbors, Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine. We evaluated their performances and presented detailed results and limitations. The research findings indicate that demographic data can be used to predict the likelihood and severity of cybersickness. This research provides critical insights into future research directions, including data collection design and optimization suggestions. It opens new avenues for personalized and inclusive VR design, potentially reducing barriers to VR adoption and enhancing user comfort and safety.
A Generation of Enhanced Data by Variational Autoencoders and Diffusion Modeling
Authors: Kim Young-Jun , Lee Seok-Pil
Published: March 2024

In the domain of emotion recognition in audio signals, the clarity and precision of emotion delivery are of paramount importance. This study aims to augment and enhance the emotional clarity of waveforms (wav) using a technique called stable diffusion. Datasets from EmoDB and RAVDESS, two well-known repositories of emotional audio clips, were utilized as the main sources for all experiments. We used the ResNet-based emotion recognition model to determine the emotion recognition of the augmented waveforms after emotion embedding and enhancement, and compared the enhanced data before and after the enhancement. The results showed that applying a mel-spectrogram-based diffusion model to the existing waveforms enlarges the salience of the embedded emotions, resulting in better identification. This augmentation has significant potential to advance the field of emotion recognition and synthesis, paving the way for improved applications in these areas.
A Lightweight Identity-Based Network Coding Scheme for Internet of Medical Things
Authors: Wang Kuan , Song Mingxuan , Bian Genqing , Shao Bilin , Huang Kaiqi
Published: March 2024

Network coding is a potent technique extensively utilized in decentralized Internet of Things (IoT) systems, including the Internet of Medical Things (IoMT). Nevertheless, the inherent packet-mixing characteristics of network coding expose data transmission to pollution attacks, potentially compromising the integrity of original files. The homomorphic signature scheme serves as a robust cryptographic tool that can bolster network coding’s resilience against such attacks. However, current schemes are computationally intensive for signature verification, making them impractical for IoMT environments. In this study, we propose a lightweight identity-based network coding scheme (IBNS) that minimizes computational overhead during the signing and verification processes. This scheme has been demonstrated to be secure against adaptive chosen-message attacks and is well-suited for IoMT applications. Furthermore, we assess the performance of our IBNS through both theoretical and experimental analyses. Simulation outcomes confirm that our scheme outperforms previous ones in terms of practicality and efficiency.
A Zero False Positive Rate of IDS Based on Swin Transformer for Hybrid Automotive In-Vehicle Networks
Authors: Wang Shanshan , Zhou Hainan , Zhao Haihang , Wang Yi , Cheng Anyu , Wu Jin
Published: March 2024

Software-defined vehicles (SDVs) make automotive systems more intelligent and adaptable, and this transformation relies on hybrid automotive in-vehicle networks that refer to multiple protocols using automotive Ethernet (AE) or a controller area network (CAN). Numerous researchers have developed specific intrusion-detection systems (IDSs) based on ResNet18, VGG16, and Inception for AE or CANs, to improve confidentiality and integrity. Although these IDSs can be extended to hybrid automotive in-vehicle networks, these methods often overlook the requirements of real-time processing and minimizing of the false positive rate (FPR), which can lead to safety and reliability issues. Therefore, we introduced an IDS based on the Swin Transformer to bolster hybrid automotive in-vehicle network reliability and security. First, multiple messages from the traffic assembly are transformed into images and compressed via two-dimensional wavelet discrete transform (2D DWT) to minimize parameters. Second, the Swin Transformer is deployed to extract spatial and sequential features to identify anomalous patterns with its attention mechanism. To compare fairly, we re-implemented up-to-date conventional network models, including ResNet18, VGG16, and Inception. The results showed that our method could detect attacks with 99.82% accuracy and 0 FPR, which saved 14.32% in time costs and improved the accuracy by 1.60% compared to VGG16 when processing 512 messages.
Thermodynamic Analysis and Comparison of Power Cycles for Small Modular Reactors
Authors: Kindra Vladimir , Maksimov Igor , Zlyvko Olga , Rogalev Andrey , Rogalev Nikolay
Published: March 2024

Small nuclear power plants can provide a stable, carbon-free energy supply to civil infrastructure and industrial enterprises in remote regions isolated from unified energy systems. More than 70 projects of small modular reactors are currently being developed by IAEA member countries; several low-power power units are already supplying thermal and electrical energy to consumers. One of the main limitations standing in the way of widespread dissemination of this technology is the high specific capital cost of a low-power nuclear power plant; therefore, new scientific and technical solutions are needed in this industry. Increasing the thermodynamic efficiency of power cycles of small modular reactors can become a driver for reducing the cost of supplied electrical energy. This paper presents the results of a comprehensive thermodynamic analysis of existing and promising power cycles for small modular reactors. In addition to traditional steam power cycles, cycles using non-traditional working fluids, including carbon dioxide, freons, and helium cycles, are considered. Optimal sets of thermodynamic parameters were determined to ensure maximum net efficiency of electricity production. For water-cooled reactor plants, a maximum efficiency of 33.5% at an initial temperature of 300 °C could be achieved using a steam turbine cycle. It was revealed that for reactor plants with liquid metal and liquid salt coolant in the range of initial temperatures above 550–700 °C, the maximum thermal efficiency was provided by the Brayton recompression cycle with a carbon dioxide coolant: the net electrical efficiency exceeded the level of steam turbine plants, with intermediate superheating of the steam, and could reach a value of 49.4% at 600 °C. This makes the use of these cycles promising for low-power nuclear power plants with a high initial temperature. In small gas-cooled reactor plants with a helium coolant, the use of a binary cycle consisting of a helium Brayton cycle and a steam-powered Rankine cycle provided an efficiency of 44.3% at an initial helium temperature of 700 °C and 52.9% at 1000 °C. This was higher than in the Brayton cycle with a recuperator, with a minimum temperature difference in the heat exchanger of 20 °C: the efficiency was 40.2% and 52%, respectively. Also, the transition to power cycles with non-traditional working fluids will lead to a change in the operating conditions of turbomachines and heat exchangers.
Techno-Economic Analysis of Grid-Connected Hydrogen Production via Water Electrolysis
Authors: Zhu Pengcheng , Mae Masahiro , Matsuhashi Ryuji
Published: March 2024

As the global energy landscape transitions towards a more sustainable future, hydrogen has emerged as a promising energy carrier due to its potential to decarbonize various sectors. However, the economic competitiveness of hydrogen production by water electrolysis strongly depends on renewable energy source (RES) availability. Thus, it is necessary to overcome the challenges related to the intermittent nature of RESs. This paper presents a comprehensive techno-economic analysis of complementing green hydrogen production with grid electricity. An evaluation model for the levelized cost of hydrogen (LCOH) is proposed, considering both CO2 emissions and the influence of RES fluctuations on electrolyzers. A minimum load restriction is required to avoid crossover gas. Moreover, a new operation strategy is developed for hydrogen production plants to determine optimal bidding in the grid electricity market to minimize the LCOH. We evaluate the feasibility of the proposed approach with a case study based on data from the Kyushu area in Japan. The results show that the proposed method can reduce the LCOH by 11% to 33%, and increase hydrogen productivity by 86% to 140%, without significantly increasing CO2 emission levels.
Thermal Management for a Stadium Power Supply Container Using a Rack-Level Air Cooling Strategy
Authors: Dong Yue , Ding Yi , Elfeky Karem Elsayed , Qi Yu , Chu Wenxiao , Wang Qiuwang
Published: March 2024

This study investigates the airflow and thermal management of a compact electric energy storage system by using computational fluid dynamic (CFD) simulation. A porous medium model for predicting the flow resistance performance of the battery modules in a battery cabinet is developed. By studying the influence of rack shapes, the effects of heat exchanger arrangements and other parameters on the airflow and battery thermal distribution are analyzed. When applying a larger bottom air channel, the inlet flow uniformity of each battery cabin in the cabinet increases by 5%. Meanwhile, temperature standard deviation decreases by 0.18 while raising the flow rate from 3 m/s to 8 m/s, indicating better temperature uniformity in the battery cabin. When the charge–discharge ratio reaches 0.5 C, the temperature deviation of the entire cabinet significantly increases, reaching 8 K. Furthermore, a rack-level thermal management scheme is proposed to effectively reduce the thermal deviation of the container electric energy storage system and improve the overall temperature uniformity. Results reveal that the rack-level thermal management of the wavy cabinet in the electric storage container can effectively improve the thermal uniformity of the distributed battery cabin, and the overall thermal deviation is controlled within 1.0 K.
Characteristics of High-Pressure Injection Pump Operated with Renewable Fuel for Diesel Engines
Authors: Chiavola Ornella , Palmieri Fulvio , Verdoliva Francesco
Published: March 2024

The use of renewable fuels for internal combustion engines marks a significant stride towards sustainability in transportation and power generation. Ensuring the compatibility of these fuels with existing diesel engines and infrastructure is paramount for a smooth transition. Diesel engines capable of harnessing the advantages of alternative fuels without extensive modifications offer a pragmatic approach to sustainable power for vehicles and industries. This article investigates the impact of carbon-neutral renewable fuel types (hydrogenated vegetable oils, or HVOs, and biodiesel) on the operation of a high-pressure injection pump, focusing on rotational speed and delivery pressure as key parameters. Our investigation, based on pump operating cycle analysis and volumetric efficiency measurements, aimed to investigate to what extent shaft speed and fuel type influence pump performance. Hydrogenated carbon-neutral fluids (HVOs) demonstrate adequate hydraulic capabilities compared to conventional fossil fuels. The findings underscore that while the fluids under consideration vary in viscosity and compressibility modulus, it is the compressibility modulus that predominantly affects the pump’s operation cycle. Diesel (fossil) fluid exhibits intermediate characteristics compared to hydrogenated fossil diesel fluids and biodiesel. The differences in terms of volumetric efficiency are finally reported, showing that hydrogenated carbon-neutral fluids do not significantly hinder the pump’s flow rate transfer capability.
Replacing Natural Gas with Biomethane from Sewage Treatment: Optimizing the Potential in São Paulo State, Brazil
Authors: Renato Natalia dos Santos , Oliveira Augusto Cesar Laviola de , Ervilha Amanda Martins Teixeira , Antoniazzi Sarah Falchetto , Moltó Julia , Conesa Juan Antonio , Borges Alisson Carraro
Published: March 2024

The search for cleaner and more sustainable energy sources is increasingly growing. Aligning this demand with another environmental problem, such as sewage treatment/disposal, is a strategic priority. In light of this, the aim of this study was to estimate the energy potential of sewage generated in the Brazilian state of São Paulo (SP) by using it to produce biomethane. The study also evaluated the viability of using this byproduct of sewage treatment (biomethane) as a substitute for natural gas (NG), as both of them have similar lower heat values. To do this, information was gathered regarding the population, gross domestic product per capita, sewage collected, and natural gas consumption for each of the state’s 645 cities, and, based on this, the sewage energy potential, the amount of NG to be substituted by biomethane, and the reduction in CO2 emissions were calculated. Moreover, in order to address a possible allocation of biomethane that could potentially be produced in each SP city and sent to currently NG-consuming cities, an optimization algorithm was proposed. The results indicated a sewage energy potential of 4.68 × 109 kWh/yr for the entire SP state, which would be enough to supply around 10% of the energy value of all the NG currently consumed. It was also observed that from 130 cities with NG consumption, 10 could produce enough biomethane to fully satisfy the natural gas demand. In the elected scenario of optimization, 291 cities were found to be capable of supplying the demand of 26 cities that currently use NG. The potential to reduce CO2 emissions is between 1.81 × 106 and 2.42 × 106 ton/yr, and this range could increase if sewage treatment coverage grows. Despite the challenges inherent in extrapolating a potential study to scenarios that require significative investment, the results obtained are useful for formulating public policies for decarbonization in the near future.
Preventive-Security-Constrained Optimal Power Flow Model Considering IPFC Control Modes
Authors: Cai Hui , Hu Chunke , Wu Xi
Published: March 2024

The interline power flow controller (IPFC) is one of the most versatile integrated flexible alternating current transmission systems (FACTS) controllers and can realize power flow control for multiple transmission lines in modern power systems. However, control characteristics are ignored in conventional IPFC models, in which unreasonable assumptions about injected voltages may lead to security problems in realistic operation. Besides, preventive security constraints considering IPFC control modes are not included in optimal power flow (OPF) control of the system with IPFC, squandering IPFC control potential. To solve these problems, a preventive-security-constrained optimal power flow (PSCOPF) model considering IPFC control modes is proposed in this paper. IPFC control characteristics under different control modes are analyzed and employed as constraints of the optimization model. The iterative updates of converter output voltages for different control modes are derived respectively for power flow calculation, and the power and voltages required in the objective function and constraints of the proposed model can then be obtained. Through optimal selection of IPFC control modes and control parameters, the proposed model can better reconcile the economical and secure operation of the system. Numerical results demonstrate the efficient performance and superiority of the PSCOPF model considering IPFC control modes.
Reservoir Characteristics of the Main Coal Seams in the Longtan Formation, Guxu Coal Mining Area, Sichuan
Authors: Zhang Yufa , Zhang Yinde , Zhou Wen , He Jianhua
Published: March 2024

To facilitate the efficient exploration and development of coalbed methane of the Longtan Formation in the Guxu coal mining area, Sichuan, it is essential to evaluate the reservoir characteristics of the main production layers. In this study, scanning electron microscopy, low-temperature liquid nitrogen adsorption experiments, high-pressure mercury pressure experiments, and isothermal adsorption experiments were conducted to investigate the reservoir characteristics of favorable coalbed methane reservoirs. The results indicate that the main coal seams in the Longtan Formation are medium-to-high ash content anthracite with a well-developed pore–fracture system. The pore size distribution exhibits both unimodal and bimodal types, while the pore morphology includes impermeable pores closed at one end, open permeable pores, and ink bottle-shaped pores. It shows that the middle part of Longtan Formation acts as an enrichment zone for coalbed methane, which is characterized by a stronger adsorption capacity, high Langmuir volume, high gas content, and high gas saturation distribution. The pH value, mineralization degree, and hydrogen–oxygen isotope of the produced water in the main coal seams indicate that the enrichment zone is typically located in a stagnant flow zone with a reducing environment and favorable storage conditions.
Using Deep Learning to Detect Anomalies in On-Load Tap Changer Based on Vibro-Acoustic Signal Features
Authors: Dabaghi-Zarandi Fataneh , Behjat Vahid , Gauvin Michel , Picher Patrick , Ezzaidi Hassan , Fofana Issouf
Published: March 2024

An On-Load Tap Changer (OLTC) that regulates transformer voltage is one of the most important and strategic components of a transformer. Detecting faults in this component at early stages is, therefore, crucial to prevent transformer outages. In recent years, Hydro Quebec initiated a project to monitor the OLTC’s condition in power transformers using vibro-acoustic signals. A data acquisition system has been installed on real OLTCs, which has been continuously measuring their generated vibration signal envelopes over the past few years. In this work, the multivariate deep autoencoder, a reconstruction-based method for unsupervised anomaly detection, is employed to analyze the vibration signal envelopes generated by the OLTC and detect abnormal behaviors. The model is trained using a dataset obtained from the normal operating conditions of the transformer to learn patterns. Subsequently, kernel density estimation (KDE), a nonparametric method, is used to fit the reconstruction errors (regarding normal data) obtained from the trained model and to calculate the anomaly scores, along with the static threshold. Finally, anomalies are detected using a deep autoencoder, KDE, and a dynamic threshold. It should be noted that the input variables responsible for anomalies are also identified based on the value of the reconstruction error and standard deviation. The proposed method is applied to six different real datasets to detect anomalies using two distinct approaches: individually on each dataset and by comparing all six datasets. The results indicate that the proposed method can detect anomalies at an early stage. Also, three alarms, including ignorable anomalies, long-term changes, and significant alterations, were introduced to quantify the OLTC’s condition.
Acoustic Characterization of Potential Quiet Areas in Dortmund, Germany
Authors: Lawrence Bryce T. , Frücht Andreas , Heying Damian , Schröer Kai , Gruehn Dietwald
Published: March 2024

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise ‘hot spots’. Sound observations (n = 282,764) were collected in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n = 70) and 23 noise hot spots between 27 April 2022 and 2 March 2023. We found that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sports and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day but expands during the day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.
Tourists’ Preferences and Willingness to Pay for Biodiversity, Concession Activity and Recreational Management in Wuyishan National Park in China: A Choice Experiment Method
Authors: Liu Jiayu , Wu Yining , Jiang Xuemei , Jin Dian
Published: March 2024

It is necessary for national parks to exert multiple forms of influence. One innovative function of national parks is leisure and recreation. By optimizing the recreation function, national parks can enhance public participation and tourists’ willingness to pay to achieve sustainable development. This paper surveys 435 tourists in Wuyishan National Park with a discrete choice experiment, applies mixed logit models to explore their preferences for recreational attributes from three dimensions and analyzes the heterogeneity. The findings indicate that attributes such as the number of concession activities, transportation variety, the density of recreational facilities, biodiversity, sightseeing zone and combined ticket fee significantly impact tourists’ willingness to pay. Higher-educated, younger and willing-to-revisit tourists are more likely to pay for the park and hope to optimize the facilities and activities. The estimated recreational value of the park is RMB 164.51 (about USD 22.75) per person. Here, heterogeneity exist: higher-educated tourists ask for lager sightseeing zone, younger ones prefer more diverse activities and those who are willing to revisit incline to variety means of transportation.
Effect of Heat Treatment on Hygroscopicity of Chinese Fir (Cunninghamia lanceolata [Lamb.] Hook.) Wood
Authors: Gao Yulei , Li Zhu , Zhao Liyuan , Lyu Jianxiong
Published: March 2024

Chinese fir (Cunninghamia lanceolata [Lamb.] Hook.) is a widely planted species of plantation forest in China, and heat treatment can improve its dimensional stability defects and improve its performance. The wood samples were heat-treated at various temperatures (160, 180, 200, and 220 °C) for 2 h. To clarify the effect of heat treatment on wood hygroscopicity, the equilibrium moisture content (EMC) was measured, the moisture adsorption and desorption rates were determined, the hygroscopic hysteresis was examined, and the Guggenheim, Anderson, and de Boer (GAB) model was fitted to the experimental data. The moisture absorption isotherms of all samples belonged to the Type II adsorption isotherm, but the shape of the desorption isotherm was more linear for heat-treated wood samples, especially when the heat treatment temperature was higher. According to the results analyzed with ANOVA, there were significant differences in equilibrium moisture content between the control samples and the heat-treated samples under the conditions of 30%, 60%, and 95% relative humidity (RH, p < 0.05), and the results of multiple comparisons were similar. The decrease in hygroscopicity was more pronounced in wood treated at higher temperatures. The EMC of the 160–220 °C heat-treated samples of the control samples was 14.00%, 22.37%, 28.95%, and 39.63% lower than that of the control sample at 95% RH. Under low RH conditions (30%), water is taken up mainly via monolayer sorption, and multilayer sorption gradually predominates over monolayer sorption with the increase in RH. The dynamic vapor sorption (DVS) analysis indicated that the heat-treated wood revealed an increase in isotherm hysteresis, which was due to the change in cell wall chemical components and microstructure caused by heat treatment. In addition, the effective specific surface area of wood samples decreased significantly after heat treatment, and the change trend was similar to that of equilibrium moisture content.
Global Trends and Innovations in Forest Ecological Compensation: An Interdisciplinary Analysis
Authors: Shi Xiaoliang , Zhang Jiayi , Lu Ji , Zhao Tierui , Yang Haiou , Aria Aruna , Qiu Yingying , Yu Lin , Ni Yan
Published: March 2024

Forest ecological benefit compensation protects the ecological environment, increases enthusiasm among forest ecological builders, protects the legitimate economic interests of forest landowners, and maintains environmental security. In this study, a systematic analysis was conducted on 4062 publications in the field of international forest ecological benefit compensation identified using the Web of Science core collection Science Citation Index Expanded (SCI-E) and Social Science Citation Index (SSCI) databases alongside CiteSpace optical measurement software. The research status and influence of countries were tracked, scientific research institutions and important authors were identified, and interdisciplinary and scientific hotspots were analyzed. The results show that the number of studies in this field has significantly increased over time and has strong development potential. The journals and articles presenting cutting-edge research in this field are all considered highly professional. The selected research is highly interdisciplinary but primarily involves environmental science and ecology. Despite the publication volume of China ranking second among the top 10 countries, its average citation volume was the lowest, indicating the urgent need to increase the international influence of China. Forest biodiversity, ecosystem services, and climate change are current research hotspots in this field. Dynamic changes in forest ecological benefit compensation and the driving forces behind its evolution are of great concern to researchers.