bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Assessment of the Effect of Positioning Techniques and Ground Control Point Distribution Models on the Accuracy of UAV-Based Photogrammetric Production
Authors: Atik Muhammed Enes , Arkali Mehmet
Published: December 2024

Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the İstanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs.
Multiple UAV Swarms Collaborative Firefighting Strategy Considering Forest Fire Spread and Resource Constraints
Authors: Zhu Pei , Song Rui , Zhang Jiangao , Xu Ziheng , Gou Yaqi , Sun Zhi , Shao Quan
Published: December 2024

To address the demands of efficient forest fire detection and suppression, an adaptive multiple UAV swarm collaborative firefighting strategy considering dynamic forest fire spread and resource constraints was proposed in this paper. The multiple UAV swarm adaptive information-driven collaborative search (MUSAIDCS) algorithm and the resource-limited firefighting model were established. A temperature change-driven adaptive step-length search strategy is proposed to improve the accuracy of the search and detection of fire spots. The critical water flow rate required for fire suppression is calculated to evaluate the firefight performance, and an emergency bidding algorithm is applied to enable multiple UAV swarms collaborative firefighting under limited resources, including different payloads per UAV and swarm number. The comparative simulations for four search strategies indicate that the MUSAIDCS search strategy can significantly reduce task completion time and improve firefighting efficiency compared with the other traditional search strategies. Increasing payload quantity per UAV and swarm number can further enhance task completion efficiency and firefighting effectiveness. This study demonstrates that a resource-constrained collaborative firefighting strategy enables the dynamic allocation of UAV swarm resources under limited conditions and then optimizes firefighting performance within constraints.
Improved Entropy-Based Condition Monitoring for Pressure Pipeline Through Acoustic Denoising
Authors: Wan Yu , Lin Shaochen , Jin Chuanling , Gao Yan , Yang Yang
Published: December 2024

During long-term operation in complex environments, the pressure pipeline systems are prone to damage and faults, and serious safety accidents may occur without real-time condition monitoring. Moreover, in traditional non-contact monitoring approaches, acoustic signals are widely employed for condition monitoring for pressure pipelines, which are easily contaminated by background noise and provide unsatisfactory accuracy. As a tool for quantifying uncertainty and complexity, signal entropy is applied to detect abnormal conditions. Based on the characteristics of entropy and acoustic signals, an improved entropy-based condition monitoring method is proposed for pressure pipelines through acoustic denoising. Specifically, this improved entropy-based noise reduction model is proposed to reduce the noise of monitoring acoustic signals through adversarial training. Based on the denoising of acoustic signals, an abnormal sound detection method is proposed to realize condition monitoring for pressure pipelines. In addition, the experimental platform is built to test the effectiveness and reliability of the proposed method. The results indicate that the quality of signal denoising can reach over 3 dB, while the accuracy of condition monitoring is about 92% for different conditions. Finally, the superiority of the proposed method is verified by comparing it with other methods.
Discrimination of the Gypsy Population in the University Environment
Authors: Aparicio-Gervás Jesús María , Carbonero-Martín Miguel Ángel , Valdivieso-León Lorena , Vázquez-Ramil Raquel
Published: December 2024

The Roma population is one of the most discriminated social groups. Ignorance of their culture, traditions, etc. generates prejudices and stereotypes that hinder the processes of intercultural coexistence. The aim is to make the Roma people visible and to intervene in the university population, promoting intercultural and inclusive learning spaces to reduce behaviors that generate inequalities. Students from the University of Valladolid were involved, 1255 students in particular, distributed in control and experimental groups. The instrument consists of 40 closed questions and one open-ended. A quantitative analysis was carried out in the anti-Gypsy attitudes categories with t-tests. Item to item differences were not significant in categories A, B and C, but there were differences in D between the experimental and the control groups, which confirms important trends in the target population. Prejudices towards the Roma population continue to exist. University students who receive training have fewer prejudices than students who do not, although other variables can influence the process. Of the students, 72.2% are disturbed by the same negative behaviors, regardless of the ethnic group, and half of them are disturbed by behaviors based on prejudices. It is confirmed the need for preventive interventions to avoid the establishment of erroneous beliefs about the Roma community.
CRP-RAG: A Retrieval-Augmented Generation Framework for Supporting Complex Logical Reasoning and Knowledge Planning
Authors: Xu Kehan , Zhang Kun , Li Jingyuan , Huang Wei , Wang Yuanzhuo
Published: December 2024

The Retrieval-Augmented Generation (RAG) framework enhances Large Language Models (LLMs) by retrieving relevant knowledge to broaden their knowledge boundaries and mitigate factual hallucinations stemming from knowledge gaps. However, the RAG Framework faces challenges in effective knowledge retrieval and utilization; invalid or misused knowledge will interfere with LLM generation, reducing reasoning efficiency and answer quality. Existing RAG methods address these issues by decomposing and expanding queries, introducing special knowledge structures, and using reasoning process evaluation and feedback. However, the linear reasoning structures limit complex thought transformations and reasoning based on intricate queries. Additionally, knowledge retrieval and utilization are decoupled from reasoning and answer generation, hindering effective knowledge support during answer generation. To address these limitations, we propose the CRP-RAG framework, which employs reasoning graphs to model complex query reasoning processes more comprehensively and accurately. CRP-RAG guides knowledge retrieval, aggregation, and evaluation through reasoning graphs, dynamically adjusting the reasoning path based on evaluation results and selecting knowledge-sufficiency paths for answer generation. CRP-RAG outperforms the best LLM and RAG baselines by 2.46 in open-domain QA, 7.43 in multi-hop reasoning, and 4.2 in factual verification. Experiments also show the superior factual consistency and robustness of CRP-RAG over existing RAG methods. Extensive analyses confirm its accurate and fact-faithful reasoning and answer generation for complex queries.
Improved Soft-Starting Method for Doubly Fed Induction Machines Based on Standstill Rotor-Side Synchronization
Authors: Mahtani Kumar , Guerrero José M. , Sánchez José A. , Platero Carlos A.
Published: December 2024

This paper addresses the challenge of developing a cost-effective and efficient soft-starting method for doubly fed induction machines (DFIMs), a critical requirement for various industrial applications, such as pumped-storage hydropower. The research aims to improve a previously developed starting method by introducing a rotor-side synchronization technique at standstill conditions, which simplifies the starting process and eliminates the need for additional equipment such as autotransformers, resistors, or auxiliary converters. The proposed method begins with the stator winding being fed directly from the power system, while the rotor-side converter adjusts the voltage and frequency to achieve synchronization. Once synchronized, the rotor frequency is gradually reduced by the converter, resulting in a smooth acceleration of the machine. The methodology is validated through a combination of simulations and experimental testing, demonstrating the effectiveness of the proposed approach. The results reveal smooth startup dynamics, with significant reductions in electrical stress, operational complexity, and converter sizing requirements compared to existing methods. Notably, the magnetizing current is supplied directly by the power system through the stator, reducing the burden on the rotor converter by 60% compared to the previous method. The conclusions highlight the method’s robustness and its potential as a superior alternative to existing DFIM starting techniques.
Automatic Measurement of Comprehensive Skin Types Based on Image Processing and Deep Learning
Authors: Ran Jianghong , Dong Guolong , Yi Fan , Li Li , Wu Yue
Published: December 2024

The skin serves as a physical and chemical barrier, effectively protecting us against the external environment. The Baumann Skin Type Indicator (BSTI) classifies skin into 16 types based on traits such as dry/oily (DO), sensitive/resistant (SR), pigmented/nonpigmented (PN), and wrinkle-prone/tight (WT). Traditional assessments are time-consuming and challenging as they require the involvement of experts. While deep learning has been widely used in skin disease classification, its application in skin type classification, particularly using multimodal data, remains largely unexplored. To address this, we propose an improved Inception-v3 model incorporating transfer learning, based on the four-dimensional classification of the Baumann Skin Type Index (BSTI), which demonstrates outstanding accuracy. The dataset used in this study includes non-invasive physiological indicators, BSTI questionnaires, and skin images captured under various light sources. By comparing performance across different light sources, regions of interest (ROI), and baseline models, the improved Inception-v3 model achieved the best results, with accuracy reaching 91.11% in DO, 81.13% in SR, 91.72% in PN, and 74.9% in WT, demonstrating its effectiveness in skin type classification. This study surpasses traditional classification methods and previous similar research, offering a new, objective approach to measuring comprehensive skin types using multimodal and multi-light-source data.
Double-Virtual-Vector-Based Model Predictive Torque Control for Dual Three-Phase PMSM
Authors: Yuan Qingqing , Xiao Rongyan , Wang Jingxia , Xia Kun , Yu Wei
Published: December 2024

To overcome the drawbacks of large torque ripples and high harmonic contents in a dual three-phase permanent magnet synchronous motor (PMSM) used in electric vehicle drive systems, a double-virtual-vector-based model predictive torque control (DVV-MPTC) strategy was proposed in this paper. Firstly, 12 virtual voltage vectors were constructed to minimize harmonic interference as much as possible. Then, the DVV-MPTC strategy is proposed to solve the problem of large torque ripples caused by single-virtual-vector-based MPTC (SVV-MPTC) method. On the other hand, an enhancement to the cost function was also introduced to resolve the challenges of tuning weight coefficients. Experimental comparisons between traditional direct torque control (DTC), SVV-MPTC method, and the proposed DVV-MPTC strategy were carried out, which show that the latter achieves significant improvements. In particular, it can reduce both harmonic components and torque ripple compared to traditional control strategies, resulting in a more efficient and stable performance for the electric drive system.
AI-Based Malicious Encrypted Traffic Detection in 5G Data Collection and Secure Sharing
Authors: Han Gang , Zhang Haohe , Zhang Zhongliang , Ma Yan , Yang Tiantian
Published: December 2024

With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex network security environment and more diverse attack methods. Unlike traditional networks, 5G networks feature higher connection density, faster data transmission speeds, and lower latency, which are widely applied in scenarios such as smart cities, the Internet of Things, and autonomous driving. The vast amounts of sensitive data generated by these applications become primary targets during the processes of collection and secure sharing, and unauthorized access or tampering could lead to severe data breaches and integrity issues. However, as 5G networks extensively employ encryption technologies to protect data transmission, attackers can hide malicious content within encrypted communication, rendering traditional content-based traffic detection methods ineffective for identifying malicious encrypted traffic. To address this challenge, this paper proposes a malicious encrypted traffic detection method based on reconstructive domain adaptation and adversarial hybrid neural networks. The proposed method integrates generative adversarial networks with ResNet, ResNeXt, and DenseNet to construct an adversarial hybrid neural network, aiming to tackle the challenges of encrypted traffic detection. On this basis, a reconstructive domain adaptation module is introduced to reduce the distribution discrepancy between the source domain and the target domain, thereby enhancing cross-domain detection capabilities. By preprocessing traffic data from public datasets, the proposed method is capable of extracting deep features from encrypted traffic without the need for decryption. The generator utilizes the adversarial hybrid neural network module to generate realistic malicious encrypted traffic samples, while the discriminator achieves sample classification through high-dimensional feature extraction. Additionally, the domain classifier within the reconstructive domain adaptation module further improves the model’s stability and generalization across different network environments and time periods. Experimental results demonstrate that the proposed method significantly improves the accuracy and efficiency of malicious encrypted traffic detection in 5G network environments, effectively enhancing the detection performance of malicious traffic in 5G networks.
Energy Harvesting Power Converter for Variable Impedance and Voltage Sources
Authors: Wieczorek Grzegorz , Oliwa Wojciech , Rymarski Zbigniew
Published: December 2024

This article describes a converter, along with a novel control algorithm, for harvesting energy from unconventional energy sources, such as stray currents from electrified railroad lines. Such energy sources are often characterized by high resistance and have variable open-circuit voltage and source resistance. The converter was, and the model was tested in detail using both simulations in LTSpice and in practice, using a prototype. A high correspondence of the presented model with simulations and actual measurements was observed. The device and algorithm operate in maximum power transfer mode and allow up to 90% of the available energy to be recovered and stored in a battery, which is an integral part of the converter. To the best of the authors’ knowledge, this is the first such converter described. Since all design recommendations and all the dependencies needed to calculate the control parameters are provided, the presented control algorithm can be easily implemented in a converter meeting the general requirements of a buck–boost converter.
A Non-Integer Ratio Impedance Conversion Broadband RF Power Amplifier
Authors: Yang Yuekuan , Nan Jingchang , Cong Mifang , Liu Jing , Dai Tao , Cui Yuchen
Published: December 2024

This paper uses the non-integer ratio coaxial line impedance transform matching method to design a wideband RF power amplifier. The motivation for the research stems from the limitations of the fixed conversion ratio of traditional impedance converters at RF frequencies and the challenges of low-frequency performance degradation. A fixed conversion ratio and low-frequency performance often limit traditional impedance matching methods. The model enables flexible and efficient wideband impedance matching, covering a wide frequency range of 2–650 MHz. The results show that the power amplifier has an output power of more than 48.5 dBm, a gain of more than 16 dB, and a drain efficiency (DE) of more than 55% in the frequency band range of 2–650 MHz, which is superior to similar designs in terms of low-frequency performance and bandwidth.
STE-YOLO: A Surface Defect Detection Algorithm for Steel Strips
Authors: Li Dongming , Wang Erfu , Li Zhiyi , Yin Yingying , Zhang Lijuan , Zhao Chunxi
Published: December 2024

To accurately detect defects, we propose an enhanced model based on YOLOv8, named STE-YOLO. To address the aforementioned challenges, this paper adopts YOLOv8 as the improved model. The structure of this paper is as follows: We enhance the model’s feature extraction and small detail recognition by integrating GhostConv into partial convolutions. In order to address the attention bias of the model, we introduce a Bottleneck Transformer self-attention convolution layer that effectively improves localization box accuracy. For the problem of defect category mismatches, we exploit the C2f-LSKA attention mechanism in the model head to address this issue. The experimental results indicate that the improved model achieves a mean average precision (mAP) of 79.0%, compared to 65.8% for the original model, marking an improvement of 13.1%. STE-YOLO significantly increases the precision of detecting surface defects on strip steel.
Complexity Reduction for Converter-Driven Stability Analysis in Transmission Systems
Authors: Yellisetti Viswaja , Moser Albert
Published: December 2024

The high penetration of power electronic converters with complex control systems is changing the power system dynamics, introducing new challenges such as converter-driven stability incidents. Traditional stability analysis methods, suitable for classical problems like voltage, frequency, and rotor angle stability in large systems, are insufficient for addressing the fast control dynamics of converters, which involve electromagnetic phenomena. These phenomena require detailed converter and network modeling, which can be performed in both the frequency and time domains, enabling the respective stability analyses to be carried out. However, frequency domain methods, based on small-signal impedances linearized at a single operating point, inherently ignore time domain phenomena like switching events and nonlinear behaviors. In contrast, time domain electromagnetic transient (EMT) simulations are effective for analyzing converter-driven stability but are computationally intensive when applied to large transmission systems with numerous use cases. Therefore, to reduce the simulation complexity in EMT tools, a complexity reduction procedure is proposed in this paper. Leveraging the advantages of the frequency domain, such as faster simulation times and information on wideband frequency characteristics of the system, this procedure utilizes the small-signal impedances and introduces a method for network reduction. The procedure also uses the frequency domain stability analysis method to screen for critical network use cases. Primarily, this procedure is a frequency domain toolchain encompassing frequency domain stability analysis and frequency domain network reduction. The result of the toolchain is a reduced network size and reduced network use cases that can be used for EMT simulations. The procedure is applied to an IEEE 39 bus system, where converter-driven stability is evaluated for two use cases. Furthermore, the network reduction method is tested on a critical use case, demonstrating reductions in network size and computation times without compromising the quality of stability analysis results.
Collaborative Optimal Configuration of Active–Reactive Flexible Resources Based on Wasserstein Confidence Set
Authors: Lin Xiaoke , Du Zhaobin , Cheng Lanfen , Xuan Peizheng , Zhou Ziqin
Published: December 2024

Flexible resources (FRs) have significant potential in ensuring the dynamic balance between supply and demand as well as enhancing the security of active distribution networks (ADNs). However, determining the optimal FR capacity in an economically reasonable manner remains a challenging task. This paper addresses the lack of representativeness of wind turbine (WT) and photovoltaic (PV) power output scenarios in the planning stage by generating a basic set of joint WT-PV output scenarios using random sampling. Subsequently, a Wasserstein confidence set (WCS) is established based on data-driven technology to better represent the unknown distribution of the actual WT-PV joint fluctuations. This provides a more detailed description of the scenario set, enabling the precise quantification of the risk of resource allocation scenarios and enhancing the flexibility and rigor of the subsequent optimal configuration model (OCM). To improve the coordination of active–reactive FRs, a bi-level OCM with multi-timescale considerations is developed. Compared to traditional configuration methods, the proposed model not only improves economic efficiency but also ensures that system voltage remains within safe limits after configuration. The effectiveness and superiority of the proposed optimal configuration method are demonstrated through simulations on an improved 33-bus test system, where the model achieved a 9.208% reduction in annual cost compared to robust methods while maintaining voltage quality and avoiding overvoltage or equipment overloads.
BGI-YOLO: Background Image-Assisted Object Detection for Stationary Cameras
Authors: Lee Youn Joo , Jung Ho Gi , Suhr Jae Kyu
Published: December 2024

This paper proposes a method enhancing the accuracy of object detectors by utilizing background images for stationary camera systems. Object detection with stationary cameras is highly valuable across various applications, such as traffic control, crime prevention, and abnormal behavior detection. Deep learning-based object detectors, which are mainly used in such cases, are developed for general purposes and do not take advantage of stationary cameras at all. Previously, cascade-based object detection methods utilizing background have been studied for stationary camera systems. These methods typically consist of two stages: background subtraction followed by object classification. However, their object detection performance is highly dependent on the accuracy of the background subtraction results, and numerous parameters must be adjusted during background subtraction to adapt to varying conditions. This paper proposes an end-to-end object detection method named BGI-YOLO, which uses a background image simply by combining it with an input image before feeding it into the object detection network. In our experiments, the following five methods are compared: three candidate methods of combining input and background images, baseline YOLOv7, and a traditional cascade method. BGI-YOLO, which combines input and background images at image level, showed a detection performance (mAP) improvement compared to baseline YOLOv7, with an increase of 5.6%p on the WITHROBOT S1 dataset and 2.5%p on the LLVIP dataset. In terms of computational cost (GFLOPs), the proposed method showed a slight increase of 0.19% compared to baseline YOLOv7. The experimental results demonstrated that the proposed method is highly effective for improving detection accuracy without increasing computational cost.
SRdetector: Sequence Reconstruction Method for Microservice Anomaly Detection
Authors: Ge Haixin , Ji Xin , Peng Fang , Chen Ruibo , Xiang Nan , Zhang Kui , Wu Wenjun
Published: December 2024

With the expansion of microservice-based applications over time, the number of microservices rises, resulting in an augmentation of the volume of performance metrics. Consequently, selecting the appropriate performance metrics for anomaly detection becomes a critical challenge. Since these performance metrics are typically strongly correlated with timestamps, they form time series data comprising timestamp–value pairs. To address this, we propose SRdetector, a feature-enhanced Transformer-based model that adopts a time series forecasting approach to detect anomalies in microservices. Furthermore, we integrate a dynamic weight adjustment mechanism into the original Transformer attention mechanism to assign weights to different performance and temporal features. This enables the model to dynamically learn the significance of various features at different time intervals, effectively serving as a feature selection method for microservice performance metrics. Finally, anomaly detection in microservices is conducted by evaluating the predicted performance metric data based on confidence intervals.
Privacy-Preserving Federated Learning-Based Intrusion Detection System for IoHT Devices
Authors: Mosaiyebzadeh Fatemeh , Pouriyeh Seyedamin , Han Meng , Liu Liyuan , Xie Yixin , Zhao Liang , Batista Daniel Macêdo
Published: December 2024

In recent years, Internet of Healthcare Things (IoHT) devices have attracted significant attention from computer scientists, healthcare professionals, and patients. These devices enable patients, especially in areas without access to hospitals, to easily record and transmit their health data to medical staff via the Internet. However, the analysis of sensitive health information necessitates a secure environment to safeguard patient privacy. Given the sensitivity of healthcare data, ensuring security and privacy is crucial in this sector. Federated learning (FL) provides a solution by enabling collaborative model training without sharing sensitive health data with third parties. Despite FL addressing some privacy concerns, the privacy of IoHT data remains an area needing further development. In this paper, we propose a privacy-preserving federated learning framework to enhance the privacy of IoHT data. Our approach integrates federated learning with ϵ-differential privacy to design an effective and secure intrusion detection system (IDS) for identifying cyberattacks on the network traffic of IoHT devices. In our FL-based framework, SECIoHT-FL, we employ deep neural network (DNN) including convolutional neural network (CNN) models. We assess the performance of the SECIoHT-FL framework using metrics such as accuracy, precision, recall, F1-score, and privacy budget (ϵ). The results confirm the efficacy and efficiency of the framework. For instance, the proposed CNN model within SECIoHT-FL achieved an accuracy of 95.48% and a privacy budget (ϵ) of 0.34 when detecting attacks on one of the datasets used in the experiments. To facilitate the understanding of the models and the reproduction of the experiments, we provide the explainability of the results by using SHAP and share the source code of the framework publicly as free and open-source software.
Design of Multi-Time Programmable Intellectual Property with Built-In Error Correction Code Function Based on Bipolar–CMOS–DMOS Process
Authors: Li Longhua , Kwon Soonwoo , Kim Dohoon , Kim Dongseob , Ha Panbong , Lee Doojin , Kim Younghee
Published: December 2024

The coupling capacitor of the MTP cell used in this paper is an NCAP-type capacitor that has only a source contact, and the layout size of the unit cell is 6.184 μm × 6.295 μm (=38.93 μm2), which is 0.44% smaller than the MTP cell that uses the coupling capacitor of the conventional NMOS transistor type that has both a source contact and a drain contact. In addition, a 4 Kb MTP IP with a built-in ECC function using an extended Hamming code capable of single-error correction and double-error detection was designed for safety considerations. In this paper, a new test algorithm is proposed to test whether the ECC function operates normally in the MTP IP with a built-in ECC function, and it is confirmed through a test using logic tester equipment that the output data DOUT[7:0] and the error flag ERROR_FLAG[1:0] are exactly the same in the cases of no error, a single-bit error, and a double-bit error. In addition, by sharing a current-controlled ring oscillator circuit that uses a current-starved inverter in the VPP, VNN, and VNNL charge pumping circuits that share a single ring oscillator in the erase and program operation modes of the MTP IP and using the regulated VPVR as power, the pumping capacitor size is reduced, and a new technology to reduce ripple voltage variation is proposed. Meanwhile, in the VNN level detector circuit that detects whether the VNN has reached the target voltage, a folded-cascode CMOS OP-AMP whose output swing voltage is almost VDD is used instead of a differential amplifier circuit with a PMOS differential input pair to ensure that normal VNN level detection operation occurs.
Optimized Frontier-Based Path Planning Using the TAD Algorithm for Efficient Autonomous Exploration
Authors: Buriboev Abror , Choi Andrew Jaeyong , Jeon Heung Seok
Published: December 2024

A novel path-planning method utilizing the trapezoid, adjacent, and distance, (TAD) characteristics of frontiers is presented in this work. The method uses the mobile robot’s sensor range to detect frontiers throughout each exploration cycle, modifying them at regular intervals to produce their parameters. This well-thought-out approach makes it possible to choose objective points carefully, guaranteeing seamless navigation. The effectiveness and applicability of the suggested approach with respect to exploration time and distance are demonstrated by empirical validation. Results from experiments show notable gains over earlier algorithms: time consumption decreases by 10% to 89% and overall path distance for full investigation decreases by 12% to 74%. These remarkable results demonstrate the efficacy of the suggested approach and represent a paradigm change in improving mobile robot exploration in uncharted territory. This research introduces a refined algorithm and paves the way for greater efficiency in autonomous robotic exploration. This study opens the door for more effective autonomous robotic exploration by introducing an improved algorithm.
Numerical Study of Nanoparticle Coagulation in Non-Road Diesel Engine Exhaust Based on the Principle of Split-Stream Rushing
Authors: Guo Yuchen , Wu Pei , Su He , Xue Jing , Zhang Yongan , Huang Peiyan
Published: December 2024

Diesel engines employed in non-road machinery are significant contributors to nanoparticulate matters. This paper presents a novel device based on the principle of split-stream rushing to mitigate particulate matter emissions from these engines. By organizing and intensifying the airflow movement of the jet in the rushing region, the probability of collisions between nanoparticles is enhanced. This accelerates the growth and coagulation of nanoparticles, reducing the number density of fine particulate matter. This, in turn, facilitates the capture or sedimentation of particulate matter in the diesel engine exhaust aftertreatment system. The coagulation kernel function tailored for diesel engine exhaust nanoparticles is developed. Then, the particle balance equation is solved to investigate the evolution and coagulation characteristics. Afterwards, three-dimensional numerical simulations are performed to study the flow field characteristics of the split-stream rushing device and the particle evolution within it. The results show that the device achieves a maximum coagulation efficiency of 59.73%, increasing the average particle diameter from 96 nm to 121 nm. The particle number density uniformity index exceeded 0.93 in most flow regions, highlighting the effectiveness of the device in ensuring consistent particle distribution.
A Review of Ammonia Combustion and Emissions Characteristics in Spark-Ignition Engines and Future Road Map
Authors: Sharma Vikas , Panesar Angad , de Sercey Guillaume , Begg Steven
Published: December 2024

Ammonia (NH3) is gaining recognition as a viable “green” transportation fuel due to its zero-carbon characteristic, its high energy density and its widespread availability. However, NH3 has a high auto-ignition temperature, resulting in potential emissions of NOx and unburned NH3. Addressing combustion challenges requires innovative solutions, such as the application of combustion promoters to enhance NH3 combustibility. This review article focuses on the compatibility of NH3 as a fuel for spark-ignition (SI) engines, examining its combustion under various modes including pure NH3 combustion, gasoline blends, NH3/hydrogen (H2) blends, and NH3/natural gas blends in single or dual-fuel configurations. The formation of nitrogen oxides (NOx) and slip-NH3 is explored to understand emissions species such as NO and N2O. Additionally, the article highlights the limitations of NH3 as a fuel for SI combustion. The comprehensive discussion provided in this review aims to fill a critical gap in the literature regarding NH3’s feasibility as a zero-carbon fuel for SI engines, particularly in the maritime sector. By offering insights into NH3 combustion characteristics and emissions profiles, the article seeks to provide a roadmap for leveraging NH3 as a suitable non-carbon fuel to decarbonize the marine sector and advance global sustainability goals.
Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent
Authors: Chand Shyamal S. , Hredzak Branislav , Cirrincione Maurizio
Published: December 2024

The elevated penetration of renewable energy has seen a significant increase in the integration of inverter-based resources (IBRs) into the electricity network. According to various industrial standards on interconnection and interoperability, IBRs should be able to withstand variability in grid conditions. Positive sequence voltage-oriented control (PSVOC) with a feed-forward decoupling approach is often adopted to ensure closed-loop control of inverters. However, the dynamic response of this control scheme deteriorates during fluctuations in the grid voltage due to the sensitivity of proportional–integral controllers, the presence of the direct- and quadrature-axis voltage terms in the cross-coupling, and predefined saturation limits. As such, a twin delayed deep deterministic policy gradient-based voltage-oriented control (TD3VOC) is formulated and trained to provide effective current control of inverter-based resources under various dynamic conditions of the grid through transfer learning. The actor–critic-based reinforcement learning agent is designed and trained using the model-free Markov decision process through interaction with a grid-connected photovoltaic inverter environment developed in MATLAB/Simulink® 2023b. Using the standard PSVOC method results in inverter input voltage overshoots of up to 2.50 p.u., with post-fault current restoration times of as high as 0.55 s during asymmetrical faults. The designed TD3VOC technique confines the DC link voltage overshoot to 1.05 p.u. and achieves a low current recovery duration of 0.01 s after fault clearance. In the event of a severe symmetric fault, the conventional control method is unable to restore the inverter operation, leading to integral-time absolute errors of 0.60 and 0.32 for the currents of the d and q axes, respectively. The newly proposed agent-based control strategy restricts cumulative errors to 0.03 and 0.09 for the d and q axes, respectively, thus improving inverter regulation. The results indicate the superior performance of the proposed control scheme in maintaining the stability of the inverter DC link bus voltage, reducing post-fault system recovery time, and limiting negative sequence currents during severe asymmetrical and symmetrical grid faults compared with the conventional PSVOC approach.
A Review of Coal Fly Ash Utilization: Environmental, Energy, and Material Assessment
Authors: Kuźnia Monika
Published: December 2024

Global coal consumption is continuously increasing. It is still the primary fuel used in power plants. Despite policies in the European Union aimed at reducing coal consumption, there are countries in the world where coal use continues to rise (China and India are the largest consumers of coal). Coal combustion produces waste, 70% of which is fly ash. It consists mainly of SiO2 and Al2O3. Fly ash also includes Fe2O3, TiO2, MgO, K2O, and CaO. This article describes various methods of using fly ash. Fly ash can be used in the cement industry, as a filler in materials, in zeolite synthesis, in cenosphere separation, in agriculture, in water purification, in road construction as an asphalt filler, and in mine backfilling. An interesting method of using fly ash as a filler in the production of rigid polyurethane foam was also described. The article concerns potential uses in accordance with the principles of a Circular Economy. The environmental, energy, and material aspects are discussed.
Investigation on the Compressibility Factor of Hydrogen-Doped Natural Gas Using GERG-2008 Equation of State
Authors: Li Ji-Chao , Fan Yong , Pang Dan , Wu Tong , Zhang Ying , Zhou Ke
Published: December 2024

The primary methods for hydrogen transportation include gaseous storage and transport, liquid hydrogen storage, and transport via organic liquid carriers. Among these, pipeline transportation offers the lowest cost and the greatest potential for large-scale, long-distance transport. Although the construction and operation costs of dedicated hydrogen pipelines are relatively high, blending hydrogen into existing natural gas networks presents a viable alternative. This approach allows hydrogen to be transported to the end-users, where it can be either separated for use or directly combusted, thereby reducing hydrogen transport costs. This study, based on the GERG-2008 equation of state, conducts experimental tests on the compressibility factor of hydrogen-doped natural gas mixtures across a temperature range of −10 °C to 110 °C and a pressure range of 2 to 12 MPa, with hydrogen blending ratios of 5%, 10%, 20%, 30%, and 40%. The results indicate that the hydrogen blending ratio, temperature, and pressure significantly affect the compressibility factor, particularly under low-temperature and high-pressure conditions, where an increase in the hydrogen blending ratio leads to a notable rise in the compressibility factor. These findings have substantial implications for the practical design of hydrogen-enriched natural gas pipelines, as changes in the compressibility factor directly impact pipeline operational parameters, compressor characteristics, and other system performance aspects. Specifically, the introduction of hydrogen alters the compressibility factor of the transported medium, thereby affecting the pipeline’s flowability and compressibility, which are crucial for optimizing and applying the performance of hydrogen-enriched natural gas in transportation channels. The research outcomes provide valuable insights for understanding combustion reactions, adjusting pipeline operational parameters, and compressor performance characteristics, facilitating more precise decision-making in the design and operation of hydrogen-enriched natural gas pipelines.
Thermogravimetric Experimental Study on the Co-Combustion Characteristics of Coal and Salix
Authors: Ma Yinsheng , Feng Bao , Gao Li , Guo Zhenyu , Ai Yu , Sun Haoying , Zhang Yong , Pan Zhenyan , Mao Jingwen , Yan Ruyu , Ye Ningzhu , Deng Lei
Published: December 2024

To study the co-combustion characteristics of coal and Salix, thermogravimetric analysis is adopted to evaluate their co-combustion performance. The effect of blending ratios and synergistic are investigated in detail. Furthermore, kinetic analysis is performed. The results show that the incorporation of Salix into coal enhances combustion performance, with significant improvements observed at higher blending ratios. The ignition temperature decreases notably from 444 °C to 393 °C, highlighting an improvement in ignition properties. The primary weight loss peak shifts from 490 °C at a 15% biomass blend to approximately 320 °C at a 100% blend. Co-combustion demonstrates synergistic effects, with a 15% biomass blend optimizing combustion between 400 °C and 530 °C, while a 30% blend inhibits it. Additionally, temperatures above 600 °C exhibit an inhibitory effect. The activation energy is reduced to 25.38 kJ mol−1 at a 30% blend ratio and further to 23.06 kJ mol−1 at a 15% blend ratio at a heating rate of 30 K min−1. Increasing the biomass blend ratio and heating rate lowers the activation energy, which means facilitating the reaction process.
Estimation for Reduction Potential Evaluation of CO2 Emissions from Individual Private Passenger Cars Using Telematics
Authors: Mae Masahiro , Wang Ziyang , Nishimura Shoma , Matsuhashi Ryuji
Published: December 2024

CO2 emissions from gas-powered cars have a large impact on global warming. The aim of this paper is to develop an accurate estimation method of CO2 emissions from individual private passenger cars by using actual driving data obtained by telematics. CO2 emissions from gas-powered cars vary depending on various factors such as car models and driving behavior. The developed approach uses actual monthly driving data from telematics and vehicle features based on drag force. Machine learning based on random forest regression enables better estimation performance of CO2 emissions compared to conventional multiple linear regression. CO2 emissions from individual private passenger cars in 24 car models are estimated by the machine learning model based on random forest regression using data from telematics, and the coefficient of determination for all 24 car models is R2=0.981. The estimation performance for interpolation and extrapolation of car models is also evaluated, and it keeps enough estimation accuracy with slight performance degradation. The case study with actual telematics data is conducted to analyze the relationship between driving behavior and monthly CO2 emissions in similar driving record conditions. The result shows the possibility of reducing CO2 emissions by eco-driving. The accurate estimation of the reduced amount of CO2 estimated by the machine learning model enables valuing it as carbon credits to motivate the eco-driving of individual drivers.
The Influences of Microwave Irradiation and Heat Treatment on the Dynamic Tensile Response of Granite
Authors: Wang Shu , Bao Lei , Yao Pujing , Xi Jingxuan , Zhang Ting , Guo Yueqing , Wu Xinshuang , Sun Yitong
Published: December 2024

The paramount significance of temperature’s influence on rock engineering endeavors underscores its profound capacity to alter the physical and mechanical attributes of rocks. Among the most crucial techniques utilized to thermally induce damage and diminish the tensile resilience of rock materials are microwave irradiation and heat treatment. This research examines and compares the effects of these two modalities on the dynamic tensile characteristics of Fangshan granite (FG), including their implications under conditions of overload and dependencies on loading rate, utilizing the sophisticated Split Hopkinson Pressure Bar (SHPB) apparatus. In particular, the dynamic real tensile strength (RST) of Brazilian disc (BD) specimens was meticulously gauged and contrasted after subjecting them to microwave irradiation at a potent 6 kW for varying durations (1.5, 3.0, and 4.5 min) and heat treatment across distinct temperature thresholds (178 °C, 345 °C, and 473 °C). To enhance the precision of the measurements, an overload correction was implemented by affixing a strain gauge in close proximity to the core of the BD specimen. The conventional dynamic tensile strength exhibited a reduction of approximately 20 to 30% with the prolongation of microwave radiation time. Furthermore, an additional decrease in tensile strength was observed with the elevation of heat treatment temperatures, reaching a maximum reduction of up to 40%. This phenomenon can be attributed to the proliferation and expansion of microcracks within the rock matrix. It was noteworthy that the RTS, corrected for overloading effects, exhibited a comparable trend to the dynamic traditional tensile strength (TTS). Both were significantly correlated with the loading rate, with the dynamic tensile strength demonstrating an average decrease of approximately 25% when the loading rate was increased. Interpolation and fitting analyses were employed to investigate the effects of microwave radiation duration, heat treatment temperature, and loading rate on the dynamic tensile strength of FG samples. Furthermore, it was established that the overload ratio increased in conjunction with an increase in microwave radiation duration, heat treatment temperature, and loading rate, reaching a maximum value of 1.5.