bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Car Rental Prices: A Comparative Analysis of Machine Learning Models
Authors: Yang Jiseok , Kim Jinseok , Ryu Hanwoong , Lee Jiwoon , Park Cheolsoo
Published: June 2024

In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers.
Cross-Social-Network User Identification Based on Bidirectional GCN and MNF-UI Models
Authors: Huang Song , Xiang Huiyu , Leng Chongjie , Xiao Feng
Published: June 2024

Due to the distinct functionalities of various social network platforms, users often register accounts on different platforms, posing significant challenges for unified user management. However, current multi-social-network user identification algorithms heavily rely on user attributes and cannot perform user identification across multiple social networks. To address these issues, this paper proposes two identity recognition models. The first model is a cross-social-network user identification model based on bidirectional GCN. It calculates user intimacy using the Jaccard similarity coefficient and constructs an adjacency matrix to accurately represent user relationships in the social network. It then extracts cross-social-network user information to accomplish user identification tasks. The second model is the multi-network feature user identification (MNF-UI) model, which introduces the concept of network feature vectors. It effectively maps the structural features of different social networks and performs user identification based on the common features of seed nodes in the cross-network environment. Experimental results demonstrate that the bidirectional GCN model significantly outperforms baseline algorithms in cross-social-network user identification tasks. The MNF-UI (multi-network feature user identification) model can operate in situations with two or more networks with inconsistent structures, resulting in improved identification accuracy. These two user identification algorithms provide technical and theoretical support for in-depth research on social network information integration and network security maintenance.
Sliding-Mode Control of Linear Induction Motor Based on Exponential Reaching Law
Authors: Ma Shuhang , Zhao Jinghong , Xiong Yiyong , Wang Hanming , Yao Xing
Published: June 2024

As a strongly coupled, multivariable, high-order nonlinear, time-varying complex system, a linear induction motor is susceptible to outside disturbances and mismatched parameters. Because of its straightforward control mechanism and fixed PI value, the traditional PI regulator is frequently utilized in linear induction motor speed control systems. However, because of these limitations, it frequently falls short of meeting the high-performance control needs of the motor in complex operating conditions. In order to solve the contradiction between the sliding-mode motion reaching time and vibration, the sliding-mode variable structure control is applied to the linear induction motor speed control system in this paper. The sliding-mode controller is designed using the exponential reaching law method, and the designed system is analyzed through simulation. The findings demonstrate how the controller enhances the system’s robustness and disturbance resistance by bringing about the advantages of rapidity, stability, no overshooting, and a strong resistance to load disturbance.
LMA-EEGNet: A Lightweight Multi-Attention Network for Neonatal Seizure Detection Using EEG signals
Authors: Zhou Weicheng , Zheng Wei , Feng Youbing , Li Xiaolong
Published: June 2024

Neonatal epilepsy is an early postnatal brain disorder, and automatic seizure detection is crucial for timely diagnosis and treatment to reduce potential brain damage. This work proposes a novel Lightweight Multi-Attention Network, LMA-EEGNet, for diagnosing neonatal epileptic seizures from multi-channel EEG signals employing dilated depthwise separable convolution (DDS Conv) for feature extraction and using pointwise convolution followed by global average pooling for classification. The proposed approach substantially reduces the model size, number of parameters, and computational complexity, which are crucial for real-time detection and clinical diagnosis of neonatal epileptic seizures. LMA-EEGNet integrates temporal and spectral features through distinct temporal and spectral branches. The temporal branch uses DDS Conv to extract temporal features, enhanced by a channel attention mechanism. The spectral branch utilizes similar convolutions alongside a spatial attention mechanism to highlight key frequency components. Outputs from both branches are merged and processed through a pointwise convolution layer and a global average pooling layer for efficient neonatal seizure detection. Experimental results show that our model, with only 2471 parameters and a size of 23 KB, achieves an accuracy of 95.71% and an AUC of 0.9862, demonstrating its potential for practical deployment. This study provides an effective deep learning solution for the early detection of neonatal epileptic seizures, improving diagnostic accuracy and timeliness.
A Novel Approach for the Enhancement of Security through Defining the Spatial Secrecy Outage Probability in the Industrial Internet of Things
Authors: Liu Xiaokai , Xu Fangmin , Ning Lina , Zhao Chenglin , Xia Hao
Published: June 2024

The rapid development of the Industrial Internet of Things (IIoT) has brought enormous opportunities to the industrial environment, but has also posed significant challenges to network security, especially physical layer security, which is crucial for the reliable transmission of messages among interconnected devices and sensors. Focusing on enhancing the physical layer security in IIoT communication, this paper proposes a mechanism for the Spatial Secrecy Outage Probability (SSOP) to ensure the secure transmission of data. The SSOP algorithm assumes that the location of legitimate devices is known, while the eavesdropper devices have a random distribution of locations, forming the SSOP model related to the device locations from the perspective of insecure regions (ISRs) and secure regions (SRs), and the closed-form expression for its upper bound is derived. Subsequently, under the constraint of the SSOP conditions, we establish an optimization model with the objective of system throughput maximization based on the Spatial Secrecy Outage Probability (SRM-SSOP). By optimizing the transmission rate of legitimate channels or controlling the transmission power, the maximization of the system throughput can be achieved, thus reducing the consumption of communication resources. Through sophisticated modeling and simulation in MATLAB, we verified the accuracy of the definition and derived the upper bound of the SSOP. The experimental results also show that the SSOP algorithm gradually converges to a specific value as the number of antenna elements increases. In addition, we observed that, when the receiver device is aligned with the normal direction of the transmit antenna array, the SSOP reaches a minimum value. This performance is crucial for understanding and optimizing the security performance of IIoT communication systems.
IoT-Enabled Single-Camera Speed Sensor for Smart City Tasks
Authors: Gorobetz Mikhail , Timofejevs Jurijs , Potapovs Andrejs , Obusevs Artjoms
Published: June 2024

This article presents an innovative IoT-enabled single-camera speed sensor designed for smart city applications. The research encompasses the development of both hardware and software components, focusing on a computer vision and artificial neural network-based system. A novel aspect of this study is the implementation of a distance-measuring algorithm that eliminates the need for expensive LIDAR sensors traditionally used in speed cameras. Instead, the system relies on convolutional neural networks (CNN) and computer vision algorithms to estimate vehicle speed accurately. Field testing was conducted in real conditions over several months, generating sufficient data to assess the device’s ability to function under various adverse conditions. The results demonstrate the system’s capability to perform vehicle speed detection consistently across diverse conditions, showcasing its potential as a scalable solution for urban traffic management. This makes the proposed system not only more affordable but also simpler to deploy and maintain, thereby enhancing its suitability for widespread use in smart city environments.
SRFAD-Net: Scale-Robust Feature Aggregation and Diffusion Network for Object Detection in Remote Sensing Images
Authors: Liu Jing , Jing Donglin , Zhang Haijing , Dong Chunyu
Published: June 2024

The significant differences in target scales of remote sensing images lead to remarkable variations in visual features, posing significant challenges for feature extraction, fusion, regression, and classification. For example, models frequently struggle to capture features of targets across all scales, inadequately consider the weights and importance of features at different scales during fusion, and encounter accuracy limitations when detecting targets of varying scales. To tackle these challenges, we proposes a Scale-Robust Feature Aggregation and Diffusion Network (SRFAD-Net) for remote sensing target detection. This model includes a Scale-Robust Feature Network (SRFN), an Adaptive Feature Aggregation and Diffusion (AFAD) module, and a Focaler-GIoU Loss. SRFN extracts scale-robust features by constructing a multi-scale pyramid. It includes a downsampling (ADown) module that combines the advantages of average pooling and max pooling, effectively preserving background information and salient features. This further enhances the network’s ability to handle targets of varying scales and shapes. The introduced Deformable Attention(DAttention) mechanism captures target features effectively through adaptive adjustment of the receptive field’s shape and size, reducing background clutter and substantially enhancing the model’s performance in detecting distant objects. In the feature fusion stage, we propose the AFAD module, which utilizes a dimension-adaptive perceptual selection mechanism and parallel depthwise convolutional operations to precisely aggregate multi-channel information. It then employs a diffusion mechanism to spread contextual information across various scales, greatly improving the network’s ability to extract and fuse features across multiple scales. For the detection head, we adopt the Focaler-GIoU Loss, leveraging its advantages in handling non-overlapping bounding boxes, effectively alleviating the difficulty of localization caused by scale variations. We have undertaken experiments on two widely utilized aerial target datasets: the Remote Sensing Scene Object Detection Dataset (RSOD) and NWPU VHR-10, which is a high-resolution object detection dataset from Northwestern Polytechnical University. The findings of these experiments clearly illustrate that SRFAD-Net surpasses the performances of mainstream detectors.
High-Performance Wave Union Time-to-Digital Converter Implementation Based on Routing Path Delays of FPGA
Authors: Siecha Roza Teklehaimanot , Alemu Getachew , Prinzie Jeffrey , Leroux Paul
Published: June 2024

Time-to-digital converters (TDCs) with superior performance are in high demand in application domains like light detection and ranging (LIDAR), nuclear physics, and time interval counters. One of the interesting architectures for field-programmable gate array (FPGA)-based TDCs is the tapped delay line (TDL) approach with carry chains as delay elements. However, the resolution of TDL-TDCs is limited, and linearity is weakened by the ultra-wide bins that correspond to the FPGA’s long routing wires crossing into another clock area. This paper presents wave union TDC using FPGA internal routing wires as delay elements to subdivide ultra-wide bins. The Zynq Evaluation and Development (ZED) board is used to implement and test the wave union types: A (WU-A) and B (WU-B) TDCs. According to experimental data, the WU-A TDC based on an 8 × 128 matrix of counters has a resolution of 5.7 ps, an integral nonlinearity (INL) of 1.1170 LSB (RMS), and a differential nonlinearity of 0.329 LSB (RMS). WU-A TDC improves DNL and INL by 19% and 57%, respectively, over ordinary TDC. The WU-B TDC uses an average of sixteen different time measurements, resulting in an effective resolution of up to 0.356 ps, a DNL of 0.60 LSB (RMS), and an INL of 1.04 LSB (RMS). These characteristics make the TDC suitable for time-of-flight applications such as LIDAR and for other general-purpose scientific instruments.
Artificial Intelligence Application in the Field of Functional Verification
Authors: Dranga Diana , Dumitrescu Catalin
Published: June 2024

The rising interest in Artificial Intelligence and the increasing time invested in functional verification processes are driving the demand for AI solutions in this field. Functional verification is the process of verifying that the Register Transfer Layer (RTL) implementation behaves according to the specifications provided. This is performed using a hardware verification language (HVL) such as SystemVerilog combined with the Universal Verification Methodology (UVM). Reading, identifying the key elements from multiple documentations, creating the verification plan, building the verification environment, implementing the tests defined, and achieving 100% coverage are usually the steps performed in order to complete the verification process. The verification process is considered finalized when functional coverage is at 100%. There are multiple ideas on how the process can be aided by AI, such as underlining the essential information from documentation, which would help in understanding faster how the Register Transfer Layer implementation works, thus vastly reducing time. In this paper, to greatly reduce the time spent on functional verification, two Convolutional Neural Network (CNN) architectures are implemented to properly classify the information across different documents; both approaches have significant and promising results. The database used for this classification task was created by the researchers using different documentations available.
Dialogue-Rewriting Model Based on Transformer Pointer Extraction
Authors: Pu Chenyang , Sun Zhangjie , Li Chuan , Song Jianfeng
Published: June 2024

In the multi-turn dialogue scenario, users commonly encounter challenges with pronoun referents and information omission, leading to semantically incomplete representations. These issues contribute to textual incoherence, as unclear referents and missing components hinder the semantic understanding of the spoken representations of text by machines. Currently, scholars frequently resort to multiple rounds of dialogue rewriting to address the semantic challenges posed by the machine comprehension of semantically missing texts with pronoun referents and information omissions. However, existing dialogue-rewriting methods often suffer from low precision and high latency in handling such texts. To mitigate these shortcomings, this paper proposes a Transformer-based dialogue-rewriting model that utilizes pointer extraction. The method leverages a Transformer pre-training model to effectively extract the potential semantic features of text and extract the key information of text by a pointer address. By extracting keywords and appropriately replacing or inserting text, the model restores referents and missing information. The experimental findings on an open-source Chinese multi-turn dialogue-rewriting dataset demonstrate the effectiveness of the proposed method in improving both the accuracy and efficiency of rewriting compared with existing methods. Specifically, the ROUGR-1 value increased by 2.9%, while the time consumption decreased by 50% compared with the benchmark method.
Simulation of a Line Voltage Regulator in a Low-Voltage Grid That Is Subject to Strong Voltage Surges Due to the Provision of Fast Frequency Reserve
Authors: Hauschel Maximilian , Bertilsson Kent
Published: June 2024

The increasing adoption of battery storage units alongside private PV systems may prove to be a new challenge for distribution grid operators. This study explored the potential impact of marketing aggregated battery discharge power as a Fast Frequency Reserve (FFR) and its effect on the distribution grid stability. We investigated the efficacy of Line Voltage Regulators (LVRs) in mitigating voltage surges caused by simultaneous battery activation. For this purpose, a simulation was developed via Matlab (Version R2023a) to simulate the voltage at the nodes of an arbitrary distribution grid, using the feed-in and consumed power of the customers as the input. We applied the model to a distribution grid section in Sundsvall (Sweden). The results confirmed that LVRs can amplify voltage surges when their adjustments are not synchronized with the FFR activation. This study underscored the need for proactive measures to address the voltage maintenance challenges arising from the integration of battery storage units and renewable energy sources.
A Visible and Synthetic Aperture Radar Image Fusion Algorithm Based on a Transformer and a Convolutional Neural Network
Authors: Hu Liushun , Su Shaojing , Zuo Zhen , Wei Junyu , Huang Siyang , Zhao Zongqing , Tong Xiaozhong , Yuan Shudong
Published: June 2024

For visible and Synthetic Aperture Radar (SAR) image fusion, this paper proposes a visible and SAR image fusion algorithm based on a Transformer and a Convolutional Neural Network (CNN). Firstly, in this paper, the Restormer Block is used to extract cross-modal shallow features. Then, we introduce an improved Transformer–CNN Feature Extractor (TCFE) with a two-branch residual structure. This includes a Transformer branch that introduces the Lite Transformer (LT) and DropKey for extracting global features and a CNN branch that introduces the Convolutional Block Attention Module (CBAM) for extracting local features. Finally, the fused image is output based on global features extracted by the Transformer branch and local features extracted by the CNN branch. The experiments show that the algorithm proposed in this paper can effectively achieve the extraction and fusion of global and local features of visible and SAR images, so that high-quality visible and SAR fusion images can be obtained.
FCL: Pedestrian Re-Identification Algorithm Based on Feature Fusion Contrastive Learning
Authors: Li Yuangang , Zhang Yuhan , Gao Yunlong , Xu Bo , Liu Xinyue
Published: June 2024

Pedestrian re-identification leverages computer vision technology to achieve cross-camera matching of pedestrians; it has recently led to significant progress and presents numerous practical applications. However, current algorithms face the following challenges: (1) most of the methods are supervised, heavily relying on specific datasets, and lacking robust generalization capabilities; (2) it is hard to extract features because the elongated and narrow shape of pedestrian images introduces uneven feature distributions; (3) the substantial imbalance between positive and negative samples. To address these challenges, we introduce a novel pedestrian re-identification unsupervised algorithm called Feature Fusion Contrastive Learning (FCL) to extract more effective features. Specifically, we employ circular pooling to merge network features across different levels for pedestrian re-identification to improve robust generalization capability. Furthermore, we propose a feature fusion pooling method, which facilitates a more efficient distribution of feature representations across pedestrian images. Finally, we introduce FocalLoss to compute the clustering-level loss, mitigating the imbalance between positive and negative samples. Through extensive experiments conducted on three prominent datasets, our proposed method demonstrates promising performance, with an average 3.8% improvement in FCL’s mAP indicators compared to baseline results.
A Privacy-Preserving V2I Fast Authentication Scheme in VANETs
Authors: Gan Yusheng , Xie Xiaolan , Liu Yining
Published: June 2024

Due to the characteristics of high-speed movement of vehicles, authentication between vehicles and roadside units (RSUs) needs to be performed quickly. Vehicles can obtain the authentication information of the relevant RSUs from the certification authority (CA) in advance through route planning. Fast authentication can be performed when the vehicle enters the RSU range. However, in most of the current vehicle-to-infrastructure (V2I) fast authentication schemes, when the vehicle requests the authentication information of an RSU from the CA, the vehicle often needs to provide the CA with the travel path information, which leads to the CA easily obtaining the travel path of the vehicle. In addition, the CA encrypts the private keys of RSUs and sends them to vehicles as authentication keys, and the vehicles can obtain the private key information of RSUs directly after decryption. Once the private keys of RSUs are leaked, vehicular ad hoc networks (VANETs) can be attacked by malicious access. In order to protect the confidentiality of RSU private keys and the route privacy of vehicles, we propose a privacy-preserving V2I fast authentication scheme in VANETs. The scheme realizes the confidentiality of RSU private keys and the route privacy protection of vehicles by improving the oblivious transfer (OT) algorithm. Security analysis proves that our scheme has good privacy and attack resistance. Finally, performance evaluation shows that the proposed scheme performs better in terms of computational overhead and communication overhead compared to related schemes.
Insights into Modern Intrusion Detection Strategies for Internet of Things Ecosystems
Authors: Isong Bassey , Kgote Otshepeng , Abu-Mahfouz Adnan
Published: June 2024

The swift explosion of Internet of Things (IoT) devices has brought about a new era of interconnectivity and ease of use while simultaneously presenting significant security concerns. Intrusion Detection Systems (IDS) play a critical role in the protection of IoT ecosystems against a wide range of cyber threats. Despite research advancements, challenges persist in improving IDS detection accuracy, reducing false positives (FPs), and identifying new types of attacks. This paper presents a comprehensive analysis of recent developments in IoT, shedding light on detection methodologies, threat types, performance metrics, datasets, challenges, and future directions. We systematically analyze the existing literature from 2016 to 2023, focusing on both machine learning (ML) and non-ML IDS strategies involving signature, anomaly, specification, and hybrid models to counteract IoT-specific threats. The findings include the deployment models from edge to cloud computing and evaluating IDS performance based on measures such as accuracy, FP rates, and computational costs, utilizing various IoT benchmark datasets. The study also explores methods to enhance IDS accuracy and efficiency, including feature engineering, optimization, and cutting-edge solutions such as cryptographic and blockchain technologies. Equally, it identifies key challenges such as the resource-constrained nature of IoT devices, scalability, and privacy issues and proposes future research directions to enhance IoT-based IDS and overall ecosystem security.
Security Analysis of Low-Budget IoT Smart Home Appliances Embedded Software and Connectivity
Authors: Murat Kacper , Topyła Dominik , Zdulski Krzysztof , Marzęcki Michał , Bieniasz Jędrzej , Paczesny Daniel , Szczypiorski Krzysztof
Published: June 2024

This paper investigates the challenge of finding and analyzing security vulnerabilities among widely available low-budget Internet of Things smart home appliances. It considers the identification of security vulnerabilities within the appliances’ embedded software and connectivity functions over wired and wireless channels in local networks and external communications with manufacturers’ cloud services. To analyze the security of these appliances, a universal laboratory test bench is proposed and a set of methodologies for testing the security of smart home devices is described. The proposed testing platform offers a practical solution for security analysis of Internet of Things smart home devices and it can serve as a reference approach for future research. The results from the research indicated varying levels of susceptibility across different types of devices. A list of recommendations for manufacturers and others to improve the security level of these appliances is provided. The findings emphasize the need for regular security assessments of smart home devices, to maintain the protection of personal and sensitive information.
A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning
Authors: Yang Xueying , Hu Min , Huang Gang , Wang Yijun
Published: June 2024

The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller’s preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP.
An Innovative Method Based on Wavelet Analysis for Chipless RFID Tag Detection
Authors: Su Chen , Wang Xueyuan , Zou Chuanyun , Jiao Liangyu , Tao Yuchuan
Published: June 2024

Chipless RFID tags have attractive low-cost advantages. However, traditional RFID anti-collision algorithms cannot be applied due to a lack of computing and processing capabilities. Problems with multitag detection must be solved to commercialize chipless RFID tags. In this paper, an innovative method for frequency-domain chipless RFID tag detection is proposed. The tags’ scattered signals are processed via wavelet analysis, and a time–frequency plot that can read the code is obtained. When the distance between tags is too close to distinguish in the time–frequency plot, independent component analysis is used to separate individual scattered signals from mixed echo signals; then, the code is read by means of wavelet analysis. To validate the proposed method, C-shaped frequency-domain chipless RFID tag models and a multitag detection simulation scenario were constructed in selected software. The short-time matrix pencil method (STMPM), short-time Fourier transform (STFT), and the proposed method were compared. When the tag spacing is 0.05 m, the code can be read successfully. Compared with the STMPM, the proposed method greatly reduces the computational quantity and shortens the reading time. Furthermore, adjustment of the window width and search step parameters is avoided.
Isolated Work of a Multi-Energy Carrier Microgrid
Authors: Knežević Sonja , Šošić Darko
Published: June 2024

With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers’ electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers’ needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system’s operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system’s behavior, a one-week time horizon is considered.
Thermal Performance Design and Analysis of Reversed Brayton Cycle Heat Pumps for High-Temperature Heat Supply
Authors: Kim Jin-Seo , Chung In-Ho , Kim Tong-Seop , Song Chan-Ho
Published: June 2024

This study examined the performance of reversed Brayton cycle heat pumps to supply heat above 300 °C. The aim was to overcome the current temperature limitations faced by heat pump technology in industrial heat supply sectors by examining the viability of the reversed Brayton cycle. In particular, the effects of the operating conditions on the cycle performance, such as the waste and return heat temperatures, were analyzed through thermal performance analysis. The reversed Brayton cycle heat pumps showed improved performance over conventional vapor compression cycle heat pumps when a heat supply above 215 °C was required. Furthermore, integrating additional heat exchangers into the cycle configuration was proposed in this study as a method to enhance waste heat utilization and recover unused heat from industrial processes. By incorporating preheating and recuperated cycles, these modifications broaden the operational range under the same operating conditions. They also improve the coefficient of performance (COP) of the reference cycle by up to 23% and 27.4%, respectively. This study explored the potential of reversed Brayton cycle heat pumps to supply heat above 300 °C and provided fundamental guidelines for the efficient design and operation of reversed Brayton cycle heat pumps. The results are expected to enhance our understanding of the performance characteristics of reversed Brayton cycle heat pump technology and expand its use as an alternative to fossil-fuel-based heat supply systems.
Analysis of Pressure Fluctuation of a Pump-Turbine with Splitter Blades on Small Opening in Turbine Mode
Authors: Xiao Wei , Chen Liu , Ren Shaocheng , Yan Bin , Liu Zishi , Xiao Yexiang
Published: June 2024

Unstable flow in a pump-turbine can cause pressure pulsation, and the resulting vibration deteriorates the stability and operating safety of the unit. This study conducted three-dimensional numerical calculations of the overall flow passage of a pump-turbine with splitter blades under the small guide vane opening, and the unsteady flow characteristics of the turbine were investigated. The results showed that the pressure fluctuation was more severe at lower head operating conditions with lower efficiency, especially in the vaneless area (the runner blade passages). Under the lower head condition, the proportion of 12 times the rotational frequency (12 f/fn) increased in the vaneless area, and the amplitude of 1 f/fn as well as 2 f/fn became larger in the runner blade channel, with more space occupied by vortices and reflux areas. A spiral vortex rope formed in the draft tube, increasing the proportion of 0.4 f/fn and 0.7 f/fn pressure pulses.
Prediction Method of PHEV Driving Energy Consumption Based on the Optimized CNN BiLSTM Attention Network
Authors: Zhang Xuezhao , Chen Zijie , Wang Wenxiao , Fang Xiaofen
Published: June 2024

In the field of intelligent transportation, the planning of traffic flows that meet energy-efficient driving requirements necessitates the acquisition of energy consumption data for each vehicle within the traffic flow. The current methods for calculating vehicle energy consumption generally rely on longitudinal dynamics models, which require comprehensive knowledge of all vehicle power system parameters. While this approach is feasible for individual vehicle models, it becomes impractical for a large number of vehicle types. This paper proposes a digital model for vehicle driving energy consumption using vehicle speed, acceleration, and battery state of charge (SOC) as inputs and energy consumption as output. The model is trained using an optimized CNN-BiLSTM-Attention (OCBA) network architecture. In comparison to other methods, the OCBA-trained model for predicting PHEV driving energy consumption is more accurate in simulating the time-dependency between SOC and instantaneous fuel and power consumption, as well as the power distribution relationship within PHEVs. This provides an excellent framework for the digital modeling of complex power systems with multiple power sources. The model requires only 54 vehicle tests for training, which is significantly fewer than over 2000 tests typically needed to obtain parameters for power system components. The model’s prediction error for fuel consumption under unknown conditions is reduced to 5%, outperforming the standard error benchmark of 10%. Furthermore, the model demonstrates high generalization capability with an R2 value of 0.97 for unknown conditions.
Comparison between Direct and Indirect Heat Flux Measurement Techniques: Preliminary Laboratory Tests
Authors: Evangelisti Luca , Barbaro Leone , Guattari Claudia , De Cristo Edoardo , De Lieto Vollaro Roberto , Asdrubali Francesco
Published: June 2024

Direct and indirect approaches can be employed for estimating the heat flow through components in different application fields. In the building sector, the thermometric method is often applied by professionals for thermal transmittance evaluations. However, miscalculations can derive from inaccurate total heat transfer coefficients, and a consensus regarding the appropriate value to employ remains to be determined. Here, an apparatus was realized for laboratory tests and heat flux measurements were performed following direct and indirect approaches. Data acquired through a common heat flow sensor were compared with those computed through a post-processing based on radiative and convective estimations. The results were affected by the specific correlation adopted for computing the convective coefficients, with the percentage differences ranging from −9.8% to −0.4%. New measurement systems could be designed for automatically computing heat fluxes through indirect approaches, thus providing alternative solutions in the panorama of non-destructive tests for building energy diagnosis.
Combined Effect of Pressure and Temperature on Nitrogen Reduction Reaction in Water
Authors: Tranchida Giuseppe , Milazzo Rachela G. , Lombardo Salvatore A. , Privitera Stefania M. S.
Published: June 2024

The synthesis of ammonia starting from nitrogen and using electrochemical processes is considered an interesting strategy to produce ammonia in a sustainable way. However, it requires not only the development of efficient catalysts for nitrogen reduction but also the optimization of the operating conditions of the employed electrochemical devices. In this work, we optimize the kinetics and the thermodynamics of the electrocatalytic nitrogen reduction reaction in water by developing a pressurized H-cell that may operate at temperatures up to 80 °C. Ni foam with low Au loading (0.08 mg cm−2) has been adopted as a catalyst at the cathode. Ammonia has been produced during chronoamperometry experiments in a saturated N2 atmosphere and measured by the indophenol blue method. The effect of voltage, temperature, and pressure has been studied. The nitrogen reduction experiments have been repeated under saturated Ar. To remove contributions due to environmental contamination, we determined the net value as the difference between the produced ammonia in N2 and in Ar. The ammonia yield increases by increasing the temperature and the pressure. The best results have been obtained by using the combined effects of temperature and pressure. Operating at 5 bar of saturated N2 and 75 °C, a production rate of 6.73 μg h−1·cm−2 has been obtained, a value corresponding to a 5-fold enhancement, compared to that obtained under ambient conditions and room temperature.
Optimization of Impedance Relay Placement in Medium-Voltage Electrical Distribution Systems through Clustering Algorithms and Metaheuristics
Authors: Arpi Josue , Zambrano Anthony , Robles Pablo , Ochoa-Correa Danny
Published: June 2024

This study explores the feasibility of using impedance relays in electrical distribution systems, a context where their application is not as common as in transmission systems. Given the dynamic nature and complex topology of medium-voltage distribution systems, this work proposes an innovative methodology integrating clustering algorithms and metaheuristic techniques to optimize the placement of impedance relays and enhance system reliability and resilience. Using CYME simulation and the Ant Colony Optimization (ACO) method, case studies were designed to validate the effectiveness of the proposed methodology. The results demonstrated that strategic placement of impedance relays reduces failure frequency and significantly improves the system’s response to such failures. This approach allows for a more efficient configuration and quicker response, which is crucial for maintaining continuity and quality of power supply. A detailed analysis of system behavior under various fault scenarios illustrates the robustness and adaptability of the proposed solution, marking a significant advancement in the protection and optimization of electrical distribution systems.
Closing the Loop between Waste-to-Energy Technologies: A Holistic Assessment Based on Multiple Criteria
Authors: Mertzanakis Christos , Vlachokostas Christos , Toufexis Charalampos , Michailidou Alexandra V.
Published: June 2024

This paper puts forward a generic methodological framework to holistically assess WtE technologies based on the PROMETHEE approach. In addition to environmental and economic aspects, the method focuses on large-scale applicability and social preference, thus adopting economic, environmental, social, and technological criteria. Three data sources are selected, namely the scientific literature, a public survey, and an experts’ opinion survey, which is a novel combination with the aim to cover public consensus, technological applicability, and to provide alternative data sources for the economic and environmental criteria, thus enriching the methodology with the input of location specific data. The demonstration of the applicability of the proposed methodology is realized at a national level for the case of Greece. Anaerobic Digestion is shown to be the most preferable choice, recognized for its cost-effectiveness and lower environmental burden to other WtE technologies (i.e., gasification, pyrolysis, incineration). When all criteria are evaluated with equal weights, anaerobic digestion greatly outperforms incineration (net flow 0.833 versus 0.1667), while incineration only becomes the most preferred choice if the social criterion is in high focus (i.e., over 63% weight).
Disturbance-Observer-Based Second-Order Sliding-Mode Position Control for Permanent-Magnet Synchronous Motors: A Continuous Twisting Algorithm-Based Approach
Authors: Liu Yong-Chao
Published: June 2024

This paper proposes a novel composite position controller for the field-oriented control (FOC) strategy of permanent-magnet synchronous motor (PMSM) servo systems. The proposed composite position controller integrates a position controller with a disturbance observer, with each designed based on a specific second-order sliding-mode algorithm. Specifically, the continuous twisting algorithm (CTA) is employed to develop the position controller for achieving rotor position tracking, while the modified super-twisting algorithm (STA) is used to construct the disturbance observer for compensating the total disturbance in the rotor position tracking error dynamics to enhance the dynamic performance. Comparative simulation tests, conducted within an FOC strategy of PMSM servo systems, contrast the performance of the CTA-based position controller, the composite position controller using a CTA-based position controller and a standard STA-based disturbance observer, and the proposed composite position controller. The simulation results validate the proposed position controller’s effectiveness and its superiority over comparable position controllers.
Enhanced Thermal Modeling of Electric Vehicle Motors Using a Multihead Attention Mechanism
Authors: Ji Feifan , Huang Chenglong , Wang Tong , Li Yanjun , Pan Shuwen
Published: June 2024

The rapid advancement of electric vehicles (EVs) accentuates the criticality of efficient thermal management systems for electric motors, which are pivotal for performance, reliability, and longevity. Traditional thermal modeling techniques often struggle with the dynamic and complex nature of EV operations, leading to inaccuracies in temperature prediction and management. This study introduces a novel thermal modeling approach that utilizes a multihead attention mechanism, aiming to significantly enhance the prediction accuracy of motor temperature under varying operational conditions. Through meticulous feature engineering and the deployment of advanced data handling techniques, we developed a model that adeptly navigates the intricacies of temperature fluctuations, thereby contributing to the optimization of EV performance and reliability. Our evaluation using a comprehensive dataset encompassing temperature data from 100 electric vehicles illustrates our model’s superior predictive performance, notably improving temperature prediction accuracy.
Double-Swing Spring Origami Triboelectric Nanogenerators for Self-Powered Ocean Monitoring
Authors: Du Xinru , Zhang Hao , Cao Hao , Hao Zewei , Nakashima Takuji , Tanaka Yoshikazu , Jiao Pengcheng , Mutsuda Hidemi
Published: June 2024

Coastal areas often experience high population density and intense human activity owing to the considerable value of the ocean. Therefore, devices for monitoring marine disasters are crucial for ensuring the safety of human life. Herein, we develop hemispherical spring origami (SO) triboelectric nanogenerators (TENGs) (HSO-TENGs) for self-powered ocean wave monitoring. Optimization is performed using two approaches. First, swing machine experiments are conducted to investigate the monitoring performance of the HSO-TENGs regarding wave height and period with satisfactory accuracy. To increase power generation and monitoring accuracy, the internal inertia and centroid of gravity of the HSO-TENGs are optimized with respect to the structural parameters (i.e., magnet weight, hammer height, and external swing arm length). Second, numerical simulations are performed using the smoothed-particle hydrodynamics (SPH) method to determine the most suitable fixed condition for the HSO-TENGs for sensing wave changes. Subsequently, wave tank experiments are conducted on the HSO-TENGs to determine their ability to sense wave height, period, frequency, and direction. Tests related to supplying other sensors are also conducted. Eventually, the ability of the HSO-TENGs to monitor wave direction and spreading parameters is investigated in a numerical SPH circular wave tank. The results prove that the optimized HSO-TENGs can achieve powering and sensing through the same device.
Informer Short-Term PV Power Prediction Based on Sparrow Search Algorithm Optimised Variational Mode Decomposition
Authors: Xu Wu , Li Dongyang , Dai Wenjing , Wu Qingchang
Published: June 2024

The output power of PV systems is influenced by various factors, resulting in strong volatility and randomness, which makes it difficult to forecast. Therefore, this paper proposes an Informer prediction model based on optimised VMD for predicting short-term PV power. Firstly, the temporal coding of the Informer model is improved and, secondly, the original sequence is decomposed into multiple modal components using VMD, and then optimisation of the results of VMD in conjunction with the optimisation strategy of SSA improves the characteristics of the time series data. Finally, the refined data are fed into the Informer framework for modelling and prediction, utilising the self-attention mechanism and multiscale feature fusion of Informer to precisely forecast PV power. The power of PV prediction data from the SSA-VMD-Informer model and four other commonly used models is compared. Experimental results indicate that the SSA-VMD-Informer model performs exceptionally well in short-term PV power prediction, achieving higher accuracy than traditional methods. As an example, the results of predicting the PV power on 24 April in a region of Xinjiang are 1.3882 for RMSE, 0.8310 for MSE, 1.14 for SDE, and 0.9944 for R2.