bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Value of Chess Squares
Authors: Gupta Aditya , Maharaj Shiva , Polson Nicholas , Sokolov Vadim
Published: September 2023

We propose a neural network-based approach to calculate the value of a chess square–piece combination. Our model takes a triplet (color, piece, square) as the input and calculates a value that measures the advantage/disadvantage of having this piece on this square. Our methods build on recent advances in chess AI, and can accurately assess the worth of positions in a game of chess. The conventional approach assigns fixed values to pieces (= ∞, = 9, = 5, = 3, = 3, = 1). We enhance this analysis by introducing marginal valuations. We use deep Q-learning to estimate the parameters of our model. We demonstrate our method by examining the positioning of knights and bishops, and also provide valuable insights into the valuation of pawns. Finally, we conclude by suggesting potential avenues for future research.
Partial Recovery of Coherence Loss in Coherence-Assisted Transformation
Authors: Fan Zhaobing , Shan Zewen , Ma Haitao
Published: September 2023

Coherence-assisted transformation under incoherent operations is discussed. For transformation from the pure state to the mixed state, we show that the coherence loss can be partially recovered by adding auxiliary coherent states. First, we discuss the coherence-assisted transformation for qubit states and give the sufficient and necessary condition for the partial recovery of coherence loss, and the maximum of the recovery of coherence loss is also studied in this case. Second, the maximally coherent state can be obtained in the above recovery scheme, so we give the full characterization of obtaining the maximally coherent state in a qubit system. Finally, we show that the coherence-assisted transformation for arbitrary finite-dimensional main coherent states and low-dimensional auxiliary coherent states is always possible, and the coherence loss also can be partially recovered in these cases.
Iterated Clique Reductions in Vertex Weighted Coloring for Large Sparse Graphs
Authors: Fan Yi , Zhang Zaijun , Yu Quan , Lai Yongxuan , Su Kaile , Wang Yiyuan , Pan Shiwei , Latecki Longin Jan
Published: September 2023

The Minimum Vertex Weighted Coloring (MinVWC) problem is an important generalization of the classic Minimum Vertex Coloring (MinVC) problem which is NP-hard. Given a simple undirected graph G=(V,E), the MinVC problem is to find a coloring s.t. any pair of adjacent vertices are assigned different colors and the number of colors used is minimized. The MinVWC problem associates each vertex with a positive weight and defines the weight of a color to be the weight of its heaviest vertices, then the goal is the find a coloring that minimizes the sum of weights over all colors. Among various approaches, reduction is an effective one. It tries to obtain a subgraph whose optimal solutions can conveniently be extended into optimal ones for the whole graph, without costly branching. In this paper, we propose a reduction algorithm based on maximal clique enumeration. More specifically our algorithm utilizes a certain proportion of maximal cliques and obtains lower bounds in order to perform reductions. It alternates between clique sampling and graph reductions and consists of three successive procedures: promising clique reductions, better bound reductions and post reductions. Experimental results show that our algorithm returns considerably smaller subgraphs for numerous large benchmark graphs, compared to the most recent method named RedLS. Also, we evaluate individual impacts and some practical properties of our algorithm. Furthermore, we have a theorem which indicates that the reduction effects of our algorithm are equivalent to that of a counterpart which enumerates all maximal cliques in the whole graph if the run time is sufficiently long.
Analysis of Properties of the Multilayer Meander Structures for Wireless Communications
Authors: Belova-Plonienė Diana , Krukonis Audrius , Urbanavičius Vytautas , Gurskas Antanas , Abromavičius Vytautas , Katkevičius Andrius
Published: September 2023

Multilayer meander structures for wireless communications are presented in this paper. The miniaturization of meander structures is solved by positioning the meander conductor in multiple layers. The influence of the increasing number of layers and connecting vias on the operational parameters of the meander structures is investigated. Three-dimensional models of the meander structures are designed and analyzed in the CST Microwave Studio© software package. The general mathematical model of the meander structure is presented. The computer-based simulation is verified by a physical experiment and analytical calculations. The investigation shows that it is possible to miniaturize the meander structure by placing it into different layers and connecting the meander conductors with vias. The overall length of the meander structure is decreased by 48% from 16.24 mm to 8.4 mm, while the delay time td is changed only by less than 3.2% and increased till 1.145 ns, which is 35 ps. The overall dimensions of the miniaturized meander structure are 8.4 × 17.35 × 0.76 mm. The designed structure is suitable for operation at a 2.4–2.5 GHz ISM frequency band.
Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles
Authors: Kim Yongcheol , Lee Hojun , Seol Seunghwan , Park Bonggyu , Chung Jaehak
Published: September 2023

This paper presents an underwater biomimetic covert acoustic communication system that achieves high covertness and a high data rate by mimicking dolphin group whistles. The proposed method uses combined time–frequency shift keying modulation with continuous varying carrier frequency modulation, which mitigates the interference between two overlapping multiple whistles while maintaining a high data rate. The data rate and bit error rate (BER) performance of the proposed method were compared with conventional underwater covert communication through an additive white Gaussian noise channel, a modeled underwater channel, and practical ocean experiments. For the covertness test, the similarity of the proposed multiple whistles was compared with the real dolphin group whistles using the mean opinion score test. As a result, the proposed method demonstrated a higher data rate, better BER performance, and large covertness to the real dolphin group whistles.
Highly Efficient GaN Doherty Power Amplifier for N78 Sub-6 GHz Band 5G Applications
Authors: Eid Mohammed A. Elsayed , Abouelnaga Tamer G. , Ibrahim Hamed A. , Hamad Ehab K. I. , Al-Gburi Ahmed Jamal Abdullah , Alghamdi Thamer A. H. , Alathbah Moath
Published: September 2023

In this paper, a high-efficiency GaN Doherty power amplifier (DPA) for 5G applications in the N78 sub-6 GHz band is introduced. The theoretical analysis of the matching networks for the peak and carrier transistors is presented, with a focus on the impact of unequal power splitting for both transistors and the recommendation of a post-harmonic suppression network. The proposed design features an unequal Wilkinson power divider at the input and a post-harmonic suppression network at the output, both of which are crucial for achieving high efficiency. The Doherty power amplifier comprises two GaN 10 W HEMTs, measured across the 3.3 GHz to 3.8 GHz band (the N78 band), and the results reveal significant improvements in gain, output power, drain efficiency, and power-added efficiency. Specifically, the proposed design achieved a power gain of over 12 dB and 42 dBm saturated output power. It also achieved a drain efficiency of 80% at saturation and a power-added efficiency of 75.2%. Furthermore, the proposed harmonic suppression network effectively attenuated the harmonics at the output of the amplifier from the second to the fourth order to more than −50 dB, thus enhancing the device’s linearity.
Measurement and Calculation Techniques of Complex Permeability Applied to Mn-Zn Ferrites Based on Iterative Approximation Curve Fitting and Modified Equivalent Inductor Model
Authors: Szczerba Piotr , Ligenza Slawomir , Worek Cezary
Published: September 2023

In many cases, power inductors are responsible for most of the power loss, volume, and cost if applied to high-frequency power electronics applications. It is desirable to optimize their design by the proper calculation of winding and core loss. It allows faster and cheaper commercial product release, which is the key to being successful in a highly competitive market. This is only possible if existing calculation techniques and technical data given by, e.g., core manufacturers, are verified and correct; otherwise, the inductor optimization process is less precise and requires several iterations to achieve good convergence. This paper addresses existing and proposes improved measurement and calculation techniques with regard to complex permeability, one of the key quantities that define inductor behavior in the frequency domain. This is done through impedance measurement and improved definition of the equivalent inductor model. Moreover, the proposed calculation techniques fulfill the need for the simple, accurate analytical methods required in commercial designs.
Dual Event-Triggered Controller Co-Design for Networked Control Systems with Network-Induced Delays
Authors: Zhou Xuede , Wang Yan , Zhang Shenglin , Ji Zhicheng
Published: September 2023

To address the presence of network-induced delays in networked control systems (NCSs), a dual event-triggered mechanism (DETM) is used to investigate the problem of reducing network delays and controller co-design. Firstly, the DETM of the sensor–controller (SC) and the controller–actuator (CA) is adopted. By determining whether the sampled data meet the event-triggered threshold conditions for network transmission, we effectively reduce the sampled data transmitted over the network, which can reduce a network delay by reducing occupation of the network resources. Secondly, a dual event-triggered NCS model with a network-induced delay is developed, and a Lyapunov function including a DETM and network-induced delay is chosen. The functional upper limit of the Lyapunov function is estimated by combining the Wirtinger’s-based integral inequality with the reciprocally convex approach. This results in a stability criterion for systems with low conservativeness and a controller co-design method for a DETM. Finally, the availability of this method was verified through a numerical example and case study.
Ten-Port MIMO Inverted-F Antenna for LTE Bands 43/48/49 Bands Smartphone Applications
Authors: Zahid Muhammad , Khalid Aliya , Moazzam Hira , Sadaqat Hajra , Shoaib Sultan , Amin Yasar
Published: September 2023

This paper presents a design and performance analysis of a 10-element 5G massive Multiple Input Multiple Output (m-MIMO) antenna array for sub-6 GHz mobile handsets, specifically for Long Term Evolution (LTE) bands 43 (3600–3800 MHz) and 48/49 (3550–3700 MHz) applications. The proposed antenna array consists of ten closely spaced inverted-F antennas with a compact size of 20 × 9 mm2 of a single element. The proposed antenna array provides high efficiency and low correlation between the antenna elements, which result in increased data rate and enhanced signal quality. The performance of the antenna array is evaluated in terms of the radiation pattern, diversity gain, efficiency, and correlation coefficient. The simulation and measured results show that the proposed antenna array achieves an approximate peak gain of 2.8 dBi and a total efficiency of 65% at the resonance frequency of 37 GHz and a low correlation coefficient of 0.07 between the adjacent antenna elements. Moreover, the single and two-hand modes are also given in order to highlight the potential of such a structure as a smart mobile terminal. The simulated results are discovered to be in excellent agreement with the measured values. We think this structure has a bright future in the next generation of smart mobile phones based on the performance and the measured findings.
A Fast Registration Method for MEMS LiDAR Point Cloud Based on Self-Adaptive Segmentation
Authors: Li Xuemei , Liu Bin , Lv Shangsong , Li Min , Liu Chengjie
Published: September 2023

The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved Normal Distribution Transform (NDT) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the Intrinsic Shape Signature (ISS) algorithm is utilized to analyze the point cloud features and extract key points; the Four-Point Congruent Set (4PCS) algorithm is then employed to calculate the initial pose under the constraints of the key point set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using a K-D tree to obtain the density features of key points, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected datasets. The results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference.
Protective Factors for Developing Cognitive Skills against Cyberattacks
Authors: Cazares María , Fuertes Walter , Andrade Roberto , Ortiz-Garcés Iván , Rubio Manuel Sánchez
Published: September 2023

Cyberattacks capitalize on human behaviors. The prevalence of cyberattacks surged during the COVID-19 pandemic, fueled by the increased interconnectivity of individuals on online platforms and shifts in their psychological dynamics due to the pandemic’s context. The enhancement of human factors becomes imperative in formulating a robust cybersecurity strategy against social engineering in the post-COVID-19 era and in anticipation of analogous pandemics. This study aims to propose a model for delineating strategies across various phases of cyberattacks, grounded in the cyber kill chain model, while also encompassing cognitive mechanisms for adaptive responses. This approach aims to cultivate defensive cognitive factors like resilience and self-efficacy. To achieve this objective, we conducted an exploratory study adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Subsequently, we pursued a descriptive and correlational study based on prevalent attacks during the pandemic. The intention was to pinpoint proactive factors conducive to the development of cognitive capabilities to counter cyberattacks. These insights could pave the way for the creation of training programs and technological solutions aimed at mitigating the impact of such cyberattacks.
Cognitive Workload Classification in Industry 5.0 Applications: Electroencephalography-Based Bi-Directional Gated Network Approach
Authors: Afzal Muhammad Abrar , Gu Zhenyu , Afzal Bilal , Bukhari Syed Umer
Published: September 2023

In the era of Industry 5.0, effectively managing cognitive workload is crucial for optimizing human performance and ensuring operational efficiency. Using an EEG-based Bi-directional Gated Network (BDGN) approach, this study tries to figure out how to classify cognitive workload in Industry 5.0 applications. The proposed approach incorporates LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models in a hybrid architecture to leverage their complementary strengths. This research highlights the utilization of the developed model alongside the MQTT (Message Queuing Telemetry Transport) protocol to facilitate real-time end-to-end data transmission. The deployed AI model performs the classification of cognitive workload based on the received data. The main findings of this research reveal an impressive accuracy of 98% in cognitive workload classification, validating the efficacy of the suggested BDGN approach. This study emphasizes the significance of leveraging EEG-based approaches in Industry 5.0 applications for cognitive workload management.
A Novel Fractional Delay Proportional–Integral Multi-Resonant-Type Repetitive Control Based on a Farrow-Structure Filter for Grid-Tied Inverters
Authors: Liang Fen , Lee Ho-Joon , Zhao Qiangsong
Published: September 2023

The integer-order delay of proportional–integral multi-resonant-type repetitive control (PIMR-RC) cannot provide excellent control performance for grid-tied inverters when the grid frequency fluctuates. To address this issue and reduce control errors, a fractional delay PIMR-RC (FD-PIMR-RC) scheme is proposed. In addition, to reduce the computational load and memory consumption, a Farrow-structure fractional delay (FFD) filter is adopted. The digital filter with the Farrow structure is flexibly and efficiently used for fractional delay. For each new fractional delay, a large number of calculations and storage for the FFD filter coefficients are avoided, which significantly reduces the computational load and memory consumption. The parameter design of the FD-PIMR-RC scheme is provided in detail, including the implementation of fractional delay based on the Farrow structure. Then, a system stability analysis and parameter optimization are presented. Finally, simulations for the steady-state and dynamic responses are presented, and the validity of the proposed method is demonstrated.
High Area Efficiency Bidirectional Silicon-Controlled Rectifier for Low-Voltage Electrostatic Discharge Protection
Authors: Chen Yipeng , Zhao Dongyan , Zhou Shicong , Zhu Xinyu , Gao Feng , Yuan Yidong , Hu Yi , Zhao Tianting , Li Xiaojuan , Dong Shurong
Published: September 2023

Continuously scaling down and decreasing operation voltages of ICs, from the 5 V TTL-compatible voltage to 3.3 V, then 1.2 V, and now 0.8 V for low-power ICs, results in more stringent electrostatic discharge protection design requirements, such as a narrow ESD design window, low operation voltage, and high ESD robustness. Based on traditional diode string and diode-triggered silicon-controlled rectifiers, an enhanced diode-triggered silicon-controlled rectifier is proposed to meet the requirements of low-voltage integrated circuits as bidirectional electrostatic discharge protection. The new device employs an additional PMOS and NMOS in the N-well and P-well, respectively, to offer additional current paths along the surface to significantly enhance its robustness. TCAD simulation shows that the device is triggered by both the diode strings and embedded MOS, making the device turn on faster and the current distribution more uniform during the ON state owing to the additional surface current paths. The proposed new device has excellent dual-directional ESD protection performance with a figure of merit of 4.01 mA/um2, which is about a 71% improvement compared with the conventional diode-triggered silicon-controlled rectifier. It also has higher area efficiency, lower trigger voltage, lower current leakage, and a faster turn-on speed. The proposed enhanced diode-triggered silicon-controlled rectifier is an attractive ESD protection solution for ultra-low-voltage ICs.
Empowering Accessibility: BLE Beacon-Based IoT Localization
Authors: Pyt Patryk , Skrobacz Kacper , Jankowski-Mihułowicz Piotr , Węglarski Mariusz , Kamuda Kazimierz
Published: September 2023

The Internet of Things (IoT) localization empowers smart infrastructures of buildings to deliver advanced services to users leveraging mobile devices. In this context, in order to enhance the mobility of people with disabilities on the university campus, a Bluetooth Low Energy (BLE) beacon-based indoor system was developed. Particular emphasis was placed on selection of the beacon for the designed application, which was performed on the basis of the energy demand characteristics at the assumed power settings and time intervals of the emitted signal. The paper also focuses on various concepts of transmitter deployment inside buildings of the campus in order to demonstrate possible configurations in which the IoT localization will work correctly. Based on experimental determination of the signal strength reaching users’ mobile devices, the best arrangement of the system was proposed. However, the dependence of the calculated distance between the interrogated beacon and the mobile device as a function of the received signal strength is a non-deterministic function of many factors; thus, only an approximate position can be designated on the performed measurements. Nevertheless, the BLE beacon-based system, supported by additional localization algorithms integrated into the user’s mobile software, can be useful for the applications in question.
Static Hand Gesture Recognition Based on Millimeter-Wave Near-Field FMCW-SAR Imaging
Authors: Hao Zhanjun , Wang Ruidong , Peng Jianxiang , Dang Xiaochao
Published: September 2023

To address the limitations of wireless sensing in static gesture recognition and the issues of Computer Vision’s dependence on lighting conditions, we propose a method that utilizes millimeter-wave near-field SAR (Synthetic Aperture Radar) imaging for static gesture recognition. First, a millimeter-wave near-field SAR imaging system is used to scan the defined static gestures to obtain data. Then, based on the distance plane, the three-dimensional gesture is divided into multiple two-dimensional planes, constructing an imaging dataset. Finally, an HOG (Histogram of Oriented Gradients) is used to extract features from the imaging results, PCA (Principal Component Analysis) is applied for feature dimensionality reduction, and RF (Random Forest) performs classification. Experimental verification shows that the proposed method achieves an average recognition precision of 97% in unobstructed situations and 93% in obstructed situations, providing an effective means for wireless-sensing-based static gesture recognition.
A Federated Learning Method Based on Blockchain and Cluster Training
Authors: Li Yue , Yan Yiting , Liu Zengjin , Yin Chang , Zhang Jiale , Zhang Zhaohui
Published: September 2023

Federated learning (FL) is an emerging machine learning method in which all participants can collaboratively train a model without sharing their raw data, thereby breaking down data silos and avoiding privacy issues caused by centralized data storage. In practical applications, client data are non-independent and identically distributed, resulting in FL requiring multiple rounds of communication to converge, which entails high communication costs. Moreover, the centralized architecture of traditional FL remains susceptible to privacy breaches, network congestion, and single-point failures. In order to solve these problems, this paper proposes an FL framework based on blockchain technology and a cluster training algorithm, called BCFL. We first improved an FL algorithm based on odd–even round cluster training, which accelerates model convergence by dividing clients into clusters and adopting serialized training within each cluster. Meanwhile, compression operations were applied to model parameters before transmission to reduce communication costs and improve communication efficiency. Then, a decentralized FL architecture was designed and developed based on blockchain and Inter-Planetary File System (IPFS), where the blockchain records the FL process and IPFS optimizes the high storage costs associated with the blockchain. The experimental results demonstrate the superiority of the framework in terms of accuracy and communication efficiency.
PAPR Suppression for Angular-Domain-Based Massive Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing System
Authors: Liu Ting , Wang Xiaoming , Xin Yuanxue , Yang Xi
Published: September 2023

In this paper, the precoding-based peak-to-average power ratio (PAPR) reduction methods are studied for the massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in the angular domain. The expectation maximization generalized approximate message passing algorithm and the proposed optimized alternating direction method of multipliers (OADMM) scheme are adopted here to explore the system characteristics in terms of the symbol error ratio (SER) performance, the PAPR reduction efficiency, and the inter user interference (IUI). Specifically, the high PAPR problem is reduced to no more than 0.18 dB by using the inherent property of the massive MIMO-OFDM angle division multiple access (ADMA) system with relatively reduced computational complexity. Moreover, the value of SER is around 10−5 dB when the proposed technique is performed. Computer numerical simulation results verify the efficiency of the proposed technique from the perspective of SER, PAPR suppression, and IUI.
3D-Printed Dielectric Rods for Radar Range Enhancement
Authors: Bagheri Mohammad Omid , Abedi Hajar , Shaker George
Published: September 2023

A design strategy to alter the radiation characteristics of modular radar printed circuit boards without the need for expensive retooling and remanufacturing is presented in this paper. To this end, a compact radar module including microstrip array antennas integrated with a dielectric rod lens is considered for a demonstration of an X-band radar antenna gain improvement leading to radar detection range enhancement. Using travelling wave theory, the proposed lens is designed to target the excitation of HE11 mode to achieve gain improvement without disturbing reflection coefficients. Using a low-cost rapid-manufacturing 3D-printing technology, two pairs of the 3D-printed dielectric rods integrated with a dielectric housing are designed and fabricated uniformly for a commercially available off-the-shelf radar module. The radar integration with the dielectric rod lens leads to a low-cost and easy-to-fabricate long-range radar system. Compared with the radar without the rods, the design system achieved a measured 6.6 dB gain improvement of the transmitter and receiver antennas which causes doubling the detection range for both elevation and azimuth directions at 10.525 GHz.
MDBF: Meta-Path-Based Depth and Breadth Feature Fusion for Recommendation in Heterogeneous Network
Authors: Liu Hongjuan , Zhang Huairui
Published: September 2023

The main challenge of recommendation in a heterogeneous information network comes from the diversity of nodes and links and the problem of semantic expression ambiguity caused by diversity. Therefore, we propose a movie recommendation algorithm for a heterogeneous network called Meta-Path-Based Depth and Breadth Feature Fusion(MDBF). Using a random walk for depth feature learning, we can extract a depth feature meta-path that reflects the overall structure of the network. In addition, using random walks in adjacent nodes, we can extract a breadth feature meta-path, preserving the neighborhood information of a node. If there is some auxiliary information, it will be learned by its own meta-paths. Then, all of the feature sequences can be fused and learned by the Skip-gram algorithm to obtain the final feature vector. In the recommendation process, based on traditional collaborative filtering, we propose a secondary filtering recommendation. The experimental results show that, without external auxiliary information, compared to the existing state-of-the-art models, the algorithm improves each index by an average of 12% on MovieLens and 22% on MovieTweetings. The algorithm not only improves the effect of movie recommendation, but also provides application scenarios for accurate recommendation services through auxiliary information.
Large-Scale Service Function Chaining Management and Orchestration in Smart City
Authors: Tam Prohim , Kang Seungwoo , Ros Seyha , Song Inseok , Kim Seokhoon
Published: September 2023

In the core networking of smart cities, mobile network operators need solutions to reflect service function chaining (SFC) orchestration policies while ensuring efficient resource utilization and preserving quality of service (QoS) in large-scale networking congestion states. To offer this solution, we observe the standardized QoS class identifiers of smart city scenarios. Then, we reflect the service criticalities via cloning virtual network function (VNF) with reserved resources for ensuring effective scheduling of request queue management. We employ graph neural networks (GNN) with a message-passing mechanism to iteratively update hidden states of VNF nodes with the objectives of enhancing allocation of resource blocks, accurate detection of availability statuses, and duplication of heavily congested instances. The deployment properties of smart city use cases are presented along with their intelligent service functions, and we aim to activate a modular architecture with multi-purpose VNFs and chaining isolation for generalizing global instances. Experimental simulation is conducted to illustrate how the proposed scheme performs under different congestion levels of SFC request rates, while capturing the key performance metrics of average delay, acceptance ratios, and completion ratios.
A Multi-Channel Parallel Keypoint Fusion Framework for Human Pose Estimation
Authors: Wang Xilong , Shi Nianfeng , Wang Guoqiang , Shao Jie , Zhao Shuaibo
Published: September 2023

Although modeling self-attention can significantly reduce computational complexity, human pose estimation performance is still affected by occlusion and background noise, and undifferentiated feature fusion leads to significant information loss. To address these issues, we propose a novel human pose estimation framework called DatPose (deformable convolution and attention for human pose estimation), which combines deformable convolution and self-attention to relieve these issues. Considering that the keypoints of the human body are mostly distributed at the edge of the human body, we adopt the deformable convolution strategy to obtain the low-level feature information of the image. Our proposed method leverages visual cues to capture detailed keypoint information, which we embed into the Transformer encoder to learn the keypoint constraints. More importantly, we designed a multi-channel two-way parallel module with self-attention and convolution fusion to enhance the weight of the keypoints in visual cues. In order to strengthen the implicit relationship of fusion, we attempt to generate keypoint tokens to the visual cues of the fusion module and transformers, respectively. Our experimental results on the COCO and MPII datasets show that performing the keypoint fusion module improves keypoint information. Extensive experiments and visual analysis demonstrate the robustness of our model in complex scenes and our framework outperforms popular lightweight networks in human pose estimation.
Face Detection Based on DF-Net
Authors: Tang Qijian , Li Yanfei , Cai Yinhe , Peng Xiang , Liu Xiaoli
Published: September 2023

Face data have found increasingly widespread applications in daily life. To efficiently and accurately extract face information from input images, this paper presents a DF-Net-based face detection approach. A lightweight facial feature extraction neural network based on the MobileNet-v2 architecture is designed and implemented. By incorporating multi-scale feature fusion and spatial pyramid modules, the system achieves face localization and extraction across multiple scales. The proposed network is trained on the open-source face detection dataset WiderFace. The hyperparameters such as bottleneck coefficients and quality factors are discussed. Comparative experiments with other commonly used networks are carried out in terms of network model size, processing speed, and network extraction accuracy. Experimental results affirm the efficacy and robustness of this method, especially in challenging facial poses.
An Effective Method for Constructing a Robot Operating System Node Knowledge Graph Based on Open-Source Robotics Repositories
Authors: Zhao Yuxin , Mao Xinjun , Yang Yi
Published: September 2023

Robot software development can be considered as a component-driven process, and existing ROS components, such as an ROS node, can be reused to construct robot applications. By reusing the ROS node, the development process of robot software can be significantly accelerated. However, the challenges in reusing ROS nodes primarily lie in the scattered organization of ROS node information. To address this challenge, this paper proposes a method to construct an ROS node knowledge graph (RNKG) based on high-quality open-source robot projects. In order to build a high-quality knowledge graph of ROS nodes, we first constructed a high-quality dataset of open-source robot projects. Since ROS node knowledge can exist in both text and code formats, we initially separated the data in the dataset into code data and text data, and then applied different knowledge extraction methods to extract corresponding entities. Finally, we integrated a series of ROS node knowledge and organized it into a knowledge graph. To validate the effectiveness of the constructed ROS node knowledge graph, we first verified the completeness of the entities and the accuracy of relationships in the knowledge graph. Next, we evaluated the performance of the ROS node knowledge graph in assisting developers with the downstream task of finding ROS nodes. These findings suggest that our proposed method for constructing an ROS node knowledge graph is feasible and demonstrate that the ROS node knowledge graph helps search ROS nodes.
Optimizing Autonomous Vehicle Communication through an Adaptive Vehicle-to-Everything (AV2X) Model: A Distributed Deep Learning Approach
Authors: Osman Radwa Ahmed
Published: September 2023

Autonomous intelligent transportation systems consistently require effective and secure communication through vehicular networks, enabling autonomous vehicle communication. The reduction of traffic congestion, the alerting of approaching emergency vehicles, and assistance in low visibility traffic are all made possible by effective communication between autonomous vehicles and everything (AV2X). Therefore, a new adaptive AV2X model is proposed in this paper to improve the connectivity of vehicular networks. This proposed model is based on the optimization method and a distributed deep learning model. The presented approach optimizes the inter-vehicle location if required for ensuring effective communication between the autonomous vehicle (AV) and everything (X) using the Lagrange optimization algorithm. Furthermore, the system is evaluated in terms of energy efficiency and achievable data rate based on the optimal inter-vehicle position to show the significance of the proposed approach. To meet the stated goals, the ideal inter-vehicle position is predicted using a distributed deep learning model by learning from mathematically generated data and defined as a restricted optimization problem using the Lagrange optimization technique to improve communication between AV2X under various environmental conditions. To demonstrate the efficiency of the suggested model, the following characteristics are considered: vehicle dispersion, vehicle density, vehicle mobility, and speed. The simulation results show the significance of the proposed model in terms of energy efficiency and achievable data rate compared with other proposed models.
End Effect Equivalence in the 2-D Finite Element Analysis of a Line-Start Permanent Magnet Synchronous Motor with Hybrid Solid Rotor
Authors: Yan Bo , Li Xianglin , Sun Yue , Tan Yingjie
Published: September 2023

Reasonable end effect equivalence is of vital importance in the 2-D finite element analysis (FEA) for a line-start permanent magnet synchronous motor (LSPMSM) equipped with a hybrid solid rotor for improved starting and synchronous capabilities. In this work, a series of correction factors are applied to the rotor conductivity to include the end effect in the 2-D FEA model. Derivation of these correction factors completes after the separation of different torque components. A dynamic motor model of the LSPMSM is established with some parameters extracted from the 2-D FEA using the conductivity correction. Outcomes of this model exhibit good agreements with the 3-D FEA results, by which the introduced correction factors are validated to possess the acceptable precision and effectiveness.
An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
Authors: Cantillo-Luna Sergio , Moreno-Chuquen Ricardo , Lopez-Sotelo Jesus , Celeita David
Published: September 2023

This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.
The Carbon Footprint of Thermal Insulation: The Added Value of Circular Models Using Recycled Textile Waste
Authors: Violano Antonella , Cannaviello Monica
Published: September 2023

The goal of climate neutrality by 2050 drives the building sector towards stricter control of processes and products, leading to a substantial reduction of embodied carbon throughout the life cycle. Many of the most used insulation materials have a high carbon footprint, mainly due to the production phase (from cradle to gate). The need to reduce these impacts has led to the implementation of materials whose predominant raw material is recycled material in order to reduce the embodied carbon. The contribution presents the results of a research work that analysed the potential of insulation materials obtained from textile waste, evaluating not only their energy performance but also, above all, their environmental impact in terms of carbon footprint. It starts from a state-of-the-art analysis of the main traditional and new-generation thermal insulation materials, not only in relation to performance but also to environmental impacts, in order to investigate the opportunities offered using insulation materials designed according to circular models (10R) and produced with industrial and/or post-consumer waste fabrics, through a carbon footprint comparison. To support the choice of this type of insulation, a multi-criteria evaluation method is proposed through which the comparative analysis of the most significant insulation products selected is carried out.
Research on Initiating Events Analysis of Small Helium-Xenon Gas Cooled Nuclear Reactor
Authors: Zhou Jun , Wu Jianhui , Cui Yong , Zhao Hongkai , Zou Chunyan , Chen Jingen
Published: September 2023

Initiating event analysis is an essential prerequisite of conducting probabilistic safety assessment for nuclear reactors, which plays an important role in improving the core design, identifying fault, and guiding operation. In order to determine the initiating event list of SIMONS (Small Innovative helium-xenon cooled Mobile Nuclear power System), preliminary researches on the initial event of SIMONS were carried out using the MLD (Main Logic Diagram) analysis method and referring to the initial event list and initial event analysis theory of other nuclear reactors such as HTGR (High Temperature Gas-cooled Reactor), MSR (Molten Salt Reactor), and PWR (Pressurized water reactor). With employing these methods, a total of 31 initial events are identified for SIMONS based on its latest conceptual design. These initial events are then divided into six groups according to the accident types, which are core heat removal increase, core heat removal decrease, abnormal reactivity and power distribution, pipeline crevasse and equipment leakage, anticipated transients without scram, and disasters (internal and external). The obtained results can provide a theoretical basis for the further safety analysis of SIMONS.
Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning
Authors: Samende Cephas , Fan Zhong , Cao Jun , Fabián Renzo , Baltas Gregory N. , Rodriguez Pedro
Published: September 2023

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69%, improve cost savings by 23.5%, and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network.
A Design and Optimization Method of Performing Layer Combinations in Separate-Layer CO2 Flooding to Improve Oil Recovery
Authors: Li Zheng , Zhou Tiyao , Yin Hengfei , Sun Yingying
Published: September 2023

Separate-layer CO2 flooding has increasingly been used to improve the overall development of multi-layer heterogeneous reservoirs. Due to their field technical limitations, layer combinations have to be carried out to reduce the number of layer-groups required for the separate injection of CO2. Currently there are few studies of the method of designing layer combinations. In field practice, the layer combinations are often made based on the permeability ratio of the layers to be developed, but it is not possible to accurately obtain the optimal scheme in many cases. Therefore, in this paper, a new design and optimization method based on the weighted standard deviation of permeability is proposed, which comprehensively considers the effects of multiple geological and fluid properties in different layers. The new method is applied to study the layer combination schemes in separate-layer CO2 flooding in H block, Daqingzijing Oilfield, Jilin, China. The prioritization of all of the schemes is obtained via the method, and a related numerical model is also established to perform verification and quantitative analysis. The results show that designing layer combinations using the new proposed method can achieve better development effect than using the conventional permeability ratio based method. It can achieve a more uniform interlayer CO2 displacement with an obvious improvement in the CO2 swept volume in low permeability layers and a higher overall oil recovery. According to the numerical simulation results, using the optimal layer combination scheme designed via the new proposed method, the ten-year swept volume of CO2 in the low permeability layer can be increased by 16.2%, and the ten-year overall oil recovery efficiency can be increased by 3.3%, with both measures showing a remarkable improvement. The work can provide a significant reference point for the field design of layer combinations in separate-layer CO2 flooding.
Thermochemical Technologies for the Optimization of Olive Wood Biomass Energy Exploitation: A Review
Authors: Maggiotto Giuseppe , Colangelo Gianpiero , Milanese Marco , de Risi Arturo
Published: September 2023

The use of biomass can be a strategic way to realize a carbon-neutral energy plan, ensuring a fuel feedstock. Residual biomass arising from pruning is demonstrated to be an important energy resource in terms of quantity and quality. In the Salento peninsula, Apulia Region, in the south of Italy, a dramatic outbreak of Xylella fastidiosa has decimated olive trees since 2013, gaining a considerable amount of wood biomass. This paper, starting from the need to find a way to optimize the use of this available stock, reviews the main technologies on the utilization of olive wood for energy purposes. In particular, processes and products are here described, and an energy analysis compares lower heating value (LHV), higher heating value (HHV), mass yield, process operating conditions, and energy generated and spent by the process in order to find the most effective technology in order to optimize the energy use of olive biomass. The conclusions show the advantages and disadvantages of each technology. Pyrolysis performs well, showing the best results for both char HHV and syngas yield under different operating conditions. Gasification seems to be the most appropriate among conversion technologies to optimize olive tree pruning for energy purposes, as it can be used to produce both electrical and thermal energy. In terms of economic valorization, char is the most promising material representing a value-added product, the quality and versatility of which ranges from fuel to soil improvers and additives for the construction of supercapacitors. Conversely, its disadvantages are mainly represented by high ash content, which can slightly decrease the boiler efficiency. Finally, the amount of alkali metals can produce several problems, such as fouling, slagging, corrosion, etc., posing a challenge for combustion control and pollutant minimization.
Assessing the Environmental Sustainability of Deep Geothermal Heat Plants
Authors: Maar Lilli , Seifermann Stefan
Published: September 2023

The energy sector is responsible for a large share of climate-damaging emissions. Regarding the decarbonization of the energy sector, deep geothermal energy is considered to have high potential, particularly in the area of heat supply. In order to gauge the extent to which heat use from deep geothermal energy can make a positive contribution to climate protection, deep geothermal systems should be appraised using an environmental sustainability assessment. Although electricity generation from deep geothermal power plants has been evaluated in many ways in the literature with respect to its sustainability, no such sustainability evaluations of pure geothermal heat plants have been conducted so far. In order to close this research gap, this study presents a systematic approach that makes it possible to apply suitable sustainability criteria across the individual life stages of deep geothermal heat plants based on life-cycle assessment (LCA) guidelines. To demonstrate the effectiveness of the systematic approach presented here, a planned geothermal heat plant in the Upper Rhine Valley, Germany, serves as an example. Based on the estimated plant parameters and the predicted total heat yield, it was possible to determine, for example, the “energy returned on energy invested” (EROI) of the plant, which was approximately 34, and the specific CO2 emissions, which were approximately 5.6 g/kWhth.