bluuebunny/crossref_metadata_2025 huggingface dataset:
A Lightweight Image Encryption Algorithm Based on Chaotic Map and Random Substitution
Authors: Alghamdi Yousef , Munir Arslan , Ahmad Jawad
Published: September 2022

Chaotic-maps-based image encryption methods have been a topic of research interest for a decade. However, most of the proposed methods suffer from slow encryption time or compromise on the security of the encryption to achieve faster encryption. This paper proposes a lightweight, secure, and efficient image encryption algorithm based on logistic map, permutations, and AES S-box. In the proposed algorithm, SHA-2 based on the plaintext image, a pre-shared key, and an initialization vector (IV) are used to generate the initial parameters for the logistic map. The logistic map chaotically generates random numbers, which are then used for the permutations and substitutions. The security, quality, and efficiency of the proposed algorithm are tested and analyzed using a number of metrics, such as correlation coefficient, chi-square, entropy, mean square error, mean absolute error, peak signal-to-noise ratio, maximum deviation, irregular deviation, deviation from uniform histogram, number of pixel change rate, unified average changing intensity, resistance to noise and data loss attacks, homogeneity, contrast, energy, and key space and key sensitivity analysis. Experimental results reveal that the proposed algorithm is up to 15.33× faster compared to other contemporary encryption methods.
Universal and Non-Universal Features in the Random Shear Model
Authors: Cecconi Fabio , Taloni Alessandro
Published: September 2022

The stochastic transport of particles in a disordered two-dimensional layered medium, driven by correlated y-dependent random velocity fields is usually referred to as random shear model. This model exhibits a superdiffusive behavior in the x direction ascribable to the statistical properties of the disorder advection field. By introducing layered random amplitude with a power-law discrete spectrum, the analytical expressions for the space and time velocity correlation functions, together with those of the position moments, are derived by means of two distinct averaging procedures. In the case of quenched disorder, the average is performed over an ensemble of uniformly spaced initial conditions: albeit the strong sample-to-sample fluctuations, and universality appears in the time scaling of the even moments. Such universality is exhibited in the scaling of the moments averaged over the disorder configurations. The non-universal scaling form of the no-disorder symmetric or asymmetric advection fields is also derived.
Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition
Authors: Lv Zhigang , Chen Yiwei , Di Ruohai , Wang Hongxi , Sun Xiaojing , He Chuchao , Li Xiaoyan
Published: September 2022

The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.
Research on Multi-Terminal’s AC Offloading Scheme and Multi-Server’s AC Selection Scheme in IoT
Authors: Liu Jiemei , Lin Fei , Liu Kaixu , Zhao Yingxue , Li Jun
Published: September 2022

Mobile Edge Computing (MEC) technology and Simultaneous Wireless Information and Power Transfer (SWIPT) technology are important ones to improve the computing rate and the sustainability of devices in the Internet of things (IoT). However, the system models of most relevant papers only considered multi-terminal, excluding multi-server. Therefore, this paper aims at the scenario of IoT with multi-terminal, multi-server and multi-relay, in which can optimize the computing rate and computing cost by using deep reinforcement learning (DRL) algorithm. Firstly, the formulas of computing rate and computing cost in proposed scenario are derived. Secondly, by introducing the modified Actor-Critic (AC) algorithm and convex optimization algorithm, we get the offloading scheme and time allocation that maximize the computing rate. Finally, the selection scheme of minimizing the computing cost is obtained by AC algorithm. The simulation results verify the theoretical analysis. The algorithm proposed in this paper not only achieves a near-optimal computing rate and computing cost while significantly reducing the program execution delay, but also makes full use of the energy collected by the SWIPT technology to improve energy utilization.
The Effects of Regulatory Performance on the Debt–Growth Relationship: Cases of Upper-Middle-Income Economies
Authors: Abd Rahman Nur Hayati , Ismail Shafinar , Abd Samad Khairunnisa , Dwi Handayani Bestari , Rahman Yozi Aulia , Sakitri Wijang
Published: September 2022

Upper-middle-income economies (UMIE) are experiencing an economic slowdown, partly due to weak regulatory performance. This issue leads to slow growth in private sector participation, thus limiting the ability to achieve higher economic growth. At this critical point, the government’s role is to inject funds into economies, hoping that growth can be increased and sustained for an extended period. Nevertheless, injecting more funds through borrowings from external debt exposes economies to vulnerable conditions. Thus, this study aimed to examine how regulatory performance affects economic growth and moderates the debt–growth relationship in UMIE. By using the generalized method of moments (GMM) as an estimation method for 32 countries from 2004 to 2020, regulatory performance was found to adversely affect economic growth. Moreover, as regulatory performance improves, public debt is expected to enhance the economic growth of UMIE. These findings are novel, as they provide significant evidence for the importance of improving the regulatory performance of UMIE. Weak regulatory performance might force a government to become the engine of growth instead of the private sector, thus leading to the adverse effect of debt on growth in UMIE. These findings have to several policy implications, particularly regarding reducing bureaucracy and improving regulatory performance in UMIE. Future researchers could extend this study by comparing the results from different groups of economies or countries.
The Impact of COVID-19 on Faculty Technological Knowledge Development at an Academic Medical Center
Authors: Gordy Xiaoshan Zhu , DeVaul Driscoll , Morton Mary E. , Callahan Kristen B. , Burrell Angela , Schmitz Travis W. , Reulet Britney
Published: September 2022

The COVID-19 pandemic impacted the learning environments of 1.3 billion students worldwide, particularly as many traditional, in-person classes moved to virtual learning. This mixed-methods study surveyed and interviewed faculty within a US academic medical center to determine whether the pandemic stimulated technological knowledge growth. While the study collected both quantitative and qualitative data, priority was given to qualitative interview data to control for a small sample size and to gather in-depth insights. Data collection began approximately 18 months after the onset of the pandemic that occurred in spring 2020. Qualitative results garnered five major themes: collective decision-making and individual autonomy, flexible learning in the new normal, challenges with student engagement in the long-distance relationship, faculty well-being in the age of COVID-19, and opportunities evolved from challenges. Quantitative results indicated that significant differences existed between faculty self-rated competency levels of performing technology-related tasks in the pre-pandemic period and in the current period, suggesting technology knowledge gains from the forced transition to online teaching. Future research is needed to survey a broader audience and to determine whether faculty will continue to utilize technological resources learned during this pandemic to supplement in-person teaching as well as to establish a future contingency plan.
Remote Teaching during COVID-19 Emergency: Teaching and Assessment Strategies and the Role of Previous Training
Authors: Capperucci Davide , Scierri Irene Dora Maria , Salvadori Ilaria , Batini Federico , Toti Giulia , Barbisoni Giulia , Pera Eleonora
Published: September 2022

The Emergency Remote Teaching experience during the COVID-19 pandemic resulted in schools reviewing teaching methods and strategies. The Italian Society for Educational Research (SIRD) conducted a survey in which more than 16,000 Italian teachers took part. The aim of this study is to be a first step in analysing the data from the Tuscany and Umbria regions by comparing them with national data in relation to the teaching and assessment methods and strategies used, and with the previous teacher training in distance learning. Secondly, the paper aims to deepen the link between the use of certain teaching and assessment methods, the previous training, and the quality of the distance learning experience perceived by teachers. For this purpose, some items and scales from the questionnaire used in the SIRD survey and some categories from the qualitative analysis of the responses to the open-ended questions were selected. The study, first and foremost, revealed some peculiarities of the two regions, including Tuscany’s lower evaluation of the effectiveness of the experience. On the national level, a positive relationship was found between teachers using interactive teaching strategies and “alternative” assessment activities and the higher perception of both effectiveness for student learning and adequate performance assessment. Finally, the role of previous training is underlined as useful only if it is not generally technology-centred, but rather related to teaching and assessment methods in virtual learning environments.
User OCEAN Personality Model Construction Method Using a BP Neural Network
Authors: Qin Xiaomei , Liu Zhixin , Liu Yuwei , Liu Shan , Yang Bo , Yin Lirong , Liu Mingzhe , Zheng Wenfeng
Published: September 2022

In the era of big data, the Internet is enmeshed in people’s lives and brings conveniences to their production and lives. The analysis of user preferences and behavioral predictions of user data can provide references for optimizing information structure and improving service accuracy. According to the present research, user’s behavior on social networking sites has a great correlation with their personality, and the five characteristics of the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model can cover all aspects of a user’s personality. It is important in identifying a user’s OCEAN personality model to analyze their digital footprints left on social networking sites and to extract the rules of users’ behavior, and then to make predictions about user behavior. In this paper, the Latent Dirichlet Allocation (LDA) topic model is first used to extract the user’s text features. Second, the extracted features are used as sample input for a BP neural network. The results of the user’s OCEAN personality model obtained by a questionnaire are used as sample output for a BP neural network. Finally, the neural network is trained. A mapping model between the probability of the user’s text topic and their OCEAN personality model is established to predict the latter. The results show that the present approach improves the efficiency and accuracy of such a prediction.
A Non-Dominated Sorting Genetic Algorithm Based on Voronoi Diagram for Deployment of Wireless Sensor Networks on 3-D Terrains
Authors: Tang Yifeng , Huang Dechang , Li Rong , Huang Zhaodi
Published: September 2022

The deployment strategy for wireless sensor networks (WSNs) affects the quality of service (QoS). Adopting a reasonable deployment strategy can improve the QoS of WSNs. In this paper, the problem regarding sensor node deployment for WSNs on three-dimensional (3D) terrain is modeled as a multi-objective optimization problem. The coverage rate of the WSNs, their unbalanced energy consumption, and the number of sensor nodes are used as fitness functions for the optimization problem. We propose a non-dominated sorting genetic algorithm based on a Voronoi diagram (VNSGA) for solving the wireless sensor network deployment issue and improving the QoS of WSNs on 3D terrain. The proposed algorithm applies the Voronoi diagram to obtain the node-sensing radius and communication radius, which are suitable for 3D terrain with respect to calculating the fitness function of the optimization problem. The Pareto optimal solution is obtained by retaining the solution close to the reference point. The experiments compare the proposed algorithm with the Multi-Objective Particle Swarm Algorithm (MOPSO) and the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) on two terrains with different ranges. The experimental results show that the proposed algorithm outperforms the comparison algorithm on both terrains with different range sizes. The proposed algorithm can improve the coverage to 97.95% and reduce the imbalance in energy consumption to 9.13% on large range terrain.
A Survey on the Use of Deep Learning Techniques for UAV Jamming and Deception
Authors: Šimon Ondřej , Götthans Tomáš
Published: September 2022

Unmanned aerial vehicles (UAVs) can be used for a variety of illegal activities (e.g., industrial espionage, smuggling, terrorism). Given their growing popularity and availability, and advances in communications technology, more sophisticated ways to disable these vehicles must be sought. Various forms of jamming are used to disable drones, but more advanced techniques such as deception and UAV takeover are considerably difficult to implement, and there is a large research gap in this area. Currently, machine and deep learning techniques are popular and are also used in various drone-related applications. However, no detailed research has been conducted so far on the use of these techniques for jamming and deception of UAVs. This paper focuses on exploring the current techniques in the area of jamming and deception. A survey on the use of machine or deep learning specifically in UAV-related applications is also conducted. The paper provides insight into the issues described and encourages more detailed research in this area.
Research on the Identification Method of Overhead Transmission Line Breeze Vibration Broken Strands Based on VMD−SSA−SVM
Authors: Wang Cheng , Wang Shufan , Li Jiajun , Wu Jianjun , Zhong Jianwei , Liao Honghua
Published: September 2022

For the problem of overhead line broken strand identification, a VMD−SSA−SVM-based overhead transmission line broken strand identification method is proposed. First, the vibration acceleration response signal of overhead transmission line under breeze environment was simulated by Ansys simulation software. Then the variable modal decomposition of the vibration acceleration response of the transmission line is performed to obtain the intrinsic mode function corresponding to the vibration behavior of the transmission line. The Hilbert transform is used to analyze the intrinsic mode functions of each order, and the Hilbert spectrum and marginal spectrum of the intrinsic mode functions of each order are studied to extract the first six order intrinsic frequencies. Each set of intrinsic frequencies is input as a feature vector into a support vector machine optimized by the sparrow search algorithm for training to achieve the identification of broken strands in overhead transmission lines. This paper studies the inherent frequency changes and identification results of overhead transmission lines under two states of normal operation and broken 5 strands operation, and the test results show that the identification method based on VMD-SSA-SVM has good identification effect on broken strands of overhead lines.
Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
Authors: Qiu Wenduo , Yang Yimu , Feng Yan , Zhang Lin , Wu Zhiqiang
Published: September 2022

In this paper, a multiple-input multiple-output (MIMO) communication system with frequency hopping (FH) aided orthogonal frequency division multiplexing differential chaotic shift keying (OFDM-DCSK) modulation is proposed. Our objective is to improve the security of MIMO communication system which is encoded by space time block coding (STBC). In order to combat the eavesdropping or malicious attacks due to the broadcast characteristics of wireless communication system, we propose to use DCSK and FH modules to encrypt the information, and hide the user data in the chaotic sequences, where the initial value of chaotic sequences and the method of generating FH module are only shared among legitimate users. Moreover, we derive the bit error rate (BER) and the secrecy capacity of the scheme in additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. Simulation results show that the proposed scheme can effectively improve the security of MIMO communication system, which can be seen from the BER of eavesdroppers and legitimate users, and the secrecy capacity of the proposed scheme and the benchmark schemes.
Two-Neuron Based Memristive Hopfield Neural Network with Synaptic Crosstalk
Authors: Qiu Rong , Dong Yujiao , Jiang Xin , Wang Guangyi
Published: September 2022

Synaptic crosstalk is an important biological phenomenon that widely exists in neural networks. The crosstalk can influence the ability of neurons to control the synaptic weights, thereby causing rich dynamics of neural networks. Based on the crosstalk between synapses, this paper presents a novel two-neuron based memristive Hopfield neural network with a hyperbolic memristor emulating synaptic crosstalk. The dynamics of the neural networks with varying memristive parameters and crosstalk weights are analyzed via the phase portraits, time-domain waveforms, bifurcation diagrams, and basin of attraction. Complex phenomena, especially coexisting dynamics, chaos and transient chaos emerge in the neural network. Finally, the circuit simulation results verify the effectiveness of theoretical analyses and mathematical simulation and further illustrate the feasibility of the two-neuron based memristive Hopfield neural network hardware.
Systematic Comparison of Objects Classification Methods Based on ALS and Optical Remote Sensing Images in Urban Areas
Authors: Cai Hengfan , Wang Yanjun , Lin Yunhao , Li Shaochun , Wang Mengjie , Teng Fei
Published: September 2022

Geographical object classification and information extraction is an important topic for the construction of 3D virtual reality and digital twin cities in urban areas. However, the majority of current multi-target classification of urban scenes uses only a single source data (optical remote sensing images or airborne laser scanning (ALS) point clouds), which is limited by the restricted information of the data source itself. In order to make full use of the information carried by multiple data sources, we often need to set more parameters as well as algorithmic steps. To address the above issues, we compared and analyzed the object classification methods based on data fusion of airborne LiDAR point clouds and optical remote sensing images, systematically. Firstly, the features were extracted and determined from airborne LiDAR point clouds and high-resolution optical images. Then, some key feature sets were selected and were composed of median absolute deviation of elevation, normalized elevation values, texture features, normal vectors, etc. The feature sets were fed into various classifiers, such as random forest (RF), decision tree (DT), and support vector machines (SVM). Thirdly, the suitable feature sets with appropriate dimensionality were composed, and the point clouds were classified into four categories, such as trees (Tr), houses and buildings (Ho), low-growing vegetation (Gr), and impervious surfaces (Is). Finally, the single data source and multiple data sources, the crucial feature sets and their roles, and the resultant accuracy of different classifier models were compared and analyzed. Under the conditions of different experimental regions, sampling proportion parameters and machine learning models, the results showed that: (1) the overall classification accuracy obtained by the feature-level data fusion method was 76.2% compared with the results of only a single data source, which could improve the overall classification accuracy by more than 2%; (2) the accuracy of the four classes in the urban scenes can reach 88.5% (Is), 76.7% (Gr), 87.2% (Tr), and 88.3% (Ho), respectively, while the overall classification accuracy can reach 87.6% with optimal sampling parameters and random forest classifiers; (3) the RF classifier outperforms DT and SVM for the same sample conditions. In this paper, the method based on ALS point clouds and image data fusion can accurately classify multiple targets in urban scenes, which can provide technical support for 3D scene reconstruction and digital twin cities in complex geospatial environments.
Accurate Frequency Estimator for Real Sinusoid Based on DFT
Authors: Liu Zhanhong , Fan Lei , Liu Jinyu , Liu Nian , Jin Jiyu , Xing Jun
Published: September 2022

An accurate frequency estimator for real sinusoid based on Discrete Fourier Transform (DFT) is proposed. The proposed estimator is based on the interpolation of the maximum DFT spectral line and two Discrete-Time Fourier Transform (DTFT) spectral lines and can operate with both the rectangular window and the maximum sidelobe decay (MSD) window. In the coarse estimation step, the proposed estimator with the MSD window is used. According to the value of the coarse estimate, the negative frequency spectral component is removed. In the fine estimation step, the proposed estimator with the rectangular window is utilized to achieve the Cramer–Rao lower bound (CRLB). Simulation results show that the performance of this algorithm is better than that of the AM algorithm, Candan algorithm, Djukanovic algorithm, and FDIAM algorithm.
Experimental Study, Simulation and Analysis of the Fracture Failure of the Drum Shaft of a Casting Bridge Crane
Authors: Xiang Dong , Li Yan , Zhang Yuqing , Xu Feng
Published: September 2022

This study investigated the fatigue fracture of bilateral drive drum shafts in casting bridge cranes including its fracture morphology and factors, such as materials, manufacturing processes, and loads. Seven conditions were designed to test the effects of changes in the speed and torque of the drum shafts during startup, commissioning and braking under different loads. A dynamic model was developed for the structure and control system of the hoisting mechanism. Changes in the speed and torque of the motor and drum shafts were simulated under common operating conditions such as speed and load changes of the motor, control asynchrony and single-motor towing. The results showed that asynchronous motor starting and braking, motor dragging and other behaviors led the left and right drum shafts undergo oscillated torque with a value reached 2 × 105 N∙m in a period of approximately 13 s, and a residual torque about 3 × 104 N∙m was retained after braking. The torques on the drum shafts changed suddenly during the processes of starting, shifting and braking. Dynamic loading was the root cause of fatigue fracture of the drum shafts.
A 280 GHz 30 GHz Bandwidth Cascaded Amplifier Using Flexible Interstage Matching Strategy in 130 nm SiGe Technology
Authors: Trinh Van-Son , Song Jeong-Moon , Park Jung-Dong
Published: September 2022

This paper presents a 280 GHz amplifier design strategy for a robust multistage amplifier in a sub-Terahertz (sub-THz) regime in 130 nm SiGe technology. The presented 280 GHz amplifier consists of 14 stages of the cascaded common emitter (CE) amplifier which offers a compact and improved-noise design due to the absence of the area-expensive and lossy baluns at such high frequencies. The interstage-matching network was flexibly constructed with two separate resonant tanks using metal–insulator–metal (MIM) capacitors and microstrip transmission lines (MSTLs) between each stage. The measured amplifier achieved a peak power gain of 10.9 dB at 283 GHz and a 3 dB gain of bandwidth of 30 GHz between 270 and 300 GHz. The peak output power of the amplifier was 0.8 dBm with an output of 1 dB gain compression point (OP1dB) of −3.6 dBm in simulation. The 14-stage amplifier consumes an area of 0.213 mm2, including all the pads. With the proposed interstage matching approach, a well-balanced 280 GHz amplifier has been demonstrated. The proposed design strategy is widely applicable to sub-THz receivers for future wireless communication systems.
Adaptive Control Method of Sensorless Permanent Magnet Synchronous Motor Based on Super-Twisting Sliding Mode Algorithm
Authors: Qiu Haonan , Zhang Hongxin , Min Lei , Ma Tianbowen , Zhang Zhen
Published: September 2022

To solve the problem of the sensorless control method of a permanent magnet synchronous motor, based on the study of a mathematical model for a permanent magnet synchronous motor and model adaptation theory, a reference model equation and adjustable model equation are derived according to the stator current equation. The correctness of the selected linear compensator matrix is strictly proved. Then, Popov’s super-stability theory is used to derive the speed adaptive law and prove its asymptotic stability. Based on the voltage closed-loop feedback MTPA weak magnetic control strategy, a simulation model of a MRAS control system based on stator current is built and combined with the principle of MRAS. Aiming to investigate the problem that the PI adaptive law in the traditional MRAS algorithm is not robust, super-twisting sliding mode control is introduced to replace the PI adaptive law. The observer based on STSM−MRAS is designed. The simulation model of the MRAS control system based on the super-twisting sliding mode is established. Under certain working conditions, the STSM−MRAS algorithm and the traditional MRAS algorithm are simulated and compared. The results show that the STSM−MRAS algorithm can improve the steady-state performance and robustness of a sensorless control system.
Facial Action Unit Recognition by Prior and Adaptive Attention
Authors: Shao Zhiwen , Zhou Yong , Zhu Hancheng , Du Wen-Liang , Yao Rui , Chen Hao
Published: September 2022

Facial action unit (AU) recognition remains a challenging task, due to the subtlety and non-rigidity of AUs. A typical solution is to localize the correlated regions of each AU. Current works often predefine the region of interest (ROI) of each AU via prior knowledge, or try to capture the ROI only by the supervision of AU recognition during training. However, the predefinition often neglects important regions, while the supervision is insufficient to precisely localize ROIs. In this paper, we propose a novel AU recognition method by prior and adaptive attention. Specifically, we predefine a mask for each AU, in which the locations farther away from the AU centers specified by prior knowledge have lower weights. A learnable parameter is adopted to control the importance of different locations. Then, we element-wise multiply the mask by a learnable attention map, and use the new attention map to extract the AU-related feature, in which AU recognition can supervise the adaptive learning of a new attention map. Experimental results show that our method (i) outperforms the state-of-the-art AU recognition approaches on challenging benchmark datasets, and (ii) can accurately reason the regional attention distribution of each AU by combining the advantages of both the predefinition and the supervision.
Personalized Search Using User Preferences on Social Media
Authors: Bok Kyoungsoo , Song Jinwoo , Lim Jongtae , Yoo Jaesoo
Published: September 2022

In contrast to traditional web search, personalized search provides search results that take into account the user’s preferences. However, the existing personalized search methods have limitations in providing appropriate search results for the individual’s preferences, because they do not consider the user’s recent preferences or the preferences of other users. In this paper, we propose a new search method considering the user’s recent preferences and similar users’ preferences on social media analysis. Since the user expresses personal opinions on social media, it is possible to grasp the user preferences when analyzing the records of social media activities. The proposed method collects user social activity records and determines keywords of interest using TF-IDF. Since user preferences change continuously over time, we assign time weights to keywords of interest, giving many high values to state-of-the-art user preferences. We identify users with similar preferences to extend the search results to be provided to users because considering only user preferences in personalized searches can provide narrow search results. The proposed method provides personalized search results considering social characteristics by applying a ranking algorithm that considers similar user preferences as well as user preferences. It is shown through various performance evaluations that the proposed personalized search method outperforms the existing methods.
An Efficient Motion Registration Method Based on Self-Coordination and Self-Referential Normalization
Authors: Ren Yuhao , Zhang Bochao , Chen Jing , Guo Liquan , Wang Jiping
Published: September 2022

Action quality assessment (AQA) is an important problem in computer vision applications. During human AQA, differences in body size or changes in position relative to the sensor may cause unwanted effects. We propose a motion registration method based on self-coordination (SC) and self-referential normalization (SRN). By establishing a coordinate system on the human body and using a part of the human body as a normalized reference standard to process the raw data, the standardization and distinguishability of the raw data are improved. To demonstrate the effectiveness of our method, we conducted experiments on KTH datasets. The experimental results show that the method improved the classification accuracy of the KNN-DTW network for KTH-5 from 82.46% to 87.72% and for KTH-4 from 89.47% to 94.74%, and it improved the classification accuracy of the tsai-MiniRocket network for KTH-5 from 91.29% to 93.86% and for KTH-4 from 94.74% to 97.90%. The results show that our method can reduce the above effects and improve the action classification accuracy of the action classification network. This study provides a new method and idea for improving the accuracy of AQA-related algorithms.
Face Anti-Spoofing Method Based on Residual Network with Channel Attention Mechanism
Authors: Kong Yueping , Li Xinyuan , Hao Guangye , Liu Chu
Published: September 2022

The face recognition system is vulnerable to spoofing attacks by photos or videos of a valid user face. However, edge degradation and texture blurring occur when non-living face images are used to attack the face recognition system. With this in mind, a novel face anti-spoofing method combines the residual network and the channel attention mechanism. In our method, the residual network extracts the texture differences of features between face images. In contrast, the attention mechanism focuses on the differences of shadow and edge features located on nasal and cheek areas between living and non-living face images. It can assign weights to different filter features of the face image and enhance the ability of network extraction and expression of different key features in the nasal and cheek regions, improving detection accuracy. The experiments were performed on the public face anti-spoofing datasets of Replay-Attack and CASIA-FASD. We found the best value of the parameter r suitable for face anti-spoofing research is 16, and the accuracy of the method is 99.98% and 97.75%, respectively. Furthermore, to enhance the robustness of the method to illumination changes, the experiment was also performed on the datasets with light changes and achieved a good result.
Sliding Mode Observer-Based Stuck Fault and Partial Loss-of-Effectiveness (PLOE) Fault Detection of Hypersonic Flight Vehicle
Authors: Hu Changhua , Liu Meijie , Li Hongzeng , Hu Xiaoxiang
Published: September 2022

In order to improve the safety and reliability of the hypersonic flight vehicle, a sliding mode observer-based fault detection scheme is applied in this paper to handle the actuator fault detection issue, including stuck fault detection and PLOE fault detection. A dynamic linear model with uncertainty is first derived from the original nonlinear hypersonic flight vehicle model by using Taylor’s linearization approach at the equilibrium point. Secondly, the actuator fault model, reflecting stuck faults and PLOE faults, is constructed. Then, a sliding mode-based fault detection observer, considering system decomposition, is developed based on the linearized hypersonic flight vehicle model. At last, the designed sliding mode observer is applied to the original nonlinear hypersonic flight vehicle for single-input, single-style actuator fault detection. The simulation results show that stuck faults and big proportion PLOE faults can be timely and accurately detected at the fault time, and the stuck actuator fault from input 3 can cause a deadly impact to the hypersonic flight vehicle, which deserves much more attention than the actuator faults from the other three inputs. Meanwhile, the detection of a small proportion of PLOE faults encounters some difficulties and needs special attention and further investigation.
Research on Demand Analysis and Optimal Allocation of Rail Transit Hybrid Energy Storage Based on the Electric Traction Model
Authors: Xin Dixi , Li Jianlin , Ma Suliang , Liu Chang’an
Published: September 2022

With the development of power transmission technology and power electronics, electrified railroads are widely used and pose a great challenge for the power grid. Hybrid energy storage integrates different advantages of multiple energy storage and can cope with the complex energy situation of rail transit. The complementary characteristics of lithium batteries and flywheels in terms of techno-economic indicators make them the main form of hybrid energy storage. In this paper, we analyze the power demand during train operation by studying the electromechanical characteristics of the rail transit traction process. On this basis, hybrid energy storage is configured to meet the power demand, and particle swarm optimization is chosen as the solution tool to perform the capacity configuration of lithium battery and flywheel in this paper. Finally, this paper proves the feasibility of the proposed theory by arithmetic example analysis, and it is shown that the proposed scheme can achieve a high percentage of energy reuse and low application cost.
Correction: Khowja et al. Integrated Motor Drive: Mass and Volume Optimization of the Motor with an Integrated Filter Inductor. Energies 2021, 14, 4564
Authors: Khowja Muhammad Raza , Vakil Gaurang , Gerada Chris , Patel Chintan , Odhano Shafiq , Wheeler Patrick
Published: September 2022

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Research and Application of Waterflooding Mechanism at the Bottom of Ultra-Low Permeability Shallow Reservoir with Horizontal Fractures
Authors: Shi Lihua , Cheng Shiqing , Chang Yuwen , Yu Haiyang , Hou Binchi
Published: September 2022

The Yanchang Eastern Oilfield of Ordos Basin is a typical ultra-low permeability shallow reservoir. Because of the relatively low vertical pressure, horizontal artificial fractures are prone to take place in the case of oil well fracturing. Given the bigger contact surface between the horizontal fracture and the waterflood front of the water injection well, the oil well may be flooded fast in a short time, leading to a low mobilization degree of the reservoir. According to the characteristics of waterflooding of horizontal fractures, the development mode of waterflooding at the bottom of oil reservoirs was proposed. Through core sample displacement experiments and nuclear magnetic resonance online tests, combined with numerical simulation of reservoirs, field tracer test, and other comprehensive methods, the heterogeneity of reservoirs and the limit of waterflooding parameters were optimized. The research results show that the waterflooding effect is the best when the variation coefficient of permeability is less than 0.5, the permeability rush coefficient is less than 1.5, the permeability contrast is less than 5, the waterflooding rate is 0.06 mL/min, and the waterflooding pressure is 7 MPa. Through field practice, the daily oil production of a single well in the test well group increased from 0.054 t/d before the test to 0.179 t/d, the water cut decreased from 15% before the test to 10%, the formation pressure increased by 0.18 MPa, and it is predicted that the final recovery would increase by 2%. This study provides an experimental and theoretical basis for water injection development of ultra-low permeability shallow reservoirs with horizontal fracture, and also plays a good demonstration role for high-efficiency water injection development of shallow reservoirs.
Optimal Powertrain Sizing of Series Hybrid Coach Running on Diesel and HVO for Lifetime Carbon Footprint and Total Cost Minimisation
Authors: Pardhi Shantanu , El Baghdadi Mohamed , Hulsebos Oswin , Hegazy Omar
Published: September 2022

This article aims to calculate, analyse and compare the optimal powertrain sizing solutions for a long-haul plug-in series hybrid coach running on diesel and hydrotreated vegetable oil (HVO) using a co-design optimisation approach for: (1) lowering lifetime carbon footprint; (2) minimising the total cost of ownership (TCO); (3) finding the right sizing compromise between environmental impact and economic feasibility for the two fuel cases. The current vehicle use case derived from the EU H2020 LONGRUN project features electrical auxiliary loads and a 100 km zero urban emission range requiring a considerable battery size, which makes its low carbon footprint and cost-effective sizing a crucial challenge. Changing the objective between environmental impact and overall cost minimisation or switching the energy source from diesel to renewable HVO could also significantly affect the optimal powertrain dimensions. The approach uses particle swarm optimisation in the outer sizing loop while energy management is implemented using an adaptive equivalent consumption minimisation strategy (A-ECMS). Usage of HVO fuel over diesel offered an approximately 62% reduction in lifetime carbon footprint for around a 12.5% increase in overall costs across all sizing solutions. For such an unconventional powertrain topology, the fuel economy-focused solution neither achieved the lowest carbon footprint nor overall costs. In comparison, CO2−cost balanced sizing resulted in reductions close to the single objective-focused solutions (5.7% against 5.9% for the CO2 solution, 7.7% against 7.9% for the TCO solution on HVO) with lowered compromise on other side targets (CO2 reduction of 5.7% against 4.9% found in the TCO-focused solution, TCO lowering of 7.7% against 4.4% found in the CO2-focused solution).
A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook
Authors: Goudarzi Arman , Ghayoor Farzad , Waseem Muhammad , Fahad Shah , Traore Issa
Published: September 2022

Swift population growth and rising demand for energy in the 21st century have resulted in considerable efforts to make the electrical grid more intelligent and responsive to accommodate consumers’ needs better while enhancing the reliability and efficiency of modern power systems. Internet of Things (IoT) has appeared as one of the enabling technologies for smart energy grids by delivering abundant cutting-edge solutions in various domains, including critical infrastructures. As IoT-enabled devices continue to flourish, one of the major challenges is security issues, since IoT devices are connected through the Internet, thus making the smart grids vulnerable to a diverse range of cyberattacks. Given the possible cascading consequences of shutting down a power system, a cyberattack on a smart grid would have disastrous implications for the stability of all grid-connected infrastructures. Most of the gadgets in our homes, workplaces, hospitals, and on trains require electricity to run. Therefore, the entire grid is subject to cyberattacks when a single device is hacked. Such attacks on power supplies may bring entire cities to a standstill, resulting in massive economic losses. As a result, security is an important element to address before the large-scale deployment of IoT-based devices in energy systems. In this report, first, we review the architecture and infrastructure of IoT-enabled smart grids; then, we focus on major challenges and security issues regarding their implementation. Lastly, as the main outcome of this study, we highlight the advanced solutions and technologies that can help IoT-enabled smart grids be more resilient and secure in overcoming existing cyber and physical attacks. In this regard, in the future, the broad implementation of cutting-edge secure and data transmission systems based on blockchain techniques is necessary to safeguard the entire electrical grid against cyber-physical adversaries.
Dual Extended Kalman Filter for State of Charge Estimation of Lithium–Sulfur Batteries
Authors: Trilla Lluís , Canals Casals Lluc , Jacas Jordi , Paradell Pol
Published: September 2022

Lithium-Sulfur is a promising technology for the next generation of batteries and research efforts for early-stage prototype implementation increased in recent years. For the development of a suitable Battery Management System, a state estimator is required; however, lithium-sulfur behavior presents a large non-observable region that may difficult the convergence of the state estimation algorithm leading to large errors or even instability. A dual Extended Kalman Filter is proposed to circumvent the non-observability region. This objective is achieved by combining a parameter estimation algorithm with a cell model that includes non-linear behavior such as self-discharge and cell degradation. The resulting dual Kalman filter is applied to lithium–sulfur batteries to estimate their State-of-Charge incorporating the effects of degradation, temperature, and self-discharge deviations.
Errors Incurred in Local Convective Heat Transfer Coefficients Obtained through Transient One-Dimensional Semi-Infinite Conduction Modeling: A Computational Heat Transfer Study
Authors: Singh Prashant
Published: September 2022

In typical turbulent flow problems, detailed heat transfer coefficient (h) maps obtained through short-duration experiments are based on inverse heat transfer methods that take the wall temperatures measured via liquid crystals or infrared thermography as input, and an error minimization routine is adopted to determine the best value of h that satisfies the wall temperature temporal evolution under a certain change in fluid temperature. A common practice involves modeling the solid as a one-dimensional semi-infinite medium by selecting the solid material that has low thermal conductivity and low thermal diffusivity. However, in certain flow scenarios, the neglection of the lateral heat diffusion may lead to significant errors in the deduced h values. It is imperative to understand the reasons behind large errors that may be incurred by using the 1D heat conduction assumption in order to accurately determine high-resolution h maps for better heat exchanger designs in a wide range of thermal management applications. This paper presents a computational heat transfer study on different jet impingement scenarios to demonstrate the errors incurred in the determination of h when calculated under the assumption of one-dimensional (1-d) heat conduction into a solid. To this end, three different cases are studied: (a) single jet, (b) array jet (theoretical distribution), (c) array jet (experimental distribution), along with three different mainstream temperature evolution profiles representing step change, moderately fast transient and slow transient nature of flow driving the heat transfer in the solid. A known distribution of heat transfer coefficient (“true h”) for each of the three cases is considered, and three-dimensional transient heat diffusion equations were solved to populate temperatures of each node in the solid at every time step. It is found that stagnation zones’ h1d calculations were lower than the “true h” while the low heat transfer zones exhibited significantly higher h1d compared to the “true h”. For the array jet (experimental distribution) case, it was observed that errors can be as high as 10% in certain low heat transfer zones. Different data reduction procedures, configurations, and conditions explored in this study indicate that a suitable balance can be achieved if shorter time durations in transient experiments are used as a reference for tracking in h1d calculations to keep the deviations from the “true h” low.
Digital Twins’ Applications for Building Energy Efficiency: A Review
Authors: Bortolini Rafaela , Rodrigues Raul , Alavi Hamidreza , Vecchia Luisa Felix Dalla , Forcada Núria
Published: September 2022

Over the last few decades, energy efficiency has received increasing attention from the Architecture, Engineering, Construction and Operation (AECO) industry. Digital Twins have the potential to advance the Operation and Maintenance (O&M) phase in different application fields. With the increasing industry interest, there is a need to review the current status of research developments in Digital Twins for building energy efficiency. This paper aims to provide a comprehensive review of the applications of digital twins for building energy efficiency, analyze research trends and identify research gaps and potential future research directions. In this review, Sustainability and Energy and Buildings are among the most frequently cited sources of publications. Literature reviewed was classified into four different topics: topic 1. Optimization design; topic 2. Occupants’ comfort; topic 3. Building operation and maintenance; and topic 4. Energy consumption simulation.
Electricity Usage Settlement System Based on a Cryptocurrency Instrument
Authors: Bielecki Sławomir , Skoczkowski Tadeusz , Sobczak Lidia , Wołowicz Marcin
Published: September 2022

This article reviews the issue of the use of cryptocurrencies (crypto-assets, in general) for an electricity settlement system. The development of digital techniques, including blockchain-based mechanisms, has meant that an increased interest in blockchain-based solutions is to be expected. Blockchain and similar approaches are characterised by decentralisation, so they are concurrent with the trends of the transforming power sector. Decentralised energy generation based on a high proportion of prosumer installations requires the implementation of a new settlement system for grid activities related to electricity use. The first projects of such systems based on a dedicated cryptocurrency have emerged. Based on these, the general concept of such a system with its own cryptocurrency called CCE is presented, including variants implementing net-metering and net-billing. Furthermore, issues requiring interdisciplinary research work and discussion before implementing such systems were identified. A settlement system in which a cryptocurrency is linked to a unit of energy used could be a first step towards introducing a new universal means of value exchange, linked to energy as the primary measure of the value of goods.