bluuebunny/crossref_metadata_2025 huggingface dataset:
Cross-Corpus Speech Emotion Recognition Based on Multi-Task Learning and Subdomain Adaptation
Authors: Fu Hongliang , Zhuang Zhihao , Wang Yang , Huang Chen , Duan Wenzhuo
Published: January 2023

To solve the problem of feature distribution discrepancy in cross-corpus speech emotion recognition tasks, this paper proposed an emotion recognition model based on multi-task learning and subdomain adaptation, which alleviates the impact on emotion recognition. Existing methods have shortcomings in speech feature representation and cross-corpus feature distribution alignment. The proposed model uses a deep denoising auto-encoder as a shared feature extraction network for multi-task learning, and the fully connected layer and softmax layer are added before each recognition task as task-specific layers. Subsequently, the subdomain adaptation algorithm of emotion and gender features is added to the shared network to obtain the shared emotion features and gender features of the source domain and target domain, respectively. Multi-task learning effectively enhances the representation ability of features, a subdomain adaptive algorithm promotes the migrating ability of features and effectively alleviates the impact of feature distribution differences in emotional features. The average results of six cross-corpus speech emotion recognition experiments show that, compared with other models, the weighted average recall rate is increased by 1.89~10.07%, the experimental results verify the validity of the proposed model.
An Enhanced Quantum K-Nearest Neighbor Classification Algorithm Based on Polar Distance
Authors: Feng Congcong , Zhao Bo , Zhou Xin , Ding Xiaodong , Shan Zheng
Published: January 2023

The K-nearest neighbor (KNN) algorithm is one of the most extensively used classification algorithms, while its high time complexity limits its performance in the era of big data. The quantum K-nearest neighbor (QKNN) algorithm can handle the above problem with satisfactory efficiency; however, its accuracy is sacrificed when directly applying the traditional similarity measure based on Euclidean distance. Inspired by the Polar coordinate system and the quantum property, this work proposes a new similarity measure to replace the Euclidean distance, which is defined as Polar distance. Polar distance considers both angular and module length information, introducing a weight parameter adjusted to the specific application data. To validate the efficiency of Polar distance, we conducted various experiments using several typical datasets. For the conventional KNN algorithm, the accuracy performance is comparable when using Polar distance for similarity measurement, while for the QKNN algorithm, it significantly outperforms the Euclidean distance in terms of classification accuracy. Furthermore, the Polar distance shows scalability and robustness superior to the Euclidean distance, providing an opportunity for the large-scale application of QKNN in practice.
Geometrical Bounds on Irreversibility in Squeezed Thermal Bath
Authors: Zou Chen-Juan , Li Yue , Xu Jia-Kun , You Jia-Bin , Png Ching Eng , Yang Wan-Li
Published: January 2023

Irreversible entropy production (IEP) plays an important role in quantum thermodynamic processes. Here, we investigate the geometrical bounds of IEP in nonequilibrium thermodynamics by exemplifying a system coupled to a squeezed thermal bath subject to dissipation and dephasing, respectively. We find that the geometrical bounds of the IEP always shift in a contrary way under dissipation and dephasing, where the lower and upper bounds turning to be tighter occur in the situation of dephasing and dissipation, respectively. However, either under dissipation or under dephasing, we may reduce both the critical time of the IEP itself and the critical time of the bounds for reaching an equilibrium by harvesting the benefits of squeezing effects in which the values of the IEP, quantifying the degree of thermodynamic irreversibility, also become smaller. Therefore, due to the nonequilibrium nature of the squeezed thermal bath, the system–bath interaction energy has a prominent impact on the IEP, leading to tightness of its bounds. Our results are not contradictory with the second law of thermodynamics by involving squeezing of the bath as an available resource, which can improve the performance of quantum thermodynamic devices.
Scientific Method’s Application Contexts for the Development and Evaluation of Research Skills in Higher-Education Learners
Authors: Vázquez-Villegas Patricia , Mejía-Manzano Luis Alberto , Sánchez-Rangel Juan Carlos , Membrillo-Hernández Jorge
Published: January 2023

The evolution of curricula in recent decades has given rise to multiple ways of teaching the task of experimental science, through which research skills are developed. In this learning gain process, teaching the systematized steps of the scientific method has been of invaluable help. In this study, we wanted to determine under what contexts of the last century the knowledge of the scientific method has encouraged the development of research skills in higher education learners. Through a literature review yielding nineteen empirical articles in the SCOPUS and Web of Science databases between 2000 and 2022, it was found that the scientific method is rarely mentioned and is more often referred to as a set of steps or structures to solve a problem, a challenge, or to carry out an investigation or an assigned task. Problem-solving and critical thinking were the two most-cited skills developed through the knowledge and practice of the scientific method. There are skills developed in theoretical classes and others that can be developed in practical courses such as laboratories or field work. A gap was found in the literature on using the scientific method and developing research skills in learners of non-science, technology, engineering, and mathematics (STEM) fields. The findings of this review lead studies to determine and compare whether effectively teaching students the scientific method improves their understanding and development of research skills in STEM and non-STEM areas.
Digitally Enabled Experiential Learning Spaces for Engineering Education 4.0
Authors: Salinas-Navarro David Ernesto , Garay-Rondero Claudia Lizette , Arana-Solares Iván Andrés
Published: January 2023

Novel digital technologies have transformed societies, organizations, and individuals in diverse aspects of daily life, elevating their competency requirements in order to successfully develop, integrate, and generate value. To remain relevant Higher education should provide students with digitally enhanced learning experiences to build their necessary competencies. To progress in this direction, this work proposes a method that can be used to develop digitally enabled experiential learning spaces (DeELS) in engineering education so as to incorporate digital technologies into engineering problem-solving and decision-making activities, as an innovative approach to Education 4.0. Two implementation cases exemplify the digital transformation of these learning spaces in the Lean Thinking Learning Space (LTLS) for undergraduate engineering courses. The exemplification shows how students, through designing, creating and integrating digital/smart kanban systems, execute their learning activities in a DeELS. The results suggest that the students were able to satisfactorily achieve their learning outcomes through the learning experiences. Moreover, new instances of learning experiences for digital transformation were identified within the LTLS. However, future work is required regarding new instances of digital transformation learning experiences in order to make any further inferences or generalizations regarding DeELS and their contribution to competency development.
Runtime Management of Service Level Agreements through Proactive Resource Provisioning for a Cloud Environment
Authors: Nadeem Sehrish , Amin Noor ul , Zaman Sardar Khaliq uz , Khan Muhammad Amir , Ahmad Zulfiqar , Iqbal Jawaid , Khan Ajab , Algarni Abeer D. , Elmannai Hela
Published: January 2023

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.
Procedural- and Reinforcement-Learning-Based Automation Methods for Analog Integrated Circuit Sizing in the Electrical Design Space
Authors: Uhlmann Yannick , Brunner Michael , Bramlage Lennart , Scheible Jürgen , Curio Cristóbal
Published: January 2023

Analog integrated circuit sizing is notoriously difficult to automate due to its complexity and scale; thus, it continues to heavily rely on human expert knowledge. This work presents a machine learning-based design automation methodology comprising pre-defined building blocks such as current mirrors or differential pairs and pre-computed look-up tables for electrical characteristics of primitive devices. Modeling the behavior of primitive devices around the operating point with neural networks combines the speed of equation-based methods with the accuracy of simulation-based approaches and, thereby, brings quality of life improvements for analog circuit designers using the gm/Id method. Extending this procedural automation method for human design experts, we present a fully autonomous sizing approach. Related work shows that the convergence properties of conventional optimization approaches improve significantly when acting in the electrical domain instead of the geometrical domain. We, therefore, formulate the circuit sizing task as a sequential decision-making problem in the alternative electrical design space. Our automation approach is based entirely on reinforcement learning, whereby abstract agents learn efficient design space navigation through interaction and without expert guidance. These agents’ learning behavior and performance are evaluated on circuits of varying complexity and different technologies, showing both the feasibility and portability of the work presented here.
Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
Authors: Nkenyereye Lionel , Naik Ramavath Prasad , Jang Jong-Wook , Chung Wan-Young
Published: January 2023

Vehicle-to-everything services are in the implementation phase, and automakers agree that V2X would improve the safety-critical applications already deployed. 3GPP Release 12 introduces LTE-V for V2V and V2I services. The LTE-V is extended to C-V2X to support V2N. Because of the challenge of high mobility in the V2X system, cutting-edge technologies, such as SDN and small cell in 5G networks, pave the way to the next generation of vehicular networks. SDN is a network technology concept that divides the data and control planes. The OpenFlow protocol is used for communication between the control layer and the network layer in SDN. Different from wireless traditional cellular base stations, small cells are lower-power cell sites that are deployed every few blocks. Small cells can transmit data using mid- and high-band spectrums. Small cell-linked road side unit (RSU) is considered a key enabling technology because it has the capability to create a logical cluster platform residing at the edge of the network, which provides high computation performance. Accordingly, we consider a novel distributed software-defined small cell-linked road side unit vehicular network architecture (diSRsVN). Based on diSRsVN, logical software-defined on-board wireless vehicle, and topology discovery over diSRsVN are presented. The proposed architecture is evaluated under an omnet++ network simulator. The simulation results show the effectiveness of the proposed architecture, which improves the packet delivery ratio and minimizes end-to-end delay.
Multi-Object Tracking with mmWave Radar: A Review
Authors: Pearce Andre , Zhang J. Andrew , Xu Richard , Wu Kai
Published: January 2023

The boundaries of tracking and sensing solutions are continuously being pushed. A stimulation in this field over recent years is exploiting the properties of millimeter wave (mmWave) radar to achieve simultaneous tracking and sensing of multiple objects. This paper aims to provide a critical analysis of the current literature surrounding multi-object tracking and sensing with short-range mmWave radar. There is significant literature available regarding single-object tracking using mmWave radar, demonstrating the maturity of single-object tracking systems. However, innovative research and advancements are also needed in the field of mmWave radar multi-object tracking, specifically with respect to uniquely identifying multiple target tracks across an interrupted field of view. In this article, we aim to provide an overview of the latest progress in multi-target tracking. In particular, an attempt to phrase the problem space is made by firstly defining a typical multi-object tracking architecture. We then highlight the areas for potential advancements. These areas include sensor fusion, micro-Doppler feature analysis, specialized and generalized activity recognition, gait, tagging and shape profile. Potential multi-object tracking advancements are reviewed and compared with respect to adaptability, performance, accuracy and specificity. Although the majority of the literature reviewed has a focus on human targets, most of the methodologies can be applied to targets consisting of different profiles and characteristics to that of humans. Lastly, future research directions are also discussed to shed light on research opportunities and potential approaches in the open research areas.
Spiking Neural-Networks-Based Data-Driven Control
Authors: Liu Yuxiang , Pan Wei
Published: January 2023

Machine learning can be effectively applied in control loops to make optimal control decisions robustly. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering because SNNs can potentially offer high energy efficiency, and new SNN-enabling neuromorphic hardware is being rapidly developed. A defining characteristic of control problems is that environmental reactions and delayed rewards must be considered. Although reinforcement learning (RL) provides the fundamental mechanisms to address such problems, implementing these mechanisms in SNN learning has been underexplored. Previously, spike-timing-dependent plasticity learning schemes (STDP) modulated by factors of temporal difference (TD-STDP) or reward (R-STDP) have been proposed for RL with SNN. Here, we designed and implemented an SNN controller to explore and compare these two schemes by considering cart-pole balancing as a representative example. Although the TD-based learning rules are very general, the resulting model exhibits rather slow convergence, producing noisy and imperfect results even after prolonged training. We show that by integrating the understanding of the dynamics of the environment into the reward function of R-STDP, a robust SNN-based controller can be learned much more efficiently than TD-STDP.
A 121 dB SNDR Zoom ADC Using Dynamic Amplifier and Asynchronous SAR Quantizer
Authors: Jia Yangchen , Guo Jiangfei , Guo Guiliang
Published: January 2023

This paper presents a discrete-time zoom analog-to-digital converter (ADC) for low-bandwidth high-precision applications. It uses a coarse-conversion 5-bit asynchronous self-timed SAR ADC combined with a fine-conversion second-order delta-sigma modulator to efficiently obtain a high signal-to-noise distortion ratio (SNDR). An integrator circuit using a high-gain dynamic amplifier is proposed to achieve higher SNDR. The dynamic amplifier uses a switched tail current source to operate periodically, simplifying the common-mode feedback circuit, reducing unnecessary static current, and improving the PVT robustness. Dynamic error correction techniques, such as redundancy, chopping, and dynamic element matching (DEM) are used to achieve low offset and high linearity. And a 2-bit asynchronous SAR quantizer with an embedded feed-forward adder is used in the second-order delta-sigma modulator to reduce the quantization noise caused by redundancy, and further achieve higher energy efficiency. Simulation results show that the ADC achieves a peak SNDR of 121.1 dB in a 390 Hz bandwidth at a 200 kHz sampling clock while consuming only 170 μW from a 2.5 V supply and the core area is 0.55 mm2. This results in a Schreier figure of merit (FoM) of 184.7 dB.
Open Set Recognition for Malware Traffic via Predictive Uncertainty
Authors: Li Xue , Fei Jinlong , Xie Jiangtao , Li Ding , Jiang Heng , Wang Ruonan , Qi Zan
Published: January 2023

Existing machine learning-based malware traffic recognition techniques can effectively detect abnormal behaviors in the network. However, almost all of them focus on a closed-set scenario in which the data used for training and testing come from the same label space. Since sophisticated malware and advanced persistent threats are evolving, it is impossible to exhaust all attacks to train a complete recognition model under the existing technical conditions. Therefore, recognition in the real network is an open-set problem, i.e., the recognition system should identify unknown and unseen attacks at test time. In this paper, we propose an uncertainty-aware method to identify known malicious traffic accurately and handle unknown traffic effectively. This method employs predictive uncertainty in deep learning as an indicator for unknown class detection. The predictive uncertainty represents the confidence in neural network predictions. In particular, the Deep Evidence Malware Traffic Recognition (DEMTR) model is presented to provide the multi-classification probability and predictive uncertainty in open-set scenarios using evidential deep learning. We demonstrate the performance of DEMTR on the MCFP dataset. Experimental results indicate that the proposed model outperforms the baseline methods in accuracy and F1-score.
The Study of Crash-Tolerant, Multi-Agent Offensive and Defensive Games Using Deep Reinforcement Learning
Authors: Li Xilun , Li Zhan , Zheng Xiaolong , Yang Xuebo , Yu Xinghu
Published: January 2023

In the multi-agent offensive and defensive game (ODG), each agent achieves its goal by cooperating or competing with other agents. The multi-agent deep reinforcement learning (MADRL) method is applied in similar scenarios to help agents make decisions. In various situations, the agents of both sides may crash due to collisions. However, the existing algorithms cannot deal with the situation where the number of agents reduces. Based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we study a method to deal with a reduction in the number of agents in the training process without changing the structure of the neural network (NN), which is called the frozen agent method for the MADDPG (FA-MADDPG) algorithm. In addition, we design a distance–collision reward function to help agents learn strategies better. Through the experiments in four scenarios with different numbers of agents, it is verified that the algorithm we proposed can not only successfully deal with the problem of agent number reduction in the training stage but also show better performance and higher efficiency than the MADDPG algorithm in simulation.
A Novel Integrated UWB Sensing and 8-Element MIMO Communication Cognitive Radio Antenna System
Authors: Srikar D , Nella Anveshkumar , Mamidi Ranjith , Babu Ashok , Das Sudipta , Lavadiya Sunil , Algarni Abeer D. , El-Shafai Walid
Published: January 2023

In this article, a cognitive radio (CR) integrated antenna system, which has 1 sensing and 24 communication antennas, is proposed for better spectrum utilization efficiency. In the 24 communication antennas, 3 different operating band antennas are realized with an 8-element MIMO configuration. The sensing antenna linked to port 1 is able to sense the spectrum that ranges from 2 to 12 GHz, whereas the communication 8-element MIMO antennas linked with ports 2 to 9, ports 10 to 17 and ports 18 to 25 perform operations in the 2.17–4.74 GHz, 4.57–8.62 GHz and 8.62–12 GHz bands, respectively. Mutual coupling is found to be less than −12 dB between the antenna elements. Peak gain and radiation efficiency of the sensing antenna are better than 2.25 dBi and 82%, respectively, whereas the peak gains and radiation efficiencies of all communication antennas are more than 2.5 dBi and 90%, respectively. Moreover, diversity characteristics of the MIMO antenna are assessed by parameters such as DG, ECC and CCL. It is found that ECC and CCL are less than 0.42 and 0.46 bits/s/Hz, respectively, and also DG is more than 9.1 dB.
BiomacVR: A Virtual Reality-Based System for Precise Human Posture and Motion Analysis in Rehabilitation Exercises Using Depth Sensors
Authors: Maskeliūnas Rytis , Damaševičius Robertas , Blažauskas Tomas , Canbulut Cenker , Adomavičienė Aušra , Griškevičius Julius
Published: January 2023

Remote patient monitoring is one of the most reliable choices for the availability of health care services for the elderly and/or chronically ill. Rehabilitation requires the exact and medically correct completion of physiotherapy activities. This paper presents BiomacVR, a virtual reality (VR)-based rehabilitation system that combines a VR physical training monitoring environment with upper limb rehabilitation technology for accurate interaction and increasing patients’ engagement in rehabilitation training. The system utilises a deep learning motion identification model called Convolutional Pose Machine (CPM) that uses a stacked hourglass network. The model is trained to precisely locate critical places in the human body using image sequences collected by depth sensors to identify correct and wrong human motions and to assess the effectiveness of physical training based on the scenarios presented. This paper presents the findings of the eight most-frequently used physical training exercise situations from post-stroke rehabilitation methodology. Depth sensors were able to accurately identify key parameters of the posture of a person performing different rehabilitation exercises. The average response time was 23 ms, which allows the system to be used in real-time applications. Furthermore, the skeleton features obtained by the system are useful for discriminating between healthy (normal) subjects and subjects suffering from lower back pain. Our results confirm that the proposed system with motion recognition methodology can be used to evaluate the quality of the physiotherapy exercises of the patient and monitor the progress of rehabilitation and assess its effectiveness.
Capacity Estimation of Lithium-Ion Batteries Based on Multiple Small Voltage Sections and BP Neural Networks
Authors: Tian Yong , Dong Qianyuan , Tian Jindong , Li Xiaoyu
Published: January 2023

Accurate capacity estimation of onboard lithium-ion batteries is crucial to the performance and safety of electric vehicles. In recent years, data-driven methods based on partial charging curve have been widely studied due to their low requirement of battery knowledge and easy implementation. However, existing data-driven methods are usually based on a fixed voltage segment or state of charge, which would be failed if the charging process does not cover the predetermined segment due to the user’s free charging behavior. This paper proposes a capacity estimation method using multiple small voltage sections and back propagation neural networks. It is intended to reduce the requirement of the length of voltage segment for estimating the complete battery capacity in an incomplete charging cycle. Firstly, the voltage segment most possibly covered is selected and divided into a number of small sections. Then, sectional capacity and skewness of the voltage curve are extracted from these small voltage sections, and severed as health factors. Secondly, the Box–Cox transformation is adopted to enhance the correlation between health factors and the capacity. Thirdly, multiple back propagation neural networks are constructed to achieve capacity estimation based on each voltage section, and their weighted average is taken as the final result. Finally, two public datasets are employed to verify the accuracy and generalization of the proposed method. Results show that the root mean square error of the fusion estimation is lower than 4.5%.
Re-Evaluation of Oil Bearing for Wells with Long Production Histories in Low Permeability Reservoirs Using Data-Driven Models
Authors: Xue Yongchao , Cao Chong , Jin Qingshuang , Wang Qianyu
Published: January 2023

The re-evaluation of oil-bearing wells enables finding potential oil-bearing areas and estimating the results of well logging. The re-evaluation of oil bearing is one of the key procedures for guiding the development of lower production wells with long-term production histories. However, there are many limitations to traditional oil-bearing assessment due to low resolution and excessive reliance on geological expert experience, which may lead to inaccurate and uncertain predictions. Based on information gain, three data-driven models were established in this paper to re-evaluate the oil bearing of long-term production wells. The results indicated that the RF model performed best with an accuracy of 95.07%, while the prediction capability of the neural network model was the worst, with only 79.8% accuracy. Moreover, an integrated model was explored to improve model accuracy. Compared with the neural network, support vector machine, and random forest models, the accuracy of the fusion model was improved by 20.9%, 8.5%, and 1.4%, which indicated that the integrated model assisted in enhancing the accuracy of oil-bearing prediction. Combined with the long-term production characteristics of oil wells in the actual oil field, the potential target sweet spot was found, providing theoretical guidance for the effective development of lower production wells in the late period of oilfield development.
Machine Learning Prediction of Nanoparticle Transport with Two-Phase Flow in Porous Media
Authors: El-Amin Mohamed , Alwated Budoor , Hoteit Hussein
Published: January 2023

Reservoir simulation is a time-consuming procedure that requires a deep understanding of complex fluid flow processes as well as the numerical solution of nonlinear partial differential equations. Machine learning algorithms have made significant progress in modeling flow problems in reservoir engineering. This study employs machine learning methods such as random forest, decision trees, gradient boosting regression, and artificial neural networks to forecast nanoparticle transport with the two-phase flow in porous media. Due to the shortage of data on nanoparticle transport in porous media, this work creates artificial datasets using a mathematical model. It predicts nanoparticle transport behavior using machine learning techniques, including gradient boosting regression, decision trees, random forests, and artificial neural networks. Utilizing the scikit-learn toolkit, strategies for data preprocessing, correlation, and feature importance are addressed. Furthermore, the GridSearchCV algorithm is used to optimize hyperparameter tuning. The mean absolute error, R-squared correlation, mean squared error, and root means square error are used to assess the models. The ANN model has the best performance in forecasting the transport of nanoparticles in porous media, according to the results.
Axisymmetric Flow and Heat Transfer in TiO2/H2O Nanofluid over a Porous Stretching-Sheet with Slip Boundary Conditions via a Reliable Computational Strategy
Authors: Naseem Tahir , Shahzad Azeem , Sohail Muhammad , Askar Sameh
Published: January 2023

In this investigation, the motion of TiO2/H2O nano-structures towards heated and porous sheets by considering the MHD effect and partial slip at the boundary is inspected. The non-linear PDEs that correspond to the basic conservation laws are converted into ODEs with the help of suitable similarity transformation. Furthermore, the shooting method is used to solve these transformed ODEs and boundary conditions. The impact of thermophoresis properties has been shown graphically and the effect of these properties on the skin friction coefficient (Cf) and Nussetl number (Nu) are given in table form. The comparison between the present exploration and published work is carried out and validation among results is prepared. The enhancement in thermophysical parameters showed contrary results to the velocity profile of the TiO2/H2O nanofluid as compared with temperature profile. Moreover, it is observed that the higher estimation in the velocity slip parameter retards the flow and an enhancement in volume fraction increases the fluid’s temperature. Furthermore, it has been discovered that the geometry of nanoparticles has a major impact on the flow behaviour. The temperature distribution diminishes when the shape of the nanoparticles changes from platelet to spherical.
A Novel Three-Layer Symmetry Winding Configuration for Five-Phase Motor
Authors: Liu Zhengmeng , Li Wenxuan , Liu Guohai
Published: January 2023

This paper presents a new three-layer, five-phase winding configuration theory of unconventional slot-pole combinations by each layer of winding for a phase vector correction, three layers of winding superimposed together to achieve the results of three-phase symmetry. Since the single-layer unconventional winding has to have an empty slot to meet its symmetry, based on the characteristics of single-layer winding, the unconventional winding design is carried out. Based on the simulation comparison between the single-layer unconventional winding and double-layer unconventional winding, a three-layer, nine-phase unconventional winding is proposed, which is based on the theory of single-layer unconventional winding, and three layers are staggered and stacked to realize nine-phase winding, which not only increases the utilization rate of the winding slot but also improves the fault tolerance performance. In addition, a 105-slot, 20-pole, three-layer, five-phase motor is proposed for a winding configuration and performance analysis to achieve both low torque pulsation and high fault tolerance.
Study of the Relationship between the Level of Lubricating Oil Contamination with Distillation Fuel and the Risk of Explosion in the Crankcase of a Marine Trunk Type Engine
Authors: Chybowski Leszek
Published: January 2023

Fuel contamination of engine lubricating oil has been previously determined to arise from two independent phenomena: the effect on oil flash point, and the effect of changing lubrication conditions on tribological pairs. This paper combines these effects and holistically analyzes the consequences of fuel in the lubricating oil of a trunk piston engine on the risk of crankcase explosion. The author hypothesized that diesel fuel as an oil contaminant increases the risk of an explosion in the crankcase of an engine due to the independent interaction of two factors: (1) changes in the oil’s combustible properties, and (2) deterioration of the lubrication conditions of the engine’s tribological nodes, such as main bearings, piston pins, or crank bearings. An experiment was performed to evaluate the rheological, ignition, and lubrication properties of two oils (SAE 30 and SAE 40) commonly used for the recirculation lubrication of marine trunk piston engines for different levels of diesel contamination. The hypothesis was partially confirmed, and the results show that contamination of the lubricating oil with diesel fuel in an amount of no more than 10% does not significantly affect the risk of explosion in the crankcase. However, diesel concentrations above 10% call for corrective action because the viscosity index, lubricity, coefficient of friction and oil film resistance change significantly. Deterioration of the tribological conditions of the engine bearings, as seen in the change in viscosity, viscosity index, and lubricity of the oil, causes an increase in bearing temperature and the possibility of hot spots leading to crankcase explosion.
Transient Stability Improvement of Large-Scale Photovoltaic Grid Using a Flywheel as a Synchronous Machine
Authors: Marupi Masilu , Batool Munira , Alizadeh Morteza , Zanib Noor
Published: January 2023

The global climate protection policy aimed at achieving a zero greenhouse gas emissions target has led to the fast incorporation of large-scale photovoltaics into the power network. The conventional AC grid was not modeled to be incorporated with large-scale non-synchronous inverter-based energy resources (IBR). Incorporating inertia-free IBR into the grid leads to technical issues such as the degradation of system strength and inertia, therefore affecting the safety and reliability of the electrical power system. This research introduced a new solution to incorporate a flywheel in the rotor of a synchronous machine to improve the dynamic inertia control during a system disruption and to maintain the constancy of the system. The objective of this work is to enhance large-scale photovoltaic systems in such a way that they can avoid failures during a fault. A model of transient constancy with two synchronous generators and a LSPV is established in PowerWorld modeling software. A line-to-ground and three-phase fault are simulated in a system with up to 50% IBR penetration. The outcomes showed that the power network was able to ride through faults (RTFs) and that the stability of frequency and voltage are enhanced because of a flywheel that improved grid inertia and strength.
Energy Potentials of Agricultural Biomass and the Possibility of Modelling Using RFR and SVM Models
Authors: Brandić Ivan , Antonović Alan , Pezo Lato , Matin Božidar , Krička Tajana , Jurišić Vanja , Špelić Karlo , Kontek Mislav , Kukuruzović Juraj , Grubor Mateja , Matin Ana
Published: January 2023

Agricultural biomass is one of the most important renewable energy sources. As a byproduct of corn, soybean and sunflower production, large amounts of biomass are produced that can be used as an energy source through conversion. In order to assess the quality and the possibility of the use of biomass, its composition and calorific value must be determined. The use of nonlinear models allows for an easier estimation of the energy properties of biomass concerning certain input and output parameters. In this paper, RFR (Random Forest Regression) and SVM (Support Vector Machine) models were developed to determine their capabilities in estimating the HHV (higher heating value) of biomass based on input parameters of ultimate analysis. The developed models showed good performance in terms of HHV estimation, confirmed by the coefficient of determination for the RFR (R2 = 0.79) and SVM (R2 = 0.93) models. The developed models have shown promising results in accurately predicting the HHV of biomass from various sources. The use of these algorithms for biomass energy prediction has the potential for further development.
Common Reed and Maize Silage Co-Digestion as a Pathway towards Sustainable Biogas Production
Authors: Czubaszek Robert , Wysocka-Czubaszek Agnieszka , Wichtmann Wendelin , Zając Grzegorz , Banaszuk Piotr
Published: January 2023

The key factor in sustainable biogas production is a feedstock whose production has no adverse impact on the environment. Since maize cultivation harms the environment, biogas plant operators seek a more sustainable feedstock. Common reed is an invasive species mown as part of wetland conservation measures, or it can be harvested from paludiculture. This study aimed to investigate wet co-digestion of maize silage with 10%, 30%, and 50% content of common reed silage using the biochemical methane potential (BMP) test. In addition, the potential energy generated and avoided greenhouse gas (GHG) emissions were calculated. The substitution of maize silage with 10%, 30%, and 50% content of reed silage reduced the methane (CH4) yield by 13%, 28%, and 35%, respectively. A disadvantage of reed silage addition was increased ammonia (NH3) and hydrogen sulfide (H2S) concentrations in biogas. Although substituting maize silage with reed silage decreases the CH4 yield, the co-digestion of maize and reed biomass from conservation or paludiculture may positively affect environmental aspects of energy generation. The substitution of maize with reed in biogas plants decreases the area used for maize cultivation and reduces GHG emissions.
Savings as a Source of Financial Energy on the Farm—What Determines the Accumulation of Savings by Agricultural Households? Model Approach
Authors: Strzelecka Agnieszka , Zawadzka Danuta
Published: January 2023

The aim of this study was to identify and evaluate the factors influencing the propensity of farm households to accumulate savings, which in this work are treated as a source of financial energy, taking into account socioeconomic characteristics relating to the farmer and their household, as well as farm characteristics. Classification and regression tree analysis (CRT) was used to achieve this goal. The study was conducted on a group of farms in Central Pomerania (Poland) participating in the Farm Accountancy Data Network. Data on 348 farms, obtained through a survey carried out in 2020 using a direct survey questionnaire, were used for the analyses. On the basis of the application of the classification-regression tree method, it was found that income (INC) was the key factor differentiating the studied population in terms of savings, followed by the agricultural area (AREA) and the level of education of the head of the household (EDU). It was also found that, in the case of households representing a lower income class, when the head of the household had at most secondary education and was over 34.5 years of age, having a successor (SUC) was also a factor influencing the accumulation of savings; however, the direction of this relationship was negative.
Investigation of Water and Heat Transfer Mechanism in PEMFCs Based on a Two-Phase Non-Isothermal Model
Authors: Gong Dapeng , Xu Sichuan , Gao Yuan
Published: January 2023

In a proton exchange membrane fuel cell (PEMFC) system, proper management of water and heat transport is essential to improve its overall performance and durability. To comprehensively investigate the internal processes of PEMFCs, an improved two-phase non-isothermal model based on heat and water transfer mechanisms inside the fuel cell is developed. The results show that the model proposed in this work can predict the fuel cell’s performance accurately and is capable of exploring water and heat transfer phenomena inside fuel cells. Additionally, the water and heat transfer of cathodes and anodes under different relative humidity and temperatures are studied. It can be concluded that when the PEMFC operates under a constant voltage, the anode water content gradually increases, while the cathode water content gradually decreases. The maximum water content occurs at the interface between cathode catalyst layer and cathode gas diffusion layer, while the minimum value is attained at the interface between anode catalyst layer and anode gas diffusion layer. When the fuel cell operates at 0.75 V, although the water content of CCL is the highest, no back-diffusion of dissolved water occurs.
A CFD Study of Vortex-Induced Motions of a Semi-Submersible Floating Offshore Wind Turbine
Authors: Liu Yuanchuan , Ge Dunjie , Bai Xinglan , Li Liang
Published: January 2023

Vortex-induced motion (VIM) is a critical issue for floating structures made of one or more columns, due to its significant impacts on their operational stability. Supported by column-type floating platforms, floating offshore wind turbines (FOWTs) may also experience large-amplitude VIM responses in current flow. Existing research on FOWTs has mostly focused on their wind/wave induced responses, yet less attention has been paid to their responses in current flow. In this paper, the VIM of the OC4 semi-submersible FOWT platform is studied numerically over a wide range of flow velocity. Three incidence angles, i.e., 0°, 90°, and 180°, are considered and the effect of current incidence on platform VIM is analysed. Results show that the so-called lock-in phenomenon is present and that a large transverse response amplitude of more than 0.3D persists until Vr = 30, with its maximum reaching over 0.8D at Vr = 8. Meanwhile, the transverse response amplitude for cases with the incidence angle of 180° is generally smaller, with a narrower lock-in regime, than those under the other two incidence scenarios. Flow field visualisation reveals that upstream vortices continuously interact with the downstream side column when the incidence angle turns to 180°, impacting the vortex shedding process and consequently fluid forces of the downstream column.
Test Investigation and Rule Analysis of Bearing Fault Diagnosis in Induction Motors
Authors: Zhou Zhiyong , Sun Junzhong , Cai Wei , Liu Wen
Published: January 2023

In this paper, a series of tests were conducted on the bearings of induction motors to investigate vibration signal analysis-based diagnosis of bearing faults, and a thorough analysis was also conducted. In the engineering field, the kurtosis coefficient of vibration acceleration and the root mean square of vibration velocity, as well as resonant demodulated spectrum analysis of vibration acceleration, have been widely used for bearing fault diagnosis. These are integrated in almost any commercially available device for diagnosing bearing faults. However, the unsuitable use of these devices results in many false diagnoses. In light of this, they were selected as research objects and were investigated experimentally. In three induction motors, faults of different severity in the bearing outer race and cage were modeled for tests, and the corresponding results were used to evaluate the performance of the selected diagnosis methods. Some vague information in engineering was clarified, and some instructive rules were outlined to improve the bearing fault diagnosis performance. Taking the kurtosis coefficient of vibration acceleration (Ku) as an example, in engineering, Ku = 4 is generally taken as the diagnostic threshold of bearing faults. This means the following rule applies: if Ku ≤ 4, the bearing is healthy; otherwise, the bearing is faulty. However, the test results in this paper show that even if Ku ≤ 4, the bearing might be faulty; if Ku > 4, the bearing is indeed faulty. Therefore, the diagnostic rule should be improved as follows: if Ku > 4, the bearing is faulty (which can be assured), and if Ku ≤ 4, the status of the bearing is still undetermined. Thus, this paper can be helpful for researchers to gain an experimental understanding of the selected diagnosis methods and provides some improved rules on their use for reducing false diagnoses.
Organizational Resilience as a Response to the Energy Crisis: Systematic Literature Review
Authors: Ingram Tomasz , Wieczorek-Kosmala Monika , Hlaváček Karel
Published: January 2023

In this paper, we provide a literature review on the topic of organizational resilience, in relation to the energy crisis. The concept of organizational resilience refers to the capability to respond and adapt to shocks. Undoubtedly, the recent energy crisis may be considered an external shock, as it has raised energy prices and exerts a significant pressure on decision makers. Although the energy crisis has impacted organizations significantly in recent months, there is little knowledge on how companies should respond to this threat. Thus, to fill in this research gap, we apply the method of a systematic literature review (SLR), combined with text mining tools, to map the topics covered by 124 works in the field. Based on our results, we uncover several important gaps in the existing studies. We also provide suggestions on relevant future research directions that could broaden the scope of the management of energy crisis, in line with the concept of organizational resilience.
Denitrification in Microbial Fuel Cells Using Granular Activated Carbon as an Effective Biocathode
Authors: Gurung Anup , Thapa Bhim Sen , Ko Seong-Yun , Ashun Ebenezer , Toor Umair Ali , Oh Sang-Eun
Published: January 2023

Nitrate (NO3−-N) and nitrites (NO2−-N) are common pollutants in various water bodies causing serious threats not only to aquatic, but also to animals and human beings. In this study, we developed a strategy for efficiently reducing nitrates in microbial fuel cells (MFCs) powered by a granular activated carbon (GAC)-biocathode. GAC was developed by acclimatizing and enriching denitrifying bacteria under a redox potential (0.3 V) generated from MFCs. Thus, using the formed GAC-biocathode we continued to study their effect on denitrification with different cathode materials and circulation speeds in MFCs. The GAC-biocathode with its excellent capacitive property can actively reduce nitrate for over thirty days irrespective of the cathode material used. The stirring speed of GAC in the cathode showed a steady growth in potential generation from 0.25 V to 0.33 V. A rapid lag phase was observed when a new carbon cathode was used with enriched GAC. While a slow lag phase was seen when a stainless-steel cathode was replaced. These observations showed that effective storage and supply of electrons to the GAC plays a crucial role in the reduction process in MFCs. Electrochemical analysis of the GAC properties studied using electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and zeta potential showed distinct properties with different abiotic and biocathode conditions. We found that the enrichment of electrotrophic bacteria on GAC facilitates the direct electron transfer in the cathode chamber for reducing NO3−-N in MFCs as observed by scanning electron microscopy.