bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fault Detection Method Based on an Oil Temperature Forecasting Model Using an Improved Deep Deterministic Policy Gradient Algorithm in the Helicopter Gearbox
Authors: Wei Lei , Cheng Zhe , Cheng Junsheng , Hu Niaoqing , Yang Yi
Published: October 2022

The main gearbox is very important for the operation safety of helicopters, and the oil temperature reflects the health degree of the gearbox; therefore establishing an accurate oil temperature forecasting model is an important step for reliable fault detection. Firstly, in order to achieve accurate gearbox oil temperature forecasting, an improved deep deterministic policy gradient algorithm with a CNN–LSTM basic learner is proposed, which can excavate the complex relationship between oil temperature and working condition. Secondly, a reward incentive function is designed to accelerate the training time costs and to stabilize the model. Further, a variable variance exploration strategy is proposed to enable the agents of the model to fully explore the state space in the early training stage and to gradually converge in the training later stage. Thirdly, a multi-critics network structure is adopted to solve the problem of inaccurate Q-value estimation, which is the key to improving the prediction accuracy of the model. Finally, KDE is introduced to determine the fault threshold to judge whether the residual error is abnormal after EWMA processing. The experimental results show that the proposed model achieves higher prediction accuracy and shorter fault detection time costs.
Approximation of Composition and Temperature Dependent Heat Conductivity and Optimization of Thermoelectric Energy Conversion in Silicon–Germanium Alloys
Authors: Cimmelli Vito Antonio , Rogolino Patrizia
Published: October 2022

We analyze the efficiency as thermoelectric energy converter of a silicon–germanium alloy with composition and temperature dependent heat conductivity. The dependency on composition is determined by a non-linear regression method (NLRM), while the dependency on temperature is approximated by a first-order expansion in the neighborhood of three reference temperatures. The differences with respect to the case of thermal conductivity depending on composition only are pointed out. The efficiency of the system is analyzed under the assumption that the optimal energy conversion corresponds to the minimum rate of energy dissipated. The values of composition and temperature which minimize such a rate are calculated as well.
Revisiting Chernoff Information with Likelihood Ratio Exponential Families
Authors: Nielsen Frank
Published: October 2022

The Chernoff information between two probability measures is a statistical divergence measuring their deviation defined as their maximally skewed Bhattacharyya distance. Although the Chernoff information was originally introduced for bounding the Bayes error in statistical hypothesis testing, the divergence found many other applications due to its empirical robustness property found in applications ranging from information fusion to quantum information. From the viewpoint of information theory, the Chernoff information can also be interpreted as a minmax symmetrization of the Kullback–Leibler divergence. In this paper, we first revisit the Chernoff information between two densities of a measurable Lebesgue space by considering the exponential families induced by their geometric mixtures: The so-called likelihood ratio exponential families. Second, we show how to (i) solve exactly the Chernoff information between any two univariate Gaussian distributions or get a closed-form formula using symbolic computing, (ii) report a closed-form formula of the Chernoff information of centered Gaussians with scaled covariance matrices and (iii) use a fast numerical scheme to approximate the Chernoff information between any two multivariate Gaussian distributions.
Selected Data Mining Tools for Data Analysis in Distributed Environment
Authors: Moshkov Mikhail , Zielosko Beata , Tetteh Evans Teiko
Published: October 2022

In this paper, we deal with distributed data represented either as a finite set T of decision tables with equal sets of attributes or a finite set I of information systems with equal sets of attributes. In the former case, we discuss a way to the study decision trees common to all tables from the set T: building a decision table in which the set of decision trees coincides with the set of decision trees common to all tables from T. We show when we can build such a decision table and how to build it in a polynomial time. If we have such a table, we can apply various decision tree learning algorithms to it. We extend the considered approach to the study of test (reducts) and decision rules common to all tables from T. In the latter case, we discuss a way to study the association rules common to all information systems from the set I: building a joint information system for which the set of true association rules that are realizable for a given row ρ and have a given attribute a on the right-hand side coincides with the set of association rules that are true for all information systems from I, have the attribute a on the right-hand side, and are realizable for the row ρ. We then show how to build a joint information system in a polynomial time. When we build such an information system, we can apply various association rule learning algorithms to it.
Mathematical Models of Death Signaling Networks
Authors: Srinivasan Madhumita , Clarke Robert , Kraikivski Pavel
Published: October 2022

This review provides an overview of the progress made by computational and systems biologists in characterizing different cell death regulatory mechanisms that constitute the cell death network. We define the cell death network as a comprehensive decision-making mechanism that controls multiple death execution molecular circuits. This network involves multiple feedback and feed-forward loops and crosstalk among different cell death-regulating pathways. While substantial progress has been made in characterizing individual cell death execution pathways, the cell death decision network is poorly defined and understood. Certainly, understanding the dynamic behavior of such complex regulatory mechanisms can be only achieved by applying mathematical modeling and system-oriented approaches. Here, we provide an overview of mathematical models that have been developed to characterize different cell death mechanisms and intend to identify future research directions in this field.
Multi-Objective Constructal Design for Quadrilateral Heat Generation Body with Vein-Shaped High Thermal Conductivity Channel
Authors: Zhu Hongwei , Chen Lingen , Ge Yanlin , Shi Shuangshuang , Feng Huijun
Published: October 2022

Based on the quadrilateral heat generation body (HGB) proposed by previous literature, the multi-objective constructal design is performed. Firstly, the constructal design is performed by minimizing the complex function composed of the maximum temperature difference (MTD) and entropy generation rate (EGR), and the influence of the weighting coefficient (a0) on the optimal constructal is studied. Secondly, the multi-objective optimization (MOO) with the MTD and EGR as optimization objectives is performed, and the Pareto frontier with an optimal set is obtained by using NSGA-II. The optimization results are selected from the Pareto frontier through LINMAP, TOPSIS, and Shannon Entropy decision methods, and the deviation indexes of different objectives and decision methods are compared. The research of the quadrilateral HGB shows that the optimal constructal can be gained by minimizing the complex function with the objectives of the MTD and the EGR, the complex function after the constructal design is reduced by up to 2% compared with its initial value, and the complex function of the two reflects the compromise between the maximum thermal resistance and the irreversible loss of heat transfer. The Pareto frontier includes the optimization results of different objectives, and when the weighting coefficient of a complex function changes, the optimization results obtained by minimizing the complex function will also be distributed in the Pareto frontier. The deviation index of the TOPSIS decision method is 0.127, which is the lowest one among the discussed decision methods.
E-Bayesian and H-Bayesian Inferences for a Simple Step-Stress Model with Competing Failure Model under Progressively Type-II Censoring
Authors: Wang Ying , Yan Zaizai , Chen Yan
Published: October 2022

In this paper, we discuss the statistical analysis of a simple step-stress accelerated competing failure model under progressively Type-II censoring. It is assumed that there is more than one cause of failure, and the lifetime of the experimental units at each stress level follows exponential distribution. The distribution functions under different stress levels are connected through the cumulative exposure model. The maximum likelihood, Bayesian, Expected Bayesian, and Hierarchical Bayesian estimations of the model parameters are derived based on the different loss function. Based on Monte Carlo Simulations. We also get the average length and the coverage probability of the 95% confidence intervals and highest posterior density credible intervals of the parameters. From the numerical studies, it can be seen that the proposed Expected Bayesian estimations and Hierarchical Bayesian estimations have better performance in terms of the average estimates and mean squared errors, respectively. Finally, the methods of statistical inference discussed here are illustrated with a numerical example.
Mixture Complexity and Its Application to Gradual Clustering Change Detection
Authors: Kyoya Shunki , Yamanishi Kenji
Published: October 2022

We consider measuring the number of clusters (cluster size) in the finite mixture models for interpreting their structures. Many existing information criteria have been applied for this issue by regarding it as the same as the number of mixture components (mixture size); however, this may not be valid in the presence of overlaps or weight biases. In this study, we argue that the cluster size should be measured as a continuous value and propose a new criterion called mixture complexity (MC) to formulate it. It is formally defined from the viewpoint of information theory and can be seen as a natural extension of the cluster size considering overlap and weight bias. Subsequently, we apply MC to the issue of gradual clustering change detection. Conventionally, clustering changes have been regarded as abrupt, induced by the changes in the mixture size or cluster size. Meanwhile, we consider the clustering changes to be gradual in terms of MC; it has the benefits of finding the changes earlier and discerning the significant and insignificant changes. We further demonstrate that the MC can be decomposed according to the hierarchical structures of the mixture models; it helps us to analyze the detail of substructures.
Cryptanalysis of a Semi-Quantum Bi-Signature Scheme Based on W States
Authors: Yang Chun-Wei , Lin Jason , Tsai Chia-Wei , Cheng Ching-Lin
Published: October 2022

Recently, Zhao et al. proposed a semi-quantum bi-signature (SQBS) scheme based on W states with two quantum signers and just one classical verifier. In this study, we highlight three security issues with Zhao et al.’s SQBS scheme. In Zhao et al.’s SQBS protocol, an insider attacker can perform an impersonation attack in the verification phase and an impersonation attack in the signature phase to capture the private key. In addition, an eavesdropper can perform a man-in-the-middle attack to obtain all of the signer’s secret information. All of the above three attacks can pass the eavesdropping check. Without considering these security issues, the SQBS protocol could fail to ensure the signer’s secret information.
Auxiliary Graph for Attribute Graph Clustering
Authors: Li Wang , Wang Siwei , Guo Xifeng , Zhou Zhenyu , Zhu En
Published: October 2022

Attribute graph clustering algorithms that include topological structural information into node characteristics for building robust representations have proven to have promising efficacy in a variety of applications. However, the presented topological structure emphasizes local links between linked nodes but fails to convey relationships between nodes that are not directly linked, limiting the potential for future clustering performance improvement. To solve this issue, we offer the Auxiliary Graph for Attribute Graph Clustering technique (AGAGC). Specifically, we construct an additional graph as a supervisor based on the node attribute. The additional graph can serve as an auxiliary supervisor that aids the present one. To generate a trustworthy auxiliary graph, we offer a noise-filtering approach. Under the supervision of both the pre-defined graph and an auxiliary graph, a more effective clustering model is trained. Additionally, the embeddings of multiple layers are merged to improve the discriminative power of representations. We offer a clustering module for a self-supervisor to make the learned representation more clustering-aware. Finally, our model is trained using a triplet loss. Experiments are done on four available benchmark datasets, and the findings demonstrate that the proposed model outperforms or is comparable to state-of-the-art graph clustering models.
Volatility Dynamics of Non-Linear Volatile Time Series and Analysis of Information Flow: Evidence from Cryptocurrency Data
Authors: Sheraz Muhammad , Dedu Silvia , Preda Vasile
Published: October 2022

This paper aims to empirically examine long memory and bi-directional information flow between estimated volatilities of highly volatile time series datasets of five cryptocurrencies. We propose the employment of Garman and Klass (GK), Parkinson’s, Rogers and Satchell (RS), and Garman and Klass-Yang and Zhang (GK-YZ), and Open-High-Low-Close (OHLC) volatility estimators to estimate cryptocurrencies’ volatilities. The study applies methods such as mutual information, transfer entropy (TE), effective transfer entropy (ETE), and Rényi transfer entropy (RTE) to quantify the information flow between estimated volatilities. Additionally, Hurst exponent computations examine the existence of long memory in log returns and OHLC volatilities based on simple R/S, corrected R/S, empirical, corrected empirical, and theoretical methods. Our results confirm the long-run dependence and non-linear behavior of all cryptocurrency’s log returns and volatilities. In our analysis, TE and ETE estimates are statistically significant for all OHLC estimates. We report the highest information flow from BTC to LTC volatility (RS). Similarly, BNB and XRP share the most prominent information flow between volatilities estimated by GK, Parkinson’s, and GK-YZ. The study presents the practicable addition of OHLC volatility estimators for quantifying the information flow and provides an additional choice to compare with other volatility estimators, such as stochastic volatility models.
Rényi Entropy, Signed Probabilities, and the Qubit
Authors: Brandenburger Adam , La Mura Pierfrancesco , Zoble Stuart
Published: October 2022

The states of the qubit, the basic unit of quantum information, are 2 × 2 positive semi-definite Hermitian matrices with trace 1. We contribute to the program to axiomatize quantum mechanics by characterizing these states in terms of an entropic uncertainty principle formulated on an eight-point phase space. We do this by employing Rényi entropy (a generalization of Shannon entropy) suitably defined for the signed phase-space probability distributions that arise in representing quantum states.
First Digits’ Shannon Entropy
Authors: Kreiner Welf Alfred
Published: October 2022

Related to the letters of an alphabet, entropy means the average number of binary digits required for the transmission of one character. Checking tables of statistical data, one finds that, in the first position of the numbers, the digits 1 to 9 occur with different frequencies. Correspondingly, from these probabilities, a value for the Shannon entropy H can be determined as well. Although in many cases, the Newcomb–Benford Law applies, distributions have been found where the 1 in the first position occurs up to more than 40 times as frequently as the 9. In this case, the probability of the occurrence of a particular first digit can be derived from a power function with a negative exponent p > 1. While the entropy of the first digits following an NB distribution amounts to H = 2.88, for other data distributions (diameters of craters on Venus or the weight of fragments of crushed minerals), entropy values of 2.76 and 2.04 bits per digit have been found.
Transactional Interpretation and the Generalized Poisson Distribution
Authors: Makowski Marcin , Piotrowski Edward Wiktor
Published: October 2022

The aim of this paper is to study the quantum-like approach to the description of the market in the context of the principle of minimum Fisher information. We wish to investigate the validity of using squeezed coherent states as market strategies. For this purpose, we focus on the representation of any squeezed coherent state with respect to the basis of the eigenvectors of the observable of market risk. We derive a formula for the probability of being the squeezed coherent state in one of these states. The distribution that we call generalized Poisson establishes the relation between the squeezed coherent states and their description in the language of risk in quantum terms. We provide a formula specifying the total risk of squeezed coherent strategy. Then, we propose a risk of risk concept that is in fact the second central moment of the generalized Poisson distribution. This is an important numerical characterization of squeezed coherent strategies. We provide its interpretations on the basis of the uncertainty relation for time and energy.
Rényi Cross-Entropy Measures for Common Distributions and Processes with Memory
Authors: Thierrin Ferenc Cole , Alajaji Fady , Linder Tamás
Published: October 2022

Two Rényi-type generalizations of the Shannon cross-entropy, the Rényi cross-entropy and the Natural Rényi cross-entropy, were recently used as loss functions for the improved design of deep learning generative adversarial networks. In this work, we derive the Rényi and Natural Rényi differential cross-entropy measures in closed form for a wide class of common continuous distributions belonging to the exponential family, and we tabulate the results for ease of reference. We also summarise the Rényi-type cross-entropy rates between stationary Gaussian processes and between finite-alphabet time-invariant Markov sources.
Two Quantum Proxy Blind Signature Schemes Based on Controlled Quantum Teleportation
Authors: Luo Qiming , Zhang Tinggui , Huang Xiaofen , Jing Naihuan
Published: October 2022

We present a scheme for teleporting an unknown, two-particle entangled state with a message from a sender (Alice) to a receiver (Bob) via a six-particle entangled channel. We also present another scheme for teleporting an unknown one-particle entangled state with a message transmitted in a two-way form between the same sender and receiver via a five-qubit cluster state. One-way hash functions, Bell-state measurements, and unitary operations are adopted in these two schemes. Our schemes use the physical characteristics of quantum mechanics to implement delegation, signature, and verification processes. Moreover, a quantum key distribution protocol and a one-time pad are adopted in these schemes.
An Efficient Supervised Deep Hashing Method for Image Retrieval
Authors: Hussain Abid , Li Heng-Chao , Ali Muqadar , Wali Samad , Hussain Mehboob , Rehman Amir
Published: October 2022

In recent years, searching and retrieving relevant images from large databases has become an emerging challenge for the researcher. Hashing methods that mapped raw data into a short binary code have attracted increasing attention from the researcher. Most existing hashing approaches map samples to a binary vector via a single linear projection, which restricts the flexibility of those methods and leads to optimization problems. We introduce a CNN-based hashing method that uses multiple nonlinear projections to produce additional short-bit binary code to tackle this issue. Further, an end-to-end hashing system is accomplished using a convolutional neural network. Also, we design a loss function that aims to maintain the similarity between images and minimize the quantization error by providing a uniform distribution of the hash bits to illustrate the proposed technique’s effectiveness and significance. Extensive experiments conducted on various datasets demonstrate the superiority of the proposed method in comparison with state-of-the-art deep hashing methods.
Characterization of a West African Coastal Lagoon System: Case of Lake Nokoué with Its Inlet (Cotonou, South Benin)
Authors: Sintondji Sèlomè Wilfried , Sohou Zacharie , Baetens Katrijn , Lacroix Geneviève , Fiogbé Emile Didier
Published: October 2022

The purpose of this work was to investigate the physical and chemical dynamics of Lake Nokoué for its efficient management. For this purpose, two sampling campaigns per month from five stations (North, South, Central, East and West) were conducted for a period of one year (November 2020 to November 2021). Physic and chemical parameters (temperature, salinity, depth, water transparency, pH, dissolved oxygen and total dissolved solids) were measured and wet substrate samples were collected to study the granulometry. Data analysis revealed that Lake Nokoué is mainly affected by two regimes: flooding and low water. Flooding, which is not directly related to rainfall, did not begin until one month after the major rainy season in June. The sources that contributed to the flooding of Lake Nokoué were the freshwater tributaries coming mainly from the Ouèmé River and the flow of the Sô River from August to November. The inflow of fresh water contributed to the decrease in salinity and transparency of the lake from the east to the south. During the low water period (from December to July), Lake Nokoué is characterized by an increase in salinity and transparency from the south to the northeast due to the massive intrusion of sea water into the lake. The highest dissolved oxygen levels are observed in the south and center (5.92 ± 0.46 mg/L) while it varies greatly in the north and west (Eichhornia crassipes concentration zone) during flooding. The average annual depth of Lake Nokoué was 1.47 ± 0.66 m with an average annual pH of 6.85 ± 0.56.
Effectiveness of Digital Interventions for Deficit-Oriented and Asset-Oriented Psychological Outcomes in the Workplace: A Systematic Review and Narrative Synthesis
Authors: Armaou Maria , Araviaki Evangelia , Dutta Snigdha , Konstantinidis Stathis , Blake Holly
Published: October 2022

Background: Digital psychological interventions can target deficit-oriented and asset-oriented psychological outcomes in the workplace. This review examined: (a) the effectiveness of digital interventions for psychological well-being at work, (b) associations with workplace outcomes, and (c) associations between interventions’ effectiveness and their theory-base. Methods: six electronic databases were searched for randomised controlled trials (RCT) and quasi-experimental studies. The methodological quality of studies that used randomisation was conducted with the “Cochrane Collaboration’s Risk of Bias” tool, while the “JBI Critical Appraisal Checklist” was used for non-randomised studies. Studies’ theory-base was evaluated using an adaptation of the “theory coding scheme” (TSC). Due to heterogeneity, narrative synthesis was performed. Results: 51 studies were included in a synthesis describing four clusters of digital interventions: (a) cognitive behavioural therapy, (b) stress-management interventions and workplace well-being promotion, (c) meditation training and mindfulness-based interventions, and (d) self-help interventions. Studies demonstrated a high risk of contamination effects and high attrition bias. Theory-informed interventions demonstrated greater effectiveness. Cognitive behavioural therapy demonstrated the most robust evidence for reducing depression symptoms among healthy employees. With the exception of the Headspace application, there was weak evidence for meditation training apps, while relaxation training was a key component of effective stress-management interventions.
Effects of the Interculturality and Mindfulness Program (PIM) on University Students: A Quasi-Experimental Study
Authors: Chiodelli Roberto , Jesus Saúl Neves de , Mello Luana Thereza Nesi de , Andretta Ilana , Oliveira Diana Fernandes , Costa Maria Emília Santos , Russell Tamara
Published: October 2022

Rates of mental health issues have been increasing among university students. This study investigates the effects of the Interculturality and Mindfulness Program (PIM) on academic students on mindfulness, emotional regulation, depression, anxiety, stress, life satisfaction, optimism, positive solitude, and loneliness. A quasi-experimental research was conducted, with pre- and post-test comparative measurements in three groups: in-person (IG), synchronous online (OG), and passive control (CG). A diverse group of students (n = 150; mean age = 25.4 ± 8.31) participated from two universities in Portugal. When compared to the CG, both active groups (IG and OG) demonstrated a beneficial interaction effect in acceptance, positive solitude, optimism, and mindfulness. The IG demonstrated a positive interaction effect in awareness and satisfaction with life, whereas the OG indicated a favorable interaction effect in impulse. When analyzing the intra-group effects, both active groups presented a significant improvement in stress, emotion regulation, mindfulness, positive solitude, and optimism. The OG demonstrated an improvement in awareness and loneliness. The main limitations of this research are that students were not randomly assigned, and groups were heterogeneous in nationality, education level, and sex. Nonetheless, PIM has indicated beneficial results in both IG and OG, and is a promising intervention for the prevention of mental health issues (e.g., stress, difficulties in emotional regulation, and loneliness), as well as for the promotion of well-being (e.g., positive solitude, mindfulness, life satisfaction, and optimism).
Structural Ensemble Regression for Cluster-Based Aggregate Electricity Demand Forecasting
Authors: Kontogiannis Dimitrios , Bargiotas Dimitrios , Daskalopulu Aspassia , Arvanitidis Athanasios Ioannis , Tsoukalas Lefteri H.
Published: October 2022

Accurate electricity demand forecasting is vital to the development and evolution of smart grids as well as the reinforcement of demand side management strategies in the energy sector. Since this forecasting task requires the efficient processing of load profiles extracted from smart meters for large sets of clients, the challenges of high dimensionality often lead to the adoption of cluster-based aggregation strategies, resulting in scalable estimation models that operate on aggregate times series formed by client groups that share similar load characteristics. However, it is evident that the clustered time series exhibit different patterns that may not be processed efficiently by a single estimator or a fixed hybrid structure. Therefore, ensemble learning methods could provide an additional layer of model fusion, enabling the resulting estimator to adapt to the input series and yield better performance. In this work, we propose an adaptive ensemble member selection approach for stacking and voting regressors in the cluster-based aggregate forecasting framework that focuses on the examination of forecasting performance on peak and non-peak observations for the development of structurally flexible estimators for each cluster. The resulting ensemble models yield better overall performance when compared to the standalone estimators and our experiments indicate that member selection strategies focusing on the influence of non-peak performance lead to more performant ensemble models in this framework.
Risk-Based Capacitor Placement in Distribution Networks
Authors: Falaghi Hamid , Ramezani Maryam , Elyasi Hasan , Farhadi Mahdi , Estebsari Abouzar
Published: October 2022

In this paper, the problem of sizing and placement of constant and switching capacitors in electrical distribution systems is modelled considering the load uncertainty. This model is formulated as a multicriteria mathematical problem. The risk of voltage violation is calculated, and the stability index is modelled using fuzzy logic and fuzzy equations. The instability risk is introduced as the deviation of our fuzzy-based stability index with respect to the stability margin. The capacitor placement objectives in our paper include: (i) minimizing investment and installation costs as well as loss cost; (ii) reducing the risk of voltage violation; and (iii) reducing the instability risk. The proposed mathematical model is solved using a multi-objective version of a genetic algorithm. The model is implemented on a distribution network, and the results of the experiment are discussed. The impacts of constant and switching capacitors are assessed separately and concurrently. Moreover, the impact of uncertainty on the multi-objectives is determined based on a sensitivity analysis. It is demonstrated that the more the uncertainty is, the higher the system cost, the voltage risk and the instability risk are.
MobileNetV2 Combined with Fast Spectral Kurtosis Analysis for Bearing Fault Diagnosis
Authors: Xue Tian , Wang Huaiguang , Wu Dinghai
Published: October 2022

Bearings are an important component in mechanical equipment, and their health detection and fault diagnosis are of great significance. In order to meet the speed and recognition accuracy requirements of bearing fault diagnosis, this paper uses the lightweight MobileNetV2 network combined with fast spectral kurtosis to diagnose bearing faults. On the basis of the original MobileNetV2 network, a progressive classifier is used to compress the feature information layer by layer with the network structure to achieve high-precision and rapid identification and classification. A cross-local connection structure is added to the network to increase the extracted feature information to improve accuracy. At the same time, the original fault signal of the bearing is a one-dimensional vibration signal, and the signal contains a large number of non-Gaussian noise and accidental shock defects. In order to extract fault features more efficiently, this paper uses the fast spectral kurtosis algorithm to process the signal, extract the center frequency of the original signal, and calculate the spectral kurtosis value. The kurtosis map generated by signal preprocessing is used as the input of the MobileNetV2 network for fault classification. In order to verify the effectiveness and generality of the proposed method, this paper uses the XJTU-SY bearing fault dataset and the CWRU bearing dataset to conduct experiments. Through data preprocessing methods, such as data expansion for different fault types in the original dataset, input data that meet the experimental requirements are generated and fault diagnosis experiments are carried out. At the same time, through the comparison with other typical classification networks, the paper proves that the proposed method has significant advantages in terms of accuracy, model size, training speed, etc., and, finally, proves the effectiveness and generality of the proposed network model in the field of fault diagnosis.
Multilevel Features-Guided Network for Few-Shot Segmentation
Authors: Xin Chenjing , Li Xinfu , Yuan Yunfeng
Published: October 2022

The purpose of few-shot semantic segmentation is to segment unseen classes with only a few labeled samples. However, most methods ignore the guidance of low-level features for segmentation, leading to unsatisfactory results. Therefore, we propose a multilevel features-guided network using convolutional neural network techniques, which fully utilizes features from each level. It includes two novel designs: (1) a similarity-guided feature reinforcement module (SRM), which uses features from different levels, it enables sufficient guidance from the support set to the query set, thus avoiding the situation that some feature information is ignored in deep network computation, (2) a method that bridges query features at each level to the decoder to guide the segmentation, making full use of local and edge information to improve model performance. We experiment on PASCAL-5i and COCO-20i datasets to demonstrate the effectiveness of the model, the results in 1-shot setting and 5-shot setting on PASCAL-5i are 64.7% and 68.0%, which are 3.9% and 6.1% higher than the baseline model, respectively, and the results on the COCO-20i are also improved.
Design Space Exploration of Antenna Impedance and On-Chip Rectifier for Microwave Wireless Power Transfer
Authors: Hashimoto Takuma , Tanzawa Toru
Published: October 2022

This paper discusses a design methodology to efficiently determine the best combination of rectenna (rectifier and antenna) to minimize the input power under a given output condition for microwave wireless power transfer (MWPT) without any other external components, such as a matching network, for cost reduction. A linearized equivalent circuit model is expanded upon to include the microstrip line connecting the antenna and rectifier. Based on the model, the design flow is presented that has mainly three steps: (1) Determination of the equivalent rectifier input impedance and the amplitude of input voltage by running SPICE simulation, (2) Drawing contour plots of input power by rectifier candidate on the antenna impedance plane by conducting model calculation and impedance loci of antenna candidates on the contour plots, and (3) Selecting the combination of antenna and rectifier which gives the minimum input power for all the combinations. To validate the equivalent circuit model and design flow, a single-diode (SD) rectifier and a voltage-doubler (VD) rectifier were fabricated in 65 nm CMOS. The input power to generate 100 μA at 1 Vdc was measured and compared. The model, SPICE and measurement are in good agreement with each other that VD has 30–50% lower input power than SD does. In addition, the sensitivity of the parasitic elements, such as the microstrip line and the bonding wires and pads on the input power, are investigated to explore the design space for rectenna.
Novel Methodology to Assess Advanced Biofuel Production at Regional Level: Case Study for Cereal Straw Supply Chains
Authors: Ugolini Marco , Recchia Lucia , Guandalini Giulio , Manzolini Giampaolo
Published: October 2022

Nowadays, there is an urgent necessity for breakthrough technologies able to reduce process complexity and to lower costs to make the biomass-based biodiesel supply chain competitive. This paper presents an innovative methodology for the identification of secondary biomass potentials for biofuel production. The methodology, divided into four steps, defines the criteria that allow identification of promising scenarios of biomass supply chains for commercial application of innovative technologies. The model set up a scoring procedure based on carbon emission avoidance, efficient resource exploitation and sustainability indexes. Applying the four levels of analysis (biomass availability, suitability analysis, regionalization and multi-criteria analysis), the cereal straw supply chains in four European districts (Scandinavian, Hanseatic, Central European and Mediterranean) have been identified and grouped in 18 biomass supply regional clusters (BSRCs). The clusters represent the most promising hotspots where biomass availability, logistics and feedstock requirements for a novel conversion technology meet. Central European and Mediterranean districts are those with the highest cereal straw bioeconomic potentials which allow mono-feedstock supply chains and relatively high capacity of a conversion technology at 200 MWth and beyond. Central Europe has an overall bioeconomic potential of 16.249 kt (232.359 TJ) distributed over 36 regions and organized in 12 BSRCs. The Mediterranean district has an overall bioeconomic potential of 3541 kt (50.630 TJ) distributed over nine regions and organized in four BSRCs.
Numerical Simulation of Heat Transfer Enhancement in the Paths of Propulsion Systems with Single-Row Spherical and Oval Dimples on the Wall
Authors: Isaev Sergey , Nikushchenko Dmitry , Sudakov Alexandr , Tryaskin Nikita , Iunakov Leonid , Usachov Alexandr , Kharchenko Valery
Published: October 2022

Modeling the vortex intensification of turbulent heat transfer in narrow engine ducts using single-row dimples was carried out within the framework of multi-block computing technologies implemented in a special VP2/3 package. The cases of a rectangular section with the placement of vortex generators—single-row packages of oval and spherical dimples on the initial hydrodynamic section and the section of the stabilized flow are excluded. The connection between the generated vortex structures and the predominant increase in heat transfer compared to the increase in hydraulic losses for various dimple layouts has been established: zigzag, ladder, and centers shifted and in a longitudinal-transverse order. The superiority in heat removal from the thickness with inclined oval dimples is shown—1.23 times compared to spherical ones with a decay in hydraulic resistance by 1.16 times.
Wind Power Consumption Model Based on the Connection between Mid- and Long-Term Monthly Bidding Power Decomposition and Short-Term Wind-Thermal Power Joint Dispatch
Authors: Zhang Gang , Zhu Yaning , Xie Tuo , Zhang Kaoshe , He Xin
Published: October 2022

Due to the insufficient consideration of medium and long-term wind power contract power in short-term dispatch, long-term planning and real-time consumption of wind power cannot be effectively undertaken, resulting in a large amount of abandoned wind power. A way to improve the wind power absorption capacity has become an urgent problem to be studied. According to the characteristics of the market and dispatching in the process of wind-fire integration construction, this paper constructs a wind power consumption model that connects the mid- and long-term transaction power decomposition and short-term dispatch. Considering the unit output characteristics and maintenance, the monthly contract electricity is decomposed into daily electricity, and the nesting of medium and long-term transactions and short-term scheduling is realized; the second stage is a short-term multi-objective optimal scheduling model considering the decomposition of contract electricity and the output of non-bidding units to improve the real-time consumption of wind power. Finally, a province in northwest China is taken as an example to verify the effectiveness of the proposed method.
Utilization of Hydrotreated Vegetable Oil (HVO) in a Euro 6 Dual-Loop EGR Diesel Engine: Behavior as a Drop-In Fuel and Potentialities along Calibration Parameter Sweeps
Authors: d’Ambrosio Stefano , Mancarella Alessandro , Manelli Andrea
Published: October 2022

This study examines the effects on combustion, engine performance and exhaust pollutant emissions of a modern Euro 6, dual-loop EGR, compression ignition engine running on regular EN590-compliant diesel and hydrotreated vegetable oil (HVO). First, the potential of HVO as a “drop-in” fuel, i.e., without changes to the original, baseline diesel-oriented calibration, was highlighted and compared to regular diesel results. This showed how the use of HVO can reduce engine-out emissions of soot (by up to 67%), HC and CO (by up to 40%), while NOx levels remain relatively unchanged. Fuel consumption was also reduced, by about 3%, and slightly lower combustion noise levels were detected, too. HVO has a lower viscosity and a higher cetane number than diesel. Since these parameters have a significant impact on mixture formation and the subsequent combustion process, an engine pre-calibrated for regular diesel fuel could not fully exploit the potential of another sustainable fuel. Therefore, the effects of the most influential calibration parameters available on the tested engine platform, i.e., high-pressure and low-pressure EGR, fuel injection pressure, main injection timing, pilot quantity and dwell-time, were analyzed along single-parameter sweeps. The substantial reduction in engine-out soot, HC and CO levels brought about by HVO could give the possibility to implement additional measures to limit NOx emissions, combustion noise and/or fuel consumption compared to diesel. For example, higher proportion of LP EGR and/or smaller pilot quantity could be exploited with HVO, at low load, to reduce NOx emissions to a greater extent than diesel, without incurring penalties in terms of incomplete combustion species. Conversely, at higher load, delayed main injection timings and reduced rail pressure could reduce combustion noise without exceeding soot levels of the baseline diesel case.
Estimation and Comparison of SOC in Batteries Used in Electromobility Using the Thevenin Model and Coulomb Ampere Counting
Authors: Salazar Diego , Garcia Marcelo
Published: October 2022

Nowadays, due to the increasing use of electric vehicles, manufacturers are making more and more innovations in the batteries used in electromobility, in order to make these vehicles more efficient and provide them with greater autonomy. This has led to the need to evaluate and compare the efficiency of different batteries used in electric vehicles to determine which one is the best to be implemented. This paper characterises, models and compares three batteries used in electromobility: lithium-ion, lead-acid, and nickel metal hydride, and determines which of these three is the most efficient based on their state of charge. The main drawback to determine the state of charge is that there are a great variety of methods and models used for this purpose; in this article, the Thévenin model and the Coulomb Count method are used to determine the state of charge of the battery. When obtaining the electrical parameters, the simulation of the same is carried out, which indicates that the most efficient battery is the Lithium-ion battery presenting the best performance of state of charge, reaching 99.05% in the charging scenario, while, in the discharge scenario, it reaches a minimum value of 40.68%; in contrast, the least efficient battery is the lead acid battery, presenting in the charging scenario a maximum value of 98.42%, and in the discharge scenario a minimum value of 10.35%, presenting a deep discharge. This indicates that the lithium-ion battery is the most efficient in both the charge and discharge scenarios, and is the best option for use in electric vehicles. In this paper, it was decided to use the Coulomb ampere counting method together with the Thévenin equivalent circuit model because it was determined that the combination of these two methods to estimate the SOC can be applied to any battery, not only applicable to electric vehicle batteries, but to battery banks, BESS systems, or any system or equipment that has batteries for its operation, while the models based on Kalman, or models based on fuzzy mathematics and neural networks, as they are often used and are applicable only to a specific battery system.
The Performance of the High-Current Transformer during Operation in the Wide Frequencies Range
Authors: Kaczmarek Michal , Kaczmarek Piotr , Stano Ernest
Published: October 2022

This paper presents the performance of the 26 kVA inductive high-current transformer (HCT) during operation in the frequencies range of transformed harmonics from 50 Hz to 5 kHz. Performed research concerns the determination of the possibility of obtaining an order of the higher harmonic of a given RMS value in its distorted output current for the required RMS value of the main component and the maximum safe instantaneous value of the input voltage equal to 400 V. The results are presented for serial, serial-parallel and parallel configurations of primary and secondary windings (9 cases). Therefore, the most favourable configuration of the primary and secondary windings sections may be chosen. The tests are performed for the transformation of the distorted current containing a fundamental component and one higher harmonic of order from the 5th changed by the 5 up to the 100th. The constant 10% higher harmonic level in relation to the main component of the distorted secondary current is set. The measurements are performed for different resistances and inductances of the secondary winding’s load resulting from the length of the connected current track.
A First Step towards Zero Nuclear Waste—Advanced Strategic Thinking in Light of iMAGINE
Authors: Merk Bruno , Detkina Anna , Litskevich Dzianis , Patel Maulik , Noori-kalkhoran Omid , Cartland-Glover Gregory , Efremova Olga , Bankhead Mark , Degueldre Claude
Published: October 2022

Traditionally, there has been a gap between reactor operation and the consideration of nuclear waste in the final disposal. Fuel is produced, and fuel must be disposed. In the view of the reactor operator, fuel has to be cleaned in the reprocessing, and new solid fuel has to be produced in the view of the chemist. iMAGINE is designed to overcome this separation through a breakthrough development applying an optimized, integrative approach from cradle to grave of nuclear energy production as a first step to come as close as possible to the vision of zero waste nuclear power. It is described here for the first time in three steps: reactor, fuel cycle, and waste, providing the rationality behind each of the choices made to come to the overall solution to open the discussion and thinking process on what could be achieved by a very innovative approach to integrated nuclear energy production. The opportunities regarding the handling of the remaining waste are discussed with a view on the expectation of the final disposal community, the study “Nuclear waste from small modular reactors”, and the IAEA report “waste from innovative types of reactors and fuel cycles—a preliminary study”. The aim of this work is not to find answers to each of the raised points, but to identify potential approaches and promising ways to go, as well as to stimulate a discussion among experts. In the best case, this could lead to a change of track for nuclear power to become even more sustainable and an important, trusted technology to help solve the net-zero challenge.
A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters
Authors: Changmai Papul , Deka Sunil , Kumar Shashank , Babu Thanikanti Sudhakar , Aljafari Belqasem , Nastasi Benedetto
Published: October 2022

To meet the exponentially growing demand for clean and green energy, the solar photovoltaic (PV) system’s importance is increasing day by day, for which PV modeling is considered to be one of the most important work in the current state-of-the-art methods. To effectively model a PV system, accurate PV parameter estimation is of the utmost importance. In line with this, although the values of some of the parameters are provided in the manufacturer’s datasheet, the values of unknown parameters, such as shunt resistance, series resistance, the diode ideality factor, photo-generated current and diode saturation current, are not provided. To estimate these values a lot of algorithms are already reported in the literature. After careful observation of all the reported algorithms, a few best-reported algorithms are identified and their performances are compared with respect to accuracy, convergence issues, computational complexity and thermal stability. All kind of algorithms, such as numerical, analytical and evolutionary algorithms, are considered in this study, and only the best reported algorithms are considered for the comparison.