bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Target Association for UAVs Based on Triangular Topological Sequence
Authors: Li Xudong , Wu Lizhen , Niu Yifeng , Ma Aitong
Published: May 2022

Multi-UAV cooperative systems are highly regarded in the field of cooperative multi-target localization and tracking due to their advantages of wide coverage and multi-dimensional perception. However, due to the similarity of target visual characteristics and the limitation of UAV sensor resolution, it is difficult for UAVs to correctly distinguish targets that are visually similar to their associations. Incorrect correlation matching between targets will result in incorrect localization and tracking of multiple targets by multiple UAVs. In order to solve the association problem of targets with similar visual characteristics and reduce the localization and tracking errors caused by target association errors, based on the relative positions of the targets, the paper proposes a globally consistent target association algorithm for multiple UAV vision sensors based on triangular topological sequences. In contrast to Siamese neural networks and trajectory correlation, the relative position relationship between targets is used to distinguish and correlate targets with similar visual features and trajectories. The sequence of neighboring triangles of targets is constructed using the relative position relationship, and the feature is a specific triangular network. Moreover, a method for calculating topological sequence similarity with similar transformation invariance is proposed, as well as a two-step optimal association method that considers global objective association consistency. The results of flight experiments indicate that the algorithm achieves an association accuracy of 84.63%, and that two-step association is 12.83% more accurate than single-step association. Through this work, the multi-target association problem with similar or even identical visual characteristics can be solved in the task of cooperative surveillance and tracking of suspicious vehicles on the ground by multiple UAVs.
Functional Intelligence-Based Scene Recognition Scheme for MAV Environment-Adaptive Navigation
Authors: Wang Lingling , Liu Yixin , Fu Li , Wang Yaning , Tang Ning
Published: May 2022

Adaptive navigation is the core of micro aerial vehicles (MAVs) conducting autonomous flights in diverse environments. Different navigation techniques are adopted according to the availability of navigation signals in the environment. MAVs must navigate using scene recognition technology to ensure the continuity and reliability of the flight. Therefore, our work investigated the scene recognition method for MAV environment-adaptive navigation. First, we exploited the functional intelligence-adaptive navigation (FIAN) scheme by imitating the physiological decision-making process. Then, based on sufficient environment-sensitive measurements from the environment perception subsystem in FIAN, the two-level scene recognition method (TSRM) in the decision-making subsystem consisting of two deep learning frameworks, SceneNet and Mobile Net-V2 was proposed to extract scene features for accurate diverse scenes recognition. Furthermore, the four-rotor MAV-Smartphone combined (MSC) platform simulating the owl’s omni-directional head-turning behavior was built. The proposed TSRM was evaluated for accuracy, delay, and robustness compared with PSO-SVM and GIST-SVM. The results of practical flight tests through MSC platform show that TSRM has higher classification accuracy than PSO-SVM and GIST-SVM, and performs smoothly with self-regulatory adaptations under diverse environments.
Picture Fuzzy Threshold Graphs with Application in Medicine Replenishment
Authors: Das Sankar , Ghorai Ganesh , Xin Qin
Published: May 2022

In this study, a novel concept of picture fuzzy threshold graph (PFTG) is introduced. It has been shown that PFTGs are free from alternating 4-cycle and it can be constructed by repeatedly adding a dominating or an isolated node. Several properties about PFTGs are discussed and obtained the results that every picture fuzzy graph (PFG) is equivalent to a PFTG under certain conditions. Also, the underlying crisp graph (UCG) of PFTG is a split graph (SG), and conversely, a given SG can be applied to constitute a PFTG. A PFTG can be decomposed in a unique way and it generates three distinct fuzzy threshold graphs (FTGs). Furthermore, two important parameters i.e., picture fuzzy (PF) threshold dimension (TD) and PF partition number (PN) of PFGs are defined. Several properties on TD and PN have also been discussed. Lastly, an application of these developed results are presented in controlling medicine resources.
Quantum Incoherence Based Simultaneously on k Bases
Authors: Wang Pu , Guo Zhihua , Cao Huaixin
Published: May 2022

Quantum coherence is known as an important resource in many quantum information tasks, which is a basis-dependent property of quantum states. In this paper, we discuss quantum incoherence based simultaneously on k bases using Matrix Theory Method. First, by defining a correlation function m(e,f) of two orthonormal bases e and f, we investigate the relationships between sets I(e) and I(f) of incoherent states with respect to e and f. We prove that I(e)=I(f) if and only if the rank-one projective measurements generated by e and f are identical. We give a necessary and sufficient condition for the intersection I(e)⋂I(f) to include a state except the maximally mixed state. Especially, if two bases e and f are mutually unbiased, then the intersection has only the maximally mixed state. Secondly, we introduce the concepts of strong incoherence and weak coherence of a quantum state with respect to a set B of k bases and propose a measure for the weak coherence. In the two-qubit system, we prove that there exists a maximally coherent state with respect to B when k=2 and it is not the case for k=3.
Thermodynamic Optimization of the Ethylene Oligomerization Chemical Process
Authors: Yu Yajie , Xia Shaojun , Zhao Ming
Published: May 2022

The use of olefin oligomerization in the synthesis of liquid fuel has broad application prospects in military and civil fields. Here, based on finite time thermodynamics (FTT), an ethylene oligomerization chemical process (EOCP) model with a constant temperature heat source outside the heat exchanger and reactor pipes was established. The process was first optimized with the minimum specific entropy generation rate (SEGR) as the optimization objective, then multi-objective optimization was further performed by utilizing the NSGA-II algorithm with the minimization of the entropy generation rate (EGR) and the maximization of the C10H20 yield as the optimization objectives. The results showed that the point of the minimum EGR was the same as that of SEGR in the Pareto optimal frontier. The solution obtained using the Shannon entropy decision method had the lowest deviation index, the C10H20 yield was reduced by 49.46% compared with the point of reference and the EGR and SEGR were reduced by 59.01% and 18.88%, respectively.
Sending-or-Not-Sending Twin-Field Quantum Key Distribution with a Passive Decoy-State Method
Authors: Xue Ke , Shen Zhigang , Zhao Shengmei , Mao Qianping
Published: May 2022

Twin-field quantum key distribution (TF-QKD) has attracted considerable attention because it can exceed the basic rate-distance limit without quantum repeaters. Its variant protocol, sending or not-sending quantum key distribution (SNS-QKD), not only fixes the security vulnerability of TF-QKD, but also can tolerate large misalignment errors. However, the current SNS-QKD protocol is based on the active decoy-state method, which may lead to side channel information leakage when multiple light intensities are modulated in practice. In this work, we propose a passive decoy-state SNS-QKD protocol to further enhance the security of SNS-QKD. Numerical simulation results show that the protocol not only improves the security in source, but also retains the advantages of tolerating large misalignment errors. Therefore, it may provide further guidance for the practical application of SNS-QKD.
Local Budget Resilience in Times of COVID-19 Crisis: Evidence from Indonesia
Authors: Desdiani Nauli Aisyiyah , Sabrina Syahda , Husna Meila , Budiman Amalia Cesarina , Afifi Fachry Abdul Razak , Halimatussadiah Alin
Published: May 2022

The COVID-19 pandemic has put immense pressure on the fiscal situation. Central revenues have decelerated while expenditures had to be increased to facilitate economic recovery. Local governments faced even harder challenges as intergovernmental transfers were reduced and room for mobilizing alternative finances was limited. This paper employs qualitative and secondary data analysis on local budget and fiscal capacities at the subnational level in Indonesia to find insights into the implications and responses of the provincial government in a decentralized economy during the pandemic. Provinces with a high dependence on intergovernmental transfer are struggling to adopt a countercyclical mitigative fiscal policy. However, provinces with high local own-source revenue have suffered the most during the crisis as low economic activity reduced tax revenues and indirectly lead to lower fiscal space. As fiscal policy is critical to containing the pandemic and facilitating recovery, this paper proposes several strategies toward a more sustainable fiscal policy for rebuilding local government capacity in the medium-to-long term in the aftermath of the crisis.
The China Shock Impact on Labor Informality: The Effects on Brazilian Manufacturing Workers
Authors: Paz Lourenço S.
Published: May 2022

The vigorous growth of the Chinese economy together with its increasingly successful role in international trade may have profoundly impacted developing countries. This study examines the large increase in the international trade exposure of the Brazilian economy during 2000–2012 to assess the impacts of import competition on its manufacturing formal and informal labor markets. In this period, import penetration grew by more than 20 percent in Brazil, and the share of the import penetration originating in China increased from 3 to 20 percent. At the same time, the share of informal workers in manufacturing declined from 27 to approximately 15 percent. Employing a switching regression model and Brazilian household survey data, this study finds that a greater industry-level Chinese and ‘rest of the world’ import penetration increases the likelihood of jobs becoming informal at different intensities, and these effects are smaller in unskilled-labor intensive industries and manufacturing states. Additionally, both types of import penetration positively impact the average informal wage. In contrast, the estimates suggest that a larger Chinese import penetration reduces average formal wages, while imports from elsewhere have the opposite effect. The results also indicate that the magnitude of the effects on wages are moderated by the unskilled labor intensity of the industry and whether the worker is located in a manufacturing state.
Quality Teaching: Finding the Factors That Foster Student Performance in Junior High School Classrooms
Authors: Polymeropoulou Vasiliki , Lazaridou Angeliki
Published: May 2022

In this study, we examined the characteristics of secondary school teachers that are effective at promoting student performance. Using a multilevel analysis design, we examined teachers’ instruction using the effective factors in the Dynamic Model of Educational Effectiveness (D.M.E.E.). The research involved 21 teachers and 697 students. Written tests in Modern Greek were administered to the student sample at both the beginning and the end of the school year 2016–2017. An observer assessed teacher factors through three different instruments, and a student questionnaire was also used to evaluate teacher effectiveness. The data showed the effects that teachers in the sample had on the learning development of their students and highlighted effective teaching skills and weaknesses. Implications for practice to promote teacher effectiveness are drawn.
Modeling Distributed MQTT Systems Using Multicommodity Flow Analysis
Authors: Manzoni Pietro , Maniezzo Vittorio , Boschetti Marco A.
Published: May 2022

The development of technologies that exploit the Internet of Things (IoT) paradigm has led to the increasingly widespread use of networks formed by different devices scattered throughout the territory. The Publish/Subscribe paradigm is one of the most used communication paradigms for applications of this type. However, adopting these systems due to their centralized structure also leads to the emergence of various problems and limitations. For example, the broker is typically the single point of failure of the system: no communication is possible if the broker is unavailable. Moreover, they may not scale well considering the massive numbers of IoT devices forecasted in the future. Finally, a network architecture with a single central broker is partially at odds with the edge-oriented approach. This work focuses on the development of an adaptive topology control approach, able to find the most efficient network configuration maximizing the number of connections and reduce the waste of resources within it, starting from the definition of the devices and the connections between them present in the system. To reach the goal, we leverage an integer linear programming mathematical formulation, providing the basis to solve and optimize the problem of network configuration in contexts where the resources available to the devices are limited.
Video Super-Resolution Using Multi-Scale and Non-Local Feature Fusion
Authors: Li Yanghui , Zhu Hong , Hou Qian , Wang Jing , Wu Wenhuan
Published: May 2022

Video super-resolution can generate corresponding to high-resolution video frames from a plurality of low-resolution video frames which have rich details and temporally consistency. Most current methods use two-level structure to reconstruct video frames by combining optical flow network and super-resolution network, but this process does not deeply mine the effective information contained in video frames. Therefore, we propose a video super-resolution method that combines non-local features and multi-scale features to extract more in-depth effective information contained in video frames. Our method obtains long-distance effective information by calculating the similarity between any two pixels in the video frame through the non-local module, extracts the local information covered by different scale convolution cores through the multi-scale feature fusion module, and fully fuses feature information using different connection modes of convolution cores. Experiments on different data sets show that the proposed method is superior to the existing methods in quality and quantity.
100 Gbps Dynamic Extensible Protocol Parser Based on an FPGA
Authors: Wang Ke , Guo Zhichuan , Song Mangu , Sha Meng
Published: May 2022

In order to facilitate the transition between networks and the integration of heterogeneous networks, the underlying link design of the current mainstream Information-Centric Networking (ICN) still considers the characteristics of the general network and extends the customized ICN protocol on this basis. This requires that the network transmission equipment can not only distinguish general network packets but also support the identification of ICN-specific protocols. However, traditional network protocol parsers are designed for specific network application scenarios, and it is difficult to flexibly expand new protocol parsing rules for different ICN network architectures. For this reason, we propose a general dynamic extensible protocol parser deployed on FPGA, which supports the real-time update of network protocol parsing rules by configuring extended protocol descriptors. At the same time, the multi-queue protocol management mechanism is adopted to realize the grouping management and rapid parsing of the extended protocol. The results demonstrate that the method can effectively support the protocol parsing of 100 Gbps high-speed network data packets and can dynamically update the protocol parsing rules under ultra-low latency. Compared with the current commercial programmable network equipment, this solution improves the protocol update efficiency by several orders of magnitude and better supports the online updating of network equipment.
Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects
Authors: Inayat Usman , Zia Muhammad Fahad , Mahmood Sajid , Khalid Haris M. , Benbouzid Mohamed
Published: May 2022

Internet of Things (IoT) is a developing technology that provides the simplicity and benefits of exchanging data with other devices using the cloud or wireless networks. However, the changes and developments in the IoT environment are making IoT systems susceptible to cyber attacks which could possibly lead to malicious intrusions. The impacts of these intrusions could lead to physical and economical damages. This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. Learning-based methods are reviewed using different types of cyber attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. A comprehensive list of publications to date in the literature is integrated to present a complete picture of various developments in this area. Finally, future research directions are also provided in the paper.
Current Collapse Conduction Losses Minimization in GaN Based PMSM Drive
Authors: Skarolek Pavel , Lipcak Ondrej , Lettl Jiri
Published: May 2022

The ever-increasing demands on the efficiency and power density of power electronics converters lead to the replacement of traditional silicon-based components with new structures. One of the promising technologies represents devices based on Gallium-Nitride (GaN). Compared to silicon transistors, GaN semiconductor switches offer superior performance in high-frequency converters, since their fast switching process significantly decreases the switching losses. However, when used in hard-switched converters such as voltage-source inverters (VSI) for motor control applications, GaN transistors increase the power dissipated due to the current conduction. The loss increase is caused by the current-collapse phenomenon, which increases the dynamic drain-source resistance of the device shortly after the turn-on. This disadvantage makes it hard for GaN converters to compete with other technologies in electric drives. Therefore, this paper offers a purely software-based solution to mitigate the negative consequences of the current-collapse phenomenon. The proposed method is based on the minimum pulse length optimization of the classical 7-segment space-vector modulation (SVM) and is verified within a field-oriented control (FOC) of a three-phase permanent magnet synchronous motor (PMSM) supplied by a two-level GaN VSI. The compensation in the control algorithm utilizes an offline measured look-up table dependent on the machine input power.
Efficient Colour Image Encryption Algorithm Using a New Fractional-Order Memcapacitive Hyperchaotic System
Authors: Rahman Zain-Aldeen S. A. , Jasim Basil H. , Al-Yasir Yasir I. A. , Abd-Alhameed Raed A.
Published: May 2022

In comparison with integer-order chaotic systems, fractional-order chaotic systems exhibit more complex dynamics. In recent years, research into fractional chaotic systems for the utilization of image cryptosystems has become increasingly highlighted. This paper describes the development, testing, numerical analysis, and electronic realization of a fractional-order memcapacitor. Then, a new four-dimensional (4D) fractional-order memcapacitive hyperchaotic system is suggested based on this memcapacitor. Analytically and numerically, the nonlinear dynamic properties of the hyperchaotic system have been explored, where various methods, including equilibrium points, phase portraits of chaotic attractors, bifurcation diagrams, and the Lyapunov exponent, are considered to demonstrate the chaos behaviour of this new hyperchaotic system. Consequently, an encryption cryptosystem algorithm is used for colour image encryption based on the chaotic behaviour of the memcapacitive model, where every pixel value of the original image is incorporated in the secret key to strengthen the encryption algorithm pirate anti-attack robustness. For generating the keyspace of that employed cryptosystem, the initial condition values, parameters, and fractional-order derivative value(s) (q) of the memcapacitive chaotic system are utilized. The common cryptanalysis metrics are verified in detail by histogram, keyspace, key sensitivity, correlation coefficient values, entropy, time efficiency, and comparisons with other recent related fieldwork in order to demonstrate the security level of the proposed cryptosystem approach. Finally, images of various sizes were encrypted and recovered to ensure that the utilized cryptosystem approach is capable of encrypting/decrypting images of various sizes. The obtained experimental results and security metrics analyses illustrate the excellent accuracy, high security, and perfect time efficiency of the utilized cryptosystem, which is highly resistant to various forms of pirate attacks.
A Novel Anti-Risk Method for Portfolio Trading Using Deep Reinforcement Learning
Authors: Yue Han , Liu Jiapeng , Tian Dongmei , Zhang Qin
Published: May 2022

In the past decade, the application of deep reinforcement learning (DRL) in portfolio management has attracted extensive attention. However, most classical RL algorithms do not consider the exogenous and noise of financial time series data, which may lead to treacherous trading decisions. To address this issue, we propose a novel anti-risk portfolio trading method based on deep reinforcement learning (DRL). It consists of a stacked sparse denoising autoencoder (SSDAE) network and an actor–critic based reinforcement learning (RL) agent. SSDAE will carry out off-line training first, while the decoder will used for on-line feature extraction in each state. The SSDAE network is used for the noise resistance training of financial data. The actor–critic algorithm we use is advantage actor–critic (A2C) and consists of two networks: the actor network learns and implements an investment policy, which is then evaluated by the critic network to determine the best action plan by continuously redistributing various portfolio assets, taking Sharp ratio as the optimization function. Through extensive experiments, the results show that our proposed method is effective and superior to the Dow Jones Industrial Average index (DJIA), several variants of our proposed method, and a state-of-the-art (SOTA) method.
Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
Authors: Arum Steve Chukwuebuka , Grace David , Mitchell Paul Daniel
Published: May 2022

Interest in delivering cellular communication using a high-altitude platform (HAP) is increasing partly due to its wide coverage capability. In this paper, we formulate analytical expressions for estimating the area of a HAP beam footprint, average per-user capacity per cell, average spectral efficiency (SE) and average area spectral efficiency (ASE), which are relevant for radio network planning, especially within the context of HAP extended contiguous cellular coverage and capacity. To understand the practical implications, we propose an enhanced and validated recursive HAP antenna beam-pointing algorithm, which forms HAP cells over an extended service area while considering beam broadening and the degree of overlap between neighbouring beams. The performance of the extended contiguous cellular structure resulting from the algorithm is compared with other alternative schemes using the carrier-to-noise ratio (CNR) and carrier-to-interference-plus-noise ratio (CINR). Results show that there is a steep reduction in average ASE at the edge of coverage. The achievable coverage is limited by the minimum acceptable average ASE at the edge, among other factors. In addition, the results highlight that efficient beam management can be achieved using the enhanced and validated algorithm, which significantly improves user CNR, CINR, and coverage area compared with other benchmark schemes. A simulated annealing comparison verifies that such an algorithm is close to optimal.
Business Process Outcome Prediction Based on Deep Latent Factor Model
Authors: Lu Ke , Fang Xinjian , Fang Xianwen
Published: May 2022

Business process outcome prediction plays an essential role in business process monitoring. It continuously analyzes completed process events to predict the executing cases’ outcome. Most of the current outcome prediction focuses only on the activity information in historical logs and less on the embedded and implicit knowledge that has not been explicitly represented. To address these issues, this paper proposes a Deep Latent Factor Model Predictor (DLFM Predictor) for uncovering the implicit factors affecting system operation and predicting the final results of continuous operation cases based on log behavior characteristics and resource information. First, the event logs are analyzed from the control flow and resource perspectives to construct composite data. Then, the stack autoencoder model is trained to extract the data’s main feature components for improving the training data’s reliability. Next, we capture the implicit factors at the control and data flow levels among events and construct a deep implicit factor model to optimize the parameter settings. After that, an expansive prefix sequence construction method is proposed to realize the outcome prediction of online event streams. Finally, the proposed algorithm is implemented based on the mainstream framework of neural networks and evaluated by real logs. The results show that the algorithm performs well under several evaluation metrics.
Deep Learning-Based Multiple Co-Channel Sources Localization Using Bernoulli Heatmap
Authors: Lin Meiyan , Huang Yonghui , Li Baozhu , Huang Zhen , Zhang Zihan , Zhao Wenjie
Published: May 2022

Multiple sources localization (MSL) has received considerable attention in scenarios of commercial, industrial, and defense areas. In this paper, a novel deep learning-based approach with observations of received signal strength (RSS) is proposed for the localization of multiple co-channel sources. The proposed method, named MSLocNet, formulates the MSL problem as a Bernoulli heatmap regression problem, solved by a fully convolutional network (FCN). The proposed MSLocNet enables simultaneous localization of variable numbers of sources, and exhibits better localization performance. Simulations, under complex environments with shadow fading, are conducted to validate the improved localization accuracy of the proposed method over other benchmark schemes. Moreover, experiments are carried out in a real environment to verify the feasibility of the proposed method.
Optimization of Fuzzy Controller for Predictive Current Control of Induction Machine
Authors: Varga Toni , Benšić Tin , Barukčić Marinko , Štil Vedrana Jerković
Published: May 2022

An optimization procedure for type 1 Takagi–Sugeno Fuzzy Logic Controller (FLC) parameter tuning is shown in this paper. Ant colony optimization is used to obtain the optimal controller parameters, and only a small amount of post-optimization parameter adjustment is needed. The choice of controller parameters is explained, along with the methodology behind the criterion for objective function value calculation. The optimized controller is implemented as an outer-loop speed controller for Predictive Current Control (PCC) of an induction machine. The performance of the proposed control method is compared with that of several other model predictive control methods. The results show a 55% decrease in speed tracking error and 74% decrease in torque overshoot.
Cross-Layer Optimization Spatial Multi-Channel Directional Neighbor Discovery with Random Reply in mmWave FANET
Authors: Song Yifei , Zeng Liang , Liu Zeyu , Song Zhe , Zeng Jie , An Jianping
Published: May 2022

MmWave FANETs play an increasingly important role in the development of UAVs technology. Fast neighbor discovery is a key bottleneck in mmWave FANETs. In this paper, we propose a two-way neighbor discovery algorithm based on a spatial multi-channel through cross-layer optimization. Firstly, we give two boundary conditions of the physical (PHY) layer and media access control (MAC) layer for successful link establishment of mmWave neighbor discovery and give the optimal pairing of antenna beamwidth in different stages and scenarios using cross-layer optimization. Then, a mmWave neighbor discovery algorithm based on a spatial multi-channel is proposed, which greatly reduces the convergence time by increasing the discovery probability of nodes in the network. Finally, a random reply algorithm is proposed based on dynamic reserved time slots. By adjusting the probability of reply and the number of reserved time slots, the neighbor discovery time can be further reduced when the number of nodes is larger. Simulations show that as the network scale is 100 to 500 nodes, the convergence time is 10 times higher than that of the single channel algorithm.
Highly Integrated Online Multi-Channel Electrochemical Impedance Spectroscopy Measurement Device for Fuel Cell Stack
Authors: Ma Tiancai , Kang Jiajun , Lin Weikang , Xu Xinru , Yang Yanbo
Published: May 2022

Electrochemical impedance spectroscopy (EIS) can provide information about the internal state of fuel cells, which makes EIS an important tool for fuel cell fault diagnosis. However, high cost, large volume, and poor scalability are limitations of existing EIS measurement equipment. In this study, a multi-channel online fuel cell EIS measurement device was designed. In this device, based on multi-phase interleaved Boost topology and average current control, an excitation source, which can output 1~500 Hz, 10 A sinusoidal excitation current was designed and verified by model simulation. Then, based on the quadrature vector digital lock-in amplifier (DLIA) algorithm, an impedance measuring module that can achieve precise online impedance measurement and calculation was designed. A prototype was then built for the experiment. According to the experiment test, the amplitude error of the excitation source is less than 1.8%, and the frequency error is less than 0.3%. Compared with the reference data, the impedance measured by the prototype has a modulus error of less than 3.5% and a phase angle error of less than 1.5°. Moreover, the waveform control and impedance extraction function of the EIS measurement device is implemented on an embedded controller, which can cut the price and reduce the volume.
Circuit Models of Power MOSFETs Leading the Way of GaN HEMT Modelling—A Review
Authors: Bottaro Enrico , Rizzo Santi Agatino , Salerno Nunzio
Published: May 2022

Gallium nitride high-electron-mobility transistor (GaN HEMT) is a key enabling technology for obtaining high-efficient and compact power electronic systems. At the design stage of a power converter, the proper modelling of the GaN HEMT is essential to benefit from their good features and to account for the limits of the current technology. Circuit models of power MOSFETs have been deeply investigated by academia and industry for a long time. These models are able to emulate the datasheet information, and they are usually provided by device manufacturers as netlists that can be simulated in any kind of SPICE-like software. This paper firstly highlights the similarities and differences between MOSFETs and GaN HEMTs at the datasheet level. According to this analysis, the features of MOSFET circuit models that can be adopted for GaN HEMT modelling are discussed. This task has been accomplished by overviewing the literature on MOSFETs circuit models as well as analysing manufacturers netlists, thus highlighting the models MOSFETs valid or adaptable to GaN HEMTs. The study has revealed show that some models can be adapted for the GaN HEMT devices to emulate static characteristics at room temperature while the MOSFET models of dynamic characteristics can be used for GaN HEMT devices. This study enables the devices modellers to speed up the GaN HEMT modelling thanks to the use of some well-established MOSFET models. In this perspective, some suggestions to develop accurate GaN HEMT models are also provided.
A Single-Phase Transformerless Nine-Level Inverter and Its Control Strategy
Authors: Li Yuan , Huang Wenwen , Li Zhen , Sun Yue , Liu Bi
Published: May 2022

To reduce the device number per unit level of the existing nine-level inverters, a topology of single-phase transformerless nine-level inverter was proposed. The proposed topology consists of only 10 switching devices and 4 capacitors. Firstly, the working principle of the proposed topology was analyzed in detail, and the comparison with conventional nine-level topologies in terms of device switching loss and conduction loss was presented. Then, combined with one-dimensional space vector modulation, a floating capacitor voltage estimation method was presented by the analysis of the operation mode before the arrival of redundant switching state. On this basis, a floating capacitor voltage sensorless control scheme was proposed, to achieve the balance of the floating capacitor voltages. Finally, simulations and experiments verified the effectiveness and correctness of the proposed topology and floating capacitor voltage sensorless control method.
Briquettes from Pinus spp. Residues: Energy Savings and Emissions Mitigation in the Rural Sector
Authors: Morales-Máximo Mario , Rutiaga-Quiñones José Guadalupe , Masera Omar , Ruiz-García Víctor Manuel
Published: May 2022

This study analyzes the household energy needs of the indigenous community of San Francisco Pichátaro, Michoacán, Mexico, and the use of Pinus spp. wood residues for the production of briquettes. The energy and emission performances of wood briquettes were evaluated on the field and in the laboratory. On-field surveys and measurements show that most users combine the use of fuelwood and LPG for cooking and heating water, and 65% of people use fuelwood daily (40% of houses consumed more than 39 kg per week). The use of biomass waste is an energy option in rural communities and contributes to reducing firewood consumption and mitigating GHGs. Briquettes gasification to heat water reduces 74% of GHG emissions, increases the thermal efficiency by 30%, and reduces pollutant emissions of CO, CH4, and PM2.5, NMHC, EC, and OC by 50% to 75% compared to a three-stone fire. The use of briquettes on the Patsari stove showed energy savings of 12% and a 36% reduction in CO2e compared to the “U” type open fire. The briquettes could reduce the fuelwood consumption by 318 t/year. It is possible to produce briquettes at a cost similar to or cheaper than fuelwood and generate a local market (circular economy) with local benefits.
Performance Evaluation of 1.1 MW Grid-Connected Solar Photovoltaic Power Plant in Louisiana
Authors: Veerendra Kumar Deepak Jain , Deville Lelia , Ritter Kenneth A. , Raush Johnathan Richard , Ferdowsi Farzad , Gottumukkala Raju , Chambers Terrence Lynn
Published: May 2022

In this work, performance analysis and comparison of three photovoltaic technologies are carried out in the Louisiana climate. During the calendar year of 2018, the University of Louisiana at Lafayette constructed and commissioned a 1.1 MW solar photovoltaic power plant for researching solar power in southern Louisiana and for partial energy demand of the university. It was one of the largest solar photovoltaic power plants in Louisiana when constructed and receives an annual solar insolation of 4.88 kWh/m2/d at latitude minus five degrees (25°) tilt. The solar power plant has a total of 4142 modules and incorporates three module technologies. Preliminary performance data from the system level are presented. The evaluation of different technologies is based on final yield, performance ratio, and capacity factor for one year from September 2019 to August 2020. An economic analysis is carried out using levelized cost of energy for the three photovoltaic (PV) technologies. Finally, the results are compared with simulated results of System Advisor Model (SAM) and PVsyst. It was found that copper indium gallium selenide (CIGS) has better performance ratio of 0.79 compared with monocrystalline silicon and polycrystalline silicon, which have performance ratios of 0.77 and 0.73, respectively. The simulation results correlated with the actual performance of the plant.
Investigations on the Effect of Pre-Treatment of Wheat Straw on Ash-Related Issues in Chemical Looping Gasification (CLG) in Comparison with Woody Biomass
Authors: Lebendig Florian , Funcia Ibai , Pérez-Vega Rául , Müller Michael
Published: May 2022

Biomass chemical looping gasification (BCLG) is a promising autothermic route for producing sustainable, N2-free, and carbon neutral syngas for producing liquid biofuels or high value hydrocarbons. However, different ash-related issues, such as high-temperature corrosion, fouling and slagging, bed agglomeration, or poisoning of the oxygen carrier might cause significant ecologic and economic challenges for reliable implementation of BCLG. In this work, lab-scale investigations under gasification-like conditions at 950 °C and thermodynamic modelling were combined for assessing the influence of composition, pre-treatment methods, such as torrefaction and water-leaching, and Ca-based additives on the release and fate of volatile inorganics, as well as on ash melting behavior. A deep characterization of both (non-)condensable gas species and ash composition behavior, joint with thermodynamic modelling has shown that different pre-treatment methods and/or Ca-additives can significantly counteract the above-mentioned problems. It can be concluded that torrefaction alone is not suitable to obtain the desired effects in terms of ash melting behavior or release of problematic volatile species. However, very promising results were achieved when torrefied or water-leached wheat straw was blended with 2 wt% CaCO3, since ash melting behavior was improved up to a similar level than woody biomass. Generally, both torrefaction and water-leaching reduced the amount of chlorine significantly.
Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis
Authors: Jan Faheem , Shah Ismail , Ali Sajid
Published: May 2022

In recent years, efficient modeling and forecasting of electricity prices became highly important for all the market participants for developing bidding strategies and making investment decisions. However, as electricity prices exhibit specific features, such as periods of high volatility, seasonal patterns, calendar effects, nonlinearity, etc., their accurate forecasting is challenging. This study proposes a functional forecasting method for the accurate forecasting of electricity prices. A functional autoregressive model of order P is suggested for short-term price forecasting in the electricity markets. The applicability of the model is improved with the help of functional final prediction error (FFPE), through which the model dimensionality and lag structure were selected automatically. An application of the suggested algorithm was evaluated on the Italian electricity market (IPEX). The out-of-sample forecasted results indicate that the proposed method performs relatively better than the nonfunctional forecasting techniques such as autoregressive (AR) and naïve models.
Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand
Authors: Sultana Nahid , Hossain S. M. Zakir , Almuhaini Salma Hamad , Düştegör Dilek
Published: May 2022

This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.
Nano-Enhanced Phase Change Materials for Thermal Energy Storage: A Bibliometric Analysis
Authors: Mohammadpour Javad , Lee Ann , Timchenko Victoria , Taylor Robert
Published: May 2022

The high latent heat thermal energy storage (LHTES) potential of phase change materials (PCMs) has long promised a step-change in the energy density for thermal storage applications. However, the uptake of PCM systems has been limited due to their relatively slow charging response, limited life, and economic considerations. Fortunately, a concerted global research effort is now underway to remove these remaining technical challenges. The bibliometric analysis of this review reveals that a major focus is now on the development of nano-enhanced phase change materials (NePCM), which have the potential to mitigate many of these technical challenges for PCM-based thermal energy storage systems. As such, our bibliometric analysis has zeroed in on research in the field of thermal energy storage using NePCMs since 1977. It was found that journal articles were the most frequently used document type, representing 79% of the records and that the pace of new work in this specific area has increased exponentially over these two decades, with China accounting for the highest number of citations and the most publications (168), followed by India and Iran. China has also played a central role in the collaboration network among the most productive countries, while Saudi Arabia and Vietnam show the highest international collaboration level.
Numerical Research of Flue Gas Denitrification Using the SNCR Method in an OP 650 Boiler
Authors: Hernik Bartłomiej
Published: May 2022

The presence of Poland in the European Union obliges the domestic economy and the professional energy sector to improve the condition of the natural environment by reducing the emissions of harmful substances into the environment. One of the substances that have a negative impact on the environment is nitrogen oxides. The results of numerical calculations of flue gas denitrification using the SNCR method in an OP 650 boiler are presented in the paper. The method of verifying the combustion of the numerical model, in terms of measurement and calculations with a zero-dimensional model, is presented. Then, the results of numerical tests of flue gas denitrification using the SNCR method with the use of urea solution injection in a specific temperature window for various nozzle positions are presented. In this paper, three variants of the reagent’s injection into the furnace chamber were carried out, depending on the height of the position of the nozzle. It is shown in this paper that thanks to combined NOx reduction systems, it is possible to adjust the emission of nitrogen oxides to a level below 200 mg/m3n with an oxygen content of 6% in the dry flue gas, with relatively low investment costs.
Classification of Multiple Power Quality Disturbances by Tunable-Q Wavelet Transform with Parameter Selection
Authors: Yang Lin , Guo Linming , Zhang Wenhai , Yang Xiaomei
Published: May 2022

Identifying power quality (PQ) disturbances is an important prerequisite for developing mitigation measures to improve PQ. However, the coupling of multiple PQ disturbances in the noise condition makes it difficult to achieve effective feature extraction and classification. This article proposes a novel method to identify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. The improved TQWT is proposed to automatically select the proper tuning parameters by screening the spectral information of PQ signals. Then, the improved TQWT is used to decompose PQ disturbances into sub-bands for further feature extraction. Optimum feature selection and classification are implemented in XGBoost. Classification accuracies of 26 categories of synthetic PQ disturbances under different noisy levels are tested and compared with existing methods. Results indicate that the proposed method is efficient and noise-resistant, and the classification accuracy can reach 97.63% under 20 dB noise, and keep above 99% under lower level noise.
The Role of Healthcare Employees’ Pro-Environmental Behavior for De-Carbonization: An Energy Conservation Approach from CSR Perspective
Authors: Xu Li , Cherian Jacob , Zaheer Muhammad , Sial Muhammad Safdar , Comite Ubaldo , Cismas Laura Mariana , Cristia Juan Felipe Espinosa , Oláh Judit
Published: May 2022

Buildings worldwide use a large amount of energy and, hence, contribute to increasing the level of greenhouse gases emission (GHG). It was realized that most electrical energy is used in buildings for heating, cooling, and ventilation purposes. To deal with environmental issues, the concepts of renewable energies and clean or green energy sources have been a part of academic discussions. A review of the literature unveils that most of the prior research in the GHG domain focused on the production side of producing renewable energy by opting for different cleaner energy sources (for example, solar energy). Although such studies have contributed significantly to advancing the field, it is also important to change the energy consumption behavior at the level of individuals for decarbonization. However, such a debate to promote the pro-environmental behavior (PEB) of employees from the perspective of energy consumption remains an understudied area. Against this backdrop, this research was carried out to promote PEB at the level of employees through corporate social responsibility (CSR) and green organizational practices in the healthcare sector of an emerging economy. The current research also focused on personal employee values, especially altruistic values, to spur their PEB. The data for the current work was collected from employees of different hospitals by employing a survey strategy (n = 441). To analyze the data, structural equation modeling was considered. The results showed that CSR directly and indirectly (via green organizational practices) influences employees’ PEB, whereas the altruistic values of employees produce a significant conditional indirect effect on the above relationship. The current research offers different implications for theory and practice, which are discussed in detail.