Integrated generalized zero-shot learning for fine-grained classification
- Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 122, no. (2022), p.
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- Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
Thermodynamic guiding principles for designing nonstoichiometric redox materials for solar thermochemical fuel production : ceria, perovskites, and beyond
- Authors: Li, Sha , Wheeler, Vincent , Kumar, Apurv , Venkataraman, Mahesh , Muhich, Chrisopher
- Date: 2022
- Type: Text , Journal article
- Relation: Energy Technology Vol. 10, no. 1 (2022), p.
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- Description: Two-step solar thermochemical water splitting is a promising pathway for renewable fuel production due to its potential for high thermal efficiency via full-spectrum sunlight utilization. Such a promise critically relies on simultaneous innovation in the redox materials and the reactor systems. Most prior efforts on material design are focused on improving the fuel yield at lower reduction temperatures. However, developing materials with both high fuel output and efficiency remains a key challenge, requiring a rigorous understanding of the effects of material thermodynamic properties. Herein, a generic thermodynamic framework is described to decipher the material effects by studying both the state-of-the-art and hypothetical materials within a counterflow reactor system. A global efficiency map is presented for redox materials, revealing inevitable tradeoffs among competing factors such as thermal losses, sweep gas and oxidizer demand, solid preheating, and reduction enthalpy. The choice of the most efficient material is closely linked to the system conditions. Ceria-based materials outperform perovskites under most scenarios, and the optimal hypothetical materials tend to favor higher reduction enthalpies and entropies than existing materials. This work offers a valuable material design roadmap to identify solutions toward efficient solar fuel production. © 2021 Wiley-VCH GmbH. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Apurv Kumar” is provided in this record**
A low-cost efficient system for monitoring microalgae density using gaussian process
- Authors: Nguyen, Dung , Nguyen, Linh , Viet Le, Dung
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Instrumentation and Measurement Vol. 70, no. (2021), p.
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- Description: This article presents a low-cost system for efficiently monitoring the density of microalgae in a closed cultivation system, such as a photobioreactor. In fact, microalgal density can be accurately determined by manually counting methods, such as the direct microscopic count technique. However, the manual approaches are cumbersome, time-consuming, and impractical to be implemented in a closed cultivation system. Therefore, in the proposed monitoring system, microalgae are first proposed to be pumped from a culturing tank into a sample container placed inside a dark box. A low-cost camera is utilized to capture images of microalgae through the transparent sample container under artificial light. It is then proposed to represent microalgal density through two average pixel values of red and green color channels of the corresponding image. Moreover, the Gaussian process (GP) is exploited to statistically learn a data-driven model of microalgae density given the measured images. The learned model can then be used to effectively predict the density of microalgae where only their corresponding image data are required. The proposed approach was evaluated in a real-world closed bioreactor system of culturing Chlorella vulgaris microalgae, where the model was trained by 100 images selected randomly from 125 ones. In 10 000 random runs, the accuracy of the estimated density results is about 8.6% (±1.8%). © 1963-2012 IEEE.
A novel OFDM format and a machine learning based dimming control for lifi
- Authors: Nowrin, Itisha , Mondal, M. , Islam, Rashed , Kamruzzaman, Joarder
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 17 (2021), p.
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- Description: This paper proposes a new hybrid orthogonal frequency division multiplexing (OFDM) form termed as DC‐biased pulse amplitude modulated optical OFDM (DPO‐OFDM) by combining the ideas of the existing DC‐biased optical OFDM (DCO‐OFDM) and pulse amplitude modulated discrete multitone (PAM‐DMT). The analysis indicates that the required DC‐bias for DPO‐OFDM-based light fidelity (LiFi) depends on the dimming level and the components of the DPO‐OFDM. The bit error rate (BER) performance and dimming flexibility of the DPO‐OFDM and existing OFDM schemes are evaluated using MATLAB tools. The results show that the proposed DPO‐OFDM is power efficient and has a wide dimming range. Furthermore, a switching algorithm is introduced for LiFi, where the individual components of the hybrid OFDM are switched according to a target dimming level. Next, machine learning algorithms are used for the first time to find the appropriate proportions of the hybrid OFDM components. It is shown that polynomial regression of degree 4 can reliably predict the constellation size of the DCO‐OFDM component of DPO‐OFDM for a given constellation size of PAM‐DMT. With the component switching and the machine learning algorithms, DPO‐OFDM‐based LiFi is power efficient at a wide dimming range. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
A smart priority-based traffic control system for emergency vehicles
- Authors: Karmakar, Gour , Chowdhury, Abdullahi , Kamruzzaman, Joarder , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 14 (2021), p. 15849-15858
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- Description: Unwanted events on roads, such as incidents and increased traffic jams, can cause human lives and economic loss. For efficient incident management, it is essential to send Emergency Vehicles (EVs) to the incident place as quickly as possible. To reduce incidence clearance time, several approaches exist to provide a clear pathway to EVs mainly fitted with RFID sensors in the urban areas. However, they neither assign priority to the EVs based on the type and severity of an incident nor consider the effect on other on-road traffic. To address this issue, in this paper, we introduce an Emergency Vehicle Priority System (EVPS) by determining the priority level of an EV based on the type and the severity of an incident, and estimating the number of necessary signal interventions while considering the impact of those interventions on the traffic in the roads surrounding the EV's travel path. We present how EVPS determines the priority code and a new algorithm to estimate the number of green signal interventions to attain the quickest incident response while concomitantly reducing impact on others. A simulation model is developed in Simulation of Urban Mobility (SUMO) using the real traffic data of Melbourne, Australia, captured by various sensors. Results show that our system recommends appropriate number of intervention that can reduce emergency response time significantly. © 2001-2012 IEEE.
ADMM-based adaptive sampling strategy for nonholonomic mobile robotic sensor networks
- Authors: Le, Viet-Anh , Nguyen, Linh , Nghiem, Truong
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Sensors Journal Vol. 21, no. 13 (2021), p. 15369-15378
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- Description: This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positions, which enables the sampling optimization problem to be formulated by the use of the log determinant of a predicted covariance matrix at next sampling locations. The control, movement and nonholonomic dynamics constraints of the mobile sensors are also considered in the adaptive sampling optimization problem. In order to tackle the nonlinearity and nonconvexity of the objective function in the optimization problem we first exploit the linearized alternating direction method of multipliers (L-ADMM) method that can effectively simplify the objective function, though it is computationally expensive since a nonconvex problem needs to be solved exactly in each iteration. We then propose a novel approach called the successive convexified ADMM (SC-ADMM) that sequentially convexify the nonlinear dynamic constraints so that the original optimization problem can be split into convex subproblems. It is noted that both the L-ADMM algorithm and our SC-ADMM approach can solve the sampling optimization problem in either a centralized or a distributed manner. We validated the proposed approaches in 1000 experiments in a synthetic environment with a real-world dataset, where the obtained results suggest that both the L-ADMM and SC-ADMM techniques can provide good accuracy for the monitoring purpose. However, our proposed SC-ADMM approach computationally outperforms the L-ADMM counterpart, demonstrating its better practicality. © 2001-2012 IEEE.
An adaptive hierarchical sliding mode controller for autonomous underwater vehicles
- Authors: Van Vu, Quang , Dinh, Tuan , Van Nguyen, Thien , Tran, Hoang , Nguyen, Linh
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 18 (2021), p.
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- Description: The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Linh Nguyen" is provided in this record**
Cross-compiler bipartite vulnerability search
- Authors: Black, Paul , Gondal, Iqbal
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 11 (2021), p.
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- Description: Open-source libraries are widely used in software development, and the functions from these libraries may contain security vulnerabilities that can provide gateways for attackers. This paper provides a function similarity technique to identify vulnerable functions in compiled programs and proposes a new technique called Cross-Compiler Bipartite Vulnerability Search (CCBVS). CCBVS uses a novel training process, and bipartite matching to filter SVM model false positives to improve the quality of similar function identification. This research uses debug symbols in programs compiled from open-source software products to generate the ground truth. This automatic extraction of ground truth allows experimentation with a wide range of programs. The results presented in the paper show that an SVM model trained on a wide variety of programs compiled for Windows and Linux, x86 and Intel 64 architectures can be used to predict function similarity and that the use of bipartite matching substantially improves the function similarity matching performance. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Deep matrix factorization for trust-aware recommendation in social networks
- Authors: Wan, Liangtian , Xia, Feng , Kong, Xiangjie , Hsu, Ching-Hsien , Huang, Runhe , Ma, Jianhua
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Network Science and Engineering Vol. 8, no. 1 (2021), p. 511-528
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- Description: Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users. © 2013 IEEE.
Detecting outlier patterns with query-based artificially generated searching conditions
- Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
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- Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.
Dissolved gas analysis for power transformers within distributed renewable generation-based systems
- Authors: Cui, Huize , Yang, Liuqing , Zhu, Yuanwei , Li, Shengtao , Abu-Siada, Ahmed , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Dielectrics and Electrical Insulation Vol. 28, no. 4 (2021), p. 1349-1356
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- Description: In this paper, a series of laboratory experiments are conducted to investigate the effect of momentary small variations in the transformer operating temperature on the dissolved gas analysis (DGA) measurement. With the increased penetration level of renewable energy sources of intermittent characteristics into electricity grids, operating power transformers are expected to experience frequent temperature variations. Sampling transformer oil during such temperature variation leads to inaccurate diagnosis. Experimental results reveal that gas evolution in transformer oil is greatly affected by the small variations in the operating temperature. Such small variation can be a result of the intermittent generation characteristics of renewable energy sources. Hence, false analysis may be reported if oil is sampled during generation or load fluctuation events. Experimental results are explained through chemical equilibrium constant theory, which indicates that dissolved gases reflect the change in aging rate of the transformer oil-paper insulation system. These results suggest a new paradigm for DGA process through correlating measurements with the transformer operating temperature through the generation and load profiles at the instant of oil sampling. © 1994-2012 IEEE.
Diversified and scalable service recommendation with accuracy guarantee
- Authors: Wang, Lina , Zhang, Xuyun , Wang, Tian , Wan, Shaohua , Pang, Shaoning
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 5 (2021), p. 1182-1193
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- Description: As one of the most successful recommendation techniques, neighborhood-based collaborative filtering (CF), which recommends appropriate items to a target user by identifying similar users or similar items, has been widely applied to various recommender systems. Although many neighbor-based CF methods have been put forward, there are still some open issues that have remained unsolved. First, the ever-increasing volume of user-item rating data decreases the recommendation efficiency significantly as a recommender system needs to analyze all the rating data when searching for similar neighbors or similar items. In this situation, users' requirements on quick response may not be met. Second, in neighbor-based CF methods, more attention is paid to the recommendation accuracy while other key indicators of recommendation performances are often ignored, i.e., recommendation diversity (RD), which probably produces similar or redundant items in the recommended list and decreases users' satisfaction. Considering these issues, a diversified and scalable recommendation method (called DR_LT) based on locality-sensitive hashing and cover tree is proposed in this article, where the item topic information is used to optimize the final recommended list. We show the effectiveness of our proposed method through a set of experiments on MovieLens data set that clearly shows the feasibility of our proposal in terms of item recommendation accuracy, diversity, and scalability. © 2014 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Shaoning Pang” is provided in this record**
Effect of reactant types (steam, CO2 and steam + CO2) on the gasification performance of coal using entrained flow gasifier
- Authors: Shahabuddin, M. , Bhattacharya, Sankar
- Date: 2021
- Type: Text , Journal article
- Relation: International Journal of Energy Research Vol. 45, no. 6 (2021), p. 9492-9501
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- Description: This study investigates the gasification behaviour of bituminous coal using different reactants of CO2, steam and a mixture of CO2 and steam under entrained flow gasification conditions at temperatures of 1000°C and 1200°C with atmospheric pressure. The major gas constituents of the syngas were measured using online micro-GC with a model number Varian 490, whereas the minor pollutant gases were analysed using Kitagawa gas detection tubes. A maximum carbon conversion of 86% was achieved under steam gasification at a temperature of 1200°C compared to 74% from CO2 gasification. The higher carbon conversion from steam gasification is due to the higher gasification reactivity than CO2 gasification. At 1000°C, the lower heating value (LHV) from steam gasification was determined to be 60%, 70% and 80% higher than that of CO2 gasification using the reactant concentrations of 10, 20 and 40 vol.%, respectively. Using a stoichiometric 50/50 ratio of CO2 and steam, the yield of H2, CO and CH4 were increased by 56%, 106% and 35% compared to that of pure CO2 gasification. At 1000°C, the LHV under mixed reactant condition is close to the LHV from the pure steam gasification at 1200°C. In steam gasification, increasing the temperature by 200°C from 1000°C decreases the LHV by 17 and 10% using 10 and 20 vol.% steam. The higher heating value from steam gasification is due to the H2 and CH4-rich syngas compared to CO-rich syngas in CO2 gasification. The BET surface area of the solid char from steam gasification is about 17 and two times higher than that of CO2 gasification at 1000°C and 1200°C, respectively. © 2021 John Wiley & Sons Ltd
Efficient deterministic algorithm for huge-sized noisy sensor localization problems via canonical duality theory
- Authors: Latorre, Vittorio , Gao, David
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Cybernetics Vol. 51, no. 10 (2021), p. 5069-5081
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- Description: This paper presents a new deterministic method and a polynomial-time algorithm for solving general huge-sized sensor network localization problems. The problem is first formulated as a nonconvex minimization, which was considered as an NP-hard based on conventional theories. However, by the canonical duality theory, this challenging problem can be equivalently converted into a convex dual problem. By introducing a new optimality measure, a powerful canonical primal-dual interior (CPDI) point algorithm is developed which can solve efficiently huge-sized problems with hundreds of thousands of sensors. The new method is compared with the popular methods in the literature. Results show that the CPDI algorithm is not only faster than the benchmarks but also much more accurate on networks affected by noise on the distances. © 2013 IEEE.
Energy poverty, children's wellbeing and the mediating role of academic performance : evidence from China
- Authors: Zhang, Quanda , Appau, Samuelson , Kodom, Peter
- Date: 2021
- Type: Text , Journal article
- Relation: Energy Economics Vol. 97, no. (2021), p.
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- Description: Using data from the China Family Panel Studies, we examine the effects of energy poverty on children's subjective wellbeing. We find that energy poverty reduces children's subjective wellbeing: a standard deviation increase in energy poverty is associated with 0.353 standard deviation decrease in subjective wellbeing. This general conclusion is robust to alternative ways of measuring subjective wellbeing and energy poverty, a suite of estimation techniques, and other sensitivity checks. Additionally, we find that academic performance is an important channel through which energy poverty lowers children's subjective wellbeing. Our findings point out the need to involve children both in household practices and policy decisions that seek to address energy poverty, especially when it pertains to the children's wellbeing. © 2021 Elsevier B.V.
Enhanced power extraction from thermoelectric generators considering non-uniform heat distribution
- Authors: Fauzan, Miftah , Muyeen, S. , Islam, Syed
- Date: 2021
- Type: Text , Journal article
- Relation: Energy Conversion and Management Vol. 246, no. (2021), p.
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- Description: In this paper, a technique to enhance the performances of the thermoelectric generator under non-uniform heat distribution is developed. A large area of heat source is needed when the thermoelectric generator is used for high power applications such as powering air conditioners, household appliances, and distributed generation systems. Non-uniform heat distribution is a natural phenomenon in large surface of heat source. A model was developed and was validated with a prototype of thermoelectric panel 80 V, 2 A. Results show very good similarities between the model and the prototype outputs under various operating conditions. The error during the tests for the voltage performances was 6.5%, while the current was 1.1%. A method of maximizing power, i.e., developing a specialized maximum power point tracker (MPPT) along with blocking diodes, is proposed to overcome the effects of non-uniform heat distribution. In a typical condition, the output power dropped by 30% when a non-uniform thermal distribution is imposed to the array. The blocking diode can save power by 15%, and the MPPT expands up to 20% power when adopting this method. © 2021
Enhanced profitability of photovoltaic plants by utilizing cryptocurrency-based mining load
- Authors: Eid, Bilal , Islam, Md Rabiul , Shah, Rakibuzzaman , Nahid, Abdullah , Kouzani, Abbas , Mahmud, M.
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Applied Superconductivity Vol. 31, no. 8 (2021), p.
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- Description: The grid connected photovoltaic (PV) power plants (PVPPs) are booming nowadays. The main problem facing the PV power plants deployment is the intermittency which leads to instability of the grid. In order to stabilize the grid, either energy storage device - mainly batteries - or a power curtailment technique can be used. The additional cost on utilizing batteries make it not preferred solution, because it leads to a drop in the return on investment (ROI) of the project. A good alternative, is using a customized load (such as; cryptocurrency-based loads) which consumes the surplus energy. This paper investigating the usage of a customized load - cryptocurrency mining rig - to create an added value for the owner of the plant and increase the ROI of the project. These devices are widely used to perform the required calculations for validating the transactions on the network of the Blockchain. A comparison between the ROI of the mining rig and the battery have been conducted in this study. Based on this study the mining rig has superior ROI of 7.7% - in the case with the lowest ROI - compared to 4.5% for battery. Moreover, an improved controlling strategy is developed to combine both the battery and mining rig in the same system. The developed strategy is able to keep the profitability as high as possible during the fluctuation of the mining network. © 2002-2011 IEEE.
Extension of ZVS region of series-series WPT systems by an auxiliary variable inductor for improving efficiency
- Authors: Li, Yong , Liu, Shunpan , Zhu, Xia , Hu, Jiefeng , Zhang, Min , Mai, Ruikun , He, Zhengyou
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Power Electronics Vol. 36, no. 7 (2021), p. 7513-7525
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- Description: To maintain a stable output voltage under various operating conditions without introducing extra dc/dc converters, phase-shift (PS) control is usually adopted for wireless power transfer (WPT) systems. By using this method, however, zero-voltage switching (ZVS) operation cannot be guaranteed, especially in light-load conditions. To achieve high efficiency and reduce electromagnetic interference, it is significant for WPT systems to achieve ZVS operation of all switching devices in the whole operation range. In this article, an auxiliary variable inductor, of which the equivalent inductance can be controlled by adjusting the dc current in its auxiliary winding, is designed for series-series-compensated WPT systems under PS control to mitigate the loss arising from hard switching. As a result, a wide ZVS operation range of all switching devices can be achieved. A laboratory prototype is built to verify the theoretical analysis. The experimental results show that, under load and magnetic coupling variations, ZVS operation at fixed operation frequency as well as a constant dc output voltage can be maintained. Compared to the conventional method with only PS control, the proposed WPT can achieve higher overall efficiency in a wider load range owing to the wide ZVS operation range. © 1986-2012 IEEE.
Forced oscillation detection amid communication uncertainties
- Authors: Surinkaew, Tossaporn , Shah, Rakibuzzaman , Nadarajah, Mithulananthan , Muyeen, S. , Emami, Kianoush , Ngamroo, Issarachai
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Systems Journal Vol. 15, no. 3 (SEP 2021), p. 4644-4655
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- Description: This article proposes a novel technique for the detection of forced oscillation (FO) in a power system with the uncertainty in the measured signals. The impacts of communication uncertainties on measured signals are theoretically investigated based on the mathematical models developed in this article. A data recovery method is proposed and applied to reconstruct the signal under the effects of communication losses. The proposed FO detection with communication uncertainties is evaluated in the modified 14-machine Southeast Australian power system. A rigorous comparative analysis is made to validate the effectiveness of the proposed data recovery and FO detection methods.
Generalized bregman envelopes and proximity operators
- Authors: Burachik, Regina , Dao, Minh , Lindstrom, Scott
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Optimization Theory and Applications Vol. 190, no. 3 (2021), p. 744-778
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- Description: Every maximally monotone operator can be associated with a family of convex functions, called the Fitzpatrick family or family of representative functions. Surprisingly, in 2017, Burachik and Martínez-Legaz showed that the well-known Bregman distance is a particular case of a general family of distances, each one induced by a specific maximally monotone operator and a specific choice of one of its representative functions. For the family of generalized Bregman distances, sufficient conditions for convexity, coercivity, and supercoercivity have recently been furnished. Motivated by these advances, we introduce in the present paper the generalized left and right envelopes and proximity operators, and we provide asymptotic results for parameters. Certain results extend readily from the more specific Bregman context, while others only extend for certain generalized cases. To illustrate, we construct examples from the Bregman generalizing case, together with the natural “extreme” cases that highlight the importance of which generalized Bregman distance is chosen. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.