IEEE Access

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ISSN / EISSN : 2169-3536 / 2169-3536
Total articles ≅ 42,825
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Yang Wang, Zhipeng Lu, Abraham P. Punnen
IEEE Access pp 1-1; doi:10.1109/access.2021.3051741

The minimum weight vertex cover problem (MWVCP) is a fundamental combinatorial optimization problem with various real-world applications. The MWVCP seeks a vertex cover of an undirected graph such that the sum of the weights of the selected vertices is as small as possible. In this paper, we present an effective algorithm to solve the MWVCP. First, a master-apprentice evolutionary algorithm based on two individuals is conducted to enhance the diversity of solutions. Second, a hybrid tabu search combined configuration checking and solution-based tabu search is introduced to intensify local search procedure. Harnessing the power of the evolutionary strategy and a novel variant of hybrid tabu search, Master-Apprentice Evolutionary Algorithm with Hybrid Tabu Search, MAE-HTS, is presented. Results of extensive computational experiments using standard benchmark instances and other large-scale instances demonstrate the efficacy of our algorithm in terms of solution quality, running time, and robustness compared to state-of-the-art heuristics from the literature and the commercial MIP solver Gurobi. We also systematically analyze the role of each individual component of the algorithm which when worked in unison produced superior outcomes. In particular, MAE-HTS produced improved solutions for 2 out of 126 public benchmark instances with better running time. In addition, our MAE-HTS outperforms other state-of-the-art algorithms DLSWCC and NuMWVC for 72 large scale MWVCP instances by obtaining the best results for 64 ones, while other two reference algorithms can only obtain 27 best results at most.
Huimin Gao, Jianlin Chen, Ruisheng Diao, Jing Zhang
IEEE Access pp 1-1; doi:10.1109/access.2021.3051843

The increased penetration of distributed energy resource and power electronics-based loads cause rapid voltage fluctuations in distribution power networks, affecting secure operation of the grid and threatening high-quality power delivery to customers. It is of great importance to conduct fast and accurate voltage stability assessment for better understanding operational risks and providing effective and timely controls for mitigating the identified risks. Traditionally, Jacobian matrix-based sensitivity analysis methods are used to evaluate voltage stability, however they suffer from ill-conditioned load flow problem caused by numerical stability. To effectively resolve this issue, this paper presents a novel Holomorphic embedding method (HEM)-based approach that can effectively capture sensitivity information for stressed operating conditions when the load flow calculation becomes ill-conditioned. An effective criterion is also proposed to approximate voltage instability point using structural and partial sensitivity information with different orders. The effectiveness of the proposed method is verified via numerical simulations conducted on the IEEE 33-bus and 69-bus test systems.
GyuWon Kim, Soo Young Lee, Jong-Seok Oh, Seungchul Lee
IEEE Access pp 1-1; doi:10.1109/access.2021.3051619

The vehicle suspension control unit serves as a critical component to the vehicle system, as it ensures the steering stability and sound ride quality of the vehicle. To effectively realize control strategies, it is essential to foreknowledge the road profile and the suspension system’s internal state variables. While the mentioned variables are not practically measurable using commercial sensors, it is necessary to estimate the desired variables by utilizing observer systems. Conventional means have mainly employed model-based approaches, in which model uncertainties and high computational cost pose limitations for practical implementation. Herein, we propose a data-driven deep learning method as an alternative because no explicit physical modeling is required, and evaluation is computationally cheap. We first propose a novel encoder-decoder structured recurrent neural network model with a two-phase attention mechanism to estimate the unknown road profile and four state variables of the vehicle suspension system. Based on a simulated data set, we assess the proposed model’s qualitative and quantitative results and demonstrate that our model can achieve highly accurate estimation results with fast computation time. Besides, we validate our black-box model’s reliability by comparing its interpretation with the suspension system’s actual physical characteristics. Furthermore, we compare the proposed model with existing baseline methods, and the results show that our proposed deep learning model significantly outperforms the baseline. Lastly, we experiment with our network’s autoregressive capability and demonstrate the feasibility of estimating a sequence of future values, which has not been presented in previous works.
Pablo Aqueveque, Luciano Radrigan, Francisco Pastene, Anibal S. Morales, Ernesto Guerra
IEEE Access pp 1-1; doi:10.1109/access.2021.3051583

This paper presents the development of an easy-to-deploy and smart monitoring IoT system that utilizes vibration measurement devices to assess real-time condition of bulldozers, power shovels and backhoes, in non-stationary operations in the mining industry. According to operating experience data and the type of mining machine, total loss failure rates per machine fleet can reach up to 30%. Vibration analysis techniques are commonly used for condition monitoring and early detection of unforeseen failures to generate predictive maintenance plans for heavy machinery. However, this maintenance strategy is intensively used only for stationary machines and/or mobile machinery in stationary operations. Today, there is a lack of proper solutions to detect and prevent critical failures for non-stationary machinery. This paper shows a cost-effective solution proposal for implementing a vibration sensor network with wireless communication and machine learning data-driven capabilities for condition monitoring of non-stationary heavy machinery in mining operations. During the machine operation, 3-axis accelerations were measured using two sensors deployed across the machine. The machine accelerations (amplitudes and frequencies) are measured in two different frequency spectrums to improve each sensing location’s time resolution. Multiple machine learning algorithms use this machine data to assess conditions according to manufacturer recommendations and operational benchmarks Proposed data-driven machine learning models classify the machine condition in states according to the ISO 2372 standards for vibration severity: Good, Acceptable, Unsatisfactory, or Unacceptable. After performing field tests with bulldozers and backhoes from different manufacturers, the machine learning algorithms are able to classify machine health status with an accuracy between 85% - 95%. Moreover, the system allows early detection of "Unacceptable" states between 120 to 170 hours prior to critical failure. These results demonstrate that the proposed system will collect relevant data to generate predictive maintenance plans and avoid unplanned downtimes.
Asanka Sayakkara, Nhien-An Le-Khac
IEEE Access pp 1-1; doi:10.1109/access.2021.3051921

The increasing use of smartphones has increased their presence in legal and corporate investigations. Unlike desktop and laptop computers, forensic analysis of smartphones is a challenging task due to their limited interfaces to retrieve information of forensic value. Electromagnetic side-channel analysis (EM-SCA) has been recently proposed as an alternative window to acquire forensic insights from computers, in particularly from Internet of Things devices. Along this line, this work experimentally evaluates the potential of extracting information of forensic value from smartphones through their EM radiation. Initially, a group of smartphones representing a diverse set of system-on-chip (SoC) processors were used to acquire EM radiation traces. Later, deep learning models were trained to detect various internal software behaviours running on the SoCs. The results of this work indicates that a wide variety of insights can be extracted from smartphones through EM side-channel, increasing the potential opportunities for digital forensic investigators.
Zhen-Nan Fan, Zu-Ying Bian, Ke Xiao, Jing-Can Li, Bing Yao, Xue-Gui Gan
IEEE Access pp 1-1; doi:10.1109/access.2021.3051241

The loss and heat of a self-cooling enclosure-isolated phase bus of large generator are studied by establishing the electromagnetic-fluid-temperature field model of the bus using the finite element method. Factors such as skin effect and eddy loss, the electro-conductivity temperature effect, gas flow, and gravity are considered. The compositive calculation and analysis of the loss and temperature of the self-cooling enclosure-isolated phase bus of a 600 MW generator are conducted, and the data are compared with the test. The results show that the current and loss distribution in the conductor and sheath of the horizontal bus correlate with skin effect. The distribution of the bus temperature around the vertical center axis is symmetric, but the temperature of the top bus is higher than the bottom. If the influence of the acceleration of gravity and heat radiation is not considered, the result will become unreasonable.
Sangmin Lee, Dae-Eun Lim, Younkook Kang, Hae Joong Kim
IEEE Access pp 1-1; doi:10.1109/access.2021.3051763

Clustered multi-task learning, which aims to leverage the generalization performance over clustered tasks, has shown an outstanding performance in various machine learning applications. In this paper, a clustered multi-task sequence-to-sequence learning (CMSL) for autonomous vehicle systems (AVSs) in large-scale semiconductor fabrications (fab) is proposed, where AVSs are widely used for wafer transfers. Recently, as fabs become larger, the repositioning of idle vehicles to where they may be requested has become a significant challenge because inefficient vehicle balancing leads to transfer delays, resulting in production machine idleness. However, existing vehicle repositioning systems are mainly controlled by human operators, and it is difficult for such systems to guarantee efficiency. Further, we should handle the small data problem, which is insufficient for machine learning because of the irregular time-varying manufacturing environments. The main purpose of this study is to examine CMSL-based predictive control of idle vehicle repositioning to maximize machine utilization. We conducted an experimental evaluation to compare the prediction accuracy of CMSL with existing methods. Further, a case study in a real largescale semiconductor plant, demonstrated that the proposed predictive approach outperforms the existing approaches in terms of transfer efficiency and machine utilization.
Mingwen Qu, Tingting Chen, Shiqi Lu, Jianling Hu, Jiajun Wang, Nan Hu
IEEE Access pp 1-1; doi:10.1109/access.2021.3051644

Steady-state responses (SSRs), evoked by various patterns of periodic stimuli, comprise an important category of evoked potentials. To explore the neural generators of SSRs, a unified framework solving the inverse problem for a single subject or integrating multiple subjects is indispensable. Inspired by the phenomenon that the oscillation frequency of an SSR follows that of the periodic stimulus, we consider the problem of source localization for SSRs using the Fourier components at the stimulation frequency instead of directly using the waveform in this paper. The multi-channel electroencephalogram (EEG) Fourier components at the stimulation frequency is shown to equal multiplying the lead field matrix (LFM) by a complex-valued vector that contains the amplitudes and phases of sources in the cortex, contaminated by spontaneous EEG and electrical noise. This complex-valued inverse problem is further solved in the framework of sparse Bayesian learning, where the non-stationarity of spontaneous EEG among epochs is considered, and the joint sparsity of complex-valued source component vectors is modeled and utilized to improve the source localization performance. Expectation-maximization (EM) is employed to give the ultimate SSR source localization algorithm. By the proposed method, not only a single subject’s SSR source localization can be achieved, but also the common locations of a certain type of SSR integrating multiple subjects can be given, even when the electrode layout or number of electrodes varies among subjects. The validity and superior performance of the proposed method was verified by simulations compared with other methods. Real SSR stimulation/recording experiments were also performed, where the electric generators of 40-Hz auditory steady-state responses (ASSRs) by various stimulation patterns were investigated.
Xin-Gang Ju, Fei-Yu Lian, Hong-Yi Ge, Yu-Ying Jiang, Yuan Zhang, Degang Xu
IEEE Access pp 1-1; doi:10.1109/access.2021.3051685

To solve the problems existing in traditional biochemical methods, such as complex sample pretreatment requirements, tedious detection processes and low detection accuracies with respect to rice species and adulteration, the volatile flavor substances of five kinds of rice are detected using headspace-gas chromatography-ion mobility spectrometry (HGC-IMS) to effectively identify the quality of rice and adulterated rice. The ion migration fingerprint spectra of five kinds of rice are identified using a semi-supervised generative adversarial network (SSGAN). We replace the output layer of the discriminator in a GAN with a softmax classifier, thus extending the GAN to a semi-supervised GAN. We define additional category tags for generated samples to guide the training process. Semi-supervised training is used to optimize the network parameters, and the trained discriminant network is used for classifying HGC-IMS images. The experimental results show that the prediction accuracy of the model reaches 98.00%, which is significantly higher than the rates achieved by other models, such as a decision tree, a support vector machine (SVM), improved SVM models (LS-SVM and PCA-SVM) and local geometric structure Fisher analysis (LGSFA); 98.00% is also higher than the prediction accuracies of the VGGNet, ResNet and Fast RCNN deep learning models. The experimental results also show that the accuracy of HGC-IMS image classification for identifying adulterated rice reaches 97.30%, which is higher than those of traditional chromatographic or spectral methods. The proposed method overcomes the shortcomings of some intelligent algorithms regarding the application of ion migration spectra and is feasible for accurately predicting rice varieties and adulterated rice.
Mohammed Kharrich, Salah Kamel, Mohamed Abdeen, Omar Hazem Mohammed, Mohammed Akherraz, Tahir Khurshaid, Sang-Bong Rhee
IEEE Access pp 1-1; doi:10.1109/access.2021.3051573

In this paper, a new application of Equilibrium Optimizer (EO) is proposed for design hybrid microgrid to feed the electricity to Dakhla, Morocco, as an isolated area. EO is selected to design the microgrid system due to its high effectiveness in determining the optimal solution in very short time. EO is presented for selecting the optimal system design which can minimize the cost, improve the system stability, and cover the load at different climate conditions. Microgrid system consists of photovoltaic (PV), wind turbine (WT), battery, and diesel generator. The objective function treated in this paper is to minimize the net present cost (NPC), respecting several constraints such as the reliability, availability, and renewable fraction. The sensitivity analysis is conducted in two stages: Firstly, the impact of wind speed, solar radiation, interest rate, and diesel fuel on the NPC, and levelized cost of energy (LCOE) is analyzed. Secondly, the influence of size variation on loss of power supply probability (LPSP) is investigated. The results obtained by EO are compared with those obtained by recent metaheuristics optimization algorithms, namely, Harris Hawks Optimizer (HHO), Artificial Electric Field Algorithm (AEFA), Grey Wolf Optimizer (GWO), and Sooty Tern Optimization Algorithm (STOA). The results show that the optimal system design is achieved by the proposed EO, where renewable energy sources (PV and WT) represent 97% of the annual contribution and fast convergence characteristics are obtained by EO. The best NPC, LCOE, and LPSP are obtained via EO achieving 74327 $, 0.0917 $/kWh, and 0.0489, respectively.
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