IEEE Access

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ISSN / EISSN : 2169-3536 / 2169-3536
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O. D. Naidu, Ashok Kumar Pradhan
IEEE Access pp 1-1; doi:10.1109/access.2020.3032458

Abstract:
Locating fault position in a series compensated transmission line is a difficult task due to the non-linear characteristics of the metal-oxide varistor which is present in the protection system of the compensator. In this paper, a model-free two-terminal traveling wave-based fault location method for a series compensated line is proposed. The technique makes use of two subroutines which calculate the fault locations assuming that the fault position to be on the left and right side of the series compensator respectively. The method then selects the correct fault location of the two by a special logic based on faulted half-section identification. The first two traveling wave arrival times recorded at both the terminals of the line are used in the method. The proposed technique does not require model information of the series capacitor and transmission line. It has been verified using the PSCAD/EMTDC model of a 400-kV, 300 km transmission line which is compensated by a fixed series capacitor in the middle and line ends. The performance of the proposed method is compared with a commercially available classical two-ended techniques and setting-free method.
Peisen Xiong, Hu Liu, Yongliang Tian, Zikun Chen
IEEE Access pp 1-1; doi:10.1109/access.2020.3032583

Abstract:
Helicopters are widely used in maritime search and rescue (SAR) missions. To improve the probability of success (POS) of SAR missions, search areas should be carefully planned. However, the search area is usually determined based on the survivors’ probable locations at a given moment by existing planning methods, while the effects of the relative motion between the helicopter and the search objects are ignored, possibly leading to a significant decrease in the POS. To minimize the impact of search object motion, a time domain-based iterative planning (TIP) method is proposed in this paper to obtain the optimal search areas. The survivors’ probable locations and mean drift direction are updated iteratively, while the probability map is developed by taking survivors’ mean drift direction as a reference. Then, the optimal search area is determined by an iterative search method starting from the cell with the highest probability of containment. To evaluate the effectiveness of a search plan, an agent-based simulation environment of a maritime search mission is constructed based on the AnyLogic simulation platform. Taking a capsizing case as an example, the simulation results show that the novel TIP method minimizes the impact of search object motion on the search effectiveness and obtains higher POS values than those obtained by other methods.
Chih-Wei Lin, Mengxiang Lin, Suhui Yang
IEEE Access pp 1-1; doi:10.1109/access.2020.3032430

Abstract:
Surveillance cameras have been widely used in urban environments and are increasingly used in rural ones. Such cameras have mostly been used for security, but they can be applied to the problem of furnishing fine-grained measurements and predictions of precipitation intensity. In this study, we formulated a stacked order-preserving (OP) learning framework to train a network using time-series data. We constructed an OP module, which uses a three-dimensional (3D) convolution operation to extract features with spatial and temporal information and that are associated with ConvLSTM; this feature extraction is used to learn the short-term and OP time-series relationships between features. Furthermore, the OP modules are stacked to form a stacked OP network (SOPNet), which strengthens the relationship between features in long-term time-series image sequences. This SOPNet can be use to obtain fine-grained measurements and predictions of precipitation intensity from images captured by outdoor surveillance cameras. Our main contributions are threefold. First, the SOPNet strengthens the short-term and long-term time-series relationship between features. Second, the SOPNet simultaneously examines spatial and temporal information to measure and predict precipitation intensity. Third, we constructed a precipitation intensity database based on optical images captured by outdoor surveillance cameras. We experimentally evaluated our proposed architecture using our self-collected data set. We found that SOPNet yields better performance and greater accuracy relative to its well-known state-of-the-art counterparts with respect to various metrics.
Rabiu S. Zakariyya, Khalid. H. Jewel, A. O. Fadamiro, Oluwole J. Famoriji, Fujiang Lin
IEEE Access pp 1-1; doi:10.1109/access.2020.3032636

Abstract:
Narrowband Internet of Things (NB-IoT) is an emerging IoT cellular-based wireless technology that builds based on the existing LTE standard to extend its network coverage suited for Low Power Wide Area Network (LPWAN) connectivity. In the NB-IoT system, LTE turbo codes and Cyclic Redundancy Check (CRC) codes are adopted as the main channel coding technique for Narrowband Physical Uplink Shared Channel (NPUSCH). However, turbo codes require iterative decoding algorithm at the expense of high computation complexity of decoding hence is crucial to achieving the LPWAN requirement of NB-IoT systems. Polar codes are best suited in this regard due to its capacity approaching and complexity reduction in short block length code. In this paper, we proposed an efficient polar coding technique using the Belief Propagation (BP) decoding algorithm for uplink data transmission on the NPUSCH channel. Furthermore, the BP algorithm is incorporated with the CRC stoppage criterion to decrease the number of decoding iteration and reduce computational complexity. In this scheme, single-tone numerology of NPUSCH using 3.75 kHz and 15 kHz subcarrier spacing is adopted. Then, the encoded data is generated with different NPUSCH resources. The theoretical formulation and simulation demonstrate that the proposed scheme provides better error rate performance over the adopted LTE turbo codes and other polar decoding algorithms while reducing the computational complexity.
Anabela Marto, Miguel Melo, Alexandrino Goncalves, Maximino Bessa
IEEE Access pp 1-1; doi:10.1109/access.2020.3032379

Abstract:
Little is known about the impact of the addition of each stimulus in multisensory augmented reality experiences in cultural heritage contexts. This paper investigates the impact of different sensory conditions on a user’s sense of presence, enjoyment, knowledge about the cultural site, and value of the experience. Five different multisensory conditions, namely, Visual, Visual+Audio, Visual+Smell, and Visual+Audio+Smell conditions, and regular visit referred to as None condition, were evaluated by a total of 60 random visitors distributed across the specified conditions. According to the results, the addition of particular types of stimuli created a different impact on the sense of presence subscale scores, namely, on spatial presence, involvement, and experienced realism, but did not influence the overall presence score. Overall, the results revealed that the addition of stimuli improved enjoyment and knowledge scores and did not affect the value of the experience scores. We concluded that each stimulus has a differential impact on the studied variables, demonstrating that its usage should depend on the goal of the experience: smell should be used to privilege realism and spatial presence, while audio should be adopted when the goal is to elicit involvement.
Dian Jia, Zhaoyang Wu
IEEE Access pp 1-1; doi:10.1109/access.2020.3032462

Abstract:
With the deepening of the global economic community, various emergencies emerge in endlessly, and the risks gradually increase. People’s lives and property are threatened, which also causes a great burden on the social economy. Hitherto unknown novel coronavirus events occurred in China after the outbreak of the new coronavirus in 2019. The emergency management system is not perfect, so we start to study and improve the deficiencies of the emergency management system, but it is still difficult to effectively prevent and deal with all kinds of sudden and frequent social problems. Therefore, this paper puts forward the research of intelligent evaluation system of government emergency management based on BP neural network. In this paper, an intelligent evaluation system of government emergency management based on Internet of things environment is established, and then the system is deepened by BP neural network algorithm to avoid the interference of human factors. An objective intelligent evaluation system of government emergency management is constructed and verified by an example. We applied the system in a province, and proved that the system has strong executive ability, outstanding big data computing ability, and can objectively evaluate and analyze the government emergency management. The operability and accuracy of the intelligent evaluation system are verified. The effective evaluation content provides a new idea and method for government emergency management. And then continuously improve the emergency management measures to achieve the effect of dealing with things smoothly without panic.
David Cabezuelo, Inigo Kortabarria, Jon Andreu, Fernando Rodriguez, Adrian Arcas, Nicola Delmonte
IEEE Access pp 1-1; doi:10.1109/access.2020.3032620

Abstract:
Switched Reluctance Machines (SRM) are considered promising rare-earth free candidates for the next generation electrified vehicles. One of the main drawback of this technology is the need of a large DC-link capacitor to balance the energy transferred back and forth between the DC source and the SRM. There are interesting novel modulations to reduce the current of the DC bus, focused on the capacitor size and cost reduction but leaving aside the thermal analysis and lifetime improvements. Carrying out the required dynamic multi-physics simulations for that purpose becomes highly time consuming and complex, especially when standardized or real driving conditions are needed to be taken into account. This paper proposes a simulation methodology, simple to implement and with a relatively low computational cost, to estimate the lifetime of an automotive DC-link capacitor, with the current load it delivers as the starting point. The presented methodology has also been used to validate a novel SRM modulation technique and to compare it, in terms of reliability, with the conventional torque sharing function.
Jianhui Wang, Keyi Wan, Xu Gao, Xuhong Cheng, Yu Shen, Zheng Wen, Usman Tariq, Jalil Piran
IEEE Access pp 1-1; doi:10.1109/access.2020.3032531

Abstract:
With the increasing public attention on sustainability, conservation of energy and materials has been a general demand for wastewater treatment plants (WWTPs). To meet the demand, efficient optimal management and decision mechanism are expected to reasonably configure resource of energy and materials.In recent years, advanced computational techniques such as neural networks and genetic algorithm provided data-driven solutions to overcome some industrial problems. They work from the perspective of statistical learning, mining invisible latent rules from massive data. This paper proposes energy and materials-saving management via deep learning for WWTPs, using real-world business data of a wastewater treatment plant located in Chongqing, China. Treatment processes are modeled through neural networks, and materials cost that satisfies single indexes can be estimated on this basis. Then, genetic algorithm is selected as the decision scheme to compute overall cost that is able to simultaneously satisfy all the indexes. Empirically, experimental results evaluate that with the proposed management method, total energy and materials cost can be reduced by 10%–15%.
Luping Xiang, Yusha Liu, Thien Van Luong, Robert G. Maunder, Lie-Liang Yang, Lajos Hanzo
IEEE Access pp 1-1; doi:10.1109/access.2020.3032627

Abstract:
Deep neural network (DNN)-aided spatial modulation (SM) is conceived. In particular, a pair of DNN structures are designed for replacing the conventional model-based channel estimators and detectors. As our first prototype, the conventional DNN estimates the channel relying on the pilot symbols and then carries out data detection in a data-driven manner. By contrast, our new DeepSM scheme is proposed for operation in more realistic time-varying channels, which updates the channel state information (CSI) at each time-slot (TS) before detecting the data. Hence, our novel DeepSM scheme is capable of performing well even in highly dynamic communication environments. Finally, our simulations show that the proposed DeepSM outperforms the conventional model-based channel estimation and data detection for transmission over time-varying channels.
Chen Li, Ju Tang, Zhiqiang Zhao, Haotian Li, Yulong Miao, Fuping Zeng
IEEE Access pp 1-1; doi:10.1109/access.2020.3032599

Abstract:
SF6/N2 gas mixture can not only reduce the consumption of SF6 in power system, but also effectively alleviate SF6 greenhouse effect. Since SF6/N2 can decompose under the alternating current partial discharge(PD), its decomposition characteristics are closely related to PD attributes. Therefore, the fault diagnosis method can be established through its PD decomposition characteristics. However, at present, the decomposition characteristics of SF6/N2 mixed gas under PD have not been fully grasped, and the correlation characteristics between decomposition characteristics and PD intensity have not been obtained. To this end, this paper uses pin-plate electrodes to simulate the typical metal protrusion insulation defects in GIS equipment. SF6/N2 decomposition experiments are conducted at different PD intensity to study the correlation between the PD decomposition characteristics of SF6/N2 mixed gas and PD intensity. The results show that: the generation rate of characteristic decomposition components such as SOF2, SO2, SO2F2, SOF4, CF4, CO2, NO, NO2, and NF3 are positively correlated with PD intensity; NO2 and (SO2F2+SOF2+SOF4+SO2) can be used as the characteristic quantity to judge the whole process of PD, SO2F2 can be used as the feature to judge the early PD, and SOF2 and SOF4 can be used as the features to judge the severe PD. The results of this paper will lay the foundation for the monitoring of equipment insulation status using SF6/N2 decomposition characteristics in the future.
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