bluuebunny/crossref_metadata_2025 huggingface dataset:
Natural arrangement of AgCu bimetallic nanostructures through oleylamine reduction
Authors: Freire R. M. , Rojas-Nunez J. , Elias-Arriaga A. L. , Fujisawa K. , Troncoso L. , Denardin J. C. , Baltazar S. E.
Published: October 2020

The use of AgCu bimetallic NPs as a catalyst is highly desired. To accomplish this, the morphology of nanostructures is a key factor.
High-temperature resistant water-soluble polymers derived from exotic amino acids
Authors: Dwivedi Sumant , Nag Aniruddha , Sakamoto Shigeki , Funahashi Yasuyoshi , Harimoto Toyohiro , Takada Kenji , Kaneko Tatsuo
Published: October 2020

High-performance water-soluble polymers have a wide range of applications from engineering materials to biomedical plastics. This article discusses the synthesis of water-soluble polyimide from bio-based monomers.
Degradation of norfloxacin by copper-doped Bi2WO6-induced sulfate radical-based visible light-Fenton reaction
Authors: Zhong Xin , Wu Wen-Ting , Jie Hao-Nan , Tang Wang-Ye , Chen Dan-Yan , Ruan Tao , Bai He-Ping
Published: October 2020

In this work, a series of Cu(ii)-doped Bi2WO6 nanomaterials with good photo-response properties were facile synthesized and used to obtain efficient peroxymonosulfate (PMS) activation activity for norfloxacin (NOF) removal under visible LED light irradiation.
Multi-fold enhancement in sustainable production of biomass, lipids and biodiesel from oleaginous yeast: an artificial neural network-genetic algorithm approach
Authors: Kumar RaviRanjan , Dhanarajan Gunaseelan , Sarkar Debasis , Sen Ramkrishna
Published: October 2020

Implementation of an artificial neural network-genetic algorithm technique for improvement in sustainable production of biomass, lipids and biodiesel from oleaginous yeast.
Field-induced anti-nematic and biaxial ordering in binary mixtures of discotic mesogens and spherical magnetic nanoparticles
Authors: Peroukidis Stavros D. , Klapp Sabine H. L. , Vanakaras Alexandros G.
Published: October 2020

Using computer simulations we explore the equilibrium structure and response to external stimuli of complex magnetic hybrids consisting of magnetic particles in discotic liquid crystalline matrices.
Destabilization and phase separation of particle suspensions in emulsions
Authors: Feneuil Blandine , Jensen Atle , Carlson Andreas
Published: October 2020

The presence of particles in an emulsion affects the drainage direction and velocity of the continuous phase. We observe experimentally and numerically that particles can either stabilize the emulsion or enhance drainage.
Two-dimensional materials as novel co-catalysts for efficient solar-driven hydrogen production
Authors: Tong Rui , Ng Kar Wei , Wang Xina , Wang Shuangpeng , Wang Xuesen , Pan Hui
Published: October 2020

Electrocatalysts with high HER activity can be used as co-catalysts to improve the P-HER performance of photocatalysts.
Identification of a glycan cluster in gp120 essential for irreversible HIV-1 lytic inactivation by a lectin-based recombinantly engineered protein conjugate
Authors: Parajuli Bibek , Acharya Kriti , Nangarlia Aakansha , Zhang Shiyu , Parajuli Bijay , Dick Alexej , Ngo Brendon , Abrams Cameron F. , Chaiken Irwin
Published: October 2020

We previously discovered a class of recombinant lectin conjugates, denoted lectin DLIs (‘dual-acting lytic inhibitors’) that bind to the HIV-1 envelope (Env) protein trimer and cause both lytic inactivation of HIV-1 virions and cytotoxicity of Env-expressing cells. To facilitate mechanistic investigation of DLI function, we derived the simplified prototype microvirin (MVN)-DLI, containing an MVN domain that binds high-mannose glycans in Env, connected to a DKWASLWNW sequence (denoted ‘Trp3’) derived from the membrane-associated region of gp41. The relatively much stronger affinity of the lectin component than Trp3 argues that the lectin functions to capture Env to enable Trp3 engagement and consequent Env membrane disruption and virolysis. The relatively simplified engagement pattern of MVN with Env opened up the opportunity, pursued here, to use recombinant glycan knockout gp120 variants to identify the precise Env binding site for MVN that drives DLI engagement and lysis. Using mutagenesis combined with a series of biophysical and virological experiments, we identified a restricted set of residues, N262, N332 and N448, all localized in a cluster on the outer domain of gp120, as the essential epitope for MVN binding. By generating these mutations in the corresponding HIV-1 virus, we established that the engagement of this glycan cluster with the lectin domain of MVN*-DLI is the trigger for DLI-derived virus and cell inactivation. Beyond defining the initial encounter step for lytic inactivation, this study provides a guide to further elucidate DLI mechanism, including the stoichiometry of Env trimer required for function, and downstream DLI optimization.
Ultrasonic irradiation and SonoVue microbubbles-mediated RNA interference targeting PRR11 inhibits breast cancer cells proliferation and metastasis, but promotes apoptosis
Authors: Luo Hui , Li Jian , Lin Qi , Xiao Xiaojun , Shi Yang , Ye Xiuqin , Wei Zhanghong , Liu Yingying , Xu Jinfeng
Published: October 2020

The present study compared the effects of ultrasonic irradiation and SonoVue microbubbles (US) or Lipofectamine 3000 on the transfection of small interfering RNA for PRR11 (siPRR11) and Proline-rich protein 11 (PRR11) overexpression plasmid into breast cancer cells. SiPRR11 and PRR11 overexpression plasmid were transfected into breast cancer MCF7 cells mediated by US and Lipofectamine 3000. PRR11 expressions in breast cancer and normal tissues were determined using Gene Expression Profiling Interactive Analysis (GEPIA). The viability, proliferation, migration, invasion and apoptosis of breast cancer cells were respectively measured by MTT assay, clone formation assay, scratch wound-healing assay, Transwell assay and flow cytometry. PRR11 and epithelial-to-mesenchymal transition (EMT)-related and apoptosis-related (B-cell lymphoma 2, Bcl-2; Bcl-2-associated protein X, Bax) proteins’ expressions were detected by quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot as appropriate. As ultrasonic intensity increased, the viability of MCF7 cells was decreased. Results from GEPIA suggested that PRR11 was up-regulated in breast cancer. Silencing PRR11 mediated by US showed a higher efficiency than by Lipofectamine 3000. SiPRR11 transfected by Lipofectamine 3000 suppressed cells growth and metastasis, while promoted cell apoptosis. Moreover, E-cadherin (E-cad) and Bax expressions were high but N-cadherin (N-cad), Snail and Bcl-2 expressions were low. However, overexpressed PRR11 caused the opposite effects. More importantly, transfection of siPRR11 and PRR11 overexpression plasmid using US had a higher efficacy than using Lipofectamine 3000. US transfection of PRR11 siRNA showed better effects on inhibiting breast cancer progression. The current findings contribute to a novel treatment for breast cancer.
Cross‐layer resource allocation for critical MTC coexistent with human‐type communications in LTE: a two‐sided matching approach
Authors: Abdelsadek Mohammed Y. , Ahmed Mohamed H. , Gadallah Yasser
Published: October 2020

Cellular systems present one of the most suitable wireless technologies to efficiently serve critical machine‐type communications (MTC) that require strict quality‐of‐service (QoS) guarantees. Therefore, ultra‐reliable and low‐latency communications is a target use case in the design of the upcoming generations of cellular networks. From the radio resource management perspective, guaranteeing such stringent QoS requirements in long term evolution (LTE) networks is a challenging task, especially in the case of the coexistence of MTC with the human‐type communications (HTC). In this study, the authors address the resource allocation and scheduling problem of critical MTC that coexist with HTC in LTE. The optimisation problem is formulated such that the overall system utility is maximised while fulfilling the different QoS demands of the two sets of users. Utilising the effective bandwidth and effective capacity theories, a cross‐layer design is developed to guarantee the QoS requirements of the critical MTC. For a computationally‐efficient solution of the problem, they formulate it as a two‐sided matching process that can be used as a practical scheduling scheme. To this end, they analyse the convergence, stability, and computational complexity of the proposed methods. Results reveal the close‐to‐optimal performance of the matching‐based scheduling scheme and its superiority to other existing techniques.
C&O charging: a hybrid wireless charging method for the mine internet of things
Authors: Hu Qingsong , Cheng Yong , Song Boming , Li Binghao , Li Shiyin
Published: October 2020

Most nodes of Mine Internet of Things (Mine IoT) are powered by batteries, and wireless charging using mobile chargers (MCs) is an effective way to make nodes work sustainably. A novel hybrid charging method combining the controlled and opportunistic MCs (C&O charging) is proposed in this study. Workers (such as the repairmen and gas inspectors) carrying portable chargers are proposed to be opportunistic MCs to provide an incidental charging service for the surrounding rechargeable Mine IoT nodes while doing its own work to reduce the payload of controlled MCs. The hybrid charging model based on the incidental charging ability of the opportunistic MC is constructed and the scheduling strategy of the controlled MC and the queueing management scheme of the charging request are also proposed. The simulation results indicate that the power demands of the majority of the nodes in the maintenance areas can be met or partially met by opportunistic MCs and the charging time of C&O charging is greatly decreased compared to that of only using controlled MCs.
Wireless information and power transfer for underwater acoustic time‐reversed NOMA
Authors: Esmaiel Hamada , Qasem Zeyad A.H. , Sun Haixin , Qi Jie , Wang Junfeng , Gu Yaping
Published: October 2020

The acoustic signal transmission over the underwater channel has a limited sum rate and it consumes high power due to the properties of the underwater environment. This study attempts to use the non‐orthogonal multiple access (NOMA) technologies for underwater communications. NOMA can be an attractive candidate for underwater communication due to its high spectral efficiency, resistance for carrier frequency offset, and efficient energy consumption. To cope with the hard‐recharging capability of the underwater wireless sensor nodes caused by the ocean environment, this study proposes a novel transmission scheme called time‐reversed NOMA (TR‐NOMA) for underwater communication. In the proposed TR‐NOMA, a single‐input multiple‐output NOMA scheme with a passive‐time reversal technique is proposed to reduce the time–frequency dispersion of the underwater acoustic channels. Consequently, simultaneous wireless information and power transfer (SWIPT) can be applied for underwater TR‐NOMA. In this study, a SWIPT‐NOMA is postposed to harvest energy in downlink transmission from the transmitted signal. The bit error rate (BER) and the outage probability are used to characterise the performance of the proposed TR‐NOMA scheme and simulation results show how the proposed TR‐NOMA significantly outperforms the conventional NOMA schemes. Additionally, a mathematical framework for the average BER of TR‐NOMA is delineated.
Enhancing multi‐tenancy security in the cloud computing using hybrid ECC‐based data encryption approach
Authors: Kumar Pawan , Kumar Bhatt Ashutosh
Published: October 2020

In this study, elliptic curve cryptography (ECC) is elected for tenant authentication, data encryption, and data decryption due to its minimum key size. The proposed ECC‐based authentication approach allows the authorised person to access private data; it protects different related attacks effectively. To develop more secure data encryption, the authors have combined a nature‐inspired optimisation, such as a moth search algorithm (MSA) with ECC, to select the correct and optimal value of the elliptic curve. The proposed encryption and decryption approach combines DNA encoding with the ECC encryption algorithm. The mechanism of DNA encoded ECC provided multi‐level security with less computational power. The security analysis of the proposed method has been provided to prove its effectiveness against certain attacks, such as denial‐of‐service attack, impersonation attack, reply attack, plaintext attack and chosen‐ciphertext attack. The experimental result is evaluated based on encryption time, decryption time, throughput and key size of the security model. The average execution time of the proposed encryption and decryption is only 83.153 and 86.076 s, respectively. From the evaluation, it is clearly determined that the proposed technique provides two‐layer security with minimum key size and less storage space.
Generic evaluation of FSO system over Málaga turbulence channel with MPPM and non‐zero‐boresight pointing errors
Authors: Alshaer Nancy , Ismail Tawfik , Nasr Mohamed E.
Published: October 2020

Free space optical (FSO) communication channels are affected by fluctuations in irradiance due to atmospheric turbulence and pointing errors. Recently, a generalized statistical model knows as Málaga () was developed to describe irradiance fluctuations of the beam propagating through a turbulent medium. In this paper, an approximate finite‐series probability density function (PDF) for composite turbulence with pointing errors is verified. Considering multiple pulse‐position‐modulation (MPPM) with intensity modulation and direct detection, specific closed‐form expressions for average symbol error rate (ASER) and outage probability are obtained. Moreover, asymptotic expressions for ASER and outage probability over the Málaga channel with pointing errors are presented. Finally, ASER and outage probability expressions are deduced in three turbulence regimes, strong, moderate, and weak. Therefore, simulation results are given to confirm the accuracy of the proposed expressions. The results show that the optimum beam divergence angle is 1 mrad for zero‐boresight displacement and 2 mrad for nonzero‐boresight at a distance of 1 km. By increasing the range to 5 km, atmospheric turbulence is found to have the dominant effect over boresight displacement. Moreover, the system performance at the optimized divergence angle is studied in terms of transmit power, normalized boresight, and link distance.
Deep reinforcement learning based optimal channel selection for cognitive radio vehicular ad‐hoc network
Authors: Pal Raghavendra , Gupta Nishu , Prakash Arun , Tripathi Rajeev , Rodrigues Joel J. P. C.
Published: October 2020

Channel selection is a challenging task in cognitive radio vehicular networks. Vehicles have to sense the channels periodically. Due to this, a lot of time is wasted which could have been utilised for transmission of data. Employing road side units (RSUs) in sensing can prove to be useful for this purpose. The RSUs may select the channel and allocate it to the vehicles on demand. However, this sensing should be proactive. RSUs should know in advance the channel to be allocated when requested. For this purpose, a deep reinforcement learning algorithm namely deep reinforcement learning based optimal channel selection is proposed in this study for training the network according to the previously sensed data. Proposed protocol is simulated and results are compared with the existing methods. The packet delivery ratio is increased by 2%, throughput is increased by 1.8%, average delay is decreased by 2% and primary user collision ratio is reduced by 3.2% when compared with similar recent work by varying number of vehicles. On the other hand, when compared with similar recent work by varying channel availability, the packet delivery ratio is increased by 4.5 %, throughput by 4.3%, average delay is decreased by 3% and PU collision ratio by 5.5%.
Reduced‐order event‐triggered controller for a singularly perturbed system: An active suspension case
Authors: Bhandari Manisha , Fulwani Deepak , Bandopadhyay B. , Gupta Rajeev
Published: October 2020

For two‐time scale systems, singular perturbation theory is often used for designing a controller based only on an approximate model of its slow dynamics, assuming the fast model to be stable. In this context, the authors investigate and implement a stabilising event‐triggered feedback law for a networked singularly perturbed system, based only on an approximate model of its slow dynamics. Triggering rule guarantees the stability and the existence of a positive lower bound between two consecutive transmissions. The proposed approach has been validated for a laboratory‐scale hardware setup of an active suspension system of a quarter‐car model. The presence of fast and slow modes in a vehicle suspension system is utilised to model it as a singularly perturbed system. Experimental results indicate that in spite of the simplified structure of the controller and event‐triggered feedback, its performance is comparable to that of the full‐state feedback design with continuous feedback with the significant reduction in control execution events.
Harnessing feedback region proposals for multi‐object tracking
Authors: Prasanna Kumar Aswathy , Mishra Deepak
Published: October 2020

In the tracking‐by‐detection approach of online multiple object tracking (MOT), a major challenge is how to associate object detections on the new video frame with previously tracked objects. Two important aspects that directly influence the performance of MOT are quality of detection and accuracy in data association. The authors propose an efficient and unified MOT framework for improved object detection, followed by enhanced object tracking. The object detection and tracking are considered as two independent functions in the tracking‐by‐detection paradigm. In this study, object detection accuracy has been increased by employing a faster region‐based convolutional neural network (Faster R‐CNN) modified with the feedback region proposals from the tracker. Target association is performed by the correlation filter‐based Siamese CNN model, which finds the similarity score between the input image patches. The Siamese CNN is trained using a supervised hard sample mining strategy. An optical flow‐based motion model is employed to predict the next probable location of the targets from the tracker and these region proposals are fed back to the classifier module of Faster R‐CNN. The authors’ extensive analysis of publicly available MOT benchmark datasets and comparison with the state‐of‐the‐art tracking methods demonstrate competitive tracking performance of the proposed MOT framework.
Decentralised design of robust multi‐objective PSSs: D‐decomposition approach
Authors: Ayman Mohamed , Soliman Mahmoud
Published: October 2020

This study addresses the problem of determining the set of all robust three‐parameter power system stabilisers having the form . Graphical characterisation of stabilising power system stabilisers (PSSs) is carried out using D‐decomposition, whereas the controller–parameter space is subdivided into root‐invariant regions. Rather than Hurwitz stability, D‐decomposition can parameterise D‐stabilising PSSs that enforce pole‐clustering in a pre‐specified region D to ensure better time‐domain specifications. The convex region of D‐stabilising PSSs is sketched by mapping the contours from the s‐plane onto the controller–parameter plane by two parametric functions and with fixed . The frequency range considered for mapping is initially computed to avoid sweeping over unnecessary frequencies. Based on the geometry of the D‐stability region, analytical expressions are derived to compute optimal σ − ζ PSSs for an arbitrary operating point. Parametric uncertainties are captured by an image‐set polynomial where the region guarantying robust D‐stability of the family is investigated. A computationally effective approach based on stabilising two vertex plants is concluded from the D‐stability region. Extension to multi‐machine systems is treated where decentralised PSSs are synthesised. An iterative algorithm is suggested to modify a set of initial feasible PSSs sequentially while maximising damping indices. Simulation results confirm the efficacy of the suggested method.
Adaptive set point modulation to mitigate transients in power systems
Authors: Ghaffarzadeh Hooman , Mehrizi‐Sani Ali
Published: October 2020

This paper presents an adaptive control strategy to improve the transient response of a stressed power system apparatus. This strategy, based on the set point automatic adjustment with correction‐enabled (SPAACE), is an auxiliary control method to enhance the set point tracking of an apparatus. SPAACE modifies the set point of the controllable apparatus using a feedback signal from its output to mitigate transients that can be caused by events such as faults, load variations, and large reference set point changes. In this work, an adaptive version of SPAACE (ASPAACE), which uses an adaptive dead‐zone band, adaptive scaling factor, and exponential prediction is introduced. Moreover, this study investigates the relationship between the natural frequency of the system and the prediction horizon of the ASPAACE controller. To demonstrate the performance of ASPAACE, different case studies are presented.
Agent‐based situational awareness system for severity in closeness of voltage instability occurrence
Authors: Khalilifar Mahtab , Joorabian Mahmood , Seifosadat Ghodratollah , Shahrtash Seyed Mohammad
Published: October 2020

In this study, on the basis of a new agent‐based situational awareness platform, the amount of severity in closeness to voltage instability occurrence is determined. In the proposed platform (with the aid of the agents of the disturbance detection unit that are responsible for perception of environmental events and understanding the current situation and the agents of the early enough instability prediction unit that predicts instability occurrence), the novelty is in creating the agents of clustering units that are responsible for prediction of the future status, i.e. severity in closeness to voltage instability occurrence through determining the severity class. This clustering unit maps the reactive power‐based features obtained from unconstrained power flow analyses, to the instability time; without any need of post‐disturbance data, i.e. the method belongs to just‐after‐disturbance algorithms. Then, it estimates the severity of the closeness to instability for any test scenario according to its membership degree related to the pre‐defined severity clusters. The simulation results show acceptable overall accuracy (up to 99%) in performing early warning task for operators of power systems.
Intelligent coordinators for automatic voltage regulator and power system stabiliser in a multi‐machine power system
Authors: Khezri Rahmat , Oshnoei Arman , Yazdani Amirmehdi , Mahmoudi Amin
Published: October 2020

This study presents the design of intelligent coordinators for the automatic voltage regulator (AVR) and power system stabiliser (PSS) in a multi‐machine power system. The intelligent coordinators are designed to update the gains of AVR and PSS in severe disturbances to guarantee the stability of the studied power system. Three potent intelligent coordinators are proposed: (a) fuzzy logic coordinator, (b) artificial neural network coordinator, and (c) brain emotional learning coordinator. Since the intelligent coordinators are based on the knowledge of the experts, desirable scaling factors are considered in the output signals of the coordinators to achieve optimal results. The scaling factors are optimised using a new evolutionary approach known as the sine–cosine algorithm. To evaluate the efficiency of the proposed intelligent approaches, the performances of coordinators are analysed on a two‐area four‐machine power system. A range of power system signals, such as rotor speed, terminal voltages, acceleration power and rotor angle of generators are demonstrated to approve and compare the performance of the intelligent coordinators. The simulation results indicate that the intelligent coordinators can guarantee the stability of the power system and satisfy performance objectives, such as desired transient and steady‐state errors.
Deep learning model to detect various synchrophasor data anomalies
Authors: Deng Xianda , Bian Desong , Wang Weikang , Jiang Zhihao , Yao Wenxuan , Qiu Wei , Tong Ning , Shi Di , Liu Yilu
Published: October 2020

High‐density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high‐frequency interferences are considered in this study. The proposed model is extensively evaluated via field‐collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.
Randomised learning‐based hybrid ensemble model for probabilistic forecasting of PV power generation
Authors: Liu Wei , Xu Yan
Published: October 2020

Probabilistic forecasting of solar photovoltaic (PV) generation is critical for stochastic or robust optimisation‐based power system dispatch. This study proposes a randomised learning‐based hybrid ensemble (RLHE) model to construct the prediction intervals of probabilistic PV forecasting. Three different randomised learning algorithms, namely extreme learning machine, randomised vector functional link, and stochastic configuration network, are ensembled as a hybrid forecasting model. Besides, bootstrap is used as the ensemble learning framework to increase the diversity of training samples. For each algorithm, a decision‐making rule is designed to evaluate the credibility of the individual outputs and the incredible ones are discarded at the output aggregation step. The weight coefficients of the aggregated outputs of the three algorithms are then optimised to compute the final point forecast results. Based on the point forecast results, the prediction intervals are constructed considering both model misspecification uncertainty and data noise uncertainty. The variance in model misspecification uncertainty is directly calculated with the individual outputs and the variance in data noise uncertainty is separately trained with an RLHE model. The proposed method is tested with an open dataset and compared with several benchmarking approaches.
Sigmoidal and Gaussian functions based neural neuron technique for grid interactive solar energy system enabling power quality improvement
Authors: Pal Kanwar , Kumar Shailendra , Singh Bhim , Kandpal Tara C.
Published: October 2020

An adaptive approach based on a neural network is presented here for the control of a grid interactive solar power generating system. This control approach is based on a summation and a product neuron, which are used to process the non‐linearity of load currents. A sigmoidal and Gaussian functions are used to linearise the response of summation and product neurons. In this control, the product and summation neurons, and sigmoidal and Gaussian functions are within a single layer, which reduces the complexity of control. This control algorithm is superior over artificial neural network (ANN)‐based control algorithm, which has large number of unknown weights and layers. It reduces the computational burden and complexity of the control algorithm. It shows the inherent improved performance at non‐linear balanced and unbalanced loads. The control of net active and reactive components of the power separately improves the efficacy of the control at unbalanced non‐linear load and reduces the voltage transients in the grid voltages. The ANN‐based control also plays an active role in enhancing the power quality of the grid. The reduced complexity of control and direct power feed forward of photovoltaic array (PVA) to the grid improve the dynamic response of the grid‐connected PVA‐based system.
Hybrid AC/DC microgrids flexible reliability index by using the axiomatic design concept
Authors: Sabouhi Hamed , Doroudi Aref , Fotuhi‐Firuzabad Mahmud , Bashiri Mahdi
Published: October 2020

With the capacity increase of large‐scale DC renewable energy and DC loads, the reliability issues of hybrid AC/DC networks become increasingly prominent. On the other hand, a modern distribution network should also deliver power to individual customers based on their priority levels and the traditional reliability indices are not suitable for modern networks. Flexible reliability (FR) is a new concept that considers the loads’ importance in the reliability evaluation of distribution grids. In this study, a novel comprehensive FR index is introduced for hybrid AC/DC networks. To determine the proposed index, power networks are modelled as a topological combination of edges and nodes. An axiomatic design concept which is a systematic binary matrix‐based approach is then employed for topology analysis. The proposed index is based on the systematic counting of feasible paths from generators to loads, considering loads’ importance. The effectiveness of the proposed index is demonstrated through an illustrative example and IEEE 33‐bus distribution system.
Deep facial emotion recognition in video using eigenframes
Authors: Hajarolasvadi Noushin , Demirel Hasan
Published: October 2020

Recently, video‐based facial emotion recognition (FER) has been an attractive topic in the computer vision society. However, processing several hundreds of frames for a single video of a particular emotion is not efficient. In this study, the authors propose a novel approach to obtain a representative set of frames for a video in the eigenspace domain. Principal component analysis (PCA) is applied to a single emotional video extracting the most significant eigenframes representing the temporal motion variance embedded in the video. Given that faces are segmented and normalised, the variance captured by PCA is attributed to the facial expression dynamics. The variation in the temporal domain is mapped to the eigenspace reducing the redundancy. The proposed approach is used to extract the input eigenframes. Later, VGG‐16, ResNet50, and 2D and 3D CNN architectures called eigenFaceNet are trained on the RML, eNTERFACE′05, and AFEW 6.0 databases. The experimental results are superior to the state‐of‐the‐art by 8 and 4% for RML, eNTERFACE′05 databases, respectively. The performance achievement is also coupled with a reduction in the computational time.
Multi‐scale patches based image denoising using weighted nuclear norm minimisation
Authors: Fu Yuli , Xu Junwei , Xiang Youjun , Chen Zhen , Zhu Tao , Cai Lei , He Weihong
Published: October 2020

As a prior knowledge, non‐local self‐similarity (NSS) has been widely utilised in ill‐posed problems. Actually, similar textures appear not only in a single scale, but also in different scales. Unlike most existing patch‐based methods that only explore NSS in the same scale, a multi‐scale patches based image denoising algorithm is proposed in this study. The authors have designed a multi‐scale strategy to expand the search space of block‐matching, which will increase the probability of finding more similar patches. After that, the weighted nuclear norm minimisation (WNNM) algorithm is employed to reveal latent clean patches. With the join of the multi‐scale framework, the performance of WNNM can be improved. The proposed algorithm can be used to solve NSS‐based image restoration tasks. In this study, mainly image denoising is studied, and its effectiveness is derived through experiments on widely used test images.
Progressive learning with style transfer for distant domain adaptation
Authors: Xiang Suncheng , Fu Yuzhuo , Liu Ting
Published: October 2020

This article studies a novel transfer learning problem termed distant domain transfer learning. Different from traditional transfer learning which assumes there is a close relation between source and target data, in this study, the objective is to execute an unseen and unrelated task based on a labelled data set training previously without any samples from intermediate domains. To this end, the authors propose deep unsupervised progressive learning (DUPL) framework and its upgraded version, end‐to‐end DUPL (eDUPL). eDUPL consists of two components, i.e. (i) translating the style of labelled images from irrelevant source domain to the target domain and (ii) learning a domain adaptation model with progressive learning for testing on the target domain. In comparison, eDUPL can integrate the two components of the framework seamlessly. In general, the proposed method is easy to be implemented and can be viewed as a strong convolutional baseline for distant domain adaptation task. Comprehensive experiments based on VeRi Vehicle, CUB‐200‐2011 Birds and Oxford5k Buildings data sets are conducted and the results indicate that the proposed method robustly achieves state‐of‐the‐art performances compared with existing approaches, which demonstrates the effectiveness and superiority of the proposed algorithm.
Outlier percentage estimation for shape‐ and parameter‐independent outlier detection
Authors: Goh Michael Joon Seng , Chiew Yeong Shiong , Foo Ji Jinn
Published: October 2020

Accurate and robust three‐dimensional reconstruction of objects allows for applications in many aspects of modern life. Yet, it typically suffers from outliers and noise which often need to be post‐processed. Although many algorithms are able to effectively remove the outliers, most require a certain amount of manual tuning of the parameter(s) or to have a parameter(s) set based on the rule of thumb. New machine learning and artificial intelligence‐based methods have also been introduced but may require vast parallel computing resources as well as training data. In the present study, a novel combinatory‐distance‐based method capable of high accuracy outlier detection named as the sorted distance divergence point (SDDP) is introduced. Results show that SDDP is able to achieve an average accuracy of 98% in outlier detection. Moreover, the introduced distance function and outlier percentage allow clear labelling of inliers and outliers cloud points. Therefore, SDDP presents an attractive enhancement to existing methods; namely, the manual parameter(s) tuning may not be necessary. The adaptability and utility of SDDP is further demonstrated by incorporating SDDP with current methods, to produce a high accuracy outlier detector. When tested with 17 objects with 20–50% outliers, attain F1 and F2 scores averaging 0.960 and 0.968, respectively.
Wearable textile EBG‐inspired bandwidth‐enhanced patch antenna
Authors: Pinapati Sree Pramod , Brittain Joshua , Caldow Adrian , Fumeaux Christophe
Published: October 2020

An electromagnetic bandgap (EBG)‐inspired low‐profile wideband textile antenna is presented. By partially surrounding a standard textile patch antenna with mushroom EBG cells, a significant enhancement of the 10 dB impedance bandwidth is obtained. In particular, whereas a standard textile patch antenna exhibits a typical 10 dB bandwidth of 6% for a substrate thickness of 0.04, the proposed technique extends the 10 dB impedance bandwidth to 32%. Notably, the relatively large impedance bandwidth is achieved without extracting a heavy cost in terms of fabrication or design complexity. To make the design amenable for textile implementation, as opposed to more conventional designs which use EBG cells as a replacement for a ground plane, the proposed design hinges on the patch antenna being coplanar with a single quasi‐ring of coplanar EBG cells. The proposed antenna design is validated through measurements in free space and on‐body, where it is demonstrated to be robust to the effects of bending in the two principal planes.
Research on Agility Development of Energy Enterprises under the Background of Carbon Emission Trading
Authors: Zhao Erdong , Chen Jianmin , Chen Chuxiang , Chen Mingsong
Published: October 2020

In recent years, environmental problems caused by greenhouse gas emissions have attracted more and more attention. Under increasing cost pressure, energy enterprises have become one of the targets to control carbon emissions. Taking China Guodian Corporation as an example, it is of great significance to study the agility development of China’s energy enterprises under the carbon emission trading system.This paper uses content coding analysis method to explore the influencing factors of agility of energy-based enterprises in China and the specific degree of influence. Through research, it is found that corporate culture, leadership awareness and internal competition have a positive effect on the agility of energy-based enterprises. This study develops the relevant theories of energy-based enterprises from the perspective of agility and finds a key breakthrough for energy-based enterprises to cope with the pressure of carbon emission reduction and optimize their operations.
Optimization and evaluation of operation strategies for regional distributed energy system
Authors: Yuan Jingyi , Li Miao , Liu Xiaoyu , Xu kun
Published: October 2020

With the development of energy technologies, the electricity, cooling and heating are tightly connected to improve the overall efficiency of distributed energy systems. These systems satisfy simultaneously different energy demands at building level (hotels, offices and residential buildings). The optimal capacity of technologies from numerous alternatives and optimal operating strategy as well as electricity network connection was investigated in this paper in order to achieve economic and environmental gains. A mixed integer linear programming model was also used for multi-objective optimization to minimize total cost and pollutant emissions. The results show that distributed energy system with electricity network as well as real time control can realize the better performance through multi-optimization from regional viewpoint. However, the results are on the contrary aiming at individual hotels and offices. The performance advancement of this system can only be reflected in residential buildings.
Performance of an internet data center refrigeration system using an evaporative cooler
Authors: Zhao Shibo , Zhang Yonghui , Nie Yunqi , Qu Pengyu , Sun Wenqiang
Published: October 2020

The traditional refrigeration method of internet data center (IDC) is mostly air refrigeration, which has undesired cooling effect and high power consumption. This study addresses this problem and proposes an evaporative air cooler (EAC) suitable for IDC. Given the high specific heat capacity of water, the evaporative condensing coil and spray device are added to the evaporative cooler to enhance the heat transfer effect. Heat and mass transfer mathematical models are established to analyze the heat transfer performance. The mathematical model is used to simulate the profile of the heat and mass transfer coefficient of the EAC with the amount of spray water and air flow. The results show that when the air flow changes from 10 to 20 kg/s, the air equivalent heat transfer coefficient increases by about 41%. When the air flow rate is 20 kg/s and the spray water volume is 0.00124 kg/(mꞏs), the total heat transfer coefficient is increased by about 308% compared with the case without spray water.
Performance of compressed air energy storage system with regenerative heat exchangers
Authors: Wang Shibiao , Liang Wei , Lai Xi , Sun Wenqiang
Published: October 2020

In order to improve the heat storage and heat exchange system of advanced adiabatic compressed air energy storage (AA-CAES) system, an AA-CAES system with regenerative heat exchangers (RHEs) is studied. The RHE is used to replace the conventional complex units, including heat exchangers, high temperature tank, and low temperature tank mode. For the AA-CAES with RHEs, the energy storage system is simplified to reduce the heat loss in the heat exchange and storage processes, and thus, the output work, energy storage density, energy storage efficiency of the system are improved. The thermodynamic model is established and the influences of compression ratio distribution, expansion ratio distribution and ambient temperature on the system performance are investigated. The results show that for the AA-CAES with RHEs, when the ratio of compression ratios is 1.14, the input work of the compressor is the minimum, the energy storage efficiency is 66.42%, and the energy storage density is 3.61 kWh/m3. When the ratio of expansion ratios is 0.82, the energy storage efficiency reaches the maximum value of 67.38%, and the energy storage density reaches the maximum value of 3.66 kWh/m3.
Fixed Point Acoustic Monitoring Investigation Report for Marine Biological Resources in an Open Water Channel of Taishan Nuclear Power Plant
Authors: Chuan XU , Zhengchun HU
Published: October 2020

To disclose changes of density of fish larva and other marine organisms in an open water channel before the open net interceptor, this acoustic monitoring point was determined in front of the first open net interceptor in the open seawater channel with considerations to monitoring functions of EK80 scientific echometer to various cold-source organism and the marine organism recovery efficiency of open net interceptor. The beam direction was perpendicular downward. Floating marine organism enters into the interceptor after passing through the acoustic beam, while the quantity of swimming fish larva into the interceptor is smaller under normal marine conditions since they have certain swimming ability.
Analysis on the Commercial Grade Items Dedication of Emergency Diesel Generator Set Level Transmitter
Authors: Yang Jingyuan , Yang Chenggang , Han Dongao , Yi Zilong , Huang Jiaqi
Published: October 2020

Commercial grade items dedication provides an in-depth analysis of key characteristics of commercial grade items. Technical acceptance of commercial grade items dedication is targeted. Commercial grade items dedication can improve the quality control level of nuclear power plant equipment and effectively compensate for the deficiencies in the process control and quality uncertainties. This article sorts out the differences between ISO9001 and HAF003 and RCC-E requirements, and analyzes the document system, work process, key characteristics, acceptance methods and evaluation process of the commercial grade items dedication of the emergency diesel generator set liquid level transmitter.
“Five Targets” management strategy of formulating international standards for energy enterprises
Authors: Xu Qin , Zhang Yu , Fang Chen , Wei Xiaochuan , Yu Aiqing , Liu Chun , Wang Yufei
Published: October 2020

There are many problems in energy enterprises, such as insufficient understanding of internationalization, serious shortage of international talents, unthorough investigation of the rules of international standards organization, and inadequate transformation of existing achievements into standards, which seriously restrict the development of international standardization. The article relies on the research on management strategy of the State Grid Shanghai Electricity Research Institute, and proposes a “Five Targets” international standardized formulation system, which develops the closed-loop value chain of platform operation, professional focus, processed management, refined control, and industrialization development. The chain drives the implementation of the world’s leading strategy of energy enterprises, builds an international technology standard ecosystem, enhances the international influence and global competitiveness of energy enterprises, and opens up new areas and new prospects for building a world’s leading urban energy internet enterprise with Chinese characteristics.
Development of Standards for Hydrogen Storage and Transportation
Authors: Yang Yanmei , Xu Haigang , Lu Qiaoling , Bao Wei , Lin Ling , Ai Bin , Zhang Bangqiang
Published: October 2020

Hydrogen storage and transportation are the intermediate link of hydrogen production and the point of end-use. Standards for hydrogen storage and transportation published by ISO, CGA, NFPA, ASME, ANSI, SAC, CEN and JISC are reviewed and analysed in this paper. Numbers of standards for hydrogen embrittlement are more than the others. Standards for hydrogen piping and pipelines are only published by CGA and ASME. Chinese GB standards are mainly focused on general design and safety, gaseous hydrogen receptacles and hydrogen embrittlement. Standards for liquid hydrogen storage receptacles and safety, hydrogen piping and pipelines, and hydrogen transportation should be emphasized.
The Dynamic Input-Output Model of Distribution Network Upgrading Project Under the Background of Electric Internet of Things
Authors: Yan Zhibin , Xiang Li , Lu Ye , Zheng Wei , Qu Gaoqiang , Kang Jian , You Lisha
Published: October 2020

China is promoting the construction of electric internet of things, and a large part of the fixed asset investment in the power grid is the investment in the upgrading of the distribution network. The economic benefits of investment are related to the sustainable development of power grid companies. At present, the Chinese government is reforming transmission and distribution tariff, and the economic benefits of power grid infrastructure investment are facing greater uncertainty. This paper studies the life cycle cost of the distribution network upgrading project from construction to decommissioning and the electricity revenue for the entire operation period. The dynamic input-output model of the distribution network upgrading project is established under the background of electric internet of things. An empirical analysis is conducted in combination with the case study. Through load forecasting and cost calculation, the input and output indicators of the project are obtained, which verifies the validity and accuracy of the model.
Optimal control of pitch angle of large wind turbine based on speed differential
Authors: GAO Xin , WANG Xiaoyu , HE Jiahuan
Published: October 2020

In order to improve the power output of wind turbine at low wind speed, this paper studies the aerodynamic performance of wind turbine blades at different installation angles. It is found that the optimal pitch angle of wind turbine can be switched between different installation angles when wind speed changes by introducing wind turbine speed differential feedback signal in PI controller of variable pitch, so as to track the optimal wind energy utilization coefficient The purpose of improving wind turbine power output. In order to verify the reliability of the analysis results, bladed is used to simulate the model of a 1.5MW unit with pitch differential control signal. At the same time, the research results are applied to the actual operation of the unit. The results show that the power curve of the wind turbine unit at low wind speed can be improved and the annual power generation can be increased by 1.24%.