bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Start-up Strategy for MMC-based Medium-voltage DC Distribution System with Low Inrush Current
Authors: Yang Limin , Zhu Yiying , Lei Ming 
Published: May 2021

Abstract
To realize medium-voltage dc distribution system, the modular multilevel converter (MMC) is commonly adopted due to its high-level scalability and reliability for medium-voltage high-power energy conversion. Since the capacitors’ voltages of the MMC are insufficient when the uncontrolled pre-charging stage is finished, large inrush current exists when the MMC is unlocked for active charging, which can cause malfunction of protection and threat the successful start-up of the whole system. This paper presents a detailed analysis on the causes of inrush current during the MMC’s active charging process. Based on the analysis, a novel start-up strategy of the MMC is proposed to achieve improved controllability of the MMC’s ac-side currents for inrush current suppression. The simulation results from a full-scale simulation model demonstrate that the inrush current can be effectively suppressed during the MMC’s active charging process.
Compact Tunable Balanced Dual-band Bandpass Filter Using Tri-Stub Loaded Resonator
Authors: Yang Hong , Zhang Dewei , Zhang Junjie , Wang Xian , Pan Chenqing , Ma Zhuang , Fu Yufan 
Published: May 2021

Abstract
This letter proposes a tunable balanced dual-band bandpass filter (BPF) with high common-mode (CM) suppression based on tri-stub loaded resonator (TSLR). Two TSLRs terminated with varactor diodes are employed to obtain the tunable second differential-mode (DM) passband, which is independent of the first one. For validation, a tunable balanced dual-band BPF is designed, fabricated and measured that achieve the continuous frequency-tuning range of the second DM passband from 5.968 to 6.368 GHz with constant 3dB fractional bandwidth (FBW) of 6.8% ± 0.17% and CM rejection levels of over 30 dB. The measured results agree well with the simulated ones.
Polarization properties of mouse oocyte captured by Mueller matrix imaging
Authors: Liu M J , Tian N , Yu J 
Published: May 2021

Abstract
Non-invasive assessment of oocyte quality is of important basis in embryo transfer and other related fields. Until now, the oocyte quality assessment is mainly focus on the morphology observation. Oocyte quality is often reflected in varieties of its microstructure which can be provided by polarization imaging technology. The Mueller matrix is a label-free technique to characterize all polarization properties of samples. This paper used a Mueller matrix microscope for the first time for non-invasive imaging the mouse oocyte. By Mueller matrix analysis, we successfully examined the dichroism, phase delay and scattering depolarization of oocytes, furthermore used the above to compare the polarization properties of mouse oocytes at GV stage and MII stage and the polarization properties of matured fresh oocytes and postovulatory aged oocytes. In this paper changed phase delay were observed before and after oocyte maturation (and oocyte aging). It suggested that the Mueller matrix imaging may provide a potential breakthrough for oocyte quality assessment.
B-spline fitting of sunglasses lens contour based on adaptive sampling
Authors: Fan Xian-guang , Nie Ting , Shen Jie , Xu Ying-jie 
Published: May 2021

Abstract
To solve the problems of low accuracy and low efficiency of B-spline fitting in sunglasses lens contour cutting, a sampling method based on the characteristic function of a weighted combination of arc length and curvature square root was proposed, and then perform B-spline fitting according to the sampling points to improve the accuracy and efficiency. First, read and identify the sunglasses lens data in the DXF file, and disperse it to obtain a series of coordinate points; combine the defined feature function for uniform sampling, and adjust the parameter weight in the feature function to obtain a more reasonable sampling point for the sunglasses lens contour; Then use the LU decomposition and interpolation method to obtain the B-spline curve through all the sampling points; the maximum absolute error (MAE) and the root-mean-square error (RMSE) are used as the accuracy criteria, by increasing the number of sampling points iteratively until the accuracy is satisfied to construct a reasonable fitting curve.
Simulation Study on State Estimation for a Quadrotor Generalized Attitude Model
Authors: Yin H , Liu J C , Geng L H , Zhao S H , Ayele T B 
Published: May 2021

Abstract
This paper studies the state estimation of a quadrotor yaw-channel generalized attitude model (GAM) using the subspace identification based state estimation method under the conditions that the measurements for the control quantity and the output attitude angle are corrupted with disturbing noises and that there are no available models. The quadrotor attitude control loop for the yaw-channel is first designed by MATLAB using an observer based state feedback law. A high-order coprime factor (CF) model for the GAM is then identified from simulation data by the subspace identification method. Next, the estimation states for the GAM can be given by the similarity transform and the relations between the GAM and its CF model. Finally, the efficacy of the presented state estimation procedure and the accuracy of the state estimation are verified.
Component identification for Raman spectra with deep learning network
Authors: Wang Xin , Pan Qi-hang , Fan Xian-guang , Xu Ying-jie 
Published: May 2021

Abstract
Raman spectroscopy is widely used in the research of the molecular structure of substances because of the advantages of no invasion, no damage and no interference from water. Meanwhile, component identification for mixtures is still challenging in Raman spectra. In this paper, a graphics-based sample-generating method and a model based on deep-learning for component identification was proposed. Convolution neural network (CNN) model is an important part of deep learning network and CNN models was utilized to assess the possibility of the presence of components in samples. As is shown in the comparative studies, the model was sensitive to the relative position of the characteristic peaks and could learn spectra features in mixtures. The deep-learning based component identification method showed more robustness than conventional linear fitting methods. Therefore, the method provided a valid approach to component identification for mixtures and has the potential in spectra component analysis.
Self-attention Mechanism based Dynamic Fault Diagnosis and Classification for Chemical Processes
Authors: Chen Shuai , Luo Lin , Xia Qilei , Wang Lunjie 
Published: May 2021

Abstract
A dynamic fault detection and diagnosis technique based on deep encoder-decoder network with self-attention mechanism is proposed in this paper. Although traditional encoder-decoder networks exhibit capability in extracting the temporal dependencies, the architecture encodes the input sequence into a fixed-length internal representation. This limits the performance of these networks, especially when considering relatively long input sequences. The self-attention mechanism is used to weight the local feature vectors and retain the correlation between the local information of the signal and the process operation state, so as to extract the effective feature vectors. The extracted features are then fed into the bidirectional encoder-decoder network. The resulting deep network is not only generalizing the importance of local temporal feature, but it also allows the interpretable feature representation and classification simultaneously. The experiments on the benchmark Tennessee Eastman process show that the proposed model has better diagnostic performance on receiver operating characteristic (ROC) and precise-recall (PR) curves than the classical diagnostic method based on the long-short term memory (LSTM) network with convolution layers.
Data-Level Parallelism Oriented Memory Access and On-Chip Buffering Mechanisms for a Loop Accelerator
Authors: Zhou Weikang , Li Xianfeng , Chen Mingtao , Li Yang 
Published: May 2021

Abstract
Memory access latency is always a bottleneck for the performance improvement of data-intensive applications. Exploiting the memory access patterns of Data-Level Parallelism(DLP) is a promising way for loop accelerators to reduce the latency significantly. This paper proposes two DLP-oriented data provisioning mechanisms to alleviate memory access latency: 1) DLP-oriented memory access(DoMA) for efficiently utilizes the available memory bandwidth. 2) a data access patterns aware on-chip buffer(PABUF) for exploiting reuse in a user-transparent manner. Unlike those loop accelerators using traditional DMA to access global memory, DoMA efficiently reduces the transmission of useless data by adjusting the size of requests intelligently. In addition, PABUF, which manages data using DLP’s memory access patterns without software engineering efforts, allows the loop accelerators to access data in parallel. Experiments show that when our mechanisms are integrated into a loop accelerator based on Rocket Chip Coprocessor(RoCC), it can achieve 4.20x-10.65x(6.81x on average) speedups with negligible overhead of power and area compared to L1 Cache.
Global optimal solution on blending problem
Authors: Zheng Yingchun 
Published: May 2021

Abstract
In order to study the algorithm of the global optimal solution of the blending problem. The general optimization model for blending problems is given in this paper, at the same time, the blending problem was transformed into the concave minimization problem and linear programming problem, the relevant proof was also given. Research shows that the blending problem is formulated as a concave minimization problem and linear programming problem, can reduce the number of variables blending problem. Blending problem is transformed into a linear programming enable blending problem solving becomes simple and easy.
A Long-term Recurrent Convolutional Network for Stock Index Prediction
Authors: Yu Xinpeng , Li Dagang 
Published: May 2021

Abstract
Stock index prediction aims to predict the future price of stock indexes, which plays a key role in seeking the maximum profit from stock investment. However, It has been proven to be a very difficult task because of its innate complexity, dynamics, and uncertainty. With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as long short-term memory networks (LSTMs) to capture the complex patterns hidden in market trends. In this paper, we propose a Long-term Recurrent Convolutional Network (LRCN), which combines convolutional layers and long-range temporal recursion and is end-to-end trainable. In the LRCN model, the two-dimensional convolutional neural network (2D-CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) learns the long-term temporal dependencies to improve stock index prediction. To evaluate the effectiveness of LRCN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed LRCN can significantly outperform several existing highly competitive methods.
A comparative study on pricing methods of infrastructural assets securitization: a case study of power distributing assets of X mall
Authors: Chen Shaozhen , Zhang Yang , He Jinlin 
Published: May 2021

Abstract
Although the securitization technique was wide applied in financial practice, moreover, oversea academics have abundant experience on such field, due to securitization’s late introduced to China, legislate infrastructure such as the supervised legislation and the tax laws are far beyond perfect, not mention the study of the pricing theories on securitization, especially in pricing the securities which backed by such infrastructural assets as electric power distributing network, theoretic research in this area is even rare. This paper conducts a case study, taking the electric-power-distribution asset of a mall as an example, to examine three different pricing methods for valuating asset backed securities, with the purpose to explore more an appliable pricing method for infrastructure asset securitization.
Research on the recognition of students’ classroom learning state based on facial expressions
Authors: Lin Qiuchen , Lai Xiangwei 
Published: May 2021

Abstract
The learning state of students in the classroom can not only affect the learning effect of students, but also reflect the teaching quality of teachers. In this paper, a new image database of classroom learning state is established according to students’ learning state in real classroom environment, and an improved ResNeSt network model based on spatial attention mechanism is proposed. In this method, spatial attention mechanism is introduced into the ResNeSt network, which is complementary to the channel attention thought of the original network, so as to improve the recognition accuracy of learning state images. Using this method to identify students’ classroom learning state can provide a basis for teaching administrators to evaluate teachers’ teaching quality, as well as provide guidance for teachers to better adjust teaching models and teaching methods, and improve teaching efficiency.
Extent of geotechnical site investigations for buildings in Estonia
Authors: Leetsaar L , Korkiala-Tanttu L , Jaaniso V , Kurnitski J , Idnurm J 
Published: May 2021

Abstract
Numerous researchers have described problems encountered during the construction and performance of a building that result from inadequate site investigations (SI). The type and coverage of in-situ tests can significantly affect the quality of SI results. The aim of this study is to explore the amount and methods of SI in Estonia during the last eleven years. Site investigations data from 92 private and public buildings were collected and analyzed. Both quantitative and qualitative research methods were adopted to identify the most commonly used in-situ and laboratory methods according to different soil types and building height. The impact of the SI contractor was analysed as well. It was found that the costs of investigations constituted approximately 0.1% of the building tender cost for two-to six-storey buildings. Almost half of the SI contractors were designers too. Regardless of the contractor of SI and the soil type, the extent of SI was always similar. The majority of the investigations were conducted only in one stage. Dynamic probing was the most frequent in-situ testing method. The amount and quality of SI was found to be too low for a reliable and optimum design.
A review on immobilization strategy of cyclodextrin glucanotransferase producing Escherichia coli as whole cell biocatalyst
Authors: Abdullah A A , Azam N Z F N , Mohamad C W S R , Illias R M 
Published: May 2021

Abstract
This paper reviewed the immobilization strategy on cyclodextrin glucanotransferase (CGTase) producing Escherichia coli as whole cell biocatalyst. Biotransformation of insoluble substrate such as starch and lignocellulosic biomass with whole cell leads to the application of biological steps to chemocatalysis, which makes the whole process more effective. In this whole cell biocatalysis process, the excretion of the enzymes into the extracellular space by the cell is crucial. However, the use of free whole cell biocatalysts especially Escherichia coli often face with problem related to the cell lysis and stability. Due to that, the biotransformation process becomes inefficient. Thus, in this study the immobilization strategy was investigated to overcome the bottle necks mentioned. First, this study began with a focus in Escherichia coli, immobilization techniques, cyclodextrin, cyclodextrin glucanotransferase and recombinant Escherichia coli, in which can give a better understanding and a bigger picture in order to overcome the bottle necks mentioned. The different immobilization strategy and condition identified expected to improve the whole cell stability and reusability which lead to efficient biotransformation process. The output of the research also will elucidate the mechanism on how different immobilization material, strategy and cultural conditions improve the overall cell performance.
Scaling-up and techno-economics of ethanol production from cassava starch via separate hydrolysis and fermentation
Authors: Choo B C , Ismail K S K , Ma’Radzi A H 
Published: May 2021

Abstract
Nowadays bioethanol is being used extensively in fuel production because of its technical feasibility, economically competitive, and environmentally friendly. It is expected that biofuel will contribute to 30% of the global energy demand by 2050. Therefore, it is very important to investigate any cost-effective bioprocessing that can improve the overall production yield. The study aims to produce bioethanol from cassava starch by Kluyveromyces marxianus at a laboratory scale and a 5L fermentation rig. A separate hydrolysis and fermentation (SHF) process of cassava starch was introduced due to high sugar content in starch, using a thermoanaerobe able to reduce the cooling time after hydrolysis. A combination of 0.35% v/w amylase and 0.20% v/w amyloglucosidase used in the hydrolysis of cassava starch produced 19.18 g/L of sugar. A 15 g/L of K. marxianus showed to be the best yeast concentration which could produce the highest bioethanol, 42.85 g/L. When the laboratory scale was scaled up to 5 L fermentation, the result was comparable at 42.33 g/L. The same SHF parameters in laboratory scale had been proven to be effective in a larger scale fermentation based on similar results obtained. Simulation using SuperPro software indicated that 50.13% of starch can be converted into ethanol.
Effect of bentonite nanoclay filler on the properties of bioplastic based on sago starch
Authors: Zamrud Z , Ng W M , Salleh H M 
Published: May 2021

AbstractThe non-biodegradability of plastic packaging has led to a renewed interest in packaging materials based on biopolymers. The biopolymer used in this study which is sago starch is versatile and abundantly available in nature. In the present study, sago starch (SS)/polyvinyl alcohol (PVA) films were plasticized with glycerol (GLY) and reinforced with nanoclay bentonite (BEN) at 1 wt.%, 3 wt.% and 5 wt.% using solution casting method. The films prepared were characterized for their physic-chemical properties using conventional methods. Findings have shown that 5 wt.% BEN reinforced thermoplastic sago starch (TSS) showed highest weight loss in degradation test, greatest improvement in tensile strength and the lowest water absorption in swelling test. The introduction of BEN in the TSS showed changes in the intensification of O-H bond and Si-O bond. Furthermore, inclusion of BEN had reduced the vacant spots of SS/PVA/GLY matrices. Hence, the introduction of BEN in TSS composite had improved the film’s properties. This study suggests that SS/PVA/GLY/BEN film has the potential for packaging application to lessen the dependence on non-degradable plastic packaging.
Chemically modified palm kernel shell biochar for the removal of heavy metals from aqueous solution
Authors: Imran-Shaukat M , Wahi R , Rosli N R , Aziz S M A , Ngaini Z 
Published: May 2021

Abstract
Heavy metals eradication from water is a complicated subject, therefore a viable, resilient, and green technology is imperative. Heavy metal removal can be accomplished through easy access, economical, and efficient sorbents derived from agricultural waste. In the current study, palm kernel shell (PKS) waste was converted into biochar (PKSC) via pyrolysis. Chemical modification was performed on PKSC via acid-base treatment to refine its adsorption properties. Batch experiments were conducted to study the efficiency of PKSC and acid-base treated PKSC (MPKSC) for removal of Cr(IV), Ni(II) and Cu(II). The surface area was increased from 112.934 m 2/g to 149.670 m 2/g by acid-based treatment. Batch adsorption study showed that the MPKSC afforded high removal efficiency for Cu (99.29%), Ni (96.77%) and Cr (42.97%). The Cr(IV) and Ni(II) adsorption by PKSC, as well as Cr(IV), Ni(II) and Cu(II) adsorption by MPKSC were best represented by Freundlich isotherm. However, Cu(II) adsorption by PKSC can explained by using Langmuir isotherm. All studied heavy metals fitted the pseudo-second-order kinetic.
Torrefaction of fish waste (Sardinella fimbriata) in a fixed bed reactor: Yields and biochar characterization
Authors: Norisham S F , Munajat N F 
Published: May 2021

Abstract
Torrefaction is a promising process to upgrade biomass and produce solid biofuel. In this study, the fish by-product (Sardinella fimbriata), a fish-processing waste from the food industry highly prevalent in the east coast region of Peninsular Malaysia was investigated in a fixed bed torrefaction reactor with varying reaction temperature (200, 250, and 300°C) under a nitrogen atmosphere and single residence time (30 minutes). The distribution and yield of the biochar, bio-oil, and biogas were examined, and further characterization of the biochar was performed (proximate and ultimate, SEM, and FTIR analysis). The results indicated that with the increased torrefaction temperature, solid char yield decreases down to ∼ 52 % while bio-oil and biogas fluctuate between 7 - 28 % and 21 – 27 &, respectively. The proximate and ultimate analysis of the torrefied solid char showed that the fixed carbon, carbon content increased while volatile matter, moisture, hydrogen, and oxygen content decreased with temperature rise. From the SEM analysis, the torrefaction temperature causes an increase in biochar porosity. Lastly, from the FTIR analysis, it can be seen that there are degradation and formation of certain functional groups such as O-H, C-H, C=C, C=O, C-O, and C-C with torrefaction temperature variations.
Chemical Compositions and Physical Properties of Selected Malaysian Rice: A Review
Authors: Shakri A N Ahmad , Kasim K F , Rukunudin I B 
Published: May 2021

Abstract
Rice is the main food for most countries in Asia and, about 90% of the global rice area, production and consumption are concentrated on Asia. In Malaysia, the main varieties of rice found in retail outlets are ordinary local and imported white rice, brown unpolished rice and specialty rice. In short, conventional rice or white rice is a medium-length, white kernel with translucent endosperm. Brown rice is also called unmilled rice that is produced by removing the outermost layer of the grain. Specialty rice is the rice with special characteristics which includes pigmented rice, fragrant rice, parboiled and glutinous rice. Various rice varieties have various chemical and physical properties. This staple food is found to have received the utmost attention because of its potent antioxidant properties especially pigmented rice. These properties were related mainly to their phytochemical content which was associated with the provision of health benefits in chronic disease reduction. Therefore, this paper will comprehensively review the chemical compositions and physical properties of the Malaysian rice and its relation with health benefits.