bluuebunny/crossref_metadata_2025 huggingface dataset:
An efficient method for estimating dormant season grass biomass in tallgrass prairie from ultra-high spatial resolution aerial imaging produced with small unmanned aircraft systems
Authors: van der Merwe Deon , Baldwin Carol E. , Boyer Will
Published: April 2020

Fire is used extensively in prairie grassland management in the Flint Hills region of the midwestern United States, particularly at the end of the dormant season (March–April). A model is used to manage grassland fires in the region to avoid deterioration of air quality beyond acceptable standards. Dormant season dry biomass is an important parameter in the model. The commonly used method for producing high-quality biomass estimates relies on clipping, drying and weighing small biomass samples, which is tedious, expensive and does not scale efficiently to larger areas to provide regional estimates. Small unmanned aircraft systems (sUAS) were used to develop a reliable and more efficient method of biomass estimation based on the correlation between biomass and vegetation canopy height derived from digital surface models (DSMs). A linear regression model was developed from data collected at 11 representative sites in the Kansas Flint Hills region, and the model was validated at two sites. Biomass and canopy heights derived from DSMs were correlated, with a Pearson product moment correlation value of 0.881 (P-value <0.001). Biomass estimated from clipped vegetation at two validation sites positively correlated with model-derived biomass estimates, resulting in linear regression R2-values of 0.90 and 0.74 and Pearson moment correlation coefficients of 0.99 (P<0.001) and 0.86 (P=0.003). The described sUAS method has the potential to increase the efficiency and reliability of dormant season grassland biomass estimates.
Phylogenetic network analysis of SARS-CoV-2 genomes
Authors: Forster Peter , Forster Lucy , Renfrew Colin , Forster Michael
Published: April 2020

Significance

This is a phylogenetic network of SARS-CoV-2 genomes sampled from across the world. These genomes are closely related and under evolutionary selection in their human hosts, sometimes with parallel evolution events, that is, the same virus mutation emerges in two different human hosts. This makes character-based phylogenetic networks the method of choice for reconstructing their evolutionary paths and their ancestral genome in the human host. The network method has been used in around 10,000 phylogenetic studies of diverse organisms, and is mostly known for reconstructing the prehistoric population movements of humans and for ecological studies, but is less commonly employed in the field of virology.
Phase transformation and microstructure evolution of pearlite heat-resistant steel during heating
Authors: Li Yaqiang , He Yang , Liu Jianhua , Cui Heng , Qiu Shengtao , Zhang Pei , Zheng Guiyun
Published: April 2020

The phase transformation and microstructure evolution of pearlite heat-resistant steel during heating were observed with an ultra-high temperature confocal scanning laser microscope. The α-ferrite completely disappeared earlier than Fe3C during the formation of γ-austenite, which is inconsistent with the fact that the Fe3C should disappear completely earlier under equilibrium conditions. After the Fe3C + α→γ transformation, static recrystallisation of γ-austenite occurred, accompanied by the dissolution of cementite. During the γ→δ transformation, the δ-cell first precipitated at the triple point of the γ-austenite grain boundaries, and then the δ-cell platelet with one tip appeared in the γ-austenite grain. The law of δ/γ inter-phase boundaries was analysed based on inter-phase boundary types and element diffusion.
Microstructure and properties of laser-clad FeNiCoCrTi0.5Nb0.5 high-entropy alloy coating
Authors: Zhang Ying , Han Tengfei , Xiao Meng , Shen Yifu
Published: April 2020

FeNiCoCrTi0.5Nb0.5 high-entropy alloy coating is prepared on AISI 1045 steel by laser cladding. The cross-sectional macroscopic morphology, phase, microstructure, microhardness and wear resistance are studied systematically. The results show that FeNiCoCrTi0.5Nb0.5 coating has no porosities, cracks or other defects and is well metallurgically bonded to the substrate. The coating is composed of body-centred cubic (BCC) solid solution, face-centred cubic (FCC) solid solution and hard Laves phase. The solid solution phase and Laves phase distribute uniformly and closely in a lamellar shape to form a fine and dense eutectic structure. The microhardness of FeNiCoCrTi0.5Nb0.5 coating is about three times that of the substrate. Compared with the substrate, the coating has superior wear resistance.
Effect of α-lath size on the mechanical properties of Ti–6Al–4V using core time hydrogen heat treatment
Authors: Cho Gye-Hoon , Oh Jung-Min , Kwon Hanjung , Lim Jae-Won
Published: April 2020

When hydrogen is dissolved, brittleness occurs in the material. However, in the case of titanium and titanium alloy, hydrogen can be temporarily dissolved and removed, thereby improving the mechanical properties of titanium and titanium alloy. In this study, Core time Hydrogen Heat treatment (CHH) applies to Ti–6Al–4V alloy to improve mechanical properties. CHH was performed at 800°C and 1000°C for 2 h. Thereafter, dehydrogenation was performed for 2  h at 700°C in vacuum atmosphere to remove residual hydrogen. After the CHH at 800°C, it was found that the α-lath size in the Ti–6Al–4V was narrowed; thereby increasing the Vickers hardness and tensile strength without decreasing in elongation.
Anthropometric variables, propulsive force and biological maturation: A mediation analysis in young swimmers
Authors: Oliveira Mariana , Henrique Rafael S. , Queiroz Daniel R. , Salvina Marlene , Melo Wilson V. , Moura dos Santos Marcos André
Published: April 2020

Youth swimming performance may be influenced by anthropometric and body composition factors during growth. Propulsive force of the arm (PFA) is highly related to swimming performance as 85–90% of the propulsive power comes from the arms. The current study analyzed the mediating effect of biological maturation on the relationship between anthropometric variables and body composition with PFA in young swimmers. A total of 128 swimmers [boys: n = 53, age = 13.6 (1.8) y; girls: n = 75, age = 12.5 (1.8) y] were evaluated. Propulsive force of the arm was estimated by the tethered swimming test. Anthropometric characteristics (body mass, stature, sitting height, arm span, arm muscle area, %body fat, and fat‐free mass) were evaluated. Biological maturation was estimated by the age of peak height velocity. Mediation analyses were performed using the Preacher and Hayes bootstrapping method, adjusted for chronological age and sex. All anthropometric and body composition variables, except %body fat, were positively associated with PFA, being mediated by biological maturation. Indirect effects and 95% confidence intervals were: body mass [.20 (.09‐.34)], stature [.22 (.11‐.43)], arm span [0.17 (.05‐.31)], arm muscle area [.74 (.38‐1.19)] and fat free‐mass [.17 (.05‐.34)]. In conclusion, biological maturation mediated the positive associations between body size and composition and PFA in young swimmers.
Regulated lipid synthesis and LEM2/CHMP7 jointly control nuclear envelope closure
Authors: Penfield Lauren , Shankar Raakhee , Szentgyörgyi Erik , Laffitte Alyssa , Mauro Michael Sean , Audhya Anjon , Müller-Reichert Thomas , Bahmanyar Shirin
Published: April 2020

The nuclear permeability barrier depends on closure of nuclear envelope (NE) holes. Here, we investigate closure of the NE opening surrounding the meiotic spindle in C. elegans oocytes. ESCRT-III components accumulate at the opening but are not required for nuclear closure on their own. 3D analysis revealed cytoplasmic membranes directly adjacent to NE holes containing meiotic spindle microtubules. We demonstrate that the NE protein phosphatase, CNEP-1/CTDNEP1, controls de novo glycerolipid synthesis through lipin to prevent invasion of excess ER membranes into NE holes and a defective NE permeability barrier. Loss of NE adaptors for ESCRT-III exacerbates ER invasion and nuclear permeability defects in cnep-1 mutants, suggesting that ESCRTs restrict excess ER membranes during NE closure. Restoring glycerolipid synthesis in embryos deleted for CNEP-1 and ESCRT components rescued NE permeability defects. Thus, regulating the production and feeding of ER membranes into NE holes together with ESCRT-mediated remodeling is required for nuclear closure.
Combined deletion of Bap1, Nf2, and Cdkn2ab causes rapid onset of malignant mesothelioma in mice
Authors: Badhai Jitendra , Pandey Gaurav Kumar , Song Ji-Ying , Krijgsman Oscar , Bhaskaran Rajith , Chandrasekaran Gayathri , Kwon Min-chul , Bombardelli Lorenzo , Monkhorst Kim , Grasso Cristoforo , Zevenhoven John , van der Vliet Jan , Cozijnsen Miranda , Krimpenfort Paul , Peeper Daniel , van Lohuizen Maarten , Berns Anton
Published: April 2020

We have generated mouse models of malignant mesothelioma (MM) based upon disruption of the Bap1, Nf2, and Cdkn2ab tumor suppressor loci in various combinations as also frequently observed in human MM. Inactivation of all three loci in the mesothelial lining of the thoracic cavity leads to a highly aggressive MM that recapitulates the histological features and gene expression profile observed in human patients. The tumors also show a similar inflammatory phenotype. Bap1 deletion alone does not cause MM but dramatically accelerates MM development when combined with Nf2 and Cdkn2ab (hereafter BNC) disruption. The accelerated tumor development is accompanied by increased Polycomb repression and EZH2-mediated redistribution of H3K27me3 toward promoter sites with concomitant activation of PI3K and MAPK pathways. Treatment of BNC tumor–bearing mice with cisplatin and pemetrexed, the current frontline treatment, prolongs survival. This makes the autochthonous mouse model described here very well suited to explore the pathogenesis of MM and validate new treatment regimens for MM, including immunotherapy.
Nonlocal density functional theory of water taking into account many-body dipole correlations: binodal and surface tension of ‘liquid–vapour’ interface
Authors: Budkov Yu A , Kolesnikov A L
Published: April 2020

In this paper we formulate a nonlocal density functional theory of inhomogeneous water. We model a water molecule as a couple of oppositely charged sites. The negatively charged sites interact with each other through the Lennard–Jones potential (steric and dispersion interactions), square-well potential (short-range specific interactions due to electron charge transfer), and Coulomb potential, whereas the positively charged sites interact with all types of sites by applying the Coulomb potential only. Taking into account the nonlocal packing effects via the fundamental measure theory, dispersion and specific interactions in the mean-field approximation, and electrostatic interactions at the many-body level through the random phase approximation, we describe the liquid–vapour interface. We demonstrate that our model without explicit account of the association of water molecules due to hydrogen bonding and with explicit account of the electrostatic interactions at the many-body level is able to describe the liquid–vapour coexistence curve and the surface tension at the ambient pressures and temperatures. We obtain very good agreement with available in the literature MD simulation results for density profile of liquid–vapour interface at ambient state parameters. The formulated theory can be used as a theoretical background for describing of the capillary phenomena, occurring in micro- and mesoporous materials.
Angular dependent interatomic potential for Ti-V system for molecular dynamics simulations
Authors: Kartamyshev Andrey I. , Lipnitskii Alexey Gennadievich , Boev Anton , Nelasov Ivan , Maksimenko Vyacheslav N. , Aksyonov Dmitry Alexandrovich , Nguyen Truong Khang
Published: April 2020

An interatomic potential for the Ti-V binary alloy focusing on evolution of defects, including ones arising as a result of the irradiation process, was constructed within the Lipnitskii-Saveliev approach, which accurately takes into account three-particle interactions and the sum of all multi-particle interactions of a higher order in the framework of the centrally symmetric approximation. In the new potential, Ti-V interactions were fitted to the DFT data on set of model structures with different coordination numbers, including ones with vacancies. The properties used for fitting are accurately reproduced by the present potentials for both pure elements and alloy systems. The potential was tested on the binding energies between Ti atoms and self-point defects in bcc V, elastic moduli, thermal expansion and melting point of some alloys, and diffusion. We obtained qualitative agreement for these properties with available theoretical and experimental data. Finally, we investigated evolution of excess vacancies in the V-4 at. \% Ti alloy at 700 K, which are typical conditions of vanadium-based alloys for fusion applications. We found that no vacancy loop is formed in the alloy in contrast to the pure V, which agrees with the experimental observations. The potential is expected to be especially suitable for irradiation simulations of vanadium based V-Ti alloys.
Scaling theory for Mott–Hubbard transitions: I. T = 0 phase diagram of the 1/2-filled Hubbard model
Authors: Mukherjee Anirban , Lal Siddhartha
Published: April 2020

We present a T = 0 K renormalization group (RG) phase diagram for the electronic Hubbard model in two dimensions on the square lattice at half filling. The RG procedure treats quantum fluctuations in the single particle occupation number nonperturbatively via the unitarily decoupling of one electronic state at every RG step. The resulting phase diagram thus possesses the quantum fluctuation energy scale (ω) as one of its axes. A relation is derived between ω and the effective temperature scale upto which gapless, as well as emergent gapped phases can be obtained. We find that the normal and insulating phases of the half-filled Hubbard model correspond, for any on-site repulsion, to a marginal Fermi liquid normal phase and a topologically-ordered gapped Mott insulating liquid respectively. The marginal Fermi liquid is found to arise from singular forward scattering in directions normal to the nested Fermi surface, while singular backscattering events lead to Mott liquid state. The transition between these two phases involves passage through a pseudogapped phase bookended by Fermi surface topology-changing Lifshitz transitions. The pseudogap phase is observed to arise from the electronic differentiation encoded within the nested Fermi surface, and involves the gradual gapping of the Fermi surface from antinodes to nodes via charge and spin excitations that are mutually entangled. We obtain effective Hamiltonians for various phases, as well as wavefunctions for the low-energy many-body eigenstates of the Mott liquid. Benchmarking of the ground-state energy per particle and the double-occupancy fraction for the Mott liquid against existing numerical results yields excellent agreement. Presence of a Néel ordering symmetry-breaking perturbation in the RG leads to an antiferromagnetic spin-ordered charge insulating Mott state. Our results thus offer novel insights on a variety of aspects of the Mott–Hubbard problem, and can be extended to the doped system.
Correction factors for two new reference beta radiation fields
Authors: Behrens R
Published: April 2020

Several correction factors for two new reference beta-particle radiation fields complementing ISO 6980 have been measured and calculated for both primary beta dosimetry and the operational quantities in radiation protection. The following correction factors for primary beta dosimetry for the determination of the absorbed dose to tissue, D
t, have been determined by calculations: k
ba for backscatter from the collecting electrode, k
pe for perturbation by the chamber’s side walls, k
ih for inhomogeneity inside the collecting volume, k
Sta for the stopping power ratio at different phantom depths, and k
SA for the use of the Spencer-Attix theory, while the following have been measured: k
br for the effect of bremsstrahlung and k
abs for variations in the attenuation and scattering of beta particles between the source and the collecting volume due to variations from reference conditions and for differences of the entrance window to a tissue-equivalent thickness of 0.07 mm. Furthermore, calculations were undertaken to determine the following correction factors to assess the operational quantities H
p(0.07), H
p(3), H′(0.07), and H′(3): k
rod(0.07) for the rod instead of the slab phantom, k
cyl(3) for the cylinder instead of the slab phantom, as well as k′(0.07) and k′(3) for the ICRU sphere instead of the slab phantom, while the correction factor for oblique radiation incidence has been measured. The newly determined correction factors were determined in the same way as those for several well-established beta-particle radiation fields described in two earlier publications by the author. Furthermore, they are ready to be implemented in an updated version of the ISO 6980 series and in the software of the Beta Secondary Standard BSS 2.
Decoding hand movements from human EEG to control a robotic arm in a simulation environment
Authors: Schwarz Andreas , Höller Maria Katharina , Pereira Joana , Ofner Patrick , Müller-Putz Gernot R
Published: April 2020

Objective. Daily life tasks can become a significant challenge for motor impaired persons. Depending on the severity of their impairment, they require more complex solutions to retain an independent life. Brain-computer interfaces (BCIs) are targeted to provide an intuitive form of control for advanced assistive devices such as robotic arms or neuroprostheses. In our current study we aim to decode three different executed hand movements in an online BCI scenario from electroencephalographic (EEG) data. Approach. Immersed in a desktop-based simulation environment, 15 non-disabled participants interacted with virtual objects from daily life by an avatar’s robotic arm. In a short calibration phase, participants performed executed palmar and lateral grasps and wrist supinations. Using this data, we trained a classification model on features extracted from the low frequency time domain. In the subsequent evaluation phase, participants controlled the avatar’s robotic arm and interacted with the virtual objects in case of a correct classification. Main results. On average, participants scored online 48% of all movement trials correctly (3-condition scenario, adjusted chance level 40%, alpha = 0.05). The underlying movement-related cortical potentials (MRCPs) of the acquired calibration data show significant differences between conditions over contralateral central sensorimotor areas, which are retained in the data acquired from the online BCI use. Significance. We could show the successful online decoding of two grasps and one wrist supination movement using low frequency time domain features of the human EEG. These findings can potentially contribute to the development of a more natural and intuitive BCI-based control modality for upper limb motor neuroprostheses or robotic arms for people with motor impairments.
An Emotion Classification Method Based on Energy Entropy of Principal Component
Authors: Li Hao , Mao Xia , Chen Lijiang
Published: April 2020

Emotional recognition based on electroencephalogram (EEG) has attracted more and more attention, and various methods emerge in an endless stream. An emotion classification method based on energy entropy of principal component (PCEE) is proposed in this paper. EEG data are divided into five rhythms (δ, θ, α, β and γ) by wavelet decomposition and reconstruction (WDR). Each rhythm signal uses principal component analysis (PCA) to perform dimensionality reduction on the channels (electrodes). The energy entropies of the principal components that meet the requirements are used as the classification feature. Results show that the classification accuracy can reach 87.61% by using the support vector machine (SVM) classifier.
Anomaly Detection of Manufacturing Process for Multi-Variety and Small Batch Production
Authors: Chen X , Chen F , Yin L
Published: April 2020

A series of quality control problems arise in multi-variety and small batch production due to the lack of data and other factors. In this paper, an integrated model in which control chart pattern (CCP) recognition is applied for anomaly detection is proposed to solve the problems. The integrated model is made up of four parts: feature extraction module, feature selection module, classifier module and anomaly diagnosis module. In the first module, thirteen shape features and eight statistical features of control charts are extracted. In the second module, the most representative feature set is selected by the sequential floating forward selection (SFFS) method. In the third module, a multiclass support vector machine (MSVM) which is optimized by beetle antennae search (BAS) algorithm is used to identify abnormal CCPs. In the last module, the results of pattern recognition are utilized to analyze the possible causes. The simulation results show that the CCP recognition method proposed in this paper has higher classification accuracy than other competing methods in the case of small sample with small amount of data. Finally, an example verifies that the proposed anomaly detection method is effective in multi-variety and small batch manufacturing environment.
Variation Method Overview of Image Segmentation
Authors: Tian Yuanyuan , Xue Yibing
Published: April 2020

With the research and development of image segmentation methods for several decades, the various theories and methods have been applied to the image segmentation. Among the many methods of image segmentation, the variation method is applied widely, because its model process is easier, the expansibility of the method is better and the implementation process is simple. On the basics of many achievements of variation method research of image segmentation recently, the variation method of image segmentation is divided into the model based on the boundary and the field according to various structural driving force based on energy functional. In this paper, we analyze these models based on these two methods and discuss their advantages and disadvantages. Relevant innovation algorithms and breakthrough progresses recently have been researched. Some development directions of image segmentation variation method have been given. The application field and development of variation image segmentation still have great research value.
Research on the Application of Artificial Intelligence in Judicial Trial: Experience from China
Authors: YAO Jie-jing , Hui Peng
Published: April 2020

The impact of artificial intelligence has penetrated into every aspect of life and work, and its value has been continuously explored and received great attention and recognition from business, government and academia. In general, artificial intelligence has begun to be applied in the judicial field in China, the development and practice of the intelligent assistant referee system will be of great significance and the key to the construction of a smart court, which can improve the judicial efficiency to a certain extent, prevent unjust and false cases, and ensure judicial justice. However, it is necessary to see clearly that artificial intelligence as a new thing has two sides. At present, it is still at a relatively preliminary stage and will create a series of risks. such as: the weakening of discretion, judicial discrimination, judicial dictatorship, privacy security, etc., which is essentially contrary to China’s judicial reform and rule of law construction. At the same time, in the face of the influence of artificial intelligence on judicial trial, we should have a correct attitude, prevent the worship of technology, and make clear the leading position of judges in the course of judicial trial.
A Survey on Video Dehazing Using Deep Learning
Authors: Feng Yue
Published: April 2020

With the fast development of object recognition and detection in autonomous driving and video monitoring, image with haze or raindrop can affect the result a lot. As deep learning develops, the hazed and raindrop image can lower the accuracy of object recognition and detection significantly. While hazed images cannot be managed using other image refining process. Since the noise in hazed image is signal-dependent. The object degradation in hazed image is related to object depth. So, the dehazing process depends on the input image. This paper provides a survey on single image and video dehazing methods, from end-to-end system to distributed system. General methods based on deep learning of state-of-art papers from 2010 to 2018 are summarized and compared, accompanied with their datasets of the current progress in this field. The application of these methods and relationship between these methos are also discussed in this paper.
A Novel Chinese Traditional Medicine Prescription Recommendation System based on Knowledge Graph
Authors: Wang Yinghui
Published: April 2020

As the traditional Chinese medical approach, Chinese medicine plays an extremely important role in the field of medical treatment. With the development of computers, people tend to acquire medical knowledge from the Internet in daily life. Because of the complexity of online Chinese medicine knowledge, nowadays there is no good way to organize the existing knowledge to provide convenience for doctors and patients. This paper introduces the study on the recommendation system based on the Knowledge Graph (KG). Firstly, it conducts extraction of entities such as Traditional Chinese Medicine (TCM) diseases, prescription, Chinese herbal medicine, symptoms, etc. Secondly, it transforms KG into vector space using Node2vec. At last, based on the similarities between vectors the provided system recommends prescription by adopting the diagnostic process of Traditional Chinese Medicine. The result shows that the Hit Ratio (HR) of the recommend system is as high as 80%.
Research on the Image Annotation Technology for Product Quality and Safety Inspection Data
Authors: Ning Xiuli , Li Ying , Xu Yingcheng , Lu Xiaowei
Published: April 2020

In recent years, the vicious events about quality and safety in China have continued to bring serious impacts on people’s lives and property. The effective analysis and processing of product quality and safety inspection data will provide intellectual support for the overall improvement in product quality, and the effective control of prominent quality and safety problems in China. Aiming at the phenomenon that there is a large amount of image information in the quality detection data, this paper proposed an image annotation technology based on big data fusion, conducted weight fusion to image similarity and image user similarity, calculated the total similarity of images, and made denoising treatment. The experimental results showed that the method proposed in this study could annotate the quality test data well.
PickPatch: A Regularization Method for Deep Face Recognition
Authors: Sun Linjun , He Wei , Ning Xin , Li Weijun , Shi Yuan
Published: April 2020

This paper proposes a simple and efficient regularization method, called PickPatch, for face recognition based on a deep convolutional neural network (DCNN). The proposed method randomly selects patches in the input face image and the intermediate feature maps as the activation region according to facial landmarks during the training phase. PickPatch is an approximation method that trains a series of models for different face patches and provides a combined model. This strategy introduces the idea of model combination for multiple face patches but does not change the model structure, which is both simple and efficient. Experiments on the public LFW database demonstrate that the proposed regularization method based on current deep convolutional neural networks can achieve obvious improvements of face recognition accuracy.
Optimization of Energy Efficiency for FPGA-Based Convolutional Neural Networks Accelerator
Authors: Tang Yongming , Dai Rongshi , Xie Yi
Published: April 2020

Convolutional neural network (CNN) is widely applied to image recognition with high recognition accuracy. CNN has a wider implementation in general-purpose processors and can be accelerated on FPGA. CNN has a unique way of computing, but general-purpose processors are not efficient for CNN and cannot meet energy efficiency requirements. And the previous studies on FPGA did not involve an energy-efficient implementation on FPGA. We innovatively propose energy efficiency models and implement high energy efficiency CNN on FPGA. We implemented the LeNet-5 network model on the GENESYS 2 board and compared it to the traditional processor and previous studies. By comparison, the computing throughput of CPU, GPU and FPGA are 3.831GFLOPS, 27.143GFLOPS and 19.61GFLOPS respectively, and their powers are 32.15W, 52W, 4.152W respectively. The final energy efficiency (GFLOPS/W) is 0.119GFLOPS/W, 0.522 GFLOPS/W, 4.723 GFLOPS/W, so the energy efficiency of FPGA are far superior to that of CPU and GPU. Since the energy efficiency we achieved on FPGA is also higher than that of FPL2009 and FPGA2015, and we have achieved good experimental results in energy efficiency.
VRP Problem Solving Based on Adaptive Dynamic Search Ant Colony Algorithm
Authors: Zheng Li , He Zhiming , Liang Wen
Published: April 2020

Based on ant colony algorithm to solve the defect analysis of VRP problem, an adaptive dynamic search ant colony algorithm (ADACO) is proposed. Firstly, the model is established and the combination parameters is experimentally configured. Secondly, the strategy of combining pseudo-random and adaptive transition probability are used to help the group choose a higher quality path. When the group is in a local predicament, the segmented setting of the pheromone intensity induces the group to break out of the predicament in time. Finally, multiple groups of experimental tests are performed on the “Jia-hui Fresh” cargo delivery case. The results show that, compared with the original algorithm, the ADACO algorithm has respectively improved 17.65%, 16.13% and 16.10% in terms of delivery cost, convergence algebra and CPU running time.
An Action Recognition Method Based on Deformable Convolution Network
Authors: Dong Xu , Tan Li , Zhou Lina , Song Yanyan
Published: April 2020

In order to solve the problem of behavior recognition in short video, this paper first proposes a key frame extraction algorithm based on mutual information entropy, which uses sliding window to preserve the timing information between frames. Based on the key frame extraction, a based on Deform-GoogLeNet, a two-stream CNN method for deformable convolutional networks, uses the two-stream network to extract the RGB features and optical Flow characteristics of the image separately, and uses the weighted average method to obtain the results of behavior recognition. On the public dataset Charades dataset, mAP is 22.9, which is higher than the similar fusion algorithm, which proves that the proposed algorithm has a good effect in short video behavior recognition.
Collective Intelligent Decision Making Method Based on Rationality Negotiation
Authors: Wentao Luo , Pingfa Feng , Jianfu Zhang , Dingwen Yu , Zhijun Wu , Haochen Liu
Published: April 2020

Collective intelligence without considering the rationality of the decision-making process often leads to unreasonable decisions, which brings immeasurable losses to intelligent system. To solve it, a collective intelligent decision-making method based on rationality negotiation is proposed. Each individual with decision-making right is endowed with wisdom by the adapted Q learning algorithm, so that each individual has the perceived ability and individual motivation. Rationality Negotiation Model algorithm is proposed to select a rational decision of group, which reflects collective motivation of group. Then the decision result generated by the collective motivation is transformed into knowledge feedback to each individual, so that the individual also learn the collective motivation. Finally with the repeated iterative optimization, individual action becomes reasonable in the long run. A case is studied based on industrial enterprises making decision in the face of unexpected situations influence production process. The actual verification shows that the proposed decision-making scheme for intelligent system is reasonable.
RFID as An Element of Economy 4.0 Used to Create Sustainable Supply Chains
Authors: Konecka S , Maryniak A
Published: April 2020

Currently, a necessity to create sustainable supply chains is stressed more and more often. The achievements of intelligent and innovative technologies significantly facilitate this process. Practically each of technological creations of industry 4.0 may, to a greater or lesser extent, contribute to the increase of environmental, economical and social effectiveness of supply chains. In this paper, the focus is placed on the RFID technology which has a broad scope of application in the logistics of business and non-business entities. The aim of the paper is specifying the role of RFID in the creation of sustainable supply chains as well as presenting the model connected with this issue which may be tested in various market conditions. The suggestions of empirical studies are placed within the background of other technologies of the fourth industrial revolution.
Development of Delaminate Inspection by Using Sound Sensor and Pattern Recognition Technique
Authors: Sukprasert Napaphan , Tangjitsitcharoen Somkiat
Published: April 2020

Windsurf manufacturing and inspection process have high influence for product quality. Recently windsurf manufacturer faced high nonconformity cost with delaminate problem. Current inspection process is performed by inspector skill which depends on the experience. The inspection method is proposed by using sound sensor and applying the Fast Fourier Transform to monitor the ranges of laminate and delaminate frequency. The pattern recognition technique is purposed to classify noise frequency in order to identify the laminate windsurf from the delaminate ones. This research investigates the delaminate among the polyvinyl chloride sheet, the biaxial carbon reinforced fabric and the fiber glass reinforced fabric. The area of laminate frequency in frequency domain shows the laminate point has high power spectrum density than the one from the delaminate point. The algorithm to detect the delaminate has been developed and proved by real windsurf. It’s proved that the method proposed can be identified the delaminate windsurf.
Analysis of Gap Time-Delay in Maglev Control System
Authors: Peng Lin , Hengkun Liu , Wenwu Zhou
Published: April 2020

In the maglev control system, signal filtering, A/D sampling and communication all bring time-delay to the gap signal, which will affect the performance of the levitation control. In order to analyze this effect, a single-degree-of-freedom maglev system model is established. The state feedback control method is used to control the maglev system, and the stability of the closed-loop system is analysed and he stable region of control parameters is studied. Gap time-delay is introduced into the closed-loop system model, and the influence of time-delay on the stability range of control parameters is analyzed. It is concluded that the gap time-delay will reduce the stability range of control parameters, the larger the time-delay of position signal is, the smaller the lower limit of the stability region of differential feedback coefficient is and the smaller the stability region is. The simulation results are used to verify the validity of the theoretical analysis.
Motion Analysis and Stability Control of Four-Wheeled Omnidirectional Mobile Robot under Limited Hardware Resource
Authors: Li Jun-nan , Tang Xiao-yu , Xu Fu-ren , Huang Rui-shan
Published: April 2020

Since widely used in factories, hospitals and other workplaces, the motion stability of Mecanum omnidirectional mobile platform becomes more and more important. In this paper, an improved PID algorithm based on integral separation method, anti-integral saturation method and incomplete differential method was proposed to improve the control precision and motion stability of omnidirectional mobile robot (OMR) under limited hardware resource. From the analysis of the experimental data, it can be seen that by combining the Angle data with the motion equation, the attitude of the omnidirectional mobile platform can be adjusted in a short time, and the response speed can be greatly improved.
Design and Kinematics Analysis of a Continuum Throat Surgery Robot
Authors: Peng W L , Zhang Y N , Shen L Y , Qian J W
Published: April 2020

A cable-driven continuum throat surgery robot is designed in this paper, which is a new type of bionic robot with softness and flexibility. Throat surgery robot in the market has the disadvantage of large-scale wound and discomfort, robot in this can solve these problem. Firstly, the joint of the robot is modularized by simulating the skeleton of the snake, and the modules are connected by spherical joints to simulate the high flexibility of the snake. Secondly, each part of the robot is controlled by three cables to control the bending motion in the pitch and yaw directions. Lastly, the kinematics analysis is carried out by the D-H method, including the mapping of the driving space, joint space and operation space of the joint module. According to the entity we make, it is approved that the robot has the advantages of small structural size, high positioning accuracy, and strong space flexibility for that the structure of the snake robot is integrated with the drive of the continuous robot. It is ideal for transoral throat surgery, and the operation is performed through the natural cavity to ensure the accuracy and safety of the operation, and the postoperative recovery is also faster.
Variable Parameters Stiffness Identification and Modeling for Positional Compensation of Industrial Robots
Authors: Jiao Jiachen , Tian Wei , Zhang Lin , Li Bo , Hu Junshan
Published: April 2020

Due to the weak stiffness of robot structure, the positional accuracy of industrial robots under load can hardly meet the application requirements of high-precision machining. Predicting and compensating errors by accurate stiffness modeling is an effective method to improve robot positional accuracy. Existing stiffness modeling methods use theoretical kinematic parameters and approximate the joint stiffness to a fixed value, so that the modeling accuracy is poor. Thus, this paper proposes a regular sampling point selection method by space gridding. Then, combining Levenberg-Marquardt kinematics parameter calibration and static joint stiffness identification methods, a comprehensive identification method is proposed to achieve simultaneous identifying of robot kinematics and stiffness parameters. Next, a variable parameter stiffness model could be established, according to the identification results in different workspaces. Finally, a model-based error prediction and compensation method is put forward through online sensing of external load. The error compensation is performed on a KR500 robot, and experimental results verified that the average value of absolute positional errors caused by external load, could be reduced by 44.61%, compared with the traditional compensation method.
Patterns of population displacement during mega-fires in California detected using Facebook Disaster Maps
Authors: Jia Shenyue , Kim Seung Hee , Nghiem Son V , Doherty Paul , Kafatos Menas C
Published: April 2020

The Facebook Disaster Maps (FBDM) work presented here is the first time this platform has been used to provide analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented in FBDM data. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to better access to transportation. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making.
Dendrocalamus asper productivity after beginning thinning
Authors: Hani A
Published: April 2020

Dendrocalamus asper is one of bamboo species that is highly required for industrial purposes and community. Beside as the construction materials, shoot of D. asper has a good taste. However, D. asper cultivation faces some obstacles because its big stem and clump disturb other species. Planting in agroforestry model and an appropriate D. asper clump management are the efforts to attract people to plant D. asper. Thinning is applied in D. asper management. This is expected to be able to reduce its canopy impact on the under plants and to improve D. asper productivity. Objective of this research is to find out the influence of beginning thinning on D. asper clump productivity. The research used complete random design with blocks with three treatments of thinning, i.e. a. thinning by eliminating first generation (J1), b. thinning by eliminating first and second generation (J2), c. without thinning (control/J3). The bamboo clump used in the research is two years old. 2 years old clumps. The planting spaces were 10 m x 10 m with a total of 100 clump. The research showed that J2 give the best height growth (5.18 m). It is better than control/J3 (4.5 m), and there were no differences on parameter of diameter and numbers of stem in a clump, although J2 treatment showed the highest average number of stem in a clump.
Preliminary assessment on above ground carbon stock of agroforestry and monoculture crop systems in peatlands
Authors: Siarudin M , Indrajaya Y , Hani A
Published: April 2020

Peatlands can be used for crop cultivation with various combination patterns both agroforestry and monoculture systems. Utilization of these lands not only produces high economic value products, but also contributes in providing environmental services, including carbon sequestration. This study aims to measure aboveground carbon stock of agroforestry and agricultural monoculture systems in Rasau Jaya, Kubu Raya Regency, West Kalimantan Province. The agroforestry system observed is divided based on the level of peat depth, which are 0-2 m and 3-4 m. The pattern of agroforestry at a depth of 0-2 m is a combination of timber and non timber tree species and agricultural crops, while the pattern of agroforestry at a depth of 3-4 m is a combination of timber and non-timber tree species and plantation crops. Timber tree species are Pulai (Alstonia pneumatohara) and Gerunggang (Cratoxylon arborescens Bl); the non-timber tree species is jelutung (Dyera costulata); and plantation crop species are Jengkol (Archidendron pauciflorum) and Pinang (Areca catechu); and the agricultural crops are peanuts, cayenne pepper, tomatoes, bitter melon and eggplant. Observations were also conductedon the agricultural monoculture pattern, including the eggplant, chili, bitter melon and tomato. The components of measured carbon stocks are plant biomass (trees and lower plants), wooden necromasses, non-woody necromasses, and wild plant biomass. The measurement results show a shallow peat agroforestry system (0-2 m) with a combination of one year old trees and agricultural crops contributing to carbon stock of 0.8 tons/ha. Deep peat agroforestry systems (3-4m) with a combination of timber trees, non-timber trees and plantation crops contribute to carbon stocks of 3 tons/ha. While the monoculture system of agricultural crops produce an average carbon stock of 1.1 tons/ha. Contribution of tree component in the agroforestry system are still relatively small, which is 2.6% of total carbon stock. However, it is estimated that the carbon stocks will continue to increase with increasing age.
Hydrological responses of micro-catchments under different farming practices in Banjarnegara, Indonesia
Authors: van Brenk S , Marhaento H , Satriagasa M C , Suryatmojo H
Published: April 2020

Effects of two different farming practices namely mixed-cropping (i.e. agroforestry) and mono-cropping systems on hydrological responses were investigated based on a paired catchment method. Two catchments namely Tamansari catchment (16.4 Ha) and Penanggungan catchment (3.6 Ha), both are located in Banjarnegara district, Central Java, Indonesia, were studied. Based on drone mapping, the dominant land use of the Tamansari catchment was agroforestry where trees were combined with seasonal crops like cabbage, potatoes and carrots on the same unit of land, whereas the dominant land use of the Penanggungan was seasonal crops without any trees. For both catchments, meteorological data and discharge data were available from May 2017 to March 2018, so that made it possible to compare each other. Furthermore, the hydrological responses of the two catchments were compared for their water balance and the annual, seasonal and monthly Flow Duration Curves (FDC). The results showed that both catchments had their lowest rainfall in August and their highest rainfall in February. Based on the FDC, the Tamansari generally had a steadier flow and the range of discharge values was smaller than the Penanggungan, especially on February, the wettest month of the year. In the drier periods, the two catchments behave similarly, but in general, the Tamansari produced more discharge than the Penanggungan. Finally, based on the observation, it can generally conclude that a micro catchment mostly occupied with seasonal crops like in the Penanggungan catchment reacted more extreme to rainfall than a micro catchment mostly occupied with combined trees and seasonal crops in form of agroforestry like in the Tamansari catchment.