bluuebunny/crossref_metadata_2025 huggingface dataset:
Critical cation–anion radius ratio and two-dimensional antiferromagnetism in van der Waals TMPS3 (TM = Mn, Fe, Ni)
Authors: Petkov Valeri , Ren Yang
Published: February 2022

Two-dimensional TMPS3 antiferromagnets, transition metal (TM) = Mn, Fe, Ni, are studied by high-energy x-ray diffraction and atomic pair distribution analysis over a broad temperature range. Results show that the compounds exhibit common average but distinct local atomic structure, including distinct distortions of the constituent TM–S octahedra, magnitude and direction of atomic displacements, TM–TM distances and TM–S–TM bond angles. The differences in the local structure may be rationalized in terms of the Pauling’s rule for the critical ratio of TM2+ cation and S2− anion radii for octahedral coordination. We argue that the observed differences in the local structure are behind the differences in the antiferromagnetic properties of TMPS3 compounds, including different magnetic anisotropy and Neel temperature.
Enhancement of thermoelectric performance in graphenylene nanoribbons by suppressing phonon thermal conductance: the role of phonon local resonance
Authors: Wu Cheng-Wei , Zhou Wu-Xing , Xie Guofeng , Chen Xue-Kun , Wu Dan , Fan Zhi-Qiang
Published: February 2022

Based on the method of non-equilibrium Green’s function, we investigate the thermal transport and thermoelectric properties of graphenylene nanoribbons (GRNRs) with different width and chirality. The results show that the thermoelectric (TE) performance of GRNRs significantly increases with decreasing ribbon width, which stems from the reduction of thermal conductance. In addition, by changing the ribbon width and chirality, the figure of merit (

Z
T

) can be controllably manipulated and maximized up to 0.45 at room temperature. Moreover, it is found that the

Z
T

value of GRNRs with branched structure can reach 1.8 at 300 K and 3.4 at 800 K owing to the phonon local resonance. Our findings here are of great importance for thermoelectric applications of GRNRs.
Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features
Authors: Jones Meredith A , Faiz Rowzat , Qiu Yuchen , Zheng Bin
Published: February 2022

Objective. Handcrafted radiomics features or deep learning model-generated automated features are commonly used to develop computer-aided diagnosis schemes of medical images. The objective of this study is to test the hypothesis that handcrafted and automated features contain complementary classification information and fusion of these two types of features can improve CAD performance. Approach. We retrospectively assembled a dataset involving 1535 lesions (740 malignant and 795 benign). Regions of interest (ROI) surrounding suspicious lesions are extracted and two types of features are computed from each ROI. The first one includes 40 radiomic features and the second one includes automated features computed from a VGG16 network using a transfer learning method. A single channel ROI image is converted to three channel pseudo-ROI images by stacking the original image, a bilateral filtered image, and a histogram equalized image. Two VGG16 models using pseudo-ROIs and 3 stacked original ROIs without pre-processing are used to extract automated features. Five linear support vector machines (SVM) are built using the optimally selected feature vectors from the handcrafted features, two sets of VGG16 model-generated automated features, and the fusion of handcrafted and each set of automated features, respectively. Main Results. Using a 10-fold cross-validation, the fusion SVM using pseudo-ROIs yields the highest lesion classification performance with area under ROC curve (AUC = 0.756 ± 0.042), which is significantly higher than those yielded by other SVMs trained using handcrafted or automated features only (p < 0.05). Significance. This study demonstrates that both handcrafted and automated futures contain useful information to classify breast lesions. Fusion of these two types of features can further increase CAD performance.
Reducing MRI-guided radiotherapy planning and delivery times via efficient leaf sequencing and segment shape optimization algorithms
Authors: Snyder Jeffrey E , St-Aubin Joël , Yaddanapudi Sridhar , Marshall Spencer , Strand Sarah , Kruger Stanley , Flynn Ryan , Hyer Daniel E
Published: February 2022

Objective. Extended treatment session times are an operational limitation in magnetic resonance imaging guided adaptive radiotherapy (MRIgRT). In this study a novel leaf sequencing algorithm called optimal fluence levels (OFL) and an optimization algorithm called pseudo gradient descent (PGD) are evaluated with respect to plan quality, beam complexity, and the ability to reduce treatment session times on the Elekta Unity MRIgRT system. Approach. Ten total patients were evaluated on this Institutional Review Board approved study: three with prostate cancer, three with oligometastases, two with pancreatic cancer, and two with liver cancer. Plans were generated using the clinical Monaco Hyperion optimizer and leaf sequencer and then re-optimized using OFL and PGD (OFL + PGD) while holding all IMRT constraints and planning parameters constant. All plans were normalized to ensure 95% of the PTV received the prescription dose. A paired t-test was used to evaluate statistical significance. Main Results. Plan quality in terms of dosimetric OAR sparing was found to be equivalent between the OFL + PGD and conventional Monaco Hyperion optimizer plans. The OFL + PGD plans had a reduction in optimization time of 51.4% ± 5.0% (p = 0.002) and reduction in treatment delivery time of 10.6% ± 7.5% (p = 0.005). OFL + PGD generated plans had on average 13.2% ± 12.6% fewer multi-leaf collimator (MLC) segments (p = 0.009) and 0.1 ± 0.1 lower plan averaged beam modulation (PM) (p = 0.004) relative to the Monaco Hyperion plans. Significance. The OFL + PGD algorithms more quickly generate Unity treatment plans that are faster to deliver than with the conventional approach and without compromising dosimetric plan quality. This is likely due to a delivery complexity reduction enabled by OFL + PGD relative to the Monaco Hyperion plans.
Radio-frequency biasing of ion acceleration grids with different propellants
Authors: Li Yifei , Tang Huan , Cai Guobiao , Fu Chencong , Wang Weizong
Published: February 2022

In order to ensure the space charge compensation of the plume, conventional ion thrusters need an additional neutralizer to release electrons. When a radio-frequency (RF) voltage is applied across the grid system instead of a direct-current voltage, the simultaneous extraction of ions and electrons is achieved, thereby a neutralizer is not required. In this paper, based on the non-uniform distribution of neutral gas density calculated using the angular coefficient method, the particle-in-cell Monte Carlo collision method is used to thoroughly investigate the spatial and temporal dynamics of particles and the grid system performance for different propellants (argon, krypton and xenon) in such an RF grid system. RPA and E × B probe are employed to measure the ion flux distribution functions (IFDFs) of RF ion thruster with RF biasing, which are used to compare with the simulations. The simulated linear relationship between the self-bias voltage and the RF voltage amplitude and the multi-peak behavior of IFDF under low RF frequency conditions are comparable with the experimental data. The simulated IFDFs compare well with the experiments with the deviation of energy peak position less than 7% and 10% from those by RPA and E × B probe respectively, indicating the effectiveness of the used model. Simulations show the RF grid system is able to realize the extraction of electrons for all three propellants, so as to achieve the plume neutralization without an external neutralizer through the spatial and temporal oscillations of the beams. Electrons pass through the grid twice (extracted from the upstream, and backflow from the downstream), bringing two peaks of electron current to the accelerator grid in one period. The thrust-RF voltage curves for all three propellants show obvious slope transition, when the perveance limit is reached. The low-energy ions in the plume are mainly generated by the electron impact ionization processes for Xe while by CEX collisions for Ar. A larger ion current density of Xe on the downstream surface of the accelerator grid, which may lead to possibly more serious erosions of grids, is found compared with those of Kr and Ar. This is mainly contributed by the larger density of electron impact ionization generated ions of Xe in the downstream because Xe propellant has a larger electron density and ionization cross-section.
Accurate prediction of the critical heat flux for pool boiling on the heater substrate
Authors: Hai Fengxun , Zhu Wei , Yang Xiaoyi , Deng Yuan
Published: February 2022

While the influence of liquid qualities, surface morphology, and operating circumstances on critical heat flux (CHF) in pool boiling has been extensively studied, the effect of the heater substrate has not. Based on the force balance analysis, a theoretical model has been developed to accurately predict the CHF in pool boiling on a heater substrate. An analytical expression for the CHF of a heater substrate is obtained in terms of the surface thermophysical property. It is indicated that the ratio of thermal conductivity (k) to the product of density (ρ) and specific heat (c
p) is an essential substrate property that influences the CHF. By modifying the well-known force-balance-based CHF model (Kandlikar model), the thermal characteristics of the substrate are taken into consideration. The bias of predicted CHF values are within 5% compared with the experimental results.
Quantum search of many vertices on the joined complete graph
Authors: Ji Tingting , Pan Naiqiao , Chen Tian , Zhang Xiangdong
Published: February 2022

The quantum search on the graph is a very important topic. In this work, we develop a theoretic method on searching of single vertex on the graph [Phys. Rev. Lett.
114 110503 (2015)], and systematically study the search of many vertices on one low-connectivity graph, the joined complete graph. Our results reveal that, with the optimal jumping rate obtained from the theoretical method, we can find such target vertices at the time

O

(

N

)

, where N is the number of total vertices. Therefore, the search of many vertices on the joined complete graph possessing quantum advantage has been achieved.
Disruption prediction at JET through deep convolutional neural networks using spatiotemporal information from plasma profiles
Authors: Aymerich E. , Sias G. , Pisano F. , Cannas B. , Carcangiu S. , Sozzi C. , Stuart C. , Carvalho P.J. , Fanni A. , JET Contributors
Published: February 2022

In view of the future high power nuclear fusion experiments, the early identification of disruptions is a mandatory requirement, and presently the main goal is moving from the disruption mitigation to disruption avoidance and control. In this work, a deep-convolutional neural network (CNN) is proposed to provide early detection of disruptive events at JET. The CNN ability to learn relevant features, avoiding hand-engineered feature extraction, has been exploited to extract the spatiotemporal information from 1D plasma profiles. The model is trained with regularly terminated discharges and automatically selected disruptive phase of disruptions, coming from the recent ITER-like-wall experiments. The prediction performance is evaluated using a set of discharges representative of different operating scenarios, and an in-depth analysis is made to evaluate the performance evolution with respect to the considered experimental conditions. Finally, as real-time triggers and termination schemes are being developed at JET, the proposed model has been tested on a set of recent experiments dedicated to plasma termination for disruption avoidance and mitigation. The CNN model demonstrates very high performance, and the exploitation of 1D plasma profiles as model input allows us to understand the underlying physical phenomena behind the predictor decision.
Experimental study of epoxy based graphite coating on parabolic trough solar collector
Authors: Nagaraj Abhijeeth
Published: February 2022

Parabolic Trough Solar Collector is a line concentration method employed to harness solar energy for high-temperature applications. Attempts are made to improve the heat transfer of PTSC by reducing convectional losses of the absorber tube by using covering on the absorber tube. The study attempts to improve the Parabolic Trough Solar Collector performance using epoxy-based graphite coating through experimentation. The experiment is attempted for varied flow rates of 0.3, 0.7 and 1.1 litres per minute. The study highlighted that the coating method improved the rise in thermal fluid temperature over the absorber tube covering method by 24%. The improvement in heat transfer due to the graphite coating can be accounted for by the improvement in absorptivity and insulation, leading to better heat transfer to the thermal fluid utilized in the absorber tube.
Photovoltaic acai (Euterpe oleracea Mart.) berry juice extractor machine
Authors: Feijão A T , Almeida A M G , Brito A U
Published: February 2022

This project is about the development of a product called the photovoltaic acai (Euterpe oleracea Mart.) berry juice extractor machine, which comes as a technological alternative to solve one of the problems resulting from the lack of commercial supply of electricity in remote areas of the Amazon. It is a utility model patent, which employs equipment available in the local market, designated initially to operate in conventional electrical supply however operationally configured for photovoltaic solar power systems. This machine configuration consists of an “acai mixer”, a three-phase induction motor, a three-phase variable-speed drive and a photovoltaic generator. The photovoltaic acai (Euterpe oleracea Mart.) berry juice extractor machine is characterized as a photovoltaic system isolated from the conventional power grid and it is of the type of direct coupling. The results of the tests performed in the lab revealed that the prototype could produce up to 126 L/day of acai berry juice in a single day. However, the production of only 28 L/day is able to amortize the investment within two years.
Grid-connected photovoltaic system with power smoothing
Authors: Pantaleon E , Sal y Rosas D
Published: February 2022

This paper analyzes the power smoothing of grid-connected photovoltaic (PV) systems under fast irradiance changes. The analyzed converter is composed of a triple active bridge series-resonant (TABSR) DC-DC converter cascaded with a voltage source inverter (VSI). Two controlled variables are considered: battery current and the PV voltage. The TABSR DC-DC converter is modeled as controlled current sources for the battery and the PV ports. The ramp rate (RR) control algorithm is used to smooth the PV output power profile enhancing the power delivered to the grid. Four ramp rates (10%, 5%, 2% and 1%) are used to evaluate the RR control algorithm and the battery state of charge (SOC) response. Therefore, the battery can be sized appropriately. Real data for irradiance and temperature in an overcast day is used to verify the effectiveness of the proposed system.
Soiling accumulation impact on PV modules installed at different tilted angles in São Paulo, Brazil
Authors: Misquita Í da S , Zilles R
Published: February 2022

The present study was carried out at the University of São Paulo (USP), in the capital campus, in Butantã, São Paulo. On the rooftop of the Photovoltaic Systems Laboratory (LSF). This study used twenty photovoltaic modules with a unit power of 20 Wp. Before the beginning of the study, the modules were exposed to natural light so that they would suffer the LID effect. Subsequently, all modules were calibrated to measure the solar irradiance, using IEC 60904-2 using a pyranometer as a secondary reference sensor. After calibration, the modules were arranged in pairs, with angles ranging from 0 to 45° as reference to the surface, with a difference between pairs of 5°. To evaluate the influence of rainfall, which is the main responsible for the natural cleaning of the modules. The pluviometric values were obtained from the meteorological station of the Institute of Astronomy, Geophysics and Atmospheric Sciences of the USP. The module allocated at 0°, “Dirty” was used as reference and all the results obtained were calculated in relation to it. The results for the 18 months of experiment showed the non-linear influence of soiling, acting in a stronger way for the less inclined angles, and after 10°, this influence, became less relevant, in comparison to the previous angle. The largest losses were found for 0° and 5°, the losses exclusively to soiling were 8.16 % and 6.82 % respectively. For angles greater than 10°, the soiling effect was attenuated, which resulted in less significant losses, for 15° the difference between the impact of dirt compared to 10° was only 0.1%. Rainfall also had a very relevant impact on the experiment. This study concluded that the optimal angle, where dirt accumulation was reduced, the rainfall cleaning and relative gains were optimized is in the range between 20° and 30°.
Design and thermo-energetic evaluation of a solar building heating system
Authors: González S M , Hernández A L
Published: February 2022

This paper presents the design, construction and thermo-energetic evaluation of a solar building heating system consisting of a double-pass counter-current solar air heating collector with porous matrix and a water-bed heat storage unit connected to a prototype building located at the National University of Salta, Argentina. The experimental study determined that the solar collector has an average daily efficiency of 37 % while the storage unit has an average daily efficiency of 38 %. The overall daily efficiency of the complete system is 18 %. During the test period, the night-time discharge of the storage unit ensured that the temperature of the room to be heated was always above 20 °C when the outside temperature was between 10 and 15 °C.
A general study of designing an electromagnetic induction cooking device prototype for microgrids in rural areas
Authors: Carbajal X , Sal y Rosas D
Published: February 2022

This paper presents a general study of designing an electromagnetic induction cooking device connected to batteries that operates in remote rural zones microgrids. For this work, the analytical and simulation modeling is carried out to obtain parameters of the equivalent impedance. Therefore, a full-bridge series-resonant (FBSR) DC/AC converter is designed at a specified operating frequency. The results obtained analytically are compared with the experimental data to verify the output power load and temperature measurement of the prototype.
Application of a bioclimatic tool for the hygrothermal analysis of a historic building in Lima-Peru
Authors: Alvarez S , Lefebvre G
Published: February 2022

The main objective of this paper is to know and analyze the bioclimatic strategies that would be present in colonial residential architecture in Lima. The knowledge and use of systems to achieve certain climate conditions in ancient buildings made of sun-dried bricks and thatch, both in open and closed spaces. We consider that through trial-and-error approaches, techniques would have been developed to obtain indoor comfort considering local climate conditions, available materials and other cultural conditions. Temperature, humidity and air speed measurements were taken three times a day during four days in some places of the building. Givoni’s building bioclimatic chart is used to analyze the performance of the places measured. On the one hand, different climate conditions were recorded in each patio and would be the result of its physical characteristics. In addition, differences between the performance of social spaces and private rooms were observed due to different ventilation strategies.
Modeling the effect of roof coatings materials on the building thermal temperature variations based on an artificial intelligence
Authors: May Tzuc O. , Hernandez-Pérez I. , Castro Karla M Aguilar , Jiménez Torres M. , Castillo-Téllez M , Demesa López N
Published: February 2022

This paper presents the benefits of applying the artificial neural networks technique to model the building indoor temperature variations caused by implementing roof coatings. Four of the most common roof coating used in Mexican constructions were considered. The study evaluated test cells at outdoors and instrumented them to measure the indoor temperature variation. The environmental parameters solar radiation, wind speed, ambient temperature, and relative humidity were also monitored. The results showed the viability of modeling the thermal behavior generated by the roof coatings with an accuracy greater than 90%. It implies the ability to predict internal temperatures based on the physical properties of the material and environmental variables, being a feasible tool in the search for thermal comfort for buildings in the region. In addition, the results of the sensitivity analysis identified to what extent to improve the optical properties of the material impact on the heat transferred to the interior.
Energy analysis of a nZEB standard residential building: case study in Rio de Janeiro
Authors: Vitória V H , Meiriño M J , Figueiredo G
Published: February 2022

This research aims to identify the technical feasibility of the concept of Nearly Zero Energy Building according to Brazilian standards and climatic conditions. The analysis was performed under the most up-to-date normative references and through computational energy simulation via EnergyPlus software, focusing on the building envelope model of a residential building in Rio de Janeiro. A reference model and five other construction models common in the national market were analyzed in two scenarios: firstly, in terms of natural ventilation only, and then using natural ventilation alternating with artificial cooling. Although those models were shown to be in the scope of international benchmarks about energy consumption in warm climate areas, some models stood out. In this case, the model with the lowest energy consumption presented results 22% lower than the reference model. This model was then associated with a supply of solar photovoltaic energy in order to apply one of the fundamental requirements of nZEB: the use of renewable energy sources. The results show the potential for nZEB implementation in Brazil.
Is the information provided by free satellite sources suitable for predicting or evaluating the performance of photovoltaic systems in Peru? (In memoriam to Heinrich Berg)
Authors: Espinosa-González C A , Angulo J R , Conde L A , Carhuavilca A M , Alfaro E F , Espinoza R , Töfflinger J A , Muñoz-Cerón E , de la Casa J
Published: February 2022

At present, there are free access online platforms that offer, free of charge, meteorological data on Peru; however, there is no study that validates the goodness of its use in the country. These types of tools are of the utmost interest, both in the design phase of photovoltaic systems PV and in the subsequent process of analysis and evaluation of their operation if it is intended to implement monitoring systems with Class B or C precision defined in the IEC 61724: 2017 standard. In this work, the consistency of the data offered by any of these platforms and their usefulness will be analysed when they are contrasted with experimental data on the operation of real systems.
Remaining Useful Life Prediction of Turbofan Engine Based on Temporal Convolutional Networks Optimized by Genetic Algorithm
Authors: Chen Zhengkun , Chen Baojia , Chen Xueliang
Published: February 2022

It is tough to select the parameters of the deep neural network and enhance the accuracy in the field of remaining useful life (RUL) prediction. To address the problem, one RUL prediction model optimized by a genetic algorithm, based on temporal convolutional networks (TCN), was proposed. Firstly, forward-filling sliding sampling is used to add the samples’ time step. Then the genetic algorithm is used to search the hyperparameters of the residual block of TCN. Finally, the performance of the proposed method is verified by the C-MAPSS dataset. The results show that in the two evaluation metrics of root mean square error (RMSE) and score function (SF), the proposed GA-TCN reduces them by 8.2% ∼ 27.56% and 28.24% ∼ 79.35%, respectively, when compared with other studies. The RMSE and SF metrics of the proposed method are on average 17.10% and 54.10% lower than that of other methods in four sub-datasets of the turbofan engine.
A reinforcement learning algorithm for the 2D-rectangular strip packing problem
Authors: Zhao Xusheng , Rao Yunqing , Fang Jie
Published: February 2022

The 2D packing problem is categorized as one branch of the cutting and packing problems, which is widely spread in the manufacturing industries. Over the years many meta­heuristics have been proposed and applied on the packing problem. Recently, the approach combined with machine learning serves as a novel paradigm for solving the combinatorial optimization problem. However, the machine learning approaches have very limited literature reports on the appliance of the packing problem. We propose a reinforcement learning method for the 2D-rectangular strip packing problem. The solution is represented by the sequence of the items and the layout is constructed piece by piece. We use the lowest centroid placement rule for the piece placement, then a Q-learning based sequence optimization is applied. Three groups of conditions are set for the testing, the computational results show the Q-learning approach has good effect on the compaction of the layout.
A new algorithm for location parameter estimation in DTV ground broadcast network
Authors: Bao Songjian
Published: February 2022

In the city, terrestrial digital TV network is the most perfect TV network, so the location of terrestrial broadcast signal based on digital TV has become one of the research hots-pots in recent years. Because multi-path in DTV wireless channel will cause serious signal fading, it becomes more difficult to detect DTV signal in the process of positioning, which directly affects the accuracy of positioning. Therefore, how to improve the detection accuracy of DTV signal through parameter estimation has become the focus of this paper. Based on the detailed study of DTV system based on wireless transmission channel characteristics and system structure, through the analysis based on linear least square (LS) channel estimation algorithm, the advantages and disadvantages of interference elimination is given based on the data, a new method of channel estimation, the simulation results verify the algorithm presented in this paper can well improve the channel estimation performance.
Target Detection Based on Deep Learning
Authors: Zhu Yuchen
Published: February 2022

As an important research direction of computer vision, target detection has been widely used in face recognition, intelligent driving, robot navigation and other fields. In recent years, with the deepening research on deep learning, great progress has been made in the field of computer vision, such as image acquisition, image processing and target detection. Compared with the traditional target detection algorithm based on candidate regions, it has the problems of poor timeliness and slow detection speed. Recently, the popular target detection algorithm based on regression realizes the real sense of end-to-end detection and greatly improves the detection efficiency. However, the accuracy of small target detection and dense target detection has not been solved. In the future, we still need to improve the efficiency and accuracy of recognition on the existing basis, and solve the problem of small target and dense target detection to make it more widely used in practical application scenarios. In this paper, the principle, advantages and disadvantages, accuracy and other aspects of the above algorithms are introduced in detail, the problems existing in the target detection algorithm are summarized, and the future development direction has been prospected. In short, both algorithms have advantages and disadvantages, but the regression-based target detection algorithm has better practicality and development prospects.
Phase-locked subdivision method of digital signal based on FPGA
Authors: Hou Han , Cao Guohua , Ding Hongchang , Li Kun , Wang Aijia
Published: February 2022

In order to improve the accuracy and real-time performance of the subdivision signal expression result of the photoelectric encoder in the signal dynamic measurement process, a phase-locked subdivision method of digital signal based on FPGA is proposed. The open-loop structure is used to realize the large-range detection of the grating measurement system, and the fractional frequency division method is used to realize the subdivision function of the grating moiré signal. In order to verify the effectiveness of the design, we compare the analog signal subdivision with the traditional digital phase-locked subdivision method. The experimental results show that the digital phase-locked subdivision algorithm designed in this paper can be used in the high-frequency moiré signal frequency and rate of change. The subdivision function is realized under the following conditions, which can be better applied to dynamic measurement occasions.
Research on the Distribution Strategy of Longitudinal Driving Force of Vehicle Driven by 8×8 In-wheel Motors
Authors: Yao Yifan , Li Erkang , Peng Chunlei
Published: February 2022

In order to meet the driving requirements of 8×8 in-wheel motor-driven vehicles, and to improve the adhesion performance of the vehicle at the same time, using the characteristics that each wheel can be controlled independently, a driving force distribution algorithm based on the optimal load of adhesion performance is designed. The mathematical model of the vehicle dynamics and the driving force distribution algorithm are built in Matlab/Simulink, and the effectiveness of the algorithm is verified through simulation tests, that is, it can meet the driver’s longitudinal dynamics requirements, take into account the limitations of the road and the motor, and reduce the load of the adhesion.
Optimization of composite cylinder shell via a data-driven intelligent optimization algorithm
Authors: Chen Ming , Zhang Xinhu , Shen Kechun , Pan Guang
Published: February 2022

While composite material provides huge flexibility for design, the design optimization of composite structure is time consuming with low efficiency. This work combines finite element analysis for composite cylinder shell with a data-driven intelligent optimization algorithm (Bayesian optimization algorithm) and is aimed at maximizing eigenvalue buckling load. Through minimizing number of iterations as a derivative-free global optimization algorithm, Bayesian optimization is versatile and can be further applied to design advanced composite structure with more complicated scenarios, such as complex geometries and load conditions.
The Concept of early monitoring and warning of thermal runaway of lithium-ion power battery using parameter analysis
Authors: Gao Ao , Xu Fang , Dong Wenhui
Published: February 2022

Considering the key factors affecting the fire rescue of lithium-ion power battery, such as timely early warning, monitoring, delaying the runaway process and sharing the causes of safety accidents, this paper analyses the thermal runaway mechanism of lithium-ion power battery. In addition, this paper puts forward the intelligent fire safety design of lithium-ion power battery, from the real-time monitoring technology of lithium-ion power battery safety, to bring the fire safety of lithium-ion power battery into the urban intelligent fire protection system.
Development of Parametric Modeling and Finite Element Analysis System for Cylindrical Spiral Tension Spring
Authors: Pan Yunping , Ming Yili , Wan Wenzhe
Published: February 2022

The survey shows that there are few researches on automatic modelling and finite element analysis by changing the relevant parameters of cylindrical spiral tension springs. This paper discusses the method of combining parametric technology with finite element analysis, using Visual Studio 2010 as the system development platform and Visual Basic (VB) as the program development language to establish a VB window operation interface. Automated modelling is carried out through the input feature size, and the VB event-driven feature is used to link automated modelling and automated finite element analysis to establish a parametric design and finite element analysis system for cylindrical spiral tension springs.
Modelling and Simulation of a 3DOF End Effector for Material Handling with a Delta Robot
Authors: Musa Muhizi , Li JinLiang
Published: February 2022

In order to improve workspace and material handling system of the delta robot, This paper describes a design of an End-effector with 3 degrees of freedom (3DOF) which makes the End-effector more flexible and having sufficient range of working space. This End-effector will be mounted on a delta parallel robot to perform the desired task in handling materials accordingly. The robot is able to pick and place a given material to the right place and positioning as it is desired by the user. The methodology used to design this robot was based on 3 major components which are: Sliding rails which deals with translational motion along horizontal axis, this motion carries the whole system from side to side and is going to play a role of increasing workspace in either assembling line or picking and placing system. Delta robot is in charge of manipulating its end effector in 3 dimensions along (x, y, z - axis) according to the object tracking localization as a human’s arm manipulate the hand. The end effector’s purpose is to perform rotational motion then pick the object and place as the system requires, this performance has advantage of handling materials and positioning respectively. It showed that the proposed system is safe and is feasible for application. The proposed material handling system can minimize the disadvantages of utilizing a delta robot by attachment of the presented end-effector to the delta robot.
Path Planning Method of Mobile Robot Based on Q-learning
Authors: Jiang Qi
Published: February 2022

With the progress of science and technology, mobile robots gradually play an increasingly important role in the industry, military, science and technology and other fields. Aiming at the core problem of path planning in the path planning of mobile robots, this paper studies and designs a path planning method based on a Q-learning algorithm. Q-learning is widely used in robot path planning, as it only needs the interaction between the current state and the environment to make rewards and punishments for robot actions, to make decisions on the next action. Aiming at the problems of low efficiency and slow convergence in the original Q-learning algorithm, this paper improved the algorithm to enable the robot to quickly complete the planning and get the optimal and shortest path. The grid method was used to establish the environment running program to visualize the convergence process and obtain data. Finally, software simulation is used to establish the environment and code the robot to simulate the real environment, which proves the practical value of the algorithm.
Modeling Power IoT Terminal Entity Based on OOPN
Authors: Miao Weiwei , Wei Lei , Zeng Zeng
Published: February 2022

With the continuous development and ceaselessly construction of power IoT system, a huge number of power terminal entities are connected to the power IoT system, which largely affect the performance of power IoT system. The lifecycle process analysis of power IoT terminal entity becomes more and more important. However, the traditional analysis methods often have some shortcomings such as complex process and huge workload. Therefore, it is urgently needed to find out some more effective method to analyze the state and behaviour of power IoT terminal entity. To overcome the shortcomings of traditional analysis methods, an OOPN-based power IoT terminal entity model is proposed by combining object-oriented idea with Petri net technology. The proposed model has the advantages of modularity design, object-oriented, simplified calculation and great flexibility, which is verified with a case study by selecting a transformer as an example of power IoT terminal entity and comparing the data.
Research on Flexible Capacitive Sensors for Smart Textiles
Authors: Wang Yang
Published: February 2022

Smart textiles are a new era of smart textiles that not only have traditional textile functions, but also have information collection, feedback, and multiple intelligent interaction functions with users. As a manifestation of the combination of art and technology in the textile field, smart textiles are of great significance to traditional textiles, clothing, home textiles, and wearable devices. From the perspectives of the background, technology, and development prospects of smart textiles, this article systematically analyses the application technology of smart textiles in practice. Starting from the current state of smart textile research, the article explains the promotion of new material technology to the research of smart textiles. Focus on the technical improvement of capacitive sensing equipment based on flexible fabrics, and find a sensing fabric structure with lower hysteresis, fast response time, good repeatability and stability through design experiments.
Research on control system of small intelligent drilling rig based on lithology identification
Authors: Wu Zebing , Wang Jie , Xi Kaikai , Guo Yulun , Yang Chenjuan , Jiang Mengjie
Published: February 2022

Intelligent oil drilling rig can automatically complete the whole drilling process without human intervention, and can integrate big data with drilling, geology, logging and other disciplines. Aiming at the problem of insufficient rock breaking capacity and complex and changeable drilling process, a small intelligent drilling rig control system is designed. The structural drilling parameter samples such as WOB and rotating speed obtained by the sensor are used for lithology identification in the software MATLAB by using the neural network pattern recognition toolbox and GRNN generalized regression neural network respectively, and the control block diagram of the automatic drilling system is designed in Matlab / Simulink. The neural network control algorithm is used to compare with the classical PID control, The results show that the overshoot and rise time are significantly reduced, and the effect is good. When applied to the oil rig to realize constant WOB drilling, it is conducive to reasonably select the bit type, timely adjust the drilling parameters and improve the drilling efficiency, so as to realize the real intelligence of the oil rig.
Intelligent Control Technology of Tire Tread Pattern Draft Angle for Intelligent Manufacturing
Authors: Li Heng , Chen Yazhou , Huang Zhengxiong , Chen Quan
Published: February 2022

In view of the problem that the tire pattern design may not meet the production requirements, the designer needs to modify the tire pattern draft angle, and the existing pattern draft angle modification process is cumbersome, and the designer’s experience requirements are relatively high, based on the tire pattern draft angle adjustment Experience has developed a tire pattern draft angle intelligent control system for intelligent manufacturing, and developed a corresponding program based on UG10.0 based on this system. The program can automatically determine the type of pattern contour surface, can judge the contour surface unevenness based on the projection method, can automatically recommend the appropriate draft angle, and realize the rapid modification of the draft angle in batches. The actual application of the enterprise shows that this program can greatly improve the efficiency of tire pattern design.
Design of a Catapulted Self-deployable UAV
Authors: Lyu Tian , Qin Shan , Tian ZiAng , Zhang QiYue , Xu YunJing , Lin KeXin
Published: February 2022

Unmanned Aerial Vehicle (UAV) is playing a gradually enhanced role in fields of recon and information acquisition, however restricted departure condition of fixed wing aircrafts, take-off preparation time of multirotor aircrafts etc. have limited its further applications. This research aims to combine advantage of hovering and self-deployable departure to reconcile the shortcomings, meanwhile adapts to drone swarm trend. A catapulted self-deployable quadrotor is designed using 3D modelling software, and later a compatible self-deployable control algorithm is developed using STM32F103 microcontroller, along with its circuitry. Eventually, a prototype of the design is 3D printed, assembled and tested. This design shows merits of easy to carry, low requirements for take-off conditions and good hovering performance and is compatible for multi-UAVs cooperation tendency.
Electrical automation control technology of electromechanical equipment based on artificial intelligence technology
Authors: Liu Yong , Zhao Yan
Published: February 2022

Artificial intelligence technology is the basic measure to ensure the good operation of electrical automation control system of electromechanical equipment. Because artificial intelligence technology is widely used, it mainly uses auxiliary electrical automation control systems such as data real-time monitoring, data acquisition and data mining in electrical automation control technology of electromechanical equipment. The application of artificial intelligence auxiliary system in electrical automation is mainly data acquisition, which integrates and analyzes the data generated in the production process of electrical automation system equipment. During the operation of the electrical automation system of electromechanical equipment, the data generated is extensive, the amount of calculation is large and the analysis is complex. Once the equipment fault occurs, it is easy to affect the electrical automation system and even the business activities of the enterprise. Therefore, the purpose of applying artificial intelligence technology to the production process of electrical automation system equipment is to centrally process the data parameters entered by electromechanical equipment, so that the accuracy of operating parameters of electrical automation control system of electromechanical equipment can be greatly improved, which is conducive to the stable development of production efficiency.
Analysis Research on Operation Condition of Mining Crane
Authors: Xu MM , Zhang ZT , Zhou Z , Zhang H , Li S , Liu YM , Jiang KS , Chen QH
Published: February 2022

Mining cranes play an important role in the transportation and installation of underground equipment. Operating swing angles will seriously affect the operations safety. To ensure the operation safety of mining cranes, this paper firstly established a mathematical model of mining crane hoisting swing, and discussed affecting factors of the oscillation. Then, the orthogonal test method has been carried out, and the optimal and serious working conditions were obtained. Finally, the accuracy of the numerical results have been cross-verified on the ADAMS virtual platform. Results show that the swing trend increases with the increase of pitch acceleration, rotary acceleration and rope length, especially the rotary angular acceleration. The vibration variation trends of Matlab numerical results and ADAMS simulation results are consistent, which cross-verifies the two extreme operation conditions are valid and reliable. It provides a useful reference for the future research.
Research on the force mechanism of the connection between the foundation ring and the anchor cage ring of the high-altitude onshore wind turbine
Authors: Xu Jianjun , Chen Kang , Yang Yunfeng , Zhang Fenghua , Meng Wenqiang , Zou Wuting , Xi Yu , Peng Huaiwu
Published: February 2022

The most widely used connection form of the wind turbine foundation and the tower is the foundation ring method. With the popularization and application of high wheel hub and large-capacity wind turbines, the anchor cage ring has gradually become the connection method of large-capacity wind turbines. Therefore, the force mechanism of the connection between the foundation ring and the anchor cage ring has become an important issue in the basic design of the wind turbine. In this paper, numerical simulation method is used to analyse the characteristics of the two connection methods and force characteristics of the foundation ring and anchor bolts. The force transmission mechanism and force characteristics of the two connection methods are summarized and the stress distribution of the foundation ring, the anchor cage ring and the nearby concrete are obtained. Finally, the basic strengthening strategies under the two connection methods are clarified.
A Novel Intelligent Radar Detection Network
Authors: Zhang Junhua
Published: February 2022

A novel Intelligent Radar Detection Network (IRDN) is proposed to be able to flexibly decompose and re-aggregate all functional elements in OODA cycle, so as to establish a new kind of all-domain kill web featuring on-demand service, flexibility, scalability and optimal kill channel, which can obtain and maintain asymmetric air defense superiority with benefits from all-domain sensing, all-domain C2 and all-domain strike capabilities.
A Simulation Method for Assembly Process of Branch Cables Based on the Minimal Energy Principle
Authors: Yang Weifeng , Liu Jianhua
Published: February 2022

To solve the simulation problem of branch cables in virtual assembly process, a cable simulation method based on the minimal energy principle is proposed. Firstly, to describe the postures and deformationss of the branch cables, a discrete Cosserat rod model is established. Next, according to the description of the minimal energy principle, each system is in the most stable state when its energy takes the minimum, so the postures of the cables could be solved by calculating the extremum of energy optimization model. After that, on the circumstance of ignoring low-speed movement of the cables, the energy could be obtained by the summation of the tensile, bending and torsional deformations potential energy. Then, the Levenberg-Marquardt algorithm is applied to solve the energy optimization model, and the postures of cables is calculated through Hermite interpolation. Finally, the verification example was established to simulate the deformations of the branch cables, which verifying the high calculating speed of algorithm and the authenticity of simulation.
Heat Transfer and Pressure Loss Simulations of Matrix Cooling Channels for Gas Turbine Airfoils
Authors: Wu Xingwei , Li Niudong , Li Feng
Published: February 2022

The paper selected 10 geometrical parameter combinations to numerically investigate the dependencies of heat transfer enhancement, flow resistances and overall thermal performance on three geometrical factors including rib inclination angle, channel blockage ratio and subchannel number. The investigated Reynolds number starts at 5,000 and can be as high as 90,000. The turbulence model selected was Transition SST model. The results show that the matrix cooling channels of 30deg rib angle have greater heat transfer enhancement and much higher pressure loss than that of 60deg rib angle. Moreover, matrix cooling channels of higher channel blockage ratio have greater heat transfer enhancement and higher flow friction, while the relationship between the overall thermal performance and the channel blockage ratio depends on the Reynolds number. It was also concluded that the heat transfer performance factor of matrix cooling channel does not change linearly with the subchannel number and the effect of subchannel number on heat transfer performance is less significant than the other two factors. Besides, matrix cooling channels of higher subchannel number have higher pressure loss and poorer overall thermal performance.