bluuebunny/crossref_metadata_2025_split huggingface dataset:
Topology Control of WSNs Based on Persistent Homology
Authors: Yu Jintao , Lu Xiaowei , Gao Zhenhua , Li Shizhou , Li Lei , Li Congcong , Tong He 
Published: August 2022

Abstract
WSNs can easily lead to the failure or even collapse of the whole network due to energy constraints. Therefore, in order to reduce a series of losses caused by energy consumption, it is necessary to take corresponding topology control to improve the overall performance of the network and make WSNs more secure and reliable. In the research of WSNs, one of the most important technical ways to improve the overall performance of the network is to optimize the topology of the network. In this paper, a topology control method of wireless sensor network based on continuous coherence is proposed. Under the condition of ensuring that the network topology remains unchanged, the energy loss in the network is reduced by sleeping the redundant nodes in the network, so as to improve the network efficiency. The experimental results confirm the effectiveness of the method.
Water Allocation Model Based on Improved TOPSIS Method
Authors: Feng Dingrong , Chen Zheng , Yang Yanxiong , Qiu Ruofeng , Qi Yunfei , Liu Tao 
Published: August 2022

Abstract
In this paper, in order to improve traditional TOPSIS water weight distribution model, the analytic hierarchy process combined with coefficient of variation method is used to calculate the weight. In the improved model, vertical distance is adopted to calculate the result, which avoids some defects caused by Euclidean distance. In the process of practical application, improved TOPSIS method selected 12 evaluation indicators with water resources development, social benefits, economic benefits, ecological environmental benefits and other aspects of evaluation, making the model more fit the reality. The results show that when the method is applied to water right allocation in Arizona, the calculated results are in agreement with the practice.
FAIAD: Feature Adaptive-based Image Anomaly Detection
Authors: Zhang Qianqian , Wei Hongyang , Du Xusheng , Li Xue , Yu Jiong 
Published: August 2022

Abstract
Image abnormality detection is a hot research topic in the field of data mining, and it has great application value in the fields of industrial appearance defect detection and medical image analysis. To address the problem of poor performance of anomaly detection models caused by incomplete feature extraction, we propose a feature-adaptive image anomaly detection model. FAIAD first trains the initial feature extraction model by pre-training the model. Then introduce feature adaptation methods to improve image feature extraction performance. The last step is to calculate the accuracy of image anomaly detection. In order to explore the feature extraction effects of different neural networks, this paper designs three kinds of backbone network comparison experiments. Experimenting on both Cifar-10 and Fashion-MNIST datasets, the accuracy of our model improved by 3.5% and 2.3%, respectively, compared to the baseline model. The experimental results show that combining pre-trained models with feature adaptation methods can effectively improve the performance of anomaly detection models.
Research on Improved Random Forest Algorithm for Highly Unbalanced Data
Authors: Sun Jianming , Shen Zicheng 
Published: August 2022

Abstract
To address the problems of low prediction performance of minority class samples due to highly unbalanced data and improved prediction performance of minority class but decreased prediction performance of majority class after balanced training set, an improved algorithm combining feature fusion and random forests quantile classifier is proposed. The algorithm obtains multiple data subsets by feature segmentation through sliding windows, and then inputs the obtained data subsets into the quantile random forest for cross-validation to obtain multiple class probability vectors for each sample, and finally splices the obtained class probability vectors with the original data set as new features to form a new data set, and uses the new combination of features to better represent the relationship between minority and majority classes, thus improve the overall classification performance of the classifier. Experiments on the KEEL dataset show that the improved algorithm improves the overall sample classification performance for unbalanced data compared to other algorithms.
Research on Prediction Model of Rock and Soil Layer Information Based on Adjacent Boreholes
Authors: Li Xiang , Su Dingli , Zhong Jiagao , Zhu Yi , Hu Hesong , Xie Xiaorong , Zhao Yinghao 
Published: August 2022

AbstractAiming at the problem that the drilling core cannot be drilled and core is unable to be drilled to find the stratum due to the construction measures or large equipment occupation in the survey site, based on the thickness information of the adjacent hole position of the borehole to be measured, the BP neural network model for stratum prediction was constructed, and the training data sampling method and comprehensive analysis results were put forward, which were based on the training data sampling method and comprehensive analysis results in the front, back, left, right and circle positions of the borehole, combined with the actual investigation Engineering. The influence of soil layer training data collected by different methods on the accuracy of soil layer thickness prediction was analyzed. The results showed that: (1) By comparing the prediction results of 10 groups of actual engineering holes to be measured, it was revealed that the prediction accuracy of soil thickness had great correlation with the orientation selection. (2) In azimuth prediction, each group collects 21 adjacent boreholes as training data samples by continuous cycle of three longitudinal and seven horizontal directions. The prediction results of neural network were stable. (3) A selection strategy was proposed to analyze the preliminary prediction results of neural network. Based on the circular prediction results, the data with the difference within ±0.2m in azimuth prediction were compared and screened. The comprehensive prediction value obtained by test was very close to the true value, and the data error is within 9%, and the prediction effect was good. The research results provide a way of thinking for the prediction of small sample soil sequence, and can provide reference for geotechnical engineering investigation and design application.
Deep Learning Based Cancer Survival Analysis Algorithm
Authors: Li Mingchang , Deng Shenyang , Chen Zhihong , Gu Wanrong , Xie Xianfen , Zhang Ziye 
Published: August 2022

Abstract
Histopathological image examination is the gold-standard tool of cancer diagnosis. However, it is subjective to only rely on doctor qualitative analysis of pathological image. What’s more, it is easy to miss pathological information. The texture of pathological images is complex and highly heterogeneous, so it is difficult to extract the deep features of histopathological images. In this paper, a deep learning-based cancer survival analysis algorithm is proposed to process the histopathological image data, which can effectively extract the histopathological image features and improve the accuracy of the model. The average C-Index of the proposed method is 0.7233, which is improved by 28.0% and 19.9% compared with two state-of-the-art methods.
An Evolutionary Model for Earthquake Prediction Considering Time-Series Evolution and Feature Extraction and Its Application
Authors: Du Depeng , Cui Zhendong 
Published: August 2022

Abstract
Seismic activity presents characteristics such as relatively concentrated distribution, high destructiveness, huge secondary hazards, complex change patterns, and unpredictable future activity conditions. It is of great theoretical significance and application and promotion value to research magnitude prediction for earthquake-prone areas. This study attempts to develop an earthquake prediction model based on seismic data from 1970 to 2021 and to predict earthquakes of magnitude (4.5-6). Firstly, the data are statistically analyzed using data statistics to analyze trends, identify patterns and characteristics of change, and make decisions conducive to development. Secondly, feature extraction and selection are carried out by convolutional neural networks to select the optimal features. The CNN_BiLSTM model was constructed and compared with other prediction models (long and short-term memory neural network, back propagation neural network, and support vector regression) to obtain prediction results for sensitivity, specificity, true prediction values, false prediction values, and accuracy values were compared and analyzed. The earthquake magnitude predictions for Japan and surrounding areas using the above techniques show essential and encouraging results, thus taking an essential step towards an eventual robust prediction mechanism.
Power Transmission Line Broken Strand Repair Robot and Visual Control Method
Authors: Hu Ran , Xu Yuxiang , Jia Yin , Wu Jiong , Deng Aopan , Song Tinghan , Chen Xi 
Published: August 2022

Abstract
This paper proposes a design of machine vision based broken strand repair robot on power transmission line. Through the analysis of the task and process of repairing the broken strands, the design scheme of the broken strand repair robot mechanical structure based on the clamp block repair method is proposed. Aiming at the disadvantages of traditional visual detection methods under the influence of complex object background and strong light, a visual inspection method based on deep learning is proposed to identify the conductor, vibration damper and broken strand, and automatically locate the bifurcation point of the broken strand. Using visual measurement and control technology, control the operation manipulator to straighten, reset, reinforce and repair the forked aluminum wire. Developed a robot experimental prototype, the experimental results verify the feasibility of the robot structure design and the effectiveness of the visual control algorithm.
Analysis of Runoff Coefficient Prediction Based on LM-BP Neural Network
Authors: Wang Shaobo , Yuan Wanhua , Zhou Jiawei 
Published: August 2022

Abstract
Runoff coefficients are affected by many factors, and their complex nonlinear relationships make it difficult to calculate accurate runoff coefficients using experimental physical models. In this paper, we improved the traditional BP neural network model based on the Levenberg-Marquardt method and established an S-type/S-type mathematical model of the relationship between runoff coefficients and influencing factors to predict each surface runoff coefficient under different rainfall conditions and different subsurface conditions, and compared it with other methods. The results calculated after the actual case simulation showed that the error of LM-BP neural network simulation was within the range of 0.03~0.09, the error was smaller, the calculation results were more accurate, and the prediction of runoff coefficient had the advantages of strong generalization ability and high prediction accuracy, which was a great improvement to the traditional rainfall-runoff coefficient best-fit relational fitting relationship method. Besides, in order to reduce the problem of sample overtraining, the more detailed the hydrological information, the better.
Research on Modeling Method of Massive Launch Vehicle Time Series Data
Authors: Li Pengcheng , Chen Haidong , Li Shipeng , Lian Yanze , Qi Junqing 
Published: August 2022

Abstract
Massive time series data is produced with the newly proposed long-term test, with high data sampling frequency, usually 40Hz, and the sampling lasts for a long time, often more than one year. Therefore, it brings the storage problem of massive launch vehicle time series data. In view of the shortcomings that the traditional storage method based on files may be difficult to meet the storage requirements of high concurrent writing, high compression rate and high query speed, the data modeling method is studied in this paper. For storage method based on files, relational database storage and time series database, the data model is constructed, and the configuration parameters are optimized, and the optimal storage method is selected by the performance simulation carried out. The simulation results show that under the massive time series data scenario, the storage method based on time series database has twice the writing speed of the others; It has the highest storage compression rate, up to 80%, far exceeding the uncompressed storage method based on files and the relational database storage method with data expansion; It has the fastest time-related query speed, more than 100,000 records/s, 3 times that of relational database and 10 times that of files. Among them, the storage method based on time series database has the best performance, and meets the storage requirements of massive time series data in the future for launch vehicles.
Stability of Stochastic Delayed Differential Equations with Exogenous Disturbances via Event-Triggered Scheme
Authors: Zhang Zhongyu , Liu Linna , Fang Jianyin 
Published: August 2022

Abstract
This paper discusses the input-to-state practically stabilization (ISpS) issue for stochastic delayed differential equations (SDDEs) with exogenous disturbances. To reduce the transmission frequency of the feedback control signal, the proposed SDDSs are stabilized by an event-triggered strategy. The concept of the input-to-state practically stabilization is used to describe the dynamic performance of control target in the event-triggered schemes and exogenous disturbances. Making use of stochastic control theory, we demonstrate that the considered SDDEs is ISpS via an event-triggered feedback controller represented by linear matrix inequalities (LMIs). Moreover, lower bound of interaction time of control task is obtained to avoid the Zeno behavior. Compared with the large number of results for discrete-time stochastic systems, event-triggered control of continuous-time stochastic systems only yields a few results.
Comprehensive energy efficiency study of different climate -adapted BIPV roof forms in single-family houses for five climate zones of China based on PV energy substitution rates (PVESR)
Authors: Shi Shaohang , Song Yehao , Chu Yingnan , He Yi 
Published: August 2022

Abstract
As a type of energy production construction, building integrated photovoltaics (BIPV) roofs are contributing for achieving NZEB goals. Considering building sites’ climate attributes with possible size of PV systems can help to reduce the initial investment and energy consumption of building operation and maintenance. This paper investigates the energy production and energy consumption patterns of single-family houses, which is designed to evaluate the comprehensive energy efficiency of BIPV roofs to meet energy demands by photovoltaic energy substitution ratio (PVESR) calculations. Five typical cities were selected, corresponding to the five climate zones in China; eight forms of BIPV roofs were developed according to single-family houses’ common scale and spatial characteristics. The results show that (1) The PVESR varies significantly between different climate zones and different forms of BIPV roofs, so pre-survey of BIPV roof application sites and comparison of roof forms are relevant in preliminary architecture design. The climate zones where energy balance can be achieved are hot-summer & warm-winter zones, hot-summer & cold-winter zones and moderate zones, and the average PVESR based on the eight roof prototypes are 0.987, 1.136 and 2.272 respectively; the corresponding PVESR for cold zones and severe cold zones are 0.807 and 0.475. (2) The peak and trough values of capacity and energy use are not exactly matching in the five climate zones, especially in severe cold zones, cold zones and hot-summer & cold-winter zones’ winter. (3) The high heating demand in winter is the main reason for the failure to balance BIPV production and energy consumption in severe cold zones and cold zones. The comprehensive energy efficiency evaluation method of BIPV roofs proposed in this paper can provide data support and application guidance for form design and size determination of BIPV roofs in five climate zones in China.
Could technological improvements make microalgal biodiesel more economically feasible?
Authors: Szulczyk Kenneth R. , Cheema Muhammad A. , Ziaei Sayyed Mahdi 
Published: August 2022

Abstract
The economic feasibility and technological improvement to produce biodiesel from microalga, Chlorella vulgaris is investigated. The biodiesel would help Malaysia replace diesel with renewable and sustainable energy in the transportation sector. Accordingly, the Malaysian Agriculture and Plantation Greenhouse Gas Model is modified and extended to incorporate algal farms. The model forecasts Malaysia’s market prices and quantities of major agricultural commodities between 2024 and 2064. The state of the art is a commercial algal biodiesel industry is incorporated into a price endogenous model of Malaysia’s agriculture, and algal farms can experience technological improvement. The simulation results indicate that algal biodiesel is not economically feasible. The biodiesel price must be RM2.80 per litre or higher for the algal farms to produce biodiesel. The biodiesel price could be lowered to RM0.60 per litre if the algal farms experience a one-percent annual boost in either algal harvest yield or productivity gain. The results indicate that harvest yield improvements boost biodiesel production more than productivity improvements. The feasibility depends on algal farms to process the leftover slurry and supply the domestic animal feed markets. At last, the biodiesel offsets CO2 transportation sector emissions while the alga recycles the CO2 from fossil fuel electric power plants, which help Malaysia sequester high levels of CO2.
Benefit analysis of 15 MW biomass direct-fired power cogeneration project
Authors: Xuan-He Su , Wei Lu 
Published: August 2022

Abstract
Biomass direct-fired power generation technology has many advantages in realizing large-scale harmless utilization of biomass resources. It is an effective way to achieve the goal of carbon peak and carbon neutrality. Taking the 15MW direct-fired biomass power generation project of typical unit configuration as an example, this paper constructs a CHP system with biomass as fuel to replace the traditional coal-fired boiler for power generation, analyzes its economic, environmental and energy-saving benefits. As shown by results, compared with the conventional coal-fired power plant, the system cannot only save more than 700000 tons of coal, but also reduce more than 1610.41 tons of carbon dioxide emission, more than 26.41 tons of sulfur dioxide emission and more than 3000 tons of smoke and dust every year. The adoption of a new boiler and straw thermal power technology can competently deal with the three major problems of energy use, environmental protection and farmer’s income increment, which is of great significance to realize the effective cycle of “resource product renewable resource” economy.
Performance study of savonius style wind turbines in a solar chimney power plant under guided flow environment
Authors: Apparavoo Pavitri , Roy Sukanta , San Yam Ke 
Published: August 2022

Abstract
With the uprise in global population and rapid industrialisation in developing nations, increasing energy demand to compensate for societal and industrial needs is inevitable. However, popular energy resources such as gas, petroleum, and coal, on which the global energy mix is heavily reliant, induce environmental pollution and global warming. Thus, clean energy sources, such as wind and solar, are increasingly sought to mitigate these adverse environmental effects and identify alternative sustainable and renewable energy technologies. The existing literature suggests minimal research has been conducted on the turbine component of the solar chimney power plant (SCPP). The present research proposes the study of a vertical axis wind turbine (VAWT), namely the Savonius-style wind turbine (SSWT), for power augmentation in the SCPP under the influence of guide vanes and shields that are installed in the solar collector area. A two-dimensional numerical analysis is performed using the ANSYS Fluent software. The SSWT and SCPP models used in the simulations were validated using numerical and experimental results from previous literature data. The angle of the guide vanes was varied while the dimensions of the SCPP model and the energy extractor considered were kept constant. The optimum turbine performance was identified based on the flow analysis and the power output of the rotary component from different guide vane configurations. The present study shows an overall improvement in the turbine’s performance coefficient and power output under the influence of the guide vane and shield in the SCPP. The turbine with the guide vane and shield at an angle of 45° enhanced the performance by 22.22% and power output by 22.25% compared to the turbine without the guide vane and shield.
Influence of pressure in RO filtration of crude biodiesel
Authors: Ramanan C J , Garg S , Bora B J , Buradi A , Roy S , Sharma P , Alom N 
Published: August 2022

Abstract
Considering the virtue of the environment, the usage of biofuel blended fossil fuels for transportation has speeded across the globe. The quality of biodiesel plays an important role in the efficiency of a diesel engine. The biodiesel by transesterification method produces crude biodiesel. The crude biodiesel contains impurities like glycerol, alcohol, catalyst, and water molecules that are suspended in the Fatty Acid Methyl Ester (FAME) molecule. These impurities in the crude biodiesel affect the performance of the engine with improper spray pattern, corrosion, nozzle blockage, affecting autoignition temperature and increase the NOx emission in exhaust gas. The shelf life of the stored biodiesel also gets affected by these left-over impurities. In order to overcome and refine it, a novel filtration of crude biodiesel using reverse osmosis membrane has been discussed. ANSYS Finite Element Analysis has been carried out for this investigation. Pressure in an increasing sequence of 100 kPa to 1500 kPa has been varied for the analysis of this multiphase flow through porous medium. The changes in velocity, pressure, density, and viscosity have been observed through the obtained contours. The results include the variation of velocity, density, and viscosity with reference to pressure. The study also indicated that the minimum pressure required for the filtration is 500 kPa. Further, the increase in viscosity and density after the filtration zone, represents the increase in the volume fraction of the impurities.