bluuebunny/crossref_metadata_2025_split huggingface dataset:
Finite Element Method Analysis for Differential Case on Vehicles Based on ANSYS Software
Authors: Wang Yanbo 
Published: July 2022

Abstract
With the continuous development of the automotive industry and the rising level of the country’s economy, there are increasing demands for vehicles’ safety, economy, comfort, and durability. Therefore, it is important to take vehicles’ mass reduction and product life into consideration. Among the most popular factors: lightweight design, structural optimization, and analysis based on software are important and become growing mature in studies and research. Most scholars have focused their research on the vehicle drive system, with very few specializing in the structure and performance of the differential component. This paper will focus on and unify structural design, lightweight design of a differential case, Ansys finite element method (FEM) analysis of stress, fatigue, and distortion, and optimization to obtain a safe structure with less mass. Initially, construct a geometrical model of the differential case using 3D modelling via SolidWorks software. In addition, mechanical performance analysis of the differential case under three operating occasions is conducted based on Ansys software. Meanwhile, exploring the material selection and shape design methods. Ultimately, the results are justified, and gaining aluminium has longer life. QT450-10 ductile iron has better safety factors, strength, and stiffness. Lightweight design and analysis are realized supporting further research.
Research on the Application of Object Detection in the Installation of Spacer Bars
Authors: Zhu Yunxiang , Wang Fa , Chen Qianxi , Li Ling 
Published: July 2022

Abstract
The correct installation of spacer bars is an important part of ensuring people’s daily life. This paper studies and implements a spacer bar installation confirmation system based on a deep learning object detection algorithm, which makes up for the deficiencies in the installation. Among the object detection algorithms, Faster-RCNN has higher detection accuracy, is more accurate than one-stage and can detect multi-scale and small objects. Through historical use, it is found that Faster-RCNN is excellent in detecting multi-class image sets tasks. For personal data sets, only fine-tuning is needed to achieve better results, and it also has a fast detection rate. Therefore, this paper is based on the Faster-RCNN algorithm to realize the detection and confirmation of the plug and rubber during the installation of the spacer bar.
Unordered Image Stitching Based on the Registration Index Table
Authors: Si Zhichao 
Published: July 2022

Abstract
In order to solve the problem of large amount of calculation and time-consuming in the process of unordered image stitching, we propose an image stitching algorithm based on the image registration index table in this paper. Firstly, we construct the matching index sequence based on the similarity of the matching area. Then we search for image matching pairs according to the matching index sequence, carry out image registration and stitch directly, and generate the registration index table. For image stitching, we build a binary tree model assisted by the image registration index table, we need to search for matching pairs and carry out registration and stitching according to the registration index table generated by each layer. Compare to the current research on disordered image stitching, our method significantly reduces the times of feature extraction and matching by at least half, therefore, it greatly improves the efficiency of image stitching.
Study on projectile number of anti-aircraft gun based on future airspace window
Authors: Qiu Lei , Hao Dawei 
Published: July 2022

Abstract
The future airspace window can improve the overall damage capability of the anti-aircraft gun by configuring the weapon’s aiming point to form a regional kill surface. In the general process of airspace window shooting in the future, solving the number of projectiles in the area is an important step, and studying the optimal projectiles can effectively improve the firing efficiency of weapons. By analyzing the deficiency of determining projectile number based on firing distance, a method of projectile number solving based on firing probability density is proposed, and the feasibility of this method is verified by simulation, which provides a new reference for future shooting application of airspace window.
Prediction and Simulation of cold rolled strip thickness based on PSO-SVM-AdaBoost
Authors: Liu Chen , Li Xisheng , You Jia 
Published: July 2022

Abstract
In order to improve the quality of cold-rolled strip production products, this paper focuses on the problem of thickness prediction accuracy in the production process of cold-rolled strip steel. The rolling data is obtained from the simulation model of the MATLAB. Firstly, this paper comprehensively considers the main influencing factors affecting the cold rolling thickness, secondly, analyses the thickness related parameter variables and combines with the actual production environment, finally a cold rolling thickness prediction model based on PSO-SVM-AdaBoost is proposed. The model is based on the SVM model. The particle swarm algorithm(PSO) optimizes the penalty parameters and kernel parameters of the SVM model. The AdaBoost algorithm integrates weak predictors into strong predictors to improve the prediction performance of the algorithm. In order to illustrate the prediction effect of the model, compared with the prediction results of BP neural network, SVM, PSO-SVM and other models, the experimental results show that the PSO-SVM-AdaBoost model has better prediction performance.
Research Status and Development Trend of Image Restoration Technology
Authors: Yu Lin , Guo Junbin , Yu Chuanqiang 
Published: July 2022

Abstract
Defocus of the imaging system, relative motion between the equipment and the object or inherent defects of the equipment will lead to the degradation of image quality. Usually, image restoration is required before image processing, and the results of restoration technology affect the effect of image processing. In order to study the effect of image restoration technology, the image restoration technology was systematically sorted out. According to the different restoration models, the image restoration technology is divided into the method based on regularization and the method based on Kalman filter, and the two methods are summarized and explained respectively. By analyzing the restoration models established by the two methods and their solving process, it is found that the method based on regularization has advantages in retaining image information, and the method based on Kalman filter has advantages in terms of speed. The development trend of image restoration technology is analyzed from the summary of the two restoration methods, and suggestions and thoughts are put forward for the development of image restoration technology. In the future, image restoration technology should make use of deep learning algorithm, combined with the actual environment and industry characteristics, to achieve intelligent, practical and domain.
A New Method of Extracting RGB V-I Trajectory for Non-intrusive Load Monitoring
Authors: Liu Rui , Sun Mingyang , Zheng Zhe , Cui Wenpeng , Gong Xiangfeng , Yang Bokuan , Qi Bing 
Published: July 2022

Abstract
Existing methods of extracting V-I trajectory in RGB space used as load signatures for non-intrusive load monitoring (NILM) are complex and not suitable for edge computed devices. To solve this problem, we propose a new method to obtain the RGB V-I trajectory. The new method is simple and efficient by using the time derivatives of instantaneous reactive current and voltage to map the grayscale V-I trajectory to RGB space. Then, a lightweight CNN model, ShuffleNetV2, is selected to carry out a transfer learning study for NILM problem. Using RGB V-I trajectory obtained by using the method in this paper as load signature achieves a 5.1% improvement in F1-score than using grayscale V-I trajectory. A V-I trajectory of larger resolution avails the improvement of transfer learning performance, and the model trained on RGB V-I trajectory seems to be less affected by input resolutions for a smaller variance in F1-score among various input resolutions.
Research on High Resolution Image Processing System Based on FPGA Technology
Authors: Xiao BoXue 
Published: July 2022

Abstract
A This paper is the focus of the dissertation and focuses on some commonly used image filtering algorithms. The commonly used image filters are median filter, Gaussian filter and edge detection algorithm. In this chapter, the FPGA implementation of grayscale processing module is studied first, then median filtering and Gaussian filtering are studied, and a fast median filtering algorithm and displacement-optimized Gaussian filtering algorithm are proposed, and the feasibility of the algorithms is demonstrated by simulation experiments. Then the edge detection algorithm is studied and the FPGA method to implement each algorithm is designed, in which an improved algorithm is proposed for Laplace algorithm to improve the detection accuracy, and finally the Sobel operator is also shifted optimized to save the hardware resources of the system.
The use of bilinear interpolation filter to remove image noise
Authors: Wang Nan 
Published: July 2022

Abstract
The bilinear interpolation filter method is introduced to reduce the image noise in this work that contains the following three procedures. Firstly, the algorithm principle of bilinear interpolation filter is analyzed. On this basis, a case study is investigated to validate the effectiveness of bilinear interpolation filter. The signal-to-noise ratio, SNR, is finally applied for the image quality evaluation. Form the above procedures we find that the SNR to the image after processed by bilinear interpolation filter method is larger than that to the original image, which illustrates that the bilinear interpolation filter is an effective method to remove image noise.
A simple macro-scale artificial lateral line sensor for the detection of shed vortices
Authors: Scott Elliott , Hauert Sabine 
Published: July 2022

Abstract
Underwater robot sensing is challenging due to the complex and noisy nature of the environment. The lateral line system in fish allows them to robustly sense their surroundings, even in turbid and turbulent environments, allowing them to perform tasks such as shoaling or foraging. Taking inspiration from the lateral line system in fish to design robot sensors could help to power underwater robots in inspection, exploration, or environmental monitoring tasks. Previous studies have designed systems that mimic both the design and the configuration of the lateral line and neuromasts, but at high cost or using complex procedures. Here, we present a simple, low cost, bio-inspired sensor, that can detect passing vortices shed from surrounding obstacles or upstream fish or robots. We demonstrate the importance of the design elements used, and show a minimum 20% reduction in residual error over sensors lacking these elements. Results were validated in reality using a prototype of the artificial lateral line sensor. These results mark an important step in providing alternate methods of control in underwater vehicles that are simultaneously inexpensive and simple to manufacture.
A deep learning based classification of atmospheric circulation types over Europe: projection of future changes in a CMIP6 large ensemble
Authors: Mittermeier M , Weigert M , Rügamer D , Küchenhoff H , Ludwig R 
Published: July 2022

Abstract
High- and low pressure systems of the large-scale atmospheric circulation in the mid-latitudes drive European weather and climate. Potential future changes in the occurrence of circulation types are highly relevant for society. Classifying the highly dynamic atmospheric circulation into discrete classes of circulation types helps to categorize the linkages between atmospheric forcing and surface conditions (e.g. extreme events). Previous studies have revealed a high internal variability of projected changes of circulation types. Dealing with this high internal variability requires the employment of a single-model initial-condition large ensemble (SMILE) and an automated classification method, which can be applied to large climate data sets. One of the most established classifications in Europe are the 29 subjective circulation types called Grosswetterlagen by Hess & Brezowsky (HB circulation types). We developed, in the first analysis of its kind, an automated version of this subjective classification using deep learning. Our classifier reaches an overall accuracy of 41.1% on the test sets of nested cross-validation. It outperforms the state-of-the-art automatization of the HB circulation types in 20 of the 29 classes. We apply the deep learning classifier to the SMHI-LENS, a SMILE of the Coupled Model Intercomparison Project phase 6, composed of 50 members of the EC-Earth3 model under the SSP37.0 scenario. For the analysis of future frequency changes of the 29 circulation types, we use the signal-to-noise ratio to discriminate the climate change signal from the noise of internal variability. Using a 5%-significance level, we find significant frequency changes in 69% of the circulation types when comparing the future (2071–2100) to a reference period (1991–2020).
A cascading reaction by hydrological spatial dynamics alternation may be neglected
Authors: Hao Ying-Ying , Zhao Bin , Wu Wan-Ben , Zheng Jun-Yi 
Published: July 2022

Abstract
Water regime changes play a critical role in the structure and function of watershed ecosystems. However, most previous studies focused only on static fixed-point quantitative change at a given time, ignoring the hydrological spatial distribution states of wetting and drying and lacking dynamic indicators for characterization. Here, we constructed a new dynamic index to characterize water alternation of wetting and drying of Poyang Lake, the largest freshwater lake in China, using all available Landsat images and Google Earth Engine from 1987 to 2020. In addition, we analyzed the relative contribution of the dam to water regime changes according to geographical characteristics, and a neglected cascading reaction was found between the upstream and downstream of the basin. The results showed that the alternation of Poyang Lake significantly intensified, and varied with different years. Although the apparent regulation of the Three Gorges Dam (TGD) has no significant impact on the maximum storage of the downstream Poyang Lake and the runoff of the further Yangtze Estuary, the TGD has changed the water spatial alternation of Poyang Lake, resulting in a cascading reaction to the runoff of the Yangtze Estuary.
Size distribution and length-weight relationship of invasive species flowerhorn (Cichlasoma trimaculatum (Günther, 1867) in Lake Mahalona, South Sulawesi
Authors: Nasution S H , Akhdiana I , Muchlis A M , Cinnawara H T 
Published: July 2022

Abstract
Lake Mahalona is one of the lakes located in the Malili Complex, South Sulawesi and is included in the Natural Tourism Park Area. Cichlasoma trimaculatum are ornamental fish from the Cichlidae family that become invasive in Lake Mahalona. A total of 29 fish species were recorded in this lake, and most of them are endemic fish. It is estimated that native and endemic fish species and populations will decline due to invasive alien species intentionally or unintentionally in Lake Mahalona. The study to determine the size distribution and length-weight relationship of C. trimaculatum was conducted in Mahalona Lake. The study was conducted at 11 stations in October 2020. Experimental gill nets used to catch Bilih. The total length of C. trimaculatum ranged from 10.0 ± 1.57 cm (Kusapa and Badilla stations) to 18.0 ± 1.57 cm (Benu station), and the weight ranged from 19.0 ± 19.2 g (Kusapa station) to 140.0 ± 19.2 g (Muara station). The correlation coefficient (r) value for this fish ranges from 0.8685 - 0.9691. The growth pattern of C. trimaculatum was a negative allometric characteristic. An exception in Muara dan Tandu Mata stations was positive allometric.
Enrichment of commercial feed with new formula products on the growth, yield, and mortality of the giant gourami Osphronemus goramy
Authors: Syandri H , Azrita  
Published: July 2022

Abstract
Background and objective: The giant gurami (Osphronemus goramy) is considered the most desirable freshwater species in Indonesia, mainly for food security. This study evaluated the effectiveness of newly formulated products containing water, coconut, palm sap sugar, and fungus used for enriched commercial feed and their impact on gurami sago’s (local strain) growth performance, yield, and mortality. Methods: A total of 100 g of palm sugar are cooked with 1,000 ml of freshwater for fifteen minutes at a temperature of 60 °C. Furthermore was added 2,000 ml of mature coconut water. For every 1,000 ml of a mixture of mature coconut water and palm sap sugar solution was added 2 g of Aspergillus niger (called product P1), 2 g of Rhizopus oligosporus (product P2), and 2 g of Saccharomyces cerevisiae (product P3). Commercial fish feed pellets enriched with P1, P2, and P3 were designated as P1, P2, and P3 diets. At the same time, the commercial feed added with freshwater is called P4 feed (placebo). The dosage of each product is 300 ml/kg of feed. Juvenile sago gourami (initial weight 50±2.5 g and total length 13.2±0.4 cm) were stocked in triplicate (0.5x0.5x0.5 m) in a freshwater concrete pond with a stocking density of 30 individuals. /net, an initial feeding rate of 3% per day for 90 days of the experiment. Results: The weight gain ranged from 172.43 to 215.6%, the specific growth rate increased from 0.60 and 0.75%/day. The coefficient of thermal growth increased from 27.26 to 32.83. At the same time, yield ranging from 14.88 to 21.03 g/L and mortality for 90 days of the experiment decreased from 22.22% to 6.66%. The coefficient of variation in weight was between 0.68% and 1.30%. Conclusion: Giant gourami juvenile survived and grew well in diet P2, moderate growth in diet P3, and diet P1; the lowest growth was recorded in diet P4.
Optimizing crude protein production from minute duckweed (Lemna perpusilla Torr) grown in varied NPK based medium
Authors: Satya A , Chrismadha T , Satya A D M , Satya I A 
Published: July 2022

Abstract
A suitable growth medium has to be developed to implement the potential use of minute duckweed (L. perpusilla Torr.). This tiny floating aquatic plant has been widely promoted as a source of protein. This research aims to find the optimal NPK based media for enhancing plant protein productivity. The duckweed was grown in twelve various NPK based media formulations (respectively named as Box-A to box-L, each has a surface area of 0.0945 m2), and the crude protein productivity was examined. Determination of crude protein through the total biomass nitrogen (TN) analysis was performed using the modified Salicylic acid method, which combined alkaline persulphate digestion before the absorbance measurement with a UV-Vis spectrophotometer. The highest crude protein productivity in dry weight was 18.02 mg/m2.day or estimated equal to 65.76 kg/ha/year found on the Box D (consisted of blending Terminalia catappa leaf extract as bioalgacide agent and trace elements in NPK of 100 mg/L). Meanwhile, the highest Total Nitrogen removal (98.6%) was observed on box F (mixed of NPK 100 mg/L, Terminalia catappa leaf extract, trace elements, and molasses solutions). The alternative formulation was blending NPK 300 mg/L with trace element (box L) that enable to yield of crude protein as much as 30.97 mg/m2.day or estimated equal to 113.04 kg/ha.year. These findings proved that duckweed biomass could be potentially used as a protein source material and improve water quality in aquaculture. Further, the use of three-dimensional models to demonstrate the effect of the remaining concentration of TN and TP in medium against crude protein content in the biomass was also discussed.
Composition and Abundance of Phytoplankton in Segara Anakan Lagoon and Cibeureum River, Central Java
Authors: Sulawesty F , Larashati S , Triyanto  , Dina R , Samir O , Widoretno M R 
Published: July 2022

Abstract
Segara Anakan Lagoon and Cibereum River are located south of Java Island, precisely on the border between West Java Province and Central Java. This study aimed to reveal phytoplankton community structure related to water quality. Observations were made in May 2021 at seven locations. Ten liters of water were filtered using a plankton net with a mesh size of 53 μm and preserved using Lugol. The parameters observed were the abundance and composition of phytoplankton, dissolved oxygen, pH, temperature, turbidity, conductivity, total dissolved solids, and salinity. Phytoplankton community structure was analyzed using the Diversity Index, Evenness Index, and Dominance Index. The phytoplankton found consisted of Bacillaryophyta, Chlorophyta, Cyanophyta, Dinophyta, and Euglenophyta. Bacillaryophyta had more diversity of species than other phyla, with an abundance range from 38–4,037 individuals/L. Station 7 (at Cibereum River) had a different phytoplankton composition, with a higher abundance and species number, and Oscillatoria sp. as a dominant species. This station has low diversity and evenness index values (0.649 and 0.054, respectively), and the dominance index close to 1 (0.858) indicated unstable habitat for the phytoplankton community. Dissolved oxygen, pH, conductivity, total dissolved solids, and salinity were lower, while turbidity was higher than other stations.
The Ability of Some Vegetables to Reduce Nutrients from Fish Culture Waste to Support Environmentally Friendly Floating Net Cage Culture
Authors: Astuti L P , Warsa A , Krismono  
Published: July 2022

AbstractAquaculture activities with floating net cages are considered sources of organic pollution through the accumulation of uneaten feed and excretion of fish metabolism in reservoir waters. Thus it is required to have aquaculture technology that can reduce the pollutant load of floating net cages. One of the technologies used is plants to absorb nutrients, such as aquaponics technology applied in reservoir waters. This study aimed to determine the ability of vegetables to reduce nutrients from aquaculture activities. The study was conducted in 2016 in a laboratory-scale experiment using green spinach (Amaranthus hybridusL), red spinach (Amaranthus tricolorL), pakcoy (Brassica rapa), and water spinach (Ipomea reptans) to treat nutrient waste from a carp fish culture. The results showed that the highest NO2removal efficiency was pakcoy (98.44%), followed by water spinach (88.15%). The highest removal efficiency of NO3was water spinach (69.49%). The highest removal efficiency of PO4was also water spinach (48.38%), while the highest removal efficiency of total organic matter was in water spinach (42.86%) and pakcoy (51.28%). Water spinach plants have the highest ability to absorb nutrients N, P, and organic matter so that they can be used as nutrient-reducing plants in friendly floating net cage culture.