bluuebunny/crossref_metadata_2025_split huggingface dataset:
A damping method of grid strapdown inertial navigation system for polar region
Authors: Yuan Kefei , Yang Xiaohui , Tian Guopeng , Ben Yueyang 
Published: August 2024

Abstract
The conventional north-pointing inertial navigation system (INS) always points to the geographic north. However, when the system crosses the pole in the polar region, the geographic north quickly changes with 180 degrees, and the rate of change of the geographic north direction will be infinite. Secondly, when the latitude approaches to 90 degrees, which will lead to singularities in math for the angular velocity caused by vehicle motion. With its special definition and mechanization, the grid coordinate system can solve the above problems. Nevertheless, the error analysis of the grid strapdown INS shows that there are Schuler periodic and Foucault periodic oscillations, which badly degrade the accuracy of the strap-down INS. To address the problem, the external horizontal damping technology is adopted in this paper, wherein the external velocity is provided by the Doppler velocity log (DVL). Finally, the feasibility and the effectiveness of the external damping technology were verified by simulation.
Lidar pose estimation method based on factor graph optimization
Authors: Zhou Lin , Qiu Jian , Han Peng , Liu Dongmei , Luo Kaiqing 
Published: August 2024

Abstract
In the realm of computer vision and robotics, lidar SLAM is an important technical means. Lidar SLAM frequently serves to ascertain the spatial coordinates and orientation of mobile robots amidst their surroundings. In this paper, an adaptive factor graph optimization of the lidar pose estimation method using only point-to-line constraints is proposed. The algorithm only employs the measure of distance between a point and a line to construct the constraint problem and solves the pose through point-to-line ICP and the pose with higher accuracy by optimizing the factor graph. The robustness of mobile robots in indoor scenes is improved. According to different application scenarios, including street and gate, comparative experiments are carried out on the open-source data set M2DGR containing lidar data. Empirical findings demonstrate that the algorithm put forth enhances the precision of the lidar odometer when confronted with outdoor scenarios, thereby bolstering the resilience of mobile robots traversing urban thoroughfares.
A top-view indoor localization based on discrete distillation of CLIP
Authors: Zhao Aojie , Liu Yifan , Cheng Kun , Ma Aiping , Yu Jianguo 
Published: August 2024

Abstract
Indoor robot localization is a challenging problem in computer vision due to sensor obstacles in a crowded environment. Pure vision localization is increasingly popular since it does not require sensors other than low-cost cameras. We adopt a top-view camera setup, effectively avoiding the problem of positioning failure due to potential occlusion of front-view cameras. We use the distilling of a pre-trained large-scale vision language CLIP model to improve the performance degradation caused by the small data set size. Our solution achieved promising performance in our customized classification-based localization test data.
Digital twin for subcritical extraction driven by data-mechanism fusion
Authors: Yan Yuhang , Zhang Huibing , Pan Fang , Jai Fei 
Published: August 2024

Abstract
Addressing the key issue of low automation in the production process due to the complex associations involved in sub-critical extraction, this study focuses on sub-critical extraction as the subject matter. It integrates data models with mechanism models to construct a multidimensional digital twin model and conduct systematic research. By combining simulation methods with real-time experimental data, the study establishes multilevel machine characteristics of data, behavior, and mechanisms, and identifies data patterns. From the perspectives of the physical production line, virtual production line, and data transmission, it builds an interactive mapping platform for multi-source data and carries out corresponding application research.
Risk early warning model of tunnel engineering based on computer vision and CNN
Authors: Zhong Ming 
Published: August 2024

Abstract
Safety management in tunnel construction is important to ensure the safety of construction personnel and equipment. This paper proposes a tunnel engineering risk early warning model based on computer vision and convolutional neural network (CNN), aiming at realizing real-time monitoring and risk early warning of the construction site using modern image processing technology and deep learning algorithm. Many image data of tunnel engineering construction sites were collected, and the data were preprocessed and marked. Then, the paper designs a depth CNN model for feature extraction and risk identification of construction site images. Through the performance evaluation of the model on the training set and test set, the effectiveness and feasibility of the model in risk early warning are verified. The experimental results show that the proposed model achieves high accuracy, recall, and F1 score on the test set, which is superior to the traditional method based on image features and artificial rules. At the same time, we analyzed the prediction results of the model on the actual construction site images. We found that the model can effectively identify different risk factors and has strong generalization ability and practicability. To sum up, this study provides a new technical means and solution for the safety management of tunnel engineering, which has important theoretical and practical significance.
Precipitation nowcasting method based on Spatial-Temporal Dual Discriminators
Authors: Li Wenfeng , Zhou Xiao 
Published: August 2024

Abstract
The prediction of radar echo sequence images is a spatiotemporal sequence forecasting problem, which is one of the main challenges in precipitation nowcasting. Addressing issues such as poor extraction of spatiotemporal features by previous models and blurry image sequence predictions, this study proposes a Spatial-Temporal dual Discriminator Precipitation Nowcasting Model (STD-SNGAN) based on spectral normalization generative adversarial networks (SNGAN). The model utilizes multi-scale convolution modules (Inception) to extract spatial features from radar echo images and convolutional gated recurrent units (ConvGRU) to extract temporal features between feature maps. By introducing a hidden feature sampler to enhance the extraction capability of the convolutional gated recurrent units and designing spatial-temporal dual discriminators to constrain the generator’s predicted samples, the spatiotemporal forecasting ability is enhanced. Experimental results demonstrate that the proposed STD-SNGAN model outperforms other algorithms in critical success index (CSI) and probability of detection (POD) for high echo intensity and long-term regions.
Productivity evaluation method of offshore fractured reservoir based on artificial intelligence ChatGPT
Authors: Li Tingli , Yuan Zhiqian , Yang Wei 
Published: August 2024

Abstract
At present, the analogy method is usually used to determine the production capacity. Due to the strong heterogeneity of fractured reservoirs in the Bohai Sea, the prediction error is large and cannot meet the needs of scheme deployment. Therefore, based on the test productivity, the characteristic parameter λ was introduced to characterize fractured reservoir productivity, and the relevant data affecting productivity were preprocessed by the K-nearest algorithm and Z-Score standardization method. Then, the feature vector of parameter λ was selected by the average impurity reduction MDI feature selection method. Finally, in the GPT Building module of ChatGPT, the reservoir parameters and productivity test data are used to train GPT and the deep forest prediction model with parameter λ is built intelligently by GPT. This method is applied to the CFD oilfield productivity evaluation in the Bohai Sea, and the prediction error caused by the difference in reservoir physical properties is reduced. The coincidence rate between prediction and actual is more than 85%, which provides a new idea and method for the productivity evaluation of offshore fractured reservoirs.
Detection of rice pests based on RepMob-Yolo
Authors: Feng Jiale , Li Youxin , Li Zhizhong 
Published: August 2024

Abstract
Aiming at the disadvantages of high labor intensity, poor real-time performance, and low efficiency of traditional methods for identifying rice pests, a rice pest identification model based on RepMob-Yolo is proposed. MobileNetV3 is used as the backbone network, and the RepMob structure is used to replace the conventional downsampling structure in MobileNetV3, the GELU is used to replace the ReLU and the last four convolutional layers of MobileNetV3, the global average pooling layer and the fully connected layer are pruned to realize the lightweight design of the model. The DANet module is embedded to realize the extraction of effective information by the model, extract the semantic information of higher key features, and improve the recognition accuracy. The FPN structure is combined with the RepMob structure for feature extraction and prediction, which improves the operation speed of the model. The experimental results show that RepMob-Yolo achieves 97.68% accuracy, 96.00% recall, 96.82% prediction rate, and 146.37 f/s frame rate in the identification of public pest datasets, which provides technical support for accurate and real-time identification of rice pests.
Evaluation decision on green degree combination of typical welding processes for large-diameter thick plates
Authors: Di Fei , Zhang Kexu , Wu Jinjun , Li Jingying , Wang Ying , Qu Haoyuan 
Published: August 2024

Abstract
Aiming at the environmental impact of large-diameter thick plate manufacturing scenarios in the welding process, a comprehensive evaluation method combining AHP and TOPSIS is proposed to analyze and make decisions on the green welding process suitable for large-diameter thick plates. The optimal green welding process evaluation and decision-making index system for large-diameter thick plates is constructed. The system can be flexibly applied to different large-diameter plate welding scenarios by focusing on different optimization objectives, providing welders with a more suitable green welding process. The constructed model is applied to the seashore factory welding scenarios and general welding scenarios for comparative decision-making, and the effectiveness and feasibility of the proposed comprehensive decision-making evaluation method are verified by the actual data calculation results.
Design and implementation of power socket soldering robot arm system
Authors: Liu Jingqiao , Wu Shuqin , Lin Zicheng , Zhang Huimin , Fan Yong , Xie Wu 
Published: August 2024

Abstract
In order to solve the problem of high cost and low efficiency in manual welding of power socket welding components, a soldering robot arm system was developed. The gripper, 5-DOF manipulator, clamp, support, and control system were designed. The coordinate system of the robot arm was constructed, and the working space of the robot arm was analyzed by using the MATLAB robot toolbox. It was concluded that the x0 coordinate range of the welding working area was [175mm, 215mm], the y0 coordinate range was [55mm, -55mm], and the z0 coordinate was 90mm. At the same time, the motion trajectory of the robot arm was determined. Using Raspberry PI as a controller, the control system is developed by programming Python language. The prototype is made, and a welding test is carried out. The test shows that the system has completed the welding operation, and the welding effect is good.
Prediction of melting characteristics of sludge coal blending based on Multi-task Learning
Authors: Yu Zhiao , Zhang Kaibing , Li Bing , Tong Zhenbo 
Published: August 2024

Abstract
In response to the urgent need for sustainable energy solutions, this study pioneers the use of a Multi-task Learning (MTL) framework to predict the melting characteristics of sludge-coal mixtures, a key aspect in optimizing co-firing processes. The integration of sludge, a byproduct of wastewater treatment, with coal in co-firing processes, presents a promising avenue to enhance energy efficiency while reducing greenhouse gas emissions. Addressing the complexities of predicting combustion behavior due to variable chemical compositions and temperatures, the MTL approach enables learning from diverse datasets, ensuring accurate and generalizable predictions. Outperforming conventional machine learning models, our framework signifies a breakthrough in predictive accuracy. Despite facing challenges such as dataset limitations, the research underscores the framework’s potential for broad industrial application, aiming for future improvements that include dataset expansion and real-time data integration. This study not only advances machine learning applications in energy and environmental domains but also contributes to the global pursuit of sustainability.
Image style migration method based on transformer generative adversarial network combined with contrast learning strategy
Authors: Chen Xing 
Published: August 2024

Abstract
Aiming at the problems of large loss of image patch embedding information and lack of stylized features in the generated image during transformer style migration, a style migration model combining contrast learning with transformer backbone generative adversarial network is established. Firstly, overlapping patch embedding is used to serialize the input images to get richer features; then the style and content contrast loss of contrast learning is used to mine the non-significant information between stylized images to reduce the style and content loss; finally, a multi-scale discriminator discriminates the true and false of stylized images to optimize the generator to get more stylistic features. The results on MS-COCO and WikiArt datasets show that the model improves the effectiveness and transformation efficacy of stylized images in style migration.
Indicator and sign recognition in real electricity scenario
Authors: Wang Baigen , Dong Lanfang , Ma Tao , Huang Kexuan 
Published: August 2024

Abstract
A multitude of instruments and equipment are contained within the electricity scenario, and their operation leads to fluctuations in a wide range of parameters. These changes are typically represented through indicators and signs. This study introduces an efficient and robust method for recognizing these indicators and signs, utilizing a variety of image processing techniques, including the computation of image mean and variance, as well as template matching. Given the plethora of indicators and signs present on substation equipment, it is crucial to discern the illumination status of indicators at different locations and relay this information to the system based on the indicator’s significance. Considering the indoor setting, which is relatively uncomplicated, this study employs the variance and mean of the indicator area image for judgment. As for identifiers, which often vary in shape and color, this study uses template matching for identification, subsequently reporting the operational status of the equipment marked by this area. Experimental results demonstrate that compared to deep learning methods, traditional image processing algorithms yield superior outcomes while significantly reducing computational costs and time.
Predictive algorithm of instrument health condition based on linear regression and radial basis function neural network
Authors: Jiang Wenrui , Wang Yi , Lin Yiyong , Zhao Lifen , Luo Pingtao 
Published: August 2024

Abstract
Enterprises and institutions engaged in social production activities need to apply for mandatory verification of general instruments and meters on production equipment according to law, and its verification period is determined by national or local verification and calibration procedures, but the health condition in the calibration intervals is difficult to know. To improve equipment reliability, this paper relies on the linear regression model to research the health condition of general instruments and meters, uses MATLAB tools to analyze the data samples of typical equipment pressure gauge, and combines radial basis function neural network to form a complete algorithm, and finally provides a complete implementation scheme for the health condition prediction of general instruments and meters in service.
Potential for developing an intercropping system on oil palm fields in peatlands
Authors: Arifin Y F , Noor I , Budiman A , Wibowo A D 
Published: August 2024

Abstract
Planting using a monoculture method is believed to impact the diversity of plant species on oil palm plantations, especially on peatlands, so the discourse on cultivating oil palm plantations using a polyculture system is to enrich species and avoid fears of environmental damage. It is necessary to conduct a study to determine the potential impact of an intercropping system between oil palm and other crops on the Land Equivalent Ratio (LER) value, intra and interspecific competition between plants, and reducing soil fertility. Data was obtained through a literature review and Forum Group Discussion (FGD) in a quadruple helix system comprising academia, farmer groups, district plantation services, and companies. Various literature studies showed that polyculture planting with an intercropping system reduces or inhibits palm oil production in peatland, but it can increase LER > 1.5. This is in line with the results of the FGD, which shows that planting using an intercropping system causes delays in palm oil production; consequently, the farmers and palm oil companies prefer monoculture planting. However, from the results of the FGD, a solution was obtained to overcome the above problems by arranging the planting distance when planting was planned, namely with a broader distance of 10 x 10 m or with a population of 100-110 plants per hectare, so that there is enough space to carry out intercropping and reduce competition. The results showed that polyculture planting with an intercropping system could also enrich plant species, apart from having the potential to increase LER > 1.5.
Life cycle assessment of oil palm plantation in Riau province: evaluating environmental impacts
Authors: Yudha Y S , Biney M , Tarigan S D 
Published: August 2024

AbstractIndonesia possesses palm oil as one of its strategic commodities, and it plays a crucial role in its economic growth. Over the years, the expansion of land and the production of Fresh Fruit Bunches (FFB) of palm oil has consistently increased, accompanied by a rise in land area and crude palm oil (CPO) production. The pivotal role of Indonesia as the world’s largest palm oil producer must be highlighted through sustainability studies, especially in environmental aspects. Life cycle assessment is a technique utilized to evaluate the environmental implications of products, processes, and services, spanning their complete life cycle from the extraction of raw materials to waste management. Life Cycle Assessment (LCA) encompasses various environmental impact categories, providing a comprehensive understanding of the ecological contribution of a product or service. This research assesses the environmental impact of oil palm plantation activities in Riau province, Indonesia. The research’s boundary is set to a cradle-to-gate analysis within the oil palm plantation subsystem. The Life Cycle Assessment was conducted utilizing the SimaPro 9.5.0.0 software employing the ReCiPe 2016 method. The total emissions generated during the production process of 5 tons of Fresh Fruit Bunches (FFB) amount to 1634.22 kg CO2eq/5 tons of FFB. The major contributors to emissions are the production and use of inorganic fertilizers. Specifically, the types of fertilizers responsible for the high greenhouse gas emissions are KCl, followed by phosphorus and nitrogen fertilizers. Besides the potential for global warming, human carcinogenic toxicity is another potential impact, primarily from fertilization. The findings in this study can serve as scientific evidence in formulating solutions for sustainable oil palm plantations, particularly in reducing greenhouse gas emissions. An environmentally conscious approach to lowering emissions requires meticulous scrutiny and implementation of precision farming. It chiefly involves keeping a close watch on the quantity of fertilizer used in plantation settings and promoting using fertilizers with lower carbon footprints.
Does consumer scepticism impact purchase intention toward palm oil as a food product?
Authors: Haro A , Setiawan M , Sunaryo  , Hussein AS 
Published: August 2024

Abstract
In recent years, the production of vegetable oil crops has expanded more quickly than other food or industrial crops. With a negative impact on the environment and the viability of producing communities, palm oil primarily grown in tropical regions has emerged as the most traded and produced oil globally. Palm oil is increasingly used in many products despite being linked to several troubling ecological, social, and health-related issues. Customers are given a wrong impression of the industry by popular opinion. To examine the reasons behind this disparity in the instance of palm oil, the current study uses an online survey to investigate the influence of consumer attitude and scepticism toward Indonesian consumers’ purchase intention (N = 200). The result showed that consumers’ intention to purchase palm oil as a food product has been influenced by increasing consumer attitude and decreasing consumer scepticism. An increase in consumer intentions to buy palm oil as a food product is driven by this alignment, which gets stronger. The findings of this study provide important new information for developing production and marketing strategies that will improve consumers’ behavioural intention to use vegetable oils.
Characteristics of Arabica and Robusta Spent Coffee Grounds Oil Extract with Different Solvents
Authors: Kresna M S , Windy R , Suci A P , Bovi W H 
Published: August 2024

Abstract
High coffee consumption contributes to waste, such as spent coffee grounds (SCG). SCG contains up to 20 % oil, with the main oil content being linoleic and palmitic fatty acids. So, SCG has the potential to be used in valuable products. This study aims to characterise Arabica and Robusta SCG oil using maceration methods with different solvents (hexane, ethanol, and petroleum benzene) to obtain maximum coffee oil as an alternative. Arabica and Robusta SCG characterisation comprises the composition content, oil yield, fatty acid profile, and viscosity. The results showed that Arabica SCG oil yield was higher than Robusta, with hexane becoming the most efficient solvent to extract the oil, with 28,87% and 25,57%, respectively. The lowest free fatty acid (FFA) found in Arabica SCG oil extract using hexane is 1,95%, which consists of palmitic acid (9.41%), linoleic acid (5.37%), and stearic acid (4.7%). The highest viscosity is Arabica SCG oil extract using hexane, which is caused by the large amount of long-chain fatty acids extracted by the solvent, such as palmitic acid and linoleic acid. The FFA content of Arabica SCG oil extract using hexane has the potential to be edible oil (cooking oil) and biodiesel.