Merging Method Research Articles

A Collaborative Merging Method for Connected and Automated Vehicle Platoons in a Freeway Merging Area with Considerations for Safety and Efficiency.

To solve the problems of congestion and accident risk when multiple vehicles merge into the merging area of a freeway, a platoon split collaborative merging (PSCM) method was proposed for an on-ramp connected and automated vehicle (CAV) platoon under a mixed traffic environment composed of human-driving vehicles (HDV) and CAVs. The PSCM method mainly includes two parts: merging vehicle motion control and merging effect evaluation. Firstly, the collision avoidance constraints of merging vehicles were analyzed, and on this basis, a following-merging motion rule was proposed. Then, considering the feasibility of and constraints on the stability of traffic flow during merging, a performance measurement function with safety and merging efficiency as optimization objectives was established to screen for the optimal splitting strategy. Simulation experiments under traffic demand of 1500 pcu/h/lane and CAV ratios of 30%, 50%, and 70% were conducted respectively. It was shown that under the 50% CAV ratio, the average travel time of the on-ramp CAV platoon was reduced by 50.7% under the optimal platoon split strategy compared with the no-split control strategy. In addition, the average travel time of main road vehicles was reduced by 27.9%. Thus, the proposed PSCM method is suitable for the merging control of on-ramp CAV platoons under the condition of heavy main road traffic demand.

Ensemble of 6 DoF Pose estimation from state-of-the-art deep methods.

Deep learning methods have revolutionized computer vision since the appearance of AlexNet in 2012. Nevertheless, 6 degrees of freedom pose estimation is still a difficult task to perform precisely. Therefore, we propose 2 ensemble techniques to refine poses from different deep learning 6DoF pose estimation models. The first technique, merge ensemble, combines the outputs of the base models geometrically. In the second, stacked generalization, a machine learning model is trained using the outputs of the base models and outputs the refined pose. The merge method improves the performance of the base models on LMO and YCB-V datasets and performs better on the pose estimation task than the stacking strategy.

Event-driven sorting algorithm-based Monte Carlo method with neighbour merging method for solving aerosol dynamics

A new event-driven sorting algorithm-based merging Monte Carlo (SAMMC) method is proposed and developed for solving the general dynamic equation in aerosol dynamics. A neighbour merging method is proposed to maintain a constant-volume and constant-number scheme with minimal interference to the numerical particle population, where absolute volume difference (AVD) and relative volume difference (RVD) are used as the crucial merging criteria. The SAMMC method can be used for simulating all aerosol dynamic processes with very high computational accuracy, especially effective in those aerosol dynamic processes generating additional numerical particles. In the present study, comprehensive computational conditions are used to study their impacts on computational accuracy and efficiency by comparing the SAMMC method to previous MC methods and analytical solutions. Numerical results show that the SAMMC method has excellent agreement with analytical solutions for all specified cases of different aerosol dynamic processes and shows higher computational accuracy than equal-weight-based MC methods. In addition, the computational accuracy of the SAMMC method in the total particle number concentration is much higher than those of the weighted fraction Monte Carlo (WFMC) method and sorting algorithm-based merging weighted fraction Monte Carlo (SAMWFMC) method in non-homogeneous coagulation. The SAMMC method can also achieve the same computational precision as the multi-Monte Carlo (MMC) method at only slightly higher computational cost in homogeneous coagulation. More importantly, the SAMMC method can deal with breakage-related processes and simultaneous coagulation and nucleation with very high computational accuracy and efficiency, while the numerical results of the MMC method may significantly deviate from analytical solutions due to the introduction of systematic errors. Furthermore, the RVD can achieve higher computational accuracy in multi-breakage modelling than AVD, but AVD and RVD have almost the same computational efficiency and accuracy in other aerosol dynamic processes.

An improved differential bond energy algorithm with fuzzy merging method to improve the document clustering for information mining

AbstractThe vast and diversified text materials on the internet in recent years have drastically increased the importance of information mining. By organizing documents into cohesive groupings, a document clustering method is a suitable tool for dealing with massive amounts of documents. Text documents, on the other hand, include sparse and uninformative features like noise, unrelated, and unneeded features, which reduce the efficiency of the document clustering methods. For noise removal, this work employs the hyperclique pattern‐based data cleaner pre‐processing approach. Then, differential bond energy algorithm (DBEA) is combined with a fuzzy merging approach and termed improved differential bond energy algorithm with fuzzy merging (IDBEFM) to handle the issues present in text document clustering. It seeks to discover and display natural variable clusters among large amounts of data. IDBEFM clusters are documented in three steps: in the first step, a cluster similarity matrix is instantiated by applying the DBEA. The second step develops an innovative technique for automatically dividing the cluster matrix into compact cohesive clusters, and in the third step, a fuzzy merging approach is used for combining identical clusters by the correlations and interrelationships between the resultant clusters. The test findings demonstrated that the accomplishment of the proposed technique is much superior to standard clustering algorithms.

Evaluation of Radar Precipitation Products and Assessment of the Gauge-Radar Merging Methods in Southeast Texas for Extreme Precipitation Events

Many radar-gauge merging methods have been developed to produce improved rainfall data by leveraging the advantages of gauge and radar observations. Two popular merging methods, Regression Kriging and Bayesian Regression Kriging were utilized and compared in this study to produce hourly rainfall data from gauge networks and multi-source radar datasets. The authors collected, processed, and modeled the gauge and radar rainfall data (Stage IV, MRMS and RTMA radar data) of the two extreme storm events (i.e., Hurricane Harvey in 2017 and Tropical Storm Imelda in 2019) occurring in the coastal area in Southeast Texas with devastating flooding. The analysis of the modeled data on consideration of statistical metrics, physical rationality, and computational expenses, implies that while both methods can effectively improve the radar rainfall data, the Regression Kriging model demonstrates its superior performance over that of the Bayesian Regression Kriging model since the latter is found to be prone to overfitting issues due to the clustered gauge distributions. Moreover, the spatial resolution of rainfall data is found to affect the merging results significantly, where the Bayesian Regression Kriging model works unskillfully when radar rainfall data with a coarser resolution is used. The study recommends the use of high-quality radar data with properly spatial-interpolated gauge data to improve the radar-gauge merging methods. The authors believe that the findings of the study are critical for assisting hazard mitigation and future design improvement.

A framework for merging precipitation retrievals and gauge-based observations based on a novel concept namely virtual gauges

The merging of multi-source precipitation retrievals (PRs) and gauge-based observations (GO) provides a new opportunity for precipitation field estimation. However, the uncertainties associated with PRs may become relatively more evident when the gauge network better captures the spatial distribution of the rainfall fields. To dynamically balance the utilisation of PRs, we propose a merging framework based on a novel concept; namely, the virtual gauge, where the grid cells utilising PRs are regarded as virtual gauges, and the framework is henceforth referred to as VG. The main steps were as follows: i) determine the locations of virtual gauges from multi-source PRs, ii) estimate rainfall at virtual gauges using a basic merging method, and iii) spatially interpolate using both actual and virtual gauges. Accordingly, the case study employed random forest and inverse distance weight as the basic merging and interpolation methods of VG. Evaluation using real world data over a region where nearly each 0.1° grid cell contains a ground gauge indicates that VG improves around 7–11% over its basic methods and improves around 32–240% over its inputting PRs. VG performed better and was more stable than the basic methods under various gauge densities, rainfall intensities, and rainfall distributions. The results showed that VG could supplement the spatial information of rainfall fields missed by the gauge network and reduce interference caused by the uncertainties of PRs. Overall, the framework integrates the advantages of existing merging and spatial interpolation methods by adjusting the number, locations, and rainfall estimations of virtual gauges.

Konsep al-Talfīq dan Hukumnya dalam Satu Ibadah Menurut Perspektif Empat Mazhab

This study aims to know and understand the concepts and laws of al-Talfīq in worship according to the perspectives of the four schools of thought. The problems that researchers raise in this study are; what are the concepts and laws of al-Talfīq in worship according to the perspective of the four schools of thought? This research uses a type of qualitative descriptive research (non-statistical), which focuses on the study of manuscripts and texts, using historical normative and philosophical approaches. The research results found are as follows; Talfiq is a method of merging two or more schools of thought to issue a new opinion (qaul al-Jadi). In addition, al-Talfiq is sometimes used by someone to get out of a difficult position by taking many different opinions without any superiority (tarjīḥ) between them. Regarding the law, the scholars have different opinions, some allow it absolutely, absolutely forbid it, and some allow it and some others prohibit it depending on certain criteria or conditions. Regarding the law, the scholars have different opinions, some allow it absolutely, absolutely forbid it, and some allow it with conditions, that is, there is no reason to seek the lightest thing without excuse, it doesn't cancel the legal judge, and it doesn't disagree with the consensus opinion.

Effects of coal bedding dip angle on hydraulic fracturing crack propagation

As a commonly used and effective technology for increasing the permeability of coal–rock reservoirs, hydraulic fracturing has been widely used in engineering sites to realize the efficient exploitation and utilization of gas resources in coal–rock reservoirs. The core of hydraulic fracturing is the initiation, propagation, and path of hydraulic cracks. In this paper, the combination of true triaxial physical test and numerical simulation is used to study the influence of coal bedding characteristics on the crack propagation of hydraulic fracturing and to discuss the important role of bedding in hydraulic crack formation. Results show that the control effect of the coal bedding dip angle on the hydraulic crack propagation under the same stress conditions is stronger than that of the maximum principal stress, and the control effect of the bedding on the crack propagation is weaker under the bedding dip angles of 0° and 90°. Reasonable fracturing fluid displacement setting is conducive to the formation of complex hydraulic fracture network structure, small displacement is conducive to the opening of primary natural fractures, and large displacement is conducive to hydraulic cracks that pass through the structural surface and the coal–rock interface. Global and local methods of finite element mesh embedding zero-thickness cohesion element and a pore-pressure node merging method to simulate fracturing are established using Python language and ABAQUS numerical analysis platform, respectively. The numerical simulation results suggest that the main fractures are formed along the principal stress direction, and the secondary branch fractures are formed along the bedding direction under the condition wherein the coal bedding dip angle is 30°. Under the conditions of different stress fields and fracturing fluid discharges, the controlling effect of bedding on hydraulic fracture is closely related to the fracturing parameters.

Machine learning methods for results merging in patent retrieval

PurposeIn federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.Design/methodology/approachThe methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.FindingsThe effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.Originality/valueIn this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

Obstacle Avoidance Design of Aerial Robot Combining A star algorithm Algorithm and Optical Dynamic Capture Method

The traditional A star obstacle avoidance algorithm is based on binocular cameras for indoor positioning, which requires rich obstacle feature points and has the risk of explosion in a single indoor environment. In this paper, the method of merging the A star algorithm and optical dynamic capture is proposed. By pasting Marker points on key moving parts, the actual position of the aerial robot is calculated with the optical dynamic capture system, and compared with the target position planned by the A star algorithm and corrected in real time during a flight. Through the experimental analysis of the original acceleration of the aerial robot and different position between the target point and the actual point in the flight process, the stability and flight accuracy of the aerial robot in the flight process can be significantly improved, which has good practical application value.

Research on Palletizing and Packing Based on Heuristic Algorithm

Based on the heuristic packing algorithm, this paper proposes a 3D packing algorithm that can run efficiently to improve the efficiency of palletizing and packing. The design of the algorithm starts from three parts: block construction, remaining space processing, and optimal block search. Firstly, the goods are packed to meet the constraints of the direction of the goods and the constraints of the support of the goods, and various composite blocks are generated. Then, set the remaining space selection, division, and merging methods that meet the priority constraint rules. Then, a sliding window and integer-split heuristic search are used to select the optimal block for each generated space. Finally, the feasibility of the algorithm is proved by experiments.

Mesh Model-Based Merging Method for DP-LDPC Code Pair

For the double protograph low-density parity-check (DP-LDPC) code pair, indicating the source and channel codes utilized in the joint source channel coding, the coding optimization is generally implemented by the differential evolution searching with high complexity. In this paper, a new structured design of the DP-LDPC code pair is proposed based on Graph theory to reduce the searching complexity, namely mesh model-based merging (M3) method. Firstly, four basic mesh models are designed to merge the protographs at any dimension. According to the different structures of the source and channel codes, the merging boundary constraint is statistically established to limit the using number of basic models. Then, a prior sorting algorithm is proposed to arrange the selection priorities of basic models for source and channel codes, respectively, which greatly refines the merging accuracy. Since most of the degrees are symmetric and predetermined in the merged protographs, the searching complexity is significantly reduced when the merged protographs are used as the initial input. Finally, simulation results demonstrate that the merging DP-LDPC code pairs show better performances compared to the existing code pairs. Furthermore, the proposed M3 method is hardware-friendly in Internet of Things because of its simple structure.

An Ensemble and Multi-View Clustering Method Based on Kolmogorov Complexity

The ability to build more robust clustering from many clustering models with different solutions is relevant in scenarios with privacy-preserving constraints, where data features have a different nature or where these features are not available in a single computation unit. Additionally, with the booming number of multi-view data, but also of clustering algorithms capable of producing a wide variety of representations for the same objects, merging clustering partitions to achieve a single clustering result has become a complex problem with numerous applications. To tackle this problem, we propose a clustering fusion algorithm that takes existing clustering partitions acquired from multiple vector space models, sources, or views, and merges them into a single partition. Our merging method relies on an information theory model based on Kolmogorov complexity that was originally proposed for unsupervised multi-view learning. Our proposed algorithm features a stable merging process and shows competitive results over several real and artificial datasets in comparison with other state-of-the-art methods that have similar goals.

Parallel incremental association rule mining framework for public opinion analysis

Internet public opinion association rule mining (POARM) has garnered significant attention from a larger group of netizens. However, most POARM methods have been applied to post-event analysis, which has poor timeliness and a low efficiency. Therefore, the real-time monitoring of public opinion association rules is lacking. To address this problem, we propose the parallel Incremental POARM Framework (IPOARM), which improves the timeliness of association rule mining in two ways: 1) using an incremental merge method to consider both inserted and deleted public opinion transaction sets and reuse previous frequent itemsets to reduce redundant computation and 2) employing a parallel implementation of big data process platforms. Moreover, the flexible association rule mining (ARM) algorithm selection structure of IPOARM enables users to freely select suitable ARM algorithms. We represent four classic transaction sets as public opinion transaction sets and compare the IPOARM framework with two novel incremental association rules mining algorithms. Our evaluations indicate that the IPOARM framework can discover Internet public opinion association rules quickly, implying that it can be easily integrated into existing big data processing platforms and that it significantly improves the mining accuracy and efficiency by 12.756% and 29.371%, respectively.

Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors

Human activity recognition (HAR) has been applied to several advanced applications, especially when individuals may need to be monitored closely. This work focuses on HAR using wearable sensors attached to various locations of the user body. The data from each sensor may provide unequally discriminative information and, then, an effective fusion method is needed. In order to address this issue, inspired by the squeeze-and-excitation (SE) mechanism, we propose the merging-squeeze-excitation (MSE) feature fusion which emphasizes informative feature maps and suppresses ambiguous feature maps during fusion. The MSE feature fusion consists of three steps: pre-merging, squeeze-and-excitation, and post-merging. Unlike the SE mechanism, the set of feature maps from each branch will be recalibrated by using the channel weights also computed from the pre-merged feature maps. The calibrated feature maps from all branches are merged to obtain a set of channel-weighted and merged feature maps which will be used in the classification process. Additionally, a set of MSE feature fusion extensions is presented. In these proposed methods, three deep-learning models (LeNet5, AlexNet, and VGG16) are used as feature extractors and four merging methods (addition, maximum, minimum, and average) are applied as merging operations. The performances of the proposed methods are evaluated by classifying popular public datasets.

Merging multisatellite precipitation products using stacking method and the censored-shifted gamma ensemble model output statistics in china's Beimiaoji basin

High-quality precipitation data are crucial for hydrological analyses, water resource management, and drought monitoring. Merging precipitation products from different sensors and algorithms provides more reliable spatial information and can obtain high-quality precipitation data. To obtain high-spatiotemporal-resolution precipitation, in this study, we proposed a method to merge multisource precipitation products by downscaling coarse resolution products(geographically weighted regression (GWR) algorithm) and then eliminating biases in individual datasets(stacking algorithm), and finally merging the bias-adjusted precipitation by weighted average(the ensemble model output statistics-censored, shifted gamma (EMOS-CSG) algorithm). The multisource precipitation products included the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation 3B42 in Real-Time (TMPA-3B42RT), Climate Precipitation Center Morphing Technique (CMORPH), Global Satellite Mapping of Precipitation Near-Real-Time (GSMaP_NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products). The merging method was applied to the Beimiaoji basin from April to October in the 2016–2019 period. The precipitation observations at 38 gauges were spatially and randomly divided into two parts, 28 gauges were used for the training, while the other for the performance evaluations. The results showed that (1) the daily merged precipitation product had a better performance than the original satellite products in terms of the six considered statistical indexes, with the lowest root mean square error (RMSE) at 4.33 mm and the highest correlation coefficient (CC) at 0.64; (2) the utilized merging method not only increased the spatial resolution to 1 km but also captured more detailed precipitation distribution information; and (3) considering the influence of the gauge density, the performance of the merged product was still improving after the number of gauge stations exceeds 16, but the improvement was not significant.

A Novel Merging Method for Generating High-Quality Spatial Precipitation Information over Mainland China

Accurate estimation of precipitation is critically important for a variety of fields, such as climatology, meteorology, and water resources. However, the availability of precipitation measurements has proved to be spatially inadequate for many applications. In this study, to acquire high-quality precipitation fields with enhanced accuracy and a fine-scale spatial resolution of 1 km × 1 km, we developed a new data fusion method by establishing an energy function model using the downscaled Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) precipitation product and high-density station observation in mainland China. Our merging approach was inspired by the interdisciplinary research framework integrating the methods in the fields of image processing, earth science, and machine learning. Cross-validation analyses were performed for the monthly precipitation over the period 2009–2018. It was found that the results of the newly developed method were more accurate than the original IMERG products in terms of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (CC), and Kling–Gupta efficiency (KGE). The merging precipitation results exhibit consistent spatial patterns with the original IMERG products, yet have good agreement with station observations. The gauge observations were the major source of the prediction skill of precipitation for the proposed method, and the downscaled-IMERG precipitation products added additional spatial details in the final merging results. Results indicate that the proposed merging method can reproduce the spatial details of the precipitation fields as well as enhance their accuracy. In addition, the time evolution of the error index indicates that the improvement in the merged result was stable over time, with KGE improving by 14% on average. The developed approach provides a promising way of estimating precipitation with high spatial resolution and high accuracy, which will benefit hydrological and climatological studies.

PRAM: A Practical Sybil-Proof Auction Mechanism for Dynamic Spectrum Access With Untruthful Attackers

Auction is becoming increasingly popular for dynamic spectrum access (DSA), while it is extremely vulnerable to sybil attacks. Existing studies on sybil-proof DSA auction impractically assume that attackers bid truthfully based on true appraisals. This paper, for the first time, considers untruthful attackers and investigates the sybil-proof auction design in such more hazardous scenarios. To justify the new assumption, we first show that attackers obtain higher utilities by bidding untruthfully, especially in networks with inadequate channels. Based on this novel finding, we then design a practical sybil attack model named EqualSumBid Sybil, where attackers follow an equal-sum rule (i.e., the sum bid value of the multiple identities of an attacker equals the bid value when it bids with only one identity) instead of their true appraisals. To ensure efficient DSA under the new attack, we finally propose the PRAM, a Practical sybil-pRoof Auction Mechanism, where suspicious identity merging and bid-independent bidder sorting methods are introduced to alleviate the effect of untruthfulness on spectrum auction. Furthermore, winner selection and payment methods are designed to resist the EqualSumBid Sybil attack. Theoretical analyses and numerical results show that PRAM not only resists the EqualSumBid Sybil attack but also achieves individual rationality and truthfulness.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

Exploring data merging methods for a distributed processing system

The ALICE experiment at the CERN LHC (Large Hadron Collider) is undertaking a major upgrade during the LHC Long Shutdown 2 in 2019-2021, which includes a new computing system called O2 (Online-Offline). The raw data input from the ALICE detectors will increase a hundredfold, up to 3.5 TB/s. By reconstructing the data online, it will be possible to compress the data stream down to 100 GB/s before storing it permanently.The O2 software is a message-passing system. It will run on approximately 500 computing nodes performing reconstruction, compression, calibration and quality control of the received data stream. As a direct consequence of having a distributed computing system, locally generated data might be incomplete and could require merging to obtain complete results.This paper presents the O2 Mergers, the software designed to match and combine partial data into complete objects synchronously to data taking. Based on a detailed study and results of extensive benchmarks, a qualitative and quantitative comparison of different merging strategies considered to reach the final design and implementation of the software is discussed.