Data Partitioning Research Articles

A Clustering Visualization Method for Density Partitioning of Trajectory Big Data Based on Multi-Level Time Encoding

The proliferation of the Internet and the widespread adoption of mobile devices have given rise to an immense volume of real-time trajectory big data. However, a single computer and conventional databases with limited scalability struggle to manage this data effectively. During the process of visual rendering, issues such as page stuttering and subpar visual outcomes often arise. This paper, founded on a distributed architecture, introduces a multi-level time encoding method using “minutes”, “hours”, and “days” as fundamental units, achieving a storage model for trajectory data at multi-scale time. Furthermore, building upon an improved DBSCAN clustering algorithm and integrating it with the K-means clustering algorithm, a novel density-based partitioning clustering algorithm has been introduced, which incorporates road coefficients to circumvent architectural obstacles, successfully resolving page stuttering issues and significantly enhancing the quality of visualization. The results indicate the following: (1) when data is extracted using the units of “minutes”, “hours”, and “days”, the retrieval efficiency of this model is 6.206 times, 12.475 times, and 18.634 times higher, respectively, compared to the retrieval efficiency of the original storage model. As the volume of retrieved data increases, the retrieval efficiency of the proposed storage model becomes increasingly superior to that of the original storage model. Under identical experimental conditions, this model’s retrieval efficiency also outperforms the space–time-coded storage model; (2) Under a consistent rendering level, the clustered trajectory data, when compared to the unclustered raw data, has shown a 40% improvement in the loading speed of generating heat maps. There is an absence of page stuttering. Furthermore, the heat kernel phenomenon in the heat map was also resolved while enhancing the visualization rendering speed.

Enhancing The Performance of Intrusion Detection Using CNN And Reduction Techniques

There have been several security solutions based on artificial intelligence (AI), such as intrusion detection systems (IDS),cyberattacks are increasing because big data is increasing by using the internet on all sides of life, therefore, unbalanced data poses a serious problem in intrusion detection systems. The proposed detection system that is based on deep learning Convolutional Neural Network(CNN )partitions data into training and testing., Creating the classifier model for the Principal Component Analysis (PCA )technique of reducing features, is required for the development of intelligent analytic tools that need data pre-treatments and deep learning algorithm-performance enhancement. The UNSW-NB15 data set is used According to experimental findings, We employed a number of evaluation tools to assess the proposed NIDCNN strategy relying on the UNSW-NB15 data set that takes 30% of it for testing and after processing this part of the data became used to evaluate the proposed system. Measures such as a classifier's F-Score, precision, and sensitivity (Recall) are evaluated. Classifier performs better than the other approaches at determining if the data stream is normal or malicious. which is used to assess deep learning's effectiveness, the suggested model results from a high level of accuracy. The experimental findings demonstrate the suggested system's ability to accelerate the intrusion detection process while reducing memory and CPU usage. Experimental results prove the theoretical considerations.Because the UNSW-NB15 data set contains a wide range of patterns that accurately represent contemporary real network traffic, New NIDS algorithms can therefore be assessed using it.

Summarising multiple clustering-centric estimates with OWA operators for improved KNN imputation on microarray data

As part of celebrating the success of OWA operators and their contributions over the past decades, this work presents an original investigation of exploiting OWA in dealing with missing value imputation witnessed in microarray experimental data. This task is significant in life science and its realisation to humanity. Both argument-independent and -dependent variants of such operators are applied to summarise a collection of estimates, determined through the concept of clustering-centric KNN imputation. This provides an innovative alternative to the state-of-the-art model that makes use of a single clustering to identify neighbours of a particular instance of interest. Instead of manually specify data partition, the proposed approach works by selecting a subset of diverse clusterings or committees from a candidate pool, which has been prepared using k-means and different (and popular) generation strategies invented for ensemble clustering. It is automated through a greedy forward-search looking for a desired number of committee members. Based on published gene expression datasets and different experimental settings, the resulting model generally outperforms its baselines, being competitive to related methods found in the literature. Further extensions to iterative refinement and supervised imputation are also discussed in addition to the analysis of algorithmic parameters.

Soft sensor method for endpoint carbon content and temperature of BOF based on multi-cluster dynamic adaptive selection ensemble learning

Abstract The accurate control of the endpoint in converter steelmaking is of great significance and value for energy saving, emission reduction, and steel quality improvement. The key to endpoint control lies in accurately predicting the carbon content and temperature. Converter steelmaking is a dynamic process with a large fluctuation of samples, and traditional ensemble learning methods ignore the differences among the query samples and use all the sub-models to predict. The different performances of each sub-model lead to the performance degradation of ensemble learning. To address this issue, we propose a soft sensor method based on multi-cluster dynamic adaptive selection (MC-DAS) ensemble learning for converter steelmaking endpoint carbon content and temperature prediction. First, to ensure the diversity of the ensemble learning base model, we propose a clustering algorithm with different data partition characteristics to construct a pool of diverse base models. Second, a model adaptive selection strategy is proposed, which involves constructing diverse similarity regions for individual query samples and assessing the model’s performance in these regions to identify the most suitable model and weight combination for each respective query sample. Compared with the traditional ensemble learning method, the simulation results of actual converter steelmaking process data show that the prediction accuracy of carbon content within ±0.02% error range reaches 92.8%, and temperature within ±10°C error range reaches 91.6%.

Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing

Brain-Computer Interfacing (BCI) has shown promise in Machine Learning (ML) for emotion recognition. Unfortunately, how data are partitioned in training/test splits is often overlooked, which makes it difficult to attribute research findings to actual modeling improvements or to partitioning issues. We introduce the “data transfer rate” construct (i.e., how much data of the test samples are seen during training) and use it to examine data partitioning effects under several conditions. As a use case, we consider emotion recognition in videos using electroencephalogram (EEG) signals. Three data splits are considered, each representing a relevant BCI task: subject-independent (affective decoding), video-independent (affective annotation), and time-based (feature extraction). Model performance may change significantly (ranging e.g. from 50% to 90%) depending on how data is partitioned, in classification accuracy. This was evidenced in all experimental conditions tested. Our results show that (1) for affective decoding, it is hard to achieve performance above the baseline case (random classification) unless some data of the test subjects are considered in the training partition; (2) for affective annotation, having data from the same subject in training and test partitions, even though they correspond to different videos, also increases performance; and (3) later signal segments are generally more discriminative, but it is the number of segments (data points) what matters the most. Our findings not only have implications in how brain data are managed, but also in how experimental conditions and results are reported.

Training feedforward neural nets in Hopfield-energy-based configuration: A two-step approach

We introduce Hopfield-Energy-Based Learning, a general learning framework that is inspired by energy-based models, to train feedforward neural nets. Our approach includes two training phases applied iteratively: first, the minimization of the internal energy, which captures dependencies between input samples and network parameters, is carried out in an unsupervised manner; second, the problem-dependent supervised external energy (e.g., cross-entropy loss) combined with partially reversed internal energy gradients are back-propagated in a standard manner. The intuition is that the first stage helps parameters to settle into the state that simply partitions data into clusters; while in the second stage, the network is allowed to deviate from that clustering a bit (hence gradient reversal) in order to converge to parameters that ultimately perform well on the task at hand. Notably, the data used for the two steps might not be one and the same (e.g., can come from different domains) and the approach naturally tailors itself to solve unsupervised domain adaptation problems without adopting any distribution alignment techniques. We also show that the proposed training strategy substantially improves the performance of several ConvNets on standard supervised classification tasks; showing improvements of at least 1.2% (2.64% on CIFAR-10, 4.5% on CIFAR-100, and 1.35% on ImageNet). Our formulation is general, performs well in practice, and holds promise for scenarios where labeled data is limited.

Prediction and model evaluation for space–time data

ABSTRACT Evaluation metrics for prediction error, model selection and model averaging on space–time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space–time prediction problems. Motivated by air pollution data collected during California wildfires in 2008, this manuscript attempts a formalization of the true prediction error associated with spatial interpolation. We investigate a variety of cross-validation (CV) procedures employing both simulations and case studies to provide insight into the nature of the estimand targeted by alternative data partition strategies. Consistent with recent best practice, we find that location-based cross-validation is appropriate for estimating spatial interpolation error as in our analysis of the California wildfire data. Interestingly, commonly held notions of bias-variance trade-off of CV fold size do not trivially apply to dependent data, and we recommend leave-one-location-out (LOLO) CV as the preferred prediction error metric for spatial interpolation.

Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.

In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen's Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen's Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.

An advanced ensemble clustering approach for data partitioning and mining to optimize performance in variable refrigerant flow systems

This study presents a data mining-based approach to develop clustering ensemble models for data partitioning of variable refrigerant flow (VRF) system, with the aim of improving the quality of pattern detection and laying a solid foundation for further data mining. This approach mainly includes three parts. First, data preprocessing combines handling outliers, feature filtering, stable data filtering and data normalization. Secondly, consensus clustering is used to obtain an appropriate number of clusters. To generate base clusterings, we applied three clustering algorithms - k-means, self-organizing maps, and fuzzy C-means - in a 1:1:1 ratio. These base clusterings were selected based on the adjusted rand index as the underlying selection criterion. For clustering ensemble, we utilized three integration methods, namely K-modes, latent class analysis, and linkage clustering ensemble. Finally, analyze the metric difference and time consumption of the integration clustering results of different methods, and obtain a suitable data partitioning scheme for VRF system. This case study examines the utilization of the proposed method to analyze the operation data of a VRF system in an office building. The results indicate that the quality of the clustering derived by the ensemble clustering model is notably superior to that of the individual base clustering. Moreover, the latent class analysis method demonstrates remarkable advantages in achieving top-quality clustering outcomes. These findings suggest that the proposed method can be effective in optimizing VRF system operation while leveling up the accuracy of data analysis. Ensemble models have considerable merit in devising fault identification and diagnosis techniques, optimizing operation, and exploring the links between VRF systems and smart grids.

Development of a hybrid VRF system energy consumption prediction model based on data partitioning and swarm intelligence algorithm

Accurately forecasting energy consumption is beneficial and pivotal for effectively managing variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive representation of the operating condition and the impact of possible faults. Energy consumption prediction is fundamental in energy conservation tasks such as fault diagnosis. Currently, many energy consumption prediction model have been applied for HVAC system widely. However black-box energy prediction models applied to VRF systems are still relatively few and crude. This study therefore proposed a hybrid energy consumption prediction method for VRF systems based on data partitioning and swarm intelligence algorithm. The model was based on back propagation neural network (BPNN), and utilized data partitioning techniques to identify the energy consumption patterns of the VRF system. For each pattern, a BPNN sub-model was trained using operating and energy consumption data of a VRF system. Then, swarm intelligence algorithm is adopted to determine the optimal architecture of each BPNN sub-model. The results demonstrate that the proposed hybrid models can achieve better prediction performance than single models like BP Neural Network. Root mean square error and mean absolute percentage error of the proposed predictive model is 202.49 and 1.95%, which has a 31.3% and 45.5% decrease compared to single BPNN model. At the same time, it is enlightening for other researchers to study the potential of data partitioning algorithm and swarm intelligence algorithm in the field of VRF system energy consumption prediction.

Pediatric reference intervals of the hemoglobin fractions HbA2, HbF and HbA0 using high-performance liquid chromatography and capillary electrophoresis

Introductionβ-thalassemia is a common genetic disorder with an estimated prevalence of 80–90 million carriers worldwide. As elevated hemoglobin A2 (HbA2) is a primary feature of carriers, hemoglobin fraction analysis is a common technique used for initial screening. However, pediatric reference intervals (RIs) are scarce. Hence, the aim was to establish pediatric RIs of hemoglobin fractions using high-performance liquid chromatography (HPLC) and capillary electrophoresis (CE). MethodsSamples were collected from assumed healthy children and adolescents of 1–18 years. Analyses were conducted using the Tosoh Automated Glycohemoglobin Analyzer HLC-723®G11 (Tosoh G11, HPLC) and the Capillarys 3 Octa (CE). Data were investigated for need of partitioning by both age (1–6 years vs. 6–18 years) and sex. ResultsIn total, 189 and 196 subjects were included in the statistical analysis of HPLC and CE, respectively. The 95% RI of HbA2 was 2.00–2.90% by HPLC and 2.2–3.0% by CE. Partitioning of data was not clinically relevant by HPLC. However, partitioning by age was suggested by CE. ConclusionRIs of hemoglobin fractions in individuals of 1–18 years using commercially available HPLC and CE equipment were reported. This is the first report of a pediatric RI of HbA2 using the Tosoh G11.

GraphAGILE: An FPGA-Based Overlay Accelerator for Low-Latency GNN Inference

This paper presents GraphAGILE, a domain-specific FPGA-based overlay accelerator for graph neural network (GNN) inference. GraphAGILE consists of (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a novel unified architecture design</i> with an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">instruction set</i> , and (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a compiler</i> built upon the instruction set that can quickly generate optimized code. Due to the proposed instruction set architecture (ISA) and the compiler, GraphAGILE does not require any FPGA reconfiguration when performing inference on various GNN models and input graphs. For the architecture design, we propose a novel hardware module named Adaptive Computation Kernel (ACK), that can execute various computation kernels of GNNs, including general matrix multiplication (GEMM), sparse-dense matrix multiplication (SpDMM), and sampled dense-dense matrix multiplication (SDDMM). The compiler takes the specifications of a GNN model and the graph meta data (e.g., the number of vertices and edges) as input, and generates a sequence of instructions for inference execution. We develop the following compiler optimizations to reduce inference latency: (1) computation order optimization that automatically reorders the computation graph to reduce the total computation complexity, (2) layer fusion that merges adjacent layers to reduce data communication volume, (3) data partitioning with a partition-centric execution scheme that partitions the input graph to fit the available on-chip memory of FPGA, (4) kernel mapping that automatically selects execution mode for ACK, and performs task scheduling to overlap computation with data communication and achieves dynamic load balance. We implement GraphAGILE on a state-of-the-art FPGA platform, Xilinx Alveo U250. GraphAGILE can execute widely used GNN models, including GCN, GAT, GIN, GraphSAGE, SGC and other GNN models supported by GraphGym. Experimental results show that GraphAGILE achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$47.1\times$</tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$3.9\times$</tex-math></inline-formula> ) reduction in end-to-end latency, including the latency of compilation and hardware execution, compared with the state-of-the-art implementations on CPU (GPU), and achieves up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2.9\times$</tex-math></inline-formula> reduction in hardware execution latency compared with the state-of-the-art FPGA accelerators.

EfShard: Toward Efficient State Sharding Blockchain via Flexible and Timely State Allocation

State sharding is a promising approach to address the scalability issue in the blockchain system. However, the previous sharding schemes using the fixed data partitioning mechanism bring high proportion of costly cross-shard transactions and cannot effectively handle the workload imbalance that occurs in practice, which slows down the performance. To address these issues, we propose EFSHARD, an efficient state sharding blockchain system that enables allocating states flexibly and timely across shards according to recent transactions. Firstly, we propose a hierarchical state partition to enable flexible mapping of states to shards. Second, we design a new state transfer protocol to efficiently migrate states across shards while guaranteeing consistency and liveness. Then, we provide a greedy-based state allocation algorithm to decide when and how to migrate states. The allocation mechanism groups highly correlated state data into the same shard to reduce the proportion of cross-shard transactions and distributes state data to shards with relatively low load to balance workload, thus improving the performance. In the end, we conduct extensive experiments to evaluate EFSHARD and the results demonstrate that EFSHARD outperforms state-of-the-art approaches in terms of transaction throughput, confirmation latency, workload balance, and queue size of transaction pool.

Obtaining reference values for nutrients in vineyard soils through boundary line approach using Bayesian segmented quantile regression on commercial farm data

Inefficient use of fertilizers in agriculture is a major issue in global food production as it hampers economic viability of farms and offers great risk of environmental pollution. Fertilizing recommendations are guided by plant nutritional assessments, which are compared to reference values so that decisions can be made regarding which nutrient and how much is to be supplied. Traditionally, these values are obtained by field trials that are particularly long and expensive for perennial crops such as grapevines. This research evaluated the use of boundary line (BL) approach and Bayesian segmented quantile regression (BSQR) in determining reference values for nutrients in vineyard soils using commercial farm data. Critical levels (CL) and sufficiency ranges (SR) of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), copper (Cu), zinc (Zn), boron (B) and manganese (Mn) were estimated using datasets from a winery in the Campanha Gaúcha region, Rio Grande do Sul, Brazil. Results indicated lower P and higher K, S, Zn and Mn levels, as compared to current state recommendations. Great inconsistencies were found in CL estimates for Cu and, to a lesser extend, Ca and Mg. Model assessments indicated that BSQR estimates were highly sensitive to quantile selection but poorly affected by changes in the prior. Data simulated using fitted models were consistently over-dispersed. Allowing for variable slopes and intercepts may partition data variability more properly, leading to better fit, higher parameter precision and greater accuracy of estimates. Despite current limitations, the BSQR model provides a flexible framework for BL analysis, enabling the use of commercial datasets in establishing reference values to support group- and site-specific recommendations, a valuable contribution to increase efficiency and sustainability of grapevine fertilization.

Soft Semi-Supervised Deep Learning-Based Clustering

Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing semi-supervised clustering approaches are relatively scarce compared to the contributions made to enhance the state-of-the-art fully unsupervised clustering approaches. In this paper, we propose a novel semi-supervised deep clustering approach, named Soft Constrained Deep Clustering (SC-DEC), that aims to address the limitations exhibited by existing semi-supervised clustering approaches. Specifically, the proposed approach leverages a deep neural network architecture and generates fuzzy membership degrees that better reflect the true partition of the data. In particular, the proposed approach uses side-information and formulates it as a set of soft pairwise constraints to supervise the machine learning process. This supervision information is expressed using rather relaxed constraints named “should-link” constraints. Such constraints determine whether the pairs of data instances should be assigned to the same or different cluster(s). In fact, the clustering task was formulated as an optimization problem via the minimization of a novel objective function. Moreover, the proposed approach’s performance was assessed via extensive experiments using benchmark datasets. Furthermore, the proposed approach was compared to relevant state-of-the-art clustering algorithms, and the obtained results demonstrate the impact of using minimal previous knowledge about the data in improving the overall clustering performance.

Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations

Spatial clusters contain biases and artifacts, whether they are defined via statistical algorithms or via expert judgment. Graph‐based partitioning of spatial data and associated heuristics gained popularity due to their scalability but can define suboptimal regions due to algorithmic biases such as chaining. Despite the broad literature on deterministic regionalization methods, approaches that quantify regionalization probability are sparse. In this article, we propose a local method to quantify regionalization probabilities for regions defined via graph‐based cuts and expert‐defined regions. We conceptualize spatial regions as consisting of two types of spatial elements: core and swing. We define three distinct types of regionalization biases that occur in graph‐based methods and showcase the use of the proposed method to capture these types of biases. Additionally, we propose an efficient solution to the probabilistic graph‐based regionalization problem via performing optimal tree cuts along random spanning trees within an evidence accumulation framework. We perform statistical tests on synthetic data to assess resulting probability maps for varying distinctness of underlying regions and regionalization parameters. Lastly, we showcase the application of our method to define probabilistic ecoregions using climatic and remotely sensed vegetation indicators and apply our method to assign probabilities to the expert‐defined Bailey's ecoregions.

A novel approach for large‐scale environmental data partitioning on cloud and on‐premises storage for compute continuum applications

SummaryCloud‐based services have proved useful in several research fields, such as engineering, health science, and astrophysics, to mention a few examples. The computational environmental science community developed a strong need for cloud facilities to store, process, and manage data from observations and numerical models for simulations and forecasts. Weather forecast models and global sensor networks deal with multidimensional geo‐referenced data∖sets. However, environmental data consumer applications usually require a relatively small amount of multidimensional input data slice to analyze a specific area or time interval. Hence, reducing data dimension for information retrieval is mandatory. This paper presents a twofold solution: a technique to load and retrieve the sliced multidimensional data set on different cloud services such as Amazon Web Service (AWS), Google Cloud Platform, and Microsoft Azure. The experimental results performed on these cloud services highlight that the proposed method can significantly speed up the process of loading and retrieving the data slices compared to working with the entire data set in bulk or OPeNDAP server.

A Modified Stochastic Model for Rainfall Prediction Using Fuzzy Aquila Optimization

In recent years, rainfall prediction has received major attention in research areas because of its demanding applications in pollution control management and flood control management. Despite having numerous learning-based approaches to calculate future rainfall trends, it remains inefficient to predict rainfall occurrences by learning linear and nonlinear data patterns of historical weather information (i.e., exact prediction value is complicated to be predicted). These complications are addressed with the evolution of stochastic models which have a greater ability to minimize prediction bias and represent long-term weather variability. Therefore, this paper proposes a novel modified stochastic fuzzy Aquila (MSFA) algorithm to make precise predictions regarding future trends by evaluating rainfall time series data. The proposed MSFA algorithm is applied in rainfall prediction applications in evaluating the effectiveness of the proposed stochastic model. Here, 10 features of the open weather dataset collected from Tamil Nadu are provided as input for the proposed rainfall prediction design. The data inconsistencies such as undesirable format and missing values are structured using preprocessing procedures, namely data arrangement, null value removal, and data partitioning. The preprocessed data are fed into the proposed MSFA algorithm which learns the data features more precisely and predicts the probable occurrence of rainfall. To evaluate the performances of the proposed MSFA algorithm, the metrics such as mean absolute error (MAE), coefficient of determination, root mean squared logarithmic error (RMSLE), and root mean square error (RMSE) are analyzed. The experimental results illustrate that the proposed MSFA algorithm achieves superior performance in terms of all metrics.

CBFLNet: Cross-boundary feature learning for large-scale point cloud segmentation

Large-scale point cloud semantic segmentation presents a crucial yet challenging task. Current point cloud analysis approaches typically partition data into volumetric blocks, independently assigning labels to each point within these blocks. However, this strategy often compromises segmentation accuracy at block boundaries due to the isolated processing of each block, thus hindering the model’s contextual understanding. To address this issue, we present CBFLNet, an innovative semantic segmentation model that enables offset-free feature upsampling and cross-boundary feature learning. CBFLNet facilitates interaction between adjacent blocks, extending its receptive field beyond the input block. As a result, it notably mitigates errors in block boundary segmentation. CBFLNet is composed of three modules: an explicit local representation module, a symmetric sampling module, and a cross-boundary feature fusion module. The explicit local representation module is a plug-and-play module that takes two overlapping point cloud blocks as input to construct an approximate local spatial representation. In the symmetric sampling module, point features are symmetrically downsampled and upsampled, effectively avoiding feature offset caused by interpolation. Lastly, the cross-boundary feature fusion module enables cross-boundary local feature learning and multi-scale feature fusion. CBFLNet demonstrates a significant performance improvement, achieving a 0.9% mIoU increase over state-of-the-art methods on the S3DIS dataset and exhibiting competitive performance on the ScannetV2 dataset.

Estimation of Parameters With Partitioned Data From Large Amplitude Maneuvers of an Aircraft

In this paper data partitioning method is applied to concatenated Large amplitude manoeuvres (LAM) of a fighter aircraft to estimate its stabiLity and control derivatives. Stepwise multiple linear regression (SMLR) is utilized to determine the model structure within each partitioned data set. It is shown that the procedure with data generated from a research simulator, yields a useful method for estimating the pitch up tendency exhibited b), the aircraft during certain angle of attack (AOA) range. The SMLR is also applied to estimate non-linear aerodynamic derivatives of the aircraft from LAM data. The method is applied to reaL LAM data of the aircraft and gives reasonably consistent estimates of the aerodynamic derivatives. Results using model error method for the longitudinal derivatives from LAMs are also presented.