High-dimensional Feature Vectors Research Articles

Position detection of elements in off-axis three-mirror space optical system based on ResNet50 and LSTM.

Position detection of elements, especially with high-precision and high-efficiency, continue to present challenges in the off-axis three-mirror space optical system to ensure the imaging quality. To test the position of elements in an off-axis three-mirror system, a method based on ResNet50 and Long short-term memory (LSTM) is proposed in this paper. In the proposed method, point spread function (PSF) with different fields of view is extracted as a high dimensional feature vector by the ResNet50 network to achieve high efficiency. The same position information in the high dimensional feature vector from different image plane is obtained by the LSTM module to achieve high precision. To evaluate the performance of the proposed method, three examples of position detection were conducted. The results indicated that the probabilities of achieving detection accuracy better than 10 µm for eccentricity and 10'' for tilt, across single-dimensional variation of components, multi-dimensional random variation, and detector variation, are 100%, 94%, and 100%, respectively. Unlike the other detection method, the positions of elements are obtained with only one calculation and are close to the final results, which saves both time and resources for adjustment. The findings indicate that the method is an accurate and effective method for detecting position of elements in off-axis three-mirror space optical system.

Efficient Image Retrieval in Fashion: Leveraging Clustering and Principal Component Analysis for Search Space Reduction

In this study, a novel approach using clustering techniques and Principal Component Analysis (PCA) for reducing the search space in fashion image retrieval systems is introduced. The study focuses on extracting high-dimensional feature vectors from images of clothing items and finding the same or the most similar product using these feature vectors, thereby narrowing the search space. The proposed method employs unsupervised learning algorithms to analyze high-dimensional fashion image feature vectors, grouping them into meaningful clusters. This enhances search efficiency and improves user experience. By reducing the dimensionality of feature vectors with PCA, computational costs are minimized. Experimental results demonstrate that the proposed method significantly accelerates computation time while maintaining an acceptable level of accuracy.

A Memory-Efficient Federated Kernel Support Vector Machine for Edge Devices.

A federated learning (FL) scheme (denoted as Fed-KSVM) is designed to train kernel support vector machines (SVMs) over multiple edge devices with low memory consumption. To decompose the training process of kernel SVM, each edge device first constructs high-dimensional random feature vectors of its local data, and then trains a local SVM model over the random feature vectors. To reduce the memory consumption on each edge device, the optimization problem of the local model is divided into several subproblems. Each subproblem only optimizes a subset of the model parameters over a block of random feature vectors with a low dimension. To achieve the same optimal solution to the original optimization problem, an incremental learning algorithm called block boosting is designed to solve these subproblems sequentially. After training of the local models, the central server constructs a global SVM model by averaging the model parameters of these local models. Fed-KSVM only increases the iterations of training the local SVM models to save the memory consumption, while the communication rounds between the edge devices and the central server are not affected. Theoretical analysis shows that the kernel SVM model trained by Fed-KSVM converges to the optimal model with a linear convergence rate. Because of such a fast convergence rate, Fed-KSVM reduces the communication cost during training by up to 99% compared with the centralized training method. The experimental results also show that Fed-KSVM reduces the memory consumption on the edge devices by nearly 90% while achieving the highest test accuracy, compared with the state-of-the-art schemes.

TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM.

The emergence of monkeypox outside its endemic region in Africa has raised significant concerns within the public health community due to its rapid global dissemination. Early clinical differentiation of monkeypox from similar diseases, such as chickenpox and measles, presents a challenge. The Monkeypox Skin Lesion Dataset (MSLD) used in this study comprises monkeypox skin lesions, which were collected primarily from publicly accessible sources. The dataset contains 770 original images captured from 162 unique patients. The MSLD includes four distinct class labels: monkeypox, measles, chickenpox, and normal. This paper presents an ensemble model for classifying the monkeypox dataset, which includes transformer models and support vector machine (SVM). The model development process begins with an evaluation of seven convolutional neural network (CNN) architectures. The proposed model is developed by selecting the top four models based on evaluation metrics for performance. The top four CNN architectures, namely EfficientNetB0, ResNet50, MobileNet, and Xception, are used for feature extraction. The high-dimensional feature vectors extracted from each network are then concatenated and optimized before being inputted into the SVM classifier. The proposed ensemble model, in conjunction with the SVM classifier, achieves an accuracy of 95.45b%. Furthermore, the model demonstrates high precision (95.51%), recall (95.45%), and F1 score (95.46%), indicating its effectiveness in identifying monkeypox lesions. The results of the study show that the proposed hybrid framework achieves robust diagnostic performance in monkeypox detection, offering potential utility for enhanced disease monitoring and outbreak management. The model's high diagnostic accuracy and computational efficiency indicate that it can be used as an additional tool for clinical decision support.

An Innovative Neighbor Attention Mechanism Based on Coordinates for the Recognition of Facial Expressions.

For solving the facial expression recognition (FER) problem, we introduce a novel feature extractor called the coordinate-based neighborhood attention mechanism (CNAM), which uses the coordinate attention (CA) method to capture the directional relationships in separate horizontal and vertical directions, the input features from a preprocessing unit, and then passes this to two residual blocks, one consisting of the neighborhood attention (NA) mechanism, which captures the local interaction of features within the neighborhood of a feature vector, while the other one contains a channel attention implemented by a multilayer perceptron (MLP). We apply the feature extractor, the CNAM module, to four FER benchmark datasets, namely, RAF-DB, AffectNet(7cls), AffectNet(8cls), and CK+, and through qualitative and quantitative analysis techniques, we conclude that the insertion of the CNAM module could decrease the intra-cluster distances and increase the inter-cluster distances among the high-dimensional feature vectors. The CNAM compares well with other state-of-the-art (SOTA) methods, being the best-performing method for the AffectNet(7cls) and CK+ datasets, while for the RAF-DB and AffectNet(8cls) datasets, its performance is among the top-performing SOTA methods.

Wushu Movement Recognition System Based on DTW Attitude Matching Algorithm

Motion recognition technology is widely used in intelligent video surveillance, human-computer interaction and other fields. With the development of computer vision technology, improving the accuracy and efficiency of motion recognition has become the focus of research. The purpose of this study is to improve the performance of Wushu movement recognition through improved dynamic time warping algorithm and hierarchical model. Firstly, a high-dimensional feature vector is constructed by using the position, velocity and Angle changes of human bone joints. The actions are subdivided by the hierarchical model, and matched and recognized by the max-minimum dynamic time regularization model. Meanwhile, the K-class mean algorithm is combined to optimize the type of tree core, improve the performance of the model, reduce the interference of noise nodes, and effectively classify Wushu actions. Experimental verification was carried out on four public data sets of KTH, Olympic Sports, Hollywood2 and HMDB51. The experimental results showed that the recognition rate of the proposed model in KTH data set was 95.2%, and that in Olympic Sports data set was 91.4%. The Hollywood2 dataset was 66.7%, and the HMDB51 dataset was 61.2%. Comparing the results of different algorithms, the proposed method improved the recognition performance by 10% compared with long short-term memory network and gated cycle unit. Compared with one-dimensional convolutional neural network, the time of the proposed method was 15s longer, but the recognition rate was 1.6% higher. The results showed that the proposed method had significant performance advantages in diverse and complex action recognition tasks. Meanwhile, the results emphasized the factors to be considered in the design of the model, demonstrating its effectiveness in the application of Wushu movement recognition.

Exploring Feature Selection and Classification Techniques to Improve the Performance of an Electroencephalography-Based Motor Imagery Brain-Computer Interface System.

The accuracy of classifying motor imagery (MI) activities is a significant challenge when using brain-computer interfaces (BCIs). BCIs allow people with motor impairments to control external devices directly with their brains using electroencephalogram (EEG) patterns that translate brain activity into control signals. Many researchers have been working to develop MI-based BCI recognition systems using various time-frequency feature extraction and classification approaches. However, the existing systems still face challenges in achieving satisfactory performance due to large amount of non-discriminative and ineffective features. To get around these problems, we suggested a multiband decomposition-based feature extraction and classification method that works well, along with a strong feature selection method for MI tasks. Our method starts by splitting the preprocessed EEG signal into four sub-bands. In each sub-band, we then used a common spatial pattern (CSP) technique to pull out narrowband-oriented useful features, which gives us a high-dimensional feature vector. Subsequently, we utilized an effective feature selection method, Relief-F, which reduces the dimensionality of the final features. Finally, incorporating advanced classification techniques, we classified the final reduced feature vector. To evaluate the proposed model, we used the three different EEG-based MI benchmark datasets, and our proposed model achieved better performance accuracy than existing systems. Our model's strong points include its ability to effectively reduce feature dimensionality and improve classification accuracy through advanced feature extraction and selection methods.

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage.

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

Quantitative Stylistic Analysis of Middle Chinese Texts Based on the Dissimilarity of Evolutive Core Word Usage

Stylistic analysis enables open-ended and exploratory observation of languages. To fill the gap in the quantitative analysis of the stylistic systems of Middle Chinese, we construct lexical features based on the evolutive core word usage and scheme a Bayesian method for feature parameters estimation. The lexical features are from the Swadesh list, each of which has different word forms along with the language evolution during the Middle Ages. We thus count the varied word of those entries along with the language evolution as the linguistic features. With the Bayesian formulation, the feature parameters are estimated to construct a high-dimensional random feature vector to obtain the pair-wise dissimilarity matrix of all the texts based on different distance measures. Finally, we perform the spectral embedding and clustering to visualize, categorize, and analyze the linguistic styles of Middle Chinese texts. The quantitative result agrees with the existing qualitative conclusions and, furthermore, betters our understanding of the linguistic styles of Middle Chinese from both the inter-category and intra-category aspects. It also helps unveil the special styles induced by the indirect language contact.

Prediction of photovoltaic power generation based on a hybrid model

In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the prediction accuracy of photovoltaic power generation, a photovoltaic power generation forecasting method is proposed based on a hybrid model of the convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China’s photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time-series feature vector, which is an input for the XGBoost. A photovoltaic power generation prediction model is established based on CNN-XGBoost by training CNN and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability compared with other methods.

Learning scene-vectors for remote sensing image scene classification

Representing the scenes by learning the subtle variations in the spatial content of different classes is crucial for scene classification in remote sensing images. In this paper, we propose a scene attribute modeling to obtain a discriminative and compact representation for scene classification. First, we construct a scene attribute model (SAM) by training a Gaussian mixture model (GMM) using convolutional features to capture the scene attributes implicitly. Then, we perform a maximum a posteriori (MAP) adaptation to enhance the contribution of significant attributes in each scene resulting in a high-dimensional feature vector which contains redundant attributes from all the scenes. Hence, we use factor analysis to obtain a compact representation of the high-dimensional feature vector termed scene-vector, which retains only the significant attributes specific to a scene. The proposed approach is demonstrated on three benchmark datasets, namely, UC Merced Land Use, AID, and NWPU-RESISC45 datasets. We further show that, being a compact representation, our scene-vector outperforms state-of-the-art methods for scene classification in remote sensing images.

Personalized Dynamic Pricing Based on Improved Thompson Sampling

This study investigates personalized pricing with demand learning. We first encode consumer-personalized feature information into high-dimensional vectors, then establish the relationship between this feature vector and product demand using a logit model, and finally learn demand parameters through historical transaction data. To address the balance between learning and revenue, we introduce the Thompson Sampling algorithm. Considering the difficulty of Bayesian inference in Thompson Sampling owing to high-dimensional feature vectors, we improve the basic Thompson Sampling by approximating the likelihood function of the logit model with the Pólya-Gamma (PG) distribution and by proposing a Thompson Sampling algorithm based on the PG distribution. To validate the proposed algorithm’s effectiveness, we conduct experiments using both simulated data and real loan data provided by the Columbia University Revenue Management Center. The study results demonstrate that the Thompson Sampling algorithm based on the PG distribution proposed outperforms traditional Laplace approximation methods regarding convergence speed and regret value in both real and simulated data experiments. The real-time personalized pricing algorithm developed here not only enriches the theoretical research of personalized dynamic pricing, but also provides a theoretical basis and guidance for enterprises to implement personalized pricing.

Motion intention recognition of the affected hand based on the sEMG and improved DenseNet network

The key to sEMG (surface electromyography)-based control of robotic hands is the utilization of sEMG signals from the affected hand of amputees to infer their motion intentions. With the advancements in deep learning, researchers have successfully developed viable solutions for CNN (Convolutional Neural Network)-based gesture recognition. However, most studies have primarily concentrated on utilizing sEMG data from the hands of healthy subjects, often relying on high-dimensional feature vectors obtained from a substantial number of electrodes. This approach has yielded high-performing sEMG recognition systems but has failed to consider the considerable inconvenience that the abundance of electrodes poses to the daily lives and work of patients. In this paper, we focused on transradial amputees and used sEMG data from the Ninapro DB3 database as our dataset. Firstly, we introduce a STFT (Short-Time Fourier Transform)-based time-frequency feature fusion map for sEMG. This map includes both time-frequency features and the time-frequency localization of sEMG signals. Secondly, we propose an Improved DenseNet (Dense Convolutional Network) model for recognizing motion intentions in the affected hand of amputees based on their sEMG signals. Finally, addressing the issue of optimizing the number of electrodes carried by amputees, we introduce the PCMIRR (Pearson Correlation and Motion Intention Recognition Rate) algorithm. This algorithm optimizes the number of channels by considering the Pearson correlation between the sEMG channels of amputees and the recognition rate of motion intentions in the affected hand based on single-channel sEMG data. The experimental results reveal that the recognition accuracy, recall, and F1 score achieved by the Improved DenseNet model were 93.82%, 93.61%, and 93.65%, respectively. When the number of electrodes was optimized to 8, the recognition accuracy reached 94.50%. In summary, this paper ultimately attained precise recognition of motion intentions in amputees' affected hands while utilizing the minimum number of sEMG channels. This method offers a novel approach to sEMG-based control of bionic robotic hands.

Deep learning-based segmentation of breast masses using convolutional neural networks

Automatic breast tumor segmentation based on convolutional neural networks (CNNs) is significant for the diagnosis and monitoring of breast cancers. CNNs have become an important method for early diagnosis of breast cancer and, thus, can help decrease the mortality rate. In order to assist medical professionals in breast cancer investigation a computerized system based on two encoder-decoder architectures for breast tumor segmentation has been developed. Two pre-trained DeepLabV3+ and U-Net models are proposed. The encoder generates a high-dimensional feature vector while the decoder analyses the low-resolution feature vector provided by the encoder and generates a semantic segmentation mask. Semantic segmentation based on deep learning techniques can overcome the limitations of traditional algorithms. To assess the efficiency of breast ultrasound image segmentation, we compare the segmentation results provided by CNNs against the Local Graph Cut technique (a semi-automatic segmentation method) in the Image Segmenter application. The output segmentation results have been evaluated by using the Dice similarity coefficient that compares the ground truth images provided by the specialists against the predicted segmentation results provided by the CNNs and Local Graph Cut algorithm. The proposed approach is validated on 780 breast ultrasonographic images of the BUSI public database of which 437 are benign and 210 are malignant. The BUSI database provides classification (benign or malignant) labels for ground truth in binary mask images. The average Dice scores computed between the ground truth images against CNNs were as follows: 0.9360 (malignant) and 0.9325 (benign) for the DeepLabV3+ architecture and of 0.6251 (malignant) and 0.6252 (benign) for the U-Net, respectively. When the segmentation results provided by CNNs were compared with the Local Graph Cut segmented images, the Dice scores were 0.9377 (malignant) and 0.9204 (benign) for DeepLabV3+ architecture and 0.6115 (malignant) and 0.6119 (benign) for U-Net, respectively. The results show that the DeepLabV3+ has significantly better segmentation performance and outperforms the U-Net network.

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer.

The aim of this study was to investigate the potential of multiparametric 18F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients. A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language. Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91 % and specificity of 92 %. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83 %, specificity of 67 % and an AUC of 0.82. M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data. · Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers. · Prediction of M-stage is superior to N-stage. · Multiparametric PET/MRI displays a valuable platform forradiomics analyses .

Multimodal Sentiment Analysis Based on Composite Hierarchical Fusion

Abstract In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios.

Characterizing conformational states in GPCR structures using machine learning

G protein-coupled receptors (GPCRs) play a pivotal role in signal transduction and represent attractive targets for drug development. Recent advances in structural biology have provided insights into GPCR conformational states, which are critical for understanding their signaling pathways and facilitating structure-based drug discovery. In this study, we introduce a machine learning approach for conformational state annotation of GPCRs. We represent GPCR conformations as high-dimensional feature vectors, incorporating information about amino acid residue pairs involved in the activation pathway. Using a dataset of GPCR conformations in inactive and active states obtained through molecular dynamics simulations, we trained machine learning models to distinguish between inactive-like and active-like conformations. The developed model provides interpretable predictions and can be used for the large-scale analysis of molecular dynamics trajectories of GPCRs.

Extrapolation and Interpolation of Information in Computer Science

Proposed method is dealing with multi-dimensional data modeling, extrapolation and interpolation using the set of high dimensional feature vectors. Identification of handwriting, signature, faces or fingerprints need data modeling and each model of the pattern is built by a choice of characteristic key points and multi-dimensional modeling functions. Novel modeling via nodes combination and parameter γ as N-dimensional function enables data parameterization and interpolation for feature vectors. Multi-dimensional data is modelled and interpolated via different functions for each feature: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.

Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE

For the problem that the fault states of parallel shaft gearboxes are difficult to identify, a diagnostic method is proposed to optimize variational modal decomposition (VMD) and t-distributed stochastic neighbor embedding (t-SNE) using an improved dung beetle optimization algorithm I have checked and revised all. (IDBO). IDBO is obtained by amplifying dung beetle optimization (DBO) using strategies such as chaos mapping, Levy flight policy, and dynamic adaptive weighting. IDBO is employed to optimize VMD, extracting decomposed eigenvalues restructured into high-dimensional feature vectors. Subsequently, we employ the t-SNE algorithm for dimensionality reduction to eliminate redundancy, obtaining two-dimensional vectors. Finally, these vectors are input into a support vector machine (SVM) for fault diagnosis. We apply IDBO, grey wolf optimization (GWO), DBO, and the sparrow search algorithm (SSA) to both benchmark functions and VMD, conducting a performance comparison. The results demonstrate that IDBO exhibits superior convergence speed and global search capability, effectively suppressing modal aliasing issues in VMD, thereby enhancing the algorithm’s robustness. Through experimental fault diagnosis on a gear transmission system, we compare our proposed method with EMD + t-SNE and traditional VMD + t-SNE feature extraction approaches. The experimental results indicate that the fault diagnosis accuracy reaches 100% after processing the fault signals with IDBO-VMD + t-SNE. This method proves to be an effective fault diagnosis approach specifically tailored for parallel-axis gearboxes, providing a reliable means to enhance diagnostic accuracy.

Identifying the Causes of Ship Collisions Accident Using Text Mining and Bayesian Networks

Under the backdrop of the robust growth of the global economy, the water transport industry is experiencing rapid development, resulting in an increase in ship collisions and a critical water traffic safety situation. This study uses text mining techniques to gather a corpus of data. The corpus includes human factors, ship factors, natural environmental factors, and management factors, which are used as target data to obtain a high-dimensional sparse original feature vector space set comprising eigenvalues and eigenvalue weight attributes. Chi-square statistics are utilised to reduce dimensionality, resulting in a final set of 33-dimensional text feature items that determine the causal factors of ship collision risk. Taking the four steps involved in the collision process as the primary focus, a Bayesian network structure for ship collision risk is constructed based on the “human-ship-environment-management” system. By incorporating existing ship collision accident/danger reports, conditional probability tables are computed for each node in the Bayesian network structure, enabling the modelling of ship collision risk. The model is validated through an example, revealing that, under relevant conditions, the probability of collision exceeds 90 %. This finding demonstrates the validity of the model and allows one to deduce the primary cause of ship collision accidents.