Construction Characteristics Research Articles

Multi-layer and multi-source features stacking ensemble learning for user profile

Electricity bill-sensitive user profiling in the power industry is gradually being recognized as a research hotspot. A multi-layer feature construction method has been proposed, separating the mining of textual and numerical information, addressing the insufficient exploration of textual data in existing user profile processing methods. The complexity of electricity user profiles is addressed through the introduction of a two-stage predictive model based on Stacking ensemble learning. In the first stage, user sensitivity is predicted by utilizing the advantages of MLP (Multi-Layer Perceptron), CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory), and XGB (Extreme Gradient Boosting) in global, local, and missing value handling, respectively. In the second stage, electricity-sensitive users are identified by employing RF (Random Forest). The experimental results show that the MLS-SEL user profile model is higher than the models MVEM, SG and SMUPM in terms of both F1 value and accuracy rate. It is implied that users who could be more sensitive to fluctuations in electricity costs have been identified more accurately.

High-precision retrieval of offshore sea surface temperature: A machine learning framework based on MODIS and in-situ measurements

High-precision sea surface temperature (SST) data is essential for monitoring and management of offshore environment. SST retrievals from thermal infrared remote sensing have the advantages in time frequency and space coverage over measurements from ships and buoys. However, traditional SST retrieval models are not fully applicable for offshore waters where seawater conditions change a lot, due to simple functional forms and empirical fitting of model parameters. Machine learning (ML) is introduced because it doesn’t need to define specific model rules or unknown parameters, which is more intelligent to deal with complex retrieval problems in a data-driven way. But the applicability of ML has not been investigated in the North Yellow Sea of China, and the selection of driving features for model construction has not been fully discussed. Here, we designed a high-precision SST retrieval framework based on random forest (RF) with optimal feature combination using MODIS data and in-situ SST measurements during 2013–2020 in the northern part of the North Yellow Sea. The performances of different feature combinations were assessed based on model testing accuracies and the RF output feature importance. Compared with previous ML-based SST retrieval studies, we added two time-related features (i.e., day of the year and month) and one feature as initial temperature (i.e., SST retrieval from split-window algorithm), which reduced the testing error by around 15 % and 11 %, respectively. The retrieval results were validated with in-situ measurements, showing that the RF model achieved better accuracies (R2 = 0.987, SD = 0.842 °C, MAE = 0.675 °C, RMSE = 0.841 °C) than the improved split-window algorithm (named as ISW) and deep neural network. Compared with ISW, the spatial distribution of RF retrieval map exhibited similar variation across four seasons, mainly differed in the coastal areas or at the formation of temperature fronts. Note that the constructed RF model was still reliable when applied to the independent samples in 2021. This study contributes to improve the accuracy of offshore SST retrieval under different seawater conditions and provides references for SST monitoring.

Integrating GIN-based multimodal feature transformation and multi-feature combination voting for irony-aware cyberbullying detection

With the increasing diversity of expressions, irony-aware cyberbullying has emerged as a significant issue in online social networks. However, detecting irony-aware cyberbullying is challenging, as it requires a comprehensive understanding of context and external factors beyond literal meanings. To take full advantage of multiple features of multimodal data to detect challenging irony-aware cyberbullying, we propose an integration framework (GINBV_MFCV). The multimodal feature construction method with Graph Isomorphism Network (GIN) feature transformation (GINBV) leverages the message passing and aggregation operations of GIN to extract the potential representations of text-image features, which enriches the structural information of multimodal data. In addition, the multi-feature combination voting strategy (MFCV) soft-votes the prediction results of constructed multimodal features and multiple combinations of GIN, Bidirectional Encoder Representations from Transformers (BERT), and Vision Transformers (ViT) embedded features to reduce the data structure information bias, which has a positive effect on irony-aware cyberbullying detection. Experimental results on a real-world dataset from Weibo demonstrate that GINBV_MFCV achieves an F1-score of 83.29% and an AUC of 91.21% in irony-aware cyberbullying detection, improving 8.65% and 15.73% over the baseline algorithm, respectively. These promising results confirm the potential of GINBV_MFCV for detecting irony-aware cyberbullying.

Classification method of traditional Chinese medicine compound decoction duration based on multi-dimensional feature weighted fusion

This paper extends a text classification method utilizing natural language processing (NLP) into the field of traditional Chinese medicine (TCM) compound decoction to effectively and scientifically extend the TCM compound decoction duration. Specifically, a TCM compound decoction duration classification named TCM-TextCNN is proposed to fuse multi-dimensional herb features and improve TextCNN. Indeed, first, we utilize word vector technology to construct feature vectors of herb names and medicinal parts, aiming to describe the herb characteristics comprehensively. Second, considering the impact of different herb features on the decoction duration, we use an improved Term Frequency-Inverse Word Frequency (TF-IWF) algorithm to weigh the feature vectors of herb names and medicinal parts. These weighted feature vectors are then concatenated to obtain a multi-dimensional herb feature vector, allowing for a more comprehensive representation. Finally, the feature vector is input into the improved TextCNN, which uses k-max pooling to reduce information loss rather than max pooling. Three fully connected layers are added to generate higher-level feature representations, followed by softmax to obtain the final results. Experimental results on a dataset of TCM compound decoction duration demonstrate that TCM-TextCNN improves accuracy, recall, and F1 score by 5.31%, 5.63%, and 5.22%, respectively, compared to methods solely rely on herb name features, thereby confirming our method’s effectiveness in classifying TCM compound decoction duration.

Assembly feature construction method of equipment mesh model for digital twin workshops

The digital twin workshop is the basic unit for realizing smart manufacturing. Its model is the prerequisite for applying digital twins in workshops. Geometric model assembly is the primary concern in workshop-level digital twin modeling. However, current research lacks sufficient focus on the efficiency and application modes of equipment mesh model assembly features in geometric model assembly, hindering the construction of the workshop-level digital twin model. The equipment-level digital twin model typically only contains assembly information between individual parts and is generally represented by polygonal meshes; the lack of assembly information between equipment models impedes the assembly of the workshop-level digital twin model. To improve the efficiency of workshop-level digital twin modeling and the reusability of the original 3D assembly information, this paper proposes an assembly feature construction method of equipment mesh model for digital twin workshops. A 3D assembly information model is constructed to describe the information related to the equipment geometric model and the assembly features. Then, the assembly features of the equipment mesh model are gradually positioned preliminarily and precisely, achieving precise mapping of the assembly features in the 3D information model to the equipment mesh model and efficient construction of equipment mesh model assembly features. Finally, with the construction and application of equipment mesh model assembly features in an arc welding workshop and storage workshop as an example, the feasibility of the method in improving the modeling efficiency is verified through relevant comparative experiments.

Directional region-based feature point matching algorithm based on SURF.

Feature point matching is one of the fundamental tasks in binocular vision. It directly affects the accuracy and quality of 3D reconstruction. This study proposes a directional region-based feature point matching algorithm based on the SURF algorithm to improve the accuracy of feature point matching. First, same-name points are selected as the matching reference points in the left and right images. Then, the SURF algorithm is used to extract feature points and construct the SURF feature point descriptors. During the matching process, the location relationship between the query feature point and the reference point in the left image is directed to determine the corresponding matching region in the right image. Then, the matching is completed within this region based on Euclidean distance. Finally, the grid-based motion statistics algorithm is used to eliminate mismatches. Experimental results show that the proposed algorithm can substantially improve the matching accuracy and the number of valid matched points, particularly in the presence of a large amount of noise and interference. It also exhibits good robustness and stability.

Clothing preferences of young males (18-26 years) for designing of khadi kurtas with different construction features

Clothing preferences of young males (18-26 years) for designing of khadi kurtas with different construction features

A Physics-Guided Graph Convolution Neural Network for Optimal Power Flow

The data-driven method with strong approximation capabilities and high computational efficiency provides a promising tool for optimal power flow (OPF) calculation with stochastic renewable energy. However, the topology change dramatically increases the learning difficulties and the demand for learning samples. In this work, we propose a physics-guided graph convolution neural network (GCNN) for OPF calculation with consideration of varying topologies, including the physics-guided graph convolution kernel, feature construction, and loss function formulation. Specifically, a physics-embedded graph convolution kernel is derived by aggregating the features from local neighborhoods utilizing the nodal OPF model formulation. An iterative feature construction method is also developed that encodes both the physical feature and practical constraints into the node vector. Finally, a correlative learning loss function to optimize the unbalanced power injection is developed. Extensive numerical results carried out on various IEEE test systems show that the prediction accuracy of OPF using the proposed method under varying topology changes can be improved by an average of 13.30% and up to 32.63% compared with state-of-the-art methods.

Features of design and construction works in the context of digital transformation of industrial pro duction

The analysis results of the scientific and technological progress influence on technologies for performing design and construction works are presented. A model of the design and construction works performing, based on a service-oriented approach to the creation of complex technical systems, which takes into account the features of the digital transformation of business models for modern design organizations is proposed. The question about the relevance of developing new methods for assessing the effectiveness of design and construction activities is raised. Keywords design and construction works, digital transformation, Industry 4.0, process model, efficiency assessment

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.

L-VSM: Label-Driven View-Specific Fusion for Multiview Multilabel Classification.

In the task of multiview multilabel (MVML) classification, each instance is represented by several heterogeneous features and associated with multiple semantic labels. Existing MVML methods mainly focus on leveraging the shared subspace to comprehensively explore multiview consensus information across different views, while it is still an open problem whether such shared subspace representation is effective to characterize all relevant labels when formulating a desired MVML model. In this article, we propose a novel label-driven view-specific fusion MVML method named L-VSM, which bypasses seeking for a shared subspace representation and instead directly encodes the feature representation of each individual view to contribute to the final multilabel classifier induction. Specifically, we first design a label-driven feature graph construction strategy and construct all instances under various feature representations into the corresponding feature graphs. Then, these view-specific feature graphs are integrated into a unified graph by linking the different feature representations within each instance. Afterward, we adopt a graph attention mechanism to aggregate and update all feature nodes on the unified graph to generate structural representations for each instance, where both intraview correlations and interview alignments are jointly encoded to discover the underlying consensuses and complementarities across different views. Moreover, to explore the widespread label correlations in multilabel learning (MLL), the transformer architecture is introduced to construct a dynamic semantic-aware label graph and accordingly generate structural semantic representations for each specific class. Finally, we derive an instance-label affinity score for each instance by averaging the affinity scores of its different feature representations with the multilabel soft margin loss. Extensive experiments on various MVML applications have verified that our proposed L-VSM has achieved superior performance against state-of-the-art methods. The codes are available at https://gengyulyu.github.io/homepage/assets/codes/LVSM.zip.

TMFO-AGGRU: A Graph Convolutional Gated Recurrent Network for Metro Passenger Flow Forecasting

Accurately exploiting the spatial correlation of passenger flow related to the metro network is important for improving metro passenger flow prediction. In this paper, a novel metro passenger flow forecasting model termed TMFO-AGGRU is proposed based on parameter estimation optimization for a graph convolutional gated recurrent neural network. First, we model a graph convolutional gated recurrent neural network, which is capable of real-time spatial and temporal information training on the passenger flow at a target station. Second, we design graph convolution operations to replace the linear operations of gated recurrent neural networks. To fully utilize the weighted distribution of output data based on different traffic characteristics, we convert the adjacent static matrices into dynamic characteristic matrices using feature learning and construct the attention mechanism module. Last, an adaptive moth-flame optimization method based on T-variance is proposed in this paper to achieve dynamic optimization of structural parameters and to avoid affecting the accuracy of prediction due to improper parameter selection. Experimental results show that the proposed TMFO-AGGRU effectively improves the prediction accuracy and convergence speed, which outperforms traditional metro passenger flow prediction methods.

АКТУАЛЬНІ ПРОБЛЕМИ БЮДЖЕТУВАННЯ ВИТРАТ БУДІВЕЛЬНИХ ПІДПРИЄМСТВ В УКРАЇНІ

The article is devoted to the study of the current problems of developing the budget structure and budgeting the costs of construction enterprises. The analysis of the dynamics of construction in Ukraine for 2012-2022, conducted according to statistical data, showed an increase in production volumes, despite the impact of negative factors. It is emphasized that the economic and managerial aspects of the activity of construction enterprises, including drawing up a consolidated budget, are largely determined by the specific features of construction as a branch of the economy. When developing the list and structure of operating budgets, construction enterprises need to take into account both their own production capacity and the rhythm of the supply of raw materials, materials and components, and a number of influencing factors. It is important to make assumptions about the probability of external influence on the components of construction works by other participants in the construction process (investors, designers, subcontractors, etc.). Thus, changes in the schedule of investment receipts, non-compliance with the terms of the contract by suppliers of raw materials or materials, inaccuracy of the developed estimate documentation can lead to additional costs. Since construction is one of the most material-intensive industries, preparation of the budget for material costs is of priority. It is noted that the budgeting of the wage component and deductions for social events should contribute to the motivation of workers at existing enterprises and reduce the turnover of qualified personnel in conditions of labor shortage. Due to budgeting, it is possible to carry out preliminary control over costs, in order to prevent irrational use of resources and to check compliance with the accepted standards of costs for individual construction works. It was determined that the budgeting process, as an element of financial planning, has prospects for use in calculating the forecasted values of the revenues of enterprises of the relevant branch of the national economy, which is especially important in the conditions of recession of the country's economy in a state of war.

Architectural and Constructive Features of Museums and Exhibition Institutions: Innovative Transformation of Space in the Conditions of War

Architectural and Constructive Features of Museums and Exhibition Institutions: Innovative Transformation of Space in the Conditions of War

Contractual Chains

This paper develops a model of private bilateral contracting, in which an exogenous network determines the pairs of players who can communicate and contract with each other. After contracting, the players interact in an underlying game with globally verifiable productive actions and externally enforced transfers. The paper investigates whether such decentralized contracting can internalize externalities that arise due to parties being unable to contract directly with others whose productive actions affect their payoffs. The contract‐formation protocol, called the “contracting institution,” is treated as a design element. The main result is positive: There is a contracting institution that supports efficient equilibria for any underlying game and connected network. A critical property is that the institution allows for sequential contract formation or revision. The equilibrium construction features assurance contracts and cancellation penalties.

ARCHAEOLOGICAL RESEARCH AT THE TASTAK CEMETERY IN 2023 (ABAI REGION, URZHAR DISTRICT)

The article introduces for the first time the results of archaeological research at the Tastak cemetery in the field season of 2023, located in the southern foothills of the Tarbagatai Range. The mentioned region in archaeological terms is a little-studied region. In the process of field stationary works, recon-naissance of the area was carried out and a topographic plan of the cemetery was taken. Two kurgans (no. 7 and no. 11) were investigated during this period. According to the planographic arrangement of the sites, the structure of the overmound and intra-mound construction, as well as the position and orientation of the burials corresponds to the classical Saka period. Some constructive features of the structure were revealed: stone shell, grave fence. The noted constructive features of the construction can be connected with ancient migration processes in this period. In the course of comparative-typological analysis, which includes a versatile characterisation of the archaeological source, identification of dated analogies, as well as 14C radiocarbon AMS dating, the investigated sites are generally placed in the IV century BC. Despite the fact that the burials were looted earlier, the materials we obtained during the study of the site will certainly make a great contribution to the study of material and spiritual culture of the Saka tribes of East Kazakhstan.

MDKLoss: Medicine domain knowledge loss for skin lesion recognition.

Methods based on deep learning have shown good advantages in skin lesion recognition. However, the diversity of lesion shapes and the influence of noise disturbances such as hair, bubbles, and markers leads to large intra-class differences and small inter-class similarities, which existing methods have not yet effectively resolved. In addition, most existing methods enhance the performance of skin lesion recognition by improving deep learning models without considering the guidance of medical knowledge of skin lesions. In this paper, we innovatively construct feature associations between different lesions using medical knowledge, and design a medical domain knowledge loss function (MDKLoss) based on these associations. By expanding the gap between samples of various lesion categories, MDKLoss enhances the capacity of deep learning models to differentiate between different lesions and consequently boosts classification performance. Extensive experiments on ISIC2018 and ISIC2019 datasets show that the proposed method achieves a maximum of 91.6% and 87.6% accuracy. Furthermore, compared with existing state-of-the-art loss functions, the proposed method demonstrates its effectiveness, universality, and superiority.

A Semantic-Based Hoist Mutation Operator for Evolutionary Feature Construction in Regression

A Semantic-Based Hoist Mutation Operator for Evolutionary Feature Construction in Regression

Research on hybrid remanufacturing process chain of laser cladding, CNC machining and ultrasonic rolling for aero-engine blades

There are high geometric accuracy and mechanical performance requirements in the repair and remanufacturing process for aero-engine blades. Therefore, a synergy of multiple processes is necessary for blade repair. This paper mainly studies the coupling relationship between multiple processes of aero-engine blade repair and the influence and contribution of each process to the final surface integrity of the blade. Firstly, a hybrid remanufacturing process chain of laser cladding, CNC machining and ultrasonic rolling is proposed for the requirements of blade repair. Then, a numerical model of the geometric feature construction and transient thermal and structural behavior in the hybrid remanufacturing process chain for blades is established. Finally, the interaction law among the three processes of laser cladding, CNC machining and ultrasonic rolling is analyzed. The results show that a larger powder feed rate is allowed in the laser cladding process of the hybrid remanufacturing process chain for blades. Thermal deformation and residual stress caused by high powder feed rate and thin-walled structure can be optimized by CNC machining and ultrasonic rolling. In addition, the influence mechanism of the three processes on the final surface integrity is revealed. It is shown that the effects of the hybrid remanufacturing process parameters on the remanufacturing quality are different from that of the traditional process. The feasibility of the hybrid remanufacturing process chain for blades is illustrated by the verification experiment on the damaged blade.

A Multitree Genetic Programming-Based Feature Construction Approach to Crop Classification Using Hyperspectral Images

A Multitree Genetic Programming-Based Feature Construction Approach to Crop Classification Using Hyperspectral Images