During the deep peak shaving period, the boiler needs to operate at a lower load, so higher requirements were set for the boiler's stable combustion. In order to better evaluate and compare the... Show More

During the deep peak shaving period, the boiler needs to operate at a lower load, so higher requirements were set for the boiler's stable combustion. In order to better evaluate and compare the stable ignition capacity of bluff-body burners and slotted bluff-body burners, guidance and calculation support for the design of boiler deep peak shaving were needed. This study adopted the reflux heating chain ignition analysis method to study the differences in the steady combustion mechanism between the bluff-body burner and the slotted bluff-body burner. A thermal combustion experiment was conducted in employing the single angle coal powder combustion furnace. The experimental results were compared against the theoretical analysis results. In addition, a study was conducted on the application of slotted bluff-body burners in the deep peak shaving of a 330MW unit power plant boiler. The results showed that as long as the small slot flow can catch fire, the mixed temperature of the reflux fluid of the slotted bluff-body burner will be higher than that of the bluff body burner. This will enhance its steady combustion ability. The combustion test results were found to be consistent with the analysis results, indicating that when the slotted bluff-body burner is used on the 330MW unit, the boiler combustion is in good condition. In this case, it has a stable combustion capacity of 66MW (20% economic continuous rating) without oil injection at low loads. This study revealed the advantages of the slotted bluff-body burner in terms of its steady combustion mechanism compared to traditional bluff-body burners. It verified the feasibility for boiler deep peak shaving in practical applications. This is of great significance for improving the flexibility of coal-fired power generation units, enhancing the flexibility of the power system, and promoting carbon reduction.

To improve transportation efficiency, this paper analyzes the factors of transportation structure from the two levels of transportation—input and system output. An epsilon-based measure model of... Show More

To improve transportation efficiency, this paper analyzes the factors of transportation structure from the two levels of transportation—input and system output. An epsilon-based measure model of non-expected output is introduced, and the environmental benefits of transportation are considered. This model is used to analyze the regional transportation efficiency of 30 provinces and cities in China. Tobit regression and geographically weighted regression are applied to analyze the causes and spatial variation of differences in the efficiency of the transportation structure, and corresponding structural adjustment strategies are proposed. The results show that the regression coefficients of the share of secondary industry output in GDP, population density, and social fixed asset investment exert the most significant effects on transportation structure efficiency. The spatial distribution of sub-variable coefficients shows that spatial heterogeneity exists in the degree of influence of various socio-economic factors on the transportation structure efficiency in different regions.

To address issues related to the insufficient representation of text semantic information and the lack of deep fusion between internal modal information and intermodal information in current... Show More

To address issues related to the insufficient representation of text semantic information and the lack of deep fusion between internal modal information and intermodal information in current multimodal sentiment analysis (MSA) methods, a new method integrating multi-layer attention interaction and multi-feature enhancement (AM-MF) is proposed. First, multimodal feature extraction (MFE) is performed based on RoBERTa, ResNet, and ViT models for text, audio, and video information, and high-level features of the three modalities are obtained through self-attention mechanisms. Then, a cross modal attention (CMA) interaction module is constructed based on transformer, achieving feature fusion between different modalities. Finally, the use of a soft attention mechanism for the deep fusion of internal and intermodal information effectively achieves multimodal sentiment classification. The experimental results CH-SIMS and CMU-MOSEI datasets show that the classification results of proposed MSA method are significantly superior to other advanced comparative methods.

In this study, the authors perform slant correction on fixed-format dial images using the Hough transform, followed by their “scaling” using binary wavelets to achieve coarse segmentation for... Show More

In this study, the authors perform slant correction on fixed-format dial images using the Hough transform, followed by their “scaling” using binary wavelets to achieve coarse segmentation for characters or numbers. Simultaneously, they propose a binary threshold iteration method that accurately determines the position of each character or number even in the presence of adhesion or fragmentation, enabling precise segmentation. They employ their proposed approach to segment digits and characters displayed on 98 fixed-format dials. Experimental results demonstrate a recognition rate of 98.5% for both letters and numbers, highlighting significant practical value and real-world implications.

The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the... Show More

The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the operation and maintenance of the power system. This article presents an approach employing graph neural network (GNN) to enhance the precision and efficiency of power system fault diagnosis and prediction. The system's efficacy lies in its ability to capture the intricate interconnections and dynamic variations within the power system by conceptualizing it as a graph structure and harnessing the capabilities of GNN. In this study, the authors introduce a substitution for the pooling layer with a convolution operation. A central role is played by the global average pooling layer, connecting the convolution layer and the fully connected layer. The fully connected layer carries out nonlinear computations, ultimately providing the classification at the top-level output layer. In experiments and tests, we verified the performance of the system.

This integration enables the system to collect and monitor information from remote sources efficiently. During the course of this research, a novel predictive PID approach was developed, splitting... Show More

This integration enables the system to collect and monitor information from remote sources efficiently. During the course of this research, a novel predictive PID approach was developed, splitting the control architecture into two tiers. The upper tier utilizes the extreme learning machine (ELM) as an intelligent predictive model, while the lower tier integrates an enhanced single-neuron adaptive predictive PID control algorithm, combining the strengths of ELM and PID control. The research findings suggest that the AI algorithm-based instrument automatic monitoring and control system holds significant promise. This technology has the potential to enhance production efficiency, reduce energy consumption, improve environmental monitoring, and provide superior safety and quality control.

Chinese financial institutions (CFIs) are increasingly embracing artificial intelligence (AI) for their financial decision-making driven by AI's capacity to mitigate risks and enhance efficiency and... Show More

Chinese financial institutions (CFIs) are increasingly embracing artificial intelligence (AI) for their financial decision-making driven by AI's capacity to mitigate risks and enhance efficiency and accuracy. However, there remain ethical challenges related to the integration of AI in financial decision-making. This study develops the AI ethics best practices model (AB-PraM) to mitigate ethical concerns and enhance the application of AI in financial decision-making. By employing a quantitative methodology, this research collected questionnaire data from 320 financial experts in CFIs. Structural equation modelling (SEM) was adopted to identify AI ethics best practices for the implementation of the AB-PraM. The findings of this research will mitigate AI ethics challenges in CFIs and provide a practical framework for transparent and accountable decision-making in alignment with ethical standards and regulations.

In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based... Show More

In order to solve the problems that most models are complex, time-consuming, and have difficulty in identifying image errors, an image identification and error correction method of test report based on deep reinforcement learning and the internet of things platform in the smart lab was proposed. Firstly, a smart lab architecture was designed based on the internet of things platform, achieving efficient operation of the laboratory through cloud edge collaboration. Then, the depth separable convolution improved convolutional neural network is used to extract image features, and the features are input into bidirectional recurrent neural networks (BiLSTM) for analysis to complete image recognition. Finally, the ICNN-BiLSTM model is used as the agent of reinforcement learning, and image error correction is completed by identifying the distance between the image and the key points of the reference image. Based on the Python platform, the proposed method was experimentally demonstrated, and the results showed that its average error correction accuracy reached 96.75%, with a processing time of 15.37s.

Traditional methods often fall short in modeling the nonlinear, seasonally variable nature of urban water demand. This proposed solution is an integrated ARIMA-LSTM deep learning model, combining... Show More

Traditional methods often fall short in modeling the nonlinear, seasonally variable nature of urban water demand. This proposed solution is an integrated ARIMA-LSTM deep learning model, combining ARIMA's proficiency in linear trend and seasonal modeling with LSTM's strength in capturing nonlinear time dependencies. In these experiments, the authors trained and evaluated using daily water demand data from 2015 to 2020, with its performance validated for the year 2021. The ARIMA-LSTM model demonstrates promising results, outperforming individual models in terms of accuracy. In validation, it achieves a high coefficient of determination (R2) of 0.98 and a significantly low root mean square error (RMSE) of 2.94. These metrics indicate an excellent fit to the data and a high level of precision in its predictions. The significance of this research lies in its potential to advance the field of urban water demand forecasting, ultimately contributing to better water resource management and sustainability in urban areas.

Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete... Show More

Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete, which disregard the legitimate miners' investments in computational power and energy consumption. In order to assess the efficiency of the Bitcoin system in real-time and promptly detect malicious miners in the network, this paper proposes a data collection framework called BitTrace, which addresses the issues of low efficiency, lack of timeliness, and data loss in traditional data collection frameworks. BitTrace enables real-time collection and analysis of the blockchain formation process, storing it as structured data. Furthermore, the paper discusses factors that influence the efficiency of data collection and proposes a topological control scheme based on the DPC algorithm to enhance the integrity and efficiency of data collection. Researchers can explore various research areas and applications, such as selfish mining detection and legitimate mining strategy research.

Existing models still exhibit a deficiency in capturing more detailed contextual information when processing architectural images. This paper introduces a model for architectural image segmentation... Show More

Existing models still exhibit a deficiency in capturing more detailed contextual information when processing architectural images. This paper introduces a model for architectural image segmentation and retrieval based on an image segmentation network. Primarily, spatial attention is incorporated into the U-Net segmentation network to enhance the extraction of image features. Subsequently, a dual-path attention mechanism is integrated into the U-Net backbone network, facilitating the seamless integration of information across different spaces and scales. Experimental results showcase the superior performance of the proposed model on the test set, with average dice coefficient, accuracy, and recall reaching 94.67%, 95.61%, and 97.88%, respectively, outperforming comparative models. The proposed model can enhance the U-Net network's capability to identify targets within feature maps. The amalgamation of image segmentation networks and attention mechanisms in artificial intelligence technology enables precise segmentation and retrieval of architectural images.

Aimed at the problem of large errors in traditional power cable temperature measurement methods, a method based on edge computing and radio frequency identification (RFID) is proposed. Firstly, a... Show More

Aimed at the problem of large errors in traditional power cable temperature measurement methods, a method based on edge computing and radio frequency identification (RFID) is proposed. Firstly, a RFID electronic label design scheme was constructed utilizing the radio frequency signal between alternating electromagnetic fields to achieve the communication and information identification between two devices. Then, the design of power cable edge intelligent terminal is analyzed from the aspects of hardware and software. On this basis, the early warning criterion formula of power cable temperature fault state is abstracted by using edge computing algorithm. Based on edge computing and RFID, the corresponding temperature measurement method is proposed. Finally, the proposed power cable temperature measurement method is compared with other three methods through simulation experiments. The results indicate that the temperature measurement error of the proposed method is the minimum under different currents and in cross-sectional areas. Compared to the other three methods, the maximum improvement is 5.15% and 7.92%, while the minimum improvement is 1.05% and 1.45%, which outperforms the comparable algorithms in terms of performance.

Multi-depot vehicle routing problem with time windows (MDVRPTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive... Show More

Multi-depot vehicle routing problem with time windows (MDVRPTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive problems instantly. Thus, this article presents a novel reinforcement learning algorithm integrated with a multi-head attention mechanism and a local search strategy to solve the problem efficiently. The routing optimization was regarded as a vehicle tour generation process and an encoder-decoder was used to generate routes for vehicles departing from different depots iteratively. A multi-head attention strategy was employed for mining complex spatiotemporal correlations within time windows in the encoder. Then, a decoder with multi-agent was designed to generate solutions by optimizing reward and observing transition state. Meanwhile, a local search strategy was employed to improve the quality of solutions. The experiments results demonstrate that the proposed method can significantly outperform traditional methods in effectiveness and robustness.

The presence of structure heterogeneity in regional innovation networks reflects the complexity and diversity of knowledge diffusion and collaborative R& D relationships. This article introduces a... Show More

The presence of structure heterogeneity in regional innovation networks reflects the complexity and diversity of knowledge diffusion and collaborative R& D relationships. This article introduces a network model based on the multiple systems generating functions mathematical algorithm to analyze the resilience of interacting networks under different link patterns. The percolation threshold is illustrated at two different levels: the subcritical and supercritical states. The algorithm is then tested on both simulated networks and real-world networks. The results of the simulation study highlight the crucial role of linking between sub-networks and emphasize the effectiveness of a moderate degree protection strategy.

Aiming at the problem that the environmental planning path of intelligent logistics vehicles on urban roads and remote mountainous areas cannot fit the actual driving scene well. This study creates... Show More

Aiming at the problem that the environmental planning path of intelligent logistics vehicles on urban roads and remote mountainous areas cannot fit the actual driving scene well. This study creates the algorithm model that combines an ant colony algorithm with a dynamic window algorithm and a Bessel smoothing strategy. Compared to the traditional colony algorithm with the same parameters, this fusion algorithm makes the path smoother by 72.2% when used on an urban highway. It also follows the right-hand rule for right-turn intersections. When the vehicle's height is determined in a mountain environment, this fusion algorithm reduces the driving's mean square deviation of height by 81.5% and shortens the path distance by 38.7%. The fusion algorithm can plan the target path of intelligent logistics vehicles and has the characteristics of scenarios available, multiple factors coordinated, and driving safety. It has provided certain research value and ideas for the digital transformation of the logistics industry.

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors... Show More

The construction of subway projects involves tight engineering cycles, multiple technical challenges, and complex coordination among various stakeholders. Due to the influence of uncertain factors during the construction process, the investment in subway project construction exhibits non-linear changes over time. Investment decision-making is the process through which the investment entity determines its investment activities. For typical investment entities, project investment decision-making primarily entails analyzing and evaluating proposed engineering projects based on investigation, analysis, and argumentation, ultimately deciding whether to invest. With the widespread application of information technology (IT) across various fields, decision support systems (DSS) have emerged to enhance the decision-making capabilities of enterprise management. This article designs an intelligent subway project investment DSS, leveraging data mining (DM) technology to integrate DSS with a data warehouse (DW).

This study aims to address the issues of image noise and distortion in machine dial recognition via initial denoising. A wavelet local threshold denoising method that combines high-frequency wavelet... Show More

This study aims to address the issues of image noise and distortion in machine dial recognition via initial denoising. A wavelet local threshold denoising method that combines high-frequency wavelet coefficients with wavelet decomposition coefficients in various directions is proposed. This method shows good results on 102 images of car dashboards, aircraft instrument panels, spacecraft displays, and dial instruments on robots. Although a few denoised images exhibit distortion due to intense lighting or heavy contamination, the denoising accuracy for the remaining images is 98.04%, demonstrating substantial practical value. Future research will concentrate on addressing complex image noise and structures.

With the deepening of globalization and the rapid development of science and technology, the world economic situation has become increasingly complex. The financial risk of an enterprise is directly... Show More

With the deepening of globalization and the rapid development of science and technology, the world economic situation has become increasingly complex. The financial risk of an enterprise is directly related to its survival and development. This research refines the XGBoost algorithm by employing a reinforcement learning framework. Initially, the iterative process of the Q-value table is honed, integrating a time discount factor to accelerate convergence in hyperparameter optimization within the traditional Q-learning algorithm. Ultimately, optimization of the hyperparameters of the XGBoost algorithm is accomplished through the enhanced Q-learning algorithm and the Granger causal network model. Experimental outcomes reveal that the precision and recall of the refined Q-learning algorithm on the German Credit Data dataset stand at 85.74% and 92.33%, respectively. This model adeptly elucidates the origins and transmission mechanisms of financial risks, assisting companies in acquiring a nuanced comprehension of their financial situation and the broader market milieu.

A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data... Show More

A malicious traffic sample adaptive enhancement device based on Deep Convolutional Generative Adversarial Network (DCGAN) is designed to address the issue of imbalanced network traffic data distribution, aiming to enhance the accuracy and efficiency of anomaly detection. By leveraging generative adversarial network technology, this device can generate samples similar to real malicious traffic to balance the training dataset. It utilizes the generator and discriminator of the Deep Convolutional Generative Adversarial Network (DCGAN), combined with the residual network (ResNet) in the CNN model, to enhance the quality of generated samples. The device can switch states to adapt to various network environments and has been experimentally validated for its effectiveness and feasibility.Moreover, employing an adaptive device, the samples of malicious traffic are adjusted. Experimental analysis demonstrates that the device significantly enhances the accuracy of anomaly traffic detection, improves robustness, and provides robust support for network security protection.

A new OPGW state evaluation method based on Multi-Source Information Fusion (MSIF) and Quantum Particle Swarm Optimization & Deep Q-learning (QPSO-DQN) is proposed. Firstly, using MSIF to integrate... Show More

A new OPGW state evaluation method based on Multi-Source Information Fusion (MSIF) and Quantum Particle Swarm Optimization & Deep Q-learning (QPSO-DQN) is proposed. Firstly, using MSIF to integrate and unify historical data and real-time monitoring data of OPGW, more comprehensive and accurate OPGW status information was obtained. Then, utilizing the advantages of deep reinforcement learning (DRL) algorithm DQN in handling highly nonlinear problems, various influencing factors related to the operation of OPGW were addressed. Finally, DQN was improved by introducing the QPSO optimization algorithm, which transformed the Q-value function solving in DQN into a function fitting problem and used QPSO as an intelligent agent to fit the function, achieving accurate evaluation of the OPGW operating status. The simulation experiment results show that the proposed method has the highest accuracy in ice weight detection, temperature detection, frequency detection, and optical power detection on the same dataset, reaching 98.85%, 98.97%, 98.13%, and 98.97%, respectively.

In the context of rapid urbanization, the challenge of effective garbage disposal has become increasingly significant. Traditional methods for addressing illegal littering by pedestrians are not... Show More

In the context of rapid urbanization, the challenge of effective garbage disposal has become increasingly significant. Traditional methods for addressing illegal littering by pedestrians are not only inefficient but also resource-intensive, demanding considerable manpower and materials. This study introduces a deep learning-based approach for detecting improper garbage disposal behavior. Leveraging advanced deep learning technologies, this approach focuses on object detection, tracking, and human posture analysis to identify and alert against illegal dumping activities captured in video footage, specifically targeting incidents outside designated times or areas. The primary aim is to facilitate prompt detection and mitigate associated health risks. The system uses three deep learning models, YOLOv5, DeepSORT and MobilePose. YOLOv5 is used to identify the human body and garbage bag, DeepSORT is used to track the two, and MobilePose identifies the key points of the human body for posture estimation. The tracking algorithm is used to determine whether to throw garbage.

In the realm of computer vision, recognizing pedestrian attributes is a crucial task, with the objective of deducing the identity characteristics of individuals on foot. This differs from... Show More

In the realm of computer vision, recognizing pedestrian attributes is a crucial task, with the objective of deducing the identity characteristics of individuals on foot. This differs from conventional pedestrian detection methods in that it not only identifies the presence of pedestrians but also examines their attributes such as gender, age, and attire by scrutinizing their visual features. Not only can recognition identify the existence of pedestrians, but it can also delve into the analysis of pedestrian attributes. This paper proposes, explores, and implements a pedestrian attribute recognition algorithm grounded in deep convolutional neural networks. First, several datasets containing large-scale pedestrian images and their corresponding data are used for the recognition of pedestrian attributes. containing large-scale pedestrian images and their corresponding attribute labels are utilized. Through data pre-processing and enhancement techniques, the noise and inconsistency of pedestrian images are reduced. techniques, the noise and inconsistency of the data are.

The article provides an in-depth analysis of Nvidia's technological evolution and its profound impact on Machine Learning, Big Data, and Artificial Intelligence (AI) on a global scale. Nvidia has... Show More

The article provides an in-depth analysis of Nvidia's technological evolution and its profound impact on Machine Learning, Big Data, and Artificial Intelligence (AI) on a global scale. Nvidia has emerged as a trailblazer, reshaping computational capabilities, and establishing itself as a prominent player in a fiercely competitive landscape. The examination meticulously scrutinizes the role of Nvidia's graphics processing unit (GPU) technologies in spearheading a transformative computing revolution, emphasizing the collaborative prowess inherent in Nvidia's developer ecosystem. The analysis extends to the dynamics of GPU innovation, its disruptive influence on the market, and the robust innovation engine ingrained within Nvidia's culture of calculated risk-taking. Internal and external factors contributing to Nvidia's remarkable success, and its consequential industry dominance are thoroughly investigated. Special attention is directed towards Nvidia's strategic development, technological advancements, influence on the industry, global footprint, and anticipated future implications.

Many existing fine-grained sentiment analysis (FGSA) methods have problems such as easy loss of fine-grained information, difficulty in solving polysemy and imbalanced sample categories. Therefore... Show More

Many existing fine-grained sentiment analysis (FGSA) methods have problems such as easy loss of fine-grained information, difficulty in solving polysemy and imbalanced sample categories. Therefore, a Transformer based FGSA method for Weibo comment text is proposed. Firstly, the RoBERTa model with knowledge augmentation was used to dynamically encode the text so as to solving the polysemy issue. Then, BiLSTM is used to effectively capture bidirectional global semantic dependency features. Next, Transformer is used to fuse multi-dimensional features and adaptively strengthen key features to overcome the problem of fine-grained information loss. Finally, an improved Focal Loss function is utilized for training to solve the issue of imbalanced sample categories. As demonstrated by the experimental outcomes on the SMP2020-EWECT, NLPCC 2013 Task 2, NLPCC 2014 Task 1, and weibo_senti_100k datasets, the suggested method outperforms the alternatives for advanced comparison methods.

Visual Acuity (VA) assessment is crucial for early vision screening, yet traditional methods are manual and time-consuming. Despite the advancements in human-computer interaction (HCI), there is no... Show More

Visual Acuity (VA) assessment is crucial for early vision screening, yet traditional methods are manual and time-consuming. Despite the advancements in human-computer interaction (HCI), there is no existing system fully addresses accuracy, efficiency and adaptability. This study introduces an intelligent VA assessment system that combines MediaPipe-based static gesture recognition with a novel Naive Bayes Classifier (NBC)-based VA Thresholds Determination (VATD) scheme. This integration offers a non-contact, user-friendly approach for rapid and precise VA testing. The VATD scheme is designed to significantly reduce the number of test trials; thereby, substantially improving the efficiency. Experimental validation confirms the system's high accuracy (96.72%) within a ±0.1 deviation from standard methods, achieves a 68% reduction in test time compared to traditional methods, and offers a 27% efficiency improvement over ANN-based systems. This system promises to enhance VA assessments, particularly for children and adolescents, with its speed, accuracy, and broader applicability.

This paper introduces the cultural dimension of product packaging, elucidates the quantification principle and methodology governing customer aesthetic experiences, propounds an innovative packaging... Show More

This paper introduces the cultural dimension of product packaging, elucidates the quantification principle and methodology governing customer aesthetic experiences, propounds an innovative packaging style design approach, and presents an evaluation model based on an enhanced neural network. Using tea packaging design as an illustrative case, the methodology initially aligns adjectives, subsequently computes the emotional depth value, and ultimately derives the corresponding correlation between customer emotional experiences and design elements through a neural network. Subsequently, the designed model is validated using tea packaging design as a practical example. The outcomes demonstrate the model's accuracy and efficacy in establishing the mapping between design elements and aesthetic experiences, offering novel insights for the evolution of packaging design in the contemporary market landscape.

The Chinese ship trading market has undergone remarkable development, transitioning into a global exemplar of ship transactions. Nevertheless, this market continues to confront a series of... Show More

The Chinese ship trading market has undergone remarkable development, transitioning into a global exemplar of ship transactions. Nevertheless, this market continues to confront a series of challenges, encompassing low transaction rates, delayed transaction values, and market instability. Blockchain, with its secure, transparent, and tamper-resistant technological features, presents a potential solution for the ship trading market. Through its decentralized architecture, blockchain technology can reduce platform operational costs, enhancing market competitiveness. Concurrently, the utilization of asymmetric encryption technology can enhance the security of platform data, effectively addressing privacy concerns. Furthermore, through a sharing mechanism, blockchain can aid in establishing a unified credit evaluation system, thereby increasing market trust to address the absence of a credit evaluation system. Moreover, blockchain technology can be employed to construct a risk identification mechanism, fortifying the platform's regulatory model to address regulatory deficiencies.

Airport management takes airport information management as its key link, and it is also the key to ensure the safe operation of airports. As a modern information management and processing... Show More

Airport management takes airport information management as its key link, and it is also the key to ensure the safe operation of airports. As a modern information management and processing technology, cloud computing plays an indispensable role in the fields of efficient information processing, strengthening management and improving work efficiency. Under the development background of the information age, the management mode of airport operation and maintenance has also changed to some extent. In the growth of the new era, airport operation and maintenance managers need to introduce new technologies and then combine more advanced technologies with the airport operation and maintenance system (OMS) so as to promote the normal operation of the airport OMS based on advanced data acquisition, analysis, and integration technologies. This article studies the application of cloud computing technology and designs a virtual cloud OMS based on deep learning (DL) from the perspective of the security requirements of the management information system (MIS) to realize the fault diagnosis and identification of the airport MIS. The fault diagnosis technology of the airport MIS based on virtual cloud OMS and DL proposed in this study has broad prospects in practical application. It can be applied to airport operation and maintenance management, security, and other links, providing intelligent support for airport operation.

In the era of knowledge economy, enterprises are facing unprecedented competition and challenges. The process of globalization has accelerated the communication and integration between different... Show More

In the era of knowledge economy, enterprises are facing unprecedented competition and challenges. The process of globalization has accelerated the communication and integration between different cultures, making cross-cultural management an indispensable ability for enterprises. Especially in the field of human resource management, it is of great significance to build a competency model based on data mining technology to improve the cross-cultural communication ability, teamwork ability, and innovation ability of enterprise managers. The application of competency theory in the field of human resource management has the potential to completely revolutionize traditional management practices, enabling better adaptation to the continuous changes in the management environment. It plays a significant role in promoting the reform of the human resource management system. The aim of this paper is to build a competency model of human resource managers based on data mining technology and verify its effectiveness in cross-cultural background. It is assumed that the model can accurately evaluate the ability level of human resource managers and provide reference for enterprises to select and train outstanding talents. The experimental results demonstrate that the model achieves an accuracy of approximately 89%, a 93.3% improvement in efficiency, and a salary range exceeding 90%. The model is deemed reasonable, feasible, and highly precise in terms of competency assessment.

Sina Weibo has evolved into a daily social tool for people, yet effectively leveraging its data for sentiment analysis remains a challenging task due to the presence of information beyond text, such... Show More

Sina Weibo has evolved into a daily social tool for people, yet effectively leveraging its data for sentiment analysis remains a challenging task due to the presence of information beyond text, such as emojis or images. In this paper, we propose an attention graph convolutional network (AGCN) for fine-grained sentiment classification of Weibo posts. Utilizing an attention network based on cosine similarity, the rich emotional information embedded in emoji features interacts with the textual content, effectively enhancing the capability to represent emotions in the text. Leveraging the characteristics of attention networks to construct a graph structure effectively enables graph convolutional networks to capture higher-order relationships between words in textual features. This approach addresses the challenge of extracting sentiment tendencies from Weibo comments. Experimental results on public data sets demonstrate the effectiveness of AGCNs.

The rapid development of social media has allowed people to access information through multiple channels, but social media has also become a breeding ground for rumors. Rumor detection models can... Show More

The rapid development of social media has allowed people to access information through multiple channels, but social media has also become a breeding ground for rumors. Rumor detection models can effectively assess the credibility of information. However, current research mainly relies on text or combined text and image features, which may not be sufficient to capture complex feature information. Therefore, this paper proposes a rumor detection model based on the graph convolutional network (GCN) and multi-modal features. The proposed model constructs a knowledge graph (KG) and leverages the GCN to extract complex relationships between its nodes. Then, an interactive attention network is adopted to deeply integrate features. Furthermore, ResNet101 is utilized to extract salient features from images, addressing the challenges related to fully utilizing additional feature information and capturing text and image features at a deeper level to some extent. Multiple experiments conducted on datasets from Twitter and Weibo platforms demonstrate the efficacy of the proposed approach.

During periods of extreme weather conditions, airport closures, or other unforeseen circumstances, air-lines frequently encounter disruptions in their flight schedules, posing flight recovery as a... Show More

During periods of extreme weather conditions, airport closures, or other unforeseen circumstances, air-lines frequently encounter disruptions in their flight schedules, posing flight recovery as a recurrent operational challenge. This paper aims to explore the impact of passenger transfer costs on flight recovery across various route types. Drawing on established methods used to calculate aircraft maintenance and crew assignment costs, this study utilizes an improved large-scale neighborhood search algorithm to perform a large number of calculations on a realistic dataset of airlines, meticulously analyzes the amalgamation of passenger transfer costs across different route types and proposes tailored strategies to mitigate disruptions. Through simulation experiments, the research evaluates the influence of various passenger transfer methods on flight recovery across diverse route types, with the goal of furnishing airlines with more adaptable and targeted decision support.

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a... Show More

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.

With the increasing popularity of e-commerce live streaming, comprehensive analysis of live barrage text has become increasingly crucial. This study presents a thematic analysis method for... Show More

With the increasing popularity of e-commerce live streaming, comprehensive analysis of live barrage text has become increasingly crucial. This study presents a thematic analysis method for categorizing e-commerce live streaming barrage text using latent Dirichlet allocation (LDA) topic modeling, combined with the advantages of the Bidirectional Encoder Representations from Transformers (BERT) and TextCNN models. The LDA algorithm is initially used to extract topics from the barrage text, and then a dataset comprising six designated categories is assembled. Subsequently, a BERT-TextCNN hybrid model is trained, merging BERT's profound semantic comprehension with TextCNN's ability to extract local features. Empirical evidence shows that the proposed model notably improves classification accuracy and efficiency, providing valuable theoretical and practical insights for crafting strategies and optimizing user experience on e-commerce live streaming platforms.

To compensate for the fact meteorological observation stations in Xinjiang are sparse, and spatial and temporal resolution of precipitation monitoring is insufficient in existing studies, in this... Show More

To compensate for the fact meteorological observation stations in Xinjiang are sparse, and spatial and temporal resolution of precipitation monitoring is insufficient in existing studies, in this study the authors proposed a precipitation inversion model that is based on infrared observation data from the Fengyun-4A satellite and the machine learning method. By combining multichannel satellite remote sensing data with ground meteorological observations, they constructed various machine learning models, such as deep forest, random forest, LightGBMs (light gradient-boosting machines), and XGBoost (extreme gradient boosting), using root-mean-square error, mean absolute error, and the coefficient of determination to evaluate and compare the model performance. The trained deep forest model was used to invert the precipitation in Xinjiang from June to August2023. The results show that the machine learning method is effective in exploiting the nonlinear relationship between the satellite observation features and the ground precipitation, and the inversion results are in good agreement with the ground observation data. Among these models, the deep forest model performs best in daytime conditions, and LightGBM is slightly better in nighttime conditions.

Based on the induction of potentially utilisable micro-spaces in typical old residential communities, this study selects the communities located at 19 and 22 Yuhe Street, Taiyuan City, Shanxi... Show More

Based on the induction of potentially utilisable micro-spaces in typical old residential communities, this study selects the communities located at 19 and 22 Yuhe Street, Taiyuan City, Shanxi Province, as typical research objects. By employing space syntax analysis, the spatial properties of these micro-spaces are examined and matched with the activity needs of various age groups within the community. By leveraging the characteristics of activity periods for all age groups, the study also proposes a composite design for less disruptive activity spaces. Consequently, optimisation suggestions for the renovation of public spaces in these two communities are presented, aiming to specifically meet the activity needs of residents of all ages within the limited space of the community. This research provides an analytical framework and methodological approach for the renovation strategies of old residential communities.

Since many different emotional expressions can be found in artistic and design compositions, it can be challenging to effectively extract and analyze emotional information from such a wide range of... Show More

Since many different emotional expressions can be found in artistic and design compositions, it can be challenging to effectively extract and analyze emotional information from such a wide range of artworks. This study uses deep learning approaches to extract and cluster emotional aspects from art and design works to address this problem. The suggested method uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for clustering and combines the VGG-16 and Bi-LSTM models for feature extraction from images. The suggested approach works better than existing models in extracting emotional information pieces, according to experimental results. With a Macro-F1 assessment score of 0.9241, the suggested technique can efficiently examine emotional inclinations in artistic and design works in practical applications. In conclusion, this study discusses the potential applications of the suggested emotion element extraction and clustering method in the field of emotional analysis in art and design, offering fresh approaches to issues in this area.

The existing social network public opinion analysis methods have problems such as poor semantic expression quality and weak detection ability in short texts. Therefore, a social network public... Show More

The existing social network public opinion analysis methods have problems such as poor semantic expression quality and weak detection ability in short texts. Therefore, a social network public opinion analysis method based on BERT-BMA is proposed. To normalize the comment text, the rumor text is initially transferred to a word vector matrix using the BERT (Bidirectional Encoder Representations from Transformer) model. The BiLSTM-based network architecture is subsequently employed to acquire the trace features of data transmission. Ultimately, this study employs the multi-head attention mechanism to extract feature information that is more significant in the analysis of online public opinion by mining the dependency relationships between users, resulting in increasing ability to detect public opinion emergencies. The experimental outcomes indicate that the results on the Twitter data set and Weibo dataset are superior to other comparative models.

Authentication ensures the privacy of patients by enabling access control within wireless medical sensor networks. However, many schemes do not consider the resource-constrained environments, making... Show More

Authentication ensures the privacy of patients by enabling access control within wireless medical sensor networks. However, many schemes do not consider the resource-constrained environments, making those protocols unusable. Meanwhile, sensitive patient information continues to be stored and accessed in a central location, increasing the risk of “single points of failure.” To solve this problem, the researchers designed a lightweight anonymous authentication protocol based on blockchain technology and fuzzy extraction. First, they created a multiround session key negotiation mechanism. Then, they utilized the fuzzy extraction function to extract and recover multimodal biometric features. Decentralization was achieved through blockchain and smart contracts. Simultaneously, the researchers also provided a formal security proof by Burrows-Abadi-Needham logic. Finally, experiments using the Java Pairing-Based Cryptography Library show that this scheme outperforms the comparison schemes in terms of computational overhead and communication overhead.

Due to the increasing load of the power grid and the rapid expansion of cable line construction, monitoring cable insulation performance has become very important. To address this challenge, this... Show More

Due to the increasing load of the power grid and the rapid expansion of cable line construction, monitoring cable insulation performance has become very important. To address this challenge, this paper proposes an online monitoring system for cable insulation in distribution networks based on line conduction characteristics. The system combines advanced signal acquisition technology, data processing methods, and deep learning (DL) algorithms to enable real-time monitoring and accurate evaluation of cable insulation status. The results demonstrate that the system excels in real-time monitoring of cable insulation status. Compared to traditional methods, the system proposed in this paper shows significant advantages in key indicators such as missed detection rate, fault location accuracy, recognition speed, and recall rate. Specifically, the system effectively reduces the missed detection rate, improves the accuracy of fault location, accelerates identification speed, and enhances the recall rate, enabling more comprehensive detection of insulation faults.

Car segmentation on Thailand's expressways poses challenges for traditional models due to unique characteristics, often resulting in predictive inaccuracies. Manual data analytics in this field is... Show More

Car segmentation on Thailand's expressways poses challenges for traditional models due to unique characteristics, often resulting in predictive inaccuracies. Manual data analytics in this field is time-consuming and human centric. This research introduces an Automated Hybrid Machine Learning (AHML) framework leveraging advancements in AutoML, tailored for personalized customer segmentation in Thailand's expressway industry. The framework streamlines and automates the machine learning process, aiming to expedite model construction while enhancing performance. By employing clustering as an initial step followed by the Random Forest classifier as a hybrid classification approach, significant performance improvements are achieved compared to existing methods. Specifically, the model outperforms by 9.15% and 12.84% in both clusters, respectively. This research highlights the potential of the framework to address complex segmentation challenges and advance personalized customer targeting.

This study proposes a novel deep-stacked fully interpretable and more generalization Takagi-Sugeno-Kang fuzzy system (D-FIMG-TSK) to obtain short fuzzy rules with high interpretability in each layer... Show More

This study proposes a novel deep-stacked fully interpretable and more generalization Takagi-Sugeno-Kang fuzzy system (D-FIMG-TSK) to obtain short fuzzy rules with high interpretability in each layer and simultaneously earn more generalization capability for high-dimensional data classification tasks. With the help of both discriminative and residual information, D-FIMG-TSK combines several fully interpretable TSK classifier FIMG-TSK in a stacked manner to conveniently move apart the manifolds existing in the original data space to achieve better linear separability. Besides, a feature selection method is designed to make feature selection from original feature sets on all layers to guarantee the classification performances. On the training process of D-FIMG-TSK, the importance of all original features and the antecedent and consequent parts of all fuzzy rules in each FIMG-TSK can be determined at the same time. The effectiveness of D-FIMG-TSK is manifested by the experimental results on eight binary high-dimensional classification datasets.

The traditional approach to logistics path planning is hindered by lengthy procedures. In this study, we explore the multi-objective optimization of logistics management, considering the... Show More

The traditional approach to logistics path planning is hindered by lengthy procedures. In this study, we explore the multi-objective optimization of logistics management, considering the conventional path and time efficiency indices alongside shelf safety and stability as additional objective functions. Based on particle swarm optimization (PSO), we optimize objective functions for internal path planning, scheduling timeliness, and shelf safety and stability. We then determine optimal routes under varying order demands using PSO and ultimately optimize the final path using dynamic programming and spline function restrictions to meet actual demand. Empirical results indicate that the proposed solution method outperforms other calculation methods, such as genetic algorithm (GA) and simulated annealing (SA), demonstrating over 10% improvement in time and total distance consumption. Further practical application tests demonstrate that the model in this study has a beneficial impact on all five distinct types of orders through efficient deployment optimization.