Intelligent Analysis Research Articles

Application and practice of industrial IoT cloud platform in oilfield intelligent transformation

Abstract Under the context of global energy transformation and low-carbon development, the traditional petroleum industry is facing a series of challenges such as technological innovation, market competition, environment protection, etc. Therefore, there is an urgent demand to realize green, safe, economical, and efficient development of oil and gas through oilfield intelligent transformation. This study develops a novel cloud platform covering the whole business based on the technical architecture of the industrial Internet of Things (IoT) for assisting the intelligent transformation of oilfields. Since the application of the IoT-based cloud platform in Qingcheng Oilfield, functions such as automatic collection of whole-domain data, remote monitoring, early warning, as well as intelligent analysis and decision-making have been realized. Furthermore, compared with the traditional platform, the new platform has improved eight indicators, such as data processing efficiency and sharing capacity, by more than 20%. Consequently, the developed IoT-based cloud platform can significantly improve the production and management level of the oilfield, and efficiently support oilfield intelligent transformation.

Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond

The application of AI to analytical and separative sciences is a recent challenge that offers new perspectives in terms of data prediction. In this work, we report an AI-based software, named Chrompredict 1.0, which based on chromatographic data of a novel mesogenic crown ether stationary phase (CESP). Its molecular design represents a significant advancement due to the unique combination of properties and binding capabilities, including the formation of a cavity, mesogenic behavior via mobile chains, and a range of polar and non-polar interactions (aromatic rings, N=N and O=O double bonds, alkyl chains, π–π interactions, and hydrogen bonding). The mesogenic phase is effective in both normal and reversed-phase chromatography, enhancing the software's adaptability across diverse datasets.Here we introduce for the first time an unprecedented scientific approach, integrating deep learning techniques with the novel CESP, which demonstrates exceptional thermal and analytical performance in both liquid chromatography modes, especially in the separation of complex hydrocarbon isomers. This ability enables the results obtained with CESP to extend across various types of stationary phases.Leveraging these insights, a comprehensive chromatographic dataset on a series of aromatic and polyaromatic molecules interacting with our CESP was used to train a Deep Learning Model (DLM). This model is embedded within a user-friendly software, Chrompredict 1.0, designed for predicting chromatographic parameters (MAE = 0.042, R² = 0.95) by selecting chemical descriptors directly from SMILES notation. It offers a deeper understanding of molecular structure and interactions through exploratory data analysis, identifying key factors affecting model accuracy and chromatographic behavior.Users can configure hyperparameters, choose from six machine learning models, and compare their performance with DLM. Chrompredict 1.0 excels in retention behavior prediction for compounds with known structures, and it accurately predicts chromatographic retention and thermal characteristics for different temperatures in HPLC and GC. The model has been successfully tested with METLIN database of 1,023 small molecules of diverse structures and polarities (R² > 0.75, error range ±7.8 s). Overall, the CESP, combined with Chrompredict 1.0, offers a robust tool for intelligent chromatographic analysis, encompassing chemo-informatics, statistical analysis, and graphical capabilities across a broad range of compounds and stationary phases.

Integration of big data analytics in the investigation of the relationship between acromegaly and cancer

ObjectiveTo evaluate the association between acromegaly and cancer and different types of cancer by using natural language processing systems and big data analytics. Material and methodsWe conducted an observational, retrospective study utilizing data from the electronic health records (EHRs) of Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain. Information from the EHRs was extracted using artificial intelligence techniques and analyzed using Savana Manager 4.0 software. ResultsOut of a total of 708,047 registered patients (54.7% females), 544 patients (0.08%; 330 women, 60.7%; mean age at diagnosis 53.0±15.8 yr) were diagnosed with acromegaly. The incidence of cancer was higher in patients with acromegaly vs those without this condition (7.7% vs 3.9%, p<0.001; OR, 2.047, 95%CI, 1.493–2.804). Male acromegalic patients had a higher prevalence of cancer vs females (57.1% vs 42.9%, p=0.012). A significantly higher prevalence of colorectal cancer (2.9% vs 1.4%, p=0.006), bladder cancer (1.1% vs 0.3%, p=0.005), and lymphoma (1.1% vs 0.3%, p=0.009) was observed in patients with acromegaly vs those without the condition. Acromegalic men had significantly higher prevalence rates of colorectal cancer (4.7% vs 1.3%, p=0.001), bladder cancer (2.8% vs 0.4%, p<0.001), breast cancer (0.9% vs 0.2%, p=0.042), gastric cancer (0.9% vs 0.1%, p=0.011), lymphoma (1.4% vs 0.3%, p=0.037), and liver cancer (0.9% vs 0.1%, p=0.012) vs non-acromegalic men. On the other hand, acromegalic women showed a higher prevalence of thyroid cancer (1.2% vs 0.4%, p=0.043) vs non-acromegalic women. ConclusionOur study, based on artificial intelligence techniques and analysis of real-world data and information, revealed a significant association between acromegaly and cancer in our hospital population, mainly acromegalic men, with a higher frequency of colorectal cancer, bladder cancer and lymphoma in particular.

Intelligent Music Emotion Analysis and Creation Based on Reinforcement Learning Model

Intelligent Music Emotion Analysis and Creation Based on Reinforcement Learning Model

Back analysis of geomechanical parameters based on a data augmentation algorithm and machine learning technique

Accurate geomechanical parameters are key factors for stability evaluation, disaster forecasting, structural design, and supporting optimization. The intelligent back analysis method based on the monitored information is widely recognized as the most efficient and cost-effective technique for inverting parameters. To address the low accuracy of measured data, and the scarcity of comprehensive datasets, this study proposes an innovative back analysis framework tailored for small sample sizes. We introduce a multi-faceted back analysis approach that combines data augmentation with advanced optimization and machine learning techniques. The auxiliary classifier generative adversarial network (ACGAN)-based data augmentation algorithm is first employed to generate synthetic yet realistic samples that adhere to the underlying probability distribution of the original data, thereby expanding the dataset and mitigating the impact of small sample sizes. Subsequently, we harness the power of optimized particle swarm optimization (OPSO) integrated with support vector machine (SVM) to mine the intricate nonlinear relationships between input and output variables. Then, relying on a case study, the validity of the augmented data and the performance of the developed OPSO-SVM algorithms based on two different sample sizes are studied. Results show that the new datasets generated by ACGAN almost coincide with the actual monitored convergences, exhibiting a correlation coefficient exceeding 0.86. Furthermore, the superiority of the OPSO-SVM algorithm is also demonstrated by comparing the displacement prediction capability of various algorithms through four indices. It is also indicated that the relative error of the predicted displacement values reduces from almost 20% to 5% for the OPSO-SVM model trained with 25 samples and that trained with 625 samples. Finally, the inversed parameters and corresponding convergences predicted by the two OPSO-SVM models trained with different samples are discussed, indicating the feasibility of the combination application of ACGAN and OPSO-SVM in back analysis of geomechanical parameters. This endeavor not only facilitates the progression of underground engineering analysis in scenarios with limited data, but also serves as a pivotal reference for both researchers and practitioners alike.

Deep Learning Based Method for Dynamic Tracking of Bubbles in Microfluidic Gas-Driven Water Experiments

Using microfluidic chips for gas displacement experiments is an important approach for studying CO2 displacement mechanisms and gas-liquid exchange processes. Analyzing the micro-scale distribution of gas and water during gas displacement enables the optimization of injection strategies, offering valuable guidance for enhancing oil and gas recovery rates. Traditional image processing techniques often face challenges in effectively handle the complex gas-liquid flow dynamics and irregular bubble shapes encountered in gas displacement experiments. To address this challenge, this paper proposes a gas bubble detection and tracking method for gas-driven water microfluidic experiments based on domain adaptation in deep learning and improved YOLOv8. Using the CycleGAN style transfer network to generate numerous simulated bubble training samples significantly reduces the manual labeling workload for bubbles. Additionally, the SE attention mechanism is integrated into the YOLOv8 backbone network to enhance the feature extraction capability of bubbles, and the Fast-PConv Head structure is incorporated into the detection head to enhance detection efficiency. Experimental results demonstrate that using just 20 annotated experimental images, the bubble detection accuracy of the proposed method can reach 94%. Lastly, DeepSORT is employed to track bubbles and visualize the dynamic trends of bubble changes in the form of visual charts. The deep learning approach proposed in this paper provides a new perspective for intelligent analysis of bubbles in microfluidic chip experiments, and future work aiming to its application to the complex multiphase flow field in porous media.

S573 Predictive Artificial Intelligence Analysis of Determinants of Health Outcomes in Hospitalized Patients with Eosinophilic Esophagitis

S573 Predictive Artificial Intelligence Analysis of Determinants of Health Outcomes in Hospitalized Patients with Eosinophilic Esophagitis

An automatic workflow for the quantitative evaluation of bit wear based on computer vision

As global oil exploration ventures into deeper and more complex territories, drilling bit wear and damage have emerged as significant constraints on drilling efficiency and safety. Despite the publication of official bit wear evaluation standards by the International Association of Drill Contractors (IADC), the current lack of quantitative and scientific evaluation techniques means that bit wear assessments rely heavily on engineers' experience. Consequently, forming a standardized database of drilling bit information to underpin the mechanisms of bit wear and facilitate optimal design remains challenging. Therefore, an efficient and quantitative evaluation of bit wear is crucial for optimizing bit performance and improving penetration efficiency.This paper introduces an automatic standard workflow for the quantitative evaluation of bit wear and the design of a comprehensive bit information database. Initially, a method for acquiring images of worn bits at the drilling site was developed. Subsequently, the wear classification and grading models based on computer vision were established to determine bit status. The wear classification model focuses on the positioning and classification of bit cutters, while the wear grading model quantifies the extent of bit wear. After that, the automatic evaluation method of the bit wear is realized. Additionally, bit wear evaluation software was designed, integrating all necessary functions to assess bit wear in accordance with IADC standards. Finally, a drilling bit database was created by integrating bit wear data, logging data, mud-logging data, and basic drilling bit data.This workflow represents a novel approach to collecting and analyzing drilling bit information at drilling sites. It holds potential to facilitate the creation of a large-scale information database for the entire lifecycle of drilling bits, marking the inception of intelligent analysis, design, and manufacture of drilling bits, thereby enhancing performance in challenging drilling conditions.

Development and Application of Drilling Intelligent Assistant Decision System

Abstract In order to systematically harness the substantial volume of data generated during the drilling process and achieve intelligent analysis and auxiliary decision-making for real-time risk monitoring and warnings during drilling, an intelligent drilling auxiliary decision-making system has been developed. This system integrates various technologies, including physical models, intelligent algorithms, and trend analysis. By analyzing the characteristics of logging data under different operating conditions, an intelligent algorithm capable of automatically distinguishing six types of drilling operating conditions has been established. Taking into account the impact of rock cuttings on the stress on the pipe string and annular pressure loss during the drilling process, coupled frictional torque and cyclic pressure loss calculation models were developed under coupled conditions. Building upon this foundation and considering the trend of deviation between predicted and measured parameters, a monitoring algorithm for detecting stuck drilling, wellbore leakage, and overflow incidents was formulated. The development of the drilling intelligent decision-making system was completed based on a C/S three-layer architecture and a data center. This system has been successfully applied in more than 50 exploration wells of various types, including shale oil and gas horizontal wells and deep wells. The static analysis results demonstrated a compliance rate of 91.5% with on-site monitoring, playing a crucial role in drilling parameter optimization and risk monitoring. The practicality and feasibility of the system have been verified, providing essential support for efficient and safe drilling production.

Application of Supervised Pattern Fault Identification Technology In Y Reservoir

Abstract The Y reservoir in the Tarim Basin is a fault-controlled buried hill reservoir. Affected by factors such as deep embedding depth and high degree of stratum fragmentation, the signal-to-noise ratio of is low and the imaging effect of faults is poor. Therefore, it is very difficult to identify faults accurately. The conventional geometric attribute plane is extremely messy, the interference information is serious, and the fault information is difficult to be effectively identified, which cannot solve the problem of fine identification of buried hill reservoir faults. Aiming at the demand of fault characterization in reservoir area, combined with the artificial intelligence analysis method of big data, a fault identification technology under supervised pattern is proposed. Firstly, through cascade denoising and fault space imaging enhancement processing, the signal-to-noise ratio of seismic data and the fault imaging effect is improved. Secondly, the fault types are divided according to the discontinuous characteristics of the seismic axis, and different types of faults are characterized by multi-type sensitive attributes. Finally, the training samples are established according to the fault type. Through the iterative constraint training method, the fault identification work under the supervised pattern is carried out, and the fine characterization of different types of faults in Y reservoir is completed. Relying on the artificial intelligence big data analysis method, this technology has the innovation of full-type fault information utilization and supervised constraint training, which plays an important guiding role in the fine identification of faults in low signal-to-noise ratio areas.

Rapid On-Site Histology of Lung and Pleural Biopsies Using Higher Harmonic Generation Microscopy and Artificial Intelligence Analysis

Lung cancer is one of the most prevalent and lethal cancers. To improve health outcomes while reducing health care burden, it becomes crucial to move toward early detection and cost-effective workflows. Currently, there is no method for the on-site rapid histologic feedback on biopsies taken in diagnostic, endoscopic, or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-based technique that provides images of unprocessed tissue. In this study, we report the feasibility of an HHG portable microscope in the clinical workflow in terms of acquisition time, image quality, and diagnostic accuracy in suspected pulmonary and pleural malignancy. One hundred nine biopsies of 47 patients were imaged and a biopsy overview image was provided within a median acquisition time of 6 minutes after excision. The assessment by pathologists and an artificial intelligence algorithm showed that image quality was sufficient for a malignancy or nonmalignancy diagnosis in 97% of the biopsies, and 87% of the HHG images were correctly scored by the pathologists. HHG is therefore an excellent candidate to provide a rapid pathology outcome on biopsy samples, enabling immediate diagnosis and (local) treatment.

Human Resource Competence Analysis of Artificial Intelligence In Bureau of Research and Strategy POLRI

The rapid development of Artificial Intelligence technology has recently had a significant impact on various sectors of life, including in the police sector. Biro jianstra POLRI as one of the work units within the Indonesian National Police is required to be able to adapt quickly to these changes in order to be able to‪Rus provides excellent service to the community. The research method used in this study is a qualitative method with a literature study approach. Data collection is carried out through a review of relevant documents related to HR competencies and the development of Arti‪ficial Intelligence in the Birojianstra POLRI environment. The results of this study indicate that improving HR competency is an important strategy that must be carried out by Birojianstra POLRI development of Artificial Intelligence

Information and mathematical model of quantum communication channel state control processes

The most protected and stable communication systems today are quantum channels of information transmission and processing. Thanks to the unique properties of photons as information elements, it becomes possible to monitor and analyze the state of information flows in communication or information transmission channels. Physical attributes such as spin, polarization, radiation frequency, phase synchronization, and the quantum entanglement effect can be tracked and interpreted online to improve the quality and reliability of information in computer systems. In order to effectively use information in support or decision support systems, it is necessary to carefully formalize the processes and indicators of quantum systems for the creation of information processing and transmission, for which information and mathematical models should be created that describe the state of the quantum communication channel (QCC).The information model should allow the convolution of the information space. The mathematical model must prove the processes of tracking the states of quantum information and provide a description of the phase state of indicators of the quantum environment. A lock in a closed space with established cause-and-effect relationships is equal to a system of clear logic.The authors summarize the experience of developing and implementing the method of simulated dynamic modeling of events in an abstract communication channel, which allows formalizing and classifying cause-and-effect relationships of quantum carriers in the analyzed channels. It is proposed to use a unified neural network for the organization of SPPR in quantum-mechanical information transmission systems. Such a network could provide an automatic intelligent system state analysis mode. Such an analysis makes it possible to classify the aggregates of current system parameters to the level of diagnostics of the state of information flows and conclusions based on such diagnostics with the support of decision-making about the quality and reliability of the transmitted information. Such a system, working in OLAP (Online analytical processing) mode, could automatically manage the process of generation and transmission of information, reacting without human intervention to emerging critical errors or attempts at unauthorized system hacking. The observer effect leads to the fact that an attempt to measure the state of a photon inevitably causes an almost instantaneous change in this state. Attempting to parallelize a photon has the same consequences. This cannot be unnoticeable during further authorized acceptance of information. The analyzed quantum communication channel (QCM) consists of a set of technological elements distributed in space. The channel works in its own time, which is formed by clock pulses and creates a flow of information.

P-TIMA: a framework of T witter threat intelligence mining and analysis based on a prompt-learning NER model

Abstract Open-source information platforms such as Twitter continuously provide the latest threat intelligence, including new vulnerabilities and in-the-wild exploitations of advanced persistent threat (APT) groups. Automated extraction of threat intelligence from Twitter has become crucial for defenders to access up-to-date threat knowledge. However, existing studies mainly rely on supervised learning methods to extract threat intelligence knowledge, such as entities, which require a large amount of annotated data. This paper presents Threat Intelligence Mining and Analysis based on Prompt Learning (P-TIMA), a framework specifically crafted for extracting and analyzing threat intelligence from Twitter. P-TIMA employs our innovative few-shot entity recognition method, SecEntPrompt (SEP), built on prompt learning, to extract vulnerability intelligence from Twitter. Additionally, P-TIMA analyzes and profiles the overarching vulnerability intelligence obtained from Twitter, along with in-the-wild exploitation intelligence of APT groups. The SEP improves the average entity recognition F1 score by 3.62-4.40 compared with the best-performing comparison model and outperforms the method based on the large language model on recognition performance and inference time. To validate our framework, we apply P-TIMA to extract vulnerability-related threat intelligence from real Twitter data. Through case studies, we then analyze trends in vulnerability threats and the exploitation capabilities of APT groups. In conclusion, our framework provides a more efficient and accurate method for extracting threat intelligence from Twitter, enabling defenders to stay up-to-date with the latest threat trends and helping them improve their defense strategies against cyber attacks.

Intelligent Dance Motion Evaluation: An Evaluation Method Based on Keyframe Acquisition According to Musical Beat Features.

Motion perception is crucial in competitive sports like dance, basketball, and diving. However, evaluations in these sports heavily rely on professionals, posing two main challenges: subjective assessments are uncertain and can be influenced by experience, making it hard to guarantee timeliness and accuracy, and increasing labor costs with multi-expert voting. While video analysis methods have alleviated some pressure, challenges remain in extracting key points/frames from videos and constructing a suitable, quantifiable evaluation method that aligns with the static-dynamic nature of movements for accurate assessment. Therefore, this study proposes an innovative intelligent evaluation method aimed at enhancing the accuracy and processing speed of complex video analysis tasks. Firstly, by constructing a keyframe extraction method based on musical beat detection, coupled with prior knowledge, the beat detection is optimized through a perceptually weighted window to accurately extract keyframes that are highly correlated with dance movement changes. Secondly, OpenPose is employed to detect human joint points in the keyframes, quantifying human movements into a series of numerically expressed nodes and their relationships (i.e., pose descriptions). Combined with the positions of keyframes in the time sequence, a standard pose description sequence is formed, serving as the foundational data for subsequent quantitative evaluations. Lastly, an Action Sequence Evaluation method (ASCS) is established based on all action features within a single action frame to precisely assess the overall performance of individual actions. Furthermore, drawing inspiration from the Rouge-L evaluation method in natural language processing, a Similarity Measure Approach based on Contextual Relationships (SMACR) is constructed, focusing on evaluating the coherence of actions. By integrating ASCS and SMACR, a comprehensive evaluation of dancers is conducted from both the static and dynamic dimensions. During the method validation phase, the research team judiciously selected 12 representative samples from the popular dance game Just Dance, meticulously classifying them according to the complexity of dance moves and physical exertion levels. The experimental results demonstrate the outstanding performance of the constructed automated evaluation method. Specifically, this method not only achieves the precise assessments of dance movements at the individual keyframe level but also significantly enhances the evaluation of action coherence and completeness through the innovative SMACR. Across all 12 test samples, the method accurately selects 2 to 5 keyframes per second from the videos, reducing the computational load to 4.1-10.3% compared to traditional full-frame matching methods, while the overall evaluation accuracy only slightly decreases by 3%, fully demonstrating the method's combination of efficiency and precision. Through precise musical beat alignment, efficient keyframe extraction, and the introduction of intelligent dance motion analysis technology, this study significantly improves upon the subjectivity and inefficiency of traditional manual evaluations, enhancing the scientificity and accuracy of assessments. It provides robust tool support for fields such as dance education and competition evaluations, showcasing broad application prospects.

INTEGRATING ARTIFICIAL INTELLIGENCE (AI) into CORPORATE GOVERNANCE SYSTEMS

Integrating AI, or Artificial Intelligence, into companies’ management is a big deal. It can change things for the better. This piece looks at how it affects corporate governance. It shows that AI can help make decisions easier, improve how we manage risks, and even make it simpler for companies to connect with their stakeholders. The cool thing about AI is that it lets boards and leaders make better and faster choices using intelligent data analysis and predictions. Plus, AI automation makes following rules easier and boosts transparency. This helps create a culture of accountability, too. However, there are some tricky challenges to think about. There are ethical questions, data privacy concerns, and algorithm bias. We need to address these challenges quickly! The development of solid rules for governance is critical here. Focusing on mixing innovation with ethical regulations is essential to keep growing in a way that builds trust and maintains accountability with everyone involved.

Multifunctional Human-Computer Interaction System Based on Deep Learning-Assisted Strain Sensing Array.

Continuous and reliable monitoring of gait is crucial for health monitoring, such as postoperative recovery of bone joint surgery and early diagnosis of disease. However, existing gait analysis systems often suffer from large volumes and the requirement of special space for setting motion capture systems, limiting their application in daily life. Here, we develop an intelligent gait monitoring and analysis prediction system based on flexible piezoelectric sensors and deep learning neural networks with high sensitivity (241.29 mV/N), quick response (66 ms loading, 87 ms recovery), and excellent stability (R2 = 0.9946). The theoretical simulations and experiments confirm that the sensor provides exceptional signal feedback, which can easily acquire accurate gait data when fitted to shoe soles. By integrating high-quality gait data with a custom-built deep learning model, the system can detect and infer human motion states in real time (the recognition accuracy reaches 94.7%). To further validate the sensor's application in real life, we constructed a flexible wearable recognition system with human-computer interaction interface and a simple operation process for long-term and continuous tracking of athletes' gait, potentially aiding personalized health management, early detection of disease, and remote medical care.

The Application of the Devil’s Advocacy Technique to Intelligence Analysis

Understanding evolving situations and predicting future events are significant challenges in the intelligence sphere. These necessitate advanced techniques for intelligence collection and robust evaluation procedures to enhance the accuracy of predictions. The Devil’s Advocacy technique can be employed to improve analysis procedures by challenging prevailing views and mitigating the risk of cognitive biases. This paper examines the effectiveness of the Devil’s Advocacy technique when applied to intelligence analysis, by evaluating its strengths, weaknesses, and applicability, through a comprehensive literature review and empirical research involving experienced practitioners in New Zealand. Previous studies examining the Devil’s Advocacy technique primarily focused on its contribution to decision-making on the final intelligence product and on the perceptions of the devil’s advocate. These are important, but this paper is also concerned with the analysts’ experience of practically applying the technique for intelligence analysis. This paper contends that the Devil’s Advocacy technique is an attractive complementary technique which is effective in specific situations. It also reveals that New Zealand intelligence practitioners define, perceive, and apply the Devil’s Advocacy technique differently across their organisations. It therefore argues that New Zealand practitioners may have an inconsistent understanding of the technique, indicating potential gaps in the training and education of New Zealand intelligence professionals. However, the inherently subjective nature of the technique itself could also be a contributing factor.

Efficient Pulp and Paper Wastewater Treatment Using Green Tea-Synthesized Magnetite: Artificial Intelligence and Kinetic Analysis

The pulp and paper industry generates pollutant-laden wastewater, requiring effective treatment to prevent environmental damage and enable sustainable reuse. This study investigated the use of green tea-synthesized magnetite (GT-Fe3O4) for treating pulp and paper wastewater. Characterization techniques, including SEM, XRD, FTIR and EDX, were employed to analyze the synthesized magnetite. Batch adsorption studies were conducted to examine the optimal operating conditions, including pH, contact time, adsorbent dose, and stirring rate, for achieving the best treatment performance. The treatment focused on reducing chemical oxygen demand (COD), total suspended solids (TSS), and turbidity in the wastewater. The adsorption data were evaluated using kinetic models, with the pseudo-second-order model providing the best fit. Additionally, an artificial intelligence prediction model (AIP) with a 4–10–1 structure was employed to predict the removal efficiencies. The optimal conditions for the treatment process were determined to be a pH of 6, a contact time of 60min, a GT-Fe3O4 dose of 0.7g/L, and a stirring rate of 150rpm at room temperature. Under these conditions, the removal efficiencies for COD, TSS, and turbidity were 70.5%, 65.4%, and 38.8%, respectively. The AIP model achieved an R² value range of 0.978 to 0.982, showing high prediction accuracy. The study demonstrates that GT-Fe3O4 effectively reduces pollutants in pulp and paper wastewater.

Improved GAN-based deep learning approach for strain field prediction and failure analysis of precast bridge slab joints

Nonlinear finite element analysis (FEA) is crucial for understanding complex stress-strain behaviors and assessing potential structural failure risks. Building on the recent success of deep learning (DL) methods in physical field predictions, there has been growing interest in forecasting internal force fields of structures beyond the existing data scope. In this study, a data-driven strain field analysis model based on an improved Generative Adversarial Network (GAN) is proposed. To enrich the input data, this novel approach incorporates the Signed Distance Field (SDF) derived from the characteristics of the original Finite Element (FE) models. The generator, based on U-Net, is enhanced with channel attention mechanisms and loss penalty terms, forming the core of the improved GAN. The effectiveness of the proposed method is demonstrated using two parallel datasets comprising 3-Headed bar tension models across two planes. Subsequently, the generated strain field is subjected to feature extraction using the 2D-Principal Component Analysis (2D-PCA) method, followed by structural failure determination through the K-Nearest Neighbor (KNN) algorithm. The results indicate that the proposed improved approach achieves a significant reduction in Root Mean Square Error (RMSE) of 20.71 % and 13.21 % across the two datasets, with the structural failure determination method reaching an F1-score of 0.971. Thus, the intelligent strain field analysis method introduced here effectively predicts strain distributions and promptly identifies failure states, offering valuable insights for further numerical analysis and structural design.