Search Results (985)

Sustainable innovation in the field of artificial intelligence (AI) is essential for the development of Industry 4.0. Recognizing the innovation abilities of researchers is fundamental to achieving sustainable innovation within organizations. This study proposes a method for identifying the core innovative competency field of researchers through text mining, which involves the extraction of core competency tags, topic clustering, and calculating the relevance between researchers and topics. Using AI as a case study, the research identifies the core innovative competency field of researchers, uncovers opportunities for sustainable innovation, and highlights key innovators. This approach offers deeper insights for AI R&D activities, providing effective support for promoting sustainable innovation. Compared to traditional expertise identification methods, this approach provides a more in-depth and detailed portrayal of researchers’ expertise, particularly highlighting potential innovation domains with finer granularity. It is less influenced by subjective factors and can be conveniently applied to identify the core innovative competency field of researchers in any other research field, making it especially suitable for interdisciplinary areas. By offering a precise and comprehensive understanding of researchers’ capability fields, this method enhances the strategic planning and execution of innovative projects, ensuring that organizations can effectively leverage the expertise of their researchers to drive forward sustainable innovation. Full article

Accurately obtaining the geological characteristic digital model of a coal seam and surrounding rock in front of a fully mechanized mining face is one of the key technologies for automatic and continuous coal mining operation to realize an intelligent unmanned working face. The research on how to establish accurate and reliable coal seam digital models is a hot topic and technical bottleneck in the field of intelligent coal mining. This paper puts forward a construction method and dynamic update mechanism for a digital model of coal seam autonomous cutting by a coal mining machine, and verifies its effectiveness in experiments. Based on the interpolation model of drilling data, a fine coal seam digital model was established according to the results of geological statistical inversion, which overcomes the shortcomings of an insufficient lateral resolution of lithology and physical properties in a traditional geological model and can accurately depict the distribution trend of coal seams. By utilizing the numerical derivation of surrounding rock mining and geological SLAM advanced exploration, the coal seam digital model was modified to achieve a dynamic updating and optimization of the model, providing an accurate geological information guarantee for intelligent unmanned coal mining. Based on the model, it is possible to obtain the boundary and inclination information of the coal seam profile, and provide strategies for adjusting the height of the coal mining machine drum at the current position, achieving precise control of the automatic height adjustment of the coal mining machine. Full article

The gravimetric method for determining coal gas sorption has many advantages and limitations. The article presents the influence of various factors on the results of methane sorption on coal. In mining practice, in addition to sorption properties of coal, knowledge of methane sorption capacity and effective diffusion coefficient determined when assuming a unipore sorption/desorption model are crucial for predicting sudden releases of methane from coal seams to a mine ventilation environment. In Poland, determining sorption capacities of coals for methane is mandatory when starting mining operations in new parts of coal deposits threatened by outbursts. Traditionally, gravimetric microbalances, such as intelligent gravimetric analysis (IGA), are used to determine adsorption capacity and desorption rate. Recently, newer microbalances XEMIS have been introduced to the market. Two gas laboratories, AGH in Krakow and CLP-B in Jastrzebie-Zdroj, respectively, compared experimental adsorption isotherms using XEMIS microbalances with mutually exchanged coal samples. Both sorption capacity at the pressure of 1 bar (a1bar) and effective diffusion coefficient (De) were independently determined for the coal samples tested. The results obtained are comparable despite the use of different microbalance XEMIS models. The conducted studies and comparative evaluation of the results allowed for assessing procedures for determining sorption properties using XEMIS microbalances. The exchange of laboratory experiences also allowed for the identification of methodology factors crucial for the development of a uniform procedure for conducting similar studies with XEMIS microbalance. The proposed factors for testing the sorption behavior of methane in coal structures may be helpful in mining practice. Full article

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems and lays the foundation for guiding component repair and maintenance. Full article

In ventilation systems of metal mines, the real-time measurement of the airflow field and a reduction in pollutants are necessary for clean environmental management and human health. However, the limited quantitative data and expensive detection technology hinder the accurate assessment of mine ventilation effectiveness and safety status. Therefore, we propose a new method for constructing a mine intelligent ventilation system with a global scheme, which can realize the intelligent prediction of unknown points in the mine ventilation system by measuring the airflow parameters of multiple known points. Firstly, the nodal wind pressure method combined with the Hardy–Cross iterative algorithm is used to solve the mine ventilation network, and the airflow parameters under normal operation and extreme working conditions are simulated, based on which an intelligent ventilation training database is established. Secondly, we compared the airflow parameter prediction ability of three different machine learning models with different neural network models based on the collected small-sample airflow field dataset of a mine roadway. Finally, the depth learning method is optimized to build the intelligent algorithm model of the mine ventilation system, and a large number of three-dimensional simulation data and field measurement data of the mine ventilation system are used to train the model repeatedly to realize the intelligent perception of air flow parameters of a metal mine ventilation network and the construction of an intelligent ventilation system. The results show that the maximum error of a single airflow measurement point is 1.24%, the maximum overall error is 3.25%, and the overall average error is 0.51%. The intelligent algorithm has a good model training effect and high precision and can meet the requirements of the research and application of this project. Through case analysis, this method can predict the airflow parameters of any position underground and realize the real-time control of mine safety. Full article

Mine ventilation is crucial for ensuring safe production in mines, as it is integral to the entire underground mining process. This study addresses the issues of high energy consumption, regulation difficulties, and unreasonable regulation schemes in mine ventilation systems. To this end, we construct an optimization model for mine ventilation network regulation using mixed-integer nonlinear programming (MINLP), focusing on objectives such as minimizing energy consumption, optimal regulation locations and modes, and minimizing the number of regulators. We analyze the construction methods of the mathematical optimization model for both selected and unselected fans. To handle high-order terms in the MINLP model, we propose a variable discretization strategy that introduces 0-1 binary variables to discretize fan branches’ air quantity and frequency regulation ratios. This transformation converts high-order terms in the constraints of fan frequency regulation into quadratic terms, making the model suitable for solvers based on globally accurate algorithms. Example analysis demonstrate that the proposed method can find the optimal solution in all cases, confirming its effectiveness. Finally, we apply the optimization method of ventilation network regulation based on MINLP to a coal mine ventilation network. The results indicate that the power of the main fan after frequency regulation is 71.84 kW, achieving a significant energy savings rate of 65.60% compared to before optimization power levels. Notably, ventilation network can be regulated without adding new regulators, thereby reducing management and maintenance costs. This optimization method provides a solid foundation for the implementation of intelligent ventilation systems. Full article

Wearable flexible strain sensors require different performance depending on the application scenario. However, developing strain sensors based solely on experiments is time-consuming and often produces suboptimal results. This study utilized sensor knowledge to reduce knowledge redundancy and explore designs. A framework combining knowledge graphs and graph representational learning methods was proposed to identify targeted performance, decipher hidden information, and discover new designs. Unlike process-parameter-based machine learning methods, it used the relationship as semantic features to improve prediction precision (up to 0.81). Based on the proposed framework, a strain sensor was designed and tested, demonstrating a wide strain range (300%) and closely matching predicted performance. This predicted sensor performance outperforms similar materials. Overall, the present work is favorable to design constraints and paves the way for the long-awaited implementation of text-mining-based knowledge management for sensor systems, which will facilitate the intelligent sensor design process. Full article

The construction field currently suffers from low productivity, a lack of expertise among practitioners, weak innovation, and lack of predictability. The digital twin, an advanced digital technology, empowers the construction sector to advance towards intelligent construction and digital transformation. It ultimately aims for highly accurate digital simulation to achieve comprehensive optimization of all phases of a construction project. Currently, the process of digital twin applications is facing challenges such as poor data quality, the inability to harmonize types that are difficult to integrate, and insufficient data security. Further research on the application of digital twins in the construction domain is still needed to accelerate the development of digital twins and promote their practical application. This paper analyzes the commonly used architectures for digital twins in the construction domain in the literature and summarizes the commonly used technologies to implement the architectures, including artificial intelligence, machine learning, data mining, cyber–physical systems, internet of things, virtual reality, augmented reality applications, and considers their advantages and limitations. The focus of this paper is centered on the application of digital twins in the entire lifecycle of a construction project, which includes the design, construction, operation, maintenance, demolition and restoration phases. Digital twins are mainly moving towards the integration of data and information, model automation, intelligent system control, and data security and privacy. Digital twins present data management and integration challenges, privacy and security protection, technical manpower development, and transformation needs. Future research should address these challenges by improving data quality, developing robust integration methodologies, and strengthening data security measures. Full article

In the process of mining, a large area of hard roof will be exposed above a goaf and may suddenly break. This can easily induce rock burst and has a significant impact on production safety. In this study, based on the engineering background of the hard roof of the 2102 working face in the Balasu coal mine, the spatial and temporal characteristics of the strain energy of the roof during the initial mining process were explored in depth. Based on a theoretical calculation, it is proposed that hydraulic fracturing should be carried out in the medium-grained sandstone layer that is 4.8–22.43 m above the roof, and that the effective fracturing section in the horizontal direction should be within 30.8 m of the cutting hole of the working face. The elastic strain energy fish model was established in FLAC^3D to analyze the strain energy accumulation of the roof during the initial mining process. The simulation and elastic strain energy results show that, as the working face advances to 70–80 m, the hard roof undergoes significant bending deformation. The energy gradient increases with the rapid accumulation of strain energy to a peak value of 140.54 kJ/m³. If the first weighting occurs at this moment in time, the sudden fracture of the roof will be accompanied by the release of elastic energy, which will induce rock burst. Therefore, it is necessary to implement roof cutting and pressure relief before reaching the critical step of 77 m. To this end, the comprehensive hydraulic fracturing technology of ‘conventional short drilling + directional long drilling’ is proposed. A field test shows that the hydraulic fracturing technology effectively weakens the integrity of the rock layer. The first weighting interval is 55 m, and it continues until the end of the pressure at the 70 m position. The roof collapses well, and the mining safety is improved. This study provides an important reference for hard roof control. Full article

With the continuous development of the mining industry and advancements in deep mining technology, mine environment optimization has become key to ensuring safety and improving the efficiency of mining. The high-temperature environment, particularly in deep mines, not only poses a serious threat to miners’ health but also significantly reduces operational efficiency. These issues have been determined based on the current application status and development trends of mine cooling technology, including traditional mechanical and non-mechanical cooling technologies, as well as emerging roadway insulation materials and mine cooling clothing applications. By comparing the advantages and disadvantages of each technology, the main challenges related to the use of current mine cooling technologies are pointed out, including the low energy efficiency ratio, high cost, and difficult implementation. Finally, this paper looks forward to the future development directions of mine cooling technologies, emphasizing the importance of intelligent, energy-saving, and environment-improving comprehensive system management and, in turn, promoting the progress and application of mine environment optimization technology and supporting safe and efficient deep mining. Full article

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset’s value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence. Full article

Various technologies with AI (Artificial Intelligence), DS (Data Science), and/or IoT (Internet of Things) have been starting to be pervasive in e-tourism (i.e., smart tourism). However, most of them for a target (e.g., what to do in such a tourism spot as Hikone Castle) utilize their “typical/major signals” (e.g., taking a photo) as summarized knowledge based on “The Principle of Majority”, and tend to filter out not only their noises but also their valuable “peculiar/minor signals” (e.g., view Sawayama Castle) as instantiated knowledge. Therefore, as a challenge to salvage not only “typical signals” but also “peculiar signals” without noises for e-tourism, this paper compares various methods of ML (Machine Learning) to text-classify a tweet as being a “tourism tweet” or not, to precisely mine tourism tweets as not summarized but instantiated knowledge. In addition, this paper proposes a MAS (Multi-Agent Simulation), powered with artisoc, for visualizing “tourism tweets”, including not only “typical signals” but also “peculiar signals”, whose number can be enormous, as not summarized but instantiated knowledge, i.e., instances of them without any summarization, and validates the effects of the proposed MAS by conducting some experiments with subjects. Full article

Existing skeleton-based action recognition methods face the challenges of insufficient spatiotemporal feature mining and a low efficiency of information transmission. To solve these problems, this paper proposes a model called the Spatial–Temporal Attention Residual Network for 3D human action recognition (3D-STARNET). This model significantly improves the performance of action recognition through the following three main innovations: (1) the conversion from skeleton points to heat maps. Using Gaussian transform to convert skeleton point data into heat maps effectively reduces the model’s strong dependence on the original skeleton point data and enhances the stability and robustness of the data; (2) a spatiotemporal attention mechanism (STA). A novel spatiotemporal attention mechanism is proposed, focusing on the extraction of key frames and key areas within frames, which significantly enhances the model’s ability to identify behavioral patterns; (3) a multi-stage residual structure (MS-Residual). The introduction of a multi-stage residual structure improves the efficiency of data transmission in the network, solves the gradient vanishing problem in deep networks, and helps to improve the recognition efficiency of the model. Experimental results on the NTU-RGBD120 dataset show that 3D-STARNET has significantly improved the accuracy of action recognition, and the top1 accuracy of the overall network reached 96.74%. This method not only solves the robustness shortcomings of existing methods, but also improves the ability to capture spatiotemporal features, providing an efficient and widely applicable solution for action recognition based on skeletal data. Full article

Visual object tracking technology is widely used in intelligent security, automatic driving and other fields, and also plays an important role in frontier fields such as human–computer interactions and virtual reality. The memory network improves the stability and accuracy of tracking by using historical frame information to assist in the positioning of the current frame in object tracking. However, the memory network is still insufficient in feature mining and the accuracy and robustness of the model may be reduced when using noisy observation samples to update it. In view of the above problems, we propose a new tracking framework, which uses the attention mechanism to establish a feature-enhanced memory network and combines cross-attention to aggregate the spatial and temporal context information of the target. The former introduces spatio-temporal adaptive attention and cross-spatial attention, embeds spatial location information into channels, realizes multi-scale feature fusion, dynamically emphasizes target location information, and obtains richer feature maps. The latter guides the tracker to focus on the area with the largest amount of information in the current frame to better distinguish the foreground and background. In addition, through the memory quality selection mechanism, the accuracy and richness of the feature samples are improved, thereby enhancing the adaptability and discrimination ability of the tracking model. Experiments on benchmark test sets such as OTB2015, TrackingNet, GOT-10k, LaSOT and UAV 123 show that this method achieves comparable performance with advanced trackers. Full article

Big data and artificial intelligence have promoted mining innovation and sustainable development, and the transportation used in open-pit mining has increasingly incorporated unmanned driving, real-time information sharing, and intelligent algorithm applications. However, the traditional manual scheduling used for mining transportation often prioritizes output over efficiency and quality, resulting in high operational expenses, traffic jams, and long lines. In this study, a novel scheduling model with multi-objective optimization was created to overcome these problems. Production, demand, ore grade, and vehicle count were the model’s constraints. The optimization goals were to minimize the shipping cost, total waiting time, and ore grade deviation. An enhanced multi-objective beluga whale optimization (IMOBWO) algorithm was implemented in the model. The algorithm’s superior performance was demonstrated in ten test functions, as well as the IEEE 30-bus system. It was enhanced by optimizing the population initialization, improving the adaptive factor, and adding dynamic domain perturbation. The case analysis showed that, in comparison to the other three conventional multi-objective algorithms, IMOBWO reduced the shipping cost from 7.65 to 0.84%, the total waiting time from 35.7 to 7.54%, and the ore grade deviation from 14.8 to 3.73%. The implementation of this algorithm for truck scheduling in open-pit mines increased operational efficiency, decreased operating costs, and advanced intelligent mine construction and transportation systems. These factors play a significant role in the safety, profitability, and sustainability of open-pit mines. Full article