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Improving the ability and accuracy of intelligent state prediction of large and complex equipment is one of the important directions of current intelligent operation and maintenance technology research. Due to the influence of insufficient analysis of equipment degradation characteristics, single function of traditional prediction model, and difficulty in determining the optimal parameters of the model make the prediction effect poor. In this paper, a state prediction model fusion optimization strategy is proposed for lithium mill equipment as an example. Based on the process flow and vibration mechanism, the inherent vibration characteristics of the roller bearing system are analyzed, and the degradation characteristics of the roller bearing under resonance conditions are explored from the finite element equivalent model, so as to determine the equipment operation stage and the starting point of degradation. The state prediction task is divided into degradation phase and residual life prediction phase, and Time-Convolutional Denoising Autoencoder (TCDAE) and two-layer Sparse Auto Encoder (SAE) are designed for data feature enhancement and degradation feature fusion and dimensionality reduction. Construct BO-BiGRU state prediction model to mine the feature information hidden in the whole time series of data points and adjust the model parameters adaptively using Bayesian Optimization method. The novelty of this study is to analyze the degradation characteristics of key components, correct the theoretical degradation starting point by using the degradation trend formula, and establish a unified framework from monitoring data to condition prediction. Compared with the original model constructed by the above algorithm, the fusion model proposed in this paper has significantly improved performance. The data analysis shows that the prediction accuracy after model fusion is substantially improved, and the accuracy after TCDAE feature enhancement is improved by about 10.2%, the accuracy after two-layer SAE model fusion and dimensionality reduction improved by about 9.8%, and the state accuracy after BO-BiGRU model improved by about 11.6%. The crux to the research depends on the construction of a state prediction model, which is based on the analysis of the bearing degradation process and the effective integration of algorithms. Predictive maintenance of critical components also improves product quality. [ABSTRACT FROM AUTHOR]

Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems. [ABSTRACT FROM AUTHOR]

Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems. [ABSTRACT FROM AUTHOR]

Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems. [ABSTRACT FROM AUTHOR]

Industrial control systems (ICSs), as critical national infrastructures, are increasingly susceptible to sophisticated security threats. To address this challenge, our study introduces the CAE-T, a deep convolutional autoencoding transformer network designed for efficient anomaly detection and real-time fault monitoring in ICS. The CAE-T utilizes unsupervised deep learning, employing a convolutional autoencoder for spatial feature extraction from multidimensional time-series data, and combines this with a transformer architecture to capture long-term temporal dependencies. The design of the model facilitates rapid training and inference, while its dual-component approach, utilizing an optimization function based on support vector data description (SVDD), enhances detection accuracy. This integration synergistically combines spatiotemporal feature extraction, significantly improving the robustness and precision of anomaly detection in ICS environments. The CAE-T model demonstrated notable performance enhancements across three industrial control system datasets. Notably, the CAE-T model achieved approximately a 70.8% increase in F1 score and a 9.2% rise in AUC on the WADI dataset. On the SWaT dataset, the model showed improvements of approximately 2.8% in F1 score and 5% in AUC. The power system dataset saw more modest gains, with an approximately 0.1% uptick in F1 score and a 1% increase in AUC. These improvements validate the CAE-T model's efficacy and robustness in anomaly detection across various scenarios. [ABSTRACT FROM AUTHOR]

Aiming at the problems of complex networking, high power consumption, and short communication distance of the communication technology used in the existing multi-loom data monitoring system, this paper adopts a LoRa technology with the advantages of strong anti-jamming ability, low operating power consumption, and long communication distance, and proposes a multi-loom data monitoring system program based on LoRa technology. Due to the large number of looms in the industrial field, multiple LoRa terminal nodes need to report data to the LoRa gateway nodes at the same time, the transmission frequency is relatively high and the channel load is heavy, and there is the problem of low channel utilization under the traditional data anti-collision algorithm. Aiming at the above problems, this paper proposes a communication mechanism based on the combination of binary tree conflict decomposition algorithm and TDMA, which can ensure that the communication of static time-slot type terminal nodes is free of data collision and reduce the node's power consumption, and at the same time, it can ensure that the terminal nodes that do not receive the receive answer frame can use the competition to report the data again within the dynamic time-slot assigned, so that the packets involved in the collision can be successfully transmission. In this paper, the remote monitoring system is designed in terms of both hardware and software systems. In order to prove the feasibility of the solution, practical tests show that the terminal nodes and gateway nodes can realize 100% no packet loss within 400 m in the loom industrial field environment, and the designed terminal nodes have low power consumption. It has some reference value in loom data monitoring. [ABSTRACT FROM AUTHOR]

Recovering low-quality waste heat using industrial waste heat is challenging, and the reuse technology needs to erupt. Moreover, the gas source of low-quality waste heat is relatively volatile, which makes it challenging to keep the actual working condition of the plant stable. Therefore, it is inspiring to research the robustness of root-waste heat power generation processed measurement and control system to improve the stability of the plant operation. Hence, in this paper, we have applied uncertainty theory to analyze it and formulate the uncertainty model based on the Bode diagram. We also proposed a control method based on the uncertainty model, which combines robust control and internal model control to make the roots waste heat power generation system operate stably under the effect of external disturbances and changes of internal structure or parameters in actual operation. Experimental results show that the robust internal model control method has a speed deviation of no more than 7.9 r/min compared with the PID control method. The adjustment time to track the set value does not exceed 73.1 seconds within the allowed fluctuation range. The fluctuation variance is 30.95% of that of the PID controller. The dynamic performance is better, with strong anti-interference capability and significantly improved tracking performance. It ensures the stability of the roots-type waste heat utilization system, which is essential for future intelligent grid-connected power generation. [ABSTRACT FROM AUTHOR]

The rapier loom works in a complex environment and operates at high speeds. It is inevitable that its performance will deteriorate during the production process, which in turn will cause faults. The development of maintenance has undergone the transition from "regular maintenance" and "post-event maintenance" to "predictive maintenance". In order to achieve the synergistic optimization goal of ensuring operational safety and reducing operational costs, a predictive maintenance method driven by the fusion of digital twin and deep learning is proposed based on the idea of "combining the real with the virtual and controlling the real". Firstly, a digital twin system structure model of rapier weaving machine is constructed, and the overall architecture of digital twin is proposed according to the full operation cycle of rapier weaving machine. Then, the digital twin-driven process parameter evaluation and prediction and health state evaluation and prediction are investigated separately. In order to achieve the evaluation and prediction of process parameters to ensure the efficiency of weaving machine operation, the prediction method of IWOA optimized BP neural network driven by twin data is proposed and the model is updated and optimized based on the martingale distance approach. In order to achieve health state assessment and prediction, we use health index as an evaluation index to characterize the health condition of spindles, and use BiLSTM network to achieve prediction of remaining spindle life and then make maintenance decisions. The results show that there are greater advantages to combining deep learning and digital twin technology for intelligent predictive maintenance of rapier loom. [ABSTRACT FROM AUTHOR]

In order to solve the problem that Switched Reluctance Motor (SRM) generates torque pulsation phenomenon during operation, which reduces the stability of loom spindle operation, this paper proposes and designs a multi-algorithm fusion-based SRM control strategy from the point of view of control strategy research. Combined with the operating characteristics of the loom, the causes of SRM torque pulsation are analyzed from the point of view of SRM control strategy, and combined with the spindle control indexes, the voltage chopper control and torque distribution function are introduced to construct the SRM control strategy scheme for the loom. On this basis, an optimization strategy based on the fusion of fuzzy control algorithm, particle swarm algorithm and simulated annealing algorithm is proposed to optimize the torque distribution function, and the algorithmic process of SRM control strategy is verified through comparative tests. The results show that the control strategy can make its torque pulsation reduced to less than 10%, the speed rise time is less than 0.1 s, and the relative error of the speed is less than 0.05%, which meets the index requirements of the spindle drive. This proves that the SRM torque pulsation can be reduced by the multi-algorithm fusion control strategy without increasing the hardware cost, which provides a useful reference for solving the SRM torque pulsation problem under the requirement of low cost. [ABSTRACT FROM AUTHOR]

The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation. [ABSTRACT FROM AUTHOR]

The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method's decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management. [ABSTRACT FROM AUTHOR]

The mechanical structures of the rapier loom are strongly coupled, resulting in faults that are characterized by strong coupling, hierarchy, phase dynamics, and a transient nature. However, current fault diagnosis methods using a single approach are not satisfactory. Additionally, fault diagnosis of the entire operation cycle of the rapier loom equipment is lacking. This paper proposes a fault tree diagnosis method with probabilistic neural network optimization to build a complete fault diagnosis system for rapier looms and improve their intelligent diagnosis capability. The method has strong fault tolerance and self-adaptive capability, allowing for accurate location of the root cause of the fault from multiple fault sources. By accumulating fault samples and continuously improving the diagnosis network, the accuracy of diagnosis can be further enhanced. Initially, the failure mechanism of key subsystems of rapier loom is analyzed. A fault tree model is established for each subsystem based on expert experience and historical data. The model identifies the characteristic sign quantities of typical fault types and serves as the basic input for fault identification. A probabilistic neural network is used to train the fault sample set and complete the diagnosis of the cause of the fault. According to field experiments, the proposed method has demonstrated a significant improvement in the efficiency of locating and identifying fault signs in rapier looms. This improvement allows for accurate and quick identification of faults. [ABSTRACT FROM AUTHOR]

Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value. [ABSTRACT FROM AUTHOR]

Fault diagnosis of rapier loom is an inevitable requirement to meet the demand of intelligent manufacturing. Facing the strong noise interference caused by complex working environment, accurate and reliable vibration signal detection of blade loom spindle is the key to realize the rapier loom fault diagnosis. This paper proposes a method to extract the spindle vibration signal of the rapier loom by Adaptive Piecewise Hybrid Stochastic Resonance (APHSR) after the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Firstly, ICEEMDAN is used to pre-process the weak vibration signal containing noise, decompose the signal into multiple IMF components and display the high and low frequency signal characteristics of the original signal. Then, the energy density method and the correlation coefficient method are used to remove high and low noise, respectively, to filter the optimal IMF components, and then the signal containing valid information is reconstructed. Finally, the reconstructed signal is input to APHSR for noise-assisted enhancement after scale transformation to restore the faint vibration signal feature frequencies and achieve effective feature extraction. Through the simulation experiment and the engineering fault experiment analysis, comparing ICEEMDAN-APHSR with CEEMDAN-SR, ICEEMDAN-SR, CEEMDAN-APHSR methods. The difference between the spectrum amplitude, the spectrum amplitude and the maximum noise and the maximum signal to noise ratio (SNR) of the fault feature frequency of the rapier loom spindle bearing increased by 3.3668 dB,1.7205 dB,2.3952 dB, respectively. The results show that ICEEMDAN-APHSR method can accurately extract the fault feature frequency of the spindle bearing of rapier loom, and effectively solves the problem of extracting the weak vibration signal feature of rapier loom in the background of strong noise. This method is beneficial to the future research of rapier loom fault diagnosis, and is of great significance to promote the maintenance of loom equipment and production safety and quality. [ABSTRACT FROM AUTHOR]

The foul odor of foul gas has many harmful effects on the environment and human health. In order to accurately assess this impact, it is necessary to identify specific malodorous components and levels. In order to meet the qualitative and quantitative identification of the components of malodorous gas, an electronic nose system is developed in this paper. Both principal component analysis (PCA) and linear discriminant analysis (LDA) were used to reduce the dimensionality of the collected data. The reduced-dimensional data are combined with a support vector machine (SVM) and backpropagation (BP) neural network for classification and recognition to compare the recognition results. Regarding qualitative recognition, this paper selects the method of LDA combined with the BP neural network after comparison. Experiments show that the qualitative recognition rate of this method in this study can reach 100%, and the amount of data after LDA dimensionality reduction is small, which speeds up the pattern speed of recognition. Regarding quantitative identification, this paper proposes a prediction experiment through Partial least squares (PLS) and BP neural networks. The experiment shows that the average relative error of the trained BP network is within 6%. Finally, the experiment of quantitative analysis of malodorous compound gas by this system shows that the maximum relative error of this method is only 4.238%. This system has higher accuracy and faster recognition speed than traditional methods. [ABSTRACT FROM AUTHOR]

In the control system of a lithium battery rolling mill, the correction system was crucial. This was because the correction system had a significant impact on the performance of the lithium battery rolling mill, including high precision and efficient rolling quality. However, the non-linearity of the correction system and the uncertainty of the correction system made it a challenging problem to achieve a high precision correction control. The contribution and innovation of this paper was a genetic fuzzy PID control strategy based on Kalman filter, which was proposed and applied to the control of lithium battery rolling mill correction technology. In order to achieve intelligent control of a high-precision electrode rolling mill correction system, an algorithm fusion control scheme was proposed. Firstly, a novel and detailed correction system model was presented. Next, the initial PID parameters of the correction were optimized by means of a genetic algorithm so that the PID parameters could be adapted to the correction control process and then optimized again by adding an extended Kalman filter. Finally, the lithium battery rolling mill correction control system was validated, tested and commissioned in the field. The results showed that the designed algorithm could meet the working requirements of the lithium battery rolling mill and that it improved the accuracy of the correction system. In the actual lithium battery rolling mill production process, the algorithm was compared with a conventional PID. Compared with the common single algorithm, the fusion algorithm proposed in this paper was a complete set of high precision correction control system algorithm to solve the high precision problem faced by the correction system in the actual lithium battery rolling mill correction system. [ABSTRACT FROM AUTHOR]

Deep learning methods have been widely applied to seismic denoising, outperforming existing methods in efficiency and generalization. For internal multiple suppression, however, deep learning models face the challenge of low generalization owing to the variability of internal multiples. This diminishes the advantages of deep learning methods over current processing flows. To overcome this, we redesign a convolutional neural network (CNN) method in the aspect of label generation and training process to suppress internal multiples. We apply the virtual event (VE) method to a small amount of data and take removed internal multiple data as labels to accelerate the network training, which is multiple learning. Instead of training a pervasive model to apply to all datasets, we rely on transfer learning to generalize. We finetune the synthetic data training model on target datasets to obtain the model applicable to the dataset with a low requirement of training data and time. Tests on synthetic and field data demonstrate the effects of multiple learning and transfer learning, as well as the competitive demultipling performance of our method compared with both the VE method and the original CNN in efficiency and primary-preserving ability. [ABSTRACT FROM AUTHOR]

Objective: The treatment of patients with primary Sjögren's syndrome is a clinical challenge. Gene expression profile analysis and comprehensive network methods for complex diseases can provide insight into molecular characteristics in the clinical context. Materials and Methods: We downloaded gene expression datasets from the Gene Expression Omnibus (GEO) database. We screened differentially expressed genes (DEG) between the pSS patients and the controls by the robust rank aggregation (RRA) method. We explored DEGs' potential function using gene function annotation and PPI network analysis. Results: GSE23117, GSE40611, GSE80805, and GSE127952 were included, including 38 patients and 30 controls. The RRA integrated analysis determined 294 significant DEGs (241 upregulated and 53 downregulated), and the most significant gene aberrantly expressed in SS was CXCL9 (p = 6.39E‐15), followed by CXCL13 (p = 1.53E‐13). Immune response (GO:0006955; p = 4.29E‐32) was the most significantly enriched biological process in GO (gene ontology) analysis. KEGG pathway enrichment analysis showed that cytokine–cytokine receptor interaction (hsa04060; p = 6.46E‐10) and chemokine signaling pathway (hsa04062; p = 9.54E‐09) were significantly enriched. We defined PTPRC, CD86, and LCP2 as the hub genes based on the PPI results. Conclusion: Our integrated analysis identified gene signatures and helped understand molecular changes in pSS. [ABSTRACT FROM AUTHOR]

To develop and verify an automatic classification method using artificial intelligence deep learning to determine the bone mineral density level of the implant site in oral implant surgery from radiographic data obtained from cone beam computed tomography (CBCT) images. Seventy patients with mandibular dentition defects were scanned using CBCT. These Digital Imaging and Communications in Medicine data were cut into 605 training sets, and then the data were processed with data standardization, and the Hounsfiled Unit (HU) value level was determined as follows: Type 1, 1000–2000; type 2, 700–1000; type 3, 400–700; type 4, 100–400; and type 5, − 200–100. Four trained dental implant physicians manually identified and classified the area of the jaw bone density level in the image using the software LabelMe. Then, with the assistance of the HU value generated by LabelMe, a physician with 20 years of clinical experience confirmed the labeling level. Finally, the HU mean values of various categories marked by dental implant physicians were compared to the mean values detected by the artificial intelligence model to assess the accuracy of artificial intelligence classification. After the model was trained on 605 training sets, the statistical results of the HU mean values of various categories in the dataset detected by the model were almost the same as the HU grading interval on the data annotation. This new classification provides a more detailed solution to guide surgeons to adjust the drilling rate and tool selection during preoperative decision-making and intraoperative hole preparation for oral implant surgery. [ABSTRACT FROM AUTHOR]

The recovery of low-quality waste heat is a major problem in energy utilization. In order to solve this problem and improve energy utilization, the research group designed a low-quality waste heat power generation device with Roots power machine as the core. However, the device has poor ability to adjust the rotation speed and it is difficult to generate electricity stably. The fundamental reason is that the system has many variables and strong coupling. According to the actual working conditions, the power of the device is 10 kW, and the fluctuation range should be within ± 7%. On the one hand, it can be improved by hardware, on the other hand, the design of software is also very critical. At present, through the investigation of domestic and foreign researches on the control system, it is found that the stability of the system is gradually improved, but the problem of strong coupling between variables has not been effectively solved. Therefore, the research group modeled the variables in the system and obtained a coupled model. Based on the couple model, the research group introduced nonlinear multi-model adaptive closed-loop decoupling control and designed a control system. The simulation results show that the maximum overshoot of the control system is 3.9%, the adjustment time is also reduced, and it is stable in low quality waste heat recovery device. Experimental results show that under the control of the system, the rotational speed of roots motor can keep stable, the maximum deviation is not more than 21.4 r/min, and the fluctuation range is within ± 7%, which meets the requirements of the index. This has laid the foundation for the follow-up research of grid-connected power generation. [ABSTRACT FROM AUTHOR]

The intelligent control strategy of electromagnetic clutch actuator is analyzed in detail in this paper. The start - stop control of the loom is realized by an electromagnetic clutch. The existing control method of electromagnetic clutch of loom is high and low pressure control strategy. The operator sets the braking advance angle according to experience, to realize the accurate braking of the spindle, but it is difficult to realize the fast and accurate control. In order to achieve good performance, it is very important to develop a fast and accurate loom braking system. Aiming at the fabric defects caused by the elongation of the warp when the loom is stopped, a method of stabilizing the excitation current of the electromagnetic clutch by using the neural adaptive PID (proportional integral differential) controller is proposed to improve the control precision of the actuator. The experimental results show that the proposed control algorithm is feasible and can effectively realize the adaptive control of the spindle braking Angle within the allowable error range. [ABSTRACT FROM AUTHOR]

At present, the global demand for lithium batteries is still in a high growth state, and the traditional lithium battery pole mill control system is still dominated by ARM (Artificial Intelligence Enhanced Computing), DSP (Digital Signal Processing), and other single-chip control methods. There are problems such as poor anti-interference ability and insufficient real-time online analysis of production data. This paper adopts the dual-chip control system architecture based on "ARM+DSP", starting from the mechanical characteristics and operating signal features of the pole mill. The hardware system adopts a three-unit joint control hardware structure, which separates the control unit from the data processing unit and improves the operation of the system. The software system adopts fuzzy PID algorithm to realize deflection control and tension control, and verifies that the Fuzzy PID (Proportion Integration Differentiation) control algorithm can effectively improve the anti-interference ability of the deflection system and tension system. The results show that the data loss rate is low with the SPI communication between DSP and ARM. The tension error of the "ARM+DSP" control system does not exceed 5%, and the deviation of the correction band is within ±4mm. The dedicated dual-chip hardware architecture effectively improves the robustness and operation efficiency of the pole mill, solves the problem of low tension control accuracy, and provides a theoretical basis for the application of the dual-roll mill. [ABSTRACT FROM AUTHOR]

Traditional proportional–integral–derivative (PID) control performance optimization is an essential method to improve a loom's warp tension control performance. This work proposes an improved genetic algorithm optimized PID control scheme to overcome the decline in control performance of the traditional PID control algorithm in a loom's warp tension control system. Through the decoupling analysis of loom motion mechanism, the establishment of warp tension model and the optimization of fitness evaluation mechanism of genetic algorithm can effectively overcome the problems of local optimal solution and algorithm degradation of genetic algorithm. Simulation experiments were carried out with the traditional PID, the integral separation PID, and the genetic PID in warp tension control. The results show the advantage of the genetic-PID algorithm to control warp tension stability. Ultimately, according to the functional characteristics of the loom mechanism, a tension control platform for experimental studies was established. The test results show that the maximum fluctuation range of warp tension is within [−2, +6] at the test speed of 850 rpm, which meets the requirements of long-term stable and reliable control of warp tension under different weaving conditions. [ABSTRACT FROM AUTHOR]

The electrical control system of rapier weaving machines is susceptible to various disturbances during operation and is prone to failures. This will seriously affect the production and a fault diagnosis system is needed to reduce this effect. However, the existing popular fault diagnosis systems and methods need to be improved due to the limitations of rapier weaving machine process and electrical characteristics. Based on this, this paper presents an in-depth study of rapier loom fault diagnosis system and proposes a rapier loom fault diagnosis method combining edge expert system and cloud-based rough set and Bayesian network. By analyzing the process and fault characteristics of rapier loom, the electrical faults of rapier loom are classified into common faults and other faults according to the frequency of occurrence. An expert system is built in the field for edge computing based on knowledge fault diagnosis experience to diagnose common loom faults and reduce the computing pressure in the cloud. Collect loom fault data in the cloud, train loom fault diagnosis algorithms to diagnose other faults, and handle other faults diagnosed by the expert system. The effectiveness of loom fault diagnosis is verified by on-site operation and remote monitoring of the loom human-machine interaction system. Technical examples are provided for the research of loom fault diagnosis system. [ABSTRACT FROM AUTHOR]

At present, the rapier loom has gradually become the mainstream equipment in the manufacturing industry. In order to make the rapier loom realize automated production and further improve the production efficiency of the rapier loom, improve the programmability of the system, and reduce the cost of system maintenance. The thesis developed a rapier loom control system based on embedded soft PLC, and carried out experiments and applications in the field. The contribution and innovation of this paper is to develop a complete low-cost control system, and through a genetic algorithm optimized PID algorithm to complete the more effective control of the loom tension system. The embedded soft PLC system proposed in this paper reduces the overall maintenance cost of the system and improves the programmability of the system. This text carries on the systematic scheme design to the embedded soft PLC from the hardware system and the software system respectively. First, according to the actual requirements, this article designs the overall scheme of the embedded software PLC hardware system with STM32F407ZGT6 as the core. Then this article is based on the embedded soft PLC hardware platform, according to the international standard of industrial control programming, writes the embedded soft PLC low-level driver software. Secondly, this article analyzes the factors that affect the warp tension during the operation of the rapier loom, and proposes the use of genetic algorithm to optimize the warp tension control method of the traditional PID algorithm. Finally, we conducted verification tests and on-site application debugging for the entire set of rapier loom embedded soft PLC control system. We controlled the warp tension as the main experimental object. The results show that this control system effectively improves the control accuracy of the warp tension of the rapier loom and meets the actual needs of industrial applications. The whole system has a good application prospect in the warp tension control of rapier looms. [ABSTRACT FROM AUTHOR]

The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom's fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms. [ABSTRACT FROM AUTHOR]

In textile machines, the stability of warp tension is one of the decisive factors for the reliability, stability and product quality of weaving process. In order to meet the improving requirement for weaving efficiency and fabric quality, it is proposed that a fuzzy optimization integral separation PID warp tension control scheme based on process sampling to improve the warp tension control level of loom. Aiming at the problems of time-varying, nonlinear and variable coupling in the warp tension control system of loom, the forming mechanism of warp tension is modeled and analyzed, and the sampling scheme of warp tension based on process is proposed. Based on the periodic change of warp tension at macro level and continuous fluctuation at micro level, the integral separation control and fuzzy optimization theory are introduced to optimize the control effect of the control system on the basis of classical PID control algorithm. Finally, the simulation and experiment show that the scheme can improve the tension controls performance and effectively reduce the tension error fluctuation. [ABSTRACT FROM AUTHOR]

Numerical methods have been widely applied to simulate seismic wave propagation. However, few studies have focused on internal multiples modeling. The formation mechanism and response of internal multiples are still unclear. Therefore, we develop a weighted-optimised-based internal multiples simulation method under 3D conditions. Using a one-way wave equation and full-wavefield method, the different-order internal multiples are computed numerically in a recursive manner. The traditional Fourier finite-difference (FFD) method has low numerical accuracy in a horizontal direction. A globally optimised FFD (OFFD) method is used to improve the lateral propagation accuracy of the seismic waves. Meanwhile, we adopt an adaptive variable-step technique to improve computational efficiency. The 3D internal multiples modeling technique is capable of calculating the different-order multiple reflections in complex structures. We use the present method to simulate internal multiples in several models. Theoretical analyses are consistent with the numerical results. Numerical examples demonstrate that the 3D internal multiples modeling technique has superior performance when adapting to lateral velocity changes and steep dip. This also implies that our method is fit for the simulation of internal multiples propagation in a 3D complex medium and can assist in identifying the internal multiples from full-wavefield data. [ABSTRACT FROM AUTHOR]

In the industrial field, the lithium battery industry has a long history and a large market scale. Lithium battery electrode strip rolling mill belongs to the high-end production equipment in the lithium battery industry. However, due to its complex structure, the tension of lithium battery electrode mill is prone to large fluctuation. This will lead to the phenomenon of wrinkle and looseness, which will affect the quality of the electrode strip. At present, the tension control method of lithium battery electrode mill mostly adopts traditional Proportional-Integral-Differential(PID) control. Under this control mode, the production speed and precision of lithium battery electrode mill need to be improved. In this paper, the fuzzy PID tension control method of lithium battery electrode mill based on genetic optimization is studied. Based on fuzzy theory and PID control method, a tension fuzzy PID model is established for experimental verification, and the initial parameters and fuzzy rules of fuzzy PID are optimized by Genetic Algorithm(GA). This method has better stability, can improve the precision of strip tension control, make the tension more stable when the rolling mill is running, and help to improve the quality of electrode strip production. [ABSTRACT FROM AUTHOR]

Monolithic nanoporous silver (np‐Ag) fabricated by a facile electrochemical method is attractive for electrocatalytic applications. However, the conversion mechanism between Ag and AgCl remains unclear as a result of the lack of appropriate electron microscopy evidence. Herein, systematic scanning electron microscopy (SEM) of the surface and cross‐sectional views were carried out during the anodic formation and reduction of the AgCl layer. To avoid the thermal decomposition of AgCl and the destruction of the cross‐sectional morphology, Ar ion polishing at −140 °C under the cooling of liquid N2 was adopted. Our data show that the reduction process of the AgCl layer includes three processes: trigger, expansion, and shrinkage. A funnel‐like transient state was observed for the first time, leading to a new formation mechanism for the resultant np‐Ag. Outstanding performance in electrocatalytic reduction of CO2 to CO was observed for one of the nanoporous Ag films presumably due to its unique nanostructure to promote initial electron transfer and active‐site exposure. The selectivity of CO between −0.31 and −0.56 V was higher than 90 %, and reached a maximum of 96 % at −0.36 V. [ABSTRACT FROM AUTHOR]

Artificial photosynthetic systems store solar energy in chemical fuels via CO2 reduction or renewable hydrogen evolution from water splitting. But only relatively moderate CO2-reducing solar-to-fuel efficiency (ηSTF < ca. 18%) was achieved previously even using the most active gold-based electrocatalyst for the CO2 reduction reaction (CO2RR), which is much lower than the current highest solar-to-hydrogen efficiency (ηSTH = 30%). Here, we report a strategy based on a monolithic nanoporous silver-based membrane electrocatalyst (npm-Ag) to increase the density of active sites while breaking the three-phase diffusion-limit length of previous membrane electrodes, improving the effective thickness of the catalyst layer from tens of nanometers to several micrometers without any polymer additive. The npm-Ag showed exceptionally selective reduction of CO2 to CO at low overpotentials (ca. 80%@40 millivolts, ca. 100%@90–290 millivolts). The catalyst also allowed a nearly full use of the photocurrent of the state-of-the-art solar cells and gave a projected maximum ηSTF of ca. 25%. Combined with a NiFe-based O2-evolving anode, a stable CO2-reducing photosynthetic system with an ηSTF of ca. 20.1% was demonstrated experimentally for 28 hours in the absence of a DC/DC converter. [ABSTRACT FROM AUTHOR]

In this paper, the design of the remote monitoring system for the industrial interconnection of the intelligent high-speed loom is carried out from the hardware system and the software system. The overall design scheme of the system is as follows. The transmission control protocol/internet protocol communication protocol based on the socket programming interface is used for data transmission between the local executor and the cloud server. The browser/server architecture is selected for data communication between the web server and remote client. The data interaction between the web server and the remote client is realized by Ajax (Asynchronous JavaScript and XML). Finally, the remote monitoring function of the industrial interconnection of the intelligent high-speed loom is realized. The effective communication distance of the hardware circuit control board of the intelligent high-speed loom monitoring system is 150 m, which meets the needs of the industrial field. The client-side of the system can be compatible with different browsers and can monitor the running state of high-speed loom in real-time. Once the loom breaks down, the system can judge the cause of the failure through algorithm analysis, and the visual interface can achieve a better human–computer interaction effect, which can meet the needs of the industry for the intelligent remote monitoring system of the high-speed loom. The effective communication distance of the hardware circuit control board of the intelligent high-speed loom monitoring system is 150 m, which meets the needs of the industrial field. [ABSTRACT FROM AUTHOR]

The textile industry has a long history and a large market scale around the world. High-speed loom belongs to the high-end production equipment of the textile industry with the characteristics of high precision, high speed and high efficiency. However, due to its expensive cost and complex structure, there might be significant loss once a high-speed loom breaks down. At present, the monitoring and troubleshooting of high-speed loom operation mainly depend on the experience of maintenance people to carry out inspections, which is inefficient, time-consuming, laborious and less efficient. In this paper, a fault diagnosis method for high-speed loom based on rough set and Bayesian network is investigated. Rough set theory is applied to reduce the attributes of fault causes and results and find the minimum reduction and classification rules. Then, a Bayesian fault diagnosis network model is built, and the probability of each fault cause is calculated to find the maximum probability. Finally, the diagnosis results are obtained. The experimental results have demonstrated the reliability and convenience of the faults diagnosis method for the high-speed loom. [ABSTRACT FROM AUTHOR]

In this paper, a new type of low-pressure steam generator, roots-type power machine, is proposed to solve the shortcomings of medium- and low-temperature waste heat energy recovery. The rotor is systematically studied by means of theoretical analysis, numerical simulation and experimental verification. Firstly, the motion law of roots-type power machine is analyzed theoretically. According to the rotor profile characteristics, the rotor profile structure is divided and the parameter equation is established. In order to verify the feasibility of the designed rotor, the pressure field, velocity field and mass flow field inside the roots-type power machine where the rotor is located are analyzed by numerical simulation method. Finally, a test platform is built to test the power output characteristics, load characteristics and flow characteristics of the roots-type power machine. The feasibility of the rotor profile design method and the reliability of the rotor mathematical model are verified. The results show that the design of the rotor profile can meet the power generation requirements of the roots-type power machine. The rotor profile design method provides a new idea and strong theoretical guidance and basis for the design of the rotor of the subsequent roots-type power machine. Moreover, the development of the roots-type power machine will have a profound impact on the recycling and energy saving of low-temperature waste heat resources. [ABSTRACT FROM AUTHOR]

The experimental platform of the SCM with STM32 as the core is designed as a supplement to the learning of 51 SCM. With the modular design and in consideration of students' learning ability and the need to master the peripherals of STM32 chip, the corresponding modules and 17 corresponding experiments are designed. The socket connection mode makes the connection between the experimental modules more reliable and stable. The experimental platform has been used in the teaching of SCM, which improves students' interest in learning SCM and trains their ability of independent innovation and engineering practice. [ABSTRACT FROM AUTHOR]

The interactions between femtosecond (fs) laser pulses and a thin Au film deposited on a silica glass substrate were systematically investigated based on experimental data. Different structures, including microholes, nanoholes, and nanobumps, are obtained when pulses with different energies are incident on the surface of a gold film. The experimental results are discussed according to specific experimental parameters. Two physical models were constructed in order to explain the experimental results. The formation of nanoholes in a silica substrate is attributed to etching by higher order harmonic generations (HHG) when the femtosecond laser pulse interacts with the generated plasma layer, while the formation of nanobumps on the surface of an Au film is attributed to the elastic and plastic characteristics of the metal film under laser pulse irradiation. [ABSTRACT FROM AUTHOR]

It is always a goal of scientists to develop new techniques to identify the composition of mantle materials and understand geodynamic processes accurately. Ferropericlase (Mg,Fe)O is a prominent mineral in the lower mantel. It is a common practice in the research community to prepare ferropericlase using a solid-phase synthesis method or high-pressure experiment synthesis method. This conventional method contains a number of ambiguities a great deal of time is needed. In this paper, we have addressed the drawbacks of the conventional technique using a liquid-phase synthesis method to prepare ferropericlase. During the experiment, oxalic acid was added to a mixed solution of ferrous sulfate and magnesium chloride and mixed according to the molar ratio. The formed magnesium iron oxalate precipitate was sintered and reduced into the final sample. Furthermore, the final sample was analyzed using XRD and SEM. Compared to the solid-phase method, this coprecipitation method could produce ferropericlase with a shorter sintering time, lower sintering temperature, and a reduction in the amount of gas consumed. XRD and SEM results show that the liquid-phase method produced samples with better composition homogeneity. [ABSTRACT FROM AUTHOR]