Search

Aiming at the problem of traditional methods which lead to low detection accuracy owing to limited sample size and imbalanced sample, this paper proposes a novel insulator self-explosion detection algorithm based on mixed samples and transfer learning. Firstly, the segmentation of insulator and background region is realized by using template matching and grabcut algorithm. Secondly, the segmented insulator image is merged with the new background image at pixel level to generate the insulator artificial sample with distinct texture. Thirdly, this paper introduces an improved Faster R-CNN model that utilizes ResNet50 for feature extraction, replacing the traditional VGG16 network. This modification significantly improves the ability of the network to extract insulator texture features. By introducing the focal loss function to address the imbalance between positive and negative samples, the detection accuracy of the network is further improved. Finally, the introduction of depthwise separable convolution helps reduce model complexity, resulting in a more lightweight architecture. The model is trained by a dataset that includes both synthetic and real samples. The real insulators are then employed for evaluation. The experimental results show that even with a reduced number of artificial samples, the algorithm proposed in this study achieves a mean average precision of 95.26%. Additionally, the average detection time is 2.31s. These results show that compared with the original Faster R-CNN method, the proposed method has improved the detection accuracy and speed. [ABSTRACT FROM AUTHOR]

The real-time dynamic coupling under the virtual-coupling technology guarantees running multi-group trains in different sections based on passenger flow's spatial and temporal distribution characteristics. It enables more flexible transport organization modes such as full-length and short-turn routing and the express/local stop mode. Utilizing the redundant capacity of the airport line to carry out coordinated transportation of passengers and goods is an essential means of relieving urban traffic pressure, saving energy, and protecting the environment. To promote urban rail transit to achieve the goal of "double-carbon," the optimization of passenger and freight cooperative transport schemes based on the virtual coupling technology for the airport line of the rail transit is investigated. Based on the train operation of full-length and short-turn routing under the virtual-coupling technology, according to the unbalanced distribution characteristics of passenger and freight flows in urban rail transit, this paper constructs an optimization model of urban rail transit capacity allocation to minimize passenger travel costs, enterprise operation costs, and carbon emission costs, and considering the constraints of the carbon emission policy, with the constraints such as the train operation safety, the constraints of the balance of the passenger flow and freight flow loading. The multi-objective model to solve the optimization problem has been designed. Given the complexity of the multi-objective model solution, an improved nondominated sorting genetic algorithm is designed, and the algorithm makes use of the screening function of the constraints during the initial population generation process, which effectively balances the relationship between the whole process of screening and time-consumption of the NSGA-II algorithm. Taking Wuxi Metro Line 3 as an example, the results show that: (i) Compared with the traditionally large and small crossing mode, full-length and short-turn routing of virtual coupling Trains proposed in this paper can reduce the average waiting time of passengers by about 2.01min, the cost of carbon emission by 15.7% and the cost of enterprise operation by 20.2%, and at the same time, improve the average total rate by about 50%, which verifies that the virtual formation technology is conducive to improving the efficiency of passenger travel and reducing the operating cost and the carbon cost. It is also beneficial for improving passenger traveling efficiency. (ii)The single-alignment fixed configuration mode, compared with the single-flow mode, can increase the average full-load rate by 26.6% while increasing the passenger travel cost by only 0.98% and reducing the carbon emission cost by a small margin; the virtual configuration technology based-on the large and smallalignment operation and transport scheme increases the passenger travel cost by about 0.3% and further reduces the carbon emission cost by 1.9%. They further reduced the carbon emission cost by 1.9% while increasing the average total occupancy rate by about 5.8%. The proposed models and algorithms provide some decision support for urban rail transit operators to optimize transport schemes under different carbon emission policies and the virtual coupling operation mode. [ABSTRACT FROM AUTHOR]

This paper evaluates the influence of natural ventilation on the longitudinal temperature attenuation of cable tunnel wall fires. It took into account the effects of different fuel quantities, cable types, cable layers, and the number of cables per layer. Experimental tests were carried out in a cable tunnel. Results indicated that under natural ventilation the cable burns well. The cable total mass loss rate varies under different conditions. Moreover, under natural ventilation, cable combustion was affected by many factors. This paper analyzed the effect of natural ventilation on longitudinal dimensionless temperature attenuation. A mathematical model was proposed for longitudinal dimensionless temperature attenuation under natural ventilation cases. In addition, a predictive model for maximum temperature rise below the ceiling was proposed to address the effects of heat release rate (HRR) and ventilation on cable tunnel fires. The experimental results were compared with previous research results. Finally, this paper established a calculation equation for dimensionless fire source heat release rate. This paper provides guidance for the fire prevention of cable tunnels. [ABSTRACT FROM AUTHOR]

Due to the rapid global environmental changes, sustainability-related issues have garnered significant attention from various stakeholders. Addressing how to reduce and balance damages caused by various factors is an important focus of sustainability-related research. To this end, this paper first proposes several evaluating mechanisms for reducing and balancing damages under multiple criteria fuzzy behavior, and then adopts some axioms to analyze these mechanisms concurrently in terms of mathematical correctness and practical application by means of axiomatic processes. In order to modify the discrimination and relative effects caused by participants and its energy levels respectively, two weighted evaluating mechanisms and corresponding characterizations are also introduced. Besides, some more interpretations for these axioms and relative axiomatic processes are discussed throughout this paper. [ABSTRACT FROM AUTHOR]

The establishment of the NOx concentration prediction model is a prerequisite for the selective catalytic reduction (SCR) flue gas denitriflcation systems in thermal power plants to overcome measurement delays in continuous emission monitoring systems and achieve precise control. This paper proposes a NOx emission prediction method for thermal power plants based on the attention mechanism and the gated recurrent unit (Attention-GRU) to address the issues of low accuracy and cumbersome feature selection process in NOx emission prediction models established for the SCR system under complex working conditions. Firstly, this article utilizes the GRU to extract data features of NOx emissions from thermal power plants at the time scale, in order to establish a long-term prediction model. Secondly, the feature dimension clustering the k-means algorithm can effectively improve the learning efficiency of temporal features and reduce the computational complexity of subsequent algorithms. Finally, the parameter attention mechanism is introduced to autonomously select favorable temporal features for predicting NOx emissions in thermal power plants, replacing the complex and time-consuming data screening process. This paper has also collected historical datasets of the SCR system form a thermal power plant under complex and simple working conditions to verify the effectiveness of Attention-GRU. The experimental results show that compared with existing methods (see, e.g., ELM, RF, SVM, BPNN, LSTM, GRU, BiLSTM, and CNN-LSTM), our method has higher prediction accuracy of NOx emissions from thermal power plants, which helps to improve the control performance of SCR systems to reduce atmospheric pollution. At the same time, this article has also found that the attention mechanism can significantly reduce the dependence of existing methods on the feature selection process, effectively solve the interference problem of noise data on model feature extraction, and further improve the prediction accuracy of NOx emissions in thermal power plants. [ABSTRACT FROM AUTHOR]

We studied a paper to apply a method of maximizing deviations to multiple attribute decision-makings under intuitionistic fuzzy environment that have found several new methods and theorems for maximum problem under some specific conditions with insufficient information environment. We showed that the Lagrange multiplication method used by the paper can be replaced by our simplify approach with the Cauchy Schwarz inequality. The purpose of this paper is fourfold. First, the iteration method for the problem within the range of weights is well developed and with appropriate explanation if the weight vector of attributes is bounded. Second, if the weight vector of attributes is completely unknown, we could directly and swiftly derive the weight by the Cauchy-Schwarz inequality such that the complicated approach by the Lagrange multiplication method becomes redundant. Third, we prove the results of score function and rank for one-norm will not be preserved in the two-norm. Fourth, the same numerical examples are examined again and have different outcome to demonstrate our findings is superior to the previously published results. [ABSTRACT FROM AUTHOR]

In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users need from this vast data has become challenging. To tackle this problem, recommending preferred information to users is becoming increasingly important. However, accurately recommending information by analyzing existing models such as GraphRec is still a challenging problem. A method called PSO_KFCM is proposed in this paper to solve this problem better. The technique combines Particle Swarm Optimization (PSO) with hybrid optimization and the kernel fuzzy C-means clustering technique to cluster similar recommendation data into one class. This way, the complexity and randomness of the recommendation data are reduced. It improves the speed and accuracy of the model prediction, which lays a solid foundation for the subsequent recommendation. Various factors will impact the recommendation process, and channel and spatial characteristics are essential. CBAM attention is added to the original attention mechanism to fully utilize these features in the recommendation data to enhance its performance. Furthermore, this paper proposes a social recommendation prediction method that combines CBAM attention and PSO_KFCM clustering and introduces a new social model called TTYGNN. The TTYGNN model optimizes the recommendation effect while maintaining the original advantages, enabling users to obtain the required information more quickly and accurately. To verify the effectiveness and practicality of the proposed model, extensive experimental comparisons were conducted on two widely used datasets. The results show that the TTYGNN model outperforms similar methods in all indicators, proving its superiority in information recommendation. [ABSTRACT FROM AUTHOR]

Quantum error correction lies at the heart of building reliable quantum information processing systems. Stabilizer codes, a fundamental class of quantum errorcorrecting codes, play a pivotal role in mitigating the adverse effects of noise and decoherence in quantum systems. This paper introduces a novel construction of quantum stabilizer codes using Hadamard difference sets, an elegant mathematical concept derived from combinatorial design theory. In this paper, the construction of the quantum stabilizer codes over non- cyclic Hadamard difference sets with parameters (4m²,2m²-m, m²-m), where m is a positive integer is discussed. Firstly, the parity check matrices are constructed from the Circulant permutation matrices with the help of Hadamard difference sets and then, the Symplectic inner product condition for Hadamard difference sets over binary operation for parity check matrices are obtained to affirm the commutative condition for Stabilizer operators which is vital for the error detection. For application, we constructed a Hadamard difference sets with parameters (16,6,2) for m = 2 of ordered pair of the group Z2 Z8 × (non-cyclic group) and quantum stabilizer codes are obtained by parity-check matrix. [ABSTRACT FROM AUTHOR]

ERP provides a powerful guarantee and a basic platform for the centralized management of group funds. This paper uses Likert's five-point method to design questionnaires, obtain original data, and apply structural equation models. Then it establishes a framework for centralized management of corporate group funds in an ERP environment, collects data through questionnaires, and explores the framework for centralized management of corporate group funds under the ERP environment. It uses linear regression models to describe experts' recognition of the capital management framework by establishing a structural equation model to test the key factors influencing the construction of a centralized fund management system. Results show that the factors influencing the construction of the centralized fund management framework include: risk control, fund budget control, fund management decision-making system, online banking, financial, logistics and other modules in formation integration, and fund monitoring. This paper can help companies not only sort out the contexts, and strengthen capital management, but also improve the efficiency and effectiveness of financial monitoring, and increase the risk control of monetary capital management. It has important practical significance for strengthening group capital management and reducing capital costs. [ABSTRACT FROM AUTHOR]

This paper studies the optimization design of capillary heat exchangers and their innovative engineering applications in buildings. The purpose of this study is to use a capillary heat exchanger as the front-end device of the heat pump in coastal areas to extract energy to facilitate the application of renewable energy in buildings. Firstly, in this paper, the main factors of capillary heat transfer efficiency were explored in areas near the coast. Secondly, for the sake of improve the heat transfer efficiency of the capillary heat exchanger, numerical calculations were carried out on the heat transfer of the capillary in the case of inconsistent operating patterns in the summer and winter months, respectively. The results showed that when the daily working time of the capillary heat exchanger was 8 hours, the temperature difference of the capillary inlet and outlet was 2.1 ℃. And the heat transfer rate was 87.1 W/m² . In summer, the temperature difference of the capillary inlet and outlet was 2.6 ℃. And the heat transfer rate was 107.8 W/m² . This article helps to promote innovative and sustainable technologies in coastland development, such as capillary heat exchangers and heat pumps, which could used in renewable energy extraction and building utilization in near coastal areas. [ABSTRACT FROM AUTHOR]

The advancement of new energy vehicles has led to more demanding standards for detecting defects in cylindrical coated lithium batteries. The current research lacks robustness and has low performance. This paper seeks to provide real-time defect identification in cylindrical coated lithium batteries and improve the object detection method of the YOLOv5s model. This paper presents an MGSEC3 module with multi-scale feature extraction and integration of the SENet network in the YOLOv5 Backbone network. This module aims to reduce computational burden and enhance feature extraction efficiency as much as possible. At the same time, the CARAFE operator is utilized to enhance the up-sampling operator in order to reduce the loss of feature information. In addition, enhancements to the loss function enhance both the detection performance and convergence speed. The enhanced YOLOv5s model achieved an average detection rate of 82.4% on the custom cylindrical coated lithium battery dataset, 2.3% higher than the original YOLOv5s. This paper significantly enhances the precision and efficiency of flaw identification in cylindrical coated lithium batteries. [ABSTRACT FROM AUTHOR]

Civil aircraft represent intricate technical systems, and their flight test is a system engineering process. This process is essential for verifying their compliance with airworthiness regulations and operational requirements. In this process, planning flight test missions is a type of combinatorial optimization problem. The solution gives an optimal allocation plan adhering to operational constraints. Assigning flight test missions for civil aircraft involves numerous uncertainties and very often, requires unforeseen adjustments. The original missions need to be reassigned due to these adjustments in a scientific way. In this paper, considering the scenarios that emerged during real civil aircraft flight tests, a multi-objective flight test mission planning model is established based on the Flexible Job Shop Problem (FJSP) model. The model is specifically tailored to the flight text mission reassignment. Examples of flight test scenarios that require mission reassignments are addressed using the FJSP model proposed in this paper and optimized using the Particle Swarm Optimization algorithm. Two rounds of initial population updates were conducted to obtain overall extreme values. The results verify the feasibility of obtaining a viable solution for the flight test mission assignment problem under complex constraints derived from real engineering requirements. These solutions can be used to address similar types of engineering problems. [ABSTRACT FROM AUTHOR]

In this paper, we examine an individual consistency control in consensus building for group decision-making that was based on a paper of Li, Rodriguez, Martinez, Dong, and Herrera. we point out their important but questionable results and then present our comments. Our results will help researchers understand the structure of individual consistency control in consensus building under fuzzy environments. [ABSTRACT FROM AUTHOR]

Aiming at the problems of large lag and inertia in the PH control system of gypsum wet desulfurization in thermal power units, this paper proposes a cascade multi-step feed forward predictive control strategy based on proportional integral derivative (PID). Firstly, the paper establishes mechanism model through the transfer function of PH value influence factor to ensure a strong correlation between the control system parameters and the PH value; Secondly, based on the traditional PID control strategy, the paper introduces cascade and multi-step feed forward control mechanisms to solve the strong inertia of the control system; Then, the three-step predictive value of SO2 content in raw flue gas based on long short term memory network (LSTM) is introduced as the feed forward value, so as to better eliminate system lag characteristics and track the PH setting value; Finally, the simulation experiment is carried out using the desulfurization data of the plant. The simulation results show that, compared with the traditional PID and cascade PID control strategies, the control strategy proposed in this paper achieves more accurate and stable control of the PH value of desulfurization slurry in thermal power units, and improve the desulfurization efficiency. [ABSTRACT FROM AUTHOR]

The evolution of Digital Predistortion (DPD) for Power Amplifier (PA) linearization in wireless communications has seen a natural progression towards increasingly complex DPD models. This complexity directly correlates with improved accuracy in modeling PAs, thereby enhancing the linearization performance of DPD systems. As the demand for bandwidth and PA operation frequencies continues to rise, there is a growing trend towards achieving higher accuracy at the expense of increased complexity. This trend poses challenges for DPD design, necessitating the introduction of more complex algorithms to ensure effective PA linearization. This phenomenon is especially apparent in Volterra Series DPD algorithms, which are widely used for modelling nonlinear systems like PAs. This paper showcases the steps of reducing the modelling operations complexity of the Simplified Volterra (SV) method. The treated SV method presented in this paper is compared against the original SV method through simulation. The DPD model operations complexity is evaluated using the number of required multiplication operations. The DPD PA linearization performance is evaluated with respective model’s Normalized Mean Square Error (NMSE). A 57% reduction on the number of multiplication operations is observed in the treated SV against the original SV. Measured NMSE is similar for both models, indicating the treated SV model achieves this reduction in complexity without sacrificing linearization performance. [ABSTRACT FROM AUTHOR]

Researching and improving the low-temperature cold-start performance of PEMFC plays an important role in the promotion and application of PEMFC. Among them, auxiliary preheating is one of the key technologies to achieve fast cold start of PEMFC. In order to reveal the influence law of auxiliary preheating parameters on the cold start of PEMFC, the auxiliary preheating control strategy is proposed. In this paper, a mathematical model for PEMFC performance analysis is first established. On this basis, the influence law of preheating exhaust gas temperature and flow rate on PEMFC temperature rise process is studied. Based on the Screening optimization algorithm, the input and output parameters of the preheating process were optimized, and the optimal auxiliary preheating control parameters were determined under different starting temperatures. Finally, a start-up auxiliary preheating control strategy for different low-temperature environments is designed and performance verification is carried out. The results show that the auxiliary preheating control strategy proposed in this paper reduces the start-up time by 43% at -10 compared without the auxiliary preheating control. The auxiliary strategy designed in this paper is able to achieve normal startup of the PEMFC at -20 and -30. However, the PEMFC startup fails in both without the auxiliary modes. [ABSTRACT FROM AUTHOR]

We provide a detailed examination of inventory models with compounding interest that derived an approximated closed-form optimal solution. The goal of our paper is threefold. First, we find a criterion to guarantee the existence of the original optimal solution. Under the criterion, we prove the uniqueness of the original optimal solution. Second, because the source paper did not consider the zeros of the first derivative, we point out that the complicated discussion of the second derivative proposed by the source paper is useless in his derivations. Third, we provide our approximated closed-form optimal solution that is more accurate than the approximated closed-form optimal solution in the source paper. Numerical examples cited from the source paper support our claim. Our paper will provide significant improvements to the source paper. [ABSTRACT FROM AUTHOR]

The quasi-interpolation operator is widely used in numerical approximation and numerical solutions of differential equations. This paper proposes a new multiquadric(MQ) quasi-interpolate and formulates a meshfree method for the Korteweg-de Vries(KdV) equation based on the proposed multiquadric quasi-interpolate. More specifically, based on the multiquadric function, a new univariate multiquadric(MQ) quasi-interpolation scheme is structured, which possesses high accuracy, simple structure, and ease of programming. Moreover, the error estimation of the new quasi-interpolate is shown in detail. Next, a meshfree method for the Korteweg-de Vries (KdV) is proposed by using the novel multiquadric(MQ) quasi-interpolation operator. In the spatial direction, the derivative is approximated by the proposed multiquadric quasi-interpolate, and the forward divided difference approximates the temporal derivative. Several numerical examples are presented at the end of the paper to verify the expected approximation capability, and the experiment results show that the meshfree method (based on the new multiquadric(MQ) quasi-interpolation operator) is valid. [ABSTRACT FROM AUTHOR]

The purpose of this article is is to investigate the critical speed and back-layer length of smoke propagation during fires of lithium-ion battery vehicles in tunnels. The combustion of lithium-ion battery vehicles in tunnels is numerically simulated. This article uses heat release rate (HRR) and ventilation rate to predict the length of smoke counterflow in tunnel fires with new energy vehicles. Temperature is an important parameter for calculating smoke counterflow. The temperature in the tunnel is measured by thermocouples. The results showed that dimensionless critical velocity influences dimensionless HRR. The numerical simulation results indicated that there is a certain correlation between the HRR variable, which corresponds to the dimensionless smoke counterflow length and the critical speed in the tunnel. This article proposed a computational model for predicting the length of smoke counterflow. This paper contributes to the prevention of tunnel fires and reduction of social and economic losses. Therefore, this paper guides the propagation of smoke under the roof of lithium-ion battery vehicle fires in tunnels. [ABSTRACT FROM AUTHOR]

In response to the suddenness, uncertainty, and complexity of public emergencies, this paper presents an analytical framework for the evolution of public emergencies based on the research idea of "event-relationship-logic". The aim is to comprehensively grasp, comprehend, and predict the development logic and internal mechanisms of public emergencies, assisting emergency departments in promptly understanding the progression of events. Building upon this foundation, this paper proposes a complete process for constructing an Event Logic Graph (ELG) in the field of public emergencies and introduces a multi-structured convolutional neural network model that incorporates word position features to extract causality between events. Experimental findings indicate that the multistructured convolutional neural network model demonstrates higher accuracy and effectiveness compared to the traditional template matching method. Furthermore, through the empirical analysis, we find that the evolutionary path of the causal event chain is relatively short, and the evolutionary motivation of public emergencies is intricate throughout their entire lifecycle. Consequently, emergency departments should focus more on key nodes, central nodes, and intermediate nodes within the causal event chain and adeptly identify these specific nodes to swiftly uncover correlations between events. Additionally, public emergencies easily convert into public crisis, underscoring the necessity for emergency departments to comprehend the evolutionary motivation and inducement factors of public emergencies. [ABSTRACT FROM AUTHOR]

Let G = (V,E) be a simple, connected and un-directed graph. We introduce a new notion of rainbow vertex antimagic coloring. This is a natural expansion of rainbow vertex coloring combined with antimagic labeling. For f: E(G) → {1, 2, . . ., |E(G)|}, the weight of a vertex v ∈ V (G) against f is wf (v) = Σe∈E(v)f(e), where E(v) is the set of vertices incident to v. The function f is called vertex antimagic edge labeling if every vertex has distinct weight. A path is considered to be a rainbow path if for each vertex u and v, all internal vertices on the u - v path have different weights. The rainbow vertex antimagic connection number of G, denoted by rvac(G), is the smallest number of colors taken over all rainbow colorings induced by rainbow vertex antimagic labelings of G. In this paper we aim to discover some new lemmas or theorems regarding to rvac(G). Furthermore, to see the robust application of rainbow vertex antimagic coloring, at the end of this paper we will illustrate the implementation of RVAC on spatial temporal graph neural networks (STGNN) multistep time series forecasting on subsidized diesel consumption of some petrol stations. [ABSTRACT FROM AUTHOR]

This study examines several papers to study inventory models with back orders to provide a simpler solution procedure for EOQ/EPQ inventory models by using Arithmetic Geometric Mean inequality or Cauchy Bunyakovsky Schwarz inequality. We will point out that a paper implicitly adopted the fill rate from a published article without proper citation to convert two-variable minimum problems into one-variable problems. Hence, his simpler solution procedure is standing on the shoulder of a giant. We also provide detailed examinations for other three related papers that was related to our studied article to point out their contributions and questionable results. [ABSTRACT FROM AUTHOR]

There are two standard methods in instance segmentation: top-down and bottom-up. The top-down approach performs object detection to generate candidate proposals and then performs pixel-level segmentation for each proposal. It is accurate and flexible, capable of handling objects of different sizes and shapes. However, it is computationally complex and relies on object detection accuracy. The bottom-up approach first performs pixel-level clustering or segmentation and then combines candidate instances to obtain the final segmentation result. It can handle overlapping cases and has lower computational complexity, but it may need to localize accurately, and segment instances, and the segmentation granularity is coarser. In this paper, the Urban Street Scene Instance Segmentation (UISNet) algorithm is proposed. Firstly, the feature extraction network is the foundation of UISNet, which uses EfficientNet as the backbone network. Secondly, MPAFPN is the feature pyramid network part of UISNet, used for multi-scale feature fusion. By using EfficientNet and MPAFPN as the backbone network and bottleneck layers, the accuracy of UISNet is improved by 4% compared to ResNet and FPN. In the inference phase, this paper introduces an innovative dual-branch design that combines top-down and bottom-up strategies. One branch is the bounding box aggregation branch, which generates highdimensional information such as the shape and orientation of bounding boxes based on the FCOS Head. The other branch is the mask decoding branch, which creates mask prediction results. These two branches are fused using the Mask FCN Header to obtain the final instance segmentation result. With this dual-branch design, the model can effectively utilize the information from both top-down and bottom-up approaches, thereby improving the accuracy and robustness of instance segmentation. Through experimental comparisons, the proposed network model in this paper achieves the best performance in terms of accuracy compared to other instance segmentation networks, with an accuracy of 36.28%. Moreover, the proposed model performs better in urban street scenes, enhancing object detection and segmentation and offering more reliable and efficient solutions for applications such as autonomous driving and intelligent transportation. [ABSTRACT FROM AUTHOR]

Lotka-Volterra model continues to be of interest to numerous researchers, both in terms of its mathematical development and applications. This paper discusses an analytical solution of the Lotka-Volterra model near its equilibrium point. This solution, which is the main result of this paper, has not been previously explored by other researchers. The proposed solution is in the form of a series expansion with respect to parameter (1), representing a perturbed solution around the equilibrium point. The analytical solution is evaluated by comparing to both the corresponding exact orbit, which is directly derived from the model, and the corresponding numerical solution. Comparisons with the exact orbit reveal that the orbit formed by the analytical solution closely approximates the exact one. The orbits formed by the analytical solution are closer to the exact ones as approaches zero. Similarly, the analytical solution is also closer to the numerical solution as approaches zero. Additionally, as the solutions exhibit wave-like behavior, further observations are made regarding the 'amplitude' and 'wavelength' of the analytical solution. The wavelength and amplitude of the analytical solutions are closer to the ones of the numerical solutions as tends to zero. The amplitude and wavelength are crucial for optimizing harvesting strategies when applying the Lotka-Volterra model in integrated farming systems. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present a complete and simplified solution procedure for the inventory model under trapezoidal type demand, Weibull-distributed deterioration, and partial backlogging. A published paper already provide a solution approach for this kind of inventory models. However, we will show that his approach is too complicated that depending on the partition the domain of a trapezoidal type demand to result in three sub-optimal problems. However, this paper will show that the already exist approach is correct but tedious. Moreover, a generalization of this kind of inventory systems from a trapezoidal type demand to any positive demand under a condition of generalized backlogged rate is also developed by this paper. [ABSTRACT FROM AUTHOR]

Belt conveyors often work in more complex environments, although many are indoors. However, due to the structure of the plant and other reasons, it is difficult to install the camera directly above the belt when using the camera to monitor the belt or identify faults. At this time, the staff can only find a location as far as possible can play a role in monitoring the belt conveyor to install the camera. In this case it is more difficult to analyze whether the belt is running away. On the other hand, the previous belt deflection detection method is through the artificial delineation of the standard position of the belt, the actual position of the belt and the standard position for comparison, which to a large extent there are human factors interference, resulting in the detection results are not precise enough. Therefore, in this paper, for the belt that can only monitor the type of unilateral rollers, we propose a method to recognize whether the belt is empty or not, and then determine the standard position of the belt through a multi-task learning model with joint target detection and semantic segmentation. This enables adaptive recognition of the standard position of the belt. The degree and direction of belt deflection is determined by obtaining the line connecting the straight line at the edge of the belt and the uppermost corner point of the rollers. The overall accuracy of the system is 94.9%, which is obtained by experimenting with the multi-task learning model and the belt runout judgment model respectively. It can be concluded that the belt runout detection method proposed in this paper can be a good solution to the situation where the camera installation position is not ideal. It reduces the failure rate of the belt conveyor and provides a guarantee for the normal operation of the belt conveyor. [ABSTRACT FROM AUTHOR]

Locating software faults during debugging is crucial yet challenging. Automated techniques, such as spectrum-based fault localization (SBFL), aid developers in efficiently localizing faults by analyzing program execution information and utilizing statistical approaches to rank program entities according to their suspiciousness. SBFL is also known as lightweight fault localization because of its scalability and minimal computational overhead. While essential, there has been limited research on how test suites affect fault localization. In this paper, we show how test suites impact fault localization and how they can be optimized for better results. SBFL techniques have some inherent limitations, especially in diagnosing faults within loop bodies or iteration statements. Additionally, identical suspiciousness levels can result in ties. While SBFL techniques effectively rank faulty program entities among the Top-N suspicious entities, they might not consistently position the faulty entity within the initial few positions. To address these research gaps, this paper proposes a hybrid approach that combines test suite optimization, statement execution frequency, and fault context concepts to enhance the performance of existing SBFL techniques in single fault scenarios. We evaluate our approach using three popular SBFL methods (Ochiai, Jaccard, and DStar) on Siemens benchmarks and four large real-world programs (flex, grep, sed, and space) with their test suites. The results demonstrate a significant enhancement in fault localization performance when applying our proposed approach to existing SBFL methods. For example, when applied to Ochiai, it reduces examined statements by 62.76% and 65.23% on average for the two test suites, respectively. Furthermore, it identifies 52% of faults by examining only 1% or less of the code and locates 60% of faults by analyzing only 0.1% or less of the code in Siemens and four large real-world programs, respectively. Similar improvements are observed when our approach is applied to Jaccard and DStar methods on the same test suites. We also show that our results are statistically significant, validating that our approach substantially improves the performance of existing SBFL techniques. [ABSTRACT FROM AUTHOR]

Positioning technology is one of the key steps of automatic docking. At present, vehicle docking technology is widely used in airport baggage cars, towing trailers and other fields, and the docking process is mostly done manually. In order to realize the automatic docking of vehicles in mixed scenes, this paper proposes a fast positioning method for docking vehicles in mixed traffic scenes. First, using the regression relationship between the template size in the camera's field of view and the actual distance, FFT template matching and threshold judgment methods are employed to locate, track, and match the template, as well as verify the threshold results, in order to achieve template long-distance positioning and ROI extraction. Then, based on the regression relationship between AprilTag pixels in the camera's field of view and actual distance, the experiment of AprilTag minimum recognition pixel is designed. Finally, the real-time pixels of AprilTag are calculated from the real-time ranging results, and compared with the minimum recognition pixel of AprilTag, it is judged whether the recognition conditions are met, so as to carry out the subsequent AprilTag positioning. The experimental results demonstrate that the method proposed in this paper can achieve rapid and accurate positioning of a target docking vehicle at a long distance, while reducing the mismatch rate. On this basis, by designing the identification conditions of AprilTag, the rapid identification and positioning of AprilTag is realized, which reduces the traversal time of the algorithm. The feasibility of this positioning method is verified by the attitude angle error experiment, which provides supporting conditions for the subsequent docking path planning and vehicle control. [ABSTRACT FROM AUTHOR]

Radiographic testing is the most common method of non-destructive testing to detect discontinuities in industrial piping welds. Human interpretation of radiographic films is time-consuming and requires high degrees of expertise. Despite the significant advancements of deep learning techniques in related fields, such as medical radiography, prior research endeavors focused on weld discontinuities were constrained by the limitations stemming from a lack of training data and their inadequate representation of real-world conditions in the field. This paper introduces a comprehensive system that automatically detects welding zones, assesses film quality, and classifies weld discontinuities for the piping process. The proposed framework demonstrates superior generalization capabilities that bypass a single industry or piping size. The key advantages of our technique lie in its enhanced accuracy, rapid processing, and automatic interpretation of welding films across a wide range of image qualities. Consequently, it achieves remarkable detection and classification accuracy, offering substantial benefits for welding inspection and quality assessment. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of finding the state transformation and feedback law for linearizing nonlinear systems. The construction of the state transformation and feedback law is proposed for two types of nonlinear systems. The steps are easy for practitioners to understand and follow. The result in this paper generalizes the well-known feedback linearization result for triangular systems by Meyer, Su, and Hunt. Two numerical examples are given to illustrate the state transformation and feedback law's construction process. [ABSTRACT FROM AUTHOR]

Swift identification of a set of influential load nodes within power systems is an important task for effective modern power grid operation and security. This paper suggests a suitable approach of Node Strength (NS) or Bus Strength (BS) scheme for effective and swift identification of sets of influential load nodes within power systems. This effective algorithm relies on the Network Structural Characteristics Theory (NSCT) of power systems. The NS scheme uses circuit theory laws to present the linear relationship among the network structure, the voltage and current in the network. The traditional Complex Network Theory (CNT) or Centrality-Based (Degree Centrality (DC), Betweeness Centrality (BC), Closeness Centrality (CC), PageRank Centrality (PRC) and Eigenvector Centrality (EC)) methods for identifying influential load nodes within power systems are also presented in the paper. Thereafter, the numerical values of the NS and each of the CNT-based methods associated with the load nodes, are determined. The relative importance of each load node is then ascertained through ranking (in the order of magnitude or priority) of the values obtained for NS and each of the CNT-based methods. Based on the order of priority, the sets of influential load nodes in the network are identified. Furthermore, performance analyses, using statistical correlation coefficient as well as structural risk assessment, are used to establish the best method suitable for quick identification of a set of influential load nodes in the system. The results obtained are corroborated with the network robustness assessment to further validate the proposed approach. The study uses IEEE 5-bus, IEEE 14-bus and Nigerian 28-bus networks as case studies. The comparison of results obtained show that the NS or BS scheme suggested is more suitable for quick identification of sets of influential load nodes in a relatively large-sized power network. [ABSTRACT FROM AUTHOR]

In this paper, a low quiescent current structure with self-regulation impedance buffer and bandgap amplifier is presented. With the bandgap amplifier, the function of voltage reference and error amplifier can be achieved simultaneously, which can efficiently reduce the consumption. The low-dropout regulator has been implemented in a 0.18µm process with die size 0.03mm². The circuit module designed in this paper are simulated by the Cadence software and the simulation results are validated. Experimental result shows that the overshoot and undershoot of line transient response are less than 30mV/V. The load regulation is about 0.1%/A, and line regulation is about 0.07mV/V at no load condition. Through the test results, it is found that the simulation results are consistent with the measured data. This conclusion ensures the stability and reliability of this module in the subsequent application scenarios. [ABSTRACT FROM AUTHOR]

This paper studies obstacle and collision avoidance strategies for nonlinear second-order multi-agent systems (MAS) formation control. Due to the uncertainties and complexities in nonlinear systems, including external disturbances and communication delays, radial basis function (RBF) neural network control is employed to address the control requirements of nonlinear terms in the system. In addition, as traditional artificial potential fields (APF) based obstacle avoidance algorithms have limitations, this paper applies an improved APF algorithm for collision avoidance and obstacle avoidance in multi-agent systems formation control. The stability and feasibility of the proposed approach are proved based on the Lyapunov stability theory. Simulation experiments further validate the effectiveness of the formation control strategy. [ABSTRACT FROM AUTHOR]

As China's electricity market continues to evolve, the increasing number and diversity of market participants have led to a rapid increase in data volume. An immediate challenge is designing a robust blockchain system that ensures not only the transparency, credibility, tamper-resistance, traceability, privacy, and security of substantial market data in a complex environment, but also provides trusted data support for intelligent dispatching within the power market. This paper proposes a solution to this pressing issue through Blockchain as a Service (BaaS). BaaS leverages the capabilities of cloud computing platforms to enhance the ease and efficiency of deploying and operating blockchain systems. Specifically, the paper introduces a BaaS system built upon an integrated intelligent scheduling and operation cloud-edge platform (CEP). It incorporates technological solutions such as data on-chain, trusted comparison, edge cluster data transmission, optimized transaction authentication, and lightweight storage nodes. Rigorous testing has conclusively demonstrated that the proposed BaaS system meets the requirements of the digital transformation of the China Southern Power Grid, and provides robust technical support for its future practical applications. [ABSTRACT FROM AUTHOR]

We propose to study a solution approach for an inventory model regarding the use of the algebraic method to solve inventory problems. His process is more straightforward than other analytic methods based on calculus. However, there are questionable results contained in his approach. The purpose of this paper is threefold. First, we point out the problems included in his process. Second, we prepare our corrected solution approach by the algebraic method. Third, for completeness, we list the questionable results in his paper. [ABSTRACT FROM AUTHOR]

We study the paper of Ishii and Konno for a stochastic newsboy problem with fuzzy shortages cost. The purpose of this note is threefold. First, we review their approach and point out their questionable results. Second, we revise their system. Third, by the same numerical example with fuzzy shortage cost in their paper, we provide an illustrative example to demonstrate our findings. We suggest researchers do not adopt the comparison between fuzzy numbers proposed by Ishii and Konno. [ABSTRACT FROM AUTHOR]

The local resolving dominating set studied in this paper is a notion that combines two concepts in graph theory, the local metric dimension and dominations in graphs. Let G and H be connected graphs of orders n and m, respectively; and x a vertex in H hereafter referred to as a linkage vertex. The comb product of G and H denoted by G.H, is a graph obtained by taking one copy of G and n copies of H and attaching the i-th copy of H at the vertex x to the i-th vertex of G. In this paper, we determine the local resolving dominating set of the comb products G. Sn with two different linkage vertices, G. Kn, G. Km;n and G. Cm graphs. [ABSTRACT FROM AUTHOR]

Deep learning is proliferating within the field of computer vision. Road damage detection technology, as its offshoot, already plays vital role in road maintenance and traffic safety. With road damage such as potholes and cracks, accurate and efficient detection results are essential for timely road safety repair and maintenance. Therefore, road damage detection algorithms based on deep learning have attracted wide attention. YOLOv5 is an advanced target detection algorithm known for its efficient detection speed and good accuracy. However, there is still room for further improvement in its performance for road damage detection. The ability of multi-scale damage detection and spatial structure capture is not perfect. Therefore, this paper proposes three improvement points to improve the accuracy of road damage detection based on YOLOv5. The first introduced module is the Non-linear Spatial Pyramidal Pooling-Fast (NSPPF) module. This module allows for better capture of detailed features of road damage areas. Non-linear transformation and fast pyramid operation improve the sensing ability and multi-scale damage detection ability. Secondly, a combination of the CoordConv and SK attention modules is constructed. The CoordConv module fuses coordinate information with features to provide a more spatially informed representation. The SK attention module also learns correlations between global and local features, enhancing the model's ability to detect damages at different scales. This paper can better capture road injuries' spatial structure and context information by combining these two modules. Finally, experimental results on the RDD2020 dataset demonstrate the effectiveness of our model. Compared to the baseline model, the proposed improvement algorithm increases the accuracy by 2.2%, resulting in a mAP of 58.2%. This demonstrates its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Urban rail transit has the potential to utilize surplus capacity to carry out urban freight transportation during off-peak periods. Based on the characteristics of virtual coupling operation and organization mode, this paper comprehensively considers the competition between passenger flow and freight flow, takes the train stopping plan and train carriage allocation as the main decision variables, and takes the minimization of passenger waiting time and the maximization of average full load rate as dual objectives to construct a multi-objective optimization model of passengerfreight collaborative transport scheme of urban rail transit under virtual coupling conditions. The multi-objective optimization model is developed to obtain the system optimization of the train stopping plan, running plan, carriage allocation plan, and passenger-freight collaborative transport plan. Numerical experiments are designed to verify the model's validity, using Wuxi Metro Line 3 as an example, and the NSGA-II algorithm solves all experiments. The results of the algorithm show that, compared to the traditional single passenger transport with a fixed large coupling, the scheme in this paper can simultaneously improve the average full capacity of the whole line by about 38.5% during the study period with only 0.65 minutes increase in the average passenger waiting time for the same number of rolling stocks, which can effectively shorten passenger travel time and improve the quality of passenger service in urban rail transit while ensuring transport efficiency. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low accuracy, false detection, missed detection, and low real-time detection of current traffic sign detection, this paper proposes an improved traffic sign detection algorithm based on the YOLOv8s algorithm. Firstly, this paper proposes a double-layer semicomposite backbone network structure (DSCB), which uses the auxiliary backbone network to extract features, and then transmits the extracted features to the backbone network to enhance the ability of the backbone network to extract target features. At the same time, the deformable convolution is integrated into the DC2f structure of the auxiliary backbone network to enhance the generalization performance of the network. Secondly, the coordinate attention mechanism is used after the SPPF layer. The coordinate attention mechanism can better retain the coordinate position information of small targets, reduce the miss rate of the model, and increase detection accuracy. Finally, this paper introduces a new CAB module to learn and aggregate the output of each layer of the feature pyramid for global spatial context to enhance the feature representation ability further. The experimental results show that the improved algorithm achieves 90.51% detection accuracy, 82.00% recall rate, 89.51% mAP@0.5 on the TT100K dataset, and the FPS reaches 106. Compared with the original algorithm model, the detection accuracy is increased by 2.27%, and the recall rate is increased by 2.48%. mAP@0.5 is increased by 2.01%, and FPS is increased by 1. The improved traffic sign detection algorithm meets the requirements in detection accuracy and real-time detection. [ABSTRACT FROM AUTHOR]

We propose a new inventory system with an amelioration rate satisfying the Weibull distribution, purchasing cost, ordering cost, and amelioration cost under a fuzzy environment. We apply the function principle to aggregate those fuzzy costs. Then, we refer to the median rule to defuzzy our fuzzy model to a crist model to locate the optimal solution. Depending on the scale parameter, we divide our solution procedure into two Cases. For each Case, we find conditions to find the optimal solution. The same numerical example proposed by a previously published paper is examined to illustrate that our solution process is more effective than the previously published paper. [ABSTRACT FROM AUTHOR]

This paper employs a recent algorithm capable of determining an optimal regularization technique based on a meshless method of fundamental solutions for the onedimensional inverse Stefan problem, where the boundary data is reconstructed on the fixed boundary. The inverse problem is ill-posed. For comparison purposes, we employ Tikhonov, Truncated Singular Value Decomposition, and Randomized Singular Value Decomposition regularization methods to obtain a more accurate solution. The numerical results of three different benchmark examples are presented in this paper. [ABSTRACT FROM AUTHOR]

This paper focuses on the S-asymptotically Periodic oscillation for a type of fractional-order fuzzy Cohen-Grossberg neural networks (CGNNs) by employing some properties of Mittag-Leffler mappings and fixed point theorems. Further, the global asymptotic stability of CGNNs is received. For CGNNs, our works in this paper not only enrich its theoretical achievements, but also expand its application scope. [ABSTRACT FROM AUTHOR]

Detecting surface defects in strip steel is an essential step in the production process, and it has consistently held a prominent position in both domestic and international contexts. Stripping surface defects has a significant impact on the product's overall appearance. Moreover, it plays a crucial role in maintaining the strip product's wear resistance, corrosion resistance and fatigue strength. Failing to address these defects would inevitably result in a reduced service life for the strip product. This paper introduces a classification and diagnosis approach using the Multi-Layer Perceptron, optimized by the Bio geography-Based Optimization algorithm (BBO), for the purpose of diagnosing strip surface defects. The multi-layer perceptron is trained by using the BBO algorithm to find the best connection weight and bias value, so that it can identify the training set and test set, and make adjustments according to different needs. This paper carried out simulation experiments on strip surface defect data set in UC I data set. In this research, we evaluated the effectiveness of the proposed approach by benchmarking it against five alternative optimization algorithms. These included particle swarm optimization, ant colony optimization, distribution estimation algorithm, genetic algorithm, and extreme value search algorithm. It becomes evident that the accuracy and speed of the proposed method have experienced substantial enhancements. The application of the BBO algorithm in optimizing the multi-layer perceptron has been demonstrated to effectively address the challenge of diagnosing strip defects. [ABSTRACT FROM AUTHOR]

Cloud computing has transformed data storage, management, and processing by offering scalable and flexible resources via the internet. A key component of this technology is the efficient allocation and management of resources, particularly through task scheduling at the level of virtual machines (VMs). Task scheduling is critical for maximizing resource utilization and system performance in cloud environments. However, it presents significant challenges due to the dynamic and distributed nature of these environments. Effective task scheduling algorithms are necessary to balance load, minimize response time, and optimize resource usage, making it a crucial area for ongoing research and development in cloud computing. This paper addresses the challenge of task scheduling in cloud computing by employing an analytical approach based on queuing theory. We model the system using a generalized processor sharing (GPS) queue and evaluate its performance through heavy traffic approximation. This method allows us to derive performance metrics for queuing systems prone to congestion, considering general interarrival and service time distributions, thus providing a comprehensive analysis of scheduling efficiency. [ABSTRACT FROM AUTHOR]

This paper deals with the issue of performanceguaranteed control for discrete-time systems under communication constraints. To alleviate communication burdens, an event-triggered mechanism and quantized data-based protocol is presented. The protocol integrates the advantages of the dualthreshold event-triggered mechanism and signal quantization, leading to a reduced channel occupancy rate compared to the normal weighted try-once-discard protocol and dual-threshold event-triggered protocol. Building upon this protocol, a state feedback controller relying on scheduling signals is proposed. A criterion on stability and energy-to-peak performance is established using the Lyapunov function method and some inequality approaches. Then, a numerically tractable method for determining the desired controller gain via solving linear matrix inequalities. Finally, a double-sided linear switched reluctance machine system is used as an example to illustrate the effectiveness of the designed protocol and the controller. [ABSTRACT FROM AUTHOR]

Current automatic segment extraction techniques for identifying target characters in videos have several limitations, including low accuracy, slow processing speeds, and poor adaptability to diverse scenes. This paper introduces an optimized algorithm to address these issues that enhance the RetinaFace and FaceNet models. We selected RetinaFace for face detection, employing MobileNetV1-0.25 as the backbone network and simplifying its Feature Pyramid Network (FPN) structure to boost detection speeds. Analysis of 460 images with a 720P resolution demonstrated an average speed improvement of 20.6%. For face recognition, we utilized FaceNet with MobileNetV3 as the backbone, augmenting its feature extraction capability by integrating four Receptive Field Block (RFB) structures and replacing the Squeeze-and-Excitation (SE) module with the Convolutional Block Attention Module (CBAM). Experimental results indicate that our enhancements elevate the maximum accuracy to 97%, outperforming the original model. Additionally, we integrated these refined algorithms and conducted disintegration experiments on segment extraction in 10 videos, evaluating various metrics. The findings show improvements in both precision and recall. We also compared our algorithm against the Dlib model; our system achieved an overall interception accuracy of 79.94%, surpassing Dlib's 75.55%. This confirms the enhanced performance and feasibility of our proposed algorithm. [ABSTRACT FROM AUTHOR]

The particle swarm optimization (PSO) path planning algorithm often suffers from low population diversity and rapid early convergence, which can lead to the algorithm easily falling into local optima and affecting its stability. This paper proposes an improved PSO algorithm with two key enhancements: (1) the introduction of a particle reactivation module during the iteration process, and (2) the incorporation of the Simulated Annealing (SA) concept during the global optimal solution update phase. These improvements enhance the diversity of the particle population, slow down the early convergence rate of the algorithm, reduce the probability of the algorithm getting trapped in local optima, and increase its overall stability. Experiments were conducted to compare the proposed algorithm with the standard PSO algorithm, genetic algorithms, and other improved PSO path planning algorithms. The results indicate that the improved algorithm shows superior performance in both average path length and algorithm stability. [ABSTRACT FROM AUTHOR]

Object detection algorithms are widely used but involve significant amounts of computations, posing challenges for adaptation to resource-constrained application scenarios, such as Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). To address these challenges, this paper proposes an object detection accelerator architecture with multi-channel parallel computation. In this architecture, the computationally intensive modules, including the convolution layer, pooling layer, and upsampling layer, are accelerated using hardware, and other modules are dealt with CPU embedded in the FPGA. The methods of pipeline design, loop expansion, data reordering, and other technologies are fully utilized to design hardware acceleration modules. A data transmission architecture is designed, incorporating multi-channel transmission along with ping-pong buffering and employing a blocking strategy for off-chip data access. Furthermore, the architecture incorporates multiple acceleration IP cores to minimize data transmission delays. Based on this architecture, the tiny-YOLOv4 model is optimized and implemented on FPGA as a hardware accelerator for object detection. The network model is enhanced by integrating the convolutional layer with normalization, and different attention mechanisms are applied to improve feature extraction, thereby reducing computational load and enhancing accuracy. The performance of the tiny-YOLOv4 FPGA-based accelerator is validated using the SIMD dataset. Experimental results demonstrate that the hardware accelerator performs exceptionally, consuming only 2.4W and surpassing existing alternatives. Such adaptability facilitates its integration into complex environments such as intelligent transportation systems. [ABSTRACT FROM AUTHOR]

In order to improve the security, ease of use and flexibility of software products, this paper proposes a software cloud authorized protection mechanism based on specific time encryption. This mechanism sends the encrypted and packed executable file to the cloud server, so that only legitimate users can unpack, decrypt and run the software in the cloud within a specific time interval. On this basis, this article gives a formal definition of the solution model and constructs a specific implementation solution based on the random oracle model. Security analysis shows that this solution can not only prevent attackers from using cloud servers and time servers to illegally obtain software usage rights, but also reduce time overhead and costs, thereby effectively protecting the legitimate rights and interests of developers and users. [ABSTRACT FROM AUTHOR]