Evaluation Function Research Articles

Method for robot kinematic parameters identification based on position and orientation data obtained with laser tracker.

How to quickly and accurately identify the kinematic parameter errors is an important prerequisite for robot accuracy compensation. The laser tracker is used to measure and identify the kinematic parameters of the robot. The influence of laser tracker layout in robot pose measurement is analyzed, and the evaluation function of laser tracker layout is constructed to determine the optimal layout position. Then, the mapping relationship between the position and orientation deviation of the robot end and the deviation of each kinematic parameter is established, and the kinematic parameter identification model integrating the position and orientation information is constructed. The identification model is compared to the identification model based on position information to clarify the intrinsic reason of high accuracy. Next, aiming at the problem that the genetic algorithm is easy to prematurely converge to the local optimal solution, a hybrid genetic algorithm is constructed by introducing the simplex method to determine the optimal calibration pose set of the robot. On this basis, the results of robot kinematics parameter identification based on position measurement data, pose measurement data, and optimal calibration pose set are compared and analyzed through experiments, which verifies the effectiveness of the robot kinematics parameter identification model based on pose measurement data and optimization strategy.

FusionOC: Research on optimal control method for infrared and visible light image fusion

Infrared and visible light image fusion can solve the limitations of single-type visual sensors and can boost the target detection performance. However, since the traditional fusion strategy lacks the controllability and feedback mechanism, the fusion model cannot precisely perceive the relationship between the requirements of the fusion task, the fused image quality, and the source image features. To this end, this paper establishes a fusion model based on the optimal controlled object and control mode called FusionOC. This method establishes two types of mathematical models of the controlled objects by verifying the factors and conflicts affecting the quality of the fused image. It combines the image fusion model with the quality evaluation function to determine the two control factors separately. At the same time, two proportional-integral-derivative (PID) control and regulation modes based on the backpropagation (BP) neural network are designed according to the control factor characteristics. The fusion system can adaptively select the regulation mode to regulate the control factor according to the user requirements or the task to make the fusion system perceive the connection between the fusion task and the result. Besides, the fusion model employs the feedback mechanism of the control system to perceive the feature difference between the fusion result and the source image, realize the guidance of the source image feature to the entire fusion process, and improve the fusion algorithm's generalization ability and intelligence level when handling different fusion tasks. Experimental results on multiple public datasets demonstrate the advantages of FusionOC over advanced methods. Meanwhile, the benefits of our fusion results in object detection tasks have been demonstrated.

Research on the application technology of expert knowledge graph based on genetic search algorithm

Abstract The present study investigates the application of a genetic search algorithm on an expert knowledge graph. By employing binary coding for candidate experts in the expert database, the matching evaluation function is utilized to define the environmental fitness index, thereby optimizing the selection of expert groups. Experimental results demonstrate that the GES algorithm exhibits a superior matching degree and reduced time consumption when addressing science and technology item-matching requirements in review work. In comparison with the traditional BFS algorithm and GIS algorithm, the GES algorithm showcases enhanced potential for application and stability when confronted with large datasets. This paper further analyzes the impact of factors such as the total number of experts, number of fields, and number of scientific and technological projects on algorithm performance, confirming that adjustment of algorithm parameters can effectively enhance matching degree while reducing time consumption, thus providing technical support for practical implementation.

Solving Max-cut Problem with a mixed penalization method for Semidefinite Programming

This paper is an attempt to exploit the opportunity of Semi Definite Programming (SDP), which is an area of convex and conic optimization. Indeed, Numerous NP-hard problems can be solveby using this approach. Hence, we intend to investigate the strength of SDP to model and provide tight relaxations of combinatorial and quadratic problems in order to present a new polynomial time algorithm for solving this robust model. This algorithm was firstly use to solve the nonlinear programs, the reason for which we search to extend it to the SDP programs. Actually, this algorithm designs the combination of two penalization methods. The first one is a primal-dual interior point (PDIM) method while the second one is a primal-dual exterior point (PDEM) method. Unlike the first method, which converges globally, the second one, also called the primal dual nonlinear rescaling method, has local super linear/quadratic convergence. Therefore, it seems appropriate to use a mixed algorithm based on the interior-exterior point method (IEPM). In fact, this resolution starts from the interior method, and at a certain level of execution, it proceeds to exterior method. Hence, a convergence evaluation function is use to know the level of permutation. Through evaluation, it has been approve that our approach is use to solve some instances of max-cut problem. This problem is a central graph theory model that occurs in many real problems and it is one of many NP-hard problems, which has attracted many researchers over the years. Then, we have used a semi definite programming solver SDPA (Semi Definite Programming Algorithm) that is modify to include the exterior point method subroutine. From the computational performance, we conclude that as the problem size increases, interior-exterior point algorithm gets relatively faster. The numerical results obtained are promising.

Research on irregular sponge layout system based on heuristic algorithms

Abstract A layout optimization method that integrates No-Fit Polygons (NFP) positioning and a genetic algorithm is proposed for the two-dimensional irregular sponge packing problem. Firstly, according to the geometric characteristics of the parts, they are classified and encoded to calculate the NFP between part classes, avoiding redundant NFP calculations. Simultaneously, heuristic rules aimed at minimizing the packing height are introduced to provide a comprehensive evaluation function for part positioning strategies, thus improving packing efficiency. Secondly, in the optimization of part sorting, a genetic algorithm is introduced to optimize the sponge packing algorithm. Finally, several standard cases provided by the European Special Interest Group on Cutting and Packing (ESICUP) are used to test the proposed packing method. The results show that compared to the traditional Bottom-Left algorithm, the computational efficiency of the proposed packing method is improved by 4.20% to 22.73%.

Research on Cooperative Tracking of Multiple Agents on Heterogeneous Ground

Abstract Pursuit-evasion scenarios are typical and significant area of study multi-agent behaviours. While previous research has primarily focused on training pursuit strategies on idealized flat, this paper explores pursuit strategies on heterogeneous ground that rely on ground features from the effects of friction and viscous forces. Since agent interacts with heterogeneous ground and changes its acceleration, we perform multi-agent modelling. Aiming at the multi-agent deep deterministic policy gradient (MADDPG) algorithm’s low training efficiency and slow convergence speed in pursuit learning, we use the prioritized experience selection mechanism of MADDPG algorithm (PES-MADDPG) which improve the experience extraction mechanism based on the policy evaluation function error and the experience extraction training frequency. With this approach, the design of rewards results in faster convergence of reward values. The experimental findings confirm that the suggested method delivers better performance and effectiveness for pursuers and evaders on heterogeneous ground, and both can acquire the relevant movement strategies through training.

Fast autofocusing in off-axis digital holography based on search region segmentation and dichotomy

Autofocusing is a critical step in digital holography for reconstructing high-quality intensity and phase information, especially for the dynamic detection of moving objects. Some commonly used autofocusing methods in digital holography are time-consuming as they search over a large range to find a unique reconstruction distance. In this study, we propose a new fast autofocusing method based on search region segmentation and dichotomy. First, a new search region is obtained by segmenting the original region and solving a system of inequality equations. Second, improving the focusing evaluation function and combining this function with the dichotomy method transforms the discriminating minimum of the evaluation function into the detection of the zero-crossing position, reducing the number of searches required for the autofocusing method. The effectiveness of the proposed autofocusing method is validated through numerical simulations and experiments. The proposed method can significantly improve computational efficiency while guaranteeing the detection accuracy of the focusing plane compared with conventional methods.

Prediction of Floor Failure Depth Based on Dividing Deep and Shallow Mining for Risk Assessment of Mine Water Inrush

Understanding and predicting floor failure depth is crucial for both mitigating mine water inrush hazards and safeguarding groundwater resources. Mining activities can significantly disturb the geological strata, leading to shifts and damage that may result in floor cracks. These disruptions can extend to confined aquifers, thereby increasing the risk of water inrushes. Such events not only pose a threat to the safety of mining operations but also jeopardize the sustainability of surrounding groundwater systems. Therefore, accurately predicting floor failure depth to take effective coal seam floor management measures is the key to reducing the impact of coal seam mining on water resources. Seventy-eight sets of data on coal seam floor failure depth in China were collected, and the main controlling factors were considered: mining depth (D1), working face inclination length (D2), coal seam inclination (D3), and mining thickness (D4). Firstly, the distance evaluation function based on Euclidean distance was constructed as the clustering effectiveness index, and the optimal cluster number K = 3 was determined. The collected data were clustered into three categories using the K-means clustering algorithm. It was found that the clustering results were positively correlated with the size of D1, indicating that D1 played a dominant role in the clustering. The D1 dividing points of the three types of samples were between 407.7~414.9 m and 750~900 m. On this basis, the grey correlation analysis method was used to analyze the order of the influence weights of the main controlling factors of coal seam floor failure depth. For the first group, the order was D2 > D1 > D3 > D4, while, in the other two, it was D1 > D2 > D3 > D4. D1 emerged as the most influential factor, surpassing D2. Therefore, D1 between 407.7 and 414.9 m could be used as the boundary, the first group could be classified as shallow mining, and the second and third groups could be classified as deep mining. Based on this boundary, CatBoost prediction models for the depth of coal seam floor failure in deep and shallow parts were constructed and the prediction results of the model test set were compared with the calculation results of the empirical formula. These models exhibited superior accuracy with a lower mean squared error (MSE) and mean absolute error (MAE) and a higher R-squared (R2) compared to the empirical formula. This study helps to enhance the understanding of coal seam floor behavior, guide floor management, and protect groundwater resources by defining deep and shallow mining to accurately predict floor failure depth.

Adaptation of the REINVENT neural network architecture to generate potential HIV-1 entry inhibitors

Objectives. The main purpose of this work is to adapt the architecture of the REINVENT neural network to generate potential inhibitors of the HIV-1 envelope protein gp120 using in the learning process with reinforcement of molecular docking on GPUs.Methods. To modify the initial network model, molecular docking on GPUs implemented in the learning process with reinforcement was used, and an algorithm was developed that allows converting the representations of connections generated by the SMILES network into the PDBQT format necessary for docking. To accelerate the learning of the neural network in the modified version of the REINVENT model, the AutoDock-Vina-GPU-2.1 docking program was used, and to clarify the results of its work, the procedure for revaluing the affinity of compounds to the target using the RFScore-4 evaluation function was used.Results. Using a modified version of the REINVENT model, more than 60,000 compounds were obtained, of which about 52,000 molecules have a binding energy value to the HIV-1 gp120 protein comparable to the value calculated for the HIV-1 inhibitor NBD-14204, used in calculations as a positive control. Of the 52,000 compounds selected, about 34,000 molecules satisfy the restrictions imposed on a potential drug to ensure its bioavailability when taken orally.Conclusion. The results obtained allow us to demonstrate the effectiveness of an adapted neural network by the example of designing new potential inhibitors of the gp120 HIV-1 protein capable of blocking the CD4- binding site of the gp120 virus envelope protein and preventing its penetration into host cells.

Resilience evaluation of train control on-board system considering component failure correlations: Based on Apriori-Multi Layer-Copula Bayesian Network model

The failure of complex system components is an important factor affecting system resilience, and it is not only affected by their own basic life parameters, but may also be affected by the failure of other components. In order to investigate the impact of component failure correlations on the resilience evaluation of Train Control on Board System (TCOBS), we propose the Apriori-Multi Layer-Copula Bayesian Network (AMLCBN) model. Firstly, the definition and evaluation function of TCOBS component resilience are provided. Then, build a TCOBS Bayesian Network and perform hierarchical processing on the network to clarify the position of Copula functions in the Bayesian Network. The Copula function is used to evaluate the correlations among component failures, and the Copula Bayesian Network is used to infer TCOBS resilience. We use Apriori to calculate the correlation coefficient matrix in the Copula function. Finally, a case study is conducted by taking CTCS-3OBS as an example, the results show that among the components of TCOBS, BTM Ant has low resilience and high importance. Considering the correlation among component failures, the TCOBS resilience evaluation results will increase, and those components with higher importance will become more important, while those with lower importance will become less important.

Construction of an Evaluation Index System for Innovation and Entrepreneurship Education in Vocational Colleges from Students’ Perspective

Innovation and entrepreneurship education is an essential content in the current teaching of colleges and universities and an important driving force to promote students’ employment. Currently, colleges and universities continue to strengthen the innovation of innovation and entrepreneurship teaching, and the construction of the evaluation system of innovation and entrepreneurship education is lagging. The evaluation system is an important evaluation standard for innovation and entrepreneurship teaching and the basis for educational innovation, which is also a critical part of teaching that requires attention. This paper takes the construction of the evaluation index system of innovation and entrepreneurship education in vocational colleges as the research goal and understands the function of innovation and entrepreneurship evaluation as well as the evaluation principle through the literature analysis method. It also analyzes the evaluation index system of innovation and entrepreneurship teaching from students’ perspective to provide an effective reference for the implementation of innovation and entrepreneurship education in higher vocational colleges.

A Dynamic Lane-Changing Trajectory Planning Algorithm for Intelligent Connected Vehicles Based on Modified Driving Risk Field Model

A dynamic LC trajectory planning algorithm based on the modified driving risk field is proposed to address the issue of dynamic changes during the lane-changing (LC) process. First, a modified driving risk field (MDRF) model is constructed for LC scenarios. Then, according to the state of the target vehicle and discrete sampling points, a series of LC candidate trajectories were generated based on the quintic polynomial. After eliminating candidate trajectories that do not meet the constraints, the MDRF was utilized as a safety evaluation function. Additionally, comfort and smoothness evaluation functions were combined to evaluate candidate LC trajectories in order to obtain the optimal LC reference trajectory. Then, this paper proposes a dynamic LC trajectory planning algorithm, addressing the challenges of complex traffic scenarios and dynamic changes in adjacent vehicle states. Utilizing the optimal reference trajectory as a basis, a dynamic segmented algorithm is applied to the x–t and y–x curves, constructing an optimized objective function that considers the MDRF. Under multiple constraints, including the continuity and smoothness of the lateral and longitudinal trajectories, the penalty function approach is employed to solve the optimization objective function, yielding the optimal LC trajectory adapted to real-time changes in the traffic state. Finally, the proposed dynamic LC trajectory planning algorithm was validated under four different scenarios by using MATLAB 2023b. The simulation results indicate the safety, continuity, and dynamic feasibility of the proposed algorithm. Moreover, it demonstrates strong adaptability and flexibility in challenging dynamic LC scenarios.

Practical twin-field quantum key distribution parameter optimization based on quantum annealing algorithm

Abstract Twin-field quantum key distribution (TF-QKD) is widely studied since it can surpass the key capacity of repeaterless QKD, whereas electromagnetic interference (EMI) is one of the main challenges in its practical applications. This study is based on the Faraday–Michelson TF-QKD. Analyze the effect of EMI on the rotation angle of the Faraday mirror causing an additional quantum bit error rate (QBER). Moreover, the quantum annealing algorithm (QA) based on quantum tunneling mechanism is applied to the optimization of practical TF-QKD. Mapping the secure key rate of TF-QKD into the evaluation function of QA and the transverse magnetic field is introduced to construct the kinetic energy term, which can realize the quantum tunneling effect. Meanwhile, the QA is improved in terms of chaotic optimization to obtain the dynamic initial value of the algorithm, the design of a perturbation method to skip the locally optimal solution, and the use of suitable temperature and magnetic field decay functions. Optimizing TF-QKD with QA, the standard deviation of QBER fluctuation caused by EMI is reduced to 0.206, the mean square error of the signal-state pulse intensity is only 3.38 × 10 − 4 , and the optimization accuracy of the secure key rate can reach 99.8 % . Additionally, this optimization method shortens the runtime and reduces computational resource consumption, making it highly efficient for practical implementations.

Konsep Administrasi Pendidikan

This paper explains the concept of educational administration which is a support for achieving educational goals effectively and efficiently. Administration in education that is orderly and organized is needed to improve the ability to manage education for principals and teachers. The purpose of this paper is to explain the meaning, urgency, purpose, function, process and scope of educational administration. The research method used in this research uses the library research method, namely research based on processed manuscripts (manuscript studies), facts and primary and secondary data (supporting data) related to the focus of the research. The results of this study indicate that educational administration is essentially a tool to achieve optimal educational goals. Because the school is a sub-system of the national education system. The educational administration process includes the functions of organizational planning, coordination, communication, supervision, financing supervision, and evaluation. All these functions are very closely related to each other.

Optimizing algorithm for high-precision imaging stitching systems based on spline surfaces

As optical systems continue to advance, non-uniform rational B-spline (NURBS) surfaces increasingly being considered in asymmetric optical systems due to their localized control characteristics. However, the representation of NURBS surfaces has complicated the analysis of these systems, leading to a significant computational burden. To address this challenge, we propose an optimizing algorithm for imaging optical systems based on high-precision ray tracing of NURBS surfaces. This method initiates with getting a knot grid as prior information, in conjunction with the Newton-Raphson algorithm, to obtain high-precision numerical solutions for the intersection of rays with a NURBS surface. Building upon this methodology, we introduce an optimization technique that includes shape evaluation to generate an evaluation function specific to NURBS surfaces. This approach is then applied within a rapid optimization process that accounted for the region of ray influence. Under consistent control point grids and sampling ray conditions, we present an off-axis four-mirror system to showcase that our algorithm has achieved a computational efficiency improvement of approximately 14 times compared to the previous method. This high-precision imaging design based on spline surfaces fulfills the need for efficient and accurate algorithms for NURBS surface applications in various imaging systems, providing guidance for practical applications.

A Novel Visual System for Conducting Safety Evaluations of Operational Tunnel Linings

Based on the lining structure of an operational tunnel, the AHP and Fuzzy mathematical models were used to determine the weight of the evaluation index and solve the membership matrix. The weighted-average Fuzzy comprehensive function was used to combine the two, and the Fuzzy–AHP evaluation model was built and programmed. According to the self-developed Fuzzy–AHP evaluation-programmed model, a visualized structure safety evaluation system for operational tunnels was developed by using MATLAB. The system’s functional design, program development, and computational visualized interface were implemented, and key codes were provided. The system can be divided into four modules: data management, fuzzy computation, predictive analysis and key disease indexes to focus on. In addition, the system can easily edit and modify the evaluation function, which includes not only the Fuzzy evaluation but also other types of evaluation functions applicable to other practical engineering projects, improving the applicability of the system. After that, the system was applied to the structure safety evaluation of a mountain tunnel, which provided the evaluation results and key indexes to focus on in the tunnel. Finally, the rationality of the system design was verified by constructing the corresponding BP–RBF combined neural network. This study provides a reference for the establishment of an intelligent structure safety warning system for operational tunnels.

Machine Learning-Based Surrogate Model for Genetic Algorithm with Aggressive Mutation for Feature Selection

The genetic algorithm with aggressive mutations GAAM, is a specialised algorithm for feature selection. This algorithm is dedicated to the selection of a small number of features and allows the user to specify the maximum number of features desired. A major obstacle to the use of this algorithm is its high computational cost, which increases significantly with the number of dimensions to be retained. To solve this problem, we introduce a surrogate model based on machine learning, which reduces the number of evaluations of the fitness function by an average of 48% on the datasets tested, using the standard parameters specified in the original paper. Additionally, we experimentally demonstrate that eliminating the crossover step in the original algorithm does not result in any visible changes in the algorithm’s results. We also demonstrate that the original algorithm uses an artificially complex mutation method that could be replaced by a simpler method without loss of efficiency. The sum of the improvements resulted in an average reduction of 53% in the number of evaluations of the fitness functions. Finally, we have shown that these outcomes apply to parameters beyond those utilized in the initial article, while still achieving a comparable decrease in the count of evaluation function calls. Tests were conducted on 9 datasets of varying dimensions, using two different classifiers.

Port environmental path planning based on key obstacles

This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles. Simulation experiments are conducted using Matlab to demonstrate the effectiveness of the improved algorithm. Moreover, the performance of the hybrid algorithm is compared with five mainstream algorithms in a real simulated port environment, and the final results show the significant enhancement of this paper’s algorithm in several key performance metrics. Thus, this study provides a feasible strategy for improved path planning efficiency for AGV in the port environment.

Integrated testability modeling method of complex systems for fault feature selection and diagnosis strategy optimization

The efficacy of system fault diagnosis is intricately linked with the testability design of the system, particularly in intricate systems encompassing numerous components exhibiting diverse failure modes. However, current research shows a dearth of effective testability models and algorithms for generating diagnostic strategies in multi-valued attribute systems (MVAS) with uncertainty. To address this, the present paper introduces a novel method for testability modeling for complex systems. This approach incorporates signal features in lieu of raw sensor signals within the testability modeling process and accounts for the uncertainties surrounding test outcomes. The context of complex MVAS, characterized by inherent uncertainty, the paper proposes a novel method for constructing a four-value dependency matrix (D-matrix). Furthermore, the paper presents a novel sequential diagnosis strategy optimization approach based on a heuristic evaluation function for the multivalued D-matrix with uncertainty. The proposed methodology has been rigorously validated using a real-world case study involving an aero-engine fuel metering device system. Comparative experiments show that the proposed method can achieve better diagnostic performance in the shortest time.

Fuzzy implications-based transformation approaches from semi-three-way decision spaces to three-way decision spaces and their applications

Three-way decision spaces, as an important component of three-way decisions, greatly enrich their theoretical development and application prospects. Meanwhile, fuzzy implications, as a vital class of fuzzy logic connectives, have made great contributions to the solution of practical problems, especially complex decision-making problems. This paper considers the collaborative effect of the two, which inject new vitality into the theoretical development and application prospects of fuzzy implications and three-way decision spaces. As a vital component of three-way decision spaces, (semi-)decision evaluation functions have been widely studied based on fuzzy logic connectives and become a research hotspot. Specifically, this paper focuses on fuzzy implications-based transformation approaches from semi-three-way decision spaces to three-way decision spaces and their applications. Firstly, we present some novel fuzzy implications-based transformation approaches from semi-decision evaluation functions to decision evaluation functions, and construction approaches of semi-decision evaluation functions involving the existing semi-decision evaluation functions, fuzzy sets, interval-valued fuzzy sets and fuzzy relations. Secondly, we discuss the relationship between our approaches and the known construction approaches of three-way decision spaces. Notably, our approaches cover all existing approaches except the uninorms-based approaches. Finally, by the experiment results, we obtain our approaches are feasible, effective, superior to the known three-way decision spaces approaches and have good anti-noise ability. And, the parameter ρ of our approaches is also effective and stable.