Evaluation Function Research Articles

Trend Prediction of Vibration Signals for Pumped-Storage Units Based on BA-VMD and LSTM

Under “dual-carbon” goals and rapid renewable energy growth, increasing start-stop frequency poses new challenges to safe operations of pumped-storage power plant equipment. Ensuring equipment safety and predictive maintenance under complex conditions urgently requires vibration warnings and trend forecasting for pumped-storage units. In this study, the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. We monitored the vibration-signal data of hydroelectric units over a long period of time, and the measured vibration-signal characteristics of pumped-storage units in a strong background-noise environment are accurately obtained using a noise-reduction method that integrates BA-VMD and wavelet thresholding. In this paper, a BP neural network prediction model, a support vector machine (SVM) prediction model, a convolutional neural network (CNN) prediction model, and a long short-term memory network (LSTM) prediction model are used to predict the trend of vibration signals of the pumped-storage unit under different operating conditions. The model prediction effect is analyzed by using the different error evaluation functions, and the prediction results are compared with the predicted results of the four different methods. By comparing the prediction effects of the four different methods, it is concluded that LSTM has higher prediction accuracy and can predict the vibration trends of hydropower units more accurately.

Investigation of microwave power transfer near sea surface with 2D parabolic equation method

AbstractDue to the existence of the evaporation duct near‐sea‐surface described as an inhomogeneous refractive index of atmosphere, microwaves can be trapped in the duct and propagate along it. To investigate wireless power transfer (WPT) over the sea surface, we utilize the two‐dimensional parabolic equation to calculate the electric field distribution and to obtain the transferred power. By analysing the propagation results with specific evaluation functions, we obtained the proper conditions for long‐distance WPT over the sea surface. When the initial field distributes uniformly on the one‐meter aperture, the frequency is 12 GHz, and the centre position is 4 m, a unique focusing spot can be obtained in the duct and nearly 5%, 8%, and 12% of the initial power remains at a distance of 10 km with a receiving aperture of 1, 2, and 3 m, respectively. The presented WPT scheme near the sea surface has potential applications in the power supply or charging for ships or small islands.

Using reinforcement learning to autonomously identify sources of error for agents in group missions

When deploying agents to execute a mission with collective behavior, it is common for accidental malfunctions to occur in some agents. It is challenging to distinguish whether these malfunctions are due to actuator failures or sensor issues based solely on interactions with the affected agent. However, we humans know that if we cause a group behavior where other agents collide with a suspected malfunctioning agent, we can monitor the presence or absence of a positional change and identify whether it is the actuator (position changed) or the sensor (position unchanged) that is broken. We have developed artificial intelligence that can autonomously deploy such “information acquisition strategies through collective behavior” using machine learning. In such problems, the goal is to plan collective actions that result in differences between the hypotheses for the state [e.g., actuator or sensor]. Only a few of the possible collective behavior patterns will lead to distinguishing between hypotheses. The evaluation function to maximize the difference between hypotheses is therefore sparse, with mostly flat values across most of the domain. Gradient-based optimization methods are ineffective for this, and reinforcement learning becomes a viable alternative. By handling this maximization problem, our reinforcement learning surprisingly gets the optimal solution, resulting in collective actions that involve collisions to differentiate the causes. Subsequent collective behaviors, reflecting this situation awareness, seemed to involve other agents assisting the malfunctioning agent.

Research on Global Off-Road Path Planning Based on Improved A* Algorithm

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments.

Scientific terms as a part of comparative tropes of modern Russian prose

Modern Russian prose is characterized by the active use of scientific terms from different fields of knowledge, which act as images of comparison for comparative tropes. Such tropes are marked by stylistic emphasis, as they contain new information for the addressee. Comparative tropes are defined as metaphors and similes of various structural types. The purpose of our work is to analyze the use of scientific terms as images of comparison for comparative constructions and determine their functions in the texts of modern Russian prose. The material for the study is the works of E. Vodolazkin, A. Ivanov, A. Ilichevsky, A. Matveeva, V. Pelevin, D. Rubina, A. Salnikova, O. Slavnikova, M. Stepnova, O. Vasyakina and others. The article discusses terms in tropes representing different fields of science: physics, chemistry, mathematics, medicine, biology, physiology, linguistics, literary criticism, art history, and computer science. The most active in the comparative tropes of modern prose are biological, medical, physical and computer terms. The productivity of these terms is associated with the rapid development of these areas of knowledge and their role in modern society. Regarding parts of speech, most of them are substantives. By metaphorizing, terms are subject to determinologization. In prose texts, terms often have distributors that clarify, specify or modify the image of comparison. In modern prose, scientific terms as part of tropes perform a number of functions: the function of figurative characteristics of a person, object or phenomenon, the evaluative function, the text-forming and conceptualizing functions. Scientific terms as part of metaphors and similes replenish and update the literature language.

A method for optimizing different geometric shields of D-T neutron generators by combining BP neural network and Analytic Hierarchy Process

In this paper, an optimization of shielding structures with different geometries is established for the D-T neutron generator system by combining Back Propagation (BP) neural network and Analytic Hierarchy Process (AHP). The D-T neutron generator (Model NG-9) used in the system was developed independently by Northeast Normal University. After investigating the rule of shielding performance among spherical, cylindrical and cubic geometries, the spherical shield is selected for BP neural network prediction to determine the total dose rate through it. Information about spherical multilayer-shielding structures and properties calculated by MCNP code is used to train the neural network. The predicted result serves as a parameter of the evaluation function, which provides a comprehensive assessment of the dose rate penetrated the shield, the shielding mass, and the shielding volume. Together with AHP, the weight factors are determined for all the optimization objectives to construct the evaluation function. By comparing its values, the optimal shielding structures for spherical, cylindrical and cubic materials are found. Against MCNP simulated values, the total dose rates’ errors of the optimal shielding structures for the sphere, cylinder, and cube are 1.72 %, -4.94 %, and -5.17 %, respectively. This result demonstrates that the combination of BP neural network and AHP is more effective in addressing multi-objective optimization problems related to the design of radiation shielding for various geometries.

On the Integration of "Teaching, Learning and Evaluation" from the Perspective of Core Literacy and its Construction Paths

In the curriculum standards for various subjects in compulsory education issued in April 2022, it is advocated that the "teaching, learning, and evaluation" be integrated, and teachers are required to transform their educational approach and pay attention to the educational function of evaluation. The integration of "teaching, learning and evaluation" is not only the basic requirement of the new curriculum scheme, but also an important path to deepen the reform of basic education curriculum teaching, but also an important measure to develop students' core quality and implement the fundamental task of cultivating morality and people. However, from the current teaching practice, there are still many problems in the integration process of "teaching, learning and evaluation". This study makes a specific analysis of this, and tries to put forward the strategy of constructing the integration of "teaching, learning and evaluation", explore the effective path to promote teaching, so that the integration of "teaching, learning and evaluation" can be implemented.

A hardware architecture for single and multiple ellipse detection using genetic algorithms and high-level synthesis tools

Ellipse detection techniques are often developed and validated in software environments, neglecting the critical consideration of computational efficiency and resource constraints prevalent in embedded systems. Furthermore, programmable logic devices, notably Field Programmable Gate Arrays (FPGAs), have emerged as indispensable assets for enhancing performance and expediting various processing applications. In the realm of computational efficiency, hardware implementations have the flexibility to tailor the required arithmetic for various applications using fixed-point representation. This approach enables faster computations while upholding adequate accuracy, resulting in reduced resource and energy consumption compared to software applications that rely on higher clock speeds, which often lead to increased resource and energy consumption. Additionally, hardware solutions provide portability and are suitable for resource-constrained and battery-powered applications. This study introduces a novel hardware architecture in the form of an intellectual property core that harnesses the capabilities of a genetic algorithm to detect single and multi ellipses in digital images. In general, genetic algorithms have been demonstrated to be an alternative that shows better results than those based on traditional methods such as the Hough Transform and Random Sample Consensus, particularly in terms of accuracy, flexibility, and robustness. Our genetic algorithm randomly takes five edge points as parameters from the image tested, creating an individual treated as a potential candidate ellipse. The fitness evaluation function determines whether the candidate ellipse truly exists in the image space. The core is designed using Vitis High-Level Synthesis (HLS), a powerful tool that converts C or C＋＋functions into Register-Transfer Level (RTL) code, including VHDL and Verilog. The implementation and testing of the ellipse detection system were carried out on the PYNQ-Z1, a cost-effective development board housing the Xilinx Zynq-7000 System-on-Chip (SoC). PYNQ, an open-source framework, seamlessly integrates programmable logic with a dual-core ARM Cortex-A9 processor, offering the flexibility of Python programming for the onboard SoC processor. The experimental results, based on synthetic and real images, some of them with the presence of noise processed by the developed ellipse detection system, highlight the intellectual property core’s exceptional suitability for resource-constrained embedded systems. Notably, it achieves remarkable performance and accuracy rates, consistently exceeding 99% in most cases. This research aims to contribute to the advancement of hardware-accelerated ellipse detection, catering to the demanding requirements of real-time applications while minimizing resource consumption.

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%.

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.

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.