Evaluation Function Research Articles

Design of a Gomoku AI Based on the Alpha-Beta Pruning Search Algorithm

Abstract. This paper presents the implementation of a Gomoku AI based on the alpha-beta pruning search algorithm and the Negamax algorithm. Gomoku, a traditional board game known for its strategic depth, poses significant challenges in AI development due to the exponential increase in possible moves. The AI leverages the computational efficiency of Alpha-Beta Pruning, which enhances the Negamax algorithm by reducing the number of nodes that need to be evaluated in the game tree. This combination allows for faster decision-making without compromising accuracy. Additionally, a value evaluation function is used to assess board states and guide the AI in selecting optimal moves. In the results the performance of the AI was tested through simulations, demonstrating great performance in move selection and computational efficiency compared to traditional methods. The paper also explores potential improvements, including the integration of reinforcement learning (RL) techniques to further enhance the AI's adaptability and strategic decision-making capabilities.

Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.

Model Checking Spatial Reachability Specifications of Public Transport Networks

Well-designed spatial configurations of public transport stops and routes in big cities contribute to enhancing daily travel services for citizens, effectively mitigating traffic congestion, and addressing other pertinent challenges. When examining spatial layouts of public transport networks (PTNs), various reachability demands between stops or urban Points of Interest (POIs) are crucial issues should be firstly taken into account. Existing methods to investigate spatial reachability properties of PTNs generally need to construct some evaluation functions, or survey reachability metrics through some network analysis techniques. These methods are often impractical, as the functional relations always cannot be accurately defined, or some global network metrics cannot provide explicit evidences for PTN layout planning or optimization.In this paper, we introduce spatial model checking techniques to the formal verification of the reachability specifications of PTN to guarantee the rationality of PTN layout. First, we extend closure space structure by incorporating attribute labeling functions and logical propositions for public transport stops and routes to develop a formal spatial verification model for PTN spatial layout. Second, we propose several novel reachability operators based on the logical operators of the Spatial Logic for Closure Space (SLCS) to facilitate the logical characterization of reachability specifications. Third, we perform the verification of the transformed reachability formulas by the spatial model checker topochecker. Examples demonstrate the effectiveness of our approach and indicate that it can perform automatic, descriptive and comprehensible verification of the reachability properties PTN layouts.

Pose estimation of bolster spring based on projection roundness and genetic algorithm in narrow space

Precisely identifying the pose of the bolster spring is vital for its effective disengagement. Addressing the challenge of detecting various positional arrangements of the bolster spring within the constrained space of a bogie frame, a method of estimating the pose of bolster spring based on projected roundness and genetic algorithm is proposed. This approach not only reduces the computational complexity, but also improves the precision of pose estimation. Firstly, Statistical filtering and Random Sample Consensus (RANSAC) are employed for the segmentation of the external spring point cloud and for preliminary orientation estimation. Subsequently, the convergence performance of the genetic algorithm is enhanced through optimization of the selection operator, implementation of an adaptive genetic probability adjustment strategy, and refinement of the encoding method. Finally, an evaluation function based on the projected roundness of the external spring is developed and integrated with an improved adaptive genetic algorithm (IAGA) to achieve precise pose estimation of the bolster spring. Experimental results indicate that the proposed method significantly improves pose estimation accuracy compared to traditional approaches, with an additional time cost of less than 1.5 s. The method achieves a RMSE of 1.71 mm for position and 0.84° for orientation, providing an efficient and accurate pose estimation solution for engineering applications.

The Simulation of Mode Control for a Photonic Lantern Adaptive Amplifier

A photonic lantern is a low-loss device that connects a single multimode waveguide to multiple single-mode waveguides and can enhance the beam quality of a fiber laser by adaptively controlling the optical parameters (amplitude, phase, polarization) at the input. In this work, we combined the gains and losses of individual modes within the fiber amplifier and introduced a mode content parameter at the amplifier’s output as an evaluation function to simulate mode control effects. Mode competition within the gain fiber can degrade the control effect of the fundamental mode and lead to it taking a longer time for the control to converge. Optimal parameters, such as the gain fiber length and pumping method, were identified to improve control effectiveness. Specifically, an optimal gain fiber length of 8 m was determined, and backward pumping was found to achieve higher pumping efficiency and better control results. The system demonstrated significant power amplification potential and could stabilize mode control under different pumping powers ranging from 50 W to 5 kW. In conclusion, our research demonstrates that an adaptive fiber amplifier based on a photonic lantern can achieve a stable, high-power, large-mode-field, near-fundamental-mode output from the gain fiber. Although mode competition within the gain fiber can degrade the control effect of the fundamental mode and cause the control to take a longer time to converge, these aspects should be further studied to improve the control’s effectiveness. These findings contribute to the development of advanced simulation models that guide high-power mode control experiments and deepen our understanding of physical processes in science and technology.

Global Path Optimal Planning Method for Autonomous Vehicles Based on Image Feature Extraction

In order to solve the problem of autonomous vehicles driving safely on the road while searching for optimal paths to avoid traffic congestion and obstruction, this paper establishes an optimal path planning model based on image feature extraction. The model consists of four parts: the selection of key turning points, interpolation between turning points, definition of the evaluation function, and global optimal path search. First, based on the map information provided by the network, an image feature detection algorithm was proposed, and the key turning points of the map route were selected and stored in an open table. Then, based on mechanical analysis theory, the cubic interpolation between two key turning points of the optimal path was defined. This allows the car to drive along a continuous curve with a certain arc radius, enabling it to drive smoothly, effectively avoid obstacles, and ensure safety. Then, an evaluation function is defined as needed, and different evaluation functions are specified for different road sections to avoid excessive local deviations in planning and to reduce the impact of known obstacles on the path. Finally, the cost of the search path is calculated based on the evaluation function. According to the principle of minimum cost, a globally optimal heuristic search algorithm is proposed to find the globally optimal path. Experimental results show that the proposed global optimal path planning algorithm has excellent performance, with an average accuracy of 96.71% and a search speed of 2.78 ms. The model not only provides accurate path planning and fast path selection capabilities but also has strong robustness against interference.

Investigation on the Optimization Strategy of DWA Algorithm for Path Planning of the USVs with the Consideration of Environmental Factors

Unmanned surface vessels (USVs) are generally subjected to wind force, wave and undercurrent action. Some unidentified objects such as underwater obstacles and submerged reefs should be monitored and evaded with converged communication, autonomous control and network system so as to achieve autonomous and automatic avoidance of danger and real-time control of flight path. Next, based on performing interference mechanics analysis on the USVs under the disturbance of wind force and wave on water surface, the relationship parameters between the bow angle, the deviation angle, safety evaluation function and the external force were obtained, and the comprehensive information of water surface environment was evaluated in combination with the dynamic window approach (DWA) algorithm. Further, the related model was established for simulation by using the simulation software MATLAB, and both reliability and stability of the modified DWA algorithm were designed and examined. Accordingly, the modified DWA algorithm can enhance the safety of the USVs in autonomous path planning and achieve rapid obstacle avoidance and autonomous path planning of the USVs. Our simulation revealed that the modified DWA algorithm can safely and rapidly correct the bow angle and the deviation angle, optimize the path trajectory and reduce the risk of roll-over of the USVs under sudden external force.

Dynamic Path Planning Method for Unmanned Surface Vessels in Complex Traffic Conditions of Island Reefs Waters

Unmanned Surface Vehicles (USVs) operating in complex traffic conditions in island reef waters often require different types of algorithms. Therefore, selecting a dynamic path-planning algorithm with strong adaptability has become a new challenge. This paper proposes a dynamic adaptive path planning algorithm for USVs, incorporating an improved Dynamic Window Approach (DWA) with fuzzy logic and the International Regulations for Preventing Collisions at Sea (COLREGS). The algorithm is designed by integrating three key aspects: evaluation function, fuzzy control, and COLREGS. First, to enable USVs to approach the target point more safely and quickly during navigation, an additional target point attraction sub-function is introduced, extending the original evaluation function. Furthermore, to ensure robust dynamic path planning for USVs across various water environments, such as narrow channels, reef-laden waters, and open seas, fuzzy logic is integrated with the improved DWA algorithm. Since USVs must comply with COLREGS during navigation, the algorithm incorporates these regulations, enhancing the DWA algorithm with fuzzy logic to ensure compliance. Finally, simulation experiments validate the proposed algorithm, demonstrating that the planned paths are safer and more stable, ensuring the safe navigation of USVs in compliance with COLREGS.

Improved A-STAR Algorithm for Power Line Inspection UAV Path Planning

The operational areas for unmanned aerial vehicles (UAVs) used in power line inspection are highly complex; thus, the best path planning under known obstacles is of significant research value for UAVs. This paper establishes a three-dimensional spatial environment based on the gridding and filling of two-dimensional maps, simulates a variety of obstacles, and proposes a new optimization algorithm based on the A-STAR algorithm, considering the unique dynamics and control characteristics of quadcopter UAVs. By utilizing a novel heuristic evaluation function and uniformly applied quadratic B-spline curve smoothing, the planned path is optimized to better suit UAV inspection scenarios. Compared to the traditional A-STAR algorithm, this method offers improved real-time performance and global optimal solution-solving capabilities and is capable of planning safer and more realistic flight paths based on the operational characteristics of quadcopter UAVs in mountainous environments for power line inspection.

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.

Research Status and Hotspot Analysis of Curriculum Evaluation in Chinese Higher Vocational Education

This study employs CiteSpace, an information visualization software, to analyze the research status and trends in curriculum evaluation within Chinese higher vocational education. By collecting data from the China National Knowledge Infrastructure (CNKI) database over the past two decades, the study generates knowledge maps and tables to reveal the spatiotemporal characteristics, core authors, research institutions, and hotspots in this field. The analysis shows distinct stages of development, with an initial development stage (2004-2007), an explosive growth stage (2008-2015), and a fluctuating development stage (2016-2023). The study identifies 14 core authors and the main research institutions, highlighting the need for stronger collaborative relationships. Through keyword co-occurrence and clustering analysis, the research hotspots are found to focus on the connotation evolution, realistic dilemmas, and functions of curriculum evaluation in Chinese higher vocational education. The study concludes by discussing the theoretical extension, categorical characteristics, dynamic improvement of indicators, and functional effects of teaching evaluation in Chinese higher vocational education. Future prospects emphasize the importance of expanding the evaluation system, adapting to industry needs, and developing intelligent teaching models to enhance the quality and relevance of higher vocational education in China.

Nonlinear vibration analysis of sandwich plates with inverse-designed 3D auxetic core by deep generative model

Building on a deep generative model (DGM), this paper introduces an innovative sandwich plate structure featuring an inverse-designed auxetic 3D lattice core and conducts a detailed investigation of its nonlinear vibration characteristics and effective Poisson's ratios under various parameter settings. By incorporating a conditional estimator and quality loss evaluation functions, the enhanced conditional generative adversarial networks are capable of designing 3D truss auxetic topologies that achieve customized negative Poisson's ratios without reliance on subjective experience. Additionally, lattice specimens are created using 3D metal printing, and the mechanical properties of these DGM-based 3D auxetic structures are validated through vibration experiments and finite element models. These structures exhibit significantly superior natural frequencies compared to those obtained through conventional topology optimization methods reported in existing literature. The study also explores the impact of different functionally graded configurations, temperature variations, boundary conditions, and dimensional parameters on the natural frequency, nonlinear vibration response, and effective Poisson's ratio of the inverse designed auxetic sandwich plates.

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.

Impact assessment of social entrepreneurship

The purpose of the article is to elucidate the main methodological and normative aspects in assessing the impact of the activity of social enterprises in the Republic of Moldova. The methodological approach is based on the PDCA quality management cycle and the role of the monitoring and evaluation function in the managerial cycle of any activity. Currently, in the Republic of Moldova, the social entrepreneurship activity is at the embryonic stage of development. Thus, in order to boost the development of this sector and achieve the desired economic and social impact, there is a need to increase the quality of managerial procedures both at the macro level – that of sectoral public policies, and at the micro level – that of the management of social enterprises. In the process of evaluating the impact of social entrepreneurship, the author relies not only on the economic and social effects, but also on the principles of sustainable development.