Negotiation requires dynamically balancing self-interest and cooperation to maximize one’s own utility. Yet, existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior...
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In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to ef...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.
Distributed underwater acoustic positioning systems offer the advantages of high positioning accuracy and flexible coverage. Classical non-cooperative target positioning methods in underwater acoustics require the agg...
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ISBN:
(数字)9798331542887
ISBN:
(纸本)9798331542894
Distributed underwater acoustic positioning systems offer the advantages of high positioning accuracy and flexible coverage. Classical non-cooperative target positioning methods in underwater acoustics require the aggregation of time-domain data from various nodes, leading to high network pressure, and positioning accuracy is degraded by multipath interference. To address this issue, this paper presents a distributed positioning method for non-cooperative underwater targets based on Multipath Time Delay Differences (MPD). This method leverages a functional mapping relationship between multipath time delay differences, positions of measurement units, and the target position, utilizing the principle of multipath propagation. An objective function is constructed using the least mean square criterion, and the target position is obtained through an optimization algorithm. Simulation analysis results demonstrate that, compared to traditional positioning methods based on Time Difference of Arrival (TDOA), Time of Arrival (TOA), and Direction of Arrival (DOA), the proposed method based on multipath time delay differences exhibits significant improvements in positioning accuracy, particularly in depth accuracy. Furthermore, this method eliminates the need to aggregate time-domain signals from each measurement unit, significantly reduced network data transmission pressure and enhanced the stability and reliability of the positioning system.
Stochastic optimization problems, which involve random variables in the optimization process, are commonly seen in many applications such as engineering design and logistics management. The challenge of stochastic opt...
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ISBN:
(数字)9798331519667
ISBN:
(纸本)9798331519674
Stochastic optimization problems, which involve random variables in the optimization process, are commonly seen in many applications such as engineering design and logistics management. The challenge of stochastic optimization is how to accurately evaluate the value of the objective function, while using a large number of Monte Carlo simulations for evaluation can result in significant computational costs. In this paper, we propose a surrogate-assisted continuous ant colony optimization method for stochastic optimization (SAACO). First, instead of using a lot of Monte Carlo simulations, SSACO estimates the approximated fitness values of the individuals in the population by a surrogate model. Second, the continuous ant colony optimization is used as the optimizer, where the population is evolved by constructing a Gaussian kernel function from global information to construct the promising solutions. The surrogate model is a forward model from the solution space to the objective space, while the Gaussian kernel function can be regarded as an inverse model from the objective space to the solution space. By combining two models, the performance of solving stochastic optimization problems can be improved. Experimental results demonstrate the promising performance of the proposed SAACO.
We study weighted group search on a disk, where two unit-speed agents must locate a hidden target exactly distance 1 away (within a unit-radius disk), starting from the same point. Agents share findings instantly via ...
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In this paper, a neurodynamic approach with communication delay is proposed for the sake of solving distributed optimization problems. First, the relationship between the equilibrium of the algorithm and the optimal s...
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ISBN:
(数字)9798331516147
ISBN:
(纸本)9798331516154
In this paper, a neurodynamic approach with communication delay is proposed for the sake of solving distributed optimization problems. First, the relationship between the equilibrium of the algorithm and the optimal solution of the distributed optimization problem is established. Then, sufficient conditions for the equilibrium to converge to the optimal solution of the optimization problem are provided in the form of linear matrix inequalities, in the case of communication delay. In addition, numerical simulation verifies the validity of the neurodynamic approach with communication time delay.
DC motors find extensive application in various industries because of their accuracy in controlling torque and speed, which makes them crucial for machine operation, transportation and automation systems. Sliding mode...
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ISBN:
(数字)9798331543952
ISBN:
(纸本)9798331543969
DC motors find extensive application in various industries because of their accuracy in controlling torque and speed, which makes them crucial for machine operation, transportation and automation systems. Sliding mode control (SMC) offers enhanced resilience to system uncertainties and disturbances, despite the widespread use of conventional PID controllers. This study looks at how optimization algorithms, specifically the Chess Optimization Algorithm (COA) and Particle Swarm Optimization (PSO), can be used to fine-tune SMC parameters in order to make a linear DC motor work better. The study uses Integral Absolute Error (IAE) and Integral Square Error (ISE) as objective functions to look at things like rise time, overshoot, settling time and steady-state error that happen over time. The results show that COA is better than PSO because it has a shorter rise time (1.1245s for IAE and 1.4843s for ISE), less overshoot (7.0404x10 -5 % for IAE and 8.7887x10 -5 % for ISE) and less errors, even though PSO has slightly shorter settling times for ISE. The Chess Optimization Algorithm exhibits remarkable stability and accuracy, positioning it as a highly effective approach for optimizing intricate control systems such as SMC in linear DC motors.
In the planning of an Alternating Current/Direct Current (AC/DC) microgrid, there are too many interference factors, which leads to the difference between the planning results and the actual situation. Therefore, an o...
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ISBN:
(数字)9798331518806
ISBN:
(纸本)9798331518813
In the planning of an Alternating Current/Direct Current (AC/DC) microgrid, there are too many interference factors, which leads to the difference between the planning results and the actual situation. Therefore, an optimal planning method for AC/DC microgrid based on node coupling degree and power balance degree is proposed. Under the analysis of node coupling degree and power balance degree, the optimal planning structure of the AC/DC microgrid is constructed to achieve maximum power output. Based on the objective function of the upper planning structure and the objective function of the lower planning structure, the objective function of AC/DC microgrid optimization planning is calculated, the data of AC/DC microgrid is dimensionless, the standardization rules are formulated, and the optimal planning method flow of AC/DC microgrid is designed to realize the optimal planning design. The experimental results show that the utilization rate of this method is above 90%, and the application effect is good, which can meet the power target of AC/DC microgrid optimization planning.
An optimal charging profile for Li-ion batteries is proposed in this paper. The objective of the charging process is to minimize the charging time of a Li-ion battery while concurrently minimizing its energy losses. T...
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ISBN:
(数字)9798331533946
ISBN:
(纸本)9798331533953
An optimal charging profile for Li-ion batteries is proposed in this paper. The objective of the charging process is to minimize the charging time of a Li-ion battery while concurrently minimizing its energy losses. To address this challenge, a multi-objective evolutionary optimization algorithm, SPEA2, is employed. For this purpose, the extraction of electrical parameters for the equivalent circuit model of the Li-ion battery is addressed. Thereafter, the polarization voltages of the Li-ion battery are utilized as a key criterion in the SPEA2 optimization algorithm. Additionally, a power loss analysis of the onboard charger converter is conducted, and simulation results are presented to validate the theoretical analysis. The results demonstrate that the proposed charging method has reduced the charging time by $\mathbf{1 6. 1 7 \%}$ and the power losses of the charger system by 6.08% compared to the Constant Current-Constant Voltage (CC-CV) charging profile.
Timely security patches are crucial in reducing vulnerabilities across computing infrastructures, yet excessive downtime or violations of concurrency constraints can adversely affect service availability and overall p...
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ISBN:
(数字)9798331523657
ISBN:
(纸本)9798331523664
Timely security patches are crucial in reducing vulnerabilities across computing infrastructures, yet excessive downtime or violations of concurrency constraints can adversely affect service availability and overall performance. In this work, we optimize the problem of constrained patch scheduling using a multi-optimizer metaheuristic approach. We first model patch scheduling as an optimization problem aiming to minimize both total downtime and vulnerability exposure while also respecting concurrency limits, which restrict the number of machines simultaneously offline. The objective function incorporates a penalty factor to discourage over-concurrent patching. We compare eight modern metaheuristic algorithms with variable patch durations. Extensive experiments are conducted, analyzing best objective scores, downtime, exposure, concurrency penalty, computational time, and convergence trends. **Our findings indicate that Equilibrium Optimizer (EO) achieves the lowest final cost (6.5351), followed closely by Multi-Verse Optimizer (MVO) (6.8295). Whereas Grey Wolf Optimizer (GWO) and Moth Flame Optimization (MFO), yield moderate results, while Whale Optimization Algorithm (WOA) exhibits the highest cost (10.3944). These outcomes underscore the impact of concurrency-limited patching constraints on performance and emphasize the benefits of selecting optimizers that effectively balance exploration and exploitation for scheduling in security-critical environments.
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