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Full-Text Articles in Physical Sciences and Mathematics
Complete Solution Of The Lady In The Lake Scenario, Alexander Von Moll, Meir Pachter
Complete Solution Of The Lady In The Lake Scenario, Alexander Von Moll, Meir Pachter
Faculty Publications
In the Lady in the Lake scenario, a mobile agent, L, is pitted against an agent, M, who is constrained to move along the perimeter of a circle. L is assumed to begin inside the circle and wishes to escape to the perimeter with some finite angular separation from M at the perimeter. This scenario has, in the past, been formulated as a zero-sum differential game wherein L seeks to maximize terminal separation and M seeks to minimize it. Its solution is well-known. However, there is a large portion of the state space for which the canonical solution does not …
Unified Multi-Objective Genetic Algorithm For Energy Efficient Job Shop Scheduling, Hongjong Wei, Shaobo Li, Huageng Quan, Dacheng Liu, Shu Rao, Chuanjiang Li, Jianjun Hu
Unified Multi-Objective Genetic Algorithm For Energy Efficient Job Shop Scheduling, Hongjong Wei, Shaobo Li, Huageng Quan, Dacheng Liu, Shu Rao, Chuanjiang Li, Jianjun Hu
Faculty Publications
In recent years, people have paid more and more attention to traditional manufacturing’s environmental impact, especially in terms of energy consumption and related emissions of carbon dioxide. Except for adopting new equipment, production scheduling could play an important role in reducing the total energy consumption of a manufacturing plant. Machine tools waste a considerable amount of energy because of their underutilization. Consequently, energy saving can be achieved by switching machines to standby or off when they lay idle for a comparatively long period. Herein, we first introduce the objectives of minimizing non-processing energy consumption, total weighted tardiness and earliness, and …
Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson
Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson
Faculty Publications
In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter …
Optimal Policy For Sequential Stochastic Resource Allocation, Kalyanam Krishnamoorthy, Meir Pachter, David W. Casbeer
Optimal Policy For Sequential Stochastic Resource Allocation, Kalyanam Krishnamoorthy, Meir Pachter, David W. Casbeer
Faculty Publications
A gambler in possession of R chips/coins is allowed N(>R) pulls/trials at a slot machine. Upon pulling the arm, the slot machine realizes a random state i ɛ{1, ..., M} with probability p(i) and the corresponding positive monetary reward g(i) is presented to the gambler. The gambler can accept the reward by inserting a coin in the machine. However, the dilemma facing the gambler is whether to spend the coin or keep it in reserve hoping to pick up a greater reward in the future. We assume that the gambler has full knowledge of the reward distribution function. We …
Rescaling The Energy Function In Hopfield Networks, Tony R. Martinez, Xinchuan Zeng
Rescaling The Energy Function In Hopfield Networks, Tony R. Martinez, Xinchuan Zeng
Faculty Publications
In this paper we propose an approach that rescales the distance matrix of the energy function in the Hopfield network for solving optimization problems. We rescale the distance matrix by normalizing each row in the matrix and then adjusting the parameter for the distance term. This scheme has the capability of reducing the effects of clustering in data distributions, which is one of main reasons for the formation of invalid solutions. We evaluate this approach through a large number (20,000) simulations based on 200 randomly generated city distributions of the 10-city traveling salesman problem. The result shows that, compared to …