محمد حسين ابراهيم دوش

We propose a Partitioned Action Space and Partitioned Circle Items (PAS-PCI) approach for the problem of packing unequal circle items in a single two-dimensional square container such that the size of the container is minimized. The PAS is used to partition the narrow action space on the long side to find two equal action spaces to fully utilize the unoccupied spaces. The PCI are used to partition the circle items into four groups based on item sizes for the basin-hopping strategy. Starting from several random patterns, PAS-PCI runs the following potential descent method and basin-hopping strategy iteratively. It finds patterns with the local minimum potential energy by the limited-memory BFGS (LBFGS) algorithm, then it selects the circular items having the most deformations and moves them to some large vacant space or randomly chosen vacant space. By adapting the action space defined for the rectangular packing problem, we approximate each circular item as a rectangular item, thus making it much easier to find comparatively larger vacant spaces for any given pattern. The tabu strategy is adopted to prevent cycling and enhance diversity during the search procedure. Several other strategies, such as randomly swapping pair-wise similar circle items in each set, are combined to increase the diversity of the patterns. Experimental results show that the PAS-PCI algorithm is effective for the addressed problem.

The paper proposes a new two-dimensional circular bin packing problem (2D-CBPP) that is closely related to the well-known 2D rectangular bin packing problem and the single container circle packing problem. Inspired by Gold corner, silver side and strawy void for Chinese Go game, a greedy algorithm based on corner occupying action (GACOA) is proposed for solving the 2D-CBPP. We define the corner occupying action to pack the outside circles into a bin as compactly as possible, such that the number of used bins is minimized. As there are no existing benchmarks for this proposed problem, we generate two sets of benchmark instances with equal and unequal circles respectively. Experimental results show that the proposed algorithm performs quite well on these 2D-CBPP instances.

We address the NP-hard problem of finding a non-overlapping dense packing pattern for n Unequal Circle items in a two-dimensional Square Container (PUC-SC) such that the size of the container is minimized. Based on our previous work on an Action Space based Global Optimization (ASGO) that approximates each circle item as a square item to efficiently find the large unoccupied spaces, we propose an optimization algorithm based on the Partitioned Action Space and Partitioned Circle Items (PAS-PCI). The PAS is to partition the narrow action space on the long side to find two equal action spaces to fully utilize the unoccupied spaces. The PCI is to partition the circle items into four groups based on size for the basin hopping strategy. Experiments on two sets of benchmark instances show the effectiveness of the proposed method. In comparison with our previous algorithm ASGO on the 68 tested instances that ASGO published, PAS-PCI not only gains smaller containers in 64 instances and matches the other 4 but also runs faster in most instances. In comparison with the best record of the Packomania website on a total of 98 instances, PAS-PCI finds smaller containers on 82 and matches the other 16. Note that we updated 19 records for (47-48, 51-54, 57, 61-72) that had been kept unchanged since 2013.

Association rule mining, one of the most important and well researched techniques of data mining, It was first introduced in 1993. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories

A fault is a non-desirable deviation of a system or one of its components from normal behaviour. Fault diagnosis is the process of detecting, isolating, and identifying such a fault. In this paper, a method to diagnose the fault in Unmanned Aircraft Vehicles (UAVs) systems is proposed. These systems consist of several components along with sensors to measure some properties such as current, voltages, and temperatures. In such a system, faults are unavoidable and could lead to the failure of the whole system without timely treatment, and cause human losses. The proposed method adopts a deep learning technique to build a model called diagnose. Convolutional Neural Network (CNN) made.

Microarray technology is a novel method to monitor the levels of expression of a huge number of genes simultaneously.this study aims at (1) identifying the most important genes in the molecular senescence of the hippocampus and retina, where both with accelerated neurologicalsenescence (S10 and 8) models were obtainable. By using feature selection to reduce the size of high dimensional data.