Optimalisasi Distribusi Harga Tiket Pesawat berdasarkan Kepadatan Rute Menggunakan Algoritma Genetika

The development of the aviation industry in Indonesia in the past decade has risen sharply. One of the impacts of the development of the aviation industry was the presence of a multilevel tariff concept. Where, the concept is the variation in ticket prices in one class with slightly different facilities such as the difference in penalty fees for making refunds and rebooking. The concept of multilevel rates is usually referred to as sub-class rates. One application of the sub-class tariffs in economic classes is divided into four types of sub-classes special promo sub-classes, promo sub-classes, then affordable sub-class and flexible sub-class. One optimization method of getting a combination that meets the requirements without having to try all possibilities is the Genetic Algorithm. The chromosomes built represent 10 subclasses on 9 routes so that they have 90 genes. The use of genetic algorithms originated from the generation of an initial population of 8 chromosomes with a length of 90 genes performed randomly, evaluation of the compatibility function was then selected using the Rank based fitness technique, crosses using Multi-Point Crossover, mutations with the Mutation Insertion technique. The system built was tested with two conditions each of eight tests with 100 generations. First, the test uses the mutation method of three subclass codes on four routes at a capacity of 150 seats, obtained the largest match value of Rp. 750,752,200 and the smallest Rp. 662,283,100. And testing with the mutation method of three subclass codes on eight routes of 150 seat capacity obtained the largest match value of Rp. 763,265,300 and the smallest Rp. 547,396,200. The results of testing the mutation method on eight routes resulted in a higher match value compared to the mutation method on four routes. The system has been implemented in software so that it can provide recommendations on the number of ticket passes distributed in the economic subclass.

Zero-shot Learning with Many Classes by High-rank Deep Embedding Networks

Zero-shot learning (ZSL) is a recently emerging research topic which aims to build classification models for unseen classes with knowledge from auxiliary seen classes. Though many ZSL works have shown promising results on small-scale datasets by utilizing a bilinear compatibility function, the ZSL performance on large-scale datasets with many classes (say, ImageNet) is still unsatisfactory. We argue that the bilinear compatibility function is a low-rank approximation of the true compatibility function such that it is not expressive enough especially when there are a large number of classes because of the rank limitation. To address this issue, we propose a novel approach, termed as High-rank Deep Embedding Networks (GREEN), for ZSL with many classes. In particular, we propose a feature-dependent mixture of softmaxes as the image-class compatibility function, which is a simple extension of the bilinear compatibility function, but yields much better results. It utilizes a mixture of non-linear transformations with feature-dependent latent variables to approximate the true function in a high-rank way, which makes GREEN more expressive. Experiments on several datasets including ImageNet demonstrate GREEN significantly outperforms the state-of-the-art approaches.

Landmark Selection for Zero-shot Learning

Zero-shot learning (ZSL) is an emerging research topic whose goal is to build recognition models for previously unseen classes. The basic idea of ZSL is based on heterogeneous feature matching which learns a compatibility function between image and class features using seen classes. The function is constructed based on one-vs-all training in which each class has only one class feature and many image features. Existing ZSL works mostly treat all image features equivalently. However, in this paper we argue that it is more reasonable to use some representative cross-domain data instead of all. Motivated by this idea, we propose a novel approach, termed as Landmark Selection(LAST) for ZSL. LAST is able to identify representative cross-domain features which further lead to better image-class compatibility function. Experiments on several ZSL datasets including ImageNet demonstrate the superiority of LAST to the state-of-the-arts.

Building the Mathmatical Model of Elastic Deformation for Rolls System by Four-High Mill Strip Rolling with a Composite Back-Up Roll

For buliding the mathmatical model of elastic deformation for rolls system with a composite back-up roll assemblled by the shrinkage fit, the roll sleeve and roll mandrel was studied individually, the bending force was acted on the working roll and the load acted on the contact zone was discreted segment by segment, then the deflection model of the working roll and the roll sleeve and roll mandrel was put forward based on the influence function method. According to the force equilibrim function and the deformation compatibility function between the roll sleeve, the roll mandrel and working roll, the solution matrix was listed and the solving programe was compiled to solve the deflection of working roll and composite back-up roll, the pressure between the working roll and the roll sleeve and the cross-section thickness of rolled strip. Therefore, it is an important theoretical foundation to improve the manufacturing technology and practical application of the composite back-up roll.

The Homologue of het-c of Neurospora crassa Lacks Vegetative Compatibility Function in Fusarium proliferatum

ABSTRACT For two fungal strains to be vegetatively compatible and capable of forming a stable vegetative heterokaryon they must carry matching alleles at a series of loci variously termed het or vic genes. Cloned het/vic genes from Neurospora crassa and Podospora anserina have no obvious functional similarity and have various cellular functions. Our objective was to identify the homologue of the Neurospora het-c gene in Fusarium proliferatum and to determine if this gene has a vegetative compatibility function in this economically important and widely dispersed fungal pathogen. In F. proliferatum and five other closely related Fusarium species we found a few differences in the DNA sequence, but the changes were silent and did not alter the amino acid sequence of the resulting protein. Deleting the gene altered sexual fertility as the female parent, but it did not alter male fertility or existing vegetative compatibility interactions. Replacement of the allele-specific portion of the coding sequence with the sequence of an alternate allele in N. crassa did not result in a vegetative incompatibility response in transformed strains of F. proliferatum. Thus, the fphch gene in Fusarium appears unlikely to have the vegetative compatibility function associated with its homologue in N. crassa. These results suggest that the vegetative compatibility phenotype may result from convergent evolution. Thus, the genes involved in this process may need to be identified at the species level or at the level of a group of species and could prove to be attractive targets for the development of antifungal agents.