Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99

On the Traveling Salesman Problem in Nautical Environments: an Evolutionary Computing Approach to Optimization of Tourist Route Paths in Medulin, Croatia

The Traveling salesman problem (TSP) defines the problem of finding the optimal path between multiple points, connected by paths of a certain cost. This paper applies that problem formulation in the maritime environment, specifically a path planning problem for a tour boat visiting popular tourist locations in Medulin, Croatia. The problem is solved using two evolutionary computing methods – the genetic algorithm (GA) and the simulated annealing (SA) - and comparing the results (are compared) by an extensive search of the solution space. The results show that evolutionary computing algorithms provide comparable results to an extensive search in a shorter amount of time, with SA providing better results of the two

Implementing genetic algorithms to CUDA environment using data parallelization

Računarske metode rješavanja paralelnih problema korištenjem grafičkih obradnih jedinica (GPUs) zadnjih su godina pobudile veliki interes. Paralelno izračunavanje može se primijeniti na genetske algoritme (GAs) u odnosu na proces evaluacije jedinki u populaciji. Ovaj rad opisuje još jednu metodu primjene GAs na CUDA okruženje gdje je CUDA računarsko okruženje opće namjene za GPUs koje daje NVIDIA. Osnovna karakteristika ovog istraživanja leži u tome da se paralelna obrada koristi ne samo za jedinke nego i za gene u jedinki. Predložena implementacija se procjenjuje kroz osam ispitnih funkcija. Ustanovili smo da predložena metoda implementacije daje 7,6-18,4 puta brže rezultate od onih kod primjene CPU.Computation methods of parallel problem solving using graphic processing units (GPUs) have attracted much research interests in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the evaluation process of individuals in a population. This paper describes yet another implementation method of GAs to the CUDA environment where CUDA is a general-purpose computation environment for GPUs provided by NVIDIA. The major characteristic point of this study is that the parallel processing is adopted not only for individuals but also for the genes in an individual. The proposed implementation is evaluated through eight test functions. We found that the proposed implementation method yields 7,6-18,4 times faster results than those of a CPU implementation

Implementing genetic algorithms to CUDA environment using data parallelization

Računarske metode rješavanja paralelnih problema korištenjem grafičkih obradnih jedinica (GPUs) zadnjih su godina pobudile veliki interes. Paralelno izračunavanje može se primijeniti na genetske algoritme (GAs) u odnosu na proces evaluacije jedinki u populaciji. Ovaj rad opisuje još jednu metodu primjene GAs na CUDA okruženje gdje je CUDA računarsko okruženje opće namjene za GPUs koje daje NVIDIA. Osnovna karakteristika ovog istraživanja leži u tome da se paralelna obrada koristi ne samo za jedinke nego i za gene u jedinki. Predložena implementacija se procjenjuje kroz osam ispitnih funkcija. Ustanovili smo da predložena metoda implementacije daje 7,6-18,4 puta brže rezultate od onih kod primjene CPU.Computation methods of parallel problem solving using graphic processing units (GPUs) have attracted much research interests in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the evaluation process of individuals in a population. This paper describes yet another implementation method of GAs to the CUDA environment where CUDA is a general-purpose computation environment for GPUs provided by NVIDIA. The major characteristic point of this study is that the parallel processing is adopted not only for individuals but also for the genes in an individual. The proposed implementation is evaluated through eight test functions. We found that the proposed implementation method yields 7,6-18,4 times faster results than those of a CPU implementation

Mbeann: Mutation-based evolving artificial neural networks

Abstract. A novel approach to topology and weight evolving artificial neural networks (TWEANNs) is presented. Compared with previous TWEANNs, this method has two major characteristics. First, a set of genetic operations may be designed without recombination because it often generates an offspring whose fitness value is considerably worse than its parents. Instead, two topological mutations whose effect on fitness value is assumed to be nearly neutral are provided in the genetic operations set. Second, a new encoding technique is introduced to define a string as a set of substrings called operons. To examine our approach, computer simulations were conducted using the standard reinforcement learning problem known as the double pole balancing without velocity information. The results obtained were compared with NEAT results, which is recognised as one of the most powerful techniques in TWEANNs. It was found that our proposed approach yields competitive results, especially when the problem is difficult

Seismic Exploration Using Active Sources at Kuchierabujima Volcano, Southwest Japan

Seismic exploration using artificial sources was conducted at Kuchierabujima volcano, southwest Japan in November 2004 by 40 participants from 9 national universities andJapan Meteorological Agency to investigate the subsurface seismic structure. The exploration was the 11th joint experiment under the National Project for Prediction of Volcanic Eruptions. A total of 183 temporal stations equippedwith a 2 Hz vertical component seismometer (including 75 3component seismometers) and a portable data logger were deployed on Kuchierabu Island. Dynamite shots with charges of 10-115 kg were detonated at 19 locations, and seismic signals were successfully recorded. To reveal the P-wave velocity structure, 2955 arrival times of the first motion were picked from the seismograms, and 2187 were classified into ranks A and B. From the record sections and the arrival time data, characteristics reflecting the geological structure were identified. Refracted waves of 5 km/s were observed at stations＞5km from the shot points. Apparent velocities near the shot points depend on the surface geology around the shots. P-wave arrived earlier at stations near the summits. Strongly scattered waves were observed similarly near the summits

The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome.

X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X's gene content, gene expression, and evolution