An improved search space resizing method for model identification by standard genetic algorithm

In this paper, a new improved search space boundary resizing method for an optimal model’s parameter identification for continuous real time transfer function by standard genetic algorithms (SGAs) is proposed and demonstrated. Premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of the search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model’s parameters for the identified transfer function. This new method is applied and examined on two processes, a third-order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method’s efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations

Quantum parallel dense coding of optical images

We propose quantum dense coding protocol for optical images. This protocol extends the earlier proposed dense coding scheme for continuous variables [S.L.Braunstein and H.J.Kimble, Phys.Rev.A 61, 042302 (2000)] to an essentially multimode in space and time optical quantum communication channel. This new scheme allows, in particular, for parallel dense coding of non-stationary optical images. Similar to some other quantum dense coding protocols, our scheme exploits the possibility of sending a classical message through only one of the two entangled spatially-multimode beams, using the other one as a reference system. We evaluate the Shannon mutual information for our protocol and find that it is superior to the standard quantum limit. Finally, we show how to optimize the performance of our scheme as a function of the spatio-temporal parameters of the multimode entangled light and of the input images.Comment: 15 pages, 4 figures, RevTeX4. Submitted to the Special Issue on Quantum Imaging in Journal of Modern Optic

PID controller tuning for a multivariable glass furnace process by genetic algorithm

Standard genetic algorithms (SGAs) are investigated to optimise discrete-time proportional-integral-derivative (PID) controller parameters, by three tuning approaches, for a multivariable glass furnace process with loop interaction. Initially, standard genetic algorithms (SGAs) are used to identify control oriented models of the plant which are subsequently used for controller optimisation. An individual tuning approach without loop interaction is considered first to categorise the genetic operators, cost functions and improve searching boundaries to attain the desired performance criteria. The second tuning approach considers controller parameters optimisation with loop interaction and individual cost functions. While, the third tuning approach utilises a modified cost function which includes the total effect of both controlled variables, glass temperature and excess oxygen. This modified cost function is shown to exhibit improved control robustness and disturbance rejection under loop interaction. © 2015 Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelber

An improved search space resizing method for model identification by Standard Genetic Algorithm

.In this paper, a new improved search space boundary resizing method for an optimal model's parameter identification by Standard Genetic Algorithms (SGAs) is proposed and demonstrated. The premature convergence to local minima, as a result of search space boundary constraints, is a key consideration in the application of SGAs. The new method improves the convergence to global optima by resizing or extending the upper and lower search boundaries. The resizing of search space boundaries involves two processes, first, an identification of initial value by approximating the dynamic response period and desired settling time. Second, a boundary resizing method derived from the initial search space value. These processes brought the elite groups within feasible boundary regions by consecutive execution and enhanced the SGAs in locating the optimal model's parameters for the identified transfer function. This new method is applied and examined on two processes, a third order transfer function model with and without random disturbance and raw data of excess oxygen. The simulation results assured the new improved search space resizing method's efficiency and flexibility in assisting SGAs to locate optimal transfer function model parameters in their explorations. © 2015 Chinese Automation and Computing Society in the UK - CAC

Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions

Preparation of high-quality datasets for the urban scene understanding is a labor-intensive task, especially, for datasets designed for the autonomous driving applications. The application of the coarse ground truth (GT) annotations of these datasets without detriment to the accuracy of semantic image segmentation (by the mean intersection over union - mIoU) could simplify and speedup the dataset preparation and model fine tuning before its practical application. Here the results of the comparative analysis for semantic segmentation accuracy obtained by PSPNet deep learning architecture are presented for fine and coarse annotated images from Cityscapes dataset. Two scenarios were investigated: scenario 1 - the fine GT images for training and prediction, and scenario 2 - the fine GT images for training and the coarse GT images for prediction. The obtained results demonstrated that for the most important classes the mean accuracy values of semantic image segmentation for coarse GT annotations are higher than for the fine GT ones, and the standard deviation values are vice versa. It means that for some applications some unimportant classes can be excluded and the model can be tuned further for some classes and specific regions on the coarse GT dataset without loss of the accuracy even. Moreover, this opens the perspectives to use deep neural networks for the preparation of such coarse GT datasets.Comment: 10 pages, 6 figures, 2 tables, The Second International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2019) 26-27 January 2019, Kiev, Ukrain

Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition

In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion

Cognitive networks: brains, internet, and civilizations

In this short essay, we discuss some basic features of cognitive activity at several different space-time scales: from neural networks in the brain to civilizations. One motivation for such comparative study is its heuristic value. Attempts to better understand the functioning of "wetware" involved in cognitive activities of central nervous system by comparing it with a computing device have a long tradition. We suggest that comparison with Internet might be more adequate. We briefly touch upon such subjects as encoding, compression, and Saussurean trichotomy langue/langage/parole in various environments.Comment: 16 page

Npas4 is activated by melatonin, and drives the clock gene Cry1 in the ovine pars tuberalis

Seasonal mammalsintegrate changes in the duration of nocturnal melatonin secretion to drive annual physiologic cycles. Melatonin receptors within the proximal pituitary region, the pars tuberalis (PT), are essential in regulating seasonal neuroendocrine responses. In the ovine PT, melatonin is known to influence acute changes in transcriptional dynamics coupled to the onset (dusk) and offset (dawn) of melatonin secretion, leading to a potential interval-timing mechanism capable of decoding changes in day length (photoperiod). Melatonin offset at dawn is linked to cAMP accumulation, which directly induces transcription of the clock gene Per1. The rise of melatonin at dusk induces a separate and distinct cohort, including the clock-regulated genes Cry1 and Nampt, but little is known of the upstream mechanisms involved. Here, we used next-generation sequencing of the ovine PT transcriptome at melatonin onset and identified Npas4 as a rapidly induced basic helix-loop-helix Per-Arnt-Sim domain transcription factor. In vivo we show nuclear localization of NPAS4 protein in presumptive melatonin target cells of the PT (α-glycoprotein hormone-expressing cells), whereas in situ hybridization studies identified acute and transient expression in the PT of Npas4 in response to melatonin. In vitro, NPAS4 forms functional dimers with basic helix loop helix-PAS domain cofactors aryl hydrocarbon receptor nuclear translocator (ARNT), ARNT2, and ARNTL, transactivating both Cry1 and Nampt ovine promoter reporters. Using a combination of 5'-deletions and site-directed mutagenesis, we show NPAS4-ARNT transactivation to be codependent upon two conserved central midline elements within the Cry1 promoter. Our data thus reveal NPAS4 as a candidate immediate early-response gene in the ovine PT, driving molecular responses to melatonin

Emotions in business-to-business service relationships

Emotion in business-to-business service relationships regarding cargo services is explored. The service relationship is characterised by mutual trust and cooperation. Contact is mainly via telephone or e-mail with some face-to-face interactions and participants providing a complex, multi-skilled seamless service. Experience rather than training plays a vital role with long-term service relationships built up and maintained. Emotional sensitivity is acquired partly by experience and a repeat customer base but mainly through a genuine desire to help and get to know others. In contrast to the view of emotional labour bringing managerial control or adverse affects to service staff, the emotion engendered by this work is authentic expression bringing personal satisfaction