Color detection of printing based on improved superpixel segmentation algorithm.

We propose an improved superpixel segmentation algorithm based on visual saliency and color entropy for online color detection in printed products. This method addresses the issues of low accuracy and slow speed in detecting color deviations in print quality control. The improved superpixel segmentation algorithm consists of three main steps: Firstly, simulating human visual perception to obtain visually salient regions of the image, thereby achieving region-based superpixel segmentation. Secondly, adaptively determining the superpixel size within the salient regions using color information entropy. Finally, the superpixel segmentation method is optimized using hue angle distance based on chromaticity, ultimately achieving a region-based adaptive superpixel segmentation algorithm. Color detection of printed products compares the color mean values of post-printing images under the same superpixel labels, outputting labels with color deviations to identify areas of color differences. The experimental results show that the improved superpixel algorithm introduces color phase distance with better segmentation accuracy, and combines it with human visual perception to better reproduce the color information of printed materials. Using the method described in this article for printing color quality inspection can reduce data computation, quickly detect and mark color difference areas, and provide the degree of color deviation.

Unbalanced Current Identification of Three-Core Power Cables Based on Phase Detection of Magnetic Fields.

Identifying unbalanced phase currents is crucial for control and fault alarm rates in power grids, especially in urban distribution networks. The zero-sequence current transformer, specifically designed for measuring unbalanced phase currents, offers advantages in measurement range, identity, and size, compared to using three separate current transformers. However, it cannot provide detailed information on the unbalance status beyond the total zero-sequence current. We present a novel method for identifying unbalanced phase currents based on phase difference detection using magnetic sensors. Our approach relies on analyzing phase difference data from two orthogonal magnetic field components generated by three-phase currents, as opposed to the amplitude data used in previous methods. This enables the differentiation of unbalance types (amplitude unbalance and phase unbalance) through specific criteria and allows for the simultaneous selection of an unbalanced phase current in the three-phase currents. In this method, the amplitude measurement range of magnetic sensors is no longer a critical factor, allowing for an easily attainable wide identification range for current line loads. This approach offers a new avenue for unbalanced phase current identification in power systems.

Amplification of input differences by dynamic heterogeneity in the spiral ganglion.

A defining feature of type I primary auditory afferents that compose ∼95% of the spiral ganglion is their intrinsic electrophysiological heterogeneity. This diversity is evident both between and within unitary, rapid, and slow adaptation (UA, RA, and SA) classes indicative of specializations designed to shape sensory receptor input. But to what end? Our initial impulse is to expect the opposite: that auditory afferents fire uniformly to represent acoustic stimuli with accuracy and high fidelity. Yet this is clearly not the case. One explanation for this neural signaling strategy is to coordinate a system in which differences between input stimuli are amplified. If this is correct, then stimulus disparity enhancements within the primary afferents should be transmitted seamlessly into auditory processing pathways that utilize population coding for difference detection. Using sound localization as an example, one would expect to observe separately regulated differences in intensity level compared with timing or spectral cues within a graded tonotopic distribution. This possibility was evaluated by examining the neuromodulatory effects of cAMP on immature neurons with high excitability and slow membrane kinetics. We found that electrophysiological correlates of intensity and timing were indeed independently regulated and tonotopically distributed, depending on intracellular cAMP signaling level. These observations, therefore, are indicative of a system in which differences between signaling elements of individual stimulus attributes are systematically amplified according to auditory processing constraints. Thus, dynamic heterogeneity mediated by cAMP in the spiral ganglion has the potential to enhance the representations of stimulus input disparities transmitted into higher level difference detection circuitry.NEW & NOTEWORTHY Can changes in intracellular second messenger signaling within primary auditory afferents shift our perception of sound? Results presented herein lead to this conclusion. We found that intracellular cAMP signaling level systematically altered the kinetics and excitability of primary auditory afferents, exemplifying how dynamic heterogeneity can enhance differences between electrophysiological correlates of timing and intensity.

Evolution of computational models in BioModels Database and the Physiome Model Repository.

BACKGROUND: A useful model is one that is being (re)used. The development of a successful model does not finish with its publication. During reuse, models are being modified, i.e. expanded, corrected, and refined. Even small changes in the encoding of a model can, however, significantly affect its interpretation. Our motivation for the present study is to identify changes in models and make them transparent and traceable. METHODS: We analysed 13734 models from BioModels Database and the Physiome Model Repository. For each model, we studied the frequencies and types of updates between its first and latest release. To demonstrate the impact of changes, we explored the history of a Repressilator model in BioModels Database. RESULTS: We observed continuous updates in the majority of models. Surprisingly, even the early models are still being modified. We furthermore detected that many updates target annotations, which improves the information one can gain from models. To support the analysis of changes in model repositories we developed MoSt, an online tool for visualisations of changes in models. The scripts used to generate the data and figures for this study are available from GitHub https://github.com/binfalse/BiVeS-StatsGenerator and as a Docker image at https://hub.docker.com/r/binfalse/bives-statsgenerator/ . The website https://most.bio.informatik.uni-rostock.de/ provides interactive access to model versions and their evolutionary statistics. CONCLUSION: The reuse of models is still impeded by a lack of trust and documentation. A detailed and transparent documentation of all aspects of the model, including its provenance, will improve this situation. Knowledge about a model's provenance can avoid the repetition of mistakes that others already faced. More insights are gained into how the system evolves from initial findings to a profound understanding. We argue that it is the responsibility of the maintainers of model repositories to offer transparent model provenance to their users.

COMODI: an ontology to characterise differences in versions of computational models in biology.

BACKGROUND: Open model repositories provide ready-to-reuse computational models of biological systems. Models within those repositories evolve over time, leading to different model versions. Taken together, the underlying changes reflect a model's provenance and thus can give valuable insights into the studied biology. Currently, however, changes cannot be semantically interpreted. To improve this situation, we developed an ontology of terms describing changes in models. The ontology can be used by scientists and within software to characterise model updates at the level of single changes. When studying or reusing a model, these annotations help with determining the relevance of a change in a given context. METHODS: We manually studied changes in selected models from BioModels and the Physiome Model Repository. Using the BiVeS tool for difference detection, we then performed an automatic analysis of changes in all models published in these repositories. The resulting set of concepts led us to define candidate terms for the ontology. In a final step, we aggregated and classified these terms and built the first version of the ontology. RESULTS: We present COMODI, an ontology needed because COmputational MOdels DIffer. It empowers users and software to describe changes in a model on the semantic level. COMODI also enables software to implement user-specific filter options for the display of model changes. Finally, COMODI is a step towards predicting how a change in a model influences the simulation results. CONCLUSION: COMODI, coupled with our algorithm for difference detection, ensures the transparency of a model's evolution, and it enhances the traceability of updates and error corrections. COMODI is encoded in OWL. It is openly available at http://comodi.sems.uni-rostock.de/ .

Primary visual cortex represents the difference between past and present.

The visual system is confronted with rapidly changing stimuli in everyday life. It is not well understood how information in such a stream of input is updated within the brain. We performed voltage-sensitive dye imaging across the primary visual cortex (V1) to capture responses to sequences of natural scene contours. We presented vertically and horizontally filtered natural images, and their superpositions, at 10 or 33 Hz. At low frequency, the encoding was found to represent not the currently presented images, but differences in orientation between consecutive images. This was in sharp contrast to more rapid sequences for which we found an ongoing representation of current input, consistent with earlier studies. Our finding that for slower image sequences, V1 does no longer report actual features but represents their relative difference in time counteracts the view that the first cortical processing stage must always transfer complete information. Instead, we show its capacities for change detection with a new emphasis on the role of automatic computation evolving in the 100-ms range, inevitably affecting information transmission further downstream.