A Quick Maximum Power Point Tracking Method Using an Embedded Learning Algorithm for Photovoltaics on Roads

This chapter presents a new approach to realize quick maximum power point tracking (MPPT) for photovoltaics (PVs) bedded on roads. The MPPT device for the road photovoltaics needs to support quick response to the shadow flickers caused by moving objects. Our proposed MPPT device is a microconverter connected to a short PV string. For real-world usage, several sets of PV string connected to the proposed microconverter will be connected in parallel. Each converter uses an embedded learning algorithm inspired by the insect brain to learn the MPPs of a single PV string. Therefore, the MPPT device tracks MPP via the perturbation and observation method in normal circumstances and the learning machine learns the relationships between the acquired MPP and the temperature and magnitude of the Sun irradiation. Consequently, if the magnitude of the Sun beam incident on the PV panel changes quickly, the learning machine yields the predicted MPP to control a chopper circuit. The simulation results suggested that the proposed MPPT method can realize quick MPPT

Mammutprojekt Verkehrswende und aktuelle verkehrspolitische Fragen

Der Verkehrssektor muss seinen Beitrag zum Klimaschutz leisten. So wichtig der Umstieg auf elektrische Antriebe auf der Basis Erneuerbarer Energien auch ist: es geht nicht ohne einen Wandel des Verkehrsverhaltens und eine weitgehende Veränderung des Rechtsrahmens. Danach sieht es derzeit nicht aus, daher ist es nötig, die überfälligen Veränderungen probeweise und örtlich sowie zeitlich begrenzt zu versuchen

CA-ARBAC: privacy preserving using context-aware role-based access control on Android permission system

Existing mobile platforms are based on manual way of granting and revoking permissions to applications. Once the user grants a given permission to an application, the application can use it without limit, unless the user manually revokes the permission. This has become the reason for many privacy problems because of the fact that a permission that is harmless at some occasion may be very dangerous at another condition. One of the promising solutions for this problem is context-aware access control at permission level that allows dynamic granting and denying of permissions based on some predefined context. However, dealing with policy configuration at permission level becomes very complex for the user as the number of policies to configure will become very large. For instance, if there are A applications, P permissions, and C contexts, the user may have to deal with A × P × C number of policy configurations. Therefore, we propose a context-aware role-based access control model that can provide dynamic permission granting and revoking while keeping the number of policies as small as possible. Although our model can be used for all mobile platforms, we use Android platform to demonstrate our system. In our model, Android applications are assigned roles where roles contain a set of permissions and contexts are associated with permissions. Permissions are activated and deactivated for the containing role based on the associated contexts. Our approach is unique in that our system associates contexts with permissions as opposed to existing similar works that associate contexts with roles. As a proof of concept, we have developed a prototype application called context-aware Android role-based access control. We have also performed various tests using our application, and the result shows that our model is working as desired

Cytotoxicity of replication-competent adenoviruses powered by an exogenous regulatory region is not linearly correlated with the viral infectivity/gene expression or with the E1A-activating ability but is associated with the p53 genotypes

BackgroundReplication-competent adenoviruses (Ad) produced cytotoxic effects on infected tumors and have been examined for the clinical applicability. A biomarkers to predict the cytotoxicity is valuable in a clinical setting.MethodsWe constructed type 5 Ad (Ad5) of which the expression of E1A gene was activated by a 5′ regulatory sequences of survivin, midkine or cyclooxygenase-2, which were highly expressed in human tumors. We also produced the same replication-competent Ad of which the fiber-knob region was replaced by that of Ad35 (AdF35). The cytotoxicity was examined by a colorimetric assay with human tumor cell lines, 4 kinds of pancreatic, 9 esophageal carcinoma and 5 mesothelioma. Ad infectivity and Ad-mediated gene expression were examined with replication-incompetent Ad5 and AdF35 which expressed the green fluorescence protein gene. Expression of cellular receptors for Ad5 and AdF35 was also examined with flow cytometry. A transcriptional activity of the regulatory sequences was investigated with a luciferase assay in the tumor cells. We then investigated a possible correlation between Ad-mediated cytotoxicity and the infectivity/gene expression, the transcriptional activity or the p53 genotype.ResultsWe found that the cytotoxicity was greater with AdF35 than with Ad5 vectors, but was not correlated with the Ad infectivity/gene expression irrespective of the fiber-knob region or the E1A-activating transcriptional activity. In contrast, replication-competent Ad produced greater cytotoxicity in p53 mutated than in wild-type esophageal carcinoma cells, suggesting a possible association between the cytotoxicity and the p53 genotype.ConclusionsSensitivity to Ad-mediated cytotoxic activity was linked with the p53 genotype but was not lineally correlated with the infectivity/gene expression or the E1A expression

Incremental Leaning and Model Selection for Radial Basis Function Network through Sleep

The model selection for neural networks is an essential procedure to get not only high levels of generalization but also a compact data model. Especially in terms of getting the compact model, neural networks usually outperform other kinds of machine learning methods. Generally, models are selected by trial and error testing using whole learning samples given in advance. In many cases, however, it is difficult and time consuming to prepare whole learning samples in advance. To overcome these inconveniences, we propose a hybrid on-line learning system for a radial basis function (RBF) network that repeats quick learning of novel instances by rote during on-line periods (awake phases) and repeats pseudo rehearsal for model selection during out-of-service periods (sleep phases). We call this system Incremental Learning with Sleep (ILS). During sleep phases, the system basically stops the learning of novel instances, and during awake phases, the system responds quickly. We also extended the system so as to shorten the periodic sleep periods. Experimental results showed the system selects more compact data models than those selected by other machine learning systems

Calibration of a Structured Light System Using Planar Objects

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