A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.     

Static output-feedback control under information structure constraints

An important challenge in the static output-feedback control context is to provide an isolated gain matrix possessing a zero–nonzero structure, mainly in problems presenting information structure constraints. Although some previous works have contributed some relevant results to this issue, a fully satisfactory solution has not yet been achieved up to now. In this note, by using a Linear Matrix Inequality approach and based on previous results given in the literature, we present an efficient methodology which permits us to obtain an isolated static output-feedback gain matrix having, simultaneously, a zero–nonzero structure imposed a priori.     

Applicability of time-of-flight-based ground and multispectral aerial imaging for grapevine canopy vigour monitoring under direct root-zone deficit irrigation

ABSTRACT

This study investigates the applicability of ground-based 3D time-of-flight (ToF) imaging and small unmanned aerial system (UAS) integrated multispectral imaging as a rapid grapevine canopy vigour mapping tool for decision support during crop production management. Direct root-zone deficit irrigation was applied to grapevines with continuous and pulse irrigation techniques at 15%, 30%, and 60% rates as that of standard irrigation rate (100%) established by the grower in a commercial production. The control block was irrigated continuously at 100% standard irrigation rate. Field plots were imaged using ground platform integrated with 3D ToF imaging sensor and small UAS-integrated multispectral camera at 128 and 65 days before the harvest to estimate the canopy vigour variability associated with irrigation treatments. Customized as well as standard methods (convex hull and voxel grid) were utilized to extract canopy attributes (e.g. volume) from the 3D ToF imaging sensor data. The multispectral images were processed to extract normalized and green normalized difference vegetation index images. Resulting data were used to estimate canopy area ratio i.e. the ratio between the grapevine canopy area with respect to the total area in selected region of interest. A significant correlation (r = 0.34) between canopy volume estimated by customized algorithm and aerial canopy area ratio was observed. Custom canopy volume estimates were also highly correlated (r = 0.79) with voxel grid derived canopy volume data. Overall, 60% continuous direct root zone deficit irrigation appeared to produce canopy volume/vigour comparable with those under control treatment. Results also suggest that grower can utilize either or both (ground and aerial) grapevine canopy mapping techniques for effective management.     

Parallelization of the FMM on distributed-memory GPGPU systems for acoustic-scattering prediction

In this work, we carry out the parallelization of the single level Fast Multipole Method (FMM) for solving acoustic-scattering problems (using the Helmholtz equation) on distributed-memory GPGPU systems. With the aim of enlarging the scope of feasible simulations, the presented solution combines the techniques developed for our distributed-memory CPU solver with our shared-memory GPGPU solver. The performance of the developed solution is proved using two different GPGPU clusters: the first one consists of two workstations with NVIDIA GTX 480 GPUs linked by a Gigabit Ethernet network, and the second one comprises four nodes with NVIDIA Tesla M2090 GPUs linked by an Infiniband network.     

The ECHORD project proposals analysis – Research profiles,collaboration patterns and research topic trends

This paper investigates the research profiles, collaboration patterns and research topic trends which can be identified in the proposals submitted to the ECHORD (European Clearing House for Open Robotics Development) FP7 project. On a country level, clusters were identified and characterized by patterns of proposal production per inhabitant, score and international cooperation. Belgium and Sweden constitute a cluster characterized by high proposal production, with very high scores and extensive international collaboration. Belgium also excels from another cluster analysis, being as the only country where 100% of proposals involve industry–academia cooperation and obtain scores above 10. Other findings show that single partner proposals have significantly lower quality than multi-partner proposals but, on the other hand, the number of countries involved shows no influence on the quality of the proposals. Despite the high number of industrial participants present on the proposals, it is observed that they play secondary roles in the proposals, with a very low number projects leaded by companies. Also, it is observed that partnerships between research institutions (non-universities) are the most successful. Concerning topics of the proposals, the technology human–robot interface and the product vision robot for small-scale manufacturing are the most significant. Finally, the paper shows clusters of institutions extracted from the giant network of relations obtained from the ECHORD set of proposals.     

A neural network based intelligent system for tile prefetching in web map services

Web mapping has become a popular way of distributing interactive digital maps over the internet. Instead of dynamically generating map images on the fly, those can be pre-generated and served from a server-side cache for faster retrieval. However, these caches can grow unmanageably in size when the cartography covers mid to large areas for multiple rendering scales. This forces modest organizations to use partial caches containing just a subset of the total tiles, and makes their services less attractive than other mapping services like Google Maps or Microsoft Bing Maps. This work proposes a neural-network-based intelligent system that predicts which areas are likely to be requested in the future from a catalog of geographic features and a short history of past requests. These priority regions can be used by a tile prefetching policy to achieve an optimal population of the cache. Neural networks are trained and validated using supervised learning with real data-sets from a public nation-wide web map service. Trace-driven simulations demonstrate that accurate long-term predictions, up to 90% in terms of cache-hit ratio, can be obtained with the proposed model by prefetching a low fraction, only the 20% of the total tiles, and with a short training period.     

Fisher’s decision tree

Univariate decision trees are classifiers currently used in many data mining applications. This classifier discovers partitions in the input space via hyperplanes that are orthogonal to the axes of attributes, producing a model that can be understood by human experts. One disadvantage of univariate decision trees is that they produce complex and inaccurate models when decision boundaries are not orthogonal to axes. In this paper we introduce the Fisher’s Tree, it is a classifier that takes advantage of dimensionality reduction of Fisher’s linear discriminant and uses the decomposition strategy of decision trees, to come up with an oblique decision tree. Our proposal generates an artificial attribute that is used to split the data in a recursive way.The Fisher’s decision tree induces oblique trees whose accuracy, size, number of leaves and training time are competitive with respect to other decision trees reported in the literature. We use more than ten public available data sets to demonstrate the effectiveness of our method.     

Towards a satisfactory conversion of messages among agent-based information systems

Over the last years, there has been a change of perspective concerning the management of information systems, since they are no longer isolated and need to communicate with others. However, from a semantic point of view, real communication is difficult to achieve due to the heterogeneity of the systems. We present a proposal which, considering information systems are represented by software agents, provides a framework that favors a semantic communication among them, overcoming the heterogeneity of their agent communication languages. The main components of the framework are a suite of ontologies – conceptualizing communication acts – that will be used for generating the communication conversion, and an Event Calculus interpretation of the communications, which will be used for formalizing the notion of a satisfactory conversion. Moreover, we present a motivating example in order to complete the explanation of the whole picture.     

Characterization of limit cycles for self-regulating and integral processes with PI control and send-on-delta sampling

This work is focused on the study of limit cycles that appear in a control scheme which is based on the use of a PI controller with an event-based send-on-delta sampling (SOD). The processes investigated are integrator processes plus time delay (IPTD) and first and second order processes plus time delay, which are of interest because they are frequently used to model many industrial processes. The SOD sampling is characterized as a non-linearity of n levels with hysteresis. An algorithm to calculate the limit cycles properties is proposed, and then the results obtained in simulations are compared with experiments performed on a real plant, a distributed solar collector field at the Solar Platform of Almería (PSA, Spain).