Application of a Natural Language Interface to the Teleoperation of a Mobile Robot

IFAC Intelligent Components for Vehicles, Seville, Spain, 1998This paper describes the application of a natural language interface to the teleoperation of a mobile robot. Natural language communication with robots is a major goal, since it allows for non expert people to communicate with robots in his or her own language. This communication has to be flexible enough to allow the user to control the robot with a minimum knowledge about its details. In order to do this, the user must be able to perform simple operations as well as high level tasks which involve multiple elements of the system. For this ones, an adequate representation of the knowledge about the robot and its environment will allow the creation of a plan of simple actions whose execution will result in the accomplishment of the requested tas

Inferring gene regression networks with model trees

Background: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results: We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions: REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of REGNET.Ministerio de Ciencia e Innovación TIN2011-68084-C02-00Ministerio de Ciencia e Innovación PCI2006-A7-0575Junta de Andalucia P07-TIC- 02611Junta de Andalucía TIC-20

Inferring gene regression networks with model trees

<p>Abstract</p> <p>Background</p> <p>Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities.</p> <p>Results</p> <p>We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>, is experimentally tested on two well-known data sets: <it>Saccharomyces Cerevisiae </it>and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database) is used as control to compare the results to that of a correlation-based method. This experiment shows that R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods.</p> <p>Conclusions</p> <p>R<smcaps>EG</smcaps>N<smcaps>ET</smcaps> generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear regressions to separate areas of the search space favoring to infer localized similarities over a more global similarity. Furthermore, experimental results show the good performance of R<smcaps>EG</smcaps>N<smcaps>ET</smcaps>.</p

Automatic Semiqualitative Analysis: Application to a Biometallurgical System

The aim of this work is the representation and analysis of semiqualitative models. Their qualitative knowledge is represented by means of qualitative operators and envelope functions. A semiqualitative model is transformed into a family of quantitative models. In this paper the analysis of a model is proposed as a constraint satisfaction problem. Constraint satisfaction is an umbrella term for a variety of techniques of Artificial Intelligence and related disciplines. In this paper attention is focused on intervals consistency techniques. The semiqualitative analysis is automatically made by means of consistency techniques. The presented method is applied to a industrial biometallurgical system in order to show how increase the capacity of production

Including Qualitative Knowledge in Semiqualitative Dynamical Systems

A new method to incorporate qualitative knowledge in semiqualitative systems is presented. In these systems qualitative knowledge may be expressed in their parameters, initial conditions and/or vector fields. The representation of qualitative knowledge is made by means of intervals, continuous qualitative functions and envelope functions. A dynamical system is defined by differential equations with qualitative knowledge. This definition is transformed into a family of dynamical systems. In this paper the semiqualitative analysis is carried out by means of constraint satisfaction problems, using interval consistency techniques

Analisis Anomali Temperatur Permukaan Tanah dan Awan Gempa Berkaitan dengan Gempa Palu 2018

Anomali temperatur permukaan tanah dan awan gempa yang berkaitan gempa bumi yang terjadi di Palu 28 September 2018 telah diteliti  menggunakan data temperatur permukaan tanah dan permukaan air laut dari Moderate Resolution Imaging Spectroradiometer (MODIS) dan data awan dari satelit Multifunction Transport Satellite (MTSAT). Data temperatur udara dari European Centre for Medium Range Weather Forecasts (EMCWF) juga digunakan untuk memastikan bahwa anomali temperatur bukan disebabkan oleh aktivitas cuaca. Anomali temperature permukaan tanah diamati selama 5 tahun dari 2014-2018 dan awan gempa diamati 3 bulan sebelum terjadi gempa bumi. Penelitian ini menemukan adanya kenaikan temperatur permukaan tanah dan air laut sebagai prekursor gempa Palu 2018. Pada saat terjadi gempa kenaikan temperatur permukaan tanah pada siang hari sebesar 2,2 K melebihi batas nilai sebagai prekursor gempa bumi (>2 K) tetapi kenaikan temperatur teramati setiap tahun. Selain itu, anomali temperatur permukaan laut hanya 0,25 K masih lebih kecil dari anomali sebagai prekursor gempa bumi (>2 K). Selama itu tidak ditemukan juga adanya kemunculan awan gempa sebelum gempa terjadi. Dengan demikian gempa Palu 2018 tidak diiringi oleh kenaikan temperatur permukaan tanah dan air laut serta kemunculan awan gempa. Anomaly land surface temperature and earthquake cloud that related the 2018 Palu earthquake were examined using land and sea surface temperatures from Moderate Resolution Imaging Spectroradiometer (MODIS)  and cloud from the Multifunction Transport Satellite (MTSAT) satellite data.  Air temperature data from the European Center for Medium-Range Weather Forecasts (EMCWF) were also used to ensure that temperature anomalies are not caused by weather activity. Land surface temperature anomalies were observed for five years from 2014-2018, and earthquake clouds were observed for three months before the earthquake. This study find an increase in the surface temperature of land and seawater as a precursor to the 2018 Palu earthquake. During the earthquake, there was an increase in land surface temperature by 2,2 K, which exceeds the limit value of anomaly land surface temperature as an earthquake precursor (> 2 K), but such an increase is observed every year. In addition, sea surface temperature anomaly is only 0,25 K, which is much smaller than the value as an earthquake precursor  (> 2 K). It was also found that there is no earthquake cloud before the Palu earthquake. Thus, the 2018 Palu earthquake was not accompanied by an increase in land and sea surface temperatures and the appearance of earthquake clouds