Improved Estimation of Sir in Mobile Cdma Systems by Integration of Artificial Neural Network and Time Series Technique

Abstract: This study presents an integrated Artificial Neural Network (ANN) and time series framework to estimate and predict Signal to Interference Ratio (SIR) in Direct Sequence Code Division Multiple Access (DS/CDMA) systems. It is difficult to model uncertain behavior of SIR with only conventional ANN or time series and the integrated algorithm could be an ideal substitute for such cases. Artificial Neural Network (ANN) approach based on supervised multi layer perceptron (MLP) network are used in the proposed algorithm. All type of ANN-MLP are examined in present study. At last, Coefficient of Determination (R ) is used for selecting preferred model from different 2 constructed MLP-ANN. One of unique feature of the proposed algorithm is utilization of Autocorrelation Function (ACF) to define input variables whereas conventional methods which use trial and error method. This is the first study that integrates ANN and time series for improved estimation of SIR in mobile CDMA systems

Forecasting peak load electricity demand using statistics and rule based approach

Problem statement: Forecasting of electricity load demand is an essential activity and an important function in power system planning and development. It is a prerequisite to power system expansion planning as the world of electricity is dominated by substantial lead times between decision making and its implementation. The importance of demand forecasting needs to be emphasized at all level as the consequences of under or over forecasting the demand are serious and will affect all stakeholders in the electricity supply industry. Approach: If under estimated, the result is serious since plant installation cannot easily be advanced, this will affect the economy, business, loss of time and image. If over estimated, the financial penalty for excess capacity (i.e., over-estimated and wasting of resources). Therefore this study aimed to develop new forecasting model for forecasting electricity load demand which will minimize the error of forecasting. In this study, we explored the development of rule-based method for forecasting electricity peak load demand. The rule-based system synergized human reasoning style of fuzzy systems through the use of set of rules consisting of IF-THEN approximators with the learning and connectionist structure. Prior to the implementation of rule-based models, SARIMAT model and Regression time series were used. Results: Modification of the basic regression model and modeled it using Box-Jenkins auto regressive error had produced a satisfactory and adequate model with 2.41% forecasting error. With rule-based based forecasting, one can apply forecaster expertise and domain knowledge that is appropriate to the conditions of time series. Conclusion: This study showed a significant improvement in forecast accuracy when compared with the traditional time series model. Good domain knowledge of the experts had contributed to the increase in forecast accuracy. In general, the improvement will depend on the conditions of the data, the knowledge development and validation. The rule-based forecasting procedure offered many promises and we hoped this study can become a starting point for further research in this field

A comparison of different fuzzy inference systems for prediction of catch per unit effort (CPUE) of fish

60-69Present work was aimed to design Mamdani- Fuzzy Inference System (FIS), Sugeno -FIS and Sugeno-Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the prediction of CPUE of fish. The system was implemented using MATLAB fuzzy toolbox. A prediction of CPUE was made using the models trained. The accuracy of fuzzy inference system models was compared using mean square error (MSE) and average error percentage. Comparative study of all the three systems provided that the results of Sugeno-ANFIS model (MSE =0.05 & Average error percentage=11.02%) are better than the two other Fuzzy Inference Systems. This ANFIS was tested with independent 28 dataset points. The results obtained were closer to training data (MSE=0.08 and Average error percentage=13.45%)

Análisis exploratorio de las variaciones estacionales e intraestacionales de los principales tipos polínicos en la atmósfera de la ciudad de Sunchales, Argentina

Introducción y objetivos: El estudio de la variabilidad estacional e intraestacional de la concentración de polen en el aire es de suma importancia para comprender las relaciones con la vegetación emisora y los parámetros atmosféricos que modulan el transporte de polen. Esta investigación tiene como objetivo estudiar estas variabilidades en Sunchales, una ciudad ubicada en el centro-este de Argentina. M&M: El monitoreo atmosférico se realizó con una trampa Burkard durante dos temporadas en 2012 y 2013 en las afueras de la ciudad. Resultados & Conclusiones: Los períodos de polinización de los tipos de polen estudiados muestran un retraso en 2013 en comparación con el año anterior, presuntamente relacionado con una mayor cantidad de unidades de calor acumuladas en 2012. Sin embargo, la integral polínica para el período 2013 fue 1,4 veces mayor que 2012, hecho que no se explica por la precipitación acumulada sino por la hora del día en que ocurren los hidrometeoros. Las concentraciones de polen categorizadas en rangos muestran que los valores mayores coinciden con la ubicación urbana de las fuentes arbóreas mientras que las herbáceas muestran una asociación con un origen rural. En cuanto a la variabilidad intraestacional, la mayor proporción de la varianza del polen en el aire se acumula en la escala sinóptica (80 - 60%) con períodos entre 3 y 10 días. Durante 2012 predominaron las ondas largas (> 5,5 días) mientras que en 2013 predominaron las ondas medias (3,9 - 5,5 días).Background and aims: The study of the seasonal and intra-seasonal variability of the airborne pollen concentration is of paramount importance to understand the relationships with the emitting vegetation and the atmospheric parameters that modulate pollen transport. This research aims to study these variabilities in Sunchales, a city located in the center-east of Argentina. M&M: Atmospheric monitoring was carried out with a Burkard trap during two seasons in 2012 and 2013 on the outskirts of the city. Results & Conclusions: The pollination periods of the studied pollen types show a delay in 2013 compared to the previous year, presumably related to a greater amount of cumulative heat units in 2012. However, the integral pollen for the period 2013 was 1.4 times higher than 2012, a fact that is not explained by accumulated precipitation but by the time of day when the hydrometeors occur. Binned pollen concentrations show that the highest concentrations coincide with the urban location of the tree sources while the herbaceous ones show an association with a rural origin. Regarding the intra-seasonal variability, the highest proportion of the airborne pollen variance accumulates on the synoptic-scale (80 - 60%) with periods between 3 and 10 days. During 2012 long waves predominated (> 5.5 days) while in 2013 medium waves prevailed (3.9 - 5.5 days).Fil: Perez, Claudio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Covi, Mauro. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gassmann, María Isabel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Ulke, Ana Graciela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentin

Recent developments in monitoring and modelling airborne pollen, a review

Public awareness of the rising importance of allergies and other respiratory diseases has led to increased scientific effort to accurately and rapidly monitor and predict pollen, fungal spores and other bioaerosols in our atmosphere. An important driving force for the increased social and scientific concern is the realisation that climate change will increasingly have an impact on worldwide bioaerosol distributions and subsequent human health. In this review we examine new developments in monitoring of atmospheric pollen as well as observation and source-orientated modelling techniques. The results of a Scopus® search for scientific publications conducted with the terms ‘Pollen allergy’ and ‘Pollen forecast’ included in the title, abstract or keywords show that the number of such articles published has increased year on year. The 12 most important allergenic pollen taxa in Europe as defined by COST Action ES0603 were ranked in terms of the most ‘popular’ for model-based forecasting and for forecasting method used. Betula, Poaceae and Ambrosia are the most forecast taxa. Traditional regression and phenological models (including temperature sum and chilling models) are the most used modelling methods, but it is notable that there are a large number of new modelling techniques being explored. In particular, it appears that Machine Learning techniques have become more popular and led to better results than more traditional observation-orientated models such as regression and time-series analyses

Penggunaan intelligence algorithm untuk penilaian keamanan kerja operator berdasarkan indikator HSEE (health, safety, enviroment; ergomics) studi kasus : PT. Dempo Laser Metalindo Indonesia

Penilaian tingkat keamanan kerja digunakan untuk mengetahui kemungkinan atau peluang seorang individu dapat kehilangan pekerjaannya yang disebabkan oleh kecelakaan kerja. Perancangan kuesioner digunakan untuk mendapatkan data yang akurat sesuai kebutuhan. Pertanyaan pada kuisioner yang berhubungan dengan faktor HSEE digunakan sebagai input variabel sedangkan faktor job security digunakan sebagai output variabel. Analisis data dilakukan dengan metode Adaptive Network Based Fuzzy Inference System (ANFIS). Kriteria nilai MAPE paling minimum didapatkan pada model ANFIS dengan fungsi input GaussMF, empat MF, operator AND (min), dan fungsi output linear. Berdasarkan model ANFIS tersebut maka diketahui sebagian besar responden menyatakan puas terhadap kondisi keamanan kerja. ========== HSEE concept aims to reduce the level of occupational accidents, health problems, and environmental impact. Job Security is the probability or opportunities an individual can lose his job due to workplace accident. Based on observations in PT Dempo Laser Metalindo Indonesia, note that there were no instructions or documentation of job security regulations on the production floor. Design of questionnaire used to obtain accurate data according to needs. The question associated with HSEE factor used as input variabel while job security used as output variable. Data analysis done with Adaptive Network Based Fuzzy Inference System (ANFIS). Minimum criteria MAPE value obtained with an input function GaussMF, four MF, operator AND (min), and output function linear. Based on the ANFIS model then known the majority of respondents said satisfied with the condition of job security

Modelización del ciclo fenológico reproductor del olivo (Olea europaea L.)

El olivo (Olea europaea L.) es la principal especie arbórea con interés agronómico en el área Mediterránea. Esta especie posee un ciclo reproductor que muestra variaciones que responden tanto a su propia genética como al clima. La presente tesis tiene como objetivo el análisis detallado y modelización del ciclo reproductor del olivo y su respuesta a diferentes variables ambientales en el Sur de la Península Ibérica. Para ello este trabajo se basa en el estudio de una base de datos fenológicos, aerobiológicos y meteorológicos que abarcan 30 años en la provincia de Córdoba (Andalucía, España), que es la segunda provincia productora de aceite de oliva de Andalucía, principal región productora en el mundo. Un conocimiento más profundo sobre los principales factores que controlan las variaciones anuales de la floración y producción de fruto de esta especie es de gran interés no sólo a nivel agronómico, sino también médico, dado el carácter alergógeno de sus granos de polen, y ecológico, ya que el acebuche, Olea europaea var. sylvestris Brot., es un arbusto característico y bioindicador del ecosistema mediterráneo. Aunque gran parte de la tesis se centra en el comportamiento y respuesta fenológica del olivo en la provincia de Córdoba, el capítulo IV de esta tesis aborda un estudio global sobre la producción de aceituna en el área Mediterránea, analizando datos de Andalucía (España), Italia y Túnez. Se han analizado estadísticamente las relaciones que mantienen diferentes factores ambientales con los aspectos más críticos del ciclo reproductor del olivo (la intensidad de la floración, la fenología floral y la producción de fruto). A partir de éste análisis se han desarrollado modelos descriptivos y predictivos del ciclo reproductor, estudiando desde sus primeras fases hasta la producción final de cosecha, con un gran interés científico y una alta aplicabilidad y transferencia a la sociedad, ya que permite la predicción de importantes eventos biológicos, como fechas, duración e intensidad de la floración, así como la producción de fruto con varios meses de antelación. En los capítulos I y II, “Biometeorological and autoregressive indices for predicting olive pollen intensity” y “Year clustering analysis for modelling olive flowering phenology”, se analizan las características de la intensidad de la floración,...The olive (Olea europaea L.) is the leading commercial tree crop in the Mediterranean area. Its reproductive cycle displays considerable variations due to inherent genetic factors but also to climate response. This thesis provides a detailed analysis and modelling of the olive reproductive cycle and its response to a range of environmental variables in the southern Iberian Peninsula. Analysis was based on phenological, aerobiological and meteorological data recorded over the last 30 years in the province of Córdoba (Andalusia, Spain), the second-largest olive-oil-producing province in Andalusia, which is in turn the world’s largest producing region. A more thorough knowledge of the factors governing year-on-year changes in olive flowering and fruit production is clearly of agricultural interest. It is also useful for medical purposes—since olive pollen is highly allergenic—and for ecological reasons, given that the wild olive Olea europaea var. sylvestris Brot., is a characteristic shrub used as a bioindicator for Mediterranean ecosystem. Although the thesis focuses mainly on the behaviour and phenological response of the olive tree in the province of Córdoba, Chapter IV offers an overview of olive production in the Mediterranean area, drawing on data for Andalusia (Spain), Italy and Tunisia. A statistical analysis is made of the correlations between various environmental factors and critical features of the olive reproductive cycle (flowering intensity, floral phenology and fruit production). The results are used in the construction of models to describe and predict the reproductive cycle, from the earliest phases through to harvest. These models are of considerable scientific interest and can readily be transferred for social applications, since they enable the prediction—several months in advance—of major biological events such as the timing, duration and intensity of flowering and the volume of fruit production. Chapters I and II “Biometeorological and autoregressive indices for predicting olive pollen intensity” and “Year clustering analysis for modelling olive flowering phenology” analyse variables relating to flowering intensity, expressed in this anemophilous species by the Pollen Index. The first chapter focuses specifically on the construction of indices to account for year-on-year variations in olive flowering intensity, while in the second chapter a cluster analysis is used to group years with..

Advanced Data Analytics Methodologies for Anomaly Detection in Multivariate Time Series Vehicle Operating Data

Early detection of faults in the vehicle operating systems is a research domain of high significance to sustain full control of the systems since anomalous behaviors usually result in performance loss for a long time before detecting them as critical failures. In other words, operating systems exhibit degradation when failure begins to occur. Indeed, multiple presences of the failures in the system performance are not only anomalous behavior signals but also show that taking maintenance actions to keep the system performance is vital. Maintaining the systems in the nominal performance for the lifetime with the lowest maintenance cost is extremely challenging and it is important to be aware of imminent failure before it arises and implement the best countermeasures to avoid extra losses. In this context, the timely anomaly detection of the performance of the operating system is worthy of investigation. Early detection of imminent anomalous behaviors of the operating system is difficult without appropriate modeling, prediction, and analysis of the time series records of the system. Data based technologies have prepared a great foundation to develop advanced methods for modeling and prediction of time series data streams. In this research, we propose novel methodologies to predict the patterns of multivariate time series operational data of the vehicle and recognize the second-wise unhealthy states. These approaches help with the early detection of abnormalities in the behavior of the vehicle based on multiple data channels whose second-wise records for different functional working groups in the operating systems of the vehicle. Furthermore, a real case study data set is used to validate the accuracy of the proposed prediction and anomaly detection methodologies