Checking sequence construction using adaptive and preset distinguishing sequences

Methods for testing from finite state machine-based specifications often require the existence of a preset distinguishing sequence for constructing checking sequences. It has been shown that an adaptive distinguishing sequence is sufficient for these methods. This result is significant because adaptive distinguishing sequences are strictly more common and up to exponentially shorter than preset ones. However, there has been no study on the actual effect of using adaptive distinguishing sequences on the length of checking sequences. This paper describes experiments that show that checking sequences constructed using adaptive distinguishing sequences are almost consistently shorter than those based on preset distinguishing sequences. This is investigated for three different checking sequence generation methods and the results obtained from an extensive experimental study are given

Using synchronizing heuristics to construct homing sequences

Computing a shortest synchronizing sequence of an automaton is an NP-Hard problem. There are well-known heuristics to find short synchronizing sequences. Finding a shortest homing sequence is also an NP-Hard problem. Unlike existing heuristics to find synchronizing sequences, homing heuristics are not widely studied. In this paper, we discover a relation between synchronizing and homing sequences by creating an automaton called homing automaton. By applying synchronizing heuristics on this automaton we get short homing sequences. Furthermore, we adapt some of the synchronizing heuristics to construct homing sequences

Forecasting Dengue, Chikungunya and Zika cases in Recife, Brazil: a spatio-temporal approach based on climate conditions, health notifications and machine learning

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation

Importance of automatization on dry funnel deposited specimens for liquefaction testing

Yenigün, Şenay (Dogus Author) -- Conference full title: 5th Geotechnical Earthquake Engineering and Soil Dynamics Conference: Slope Stability and Landslides, Laboratory Testing, and In Situ Testing, GEESDV 2018; Austin; United States; 10 June 2018 through 13 June 2018.Dry funnel deposition is one of the most commonly utilized specimen reconstitution methods for both triaxial and simple shear testing of sandy soils. The basic procedure of the method is simple, where the dry soil is deposited through a funnel, which is raised gently along the axis of symmetry of the specimen allowing the soil to gradually fill the space encapsulated by a split mold or stack of rings. The specimen can then be saturated by CO2 flushing and de-aired water percolation. During funnel raising process, experimentalists could lose the control of the raising speed, vertical alignment (i.e. asymmetrical raising), or even could shake the funnel, which could influence the initial fabric and therefore the dynamic response of specimens. In this study, an automated funnel was designed and developed for an NGI type cyclic simple shear apparatus. The funnel has a control unit which allows various computer controlled funnel raising speeds. The funnel also has six extensions, each having 35 mm length. The present study investigates the influence of funnel raising speed and the height of the funnel (by using extensions) on relative density and cyclic liquefaction resistance of a clean and silty sand (with 10% fines content). An equation is developed showing the relationship between funnel raising speed and relative density of specimens. Accordingly, the relative density of specimens increase with increasing funnel raising speed in a logarithmic manner. Also as the number of extensions on the funnel increase, relative density of resulting specimens also increase. This is an important observation which shows that deposition process actually starts much earlier than the initial funnel movement, perhaps as soon as the experimentalist starts to deal with the soil. It was observed that both manual and automatic dry funnel deposited specimens have the same liquefaction resistance for a given relative density and CSR, implying that specimens at the same Dr have similar fabric. However, it was found that for a given funnel raising speed (frs), the fabric achieved by automatic dry funnel deposition is systematically looser than the one achieved by manual dry funnel deposition. Possible reasons are discussed

Estimation of Monthly Pan Evaporation Using Different Artificial Intelligence Methods in Adana Station

Buharlaşma, hidrolojik ve meteorolojik çalışmalarda önemli bir parametre olarak karşımıza çıkmakta ve buharlaşma tahmininin doğru yapılması ise su kaynaklarının geliştirilmesi, kontrol edilmesi ve yönetimi gibi çeşitli amaçlar için önem arz etmektedir. Son yıllarda, yapay zeka yöntemleri kullanan araştırmacılar arasında, hidroloji ve su kaynakları yönetimi konusu giderek daha popüler hale gelmektedir. Bu çalışmada, aylık ortalama buharlaşma tahminini elde etmek için Yapay Sinir Ağı (YSA), Bulanık Mantık Yapay Sinir Ağı (ANFIS) ve Gen Ekspresyon Programlama (GEP) yöntemleri kullanılmıştır. Aylık ortalama sıcaklık (Co), nem (%), rüzgar hızı (m/s), basınç (hPa), güneşlenme şiddeti (cal / cm2) ve güneşlenme süresi (saat) iklimsel verileri kullanılarak, Adana istasyonundaki aylık ortalama buharlaşma tahmin edilmiştir. Farklı girdi parametreleri kombinasyonları oluşturularak, YSA, ANFIS ve GEP metotları kullanılarak elde edilen sonuçlar karşılaştırılmıştır. Elde edilen sonuçlar neticesinde, kullanılan tüm metotların buharlaşma tahmininin kabul edilebilir derecede başarılı olduğu ancak ANFIS metodunda 6 girdili kombinasyonun, oluşturulan tüm modeller içerisinde en başarılı sonucu verdiği belirlenmiştirEvaporation is a primary process of water and heat loss for most of lakes and therefore a main component in both their energy and water budgets. Accurate estimation of evaporation is necessary for water and energy budget studies, water quality surveys, watermanagement and planning of hydraulic constructions. Evaporation is emerging as an important parameter in hydrological and meteorological studies and also it is important to estimate evaporation correctly for the development, controlling and management of the water resources. In recent years, artificial intelligence methods are becoming more popular among the researchers in hydrology and water resources management. In this study, Artificial Neural Network (ANN), Adaptive Network Based Fuzzy Inference Systems (ANFIS) and Gene Expressing Programming (GEP) were used to obtain the estimated monthly average evaporation. Using climatic data of monthly average temperature (oC), humidity (%) wind speed (m/s), pressure (hPa), solar radiation (cal / cm2) and sunshine duration (hours), the average monthly pan evaporation in Adana station was estimated. Creating different combinations of input parameters the results obtained from ANN, ANFIS and GEP were compared. According to the result obtained from different methods, all methods were found to be successful in estimating the evaporation but ANFIS method with 6 input combination is determined to be most successful in all models created