Estimating fuzzy membership functions parameters by the levenberg-marquardt algorithm

In previous papers from the authors fuzzy model identification methods were discussed. The bacterial algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt algorithm was also proposed for determining membership functions in fuzzy systems. In this paper the Levenberg-Marquardt technique is improved to optimise the membership functions in the fuzzy rules without Ruspini-partition. The class of membership functions investigated is the trapezoidal one as it is general enough and widely used. The method can be easily extended to arbitrary piecewise linear functions as well.

Estimating bottom properties with a vector sensor array during MakaiEx 2005

Nowadays, vector sensors which measure both acoustic pressure and particle velocity begin to be available in underwater acoustic systems, normally configured as vector sensor arrays (VSA). The spatial filtering capabilities of a VSA can be used, with advantage over traditional pressure only hydrophone arrays, for estimating acoustic field directionality as well as arrival times and spectral content, which could open up the possibility for its use in bottom properties' estimation. An additional motivation for this work is to test the possibility of using high frequency probe signals (say above 2 kHz) for reducing size and cost of actual sub bottom profilers and current geoacoustic inversion methods. This work studies the bottom related structure of the VSA acquired signals, regarding the emitted signal waveform, frequency band and source-receiver geometry in order to estimate bottom properties, specially bottom reflection coefficient characteristics. Such a system was used during the Makai 2005 experiment, off Kauai I., Hawai (USA) to receive precoded signals in a broad frequency band from 8 up to 14 kHz. The agreement between the observed and the modelled acoustic data is discussed and preliminary results on the bottom reflection estimation are presented.

Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management

Estimating depth and explosive charge weight for an extremely shallow underwater explosion of the ROKS Cheonan sinking in the Yellow Sea

We estimated the detonation depth and net explosive weight for a very shallow underwater explosion using cutoff frequencies and spectral analysis. With detonation depth and a bubble pulse the net explosive weight for a shallow underwater explosion could simply be determined. The ray trace modeling confirms the detonation depth as a source of the hydroacoustic wave propagation in a shallow channel. We found cutoff frequencies of the reflection off the ocean bottom to be 8.5 Hz, 25 Hz, and 43 Hz while the cutoff frequency of the reflection off the free surface to be 45 Hz including 1.01 Hz for the bubble pulse, and also found the cutoff frequency of surface reflection to well fit the ray-trace modeling. We also attempted to corroborate our findings using a 3D bubble shape modeling and boundary element method. Our findings led us to the net explosive weight of the underwater explosion offshore of Baengnyeong-do for the ROKS Cheonan sinking to be approximately 136 kg TNT at a depth of about 8 m within an ocean depth of around 44 m. © 2015 Elsevier B.V.

Problems in the use of benzoic acid for estimating the internal pH of yeasts

Some yeasts have the peculiar ability to grow in the presence of weak acids at rather low pH. These conditions are predominant in preserved foods and beverages such as fruit concentrates, juices, wine, where these yeasts are responsible for spoilage. The main preservatives currently utilized by food industries are sorbic, propionic, benzoic acids and SO2. It is usually assumed that weak acids diffuse through the plasma membrane in the undissociated form. In the cytoplasm, where the pH is higher, dissociation occurs resulting in accumulation of the lipid-insoluble anion and internal acidification. This is probably a very general mechanism of preventing microbial growth in foods.