Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

An optimization model for guiding the petrochemical industry development in Saudi Arabia

A mixed integer linear programming model is formulated for determining the optimum plan for the expansion of the Saudi Arabian petrochemical industry. The products selected for consideration fall into four categories: propylene derivatives, ethylene derivatives, synthesis gas derivatives, and aromatic derivatives. The model incorporates new variables and constraints, and realistic estimates of production costs, which are calculated based on local conditions in Saudi Arabia. For each production process, the unit production cost is assumed to be a function of production capacity. The input data for each product includes relevant production technologies, capacities, local production costs, and selling price. The solution of the model gives the recommended products under different scenarios of available capital investment and feedstock. The results are reported and analyzed.     

Energy-efficient and fault-tolerant drone-BS placement in heterogeneous wireless sensor networks

Wireless Networks - This paper introduces a distributed and energy-aware algorithm, called Minimum Drone Placement (MDP) algorithm, to determine the minimum number of base stations mounted on...     

Magnetron sputtered Dy_2O_3 with chromium and copper contents for antireflective thin films with enhanced absorption

Dy_2O_3 is a rare earth oxide having a number of advanced applications in various fields including protective or antireflective coatings, Main objective of this novel research work is to check the effect of Cr and Cu addition on different properties of Dy_2O_3 and achievement of antireflective thin films with enhanced abso rption. Thin films of these materials we re deposited using DC magnetron with reactive cosputtering. XRD studies reveals the crystalline nature of thin films having Dy_2O_3(222) reflection in all samples with Cr_2O_3(116) and CuO(111) reflections in Cr and Cu containing compositions. Field emission scanning electron microscopy demonstrates the homogeneous deposition of thin films with uniform shape, size and distribution of grains. Refractive index, extinction coefficient and absorption coefficient significantly increase while optical reflectance decreases with Cr and Cu mediation corroborating an improved antireflective mechanism. The imaginary part of dielectric constant is found to increase slightly with low tangent loss for Cr containing composition co nsidered favorable for energy storage applications.     

Strained fermented milks – A review of existing legislative provisions,survey of nutritional labelling of commercial products in selected markets and terminology of products in some selected countries

A survey of the information contained in the labels of 109 commercial concentrated fermented milks from the Eastern Mediterranean, Australasia, the United Kingdom, Ireland, the United States of America and Canada plus Skyr (from Iceland), Ymer (from Denmark) and Chakka (from India) was carried out in late 2012 and early 2013. There were substantial differences in composition. The carbohydrate, fat and protein contents ranged between 1–12, 0–20 and 3.3–11 g/100 g, respectively. Considering the compositional data of the product and existing legislative provisions, a typical strained product should have a protein content of ≥8 g/100 and ~5 g/100 g of carbohydrates, and it would be appropriate to name such products as ‘strained yoghurt’, ‘Greek strained yoghurt’ and/or ‘Labneh’. A higher content of carbohydrate would suggest that the product was made using the product formulation method (i.e. without straining the fermentate), or the milk base had been fortified with skimmed milk before straining the fermentate to enhance the yield of the product, and such products should be known, for example, as ‘Greek‐style yoghurt’. It was also observed in some of the samples that the nomenclature of the starter cultures did not conform to recommendations of the International Union of Microbiological Societies (IUMS). Manufacturers could address such issues to minimise consumer confusion.     

Machine learning based optimized live virtual machine migration over WAN links

Live virtual machine migration is one of the most promising features of data center virtualization technology. Numerous strategies have been proposed for live migration of virtual machines on local area networks. These strategies work perfectly in their respective domains with negligible downtime. However, these techniques are not suitable to handle live migration over wide area networks and results in significant downtime. In this paper we have proposed a Machine Learning based Downtime Optimization (MLDO) approach which is an adaptive live migration approach based on predictive mechanisms that reduces downtime during live migration over wide area networks for standard workloads. The main contribution of our work is to employ machine learning methods to reduce downtime. Machine learning methods are also used to introduce automated learning into the predictive model and adaptive threshold levels. We compare our proposed approach with existing strategies in terms of downtime observed during the migration process and have observed improvements in downtime of up to 15 %.     

Enhancing speed of SIMON: A light-weight-cryptographic algorithm for IoT applications

Multimedia Tools and Applications - Multimedia communication is revolutionizing all major spheres of human life. The advent of IoT and its applications in many fields like sensing, healthcare and...     

Design and development of an innovative individualized adaptive and intelligent e-learning system for teaching–learning of probability unit: Details of UZWEBMAT

In this study, an innovative adaptive and intelligent web based e-learning system, UZWEBMAT (Turkish abbreviation of Adaptive and INtelligent WEB based MAThematics teaching–learning system) was designed, developed and implemented. This e-learning system was intended for learning and teaching secondary school level permutation-combination-binomial expansion and probability subjects. Content which was prepared according to Turkish curriculum for secondary school mathematics course was transformed into learning objects in three different ways in accordance with VAK (Visual–Auditory–Kinesthetic) learning styles. Primary/secondary/tertiary learning styles of learners registering the system are determined and each learner receives the content appropriate for his/her dominant learning style. Also, they can be directed to contents of other styles according to their performances thanks to an expert system. Learning objects constituting the content were prepared according to constructivist approach. An active role for the learner was the purpose. Tips and intelligent solution supports within the learning objects were presented with expert system support to the learners. With this structure, UZWEBMAT bears the characteristics of intelligent tutoring system as well as an adaptive e-learning environment. All the movements of learners studying with UZWEBMAT are recorded and the necessary information is reported to both learners and teachers in a visualized way.     

Determination of palaeotectonic and neotectonic features around the Menderes Massif and the Gediz Graben (western Turkey) using Landsat TM image

Western Turkey contains both spectacular palaeotectonic and neotectonic features of the Anatolian block. The first of these features is the Menderes Massif, a huge metamorphic terrain comprising various lithologies; the second is the Gediz Graben, one of the most well‐known extensional features that formed during the neotectonic period of Turkey's complex geological history. In the study area, approximately E–W‐trending Neogene grabens, such as the Gediz, obliquely subdivide palaeotectonic massifs (such as the NE–SW‐oriented Menderes Massif) and mélange rocks. The terms Menderes Massif and Gediz Graben indicate different products of different tectonic regimes.

In this study, lithological components of the region were determined using a Landsat Thematic Mapper (TM) image. Band‐ratioing and principal component analysis (PCA) were chosen for lithological discrimination of the outcropping geological units. After processing, PCA gave the best results in terms of lithological differentiation. Furthermore, certain band‐ratio colour composites (5/7, 5/4, 4/1) are sensitive to lithological differences in RGB (red, green and blue) space and thus provide a general understanding of the distribution of rock‐forming minerals, such as known hydroxyl‐bearing and ferric iron minerals, as well as the vegetative characteristics of the region. However, a structural‐analysis study, including visual inspection and edge‐enhancement techniques, played a complementary role in the geological analysis of the region. Outcrops of the Menderes Massif metamorphic rocks, mélange rocks and Neogene cover associated with the Gediz Graben are favourable for remote sensing studies in so far as they allow ease of interpretation and geological evaluation by researchers. One of the most notable results derived from this study was the discrimination of younger neotectonic, fluviatile occurrences from the palaeotectonic Menderes Massif metamorphic rocks and Izmir–Ankara Suture Zone mélange rocks. Additionally, boundaries of the active Gediz Graben have been delineated.