Search results for: bypass diode.

Authors: Jorge Rodriguez-Guerra, Carlos Calleja, Aron Pujana, Iker Elorza, Ana Maria Macarulla

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Society demands more reliable manufacturing processes capable of producing high quality products in shorter production cycles. New control algorithms have been studied to satisfy this paradigm, in which Fault-Tolerant Control (FTC) plays a significant role. It is suitable to detect, isolate and adapt a system when a harmful or faulty situation appears. In this paper, a general overview about FTC characteristics are exposed; highlighting the properties a system must ensure to be considered faultless. In addition, a research to identify which are the main FTC techniques and a classification based on their characteristics is presented in two main groups: Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant Controllers (PFTCs). AFTC encompasses the techniques capable of re-configuring the process control algorithm after the fault has been detected, while PFTC comprehends the algorithms robust enough to bypass the fault without further modifications. The mentioned re-configuration requires two stages, one focused on detection, isolation and identification of the fault source and the other one in charge of re-designing the control algorithm by two approaches: fault accommodation and control re-design. From the algorithms studied, one has been selected and applied to a case study based on an industrial hydraulic-press. The developed model has been embedded under a real-time validation platform, which allows testing the FTC algorithms and analyse how the system will respond when a fault arises in similar conditions as a machine will have on factory. One AFTC approach has been picked up as the methodology the system will follow in the fault recovery process. In a first instance, the fault will be detected, isolated and identified by means of a neural network. In a second instance, the control algorithm will be re-configured to overcome the fault and continue working without human interaction.

Keywords: Fault-tolerant control, electro-hydraulic actuator, fault detection and isolation, control re-design, real-time.

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Authors: Siming Wang, Qing Ni, Yu Wu, Ruihai Xu, Hong Ye

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Porous electric heaters, compared to conventional electric heaters, exhibit excellent heating performance due to their large specific surface area. Porous electric heaters employ porous metallic materials or conductive porous ceramics as the heating element. The former attains a low heating power with a fixed current due to the low electrical resistivity of metal. Although the latter can bypass the inherent challenges of porous metallic materials, the fabrication process of the conductive porous ceramics is complicated and high cost. This work proposed a porous ceramic electric heater with dielectric honeycomb ceramic as a substrate and surface conductive coating as a heating element. The conductive coating was prepared by the sol-gel method using silica sol and methyl trimethoxysilane as raw materials and graphite powder as conductive fillers. The conductive mechanism and degradation reason of the conductive coating was studied by electrical resistivity and thermal stability analysis. The heating performance of the proposed heater was experimentally investigated by heating air and deionized water. The results indicate that the electron transfer is achieved by forming the conductive network through the contact of the graphite flakes. With 30 wt% of graphite, the electrical resistivity of the conductive coating can be as low as 0.88 Ω∙cm. The conductive coating exhibits good electrical stability up to 500 °C but degrades beyond 600 °C due to the formation of many cracks in the coating caused by the weight loss and thermal expansion. The results also show that the working medium has a great influence on the volume power density of the heater. With air under natural convection as the working medium, the volume power density attains 640.85 kW/m3, which can be increased by 5 times when using deionized water as the working medium. The proposed honeycomb ceramic electric heater has the advantages of the simple fabrication method, low cost, and high-volume power density, demonstrating great potential in the fluid heating field.

Keywords: Conductive coating, honeycomb ceramic electric heater, high specific surface area, high volume power density.

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Authors: A. U. Moreh, M. Momoh, H. N. Yahya, B. Hamza, I. G. Saidu, S. Abdullahi

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This work studies the effect of thickness on structural and electrical properties of CuAlS2 thin films grown by two stage vacuum thermal evaporation technique. CuAlS2 thin films of thicknesses 50nm, 100nm and 200nm were deposited on suitably cleaned corning 7059 glass substrate at room temperature (RT). In the first stage Cu-Al precursors were grown at room temperature by thermal evaporation and in the second stage Cu-Al precursors were converted to CuAlS2 thin films by sulfurisation under sulfur atmosphere at the temperature of 673K. The structural properties of the films were examined by X-ray diffraction (XRD) technique while electrical properties of the specimens were studied using four point probe method. The XRD studies revealed that the films are of crystalline in nature having tetragonal structure. The variations of the micro-structural parameters, such as crystallite size (D), dislocation density ( ), and micro-strain ( ), with film thickness were investigated. The results showed that the crystallite sizes increase as the thickness of the film increases. The dislocation density and micro-strain decreases as the thickness increases. The resistivity (  ) of CuAlS2 film is found to decrease with increase in film thickness, which is related to the increase of carrier concentration with film thickness. Thus thicker films exhibit the lowest resistivity and high carrier concentration, implying these are the most conductive films. Low electrical resistivity and high carrier concentration are widely used as the essential components in various optoelectronic devices such as light-emitting diode and photovoltaic cells.

Keywords: Crystalline, CuAlS2, evaporation, resistivity, sulfurisation, thickness.

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: Metagenomics, phenotype prediction, deep learning, embeddings, multiple instance learning.

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Authors: A. M. Tarola, R. Preti, A. M. Girelli, P. Campana

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Safeguarding, studying and enhancing biodiversity play an important and indispensable role in re-launching agriculture. The ancient local varieties are therefore a precious resource for genetic and health improvement. In order to protect biodiversity through the recovery and valorization of autochthonous varieties, in this study we analyzed 12 samples of four ancient apple cultivars representative of Friuli Venezia Giulia, selected by local farmers who work on a project for the recovery of ancient apple cultivars. The aim of this study is to evaluate the polyphenolic profile and the antioxidant capacity that characterize the organoleptic and functional qualities of this fruit species, besides having beneficial properties for health. In particular, for each variety, the following compounds were analyzed, both in the skins and in the pulp: gallic acid, catechin, chlorogenic acid, epicatechin, caffeic acid, coumaric acid, ferulic acid, rutin, phlorizin, phloretin and quercetin to highlight any differences in the edible parts of the apple. The analysis of individual phenolic compounds was performed by High Performance Liquid Chromatography (HPLC) coupled with a diode array UV detector (DAD), the antioxidant capacity was estimated using an in vitro essay based on a Free Radical Scavenging Method and the total phenolic compounds was determined using the Folin-Ciocalteau method. From the results, it is evident that the catechins are the most present polyphenols, reaching a value of 140-200 μg/g in the pulp and of 400-500 μg/g in the skin, with the prevalence of epicatechin. Catechins and phlorizin, a dihydrohalcone typical of apples, are always contained in larger quantities in the peel. Total phenolic compounds content was positively correlated with antioxidant activity in apple pulp (r² = 0,850) and peel (r² = 0,820). Comparing the results, differences between the varieties analyzed and between the edible parts (pulp and peel) of the apple were highlighted. In particular, apple peel is richer in polyphenolic compounds than pulp and flavonols are exclusively present in the peel. In conclusion, polyphenols, being antioxidant substances, have confirmed the benefits of fruit in the diet, especially as a prevention and treatment for degenerative diseases. They demonstrated to be also a good marker for the characterization of different apple cultivars. The importance of protecting biodiversity in agriculture was also highlighted through the exploitation of native products and ancient varieties of apples now forgotten.

Keywords: Apple, biodiversity, polyphenols, antioxidant activity, HPLC-DAD, characterization.

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Authors: Tatsuya Kasuga, Hidehisa Shimada, Kimio Oguchi

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Recent rapid progress in ICT (Information and Communication Technology) has advanced the penetration of sensor networks (SNs) and their attractive applications. Agriculture is one of the fields well able to benefit from ICT. Plant factories control several parameters related to plant growth in closed areas such as air temperature, humidity, water, culture medium concentration, and artificial lighting by using computers and AI (Artificial Intelligence) is being researched in order to obtain stable and safe production of vegetables and medicinal plants all year anywhere, and attain self-sufficiency in food. By providing isolation from the natural environment, a plant factory can achieve higher productivity and safe products. However, the biggest issue with plant factories is the return on investment. Profits are tenuous because of the large initial investments and running costs, i.e. electric power, incurred. At present, LED (Light Emitting Diode) lights are being adopted because they are more energy-efficient and encourage photosynthesis better than the fluorescent lamps used in the past. However, further cost reduction is essential. This paper introduces experiments that reveal which color of LED lighting best enhances the growth of cultured radish sprouts. Radish sprouts were cultivated in the experimental environment formed by a hydroponics kit with three cultivation shelves (28 samples per shelf) each with an artificial lighting rack. Seven LED arrays of different color (white, blue, yellow green, green, yellow, orange, and red) were compared with a fluorescent lamp as the control. Lighting duration was set to 12 hours a day. Normal water with no fertilizer was circulated. Seven days after germination, the length, weight and area of leaf of each sample were measured. Electrical power consumption for all lighting arrangements was also measured. Results and discussions: As to average sample length, no clear difference was observed in terms of color. As regards weight, orange LED was less effective and the difference was significant (p < 0.05). As to leaf area, blue, yellow and orange LEDs were significantly less effective. However, all LEDs offered higher productivity per W consumed than the fluorescent lamp. Of the LEDs, the blue LED array attained the best results in terms of length, weight and area of leaf per W consumed. Conclusion and future works: An experiment on radish sprout cultivation under 7 different color LED arrays showed no clear difference in terms of sample size. However, if electrical power consumption is considered, LEDs offered about twice the growth rate of the fluorescent lamp. Among them, blue LEDs showed the best performance. Further cost reduction e.g. low power lighting remains a big issue for actual system deployment. An automatic plant monitoring system with sensors is another study target.

Keywords: Electric power consumption, LED color, LED lighting, plant factory.

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