Energy inefficiency diagnosis in industrial process through one-class machine learning techniques

In the era of Industry 4.0, the ease of access to precise measurements in real-time and the existence of machine-learning (ML) techniques will play a vital role in building practical tools to isolate inefficiencies in energy-intensive processes. This paper aims at developing an abnormal event diagnosis (AED) tool based on ML techniques for monitoring the operation of industrial processes. This tool makes it easier for operators to accomplish their tasks and to make quick and accurate decisions to ensure highly efficient processes. One of the most popular ML techniques for AED is the multivariate statistical control (MSC) method; it only requires the dataset of the normal operating conditions (NOC) to detect and identify the variables that contribute to abnormal events (AEs). Despite the popularity of MSC, it is challenging to select the appropriate method for detecting and isolating all possible abnormalities a complex industrial process can experience. To address this limitation and improve efficiency, we have developed a generic methodology that integrates different ML techniques into a unified multiagent based approach, the selected ML techniques are supposed to be built using only the normal operating condition. For the sake of demonstration, we chose a combination of two ML methods: principal component analysis and k-nearest neighbors (k-NN). The k-NN was integrated into the proposed multiagent to take into account the nonlinearity and multimodality that frequently occur in industrial processes. In addition, we modified a k-NN method proposed in the literature to reduce computation time during real-time detection and isolation. Finally, the proposed methodology was successfully validated to monitor the energy efficiency of a reboiler located in a thermomechanical pulp mill.

     

Feature Selection and Extraction Methods for Power Systems Transient Stability Assessment Employing Computational Intelligence Techniques

One of the important aspects in achieving better performance for transient stability assessment (TSA) of power systems employing computational intelligence (CI) techniques is by incorporating feature reduction techniques. For small power system the number of features may be small but when larger systems are considered the number of features increased as the size of the systems increases. Apart from employing faster CI techniques to achieve faster and accurate TSA of power system, feature reduction techniques are needed in reducing the input features while preserving the needed information so as to make faster training of the CI technique. This paper presents feature reductions techniques used, namely correlation analysis and principle component analysis, in reducing number of input features presented to two CI techniques for TSA, namely probabilistic neural network (PNN) and least squares support vector machines (LS-SVM). The proposed feature reduction techniques are implemented and tested on the IEEE 39-bus test system and 87-bus Malaysia’s power system. Numerical results are presented to demonstrate the performance of the feature reduction techniques and its effects on the accuracies and time taken for training the two CI techniques.     

RFID-based interactive multimedia system for the children

In this paper, we present an interactive edutainment system for the children that leverages multimedia and RFID technologies in a seamless manner. The proposed system allows children to learn about new objects/entities by tapping on physical objects through a specially designed RFID-Bluetooth based Tangible User Interface (TUI) tool. The output of the system is delivered as a set of appropriate multimedia representations related to the objects being tapped. The TUI uses RFID technology for object identification and Bluetooth communication to transmit data to the computer where the system??s software is running. We incorporated our system in three games that allow children of different ages to benefit from the system??s functionalities and encourage them to interact with it.     

A secure cookie scheme

Cookies are the primary means for web applications to authenticate HTTP requests and to maintain client states. Many web applications (such as those for electronic commerce) demand a secure cookie scheme. Such a scheme needs to provide the following four services: authentication, confidentiality, integrity, and anti-replay. Several secure cookie schemes have been proposed in previous literature; however, none of them are completely satisfactory. In this paper, we propose a secure cookie scheme that is effective, efficient, and easy to deploy. In terms of effectiveness, our scheme provides all of the above four security services. In terms of efficiency, our scheme does not involve any database lookup or public key cryptography. In terms of deployability, our scheme can be easily deployed on existing web services, and it does not require any change to the Internet cookie specification. We implemented our secure cookie scheme using PHP and conducted experiments. The experimental results show that our scheme is very efficient on both the client side and the server side.A notable adoption of our scheme in industry is that our cookie scheme has been used by Wordpress since version 2.4. Wordpress is a widely used open source content management system.     

Environmental-aware virtual data center network

Cloud computing services have recently become a ubiquitous service delivery model, covering a wide range of applications from personal file sharing to being an enterprise data warehouse. Building green data center networks providing cloud computing services is an emerging trend in the Information and Communication Technology (ICT) industry, because of Global Warming and the potential GHG emissions resulting from cloud services. As one of the first worldwide initiatives provisioning ICT services entirely based on renewable energy such as solar, wind and hydroelectricity across Canada and around the world, the GreenStar Network (GSN) was developed to dynamically transport user services to be processed in data centers built in proximity to green energy sources, reducing Greenhouse Gas (GHG) emissions of ICT equipments. Regarding the current approach, which focuses mainly in reducing energy consumption at the micro-level through energy efficiency improvements, the overall energy consumption will eventually increase due to the growing demand from new services and users, resulting in an increase in GHG emissions. Based on the cooperation between Mantychore FP7 and the GSN, our approach is, therefore, much broader and more appropriate because it focuses on GHG emission reductions at the macro-level. This article presents some outcomes of our implementation of such a network model, which spans multiple green nodes in Canada, Europe and the USA. The network provides cloud computing services based on dynamic provision of network slices through relocation of virtual data centers.     

Filtering for bilinear systems with a lipschitz nonlinearity using LPV approach

This paper deals with the H _?? filtering problem for a class of nonlinear systems. This class of nonlinear systems is composed of a bilinear part and of a lipschitzian one. The use of an unbiasedness condition for the bilinear part (called quasi unbiasedness condition) permits to parameterize the filter matrices through a single gain. Two LPV (Linear Parameter Varying) extensions of the bounded real lemma are used to solve the filtering problem. This approach reduces the conservatism inherent to the boundedness of the inputs. Then the filtering solution is expressed in terms of LMI (Linear Matrix Inequality) to be verified at the vertices of a polytope. A numerical example is finally given to illustrate our approach.     

Experimental and Established Oximes as Pretreatment before Acute Exposure to Azinphos-Methyl

Poisoning with organophosphorus compounds (OPCs) represents an ongoing threat to civilians and rescue personal. We have previously shown that oximes, when administered prophylactically before exposure to the OPC paraoxon, are able to protect from its toxic effects. In the present study, we have assessed to what degree experimental (K-27; K-48; K-53; K-74; K-75) or established oximes (pralidoxime, obidoxime), when given as pretreatment at an equitoxic dosage of 25% of LD₀₁, are able to reduce mortality induced by the OPC azinphos-methyl. Their efficacy was compared with that of pyridostigmine, the only FDA-approved substance for such prophylaxis. Efficacy was quantified in rats by Cox analysis, calculating the relative risk of death (RR), with RR=1 for the reference group given only azinphos-methyl, but no prophylaxis. All tested compounds significantly (p ≤ 0.05) reduced azinphos-methyl-induced mortality. In addition, the efficacy of all tested experimental and established oximes except K-53 was significantly superior to the FDA-approved compound pyridostigmine. Best protection was observed for the oximes K-48 (RR = 0.20), K-27 (RR = 0.23), and obidoxime (RR = 0.21), which were significantly more efficacious than pralidoxime and pyridostigmine. The second-best group of prophylactic compounds consisted of K-74 (RR = 0.26), K-75 (RR = 0.35) and pralidoxime (RR = 0.37), which were significantly more efficacious than pyridostigmine. Pretreatment with K-53 (RR = 0.37) and pyridostigmine (RR = 0.52) was the least efficacious. Our present data, together with previous results on other OPCs, indicate that the experimental oximes K-27 and K-48 are very promising pretreatment compounds. When penetration into the brain is undesirable, obidoxime is the most efficacious prophylactic agent already approved for clinical use.     

Corrosion inhibition of carbon steel in hydrochloric acid by cationic arylthiophenes as new eco-friendly inhibitors: Experimental and quantum chemical study

This study describes the adsorption behavior of three arylthiophene derivatives namely: 2-(4-amidino-3-fluorophenyl)-5-[4-methoxy phenyl] thiophene dihydrochloride salt (MA-1217), 2-(4-amidinophenyl)-5-[4-chlorophenyl] thiophene dihydrochloride salt (MA-1316) and 2-(4-amidino-3-fluorophenyl)-5-[4-chlorophenyl]thiophene dihydrochloride salt (MA-1312) at C-steel in 1.0 mol·L^-1 HCl interface using experimental and theoretical studies. Electrochemical and mass loss measurements showed that the inhibition efficiency (IE) of the arylthiophene derivatives increases with increasing concentrations and exhibited maximum efficiency 89% at 21×10^-6 mol·L^-1 (MA-1217) by mass loss method. The investigated arylthiophene derivatives obey the Langmuir adsorption isotherm. From polarization studies the arylthiophene derivatives act as mixed-type inhibitors. Surface analysis were carried out and discussed. The mode of orientation and adsorption of inhibitor molecules on C-steel surface was studied using molecular dynamics (MD) simulations. Quantum chemical parameters as well as the radial distribution function indices and binding energies confirm the experimental results.     

The Multifunctional Sactipeptide Ruminococcin C1 Displays Potent Antibacterial Activity In Vivo as Well as Other Beneficial Properties for Human Health

The world is on the verge of a major antibiotic crisis as the emergence of resistant bacteria is increasing, and very few novel molecules have been discovered since the 1960s. In this context, scientists have been exploring alternatives to conventional antibiotics, such as ribosomally synthesized and post-translationally modified peptides (RiPPs). Interestingly, the highly potent in vitro antibacterial activity and safety of ruminococcin C1, a recently discovered RiPP belonging to the sactipeptide subclass, has been demonstrated. The present results show that ruminococcin C1 is efficient at curing infection and at protecting challenged mice from Clostridium perfringens with a lower dose than the conventional antibiotic vancomycin. Moreover, antimicrobial peptide (AMP) is also effective against this pathogen in the complex microbial community of the gut environment, with a selective impact on a few bacterial genera, while maintaining a global homeostasis of the microbiome. In addition, ruminococcin C1 exhibits other biological activities that could be beneficial for human health, as well as other fields of applications. Overall, this study, by using an in vivo infection approach, confirms the antimicrobial clinical potential and highlights the multiple functional properties of ruminococcin C1, thus extending its therapeutic interest.