Safe semi supervised multi-target regression (MTR-SAFER) for new targets learning

Multimedia Tools and Applications - Multi-target regression (MTR) is a challenging research problem which aims to predict more than one continuous variable as output in a pattern. In recent time, a...     

A Dynamic Maintenance Planning Framework Based on Fuzzy TOPSIS and FMEA: Application in an International Food Company

A maintenance planning framework is developed in this study to reduce and stabilize the maintenance costs of the manufacturing companies. The framework is based on fuzzy technique for order preference by similarity to ideal solution(TOPSIS) and failure mode and effects analysis (FMEA) techniques and supports maintenance planning decisions in a dynamic way. The proposed framework is general and can easily be adapted to a host of manufacturing environments in a variety of sectors. To determine the maintenance priorities of the machines, fuzzy TOPSIS technique is employed. In this regard, ‘risk priority number’ obtained by FMEA and ‘current technology’, ‘substitutability’, ‘capacity utilization’, and ‘contribution to profit’ are used as the criteria. Performance of the resulting maintenance plan is monitored, and maintenance priorities of the machines are updated by the framework. To confirm the viability of the proposed framework, a real‐world implementation in an international food company is presented. The results of the application reveal that the proposed maintenance planning framework can effectively and efficiently be used in practice. Copyright © 2015 John Wiley & Sons, Ltd.     

Controlling Congestion in Wireless Sensor Networks Through Imperialist Competitive Algorithm

Wireless Personal Communications - Congestion control is one of the most important in wireless sensor networks (WSNs) due to inherent limited resources. In WSNs congestion leads to the loss of...     

Lightly supervised alignment of subtitles on multi-genre broadcasts

This paper describes a system for performing alignment of subtitles to audio on multigenre broadcasts using a lightly supervised approach. Accurate alignment of subtitles plays a substantial role in the daily work of media companies and currently still requires large human effort. Here, a comprehensive approach to performing this task in an automated way using lightly supervised alignment is proposed. The paper explores the different alternatives to speech segmentation, lightly supervised speech recognition and alignment of text streams. The proposed system uses lightly supervised decoding to improve the alignment accuracy by performing language model adaptation using the target subtitles. The system thus built achieves the third best reported result in the alignment of broadcast subtitles in the Multi–Genre Broadcast (MGB) challenge, with an F1 score of 88.8%. This system is available for research and other non–commercial purposes through webASR, the University of Sheffield’s cloud–based speech technology web service. Taking as inputs an audio file and untimed subtitles, webASR can produce timed subtitles in multiple formats, including TTML, WebVTT and SRT.     

The novel functional nucleic acid iRed effectively regulates target genes following cytoplasmic delivery by faint electric treatment

An intelligent shRNA expression device (iRed) contains the minimum essential components needed for shRNA production in cells, and could be a novel tool to regulate target genes. However, general delivery carriers consisting of cationic polymers/lipids could impede function of a newly generated shRNA via electrostatic interaction in the cytoplasm. Recently, we found that faint electric treatment (fET) of cells enhanced delivery of siRNA and functional nucleic acids into the cytoplasm in the absence of delivery carriers. Here, we examined fET of cells stably expressing luciferase in the presence of iRed encoding anti-luciferase shRNA. Transfection of lipofectamine 2000 (LFN)/iRed lipoplexes showed an RNAi effect, but fET-mediated iRed transfection did not, likely because of the endosomal localization of iRed after delivery. However, fET in the presence of lysosomotropic agent chloroquine significantly improved the RNAi effect of iRed/fET to levels that were higher than those for the LFN/iRed lipoplexes. Furthermore, the amount of lipid droplets in adipocytes significantly decreased following fET with iRed against resistin in the presence of chloroquine. Thus, iRed could be a useful tool to regulate target genes following fET-mediated cytoplasmic delivery with endosomal escape devices.     

Analyzing Vulnerability of Asynchronous Pipeline to Soft Errors: Leveraging Formal Verification

Glitches due to the secondary neutron particles from cosmic rays cause soft errors in integrated circuits (IC) that are becoming a major threat in modern sub 45nm ICs. Therefore, researchers have developed many techniques to mitigate the soft errors and some of them utilize the built in error detection schemes of low-power asynchronous null conventional logic (NCL). However, it requires extensive simulations and emulations for careful and complete analysis of the design, which can be costly, time consuming and cannot encompass all the possible input conditions. In this paper, we propose a framework to improve the soft error tolerant asynchronous pipelines by identifying and formally analyzing the vulnerable paths using the nuXmv model checker. The proposed framework translates the design behavior and specification into a state-space model and the potential vulnerabilities against soft errors in the pipeline as linear temporal logical (LTL) properties. These formally specified properties are then verified on the state-space model and in case of failure counterexamples are obtained. These counterexamples can then be further analyzed to obtain the soft error propagation paths and thus give insights about soft error tolerant approaches to the designers. For illustration, this work provides an analysis and comparison of three state-of-the-art asynchronous pipelines. Formal model and analysis of all the pipelines show that the soft error hardened pipeline is comparatively superior against soft errors but at the expense of almost two times area overhead.     

Improving the laser cutting process design by machine learning techniques

In the field of manufacturing engineering, process designers conduct numerical simulation experiments to observe the impact of varying input parameters on certain outputs of the production process. The disadvantage of these simulations is that they are very time consuming and their results do not help to fully understand the underlying process. For instance, a common problem in planning processes is the choice of an appropriate machine parameter set that results in desirable process outputs. One way to overcome this problem is to use data mining techniques that extract previously unknown but valuable knowledge from simulation results. This paper presents a hybrid machine learning approach for applying clustering and classification techniques in a laser cutting planning process. In a first step, a clustering algorithm is used to divide large parts of the simulation data into groups of similar performance values and select those groups that are of major interest (e.g. high cut quality results). Next, classification trees are used to identify regions in the multidimensional parameter space that are related to the found groups. The evaluation shows that the models accurately identify multidimensional relationships between the input parameters and the output values of the process. In addition to that, a combination of appropriate visualization techniques for clustering with interpretable classification trees allows designers to gain valuable insights into the laser cutting process with the aim of optimizing it through visual exploration.     

Towards Probabilistic Formal Analysis of SATS-Simultaneously Moving Aircraft (SATS-SMA)

The objective of NASA’s Small Aircraft Transportation System (SATS) Concept of Operations (ConOps) is to facilitate high volume operation of advanced small aircraft operating in non-towered, non-radar airports. This system can provide improved and accessible air travel at a lower cost. Given the safety-critical nature of SATS, its analysis accuracy is extremely important. However, the commonly used analysis techniques, like pilot/computer simulation and traditional model checking, do not ascertain an error-free and complete verification of SATS due to the wide range of possibilities involved in SATS or the inability to capture the randomized and unpredictable aspects of the SATS ConOps environment in their models. Another limitation of these studies is that a limited speed range was used in the analysis. To overcome these limitations, we propose to formulate the SATS ConOps as a fully synchronous and probabilistic model, i.e., SATS-SMA, that supports simultaneously moving aircraft. The distinguishing features of our work include the preservation of safety of aircraft while providing a precise timing model, which is closer to reality compared to the previous hybrid analyses. Important insights related to the aircraft take-off and landing operations during the instrument meteorological conditions are also presented.