Automatic detection of depth of cut during end milling operation using acoustic emission sensor

Any shortfall in the required depth during milling machining can affect the dimensional accuracy of the part produced and can cause a catastrophic failure to the machine. Corrective remedies to fix the dimensions inaccuracy will increase the machining time and costs. In this work, a depth-of-cut monitoring system was proposed to detect depth of cut in real time using an acoustic emission sensor and prediction model. The characteristics of the sensor signal obtained in machining processes can be complex in terms of both nonlinearity and nonstationarity. To overcome this complexity, a regression model and an artificial neural network model were used to represent the relationship between the acoustic emission signal and the depth of cut. The model was tested under different machining cases and found to be efficient in predicting the depth of cut.     

Why do fish strand? An analysis of ten years of flow reduction monitoring data from the Columbia and Kootenay rivers,Canada

Stranding of fish due to flow reductions has been documented in the near shore of the Columbia and Kootenay Rivers, Canada, and can result in sub‐lethal or lethal effects on fish. Ten years (1999–2009) of monitoring data have been collected at sites below two hydro‐electric dams (Hugh‐L‐Keenleyside and Brilliant Dam) following flow reductions. A generalized linear mixed effects model analysed the probability of a stranding event in relation to environmental and operational variables including the rate of change in the water levels, the duration of shoreline inundation prior to a reduction (wetted history), the river stage, the magnitude of the reduction, distance downstream from the dam, time of day, day of year (season) and whether a site had been physically altered to mitigate stranding. The results demonstrated statistically significant effects on stranding risk from minimum river stage, day of the year and whether a site had been physically re‐contoured. The combination of investigated factors giving the highest probability of stranding was a large magnitude reduction completed in the afternoon in midsummer, at low water levels when the near shore had been inundated for a long period. This research is significant in its approach to assessing years of ecosystem scale monitoring data and using the modelling results to determine ways for these findings to be applied in regulated river management to minimize fish stranding. It also highlighted data gaps that require addressing and provides ecosystem scale results to compare with stranding studies carried out in mesocosms. © 2014 The Authors. River Research and Applications published by John Wiley & Sons Ltd.     

Adaptive augmented Lagrangian methods: algorithms and practical numerical experience

In this paper, we consider augmented Lagrangian (AL) algorithms for solving large-scale nonlinear optimization problems that execute adaptive strategies for updating the penalty parameter. Our work is motivated by the recently proposed adaptive AL trust region method by Curtis et al. [An adaptive augmented Lagrangian method for large-scale constrained optimization, Math. Program. 152 (2015), pp. 201–245.]. The first focal point of this paper is a new variant of the approach that employs a line search rather than a trust region strategy, where a critical algorithmic feature for the line search strategy is the use of convexified piecewise quadratic models of the AL function for computing the search directions. We prove global convergence guarantees for our line search algorithm that are on par with those for the previously proposed trust region method. A second focal point of this paper is the practical performance of the line search and trust region algorithm variants in Matlab software, as well as that of an adaptive penalty parameter updating strategy incorporated into the Lancelot software. We test these methods on problems from the CUTEst and COPS collections, as well as on challenging test problems related to optimal power flow. Our numerical experience suggests that the adaptive algorithms outperform traditional AL methods in terms of efficiency and reliability. As with traditional AL algorithms, the adaptive methods are matrix-free and thus represent a viable option for solving large-scale problems.     

A deep bidirectional LSTM approach for video-realistic talking head

This paper proposes a deep bidirectional long short-term memory approach in modeling the long contextual, nonlinear mapping between audio and visual streams for video-realistic talking head. In training stage, an audio-visual stereo database is firstly recorded as a subject talking to a camera. The audio streams are converted into acoustic feature, i.e. Mel-Frequency Cepstrum Coefficients (MFCCs), and their textual labels are also extracted. The visual streams, in particular, the lower face region, are compactly represented by active appearance model (AAM) parameters by which the shape and texture variations can be jointly modeled. Given pairs of the audio and visual parameter sequence, a DBLSTM model is trained to learn the sequence mapping from audio to visual space. For any unseen speech audio, whether it is original recorded or synthesized by text-to-speech (TTS), the trained DBLSTM model can predict a convincing AAM parameter trajectory for the lower face animation. To further improve the realism of the proposed talking head, the trajectory tiling method is adopted to use the DBLSTM predicted AAM trajectory as a guide to select a smooth real sample image sequence from the recorded database. We then stitch the selected lower face image sequence back to a background face video of the same subject, resulting in a video-realistic talking head. Experimental results show that the proposed DBLSTM approach outperforms the existing HMM-based approach in both objective and subjective evaluations.     

Computing the Ramsey number R(4,3,3) using abstraction and symmetry breaking

The number R(4, 3, 3) is often presented as the unknown Ramsey number with the best chances of being found “soon”. Yet, its precise value has remained unknown for almost 50 years. This paper presents a methodology based on abstraction and symmetry breaking that applies to solve hard graph edge-coloring problems. The utility of this methodology is demonstrated by using it to compute the value R(4, 3, 3) = 30. Along the way it is required to first compute the previously unknown set \(\mathcal {R}(3,3,3;13)\) consisting of 78,892 Ramsey colorings.     

Infiltrating the zebrafish swarm: design,implementation and experimental tests of a miniature robotic fish lure for fish–robot interaction studies

Robotic fish are nowadays developed for various types of research, such as bio-inspiredrobotics, biomimetics and animal behavior studies. In the context of our research on the social interactions of the zebrafish Danio Rerio, we developed a miniature robotic fish lure for direct underwater interaction with the living fish. This remotely controlled and waterproof device has a total length of 7.5 cm with the same size ratio as zebrafish and is able to beat its tail with different frequencies and amplitudes, while following the group of living animals using a mobile robot moving outside water that is coupled with the robotic lure using magnets. The robotic lure is also equipped with a rechargeable battery and can be used autonomously underwater for experiments of up to 1 h. We performed experiments with the robot moving inside an aquarium with living fish to analyze its impact on the zebrafish behavior. We found that the beating rate of the tail increased the attractiveness of the lure among the zebrafish shoal. We also demonstrated that the lure could influence a collective decision of the zebrafish shoal, the swimming direction, when moving with a constant linear speed inside a circular corridor. This new robotic fish design and the experimental results are promising for the field of fish–robot interaction.     

Multigrid gradient vector flow computation on the GPU

Gradient vector flow (GVF) is a feature-preserving spatial diffusion of image gradients. It was introduced to overcome the limited capture range in traditional active contour segmentation. However, the original iterative solver for GVF, using Euler’s method, converges very slowly. Thus, many iterations are needed to achieve the desired capture range. Several groups have investigated the use of graphic processing units (GPUs) to accelerate the GVF computation. Still, this does not reduce the number of iterations needed. Multigrid methods, on the other hand, have been shown to provide a much better capture range using considerable less iterations. However, non-GPU implementations of the multigrid method are not as fast as the Euler method when executed on the GPU. In this paper, a novel GPU implementation of a multigrid solver for GVF written in OpenCL is presented. The results show that this implementation converges and provides a better capture range about 2–5 times faster than the conventional iterative GVF solver on the GPU.     

Adaptive water management in Israel: structure and policy options

Difficulties in integrating technical, economic and institutional factors present a major gap in analytical capacity to guide water policy. This article presents an integrated framework to support water policy and guide water management choices, with application to Israel. That framework rests on the theory of economic policy originally developed by Tinbergen. It sees national water challenges as consisting of external factors, constraints, policy instruments and targets. The need for a modern implementation of the theory of economic policy is motivated by emerging environmental requirements, scarce water, growing demands for domestic use, and ongoing needs to implement existing and potential peace agreements.     

SOA-enabled compliance management: instrumenting, assessing, and analyzing service-based business processes

Facilitating compliance management, that is, assisting a company’s management in conforming to laws, regulations, standards, contracts, and policies, is a hot but non-trivial task. The service-oriented architecture (SOA) has evolved traditional, manual business practices into modern, service-based IT practices that ease part of the problem: the systematic definition and execution of business processes. This, in turn, facilitates the online monitoring of system behaviors and the enforcement of allowed behaviors—all ingredients that can be used to assist compliance management on the fly during process execution. In this paper, instead of focusing on monitoring and runtime enforcement of rules or constraints, we strive for an alternative approach to compliance management in SOAs that aims at assessing and improving compliance. We propose two ingredients: (i) a model and tool to design compliant service-based processes and to instrument them in order to generate evidence of how they are executed and (ii) a reporting and analysis suite to create awareness of a company’s compliance state and to enable understanding why and where compliance violations have occurred. Together, these ingredients result in an approach that is close to how the real stakeholders—compliance experts and auditors—actually assess the state of compliance in practice and that is less intrusive than enforcing compliance.