Critical heat flux experiments in a heated rod bundle with upward crossflow of Freon 114

Critical heat flux (CHF) data were obtained for upward crossflow of R-114 in a heated staggered rod bundle. Data were obtained over a broad range of mass fluxes (135 to 1,221 kg/m{sup 2} sec), inlet subcooling (0 to 55 C), and qualities ({minus}0.42 to 0.92). The present work extends the available database to higher quality, inlet subcooling, and mass flux. The test section is 3.43 cm x 15.24 cm (1.35 in. x 6 in.) in cross section with a total length of 55.88 cm (22 inches) from the top of the inlet flow straightener to the perforated plate at the test section exit. The rod bundle has a triangular pitch with a diameter (D) of 0.635 cm (0.25 in), and a pitch to diameter (P/D) ratio of 1.5. The rod bundle has 165 rods with a 15.24 cm (6 in.) heated length arranged in 55 rows of three rods each. Unheated half rods were positioned on the walls of the test section to maintain the regular rod arrangement and prevent flow bypass along the gaps between the window and the first column of heated rods. A single instrumented heater was positioned five rows upstream from the bundle exit to determine CHF. The last three rows of rods in the bundle were unheated to prevent undetected dryout downstream of the CHF position. Temperature excursions due to CHF were sensed using four imbedded thermocouples (TC) in the heater rod. The four TC temperatures were continuously monitored on a strip chart recorder. The rod heat was gradually increased until CHF was detected. Overall, the data are in good agreement with the Jensen and Tang correlation in the range of application of this correlation. The local minima in CHF which occurs near zero quality is slightly lower in the present experiment than for the Jensen and Tang correlation. At high quality, CHF drops off more rapidly than the Jensen-Tang prediction. Data are now available to extend the existing correlations to higher quality, and higher inlet subcooling

Restoring Speech Following Total Removal of the Larynx

By speech articulator movement and training a transformation to audio we can restore the power of speech to someone who has lost their larynx. We sense changes in magnetic field caused by movements of small magnets attached to the lips and tongue. The sensor transformation uses recurrent neural networks

ChunkitApp : Investigating the relevant units of online speech processing

Peer reviewe

Direct Speech Reconstruction From Articulatory Sensor Data by Machine Learning

This paper describes a technique that generates speech acoustics from articulator movements. Our motivation is to help people who can no longer speak following laryngectomy, a procedure that is carried out tens of thousands of times per year in the Western world. Our method for sensing articulator movement, permanent magnetic articulography, relies on small, unobtrusive magnets attached to the lips and tongue. Changes in magnetic field caused by magnet movements are sensed and form the input to a process that is trained to estimate speech acoustics. In the experiments reported here this “Direct Synthesis” technique is developed for normal speakers, with glued-on magnets, allowing us to train with parallel sensor and acoustic data. We describe three machine learning techniques for this task, based on Gaussian mixture models, deep neural networks, and recurrent neural networks (RNNs). We evaluate our techniques with objective acoustic distortion measures and subjective listening tests over spoken sentences read from novels (the CMU Arctic corpus). Our results show that the best performing technique is a bidirectional RNN (BiRNN), which employs both past and future contexts to predict the acoustics from the sensor data. BiRNNs are not suitable for synthesis in real time but fixed-lag RNNs give similar results and, because they only look a little way into the future, overcome this problem. Listening tests show that the speech produced by this method has a natural quality that preserves the identity of the speaker. Furthermore, we obtain up to 92% intelligibility on the challenging CMU Arctic material. To our knowledge, these are the best results obtained for a silent-speech system without a restricted vocabulary and with an unobtrusive device that delivers audio in close to real time. This work promises to lead to a technology that truly will give people whose larynx has been removed their voices back

Stress Corrosion Crack Detection on HU-25 Guardian Aircraft

Several ultrasonic inspection methods were developed at the Federal Aviation Administration's Airworthiness Assurance NDI Validation Center (AANC) to easily and rapidly detect hidden stress corrosion cracks in all vertical windshield posts on the US Coast Guard (USCG) HU-25 Guardian aircraft. The inspection procedure locates cracks as small as 2.0 millimeters emanating from internal fastener holes and determines their length. A test procedure was developed and a baseline assessment of the USCG fleet was conducted. Inspection results on twenty-five aircraft revealed a good correlation with results made during subsequent structural disassembly and visual inspection

Evaluating Lifeworld as an emancipatory methodology

Disability research is conducted within a highly politicised ‘hotbed’ of competing paradigms and principles. New researchers, who want to work within the social model, are soon faced with complex and challenging methodological and philosophical dilemmas. The social model advocates research agendas that are focused on the emancipation and empowerment of disabled people but, in reality, these are rarely achieved. To be successful researchers need to engage with innovative and creative methodologies and to share their experiences of these within environments that welcome challenge and debate. This paper focuses on Lifeworld and assesses its value as a tool for emancipatory research. Using examples from a study with parents, whose children were in the process of being labelled as having autism, the paper illustrates how the principles that ‘underpin’ the methodology offered a supportive framework for a novice researcher

Towards an intraoral-based silent speech restoration system for post-laryngectomy voice replacement

© Springer International Publishing AG 2017,Silent Speech Interfaces (SSIs) are alternative assistive speech technologies that are capable of restoring speech communication for those individuals who have lost their voice due to laryngectomy or diseases affecting the vocal cords. However, many of these SSIs are still deemed as impractical due to a high degree of intrusiveness and discomfort, hence limiting their transition to outside of the laboratory environment. We aim to address the hardware challenges faced in developing a practical SSI for post-laryngectomy speech rehabilitation. A new Permanent Magnet Articulography (PMA) system is presented which fits within the palatal cavity of the user’s mouth, giving unobtrusive appearance and high portability. The prototype is comprised of a miniaturized circuit constructed using commercial off-the-shelf (COTS) components and is implemented in the form of a dental retainer, which is mounted under roof of the user’s mouth and firmly clasps onto the upper teeth. Preliminary evaluation via speech recognition experiments demonstrates that the intraoral prototype achieves reasonable word recognition accuracy and is comparable to the external PMA version. Moreover, the intraoral design is expected to improve on its stability and robustness, with a much improved appearance since it can be completely hidden inside the user’s mouth

A silent speech system based on permanent magnet articulography and direct synthesis

In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies

Voice restoration after laryngectomy based on magnetic sensing of articulator movement and statistical articulation-to-speech conversion

© Springer International Publishing AG 2017.In this work, we present a silent speech system that is able to generate audible speech from captured movement of speech articulators. Our goal is to help laryngectomy patients, i.e. patients who have lost the ability to speak following surgical removal of the larynx most frequently due to cancer, to recover their voice. In our system, we use a magnetic sensing technique known as Permanent Magnet Articulography (PMA) to capture the movement of the lips and tongue by attaching small magnets to the articulators and monitoring the magnetic field changes with sensors close to the mouth. The captured sensor data is then transformed into a sequence of speech parameter vectors from which a time-domain speech signal is finally synthesised. The key component of our system is a parametric transformation which represents the PMA-tospeech mapping. Here, this transformation takes the form of a statistical model (a mixture of factor analysers, more specifically) whose parameters are learned from simultaneous recordings of PMA and speech signals acquired before laryngectomy. To evaluate the performance of our system on voice reconstruction, we recorded two PMA-and-speech databases with different phonetic complexity for several non-impaired subjects. Results show that our system is able to synthesise speech that sounds as the original voice of the subject and also is intelligible. However, more work still need to be done to achieve a consistent synthesis for phonetically-rich vocabularies