The 30-kW ammonia arcjet technology

The technical results are summarized of a 30 kW class ammonia propellant arcjet technology program. Evaluation of previous arcjet thruster performance, including materials analysis of used thruster components, led to the design of an arcjet with improved performance and thermal characteristics. Tests of the new engine demonstrated that engine performance is relatively insensitive to cathode tip geometry. Other data suggested a maximum sustainable arc length for a given thruster configuration, beyond which the arc may reconfigure in a destructive manner. A flow controller calibration error was identified. This error caused previously reported values of specific impulse and thrust efficiency to be 20 percent higher than the real values. Corrected arcjet performance data are given. Duration tests of 413 and 252 hours, and several tests 100 hours in duration, were performed. The cathode tip erosion rate increased with increasing arc current. Elimination of power source ripple did not affect cathode tip whisker growth. Results of arcjet modeling, diagnostic development and mission analyses are also discussed. The 30 kW ammonia arcjet may now be considered ready for development for a flight demonstration, but widespread application of 30 kW class arcjet will require improved efficiency and lifetime

Vibrotactile Signal Generation from Texture Images or Attributes using Generative Adversarial Network

Providing vibrotactile feedback that corresponds to the state of the virtual texture surfaces allows users to sense haptic properties of them. However, hand-tuning such vibrotactile stimuli for every state of the texture takes much time. Therefore, we propose a new approach to create models that realize the automatic vibrotactile generation from texture images or attributes. In this paper, we make the first attempt to generate the vibrotactile stimuli leveraging the power of deep generative adversarial training. Specifically, we use conditional generative adversarial networks (GANs) to achieve generation of vibration during moving a pen on the surface. The preliminary user study showed that users could not discriminate generated signals and genuine ones and users felt realism for generated signals. Thus our model could provide the appropriate vibration according to the texture images or the attributes of them. Our approach is applicable to any case where the users touch the various surfaces in a predefined way.Comment: accepted for EuroHaptics 2018: Haptics: Science, Technology, and Applications, pp.25-3

Neural Networks for Information Retrieval

Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of information available can be overwhelming both for junior students and for experienced researchers looking for new research topics and directions. Additionally, it is interesting to see what key insights into IR problems the new technologies are able to give us. The aim of this full-day tutorial is to give a clear overview of current tried-and-trusted neural methods in IR and how they benefit IR research. It covers key architectures, as well as the most promising future directions.Comment: Overview of full-day tutorial at SIGIR 201

Geometric deep learning

The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics. The text in the course materials is primarily based on previously published work. With these notes we gather and provide a clear picture of the key concepts and techniques that fall under the umbrella of geometric deep learning, and illustrate the applications they enable. We also aim to provide practical implementation details for the methods presented in these works, as well as suggest further readings and extensions of these ideas

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

The multidisciplinary, theory-based co-design of a new digital health intervention supporting the care of oesophageal cancer patients

Objective: Oesophageal cancer patients have complex care needs. Cancer clinical nurse specialists play a key role in coordinating their care but often have heavy workloads. Digital health interventions can improve patient care but there are few examples for oesophageal cancer. This paper aims to describe the multidisciplinary co-design process of a digital health intervention to improve the experience of care and reduce unmet needs among patients with oesophageal cancer. Methods: A theory-based, multi-disciplinary, co-design approach was used to inform the developmental process of the digital health intervention. Key user needs were elicited using mixed methodology from systematic reviews, focus groups and interviews and holistic need assessments. Overarching decisions were discussed among a core team of patients, carers, health care professionals including oncologists and cancer clinical nurse specialists, researchers and digital health providers. A series of workshops incorporating a summary of findings of key user needs resulted in the development of a minimum viable product. This was further refined after a pilot study based on feedback from end users. Results: The final digital health intervention consists of a mobile app feature for patients and carers connected to a dashboard with supporting additional features for clinical nurse specialist. It contains a one-way messaging function for clinical nurse specialists to communicate with patients, functions for patients to record weight and holistic need assessment results which could be viewed by their clinical nurse specialists as well as a library of informative articles. Conclusions: The multidisciplinary co-design of a digital health intervention providing support for oesophageal cancer patients and health care professionals has been described. Future studies to establish its impact on patient outcomes are planned

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Health Care Practitioners’ Determinants of Telerehabilitation Acceptance

Background: Pulmonary rehabilitation is a multidisciplinary patient-tailored intervention that aims to improve the physical and psychological condition of people with chronic respiratory diseases. Providing pulmonary rehabilitation (PR) services to the growing population of patients is challenging due to shortages in health care practitioners and pulmonary rehabilitation programs. Telerehabilitation has the potential to address this shortage in practitioners and PR programs as well as improve patients’ participation and adherence. This study’s purpose was to identify and evaluate the influences of intention of health care practitioners to use telerehabilitation. Methods: Data were collected through a self-administered Internet-based survey. Results: Surveys were completed by 222 health care practitioners working in pulmonary rehabilitation with 79% having a positive intention to use telerehabilitation. Specifically, perceived usefulness was a significant individual predictor of positive intentions to use telerehabilitation. Conclusion: Perceived usefulness may be an important factor associated with health care providers’ intent to use telerehabilitation for pulmonary rehabilitation

Development and Validation of the Tele-Pulmonary Rehabilitation Acceptance Scale

BACKGROUND: Using telehealth in pulmonary rehabilitation (telerehabilitation) is a new field of health-care practice. To successfully implement a telerehabilitation program, measures of acceptance of this new type of program need to be assessed among potential users. The purpose of this study was to develop a scale to measure acceptance of using telerehabilitation by health-care practitioners and patients. METHODS: Three objectives were met (a) constructing a modified scale of the technology acceptance model, (b) judging the items for content validity, and (c) judging the scale for face validity. Nine experts agreed to participate and evaluate item relevance to theoretical definitions of domains. To establish face validity, 7 health-care practitioners and 5 patients were interviewed to provide feedback about the scale's clarity and ease of reading. RESULTS: The final items were divided into 2 scales that reflected the health-care practitioner and patient responses. Each scale included 3 subscales: perceived usefulness, perceived ease of use, and behavioral intention. CONCLUSIONS: The 2 scales, each with 3 subscales, exhibited evidence of content validity and face validity. The 17-item telerehabilitation acceptance scale for health-care practitioners and the 13-item telerehabilitation acceptance scale among patients warrant further psychometric testing as valuable measures for pulmonary rehabilitation programs

Real-time embedded intelligence system : emotion recognition on Raspberry Pi with Intel NCS

Convolutional Neural Networks (CNNs) have exhibited certain human-like performance on computer vision related tasks. Over the past few years since they have outperformed conventional algorithms in a range of image processing problems. However, to utilise a CNN model with millions of free parameters on a source limited embedded system is a challenging problem. The Intel Neural Compute Stick (NCS) provides a possible route for running largescale neural networks on a low cost, low power, portable unit. In this paper, we propose a CNN based Raspberry Pi system that can run a pre-trained inference model in real time with an average power consumption of 6.2W. The Intel Movidius NCS, which avoids requirements of expensive processing units e.g. GPU, FPGA. The system is demonstrated using a facial image-based emotion recogniser. A fine-tuned CNN model is designed and trained to perform inference on each captured frame within the processing modules of NCS