Explosive Burning of a Mechanically Activated Al and CuO Thermite Mixture

The results of experiments to determine the role of structural schemes for the ignition of a mechanically activated thermite mixture Al–CuO and the formation of its combustion flame are presented. The reaction initiated in the bulk of the experimental assembly transforms into torch combustion in an open space. The dynamics of the volume of the flame reaction region was determined. The stage of flame formation has a stochastic character, determined by the random distribution of the reaction centres in the initial volume of the components. A high-speed camera, a pyrometer and electro contact sensors were used as diagnostic tools. The ultimate goal of the study was to optimize the conditions for the flame formation of this mixture for its effective use with a single ignition of various gas emissions.

Design and Implementation of a Smart Home in a Box to Monitor the Wellbeing of Residents With Dementia in Care Homes

There is a global challenge related to the increasing number of People with Dementia (PwD) and the diminishing capacity of governments, health systems, and caregivers to provide the best care for them. Cost-effective technology solutions that enable and ensure a good quality of life for PwD via monitoring and interventions have been investigated comprehensively in the literature. The objective of this study was to investigate the challenges with the design and deployment of a Smart Home In a Box (SHIB) approach to monitoring PwD wellbeing within a care home. This could then support future SHIB implementations to have an adequate and prompt deployment allowing research to focus on the data collection and analysis aspects. An important consideration was that most care homes do not have the appropriate infrastructure for installing and using ambient sensors. The SHIB was evaluated via installation in the rooms of PwD with varying degrees of dementia at Kirk House Care Home in Belfast. Sensors from the SHIB were installed to test their capabilities for detecting Activities of Daily Living (ADLs). The sensors used were: (i) thermal sensors, (ii) contact sensors, (iii) Passive Infrared (PIR) sensors, and (iv) audio level sensors. Data from the sensors were collected, stored, and handled using a ‘SensorCentral’ data platform. The results of this study highlight challenges and opportunities that should be considered when designing and implementing a SHIB approach in a dementia care home. Lessons learned from this investigation are presented in addition to recommendations that could support monitoring the wellbeing of PwD. The main findings of this study are: (i) most care home buildings were not originally designed to appropriately install ambient sensors, and (ii) installation of SHIB sensors should be adapted depending on the specific case of the care home where they will be installed. It was acknowledged that in addition to care homes, the homes of PwD were also not designed for an appropriate integration with ambient sensors. This study provided the community with useful lessons, that will continue to be applied to improve future implementations of the SHIB approach.

A novel autonomous strategy for multi-bolt looseness detection using smart glove and Siamese double-path CapsNet

Recently, the issue of bolt looseness has attracted more attention due to its severe consequences. Among different methods for bolt looseness detection, the active sensing method that is based on stress wave signals is preferred since it is low cost and high robust. However, current active sensing method depends on permanent contact sensors, which may be impractical. Moreover, the investigation of multi-bolt looseness detection via the active sensing is very limited so far. With the above deficiency in mind, we propose a new robotic-assisted active sensing method based on our newly designed PZT-enabled smart gloves (SGs) and position-based visual servoing (PBVS) technique. Particularly, another main contribution is that we develop a new Siamese CapsNet to classify stress wave signals under different cases for multi-bolt looseness detection. Compared to machine learning (ML) and traditional deep learning techniques such as Convolutional Neural Networks (CNN), the proposed Siamese CapsNet model can achieve better performance and realize the recognition of signals that is never used during the training, which is impossible for common classification methods. Finally, an experiment is conducted to verify the effectiveness of the proposed method and Siamese CapsNet, which can guide future research significantly.

High-Efficiency Multi-Sensor System for Chair Usage Detection

Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%.

Noninvasive real-time breath test for controlling hormonal background of the human body: Detection of serotonin and melatonin with quantum point-contact sensors

Significant progress in development of noninvasive diagnostic tools based on breath analysis can be expected if one employs a real-time detection method based on finding a spectral breath profile which would contain some energy characteristics of the analyzed gas mixture. Using the fundamental energy parameters of a quantum system, it is possible to determine with a high accuracy its quantitative and qualitative composition. Among the most efficient tools to measure energy characteristics of quantum systems are sensors based on Yanson point contacts. This paper reports the results of serotonin and melatonin detection as an example of testing the human hormonal background with point-contact sensors, which have already demonstrated their high efficiency in detecting carcinogenic strains of Helicobacter pylori and selective detection of complex gas mixtures. When comparing the values of serotonin and melatonin with the characteristic parameters of the spectral profile of the exhaled breath of each patient, high correlation dependences of the concentration of serotonin and melatonin with a number of characteristic parameters of the response curve of the point-contact sensor were found. The performed correlation analysis was complemented with the regression analysis. As a result, empiric regression relations were proposed to realize in practice the new non-invasive breath test for evaluation of the human hormonal background. Registration of the patient’s breath profile using point-contact sensors makes it possible to easily monitor the dynamics of changes in the human hormonal background and perform a quantitative evaluation of serotonin and melatonin levels in the human body in real time without invasive interventions (blood collection) and expensive equipment or reagents.

Analysis of the vibro-acoustic data from test rig -comparison of acoustic and vibrational methods

Detection of damage is a significant issue in providing efficiency and safety in industrial processes. In underground mining much research effort is made for developing an automatic system of diagnosing the machinery using robots. One of the major groups of equipment utilized and maintained in the mines is rotating machinery. Local damage occurring in such machines commonly have a cyclostationary character in short term as any change in their characteristics is expected to repeat periodically. In most cases they can be easily detected based on vibration signals measured with contact sensors (accelerometers). However if mobile robots such as UAV (unmanned aerial vehicles) are planned to be used, remote measurement is firmly preferred. In this paper we compare vibrational detection with a novel approach based on analysing an acoustic signal recorded by a microphone.

Balancing inverted pendulum cart on inclines using accelerometers

The balance of inverted pendulum on inclined surfaces is the precursor to their control in unstructured environments. Researchers have devised control algorithms with feedback from contact (encoders - placed at the pendulum joint) and non-contact (gyroscopes, tilt) sensors. We present feedback control of Inverted Pendulum Cart (IPC) on variable inclines using non-contact sensors and a modified error function. The system is in the state of equilibrium when it is not accelerating and not falling over (rotational equilibrium). This is achieved when the pendulum is aligned along the gravity vector. The control feedback is obtained from non-contact sensors comprising of a pair of accelerometers placed on the inverted pendulum and one on the cart. The proposed modified error function is composed of the dynamic (non-gravity) acceleration of the pendulum and the velocity of the cart. We prove that the system is in equilibrium when the modified error is zero. We present algorithm to calculate the dynamic acceleration and angle of the pendulum, and incline angle using accelerometer readings. Here, the cart velocity and acceleration are assumed to be proportional to the motor angular velocity and acceleration. Thereafter, we perform simulation using noisy sensors to illustrate the balance of IPC on surfaces with unknown inclination angles using PID feedback controller with saturated motor torque, including valley profile that resembles a downhill, flat and uphill combination. The successful control of the system using the proposed modified error function and accelerometer feedback argues for future design of controllers for unstructured and unknown environments using all-accelerometer feedback.

Thermal camera reliability study : FLIR Pro One

The purpose of the effort described herein is to verify the reliability of the FLIR One Pro Gen 3 (FLIR One) unit through systematic experiments that compare the temperature perceived by the unit to the temperature measured by contact sensors on different materials through a range of temperatures.