Abstract: The conservation of marine biodiversity keeps ecosystems in balance and ensures the sustainable use of resources. In this context, technological resources have been used for monitoring marine species to allow biologists to obtain data in real-time. There are different mobile applications developed for data collection for monitoring purposes, but these systems are designed to be utilized only on third-generation (3G) phones or smartphones with Internet access and in rural parts of the developing countries, Internet services and smartphones are scarce. Thus, the objective of this work is to develop a system to monitor marine turtles using Unstructured Supplementary Service Data (USSD), which users can access through basic mobile phones. The system aims to improve the data collection mechanism and enhance the effectiveness of current systems in monitoring sea turtles using any type of mobile device without Internet access. The system will be able to report information related to the biological activities of marine turtles. Also, it will be used as a platform to assist marine conservation entities to receive reports of illegal sales of sea turtles. The system can also be utilized as an educational tool for communities, providing knowledge and allowing the inclusion of communities in the process of monitoring marine turtles. Therefore, this work may contribute with information to decision-making and implementation of contingency plans for marine conservation programs.
Abstract: The three most important components in the cognitive architecture for cognitive robotics is memory representation, memory recall, and action-selection performed by the executive. In this paper, action selection, performed by the executive, is defined as a memory quantification and optimization process. The methodology describes the real-time construction of episodic memory through semantic memory optimization. The optimization is performed by set-based particle swarm optimization, using an adaptive entropy memory quantification approach for fitness evaluation. The performance of the approach is experimentally evaluated by simulation, where a UAV is tasked with the collection and delivery of a medical package. The experiments show that the UAV dynamically uses the episodic memory to autonomously control its velocity, while successfully completing its mission.
Abstract: Internet of Health Things (IoHT) has already proved to be a persuasive means to support a proper assessment of the living conditions by collecting a huge variety of data. For a customized health management of a senior patient, IoHT provides the capacity to build a dynamic solution for sustaining the shift inside the patient-physician relationship by allowing a real-time and continuous remote monitoring of the health status, well-being, safety and activities of the senior, especially in a non-clinical environment. Thus, is created a win-win solution in which both the patient and the physician enhance their involvement and shared decision-making, with significant outcomes. Health monitoring systems in smart environments are becoming a viable alternative to traditional healthcare solutions. The ongoing “Non-invasive monitoring and health assessment of the elderly in a smart environment (RO-SmartAgeing)” project aims to demonstrate that the existence of complete and accurate information is critical for assessing the health condition of the seniors, improving wellbeing and quality of life in relation to health. The researches performed inside the project aim to highlight how the management of IoHT devices connected to the RO-SmartAgeing platform in a secure way by using a role-based access control system, can allow the physicians to provide health services at a high level of efficiency and accessibility, which were previously only available in hospitals. The project aims to identify deficient aspects in the provision of health services tailored to a senior patient’s specificity and to offer a more comprehensive perspective of proactive and preventive medical acts.
Abstract: The information of the impact cutting behavior of plants stalk plays an important role in the design and fabrication of plants cutting equipment. It is difficult to investigate a theoretical method for defining cutting properties of plants stalks because the cutting process is complex. Thus, it is necessary to set up an experimental approach to determine cutting parameters for a single stalk. To measure the shear force, shear energy and shear strength of plant stalk, a special impact cutting tester was fabricated. It was similar to an Izod impact cutting tester for metals but a cutting blade and data acquisition system were attached to the end of pendulum's arm. The apparatus was included four strain gages and a digital indicator to show the real-time cutting force of plant stalk. To measure the shear force and also testing the apparatus, two plants’ stalks, like buxus and privet, were selected. The samples (buxus and privet stalks) were cut under impact cutting process at four loading rates 1, 2, 3 and 4 m.s-1 and three internodes fifth, tenth and fifteenth by the apparatus. At buxus cutting analysis: the minimum value of cutting energy was obtained at fifth internode and loading rate 4 m.s-1 and the maximum value of shear energy was obtained at fifteenth internode and loading rate 1 m.s-1. At privet cutting analysis: the minimum value of shear consumption energy was obtained at fifth internode and loading rate: 4 m.s-1 and the maximum value of shear energy was obtained at fifteenth internode and loading rate: 1 m.s-1. The statistical analysis at both plants showed that the increase of impact cutting speed would decrease the shear consumption energy and shear strength. In two scenarios, the results showed that with increase the cutting speed, shear force would decrease.
Abstract: In general, issues related to design and maintenance are considered in an independent manner. However, the decisions made in these two sets influence each other. The design for maintenance is considered an opportunity to optimize the life cycle cost of a product, particularly in the nuclear or aeronautical field, where maintenance expenses represent more than 60% of life cycle costs. The design of large-scale systems starts with product architecture, a choice of components in terms of cost, reliability, weight and other attributes, corresponding to the specifications. On the other hand, the design must take into account maintenance by improving, in particular, real-time monitoring of equipment through the integration of new technologies such as connected sensors and intelligent actuators. We noticed that different approaches used in the Design For Maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and maintainability of a multi-component system. This article proposes a method of DFM that assists designers to propose dynamic maintenance for multi-component industrial systems. The term "dynamic" refers to the ability to integrate available monitoring data to adapt the maintenance decision in real time. The goal is to maximize the availability of the system at a given life cycle cost. This paper presents an approach for simultaneous optimization of the design and maintenance of multi-component systems. Here the design is characterized by four decision variables for each component (reliability level, maintainability level, redundancy level, and level of monitoring data). The maintenance is characterized by two decision variables (the dates of the maintenance stops and the maintenance operations to be performed on the system during these stops). The DFM model helps the designers choose technical solutions for the large-scale industrial products. Large-scale refers to the complex multi-component industrial systems and long life-cycle, such as trains, aircraft, etc. The method is based on a two-level hybrid algorithm for simultaneous optimization of design and maintenance, using genetic algorithms. The first level is to select a design solution for a given system that considers the life cycle cost and the reliability. The second level consists of determining a dynamic and optimal maintenance plan to be deployed for a design solution. This level is based on the Maintenance Free Operating Period (MFOP) concept, which takes into account the decision criteria such as, total reliability, maintenance cost and maintenance time. Depending on the life cycle duration, the desired availability, and the desired business model (sales or rental), this tool provides visibility of overall costs and optimal product architecture.
Abstract: Artificial Intelligence (AI) has the potential to transform
manufacturing by improving shop floor processes such as production,
maintenance and quality. However, industrial datasets are notoriously
difficult to extract in a real-time, streaming fashion thus, negating
potential AI benefits. The main example is some specialized industrial
controllers that are operated by custom software which complicates
the process of connecting them to an Information Technology (IT)
based data acquisition network. Security concerns may also limit
direct physical access to these controllers for data acquisition.
To connect the Operational Technology (OT) data stored in these
controllers to an AI application in a secure, reliable and available
way, we propose a novel Industrial IoT (IIoT) solution in this paper.
In this solution, we demonstrate how video cameras can be installed
in a factory shop floor to continuously obtain images of the controller
HMIs. We propose image pre-processing to segment the HMI into
regions of streaming data and regions of fixed meta-data. We then
evaluate the performance of multiple Optical Character Recognition
(OCR) technologies such as Tesseract and Google vision to recognize
the streaming data and test it for typical factory HMIs and realistic
lighting conditions. Finally, we use the meta-data to match the OCR
output with the temporal, domain-dependent context of the data to
improve the accuracy of the output. Our IIoT solution enables reliable
and efficient data extraction which will improve the performance of
subsequent AI applications.
Abstract: A need exists to identify the sources of risks associated with the process automation systems within petrochemical companies or similar energy related industries. These companies use many different process automation technologies in its value chain. A crucial part of the process automation system is the information technology component featuring in the supervisory control layer. The ever-changing technology within the process automation layers and the rate at which it advances pose a risk to safe and predictable automation system performance. The age of the automation equipment also provides challenges to the operations and maintenance managers of the plant due to obsolescence and unavailability of spare parts. The main objective of this research was to determine the risk sources associated with the equipment that is part of the process automation systems. A secondary objective was to establish whether technology managers and technicians were aware of the risks and share the same viewpoint on the importance of the risks associated with automation systems. A conceptual model for risk sources of automation systems was formulated from models and frameworks in literature. This model comprised six categories of risk which forms the basis for identifying specific risks. This model was used to develop a questionnaire that was sent to 172 instrument technicians and technology managers in the company to obtain primary data. 75 completed and useful responses were received. These responses were analyzed statistically to determine the highest risk sources and to determine whether there was difference in opinion between technology managers and technicians. The most important risks that were revealed in this study are: 1) the lack of skilled technicians, 2) integration capability of third-party system software, 3) reliability of the process automation hardware, 4) excessive costs pertaining to performing maintenance and migrations on process automation systems, and 5) requirements of having third-party communication interfacing compatibility as well as real-time communication networks.
Abstract: Communication signal modulation recognition
technology is one of the key technologies in the field of modern
information warfare. At present, communication signal automatic
modulation recognition methods are mainly divided into two major
categories. One is the maximum likelihood hypothesis testing method
based on decision theory, the other is a statistical pattern recognition
method based on feature extraction. Now, the most commonly used
is a statistical pattern recognition method, which includes feature
extraction and classifier design. With the increasingly complex
electromagnetic environment of communications, how to effectively
extract the features of various signals at low signal-to-noise ratio
(SNR) is a hot topic for scholars in various countries. To solve this
problem, this paper proposes a feature extraction algorithm for the
communication signal based on the improved Holder cloud feature.
And the extreme learning machine (ELM) is used which aims at
the problem of the real-time in the modern warfare to classify
the extracted features. The algorithm extracts the digital features
of the improved cloud model without deterministic information in
a low SNR environment, and uses the improved cloud model to
obtain more stable Holder cloud features and the performance of the
algorithm is improved. This algorithm addresses the problem that
a simple feature extraction algorithm based on Holder coefficient
feature is difficult to recognize at low SNR, and it also has a
better recognition accuracy. The results of simulations show that the
approach in this paper still has a good classification result at low
SNR, even when the SNR is -15dB, the recognition accuracy still
reaches 76%.
Abstract: With the rapid development of modern communication,
diagnosing the fiber-optic quality and faults in real-time is widely
focused. In this paper, a Labview-based system is proposed for
fiber-optic faults detection. The wavelet threshold denoising method
combined with Empirical Mode Decomposition (EMD) is applied to
denoise the optical time domain reflectometer (OTDR) signal. Then
the method based on Gabor representation is used to detect events.
Experimental measurements show that signal to noise ratio (SNR)
of the OTDR signal is improved by 1.34dB on average, compared
with using the wavelet threshold denosing method. The proposed
system has a high score in event detection capability and accuracy.
The maximum detectable fiber length of the proposed Labview-based
system can be 65km.
Abstract: The paper presents a methodology for real-time structural health monitoring and geophysical applications. The key elements of the system are a high performance MIMO RADAR sensor, an optical camera and a dedicated set of software algorithms encompassing interferometry, tomography and photogrammetry. The MIMO Radar sensor proposed in this work, provides an extremely high sensitivity to displacements making the system able to react to tiny deformations (up to tens of microns) with a time scale which spans from milliseconds to hours. The MIMO feature of the system makes the system capable of providing a set of two-dimensional images of the observed scene, each mapped on the azimuth-range directions with noticeably resolution in both the dimensions and with an outstanding repetition rate. The back-scattered energy, which is distributed in the 3D space, is projected on a 2D plane, where each pixel has as coordinates the Line-Of-Sight distance and the cross-range azimuthal angle. At the same time, the high performing processing unit allows to sense the observed scene with remarkable refresh periods (up to milliseconds), thus opening the way for combined static and dynamic structural health monitoring. Thanks to the smart TX/RX antenna array layout, the MIMO data can be processed through a tomographic approach to reconstruct the three-dimensional map of the observed scene. This 3D point cloud is then accurately mapped on a 2D digital optical image through photogrammetric techniques, allowing for easy and straightforward interpretations of the measurements. Once the three-dimensional image is reconstructed, a 'repeat-pass' interferometric approach is exploited to provide the user of the system with high frequency three-dimensional motion/vibration estimation of each point of the reconstructed image. At this stage, the methodology leverages consolidated atmospheric correction algorithms to provide reliable displacement and vibration measurements.
Abstract: Resistance exercise bands are a core component of any physical activity strengthening program. Strength training can mitigate the development of sarcopenia, the loss of muscle mass or strength and function with aging. Yet, the adherence of such behavioral exercise strategies in a home-based setting are fraught with issues of monitoring and compliance. Our group developed a Bluetooth-enabled resistance exercise band capable of transmitting data to an open-source platform. In this work, we developed an application to capture this information in real-time, and conducted three usability studies in two mixed-aged groups of participants (n=6 each) and a group of older adults with obesity participating in a weight-loss intervention (n=20). The system was favorable, acceptable and provided iterative information that could assist in future deployment on ubiquitous platforms. Our formative work provides the foundation to deliver home-based monitoring interventions in a high-risk, older adult population.
Abstract: It is important for a commander to have real-time information to aware situations and to make decision in the battlefield. Results of modern technique developments have brought in this kind of information for military purposes. Unmanned aerial vehicle (UAV) is one of the means to gather intelligence owing to its widespread applications. It is still not clear whether or not the mini UAV with short-range wireless transmission system is used as a reconnaissance system in Taiwanese. In this paper, previous experience on the research of the sort of aerial vehicles has been applied with a data-relay system using the ZigBee modulus. The mini UAV developed is expected to be able to collect certain data in some appropriate theaters. The omni-directional antenna with high gain is also integrated into mini UAV to fit the size-reducing trend of airborne sensors. Two advantages are so far obvious. First, mini UAV can fly higher than usual to avoid being attacked from ground fires. Second, the data will be almost gathered during all maneuvering attitudes.
Abstract: The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.
Abstract: After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.
Abstract: Efficiency of the cooling process for cryogenic
propellant boiling in engine cooling channels on space applications is
relentlessly affected by the phase change occurs during the boiling.
The effectiveness of the cooling process strongly pertains to the
type of the boiling regime such as nucleate and film. Geometric
constraints like a non-transparent cooling channel unable to use
any of visualization methods. The ultrasonic (US) technique as a
non-destructive method (NDT) has therefore been applied almost
in every engineering field for different purposes. Basically, the
discontinuities emerge between mediums like boundaries among
different phases. The sound wave emitted by the US transducer is
both transmitted and reflected through a gas-liquid interface which
makes able to detect different phases. Due to the thermal and
structural concerns, it is impractical to sustain a direct contact
between the US transducer and working fluid. Hence the transducer
should be located outside of the cooling channel which results in
additional interfaces and creates ambiguities on the applicability
of the present method. In this work, an exploratory research is
prompted so as to determine detection ability and applicability of
the US technique on the cryogenic boiling process for a cooling
cycle where the US transducer is taken place outside of the channel.
Boiling of the cryogenics is a complex phenomenon which mainly
brings several hindrances for experimental protocol because of
thermal properties. Thus substitute materials are purposefully selected
based on such parameters to simplify experiments. Aside from
that, nucleate and film boiling regimes emerging during the boiling
process are simply simulated using non-deformable stainless steel
balls, air-bubble injection apparatuses and air clearances instead
of conducting a real-time boiling process. A versatile detection
algorithm is perennially developed concerning exploratory studies
afterward. According to the algorithm developed, the phases can be
distinguished 99% as no-phase, air-bubble, and air-film presences.
The results show the detection ability and applicability of the US
technique for an exploratory purpose.
Abstract: This paper introduces a framework to digitalize lean manufacturing tools to enhance smart lean-based manufacturing environments or Lean 4.0 manufacturing systems. The paper discusses the integration between lean tools and the powerful features of recent real-time data capturing systems with the help of Information and Communication Technologies (ICT) to develop an intelligent real-time monitoring and controlling system of production operations concerning lean targets. This integration is represented in the Lean 4.0 system called Dynamic Value Stream Mapping (DVSM). Moreover, the paper introduces the practice of Radio Frequency Identification (RFID) and ICT to smartly support lean tools and practices during daily production runs to keep the lean system alive and effective. This work introduces a practical description of how the lean method tools 5S, standardized work, and poka-yoke can be digitalized and smartly monitored and controlled through DVSM. A framework of the three tools has been discussed and put into practice in a German switchgear manufacturer.
Abstract: Society demands more reliable manufacturing processes
capable of producing high quality products in shorter production
cycles. New control algorithms have been studied to satisfy this
paradigm, in which Fault-Tolerant Control (FTC) plays a significant
role. It is suitable to detect, isolate and adapt a system when a harmful
or faulty situation appears. In this paper, a general overview about
FTC characteristics are exposed; highlighting the properties a system
must ensure to be considered faultless. In addition, a research to
identify which are the main FTC techniques and a classification
based on their characteristics is presented in two main groups:
Active Fault-Tolerant Controllers (AFTCs) and Passive Fault-Tolerant
Controllers (PFTCs). AFTC encompasses the techniques capable of
re-configuring the process control algorithm after the fault has been
detected, while PFTC comprehends the algorithms robust enough
to bypass the fault without further modifications. The mentioned
re-configuration requires two stages, one focused on detection,
isolation and identification of the fault source and the other one in
charge of re-designing the control algorithm by two approaches: fault
accommodation and control re-design. From the algorithms studied,
one has been selected and applied to a case study based on an
industrial hydraulic-press. The developed model has been embedded
under a real-time validation platform, which allows testing the FTC
algorithms and analyse how the system will respond when a fault
arises in similar conditions as a machine will have on factory. One
AFTC approach has been picked up as the methodology the system
will follow in the fault recovery process. In a first instance, the fault
will be detected, isolated and identified by means of a neural network.
In a second instance, the control algorithm will be re-configured to
overcome the fault and continue working without human interaction.
Abstract: Unidentified Flying Objects (UFOs) have been an interesting topic for most enthusiasts and hence people all over the United States report such findings online at the National UFO Report Center (NUFORC). Some of these reports are a hoax and among those that seem legitimate, our task is not to establish that these events confirm that they indeed are events related to flying objects from aliens in outer space. Rather, we intend to identify if the report was a hoax as was identified by the UFO database team with their existing curation criterion. However, the database provides a wealth of information that can be exploited to provide various analyses and insights such as social reporting, identifying real-time spatial events and much more. We perform analysis to localize these time-series geospatial events and correlate with known real-time events. This paper does not confirm any legitimacy of alien activity, but rather attempts to gather information from likely legitimate reports of UFOs by studying the online reports. These events happen in geospatial clusters and also are time-based. We look at cluster density and data visualization to search the space of various cluster realizations to decide best probable clusters that provide us information about the proximity of such activity. A random forest classifier is also presented that is used to identify true events and hoax events, using the best possible features available such as region, week, time-period and duration. Lastly, we show the performance of the scheme on various days and correlate with real-time events where one of the UFO reports strongly correlates to a missile test conducted in the United States.
Abstract: Computacional recognition of sign languages aims to
allow a greater social and digital inclusion of deaf people through
interpretation of their language by computer. This article presents
a model of recognition of two of global parameters from sign
languages; hand configurations and hand movements. Hand motion
is captured through an infrared technology and its joints are built
into a virtual three-dimensional space. A Multilayer Perceptron
Neural Network (MLP) was used to classify hand configurations and
Dynamic Time Warping (DWT) recognizes hand motion. Beyond
of the method of sign recognition, we provide a dataset of
hand configurations and motion capture built with help of fluent
professionals in sign languages. Despite this technology can be
used to translate any sign from any signs dictionary, Brazilian
Sign Language (Libras) was used as case study. Finally, the model
presented in this paper achieved a recognition rate of 80.4%.
Abstract: Driven by the demand of intelligent monitoring in
rehabilitation centers or hospitals, a high accuracy real-time location
system based on UWB (ultra-wideband) technology was proposed.
The system measures precise location of a specific person, traces his
movement and visualizes his trajectory on the screen for doctors or
administrators. Therefore, doctors could view the position of the
patient at any time and find them immediately and exactly when
something emergent happens. In our design process, different
algorithms were discussed, and their errors were analyzed. In addition,
we discussed about a , simple but effective way of correcting the
antenna delay error, which turned out to be effective. By choosing the
best algorithm and correcting errors with corresponding methods, the
system attained a good accuracy. Experiments indicated that the
ranging error of the system is lower than 7 cm, the locating error is
lower than 20 cm, and the refresh rate exceeds 5 times per second. In
future works, by embedding the system in wearable IoT (Internet of
Things) devices, it could provide not only physical parameters, but
also the activity status of the patient, which would help doctors a lot in
performing healthcare.