Abstract: The present study proposes a methodology for the efficient daily management of fleet vehicles and construction machinery. The application covers the area of remote monitoring of heavy-duty vehicles operation parameters, where specific sensor data are stored and examined in order to provide information about the vehicle’s health. The vehicle diagnostics allow the user to inspect whether maintenance tasks need to be performed before a fault occurs. A properly designed machine learning model is proposed for the detection of two different types of faults through classification. Cross validation is used and the accuracy of the trained model is checked with the confusion matrix.
Abstract: Electrocardiographic (ECG) machine is an important equipment to diagnose heart problems. Besides, the ECG signals are used to detect many other features of human body and behavior. But it is not so cheap and simple in operation to be used in the countries like Bangladesh, where most of the people are very low income earners. Therefore, in this paper, we have tried to implement a simple and portable ECG machine. Since Arduino Uno microcontroller is very cheap, we have used it in our system to minimize the cost. Our designed system is powered by a 2-voltage level DC power supply. It provides wireless connectivity to have ECG data either in smartphone having android operating system or a PC/laptop having Windows operating system. To get the data, a graphic user interface has been designed. Android application has also been made using IDE for Android 2.3 and API 10. Since it requires no USB host API, almost 98% Android smartphones, available in the country, will be able to use it. We have calculated the heart rate from the measured ECG by our designed machine and by an ECG machine of a reputed diagnostic center in Dhaka city for the same people at the same time on same day. Then we calculated the percentage of errors between the readings of two machines and computed its average. From this computation, we have found out that the average percentage of error is within an acceptable limit.
Abstract: Constant potometer is an important component of pipeline anti-corrosion systems in the chemical industry. Based on Internet of Things (IoT) technology, Programmable Logic Controller (PLC) technology and database technology, this paper developed a set of a constant potometer remote monitoring management system. The remote monitoring and remote adjustment of the working status of the constant potometer are realized. The system has real-time data display, historical data query, alarm push management, user permission management, and supporting Web access and mobile client application (APP) access. The actual engineering project test results show the stability of the system, which can be widely used in cathodic protection systems.
Abstract: Electrical energy demand in the World and particularly in India, is increasing drastically more than its production over a period of time. In order to reduce the demand-supply gap, conserving energy becomes mandatory. Induction motors are the main driving force in the industries and contributes to about half of the total plant energy consumption. By effective monitoring and control of induction motors, huge electricity can be saved. This paper deals about the design and development of such a system, which employs iLON Smart Server and motor performance monitoring nodes. These nodes will monitor the performance of induction motors on-line, on-site and in-situ in the industries. The node monitors the performance of motors by simply measuring the electrical power input and motor shaft speed; coupled to genetic algorithm to estimate motor efficiency. The nodes are connected to the iLON Server through RS485 network. The web server collects the motor performance data from nodes, displays online, logs periodically, analyzes, alerts, and generates reports. The system could be effectively used to operate the motor around its Best Operating Point (BOP) as well as to perform the Life Cycle Assessment of Induction motors used in the industries in continuous operation.
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: Autonomous vehicles offer the possibility of significant benefits to social welfare. However, fully automated cars might not be going to happen in the near further. To speed the adoption of the self-driving technologies, many governments worldwide are passing laws requiring data recorders for the testing of autonomous vehicles. Currently, the self-driving vehicle, (e.g., shuttle bus) has to be monitored from a remote control center. When an autonomous vehicle encounters an unexpected driving environment, such as road construction or an obstruction, it should request assistance from a remote operator. Nevertheless, large amounts of data, including images, radar and lidar data, etc., have to be transmitted from the vehicle to the remote center. Therefore, this paper proposes a data compression method of in-vehicle networks for remote monitoring of autonomous vehicles. Firstly, the time-series data are rearranged into a multi-dimensional signal space. Upon the arrival, for controller area networks (CAN), the new data are mapped onto a time-data two-dimensional space associated with the specific CAN identity. Secondly, the data are sampled based on differential sampling. Finally, the whole set of data are encoded using existing algorithms such as Huffman, arithmetic and codebook encoding methods. To evaluate system performance, the proposed method was deployed on an in-house built autonomous vehicle. The testing results show that the amount of data can be reduced as much as 1/7 compared to the raw data.
Abstract: Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.
Abstract: This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.
Abstract: Electrical distribution networks are prone to power quality disturbances originating from the complexity of the distribution network, mode of distribution (overhead or underground) and types of loads used by customers. Data on the types of disturbances present and frequency of occurrence is needed for economic evaluation and hence finding solution to the problem. Utility companies have resorted to using secondary power quality devices such as smart meters to help gather the required data. Even though this approach is easier to adopt, data gathered from these devices may not serve the required purpose, since the installation of these devices in the electrical network usually does not conform to available PQM placement methods. This paper presents a design of a PQM that is capable of integrating into an existing DAS infrastructure to take advantage of available placement methodologies. The monitoring component of the design is implemented and installed to monitor an existing LV network. Data from the monitor is analyzed and presented. A portion of the LV network of the Electricity Company of Ghana is modeled in MATLAB-Simulink and analyzed under various earth fault conditions. The results presented show the ability of the PQM to detect and analyze PQ disturbance such as voltage sag and overvoltage. By adopting a placement methodology and installing these nodes, utilities are assured of accurate and reliable information with respect to the quality of power delivered to consumers.
Abstract: Nowadays, illegal logging has been causing many
effects including flash flood, avalanche, global warming, and etc. The
purpose of this study was to maintain the earth ecosystem by keeping
and regulate Malaysia’s treasurable rainforest by utilizing a new
technology that will assist in real-time alert and give faster response
to the authority to act on these illegal activities. The methodology of
this research consisted of design stages that have been conducted as
well as the system model and system architecture of the prototype in
addition to the proposed hardware and software that have been
mainly used such as microcontroller, sensor with the implementation
of GSM, and GPS integrated system. This prototype was deployed at
Royal Belum forest in December 2014 for phase 1 and April 2015 for
phase 2 at 21 pinpoint locations. The findings of this research were
the capture of data in real-time such as temperature, humidity,
gaseous, fire, and rain detection which indicate the current natural
state and habitat in the forest. Besides, this device location can be
detected via GPS of its current location and then transmitted by SMS
via GSM system. All of its readings were sent in real-time for further
analysis. The data that were compared to meteorological department
showed that the precision of this device was about 95% and these
findings proved that the system is acceptable and suitable to be used
in the field.
Abstract: In new energy development, wind power has boomed.
It is due to the proliferation of wind parks and their operation in
supplying the national electric grid with low cost and clean resources.
Hence, there is an increased need to establish a proactive
maintenance for wind turbine machines based on remote control and
monitoring. That is necessary with a real-time wireless connection in
offshore or inaccessible locations while the wired method has many
flaws. The objective of this strategy is to prolong wind turbine
lifetime and to increase productivity. The hardware of a remote
control and monitoring system for wind turbine parks is designed. It
takes advantage of GPRS or Wi-Max wireless module to collect data
measurements from different wind machine sensors through IP based
multi-hop communication. Computer simulations with Proteus ISIS
and OPNET software tools have been conducted to evaluate the
performance of the studied system. Study findings show that the
designed device is suitable for application in a wind park.
Abstract: This paper describes an effective solution to the task
of a remote monitoring of super-extended objects (oil and gas
pipeline, railways, national frontier). The suggested solution is based
on the principle of simultaneously monitoring of seismoacoustic and
optical/infrared physical fields. The principle of simultaneous
monitoring of those fields is not new but in contrast to the known
solutions the suggested approach allows to control super-extended
objects with very limited operational costs. So-called C-OTDR
(Coherent Optical Time Domain Reflectometer) systems are used to
monitor the seismoacoustic field. Far-CCTV systems are used to
monitor the optical/infrared field. A simultaneous data processing
provided by both systems allows effectively detecting and classifying
target activities, which appear in the monitored objects vicinity. The
results of practical usage had shown high effectiveness of the
suggested approach.
Abstract: Conventional industrial monitoring systems are
tedious, inefficient and the at times integrity of the data is
unreliable. The objective of this system is to monitor industrial
processes specifically the fluid level which will measure the
instantaneous fluid level parameter and respond by text
messaging the exact value of the parameter to the user when
being enquired by a privileged access user. The development of
the embedded program code and the circuit for fluid level
measuring are discussed as well. Suggestions for future
implementations and efficient remote monitoring works are
included.
Abstract: At present, the tendency to implement the conditionbased
maintenance (CBM), which allows the optimization of the
expenses for equipment monitoring, is more and more evident; also,
the transformer substations with remote monitoring are increasingly
used. This paper reviews all the advantages of the on-line monitoring
and presents an equipment for on-line monitoring of bushings, which
is the own contribution of specialists who are the authors of this
paper. The paper presents a study of the temperature field, using the
finite element method. For carrying out this study, the 3D modelling
of the above mentioned bushing was performed. The analysis study is
done taking into account the extreme thermal stresses, focusing at the
level of the first cooling wing section of the ceramic insulator. This
fact enables to justify the tanδ variation in time, depending on the
transformer loading and the environmental conditions. With a view
to reducing the variation of dielectric losses in bushing insulation, the
use of ferrofuids instead of mineral oils is proposed.