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Effect of rutile phase titanium oxide nanofiller on the dielectric properties of polypropylene nanocomposites
No full textPolypropylene (PP) which is recyclable and having high working temperature is considered as a potential insulation material for high voltage direct current (HVDC) insulation. The aim of this work is to investigate the effect of rutile phase TiO2 on the dielectric properties of PP nanocomposites. Rutile TiO2 was obtained using sol gel method in the laboratory. X-ray diffraction (XRD) and field emission scanning electron microscopy (FE-SEM) was employed to investigate the crystalline and morphological structure of synthesized TiO2. PP nanocomposite were obtained by mixing 0.5 wt%, 1 wt% and 3 wt% rutile TiO2 by using brabender machine. The thermal behavior of PP nanocomposites was characterized through Differential scanning calorimetry (DSC). Meanwhile, the DC breakdown strength tests were executed to study the dielectric properties of PP nanocomposites. The results of breakdown strength revealed that incorporation of rutile TiO2 significantly decreased the breakdown strength of PP for all systems. The mechanism concerning decreased in DC breakdown strength are discussed
Pattern-driven 4D printed PLA actuators
No full textFour-dimensional (4D) printing is a technology that allows fabricating complex structures that have the ability to change their shape based on various stimuli. Such structures are printed using smart materials, which mainly rely on shape memory polymers. Controlling the response of 4D printed structures becomes challenging when increasing the complexity of the design. This paper investigates the effect of the printing patterns on the deformation performance of polylactic acid (PLA) actuators. The actuators are printed using the fused deposition modelling method. Four different patterns, namely, rectilinear, grid, honeycomb, and triangular patterns are used to develop eight actuators' designs. The size of the patterns was varied in order to print the actuators using different infill percentages. The PLA actuators are activated using hot water of 85°C. The results show that the bending angle of the actuators is varied between 3.00° and 16.80°. The bending direction is affected by the size of the printing pattern, where the actuators bend towards the pattern with the bigger size. The results show that the proposed concept can be used in various pattern-based 4D printed robotics applications, where the actuation performance is tuned using the printing patterns, as well as the printing parameters
Estimation of energy gain improvement of various tracking techniques for LSS photovoltaic system
No full textThe produced electrical energy from a solar photovoltaic (PV) system can be increased via using manual or auto solar tracking strategy. Generally, compared with the fixed solar system, the auto dual (two) axis tracker will give the highest energy output improvement, followed by the auto single (one) axis tracker then the manual adjusted tilt angle (monthly and seasonally) solar system's respectively. However, the magnitude of the improvement varies from one location to another location due to the latitude angle difference between them. The improvement can be high at one location, but its low at another location. Thus, an exhaustive simulation analysis for each strategy is needed to be conducted before their energy output improvement can be determined for a specific location. Not only it will be time consuming, but also it will be impossible for a person with no PV simulation tool skills to perform this task. This research presents a simpler method for estimating the generated energy performance (the energy gain, %) of the large scale solar (LSS) system for various tracking strategies. The mathematical equations are developed from the exhaustive simulation results. The proposed method is tested on (12 selected cites) that included in our research recently as well as it also tested on another (two new cities) with the main error (between 0.0% and 0.56%) is observed
Comprehensive analysis of gate oxide short in junctionless fin field effect transistor
No full textJunctionless (JL) FinFET is one of the most promising alternatives to FinFET and planar MOSFET for future performance enhancements. The complexity of the JL FinFET manufacturing process has prompted difficulties in reliable device testing. Gate oxide short (GOS) is one of the most common faults that substantially influence circuit reliability, specifically in FinFET device structure. In this work, GOS defect model is presented for both n-channel and p-channel JL FinFET and JL FinFET-based inverter by introducing the defect as a pinhole designated by small cuboid cuts of different sizes for several coordination in the dielectric and filled with gate material. The electrical characteristics of 15nm n- and p-channel JL FinFET with fin height and width of 10nm, source/drain, channel and substrate doping concentration of 1.5×1019 cm-3, and work function of 4.76eV and 4.52eV for n- and p-channel are successfully simulated by using Synopsys Sentaurus TCAD Tools where Vth, SS, and DIBL are 0.371V, 75.7mV/dec and 42.7mV for n-channel and 0.3298V, 79.1mV/dec and 48.9mV for p-channel JL FinFET respectively that is compared with post GOS defect injection. The high-to-low delay time (tHL) is 1.61ps and low-to-high delay time (tLH) is 1.74ps for the defect-free inverter that is compared to the defected one where the tHL is 16.1 % and tLH is 22.4 % smaller than defective inverter. The findings of this research potentially result in the formation of a realistic analytical GOS fault model for circuit-level modeling
Decision support platform for production of chili using IoT, cloud computing, and machine learning approach
No full textThe chili crop is largely grown in several regions of the world, especially in Asian and African countries. It is a major source of income for both small- and large-scale farmers. Unfortunately, chili farmers have to contend with the challenge of pests and diseases and the need for timely decisions to have a bountiful production. To solve this problem, this paper proposes a chili-decision support platform (chili-DSP) that can help farmers detect diseases, and nutrient deficiency and make timely decisions. The proposed system integrates the internet of things, cloud computing, and data analytics technologies. The framework and architecture of the proposed chili-DSP are presented in this paper and the preliminary results using the convolutional neural network (CNN) for the classification of chili are presented. The result shows that CNN provides an accurate prediction of the learned data set and can be extended to larger data set for real-time classification of chili diseases. The chili-DSP is expected to provide a comprehensive feature and support that will help the chili farmers enhance the production of chili while minimizing losses
Risk analysis of water grid systems using threat modeling
Get PDFCritical infrastructure systems consist of physical and cyber assets that are essential to the operation of the economy and the government. As one of the most important critical infrastructures worldwide, the water sector has become vulnerable to new risks in the form of cyber threats that can severely impact public health, and are difficult to detect. A water grid system (WGS) plays an important role in guarding the business processes of the water sector against possible threats and risks. Threat modeling can be used to analyze threats to the WGS. It is applied to identify points of access to the assets and devices of the system, classify threats to them, assess the risks posed by them, and suggest mitigation measures. Each threat is classified based on its type according to the STRIDE methodology, and the results of the threat classification can be used to assess the level of risk by using the DREAD methodology. This yields a risk rating for each threat that can be used to devise mitigation measures to minimize the risk posed by it. Through the threat modeling stage, it is known that the high-risk threats on WGSs are tampering with a risk score of 14, denial of service threats with a risk score of 13, and repudiation threats with a risk score of 12. The results of the ranking are used to formulate recommendations in the form of mitigation controls against these threats
Mobile IoT cloud-based health monitoring dashboard application for the elderly
No full textOver the years the population are increasing and currently, there are about 8 billion people residing globally. In time, the number of elderly people will increase due to a higher life expectancy over the years. In 1950, the average life cycle is at 46 years and in 2019 the projection expands to 72.6 years. The main reason for this increase is due to better global health services and quality of life. Mobile health technologies are being implemented in most areas related to the healthcare industry to aid elderly patients by monitoring and collecting data related to the diseases and their critical level. This paper described the design and development of an IoT health monitoring system for the elderly. This IoT system consists of two sensing modules, PI and P3. PI measures the body temperature, heart rate, and oxygen saturation (SpO2), while P3 is an accelerometer sensor that detects a fall. Data gathered from these sensors are dispatched wirelessly to the Raspberry Pi gateway and are later stored in a cloud database called InfluxDB. A mobile application is built using the Flutter framework for mobile data visualization purposes. Users can view four screens in the application, including the dashboard for data sensors PI and P3, the profile page, and the notification page. The dashboard displays the elderly data embedded using Grafana. If the patient falls, an alert (OneSignal) in the notification will be sent postfall instantaneously
Livestock posture recognition using deep learning
No full textCalf posture recognition could be one of the required steps for a complete automated calf monitoring system, as sometimes the calf is required to be in a standing posture before being able to proceed to the next stage. To distinguish calf postures such as between standing or lying, machines require complicated frameworks, especially one that involves deep learning models. Previously, most of the works utilized video inputs rather than image inputs, which would make the model unnecessarily complicated compared to a conventional Convolutional Neural Network (CNN) model, which accepts image inputs. In this paper, to overcome all the problems mentioned earlier, two deep learning models with the exact same ResNet-50 based architecture have been built and trained on two different image datasets, respectively sourced from separate cameras placed at different angles to be compared and analyzed. The performance for both CNN models were 99.7% and 99.99% in accuracy, respectively, significantly better than the 92.61% accuracy of a similar work, and is adequate for a real-Time calf monitoring system
Development of visual dashboard for river monitoring system
No full textMonitoring a tributary's water depth and velocity can provide a wealth of information for ecological systems. Typically, tributaries are located deep within the jungle, and manual measurement is the norm. The research aims to develop a real-time remote monitoring system for measuring water depth and velocity. The system uses ultrasonic and water flow sensors to measure velocity and river depth. The data will be transmitted to the back-end software, including Arduino IDE and Node-RED, for data collection. The data is then stored in the cloud database InfluxDB for future use. The data can be set to be automatically deleted every 1 hour, 1 day, 1 month, or at other time intervals. The Grafana platform can produce an interactive Graphical User Interface (GUI), and the administrator can modify the visual dashboard using either their program code or existing features. It can then be made available to the general public as an observer for the online visual dashboard via the Uniform Resource Locator (URL)
Strategic framework of using drone in cities disaster response
Get PDFDrone technology has been around the world more than a decade and the application has becomes more vital, especially, during the disaster event such as COVID-19 pandemic. However, some of the countries were not ready in implementating the technology due to various reasons. Thus, this study intended to identify the technological and legislative aspects of drone application for disaster response in cities environment and to propose strategic framework on optimization of drone capabilities in cities disaster response. The literature search has been conducted in order to explore the applicability of drone technology in disaster management and disaster response. Later, further investigations were carried out using the Participatory Action Research (PAR) approach. In the PAR method, the data were collected in two phases; 1) observation and discussions and 2) interview sessions. The observation and discussions were conducted to obtain the implementation process of using the drone during the disaster event by the rescue team via recorded video. To triangulate the information gathered from the discussion, a series of interview had been made. Where, six selected stakeholders from end-user, regulatory agency and supplier were interviewed. It is found that the implementation of drone technology framework in disaster consists of six perspectives, namely; actions, internal organization, external environment, needs specification and feasibility analysis. To make the framework works, stakeholders are required to function in a cohesive and collaborative manner as illustrated in the Harmonization Triangle. The strength of cohesion and collaboration has also opened up future study for a full scope disaster management cycle phases, known as response, recovery, mitigation and preparedness