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The study related to pharmaceutical packaging has come to attention for its importance to the quality of drugs and compliance achievement. The aims of this study is to provide a conceptual view of producing a new model of pharmaceutical drug packaging design to overcome existing problems. The proposed methods for this research is to use the process mining techniques to support the construction of a new model for pharmaceutical packaging design process improvement. A case study method was selected for the methodology of the research resulted the flow of packaging design process in pharmaceutical company. The study lengthens the knowledge of drug packaging design process and concludes the importance of drug packaging design process to be investigated for compliance fulfilment purposes. This study is significant to the strong implication of drug quality and safety outcomes for the patients.

The recognition and acknowledgment of the wildfires are of high significance, especially in countries such as Australia due to its intense bionetwork. Prompt bushfire detection is crucial to fire management as elimination of fire is quicker and more effective when the fires are on a small scale. Immediate detection allows emergency management to compose decisions that will save lives, time, and any loss. The imperceptible escalation in satellite sensory technologies has paved the way for opportunities in forestry monitoring and management practices requiring greater fidelity data on burning and other fire activities. The use of satellite imagery for fire detection is vital for the identification of emission approximations and bio conservatives studies. This composition uses the open source Near Real Time (NRT) data provided by the active fire map of Fire Information for Resource Management System by NASA. We used the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument mounted on the Suomi National Polar-orbiting Partnership (NPP) satellite that compiles and processes visible images along with infrared images that depict the geographical data. A Gridded Fire Hotspot sensor was also employed to identify the major fire hotspots of the country as well as provide data on the 2019-2020 bushfire season of Australia. In this paper, the received data is then analyzed further and monthly developments are observed and compared with the statistics.

Year-by-year, we have witnessed the advancement in the population count due to sudden urban expansion that had occurred in many countries. Due to urbanization, a lot of people have migrated to these areas from rural areas and from other countries. While this is good for the economy of the country, this has also increased the air contamination in the vicinity, and NO2 is one of the primary hazardous pollutants. It is monitored using the available remote sensing technology. For this paper, the data of NO2 is obtained from the latest satellite from the Sentinel series; Sentinel – 5 Precursor (5P) Satellite launched by European Space Agency (ESA) to detect the presence of pollutants at a tropospheric level using an on-board sensor – Tropospheric Monitoring Instrument (TROPOMI).

Current methods of joint diagnosis depend on invasive, radiative methods, large set ups and requiring skilled operators, which are still not able to assess the joint during motion. Replacing these methods are acoustic methods, which are only able to diagnose joint health based on the characteristics sound emitted by a moving joint. This paper looks into informing the medical practitioner of the locations of joint damage instead, hence the feasibility of localizing upon the damaged part of the joint, which is represented by a playback of a recording of crepitus sounds emitted from a human joint in 2 dimensional settings. Three microphones, an earphone and a data acquisition device were used to implement the testing. Using the time-difference-of-arrival (TDOA) between the 3 sensors, the Angle-of-Arrival (AOA) method was used to attain the initial localization coordinates. These coordinates were then fed into a gradient descent algorithm to reduce the positioning error of the AOA method, yielding the final position results. Results show that the developed system is able to achieve localization errors of less than 1.0 cm.

The drive for more electric aircraft (MEA) architecture has resulted in the introduction of high power density electric machines for aircraft propulsion applications. The reliability of the electrical machines in such harsh environments is a challenge that is seldom addressed. The paper proposes, through simulation, the use of aging model to accurately estimate the lifetime of a machine with turn-to-turn short-circuit (TTSC) condition. The proposed aging model considers all the aging factors due to electrical, mechanical and thermal phenomena. The paper also puts forth a cumulative stress curve for various loading & environmental constraints and uses the stress curve with winding temperature to create an aging model to accurately predict and estimate the lifetime of the machine. A bottom-up approach through the developed model can be used to estimate the lifetime of the machine in real-time.

Nowadays we are finding newer and newer efficient ways to carry out tasks by using the concept of automation and machinery combined. One of the ways to accomplish such a task is by using a quadcopter. It can accomplish a vertical trip in a steady way and be utilized to screen or gather information in an explicit area, for example, mapping landscapes. Mechanical advances have diminished the expense and increment the execution of the low power microcontrollers that enabled the overall population to build up their very own quadcopter. The objective of this venture is to manufacture, change, and make enhancements in the control configuration of the quadcopter to acquire stable flight. The objective of this paper is to incorporate Wi-Fi communication in controlling the quadcopter by using an inexpensive microcontroller as a flight computer and develop an HTML webpage to receive the video-feed transmitted from the mobile phone camera fixed on the quadcopter.

Ion propulsion is based on the concept of the collisions of neutral atoms with high energy electrons which causes ionization, this ionization then releases the positive ion allowing it to accelerate through the electrodes causing them to shoot out with high speeds. Ion propulsion technology took 50 years to produce until October 24 of 1998. In this year the first ever spacecraft to utilize ion propulsion technology to propel it was the Deep Space 1. From that year on ion propulsion technology was steadily increasing in popularity due to the fact that ion propulsion does not have a need for moving parts therefore less stress on the body/fuselage of any space/aircraft. Ion propulsion technology through the use of xenon as a propellant led NASA to the proposal of their newest project named N.E.X.T. NASA's Evolutionary Xenon thruster. NEXT system opens a door to a wide range of office of space science missions such as Saturn Ring Observer, and Comet surface sample collection mission.

Any defect in the form of an axial crack or a circular defect in the propeller induces additional component in the centrifugal force and thereby producing an additional vibration characteristic in the propeller. The objective of this paper is to determine the translation of vibration to the permanent magnet synchronous machine (PMSM) which drives the propeller and in turn a triaxial accelerometer is used to detect the faults. A finite element based model is created consisting of the propeller, shaft, bearings and PMSM. The FEA modal analysis is performed to determine the natural frequency of the system and a harmonic response analysis is performed to determine the vibration harmonics at the natural frequency of the system. A comparison of the natural frequencies of the healthy system and the defective systems are obtained along with vibration characteristics for the different profiles and the load dependency of the vibration characteristics. It is observed that the x-axis and z-axis vibrations increase significantly for the defective propeller and it increases with load.

This paper presents an online technique to estimate the capacitance value of the LCL filter integrated with Active Front End Rectifier (AFER) used in marine vessels or offshore drilling/oil rigs. The proposed methodology is simple, robust and requires flux sensors in addition to the existing on-board sensors for grid-voltages, grid- and capacitor-currents measurements. The proposed online methodology is based on the fundamental components of inductor leakage fluxes, grid-voltages, grid- and capacitor-currents, which estimates both inductance and capacitance of the LCL filter. Based on the estimated values of the inductance and capacitance, a technique is proposed to estimate the resonant frequency of the LCL filter under real-time operating condition which can be used to predict the maintenance schedule before any catastrophic failure. The proposed technique has been validated in a laboratory based 11.3kW marine electric propulsion test-rig for 7.7%, 10% and 14% degradation in the capacitance value of the LCL filter under different loading conditions. The estimated capacitance values under real-time operating condition agree with the measured values under offline conditions for various degrees of capacitance degradation and loading conditions.

Wireless Sensor Network (WSN) is a physical bridge that connects the real world to the digital world. Due to recent advances in an Internet of things and Industrial 4.0, more extensive WSN devices are used in industries ranging from the aerospace system, smart grid, Unmanned Aerial Vehicle (UAV) and drone application to the military investigation. However, due to the nature of WSNs, attackers can easily deploy various security threats to make WSN network vulnerable and unstable. The easiest way to attack WSN network is through the jamming attack with a little bit knowledge of targeted communication system. Standardisation of wireless communications used in various industries makes the attackers easy to design a smart reactive jamming device. In this paper, a new method using a timing channel is proposed to defeat the reactive jammer. To validate the proposed method, a smart reactive jamming device, a transmitter and a receiver are implemented in hardware. Test results are presented, showing how the method can defeat the reactive jamming attack.

Audio-signal acquisition as part of wearable sensing adds an important dimension for applications such as understanding human behaviors. As part of a large study on work place behaviours, we collected audio data from individual hospital staff using custom wearable recorders. The audio features collected were limited to preserve privacy of the interactions in the hospital. A first step towards audio processing is to identify the foreground speech of the person wearing the audio badge. This task is challenging because of the multi-party nature of possible ambulatory interactions, lack of access to speaker information and varying channel and ambient conditions. In this paper, we present a speaker-agnostic approach to foreground detection. We propose a convolutional neural network model to predict foreground regions using a limited set of audio features. We show that these models generalize across the proxy corpora we collected in house to approximately match the deployment environment. The proxy corpora contained full audio and was used as a test-bed to analyze our models in greater detail. We also evaluated the models in the workplace setting to measure speech activity. Our experimental results show promising direction for analyzing workplace behaviors with privacy protected sensing.

Electrical and magnetic domain parameters for online condition monitoring of converter transformers used in marine power system is explored, where the traditional DGA and PD techniques are not much applicable. Current signal and leakage flux from transformer core and winding are studied, with an emulated inter-turn fault in a 10 kVA lab-scale converter transformer. It is expected that the presence of valve winding in converter transformer will pose a challenge, for condition monitoring as it has inherent harmonics. There is a need for an online monitoring technique which is simple, cost effective and rugged to diagnose the fault and raise an alarm on-board ship, as it may lack the right maintenance technician or a replacement transformer during the voyage. Method of emulating the inter-turn fault in transformer, experimental studies and the frequency domain results from electrical and magnetic sensors for condition monitoring are discussed.

For the purpose of monitoring the health conditions of electrical machines, a framework is proposed to establish the methods to provide an early warning to potential machine failures in data mining terminology. The framework consists of five stages including data segmentation, feature extraction/selection, multi-classifier ensemble, decision fusion and output, which is flexible and can be adapted for any known faults. The difference lies in the implementation choices of techniques and structures (e.g. number of classifiers) in the second to forth stage as well as the input requirements. As an example, the turn-to-turn short circuit fault of induction motor is used as the known fault in studies in this work. Simulation results show the effectiveness of the proposed techniques.

The role of transformer in electricity network reliability and safe operation is quite crucial. Therefore, their monitoring, maintenance and management is vitally important for utility operators. Simpler, non-invasive and online condition monitoring method which is sensitive to incipient faults is in demand for transformer diagnosis. Although vibration monitoring is considered as a valuable technique in industry for rotating machines, it is least explored for static electrical equipment such as transformer. In this study, core and winding vibrations in a dry type transformer is monitored using an accelerometer to diagnose the winding electrical and mechanical faults. Winding and core vibrations for various degrees of inter-turn fault, axial movement and disc displacement under different load conditions are studied.

Unmanned Ariel vehicles are very exciting for all, and can be used by anyone; it provides a new perspective to the modern world. Misuse of drone's leads to warfare then epitomizes altogether different effect and possibly followed uncontrolled threat to society at large. UAE have been coming up with regulation to restrict and monitor the use of non-commercial usage on emerging technologies. The paper aims to address regulation, requirements, restriction, and to discuss legislation on civil aviation and flight activities on drones.

In condition monitoring of electrical machines, wireless sensors enables access to internal signals that are relatively unexplored compared to the conventional external measurements. This paper demonstrates the application of wireless sensor to measure the rotor field winding current in a Brushless Synchronous Generator and compare the fault detection capability with the stator exciter field current. The faults considered are the stator armature and rotor field winding inter-turn short circuit fault. From the experimental investigation, the rotor field current measurements obtained through a wireless sensor is found to be more effective in tracking the evolution of a fault.

The overall efficiency of a marine electrical power system is reliant on its condition monitoring algorithm. A competitive system has quick fault detection functionalities and maintenance enabled. This paper examines existing method to develop an online approach for superior detection of eccentricity fault in surface-mounted permanent magnet synchronous motors, in a marine propulsion setting. Detection of eccentricity fault is best carried out by monitoring vibration, air-gap flux density and induced emf. With the introduction of search coils, the proposed methodology aims to obtain the induced emf voltage in the air-gap. Search coil were found to decisively segregate fault symptoms appearing from other asymmetric interruptions such as load imbalance and input unbalanced voltage. Vibration in the motor was monitored by placing sensors on the stator yoke to detect vibration during various levels of eccentricity fault. In this research, eccentricity fault was simulated in 2D finite element analysis (FEA) platform using Ansys Maxwell and Ansys Mechanical. The simulated results shows a robust online based fault diagnosis method applicable for marine propulsion motors.

A multiple indicator based fault diagnosis and condition identification has been proposed in this work suitable for brushless synchronous generators (BLSG). One of the important challenges in BLSG interturn fault diagnosis is to identify whether the load imbalance exists alongwith the fault. The multiple indicator approach proposed in this work, is found to be useful, especially at incipient stages of fault development. The fault indicators are derived from the analytic fault model of the BLSG, and the technique is found to work well for complex conditions when fault and load imbalances occur together. The results show the applicability of the method to accommodate varying operating conditions of the machine as well as different imbalances in load.

In this paper, a probabilistic space-time representation of complex traffic scenarios is predicted using machine learning algorithms. Such a representation is significant for all active vehicle safety applications especially when performing dynamic maneuvers in a complex traffic scenario. As a first step, a hierarchical situation classifier is used to distinguish the different types of traffic scenarios. This classifier is responsible for identifying the type of the road infrastructure and the safety-relevant traffic participants of the driving environment. With each class representing similar traffic scenarios, a set of Random Forests (RFs) is individually trained to predict the probabilistic space-time representation, which depicts the future behavior of traffic participants. This representation is termed as a Predicted-Occupancy Grid (POG). The input to the RFs is an Augmented Occupancy Grid (AOG). In order to increase the learning accuracy of the RFs and to perform better predictions, the AOG is reduced to low-dimensional features using a Stacked Denoising Autoencoder (SDA). The excellent performance of the proposed machine learning approach consisting of SDAs and RFs is demonstrated in simulations and in experiments with real vehicles. An application of POGs to estimate the criticality of traffic scenarios and to determine safe trajectories is also presented.

A marine electrical power systems fate in terms of its quality and maintenance heavily relies on the efficiency of its fault detection. This paper analyses and develops an online approach to detecting winding and demagnetization faults in surface-mounted permanent magnet synchronous motor (SPMM) in a marine propulsion setting. The proposed methodology uses search coils to determine faults that reveals data to pinpoint which slot/magnet is affected by the faults, segregating fault symptoms from other asymmetric problems such as load imbalance and input unbalanced voltage. This paper is distinctive in its acute detection of faults even when the severity of the fault is very minimal. The proposed model used for simulation in two-dimensional (2-D) Finite Element Analysis (FEA) is a 3-phase, 42 poles, 54 slots fractional slot concentrated wound, SPMM. In addition to the use of search coils, the proposed research looks into the torque profile with various conditions of fault and analyzes the machine current using Hilbert-Huang transform. The simulated results reveal potential for the proposed method to provide reliable data on fault diagnosis of marine propulsion motors.

Fault analysis of marine generators is paramount to improve reliability and accessibility of a marine electrical power system. Inter-turns fault is presumably the most common fault in a PMSM and the symptoms associated with this fault in many machines are undetectable, depending on the level of fault. This paper proposes a novel method to use search coils to determine the inter-turns fault in a Surface Mounted Permanent Magnet Machine (SPMM). This technique would reveal data to pinpoint which slot is affected by the fault and how fault symptoms can easily be segregated from other asymmetric problems such as load imbalance and input unbalanced voltage. The proposed model used for simulation in FEA using MAXWELL 15 is a 3-phase, 42 poles, 54 slots fractional slot concentrated wound (double layer), surface mount PMSM. In addition to the use of search coils, the proposed research also looks into the torque profile with various conditions of fault, analyzes the machine current using symmetrical components and discrete wavelet transform.

Ahead of an unavoidable collision, the actual crash constellation and along with it, the crash severity can significantly change based on the driver actions. To justify the use of safety measures like airbags, prior to an accident, the severity of the predicted crash must be high enough and the crash severity prediction itself must be reliable. In this work, a machine learning driven reliability estimator for crash severity predictions is presented. The reliability estimate is obtained by simulating various driver hypotheses and analyzing the corresponding crash severity distribution. A simulation framework is introduced, utilizing a two-track dynamics model and a mass-spring model, to simulate the pre-, in- and post-crash phases and automatically generate a large amount of crash data. The data are used to train a Random Forest regression model, capable of estimating the reliability of one crash severity prediction in real-time and thereby, around 105 times faster than with simulations, and with a correlation coefficient of true and predicted reliability values of 0:92.

A multiple model indicator based fault diagnosis and condition identification has been proposed and experimentally verified in this work. One of the important challenges in fault isolation is discriminating the fault with voltage imbalances, which always exists in the system. The multiple indicator approach proposed in this work, is found to be useful, especially at early stages of the fault. The fault indicators are derived from the analytic fault model of the induction machine, and the technique is found to work well for complex conditions when fault and imbalances occur together. The method is insular to load variations. The experimental results show the applicability of the method to accommodate varying operating conditions of the machine as well as the imbalances in supply.

In this paper, the SMART grid concepts and technology that can be potentially applied to address key challenges in future Naval vessels are analyzed. The IEEE standards for designing various modules for naval vessels such as Integrated Power System (IPS) are discussed and proposed potential architecture for power management module in naval vessel IPS to supply SMART grid concepts.