Multimodal convolutional neural networks for sperm motility and concentration predictions

Manual semen analysis is a conventional method to assess male infertility which includes laboratory technicians examining on parameters such as sperm motility and concentration. Manual evaluation is prone to human errors that causes precision and accuracy issues. The purpose of this research study is to adopt computer vision deep learning techniques and multimodal learning approach in sperm parameters prediction using video-based and image-based input. Convolutional neural network (CNN) has benefited technology industry in recent years, and it has been widely applied in computer vision research tasks as well. Most of the well-established model were designed and pretrained for image-based input, whereas temporal information of video-based input might not be extracted properly using these architectures. Three-dimensional CNN (3DCNN) would be an alternative as it was designed to extract motion and temporal features, which are vital for sperm motility prediction. For sperm concentration, since twodimensional CNN (2DCNN) is efficient in recognizing and extracting spatial features, Residual Network (ResNet) could be adopted for sperm concentration prediction with minimal modification on the original architecture. On the other hand, multimodal learning approach is a technique to aggregate learnt features from different deep learning architecture that adopted other forms of modalities, and provide deep learning model better insights on their tasks. Hence, multimodal learning has been introduced in this research study, where the finalized deep learning architecture received both image-based (frames extracted from video samples) and video-based (stacked frames pre-processed from video samples) input that could provide well-extracted spatial and temporal features for sperm parameters prediction. In this research study, VISEM dataset has been used because it is an open-source dataset which contains 85 sperm videos and biological analysis data from different patients. The video samples went through pre-processing stage to obtain the suitable modalities for training and validation. The developed system has been proven to be capable of improving performance which was as proposed, after the results had been compared to other similar research works. Average mean absolute error (MAE) for sperm motility was observed with high accuracy up to 8.05, and competent performance for sperm concentration with Pearson’s correlation coefficient (RP) of 0.853

Sensor fusion with Kalman filter and support vector machine for fault detection in automated guided vehicle

Industries are moving towards automation and the usage of machines such as Automated Guided Vehicle (AGV) is increasing. Thus, demands for the reliability of AGVs are increasing as they have various complex tasks to carry out. Unfortunately, AGVs are still susceptible to faults and breakdown. Therefore, fault detection is important to provide means of self-diagnosis on AGV. However, fault detections are generally threshold based which are unsatisfying in terms of accuracy and are prone to false triggering. Extended Kalman Filter (EKF) has limitations in handling nonlinear models while Unscented Kalman Filter (UKF) seems promising. Support Vector Machine (SVM) was used as a fault detection method. Thus, this research proposes a sensor fusion enhanced with SVM for fault detection on AGV. The first objective of this research is to develop a test AGV. This AGV is a two-wheeled differential driven mobile robot with multiple sensors and able to make various types of movements to emulate an industrial AGV. Next objective is to develop an enhanced sensor fusion method using EKF and UKF for fault detection with SVM on AGV. The last objective is to evaluate the performance of the developed method. Experiments were carried out where the AGV was used as a test bed for sensor fusion and fault detection. The AGV was tested in different experiment setups such as different track layout, different wheel condition, and different castor conditions. Result shows that UKF handles changes and non-linearity better than EKF. The average residual generated during the test for UKF is 0.0083 meter while for EKF is 0.0129 meter. With sensor fusion, deviations in odometry data can be compensated with the usage of a LiDAR sensor as reference. Using UKF parameters to detect fault, the accuracy achieved with SVM is 64.2% compared to 37.9% without SVM. Fault detection accuracy using EKF parameters with SVM is 82.5% while without SVM is 41.0%. As a conclusion, the results show SVM improves fault detection accuracy regardless of using UKF or EKF

Identification of the unique spheroid by rotated and scaled first order polarization tensor

In the applications of electric and electromagnetic, polarization tensor is usually used to describe the perturbation in electrical field induced by the presence of conducting objects. Due to the fact that polarization tensor carries information about geometry and conductivity of those presented conducting objects, it is possible to substantially use the polarization tensor to describe the objects including reconstructing their images. Some recent applications of polarization tensor include electrical imaging for medical or industrial purposes, characterization of objects by weakly electrosensing fish and also metal detection. Specifically, this research is concerned with the first order polarization tensor when the conducting object is a conducting spheroid. Given the first order polarization tensor of any object, a spheroid can be numerically determined so that the spheroid has the same first order polarization tensor. Here, the main purpose of this research is to investigate the rotated or the scaled first order polarization tensor that is related to a unique spheroid. In order to achieve the objectives of this research, the depolarization factors of both prolate and oblate spheroids are first investigated. It is proven that the eccentricity of both spheroids are unique based on their depolarization factors. After that, the effect of scaling on the first order polarization tensor to the volume, depolarization factors, eccentricity and also semi axes of the spheroid are revealed. Some numerical calculations to identify the volume and semi axes of the spheroid based on a few scaled first order polarization tensors are presented in this research. Furthermore, the effect of rotation on the first order polarization tensor to the conductivity, volume, depolarization factors as well as semi axes of the spheroid are also identified. By the implementation of rotation, a new and improvised flowchart is provided in finding the semi axes of a spheroid based on the different forms of the first order polarization tensor. Last but not least, this research described the uniqueness of the spheroid at a fixed conductivity based on either rotated or scaled first order polarization tensor. As a conclusion, this research would be beneficial in gaining the information about the object specifically for spheroid based on the given first order polarization tensor

Development of integrated renewable energy system for sustainable energy supply in offshore platform

Malaysia introduced renewable energy as the 5th fuel strategy in the energy-mix under the National Energy Policy in 2001. Malaysia has huge potential renewable energy resources in the form of solar, pico hydro and wind due to its geological characteristic and proximity to equator. Mai Farm in Kalumpang, Selangor is planning to implement a small scale integrated Solar PV, pico hydro, and wind for electricity to power up its facilities. The site has an average solar irradiation at 5.85 kWh/m2/day with the highest solar irradiation at 6.08 kWh/m2/day for month of October which is potential for implementation of solar PV system. The highest wind speed recorded is 5.58 m/sec on month of June. The average wind speed is 4.86 m/sec. The site has a consistent and continuous velocity of water flow coming from the nearest hill via gravity fed 3” PVC pipeline system. This research objective is to investigate the potential energy can be generated and to design an integrated renewable energy system consisting of floating solar PV, pico hydro and wind. The methodology of the research started by gathering data for geographical and meteorological information such as location, elevation, solar irradiation, wind speed and flow data for pico hydro. The load list for Mai Farm also investigated for demand and consumption. All the collected data is used as input to perform simulation and optimization of renewable energy system configurations by HOMER software. Different off-grid configurations of the integrated system proposed are investigated for its levelized cost of electricity (LCOE). The simulation shall be able to assess levelized cost of electricity (LCOE), CO2 avoidance and excess electricity resulting of the integrated renewable energy system

Fractal and probabilistic analysis on fatigue crack growth rate of metallic materials

Load-bearing and complex geometry structures such as aircraft wing spars, thick-walled chemical processing vessels, offshore platforms and jacket structures are designed based on damage-tolerant design philosophy. The design employs fracture mechanics and test data to ensure that structural cracks nucleating during the operation will not propagate before they are detected by periodic inspections. The fracture mechanics equation describing the crack tip stress field (fr-field) is expressed in terms of the far-field stress and relies on the crack geometry factor. Closed-form equations for the far-field stress and the crack geometry factors have been established for standard fracture test coupons and relatively simple structures. The unavailability of the crack geometry factor for complex structures and loading renders the use of the fracture mechanics equation impractical. Inaccurate assessment of the fatigue crack and crack growth rates could jeopardize the safety and integrity of the structures. An alternative approach employing fractal analysis to quantify the fatigue crack growth rates of single-phase metallic material is proposed and examined. The fractal approach avoids the need for the crack geometry factor when calculating the crack tip driving force. The fractal analysis is carried out on digital images of the crack with a precision of 1.19 pixel/^m2 employing the box-counting algorithm to determine the fractal dimension (dF) along the edge of the crack length. The analysis is confined to the power law crack growth rate stage (Paris crack growth regime). Compact tension, C(T) specimens fabricated from AISI 410 martensitic stainless steel provide the reference fatigue crack growth response. Results show that the crack initially exhibits a Euclidean nature (dF-1.0). The fractal dimension increases steadily with increasing crack length in Paris region with 1.05<dF <1.24. The corresponding extent of disparity in the crack tip driving force range is between 18<Afr<40 MPaVm. The fractal dimension (dF) correlates linearly with the normalized crack tip driving force range (Afr/frIC) within the Paris region. The coefficient of fractality (CF) is identified as a characteristic material parameter. This enables the multifractal crack growth rate semiempirical model to be established in terms of Paris coefficient and exponent, fractal characteristics, and fatigue fracture properties of the material. A significant statistical dispersion is noted which is typical of a fatigue response. Given this, a probabilistic model based on Walker’s crack growth rate equation considering the variability in the crack tip driving force range, AK and stress ratio, R is developed. The model's validity is examined using selected sets of fatigue crack growth curves of AI-7075-T6, Al- 2024-351 and Ti-6Al-4V alloys. A good fit of the experimental data is noted. The model variance shows a convergent trend with an increasing number of test coupons, thus providing the statistical means of establishing sample sufficiency. The probabilistic model is annexed to the fractal analysis to yield an integrated probabilistic-fractal fracture model. The application of the integrated model to the general structures that lack the crack geometry factor for fatigue crack growth analysis is demonstrated on a bell crack structure. The results are contrasted with AK estimate established through the contour integral (CI) approach using Abaqus software and a close resemblance is noted. Thus, the model could be employed for the prediction of the fatigue crack growth response of engineering structures where the crack geometry factor is not readily available

Delay model of tumor-immune system interactions with hyperthermia treatment

The interaction of the tumor-immune system was initially based on the immunosurveillance hypothesis that immune cells can identify and kill tumor cells, leading to the use of a prey-predatory model for the description of tumor-immune cell interactions. However, the current biomedical findings reveal a pathway to immunoediting, which hypothesizes the ability of tumors to inhibit, seal, and counteract effector cells. Contrary to the discovery of non-oscillating dynamic biomedicine in solid tumors, existing models show oscillating solutions. Thus, the formulation of an immunoediting model that corresponds to the interaction of the tumor-immune system is sacrosanct in the search for effective malignant tumor treatment. The research suggests an immunoediting delay model of tumor-immune system interactions that combine tumor-immune cytokines derived from tumors to counteract effector cells. Qualitative analysis of this model gives an idea of the conditions for the stability of non-aggressive (benign) tumors and the instability of aggressive (malignant) tumors. The numerical results for these two conditions do not indicate an oscillating solution. Although the elimination of tumors is seen in the case of non-aggressive tumors, the suppression of effector cells and uncontrolled growth of tumors characterize the results for aggressive tumors. To find the best treatment, a sensitivity analysis is performed to ensure the role of the model parameters in the development of the tumor. The analysis reveals the best treatment options to kill tumor cells and strengthen the performance of immune cells. The sensitivity analysis results inform the merger of hyperthermia treatments in the proposed model to investigate the effects of thermal induction on immune cell performance and tumor regression. Discrete-time delays were used to investigate whether hyperthermia treatment was safe for patients who had received other treatments, but no cure occurred. The global stability of hyperthermia treatment is obtained using the Lyapunov function. Furthermore, an optimal heat control strategy for treating malignant tumor hyperthermia is obtained to minimize the effect of heat on normal cells while ensuring the elimination of malignant tumors. This research establishes a unique thermal optimal solution that improves the performance of the effector cell without difficulty

Friction stir welding with different tool profiles on structure and hardness of dissimilar aluminium alloy AA5083 and AA6061-T6

Underwater dissimilar friction stir welding (FSW) of an aluminium alloy AA5083 and AA6061-T6 in butt joint configuration was studied. Underwater FSW is the solid-state joining operation that utilises the non-consumable tool to connect two facing workpieces without melting the workpiece substance in the underwater environment. Defect has been proven to have negative impact on overall welding engagement. It is becoming an area of increasing concern in any type of manufacturing, including welding. The present study continually looked for ways to manufacture the best underwater FSW tool pin profile for specific applications while minimising the different welding defects. The experimental method was to choose 1 sample among underwater FSW and normal FSW fabricated according to the welding process parameter and met the research criteria based on comparable studies. In addition, optical microscopy was employed to observe the microstructures and welding defects of the joints. The effects of the different tools at three profiles of straight, threaded, and tapered cylindrical tools on the structure and hardness of the joint dissimilar aluminium alloy were investigated. The presence of water is an excellent alternative to enhance durability and reduce the various types of welding defects during the welding process changes occurring in underwater FSW and normal FSW. These results have been used to understand the variation in the welding strength and the microhardness obtained in underwater FSW and normal FSW at both different tool pin profiles and values of welding speed. Results have revealed the presence of various types of defects such as groove and tunnel defects in the weld region. Metallurgical macrostructure observation on the underwater FSW and normal FSW samples revealed the presence of welding defects which were groove and tunnel in the stir zone. The tapered cylinder pin profile and 60mm/min welding speed remarkably affected welding textures resulting in a higher hardness value than the other samples. Furthermore, the optimal combination of rotational speed and welding speed can significantly improve the hardness strength of the welded joint. The study indicates that underwater FSW was achieve 77.56 HV, which is supported by the result obtained. This number was 18% greater than the hardness value achievable with the normal FSW, 63.30 HV. For underwater FSW, higher rotational speed and welding speed were suggested to establish sound welded dissimilar joints and achieve better microstructures which could collectively improve the welding strength

Simulation of performance and optimization for diesel engine fueled with higher biodiesel blend

With global energy demand that keeps on increasing by 1.2% every year, CO2 was predicted to increase as well. A short-term solution to reduce CO2 is to switch to carbon-neutral fuels and one of them is biodiesel. However, biodiesel’s higher viscosity and lower calorific value compared to pure petroleum diesel lead to higher brake specific fuel consumption (BSFC) especially for higher biodiesel blend. Even though there are many ways to reduce the BSFC, to quantify how much these ways manage to reduce it requires experiments that are costly and time consuming. At the same time, a limited simulation model can be used as the alternative to the experiments. Therefore, the objective of this research is to develop a model based on the Yanmar L70N6 engine using GT-Suite simulation software, to predict engine performance, combustion, and emissions when using biodiesel. The engine model was then used to simulate a high biodiesel blend with a variation of injection timing (IT), injection pressure (IP), and preheat biodiesel fuel (PF). In this research, experimental work was conducted to obtain baseline data for validating the simulation study based on the manufacturer’s default setting of IP (206 bar), IT (14 ºbTDC), and ambient temperature for fuel which is around 30°C. In the experiment, B10 and B30 were tested at four different speeds (1500, 2000, 2500, and 3000 rpm) with five different loads (3, 5, 7.5, 10, and 11.5 Nm) at each speed. Then, B30, B50, B70, and B100 were simulated with variations of IP (206, 220, 240, 260, 280, and 300 bars), IT (10, 12, 14, 16, 18, 20, 22, and 24 ºbTDC) and PF (30, 40, 50, 60, 70, 80, and 100°C). For model validation, the engine speed was simulated at 2000 rpm with five different loads and comparison between the simulation and the experimental results showed less than 10% differences in the BSFC of B10 (8.8%) and B30 (5.1%). The results showed that by increasing IP to 300 bar, retarding IT to 12ºbTDC, and PF to 100ºC, reduction of the BSFC was recorded from 2.1% to 5.4% meanwhile CO2 emission reduction was recorded from 3.79% to 10.7% and by combining three optimized parameters, it helps reducing BSFC, and CO2 for all blends. Among all biofuels, B100 has the lowest BSFC (8.8%) and CO2 (22.3%) at 3000 rpm and 3 Nm load. In conclusion, the objective of the research, which is to develop a reliable simulation model and improve the performance of a high biodiesel blend, has been achieved successfully

Development of integrated renewable energy system for sustainable energy supply at Mai Farm Kalumpang Selangor

Malaysia introduced renewable energy as the 5th fuel strategy in the energy-mix under the National Energy Policy in 2001. Malaysia has huge potential renewable energy resources in the form of solar, pico hydro and wind due to its geological characteristic and proximity to equator. Mai Farm in Kalumpang, Selangor is planning to implement a small scale integrated Solar PV, pico hydro, and wind for electricity to power up its facilities. The site has an average solar irradiation at 5.85 kWh/m2/day with the highest solar irradiation at 6.08 kWh/m2/day for month of October which is potential for implementation of solar PV system. The highest wind speed recorded is 5.58 m/sec on month of June. The average wind speed is 4.86 m/sec. The site has a consistent and continuous velocity of water flow coming from the nearest hill via gravity fed 3” PVC pipeline system. This research objective is to investigate the potential energy can be generated and to design an integrated renewable energy system consisting of floating solar PV, pico hydro and wind. The methodology of the research started by gathering data for geographical and meteorological information such as location, elevation, solar irradiation, wind speed and flow data for pico hydro. The load list for Mai Farm also investigated for demand and consumption. All the collected data is used as input to perform simulation and optimization of renewable energy system configurations by HOMER software. Different off-grid configurations of the integrated system proposed are investigated for its levelized cost of electricity (LCOE). The simulation shall be able to assess levelized cost of electricity (LCOE), CO2 avoidance and excess electricity resulting of the integrated renewable energy system

Process improvement and energy saving measures for sewage treatment plant

Energy in sewage treatment plant (STP) show the differential energy consumption on various stages which it depends on the indicator of the findings. To do comparison and benchmarking, the suitable key energy consumption indicator need to identify, and the suitable method need to formulate in performing the energy. This study is to perform energy saving measures (ESM) and process improvement for the STP. The study started with planning to select the STP and study on the potential of method to use. Sites visit then conducted to make an overall overview of STP and the related data was collected to do analysis. The propose ESM and process improvement analyzed in term of results feasibility, findings, and economic analysis. Introducing the Solar PV systems by do comparison type of panel and area available will determine the potential of savings. Retrofitting the lighting for existing 36W fluorescent to LED T8 14W and for 300W spotlight to 150W LED spotlight also help the STP to reduce the energy consumption. Retrofitting the Sequencing Batch Reactor (SBR) to produce biogas and the development of Combine Heat and Power (CHP) plant to generate electricity will reduce the demand of electricity from the grid. The waste heat produced later can be used for sludge dewatering process. Sludge utilization will help the STP to generate yearly income rather than allocate budget for the sludge removal from the STP. The overall result which is categorized according to the options will the analyzed to perform the best selected option. The best option is to utilize all the available potential area of Solar PV installation along with another ESM and utilization to obtain the best result. The combination of proposed improvement being analyzed according to the selected option, economic feasibility, energy savings, CO2 reduction and simple payback period with expected reducing the energy consumption and cost at the STP. As conclusion, the implementation of all the propose ESM and utilization will lead the savings in many aspects of area to the STP