Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item AI-Based Controls for Thermal Comfort in Adaptable Buildings: A Review(MDPI AG, 2024-11-04) Ahsan M; Shahzad W; Arif KDue to global weather changes and pandemics, people are more likely to spend most of their time in indoor environments. In this regard, indoor environment quality is a very important aspect of occupant well-being, which is often ignored in modern building designs. Based on our research, thermal comfort is one of the essential items in building environments that can improve the mental stability and productivity of the occupants if the building’s indoor environment is created in a way that meets the occupants’ comfort requirements. Buildings nowadays operate on adaptive or stationary models to attain thermal comfort, which is based on Fanger’s model of the Predicted Mean Vote (PMV). Based on the literature review, limited work has been carried out to enhance the quality of the inside environment, and most research work has been devoted to building energy management. Moreover, there have been no definite solutions so far that have the capability to detect the thermal comfort requirements of multiple occupants in real time. Modern buildings tend to operate on predefined set point parameters to control the indoor environment based on the measured room temperature, which can be different from the thermal comfort requirements of the occupants. This paper discusses the limitations and assumptions that are associated with the existing thermal comfort solutions and emphasises the importance of having a real-time solution to address the thermal requirements of occupants.Item Low-cost IoT based system for lake water quality monitoring(Public Library of Science (PLoS), 2024-03-28) Lal K; Menon S; Noble F; Arif KWater quality monitoring is a critical process in maintaining the well-being of aquatic ecosystems and ensuring growth of the surrounding environment. Clean water supports and maintains the health, livelihoods, and ecological balance of the ecosystem as a whole. Regular assessment of water quality is essential to ensure clean and reliable water is available to everyone. This requires regular measurement of pollutants or contaminants in water that can be monitored in real-time. Hence, this research showcases a system that consists of low-cost sensors used to measure five basic parameters of water quality that are: turbidity, total dissolved solids, temperature, pH, and dissolved oxygen. The system incorporates electronics and IoT technology that are powered by a solar charged lead acid battery. The data gathered from the sensors was stored locally on a micro-SD card with live updates that could be viewed on a mobile device when in proximity to the system. Data was gathered from three different bodies of water over a span of three weeks, precisely during the seasonal transition from autumn to winter. We adopted a water sampling technique since our low-cost sensors were not designed for continuous submersion. The results show that the temperature drops gradually during this period and an inversely proportional relationship between pH and temperature could be observed. The concentration of total dissolved solids decreased during rainy periods with a variation in turbidity. The deployed system was robust and autonomous that effectively monitored the quality of water in real-time with scope of adding more sensors and employing Industry 4.0 paradigm to predict variations in water quality.Item Electrochemical and Optical Sensors for Real-Time Detection of Nitrate in Water(MDPI, 2023-08-11) Lal K; Jaywant S; Arif KThe health and integrity of our water sources are vital for the existence of all forms of life. However, with the growth in population and anthropogenic activities, the quality of water is being impacted globally, particularly due to a widespread problem of nitrate contamination that poses numerous health risks. To address this issue, investigations into various detection methods for the development of in situ real-time monitoring devices have attracted the attention of many researchers. Among the most prominent detection methods are chromatography, colorimetry, electrochemistry, and spectroscopy. While all these methods have their pros and cons, electrochemical and optical methods have emerged as robust and efficient techniques that offer cost-effective, accurate, sensitive, and reliable measurements. This review provides an overview of techniques that are ideal for field-deployable nitrate sensing applications, with an emphasis on electrochemical and optical detection methods. It discusses the underlying principles, recent advances, and various measurement techniques. Additionally, the review explores the current developments in real-time nitrate sensors and discusses the challenges of real-time implementation.Item Partial Biodegradable Blend for Fused Filament Fabrication: In-Process Thermal and Post-Printing Moisture Resistance(MDPI AG, 9/04/2022) Harris M; Mohsin H; Naveed R; Potgieter J-G; Ishfaq K; Ray S; Guen M-JL; Archer R; Arif KDespite the extensive research, the moisture-based degradation of the 3D-printed polypropylene and polylactic acid blend is not yet reported. This research is a part of study reported on partial biodegradable blends proposed for large-scale additive manufacturing applications. However, the previous work does not provide information about the stability of the proposed blend system against moisture-based degradation. Therefore, this research presents a combination of excessive physical interlocking and minimum chemical grafting in a partial biodegradable blend to achieve stability against in-process thermal and moisture-based degradation. In this regard, a blend of polylactic acid and polypropylene compatibilized with polyethylene graft maleic anhydride is presented for fused filament fabrication. The research implements, for the first time, an ANOVA for combined thermal and moisture-based degradation. The results are explained using thermochemical and microscopic techniques. Scanning electron microscopy is used for analyzing the printed blend. Fourier transform infrared spectroscopy has allowed studying the intermolecular interactions due to the partial blending and degradation mechanism. Differential scanning calorimetry analyzes the blending (physical interlocking or chemical grafting) and thermochemical effects of the degradation mechanism. The thermogravimetric analysis further validates the physical interlocking and chemical grafting. The novel concept of partial blending with excessive interlocking reports high mechanical stability against moisture-based degradation.Item A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables(Frontiers Media, 25/10/2022) Saleem MH; Potgieter J; Arif KDeep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.Item Methamphetamine detection using nanoparticle-based biosensors: A comprehensive review(Elsevier BV, 2022-12) Lal K; Noble F; Arif KDrug abuse is a global issue, requiring diverse techniques for recognition of drug of interest. One such illicit drug that is abused worldwide is Methamphetamine (METH). It is an addictive and illicit substance that severely affects the central nervous system. Similar to many other illicit substances, recognition of METH in biological fluids and in more diverse matrices such as wastewater, is a topic of great interest to the government and law enforcement agencies. With the rise of nanotechnology that relies on exploiting the properties of certain materials at a scale down to their nanometer range in conjunction with aptamers, molecularly imprinted polymers as well as antibodies have gained much attention over the last decade. The scope and appositeness of nanomaterials have significant characteristics that are highly suitable for recognition of illicit chemical compounds such as METH. This comprehensive review focuses on the detection of METH using nanoparticles in real world samples such as biological fluids and wastewater, while discussing varieties of materials used as nanoparticles and that aid in its recognition. It also offers insights into future opportunities and challenges that come with the use of nanotechnology in sensing applications.Item Hybrid deposition additive manufacturing: novel volume distribution, thermo-mechanical characterization, and image analysis(The Brazilian Society of Mechanical Sciences and Engineering, 25/08/2022) Harris M; Mohsin H; Potgieter J-G; Arif K; Anwar S; AlFaify A; Farooq MUThe structural integrity of additive manufacturing structures is a pronounced challenge considering the voids and weak layer-to-layer adhesion. One of the potential ways is hybrid deposition manufacturing (HDM) that includes fused filament fabrication (FFF) with the conventional filling process, also known as “HDM composites". HDM is a potential technique for improving structural stability by replacing the thermoplastic void structure with a voidless epoxy. However, the literature lacks investigation of FFF/epoxy HDM-based composites regarding optimal volume distribution, effects of brittle and ductile FFF materials, and fractographic analysis. This research presents the effects of range of volume distributions (10–90%) between FFF and epoxy system for tensile, flexure, and compressive characterization. Volume distribution in tensile and flexure samples is achieved using printable wall thickness, slot width, and maximum width. For compression, the printable wall thickness, slot diameter, and external diameter are considered. Polylactic acid and acrylonitrile butadiene styrene are used to analyze the brittle and ductile FFF structures. The research reports novel application of image analysis during mechanical characterization using high-quality camera and fractographic analysis using scanning electron microscopy (SEM). The results present surprising high tensile strain (0.038 mm/mm) and compressive strength (64.5 MPa) for lower FDM-percentages (10%, 20%) that are explained using in situ image analysis, SEM, stress–strain simulations, and dynamic mechanical analysis (DMA). In this regard, the proposed work holds novelty to apply DMA for HDM. The optimal volume distributions of 70% and 80% alongside fractographic mechanisms for lower percentages (10%, 20%) can potentially contribute to structural applications and future material-based innovations for HDM.Item Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network(MDPI AG, 11/01/2023) Ali S; Alam F; Arif K; Potgieter J-GThe advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.Item Low-Cost Sensor for Continuous Measurement of Brix in Liquids(MDPI AG, 25/11/2022) Jaywant SA; Singh H; Arif KThis paper presents a Brix sensor based on the differential pressure measurement principle. Two piezoresistive silicon pressure sensors were applied to measure the specific gravity of the liquid, which was used to calculate the Brix level. The pressure sensors were mounted inside custom-built water-tight housings connected together by fixed length metallic tubes containing the power and signal cables. Two designs of the sensor were prepared; one for the basic laboratory testing and validation of the proposed system and the other for a fermentation experiment. For lab tests, a sugar solution with different Brix levels was used and readings from the proposed sensor were compared with a commercially available hydrometer called Tilt. During the fermentation experiments, fermentation was carried out in a 1000 L tank over 7 days and data was recorded and analysed. In the lab experiments, a good linear relationship between the sugar content and the corresponding Brix levels was observed. In the fermentation experiment, the sensor performed as expected but some problems such as residue build up were encountered. Overall, the proposed sensing solution carries a great potential for continuous monitoring of the Brix level in liquids. Due to the usage of low-cost pressure sensors and the interface electronics, the cost of the system is considered suitable for large scale deployment at wineries or juice processing industries.Item A Performance-Optimized Deep Learning-based Plant Disease Detection Approach for Horticultural Crops of New Zealand(Institute of Electrical and Electronics Engineers, 23/08/2022) Saleem MH; Potgieter J; Arif KDeep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.
