Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

Multiscale Investigations on the Mechanical Behaviour and Hydrogen Embrittlement of Advanced Medium Manganese Steel

The current studied medium Mn steel has demonstrated remarkable mechanical performance among other 3rd-Gen AHSS, with a yield strength of about 1140 MPa, a tensile strength of around 1600 MPa, and a uniform elongation of ~ 30%. These extraordinary mechanical properties could be attributed to the high dislocation density resulting from warm rolling and quenching processes, while the large fraction of elongated retained austenite with a wide range of stability that will be gradually transformed into martensite during deformation can provide a sustained work-hardening effect to enable the excellent ductility. The stress-strain curves indicated the pronounced plastic instability featured by localised deformation that could be reflected by the Lüder band and Portevin-Le Chatelier (PLC) bands. It was found that the discontinuous yielding behaviours were significantly affected by the applied strain rates, in which more prominent stress serration events were observed with decreasing strain rates. In addition, extensive experimental studies were conducted on the effects of strain rates on the discontinuous yielding behaviours using a series of in-situ and ex-situ macroscale and microscale characterisation methods. The experimental results indicated that loading conditions in terms of strain rate and deformation temperature essentially influence the formation of shear bands during deformation, resulting in different kinetics of martensitic transformation and therefore affecting the mechanical properties remarkably. The current studied medium Mn steel showed pronounced premature failure after hydrogen ingression, featuring a hydrogen embrittlement index (HEI) of 37%. Microstructural studies suggested that the deformation mechanism was not affected by the hydrogen, whereas the fracture mechanism was heavily altered by the release of mobile hydrogen atoms resulting from phase transformation

C-Peptide Prevents Hippocampal Apoptosis in Type 1 Diabetes

To explore mechanisms underlying central nervous system (CNS) complications in diabetes, we examined hippocampal neuronal apoptosis and loss, and the effect of C-peptide replacement in type 1 diabetic BB/W rats. Apoptosis was demonstrated after 8 months of diabetes, by DNA fragmentation, increased number of apoptotic cells, and an elevated ratio of Bax/Bcl-xL, accompanied by reduced neuronal density in the hippocampus. No apoptotic activity was detected and neuronal density was unchanged in 2-month diabetic hippocampus, whereas insulin-like growth factor (IGF) activities were impaired. In type 1 diabetic BB/W rats replaced with C-peptide, no TdT-mediated dUTP nick-end labeling (TUNEL)- positive cells were shown and DNA laddering was not evident in hippocampus at either 2 or 8 months. C-peptide administration prevented the preceding perturbation of IGF expression and reduced the elevated ratio of Bax/Bcl-xL. Our data suggest that type 1 diabetes causes a duration-dependent programmed cell death of the hippocampus, which is partially prevented by C-peptide

The Insulin-Like Growth Factor System and Neurological Complications in Diabetes

The IGF system plays vital roles in neuronal development, metabolism, regeneration and survival. It consists of IGF-I, IGF-II, insulin, IGF-I-receptor, and those of IGF-II and insulin as well as IGF-binding proteins. In the last decades it has become clear that perturbations of the IGF system play important roles in the pathogenesis of diabetic neurological complications. In the peripheral nervous system IGF-I, insulin, and C-peptide particularly in type 1 diabetes participate in the development of axonal degenerative changes and contributes to impaired regenerative capacities. These abnormalities of the IGF system appear to be less pronounced in type 2 diabetes, which may in part account for the relatively milder neurological complications in this type of diabetes. The members of the IGF system also provide anti-apoptotic effects on both peripheral and central nervous system neurons. Furthermore, both insulin and C-peptide and probably IGF-I possess gene regulatory capacities on myelin constituents and axonal cytoskeletal proteins. Therefore, replenishment of various members of the IGF system provides a reasonable rational for prevention and treatment of diabetic neurological complications

The Effects of C-peptide on Type 1 Diabetic Polyneuropathies and Encephalopathy in the BB/Wor-rat

Diabetic polyneuropathy (DPN) occurs more frequently in type 1 diabetes resulting in a more severe DPN. The differences in DPN between the two types of diabetes are due to differences in the availability of insulin and C-peptide. Insulin and C-peptide provide gene regulatory effects on neurotrophic factors with effects on axonal cytoskeletal proteins and nerve fiber integrity. A significant abnormality in type 1 DPN is nodal degeneration. In the type 1 BB/Wor-rat, C-peptide replacement corrects metabolic abnormalities ameliorating the acute nerve conduction defect. It corrects abnormalities of neurotrophic factors and the expression of neuroskeletal proteins with improvements of axonal size and function. C-peptide corrects the expression of nodal adhesive molecules with prevention and repair of the functionally significant nodal degeneration. Cognitive dysfunction is a recognized complication of type 1 diabetes, and is associated with impaired neurotrophic support and apoptotic neuronal loss. C-peptide prevents hippocampal apoptosis and cognitive deficits. It is therefore clear that substitution of C-peptide in type 1 diabetes has a multitude of effects on DPN and cognitive dysfunction. Here the effects of C-peptide replenishment will be extensively described as they pertain to DPN and diabetic encephalopathy, underpinning its beneficial effects on neurological complications in type 1 diabetes

An evolution strategy of GAN for the generation of high impedance fault samples based on Reptile algorithm

In a distribution system, sparse reliable samples and inconsistent fault characteristics always appear in the dataset of neural network fault detection models because of high impedance fault (HIF) and system structural changes. In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. First, the GANRA generates enough high-quality analogous fault data to solve a shortage of realistic fault data for the fault detection model’s training. Second, an evolution strategy is proposed to help the GANRA improve the fault detection neural network’s accuracy and generalization by searching for GAN’s initial parameters. Finally, Convolutional Neural Network (CNN) is considered as the identification fault model in simulation experiments to verify the validity of the evolution strategy and the GANRA under the HIF environment. The results show that the GANRA can optimize the initial parameters of GAN and effectively reduce the calculation time, the sample size, and the number of learning iterations needed for dataset generation in the new grid structures

Housing Development Building Management System (HDBMS) For Optimized Electricity Bills

Smart Buildings is a modern building that allows residents to have sustainable comfort with high efficiency of electricity usage. These objectives could be achieved by applying appropriate, capable optimization algorithms and techniques. This paper presents a Housing Development Building Management System (HDBMS) strategy inspired by Building Energy Management System (BEMS) concept that will integrate with smart buildings using Supply Side Management (SSM) and Demand Side Management (DSM) System. HDBMS is a Multi-Agent System (MAS) based decentralized decision making system proposed by various authors. MAS based HDBMS was created using JAVA on a IEEE FIPA compliant multi-agent platform named JADE. It allows agents to communicate, interact and negotiate with energy supply and demand of the smart buildings to provide the optimal energy usage and minimal electricity costs.  This results in reducing the load of the power distribution system in smart buildings which simulation studies has shown the potential of proposed HDBMS strategy to provide the optimal solution for smart building energy management

Learning to Learn: How to Continuously Teach Humans and Machines

Our education system comprises a series of curricula. For example, when we learn mathematics at school, we learn in order from addition, to multiplication, and later to integration. Delineating a curriculum for teaching either a human or a machine shares the underlying goal of maximizing the positive knowledge transfer from early to later tasks and minimizing forgetting of the early tasks. Here, we exhaustively surveyed the effect of curricula on existing continual learning algorithms in the class-incremental setting, where algorithms must learn classes one at a time from a continuous stream of data. We observed that across a breadth of possible class orders (curricula), curricula influence the retention of information and that this effect is not just a product of stochasticity. Further, as a primary effort toward automated curriculum design, we proposed a method capable of designing and ranking effective curricula based on inter-class feature similarities. We compared the predicted curricula against empirically determined effectual curricula and observed significant overlaps between the two. To support the study of a curriculum designer, we conducted a series of human psychophysics experiments and contributed a new Continual Learning benchmark in object recognition. We assessed the degree of agreement in effective curricula between humans and machines. Surprisingly, our curriculum designer successfully predicts an optimal set of curricula that is effective for human learning. There are many considerations in curriculum design, such as timely student feedback and learning with multiple modalities. Our study is the first attempt to set a standard framework for the community to tackle the problem of teaching humans and machines to learn to learn continuously

Cathepsin B Regulates Collagen Expression by Fibroblasts via Prolonging TLR2/NF- κ

Fibroblasts are essential for tissue repair due to producing collagens, and lysosomal proteinase cathepsin B (CatB) is involved in promoting chronic inflammation. We herein report that CatB regulates the expression of collagens III and IV by fibroblasts in response to a TLR2 agonist, lipopolysaccharide from Porphyromonas gingivalis (P.g. LPS). In cultured human BJ fibroblasts, mRNA expression of CatB was significantly increased, while that of collagens III and IV was significantly decreased at 24 h after challenge with P.g. LPS (1 μg/mL). The P.g. LPS-decreased collagen expression was completely inhibited by CA-074Me, the specific inhibitor of CatB. Surprisingly, expression of collagens III and IV was significantly increased in the primary fibroblasts from CatB-deficient mice after challenge with P.g. LPS. The increase of CatB was accompanied with an increase of 8-hydroxy-2′-deoxyguanosine (8-OHdG) and a decrease of IκBα. Furthermore, the P.g. LPS-increased 8-OHdG and decreased IκBα were restored by CA-074Me. Moreover, 87% of CatB and 86% of 8-OHdG were colocalized with gingival fibroblasts of chronic periodontitis patients. The findings indicate the critical role of CatB in regulating the expression of collagens III and IV by fibroblasts via prolonging TLR2/NF-κB activation and oxidative stress. CatB-specific inhibitors may therefore improve chronic inflammation-delayed tissue repair