Consumer satisfaction on the adoption of e-payment among millennials in Malaysia during Covid 19 pandemic

The Pandemic Covid-19 had hugely impacted businesses and economy in every sector of the world through the implementation of Lockdowns and Movement Control Order (MCO). This situation has unavoidably caused a worldwide monetary decline (Cheng, 2020 & UNDP, 2020). , The MCO situation has limited the normal face to face retailing activity and affected consumer goods and the retail industry. Stores of essential items along with meals, groceries, and healthcare experienced extended call for opportunities for serving purchasers at home, at the same time as facing demanding situations of stock, supply chain control, shipping, and maintaining their facility a secure environment (Roggeveen & Sethuraman, 2020)

Intelligent Control Strategies for an Autonomous Underwater Vehicle

The dynamic characteristics of autonomous underwater vehicles (AUVs) present a control problem that classical methods cannot often accommodate easily. Fundamentally, AUV dynamics are highly non-linear, and the relative similarity between the linear and angular velocities about each degree of freedom means that control schemes employed within other flight vehicles are not always applicable. In such instances, intelligent control strategies offer a more sophisticated approach to the design of the control algorithm. Neurofuzzy control is one such technique, which fuses the beneficial properties of neural networks and fuzzy logic in a hybrid control architecture. Such an approach is highly suited to development of an autopilot for an AUV. Specifically, the adaptive network-based fuzzy inference system (ANFIS) is discussed in Chapter 4 as an effective new approach for neurally tuning course-changing fuzzy autopilots. However, the limitation of this technique is that it cannot be used for developing multivariable fuzzy structures. Consequently, the co-active ANFIS (CANFIS) architecture is developed and employed as a novel multi variable AUV autopilot within Chapter 5, whereby simultaneous control of the AUV yaw and roll channels is achieved. Moreover, this structure is flexible in that it is extended in Chapter 6 to perform on-line control of the AUV leading to a novel autopilot design that can accommodate changing vehicle pay loads and environmental disturbances. Whilst the typical ANFIS and CANFIS structures prove effective for AUV control system design, the well known properties of radial basis function networks (RBFN) offer a more flexible controller architecture. Chapter 7 presents a new approach to fuzzy modelling and employs both ANFIS and CANFIS structures with non-linear consequent functions of composite Gaussian form. This merger of CANFIS and a RBFN lends itself naturally to tuning with an extended form of the hybrid learning rule, and provides a very effective approach to intelligent controller development.The Sea Systems and Platform Integration Sector, Defence Evaluation and Research Agency, Winfrit

Um método multivariável para avaliação da demanda de energia elétrica a curto prazo

Projetar a demanda de energia elétrica é tarefa fundamental no planejamento da operação e da expansão do sistema elétrico. A complexidade destas projeções está associada ao número de variáveis que podem influenciá-la. O enfoque tradicional de projeção utiliza variáveis climáticas como parâmetros para caracterizar a demanda de energia elétrica. No entanto, outras variáveis relevantes podem ser consideradas nestes estudos. Assim, o objetivo deste trabalho é desenvolver uma metodologia e um modelo para projeção de demanda a curto prazo considerando variáveis climáticas, conservação, econômicas e de decisão (adesão de micro e minigeração fotovoltaica). A fundamentação teórica deste trabalho está apoiada nos conceitos relativos à técnica de análise e modelagem de Dinâmica de Sistemas. A construção do modelo foi dividida em duas partes. Na primeira etapa o modelo é definido por uma projeção tradicional da demanda em função de variáveis climáticas. Na segunda etapa o modelo é modificado para a releitura da projeção em função das demais variáveis e a consolidação do comportamento dinâmico através de ligações causais realimentadas. Para avaliar a metodologia proposta foram construídos sete cenários, que representam a atuação de políticas referentes ao acionamento de bandeiras tarifárias, o nível da economia e a adesão de micro e minigeração fotovoltaica. Os resultados destes cenários validam a Dinâmica de Sistemas como uma abordagem adequada ao trato do problema da avaliação multivariável da demanda de energia elétrica a curto prazo.Electricity demand forecasting is a fundamental task for the expansion and operation of a power system. The complexity of this forecasting is associated with the number of variables that might influence it. The traditional forecasting approach uses climatic variables as parameters to describe the electricity demand. However, other relevant variables should be considered in these studies. In this way, the aim of this work is to develop a methodology and a model for short-term demand forecasting considering climatic variables, conservation, economy and decision-making (adherence of micro and small photovoltaic generation). The theoretical basis of this work rests on the concepts related to the System Dynamics analysis and modeling technique. The model construction was divided in two steps. In the first step, the model is defined by a traditional demand forecasting based on climatic variables. In the second step, the model is modified to revamp the forecasting in light of another variables and fund the dynamic behavior of the model through causal loops. To evaluate the proposed methodology were built seven scenarios which represent the policies for the tariff flags, the level of the economy and the accession of micro and small photovoltaic generation. The results of these scenarios validate the System Dynamics technique as an appropriate approach to deal with the problem of multivariable evaluation of the electricity demand in the short-term

Investigating renewable energy systems using artifcial intelligence techniques

This research investigated applying Artificial Intelegence (AI) and Machine Learning (ML) to renewable energy through three studies. The first study characterized and mapped the recent research landscape in the field of AI applications for various renewable energy systems using Natural Language Prcoessing (NLP) and ML models. It considered published documetns at Scopus database in the period (2000-2021). The second study built hybrid Catboost-CNN-LSTM architecture pipeline to predict an industrial-scale biogas plant’s daily biogas production and investigate the feedstock components importance on it. The third study investigated prediciting biogas yield of various subtrates and the significance of each organic component (carbohydrates, proteins, fats/lipids, and legnin) in biogas production using hybrid VAE-XGboost model. The first study showed seven main metatopics and ascent of "deep learning (DL)" as a prominent methodology led to an increase in intricate subjects, including the optimization of power costs and the prediction of wind patterns. Also, a growing utilization of DL approaches for the analysis of renewable energy data, particularly in the context of wind and solar photovoltaic systems. The research themes and trends observed in the first study signify substantial recent investments in advanced AI learning techniques. The developed Catboost-CNN-LSTM pipeline achived a significant results and presented a superior approach when compared to previous relevant studies by eliminating the requirement for feature engineering, enabling direct prediction of biogas yield without the need for converting it into a classification task. The VAE-XGboost pipeline could ovcercome data limitation in the field and produced significant results. It has shown that the "fats" category is the most influential group on the methane production in biogas plants, however, “proteins” illustrated the lowest impact on biogas production

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras