13,827 research outputs found
A review of abnormal behavior detection in activities of daily living
Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend
A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling
The accurate prediction of short-term electricity prices is vital for
effective trading strategies, power plant scheduling, profit maximisation and
efficient system operation. However, uncertainties in supply and demand make
such predictions challenging. We propose a hybrid model that combines a
techno-economic energy system model with stochastic models to address this
challenge. The techno-economic model in our hybrid approach provides a deep
understanding of the market. It captures the underlying factors and their
impacts on electricity prices, which is impossible with statistical models
alone. The statistical models incorporate non-techno-economic aspects, such as
the expectations and speculative behaviour of market participants, through the
interpretation of prices. The hybrid model generates both conventional point
predictions and probabilistic forecasts, providing a comprehensive
understanding of the market landscape. Probabilistic forecasts are particularly
valuable because they account for market uncertainty, facilitating informed
decision-making and risk management. Our model delivers state-of-the-art
results, helping market participants to make informed decisions and operate
their systems more efficiently
Analysis and Design of Detection for Liver Cancer using Particle Swarm Optimization and Decision Tree
Liver cancer is taken as a major cause of death all over the world. According to WHO (World Health Organization) every year 9.6 million peoples are died due to cancer worldwide. It is one of the eighth most leading causes of death in women and fifth in men as reported by the American Cancer Society. The number of death rate due to cancer is projected to increase by45 percent in between 2008 to 2030. The most common cancers are lung, breast, and liver, colorectal. Approximately 7, 82,000 peoples are died due to liver cancer each year. The most efficient way to decrease the death rate cause of liver cancer is to treat the diseases in the initial stage. Early treatment depends upon the early diagnosis, which depends on reliable diagnosis methods. CT imaging is one of the most common and important technique and it acts as an imaging tool for evaluating the patients with intuition of liver cancer. The diagnosis of liver cancer has historically been made manually by a skilled radiologist, who relied on their expertise and personal judgement to reach a conclusion. The main objective of this paper is to develop the automatic methods based on machine learning approach for accurate detection of liver cancer in order to help radiologists in the clinical practice. The paper primary contribution to the process of liver cancer lesion classification and automatic detection for clinical diagnosis. For the purpose of detecting liver cancer lesions, the best approaches based on PSO and DPSO have been given. With the help of the C4.5 decision tree classifier, wavelet-based statistical and morphological features were retrieved and categorised
Multimodal spatio-temporal deep learning framework for 3D object detection in instrumented vehicles
This thesis presents the utilization of multiple modalities, such as image and lidar, to incorporate spatio-temporal information from sequence data into deep learning architectures for 3Dobject detection in instrumented vehicles. The race to autonomy in instrumented vehicles or self-driving cars has stimulated significant research in developing autonomous driver assistance systems (ADAS) technologies related explicitly to perception systems. Object detection plays a crucial role in perception systems by providing spatial information to its subsequent modules; hence, accurate detection is a significant task supporting autonomous driving. The advent of deep learning in computer vision applications and the availability of multiple sensing modalities such as 360° imaging, lidar, and radar have led to state-of-the-art 2D and 3Dobject detection architectures. Most current state-of-the-art 3D object detection frameworks consider single-frame reference. However, these methods do not utilize temporal information associated with the objects or scenes from the sequence data. Thus, the present research hypothesizes that multimodal temporal information can contribute to bridging the gap between 2D and 3D metric space by improving the accuracy of deep learning frameworks for 3D object estimations. The thesis presents understanding multimodal data representations and selecting hyper-parameters using public datasets such as KITTI and nuScenes with Frustum-ConvNet as a baseline architecture. Secondly, an attention mechanism was employed along with convolutional-LSTM to extract spatial-temporal information from sequence data to improve 3D estimations and to aid the architecture in focusing on salient lidar point cloud features. Finally, various fusion strategies are applied to fuse the modalities and temporal information into the architecture to assess its efficacy on performance and computational complexity. Overall, this thesis has established the importance and utility of multimodal systems for refined 3D object detection and proposed a complex pipeline incorporating spatial, temporal and attention mechanisms to improve specific, and general class accuracy demonstrated on key autonomous driving data sets
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition
Sustainable eSiC reinforced composite materials – synthetization and characterization
Sustainable and light weight composite materials have received extensive attention in the application of
aerospace, automotive, agriculture and marine. Synthetic SiC is expensive and harmful to the human being. Therefore, the
aim of this study is to develop eSiC reinforced aluminium matrix sustainable composite material using waste rice husk with
the process route of powder metallurgy. Simple and cost-effective pyrolysis process was used for the extraction of low�density eSiC from agricultural waste rice husk which contains a significant amount of silica. This silica was then converted
in to environmentally friendly SiC (known as eSiC) material and used as a reinforcing agent to the lightweight composite
development. From the results, these materials showed good metallurgical bonding with better mechanical properties. It is
also observed that compared to metallic cast iron, this new composite material is better in terms of cost, material usage,
eco-friendly (no harm to the environment and people), hence, sustainable. This concept demonstrates that this new
sustainable and lightweight material can be used for aerospace, automotive and other structural applications, especially for
disk brake, liner, and shaft. This eSiC can also be used as a coating material for composite coating development
Intelligent Control Schemes for Maximum Power Extraction from Photovoltaic Arrays under Faults
Investigation of power output from PV arrays under different fault conditions is an essential task to enhance performance of a photovoltaic system under all operating conditions. Significant reduction in power output can occur during various PV faults such as module disconnection, bypass diode failure, bridge fault, and short circuit fault under non-uniform shading conditions. These PV faults may cause several peaks in the characteristics curve of PV arrays, which can lead to failure of the MPPT control strategy. In fact, impact of a fault can differ depending on the type of PV array, and it can make the control of the system more complex. Therefore, consideration of suitable PV arrays with an effective control design is necessary for maximum power output from a PV system. For this purpose, the proposed study presents a comparative study of two intelligent control schemes, i.e., fuzzy logic (FL) and particle swarm optimization (PSO), with a conventional control scheme known as perturb and observe (P&O) for power extraction from a PV system. The comparative analysis is based on the performance of the control strategies under several faults and the types of PV modules, i.e., monocrystalline and thin-film PV arrays. In this study, numerical analysis for complex fault scenarios like multiple faults under partial shading have also been performed. Different from the previous literature, this study will reveal the performance of FL-, PSO-, and P&O-based MPPT strategies to track maximum peak power during multiple severe fault conditions while considering the accuracy and fast-tracking efficiencies of the control techniques. A thorough analysis along with in-depth quantitative data are presented, confirming the superiority of intelligent control techniques under multiple faults and different PV types
Application of fuzzy controllers in automatic ship motion control systems
Automatic ship heading control is a part of the automatic navigation system. It is charged with the task of maintaining the actual ship’s course angle or actual ship’s course without human intervention in accordance with the set course or setting parameter and maintaining this condition under the effect of disturbing influences. Thus, the corrective influence on deviations from a course can be rendered by the position of a rudder or controlling influence that leads to the rotary movement of a vessel around a vertical axis that represents a problem, which can be solved with the use of fuzzy logic. In this paper, we propose to consider the estimation of the efficiency of fuzzy controllers in systems of automatic control of ship movement, obtained by analysis of a method of the formalized record of a logic conclusion and structure of the fuzzy controller. The realization of this allows to carry out effective stabilization of a course angle of a vessel taking into account existing restrictions
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
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