Activities recognition and worker profiling in the intelligent office environment using a fuzzy finite state machine

Analysis of the office workers’ activities of daily working in an intelligent office environment can be used to optimize energy consumption and also office workers’ comfort. To achieve this end, it is essential to recognise office workers’ activities including short breaks, meetings and non-computer activities to allow an optimum control strategy to be implemented. In this paper, fuzzy finite state machines are used to model an office worker’s behaviour. The model will incorporate sensory data collected from the environment as the input and some pre-defined fuzzy states are used to develop the model. Experimental results are presented to illustrate the effectiveness of this approach. The activity models of different individual workers as inferred from the sensory devices can be distinguished. However, further investigation is required to create a more complete model

Predicting remaining useful life of rotating machinery based artificial neural network

Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure

Human activities recognition based on neuro-fuzzy finite state machine

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques

Enhanced fuzzy finite state machine for human activity modelling and recognition

A challenging key aspect of modelling and recognising human activity is to design a model that can deal with the uncertainty in human behaviour. Several machine learning and deep learning techniques are employed to model the Activity of Daily Living (ADL) representing the human activity. This paper proposes an enhanced Fuzzy Finite State Machine (FFSM) model by combining the classical FFSM with Long Short-Term Memory (LSTM) neural network and Convolutional Neural Network (CNN). The learning capability in the LSTM and CNN allows the system to learn the relationship in the temporal human activity data and to identify the parameters of the rule-based system as building blocks of the FFSM through time steps in the learning mode. The learned parameters are then used for generating the fuzzy rules that govern the transitions between the system’s states representing activities. The proposed enhanced FFSMs were tested and evaluated using two different datasets; a real dataset collected by our research group and a public dataset collected from CASAS smart home project. Using LSTM-FFSM, the experimental results achieved 95.7% and 97.6% for the first dataset and the second dataset, respectively. Once CNN-FFSM was applied to both datasets, the obtained results were 94.2% and 99.3%, respectively

Fuzzy Finite State Machine for human activity modelling and recognition

Independent living is a housing arrangement designed exclusively for older adults to support them with their Activity of Daily Living (ADL) in a safe and secure environment. The provision of independent living would reduce the cost of social care while elderly residents are kept in their own homes. Therefore, there is a need for an automated system to monitor the residents to be able to understand their activities and only when abnormal activities are identified, provide human support to resolve the issue. Three main approaches are used for gathering data representing the human’s activities; ambient sensory device-based, wearable sensory device-based and camera vision device-based. Ambient sensory devices-based systems use sensors such as Passive Infra-Red (PIR) and door entry sensors to capture a user’s presence or absence within a specific area and record them as binary information. Gathering data using these sensory devices are widely accepted, as they are unobtrusive and it does not affect the ADLs. However, wearable sensory devices-based and camera vision device-based approaches are undesirable to many users especially for the older adults users as they more often forget to wear them and due to some privacy concerns. Recognising and modelling human activities from unobtrusive sensors is a topic addressed in Ambient Intelligence (AmI) research. The research proposed in this thesis aims to recognise and model human activities in an indoor environment based on ambient sensory device-based data. Different methods including statistical, machine learning and deep learning techniques are already researched to address the challenges of recognising and modelling human activities. The research in this thesis is mainly focusing on the application of Fuzzy Finite State Machine (FFSM) for human activities modelling and proposes ways for enhancing the FFSM performance to improve the accuracy of human activity modelling. In this thesis, three novel contributions are made which are outlined as follows; Firstly, a framework is proposed for combining the learning abilities of Neural Networks (NNs), Long Short-Term Memory (LSTM) neural network and Convolutional Neural Networks (CNNs) with the existing FFSM for human activity modelling and recognition. These models are referred to as NN-FFSM, LSTM-FFSM and CNN-FFSM. Secondly, to obtain the optimal feature representation from the acquired sensory information, relevant features are extracted and fuzzified with the selected membership degrees, these features are then applied to the different enhanced FFSM models. Thirdly, binary data gathered from the ambient sensors including PIR and door entry sensors are represented as greyscale images. A pre-trained Deep Convolutional Neural Network (DCNN) such as AlexNet is used to select and extract features from the generated greyscale image for each activity. The selected features are then used as inputs to Adaptive Boosting (AdaBoost) and Fuzzy C-means (FCM) classifiers for modelling and recognising the ADL for a single user. The proposed enhanced FFSM models were tested and evaluated using two different datasets representing the ADL for a single user. The first dataset was collected at the Smart Home facilities at NTU and the second dataset is a public dataset collected from CASAS smart home project

Implementing Industry 4.0 in SMEs

This open access book addresses the practical challenges that Industry 4.0 presents for SMEs. While large companies are already responding to the changes resulting from the fourth industrial revolution , small businesses are in danger of falling behind due to the lack of examples, best practices and established methods and tools. Following on from the publication of the previous book ‘Industry 4.0 for SMEs: Challenges, Opportunities and Requirements’, the authors offer in this new book innovative results from research on smart manufacturing, smart logistics and managerial models for SMEs. Based on a large scale EU-funded research project involving seven academic institutions from three continents and a network of over fifty small and medium sized enterprises, the book reveals the methods and tools required to support the successful implementation of Industry 4.0 along with practical examples

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Learning preferences for personalisation in a pervasive environment

With ever increasing accessibility to technological devices, services and applications there is also an increasing burden on the end user to manage and configure such resources. This burden will continue to increase as the vision of pervasive environments, with ubiquitous access to a plethora of resources, continues to become a reality. It is key that appropriate mechanisms to relieve the user of such burdens are developed and provided. These mechanisms include personalisation systems that can adapt resources on behalf of the user in an appropriate way based on the user's current context and goals. The key knowledge base of many personalisation systems is the set of user preferences that indicate what adaptations should be performed under which contextual situations. This thesis investigates the challenges of developing a system that can learn such preferences by monitoring user behaviour within a pervasive environment. Based on the findings of related works and experience from EU project research, several key design requirements for such a system are identified. These requirements are used to drive the design of a system that can learn accurate and up to date preferences for personalisation in a pervasive environment. A standalone prototype of the preference learning system has been developed. In addition the preference learning system has been integrated into a pervasive platform developed through an EU research project. The preference learning system is fully evaluated in terms of its machine learning performance and also its utility in a pervasive environment with real end users