Integrate design structure matrix with carbon footprint for product low carbon design

Product low-carbon design plays a significant part in reducing carbon footprint during the product life cycle. This paper proposed Design Structure Matrix integrated with Carbon Footprint (DSM-CF) to design for low carbon footprint. After the carbon footprint for product life cycle is discussed in detail, the DSM-CF is also proposed. The construction, decomposition and clustering algorithm of DSM-CF model is then put forward in detail. The impact of the carbon footprint of each design task on the entire product life cycle is analyzed, which could effectively reduce the carbon footprint of products. At the end of this paper, the DSM-CF model of the hand rehabilitation robot system is established to verify the effectiveness of the model.     

The integration of local government,residents, and insurance in coastal adaptation: An agent-based modeling approach

Previous evaluation of flood risk overlooks the behavior and capacity of private stakeholders, thus limiting the application of adaptation policies. This study presents an agent-based model, applied to Miami-Dade County, FL, as the case study, to explore the public and private interaction in coastal flood adaptation and mitigation. The decision making of individuals' adaptive behavior is simulated based on the prospected theory under households' risk perception, insurance policies, and the local flood mitigation. The NFIP and private insurance policy are simulated separately to reflect the flood insurance market. Our results show that households' risk mitigation behaviors are clustered in high-risk coastal areas, including Miami-Beach and the east coast of the County. The overall flood risk is still high in the southern part of the County. To better reflect the flooding risk and address the affordability issue, a voucher coupled house elevation program could improve the insurance take-up rates as well as reduce the overall flood risk in the area. Results also indicate that private insurance would slightly increase if the NFIP's insurance rates increase. Afterward, four adaptation scenarios in response to future sea level rises are examined by considering the voucher-based insurance program and the local adaptation actions. Compared with the high-risk reduction but low coverage mitigation plan, more extensive coverage of public adaptation would better improve the overall adaptation outcome of the County, which indicates the importance of public participation in local risk mitigation and urban governance.     

Maritime piracy situation modelling with dynamic Bayesian networks

A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a larger DBN. The application of synthetic data fabrication of maritime vessel behaviour is considered. Behaviour of various vessels in a maritime piracy situation is simulated. A means to integrate information from context based external factors that influence behaviour is provided. Simulated observations of the vessels kinematic states are generated. The generated data may be used for the purpose of developing and evaluating counter-piracy methods and algorithms. A novel methodology for evaluating and optimising behavioural models such as the proposed model is presented. The log-likelihood, cross entropy, Bayes factor and the Bhattacharyya distance measures are applied for evaluation. The results demonstrate that the generative model is able to model both spatial and temporal datasets.     

Hierarchical classification method based on selective learning of slacked hierarchy for activity recognition systems

Physical activity recognition using wearable sensors has gained significant interest from researchers working in the field of ambient intelligence and human behavior analysis. The problem of multi-class classification is an important issue in the applications which naturally has more than two classes. A well-known strategy to convert a multi-class classification problem into binary sub-problems is the error-correcting output coding (ECOC) method. Since existing methods use a single classifier with ECOC without considering the dependency among multiple classifiers, it often fails to generalize the performance and parameters in a real-life application, where different numbers of devices, sensors and sampling rates are used. To address this problem, we propose a unique hierarchical classification model based on the combination of two base binary classifiers using selective learning of slacked hierarchy and integrating the training of binary classifiers into a unified objective function. Our method maps the multi-class classification problem to multi-level classification. A multi-tier voting scheme has been introduced to provide a final classification label at each level of the solicited model. The proposed method is evaluated on two publicly available datasets and compared with independent base classifiers. Furthermore, it has also been tested on real-life sensor readings for 3 different subjects to recognize four activities i.e. Walking, Standing, Jogging and Sitting. The presented method uses same hierarchical levels and parameters to achieve better performance on all three datasets having different number of devices, sensors and sampling rates. The average accuracies on publicly available dataset and real-life sensor readings were recorded to be 95% and 85%, respectively. The experimental results validate the effectiveness and generality of the proposed method in terms of performance and parameters.     

Iterative design of MOVE: A situationally appropriate vehicle navigation system

Drivers need assistance when navigating an unfamiliar route. In-vehicle navigation systems have improved in recent years due to the technology advances, but are sometimes problematic because of information overload while driving. To address the attentional demands of reading a map while driving, we have developed the maps optimized for vehicular environments (MOVE) in-car navigation display, which provides situationally appropriate navigation information to the driver through optimization of map information.In this paper, we describe the iterative design and evaluation process that shaped the MOVE system. We describe early map reading and navigation studies that led to early designs for our system. We present a study on visual search tasks that refined the renditions used for the system. Finally, we present a study on the effectiveness of several variations of a contextually optimized route map visualization with a desktop steering system.The result of this study shows that MOVE's contextually optimized navigation information can reduce the driver's perceptual load significantly. Our laboratory experiment shows that the total map display fixation time was decreased six-fold, and the number of glances to interpret the map display were decreased about threefold, when comparing the contextually optimized display to a static display.     

HIES: Cases for hydrogen energy and I-Energy

With the increasing demand for electricity, more and more fossil fuels are used to generate electricity which leads to energy shortage and environmental pollution. Therefore, using Renewable Energy Sources (RESs) and developing sustainable smart grid have become a common global priority: Since RESs, like solar and wind energy, are inherently unstable, hydrogen energy, as a completely clean new energy, has received widespread attention: I-Energy, which combines information and energy, is a new research direction in smart grid. Furthermore, the household electricity usage accounts for 41% of the total power consumption. Therefore, Household Intelligent Electricity System (HIES), combining hydrogen energy and i-Energy, becomes smart solutions. In this paper, we review the scientific literature for hydrogen energy and i-Energy on HIES, including recognition of electricity appliances, establishment of power consumption model, human activity analysis, smart interactive terminal, home energy management system, distributed power supply and district coordinated power utilization. Finally, we summarize the challenges and give the solutions concerning HIES, and this work can give a useful reference to new energy used model and environment protection.     

Bioconvection of gravitactic micro-organisms in rectangular enclosures

This paper investigates the gravitactic bioconvection in rectangular enclosures. The governing equations are the continuity, the Navier–Stokes equations with the Boussinesq approximation and the diffusion equation for the motile micro-organisms. The control volume method is used to solve numerically the complete set of governing equations. The effects of bioconvection Peclet number from 0.1 to 10 and the aspect ratio from 1 to 5 are investigated on the onset of bioconvection. It was found that the bifurcation was subcritical in all cases. The critical Rayleigh number is decreased with increasing bioconvection Peclet number and with increasing aspect ratio.     

Predictive maintenance decision-making for variable faults with non-equivalent costs of fault severities

Predictive Maintenance is crucial for production systems as it helps maintaining the reliability and availability of components/equipment as well as preventing unexpected shutdowns during production. Traditional maintenance strategies mostly focus on the predictive diagnosis of fault types and identical maintenance decisions would be delivered for the equipment with the same fault type. It often results in “over-maintenance” as the variable fault severities may require non-equivalent costs of maintenance resources. To tackle this problem, this paper aims at developing a fault prediction model firstly predicting fault severity and fault type simultaneously and subsequently providing distinguished maintenance strategy for variable faults accordingly, through which the abnormal faults of equipment can be effectively prevented, and machines can be efficiently and economically maintained based on the model suggested decisions. The main works in this study are 1) The fault features based on monitored vibration signals are extracted from multi-domains, and most significant features are selected by L1-Support Vector Machine (L1-SVM) together with variance filtering method; 2) A parallel fault prediction model based on Back propagation Neural Network and Long Short Term Memory Neural Network (BP-LSTM) is proposed, which is used to predict the fault type and fault degree simultaneously; 3) A Deep Q-Network (DQN)-based maintenance decision-making model is established for different fault types with various fault severities.