Advanced integrated manufacture by application of sustainable technology through product lifecycle: a circular economy approach

An approach has been developed for sustainable integrated manufacture, covering the whole product development process from material acquisition till the product end of life treatment, which is part of the CIRC4Life project supported by the European Commission's Horizon 2020 programme. The approach consists of three new circular economy business models (CEBMs) including Co-creation of products/services, Sustainable consumption and Collaborative recycling/reuse. The CEBMs are supported by an eco-point approach, information and communication technologies, traceability techniques, online data mining, and other enable techniques/methods. In this paper, the overview and three new CEBMs are presented, followed by a case study of domestic lighting products, illustrating how the approach is implemented in an industrial application

Nitrogen-Mediated Graphene Oxide Enables Highly Efficient Proton Transfer

Two-dimensional (2D) graphene and graphene oxide (GO) offer great potential as a new type of cost-efficient proton-exchange membranes (PEM) for electrochemical devices. However, fundamental issues of proton transfer mechanism via 2D membranes are unclear and the transfer barrier for perfect graphene are too high for practical application. Using ab initio molecular dynamic simulations, we screened the proton transfer barrier for different un-doped and nitrogen doped GO membranes, and clarified the corresponding transfer mechanisms. More significantly, we further identify that N-mediated GO can be built into a highly efficient PEM with a proton transfer rate of seven orders of magnitude higher than an un-doped case via. a proton relay mechanism between a ketone-like oxygen and a pyridine-like nitrogen across the vacancy site. The N-doped 2D GO is also impermeable to small molecules, and hence a highly efficient PEM for practical applications

Cognitive Principles in Robust Multimodal Interpretation

Multimodal conversational interfaces provide a natural means for users to communicate with computer systems through multiple modalities such as speech and gesture. To build effective multimodal interfaces, automated interpretation of user multimodal inputs is important. Inspired by the previous investigation on cognitive status in multimodal human machine interaction, we have developed a greedy algorithm for interpreting user referring expressions (i.e., multimodal reference resolution). This algorithm incorporates the cognitive principles of Conversational Implicature and Givenness Hierarchy and applies constraints from various sources (e.g., temporal, semantic, and contextual) to resolve references. Our empirical results have shown the advantage of this algorithm in efficiently resolving a variety of user references. Because of its simplicity and generality, this approach has the potential to improve the robustness of multimodal input interpretation

Some integral inequalities on time scales

In this paper, some new integral inequalities on time scales are presented by using elementarily analytic methods in calculus of time scales.Comment: 8 page

Sustainable lighting product development underpinned by online data mining and life cycle assessment

The accurate acquisition of customer requirement information is an important part in product planning and positioning, it plays a decisive role in the success of products in the market. the rapid development of e-commerce makes increasing more consumers shopping online and a big volume of customer reviews are posted on different Websites. The online reviews contain valuable opinions of customers, enabling designers to understand their concerns. In this research, an integrated approach has been developed to mine customer requirements according to the online reviews collected from e-commerce sites to form product design specifications. The main research contents include the following aspects: (1) development of useful online review prediction and classification approach; (2) online review implicit product features and sentiment analysis based on the constructed feature and sentiment lexicon; (3) built a knowledge base containing customer requirements mined from online reviews; (4) conduct a dedicated environmental and social LCA on the proposed domestic lighting product by using a professional LCA software. In this study, multiple models and technologies/methods have been successfully implemented: review helpfulness classification model has been constructed based on the training set and test set by tuning and optimizing; proposes a new approach to implicit feature and sentiment analysis, based on explicit formal feature-emotion sentences, implicit feature sentences and implicit sentiment sentences, combined with a feature lexicon, a 1V1/1Vn sentiment-feature rule base and the feature-emotion word pairs are extracted; based on the preliminary analysis results of feature extraction and sentiment analysis, combined with KANO model to establish user requirement mining rules, and consider satisfaction, propose the user demand priority to obtain the final list of user requirements; a real industrial context with lighting product manufacturer (ONA) in Spain has involved with the lighting product life cycle analysis and development for new product. The analytical results of these studies present an in-depth modelling and analysis on the sustainable lighting product lifecycle with the aid of real manufacturing data

Application of life-cycle assessment to the eco-design of LED lighting products

An approach for integrating life-cycle assessment (LCA) into the eco-design of lighting products was developed, and LCAs of five lighting products that are currently on the market were then carried out using this approach. Based on the results of these LCAs, the sustainability requests for lighting products were derived and embedded into the product design specification (PDS), thus ensuring that any product developed according to the PDS would have the desired eco-design features. A new sustainable lighting product was then designed according to the PDS and manufactured, after which the new product underwent LCA. Upon comparing the results of the LCA of the new product with the LCA results for the existing lighting products, the newly designed product was found to provide better environmental performance than the existing products (a 27–58% reduction in environmental impact)

Analysis of RTN signals in Resistive-Switching RAM device and its correlation with device operations

Filament rupture/restoration induced by movement of defects, e.g. oxygen ions/vacancies, is considered as the switching mechanism in HfO2 RRAM. However, details of filament alteration during switching are still speculative, due to the limitations of existing experiment-based probing techniques, impeding its understanding. In this work, for the first time, an RTN-based defect tracking technique is developed for RRAM devices, which can monitor the movements of defects and statistically provide their spatial and energy profiles. The critical filament region is experimentally identified and its alteration is observed and correlated with switching operations under various operation conditions. This provides a useful tool for further development of RRAM technology

Bearing angle based cooperative source localization

© 2014 IEEE. This paper deals with the cooperative source localization problem with the goal of having an accurate estimate of the coordinate of the source cooperatively by a group of unicycle-type mobile agents. Neither absolute positioning information nor a common sense of direction is shared by the agents. Each agent gets its estimate about the source's coordinate in its own local frame based on the bearing measurements about its neighbors (that may or may not include the source) together with its own linear and angular speed information. A continuous time estimation scheme and a distributed fusion scheme are proposed for this goal such that the source's relative coordinate can be estimated at any time by each agent no matter whether it can directly detect the source or not. The globally asymptotic convergence of the estimation scheme and the fusion scheme is rigorously analyzed. Simulation results are also provided to verify the effectiveness of the proposed algorithms

Characterisation of Novel Resistive Switching Memory Devices

Resistive random access memory (RRAM) is widely considered as a disruptive technology that will revolutionize not only non-volatile data storage, but also potentially digital logic and neuromorphic computing. The resistive switching mechanism is generally conceived as the rupture/restoration of defect-formed conductive filament (CF) or defect profile modulation, for filamentary and non-filamentary devices respectively. However, details of the underlying microscopic behaviour of the resistive switching in RRAM are still largely missing. In this thesis, a defect probing technique based on the random telegraph noise (RTN) is developed for both filamentary and non-filamentary devices, which can reveal the resistive switching mechanism at defect level and can also be used to analyse the device performance issues. HfO2 is one of the most matured metal-oxide materials in semiconductor industry and HfO2 RRAM shows promising potential in practical application. An RTN-based defect extraction technique is developed for the HfO2 devices to detect individual defect movement and provide statistical information of CF modification during normal operations. A critical filament region (CFR) is observed and further verified by defect movement tracking. Both defect movements and CFR modification are correlated with operation conditions, endurance failure and recovery. Non-filamentary devices have areal switching characteristics, and are promising in overcoming the drawbacks of filamentary devices that mainly come from the stochastic nature of the CF. a-VMCO is an outstanding non-filamentary device with a set of unique characteristics, but its resistive switching mechanism has not been clearly understood yet. By utilizing the RTN-based defect profiling technique, defect profile modulation in the switching layer is identified and correlated with digital and analogue switching behaviours, for the first time. State instability is analysed and a stable resistance window of 10 for >106 cycles is restored through combining optimizations of device structure and operation conditions, paving the way for its practical application. TaOx-based RRAM has shown fast switching in the sub-nanosecond regime, good CMOS compatibility and record endurance of more than 1012 cycles. Several inconsistent models have been proposed for the Ta2O5/TaOx bilayered structure, and it is difficult to quantify and optimize the performance, largely due to the lack of microscopic description of resistive switching based on experimental results. An indepth analysis of the TiN/Ta2O5/TaOx/TiN structured RRAM is carried out with the RTN-based defect probing technique, for both bipolar and unipolar switching modes. Significant differences in defect profile have been observed and explanations have been provided