Development of novel design methodology for product mass customization based on human attributes and cognitive behaviours

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The competition in the global market is accelerating rapidly because of less technological gap, matured manufacturing level, and various changing customer needs. Increasingly customers choose products in terms of experience desires, psychological desires and whether the products can reflect their values, in addition to the main product functions. Moreover, there are a large number of small and medium sized manufacturing companies in the developing countries. OEM (Original Equipment Manufacturer) and simple mass production cannot generate good value for these manufacture companies, and they have been seeking new opportunities to create higher value for their products/services and satisfy different needs of customers. Mass customization is one of the main business forms in the future, which can best meet the needs of individual customer, especially psychological needs. The key to mass customization is to provide enough modules to meet individual needs with a limited cost increase. The problem has been how to identify the real user needs and individual differences. The purpose of this research is to develop a sound design methodology based upon the current product design theories and practices for future product innovation and sustainable growth of small and medium sized manufacturing enterprises. The research focuses on the user-product cognitive behaviours and the relationship between human attributes and product features. Orthogonal experiment, eye tracking technology and artificial neural network have been successfully applied in this research. The research has developed a user needs hierarchy model and added value hierarchy model, and a robust theoretical basis to predict and evaluate (individual) user needs for product design. The research has further made the following contributions: 1) The relationship between human attributes and product features has been established, which can help designers understand the differences of various customer groups; 2) The different effects of various influence factors on people’s cognition and preference choice based on vision have been analysed and discussed; 3) A new method to identify, cluster, and combine common needs and personalized needs in early design stage for mass customization has been developed; 4) The research results can be reused in the future design of the same or similar kind of products

Interleukin 1 Receptor and Alzheimer’s Disease-Related Neuroinflammation

Neuroinflammation as one of the pathogenic mechanisms concerning to the development of Alzheimer’s disease (AD) has aroused more attention since last decades. Amyloid beta (Aβ) peptide generation is supposed to be the initial event in AD progress, followed by neuronal impairment, neuroinflammation, and severe substantial neuronal dysfunction. Interleukin-1 receptor (IL-1R) as one of the most prevalent inflammatory mediated surface receptors, participates not only in peripheral inflammation but also in AD-related neuroinflammation. In microglia, IL-1R activation triggers the downstream signaling and the production of proinflammatory cytokines and chemokines. IL-1R signaling also participates in AD-related Aβ-induced inflammasome activation. Besides, IL-1R activation in neurons may increase APP non-amyloid pathway by modulation of APP α-secretase activity, which may prevent neurotoxic Aβ generation. Thus, the exact role of IL-1R signaling in AD development and neuronal functions is somehow tricky

Disturbance Grassmann Kernels for Subspace-Based Learning

In this paper, we focus on subspace-based learning problems, where data elements are linear subspaces instead of vectors. To handle this kind of data, Grassmann kernels were proposed to measure the space structure and used with classifiers, e.g., Support Vector Machines (SVMs). However, the existing discriminative algorithms mostly ignore the instability of subspaces, which would cause the classifiers misled by disturbed instances. Thus we propose considering all potential disturbance of subspaces in learning processes to obtain more robust classifiers. Firstly, we derive the dual optimization of linear classifiers with disturbance subject to a known distribution, resulting in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into two kinds of disturbance, relevant to the subspace matrix and singular values of bases, with which we extend the Projection kernel on Grassmann manifolds to two new kernels. Experiments on action data indicate that the proposed kernels perform better compared to state-of-the-art subspace-based methods, even in a worse environment.Comment: This paper include 3 figures, 10 pages, and has been accpeted to SIGKDD'1

Techno-economic and greenhouse gas savings assessment of decentralized biomass gasification for electrifying the rural areas of Indonesia

This study explored the feasibility of decentralized gasification of oil palm biomass in Indonesia to relieve its over-dependence on fossil fuel-based power generation and facilitate the electrification of its rural areas. The techno-feasibility of the gasification of oil palm biomass was first evaluated by reviewing existing literature. Subsequently, two scenarios (V1 and V2, and M1 and M2) were proposed regarding the use cases of the village and mill, respectively. The capacity of the gasification systems in the V1 and M1 scenarios are determined by the total amount of oil palm biomass available in the village and mill, respectively. The capacity of the gasification systems in the V2 and M2 scenarios is determined by the respective electricity demand of the village and mill. The global warming impact and economic feasibility (net present value (NPV) and levelized cost of electricity (LCOE)) of the proposed systems were compared with that of the current practices (diesel generator for the village use case and biomass boiler combustion for the mill use case) using life cycle assessment (LCA) and cost-benefit analysis (CBA). Under the current daily demand per household (0.4 kWh), deploying the V2 system in 104 villages with 500 households each could save up to 17.9 thousand tons of CO2-eq per year compared to the current diesel-based practice. If the electricity could be fed into the national grid, the M1 system with 100% capacity factor could provide yearly GHG emissions mitigation of 5.8 × 104 ton CO2-eq, relative to the current boiler combustion-based reference scenario. M1 had a positive mean NPV if the electricity could be fed into the national grid, while M2 had a positive mean NPV at the biochar price of 500 USD/ton. Under the current electricity tariff (ET) (0.11 kWh) and the biochar price of 2650 USD/ton, daily household demands of 2 and 1.8 kWh were required to reach the break-even point of the mean NPV for the V2 system for the cases of 300 and 500 households, respectively. The average LCOE of V2 is approximately one-fourth that of the reference scenario, while the average LCOE of V1 is larger than that of the reference scenario. The average LCOE of M1 decreased to around 0.06 USD/kWh for the case of a 100% capacity factor. Sensitivity analysis showed that the capital cost of gasification system and its overall electrical efficiency had the most significant effects on the NPV. Finally, practical system deployment was discussed, with consideration of policy formulation and fiscal incentives

A Novel Ranging Method Based on RSSI

AbstractThe ranging technique based on RSSI is often used in localization of wireless sensor network (WSN). Due to external interferences, the RSSI fluctuates a lot and then a novel ranging method is presented. It establishes a database of mapping relationship between the RSSI and the distance range, then the distance between the transmitter and the receiver can be drawn by summing weighted of the distance spaces obtained through querying the mapping database. Simulation results show that,this method can eliminate the negative effects on RSSI fluctuation as much as possible and provides high ranging precision. It's no environmental limitations and can be applied in range-based localization technique with high value