Manual assembly modelling and simulation for ergonomics analysis

In manufacturing industry, although automation techniques have been employed widely, many tasks still require the flexibility and intelligence of human operators, especially in the product assembly process. Insufficient industrial ergonomics in the assembly process will cause the health problems and quality and productivity losses, ultimately increase costs of the final product. The purpose of this thesis is to integrate ergonomic considerations into the manual assembly process modelling and simulation in order to provide product/process design changes before their physical prototyping. In this research, a state-of-the-art commercial software tool - DELMIA - is adopted for the ergonomics simulation and analysis. Associated with its capabilities for the ergonomics solution, a series of human related issues in the manual assembly process is simulated and studied in order to demonstrate the benefits of a virtual assembly approach to the product deign, workplace deign, time and energy saving. Due to the poor repeatability and reproducibility of digital human postures in DELMIA manipulation, a posture prediction method is developed aiming at a practical and precise ergonomics analysis. A 10-degrees-of-freedom, 4-control-points digital human model concerned with assembly features and human diversity is established. The multi-objective optimisation method is applied to assembly posture prediction in which optimisation objectives (i.e. joint discomfort and metabolic energy expenditure) and constraints corresponding to manual assembly tasks are proposed and formulated. Following the verification of the posture prediction method, a series of posture strategies under different assembly conditions are investigated towards more comfortable and energy-efficient assembly postures. Thus far, the consideration on assembly operators in assembly sequencing is insufficient though it plays a key role in the integrative product and process design. In this research, the use of new ergonomic constraints into assembly sequencing optimisation is proposed. Feasible assembly sequences are generated and evaluated based on the product geometry, assembly workstation layout, operator characteristics and working posture. A new Liverpool Assembly Sequence Planning System (LASP) is developed to achieve the integration by applying two evaluation criteria, i.e. visibility criterion, accessibility criterion or both. With LASP, possible design faults with respect to restricted visibility and obstructed accessibility is obtainable during the early design stage. Meanwhile, the optimum sequences are provided to operators automatically for ease of manual assembly, facilitating higher assembly quality and efficiency

Distributed and Robust Support Vector Machine

In this paper, we consider the distributed version of Support Vector Machine (SVM) under the coordinator model, where all input data (i.e., points in R^d space) of SVM are arbitrarily distributed among k nodes in some network with a coordinator which can communicate with all nodes. We investigate two variants of this problem, with and without outliers. For distributed SVM without outliers, we prove a lower bound on the communication complexity and give a distributed (1-epsilon)-approximation algorithm to reach this lower bound, where epsilon is a user specified small constant. For distributed SVM with outliers, we present a (1-epsilon)-approximation algorithm to explicitly remove the influence of outliers. Our algorithm is based on a deterministic distributed top t selection algorithm with communication complexity of O(k log (t)) in the coordinator model. Experimental results on benchmark datasets confirm the theoretical guarantees of our algorithms

Improving musculoskeletal model scaling using an anatomical atlas:the importance of gender and anthropometric similarity to quantify joint reaction forces

Objective: The accuracy of a musculoskeletal model relies heavily on the implementation of the underlying anatomical dataset. Linear scaling of a generic model, despite being time and cost-efficient, produces substantial errors as it does not account for gender differences and inter-individual anatomical variations. The hypothesis of this study is that linear scaling to a musculoskeletal model with gender and anthropometric similarity to the individual subject produces similar results to the ones that can be obtained from a subject-specific model. Methods: A lower limb musculoskeletal anatomical atlas was developed consisting of ten datasets derived from magnetic resonance imaging of healthy subjects and an additional generic dataset from the literature. Predicted muscle activation and joint reaction force were compared with electromyography and literature data. Regressions based on gender and anthropometry were used to identify the use of atlas. Results: Primary predictors of differences for the joint reaction force predictions were mass difference for the ankle (p<0.001) and length difference for the knee and hip (p≤0.017) . Gender difference accounted for an additional 3% of the variance (p≤0.039) . Joint reaction force differences at the ankle, knee and hip were reduced by between 50% and 67% (p=0.005) when using a musculoskeletal model with the same gender and similar anthropometry in comparison with a generic model. Conclusion: Linear scaling with gender and anthropometric similarity can improve joint reaction force predictions in comparison with a scaled generic model. Significance: The scaling approach and atlas presented can improve the fidelity and utility of musculoskeletal models for subject-specific applications

Development, validation and use of a musculoskeletal model for transtibial amputation: biomechanical evidence for increased rates of osteoarthritis of the uninjured limb

High functioning military transtibial amputees (TTAs) with well‐fitted state of the art prosthetics have gait that is indistinguishable from healthy individuals, yet they are more likely to develop knee osteoarthritis (OA) of their intact limbs. This contrasts with the information at the knees of the amputated limbs that have been shown to be at a significantly reduced risk of pain and OA. The hypothesis of this study is that biomechanics can explain the difference in knee OA risk. Eleven military unilateral TTAs and eleven matched healthy controls underwent gait analysis. Muscle forces and joint contact forces at the knee were quantified using musculoskeletal modeling, validated using electromyography measurements. Peak knee contact forces for the intact limbs on both the medial and lateral compartments were significantly greater than the healthy controls (P ≤ .006). Additionally, the intact limbs had greater peak semimembranosus (P = .001) and gastrocnemius (P ≤ .001) muscle forces compared to the controls. This study has for the first time provided robust evidence of increased force on the non‐affected knees of high functioning TTAs that supports the mechanically based hypothesis to explain the documented higher risk of knee OA in this patient group. The results suggest several protentional strategies to mitigate knee OA of the intact limbs, which may include the improvements of the prosthetic foot control, socket design, and strengthening of the amputated muscles

Manual assembly modelling and simulation for ergonomics analysis

Thesis submitted in accordance with the requirements o

Computational Methods for Protein-Protein Interaction Identification

Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanisms of diseases. This dissertation introduces the computational method to predict PPIs. In the first chapter, the history of identifying protein interactions and some experimental methods are introduced. Because interacting proteins share similar functions, protein function similarity can be used as a feature to predict PPIs. NaviGO server is developed for biologists and bioinformaticians to visualize the gene ontology relationship and quantify their similarity scores. Furthermore, the computational features used to predict PPIs are summarized. This will help researchers from the computational field to understand the rationale of extracting biological features and also benefit the researcher with a biology background to understand the computational work. After understanding various computational features, the computational prediction method to identify large-scale PPIs was developed and applied to Arabidopsis, maize, and soybean in a whole-genomic scale. Novel predicted PPIs were provided and were grouped based on prediction confidence level, which can be used as a testable hypothesis to guide biologists’ experiments. Since affinity chromatography combined with mass spectrometry technique introduces high false PPIs, the computational method was combined with mass spectrometry data to aid the identification of high confident PPIs in large-scale. Lastly, some remaining challenges of the computational PPI prediction methods and future works are discussed