Until the 1980s, the People’s Republic of China was principally opposed to United Nations peacekeeping, understanding the institution to be a thinly veiled excuse for powerful states to intervene in the sovereign affairs of others. However, the meaning the country attributes to peacekeeping has changed drastically since then. China has adopted a more pragmatic attitude and has gradually become more supportive and involved in United Nations peacekeeping. Today the country stands as a major contributor to peacekeeping, not least in terms of the number of peacekeepers it contributes to missions. However, how does China make sense of its current behaviour? This paper seeks to understand how the participation of Chinese military personnel and police in peacekeeping operations is made coherent with Chinese self-identity. The paper employs a narrative analysis that focuses on how narratives draw on master narratives about Chinese collective memory to construct participation in peacekeeping as a natural conclusion to already accepted notions about what it means to be Chinese. On the one hand, this paper confirms the findings of previous scholarship on Chinese identity and the country’s attitude on peacekeeping. Like these, this paper finds that China’s self-identity as a part of the Global South and as a great power plays a role in how China conceptualizes peacekeeping. However, on the other hand, the paper also finds dissonance in how the narrative relates peacekeeping to China’s identity as a part of the Global South. Additionally, this paper also demonstrates that the narrative draws on several master narratives that have not previously been identified as important to how China makes meaning of peacekeeping. Specifically, these are the collective memories of ‘Asian values’, China’s experiences from the Second World War, and the revolutionary history of the CPC.

In an air defence scenario decisions need to be taken with extreme precision and under high pressure. These decisions becomes even more challenging when the aircraft in question need to function as a team and coordinate their effort. Because of the difficulty of the task, and the amount of information that needs to be rapidly processed, fighter pilots can benefit greatly from computer-assisted decision making.  In this thesis this kind of decentralised multi-agent coordination problem is studied and mission assignment models, based on robust and stochastic optimisation, are evaluated. Since the information obtained by aircraft sensors often suffer from a notable amount of noise and the scenario state therefore is uncertain, a Poisson multi-Bernoulli mixture filter is implemented in order to model these noisy measurements and keep track of potential adversaries. The study finds that the filter used was more than capable of handling the scenario uncertainties and provided valuable task information to the mission assignment models. However, the preliminary robust optimisation models based entirely on the positional uncertainty of the adversaries were not sophisticated enough for such a complex coordination problem, indicating that further research is needed in this area.

The quality aspect of mechanical components is a significant factor forcustomer satisfaction. By increasing the robustness, the product will be moreresistant to non-ideal situations and the perceived quality would be increased.A concept design for the case of A-Stay mounting on articulated haulers isdeveloped to increase the robustness of the product and removing the risk formechanical failure. A modified product development process with focus onaspects of assembly and robust design is used to generate concepts anddeveloping a concept design. The process resulted in a concept that increasesthe robustness of the system, both through higher redundancy and improvedcontrol of the noise factors.

Thesis (M.Sc.(Computer Science )) -- University of Limpopo, 2020 / Language modelling (LM) work for under-resourced languages that does not consider most linguistic information inherent in a language produces language models that in adequately represent the language, thereby leading to under-development of natural language processing tools and systems such as speech recognition systems. This study investigated the influence that the orthography (i.e., writing system) of a lan guage has on the quality and/or robustness of the language models created for the text of that language. The unique conjunctive and disjunctive writing systems of isiN debele (Ndebele) and Sepedi (Pedi) were studied. The text data from the LWAZI and NCHLT speech corpora were used to develop lan guage models. The LM techniques that were implemented included: word-based n gram LM, LM smoothing, LM linear interpolation, and higher-order n-gram LM. The toolkits used for development were: HTK LM, SRILM, and CMU-Cam SLM toolkits. From the findings of the study – found on text preparation, data pooling and sizing, higher n-gram models, and interpolation of models – it is concluded that the orthogra phy of the selected languages does have effect on the quality of the language models created for their text. The following recommendations are made as part of LM devel opment for the concerned languages. 1) Special preparation and normalisation of the text data before LM development – paying attention to within sentence text markers and annotation tags that may incorrectly form part of sentences, word sequences, and n-gram contexts. 2) Enable interpolation during training. 3) Develop pentagram and hexagram language models for Pedi texts, and trigrams and quadrigrams for Ndebele texts. 4) Investigate efficient smoothing method for the different languages, especially for different text sizes and different text domains / National Research Foundation (NRF) Telkom University of Limpopo

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The traffic signal affects the life of virtually everyone every day. The effectiveness of signal systems can reduce the incidence of delays, stops, fuel consumption, emission of pollutants, and accidents. The problems related to rapid growth in traffic congestion call for more effective traffic signalization using robust feedback control methodology. Online traffic-responsive signalization is based on real-time traffic conditions and selects cycle, split, phase, and offset for the intersection according to detector data. A robust traffic feedback control begins with assembling traffic demands, traffic facility supply, and feedback control law for the existing traffic operating environment. This information serves the input to the traffic control process which in turn provides an output in terms of the desired performance under varying conditions. Traffic signalization belongs to a class of hybrid systems since the differential equations model the continuous behavior of the traffic flow dynamics and finite-state machines model the discrete state changes of the controller. A complicating aspect, due to the state-space constraint that queue lengths are necessarily nonnegative, is that the continuous-time system dynamics is actually the projection of a smooth system of ordinary differential equations. This also leads to discontinuities in the boundary dynamics of a sort common in queueing problems. The project is concerned with the design of a feedback controller to minimize accumulated queue lengths in the presence of unknown inflow disturbances at an isolated intersection and a traffic network with some signalized intersections. A dynamical system has finite L₂-gain if it is dissipative in some sense. Therefore, the H<SUB>infinity</SUB>-control problem turns to designing a controller such that the resulting closed loop system is dissipative, and correspondingly there exists a storage function. The major contributions of this thesis include 1) to propose state space models for both isolated multi-phase intersections and a class of queueing networks; 2) to formulate H<SUB>infinity</SUB> problems for the control systems with persistent disturbances; 3) to present the projection dynamics aspects of the problem to account for the constraints on the state variables; 4) formally to study this problem as a hybrid system; 5) to derive traffic-actuated feedback control laws for the multi-phase intersections. Though we have mathematically presented a robust feedback solution for the traffic signalization, there still remains some distance before the physical implementation. A robust adaptive control is an interesting research area for the future traffic signalization. / Ph. D.

This dissertation aims mainly at obtaining robust variants of Gaussian processes (GPs) that do not require using non-Gaussian likelihoods to compensate for outliers in the training data. Bayesian kernel methods, and in particular GPs, have been used to solve a variety of machine learning problems, equating or exceeding the performance of other successful techniques. That is the case of a recently proposed approach to GP-based novelty detection that uses standard GPs (i.e. GPs employing Gaussian likelihoods). However, standard GPs are sensitive to outliers in training data, and this limitation carries over to GP-based novelty detection. This limitation has been typically addressed by using robust non-Gaussian likelihoods. However, non-Gaussian likelihoods lead to analytically intractable inferences, which require using approximation techniques that are typically complex and computationally expensive. Inspired by the use of weights in quasi-robust statistics, this work introduces a particular type of weight functions, called here data weighers, in order to obtain robust GPs that do not require approximation techniques and retain the simplicity of standard GPs. This work proposes implicit weighted variants of batch GP, online GP, and sparse online GP (SOGP) that employ weighted Gaussian likelihoods. Mathematical expressions for calculating the posterior implicit weighted GPs are derived in this work. In our experiments, novelty detection based on our weighted batch GPs consistently and significantly outperformed standard batch GP-based novelty detection whenever data was contaminated with outliers. Additionally, our experiments show that novelty detection based on online GPs can perform similarly to batch GP-based novelty detection. Membership scores previously introduced by other authors are also compared in our experiments.

Biological phenomena found in nature can be learned and customized to obtain innovative engineering solutions. In recent years, biologists found that birds and bats use their mechanoreceptors to sense the airflow information and use this information directly to achieve their agile flight performance. Inspired by this phenomenon, an attitude control system for micro air vehicles using rich amount of airflow sensor information is proposed, designed and tested. The dissertation discusses our research findings on this topic. First, we quantified the errors between the calculated and measured lift and moment profiles using a limited number of micro pressure sensors over a straight wing. Then, we designed a robust pitching controller using 20 micro pressure sensors and tested the closed-loop performance in a simulated environment. Additionally, a straight wing was designed for the pressure sensor based pitching control with twelve pressure sensors, which was then tested in our low-speed wind tunnel. The closed-loop pitching control system can track the commanded angle of attack with a rising time around two seconds and an overshoot around 10%. Third, we extended the idea to the three-axis attitude control scenarios, where both of the pressure and shear stress information are considered in the simulation. Finally, a fault tolerant controller with a guaranteed asymptotically stability is proposed to deal with sensor failures and calculation errors. The results show that the proposed fault tolerant controller is robust, adaptive, and can guarantee an asymptotically stable performance even in case that 50% of the airflow sensors fail in flight.

Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers