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For the purpose of maximizing the spread of influence caused by a certain small number k of nodes in a social network, we are asked to find a k-subset of nodes (i.e., a seed set) with the best capacity to influence the nodes not in it. This problem of influence maximization (IM) has wide application, belongs to subset problems, and is NP-hard. To solve it, we should theoretically examine all seed sets and evaluate their influence spreads, which is time-consuming. Therefore, metaheuristic strategies are generally employed to gain a good seed set within a reasonable time. We observe that many algorithms for the IM problem only adopt a uniform mechanism in the whole solution search process, which lacks a response measure when the algorithm becomes trapped in a local optimum. To address this issue, we propose a phased hybrid evaluation-enhanced (PHEE) approach for IM, which utilizes two distinct search strategies to enhance the search of optimal solutions: a randomized range division evolutionary (RandRDE) algorithm to improve the solution quality, and a fast convergence strategy. Our approach is evaluated on 10 real-world social networks of different sizes and types. Experimental results demonstrate that our algorithm is efficient and obtains the best influence spread for all the datasets compared with three state-of-the-art algorithms, outperforms the time consuming CELF algorithm on four datasets, and performs worse than CELF on only two networks.

Batch process is a typical multi-phase process. Due to the interaction between the phases of the batch process, high precision control in a single phase cannot guarantee high precision control of the whole batch process. In order to solve this problem, the guaranteed cost iterative learning control (ILC) of multi-phase batch processes is studied in this paper. Firstly, through introducing the output error, the state error and the extended information, the multi-phase batch process is transformed into an equivalent 2D switched system which has different dimensions. In addition, under the measurable condition, the guaranteed cost iterative learning control law with extended information is designed. The proposed control law ensures not only the stability of the system but also the optimal control performance. Next, in order to study the stability of the system and the minimum running time under the condition of stable running, the multi-Lyapunov function method is used. By means of the average dwell time method, the sufficient conditions ensuring system to be exponentially stable are given in the form of linear matrix inequality (LMI). Finally, the injection molding process is taken as an example to make simulation, which shows the feasibility and effectiveness of the proposed method.

Achieving autonomous in complex natural environments has the potential to transform society by bringing the benefits of automation from the confines of the factory floor to the outdoors. There, it could benefit areas such as environmental monitoring and clean-up, precision agriculture, delivery of goods. A fundamental requirement for achieving these goals are sensors that can provide reliable support for navigation, e.g., a drone, in natural environments. In this thesis, sonar-based navigation has been investigated as an approach to parsimonious autonomous sensing for drones. Bats living in dense vegetation have demonstrated that autonomous navigation in a complex, natural environment based on two one-dimensional ultrasonic echo streams is feasible. Here, a biomimetic sonar head has been used to collect echo data from recreations of natural foliage in the lab under controlled conditions. This data was used to address the research question whether the grazing angle at which the sonar is looking at a surface can be estimated from the echoes -- despite the random three-dimensional nature of the scatter from the foliage. To investigate this, the echoes have been subjected to statistical analysis such as spectral coherence and cross-correlation. Most importantly, the foliage data was compared against predictions made by the Endura method (energy, duration, and range method) that has been devices for two-dimension random scatterers. The results of this analysis shows that -- despite their profoundly random nature -- echoes can be used to estimate the sonar grazing angle directly, i.e., without the need to resort to reconstructions of the foliage geometry. This opens the possibility of developing simple devices for navigation control in natural environments that can control the direction of motion at a very little computational cost. Master of Science Autonomously flying drones is a potential technology that could bring benefits to the society and improve the quality of life for humans[22]. Therefore, a study of autonomously flying in a natural environment is necessary, and this thesis will focus on drone that could recognize objects with different grazing angle and acoustic signal by collecting data from near foliage surface. For example, when a bush wall is in front of the drone, a on board computer could inform drone whether the drone airline will collide with the bush wall or the bush wall is safely out of drone’s path[5]. If on board computer reads that there will be a collision with bush wall, then drone needs to make decision (change direction or stop immediately) to avoid crush on to bush wall. A sonar based navigation system has been investigated as an approach to achieve autonomous sensing for drones, which is inspired by bats. Bats use their natural sonar system to navigate in cave or forest, hence, it is hardly to see bats slam into any obstacles while flying. Bats navigation behaviours could be reconstructed as a sonar based autonomy. Hence, this thesis is inspired by bats to determine if there is a computational way to illustrate that sonar based sensor could be a solution to achieve reactive autonomy by using different grazing angle of the surface’s acoustic signals.