APPROXIMATION ASSISTED MULTIOBJECTIVE AND COLLABORATIVE ROBUST OPTIMIZATION UNDER INTERVAL UNCERTAINTY

Optimization of engineering systems under uncertainty often involves problems that have multiple objectives, constraints and subsystems. The main goal in these problems is to obtain solutions that are optimum and relatively insensitive to uncertainty. Such solutions are called robust optimum solutions. Two classes of such problems are considered in this dissertation. The first class involves Multi-Objective Robust Optimization (MORO) problems under interval uncertainty. In this class, an entire system optimization problem, which has multiple nonlinear objectives and constraints, is solved by a multiobjective optimizer at one level while robustness of trial alternatives generated by the optimizer is evaluated at the other level. This bi-level (or nested) MORO approach can become computationally prohibitive as the size of the problem grows. To address this difficulty, a new and improved MORO approach under interval uncertainty is developed. Unlike the previously reported bi-level MORO methods, the improved MORO performs robustness evaluation only for optimum solutions and uses this information to iteratively shrink the feasible domain and find the location of robust optimum solutions. Compared to the previous bi-level approach, the improved MORO significantly reduces the number of function calls needed to arrive at the solutions. To further improve the computational cost, the improved MORO is combined with an online approximation approach. This new approach is called Approximation-Assisted MORO or AA-MORO. The second class involves Multiobjective collaborative Robust Optimization (McRO) problems. In this class, an entire system optimization problem is decomposed hierarchically along user-defined domain specific boundaries into system optimization problem and several subsystem optimization subproblems. The dissertation presents a new Approximation-Assisted McRO (AA-McRO) approach under interval uncertainty. AA-McRO uses a single-objective optimization problem to coordinate all system and subsystem optimization problems in a Collaborative Optimization (CO) framework. The approach converts the consistency constraints of CO into penalty terms which are integrated into the subsystem objective functions. In this way, AA-McRO is able to explore the design space and obtain optimum design solutions more efficiently compared to a previously reported McRO. Both AA-MORO and AA-McRO approaches are demonstrated with a variety of numerical and engineering optimization examples. It is found that the solutions from both approaches compare well with the previously reported approaches but require a significantly less computational cost. Finally, the AA-MORO has been used in the development of a decision support system for a refinery case study in order to facilitate the integration of engineering and business decisions using an agent-based approach

A New Artificial Intelligence based Internet Online English Teaching Model with Curriculum of Ideological and Political Concern

With the development of artificial intelligence and the rapid spread of the Internet, online teaching has become an increasingly popular method of education. However, in the context of the post-epidemic era of COVID-19, online teaching has become even more important, as many educational institutions have been forced to transition to this model to ensure continuity of learning. In this context, there is a growing need to develop innovative approaches to online teaching that can effectively address the challenges posed by the pandemic. Online teaching has become increasingly important for higher education institutions around the world, and it has been particularly crucial during the COVID-19 pandemic. The teaching of English at universities and colleges exhibited significant performance for online teaching. The ideology concept performs online teaching in English for politics and comprises of different strategies. English teaching, several strategies can be implemented. This research paper proposes a novel approach to integrate artificial intelligence (AI) and cloud computing technologies in the online English teaching model with a curriculum of ideological and political concern for colleges and universities. The proposed model, referred to as AIIOE, aims to enhance the quality and effectiveness of online English teaching while also providing a comprehensive education on ideological and political issues. The AIIOE model utilizes natural language processing (NLP), machine learning, and cloud computing technologies to provide a personalized and interactive learning experience to students. The proposed curriculum includes topics related to political ideology, history, and culture to enhance students' awareness and understanding of their social and political environment. The study adopts a mixed-methods approach, including a survey of English teachers, focus group interviews with students, and an analysis of students' performance in English language proficiency and ideological and political awareness. The results indicate that the AIIOE model significantly improves students' English language proficiency, knowledge of ideological and political issues, and overall learning experience. The examination is evaluated based on the ideological and political curriculum with an Internet-based online teaching mode in English teaching. With the investigation of the Internet online teaching model, the significant contribution is evaluated. Through analysis, it is concluded that the concept of the Internet Online teaching model significantly contributed to ideological and political factors

A Life Prediction Model of Multilayered PTH Based on Fatigue Mechanism.

Plated through hole (PTH) plays a critical role in printed circuit board (PCB) reliability. Thermal fatigue deformation of the PTH material is regarded as the primary factor affecting the lifetime of electrical devices. Numerous research efforts have focused on the failure mechanism model of PTH. However, most of the existing models were based on the one-dimensional structure hypothesis without taking the multilayered structure and external pad into consideration. In this paper, the constitutive relation of multilayered PTH is developed to establish the stress equation, and finite element analysis (FEA) is performed to locate the maximum stress and simulate the influence of the material properties. Finally, thermal cycle tests are conducted to verify the accuracy of the life prediction results. This model could be used in fatigue failure portable diagnosis and for life prediction of multilayered PCB

Digital Music Copyright Protection Dilemma: a Discussion on Draft Amendments of China’s Copyright Law

On March 31,2012, China’s National Copyright Administration of the People’s Republic of China (NCAC) published the Draft Amendment to the Copyright Law at its website to seek public feedback. Some articles in the current Draft Amendments, such as Articles 46 and 48, have attracted the most attention from the public, especially the music industry, because they involve unauthorized use of copyrighted material. Some musician indicated that “the draft clearly favored Internet.” This paper wants to discuss those controversial articles under the Draft Amendments and some other solution except legislation for musicians to face digital era with an aim to make a healthy development of digital music sector in China