Volume 3
Published on January 2025The title of this dissertation is to what extent can prisoner’s dilemma in game theory be used in pricing strategy? The purpose of the current study was to determine the extent of prisoner's dilemma used in pricing strategies, and how the prisoner's dilemma is used in pricing strategies. We first comprehensive analysis and evaluation of relevant literature about prisoner’s dilemma and common pricing strategies in literature review section. Then in discussion part, we use game theory models to describe and analyze the prisoner's dilemma in pricing strategies to predict various situations, make the optimal pricing strategies. In the dissertation, we found the effects of prisoner’s dilemma strategies on payoffs when pricing and explained why cooperative pricing agreements promote mutual benefits and long-term stability than non-cooperative pricing strategies. Then we analyzed the stability of cooperative pricing strategy, we also developed a pricing model based on Bayes’ theorem. Our research has important implications for economics, marketing and game theory. By applying prisoner's dilemma theory, firms can better understand the behavioral of competitors and enhance decision-making processes, optimize their pricing strategies, thereby enhancing market efficiency and profitability.
View pdf
With the continuous growth in global demand for meat and eggs, the livestock industry is becoming increasingly important in the agricultural economy. However, a critical issue that needs to be addressed is how to optimize the operational efficiency of farms and improve economic benefits through scientific methods under limited resources. This paper constructs a mathematical model for profit maximization in chicken farms based on integer programming theory. The model considers multiple factors, including chicken types, feed costs, and market prices, and uses the branch-and-bound method to determine the optimal number of chickens under different market conditions. The results show that reasonably adjusting the proportion of chickens in the farm according to current market prices can significantly increase the farm’s profit, providing important decision-making references for actual operations.
View pdf
Machine learning has brought significant advancements to the field of structural health monitoring, providing flexible and efficient solutions for detecting both local and global damage in various infrastructures. Local damage detection focuses on identifying issues such as cracks and spalling in specific areas of concrete structures, including bridges, highways, and tunnels. Techniques like artificial neural networks (ANNs) and deep neural networks (DNNs) have proven effective in surface defect recognition, demonstrating their versatility across different structural environments. Additionally, cost-effective methods leveraging devices such as smartphones have been explored for rapid road integrity assessments, offering practical and affordable solutions. On a larger scale, global damage detection involves classifying structural collapse modes and types of damage, utilizing feature extraction and deep learning models to improve the accuracy of identifying large-scale failures. These advancements highlight the growing importance of machine learning and computer vision in enhancing the resilience and real-time monitoring of critical infrastructure systems.
View pdf
The Naive Bayes algorithm is one of the most important and popular algorithms in machine learning and data mining, not only because of its simplicity but also because of its superior classification performance. The central assumption of this algorithm is known as the attribute independence assumption. This assumption allows the Naive Bayes algorithm to solve classification problems conveniently, but also limits the performance of this algorithm to a certain extent when the mixed type of variables exist in its input dataset. Recently, we proposed an improved Naive Bayes classification algorithm by combining an improved Principal Component Analysis (PCA) method. The improved PCA first calculates correlation coefficients between coupling variables using the Pearson and Kendall coefficients, where the two types of coefficients are calculated separately for quantitative and qualitative data. After coupling data is transformed into principal components, those correlated variables can be integrated into the improved Naive Bayes algorithm. When the improved Naive Bayes algorithm is applied to a classified task, it is easy to verify that the transformed principal components data are approximately independent, thereby conforming to the Naive Bayes independence assumption to a relatively greater extent. This implies that it is likely for the improved Naive Bayes algorithm to yield a more accurate classification performance, as it is more robust to the presence of noise in classification instances.
View pdf
The purpose of this thesis is to use drones and machine learning algorithms for automating crack detection in tunnel systems. With the high resolution RGB cameras and LiDAR sensor in drones, you get the imagery and structural data required to inspect tunnels. The images are then fed through CNNs together with SVMs for detecting and classification cracks in concrete and other surfaces. With this automated mechanism, the process will no longer need manual effort, and the inspection will be more precise and safer. The study shows the efficiency of this hybrid approach, which has 92% detection rate, much better than traditional inspection. And it is also very good at reducing false positives, and produces more trustworthy results. Crack severity is sorted into hairline, medium and deep cracks to make the process of maintenance and repairs easier. According to the results, paired with drones and machine learning, tunnel inspections become more effective, and data collection and analysis greatly enhanced. This method has potential use cases in infrastructure monitoring and could possibly be used for other structural damage detection tasks in high-dimensional domains.
View pdf
This paper proposes a multi-stage decision model to address the detection and disassembly of parts, semi-finished products, and finished products in electronic product production. Key decisions in the production process are optimized by implementing small and large sample sampling techniques and a variable decision model with global cost minimization. The methods include probability and accumulation models, normal distribution approximation, and traversal algorithms. The results show that rational decision-making can effectively reduce production costs and improve product quality. For small samples, the probability and accumulation models are established, and the number of samples with a defect rate greater than or less than the nominal value under 95% and 90% reliability is calculated, yielding results of 29 and 22, respectively. For large samples, the sample sizes under different error tolerances are calculated using the normal distribution approximation. Through MATLAB programming, the minimum unit cost and its corresponding decision scheme are determined through traversal-based calculations. The results show that the minimum unit costs in different cases are 30.98, 31.33, 32.35, 30.55, 29.62, and 29.43. Additionally, considering the decision problem involving multiple processes and parts, the problem is decomposed into smaller problems, with the minimum cost sum of each stage representing the global minimum cost. For a case with two processes and eight parts, the minimum unit cost is calculated to be 138. The decision scheme is that no parts or semi-finished products are tested, and the semi-finished products and finished products are disassembled. The research results provide a scientific basis for actual production, and future research can further consider uncertainties and risk factors in real production to achieve more comprehensive optimization.
View pdf