Redundancy Identification and Optimization Scheme of Branches for Sustainable Operation of Commercial Banks

With the rapid development of the internet, the number of offline customers in the bank branches decreases, and the existing layout of branches leads to the increase of operation cost, which has an impact on the sustainable operation of commercial banks. Adjusting and optimizing the layout of the physical branches of commercial banks can not only reduce the operation cost of banks and avoid the waste of resources, but is also crucial to the sustainable operation of commercial banks. First, an evaluation index system (deposit; loan; number of vouchers; maintenance, establishment, and modification of customer information; number of counter transactions) is constructed to reflect the operation performance of bank branches. Second, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used to rank the bank branches. Then, a combination of factor analysis and assignment method is used to identify redundant bank branches. Last, cluster analysis is used to find alternative schemes of redundant bank branches. Finally, Shaanxi Rural Credit Cooperatives Union in Hanzhong, Shaanxi Province, China is selected for empirical analysis. The results show that: four redundant bank branches are identified, and alternative combination schemes of the redundant bank branches are determined. The redundancy identification method in this paper is helpful for commercial banks to allocate various resources rationally and reduce operation cost, so as to ensure the sustainable operation of commercial banks.

MULTI-AGENT LEARNING FRAMEWORK FOR ENVIRONMENT REDUNDANCY IDENTIFICATION FOR MOBILE SENSORS IN AN IOT CONTEXT

<p><strong>Abstract.</strong> From an IoT point of view, the continuous growth of cheap and versatile sensor technologies has generated a massive data flow in communication networks, which most of the time carries unnecessary or redundant information that requires larger storage centers and more time to process and analyze data. Most of this redundancy is due to fact that network nodes are unable to identify environmental cues showing measurement changes to be considered and instead remain at a static location getting the same data. In this work we propose a multi-agent learning framework based on two theoretical tools. Firstly, we use Gaussian Process Regression (GPR) to make each node capable of getting information from the environment based on its current measurement and the measurements taken by its neighbors. Secondly, we use the rate distortion function to define a boundary where the information coming from the environment is neither redundant nor misunderstood. Finally, we show how the framework is applied in a mobile sensor network in which sensors decide to be more or less exploratory by means of the parameter s of the Blahut-Arimoto algorithm, and how it affects the measurement coverage in a spatial area being sensed.</p>

Multi-Modal Fusion Schemes for Image Retrieval Systems to Bridge the Semantic Gap

Image retrieval (IR) systems are used for searching of images by means of diverse modes such as text, sample image, or both. They suffer with the problem of semantic gap which is the mismatch between the user requirement and the capabilities of the IR system. The image data is generally stored in the form of statistics of the value of the pixels which has very little to do with the semantic interpretation of the image. Therefore, it is necessary to understand the mapping between the two modalities i.e. content and context. Research indicates that the combination of the two can be a worthwhile approach to improve the quality of image search results. Hence, multimodal retrieval (MMR) is an expected way of searching which attracts substantial research consideration. The main challenges include discriminatory feature extraction and selection, redundancy identification and elimination, information preserving fusion and computational complexity. Based on these challenges, in this chapter, authors focus on comparison of various MMR systems that have been used to improve the retrieval results.

Deadlock Prevention Policy with Behavioral Optimality or Suboptimality Achieved by the Redundancy Identification of Constraints and the Rearrangement of Monitors

This work develops an iterative deadlock prevention method for a special class of Petri nets that can well model a variety of flexible manufacturing systems. A deadlock detection technique, called mixed integer programming (MIP), is used to find a strict minimal siphon (SMS) in a plant model without a complete enumeration of siphons. The policy consists of two phases. At the first phase, SMSs are obtained by MIP technique iteratively and monitors are added to the complementary sets of the SMSs. For the possible existence of new siphons generated after the first phase, we add monitors with their output arcs first pointed to source transitions at the second phase to avoid new siphons generating and then rearrange the output arcs step by step on condition that liveness is preserved. In addition, an algorithm is proposed to remove the redundant constraints of the MIP problem in this paper. The policy improves the behavioral permissiveness of the resulting net and greatly enhances the structural simplicity of the supervisor. Theoretical analysis and experimental results verify the effectiveness of the proposed method.