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Automatic population density estimation is a significant study area in intelligent video monitoring. Traditional methods need design features manually, which are hard to keep pace with the current state of big data. At the same time, with the outbreak of artificial intelligence methods such as deep learning, the application of deep learning to video monitoring is also an irresistible trend. Therefore, according to the disadvantage of traditional manual feature extraction and the deficiency of single-layer convolutional neural network(CNN), a multilayer convolutional neural network(MCNN) is raised. In this article, head size changes caused by various reasons, such as penetration effect, will not affect the characteristics of CNN learning pictures. That is to say, even if we do not know the perspective of the input map, we can accurately detect the population density on the basis of adaptive kernel. The characteristic graphs of each layer are integrated to obtain the population density map. experiments reveals that this network structure can attain more accurate population estimation.

In new product development, the rapid proposal of innovative solutions represents an important phase. This in turn relies on creative ideas, their evaluation, refinement and embodiment of worthwhile directions. This study aims to describe a CBDT based process model for product conceptual design that concentrates on rapidly generating innovations with the support of decision-making rationale. Case-based decision theory (CBDT), derived from case-based reasoning, is applied in this paper as a core method to aid design engineers to make an informed decision quickly, thus accelerating the design process. In the process of utilizing CBDT to support a decision, as for the similarity function, the proper value assignment methods to the selected attribute set for calculation are discussed. In order to assist with innovative solution, aspects of the theory of inventive problem solving (TRIZ) are integrated into the case-based reasoning process. Accordingly, a CBDT-TRIZ model is developed. Quality-function deployment is used to translate customer wants into relevant engineering design requirements and thus formulating the design specification. Image-Scale is used to offer an orthogonal coordinates system to aid evaluation. Finally, a case study is used to demonstrate the validity of the proposed process model based on the design of a cordless hand-tool for garden and lawn applications.

A review of deep learning based soft computing techniques in several applications is presented. On one hand, soft computing, defined as a group of methodologies, is an important element for constructing a new generation of computational intelligent system and has gained great success in solving practical computing problems. On the other hand, deep learning has become one of the most promising techniques in artificial intelligence in the past decade. Since soft computing is an evolving collection of methodologies, by presenting the latest research results of soft computing based on deep learning, this review not only reveals a promising direction for soft computing by incorporating deep learning, but also gives some suggestions for improving the performance of deep learning with soft computing techniques.