The Approach to Accelerate Collaborative New Product Development Process through Managing Knowledge Sharing Behaviors

Knowledge sharing plays a critical role in collaborative new product development (Co-NPD) process. Through knowledge sharing, the excessive revenue of participants in Co-NPD can be easily realized. The research literature shows the way actors create a knowledge-sharing environment to create new products is a quality indicator of Co-NPD. This study summarizes which factors influence the knowledge sharing behaviors in Co-NPD, and it analyzes knowledge sharing behaviors among enterprises in Co-NPD process by evolutionary game theory. The conclusion indicates that the initial value and change tendency of revenue function parameters of knowledge sharing in Co-NPD process affects the choice of knowledge sharing strategy. According to the findings, the governance mechanism for promoting knowledge sharing is expounded to create a high performance of new product development collaboratively. The significance of the results is to help all participants achieve the expected maximum utility in the Co-NPD process by knowledge sharing behaviors. Keywords: collaborative new product development; knowledge sharing; evolutionary game theory; governance mechanis

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Dynamic model and ADRC of a novel water-air unmanned vehicle for water entry with in-ground effect

The class of vehicles that can move both in the air and underwater has been of great interest for decades. A novel water-air unmanned vehicle with double quadrotor structure is designed in this study. The air power mechanism works when the vehicle flies in the air, whereas the water power mechanism works when it moves underwater. The water entry process of water-air unmanned vehicle requires accurate attitude and height control, or the vehicle may bounce off or overturn. However, a force resisting its descent known as in-ground effect will affect its stability. The in-ground effect formula of the water entry process is derived by experiments, and the water entry dynamic model is improved at the same time. An active disturbance rejection controller (ADRC) is designed for the control of water entry attitude and height. Experimental results obtained from the comparison of the ADRC and a proportional-integral-derivative (PID) controller show that the ADRC designed in this study is more robust than the PID controller for the internal coupling and external disturbance on the vehicle. Moreover, the ADRC can meet the requirements of rapid attitude adjustment and accurate height control

Semantic Map Building Based on Object Detection for Indoor Navigation

Building a map of the environment is a prerequisite for mobile robot navigation. In this paper, we present a semantic map building method for indoor navigation of a robot using only the image sequence acquired by a mon‐ ocular camera installed on the robot. First, a topological map of the environment is created, where each key frame forms a node of the map represented as visual words (VWs). The edges between two adjacent nodes are built from relative poses obtained by performing a novel pose estimation approach, called one-point RANSAC camera pose estimation (ORPE). Then, taking advantage of an improved deformable part model (iDPM) for object detection, the topological map is extended by assigning semantic attributes to the nodes. Extensive experimental evaluations demonstrate the effectiveness of the proposed monocular SLAM method

Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection

Infrared Small Target Detection is a challenging task to separate small targets from infrared clutter background. Recently, deep learning paradigms have achieved promising results. However, these data-driven methods need plenty of manual annotation. Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field. This letter proposed a labor-efficient and cursory annotation framework with level set, which obtains a high-quality pseudo mask with only one cursory click. A variational level set formulation with an expectation difference energy functional is designed, in which the zero level contour is intrinsically maintained during the level set evolution. It solves the issue that zero level contour disappearing due to small target size and excessive regularization. Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approach achieves superior performance. Code is available at https://github.com/Li-Haoqing/COM.Comment: 4 pages, 5 figures, references adde

Contour Detection-based Discovery of Mid-level Discriminative Patches for Scene Classification

Feature extraction and representation is a key step in scene classification. In this paper, a contour detection-based mid-level features learning method is proposed for scene classification. First, a sketch tokens-based contour detection scheme is proposed to initialize seed blocks for learning mid-level patches and the patches with more contour pixels are selected as seed blocks. The procedure is demonstrated to be helpful for scene classification. Next, the seed blocks are employed to train an exemplar SVM to discover other similar occurrences and an entropy-rank criterion is utilized to mine the discriminative patches. Finally, scene categories are identified by matching the discriminative patches and testing images. Extensive experiments on the MIT Indoor-67 dataset, the 15-scene dataset and the UIUC-sports dataset show that the proposed approach yields better performance than other state-of-the-art counterparts

Hardware/software partitioning algorithm based on the combination of genetic algorithm and tabu search

To solve the hardware/software (HW/SW) partitioning problem of a single Central Processing Unit (CPU) system, a hybrid algorithm of Genetic Algorithm (GA) and Tabu Search(TS) is studied. Firstly, the concept hardware orientation is proposed and then used in creating the initial colony of GA and the mutation, which reduces the randomicity of initial colony and the blindness of search. Secondly, GA is run, the crossover and mutation probability become smaller in the process of GA, thus they not only ensure a big search space in the early stages, but also save the good solution for later browsing. Finally, the result of GA is used as initial solution of TS, and tabu length adaptive method is put forward in the process of TS, which can improve the convergence speed. From experimental statistics, the efficiency of proposed algorithm outperforms comparison algorithm by up to 25% in a large-scale problem, what is more, it can obtain a better solution. In conclusion, under specific conditions, the proposed algorithm has higher efficiency and can get better solutions