A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard m...A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in naalre. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network （WSN）. We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.展开更多
We present a rapid system for predicting beef tenderness by mimicking the human tactile sense. The detection system includes a FS pressure sensor, a power supply conversion circuit, a signal amplifier and a box in whi...We present a rapid system for predicting beef tenderness by mimicking the human tactile sense. The detection system includes a FS pressure sensor, a power supply conversion circuit, a signal amplifier and a box in which the sample is mounted. A sample of raw Longissimus dorsi (LD) muscle is placed in the measuring box; then a rod connected to the pressure sensor is pressed into the beef sample to a given depth; the reaction force of the beef sample is measured and used to predict the tenderness. Sensory evaluation and Warner-Bratzler Shear Force (WBSF) evaluation of samples from the same LD muscle are used for comparison. The new detection system agrees with established procedure 95% of the time, and the time to test a sample is less than 5 minutes.展开更多
基金National "863" Project of China (Grant no. 2007AA04Z224)
文摘A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in naalre. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network （WSN）. We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.
基金supported by the Key Project of Science and Technology Foundations of Jilin Province of China (Grant No.20060217)the Research Foundation for the Talents by the People's Government of Jilin Province
文摘We present a rapid system for predicting beef tenderness by mimicking the human tactile sense. The detection system includes a FS pressure sensor, a power supply conversion circuit, a signal amplifier and a box in which the sample is mounted. A sample of raw Longissimus dorsi (LD) muscle is placed in the measuring box; then a rod connected to the pressure sensor is pressed into the beef sample to a given depth; the reaction force of the beef sample is measured and used to predict the tenderness. Sensory evaluation and Warner-Bratzler Shear Force (WBSF) evaluation of samples from the same LD muscle are used for comparison. The new detection system agrees with established procedure 95% of the time, and the time to test a sample is less than 5 minutes.
