Search results for: Jiahao Cui
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Search results for: Jiahao Cui

3 The Research of Reliability of MEMS Device under Thermal Shock Test in Space Mission

Authors: Liu Ziyu, Gao Yongfeng, Li Muhua, Zhao Jiahao, Meng Song

Abstract:

The effect of thermal shock on the operation of micro electromechanical systems (MEMS) were examined. All MEMS device were tested before and after three different conditions of thermal shock (from -55℃ to 85℃, from -65℃ to 125℃, from -65℃ to 200℃). The micro lens showed no changes after thermal shock, which shows that the design of the micro lens can be well adapted to the application environment in the space. The design of the micro mirror can be well adapted to the space application environment. The micro-magnetometer, RF MEMS switch and the micro accelerometer exhibited degradation and parameter drift after thermal shock, potential mechanical was proposed.

Keywords: MEMS, thermal shock test, reliability, space environment

Procedia PDF Downloads 547
2 The Modification of Convolutional Neural Network in Fin Whale Identification

Authors: Jiahao Cui

Abstract:

In the past centuries, due to climate change and intense whaling, the global whale population has dramatically declined. Among the various whale species, the fin whale experienced the most drastic drop in number due to its popularity in whaling. Under this background, identifying fin whale calls could be immensely beneficial to the preservation of the species. This paper uses feature extraction to process the input audio signal, then a network based on AlexNet and three networks based on the ResNet model was constructed to classify fin whale calls. A mixture of the DOSITS database and the Watkins database was used during training. The results demonstrate that a modified ResNet network has the best performance considering precision and network complexity.

Keywords: convolutional neural network, ResNet, AlexNet, fin whale preservation, feature extraction

Procedia PDF Downloads 78
1 Time of Week Intensity Estimation from Interval Censored Data with Application to Police Patrol Planning

Authors: Jiahao Tian, Michael D. Porter

Abstract:

Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval-censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of the week. This approach presented here provides accurate intensity estimates and can also uncover day-of-week clusters that share the same intensity patterns. Anticipating where and when crimes might occur is a key element to successful policing strategies. However, this task is complicated by the presence of interval-censored data. The censored data refers to the type of data that the event time is only known to lie within an interval instead of being observed exactly. This type of data is prevailing in the field of criminology because of the absence of victims for certain types of crime. Despite its importance, the research in temporal analysis of crime has lagged behind the spatial component. Inspired by the success of solving crime-related problems with a statistical approach, we propose a statistical model for the temporal intensity estimation of crime with censored data. The model is built on Poisson regression and has special penalty terms added to the likelihood. An EM algorithm was derived to obtain maximum likelihood estimates, and the resulting model shows superior performance to the competing model. Our research is in line with the smart policing initiative (SPI) proposed by the Bureau Justice of Assistance (BJA) as an effort to support law enforcement agencies in building evidence-based, data-driven law enforcement tactics. The goal is to identify strategic approaches that are effective in crime prevention and reduction. In our case, we allow agencies to deploy their resources for a relatively short period of time to achieve the maximum level of crime reduction. By analyzing a particular area within cities where data are available, our proposed approach could not only provide an accurate estimate of intensities for the time unit considered but a time-variation crime incidence pattern. Both will be helpful in the allocation of limited resources by either improving the existing patrol plan with the understanding of the discovery of the day of week cluster or supporting extra resources available.

Keywords: cluster detection, EM algorithm, interval censoring, intensity estimation

Procedia PDF Downloads 31