A Danger Estimation System for Infants Based on a Foreground Dynamic Probability Model

This study presents a danger estimation system to prevent accidents among infants. A video camera positioned above the infant＇s crib captures video. The proposed system can monitor the behavior of infants aged zero to six months. If there is a change in behavior or any other unusual occurrence, the system alerts the person responsible to attend to the baby immediately. The proposed system operates in three phases, which are foreground color model （FC model） construction, infant detection, and degree of danger analysis. During FC model construction, the foreground color histogram is created iteratively; the background image does not have to be constructed first. A motion-history image （MHI） is also obtained based on the motion of the infant. The color and motion information supplied by the FC model and the MHI are combined to detect the infant, who is regarded as the foreground object in the input frame. Moreover, six features of infant behavior are extracted from the detected infant to measure the degree of danger faced by the infant, and the result is used to warn the baby-sitter if needed. Experimental results show that the proposed method is robust and efficient.

Dynamic probability evaluation of safety levels of earth-rockfill dams using Bayesian approach

In order to accurately predict and control the aging process of dams, new information should be collected continuously to renew the quantitative evaluation of dam safety levels. Owing to the complex structural characteristics of dams, it is quite difficult to predict the time-varying factors affecting their safety levels. It is not feasible to employ dynamic reliability indices to evaluate the actual safety levels of dams. Based on the relevant regulations for dam safety classification in China, a dynamic probability description of dam safety levels was developed. Using the Bayesian approach and effective information mining, as well as real-time information, this study achieved more rational evaluation and prediction of dam safety levels. With the Bayesian expression of discrete stochastic variables, the a priori probabilities of the dam safety levels determined by experts were combined wfth the likelihood probability of the real-time check information, and the probability information for the evaluation of dam safety levels was renewed. The probability index was then applied to dam rehabilitation decision-making. This method helps reduce the difficulty and uncertainty of the evaluation of dam safety levels and complies with the current safe decision-making regulations for dams in China. It also enhances the application of current risk analysis methods for dam safety levels.

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping(VSLAM)systems need to assume static features in the environment.However,moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption.To cope with this challenging topic,a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed.To reduce the influence of dynamic content,we incorporate the deep-learning-based object detection method in the visual odometry,then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system.Experiment with both on the TUM and KITTI benchmark dataset,as well as in a real-world environment,the results clarify that our method can significantly reduce the tracking error or drift,enhance the robustness,accuracy and stability of the VSLAM system in dynamic scenes.

Impact of Wavelength-Routed Network Physical Topology on Blocking Probability Using a Dynamic Traffic Growth Model

We investigate the impact of network topology on blocking probability in wavelength-routed networks using a dynamic traffic growth model. The dependence of blocking on different physical parameters is assessed.

Reliability Analysis of Multi-State Repairable System with Dynamic Transition Probabilities

Inadequate maintenance decisions lead to incremental overall costs. In order to minimize costs in maintenance of the multi-state repairable system, we model a preventive maintenance(PM) scheme of the multistate repairable system using non-Markov process. The periodically decreasing reliability model of the non-Markov dynamic system with dynamic transition probabilities is established to satisfy the probability change. The diesel engine system is taken as an example to illustrate the model. The reliability of the diesel engine is analyzed and its PM scheme is worked out. RENO software is used to simulate the diesel engine system. The maintenance cost of components and the optimal PM interval data of the system are obtained by using the minimal average cost as the objective function. The adaptability of PM is judged, and the optimal PM scheme is presented.

A novel variable-lag probability hypothesis density smoother for multi-target tracking

It is understood that the forward-backward probability hypothesis density （PHD） smoothing algorithms proposed recently can significantly improve state estimation of targets. However, our analyses in this paper show that they cannot give a good cardinality （i.e., the number of targets） estimate. This is because backward smoothing ignores the effect of temporary track drop- ping caused by forward filtering and/or anomalous smoothing resulted from deaths of targets. To cope with such a problem, a novel PHD smoothing algorithm, called the variable-lag PHD smoother, in which a detection process used to identify whether the filtered cardinality varies within the smooth lag is added before backward smoothing, is developed here. The analytical results show that the proposed smoother can almost eliminate the influences of temporary track dropping and anomalous smoothing, while both the cardinality and the state estimations can significantly be improved. Simulation results on two multi-target tracking scenarios verify the effectiveness of the proposed smoother.

Explicating the mechanisms of land cover change in the New Eurasian Continental Bridge Economic Corridor region in the 21st century

Land cover change has presented clear spatial differences in the New Eurasian Continental Bridge Economic Corridor(NECBEC)region in the 21 st century.A spatiotemporal dynamic probability model and a driving force analysis model of land cover change were developed to analyze explicitly the dynamics and driving forces of land cover change in the NECBEC region.The results show that the areas of grassland,cropland and built-up land increased by 114.57 million ha,8.41 million ha and 3.96 million ha,and the areas of woodland,other land,and water bodies and wetlands decreased by 74.09 million ha,6.26 million ha,and 46.59 million ha in the NECBEC region between 2001 and 2017,respectively.Woodland and other land were mainly transformed to grassland,and grassland was mainly transformed to woodland and cropland.Built-up land had the largest annual rate of increase and 50%of this originated from cropland.Moreover,since the Belt and Road Initiative(BRI)commenced in 2013,there has been a greater change in the dynamics of land cover change,and the gaps in the socio-economic development level have gradually decreased.The index of socio-economic development was the highest in western Europe,and the lowest in northern Central Asia.The impacts of socio-economic development on cropland and built-up land were greater than those for other land cover types.In general,in the context of rapid socio-economic development,the rate of land cover change in the NECBEC has clearly shown an accelerating trend since 2001,especially after the launch of the BRI in 2013.

Hybrid gray wolf optimization-cuckoo search algorithm for RFID network planning

In recent years,with the rapid development of Internet of things(IoT)technology,radio frequency identification(RFID)technology as the core of IoT technology has been paid more and more attention,and RFID network planning(RNP)has become the primary concern.Compared with the traditional methods,meta-heuristic method is widely used in RNP.Aiming at the target requirements of RFID,such as fewer readers,covering more tags,reducing the interference between readers and saving costs,this paper proposes a hybrid gray wolf optimization-cuckoo search(GWO-CS)algorithm.This method uses the input representation based on random gray wolf search and evaluates the tag density and location to determine the combination performance of the reader's propagation area.Compared with particle swarm optimization(PSO)algorithm,cuckoo search(CS)algorithm and gray wolf optimization(GWO)algorithm under the same experimental conditions,the coverage of GWO-CS is 9.306%higher than that of PSO algorithm,6.963%higher than that of CS algorithm,and 3.488%higher than that of GWO algorithm.The results show that the GWO-CS algorithm cannot only improve the global search range,but also improve the local search depth.