NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

Analysis of Panel Count Data with Time-dependent Covariates and Informative Observation Process

Panel count data occur in many clinical and observational studies and in some situations the observation process is informative. In this article, we propose a new joint model for the analysis of panel count data with time-dependent covariates and possibly in the presence of informative observation process via two latent variables. For the inference on the proposed model, a class of estimating equations is developed and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a lack-of-fit test is provided for assessing the adequacy of the model. The finite-sample behavior of the proposed methods is examined through Monte Carlo simulation studies which suggest that the proposed approach works well for practical situations. Also an illustrative example is provided.

Direct Observation of Carrier Transportation Process in InGaAs/GaAs Multiple Quantum Wells Used for Solar Cells and Photodetectors

The resonant excitation is used to generate photo-excited carriers in quantum wells to observe the process of the carriers transportation by comparing the photoluminescence results between quantum wells with and without a p-n junction. It is observed directly in experiment that most of the photo-excited carriers in quantum wells with a p-n junction escape from quantum wells and form photoeurrent rather than relax to the ground state of the quantum wells. The photo absorption coei^cient of multiple quantum wells is also enhanced by a p-n junction. The results pave a novel way for solar cells and photodetectors making use of low-dimensional structure.

Impulse Control Problem of Partially Observed Diffusion Processes

The authors investigate the problem of impulse control of a partially observed diffusion process. The authors study the impulse control of Zakai type equations. The associated value function is characterized as the only viscosity solution of the corresponding quasi-variational inequality. The authors show the optimal cost function for the problem with incomplete information can be approximated by a sequence of value functions of the previous type.

How to co-add images? I. A new iterative method for image reconstruction of dithered observations

By employing the previous Voronoi approach and replacing its nearest neighbor approx- imation with Drizzle in iterative signal extraction, we develop a fast iterative Drizzle algorithm, namedfiDrizzle, to reconstruct the underlying band-limited image from undersampled dithered frames. Compared with the existing iDrizzle, the new algorithm improves rate of convergence and accelerates the computational speed. Moreover, under the same conditions （e.g. the same number of dithers and iterations）, fiDrizzle can make a better quality reconstruction than iDrizzle, due to the newly discov- ered High Sampling caused Decelerating Convergence （HSDC） effect in the iterative signal extraction process.fiDrizzle demonstrates its powerful ability to perform image deconvolution from undersampled dithers.

Observational Features of Large-Scale Structures as Revealed by the Catastrophe Model of Solar Eruptions

Large-scale magnetic structures are the main carrier of major eruptions in the solar atmosphere. These structures are rooted in the photosphere and are driven by the unceasing motion of the photospheric material through a series of equilibrium configurations. The motion brings energy into the coronal magnetic field until the system ceases to be in equilibrium. The catastrophe theory for solar eruptions indicates that loss of mechanical equilibrium constitutes the main trigger mechanism of major eruptions, usually shown up as solar flares, eruptive prominences, and coronal mass ejections （CMEs）. Magnetic reconnection which takes place at the very beginning of the eruption as a result of plasma instabilities/turbulence inside the current sheet, converts magnetic energy into heating and kinetic energy that are responsible for solar flares, and for accelerating both plasma ejecta （flows and CMEs） and energetic particles. Various manifestations are thus related to one another, and the physics behind these relationships is catastrophe and magnetic reconnection. This work reports on recent progress in both theoretical research and observations on eruptive phenomena showing the above manifestations. We start by displaying the properties of large-scale structures in the corona and the related magnetic fields prior to an eruption, and show various morphological features of the disrupting magnetic fields. Then, in the framework of the catastrophe theory, we look into the physics behind those features investigated in a succession of previous works, and discuss the approaches they used.

CRESDA Successfully Processed the First Track Data of GF-3

The first images obtained from Gaofen-3（GF-3）,China’s first C-band high-resolution Synthetic Aperture Radar（SAR）satellite with a resolution of one meter in spatial diameter were published on August 25.This satellite undertakes an important task with its all-day,all-weather observation capability as part of the China High-resolution Earth Observation System（CHEOS）.With 12 imaging modes,

Analysis of a POMDP Model for an Optimal Maintenance Problem with Multiple Imperfect Repairs

I consider a system whose deterioration follows a discrete-time and discrete-state Markov chain with an absorbing state. When the system is put into practice, I may select operation (wait), imperfect repair, or replacement at each discrete-time point. The true state of the system is not known when it is operated. Instead, the system is monitored after operation and some incomplete information concerned with the deterioration is obtained for decision making. Since there are multiple imperfect repairs, I can select one option from them when the imperfect repair is preferable to operation and replacement. To express this situation, I propose a POMDP model and theoretically investigate the structure of an optimal maintenance policy minimizing a total expected discounted cost for an unbounded horizon. Then two stochastic orders are used for the analysis of our problem.

Optimization of dynamic sequential test strategy for equipment health management

Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.

Evolution Handoff Strategy for Real-Time Video Transmission over Practical Cognitive Radio Networks

The transmission delay of realtime video packet mainly depends on the sensing time delay(short-term factor) and the entire frame transmission delay(long-term factor).Therefore,the optimization problem in the spectrum handoff process should be formulated as the combination of microscopic optimization and macroscopic optimization.In this paper,we focus on the issue of combining these two optimization models,and propose a novel Evolution Spectrum Handoff(ESH)strategy to minimize the expected transmission delay of real-time video packet.In the microoptimized model,considering the tradeoff between Primary User's(PU's) allowable collision percentage of each channel and transmission delay of video packet,we propose a mixed integer non-linear programming scheme.The scheme is able to achieve the minimum sensing time which is termed as an optimal stopping time.In the macro-optimized model,using the optimal stopping time as reward function within the partially observable Markov decision process framework,the EHS strategy is designed to search an optimal target channel set and minimize the expected delay of packet in the long-term real-time video transmission.Meanwhile,the minimum expected transmission delay is obtained under practical cognitive radio networks' conditions,i.e.,secondary user's mobility,PU's random access,imperfect sensing information,etc..Theoretical analysis and simulation results show that the ESH strategy can effectively reduce the transmission delay of video packet in spectrum handoff process.

Distributed cooperative task planning algorithm for multiple satellites in delayed communication environment

Multiple earth observing satellites need to communicate with each other to observe plenty of targets on the Earth together. The factors, such as external interference, result in satellite information interaction delays, which is unable to ensure the integrity and timeliness of the information on decision making for satellites. And the optimization of the planning result is affected. Therefore, the effect of communication delay is considered during the multi-satel ite coordinating process. For this problem, firstly, a distributed cooperative optimization problem for multiple satellites in the delayed communication environment is formulized. Secondly, based on both the analysis of the temporal sequence of tasks in a single satellite and the dynamically decoupled characteristics of the multi-satellite system, the environment information of multi-satellite distributed cooperative optimization is constructed on the basis of the directed acyclic graph（DAG）. Then, both a cooperative optimization decision making framework and a model are built according to the decentralized partial observable Markov decision process（DEC-POMDP）. After that, a satellite coordinating strategy aimed at different conditions of communication delay is mainly analyzed, and a unified processing strategy on communication delay is designed. An approximate cooperative optimization algorithm based on simulated annealing is proposed. Finally, the effectiveness and robustness of the method presented in this paper are verified via the simulation.

A navigation method based on POMDP for smart wheelchair in uncertain environments

A navigation method based on the partially observable markov decision process （POMDP） for smart wheelchairs in uncertain environments is presented in this paper. The design key factors for the navigation system of a smart wheelchair are discussed. A kinematics model of the smart wheelchair is given, and the model and principle of POMDP are introduced. In order to respond in uncertain local environments, a novel navigation methodology based on POMDP using the sensors perception and the user＇s joystick input is presented. The state space, the action set, the observations and the sensor fusion of the navigation method are given in detail, and the optimal policy of the POMDP model is proposed. Experimental results demonstrate the feasibility of this navigation method. Analysis is also conducted to investigate performance evaluation, advantages of the approach and potential generalization of this paper.

In-situ observations of internal dissolved heavy metal release in relation to sediment suspension in lake Taihu, China

Despite laboratory experiments that have been performed to study internal heavy metal release,our understanding of how heavy metals release in shallow eutrophic lakes remains limited for lacking in-situ evidence.This study used automatic environmental sensors and a water sampling system to conduct high-frequency in-situ observations(1-hr intervals)of water environmental variables and to collect water samples(3-hr intervals),with which to examine the release of internal heavy metals in Lake Taihu,China.Under conditions of disturbance by strong northerly winds,sediment resuspension in both the estuary area and the lake center caused particulate heavy metal resuspension.However,the patterns of concentrations of dissolved heavy metals in these two areas were complex.The concentrations of dissolved Se and Mo increased in both areas,indicating that release of internal dissolved Se and Mo is triggered by sediment resuspension.The concentrations of dissolved Ni,Zn,As,Mn,Cu,V,and Co tended to increase in the estuary area but decrease in the lake center.The different trends between these two areas were controlled by pH and cyanobacteria,which are related to eutrophication.During the strong northerly winds,the decrease in concentrations of dissolved heavy metals in the lake center was attributable primarily to absorption by the increased suspended solids,and to growth-related assimilation or surface adsorption by the increased cyanobacteria.The findings of this study suggest that,short-term changes of environmental conditions are very important in relation to reliable monitoring and risk assessment of heavy metals in shallow eutrophic lakes.

Decision-making models on perceptual uncertainty with distributional reinforcement learning

Decision-making for autonomous vehicles in the presence of obstacle occlusions is difficult because the lack of accurate information affects the judgment.Existing methods may lead to overly conservative strategies and timeconsuming computations that cannot be balanced with efficiency.We propose to use distributional reinforcement learning to hedge the risk of strategies,optimize the worse cases,and improve the efficiency of the algorithm so that the agent learns better actions.A batch of smaller values is used to replace the average value to optimize the worse case,and combined with frame stacking,we call it Efficient-Fully parameterized Quantile Function(EFQF).This model is used to evaluate signal-free intersection crossing scenarios and makes more efficient moves and reduces the collision rate compared to conventional reinforcement learning algorithms in the presence of perceived occlusion.The model also has robustness in the case of data loss compared to the method with embedded long and short term memory.

Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping

Continuous global-scale mapping of human settlements in the service of international agreements calls for massive volume of multi-source,multi-temporal,and multi-scale earth observation data.In this paper,the latest developments in terms of processing big earth observation data for the purpose of improving the Global Human Settlement Layer(GHSL)data are presented.Two experiments with Sentinel-1 and Landsat data collections were run leveraging on the Joint Research Centre Earth Observation Data and Processing Platform.A comparative analysis of the results of built-up areas extraction from different remote sensing data and processing workflows shows how the information production supported by data-intensive computing infrastructure for optimization and multiple testing can improve the output information reliability and consistency within the GHSL scope.The paper presents the processing workflows and the results of the two main experiments,giving insights into the enhanced mapping capabilities gained by analyzing Sentinel-1 and Landsat data-sets,and the lessons learnt in terms of handling and processing big earth observation data.

Admission Control with Elastic QoS for Video on Demand Systems

In network service systems, satisfying quality of service (QoS) is one of the main objectives. Admission control and resource allocation strategy can be used to guarantee the QoS requirement. Based on partially observable Markov decision processes (POMDPs), this paper proposes a novel admission control model for video on demand (VOD) service systems with elastic QoS. Elastic QoS is also considered in resource allocation strategy. Policy gradient algorithm is often available to find the solution of POMDP problems, with a satisfactory convergence rate. Through numerical examples, it can be shown that the proposed admission control strategy has better performance than complete admission control strategy.

Power Allocation Optimization of Multibeam High-Throughput Satellite Communication Systems

The increasing demands in terms of high data rate and quality of services over the hybrid satellite-terrestrial relay networks(HSTRN)have pushed for the development of millimeter-wave(mmWave)band high-throughput satellites(HTS)with multibeams.The next generation of mmWave multibeam HTS communication systems(HTSCS)is viewed as the backbone network to enhance the throughput of the HSTRN.The article first investigates the basic backbone topology architecture of HTSCS,and an M-state Markov channel for the Ka/Q/V band mmWave systems is reviewed.Then,we propose a long-term optimal power allocation scheme over two in-dependent and identical spot beams based on the partially observable Markov decision process(POMDP),which can partly mitigate the negative effects of severe weather conditions.The key conditions for selecting the optimal power allocation action in the multibeam HTSCS are given.Simulation results show that our POMDP-based power allocation scheme can enhance the long-term throughput of the HTSCS.

Partition sampling strategy for robot motion planning under uncertainty

In order to solve the sensing and motion uncertainty problem of motion planning in narrow passage environment,a partition sampling strategy based on partially observable Markov decision process(POMDP)was proposed.The method combines partition sampling strategy and can improve the success rate of the robot motion planning in the narrow passage.Firstly,the environment is divided into open area and narrow area by using a partition sampling strategy,and generates the initial trajectory of the robot with fewer sampling points.Secondly,the method can calculate a local optimal solution of the initial nominal trajectory by solving POMDP problem,and iterates an overall optimal trajectory of robot motion.The proposed method follows the general POMDP solution framework,in which the belief dynamics is approximated by an extended Kalman filter(EKF),and the value function is represented by an effective quadratic function in the belief space near the nominal trajectory.Using a belief space variant of iterative linear quadratic Gaussian(iLQG)to perform the value iteration,which results in a linear control policy over the belief space that is locally optimal around the nominal trajectory.A new nominal trajectory is generated by executing the control strategy iteration,and the process is repeated until it converges to a locally optimal solution.Finally,the robot gets the optimal trajectory to safely pass through a narrow passage.The experimental results show that the proposed method can efficiently improves the performance of motion planning under uncertainty.

Sequential degradation-based burn-in test with multiple periodic inspections

Bum-in has been proven effective in identifying and removing defective products before they are delivered to customers.Most existing bum-in models adopt a one-shot scheme,which may not be sufficient enough for identification.Borrowing the idea from sequential inspections for remaining useful life prediction and accelerated lifetime test,this study proposes a sequential degradation-based bum-in model with multiple periodic inspections.At each inspection epoch,the posterior probability that a product belongs to a normal one is updated with the inspected degradation level.Based on the degradation level and the updated posterior probability,a product can be disposed,put into field use,or kept in the test till the next inspection epoch.We cast the problem into a partially observed Markov decision process to minimize the expected total bum-in cost of a product,and derive some interesting structures of the optimal policy.Then,algorithms are provided to find the joint optimal inspection period and number of inspections in steps.A numerical study is also provided to illustrate the effectiveness of our proposed model.

HDec-POSMDPs MRS Exploration and Fire Searching Based on IoT Cloud Robotics

The multi-robot systems(MRS)exploration and fire searching problem is an important application of mobile robots which require massive computation capability that exceeds the ability of traditional MRS′s.This paper propose a cloud-based hybrid decentralized partially observable semi-Markov decision process(HDec-POSMDPs)model.The proposed model is implemented for MRS exploration and fire searching application based on the Internet of things(IoT)cloud robotics framework.In this implementation the heavy and expensive computational tasks are offloaded to the cloud servers.The proposed model achieves a significant improvement in the computation burden of the whole task relative to a traditional MRS.The proposed model is applied to explore and search for fire objects in an unknown environment;using different sets of robots sizes.The preliminary evaluation of this implementation demonstrates that as the parallelism of computational instances increase the delay of new actuation commands which will be decreased,the mean time of task completion is decreased,the number of turns in the path from the start pose cells to the target cells is minimized and the energy consumption for each robot is reduced.