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The Antagonistic Weapon-Target Assignment (AGWTA) problem is a crucial decision issue in Command & Control (C2). Since this is a minimax problem, co-evolutionary algorithms can be used to solve it effectively. However, the co-evolutionary algorithm is originally designed for continuous minimax problems which loses its efficiency to discrete contexts. In this paper, a novel elitism co-evolutionary algorithm is proposed to solve the AGWTA. Firstly, an improved AGWTA model for air combat based on the attack and evasion strategies is proposed. Secondly, an elite cooperative genetic algorithm based on the framework of the co-evolutionary algorithm is put forward. In this proposed algorithm, a problem-specific coding method and evolution operator are designed. Meanwhile, an elite individual update mechanism is presented. Finally, based on the analysis of the relationship between the feasible solutions under the air combat environment, an evaluation index is proposed. Experiments show that the proposed algorithm has higher accuracy than traditional co-evolutionary algorithms for solving AGWTA problems.

The harmonic reducer of the space drive mechanism is one of the critical components of the satellite remote sensing system and its reliability is always required strictly in practice. During the manufacturing and usage processes of the harmonic reducer, uncertainties are ubiquitous and performance degradation is inevitable, which is mainly caused by the wear between the meshed tooth surfaces. They influence the reliability of harmonic reducer. In this paper, a performance margin modeling and reliability analysis for harmonic reducer considering multi-source uncertainties and wear is proposed. Firstly, based on the functional principle and the influence of wear, the performance margin model is established using hysteresis and transmission errors as key performance parameters. Then multi-source uncertainties are analyzed and quantified, including manufacturing errors, uncertainties in operational and environmental conditions, and uncertainties in performance thresholds. Finally, the reliability model is constructed based on the performance margin. A case study of a harmonic reducer is applied and the reliability sensitivity analysis is implemented to show the practicability of the proposed method. The results show that the proposed method can provide some suggestions to the design and manufacturing phases of the harmonic reducer.

In training-free Synthetic Aperture Radar (SAR) target detection, it is difficult to distinguish targets and non-targets accurately. To solve the problem, a new improved NCM clustering method with one clustering center (K=1), as called NCM-1K clustering method, is proposed in this paper. The principle of NCM-1K is to divide the potential target areas into target areas and non-target areas according to the Polar Scale-Invariant Feature Transform (PSIFT) descriptor. The proposed NCM-1K clustering method can realize the SAR target detection with free-training. Experimental results verify the feasibility and effectiveness of proposed method.

The weapon-target assignment (WTA) problem is a crucial decision issue in the process of cooperative aerial warfare (CAW). The decision strategy of fighter teams involved in the CAW is susceptible to the influence of the enemy fire attack and electronic interference, which will lead to both the antagonism and uncertainty of the decision making. In this paper, a novel antagonistic game WTA (AGWTA) model with uncertainty is introduced. The antagonism is described by a non-cooperative zero-sum game model conducted by two fighter teams. Then, a modified sensor data fusion method using belief entropy and similarity of sensor data is presented to manage the uncertainty of AGWTA. According to the characteristics of the AGWTA model, a decomposition co-evolution algorithm (DCEA-AGWTA) is proposed to obtain the non-cooperative Nash equilibrium (NCNE) strategy. The experimental results show that the modified sensor data fusion method contributes to higher reliability of target type identification and the AGWTA model is meaningful in the antagonistic and uncertain situation of CAW. In addition, the DCEA-AGWTA is effective and has a promising ability in finding the closest strategy to the NCNE strategy compared with the other three intelligent evolution-based algorithms.

With the increasing design requirements of grid-connected microgrid, the capacity configuration of microgrid should not only consider the cost, but also consider other design objectives, uncertainty of renewable energy and load demand. Therefore, a multi-objective function considering annual comprehensive cost and grid dependence is established. The k-medoids method was used to obtain the data and probability of typical and extreme days, which was added into the optimization as uncertain scenes. The Multi-Objective Cuckoo Search algorithm (MOCS) is used to solve the proposed model. Then the obtained non-dominated optimal solution set was treated with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, which can obtain the optimal compromise solution from the set. The optimal compromise solution is the final configuration of this Wind-PV-Battery microgrid.

Uncertain right censoring which describes the situation that the censoring settings of test units are unrelated to their failure time often occurs in the life testing. Besides, in the reliability evaluations using the collected time-to-failure data from the practical life testing, the small-sample situation is common since the test resources are usually limited. Under such situation, the large-sample based probability theory is not appropriate anymore to evaluate reliability. To address such problem, based on a new theory of reliability-belief reliability theory, the reliability evaluation is conducted to cope with the limited censored time-to-failure data under uncertain right censoring, where the uncertain measure is employed. Firstly, a brief introduction to the belief reliability theory is conducted. Then, the lifetime model and belief reliability evaluation under the uncertain measure are presented, and the corresponding statistics method for unknown parameter estimation is provided with objective measures. Finally, a simulation study is used to illustrate the proposed method. The results show that the proposed method can achieve more accurate and concrete reliability evaluation results with limited right-censored time-to-failure data, compared to the probability reliability method.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.

The robot-assisted natural orifice transluminal surgery (NOTES) typically involves applying the lengthy and slender continuum instruments/manipulators to get access to the target lesion sites, and then performs complex operations. However, due to the strong-nonlinearity of continuum robotic manipulators and the confined and tortuous anatomical paths, it’s difficult to establish accurate inverse kinematics (IK) model to achieve precise motion control and real-time shape sensing for accurately modeling their shapes. To tackle such difficulties, a model-free method based on neural network have been proposed to solve the IK problem and reconstruct the shape of a continuum manipulator at the same time using the training results from the electromagnetic (EM) tracking approach. For the IK problem, the relationship between the tip position and the corresponding cable lengths can be learned and for the shape estimation problem, the mapping from the cable lengths to the shape of the continuum robot can be established. A dataset of 500 random continuum manipulator postures was used to train the neural network, with recorded EM sensors-tracked positions logged synchronously to the cable lengths. Experiment results show the proposed model-free method could achieve high accuracy and reliable inverse kinematics and shape reconstruction outcomes.

This paper presents the design and development of a new standard of Urban Transit, which replaced the traditional steel wheels with rubber tire and constraint within virtual track. This transit has been in revenue services at Zhuzhou, Hunan, Mainland of China in 2017. In order to facilitate developing the precision lane keeping control system, analyzing and building the 32 meters long transit dynamic models is necessary. This paper describes in three aspects: the transit dynamic model, the simulation procedure for validation testing, and the lane keeping controller design based on model predictive control method. Through these development procedure we can enhance the control system iterative rate and reliability to facilitate the new type of transit for narrow path lane keeping and precise docking at platform.

Manually driving a 32 meters long rubber-tire transit with highly intensive concentration could easily get driver fatigue. Alongside the vision navigation method, this paper describes an automatic path tracking control method. Firstly, introduce the automatic steering control principle which is based on image recognition with optical guidance. Then, specifically describes the path tracking controller design. Finally, the control effect is verified by both joint simulation of Trucksim-MATLAB/Simulink and the real vehicle tests. The results show that the precise docking clearance between the platform and the transit is kept at 10-20cm. When driving at high speed, the stable speed is up to 72km/h, and the lateral deviation is controlled within 0.15m.

To meet the immensely diverse service requirements, heterogeneous cloud radio access network (H-CRAN) architecture and D2D communication is embraced. Consequently, the resource allocation between D2D pairs and current users is a challenge. In this paper, a joint power control and sub-channel allocation scheme is proposed. The original mixed-integer nonlinear programming problem is decomposed into power and sub-channel allocation. Geometric Vertex Search approach and 3-dimensional (3-D) matching method are used to solve them. Finally, numerical results verify the proposed scheme has about 35% and 60% improvement in total throughput comparing with other approaches.

For a dense sensor network in a smart city, efficient data aggregation can be realized by deploying readers mounted on unmanned aerial vehicles (UAVs), At high mobility and given dense sensors, the requirement of ultra-low latency cannot be met using a traditional multi-access scheme such as time-division or orthogonal-frequency-division multiple access. A technique called over-the-air computation (AirComp) has emerged to be a promising solution for high-mobility sensing, which integrates functional computation (e.g., averaging and geometric mean) and multi-access by exploiting analog waveform addition. Targeting high-mobility sensing, the technique supports ultra-fast simultaneous access and function computation. In this paper, building on multi-antenna AirComp, we present a new frame-work of wirelessly powered (WP) AirComp to enable UAVs for simultaneous data aggregation and wirelessly powering sensors, where wireless power solves a key design challenge of battery recharging for many sensors. The key feature of WP-AirComp is the leverage of down-link wireless power transfer (WPT) as an additional design dimension for reducing the sum computation error in up-link AirComp. Designing the framework involves the joint optimization of power control, energy beamforming and AirComp equalization. To derive a practical solution, we recast the non-convex problem into equivalent outer and inner problems for (inner) wireless power control and energy beamforming and (outer) AirComp equalization, respectively. The former is solved in closed form while the latter via semi-definite relaxation, which is shown to reach the global optimum with high probability. The solution reveals that the optimal power beams point to the WPT channels, and the optimal power allocation tends to equalize the round-trip attenuation over sensors.

Accelerated degradation testing (ADT) is widely applied to evaluate product reliability and lifetime before releasing products to the market. The degradation data from ADTs are usually integrated with expert experience in the development phase for high accurate reliability assessments. However, in practical applications, expert experience may only provide subjective knowledge, and the degradation data are usually scarce due to the limited test resources in conducting ADTs. So epistemic uncertainties are embedded in both expert experience and ADTs. Therefore, the integration of these two information sources with epistemic uncertainties for high accurate reliability assessments is an urgent issue to be solved. To address such issue, a novel uncertain information integration method of expert experience and ADT data based on uncertain cross-entropy is presented. First, the uncertainty theory is introduced to obtain the lifetime characteristic distribution from expert opinions and to model the degradation process from ADT data. Then, the reliability information of these two sources is integrated for reliability assessments based on uncertain cross-entropy. A case study of the integration of expert experience and stress relaxation ADT data for electrical connector is applied to verify the effectiveness of the proposed method.

As the accuracy in sensors and powerful in controller keep improving, there is more room for developing the perception of the road environment and the operation in complex traffic conditions of Connected Automated Vehicles. In this paper, we propose a control strategy with environment identification to minimize the cost but achieve the effect of expensive Multiline Lidar. We use computer vision and deep learning to train existing data sets in this paper. More specifically, we use efficient neural network trained the data in German Traffic Sign Recognition Benchmark and KITTI respectively to realize classification of traffic signs and detection of vehicles, and functions of OpenCV are used to identify and locate traffic identification lines. To plan and make decisions on the driving route, the vehicle driving simulator based on the Model Predictive Control also is used to collect, control and train the data. Finally, our method can be proved practically from the case study and data in Udacity's Self-Driving Car Nanodegree project and the road scene in real life.

In this paper, we study the model design for realizing simultaneous wireless information and power transmission (SWIPT) in multi-user universal filtered multi-carrier (UFMC) downlink system, which can transfer energy as well as information at the same time. Different from orthogonal frequency division multiplexing (OFDM) which filters the whole band, UFMC filters a serial of subcarriers and does not need cyclic prefix (CP), thus it can enhance the spectrum efficiency. So combining UFMC and SWIPT technology can improve information rate as well as harvested energy. To achieve the rate-energy region, we jointly optimize the power allocation strategy as well as the subcarrier allocation scheme in a multiuser UFMC downlink system by Lagrange duality method. At the receiver, the power splitting (PS) method is used to coordinate the energy harvesting (EH) with information decoding (ID) process. The simulation results show that UFMC-based SWIPT system outperforms OFDM-based SWIPT system under the same channel condition.

As an information hinge of various trades and professions in the era of big data, cloud data center bears the responsibility to provide uninterrupted service. To cope with the impact of failure and interruption during the operation on the Quality of Service (QoS), it is important to guarantee the resilience of cloud data center. Thus, different resilience actions are conducted in its life circle, that is, resilience strategy. In order to measure the effect of resilience strategy on the system resilience, this paper propose a new approach to model and evaluate the resilience strategy for cloud data center focusing on its core part of service providing-IT architecture. A comprehensive resilience metric based on resilience loss is put forward considering the characteristic of cloud data center. Furthermore, mapping model between system resilience and resilience strategy is built up. Then, based on a hierarchical colored generalized stochastic petri net (HCGSPN) model depicting the procedure of the system processing the service requests, simulation is conducted to evaluate the resilience strategy through the metric calculation. With a case study of a company's cloud data center, the applicability and correctness of the approach is demonstrated.

As active-active data center has sprung up since the popularization of cloud computing services, it keeps on a flourishing development due to the advanced active-active mechanism. In order to provide high-quality service to customers, the service reliability based IT infrastructure that delivers the service and implements the active-active mechanism is a key concern. In order to evaluate and further improve the service reliability of the cloud data center under active-active mechanism, modeling is absolutely necessary but complex. In this paper, a novel colored generalized stochastic petri net (CGSPN) model based on IT infrastructures is build up, which clearly reflect the dynamic behavior and service request processing procedure under the active-active mechanism. During the modeling, the hierarchical modeling method is applied. The outline net illustrates the processing procedure and the subnets consider the failure and resource usage of each node. Afterwards, the simulation is carried out through CPN Tools and applied in a use case, which verifies the validity of our approach.

With the development of science and technology, long-life products have been prevalent in the market, and it is necessary to apply accelerated testing techniques to product demonstration. At the same time, the researchers designed the accelerated acceptance sampling plan with the producer risk and consumer risk that are known, which lacked the scientific basis. Based on the knowledge of incomplete information static game and considering that the acceleration factor (AF) has been acquired, this paper takes the risk of producers and users as unknown in AASP and takes the risk equivalence as the goal and then designs AASP, which solves the problem of producer risk and consumer risk that are not scientific and strict. Finally the numerical case is given and the correctness of the method is verified.

This paper investigates resource allocation in a multi-user, multi-band and multi-antenna equipped wireless communication system, which is used for simultaneous wireless information and power transmission (SWIPT). The optimization problems of sum-rate over multiple sub-bands subject to the power spectral density requirement (PSDR) limit and the circuit power requirement (CPR) are studied in the case of two users, i.e., far-user (FU) and near-user (NU), and under 3 situations: 1) to maximize the global information sum-rate; 2) to maximize the information rate of far-user with higher priority; 3) to maximize the least of common throughput. Using convex optimization methods, the above 3 problems are solved. Simulation results are provided to demonstrate the presented solutions.

An improved three dimensional adaptive robust guidance law is proposed for the passive homing guidance in which only line-of-sight angular rate can be measured when target maneuvered. The nonlinear control system of missile is designed according to relative motion model of missile and target, which regards coupling and target acceleration as interference. Then a sliding surface and control law is designed based on approximate parallel approaching principle and variable structure control theory. The self-learning ability of RBF neural network is used to approximate the uncertain nonlinear time-varying functions in the control law online. Finally, the network weights adaptive law is proposed according to Lyapunov theory. This method can assure the excellent adaptive ability and robustness of the system. Simulation results indicate that the guidance law is better than traditional proportional navigation and variable structure guidance law in performance.

The issue of remaining useful life (RUL) prediction has already become a quite interesting topic in industrial product. The data driven RUL prediction has been applied to the current research by taking advantage of a long-short term memory (LSTM)-recurrent neural network (RNN) approach. This means that even in a specified long-short term memory bound and limited available data sets, the RUL predictions can also improve the equipment capacity. By collecting the sensor parameters from National Aeronautics and Space Administration (NASA) jet engines, the capability of this approach has been demonstrated. An appropriate selection, that is suitable for the variable measurement, has been used to feed LSTM-RNNs with the most useful RUL labels. The analysis results illustrate that, compared with traditional statistical model, the development prediction approach is likely to provide an accurate prediction for RUL equipment that can be meaningful to maintenance schemes.

The environmental factor (such as temperature, etc.) is an important error sources of product performance degradation data. In practice, the raw degradation data usually contain some noise terms caused by the fluctuation of environmental factor. Therefore, it is necessary to extract the real degradation trend before degradation modeling and life prediction. In this research, the Grey relational analysis method is utilized to quantitatively describe the correlational relationship between temperature fluctuation and raw degradation data. Then a novel temperature compensation model based on least squares support vector machine (LS-SVM) is proposed, which can be used to compensate the influence of environmental fluctuation on degradation data. An engineering case study on the degradation data of a super-luminescent diode (SLD) is employed to verify the effectiveness of the proposed method.

A cooperative maneuver decision method for multiple unmanned aerial vehicles is proposed to address the close-range air combat problem in this article. As the perfect information of adversary is impossible to obtain, a state predicted influence diagram model is established to solve the problem of the maneuver decision in the close-range air combat. The elements are analyzed comprehensively based on the proposed model. A framework of the cooperative control system for the multi unmanned aerial vehicle is put forward, and an unscented Kalman filter model is used for belief state updating with the state prediction, which could provide an impactful information of combat state. In order to fulfill with the real-time requirement of the air combat, the moving horizon control approach is used for solving the proposed model. Experiment results indicate that the proposed method could offer a novel approach effectively and reasonably to analyze the close-range air combat. The superiority of the cooperative control strategy is demonstrated as well.

Probe vehicle data, also known as floating car data or connected vehicle data, is the data collected from GPS-enabled sensors on vehicles. With the advancement in wireless communications and localization technologies, more and more vehicles are expected to be equipped with such sensors. Existing studies only focus on using small-scale probe vehicle data. In this paper, we are interested in developing a real-time parallel stream processing framework to extract traffic flow KPIs from large-scale probe vehicle data. The developed framework is implemented using Apache Storm on Amazon AWS, and can process one million probe vehicle messages per second. Various design considerations, such as data partition and delay processing are discussed. To evaluate the performance of stream processing framework, simulated probe vehicle data based on the actual traffic flows in Jurong Lake District (JLD) of Singapore, is generated using the microscopic simulation software VISSIM. The JLD data is replicated multiple times to represent the one million population of vehicles in Singapore. GPS errors and communication delays are added to represent the real situations before the data is fed to stream processing module. The estimated KPIs from our stream processing model are validated against the ground truth values under different penetration levels.

Resilience is the ability characterizing an engineering system to recover rapidly after disruptions from inside or outside the system. Resilience is realized on the basis of fixed strategies and resources, beyond which the resilience is changing in essence. This paper proposes the definition of system resilience limit, as well as the definition of system resilience concerned with resilience limit. Then the system resilience is modeled and evaluated involved with the minimal paths. Finally, the method is verified with respect to a network system with the help of Monte Carlo simulation. The method proposed in this paper is proved to be efficient in assessing system resilience, and is useful for the decision-making and optimization of resilience strategies.

Accelerated degradation testing (ADT) is commonly used to obtain degradation data under the hasher than normal usage conditions, and to assess lifetime and reliability for high reliable and long life products in a short time. The optimal design for ADT is to propose the test plan based on the performance degradation process with a reasonable optimal criteria and the constraints of test resources. Both the traditional ADT optimal design and Bayesian ADT optimal design are similar that the historical data is needed to determine the initial values or the prior distributions of the model parameters. However, it may be inaccurate to estimate the parameter values or the prior distributions only according to the historical information, which will result in large deviations between the parameter values or the prior distributions and the actual ones. Therefore, the optimal plan could not meet the statistical accuracy. In this paper, we propose a Bayesian ADT dynamic optimal design method with sequential stress. The degradation information under the most recent stress level will be treated as the prior of the next stress level. Under the assumption of Wiener process, an optimal model with the objective of quadratic loss function is built with the decision variables, e.g. stress levels and inspection times. Finally, the simulation study is introduced to illustrate the proposed method, and the results show that Bayesian ADT optimal design with sequential stress can adjust the test plan dynamically to improve the test efficiency.

With the increasing use in different areas, cloud data center has gradually showed its superiority in availability, resource utilization and disaster recovery during the service delivery, compared with traditional data center. According to SLA (Service-Level Agreement), the demand on service reliability and other related indexes are put forward. Despite efforts at fault tolerance and redundancy, the occurrence of failure in data center is still inevitable. Hence, there is a need to model and analyze the service reliability of data center. However, traditional method of reliability modeling is no longer applicable because of the complicated cloud control flows, massive-scale service sharing, and complexity real-word infrastructures. This paper proposes a new approach to model the service reliability of the IT infrastructure of active-active data center, which is a typical form of cloud data center. Firstly, we divide the process of service delivery into two stages — the request stage and execution stage. Then, two models are built for two stages, respectively. For request stage, we use the Queuing Theory and Monte Carlo Method while for execution stage, the Graph Theory and Monte Carlo Method are adopted. Based on the proposed model, the service reliability of data center can be calculated. With the data of a company active-active data center as a case study, we finally demonstrate the applicability and correctness of the model.

This paper presents a two-stage evolutionary multiobjective particle swarm optimization (TSMOPSO) algorithm to solve the multiobjective weapon-target assignment (WTA) problem. In order to improve the convergence and the rate of convergence of the proposed algorithm, a two-stage evolutionary strategy is employed. In the first stage of evolution, the population is evolved by the particle status updating rules defined in particle swarm algorithm (PSO). In the second stage of evolution, a novel evolution operator is designed to update the Pareto front solutions which is obtained in the first stage of evolution. Experiments indicate that TSMOPSO algorithm is an effective method to solve the multiobjective weapon-target assignment problem, and the convergence and the rate of convergence of this algorithm have an obvious improvement.

Multi-target threat assessment is a considerable issue of air combat tactical decision. As the conventional approaches are deficient and imperfect in the procedure of determining the attribute weights and result display of the threats, a novel threat assessment method based on Prioritized Visibility Graph Ordered Weighted Averaging (PVGOWA) operator is proposed. In this work, the relative threat function and the multitarget assessment model of air combat are established. The VGOWA operator is used to calculate the objective weights, which can fully utilize the information in the combat situation. In order to avoid the randomness and uncertainty of expert scoring in the subjective weight determination process, the POWA operator is used to get the final attribute weights by fusing the objective weights and the priorities of the attributes of each target, which are identified by the decision maker. Besides, the radar-chart model is adopted to display the assessment results. Simulation indicates that the proposed method is more reasonable and intuitional for multi-target threat assessment compared to the conventional method.

In this paper, the inner transport behavior of anode flow field structures (point-type, parallel, single-serpentine and double-serpentine) on a silicon-based micro direct methanol fuel cell (μDMFC) is investigated through both simulations and experiments. A model is established to describe the concentrations both in the flow field and the diffusion layer, as well as the pressure distribution in the flow field. Furthermore, using micro electromechanical system (MEMS) technology, a silicon-based μDMFC with the active area of 0.64cm2 is designed and fabricated to evaluate the model verification as well as the corresponding cell performance. In addition, a novel method is introduced by Polydimethylsiloxane (PDMS) to optimize the assembly. The experimental results reveal that the cell patterned with double-serpentine channel exhibits the best cell performance with the maximum power density of 11.4 mW cm−2.