Inversion of Seabed Geotechnical Properties in the Arctic Chukchi Deep Sea Basin Based on Time Domain Adaptive Search Matching Algorithm

The chirp sub-bottom profiler,for its high resolution,easy accessibility and cost-effectiveness,has been widely used in acoustic detection.In this paper,the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China.The time-domain adaptive search matching algorithm was used and validated on our established theoretical model.The misfit between the inversion result and the theoretical model is less than 0.067%.The grain size was calculated according to the empirical relationship between the acoustic impedance and the grain size of the sediment.The average acoustic impedance of sub-seafloor strata is 2.5026×10^(6) kg(s m^(2))^(-1)and the average grain size(θvalue)of the seafloor surface sediment is 7.1498,indicating the predominant occurrence of very fine silt sediment in the study area.Comparison of the inversion results and the laboratory measurements of nearby borehole samples shows that they are in general agreement.

An Improved Genetic Algorithm for Solving the Mixed⁃Flow Job⁃Shop Scheduling Problem with Combined Processing Constraints

The flexible job-shop scheduling problem(FJSP)with combined processing constraints is a common scheduling problem in mixed-flow production lines.However,traditional methods for classic FJSP cannot be directly applied.Targeting this problem,the process state model of a mixed-flow production line is analyzed.On this basis,a mathematical model of a mixed-flow job-shop scheduling problem with combined processing constraints is established based on the traditional FJSP.Then,an improved genetic algorithm with multi-segment encoding,crossover,and mutation is proposed for the mixed-flow production line problem.Finally,the proposed algorithm is applied to the production workshop of missile structural components at an aerospace institute to verify its feasibility and effectiveness.

Enabling Technology of Multiagent Manufacturing System:A Novel Mode of Self-organizing IoT Manufacturing

As the manufacturing mode focuses more on network and community,the orders and production processes are becoming highly dynamic and unpredictable.The traditional manufacturing system cannot handle those exceptional events such as rush orders and machine breakdowns.Nevertheless,the multiagent manufacturing system(MAMS)becomes a critical pattern to deal with these disturbances in a real-time way.However,due to the lack of universality,MAMS is difficult to be applied to industrial sites.A new multiagent architecture and the relay cooperation model based on a positive process relation matrix are proposed to address this paper’s issue.An optimized contract net protocol(CNP)-based negotiation mechanism is developed to improve the efficiency of collaboration in the proposed architecture.Finally,a case study of self-organizing internet of things(Io T)manufacturing system is used to test the feasibility and effectiveness of the method.It is shown that the proposed self-organizing Io T manufacturing mode outperforms the traditional manufacturing system in terms of makespan and critical machine workload balancing under disturbances through comparison.

A capture probability analytic model for the electromagnetic launched anti-torpedo torpedo

With the unique characteristics,electromagnetic launch technology is applicable to launch shipborne anti-torpedo torpedo(ATT).This paper aims to establish an analytic model to pre-evaluate the capture probability of the electromagnetic launched ATT.The mathematics model of the multi-stage coilgun and the trajectory of the ATT is established for analysis.The influence factors of the capture probability are analyzed respectively,including the entry point dispersion of the ATT and the position dispersion of the incoming torpedo.Adopting the advanced angle interception mode,the ATT search model is obtained according to the positional relationship,and the course error is synthetically calculated according to the differentiation of implicit function.A geometric method to calculate the integral boundaries of the probability density function is proposed,based on the relative motion of the ATT and the incoming target.To verify the proposed integral model,the digital simulation and comparison is conducted.The results reveal that the variation trends and the calculation value of the proposed analytic model are coincident with the statistic results from Monte Carlo method.And implications of the results regarding the analytic model are discussed.

Multi-target range and velocity measurements of a digital phased array radar system

As the core of a digital phased array radar system,a radar signal processing environment is created to measure multitarget range and velocity information. The radar echo signal is achieved by superposing target echo, noise, clutter and jamming signals linearly. Considering that these signals have many types,two typical combinations are selected to construct the multi-target echo signal and the simulated echo signal is used as the input of the signal processing environment. This environment mainly adopts pulse compression,moving target indication and detection technologies to process the echo signal.It is found that the frequency domain method is more desirable for the pulse compression effect than the time domain method,and multi-target range information can be measured from the moving target indication result after using a double delay canceller. A new moving target detecting method is proposed,which can present the positive and negative velocity accurately with the multi-target range and velocity measured simultaneously. Simulation results indicate that the potential targets are detected from the chaotic radar echo signals successfully,and their range and velocity can be figured out correctly in the built radar signal processing environment.

Interception probability simulation and analysis of salvo of two electromagnetic coil launched anti-torpedo torpedoes

Electromagnetic coil launch is an important branch of electromagnetic launch(EML)technology,which is suitable for launching anti-torpedo torpedo(ATT).This paper focuses on the EML parameters and the interception probability of the EML two ATTs salvo.Based on the launching model of a multi-stage coil launcher,the trajectory model of the ATT and the attacking torpedo,a calculation method for the EML two ATTs salvo parameters is proposed,with the conditions of capture and interception given reasonably.An adaptive particle swarm optimization(APSO)algorithm is proposed to calculate the optimal launching parameters,by designing the adaptive inertia weight and time-varying study factors.According to the analysis of the simulation with Monte Carlo method,EML will improve the interception probability effectively,and the interception probability is affected by the launching range.The results demonstrate good performance of the proposed APSO in calculating EML parameters for the two ATTs salvo in certain combat situation.Implications of these results are particular regarding the command and decision in the anti-torpedo combat.

A Process Simulation-Based Method for Engineering Change Management

Engineering change management is a special form of problem solving where many rules must be followed to satisfy the requirements of product changes.As engineering change has great influence on the cycle and the cost of product development,it is necessary to anticipate design changes(DCs)in advance and estimate the influence effectively.A process simulation-based method for engineering change management is proposed incorporating multiple assessment parameters.First,the change propagation model is established,which includes the formulation of change propagation influence,assessment score of DC solution.Then the optimization process of DC solution is introduced based on ant colony optimization(ACO),and an optimization algorithm is detailed to acquire the optimal DC solution automatically.Finally,a case study of belt conveyor platform is implemented to validate the proposed method.The results show that changed requirement of product can be satisfied by multiple DC solutions and the optimal one can be acquired according to the unique characteristics of each solution.

Physical Frame Time-Slot Switching-A New Switching Technique over DWDM

A novel technique called physical frame time-slot switching （PFTS） is discussed and its technical and application aspects are analyzed. The format of the ethernet media access control （MAC） frame is borrowed in defining the physical frame for PFTS and the transmission time for the maximum size of the MAC frame is defined as a physical frame time-slot （PFT）. Consequently, user data can be fed into PFTS and switched in a single layer sub-network in an asynchronous mode.

An adaptive distributed auction algorithm and its application to multi-AUV task assignment

The task assignment of multi-agent system has attracted considerable attention;however,the contradiction between computational complexity and assigning performance remains to be resolved.In this paper,a novel consensus-based adaptive optimization auction(CAOA)algorithm is proposed to greatly reduce the computation load while attaining enhanced system payoff.A new optimization scheme is designed to optimize the critical control parameter in the price update role of auction algorithm which can reduce the searching complexity in obtaining a better bidding price.With this new scheme,the CAOA algorithm is designed.Then the developed algorithm is applied to the multi-AUV task assignment problem for underwater detection mission in complex environments.The simulation and comparison studies verify the effectiveness and advantage of the CAOA algorithm.

FAANet: feature-aligned attention network for real-time multiple object tracking in UAV videos

Multiple object tracking(MOT)in unmanned aerial vehicle(UAV)videos has attracted attention.Because of the observation perspectives of UAV,the object scale changes dramatically and is relatively small.Besides,most MOT algorithms in UAV videos cannot achieve real-time due to the tracking-by-detection paradigm.We propose a feature-aligned attention network(FAANet).It mainly consists of a channel and spatial attention module and a feature-aligned aggregation module.We also improve the real-time performance using the joint-detection-embedding paradigm and structural re-parameterization technique.We validate the effectiveness with extensive experiments on UAV detection and tracking benchmark,achieving new state-of-the-art 44.0 MOTA,64.6 IDF1 with 38.24 frames per second running speed on a single 1080Ti graphics processing unit.

A Class of Second-Order Consensus Protocol in Multi-Agent Systems with Multiple Input Delays

This paper is concerned with the consensus problems for second-order multi-agent systems with multiple input delays. Different from all standard consensus algorithms with uniform delays, the authors aim to find the largest input-delay margin which can guarantee the consensus for the case when delays are nonuniform. Based on frequency domain analysis and matrix theory, an upper bound for maximum tolerable input-delay is given in terms of the relationship with scaling strengths and largest eigenvalue of the Lapalician matrix. Simulation results are provided to illustrate the obtained results.

Finite-time coordinated path-following control of leader-following multi-agent systems

This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite time.It is assumed that the topology for the virtual leader and followers is directed.An additional condition of the so-called barrier function is designed to make all agents move within a limited area.A novel continuous finite-time path-following control law is first designed based on the barrier function and backstepping.Then a novel continuous finite-time formation algorithm is designed by regarding the path-following errors as disturbances.The settling-time properties of the resulting system are studied in detail and simulations are presented to validate the proposed strategies.

An incremental software architecture recovery technique driven by code changes

It is difficult to keep software architecture up to date with code changes during software evolution.Inconsistency is caused by the limitations of standard development specifications and human power resources,which may impact software maintenance.To solve this problem,we propose an incremental software architecture recovery(ISAR)technique.Our technique obtains dependency information from changed code blocks and identifies different strength-level dependencies.Then,we use double classifiers to recover the architecture based on the method of mapping code-level changes to architecture-level updates.ISAR is evaluated on 10 open-source projects,and the results show that it performs more effectively and efficiently than the compared techniques.We also find that the impact of low-quality architectural documentation on effectiveness remains stable during software evolution.

Differential Privacy Trajectory Protection Method Based on Spatiotemporal Correlation

Location-based services provide service and convenience,while causing the leakage of track privacy.The existing trajectory privacy protection methods lack the consideration of the correlation between the noise sequence,the user’s original trajectory sequence,and the published trajectory sequence.And they are susceptible to noise filtering attacks using filtering methods.In view of this problem,a differential privacy trajectory protection method based on spatiotemporal correlation is proposed in this paper.With this method,the concept of correlation function was introduced to establish the correlation constraint of release track sequence,and the least square method was used to fit the user’s original track and the overall direction of noise sequence to construct noise candidate set.It ensured that the added noise sequence has spatiotemporal correlation with the user’s original track sequence and release track sequence.Also,it effectively resists attackers’denoising attacks,and reduces the risk of trajectory privacy leakage.Finally,comparative experiments were carried out on the real data sets.The experimental results show that this method effectively improves the privacy protection effect and the data availability of the release track,and it also has better practicability.

V2V Online Data Offloading Method Based on Vehicle Mobility

As people are accustomed to getting information in the vehicles,mobile data offloading through Vehicular Ad Hoc Networks(VANETs)becomes prevalent nowadays.However,the impacts caused by the vehicle mobility(such as the relative speed and direction between vehicles)have great effects on mobile data offloading.In this paper,a V2V online data offloading method is proposed based on vehicle mobility.In this mechanism,the network service process was divided into continuous and equal-sized time slots.Data were transmitted in a multicast manner for the sake of fairness.The data offloading problem was formalized to maximize the overall satisfaction of the vehicle users.In each time slot,a genetic algorithm was used to solve the maximizing problem to obtain a mobile data offloading strategy.And then,the performance of the algorithm was enhanced by improving the algorithm.The experiment results show that vehicle mobility has a great effect on mobile data offloading,and the mobile data offloading method proposed in the paper is effective.

Near-Optimal Transmission Optimization of Cache-Based SVC Vehicle Video

A problem of video streaming of scalable video coding(SVC)is studied in vehicular networks.To improve the performance of the video streaming services and alleviate the pressure on backhaul links,the small cell base stations(SBS)is proposed with caching ability to assist the content delivery.In this paper,it introduced the problem of joint optimization of caching strategy and transmission path in SBS cache cluster.An integer programming problem was formulated to maximize the average quality of experience.In order to obtain the globally optimal solution,the primal problem was first relaxed,then an adaptive algorithm was used based on the joint KKT condition,and the branch definition algorithm was applied.Extensive simulations were performed to demonstrate the efficiency of our proposed caching strategy.