Operations Research, Systems Engineering and Industrial Engineering Commons^™

A Compliant Robot Control Based On Extended Social-Force Model For Human-Following And Obstacle Avoidance, Jianwei Peng, Zhelin Liao, Hanchen Yao, Zhiyu Wan, Liqi Zhu, Houde Dai

Journal of System Simulation

Abstract: Human-robot coexisting is an essential feature of the next generation mobile robot. A compliant robot control strategy based on the extended social-force model for human-following and obstacle avoidance in coexisting-cooperative-cognitive environment is presented. The human-following controller based on impedance control can simultaneously adjust human-robot interaction force and position deviation to carry out the compliant human-following of mobile robots. Considering humanrobot- obstacle interactions, based on the extended social-force model and proxemics, a control strategy for human-friendly compliant human-following and obstacle avoidance is designed to solve the obstacle avoidance problem of robot and ensure the human comfort and improving the social …

Research On Modeling And Optimization Method Of Torpedo Anti-Jamming Attack Based On Game Confrontation, Liqiang Guo, Ma Liang, Zhang Hui, Yang Jing, Fan Xueman, Cheng Zhuo

Journal of System Simulation

Abstract: Aiming at the strong adversarial characteristics of underwater attack and defense operations and the time-consuming problem of traditional Monte Carlo method, a model of torpedo anti-jamming attack based on game confrontation is designed and an improved genetic simulated annealing algorithm for optimal model is proposed. Through the research method of simulation analysis, on the basis of the models of two torpedo salvo attack and submarine acoustic resistance defense, the attack-defense confrontation model is constructed according to the Nash equilibrium theory under zero-sum game. The initial population, fitness function, and evolutionary strategy of GA are improved by the ideas of …

Research And Application Progress Of Tracking Registration Methods In Ar Assembly, Wei Fang, Shuhong Xu, Lei Han, Zhangwenchi Li

Journal of System Simulation

Abstract: Augmented reality (AR) can superimpose virtual auxiliary information in appropriate positions at the actual work site to achieve intelligent assembly guidance of "what you see is what you operate", alleviating the difficulties for workers to recognize drawings in traditional drawing-based assembly and the problems of misassembly, missing parts and so on. Stable tracking registration in the assembly site environment is the basis for achieving the fusion of virtual and actual scene in augmented assembly visual guidance, and also a key issue in the practical application and deployment of existing augmented assembly. In view of the research and application results …

Simulation Of Pedestrian Emergency Evacuation Considering Terrorist Attack Mode, Shuchao Cao, Jialong Qian

Journal of System Simulation

Abstract: To investigate pedestrian evacuation under the sudden terrorist attack, an evacuation model is established for pedestrians taking into account the terrorist attack mode. The terrorist can take two strategies including attacking the nearest pedestrian and attacking the crowd in the model. The evacuation time, casualties and location distribution in various scenarios under different attack modes are analyzed. The results show that pedestrians need to maintain a proper escape intention when avoiding the terrorist. The closer the initial position of the terrorists to the exit, the greater the number of casualties and the longer the evacuation time. The effect of …

Improved Particle Swarm Algorithm Of Unrelated Parallel Batch Scheduling Optimization, Lizhen Du, Tao Ye, Yuhao Wang, Yajun Zhang

Journal of System Simulation

Abstract: To address the problems of population diversity loss and the tendency to fall into local optimality in the PSO (particle swarm optimization)algorithm in dealing with unrelated parallel batch scheduling problems, an improved scheduling optimization algorithm for PSO is proposed for minimizing the maximum completion time solution. A real number encoding based on the sequence of artifacts is used for the encoding operation. A new strategy based on J_B local search is designed based on the mixed integer programming model of the problem. The Metropolis criterion of the simulated annealing algorithm isintroduced into the individual extreme value search of the …

An Intelligent Driver Model Simulation Considering Both Backward Looking Effect And Velocity Difference, Yin Xu, Yun Pu, Haixu Liu, Yifan Tan

Journal of System Simulation

Abstract: Aiming at the phenomenon that driver adjusts vehicle movement by observing the following vehicles through rearview mirror in the actual car-following driving, an improved intelligent driver model accounting for both backward looking effect and velocity difference is proposed, and the critical stability condition of the new model is obtained by employing the linear stability analysis. Based on the numerical simulation experiments, the car following characteristics analysis during the acceleration process of the vehicle and the traffic safety evaluation are carried out. A small disturbance simulation under the periodic boundary condition is used to verify the conclusion consistency of stability …

Path Planning Of Mobile Robots Based On Memristor Reinforcement Learning In Dynamic Environment, Hailan Yang, Yongqiang Qi, Baolei Wu, Dan Rong

Journal of System Simulation

Abstract: In order to solve the path planning problem of mobile robots in dynamic environment, two-layer path planning algorithm based on improved ant colony algorithm and MA-DQN algorithm is proposed. Static global path planning is accomplished by ant colony algorithm that improved the probabilistic transfer function and the pheromone updating principle; the traditional DQN algorithm structure is improved by using the memristor as the synaptic structure of neural network, and then completed the local dynamic obstacle avoidance of the mobile robot. The path planning mechanism is switched according to whether there are dynamic obstacles within the sensing range of the …

Short-Term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition, Kai Zhang, Haipeng Lu, Ying Han, Lingyun Zhang, Yujie Ding

Journal of System Simulation

Abstract: Accurate short-term vehicle speed prediction helps to resolve city traffic congestion problems. Focusing on the defect that CNN cannot process non-Euclidean geometric data, GCN and BiLSTM are combined to fully process the spatiotemporal characteristics of road network information, in which the advantages of GCN integrating global features and the ability of BiLSTM to extract temporal features are considered. In order to reduce the interference of noise to the data, variational modal decomposition (VMD) is introduced and short-term vehicle speed prediction model based on VMD-GCN-BiLSTM (VGBLSTM) is proposed . Simulation results show that the prediction accuracy of VGBLSTM model is …

Obstacle Avoidance Path Planning And Simulation Of Mobile Picking Robot Based On Dppo, Junqiang Lin, Hongjun Wang, Xiangjun Zou, Po Zhang, Chengen Li, Yipeng Zhou, Shujie Yao

Journal of System Simulation

Abstract: Aiming at the autonomous decision-making difficulty of mobile picking robots in random and changeable complicated path environment during field operations, an autonomous obstacle avoidance path planning method based on deep reinforcement learning is propose. By setting the state space and action space and using the artificial potential field method to design the reward function, an obstacle penalty coefficient setting method based on collision cone collision avoidance detection is proposed to improve the autonomous collision avoidance ability. A virtual simulation system is constructed, in which the learning and training of the mobile picking robot is carried out and verified by …

Intelligent Air Defense Task Assignment Based On Assignment Strategy Optimization Algorithm, Jiayi Liu, Gang Wang, Qiang Fu, Xiangke Guo, Siyuan Wang

Journal of System Simulation

Abstract: Aiming at the insufficient solving speed of assignment strategy optimization algorithm in largescale scenarios, deep reinforcement learning is combined with Markov decision process to carry out the intelligent large-scale air defense task assignment. According to the characteristics of large-scale air defense operations, Markov decision process is used to model the agent and a digital battlefield simulation environment is built. Air defense task assignment agent is designed and trained in digital battlefield simulation environment through proximal policy optimization algorithm. The feasibility and advantage of the method are verified by taking a large-scale ground-to-air countermeasure mission as an example.

Robot Path Planning By Fusing Particle Swarm Algorithm And Improved Grey Wolf Algorithm, Menglong Cao, Wenbin Zhao, Zhiqiang Chen

Journal of System Simulation

Abstract: Aiming at the long paths and slow convergence speed of GWO algorithm in robot path planning, a hybrid PSO-GWO algorithm based on PSO algorithm and the improved GWO algorithm is proposed. By running PSO algorithm for many times, the initial wolf group size and initial fitness value are determined. A nonlinear convergence factor is introduced to balance the exploration and development capabilities of GWO algorithm, and a dynamic inertia weight factor is proposed to ensure the leadership system of alpha wolf and to promote the population communication. Levy flight and greedy strategy are used to effectively avoid the local …

Monitoring Method Research On Passenger Behavior On Escalator Based On Digital Twin, Nan Lü, Qibing Wang, Lu Jiawei, Juntong Chen, Gang Xiao

Journal of System Simulation

Abstract: In order to solve the problems that the traditional escalator cannot be monitored and analyzed in real time during operation, the management and maintenance only on escalator equipment side, and the lack of monitoring passenger dangerous behavior, a monitoring method of passenger behavior on escalator based on digital twin is proposed. By constructing the digital twin of escalators, a visual interface is designed to map the escalator running status and passenger behavior data. Through passenger video surveillance, the improved OpenPose posture recognition algorithm is used to obtain the key point data of human body. Posture recognition is classified to …

Two-Stage Robust Optimization-Based Economic Dispatch Of Virtual Power Plants Considering Cogeneration, Jinpeng Liu, Peng Jinchun, Jiaming Deng, Hushihan Liu

Journal of System Simulation

Abstract: With continuous enrichment of resources of the supply side and flexible and changeable load of the demand side of energy system to effectively cope with the complexity of system operation optimization and resource allocation, a robust optimization model of virtual power plant considering the interaction between electric and thermal units is proposed. Considering the uncertainty of renewable energy and load in virtual power plant, a two-stage robust optimization model of min-max-min structure is established, and the optimal operation economy dispatching scheme in the worst scenario is obtained. Robustness coefficient is introduced to flexibly adjust the conservativeness of the optimization …

Lidar Slam Mapping Method Adapted To Environmental Spatial Changes, Songming Jiao, Xin Yao, Hui Ding, Yufei Zhong

Journal of System Simulation

Abstract: In the environment with obvious changes in space size, aiming at the drift and other problems of the existing algorithm, Adp-lio-sam mapping method is proposed to adapt to the environment space changes, and improve the generality of lio-sam algorithm. Point cloud dewarping method is improved, and Kalman filter algorithm is used to carry out the motion compensation data by fusing lidar interframe pose interpolation and IMU interpolation. Fuzzy algorithm is used to adapt different points filtering thresholds for different spatial environments and the constraints of loop closure detection are optimized. Experimental results show that, compared with the existing …

Metaverse Concept And Its Military Application, Tan Zhao, Lin Wu, Jiuyang Tao, Shuai Li

Journal of System Simulation

Abstract: Metaverse is a concept describing the fusion and interaction between virtual and real, which has become popular in business and academia since 2021. The aim is to study possible applications of metaverse in military. We sort out the definition, characteristics and development of this concept. Then we analyze the necessity of using the concept of military metaverse from the expansion of modeling and simulation(M&S) and live-virtual-constructive(LVC) simulation. And then we study the possible improvements of the military metaverse from the actual needs of military training, operation and information resource management. We sort out the prototype products of the military …

Military Metaverse: Key Technologies, Potential Applications And Future Directions, Zhao Zhang, Yujie Guo, Xiaoning Zhao, Baoliang Sun

Journal of System Simulation

Abstract: Since its emergence, the concept of metaverse has been applied to many fields. At present, the transformation of military intelligence, digitalization and information technology is advancing in an allround way. The military enabled by metaverse technology will accelerate the process of military reform in the new era. To explore the potential application of metaverse in the military field, this paper first introduces several key technologies of metaverse and their functions in the military field, and then discusses some potential directions for the application of metaverse in weapon development and support, training and teaching of officers and soldiers, tactical command …

Target Search Planning And Algorithm For Monitoring Of Polar Disaster Areas, Fei Ding, Meinan Zhang, Hengheng Zhuang, Hairong Ma

Journal of System Simulation

Abstract: Aiming at improving the ability of safe navigation route planning and risk assessment of ships in polar waters, a target search model and method based on clustering and efficient indexing of monitoring center are proposed. By constructing a disaster monitoring scenario based on the current navigation area of the ship, a virtual electronic fence is introduced to define the monitoring area. Spectral clustering algorithm is used to divide the risk level of the fence area, extract high-risk areas, and optimize the generation of target search scenarios; Efficient determination of the matching relationship between the target vessel and the fence …

Modeling And Analysis Of Metro Emergency Decision Based On Logical Game Probability Petri Net, Zhe Yan, Wei Liu, Yuyue Du

Journal of System Simulation

Abstract: In order to solve the problem that logical Petri net can not describe dynamic game process well, logical game probabilistic Petri net is proposed. The four elements of the game are integrated into the logical Petri net, and the players of the game are defined as an attribute of Token, for which the strategy set and utility function are defined, and the information database is introduced. Probability change and vector are introduced to represent the transformation relationship of empirical probability in the process of game, and fuzzy theory is introduced on the basis of Bayes formula to solve the …

Research On Nested Named Entity Recognition In Missile Field Text, Jingwen Guan, Xiao Song, Xiaoqing Li, Tong Yang, Junhua Zhou

Journal of System Simulation

Abstract: Compared with the text recognition in conventional fields, it is difficult to recognize the large number of nested named entities in professional terms. This is also one of the care challenges in building the knowledge graph in aerospace field. For the named entity recognition technologies, bidirectional long short-term memory network plus conditional random field (BiLSTM-CRF) is often used to identify entities, which is difficult to distinguish the complex relationships such as nesting and intersection of terms in missile field. In order to solve the problem, based on the nested entity labeling of domain text, a nested named entity recognition …

Generalization Through Diversity: Improving Unsupervised Environment Design, Wenjun Li, Pradeep Varakantham, Dexun Li

Research Collection School Of Computing and Information Systems

Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e.g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board). Due to this dependence, small changes in the environment (e.g., positions of obstacles in the maze, size of the board) can severely affect the effectiveness of the policy learned by the agent. To that end, existing work has proposed training RL agents on an adaptive curriculum of environments (generated automatically) to improve performance on out-of-distribution (OOD) test scenarios. Specifically, existing research has employed the potential for the …

Reinforcing Digital Trust For Cloud Manufacturing Through Data Provenance Using Ethereum Smart Contracts, Trupti Narayan Rane

Engineering Management & Systems Engineering Theses & Dissertations

Cloud Manufacturing(CMfg) is an advanced manufacturing model that caters to fast-paced agile requirements (Putnik, 2012). For manufacturing complex products that require extensive resources, manufacturers explore advanced manufacturing techniques like CMfg as it becomes infeasible to achieve high standards through complete ownership of manufacturing artifacts (Kuan et al., 2011). CMfg, with other names such as Manufacturing as a Service (MaaS) and Cyber Manufacturing (NSF, 2020), addresses the shortcoming of traditional manufacturing by building a virtual cyber enterprise of geographically distributed entities that manufacture custom products through collaboration.

With manufacturing venturing into cyberspace, Digital Trust issues concerning product quality, data, and intellectual …

Grasp Based Metaheuristic To Solve The Mixed Fleet E-Waste Collection Route Planning Problem, Aldy Gunawan, Dang V.A. Nguyen, Pham K.M. Nguyen, Pieter. Vansteenwegen

Research Collection School Of Computing and Information Systems

The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …

Lean Manufacturing Approach To Increase Packaging Efficiency, Lina Gozali, Irsandy Kurniawan, Aldo Salim, Iveline Anne Marie, Benny Tjahjono, Yun Chia Liang, Aldy Gunawan, Nnovia Hardjo Sie, Yuliani Suseno

Research Collection School Of Computing and Information Systems

The company upon which this paper is based engages in flexible packaging production, especially pharmaceutical products with guaranteed quality, trusted by consumers. Its production process includes printing, laminating, and assembling processes. Production activities are done manually and automatically using machines, so various types of waste are often found in these processes, making the level of plant efficiency nonoptimal. This study aims to identify wastes occurring in the production process, especially the production of pollycelonium with three colour variants as the highest demand product, by applying lean manufacturing concepts. The Current Value Stream Mapping (CVSM) used to map the production process …

Learning To Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching And Rescheduling Via Reinforcement Learning, Waldy Joe, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. Our approach utilizes Multi-Agent Value Function Approximation (MAVFA) with a rescheduling heuristic to learn dispatching and rescheduling policies jointly. Often, police operations are divided into multiple sectors for more effective and efficient operations. In a dynamic setting, incidents occur throughout the day across different sectors, disrupting initially-planned patrol schedules. To maximize policing effectiveness, police agents from different sectors cooperate by sending reinforcements to support one another in their incident response and even routine patrol. This poses an interesting research challenge …

A Hybrid Metaheuristic And Computer Vision Approach To Closed-Loop Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano, Shay V. Wallace, Benji Maruyama, Gilbert L. Peterson

Faculty Publications

Fused deposition modeling (FDM) is one of the most popular additive manufacturing (AM) technologies for reasons including its low cost and versatility. However, like many AM technologies, the FDM process is sensitive to changes in the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters. The experience required to efficiently calibrate a printer to a new feedstock acts as a barrier to entry. To enable greater accessibility to non-expert users, this paper presents the first system for autonomous calibration of low-cost FDM 3D printers that demonstrates optimizing …

The Characteristics Of Successful Military It Projects: A Cross-Country Empirical Study, Helene Berg, Jonathan D. Ritschel

Faculty Publications

No abstract provided.

Imitation Improvement Learning For Large-Scale Capacitated Vehicle Routing Problems, The Viet Bui, Tien Mai

Research Collection School Of Computing and Information Systems

Recent works using deep reinforcement learning (RL) to solve routing problems such as the capacitated vehicle routing problem (CVRP) have focused on improvement learning-based methods, which involve improving a given solution until it becomes near-optimal. Although adequate solutions can be achieved for small problem instances, their efficiency degrades for large-scale ones. In this work, we propose a newimprovement learning-based framework based on imitation learning where classical heuristics serve as experts to encourage the policy model to mimic and produce similar or better solutions. Moreover, to improve scalability, we propose Clockwise Clustering, a novel augmented framework for decomposing large-scale CVRP into …

Learning Deep Time-Index Models For Time Series Forecasting, Jiale Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historicalvalue models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep timeindex models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a …

Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le

Research Collection School Of Computing and Information Systems

This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …

A Hierarchical Optimization Approach For Dynamic Pickup And Delivery Problem With Lifo Constraints, Jianhui Du, Zhiqin Zhang, Xu Wang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider a dynamic pickup and delivery problem (DPDP) where loading and unloading operations must follow a last in first out (LIFO) sequence. A fleet of vehicles will pick up orders in pickup points and deliver them to destinations. The objective is to minimize the total over-time (that is the amount of time that exceeds the committed delivery time) and total travel distance. Given the dynamics of orders and vehicles, this paper proposes a hierarchical optimization approach based on multiple intuitive yet often-neglected strategies, namely what we term as the urgent strategy, hitchhike strategy and packing-bags strategy. These multiple strategies …