Accurate Behavioral Model Research Articles

Market potential of bicycle crowdshipping: A two-sided acceptance analysis

Crowdshipping has recently emerged as a sustainable option for urban parcel delivery, where transport tasks are carried out by regular citizens who are engaged in a passenger trip, instead of the usual professional carriers. A key challenge for such as service offering is to maintain a sustainable network of customers (demand) and couriers (supply). This paper quantitatively explores how demand and supply functions for bicycle crowdshipping meet in a parcel delivery market. Earlier literature has identified factors that determine the success of this business model. We build on this to estimate the potential of the new services, by means of a market equilibrium model. A case study is presented from the Netherlands which uses tailor-made surveys, bicycle trip data and online shopping statistics. The study quantifies the importance of influencing factors. The insights can be used to design a bike crowdshipping platform which brings together the demand and the supply sides effectively. Future research could go into platform design, more accurate behavioral models as well as into advanced pricing approaches.

Design modification of iron ore bearing transfer chute using discrete element method

Purpose The dryer feed chute of the pellet plant plays an important role in the pelletizing process. The chute discharges sticky and moist iron ore fines (<1 mm) to the inline rotary dryer for further processing. Since the inception of the installation of the dryer feed chute, the poor flowability of the feed materials has caused severe problems such as blockages and excessive wear of chute liners. This leads to high maintenance costs and reduced lifetime of the liner materials. Constant housekeeping is needed for maintaining the chute and reliable operation. The purpose of this study is to redesign the dryer feed chute to overcome the above challenges. Design/methodology/approach The discrete element method (DEM) has been used to model the flow of cohesive materials through the transfer chute. Physical experiments have been performed to understand the most severe flow conditions. A DEM material model is also developed for replicating the worst-case material condition. After identifying the key problem areas, concept designs were proposed and simulated to assess the design improvements to increase the reliability of chute operation. Findings Flow simulations correlated well with the existing flow behavior of the iron ore fines inside the chute. The location of the problematic areas has been validated with that of the previously installed chute. Subsequently, design modifications have been proposed. This includes modification of deflector plate and change in slope and cross-section of the chute. DEM simulations and analysis were conducted after incorporating these design changes. A comparison in the average velocity of particle and force on chute wall shows a significant improvement using the proposed design. Originality/value Method to calibrate DEM material model was found to provide accurate prediction and modeling of the flow behavior of bulk material through the real transfer chute. DEM provided greater insight into the performance of the chute especially modeling cohesive materials. DEM is a valuable design tool to assist chute designers troubleshoot and verify chute designs. DEM provides a greater ability to model and assess chute wear. This technique can help in achieving a scientific understanding of the flow properties of bulk solids through transfer chute, hence eliminate challenges, ensuring reliable, uninterrupted and profitable plant operation. This paper strongly advocates the use of calibrated DEM methodology in designing bulk material handling equipment.

Revisiting Kelvin equation and Peng\u2013Robinson equation of state for accurate modeling of hydrocarbon phase behavior in nano capillaries

The thermodynamics of fluids in confined (capillary) media is different from the bulk conditions due to the effects of the surface tension, wettability, and pore radius as described by the classical Kelvin equation. This study provides experimental data showing the deviation of propane vapour pressures in capillary media from the bulk conditions. Comparisons were also made with the vapour pressures calculated by the Peng–Robinson equation-of-state (PR-EOS). While the propane vapour pressures measured using synthetic capillary medium models (Hele–Shaw cells and microfluidic chips) were comparable with those measured at bulk conditions, the measured vapour pressures in the rock samples (sandstone, limestone, tight sandstone, and shale) were 15% (on average) less than those modelled by PR-EOS.

Load frequency control and dynamic response improvement using energy storage and modeling of uncertainty in renewable distributed generators

Nowadays to improve the dynamic performance of the power system and control the frequency stability, load frequency control (LFC) based on droop strategy must be provided. PID controller is also used in addition to the droop method to maintain the stable load frequency. In the new power systems such as microgrids (MGs) with different low and high-inertial distributed generators (DGs), using only LFC method is not sufficient to provide the stable load frequency and balanced power sharing. Thus, modified LFC approach is needed. Energy storage element is a precious solution presented to combat the non-desirable transient conditions on load frequency and power sharing. Among different storage elements, superconducting magnetic energy storage (SMES) is selected in this paper because of fast dynamic response and desirable inertial characteristic. Using accurate modeling of SMES, the reserved power in off-peak times can be exploited in on-peak times to inject the required power in sudden load changes to provide stable frequency and balanced power sharing. Moreover, the uncertainty of renewable DGs such as photovoltaic (PV) and wind turbine (WT) can be assumed as disturbance input to the MG. Applying SMES in the MG, accurate modeling of SMES dynamic behavior in control block and modeling of DGs uncertainties with suitable probability functions in the proposed control structure leads to more stable LFC approach shown in simulation results of this paper. Inertial property improvement by SMES is also analyzed while comparing with other LFC methods, proposed modified LFC approach presents more stable load frequency. In addition, the frequency fluctuation in the proposed control structure is reduced 15–20% in average compared to other methods.

Machine Learning and Prophecy of Behavior: A Breakthrough in Artificial Intelligence

This paper addresses how the cognitive theory and machine learning methods are combined for predicting human behavior. Prediction of human behavior is a complex task. Researchers addressing this task proposes behavior models using statistical methods and rationality theory such as machine learning algorithms. This paper addresses how the potential cognitive theory and machine learning methods are combined in using by providing and predicting human behavior. We discussed two theory focal points theory and aspiration adaptation theory can be used in combination with machine learning algorithm to simplify the task and understanding the behavior. These two approaches are combined and compared in two domains. In simple domain, both machine learning models and psychological models give clear predictions and results. While in complex domains, the results states that psychological models’ results are not cleared and in learning models are also inaccurate. To overcome this problem, the hybrid model is introduced which helps in understanding of how people behave and at the same time how they wouldn’t behave reducing the space helping machine learning algorithms in searching the accurate behavior models. Hybrid model with cognitive models and machine learning have contributed in models which predicted behavior accurately as compared to machine learning models/cognitive models alone. The future researches must work in these directions using hybrid models for successful prediction of behavior.

Modeling driver behavior in the dilemma zone based on stochastic model predictive control.

Driver behavior is considered one of the most important factors in the genesis of dilemma zones and the safety of driver-vehicle-environment systems. An accurate driver behavior model can improve the traffic signal control efficiency and decrease traffic accidents in signalized intersections. This paper uses a mathematical modeling method to study driver behavior in a dilemma zone based on stochastic model predictive control (SMPC), along with considering the dynamic characteristics of human cognition and execution, aiming to provide a feasible solution for modeling driver behavior more accurately and potentially improving the understanding of driver-vehicle-environment systems in dilemma zones. This paper explores the modeling framework of driver behavior, including the perception module, decision-making module, and operation module. The perception module is proposed to stimulate the ability to perceive uncertainty and select attention in the dilemma zone. An SMPC-based driver control modeling method is proposed to stimulate decision-making behavior in the dilemma zone. The operation module is proposed to stimulate the execution ability of the driver. Finally, CarSim, the well-known vehicle dynamics analysis software package, is used to verify the proposed models of this paper. The simulation results show that the SMPC-based driver behavior model can effectively and accurately reflect the vehicle motion and dynamics under driving in the dilemma zone.

Short-term stochastic movements of electricity prices and long-term investments in power generating technologies

Modeling probability distributions for the long-term dynamics of electricity prices is of key importance to value long-term investments under uncertainty in the power sector, such as investments in new generating technologies. Starting from accurate modeling of the short-term behavior of electricity prices, we derive long-term stationary probability distributions. Then, investments in new baseload generating technologies, namely gas, coal and nuclear power, are discussed. In order to compute the stochastic Net Present Value of investments in new generating technologies, the revenues from selling electricity in power markets as well as the costs which come from buying fuels at uncertain market prices must be evaluated over very long time horizons, i.e., over the whole lifetime of the plants. Starting from accurate short-term stochastic models of fuel prices in addition to electricity prices, we provide long-run probability distributions which are used to compute revenues and costs incurring during the whole lifetime of the plants. Five sources of uncertainty are taken into account, namely electricity market prices, fossil fuel prices (natural gas and coal prices), nuclear fuel prices and hbox {CO}_{text{2 }} prices. Our evaluation model is calibrated on empirical data to account for both historical market prices and macroeconomic views about future trends of electricity and fuel prices. The full probability density of the stochastic Net Present Value is thus determined for each generation technology considered in this study.

Reliability analysis of truss structures considering complete failure paths and using the FLHB model

abstract: The study of progressive collapse of structures using numerical models requires accurate modeling of geometrical nonlinearity and material failure behavior. Numerical models must demonstrate stability, such that localized member failures do not trigger numerical instabilities. Also, algorithms should be efficient, to limit the computational burden of analyzing multiple responses when considering the effects of uncertain loads, geometric and material variables. In this scientific domain, a comprehensive non-linear ductile-damage truss-element model has been recently presented by the authors. The model accounts for the geometrical and material nonlinearities observed during progressive collapse of structural systems. In this paper, the Felipe-Leonel-Haach-Beck (FLHB) model is calibrated to describe the response of Ultra-High-Performance Fiber Reinforced Concrete (UHPFRC). Based on a limited number of UHPFRC experimental curves, statistics of FLHB model parameters are obtained. These are employed in the probabilistic analysis of failure paths of truss structures under progressive collapse. Monte Carlo Simulation and the First Order Reliability Method are employed in the probabilistic failure path analyses. Six application examples demonstrate the accuracy, robustness, and efficiency of the FLHB model in evaluation of failure paths of realistic structural systems.

Analysis and Suppression of High Speed Dv/Dt Induced False Turn-on in GaN HEMT Phase-Leg Topology

Gallium nitride high electron mobility transistor (GaN HEMT) is liable to gate false turn-on problem when the gate crosstalk voltage exceeds its threshold voltage in the widely adopted phase-leg topology due to its low threshold voltage and high switching speed. Without considering the gate loop stray inductance, gate internal resistance, nonlinearity of parasitic capacitances and power loop stray parameters, traditional false turn-on analytical method is insufficient to support accurate analysis. And it has been found that GaN HEMT gate-source parasitic capacitance C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gs</sub> previously assumed constant is otherwise highly nonlinear and has strong impacts on the gate crosstalk voltage. This paper has measured C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gs</sub> by vector network analyzer and constructed an accurate nonlinear model of C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gs</sub> , based on which an accurate GaN HEMT behavior model is further fulfilled. The accuracy of the proposed behavior model has been verified by large amounts of experiment results. The proposed GaN HEMT model is used to accurately calculate gate crosstalk voltage and switching losses. Besides, false turn-on induced extra loss has been calculated and is adopted as a criterion to evaluate the severity of false turn-on and optimal design method for false turn-on suppression has been detailed further.

Deep Inverse Reinforcement Learning for Behavior Prediction in Autonomous Driving: Accurate Forecasts of Vehicle Motion

Accurate behavior anticipation is essential for autonomous vehicles when navigating in close proximity to other vehicles, pedestrians, and cyclists. Thanks to the recent advances in deep learning and inverse reinforcement learning (IRL), we observe a tremendous opportunity to address this need, which was once believed impossible given the complex nature of human decision making. In this article, we summarize the importance of accurate behavior modeling in autonomous driving and analyze the key approaches and major progress that researchers have made, focusing on the potential of deep IRL (D-IRL) to overcome the limitations of previous techniques. We provide quantitative and qualitative evaluations substantiating these observations. Although the field of D-IRL has seen recent successes, its application to model behavior in autonomous driving is largely unexplored. As such, we conclude this article by summarizing the exciting pathways for future breakthroughs.

Cyclic shear behavior of GMB/GCL composite liner

The composite liner system consisting of geomembrane (GMB) and geosynthetic clay liner (GCL) has been widely used in landfills. Although there have been a lot of studies on the monotonic shear behavior of GMB/GCL composite liner, the dynamic test data are still very limited and consequently, the dynamic shear mechanism is not clear. A series of displacement-controlled cyclic shear tests were conducted to study the shear behavior of GMB/GCL composite liner, including the shear stress versus horizontal displacement relationships, backbone curves, and shear strengths. Hysteretic loops in the shape of parallelogram were obtained and equivalent linear analyses revealed that the secant shear stiffness decreased and the damping ratio increased with the rise in loading cycles. According to the test results, it is generally acceptable to predict the dynamic peak strength of a GMB/GCL composite liner with its static strength envelope. Furthermore, the dynamic softening mechanism and rate-dependent shear stiffnesses were well described by the proposed equations, which also facilitate the accurate modeling of the cyclic shear behavior.

From transistor level to cyber physical/hybrid systems: Formal verification using automatic compositional abstraction

Abstract In this paper we present a methodology to automatically generate an accurate behavioral model from an analog circuit description. The current machine learning method is limited to circuits with up to 80 transistors, limiting our approach to small and mid size circuit blocks due to a state explosion problem. However, if complex building blocks such as IOT systems should be modeled, the current approach needs to recoup with feasible simulation and modeling time. To come up with a solution for this problem, we extend the current method by a compositional approach. The approach is illustrated upon an example from the area of autonomous driving. Our method decomposes this large example into smaller building blocks and models each of them automatically. All models are combined into a compositional hybrid automaton of the whole complex system. Compared to the original state space, the building blocks operate on smaller and reduced state spaces and hence drastically reduce the complexity. Using a back-transformation on the compositional automaton, all values from the original state space can be reconstructed. Moreover, we perform a formal verification on the generated compositional automaton. Results from a meaningful example are presented and discussed.

The Calibration Method for Incomplete Charge Transfer in ToF Image Sensor

In this paper, a kind of calibration method for two-tap indirect time-of-flight (ToF) image sensor is proposed, which aims to reduce the distance error caused by the incomplete charge transfer. The phenomenon of two-phase charge confusion due to incomplete charge transfer makes a serious impact on the accuracy of ToF imaging especially under high modulation frequency. Current researches effort to improve the charge transfer ratio by changing pixel structure or fabrication process. The proposed method obtains a charge transfer ratio by reducing the frequency of the work timing. Then the true photon-generated charge of each phase can be calculated and the distance error can be calibrated. To verify the validity of the proposed calibration method, an accurate behavioral level model is established according to the actual ToF imaging system. The simulation results prove that the calibrated modulation contrast is improved by 60% under 100 MHz modulation and 0.1 charge transfer ratio. The calibrated modulation contrast is very close to the theoretical maximum value. When the signal strength increases or the times of charge transfer increases, the calibration effect on the distance error become more effective.

Towards accurate phase behavior modeling for hydrogen sulfide/water mixtures

Many deep gas wells contain acid gas components. Hydrogen sulfide (H2S) is one of the typical acid gases. Accurate flow simulations for H2S/H2O mixtures in reservoirs and wellbores requires a proper thermodynamic model that is capable of accurately modeling the H2S/H2O mixtures under in-situ conditions. This study aims at screening and developing cubic-equation-of-state-based thermodynamic models that can well describe the phase behavior of H2S/H2O mixtures. Peng-Robinson equation of state (PR EOS) (Peng and Robinson, 1976) and the Huron-Vidal (HV) (Huron and Vidal, 1979) mixing rule are used as the primary modeling framework. The temperature-dependent binary interaction parameter (BIP) correlations in the HV mixing rule are established by matching the measured vapor-liquid equilibria (VLE) data for H2S/H2O mixtures collected from the literature. The experimental VLE data cover a temperature range of 273.15–627.85 K and a pressure range of 0.41–303 bar, while the experimental density data cover a temperature range of 294.35–705.30 K and pressures up to 350 bar. Different volume translation strategies are examined in terms of their accuracy in reproducing the measured density data for H2S/H2O mixtures. We employ PR EOS together with the optimal BIP strategy in the HV mixing rule to reproduce the mutual solubility of H2S and H2O in VLE. The calculated results show a good agreement with the experimental data, especially at high temperatures and pressures; the average absolute percentage deviation (%AAD) of 4.90% and 4.95% can be obtained for reproducing the vapor-phase H2O solubility and the aqueous-phase H2S solubility, respectively. With the inclusion of the volume translation model proposed by Abudour et al. (2013), PR EOS together with the optimal BIP strategy in the HV mixing rule shows a good performance in estimating the aqueous-phase density for H2S/H2O VLE, i.e., an %AAD of 5.42% in reproducing the measured density data.

Explaining lecture attendance behavior via structural equation modeling: Self-Determination Theory and the Theory of Planned Behavior

Some research suggests that university lecture attendance positively correlates with academic performance. Although there are several motivational pathways which may explain attendance, few studies have examined the psychosocial factors leading to student attendance intentions and behavior. Consequently, we evaluated via structural equation modeling (SEM) two prominent motivational theories to help explain lecture attendance: Self-Determination Theory (SDT) and The Theory of Planned Behavior (TPB). Undergraduates (N = 288) from two universities completed pre-semester motivation measurements and post-semester estimates of attendance. Student grades were also examined. SDT was not found to be an accurate model of attendance intentions or behavior. By contrast, TPB was found to be an adequate model to help explain attendance intentions and behavior. Lecture attendance did not significantly correlate with grades. If educators and students are committed to increasing lecture attendance rates, our findings suggest that the enhancement of perceived behavioral control, as well as optimistic intentions, may yield the greatest benefits with respect to students meeting their lecture attendance expectations.

Mixed-Precision Deep Learning Based on Computational Memory.

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance states and perform the expensive weighted summations in place in a non-von Neumann manner. However, updating the conductance states in a reliable manner during the weight update process is a fundamental challenge that limits the training accuracy of such an implementation. Here, we propose a mixed-precision architecture that combines a computational memory unit performing the weighted summations and imprecise conductance updates with a digital processing unit that accumulates the weight updates in high precision. A combined hardware/software training experiment of a multilayer perceptron based on the proposed architecture using a phase-change memory (PCM) array achieves 97.73% test accuracy on the task of classifying handwritten digits (based on the MNIST dataset), within 0.6% of the software baseline. The architecture is further evaluated using accurate behavioral models of PCM on a wide class of networks, namely convolutional neural networks, long-short-term-memory networks, and generative-adversarial networks. Accuracies comparable to those of floating-point implementations are achieved without being constrained by the non-idealities associated with the PCM devices. A system-level study demonstrates 172 × improvement in energy efficiency of the architecture when used for training a multilayer perceptron compared with a dedicated fully digital 32-bit implementation.

A new constitutive model on biaxial tensile behavior of architectural fabrics

Accurate and concise constitutive model of biaxial tensile behavior is urgently needed for the analysis of complex membrane structure engineering. A new constitutive model is developed to describe the tensile behaviors of coated fabrics from pre-tension to breaking. The shrinkage curve is employed for the first time and stress-ratio dependence coefficient (SDC) is proposed to represent the proportion of elongation and shrinkage in biaxial monotonic tensile test. The deformation of cruciform specimen is considered as the result of the coupling effect of biaxial tension and Poisson effect. Excellent fitting results were obtained between the data points of biaxial tensile test and stress-stress-strain response surface, which R-squares of both warp and weft response surface were 0.995.

Dark count in single-photon avalanche diodes: A novel statistical behavioral model**Project supported by the Natural Science Foundation of Zhejiang Province, China (Grant No. LY17F010022) and the National Natural Science Foundation of China (Grant No. 61372156).

Dark count is one of the inherent noise types in single-photon diodes, which may restrict the performances of detectors based on these diodes. To formulate better designs for peripheral circuits of such diodes, an accurate statistical behavioral model of dark current must be established. Research has shown that there are four main mechanisms that contribute to the dark count in single-photon avalanche diodes. However, in the existing dark count models only three models have been considered, thus leading to inaccuracies in these models. To resolve these shortcomings, the dark current caused by carrier diffusion in the neutral region is deduced by multiplying the carrier detection probability with the carrier particle current at the boundary of the depletion layer. Thus, a comprehensive dark current model is constructed by adding the dark current caused by carrier diffusion to the dark current caused by the other three mechanisms. To the best of our knowledge, this is the first dark count simulation model into which incorporated simultaneously are the thermal generation, trap-assisted tunneling, band-to-band tunneling mechanisms, and carrier diffusion in neutral regions to evaluate dark count behavior. The comparison between the measured data and the simulation results from the models shows that the proposed model is more accurate than other existing models, and the maximum of accuracy increases up to 31.48% when excess bias voltage equals 3.5 V and temperature is 50 °C.

СУЧАСНІ ПІДХОДИ ДО ПЛАНУВАННЯ ВИТРАТ ТА ПРИБУТКУ НА ПІДПРИЄМСТВАХ СФЕРИ ТУРОПЕРЕЙТИНГУ

Introduction . The predominant focus of management systems in the sphere of tour operators’ activity on retrospective financial accounting information does not allow tour operators to use the optimization planning function, which negatively affects their economic efficiency. Purpose . The aim of the article is to develop modern methodical approaches to improve the system of cost and profit planning at the enterprises of tour operators on the basis of the margin approach, which involves modeling the causal links between costs, income, profit on the one hand and the relevant factors that determine them – on the other. Results . In the article there is used a systematic approach to determine the place and role of the tour operator in the value chain of the tourism industry, the characteristics of the economic process of the tour operator and its economic relations with the external environment. To plan costs and profits there is used the apparatus of the theory of economic and mathematical modeling. Based on a systematic approach, the author's descriptive model of the tour operator's activity and its relations with counterparties in the market of resources and products is developed and proposed. This model provides classification of incoming resource flows of the tour operator into two types: resources purchased on the side of primary tourist services and resources that are necessary elements to perform various functions within the business process of the tour operator. According to the classification of resource flows, it is proposed to structure the costs of the tour operator into two groups: the cost of purchased primary travel services and conversion costs. In order to identify cost-generating factors, a comprehensive model of the economic process of the tour operator and the universal model of the main process of the enterprise are proposed. It is substantiated that the main economic process of a tour operator by the nature of the relationship between the flow of resources and the flow of products (services) can be defined as convergent. It is substantiated that the activity of the development of a complex of tourism product should be considered not as a separate function within the main process, but as an element of an independent innovative process of the tour operator. It is justified that resources are consumed not in the main economic process as a whole, but during the performance of individual functions within the main process. So the primary is causal link between cost-generating factors and consumed resources at the level of a particular function. For the sphere of tour operating activity, there are substantiated the criteria for identifying variable conversion costs: costs can be proportionally adapted to fluctuations in the volume of activity of a particular function and the volume of activity of the specified function behaves in direct proportion to the volume of the realized tourist services (quantity or cost of the realized tourist packages). It is substantiated that the «standard list» of variable conversion costs for the sphere of tour operators’ activity cannot be given, because variable costs are not an objective empirical phenomenon, and their behavior depends on the subjective management decision of an individual manager, which in turn is made within specific objective circumstances. According to W. Kilger's concept, it is proposed to determine 2 indicators of marginal cost: marginal cost for short-term decisions and marginal cost for long-term decisions, which is especially relevant in the allocation of costs for the situation of purchasing primary travel services on the basis of contracts with a guaranteed amount of capacity reservation. Originality . In the article there is proposed the methodical approach to formation of an indicator of normative marginal cost of a tourist product. Based on the concept of marginal profit, there is suggested the author's approach to planning of the costs and financial results of enterprises in the sphere tour operators’ activity. There is proposed a methodical approach to determine the break-even point of the tour operator based on the comparison of the planned marginal profit and the function of the total fixed and discretionary costs of the tour operator for the planned season. All abovementioned author's methodical approaches make up a scientific novelty, the use of which in the practice of enterprises in the sphere of tour operators’ activity allows to increase the efficiency of the management system and, accordingly, the profitability of enterprises. Conclusion . Thus, the use of the concept of the marginal profit in contrast to existing approaches allows to improve the quality of information support of the management system because within the proposed approach there is a possibility of more accurate modeling of cost and profit behavior at the enterprises of tour operators’ activity compared to the alternative concepts.

Needle deformation in the process of puncture surgery: experiment and simulation

Abstract Needle insertion in soft tissue has attracted considerable attention in recent years due to its application in minimally invasive surgery such as biopsies, intravenous injection and brachytherapy. In order to ensure the accuracy of surgery, the surgical simulation process requires accurate modeling of the contact behavior between instrument and soft tissue. However, it is difficult to carry out a simulation of the needle deformation in the puncture process. Hence, in this paper, the deformation of the puncture needle specially focusses on muscle puncture surgery in the process of robot-assisted puncture is analyzed and investigated. Interaction during puncture is modeling and simulated using Abaqus to obtain the displacement curve of tissue and the force curve of needle. Insertion experiments are performed to verify the simulation results, and the insertion mechanisms of the soft tissue under different under different diameters of needles, insertion velocities, insertion depth and rotational angles are revealed and discussed. Relevant analysis is performed to direct the real surgery and make it more accurate.