High Dynamic Environment Research Articles

The influence of firm, industry and concentrated ownership on dynamic capital structure decision in emerging market

PurposeThis study aims to evaluate the impact of firm, industry level determinants and ownership concentration on the dynamic capital structure decision in Indonesia and analyses the governing theories.Design/methodology/approachThis study uses the dynamic panel model of generalized method of moments-System (one-step and two-step) by using a panel data from 2000 to 2014 to examine the relationship between the determinants and leverage. The results are robust to the various definitions of leverage, heterogeneity, autocorrelation, multicollinearity and endogeneity concern.FindingsGrowing firms and firms operating in a highly concentrated industry use high level of debt, taking advantage of the tax shield (trade-off theory). However, if the firms are operating in a highly dynamic environment, they take on less debt as to avoid bankruptcy risk. Firms in Indonesia opt for debt financing perhaps to act as a controlling mechanism to mitigate agency conflicts that may exist between the large controlling shareholders and the minority. Aged and highly profitable firms with high tangible and intangible assets and liquidity level operating in a high dynamic environment follow the pecking order theory.Research limitations/implicationsThis study does not perform each industry regression individually. All the industries are pooled together, as the main focus of this study is to examine the factors affecting leverage of firms in general without giving particular attention to individual industry.Originality/valueThe insights on the impact of ownership concentration and industry characteristics are novel especially on Indonesia, thus fill the gap in the literature.

An improved conical scaning tracking method in Satellite Communication Systems

Shipborne satellite earth stations will cause feed vibrations when the conical scanning frequency is too high under low dynamic conditions, which will cause large fluctuations in the received signal strength of the earth station in a relatively stationary environment. And too low conical scanning frequency will cause the earth station Lost the target satellite in a high dynamic environment. The tracking system adopts adaptive speed regulation strategy and initial phase estimation algorithm, and adjusts the frequency of cone scanning in real time according to the motion attitude of earth station measured by IMU, so that the earth station can adapt to different dynamic environment. In practical engineering, a Ku band tracking system of ship borne satellite earth station is designed and implemented based on FPGA. The actual test results show that the signal fluctuation of the improved cone scanning algorithm is about 2dbm lower than that of the cone scanning algorithm with a fixed frequency of 50Hz.

Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments

This paper designs a cascading vector tracking loop based on the Unscented Kalman Filter (UKF) for high dynamic environment. Constant improvement in dynamic performance is an enormous challenge to the traditional receiver. Due to the doppler effect, the satellite signals received by these vehicles contain fast changing doppler frequency shifts and the first and second derivatives of doppler frequency, which will directly cause a negative impact on the receiver’s stable tracking of the signals. In order to guarantee the dynamic performance and the tracking accuracy, this paper designs a vector carrier structure to estimate the doppler component of a signal. Firstly, after the coherence integral, the IQ values are reorganized into new observations. Secondly, the phase error and frequency of the carrier are estimated through the pre-filter. Then, the pseudorange and carrier frequency are used as the observations of the main filter to estimate the motion state of the aircraft. Finally, the current state is fed back to the carrier Numerical Controlled Oscillator (NCO) as a complete closed loop. In the whole structure, the cascading vector loop replaces the original carrier tracking loop, and the stable signal tracking of code loop is guaranteed by carrier assisted pseudo-code method. In this paper, with the high dynamic signals generated by the GNSS signal simulator, this designed algorithm is validated by a software receiver. The results show that this loop has a wider dynamic tracking range and lower tracking error than the second-order frequency locked loop assisted third-order phase locked loop in high dynamic circumstances. When the acceleration of carrier is 100 g, the convergence time of vector structure is about 100 ms, and the carrier phase error is lower than 0.6 mm.

OFM-SLAM: A Visual Semantic SLAM for Dynamic Indoor Environments

Most of the current visual Simultaneous Localization and Mapping (SLAM) algorithms are designed based on the assumption of a static environment, and their robustness and accuracy in the dynamic environment do not behave well. The reason is that moving objects in the scene will cause the mismatch of features in the pose estimation process, which further affects its positioning and mapping accuracy. In the meantime, the three-dimensional semantic map plays a key role in mobile robot navigation, path planning, and other tasks. In this paper, we present OFM-SLAM: Optical Flow combining MASK-RCNN SLAM, a novel visual SLAM for semantic mapping in dynamic indoor environments. Firstly, we use the Mask-RCNN network to detect potential moving objects which can generate masks of dynamic objects. Secondly, an optical flow method is adopted to detect dynamic feature points. Then, we combine the optical flow method and the MASK-RCNN for full dynamic points’ culling, and the SLAM system is able to track without these dynamic points. Finally, the semantic labels obtained from MASK-RCNN are mapped to the point cloud for generating a three-dimensional semantic map that only contains the static parts of the scenes and their semantic information. We evaluate our system in public TUM datasets. The results of our experiments demonstrate that our system is more effective in dynamic scenarios, and the OFM-SLAM can estimate the camera pose more accurately and acquire a more precise localization in the high dynamic environment.

An Efficient Route Failure Detection Mechanism with Energy Efficient Routing (EER) Protocol in MANET

Mobile ad-hoc network (MANET) is a network with two or more number of nodes with restricted energy constraint. The high dynamic nature in MANET attracts needs to consider the energy efficient features in their construct. The routing protocol is an important criterion to be considered for evaluating the performance of the MANET. Energy consumption plays vital role in MANET. Hence designing the scheme that supports energy efficient is much needed for the high dynamic MANET environment concerned. This paper proposes the Energy Efficient Routing (EER) protocol based on efficient route failure detection. The Scope of this paper is to suggest a fresh routing procedure for Mobile Adhoc Network minimizes unsuccessful communication. The projected procedure practices three important criterions to locate the path that assure authentic communication. The channel caliber, connection caliber and node’s residual energy is important reason for the failure of the node in MANET. Hence, the suggested routing mechanism believes these three different parameters to choose the finest node in the route. The reliable transmission and reception are attained by transferring information through route nominated by the suggested system verified by means of NS-2 simulator.

Learning HDR illumination from LDR panorama images

For indoor scenes, the fourth-order spherical harmonic function is used to model the illumination, resulting in that 48 spherical harmonic coefficients are used to represent the whole scene. The illumination contained in the low dynamic range image is insufficient, so high dynamic range environment maps are adopted in this part, and the aim is to predict spherical harmonic coefficients of the corresponding high dynamic range image from the low dynamic range image. For this problem, the MSE loss function is used in this paper. Experiments verify the effectiveness of our method. The final visual results show that our method can predict accurate spherical harmonic coefficients, and the recovered luminance is realistic.

An attitude updating algorithm using Picard iteration and higher degree polynomial

To accurately track body attitude under high dynamic environments, a new attitude updating algorithm for the strapdown inertial navigation system is proposed after further applying higher degree polynomial to the quaternion Picard iteration (QPI) algorithm. With QPI, calculation error introduced by Picard approximation can be eliminated, but the angular rate fitting error introduced by substituting polynomial for angular rate of body will still affect the accuracy of the attitude updating algorithms which are designed based on polynomial model. Hence, a five- rather three-degree polynomial constructing method using four samples of gyro outputs with coning motion constrain is designed and tested. Simulation results indicate the proposed method owns more accuracy than QPI, optimal coning algorithm, and Fourth4Rot under both low and high dynamic environments.

DVG+A* and RRT Path-Planners: A Comparison in a Highly Dynamic Environment

This work provides a deeper comparison between two path planning algorithms, the Dynamic Visibility Graph A Star (DVG+A*) and Rapidly–exploring Random Trees (RRT), when applied in a high dimension and dynamic environment, which is the RoboCup Small Size League. The algorithms were compared under two different perspectives. In the first analysis, the algorithms were evaluated according to its computational time, path length and path safety in a static environment. Afterwards, they were evaluated regarding the accumulated computational time, number of recalculated paths, total navigation time and number of collisions in a dynamic environment. The static environment results have shown that the DVG+A* has a better overall performance than RRT, except for the path safety, however, some ideas on how to improve this were discussed. In the dynamic environment the algorithms performed similarly and with a high number of collisions during the experiments. Thus, showing the importance of using an obstacle avoidance algorithm combined with the path planner. In conclusion, the results obtained showed that both algorithms aren’t suitable for highly dynamic and cluttered environments, however, due how sparse the obstacles are in the SSL, they can still be used with some care. Regarding static environments, the DVG+A* has shown the best results.

A High-Order Enhanced Attitude Algorithm Under Angular-Rate Input Condition

With the development of high-precision strapdown inertial navigation systems (SINS), it is increasingly important to improve the accuracy of navigation algorithms to match high-precision inertial sensors and meet the navigation localization requirements in high dynamic environments. However, it is difficult to further improve the accuracy of traditional second-order angular rate-based attitude algorithms due to the neglect of the triple-cross-product term and the limitation of attitude updating frequency. In this paper, a high-order rotation vector-based coning algorithm with compressed form for high accuracy attitude computation of online processing systems is proposed. The theoretical third-order terms of rotation vector differential equation in pure coning motion are deduced in detail and the angular rate vectors of previous attitude updating cycle are additionally used to estimate angular increment, coning correction term and the third-order terms. The error analysis and optimization of the coefficients for proposed algorithm are conducted by utilizing the Taylor series expansions in powers of coning frequency. Simulations under coning vibration environment and turntable experiments in laboratory were performed to verify the performance of the algorithm. The results show that the proposed algorithm can obviously reduce the noncommutativity error and obtain higher accuracy compared with the existing angular rate-based algorithms.

Fuzzy logic based forwarder selection for efficient data dissemination in VANETs

A Vehicular Ad hoc Network is a model of mobile Ad hoc network for running vehicles in which vehicles can communicate with wireless communication devices within a confined geographical region in the absence of fixed infrastructure. Also, it works as an intelligent system for road safety and traffic management by disseminating information among moving vehicles with the wireless sensor Ad hoc network, which has so many challenges and prospects since routing protocols are semi-organized for vehicular movements and constraints of road topology with rules within the limited physical connectivity. A successful broadcast is a challenging task within limited resources. The availability of the number of nodes in a range is an obstacle for rebroadcasting, can be resolved by reducing the figure with a fuzzy logic-based forwarder selection method. Signal strength can be improved in a transmission range by employing an optimum number of broadcasting points with less delay, fewer hop counts, and a high packet dissemination ratio. This optimal fuzzy broadcast algorithm is proposed for the selection of the best forwarder nodes with the consideration of inter-vehicle: distance, mobility, and density into account. Also, the performance of inter-vehicular geographic routing in high speed dynamic vehicular environment is proved with the MATLAB software, OMNeT++ tool, and SUMO environment.

A robust visual odometry based on RGB-D camera in dynamic indoor environments

To solve the accurate positioning problem of mobile robots, simultaneous localization and mapping (SLAM) or visual odometry (VO) based on visual information are widely used. However, most visual SLAM or VO cannot meet the accuracy requirements in dynamic indoor environments. This paper proposes a robust visual odometry based on deep learning to eliminate feature points matching error. However, when a camera and dynamic objects are in relative motion, the frames of camera will produce ghosting, especially in high-dynamic environments, which bring additional positioning error; in view of this problem, a novel method based on the average optical flow value of the dynamic region is proposed to identify feature points of the ghosting, and then the feature points of the ghosting and dynamic region are removed. After the remaining feature points are matched, we use a non-linear optimization method to calculate the pose. The proposed algorithm is tested on TUM RGB-D dataset, and the results show that our VO improves the positioning accuracy than other robust SLAM or VO and is strongly robust especially in high-dynamic environments.

Design of a Compact GPS/MEMS IMU Integrated Navigation Receiver Module for High Dynamic Environment

Design of a Compact GPS/MEMS IMU Integrated Navigation Receiver Module for High Dynamic Environment

Velocity updating based on two auxiliary frames

The conventional velocity updating algorithms are sufficient for the low and medium-accuracy strapdown INS. However, the accuracy of conventional algorithms is not sufficient for a high-accuracy strapdown INS and the high-dynamic environment. To solve this issue, a new velocity updating algorithm is proposed in this paper. It employs two auxiliary frames to reduce the rate of change in direction of the specific force and angular rate vectors. The first slewing frame slews at a constant rate about a fixed axis in the vehicle frame and the second slewing frame slews at a constant rate about a fixed axis in the first slewing frame. With the proper choice of slew rates, the angular rate and specific force vectors of the second slewing frame changes direction more slowly than that of the vehicle frame. Integrating of the transformed specific force vector in the second slewing frame and then translating it to the navigation frame could determine the updated velocity. The velocity updating algorithm presented in this paper does not involve sculling correction. Simulation and trial results demonstrated that this algorithm has a higher accuracy than the conventional algorithms. This algorithm plays an important role in the high-dynamic environment and a high-accuracy strapdown INS.

ROBUST VISUAL-INERTIAL ODOMETRY IN DYNAMIC ENVIRONMENTS USING SEMANTIC SEGMENTATION FOR FEATURE SELECTION

Abstract. Camera based navigation in dynamic environments with high content of moving objects is challenging. Keypoint-based localization methods need to reliably reject features that do not belong to the static background. Here, traditional statistical methods for outlier rejection quickly reach their limits. A common approach is the combination with an inertial measurement unit for visual-inertial odometry. Also, deep learning based semantic segmentation was recently successfully applied in camera based localization to identify features on common objects. In this work, we study the application of mask-based feature selection based on semantic segmentation for robust localization in high dynamic environments. We focus on visual-inertial odometry, but similarly investigate a state-of-the-art pure vision-based method as baseline. For a versatile evaluation, we use challenging self-recorded datasets based on different sensor systems. This includes a combined dataset of a real world system and its synthetic clone with a large number of humans for in-depth analysis. We further deploy large-scale datasets from pedestrian navigation in a mall with escalator scenes and vehicle navigation during the day and at night. Our results show that visual-inertial odometry performs generally well in dynamic environments itself, but also shows significant failures in challenging scenes, which are prevented by using the segmentation aid.

Accurate Attitude Determination Based on Adaptive UKF and RBF Neural Network Using Fusion Methodology for Micro-IMU Applied to Rotating Environment

Focusing on the issue of attitude tracking for low-cost and small-size Micro-Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) in high dynamic environment, an Adaptive Unscented Kalman Filter (AUKF) method combining sensor fusion methodology with Artificial Neural Network (ANN) is proposed. The different control strategies are adopted by fusing multi-MEMS inertial sensors under various dynamic situations. The AUKF attitude determination approach utilizing the MEMS sensor and Global Positioning System (GPS) can provide reliable estimation in these situations. In particular, the adaptive scale factor is used to adaptively weaken or enhance the effects on new measurement data according to the predicted residual vector in the estimation process. In order to solve the problem that the new measurement data is not available in case of GPS fault, an attitude algorithm based on Radial Basis Function (RBF)-ANN feedback correction is proposed for AUKF. The estimated deviation of predicted system state can be provided based on RBF-ANN in GPS-denied environment. The corrected predicted system state is used for the estimation process in AUKF. An experimental platform was setup to simulate the rotation of the spinning projectile. The experimental results show that the proposed method has better performance in terms of attitude estimation than other representative methods under various dynamic situations.

A Low-Cost Method of Improving the GNSS/SINS Integrated Navigation System Using Multiple Receivers

GNSS (global navigation satellite system) and SINS (strap-down inertial navigation system) integrated navigation systems have been the apparatus for providing reliable and stable position and velocity information (PV). Commonly, there are two solutions to improve the GNSS/SINS integration navigation system accuracy, i.e., employing GNSS with higher position accuracy in the integration system or utilizing the high-grade inertial measurement unit (IMU) to construct the integration system. However, technologies such as RTK (real-time kinematic) and PPP (precise point positioning) that improve GNSS positioning accuracy have higher costs and they cannot work under high dynamic environments. Also, an IMU with high accuracy will lead to a higher cost and larger volume, therefore, a low-cost method to enhance the GNSS/SINS integration accuracy is of great significance. In this paper, multiple receivers based on the GNSS/SINS integrated navigation system are proposed with the aim of providing more precise PV information. Since the chip-scale receivers are cheap, the deployment of multiple receivers in the GNSS/SINS integration will not significantly increase the cost. In addition, two different filtering methods with central and cascaded structure are employed to process the multiple receivers and SINS integration. In the centralized integration filter method, measurements from multiple receivers are directly processed to estimate the SINS errors state vectors. However, the computation load increases heavily due to the rising dimension of the measurement vector. Therefore, a cascaded integration filter structure is also employed to distribute the processing of the multiple receiver and SINS integration. In the cascaded processing method, each receiver is regarded as an individual “sensor”, and a standard federated Kalman filter (FKF) is implemented to obtain an optimal estimation of the navigation solutions. In this paper, a simulation and a field tests are carried out to assess the influence of the number of receivers on the PV accuracy. A detailed analysis of these position and velocity results is presented and the improvements in the PV accuracy demonstrate the effectiveness of the proposed method.

UAV navigation in high dynamic environments: A deep reinforcement learning approach

Unmanned Aerial Vehicle (UAV) navigation is aimed at guiding a UAV to the desired destinations along a collision-free and efficient path without human interventions, and it plays a crucial role in autonomous missions in harsh environments. The recently emerging Deep Reinforcement Learning (DRL) methods have shown promise for addressing the UAV navigation problem, but most of these methods cannot converge due to the massive amounts of interactive data when a UAV is navigating in high dynamic environments, where there are numerous obstacles moving fast. In this work, we propose an improved DRL-based method to tackle these fundamental limitations. To be specific, we develop a distributed DRL framework to decompose the UAV navigation task into two simpler sub-tasks, each of which is solved through the designed Long Short-Term Memory (LSTM) based DRL network by using only part of the interactive data. Furthermore, a clipped DRL loss function is proposed to closely stack the two sub-solutions into one integral for the UAV navigation problem. Extensive simulation results are provided to corroborate the superiority of the proposed method in terms of the convergence and effectiveness compared with those of the state-of-the-art DRL methods.

Leveraging Dynamic Stackelberg Pricing Game for Multi-Mode Spectrum Sharing in 5G-VANET

5G enabled Vehicular ad hoc network (5G-VANET) plays a promising role to support diverse intelligent transportation system (ITS) applications. There are three types of communication modes in 5G-VANET: cellular mode, reuse mode and dedicated mode, i.e., vehicle users (VUEs) communicate with each other using the cellular network spectrum directly, in an underlay sharing way, and utilizing the allocated dedicated spectrum, respectively. However, how to dynamically share the multi-mode spectrum to optimize the network performance (i.e., network throughput) in 5G-VANET is a challenging task due to the high dynamic VANET environment and network resource heterogeneity. In this paper, we propose a dynamic Stackelberg pricing game enabled multi-mode spectrum sharing solution in 5G-VANET. In specific, we develop an access price strategy for different spectrum sharing modes considering the cellular BS's revenue and whole network throughput, while VUEs can select communication modes in a distributed way and dynamically change the selections through an evolutionary game. Through testing different traffic scenarios generated by SUMO, we demonstrate the effectiveness of the proposed algorithm. Specifically, the proposed algorithm can improve the total transmission rate of VANET by at least 20% compared with the random selection method.

A robust STAP beamforming algorithm for GNSS receivers in high dynamic environment

In high dynamic environment, the output performance of the space-time adaptive processing (STAP) beamformer can be degraded dramatically since directions of arrival (DOAs) corresponding to interferences change rapidly. A novel robust STAP beamforming algorithm is proposed for global navigation satellite system (GNSS) receivers to broaden the nulls towards interferences and meanwhile fight against the steering vector (SV) mismatch caused by DOA estimation errors. Firstly, the received signal model in high dynamic environment is established for the STAP architecture. Then, the spatial-temporal interference plus noise covariance (INC) matrix is obtained by reconstructing the interference covariance matrix and noise covariance matrix respectively to avoid DOA estimation of GNSS signals, which can also remove the signal of interest (SOI) component from the sample covariance matrix (SCM). Furthermore, the spatial-temporal spectrum estimates around interferences are respectively reset to be the same as those of interferences, due to which the nulls towards interferences in the space domain can be widened in high dynamic environment. Finally, the constraints to solve weight vector is specially designed for GNSS signals. Simulation results show that the proposed algorithm can broaden the nulls effectively and also has a better output carrier-to-noise (C/No) ratio performance than the other involving algorithms.

An Improved Attitude Compensation Algorithm in High Dynamic Environment

With the advent of high-precision gyro laboratory prototypes and the widespread use of gyros in high-dynamic fields, the requirements for navigation calculations will definitely increase. The traditional cone error compensation algorithm is very effective in pure cone motion environment with small half cone angle. However not only the triple cross product term of the noncommutativity error is ignored but also the cross-product terms of the angular increment are simplified in this algorithm for convenience. Thus, the attitude error is extremely huge when the half cone angle is large or in high dynamic environments. Aiming at the above problems, this paper proposes an improved high-precision attitude compensation algorithm based on Taylor series expansion. In this improved algorithm, the neglected and simplified items indicated above are both considered carefully. On this basis, the compensation coefficients are given after deducing in detail. Our algorithm does not need to know the coefficient model in advance, so it is more practical and has advantages in deriving high-order algorithms or sculling compensation algorithms. In order to verify the performance of the algorithm, pure coning and high dynamic environment simulations are performed. And the results show that the improved attitude compensation algorithm can obtain higher precision, which proving its feasibility and effectiveness.