Earticle

Trajectory data is widely collected and utilized for scientific research and business purpose, publishing trajectory it without proper privacy policy leads to an acute threat to individual data. Recently, several methods, i.e., k-anonymity, l-diversity, t-closeness, have been studied, though they tend to protect by reducing data depends on a feature of each method. When it requires strong privacy protection, these methods have excessively reduced data utility that may affect the result of scientific research. In this research, for the first time, we suggest a novel approach to tackle this existing dilemma via an adding noise trajectory on a vector-based grid environment. Afterward, we propose an efficient algorithm to expand trajectories adding the minimum amount of noise. Compared to other methods, our experiment shows the proposed algorithm maintains excellent performance with respect to data utility and time complexity.

아프리카 돼지 열병의 세계적인 확산과 함께 가축전염병에 대한 관심이 증가하고 있다. 가 축관련 운송차량은 가축전염병 확산의 주요 원인으로 제기되고 있으나 국내에서는 가축관련 운송차량의 이동성과 관련한 실증적인 방역절차와 기준이 마련되지 않고 있는 실정이다. 이에 본 연구는 국가가축방역시스템의 축산시설 방문이력 데이터와 한국교통안전공단의 DTG(Digital Tachograph) 데이터를 활용하여 가축관련 운송차량이 이용한 도로이용정보를 추출 하고, 각 차량의 링크별 점유시간을 집계하여 노출도 지표로 제시하였다. 총 274,519행의 가축 관련 운송차량의 통행궤적이 추출되었으며 링크별, 존별 노출도를 정량적으로 도출하였다. 본 연구를 통해 가축관련 운송차량의 사전 모니터링 및 사후 방역방침 수립이 가능할 것으로 기대된다.

With the worldwide spread of African swine fever, interest in livestock epidemics is growing. Livestock transport vehicles are the main cause of the spread of livestock epidemics, but no empirical quarantine procedures and standards related to the mobility of livestock transport vehicles in South Korea. This study extracted livestock-related vehicles' trajectory by utilizing the facility visit history data from the Korea Animal Health Integrated System and the DTG (Digital Tachograph) data from the Korea Transportation Safety Authority and presented them as exposure indexes aggregating the link-time occupancy of each vehicle. As a result, a total of 274,519 livestock-related vehicle trajectories were extracted, and exposure values by link and zone were quantitatively derived. Through this study, it is expected that prior monitoring of livestock transport vehicles and the establishment of post-disaster prevention policies would be provided.

The launcher of a hard-kill type APS (Active Protection System) requires rapid and precise driving to aim at incoming threats after detection. High angular acceleration is necessary for rapid driving, which demands high energy consumption. However, the capacity of the capacitor bank and power supply unit is limited due to weight and space constraints. If energy becomes insufficient during continuous operation, the voltage of the capacitor bank can drop below the minimum operating voltage of the drive motor, leading to problems such as torque deficiency. Therefore, it is necessary to determine an allowable angular acceleration that satisfies precision within the available energy and generate a driving profile accordingly. This paper proposes a method for deriving an allowable angular acceleration by analyzing the allowable energy and validates it through simulation. We examined the allowable energy by verifying the charged voltage of the capacitor bank, formulated equations for energy at the point of maximum consumption, and derived an equation for allowable angular acceleration through numerical analysis. By applying the proposed algorithm in simulations, we confirmed that the voltage of the capacitor bank did not drop below the minimum operating voltage of the driving motor during three consecutive operations. Therefore, it is expected that the stability of the APS launcher can be improved by applying the proposed algorithm, and continuous operation with limited performance is anticipated to be possible.

최근 국제적인 테러 위협이 불특정 다수를 대상으로 발생하고 있으며, 이러한 위협에서 시민을 보호하기 위한 다양한 대책이 논의 중이다. 저렴해진 센서 기술을 활용한 사전 감시 시스템에 대한 요구가 높아지고 있으나, 보행 궤적의 고유 특성 검출 및 상세 분석 연구가 미비한 실정이다. 본 연구에서는 상용화된 보행 동선 솔루션을 활용하여, 삼성역 개찰구에서 코엑스와 직접 연결되는 연결 통로 (3-6번 출구 근처) 일대의 보행 동선 궤적 조사를 수행하 였다. 조사된 궤적 자료를 바탕으로, 궤적 자료의 정규화 기법, Clustering 방법을 중심으로 보 행 궤적을 유형화하고 배회 동선을 추출하는 분석 방법론을 제시하였다. 분석 결과, 동일 군 집내에서 유사성이 크게 떨어지는 보행 궤적의 검출 가능성을 검증하였다.

Recently terrorism targets unspecified masses and causes massive destruction, which is so-called Super Terrorism. Many countries have tried hard to protect their citizens with various preparation and safety net. With inexpensive and advanced technologies of sensors, the surveillance systems have been paid attention, but few studies associated with the classification of the pedestrians’ trajectories and the difference among themselves have attempted. Therefore, we collected individual trajectories at Samseoung Station using an analytical solution (system) of pedestrian trajectory by LiDAR sensor. Based on the collected trajectory data, a comprehensive framework of classifying the types of pedestrians’ trajectories has been developed with data normalization and “trajectory association rule-based algorithm.” As a result, trajectories with low similarity within the very same cluster is possibly detected.

본 연구는 일반긴장이론을 중심으로 청소년 지위비행의 발달궤적을 통해 집단을 나누고 각 집단별로 긴장 유형의 차이와 대처능력에 차이가 있는지를 살펴보고자 하였다. 본 연구에 사용된 자료는 한국 아동·청소년 패널데이터를 사용하였고, 그 중 3차년도 부터 6차년도의 비행궤적을 사용하였다. 연구결과, 지위비행의 발달궤적에 따른 집단은 무비행집단, 후발형비행집단, 고비행집단으로 나누어졌고 무비행집단과 후발형비행집단의 경우 공격성이 높을수록, 교사애착이 낮을수록, 비행친구 수가 많을수록, 남학생일수록 무비행집단보다는 후발형비행집단에 더 속하게 될 확률이 높아지는 것으로 나타났다. 다음으로 무비행집단과 고비행집단을 경우를 살펴보면 부모긴장이 많을수록, 친구긴장이 낮을수록, 공격성이 높을수록, 교사애착이 낮을수록, 비행친구 수가 많을수록, 남학생일수록 무비행집단보다는 고비행집단에 속하게 될 확률이 더 높아지는 것을 알 수 있었다. 특히, 무비행집단대비, 후발형비행집단 또는 고비행집단에 속한 경우 더 많은 긴장을 느끼고 비행친구가 더 많았으며, 교사애착이 더 낮은 것을 알 수 있었다. 또한 시간에 따른 독립변수의 변화가 지위비행발달궤적에 영향을 주는 것을 살펴본 결과, 부모긴장의 경우 후발형비행집단에서, 가구 수입의 경우 고비행집단에서, 비행친구 수의 경우 비행증가-중단집단과 후발형비행집단에서 유의미한 결과를 나타내는 것을 확인 할 수 있었다.

This study extracted several trajectories of juvenile status delinquency and examined whether strain as well as coping factors give significant influences on the membership of the extracted groups. Korean Child Youth Penal Study data were used for this study. Total 2,056 samples are included except missing cases. This study extracted three different developmental trajectories of juvenile status delinquency. We named those groups no-delinquency, increasing-delinquency, chronic-delinquency groups. In the time-invarying covariate analysis, the no-delinquency group was a reference group in order to compare with increasing status delinquency group. The increasing-delinquency group has more aggressive, more the number of delinquency friends, less teacher attachment and male compared to the no-delinquency group. Also, chronic-delinquency groups has more parents strain, more aggressive, greater the number of delinquency friends, less friends strain, less teacher attachment and male compared to the no-delinquency group. Especially, compared to the no-delinquency group, the increasing-delinquency group or chronic-delinquency group felt more strain, had more delinquency friends, and had less teacher attachment. Also, the time-varying covariate analysis showed that parental strain gave a significant influence on the increasing-delinquency group and family income gave a significant influence on the developmental trajectory of the chronic-delinquency group. The number of delinquency friends gave a significant influence on the change of increase-cease delinquency group as well as the increasing-delinquency group.

선점preemption은 인과관계를 반사실적 의존관계로 해명하는 반사실인과이론에 대한 문제로서 제기되었다. 최근에 선점은 인과관계를 확률의 관계로 해명하는 확률인과이론에 대한 문제로서 다시 부각되었다. 문제의 핵심은, 확률의 관계가 인과관계를 해명하는 데 필요하지도 충분하지도 않다는 것이다. 이 문제에 대해 필자는 다음을 논의한다. 첫째, 선점의 문제는 확률인과이론의 반례가 아니라 사건 수준의 인과관계를 위한 확률인과이론의 필요성을 보여주는 것이다. 필자는 두 가지 서로 다른 인과관계의 개념으로 이 필요성을 입증한다. 둘째, 사건 수준의 새로운 확률인과이론으로서 엘스Eells(1998, 2002)의 확률궤적이론과 선점 현상에 대한 이 이론의 해명을 소개한다. 셋째, 셰퍼Schaffer(2001)가 제시한 새로운 유형의 선점의 문제와 이 문제를 해결하는 셰퍼의 두 가지 분석을 소개하고, 확률궤적이론이 그 문제를 어떻게 분석할 수 있는지 보여준다. 넷째, 보다 복잡한 선점의 현상에서 세퍼의 분석과 확률궤적이론을 비교하여 인과유관성을 해명하는 데 확률궤적이론이 어떤 수월성을 가지는지 보여준다. 또 사건 수준의 인과유관성을 확률의 관계로 해명하는 데, 확률의 변화와 구체적인 기술 그리고 다양한 속성의 예화로서 사건의 개념이 필요하다는 것을 확인한다. 다섯째, 확률궤적이론이 해결해야 할 다른 문제들을 소개한다.

This paper proposes a locomotion pattern and a control method for biped robots with curved soles. First, since the contact point of a supporting leg may arbitrarily move back and forth on the ground, we derived the desired trajectory from a model called the Moving. Inverted Pendulum Model (MIPM) where the Zero Moment Point (ZMP) exists at the supporting point and can be moved intentionally. Secondly, a biped robot with curved soles is an under-actuated system since the supporting point contacting with a point on the ground has no actuator during the single supporting phase. Therefore, this paper proposes a computed-torque control for this under-actuated system using decoupled dynamic equations. A series of computer simulations with a 7-DOF biped robot with curved soles shows that the proposed walking pattern and control method are effective and allow the biped robot to walk fast and stably, and move more like human beings. Also, it is shown that the curved sole shape has superior energy consumption compared to flat soles, and greater efficiency in ascending and descending the stairs.

In the field of machine vision using a single camera mounted on a mobile robot, although the detection and tracking of moving objects from a moving observer, is complex and computationally demanding task. In this paper, we propose a new scheme for a mobile robot to track and capture a moving object using images of a camera. The system consists of the following modules: data acquisition, feature extraction and visual tracking, and trajectory generation. And a single camera is used as visual sensors to capture image sequences of a moving object. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the active camera. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to capture the moving object, the linear and angular velocities are estimated and utilized. The experimental results of tracking and capturing of the target object with the mobile robot are presented.

Robotic applications, such as automatic fish cutting, require online trajectory planning because the material properties of the object, such as the bone or flesh conditions, are not known in advance. Different trajectories are required when the material properties vary. An effective online trajectory-planning algorithm is proposed using quaternions to determine the position and orientation of a robot manipulator with a spherical wrist. Quaternions are free of representation singularities and permit computationally efficient orientation interpolations. To prevent singular configurations, the exact locations of the kinematic singularities of the PUMA 560 manipulator are derived and geometrically illustrated when a forearm offset exists and the third link length is not zero.

This paper proposes an optimal trajectory generation method for biped robots for walking up-and-down stairs using a Real-Coded Genetic Algorithm (RCGA). The RCGA is most effective in minimizing the total consumption energy of a multi-dof biped robot. Each joint angle trajectory is defined as a 4-th order polynomial of which the coefficients are chromosomes or design variables to approximate the walking gait. Constraints are divided into equalities and inequalities. First, equality constraints consist of initial conditions and repeatability conditions with respect to each joint angle and angular velocity at the start and end of a stride period. Next, inequality constraints include collision prevention conditions of a swing leg, singular prevention conditions, and stability conditions. The effectiveness of the proposed optimal trajectory is shown in computer simulations with a 6-dof biped robot model that consists of seven links in the sagittal plane. The optimal trajectory is more efficient than that generated by the Modified Gravity-Compensated Inverted Pendulum Mode (MGCIPM). And various trajectories generated by the proposed GA method are analyzed from the viewpoint of the consumption energy: walking on even ground, ascending stairs, and descending stairs.

In this study, a position and force simultaneous trajectory tracking control algorithm is proposed for a driving apparatus that consists of two pneumatic cylinders connected in series. The controller applied to the driving apparatus is composed of a non-interaction controller to compensate for interaction between cylinders and a disturbance observer aimed to reduce the effect of model discrepancy that cannot be compensated by the non-interaction controller. The effectiveness of the proposed control algorithm is proved by experimental results.

Trajectory modeling is foundational for 4D-Route modeling, conflict detection and air traffic flow management. This paper proposes a novel algorithm based Vincenty's fomulas for trajectory calculation, combined with the Dijkstra algorithm and Vincenty's formulas. Using flight plan simulations our experimental results show that our method of En-route trajectory calculation exhibits much improved performance in accuracy.

We present a procedure for the application of global orthogonal polynomial into an atmospheric re-entry maneuvering problem. This trajectory optimization is imbedded in a family of canonically parameterized optimal control problem. The optimal control problem is transcribed to nonlinear programming via global orthogonal polynomial and is solved a sparse nonlinear optimization algorithm. We analyze the optimal trajectories with respect to the performance of re-entry maneuver.

The non-minimum phase characteristic of a flexible manipulator makes tracking control of its tip difficult. The level of the tip tracking performance of a flexible manipulator is significantly affected by the characteristics of the tip reference trajectory as well as the characteristics of the flexible manipulator system. This paper addresses the question of how to best specify a reference trajectory for the tip of a flexible manipulator to follow in order to achieve the objectives of reducing : tip tracking error, residual tip vibration, and the required actuation effort at the manipulator joint. A novel method of tip-based learning controller for the flexible manipulator system is proposed in the paper, where a model of the flexible manipulator system with a command shaping filter is used to generate a smooth and realizable tip reference trajectory for a tip-based learning controller.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have garnered considerable research interest in the fields of 5G and 6G wireless communication due to their remarkable flexibility and cost-effectiveness. However, the inherent openness of wireless communication environments renders these technologies vulnerable to eavesdropping. This paper presents a penalty-based successive convex approximation algorithm and a minorize?maximization algorithm to optimize the transmission beamforming vector, RIS beamforming vector, and UAV?RIS trajectory. The objective of this study was to enhance the physical layer security performance of wireless communication systems using UAVs and RISs. Our simulation results demonstrate that the proposed technique achieves a higher security transmission rate compared to existing techniques.

Based on an introduced optimal trajectory planning method, this paper mainly deals with the accuracy analysis during the function approximation process of the optimal trajectory planning method. The basis functions are composed of Hermit polynomials and Fourier series to improve the approximation accuracy. Since the approximation accuracy is affected by the given orders of each basis function, the accuracy of the optimal solution is examined by changing the combinations of the orders of Hermit polynomials and Fourier series as the approximation basis functions. As a result, it is found that the proper approximation basis functions are the $5^{th}$ order Hermit polynomials and the $7^{th}-10^{th}$ order of Fourier series.

In this paper, we propose a deep reinforcement learning (DRL) based unmanned aerial vehicles (UAV)-assisted trajectory optimization for ultra-dense small cell networks. We assume that each UAV is equipped with a sensing radio to obtain distance information to the UEs and other UAVs in the network which are used to update the UAV’s trajectory. The proposed DRL-based system selects the optimal joint control actions for the UAVs that maximizes the system sum-rate. The simulation results show that the proposed DRL-based UAV controller provides fast UAV placement in the network with a high system performance when compared with the benchmark schemes.

Trajectory prediction is an essential element for driving autonomous vehicles, and various trajectory prediction models have emerged with the development of deep learning technology. Convolutional neural network (CNN) is the most commonly used neural network architecture for extracting the features of visual images, and the latest models exhibit high performances. This study was conducted to identify an efficient CNN backbone model among the components of deep learning models for trajectory prediction. We changed the existing CNN backbone network of multiple-trajectory prediction models used as feature extractors to various state-of-the-art CNN models. The experiment was conducted using nuScenes, which is a dataset used for the development of autonomous vehicles. The results of each model were compared using frequently used evaluation metrics for trajectory prediction. Analyzing the impact of the backbone can improve the performance of the trajectory prediction task. Investigating the influence of the backbone on multiple deep learning models can be a future challenge.

Spatio-temporal patterns extracted from historical trajectories of moving objects reveal important knowledge about movement behavior for high quality LBS services. Existing approaches transform trajectories into sequences of location symbols and derive frequent subsequences by applying conventional sequential pattern mining algorithms. However, spatio-temporal correlations may be lost due to the inappropriate approximations of spatial and temporal properties. In this paper, we address the problem of mining spatio-temporal patterns from trajectory data. The inefficient description of temporal information decreases the mining efficiency and the interpretability of the patterns. We provide a formal statement of efficient representation of spatio-temporal movements and propose a new approach to discover spatio-temporal patterns in trajectory data. The proposed method first finds meaningful spatio-temporal regions and extracts frequent spatio-temporal patterns based on a prefix-projection approach from the sequences of these regions. We experimentally analyze that the proposed method improves mining performance and derives more intuitive patterns.

Trajectory planning is a key technology for unmanned aerial vehicles (UAVs) to achieve complex flight missions. In this paper, a terminal constraints conversion-based Gauss pseudospectral trajectory planning optimization method is proposed. Firstly, the UAV trajectory planning mathematical model is established with considering the boundary conditions and dynamic constraints of UAV. Then, a terminal constraint handling strategy is presented to tackle terminal constraints by introducing new penalty parameters so as to improve the performance index. Combined with Gauss-Legendre collocation discretization, the improved Gauss pseudospectral method is given in detail. Finally, simulation tests are carried out on a four-quadrotor UAV model with different terminal constraints to verify the performance of the proposed method. Test studies indicate that the proposed method performances well in handling complex terminal constraints and the improvements are efficient to obtain better performance indexes when compared with the traditional Gauss pseudospectral method.