• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1995.04a
• /
• pp.787-796
• /
• 1995

A man model is a useful design tool for the evaluation of man machine systems and products. An arm reach trajectory prediction for such a model will be specifically useful to present human activities and, consequently, could increase the accuracy and reality of the evaluation. In this study, a three-dimensional reach trajectory prediction model was developed using an inverse kinematics technique. The upper body was modeled as a four link open kinematic chain with seven degrees of freedom. The Resolved Motion Method used for the robot kinematics problem was used to predict the joint movements. The cost function of the perceived discomfort developed using the central composite design was also used as a performance function. This model predicts the posture by moving the joints to minimize the discomfort on the constraint of the end effector velocity directed to a target point. The results of the pairwise t-test showed that all the joint coordinates except the shoulder joint's showed statistically no differences at .alpha. = 0.01. The reach trajectory prediction model developed in this study was found to accurately simulate human arm reach trajectory and the model will help understand the human arm reach movement.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.24 no.4
• /
• pp.44-52
• /
• 2016

The departure flow management is the planning tool to optimize the schedule of the departure aircraft and allows them to join smoothly into the overhead traffic flow. To that end, the arrival time prediction to the merge point for the cruising aircraft is necessary to determined. This paper proposes a trajectory prediction model for the cruising aircraft based on the machine learning approach. The proposed method includes the trajectory vectored from the procedural route and is applied to the historical data to evaluate the prediction performances.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.10 no.5
• /
• pp.206-213
• /
• 2002

This paper proposes a vehicle trajectory prediction method for application to vehicle-to-vehicle distance control. This method is based on 2-dimensional kinematics and a Kalman filter has been used to estimate acceleration of the object vehicle. The simulation results using the proposed control method show that the relative distance characteristics can be improved via the trajectory prediction method compared to the customary vehicle stop and go cruise control systems which makes the vehicle remain at a safe distance from a preceding vehicle according to the driver's preference, automatically slow down and come to a full stop behind a preceding vehicle.

• International Journal of Aeronautical and Space Sciences
• /
• v.13 no.3
• /
• pp.377-385
• /
• 2012

The Automatic Dependent Surveillance Broadcast (ADS-B) system is a key component of CNS/ATM recommended by the International Civil Aviation Organization (ICAO) as the next generation air traffic control system. ADS-B broadcasts identification, positional data, and operation information of an aircraft to other aircraft, ground vehicles and ground stations in the nearby region. This paper explores the ADS-B based trajectory prediction and the conflict detection algorithm. The multiple-model based trajectory prediction algorithm leads accurate predicted conflict probability at a future forecast time. We propose an efficient and accurate algorithm to calculate conflict probability based on approximation of the conflict zone by a set of blocks. The performance of proposed algorithms is demonstrated by a numerical simulation of two aircraft encounter scenarios.

• Journal of IKEEE
• /
• v.25 no.4
• /
• pp.766-769
• /
• 2021

In order to perform tasks such as design, control, optimization, and prediction of flight vehicle trajectories based on machine learning techniques including deep learning, a certain amount of flight vehicle trajectory data is required. However, there are cases in which it is difficult to secure more than a certain amount of flight vehicle trajectory data for various reasons. In such cases, synthetic data generation could be one way to make machine learning possible. In this paper, to explore this possibility, we generated and evaluated synthetic flight vehicle trajectory data using time-series generative adversarial neural network. In addition, various ablation studies (comparative experiments) were performed to explore the possibility of using synthetic data in the aircraft trajectory prediction task. The experimental results presented in this paper are expected to be of practical help to researchers who want to conduct research on the possibility of using synthetic data in the generation of synthetic flight vehicle trajectory data and the work related to flight vehicle trajectories.

• Journal of Navigation and Port Research
• /
• v.46 no.6
• /
• pp.576-584
• /
• 2022

AIS is widely utilized in vessel traffic services for marine traffic safety. In 2021, Korea deployed the high-speed maritime wireless communication system (LTE-Maritime) on the sea following IMO's proposal for the introduction of e-Navigation. In this paper, vessel trajectory data from AIS and LTE-Maritime were used for vessel trajectory prediction to compare and analyze the two systems. The results show that the trajectory prediction error of LTE-Maritime was smaller than that of AIS due to the granular and uniform data provided by LTE-Maritime. Additionally, it was revealed that time interval is the most important factor influencing the errors in trajectory prediction, with the prediction error of LTE-Maritime growing at a slower rate of 17% than AIS. This research contributes to the literature by quantitatively comparing AIS and LTE-Maritime systems for the first time.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.8
• /
• pp.3399-3419
• /
• 2020

In the Vehicular Ad-hoc Network (VANET), the data transmission of time-sensitive applications requires low latency, such as accident warnings, driving guidance, etc. However, frequent changes of topology in VANET will result in data transmission failures. In order to improve the efficiency of VANETs data transmission and increase the timeliness of data, this paper proposes a relay scheme based on Recurrent Neural Network (RNN) trajectory prediction, which can be used to select the optimal relay vehicle to transmit data. The proposed scheme learns vehicle trajectory in a distributed manner and calculates the predicted trajectory, and then the optimal vehicle can be selected to complete the data transmission, which ensures the timeliness of the data. Finally, we carry out a set of simulations to demonstrate the performance of the algorithm. Simulation results show that the proposed scheme enhances the timeliness of the data and the accuracy of the predicted driving trajectory.

• Journal of Korea Spatial Information System Society
• /
• v.9 no.1
• /
• pp.1-13
• /
• 2007

The necessity of future index is increasing to predict the future location of moving objects promptly for various location-based services. A representative research topic related to future index is the probability trajectory prediction technique that improves reliability using the past trajectory information of moving objects in the road network environment. However, the prediction performance of this technique is lowered by the heavy load of extensive future trajectory search in long-range future queries, and its index maintenance cost is high due to the frequent update of future trajectory. Thus, this paper proposes the Probability Cell Trajectory-Tree (PCT-Tree), a cell-based future indexing technique for efficient long-range future location prediction. The PCT-Tree reduces the size of index by rebuilding the probability of extensive past trajectories in the unit of cell, and improves the prediction performance of long-range future queries. In addition, it predicts reliable future trajectories using information on past trajectories and, by doing so, minimizes the cost of communication resulting from errors in future trajectory prediction and the cost of index rebuilding for updating future trajectories. Through experiment, we proved the superiority of the PCT-Tree over existing indexing techniques in the performance of long-range future queries.

• International Journal of Aerospace System Engineering
• /
• v.3 no.2
• /
• pp.14-21
• /
• 2016

This paper presents research on predicting the possible intercept time for a Nodong missile based on its flight trajectory. North Korea possesses ballistic missiles of various ranges, and nuclear warhead miniaturization tests and ballistic missile launch tests conducted last year and in previous years have made these missiles into a serious security threat for the international community. With North Korea's current miniaturization skills, the range of the nuclear capable Nodong missiles can be adjusted according to their use goals and operating environment by using a variety of adjustment methods such as payload, fuel mass, Isp, loft angle, cut-off, etc., and therefore precise flight trajectory prediction is difficult. In this regards, this research performs model simulations of the flight trajectory of North Korea's domestically developed Nodong missiles and uses these as a basis for predicting the possible intercept times for major ballistic missile defense systems such as PAC-3, THAAD, and SM-3.

• Journal of Construction Engineering and Project Management
• /
• v.8 no.1
• /
• pp.10-21
• /
• 2018

Many construction projects involve a plethora of safety-related problems that can cause loss of productivity, diminished revenue, time overruns, and legal challenges. Incorporating data collection and analytics methods can help overcome the root causes of many such problems. However, in a dynamic construction workplace collecting data from a large number of resources is not a trivial task and can be costly, while many contractors lack the motivation to incorporate technology in their activities. In this research, an Android-based mobile application, Preemptive Construction Site Safety (PCS2) is developed and tested for real-time location tracking, trajectory prediction, and prevention of potential collisions between workers and site hazards. PCS2 uses ubiquitous mobile technology (smartphones) for positional data collection, and a robust trajectory prediction technique that couples hidden Markov model (HMM) with risk-taking behavior modeling. The effectiveness of PCS2 is evaluated in field experiments where impending collisions are predicted and safety alerts are generated with enough lead time for the user. With further improvement in interface design and underlying mathematical models, PCS2 will have practical benefits in large scale multi-agent construction worksites by significantly reducing the likelihood of proximity-related accidents between workers and equipment.