• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권3호
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• pp.1477-1491
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• 2017

Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.

• 한국항행학회논문지
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• 제27권4호
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• pp.391-395
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• 2023

Helicopter accidents due to spatial disorientation in low visibility conditions continue to persist as a major issue. These incidents often stem from human error, typically induced by stress, and frequently result in fatal outcomes. This study employs machine learning to analyze flight data and evaluate the efficacy of a flight illusion detection algorithm, laying groundwork for further research. This study collected flight data from approximately 20 pilots using a simulated flight training device to construct a range of flight scenarios. These scenarios included three stages of flight: ascending, level, and descent, and were further categorized into good visibility conditions and 0-mile visibility conditions. The aim was to investigate the occurrence of flight illusions under these conditions. From the extracted data, we obtained a total of 54,000 time-series data points, sampled five times per second. These were then analyzed using a machine learning approach.

• 수산해양교육연구
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• 제6권2호
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• pp.198-216
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• 1994

The problem of promoting instructional effect using reorganizing the content of textbook is one of the major concerns of many education theorists and teachers. The results of many researches about above problem reveal that reorganizing the content of textbook promotes the ability of recall and problem solving of learners. The content structure of current navigation textbook revealed a categorical structure as its basic framework, though it seems to be a poor one. A categorical structure is known as providing an inferior information processing mechanism for learners than a learning hierarchy content structure is. Furthermore current content structure hasn't given any considerations to navigation in practice, spatial contexts and sequential events of ships from a harbor to another harbor. The learning hierarchy content structure has an advantage of giving learners more systematic and stronger knowledge networks than a categorical structure.

• 동의생리병리학회지
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• 제36권1호
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• pp.18-22
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• 2022

To investigate effects of cognitive function improvement whether against Taegeuk ginseng on scopolamine-induced memory impairment in rats. All experiments were conducted in three groups: the control group (CTR), the scopolamine 0.4mg/kg (SCP), and the scopolamine (SCP+T) treated with Taegeuk ginseng 100 mg/kg. Taegeuk ginseng 100 mg/kg daily was orally administered for one month and treated with scopolamine was only for 7 consecutive days on the Morris water maze task. 3 weeks after oral administration of Taegeuk ginseng, subjects were performed the Morris water maze test for 8 days and then the open-field exploration test which to assessed for cognitive function improvement. After behavioral testing, subjects were sacrificed and microdissected brains for neurochemical analysis. In the cognitive-behavioral test, long-term administration of Taegeuk ginseng improved spatial navigation learning task compared with the impeded by scopolamine treatment. In neurochemistry, the expression of the synaptic marker PSD95 (postsynaptic density protein 95) was increased in the hippocampus compared to the scopolamine group. Also, brain-derived neurotrophic factor (BDNF) expression was significantly increased in the taegeuk ginseng administration group. These data suggested that long-term administration of taegeuk ginseng might improve cognitive-behavioral functions on hippocampal related spatial learning memory, and it was correlated with neurotropic and synaptic reinforcement. In conclusion, treatment with taegeuk ginseng may positive outcome on learning and memory deficit disorders.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.302-306
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• 2021

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

• 한국건설관리학회논문집
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• 제24권5호
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• pp.73-82
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• 2023

Optimization of Construction Site Layout Planning (CSLP) heavily relies on workers' travel paths. However, traditional path generation approaches predominantly focus on the shortest path, often neglecting critical variables such as individual wayfinding tendencies, the spatial arrangement of site objects, and potential hazards. These oversights can lead to compromised path simulations, resulting in less reliable site layout plans. While Deep Reinforcement Learning (DRL) has been proposed as a potential alternative to address these issues, it has shown limitations. Despite presenting more realistic travel paths by considering these variables, DRL often struggles with efficiency in complex environments, leading to extended learning times and potential failures. To overcome these challenges, this study introduces a refined model that enhances spatial navigation capabilities and learning performance by integrating workers' visibility into the reward functions. The proposed model demonstrated a 12.47% increase in the pathfinding success rate and notable improvements in the other two performance measures compared to the existing DRL framework. The adoption of this model could greatly enhance the reliability of the results, ultimately improving site operational efficiency and safety management such as by reducing site congestion and accidents. Future research could expand this study by simulating travel paths in dynamic, multi-agent environments that represent different stages of construction.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.

• 한국항행학회논문지
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• 제27권4호
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• pp.323-329
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• 2023

본 연구는 헬리콥터 조종사 교육에 있어서 성공적인 비행훈련 성과에 영향을 미치는 요인을 살펴보고자 한다. 이를 위해 탐색적 요인분석을 이용하여 개인의 인지적 특성과 비인지적 특성을 추출하고, 계층적 회귀분석을 통해 이러한 특성(요인)들이 비행훈련 성과에 얼마나 영향을 미치는지 알아보았다. 그 결과 공간지각능력, 회복탄력성, 숙달목표지향적 학습태도 성향이 높을수록 비행 교육성과에 긍정적인 영향을 미치는 것으로 나타났다. 이는 조종사에게 특히 요구되는 공간지각능력의 중요성을 재확인할 수 있었고, 그리고 제한된 조종석 공간에서의 비행교관의 역할, 개인의 올바른 동기부여와 노력이 비행훈련 성과에 영향을 미친다는 것을 재확인시켜준다. 이러한 결과는 향후 헬기 조종사의 효과적인 비행교육에 유용한 지표가 될 것으로 기대한다.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2006년도 학술대회 2부
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• pp.633-638
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• 2006

We investigated the importance and efficiency of active and passive exploration on the recognition of objects in a variety of virtual environments (VEs). In this study, 54 participants (19 males and 35 females) were randomly allocated into one of two navigation conditions (active and passive navigation). The 3D visual display was presented through HMD and participants used joysticks to navigate VEs. The VEs consisted of exploring four rooms (library, office, lounge, and conference room), each of which had 15 objects. 'Active navigation' was performed by allowing participants to self-pace and control their own navigation within a predetermined time limitation for each room. 'Passive navigation' was conducted by forced navigation of the four rooms in random order. Total navigation duration and objects for both navigations were identical. After navigating VEs, participants were asked to recognize the objects that had been in the four rooms. Recognition for objects was measured by response time and the percentage of correct, false, hit, and miss responses. Those in the active navigation condition had a significantly higher percentage of hit responses (t (52) = 4.000 p < 0.01), and a significantly lower percentage of miss responses (t (52) = -3.763, p < 0.01) in object recognition than those in the passive condition. These results suggest that active navigation plays an important role in spatial cognition as well as providing a better explanation about the efficiency of learning in a 3D-based program.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2023년도 춘계학술대회
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• pp.252-253
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• 2023

Container terminal automation might offer many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A robust lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure lane positioning robustness. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions and tested the robustness of the lane detection method with stochastic noise.