• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1066-1079
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• 2023

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.3
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• pp.112-121
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• 2002

This paper describes a new bike system for the postural balance rehabilitation training. Virtual environment and three dimensional graphic model is designed with CAD tools such as 3D Studio Max and World Up. For the real time bike simulation, the optimized WorldToolKit graphic library is embedded with the dynamic geometry generation method, multi-thread method, and portal generation method. In this experiment, 20 normal adults were tested to investigate the influencing factors of balancing posture. We evaluated the system by measuring the parameters such as path deviation, driving velocity, COP(center for pressure), and average weight shift. Also, we investigated the usefulness of visual feedback information by weight shift. The results showed that continuous visual feedback by weight shift was more effective than no visual feedback in the postural balance control It is concluded this system might be applied to clinical use as a new postural balance training system.

• Steel and Composite Structures
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• v.29 no.6
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• pp.771-783
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• 2018

In this paper, topology optimization (TO) is applied to find a new configuration for the perforated steel plate shear wall (PSPSW) based on the maximization of reaction forces as the objective function. An infill steel plate is introduced based on an experimental model for TO. The TO is conducted using the sensitivity analysis, the method of moving asymptotes and SIMP method. TO is done using a nonlinear analysis (geometry and material) considering the buckling. The final area of the optimized plate is equal to 50% of the infill plate. Three plate thicknesses and three length-to-height ratios are defined and their effects are investigated in the TO. It indicates the plate thickness has no significant impact on the optimization results. The nonlinear behavior of optimized plates under cyclic loading is studied and the strength, energy and fracture tendency of them are investigated. Also, four steel plates including infill plate, a plate with a central circle and two types of the multi-circle plate are introduced with equal plate volume for comparing with the results of the optimized plate.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.115.2-115
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• 2002

A real-time vehicle simulation system is a key element of a driving simulator because accurate prediction of vehicle motion with respect to driver input is required to generate realistic visual, motion, sound and proprioceptive cues. In order to predict vehicle motion caused by various driving actions of the driver on board the simulator, the vehicle model should consist of complete subsystems. On this paper, a tracked vehicle dynamic model with high efficiency and effectiveness is introduced that has been implemented on a training driving simulator. The multi-body vehicle model is based on recursive formulation and has been automatically generated from a symbolic computation package develop...

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1052-1070
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• 2017

For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.

• Korean Journal of Remote Sensing
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• v.15 no.1
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• pp.39-50
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• 1999

It is complicate and time-consuming process to classify a multi-band satellite imagery according to the application. In addition, classification rate sensitively depends on the selection of training data set and features in a supervised classification process. This paper introduced a classification network adopting a fuzzy-based $\gamma$-model in order to select a training data set and to extract feature which highly contribute to an actual classification. The features used in the classification were gray-level histogram, textures, and NDVI(Normalized Difference Vegetation Index) of target imagery. Moreover, in order to minimize the errors in the classification network, the Gradient Descent method was used in the training process for the $\gamma$-parameters at each code used. The trained parameters made it possible to know the connectivity of each node and to delete the void features from all the possible input features.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3275-3285
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• 2021

A Nuclear Power Plant (NPP) is a complex dynamic system-of-systems with highly nonlinear behaviors. In order to control the plant operation under both normal and abnormal conditions, the different systems in NPPs (e.g., the reactor core components, primary and secondary coolant systems) are usually monitored continuously, resulting in very large amounts of data. This situation makes it possible to integrate relevant qualitative and quantitative knowledge with artificial intelligence techniques to provide faster and more accurate behavior predictions, leading to more rapid decisions, based on actual NPP operation data. Data-driven models (DDM) rely on artificial intelligence to learn autonomously based on patterns in data, and they represent alternatives to physics-based models that typically require significant computational resources and might not fully represent the actual operation conditions of an NPP. In this study, a feed-forward backpropagation artificial neural network (ANN) model was trained to simulate the interaction between the reactor core and the primary and secondary coolant systems in a pressurized water reactor. The transients used for model training included perturbations in reactivity, steam valve coefficient, reactor core inlet temperature, and steam generator inlet temperature. Uncertainties of the plant physical parameters and operating conditions were also incorporated in these transients. Eight training functions were adopted during the training stage to develop the most efficient network. The developed ANN model predictions were subsequently tested successfully considering different new transients. Overall, through prompt prediction of NPP behavior under different transients, the study aims at demonstrating the potential of artificial intelligence to empower rapid emergency response planning and risk mitigation strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.413-435
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• 2018

Although the accuracy of handwritten character recognition based on deep networks has been shown to be superior to that of the traditional method, the use of an overly deep network significantly increases time consumption during parameter training. For this reason, this paper took the training time and recognition accuracy into consideration and proposed a novel handwritten character recognition algorithm with newly designed network structure, which is based on an extended nonlinear kernel residual network. This network is a non-extremely deep network, and its main design is as follows:(1) Design of an unsupervised apriori algorithm for intra-class clustering, making the subsequent network training more pertinent; (2) presentation of an intermediate convolution model with a pre-processed width level of 2;(3) presentation of a composite residual structure that designs a multi-level quick link; and (4) addition of a Dropout layer after the parameter optimization. The algorithm shows superior results on MNIST and SVHN dataset, which are two character benchmark recognition datasets, and achieves better recognition accuracy and higher recognition efficiency than other deep structures with the same number of layers.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1220-1220
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• 2022

Constructionarium Ltd is a not-for-profit organisation which delivers a residential, experiential, immersive learning opportunity to university students from across the built environment education sector. Since 2002, the Constructionarium education model has been available to students in engineering, construction management and architecture at a purpose built, 19-acre multi-disciplinary training facility in Bircham Newton, England simulating real site life and reflecting site processes, practices and health and safety requirements. The unique approach of Constructionarium puts experiential learning and sustainability at the heart of everything. In a week, students develop a practical understanding of the construction process, develop transferable skills, build a team and are exposed to the latest in sustainable technologies. Experiential learning is what differentiates a Constructionarium project from regular field trips or site visits. At Constructionarium the focus is on learning by participation rather than learning through theory or watching a demonstration. The projects cannot be replicated in a classroom or on campus. Using the hands-on construction of scaled down versions of iconic structures from around the world, students learn that it requires the involvement of the whole construction team to successfully complete their project. Skills such as communication, planning, budgeting, time management and decision making are woven into a week-long interrelationship with industry professionals, academic mentors and trades workers. Working together to enhance transferable skills brings the educational environment into the reality of completing an actual construction project handled by the students. Constructionarium has used this transformational learning model to educate thousands of students from all over the United Kingdom, Europe and Asia. Texas A&M University in the United States has sent multiple teams of students from its Department of Construction Science every operational year since 2016.