• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.451-465
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• 2017

This paper presents a novel discriminative visual tracking algorithm with an adaptive incremental extreme learning machine. The parameters for an adaptive incremental extreme learning machine are initialized at the first frame with a target that is manually assigned. At each frame, the training samples are collected and random Haar-like features are extracted. The proposed tracker updates the overall output weights for each frame, and the updated tracker is used to estimate the new location of the target in the next frame. The adaptive learning rate for the update of the overall output weights is estimated by using the confidence of the predicted target location at the current frame. Our experimental results indicate that the proposed tracker can manage various difficulties and can achieve better performance than other state-of-the-art trackers.

• Journal of Power Electronics
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• v.2 no.1
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• pp.25-31
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• 2002

Servo drives with microcomputer control provide the possibility of using modern and sophisticated control algorithms. As an additional feature it is possible to implement parallel and/or redundant software and hardware structures to realise safe motion or similar security functions. Unfortunately microcomputer control also has some impact on the behaviour of servo drives. Control algorithm, cycle time, sensors and interface have to be perfectly synchronised. Special control schemes are necessary on the line side (power supply) to meet the actual requirements concerning EMC. This contribution presents experiences and results obtained from a modern digital drive system pointing out the influences of low and high accuracy position sensors and the interdependencies mentioned above.

• Electronics and Telecommunications Trends
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• v.36 no.2
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• pp.1-11
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• 2021

As sensors such as Inertial Measurement Unit, cameras, and Light Detection and Rangings have become cheaper and smaller, research has been actively conducted to implement functions automating micro aerial vehicles such as multirotor type drones. This would fully enable the autonomous flight of drones in the real world without human intervention. In this article, we present a survey of state-of-the-art development on autonomous drones. To build an autonomous drone, the essential components can be classified into pose estimation, environmental perception, and obstacle-free trajectory generation. To describe the trend, we selected three leading research groups-University of Pennsylvania, ETH Zurich, and Carnegie Mellon University-which have demonstrated impressive experiment results on automating drones using their estimation, perception, and trajectory generation techniques. For each group, we summarize the core of their algorithm and describe how they implemented those in such small-sized drones. Finally, we present our up to date research status on developing an autonomous drone.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.1
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• pp.78-83
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• 1997

The paper presents the experimental analysis of a Stewart platform-based force-torque senor. The closed-form solution of forward kinematics of the Stewart platform is derived approximately by way of a linearization technique, and the solution is used in the force analysis of the force-torque sensor. An exper- mental studies show that the proposed method including gravity compensation algorithm is valid for Stew- art platform-based force-torque sensors. The performance of the developed force-torque sensor is evaluated in view of accuracy and linearity in measurements.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.297-299
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• 2021

Text classification is one of the popular tasks in Natural Language Processing (NLP) used to classify text or document applications such as sentiment analysis and email filtering. Nowadays, state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms are the core engine used to perform these classification tasks with high accuracy, and they show satisfying results. This paper conducts a benchmarking performance's analysis of multiple SOTA algorithms on the first known labeled Korean voice phishing dataset called KorCCVi. Experimental results reveal performed on a test set of 366 samples reveal which algorithm performs the best considering the training time and metrics such as accuracy and F1 score.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.58-63
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• 2017

Emergencies and disasters can happen any time without any warning, and things can change and escalate very quickly, and often it is swift and decisive actions that make all the difference. It is a responsibility of the building facility management to ensure that a proven evacuation plan in place to cover various worst scenario to handled automatically inside the facility. To mapping out optimal safe escape routes is a straightforward undertaking, but does not necessarily guarantee residents the highest level of protection. The emergency evacuation navigation approach is a state-of-the-art that designed to evacuate human livings during an emergencies based on real-time decisions using live sensory data with pre-defined optimum path finding algorithm. The poor decision on causalities and guidance may apparently end the evacuation process and cannot then be remedied. This paper propose a cloud connected emergency evacuation system model to react dynamically to changes in the environment in emergency for safest emergency evacuation using IoT based emergency exit sign system. In the previous researches shows that the performance of optimal routing algorithms for evacuation purposes are more sensitive to the initial distribution of evacuees, the occupancy levels, and the type and level of emergency situations. The heuristic-based evacuees routing algorithms have a problem with the choice of certain parameters which causes evacuation process in real-time. Therefore, this paper proposes an evacuee routing algorithm that optimizes evacuation by making using high computational power of cloud servers. The proposed algorithm is evaluated via a cloud-based simulator with different "simulated casualties" are then re-routed using a Dijkstra's algorithm to obtain new safe emergency evacuation paths against guiding evacuees with a predetermined routing algorithm for them to emergency exits. The performance of proposed approach can be iterated as long as corrective action is still possible and give safe evacuation paths and dynamically configure the emergency exit signs to react for real-time instantaneous safe evacuation guidance.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.24 no.2
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• pp.219-228
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• 2014

Objectives: The major objective of this study was to develop a tier 2 exposure model combining tier 1 exposure model estimates and worker monitoring data and suggesting narrower exposure ranges than tier 1 results. Methods: Bayesian statistics were used to develop a tier 2 exposure model as was done for the European Union (EU) tier 2 exposure models, for example Advanced REACH Tools (ART) and Stoffenmanager. Bayesian statistics required a prior and data to calculate the posterior results. In this model, tier 1 estimated serving as a prior and worker exposure monitoring data at the worksite of interest were entered as data. The calculation of Bayesian statistics requires integration over a range, which were performed using a Riemann sum algorithm. From the calculated exposure estimates, 95% range was extracted. These algorithm have been realized on Excel spreadsheet for convenience and easy access. Some fail-proof features such as locking the spreadsheet were added in order to prevent errors or miscalculations derived from careless usage of the file. Results: The tier 2 exposure model was successfully built on a separate Excel spreadsheet in the same file containing tier 1 exposure model. To utilize the model, exposure range needs to be estimated from tier 1 model and worker monitoring data, at least one input are required. Conclusions: The developed tier 2 exposure model can help industrial hygienists obtain a narrow range of worker exposure level to a chemical by reflecting a certain set of job characteristics.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.913-919
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• 2016

In this paper, we propose the visual tracking algorithm that takes advantage of multiple random walkers. We first show the tracking method based on support vector machine as [1] and suggest a method that suppresses feature vectors extracted from backgrounds while preserve features vectors from foregrounds. We also show how to discriminate between foregrounds and backgrounds. Learned by reducing influences of backgrounds, support vector machine can clearly distinguish foregrounds and backgrounds from the image whose target objects are similar to backgrounds and occluded by another object. Thus, the algorithm can track target objects well. Furthermore, we introduce a simple method improving tracking speed. Finally, experiments validate that proposed algorithm yield better performance than the state-of-the-art trackers on the widely-used benchmark dataset with high speed.

• Structural Engineering and Mechanics
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• v.85 no.1
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• pp.119-133
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• 2023

Impact event is the key factor influencing the operational state of the mechanical equipment. Additionally, nonlinear factors existing in the complex mechanical equipment which are currently attracting more and more attention. Therefore, this paper proposes a novel hybrid-separate identification strategy to solve the force identification problem of the nonlinear structure under impact excitation. The 'hybrid' means that the identification strategy contains both l1-norm (sparse) and l2-norm regularization methods. The 'separate' means that the nonlinear response part only generated by nonlinear force needs to be separated from measured response. First, the state-of-the-art two-step iterative shrinkage/thresholding (TwIST) algorithm and sparse representation with the cubic B-spline function are developed to solve established normalized sparse regularization model to identify the accurate impact force and accurate peak value of the nonlinear force. Then, the identified impact force is substituted into the nonlinear response separation equation to obtain the nonlinear response part. Finally, a reduced transfer equation is established and solved by the classical Tikhonove regularization method to obtain the wave profile (variation trend) of the nonlinear force. Numerical and experimental identification results demonstrate that the novel hybrid-separate strategy can accurately and efficiently obtain the nonlinear force and impact force for the nonlinear structure.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.97-103
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• 2018

In oriental paintings, there is Luo-kuan that expressed in a single picture by compressing the artist's information. Such Luo-kuan includes various information such as the title of the work or the name of the artist. Therefore, information about Luo-kuan is considered important to those who collect or enjoy oriental paintings. However, most of the letters in the Luo-kuan are difficult kanji, kanzai, or various shapes, so it is difficult for the ordinary people to interpret. In this paper, we developed an Luo-kuan search application to easily check the information of the Luo-kuan. The application uses a search algorithm that analyzes the captured Luo-kuan image and sends it to the server to output information about the Luo-kuan candidates that are most similar to the Luo-kuan images taken from the database in the server. We also compared and analyzed the accuracy of the algorithm based on 170 Luo-kuan data in order to find out the ranking of the Luo-kuan that matched the Luo-kuan among the candidates. Accuracy Analysis Experimental Results The accuracy of the search algorithm of this application is confirmed to be about 90%, and it is anticipated that it will be possible to develop a platform to automatically analyze and search images in a big data environment by supplementing the optimizing algorithm and multi-threading algorithm.