• Journal of the Korea Convergence Society
• /
• v.6 no.5
• /
• pp.63-75
• /
• 2015

The aim of this study was to know the experience of emergency nurses on hospital violence and violence's effect on nurses via convergent approach of phenomenological methodology to be known for a good method to study alive human's experience. This study is a qualitative study converged the phenomenological methods and 5 emergency nurses participated in an in-depth interview. From the transcript, 41 significant statements, 17 clusters of theme, 6 categories were extracted. The extracted categories are violence's background, emotional response, physical response, social response, passive coping and active coping. The hospital violence's negative effect on emergency nurses occurred in various sides and the countermeasure is required to prevent violence from hospital setting anymore.

• ETRI Journal
• /
• v.36 no.6
• /
• pp.913-923
• /
• 2014

This paper proposes a global mapping algorithm for multiple robots from an omnidirectional-vision simultaneous localization and mapping (SLAM) approach based on an object extraction method using Lucas-Kanade optical flow motion detection and images obtained through fisheye lenses mounted on robots. The multi-robot mapping algorithm draws a global map by using map data obtained from all of the individual robots. Global mapping takes a long time to process because it exchanges map data from individual robots while searching all areas. An omnidirectional image sensor has many advantages for object detection and mapping because it can measure all information around a robot simultaneously. The process calculations of the correction algorithm are improved over existing methods by correcting only the object's feature points. The proposed algorithm has two steps: first, a local map is created based on an omnidirectional-vision SLAM approach for individual robots. Second, a global map is generated by merging individual maps from multiple robots. The reliability of the proposed mapping algorithm is verified through a comparison of maps based on the proposed algorithm and real maps.

• Journal of Information Technology Services
• /
• v.9 no.4
• /
• pp.243-259
• /
• 2010

It is expected that the next generation mobile communication system will be a service-driven developed system capable to realize the human-centric mobile convergence services. and it is different from the technology-driven development approach of the second and the third generation mobile communication systems. As a preliminary research work on such service-driven system development approach for the next generation mobile communication system. we developed the scenario based service analysis process (2SAP) framework to derive core service technologies and functionalities. In this paper. we propose the next generation mobile convergence service business model creation methodology based on research results of the 2SAP framework. To achieve this goal, we first establish a service model contains several components such as infrastructures. operations. and provision of services that are indispensible for providing next generation mobile services. Then, the next generation mobile services and its corresponding business models can be created by adding service and value flows to the developed service model after defining necessary components of business model including actors, their relationships, and roles.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.50 no.3
• /
• pp.120-130
• /
• 2001

We present an approach to parallelizing optimal power flow that is suitable for distributed implementation and is applicable to very large interconnected power systems. This approach can be used by utilities to optimize economy interchange without disclosing details of their operating costs to competitors. Recently, it is becoming necessary to incorporate contingency constraints into the formulation, and more rapid updates of telemetered data and faster solution time are becoming important to better track changes in the system. This concern led to a research to develop an efficient algorithm for a distributed optimal power flow based on the Auxiliary Problem Principle and to study the convergence rate improvement of the distributed algorithm. The objective of this paper is to find a set of control parameters with which the Auxiliary Problem Principle (Algorithm - APP) can be best implemented in solving optimal power flow problems. We employed several IEEE Reliability Test Systems, and Korea Power System to demonstrate the alternative parameter sets.

• ETRI Journal
• /
• v.41 no.2
• /
• pp.242-253
• /
• 2019

We devise a layer-wise hint training method to improve the existing hint-based knowledge distillation (KD) training approach, which is employed for knowledge transfer in a teacher-student framework using a residual network (ResNet). To achieve this objective, the proposed method first iteratively trains the student ResNet and incrementally employs hint-based information extracted from the pretrained teacher ResNet containing several hint and guided layers. Next, typical softening factor-based KD training is performed using the previously estimated hint-based information. We compare the recognition accuracy of the proposed approach with that of KD training without hints, hint-based KD training, and ResNet-based layer-wise pretraining using reliable datasets, including CIFAR-10, CIFAR-100, and MNIST. When using the selected multiple hint-based information items and their layer-wise transfer in the proposed method, the trained student ResNet more accurately reflects the pretrained teacher ResNet's rich information than the baseline training methods, for all the benchmark datasets we consider in this study.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.3
• /
• pp.874-890
• /
• 2021

With the widespread deployment of the fifth-generation (5G) communication networks, various real-time applications are rapidly increasing and generating massive traffic on backhaul network environments. In this scenario, network congestion will occur when the communication and computation resources exceed the maximum available capacity, which severely degrades the network performance. To alleviate this problem, this paper proposed an intelligent resource allocation (IRA) to integrate with the extant resource adjustment (ERA) approach mainly based on the convergence of support vector machine (SVM) algorithm, software-defined networking (SDN), and mobile edge computing (MEC) paradigms. The proposed scheme acquires predictable schedules to adapt the downlink (DL) transmission towards off-peak hour intervals as a predominant priority. Accordingly, the peak hour bandwidth resources for serving real-time uplink (UL) transmission enlarge its capacity for a variety of mission-critical applications. Furthermore, to advance and boost gateway computation resources, MEC servers are implemented and integrated with the proposed scheme in this study. In the conclusive simulation results, the performance evaluation analyzes and compares the proposed scheme with the conventional approach over a variety of QoS metrics including network delay, jitter, packet drop ratio, packet delivery ratio, and throughput.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.704-707
• /
• 2021

Pigs' aggressive behavior represents one of the common issues that occur inside pigpens and which harm pigs' health and welfare, resulting in a financial burden to farmers. Continuously monitoring several pigs for 24 hours to identify those behaviors manually is a very difficult task for pig caretakers. In this study, we propose a lightweight video-based approach for monitoring pigs' aggressive behavior that can be implemented even in small-scale farms. The proposed system receives sequences of frames extracted from an RGB video stream containing pigs and uses MnasNet with a DM value of 0.5 to extract image features from pigs' ROI identified by predefined annotations. These extracted features are then forwarded to a lightweight LSTM to learn temporal features and perform behavior recognition. The experimental results show that our proposed model achieved 0.92 in recall and F1-score with an execution time of 118.16 ms/sequence.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.2
• /
• pp.236-243
• /
• 2005

This paper presents a new stochastic approach for solving combinatorial optimization problems by using a new selection method, i.e. SA-selection, in genetic algorithm (GA). This approach combines GA with simulated annealing (SA) to improve the performance of GA. GA and SA have complementary strengths and weaknesses. While GA explores the search space by means of population of search points, it suffers from poor convergence properties. SA, by contrast, has good convergence properties, but it cannot explore the search space by means of population. However, SA does employ a completely local selection strategy where the current candidate and the new modification are evaluated and compared. To verify the effectiveness of the proposed method, the optimization of a fuzzy controller for balancing an inverted pendulum on a cart is considered.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
• /
• 2005.11a
• /
• pp.153-156
• /
• 2005

This paper concerns an efficient iterative approach for eliminating coherent interference signals in linearly constrained adaptive arrays. The Alternate Mainbeam Nulling Algorithm[1] is implemented iteratively to find an optimum weight vector. The convergence parameters in the unit gain and null constraints are calculated using steepest descent method with gradient estimation. The nulling performance of the proposed method is compared with that of conventional ones. It is shown that the proposed method performs better than conventional ones when the power of the coherent signals is large compared with a desired signal. Also, it performs consistently well for more number of interferences.

• ETRI Journal
• /
• v.30 no.2
• /
• pp.338-340
• /
• 2008

In this letter, we propose a novel approach to detecting and tracking apartment buildings for the development of a video-based navigation system that provides augmented reality representation of guidance information on live video sequences. For this, we propose a building detector and tracker. The detector is based on the AdaBoost classifier followed by hierarchical clustering. The classifier uses modified Haar-like features as the primitives. The tracker is a motion-adjusted tracker based on pyramid implementation of the Lukas-Kanade tracker, which periodically confirms and consistently adjusts the tracking region. Experiments show that the proposed approach yields robust and reliable results and is far superior to conventional approaches.