Earticle

Electroencephalogram (EEG) recording provides a new way to support human-machine communication. It gives us an opportunity to analyze the neuro-dynamics of human cognition. Machine learning is a powerful for the EEG classification. In addition, machine learning can compensate for high variability of EEG when analyzing data in real time. However, the optimal EEG electrode location must be prioritized in order to extract the most relevant features from brain wave data. In this paper, we propose an intelligent system model for the extraction of EEG data by training the optimal electrode location of EEG in a specific problem. The proposed system is basically a fuzzy system and uses a neural network structurally. The fuzzy clustering method is used to determine the optimal number of fuzzy rules using the features extracted from the EEG data. The parameters and weight values found in the process of determining the number of rules determined here must be tuned for optimization in the learning process. Genetic algorithms are used to obtain optimized parameters. We present useful results by using optimal rule numbers and non - symmetric membership function using EEG data for four movements with the right arm through various experiments.

The purpose of this study was to investigate the disaster preparedness and professional quality of life among nurses in emergency departments. The participants in this study were 56 nurses in emergency departments in two cities. The data were collected by questionnaires survey from January to August 2016. All statistical analyses were performed using SPSS ver. 23.0 program. Participants’ disaster preparedness was different according to position (t=-2.32, p=.004), type of working (t=-2.32, p=.004), and frequency of traumatic events (F=5.26, p=.009). There were significant differences by gender (t=2.88, p=.006), desire for continuous work in the emergency room (t=2.95, p=.005), and job satisfaction (F=10.81, p < .001) in compassion satisfaction. Burnout was different according to gender (t=-2.05, p=.045), choice of an emergency department (t=-2.37, p=.021), desire for continuous work in the emergency room (t=-2.31, p=.025), and job satisfaction (F=11.99, p < .001). Disaster preparedness and compassion satisfaction had positively significant correlations. Compassion satisfaction of nurses in emergency department was an important variable that should be considered to improve their disaster preparedness.

The mobile agent paradigm can be exploited in a variety of ways, ranging from low-level system administration tasks to middle ware to user-level applications. Mobile agents can be useful in building middle-ware services such as active mail systems, distributed collaboration systems, etc. An active mail message is a program that interacts with its recipient using a multimedia interface, and adapts the interaction session based on the recipient’s responses. The mobile agent paradigm is well suitable to this type of application, since it can carry a sender-defined session protocol along with the multimedia message. Mobile agent communication is possible via method invocation on virtual references. Agents can make synchronous, one-way, or future-reply type invocations. Multicasting is possible, since agents can be aggregated hierarchically into groups. A simple check-pointing facility has also been implemented. Another proposed solution is to use multi agent computer systems to access, filter, evaluate, and integrate this information. We will present the overall architectural framework, our agent design commitments, and agent architecture to enable the above characteristics. Besides, the each information needed a mobile agent system such as text, graphic, image, audio and video etc, constructed a great capacity multimedia database system. However, they have problems in establishing connections over multiple subnetworks, such as no end-to-end connections, transmission delay due to ATM address resolution, no QoS protocols. We propose a new connection management scheme in the thesis to improve the connection management involved of mobile agent systems.

Long Term Evolution-Advanced (LTE-A) is the next drive in the broadband mobile communication, which allows operators to improve networks performance and service capabilities. LTE-A targets the peak data rates of 1Gbps in the downlink and 500Mbps in the uplink. This requirement is only fulfilled by a transmission bandwidth of up to 100MHz. However the accessibility of such large part of the contiguous spectrum is uncommon in practice. Therefore LTE-A uses some new features on top of the existing LTE standards to provide very high data rate transmission. Some of the most significant features introduced in LTE-A are carrier aggregation, heterogeneous network enhancement, coordinated multipoint transmission and reception, enhanced multiple input and multiple output, and development relay nodes with universal frequency reuse. This review paper presents an overview of the above mentioned LTE-A key features and functionalities. Based on this review, in the conclusion we discuss the current technical challenges for future broadband mobile communication systems.

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

A zinc oxide thin film doped with aluminum was deposited by RF sputtering. The deposition temperature of the sputter chamber was kept constant at 350°C, the power supplied to the chamber was 75 W, the oxygen flow rate was changed to 10 sccm and 20 sccm, and the thin film deposition time was changed to 120 and 180 minutes. The structures of the deposited zinc oxide thin films were analyzed by van der Waals method using an X-ray diffractometer. As a result of X-ray diffraction, the amount of oxygen supplied to the zinc oxide thin film increased, and the surface growth of the (002), (400), (110), and (103) planes showed a change with increasing deposition time. Moreover, as the amount of oxygen supplied to the zinc oxide thin film increased, their shape was observed to be coarse, and the thin film’ s particles shape was correlated with the oxygen chemical defect introduced.

This study is based on the purpose of organizing and planning a fundraising for Daum Kakao's ‘storyfunding’ during the recent crowdfunding that uses mobile-based Internet platforms to raise funds. Analysis by project subjects shows that one planner is more efficient if the ability of one planner, such as ‘publishing’, is important. However, there were many group A types of contents that require variety of expertise such as ‘campaign’ and ‘journalism’ after funding is completed. As a result of analysis by planning purposes, there were the most funds available to produce contents. This is a sign that there are many projects that match the basic purpose of story funding. There are also many other purposes for the purpose of raising public opinion for the project or the establishment of monuments, or for the support of people, animals, or animal rights groups. And there is a private equity fund to sell or promote products. Such quantitative research and analysis will accurately determine the current situation of crowdfunding and will provide basic data such as efficient human composition and goals setting to achieve the goals of crowdfunding in the future.