Earticle

It is usually that some people who did not plan to buy anything walk into e-mart, but walk out with something in hands. Obviously, these people were stimulated and the result is purchasing something maybe are not really needed. As a consumer, sometimes we are interested in what somebody else is using, and sometimes we cannot help trying a kind of new drink when we found many of people around are drinking it. In this paper, conformity psychology’s effect in the advertisement will be checked in the hope of helping improve the advertisement effectiveness.

The purpose of this study is to find out how to use emotional activity rate in sentence. The aim is to estimate the Emotion-Codon status of emotion-DNA wiggling as well as the sequence of codons in which amino acids of human body are spread. It is to use as if the amino acids are translated from the DNA of the human body, and the emotions that cooperate with each other are diversified and refined. Through these attempts, we anticipate the change of emotions by literary works and present the direction of the synapse working and the direction of the encoding. Therefore, this study analyzed the emotional activity rate of Rated Emotion-Codon, which works as Rated Sijo. At this time, the emotional change in the human body, in which the sentence is more clearly pronounced, is predicted and detected. This result shows how emotions can activate the human body. This state of activation is an indicator of how much the sentence pushes the physiological action of the human body. It is judged that Codon is constructed precisely for the activation of the rated Sijo additive definition by this index. Our result is evidenced by the fact that three sentences of Rated Sijo show an array with the same rating as the Rated Codon's, The first sentence fuses with the sentiment between the second sentence and the third sentence of Rated Sijo. The emotional symbols of the first sentence’s space, which are fused with emotions flowing between the second and third sentence of Rated Sijo, are "A, U, J, L", which are rated-structured in the first base of the Codon table. The emotional symbols of the second sentence’s space, which are fused with emotions flowing between the first and third sentence of Rated Sijo, are "A, U, J, L", which are rated-structured in the second base of the Codon table. The emotional symbols of the third sentence’s space, which are fused with emotions flowing between the first and second sentence of Rated Sijo, are "A, U, J, L", which are rated-structured in the third base of the Codon table. The Rated Sijo, which is resiliently and precisely assigned to the human body's Rated Emotion-Codon, will require a rich experience in our Rated Life.

The research is to identify social networks of problems that have an influence on the quality of ageing people’s lives by using social network analysis, based on the premise that there are differences in networks of ageing problems in urban and rural areas. From analyzing network of ageing people’s problems using NodeXL, vertices in the networks of both urban and rural areas are well- connected. For urban areas, financial poverty is the core problem related to the quality of life. It has direct connections with illness and health, family responsibility, housing, role loss in community, and employment, which have positive or negative interactions with the quality of older people’s lives. For rural areas, on the other hand, role loss in community is the major problem. It has direct connections with the elderly abuse, financial poverty, leisure activity, divorce, isolation and loneliness from society, education, and suicide. As a result, the research shows that the problems of ageing people have strong linkages and interactive effects with a structure of network, and the networks are different depending on types of places for living.

This study investigated the effects of TBL nursing education(for the care of congestive heart failure patients) on self identity and self efficacy among nursing students. A one-group, pre-post design was utilized with 28 nursing students as the participants. The scenario of TBL nursing education was created based on PBL, and consisted of four states(1) Present a problem, (2) Problem Follow-Up Steps,(3) Present the results including presentation, and(4) Problem Conclusion and after resolution. And then In-depth interviews were conducted with volunteers on program experiences. The data were analyzed using descriptive statistics, Pearson's correlation coefficients and paired t-tests and Giorgi phenomenology analysis method was performed. The TBL program was effective in self - efficacy and self - identity (p<0.01) increased significantly after the education intervention and The meaning of the TBL experience is four (meaning that the learners embarrassed TBL, aggressive learning attitude change, effective collaboration for problem solving in a free learning environment, university classes imagined in high school), and 67 sub-components appeared as an element. The TBL program is an effective teaching method for nursing education and it can be used as basic data for the development of nursing education based on this research.

The purpose of this study was to investigate the relationship between self- identity and smart phone addiction among University students. This study used descriptive cross sectional study to analyze the relationship between self-identity and smart phone addiction. Study participants included 357 University students located in C province who selected through volunteered, but 10 missing data were excluded and finally 347 participants completed the study. The study data were collected using self-identity tool made by Park A chung in 1996 which was composed of Independence, Proprioception, Future conviction, Goal orientation, Initiate, and Intimacy and Smart phone addiction tool made by National information society agency. The self-identity’s Cronbach’s αlpha was .937 and Smart phone was .670. All data analyses were performed using SPSS 18.0. Results showed that most students were not addicted(97.1%) in Smart phone using and middle level(73.2%) of self identity. Also, there was a difference in self – identity level according to gender and grade(the ratio of smart phone general users, not at all addicted, was 90.79% for males and 98.53% for females), and there was difference in Smart phone addiction level according to gender and residence type(the ratio of Smart phone general users, not at all addicted, is 99.32% for commute, 97.04% for dormitory, and 89.66% for self governing). Also there were strongly related with self-identity and Smart phone addiction(p < .001). This results reveled that self-identity affect to Smart phone addiction, therefore it is important to raise self-identity to prevent Smart phone addiction for University students.

Currently, falling to industrial field workers is causing serious injuries. Therefore, many researchers are actively studying the fall by using acceleration sensor, gyro sensor, pressure sensor and image information.Also, as the spread of smartphones becomes common, techniques for determining the fall by using an acceleration sensor built in a smartphone are being studied. The proposed method has complexity due to fusion of various sensor data and it is still insufficient to develop practical application. Therefore, in this paper, we use acceleration sensor module built in smartphone to collect acceleration data, propose a simple falling algorithm based on accelerometer sensor data after normalization and preprocessing, and implement an Android based app.

In this paper, we propose algorithm for determining the static and dynamic posture using the acceleration sensor of smartphone. The measured acceleration values are then analyzed according to a preprocessing to the respective axis (X, Y, Z) and posture (standing, sitting, lying) presents static posture determination criterion. The proposed static posture determination condition is used for static posture determination and dynamic posture determination. The dynamic posture is determined by using regression linear equations. In addition, transition state can be grasped by SVM change in dynamic posture determination. Experimental results are presented using data and app. Experiments were performed using data collected from 10 adults.

Recently, there are many companies that use systems based on artificial intelligence. The accuracy of artificial intelligence depends on the amount of learning data and the appropriate algorithm. However, it is not easy to obtain learning data with a large number of entity. Less data set have large generalization errors due to overfitting. In order to minimize this generalization error, this study proposed DGA(Dropout Genetic Algorithm) which can expect relatively high accuracy even though data with a less data set is applied to machine learning based genetic algorithm to deep learning based dropout. The idea of this paper is to determine the active state of the nodes. Using Gradient about loss function, A new fitness function is defined. Proposed Algorithm DGA is supplementing stochastic inconsistency about Dropout. Also DGA solved problem by the complexity of the fitness function and expression range of the model about Genetic Algorithm As a result of experiments using MNIST data proposed algorithm accuracy is 75.3%. Using only Dropout algorithm accuracy is 41.4%. It is shown that DGA is better than using only dropout.

The range of problems that can be handled by the activation of big data and the development of hardware has been rapidly expanded and machine learning such as deep learning has become a very versatile technology. In this paper, mnist data set is used as experimental data, and the Cross Entropy function is used as a loss model for evaluating the efficiency of machine learning, and the value of the loss function in the steepest descent method is We applied the GradientDescentOptimize algorithm to minimize and updated weight and bias via backpropagation. In this way we analyze optimal reliability value corresponding to the number of exercises and optimal reliability value without overfitting. And comparing the overfitting time according to the number of data changes based on the number of training times, when the training frequency was 1110 times, we obtained the result of 92%, which is the optimal reliability value without overfitting.

The Internet of Things(IoT) market is expected to grow from 15.5billion to 75.4 billion by 2015-2025. As the number of IoT devices increases, the amount of data that is sent to the cloud is increasing. Today’s Cloud Computing models are not suited to handle the vast amount of data generated by IoT devices. In this paper, we propose a Cooperation Coordinator System that reduces server load and improved real-time processing capability under specific circumstances by using Band-Cloud. The cooperation coordinator system dynamically forms the cloud when cooperation is needed between mobile devices located near. It is called Band-Cloud. Band-Cloud provides services entrusted by Central Cloud. This paper describes the proposed system and shows the cooperation process using the Android-based mobile devices and Wi-Fi Direct technology. Such a system can be applied to cases where real-time processing is required in a narrow area such as a hospital ward or a school classroom.

Smartphones are fast growing in the IT market and are the most influential devices in our daily life. Smartphones are being studied for their use in body sensor networks with excellent processing power and wireless communication technology. In this paper, we propose a coordinator design that provides data collection, classification, and display using based on Android-smartphone in multiple sensor nodes. The coordinator collects data of sensor nodes that measure biological patterns using wireless communication technologies such as Bluetooth and NFC. The coordinator constructs a network using a multiple-level scheduling algorithm for efficient data collection at multiple sensor nodes. Also, to support different protocols between heterogeneous sensors, a data sheet recording wireless communication protocol information is used. The designed coordinator used Arduino to test the performance of multiple sensor node environments.