In this paper, to expand the use of standalone street lights, an algorithm for controlling LED energy consumption was proposed. The proposed method uses an LED module of a standalone street light divided into n zones. This is a method of reducing total power consumption by preventing the increase in power consumption due to high heat generation by weakly operating the entire LED according to the illuminance. When the amount of sunlight decreases, the whole LED operates weakly and then brightens, and unlike streetlight that act as streetlight, a method of dividing LEDs by area and limiting the number of LEDs operating according to illumination intensity was proposed. This is a way to use a lot of time with limited battery capacity by reducing the generation of heat that consumes the most power in streetlight. It is also a method of continuously changing the initial usage area to improve the total usage time of the LED substrate. As a result of the experiment, it was found that the proposed method extends the service time because it generates less heat than the conventional stand-alone streetlight.

With the increased attention about healthcare and management of heart diseases, smart healthcare services and related devices have been actively developed recently. R wave is the largest representative signal among ECG signals. R wave detection is very important because it detects QRS pattern and classifies arrhythmia. Several R wave detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications. In this paper, we propose R wave detection based on optimal threshold and arrhythmia classification through QRS pattern considering complexity in smart healthcare environments. For this purpose, we detected R wave from noise-free ECG signal through the preprocessing method. Also, we classify premature ventricular contraction arrhythmia in realtime through QRS pattern. The performance of R wave detection and premature ventricular contraction arrhythmia classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30 premature ventricular contraction. The achieved scores indicate the average of 98.72% in R wave detection and the rate of 94.28% in PVC classification.

The 4th industrial revolution technology has brought many changes not only in human life but also in the industrial field. ICT such as 5G and artificial intelligence and convergence/complex systems such as drones and robots are convenient for humans, and automation of all processes in the industrial field. However, these advanced information and communication technologies also have adverse functions. As advanced ICT was incorporated into military and terrorist weapon systems, more powerful and highly destructive weapon systems began to be developed. In particular, by applying advanced ICT to the production and use of terrorist tools, the terrorist method became more sophisticated and caused more damage. In this paper, we derive advanced ICT that can be abused according to the terror patterns in the 4th industrial revolution era, and present a method that is applied from preparation to execution of terrorism. The abuse of advanced ICT makes terrorism more stealthy and subtle, and increases its destructive power.

Availability is one of the important factor for developing weapon system, because it indicates the mission capability and sustainable life cycle management of weapon system. Recently, as weapon system becomes more advanced and more complex, availability estimation becomes more important to reduce the life cycle cost of weapon system. Modeling and simulation(M&S) is useful method to describe the availability of complex weapon system applying operational environment and maintenance plan. Especially agent based model(ABM) has the strength to describe interactions between agents and environments in complex system. Therefore, this paper presents the availability estimation of weapon system using agent based model. The sample data of part list and reliability analysis is applied to build availability estimation model. User agent and mechanic agent are developed to illustrate the behavior of operation and maintenance using formal specification. Storage reliability is applied to describe failure of each parts. The experimental result shows that this model is quite useful to estimate availability of weapon system. This model may estimate more reasonable availability, if full scale data of weapon system and real field data of operation is provided.

With the development of information technology, the cybersecurity threat continues as digital-related technologies are applied to the instrumentation and control system of nuclear power plants. The malfunction of the instrumentation and control system can cause economic damage due to shutdown, and furthermore, it can lead to national disasters such as radioactive emissions, so countering cybersecurity threats is an important issue. In general, the study of cybersecurity in instrumentation and control systems is concentrated on safety systems, and diverse protection systems perform protection and reactor shutdown functions, leading to reactor shutdown or, in the worst case, non-stop situations. To accurately analyze cyber threats in the diverse protection system, its linked facilities should be analyzed together. Risk analysis should be conducted by analyzing the potential impact of inter-facility cyberattacks on related facilities and the impact of cybersecurity on each configuration module of the diverse protection system. In this paper, we analyze the linkage of the diverse protection system and discuss the cybersecurity linkage threat by analyzing the availability of equipment, the cyber threat impact of the linked equipment, and the configuration module's cybersecurity vulnerability.

Recently, competition in the interior remodeling business is fierce. However, the interior remodeling business is basically B2B, and in this business, sales are basically dictated by the capabilities of salespeople. Therefore, in this study, we intend to develop a model for the core competency of salespeople in the interior remodeling business. To this end, based on the research on the existing competency and competency modeling, the competency model of the salesperson was derived using Dubois' overlay method. A total of 12 core competencies could be defined through the first and second modeling. The subject of this study was focused on LG Hausys, the most representative interior remodeling company in Korea. Based on the core competencies developed in this way, overall sales competencies can be raised through the development of a training course to enhance the sales competencies of salespeople, and a more efficient and objective HR.

The purpose of this study is to analyze the diffusion period and productivity of smartwork in an organization. Firms are increasingly interested in smartwork for non contact work and working from home because of the corona 19. The smartwork is a new technology that changes face-to-face work in an organization. It helps the work of individuals and organizations regardless of time and place. The theoretical background describes the complexity, system thinking, diffusion theory, smart work, organizational resistance, and productivity. This study analyzes the diffusion period and productivity of smart work through business simulation techniques. A simulation study progresses four stages. There are problem definition, hypothesis establishment and causal loop diagram, model construction and verification, and policy evaluation. The simulation models contain an individual's resistance variables organizational investment and leadership variables related to the operation of smartwork. The organizational investment variables include organizational culture, legal system, implement systems and technology investment. The individual resistance variables include cognitive, attitude, structure and technological resistance. The leadership includes leadership interest variables and performance linkage variables. The simulation executed the changes of a people number adopting smart work and the organizational productivity monthly. As a result of the simulation, many organization members have accepted the smart work innovation after 20 months. The organizational productivity through smart work showed very high value after 16 months. In scenario analysis, the individuals' awareness and attitude resistance showed very important variables to productivity and a personal change of smart work adoption. Meanwhile, The organizational investment showed that the high driving-force increased not productivity and the low driving-force showed decreased low productivity. Also, leadership variables showed a powerful driver for changing smart work productivity. The implication of the study has suggested extending complexity, diffusion theory and organization resistance theory based on simulation methods.

For analyzing big data, the social network is increasingly being utilized through relational data, which means the connection characteristics between entities such as people and objects. When the relational data does not exist directly, a social network can be configured by calculating relational data such as attribute similarity from attribute data of entities and using it as links. In this paper, the composition method of the social network using the attribute similarity between entities as a connection relationship, and the clustering method using subgroups for the configured social network are suggested, and the clustering effectiveness of the clustering results is evaluated. The analysis results can vary depending on the type and characteristics of the data to be analyzed, the type of attribute similarity selected, and the criterion value. In addition, the clustering effectiveness may not be consistent depending on the its evaluation method. Therefore, selections and experiments are necessary for better analysis results. Since the analysis results may be different depending on the type and characteristics of the analysis target, options for clustering, etc., there is a limitation. In addition, for performance evaluation of clustering, a study is needed to compare the method of this paper with the conventional method such as k-means.

In this study, we would like to consider the main factors that influence psychological well-being by using social network service. suggest ways to use healthy social network services, and empirically verify how behavioral approaches, in other words self-determination theory, that deals with human psychological needs, affect psychological well-being by self-esteem and satisfaction to comprehensively examine the characteristics of service and user individuals and social influences, etc. First research result, it has been confirmed that the autonomy has a positive effect on self-esteem. Second, it has been confirmed that the autonomy does not have a positive effect on satisfaction. Third, it has been confirmed that the competence has a positive effect on self-esteem. Fourth, it has been confirmed that the competence does not have a positive effect on satisfaction. Fifth, it has been confirmed that the relationship has a positive effect on self-esteem. Sixth, it has been confirmed that the relationship does not have a positive effect on satisfaction. Seventh, it has been confirmed that self-esteem has a positive effect on psychological well-being. Eighth, it has been confirmed that self-esteem has a positive effect on satisfaction. Ninth, it has been confirmed that satisfaction has a positive effect on psychological well-being. These results mean that meeting basic human psychological needs has a significant impact on psychological well-being of the social network service users. In addition, it was confirmed that increasing the desire for autonomy, competence, relationship, which are factors of self-determination, is an important factor to enhance psychological well-being.

The number of freshmen at universities is decreasing due to the recent decline in the school-age population, and the survival of many universities is threatened. To overcome this situation, universities are seeking ways to use big data within the school to improve the quality of education. A study on the prediction of dropout students is a representative case of using big data in universities. The dropout prediction can prepare a systematic management plan by identifying students who will drop out of school due to reasons such as dropout or expulsion. In the case of actual on-campus data, a large number of missing values are included because it is collected and managed by various departments. For this reason, it is necessary to construct a model by effectively reflecting the missing values. In this study, we propose a university student dropout prediction model based on eXtreme Gradient Boost that can be applied to data with many missing values and shows high performance. In order to examine the practical applicability of the proposed model, an experiment was performed using data from C University in Chungbuk. As a result of the experiment, the prediction performance of the proposed model was found to be excellent. The management strategy of dropout students can be established through the prediction results of the model proposed in this paper.