• Journal of Korean Society of Steel Construction
• /
• v.23 no.4
• /
• pp.503-514
• /
• 2011

In this research, the seismic connection details for two concrete-filled U-shape steel beam-to-H columns were proposed and cyclically tested under a full-scale cruciform configuration. The key connecting components included the U-shape steel section (450 and 550 mm deep for specimens A and B, respectively), a concrete floor slab with a ribbed deck (165 mm deep for both specimens), welded couplers and rebars for negative moment transfer, and shear studs for full composite action and strengthening plates. Considering the unique constructional nature of the proposed connection, the critical limit states, such as the weld fracture, anchorage failure of the welded coupler, local buckling, concrete crushing, and rebar buckling, were carefully addressed in the specimen design. The test results showed that the connection details and design methods proposed in this study can well control the critical limit states mentioned above. Especially, the proposed connection according to the strengthening strategy successfully pushed the plastic hinge to the tip of the strengthened zone, as intended in the design, and was very effective in protecting the more vulnerable beam-to-column welded joint. The maximum story drift capacities of 6.0 and 6.8% radians were achieved in specimens A and B, respectively, thus far exceeding the minimumlimit of 4% radians required of special moment frames. Low-cycle fatigue fracture across the beam bottom flange at a 6% drift level was the final failure mode of specimen A. Specimen B failed through the fracture of the top splice plate of the bolted splice at a very high drift ratio of 8.0% radian.

• Sleep Medicine and Psychophysiology
• /
• v.7 no.2
• /
• pp.96-101
• /
• 2000

Objectives: Through comparing sleep variables and autonomic activities among three shifts in shift workers, the authors intended to clarify which shift is most tolerable and to identify the characteristics of their psychological and physical problems. This study is also expected to help shift workers to adapt themselves to their work more effectively. Methods: Fifty one shift workers took part in this study. They were working in a rapidly rotating system in which they worked for 3 days in one shift with one day off between each shift. Based on a sleep diary, sleep latency (SL), sleep period time (SPT), and number of wake after sleep onset (NWASO) were estimated and compared among the three shifts. In assessing sleepiness, Epworth sleepiness scale (ESS) and visual analogue scale (VAS) were used. To evaluate mood states among the three shifts, profile of mood states (POMS) was administered. Heart rate variability (HRV), and the level of adrenaline and noradrenaline were measured to assess autonomic activities. HRV included low frequency power (LF), high frequency power (HF), and LF/HF. Results: SPT was significantly lengthened during the evening shift and SL was shortened during the night shift. The workers showed a drop in alertness at wake-up during morning shift and a drop in alertness at work during night shift. During night shift the subjects complained of physical fatigue and cognitive decline. Comparison of HRV showed that parasympathetic activity was most prominent during the evening shift. Secretion of adrenaline and noradrenaline decreased during the evening shift, though statistically not significant. Conclusion: We found that the evening shift was most tolerable among the three shifts. It is recommended that morning light exposure be done during the morning shift and nocturnal light exposure during the night shift.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.173-198
• /
• 2020

For a long time, many studies have been conducted on predicting the success of campaigns for customers in academia, and prediction models applying various techniques are still being studied. Recently, as campaign channels have been expanded in various ways due to the rapid revitalization of online, various types of campaigns are being carried out by companies at a level that cannot be compared to the past. However, customers tend to perceive it as spam as the fatigue of campaigns due to duplicate exposure increases. Also, from a corporate standpoint, there is a problem that the effectiveness of the campaign itself is decreasing, such as increasing the cost of investing in the campaign, which leads to the low actual campaign success rate. Accordingly, various studies are ongoing to improve the effectiveness of the campaign in practice. This campaign system has the ultimate purpose to increase the success rate of various campaigns by collecting and analyzing various data related to customers and using them for campaigns. In particular, recent attempts to make various predictions related to the response of campaigns using machine learning have been made. It is very important to select appropriate features due to the various features of campaign data. If all of the input data are used in the process of classifying a large amount of data, it takes a lot of learning time as the classification class expands, so the minimum input data set must be extracted and used from the entire data. In addition, when a trained model is generated by using too many features, prediction accuracy may be degraded due to overfitting or correlation between features. Therefore, in order to improve accuracy, a feature selection technique that removes features close to noise should be applied, and feature selection is a necessary process in order to analyze a high-dimensional data set. Among the greedy algorithms, SFS (Sequential Forward Selection), SBS (Sequential Backward Selection), SFFS (Sequential Floating Forward Selection), etc. are widely used as traditional feature selection techniques. It is also true that if there are many risks and many features, there is a limitation in that the performance for classification prediction is poor and it takes a lot of learning time. Therefore, in this study, we propose an improved feature selection algorithm to enhance the effectiveness of the existing campaign. The purpose of this study is to improve the existing SFFS sequential method in the process of searching for feature subsets that are the basis for improving machine learning model performance using statistical characteristics of the data to be processed in the campaign system. Through this, features that have a lot of influence on performance are first derived, features that have a negative effect are removed, and then the sequential method is applied to increase the efficiency for search performance and to apply an improved algorithm to enable generalized prediction. Through this, it was confirmed that the proposed model showed better search and prediction performance than the traditional greed algorithm. Compared with the original data set, greed algorithm, genetic algorithm (GA), and recursive feature elimination (RFE), the campaign success prediction was higher. In addition, when performing campaign success prediction, the improved feature selection algorithm was found to be helpful in analyzing and interpreting the prediction results by providing the importance of the derived features. This is important features such as age, customer rating, and sales, which were previously known statistically. Unlike the previous campaign planners, features such as the combined product name, average 3-month data consumption rate, and the last 3-month wireless data usage were unexpectedly selected as important features for the campaign response, which they rarely used to select campaign targets. It was confirmed that base attributes can also be very important features depending on the type of campaign. Through this, it is possible to analyze and understand the important characteristics of each campaign type.