Improvement in Calculating Engineer Standard Wage Rate and Its Appropriate Level Computation (엔지니어링 노임단가 산출기준 개선방안과 적정 노임단가 추정)
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- KSCE Journal of Civil and Environmental Engineering Research
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- v.42 no.6
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- pp.853-860
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- 2022
The purpose of this study is to suggest an improvement plan for the calculation method of the engineer standard wage rate (ESWR) and to compute a reasonable ESWR. To this end, an adequacy review of theESWR calculation criteria was conducted along with an extensive engineering industry survey. The survey results were analyzed using an effective response sample of 748 companies out of 1,000 survey samples extracted by stratifying the 5,879 survey population. The main results were as follows. ①When calculating the engineering service fee, the prime contractor's engineer wage is suitable for the ESWR. The ESWR can be estimated by the formula 'average wage÷[1-proportion of subcontract orders×(1-subcontract rate)].' ② The field survey showed that the number of monthly working days was 20.35-20.54 days at 99 % confidence interval, which was significantly different from the current standard (22 days). In addition, as a result of a legal review of the ESWR criteria, it was found that the number of working days should be calculated in accordance with the Labor Standards Act after 2022. ③ Applying government guidelines, the time difference between the wage survey and the ESWR application can be corrected by the past ESWR increase rate for a specific period. ④ Using modeling based on the analysis above, the current ESWR was 13.5-14.5 % lower than the appropriate level. A lower ESWR was driven by the non-reflection of subcontract structure (4.1 %), overestimation of monthly work days (6.8-7.8 %), and application of past wage (2.6 %). The proposed model is expected to be widely used in policy making, as it can provide a useful framework for calculating the standard wage rate in similar industries as well as calculating appropriate engineering fees.
The results of the study on the consumptine use of irrigated water in paddy fields during the growing season of rice plants are summarized as follows. 1. Transpiration and evaporation from water surface. 1) Amount of transpiration of rice plant increases gradually after transplantation and suddenly increases in the head swelling period and reaches the peak between the end of the head swelling poriod and early period of heading and flowering. (the sixth period for early maturing variety, the seventh period for medium or late maturing varieties), then it decreases gradually after that, for early, medium and late maturing varieties. 2) In the transpiration of rice plants there is hardly any difference among varieties up to the fifth period, but the early maturing variety is the most vigorous in the sixth period, and the late maturing variety is more vigorous than others continuously after the seventh period. 3) The amount of transpiration of the sixth period for early maturing variety of the seventh period for medium and late maturing variety in which transpiration is the most vigorous, is 15% or 16% of the total amount of transpiration through all periods. 4) Transpiration of rice plants must be determined by using transpiration intensity as the standard coefficient of computation of amount of transpiration, because it originates in the physiological action.(Table 7) 5) Transpiration ratio of rice plants is approximately 450 to 480 6) Equations which are able to compute amount of transpiration of each variety up th the heading-flowering peried, in which the amount of transpiration of rice plants is the maximum in this study are as follows: Early maturing variety ; Y=0.658+1.088X Medium maturing variety ; Y=0.780+1.050X Late maturing variety ; Y=0.646+1.091X Y=amount of transpiration ; X=number of period. 7) As we know from figure 1 and 2, correlation between the amount evaporation from water surface in paddy fields and amount of transpiration shows high negative. 8) It is possible to calculate the amount of evaporation from the water surface in the paddy field for varieties used in this study on the base of ratio of it to amount of evaporation by atmometer(Table 11) and Table 10. Also the amount of evaporation from the water surface in the paddy field is to be computed by the following equations until the period in which it is the minimum quantity the sixth period for early maturing variety and the seventh period for medium or late maturing varieties. Early maturing variety ; Y=4.67-0.58X Medium maturing variety ; Y=4.70-0.59X Late maturing variety ; Y=4.71-0.59X Y=amount of evaporation from water surface in the paddy field X=number of period. 9) Changes in the amount of evapo-transpiration of each growing period have the same tendency as transpiration, and the maximum quantity of early maturing variety is in the sixth period and medium or late maturing varieties are in the seventh period. 10) The amount of evapo-transpiration can be calculated on the base of the evapo-transpiration intensity (Table 14) and Tablet 12, for varieties used in this study. Also, it is possible to compute it according to the following equations with in the period of maximum quantity. Early maturing variety ; Y=5.36+0.503X Medium maturing variety ; Y=5.41+0.456X Late maturing variety ; Y=5.80+0.494X Y=amount of evapo-transpiration. X=number of period. 11) Ratios of the total amount of evapo-transpiration to the total amount of evaporation by atmometer through all growing periods, are 1.23 for early maturing variety, 1.25 for medium maturing variety, 1.27 for late maturing variety, respectively. 12) Only air temperature shows high correlation in relation between amount of evapo-transpiration and climatic conditions from the viewpoint of Korean climatic conditions through all growing periods of rice plants. 2. Amount of percolation 1) The amount of percolation for computation of planning water requirment ought to depend on water holding dates. 3. Available rainfall 1) The available rainfall and its coefficient of each period during the growing season of paddy fields are shown in Table 8. 2) The ratio (available coefficient) of available rainfall to the amount of rainfall during the growing season of paddy fields seems to be from 65% to 75% as the standard in Korea. 3) Available rainfall during the growing season of paddy fields in the common year is estimated to be about 550 millimeters. 4. Effects to be influenced upon percolation by transpiration of rice plants. 1) The stronger absorbtive action is, the more the amount of percolation decreases, because absorbtive action of rice plant roots influence upon percolation(Table 21, Table 22) 2) In case of planting of rice plants, there are several entirely different changes in the amount of percolation in the forenoon, at night and in the afternoon during the growing season, that is, is the morning and at night, the amount of percolation increases gradually after transplantation to the peak in the end of July or the early part of August (wast or soil temperature is the highest), and it decreases gradually after that, neverthless, in the afternoon, it decreases gradually after transplantation to be at the minimum in the middle of August, and it increases gradually after that. 3) In spite of the increasing amount of transpiration, the amount of daytime percolation decreases gadually after transplantation and appears to suddenly decrease about head swelling dates or heading-flowering period, but it begins to increase suddenly at the end of August again. 4) Changs of amount of percolation during all growing periods show some variable phenomena, that is, amount of percolation decreases after the end of July, and it increases in end August again, also it decreases after that once more. This phenomena may be influenced complexly from water or soil temperature(night time and forenoon) as absorbtive action of rice plant roots. 5) Correlation between the amount of daytime percolation and the amount of transpiration shows high negative, amount of night percolation is influenced by water or soil temperature, but there is little no influence by transpiration. It is estimated that the amount of a daily percolation is more influenced by of other causes than transpiration. 6) Correlation between the amount of night percoe, lation and water or soil temp tureshows high positive, but there is not any correlation between the amount of forenoon percolation or afternoon percolation and water of soil temperature. 7) There is high positive correlation which is r=+0.8382 between the amount of daily percolation of planting pot of rice plant and amount and amount of daily percolation of non-planting pot. 8) The total amount of percolation through all growin. periods of rice plants may be influenced more from specific permeability of soil, water of soil temperature, and otheres than transpiration of rice plants.
Convolutional Neural Network (ConvNet) is one class of the powerful Deep Neural Network that can analyze and learn hierarchies of visual features. Originally, first neural network (Neocognitron) was introduced in the 80s. At that time, the neural network was not broadly used in both industry and academic field by cause of large-scale dataset shortage and low computational power. However, after a few decades later in 2012, Krizhevsky made a breakthrough on ILSVRC-12 visual recognition competition using Convolutional Neural Network. That breakthrough revived people interest in the neural network. The success of Convolutional Neural Network is achieved with two main factors. First of them is the emergence of advanced hardware (GPUs) for sufficient parallel computation. Second is the availability of large-scale datasets such as ImageNet (ILSVRC) dataset for training. Unfortunately, many new domains are bottlenecked by these factors. For most domains, it is difficult and requires lots of effort to gather large-scale dataset to train a ConvNet. Moreover, even if we have a large-scale dataset, training ConvNet from scratch is required expensive resource and time-consuming. These two obstacles can be solved by using transfer learning. Transfer learning is a method for transferring the knowledge from a source domain to new domain. There are two major Transfer learning cases. First one is ConvNet as fixed feature extractor, and the second one is Fine-tune the ConvNet on a new dataset. In the first case, using pre-trained ConvNet (such as on ImageNet) to compute feed-forward activations of the image into the ConvNet and extract activation features from specific layers. In the second case, replacing and retraining the ConvNet classifier on the new dataset, then fine-tune the weights of the pre-trained network with the backpropagation. In this paper, we focus on using multiple ConvNet layers as a fixed feature extractor only. However, applying features with high dimensional complexity that is directly extracted from multiple ConvNet layers is still a challenging problem. We observe that features extracted from multiple ConvNet layers address the different characteristics of the image which means better representation could be obtained by finding the optimal combination of multiple ConvNet layers. Based on that observation, we propose to employ multiple ConvNet layer representations for transfer learning instead of a single ConvNet layer representation. Overall, our primary pipeline has three steps. Firstly, images from target task are given as input to ConvNet, then that image will be feed-forwarded into pre-trained AlexNet, and the activation features from three fully connected convolutional layers are extracted. Secondly, activation features of three ConvNet layers are concatenated to obtain multiple ConvNet layers representation because it will gain more information about an image. When three fully connected layer features concatenated, the occurring image representation would have 9192 (4096+4096+1000) dimension features. However, features extracted from multiple ConvNet layers are redundant and noisy since they are extracted from the same ConvNet. Thus, a third step, we will use Principal Component Analysis (PCA) to select salient features before the training phase. When salient features are obtained, the classifier can classify image more accurately, and the performance of transfer learning can be improved. To evaluate proposed method, experiments are conducted in three standard datasets (Caltech-256, VOC07, and SUN397) to compare multiple ConvNet layer representations against single ConvNet layer representation by using PCA for feature selection and dimension reduction. Our experiments demonstrated the importance of feature selection for multiple ConvNet layer representation. Moreover, our proposed approach achieved 75.6% accuracy compared to 73.9% accuracy achieved by FC7 layer on the Caltech-256 dataset, 73.1% accuracy compared to 69.2% accuracy achieved by FC8 layer on the VOC07 dataset, 52.2% accuracy compared to 48.7% accuracy achieved by FC7 layer on the SUN397 dataset. We also showed that our proposed approach achieved superior performance, 2.8%, 2.1% and 3.1% accuracy improvement on Caltech-256, VOC07, and SUN397 dataset respectively compare to existing work.
The duty shifts of hospital nurses not only affect nurses' physical and mental health but also present various personnel management problems which often result in high turnover rates. In this context a study was carried out from October to November 1995 for a period of two months to find out the status of hospital nurses' duty shift patterns, and preferred duty hours and fixed duty shifts. The study population was 867 RNs working in five general hospitals located in Seoul and its vicinity. The questionnaire developed by the writer was used for data collection. The response rate was 85.9 percent or 745 returns. The SAS program was used for data analysis with the computation of frequencies, percentages and Chi square test. The findings of the study are as follows: 1. General characteristics of the study population: 56 percent of respondents was (25 years group and 76.5 percent were "single": the predominant proportion of respondents was junior nursing college graduates(92.2%) and have less than 5 years nursing experience in hospitals(65.5%). For their future working plan in nursing profession, nearly 50% responded as uncertain The reasons given for their career plan was predominantly 'personal growth and development' rather than financial reasons. 2. The interval for rotations of duty stations was found to be mostly irregular(56.4%) while others reported as weekly(16.1%), monthly(12.9%), and fixed terms(4.6%). 3. The main problems related to duty shifts particularly the evening and night duty nurses reported were "not enough time for the family, " "afraid of security problems after the work when returning home late at night." and "lack of leisure time". "problems in physical and physiological adjustment." "problems in family life." "lack of time for interactions with fellow nurses" etc. 4. The forty percent of respondents reported to have '1-2 times' of duty shift rotations while all others reported that '0 time'. '2-3 times'. 'more than 3 times' etc. which suggest the irregularity in duty shift rotations. 5. The majority(62.8%) of study population found to favor the rotating system of duty stations. The reasons for favoring the rotation system were: the opportunity for "learning new things and personal development." "better human relations are possible. "better understanding in various duty stations." "changes in monotonous routine job" etc. The proportion of those disfavor the rotating 'system was 34.7 percent. giving the reasons of"it impedes development of specialization." "poor job performances." "stress factors" etc. Furthermore. respondents made the following comments in relation to the rotation of duty stations: the nurses should be given the opportunity to participate in the. decision making process: personal interest and aptitudes should be considered: regular intervals for the rotations or it should be planned in advance. etc. 6. For the future career plan. the older. married group with longer nursing experiences appeared to think the nursing as their lifetime career more likely than the younger. single group with shorter nursing experiences (