Investigation on relative contribution of flow noise sources of ship propulsion system (선박 추진시스템 유동 소음원 상대적 기여도 분석)
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- The Journal of the Acoustical Society of Korea
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- v.41 no.3
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- pp.268-277
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- 2022
In this study, each component of flow noise source of underwater propeller installed to the scale model of the KVLCC2 is investigated and the effect of each noise source on underwater-radiated noise is quantitatively analyzed. The computation domain is set to be the same as the test section of the large cavitation tunnel in the Korea Research Institute of Ship and Ocean Engineering. First, for the high-resolution computation of flow field which is noise source region, the incompressible multiphase Delayed Detached Eddy Simulation is performed. Based on flow simulation results, the Ffowcs Williams and Hawkings integral equation is used to predict underwater-radiated noise and its validity is confirmed through the comparison with the tunnel experiment result. For the quantitative comparison on the contribution of each noise source, the spectral levels of sound pressure and power levels predicted using propeller tip-vortex cavitation, blade surface and rudder surface as the integral region of noise sources are investigated. It is confirmed that the cavitation which is monopole noise source significantly contributed to the underwater-radiated noise than propeller blades and rudder which is dipole noise source, and the rudder have more contribution than propeller blades due to the influence of the propeller wake.
The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.
Purpose: Lingual nerve (LN) damage may be caused by either tumor resection or injury such as wisdom tooth extraction, Although autologous nerve graft is sometimes used to repair the damaged nerve, it has the disadvantage of necessity of another operation for nerve harvesting. Moreover, the results of nerve grafting is not satisfactory. The nerve growth factor (NGF) is well-known to play a critical role in peripheral nerve regeneration and its local delivery to the injured nerve has been continuously tried to enhance nerve regeneration. However, its application has limitations like repeated administration due to short half life of 30 minutes and an in vivo delivery model must allow for direct and local delivery. The aim of this study was to construct a well-functioning
It is difficult to evaluate the performance of process innovation of e-procurement which has large scale and complex processes. The existing evaluation methods for measuring the effects of process innovation have been mainly done with statistically quantitative methods by analyzing operational data or with qualitative methods by conducting surveys and interviews. However, these methods have some limitations to evaluate the effects because the performance evaluation of e-procurement process innovation should consider the interactions among participants who are active either directly or indirectly through the processes. This study considers the e-procurement process as a complex system and develops a simulation model based on MAS(Multi-Agent System) to evaluate the effects of e-procurement process innovation. Multi-agent based simulation allows observing interaction patterns of objects in virtual world through relationship among objects and their behavioral mechanism. Agent-based simulation is suitable especially for complex business problems. In this study, we used Netlogo Version 4.1.3 as a MAS simulation tool which was developed in Northwestern University. To do this, we developed a interaction model of agents in MAS environment. We defined process agents and task agents, and assigned their behavioral characteristics. The developed simulation model was applied to G2B system (KONEPS: Korea ON-line E-Procurement System) of Public Procurement Service (PPS) in Korea and used to evaluate the innovation effects of the G2B system. KONEPS is a successfully established e-procurement system started in the year 2002. KONEPS is a representative e-Procurement system which integrates characteristics of e-commerce into government for business procurement activities. KONEPS deserves the international recognition considering the annual transaction volume of 56 billion dollars, daily exchanges of electronic documents, users consisted of 121,000 suppliers and 37,000 public organizations, and the 4.5 billion dollars of cost saving. For the simulation, we analyzed the e-procurement of process of KONEPS into eight sub processes such as 'process 1: search products and acquisition of proposal', 'process 2 : review the methods of contracts and item features', 'process 3 : a notice of bid', 'process 4 : registration and confirmation of qualification', 'process 5 : bidding', 'process 6 : a screening test', 'process 7 : contracts', and 'process 8 : invoice and payment'. For the parameter settings of the agents behavior, we collected some data from the transactional database of PPS and some information by conducting a survey. The used data for the simulation are 'participants (government organizations, local government organizations and public institutions)', 'the number of bidding per year', 'the number of total contracts', 'the number of shopping mall transactions', 'the rate of contracts between bidding and shopping mall', 'the successful bidding ratio', and the estimated time for each process. The comparison was done for the difference of time consumption between 'before the innovation (As-was)' and 'after the innovation (As-is).' The results showed that there were productivity improvements in every eight sub processes. The decrease ratio of 'average number of task processing' was 92.7% and the decrease ratio of 'average time of task processing' was 95.4% in entire processes when we use G2B system comparing to the conventional method. Also, this study found that the process innovation effect will be enhanced if the task process related to the 'contract' can be improved. This study shows the usability and possibility of using MAS in process innovation evaluation and its modeling.
Now, safety assurance in construction sites should be accomplished by its own organization rather than control of the code or government. It is believed that the safety assurance can be considerably improved by a lecture or an education using the existing theories or literatures up to now, but it is thought that fundamental safety assurance we not able to be accomplished without developing safety devices '||'&'||' equipment or taking fundamental measures, based on the result analyzed from workers behaviors. There are various behaviors of the workers showed in construction site, but only tests for hammerusing works such as form, re-bar, stone workers directly related to the grip strength are mainly performed, investigated and measured here for the study. The above works are similar to power grip, 7th picture on seven items which are categorized for hand grip types(Ammermin 1956 ; Jones ; Kobrick 1958). Measurements of grip strength are commonly taken in anthropometric surveys. They are easy to administer but unfortunately it is rather dubious whether they yield any data that are of interest to the engineer. Very fewer controls of tools are grasped and squeesed studies showed very little overall correlation between grip strength and other measures of bodily strength (Laubach, Kromer, and Thordsen 1972), but hammer-using work which is practically progressed in construction site are mainly influenced with grip strength. According to the investigation on work measurement, it is shown that 77% of form worker are using hammer to be related to grip strength. In this study, it is particularly noticed that wearing safety gloves in construction site is required for workers safety but 20% difference between grip strength with safety gloves and without ones are commonly neglected in the site(Fig. 1). Nevertheless, safety operation with consideration of the above 20% difference is not considered in the construction site. Factors of age, kinds of work, working time, with or without safety gloves are in vestigated '||'&'||' collected at the sites for this study. Test, not at each working hour but at 14 : 00 when the almost all of the workers think the most tired, resulting from the questionaires, also when it is shown on the research report has been performed and compared for main kinds of works : form '||'&'||' re-bar work. Tests were performed with both left SE rightand of the workers simultaneously in construction site using Rand Dynamometer(Model 78010, Lafayette Instrument Co., Indiana, U.S.A) by reading grip strength on the gauge while they are pulling, and then by interviewing on their ages, works, experiences and etc., directly. The above tests have been performed for the dates of 15th march-26th May '95 with consideration of site condition. And even if various factors of ambient temperature on the testing date, working condition, individual worker's habit and worker's condition of the previous ate are concerned with the study. Those are considered as constants in this study. Samples are formwork 53, rebar 62, electrician 5, plumber 4, welding 1 from D construction Co., Ltd, ; formwork 12, re-bar 5, electrician 2, from S construction Co., Ltd, , formwork 78, re-bar 18, plumber 31, electrician 13, labor 48, plumber 31, plasterer 15, concrete placer 6, water proof worker 3, maisony 5 from B construction Co., Ltd. As In the previously mentioned, main aspect to be investigated in this study will be from '||'&'||' re-bar work because grip strength will be directly applied to these two kinds of works ; form '||'&'||' re-bar work, eventhough there are total 405 samples taken. It is thought that a frequency of accident occurrence will be mainly two work postures "looking up '||'&'||' looking down" to be mainly sorted, but this factor is not clarified in this study because It will be needed a lot of work more. Tests has been done at possible large scale of horizontally work-extended sites within one hour in order to prevent or decrease errors '||'&'||' discrepancies from time lag of the test. Additionally, the statistical package computer program SPSS PC+has been used for the study.
Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as