Recently, there are many efforts to define the field of entrepreneurship as an area of independent study. According to Shane & Venkataraman, the study of entrepreneurship is moving toward understanding the combination of entrepreneurial individual and valuable opportunity in becoming entrepreneurs. In Korea, entrepreneurship education is spreading widely on the basis of universities and in 2010 the entrepreneurship major was created in Sookmyung Women's University for the first time in Korea. The results of this study are as follows. First, there are many research about examining the relationship between entrepreneurship education and entrepreneurship intention. Nevertheless, there are lack of the study focusing on the opportunity recognition which many scholars have recognized as the independent study field of entrepreneurship domain. Therefore, the purpose of this study is to examine the effect of satisfaction of entrepreneurship major education on entrepreneurial opportunity recognition and to examine the mediating effect of entrepreneurial opportunity recognition according to educational commitment. The questionnaires were carried out for 3 weeks to entrepreneurship major students in Sookmyung Woment's University. A total of 84 surveys were collected and statistically analyzed by the R program. As a result of the analysis, it was found that the satisfaction of education positively influences the recognition of entrepreneurial opportunities. Commitment also has a full mediating effect on the recognition of entrepreneurial opportunities. The results of this analysis confirm that the ability to recognize entrepreneurial opportunity is developed by entrepreneurship education, and during the study students' commitment has an important role in the relationship between educational satisfaction and entrepreneurial opportunity recognition. The results were verified through empirical analysis. Satisfaction with entrepreneurship education and awareness of entrepreneurship opportunities through entrepreneurship can be anticipated as entrepreneurship activities in the future.
The study is enforce to study image quality evaluation of condition provide the IEC and combination of clinical conditions each quality of radiation that image quality to assess the conditions provided to IEC in the clinical environment to conduct image quality assessment of the digital radiography system in the detector have environmental limits. First, image quality evaluation was evaluated by measuring the MTF, NPS using four quality of radiation and Using MCNPX simulation lastly DQE make a image quality evaluation after calculating the particle fluence to analyze spectrum quality of radiation. Second, Using MCNPX simulation of four quality of radiation was evaluated absorbed dose rate about electronic 1 per unit air, water, muscle, bone by using Radiation flux density and energy, mass-energy absorption coefficient of matter. Results of evaluation of image quality, MTF of four quality of radiation was satisfied diagnosis frequency domain 1.0 ~ 3.0 lp/mm of general X-ray that indicated 1.13 ~ 2.91 lp/mm spatial frequency. The NPS has added filter, spatial frequency 0.5 lp/mm at standard NPS showed a tendency to decrease after increase. Unused added filter, spatial frequency 0.5 lp/mm at standard NPS showed a certain NPS result value after decrease. DQE in 70 kVp / unuesd added filter(21 mm Al) / SID 150 cm that patial frequency 1.5 lp/mm at standard showed a tendency to decrease after certain value showed. Patial frequency in the rest quality of radiation was showed a tendency to decrease after increase. Results of evaluation of absorbed dose, air < water < muscle < bone in the order showed a tendency to increase. Based on the results of this study provide to basic data that present for the image quality evaluation method of a digital radiation imaging system in various the clinical condition.
Jurassic granite from Geochang was analysed with respect to the characteristics of the rock cleavage. The comprehensive evaluation for rock cleavages was performed through the combination of the 16 parameters derived from the enlarged photomicrographs of the thin section and the spacing-cumulative frequency diagrams. The results of analysis for the representative values of these spacing parameters with respect to the rock cleavage are summarized as follows. First, the above parameters can be classified into group I (spacing frequency (N), total spacing (
According to the review and analysis of medical cases that are assigned to the Supreme Court and all local High Court in 2011 and that are presented in the media, it was found that the following categories were taken seriously, medical and pharmaceutical product liability, the third principle of trust between medical institutions, negligence and causation estimation, responsibility limit, the meaning of medical records and related judgment of disturbed substantiation, Oriental doctors' duties to explain the procedures, IMS events, whether one can claim for each medical care operated by non-physician health care institutions to the nonmedical domain in the National Health Insurance Corporation, and the basis of norms for each claim. In the cases related to medical pharmaceutical product liability, Supreme Court alleviated burden of proof for accidents with medical and pharmaceutical products prior to the practice of Product Liability Law and onset the point of negative prescription as the time of damage strikes to condition feasibility of the specific situation. In the cases related to the 3rd principle of trust between medical institutions, the Supreme Court refused to sentence the doctor who has trusted the judgment of the same third-party doctors the violations of the care duty. With respect to proof of a causal relationship and damages in a medical negligence case, the Supreme Court decided that it is unjust to deny negligence by the materials of causal relationship rejecting the original verdict and clarified that the causal relationship shall not deny the reasons to limit doctors' responsibilities. In order not put burden on patients with disadvantages in which medical records and the description of the practice or the most fundamental and important evidence to prove negligence and causation are being neglected, the Supreme Court admitted in the hospital's responsibility for the case of the neonate death of suffocation without properly listed fetal heart rate and uterine contraction monitor. On the other hand, the Seoul Western District Court has admitted alimony for altering and forging medical records. With respect to doctors' obligations to description, the Supreme Court decided that it is necessary to explain the foreseen risks by the combination of oriental and western medicines emphasizing the right of patient's self-determination. However, questions have arisen whether it is realistically feasible or not. In a case of an unlicensed doctor performing intramuscular stimulation treatment (IMS), the Supreme Court put off its decision if it was an unlicensed medical practice as to put limitation of eastern and western medical practices, but it declared that IMS practice was an acupuncture treatment therefore the plaintiff's conduct being an illegal act. In the future, clear judgment on this matter should be made. With respect to the claim of bills from non-physical health care institutions, the Supreme Court decided to void it for the implementation of the arrangement is contrary to the commitments made in the medical law and therefore, it is invalid to claim. In addition, contrast to the private healthcare professionals, who are subject to redemption according to the National Healthcare Insurance Law, the Seoul High Court explicitly confirmed that the non-professionals who receive the tort operating profit must return the unjust enrichment and have the liability for damages. As mentioned above, a relatively wide range of topics were discussed in medical field of 2011. In Korea's health care environment undergoing complex changes day by day, it is expected to see more diverse and in-depth discussions striding out to the development in the field of health care.
The social venture start-up phenomenon is found from the perspectives of social enterprise and for-profit enterprise. This study aims to fundamentally explore the start-up phenomenon of social ventures from these two perspectives. Considering the lack of prior research that researched both social and commercial perspectives at the same time, this paper analyzed using grounded theory approach of Strauss & Corbin(1998), an inductive research method that analyzes based on prior research and interview data. In order to collect data for this study, eight corporate representatives currently operating social ventures were interviewed and data and phenomena were analyzed. This progressed to a theoretical saturation where no additional information was derived. The analysis results of this study using the grounded theory approach are as follows. As a result of open coding and axial coding, 147 concepts and 70 subcategories were derived, and 18 categories were derived through the final abstraction process. In the selective coding, 'expansion of social venture entry in the social domain' and 'expansion of social function of for-profit companies' were selected as key categories, and a story line was formed around this. In this study, we saw that it is necessary to conduct academic research and analysis on the competitive factors required for companies that pursue the values of two conflicting relationships, such as social ventures, to survive with competitiveness. In practice, concepts such as collaboration with for-profit companies, value combination, entrepreneurship competency and performance improvement, social value execution competency reinforcement, communication strategy, for-profit enterprise value investment, and entrepreneur management competency were derived. This study explains the social venture phenomenon for social enterprises, commercial enterprises, and entrepreneurs who want to enter the social venture field. It is expected to provide the implications necessary for successful social venture startups.
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.
Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is
In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70