In the rapidly to change global market in recent years, as the era of merging and integrating industries and the evolution of technology have come to an era in which everything can not be solved as a single company, it is evolving into competition for the enterprise network rather than the competition for the enterprise unit. In a competitive business environment, it is necessary to provide not only for the efforts as an individual companies but also the mutual development efforts to enhance output through the innovation activities based on the interrelationship with the business partners. In spite of the recent efforts and research through core competencies and innovation activities, some of business activities were unable to achieve enough progress in business performance and this study mainly focused to improve business performance for those companies. This study targeted CEOs and Directors who participates in "manufacturing performance innovation partnership project" carried by The foundation of Large, SMEs, Agriculture, Fisheries cooperation Korea and studied the influences of innovation activities to the core competencies and business performance. Detailed variables in this study were extracted from the previous research and used for verification. The study is designed to determine the influence of individual innovation activities to the core competencies and business performance. Innovation activities as a parameter, the relationship between core competencies and business performance was examined. In the examination of the innovation activities as a meditated effect, those activities carried by SMEs (Collaboration in Technology, Manufacturing, and Management innovations with Large Scale Business) through partnership in manufacturing innovation is significantly related business performance. Therefore, the result reveals that the individual SMEs are having own limitation in the achievement of significant progress in business performance with their own capabilities, and using the innovation activities act as catalyst through the collaboration with large scale businesses would result significant progress in business performance. Mutual effort in collaborative innovation activities between large scale businesses and SMEs is one of the most critical issues in recent years in Korea and the main focus of this study is to provide analysis which demonstrates where the SMEs are required to focus in their innovation activities.
The adoption of carbon foot print system is being activated mostly in the developed countries as one of the long-term response towards tightened up regulations and standards on carbon emission in the agricultural sector. The Korean Ministry of Environment excluded the primary agricultural products from the carbon foot print system due to lack of LCI (life cycle inventory) database in agriculture. Therefore, the research on and establishment of LCI database in the agriculture for adoption of carbon foot print system is urgent. Development of LCA (life cycle assessment) methodology for application of LCA to agricultural environment in Korea is also very important. Application of LCA methodology to agricultural environment in Korea is an early stage. Therefore, this study was carried out to find out the effect of lettuce cultivation on agricultural environment by establishing LCA methodology. Data collection of agricultural input and output for establishing LCI was carried out by collecting statistical data and documents on income from agro and livestock products prepared by RDA. LCA methodology for agriculture was reviewed by investigating LCA methodology and LCA applications of foreign countries. Results based on 1 kg of lettuce production showed that inputs including N, P, organic fertilizers, compound fertilizers and crop protectants were the main sources of major emission factor during lettuce cropping process. The amount of inputs considering the amount of active ingredients was required to estimate the actual quantity of the inputs used. Major emissions due to agricultural activities were
The optimization of supercritical water oxidation (SCWO) process for decomposing nitromethane was studied by means of a design of experiments. The optimum operating region for the SCWO process to minimize COD and T-N of treated water was obtained in a lab scale unit. The authors had compared the results from a SCWO pilot plant with those from a lab scale system to explore the problems of scale-up of SCWO process. The COD and T-N in treated waters were selected as key process output variables (KPOV) for optimization, and the reaction temperature (Temp) and the mole ratio of nitromethane to ammonium hydroxide (NAR) were selected as key process input variables (KPIV) through the preliminary tests. The central composite design as a statistical design of experiments was applied to the optimization, and the experimental results were analyzed by means of the response surface method. From the main effects analysis, it was declared that COD of treated water steeply decreased with increasing Temp but slightly decreased with an increase in NAR, and T-N decreased with increasing both Temp and NAR. At lower Temp as
Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.
This paper proposes a design of a dual-band band-pass filter that integrates a λg/2 open SIR structure, a transmission line, and a fork-type structure with symmetric and asymmetric open stubs. To obtain the dual-band effect, the proposed filter uses the SIR structure and adjusts the impedance ratio of the SIR structure. Therefore, the position of the harmonics of the filter is shifted through the adjustment of the impedance ratio, and this can obtain a double-band effect. In order to obtain the dual-band characteristics, the dual-band effect is obtained by inserting a open stub between the SIR structures with the SIR structure divided in half. In addition, the second frequency response is obtained by adjusting the length of the open symmetrical stub in the fork-shaped structure. The asymmetrical open stub in the fork form achieves optimum bandwidth by adjusting the length. Therefore, the first center frequency of the proposed band-pass filter is 5.896 GHz and the bandwidth is 13.6 %. At this time, the measurement results are 0.13 dB and 33.6 dB. The second center frequency is 5.906 GHz and the bandwidth is 13.6 %. At this time, the measurement results are 0.15 dB and 19.8 dB. The reason is that when the impedance ratio (Δ) is higher than 1, the position of the harmonic is shifted to a lower frequency band. However, if the impedance ratio (Δ) is lowered by one step, the position of harmonics will move to a higher frequency band. The function of the filter designed using these characteristics can be obtained from the measurement result. The proposed band-pass filter has no coupling loss and no via energy concentration loss because there is no coupling structure of input/output and no via hole. Therefore, system integration is possible due to its excellent performance, and it is expected that dedicated short-range communication (DSRC) system applications used in traffic communication systems will be possible.
As a part of the global industrial efforts to reduce environmental pollution owing to air pollution, regulations have been established by the International Maritime Organization (IMO). The IMO has implemented various regulations such as EEXI, EEDI, and CII to reduce air pollution emissions from ships. They are also promoting measures to decrease the power consumption in ships, aiming to conserve energy. Most of the power used in ships is consumed by electric motors. Among the motors installed on ships, the engine room blower that takes up a significant load, operates at a constant irrespective of demand. Therefore, energy savings can be expected through frequency control. In this study, we demonstrated the efficacy of energy savings by controlling the frequency of the electric motor of the generator blower that supplies combustion air to the generator's turbocharger. The system was modeled based on the output data of the turboharger outlet temperature in response to the blower frequency inpu. A PI control system was established to control the frequency with the target being the turbocharger outlet temperature. By maintaining the turbocharger design standard outlet temperature and controlling the blower frequency, we achieved an annual energy saving of 15,552kW in power consumption. The effectiveness of energy savings through frequency control of blower fans was verified during the summer (April to September) and winter (March to October) periods. Based on this, we achieved annual fuel cost savings of 6,091 thousand won and reduction of 8.5 tons of carbon dioxide, 2.4 kg of SOx, and 7.8 kg of NOx air pollutants on the training ship.
Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.
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