• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.1029-1036
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• 2024

Drawing on the deep experience and understanding of the principles of nuclear safety, as well as many years of nuclear power plant design and operation, the EDF led NUWARD SMR Project is developing a design for a Small Modular Reactor (SMR) of 340 MWe composed of two 170 MWe independent units, that will supplement the offering of high-output nuclear reactors, especially in response to specific needs such as replacement of fossil-fuelled power plants. NUWARD SMR is a mix of proven and innovative design features that will make it more commercially competitive, while integrating safety features that comply with the highest international standards. Following the principles of redundancy and diversity and rigorous application of Defence in Depth (DID), with an international view on nuclear safety licensing, the Project also incorporates new safety approaches into its design development. The NUWARD SMR Project has been in development for a number of years, it entered conceptual design formally in mid-2019 and entered Basic Design in 2023. The objective of the concept design phase was to confirm the project technological choices and to define the first design configuration of the NUWARD SMR product, to document it, in order to launch pre-licensing with the French Safety Authority (ASN) and to define its estimated cost and its subsequent development and construction schedules. As a delivery milestone the Safety Options file (called the Dossier d'Options de Sûreté (DOS)) has been submitted to ASN in July 2023 for their opinion. An integral part of the NUWARD SMR Project, is not only to deliver a design suitable for France and to satisfy French regulation, but to develop a product suitable and indeed desirable, for the international market, with a first focus in Europe. In order to achieve its objectives and realise its market potential, the NUWARD SMR Project needs to define and realise its safety approach within an international environment and that is the key subject of this paper. The following paper: • Summarises the foundation principles and technological background which underpin the design; • Contextualises the key design features with regard to the international safety regulatory framework with particular emphasis on innovative passive safety aspects; • Illustrates the Project activities in preparation for first licensing in France, and also a wider international view via the ASN led Joint Early Review of the NUWARD SMR design, including Finnish and Czech Republic regulators, recently joined by the Swedish, Polish and Dutch regulators; • Articulates the collaborative approach to design development from involvement with the Project partners (the Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Naval Group, TechnicAtome, Framatome and Tractebel) to the establishment of the International NUWARD Advisory Board (INAB), to gain greater international insight and advice; • Concludes with the focus on next steps into detailed design development, standardisation of the design and its simplification to enhance its commercial competitiveness in a context of further harmonisation of the nuclear safety and licensing requirements and aspirations.

• Journal of Bio-Environment Control
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• v.4 no.1
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• pp.50-58
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• 1995

These studies were carried out to develop a system for non -destructive and continuous measurement of fresh weight and to analyse the growth response of leaf lettuce under the different nutrient solution and light condition with this system. The developed measurement system was consisted of four load cells and a microcomputer. The output from the system was highly positive correlation with the plant fresh weight above the surface of the hydroponic solution. The top fresh weight of plant could be measured within the error $\pm$ 1.0g in the range of 0 - 2000g. The top fresh weight of leaf lettuce increased 44 times at 18th day after transferring to the nutrient solution, and the maximum growth rate was observed at 13th day after transferring. The growth rate was 10.7- 29.6% per day during 18 days. Optimum concentration of the nutrient solution for the growth of lettuce was 1.4 - 2.2 mS/cm of EC level. When the light condition was changed from dark to light, the fresh weight was temporarily decreased, but the fresh weight increased under the opposite condition. Top fresh weight of leaf lettuce in the darkness normally increased within 12 hours after darkness treatment, and then slowly increased until 78 hours under continuous dark condition. After that times, the fresh weight began to decrease.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.2
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• pp.63-77
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• 2018

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.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.5
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• pp.705-715
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• 2010

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 $N_2O$ (emission to air) and ${NO_3}^-$/${PO_4}^-$ (emission to water) from fertilizers, organic compounds from pesticides and air pollutants from fossil fuel combustion in using agricultural machines. The softwares for LCIA (life cycle impact assessment) and LCA used in Korea are 'PASS' and 'TOTAL' which have been developed by the Ministry of Knowledge Economy and the Ministry of Environment. However, the models used for the softwares are the ones developed in foreign countries. In the future, development of models and optimization of factors for characterization, normalization and weighting suitable to Korean agricultural environment need to be done for more precise LCA analysis in the agricultural area.

• Korean Chemical Engineering Research
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• v.44 no.6
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• pp.659-668
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• 2006

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 $420{\sim}430^{\circ}C$, the T-N steeply decreased with an increase in NAR, however its variation was negligible at higher Temp above $450^{\circ}C$. The regression equations for COD and T-N were obtained as quadratic models with coded Temp and NAR, and they were confirmed with coefficient of determination ($r^2$) and normality of standardized residuals. The optimum operating region was defined as Temp $450-460^{\circ}C$ and NAR 1.03-1.08 by the intersection area of COD < 2 mg/L and T-N < 40 mg/L with regression equations and considering corrosion prevention. To confirm the optimization results and investigate the scale-up problems of SCWO process, the nitromethane was decomposed in a pilot plant. The experimental results from a SCWO pilot plant were compared with regression equations of COD and T-N, respectively. The results of COD and T-N from a pilot plant could be predicted well with regression equations which were derived in a lab scale SCWO system, although the errors of pilot plant data were larger than lab ones. The predictabilities were confirmed by the parity plots and the normality analyses of standardized residuals.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.252-264
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• 2023

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.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.697-703
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• 2023

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.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

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.