• Journal of Power Electronics
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• v.19 no.6
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• pp.1366-1379
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• 2019

High-gain DC/DC converters have become one of the key technologies for the grid-connected operation of new energy power generation, and its research provides a significant impetus for the rapid development of new energy power generation. Inspired by the transformer effect and the ripple-suppressed ability of a coupled inductor, a double-coupled inductor high gain DC/DC converter with a ripple absorption circuit is proposed in this paper. By integrating the diode-capacitor voltage multiplying unit into the quadratic Boost converter and assembling the independent inductor into the magnetic core of structure coupled inductors, the adjustable range of the voltage gain can be effectively extended and the limit on duty ratio can be avoided. In addition, the volume of the magnetic element can be reduced. Very small ripples of input current can be obtained by the ripple absorption circuit, which is composed of an auxiliary inductor and a capacitor. The leakage inductance loss can be recovered to the load in a switching period, and the switching-off voltage spikes caused by leakage inductance can be suppressed by absorption in the diode-capacitor voltage multiplying unit. On the basis of the theoretical analysis, the feasibility of the proposed converter is verified by test results obtained by simulations and an experimental prototype.

• Journal of Environmental Science International
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• v.33 no.6
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• pp.417-425
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• 2024

This study investigated the effects of various planting models on the joint cultivation of eggplant (Solanum melongena) and marigold (Tagetes erecta)to enhance sustainable rooftop urban farming. Rooftop agriculture is increasingly valued to boost the food supply and benefit the environment. Integrating such practices into urban planning is viewed as a way to sustainably manage resources and improve the food-energy-water cycle in cities. The experiment was conducted on a rooftop in Chungju, South Korea from May to August. Four different planting setups were used: central eggplant with peripheral marigold (SET), eggplant with a protective net (SIC), central marigold with peripheral eggplant (TES), and control with only eggplant (CON S). These models tested the effects of companion planting versus monoculture using a lightweight soil mix ideal for rooftops made from cocopeat and perlite and enriched with organic fertilizer. Measurements focused on soil conditions and plant health and assessed soil temperature, moisture, conductivity, plant height, width, and leaf size. The results indicated that the SET modelyielded the best growth. This setup benefited from marigold pest control properties and its ability to improve soil conditions by enhancing moisture and nutrient levels and aiding eggplant growth. These findings underscore the potential of mixed planting on rooftops and suggest that such approaches can be effectively incorporated into urban agriculture to boost yield and environmental sustainability. This study supports the idea that diverse planting methods can significantly affect plant growth and promote urban greening and food security.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.395-402
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• 2012

This study has been conducted to design and implement smart phone-based application which can keep records effectively to improve athletes' ability. To boost their performance, they need to receive training as realistic as possible. As a way to do that, training should be performed with some fitness equipments, sporting apparatus, facilities, and conditions. However, it is also possible to improve their ability by motivating athletes with checking and recording their performance. This study ascertains that using scoring records actually motivates athletes when comparing the result of youth basketball team using the records keeping system with that of not using it. Accordingly, smart phone-based records keeping application has been designed and developed. Also, through a survey targeting athletic coaches who have used the system, training efficiency has been measured and it turns out that it actually motivates athletes to improve their abilities.

• Food Science of Animal Resources
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• v.38 no.6
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• pp.1144-1154
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• 2018

Both the calcium and collagen in bone powder are hard to be absorbed by the body. Although enzymatic hydrolysis by protease increased the bio-availability of bone powder, it was a meaningful try to further increase $Ca^{2+}$ release, oligopeptide formation and antioxidant activity of the sheep bone hydrolysate (SBH) by lactic acid bacteria (LAB) fermentation. Lactobacillus helveticus was selected as the starter for its highest protease-producing ability among 5 tested LAB strains. The content of liberated $Ca^{2+}$ was measured as the responsive value in the response surface methodology (RSM) for optimizing the fermenting parameters. When SBH (adjusted to pH 6.1) supplemented with 1.0% glucose was inoculated 3.0% L. helveticus and incubated for 29.4 h at $36^{\circ}C$, $Ca^{2+}$ content in the fermented SBH significantly increased (p<0.01), and so did the degree of hydrolysis and the obtaining rate of oligopeptide. The viable counts of L. helveticus reached to $1.1{\times}10^{10}CFU/mL$. Results of Pearson correlation analysis demonstrated that LAB viable counts, $Ca^{2+}$ levels, obtaining rates of oligopeptide and the yield of polypeptide were positively correlated with each other (p<0.01). The abilities of SBH to scavenge the free radicals of DPPH, OH and ABTS were also markedly enhanced after fermentation. In conclusion, L. helveticus fermentation can further boost the release of free $Ca^{2+}$ and oligopeptide, enhance the antioxidant ability of SBH. The L. helveticus fermented SBH can be developed as a novel functional dietary supplement product.

• Microbiology and Biotechnology Letters
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• v.49 no.4
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• pp.501-509
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• 2021

The increasing demand for baked products has given a boost to research on isolation and selection of novel yeast strains with improved leavening activity. Twelve sourdough samples were collected from several localities of the Fez region in Morocco. The pH and total titratable acidity (TTA) values of these samples varied from 3.03-4.63 and 14-17.5 ml of 0.1 N NaOH/10 g of sourdough, respectively, while yeast counts ranged from 5.3 6.77 Log CFU/g. Thirty-two yeast isolates were obtained and evaluated for their leavening ability. Out of all isolates, four yeasts molecularly identified as Saccharomyces cerevisiae (three strains) and Kluyveromyces marxianus (one strain) showed highest specific volumes of 4.69, 4.55, 4.35 and 4.1 cm³/g, respectively. These strains were further assessed for their tolerance to high concentrations of salt, sugar, elevated temperatures, and low pH conditions. K. marxianus showed higher resistance than the S. cerevisiae. Thus, Moroccan sourdoughs harbor technologically relevant yeasts that could be used as potential starters for bread preparation.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1484-1494
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• 2017

We present a two-stage inverter with high step-up conversion ratio engaging modified finite-set Model Predictive Control (MPC) for utility-integrated photovoltaic (PV) applications. The anticipated arrangement is fit for low power PV uses, the calculated efficiency at 150 W input power and 19 times boosting ratio was around 94%. The suggested high-gain dc-dc converter based on Cockcroft-Walton multiplier constitutes the first-stage of the offered structure, due to its high step-up ability. It can boost the input voltage up to 20 times. The 3S current-source inverter constitutes the second-stage. The 3S current-source inverter hires three semiconductor switches, in which one is functioning at high-frequency and the others are operating at fundamental-frequency. The high-switching pulses are varied in the procedure of unidirectional sine-wave to engender a current coordinated with the utility-voltage. The unidirectional current is shaped into alternating current by the synchronized push-pull configuration. The MPC process are intended to control the scheme and achieve the subsequent tasks, take out the Maximum Power (MP) from the PV, step-up the PV voltage, and introduces low current with low Total Harmonic Distortion (THD) and with unity power factor with the grid voltage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5489-5511
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• 2019

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5427-5445
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• 2019

While deep neural networks have achieved remarkable performance in representation learning, a huge amount of labeled training data are usually required by supervised deep models such as convolutional neural networks. In this paper, we propose a new representation learning method, namely generative adversarial networks (GAN) based bagging deep convolutional autoencoders (GAN-BDCAE), which can map data to diverse hierarchical representations in an unsupervised fashion. To boost the size of training data, to train deep model and to aggregate diverse learning machines are the three principal avenues towards increasing the capabilities of representation learning of neural networks. We focus on combining those three techniques. To this aim, we adopt GAN for realistic unlabeled sample generation and bagging deep convolutional autoencoders (BDCAE) for robust feature learning. The proposed method improves the discriminative ability of learned feature embedding for solving subsequent pattern recognition problems. We evaluate our approach on three standard benchmarks and demonstrate the superiority of the proposed method compared to traditional unsupervised learning methods.

• Knowledge Management Research
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• v.15 no.3
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• pp.121-140
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• 2014

Many firms have been utilizing SNSs such as Facebook and Twitter actively in order to boost interactions with customers that promote product and service innovations and effective marketing. Although positive outcomes of the customer interactions in SNSs are expected, there exist few studies on the effects of interactions between customers and firms in the SNS context. This study empirically examines how customer experiences in multi-dimensional interactions (i.e., pragmatic, sociability, usability, and hedonic interaction) in corporate SNSs influence customer trust in the firm, and how customer learning benefits are associated with firm benefits such as gaining customer trust. The results indicate that all four dimensions of customer interactions in SNSs have significant effects on customer learning benefits, which in turn significantly influence customer trust in the firm. Meanwhile, the results reveal that there are also direct relationships between specific dimensions of customer interactions in SNSs and the two dimensions of customer trust (i.e., ability-based and benevolence/integrity-based). Based on the findings, this study diagnoses the status of corporate SNSs in terms of collaboration with customers and provides practical implications for firms which attempt to capitalize on the multi-dimensional customer interactions in SNSs and to facilitate innovative activities with customers.