An Establishment of the Optimum Sowing Time for a Machine Harvest of Perilla for Seed (종실용 들깨의 기계수확에 적합한 최적 파종시기 설정)
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- Journal of the Korean Society of International Agriculture
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- v.30 no.4
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- pp.370-375
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- 2018
In order to promote the mechanized cultivation of perilla for seed, which has been increasing in cultivation area and production recently as demand increases according to the health-functional effects, we carried out this experiment to determine the optimum sowing time of perilla to minimize the seed loss at harvest and increase the yield. We used two different types of perilla varieties, 'Sodam(small-branch)' and 'Deulsaem(multi-branch)', and the sowing time was June 15, June 30, July 15 and August 1. As the sowing time is late, days of growth from sowing to flowering were shortened, and they were shortened from 14, 26 and 31~32 days on June 30, July 15 and August 1 as compared with June 15, respectively. And, the stem length and culm diameter were shortened or tapered and the number of nodes tended to decrease. The number of effective branch was 82%, 61% and 56% on June 30, July 15 and August 1 as compared with June 15, respectively. Accordingly, it seems to make against in securing the yield from July 15. And, the lowest cluster height was generally shorter as the sowing time is late, and the height was below 15cm on July 15 and August 1. It seems that this may work against the machine harvest. There was a high degree of significance between the sowing time and the yield. Although, the total yield was not statistically significant among June 15, June 30 and July 15, the ratio of shattering seed at harvest was in order of July 15, August 1(30.3%)> June 15(15.3%)> June 30(13.5%). Therefore, the net yield except for shattered seed was higher in order of June 30
At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.
As medical facilities are usually built at urban areas, special concrete aggregates and evaluation methods are needed to optimize the design of concrete walls by balancing density, thickness, material composition, cost, and other factors. Carbon treatment rooms require a high radiation shielding requirement, as the neutron yield from carbon therapy is much higher than the neutron yield of protons. In this case study, the maximum carbon energy is 430 MeV/u and the maximum current is 0.27 nA from a hybrid particle therapy system. Hospital or facility construction should consider this requirement to design a special heavy concrete. In this work, magnetite is adopted as the major aggregate. Density is determined mainly by the major aggregate content of magnetite, and a heavy concrete test block was constructed for structural tests. The compressive strength is 35.7 MPa. The density ranges from 3.65 g/cm3 to 4.14 g/cm3, and the iron mass content ranges from 53.78% to 60.38% from the 12 cored sample measurements. It was found that there is a linear relationship between density and iron content, and mixing impurities should be the major reason leading to the nonuniform element and density distribution. The effect of this nonuniformity on radiation shielding properties for a carbon treatment room is investigated by three groups of Monte Carlo simulations. Higher density dominates to reduce shielding thickness. However, a higher content of high-Z elements will weaken the shielding strength, especially at a lower dose rate threshold and vice versa. The weakened side effect of a high iron content on the shielding property is obvious at 2.5 µSv=h. Therefore, we should not blindly pursue high Z content in engineering. If the thickness is constrained to 2 m, then the density can be reduced to 3.3 g/cm3, which will save cost by reducing the magnetite composition with 50.44% iron content. If a higher density of 3.9 g/cm3 with 57.65% iron content is selected for construction, then the thickness of the wall can be reduced to 174.2 cm, which will save space for equipment installation.
In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.
The purpose of this research is to explore differences between small and medium-sized companies and large-sized companies in the impact of human resource innovativeness(HRI), learning orientation(LO), and HRI-LO interaction on innovation effect and business performance. Although learning orientation has long been considered as a key factor influencing good performance of a business, little research was devoted to exploring the effect of HRI-LO interaction on innovation effect and business performance. In this study, it is investigated whether there is a synergy effect between innovative human workforce and learning orientation corporate culture, in addition to each by itself, to generate good business performance as well as a success of new innovations in the market. Research hypotheses were as follows, including H1) human resource innovativeness(HRI), learning orientation(LO), and interactions of HRI and LO(HRI-LO interaction) positively affect innovation effect, H2) there is a difference of the effect of HRI, LO, and HRI-LO interaction on innovation effect between large-sized and small-sized companies, H3) HRI, LO, HRI-LO interaction, innovation effect positively affect business performance, and H4) there is a difference of the effect of HRI, LO, HRI-LO interaction, and innovation effect on business performance between large-sized and small-sized companies. Data were obtained from 479 practitioners through a web survey since the web survey is an efficient method to collect a national data at a variety of fields. A single respondent from a company was allowed to participate in the study after checking whether they have more than 5-year work experiences in the company. To check whether a common source bias is existed in the sample, additional data from a convenient sample of 97 companies were gathered through the traditional survey method, and were used to confirm correlations between research variables of the original sample and the additional sample. Data were divided into two groups according to company size, such as 352 small and medium-sized companies with less than 300 employees and 127 large-sized companies with 300 or more employees. Data were analyzed through t-test and regression analyses. HRI which is the innovativeness of human resources in the company was measured with 9 items assessing the innovativenss of practitioners in staff, manager, and executive-level positions. LO is the company's effort to encourage employees' development, sharing, and utilizing of knowledge through consistent learning. LO was measured by 18 items assessing commitment to learning, vision sharing, and open-mindedness. Innovation effect which assesses a success of new products/services in the market, was measured with 3 items. Business performance was measured by respondents' evaluations on profitability, sales increase, market share, and general business performance, compared to other companies in the same field. All items were measured by using 6-point Likert scales. Means of multiple items measuring a construct were used as variables based on acceptable reliability and validity. To reduce multi-collinearity problems generated on the regression analysis of interaction terms, centered data were used for HRI, LO, and Innovation effect on regression analyses. In group comparison, large-sized companies were superior on annual sales, annual net profit, the number of new products/services in the last 3 years, the number of new processes advanced in the last 3 years, and the number of R&D personnel, compared to small and medium-sized companies. Also, large-sized companies indicated a higher level of HRI, LO, HRI-LO interaction, innovation effect and business performance than did small and medium-sized companies. The results indicate that large-sized companies tend to have more innovative human resources and invest more on learning orientation than did small-sized companies, therefore, large-sized companies tend to have more success of a new product/service in the market, generating better business performance. In order to test research hypotheses, a series of multiple-regression analysis was conducted. In the regression analysis examining the impact on innovation effect, important results were generated as : 1) HRI, LO, and HRI-LO affected innovation effect, and 2) company size indicated a moderating effect. Based on the result, the impact of HRI on innovation effect would be greater in small and medium-sized companies than in large-sized companies whereas the impact of LO on innovation effect would be greater in large-sized companies than in small and medium-sized companies. In other words, innovative workforce would be more important in making new products/services that would be successful in the market for small and medium-sized companies than for large-sized companies. Otherwise, learning orientation culture would be more effective in making successful products/services for large-sized companies than for small and medium-sized companies. Based on these results, research hypotheses 1 and 2 were supported. In the analysis of a regression examining the impact on business performance, important results were generated as : 1) innovation effect, LO, and HRI-LO affected business performance, 2) HRI by itself did not have a direct effect on business performance regardless of company size, and 3) company size indicated a moderating effect. Specifically, an effect of the HRI-LO interaction on business performance was stronger in large-sized companies than in small and medium-sized companies. It means that the synergy effect of innovative human resources and learning orientation culture tends to be stronger as company is larger. Referring to these result, research hypothesis 3 was partially supported whereas hypothesis 4 was supported. Based on research results, implications for companies were generated. Regardless of company size, companies need to develop the learning orientation corporate culture as well as human resources' innovativeness together in order to achieve successful development of innovative products and services as well as to improve sales and profits. However, the effectiveness of the HRI-LO interaction would be varied by company size. Specifically, the synergy effect of HRI-LO was stronger to make a success of new products/services in small and medium-sized companies than in large-sized companies. However, the synergy effect of HRI-LO was more effective to increase business performance of large-sized companies than that of small and medium-sized companies. In the case of small and medium-sized companies, business performance was achieved more through the success of new products/services than much directly affected by HRI-LO. The most meaningful result of this study is that the effect of HRI-LO interaction on innovation effect and business performance was confirmed. It was often ignored in the previous research. Also, it was found that the innovativeness of human workforce would not directly influence in generating good business performance, however, innovative human resources would indirectly affect making good business performance by contributing to achieving the development of new products/services that would be successful in the market. These findings would provide valuable managerial implications specifically in regard to the development of corporate culture and education program of small and medium-sized as well as large-sized companies in a variety of fields.
1. The 'Kao Zheng Pai(考證派) comes from the 'Zhe Zhong Pai' and is a school that is influenced by the confucianism of the Qing dynasty. In Japan Inoue Kinga(井上金娥), Yoshida Koton(吉田篁墩) became central members, and the rise of the methodology of historical research(考證學) influenced the members of the 'Zhe Zhong Pai', and the trend of historical research changed from confucianism to medicine, making a school of medicine based on the study of texts and proving that the classics were right. 2. Based on the function of 'Nei Qu Li '(內驅力) the 'Kao Zheng Pai', in the spirit of 'use confucianism as the base', researched letters, meanings and historical origins. Because they were influenced by the methodology of historical research(考證學) of the Qing era, they valued the evidential research of classic texts, and there was even one branch that did only historical research, the 'Rue Xue Kao Zheng Pai'(儒學考證派). Also, the 'Yi Xue Kao Zheng Pai'(醫學考證派) appeared by the influence of Yoshida Kouton and Kariya Ekisai(狩谷掖齋). 3. In the 'Kao Zheng Pai(考證派)'s theories and views the 'Yi Xue Kao Zheng Pai' did not look at medical scriptures like the "Huang Di Nei Jing"("黃帝內經") and did not do research on 'medical' related areas like acupuncture, the meridian and medicinal herbs. Since they were doctors that used medicine, they naturally were based on 'formulas'(方劑) and since their thoughts were based on the historical ideologies, they valued the "Shang Han Ja Bing Lun" which was revered as the 'ancestor of all formulas'(衆方之祖). 4. The lives of the important doctors of the 'Kao Zheng Pai' Meguro Dotaku(目黑道琢) Yamada Seichin(山田正珍), Yamada Kyoko(山田業廣), Mori Ritsi(森立之) Kitamura Naohara(喜多村直寬) are as follows. 1) Meguro Dotaku(目黑道琢 1739
1.The 'Kao Zheng Pai'(考證派) comes from the 'Zhe Zhong Pai(折衷派)' and is a school that is influenced by the confucianism of the Qing dynasty. In Japan Inoue Kinga(井上金峨), Yoshida Koton(古田篁墩