• Analyses & Alternatives
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• v.6 no.1
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• pp.69-114
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• 2022

This paper analyzes Taiwan's 「New Southbound Policy」 from the perspective of 'balancing' and 'bandwagoning' in international politics. Specifically, it examines the changes and characteristics of 'Southbound policies' that have continued since the period of the Lee Teng-hui(李登輝) administration, and examines the meaning of the New Southbound Policy promoted by the Tsai Ing-wen(蔡英文) administration. Taiwan's foreign policy has been strongly influenced by external variables such as U.S.-China relations. Previous Taiwanese governments have actively promoted Southbound policies to advance to Southeast Asian countries such as ASEAN with the aim of 'De-Sinicization', but have not achieved much results. This is because variables such as cooperative U.S.-China relations and strong checks from China played a role at the time. In this environment, Taiwan had to pursue an appropriate 'balancing' between the United States, China, and Southeast Asian countries. However, since the inauguration of the Trump administration, strategic competition between the U.S. and China has been maximized, creating a new space for Taiwan's foreign policy. This is because the U.S. valued cooperation with Taiwan in the process of embodying the 'Indo-Pacific Strategy' to curb China's rise. The New Southbound Policy promoted by the Tsai Ing-won administration is different from the existing Southbound policies in that it seeks to link with the U.S. India-Pacific Strategy and attempts to advance to South Asian countries such as India. From an international political point of view, the Tsai Ing-won administration's New Southbound Policy can be interpreted as a 'bandwagoning' to the United States, not a balanced strategy between the U.S. and China. Strategic competition between the U.S. and China is expected to intensify for a considerable period of time in the future, and honeymoon between Taiwan and the U.S. are also expected to continue. Taiwan's bandwagoning strategy, which actively pursues a link between the New Southbound Policy and the India-Pacific Strategy, is also expected to be maintained.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2335-2347
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• 2023

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.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.