• Journal of East-Asian Urban History
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• v.2 no.1
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• pp.113-128
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• 2020

Right before the outbreak of the War of Resistance Against Japanese Aggression (1937-1954), the Great Eastern Broadcasting Station (GEBS) was established in Shanghai under the aegis of the Ministry of Foreign Affairs of Japan (MOFA). Prior to the launching of the GEBS, Japan had not owned similar radio stations in China for years. As a result, the Embassy of Japan in China held rounds of discussions on the plan and Japanese governments, ranging from the MOFA, the Navy, the Army, the Ministry of Communications (MOC), and NHK-Japan Broadcasting Corporation (Nihon Hoso Kyoku) provided financial, technological, and equipment support. In the mid-1930s when the Sino-Japanese relations became intensified, the GEBS assumed the primary role of disseminating government policies to over 30,000 Japanese expatriates in Shanghai to make sure that they could remain settled while supporting military endeavors of the Japanese army once the war between China and Japanese broke out. After 1937, the GEBS became an essential propaganda tool to advance imperial policies of Japan. Although the station differed from the Army-controlled Greater Shanghai Broadcasting Station (Dai Shanhai Hoso Kyoku) in many aspects, it was in line with the latter in terms of advancing wartime ideology of the Japanese empire. As the Japanese-occupied areas were enlarged, target audience of the GEBS also expanded to Chinese people and foreign nationals when Japanese, English, Russian, and Shanghai-dialect news was broadcast by the station. Suffice it to say that the GEBS was closely related to wartime propaganda of the Japanese imperial expansion.

• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.464-475
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• 2023

In this study, based on an analysis of two DNA barcode markers (cytochrome c oxidase subunit I and cytochrome b genes), we performed species identification and monitored labeling compliance for 50 commercial pufferfish products sold in on-line markets in Korea. Using these barcode sequences as a query for species identification and phylogenetic analysis, we screened the GenBank database. A total of seven pufferfish species (Takifugu chinensis, T. pseudommus, T. xanthopterus, T. alboplumbeus, T. porphyreus, T. vermicularis, and Lagocephalus cheesemanii) were identified and we detected 35 products (70%) that were non-compliant with the corresponding label information. Moreover, the labels on 12 commercial products contained only the general common name (i.e., pufferfish), although not the scientific or Korean names for the 21 edible pufferfish species. Furthermore, the proportion of mislabeled highly processed products (n = 9, 81.8%) was higher than that of simply processed products (n = 26, 66.7%). With respect to the country of origin, the percentage of mislabeled Chinese products (n = 8, 80%) was higher than that of Korean products (n = 26, 66.7%). In addition, the market and dialect names of different pufferfish species were labeled only as Jolbok or Milbok, whereas two non-edible pufferfish species (T. vermicularis and T. pseudommus) were used in six commercial pufferfish products described as JolboK and Gumbok on their labels, which could be attributable to the complex classification system used for pufferfish. These monitoring results highlight the necessity to develop genetic methods that can be used to identify the 21 edible pufferfish species, as well as the need for regulatory monitoring of commercial pufferfish products.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.