日本水産工学会誌
Online ISSN : 2189-7131
Print ISSN : 0916-7617
ISSN-L : 0916-7617
最新号
選択された号の論文の10件中1~10を表示しています
  • カラマ カイリア・スワレ, 松下 吉樹
    2019 年 56 巻 1 号 p. 1-13
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    小規模漁業を支援するために世界中で係留式浮魚礁による漁場造成が行われている。本稿では浮魚礁が伝 統的に使われてきた,あるいは成功裡に導入されたアジアとインド洋の国々の状況を例示し,さらに浮魚礁 と関連する,生態系への悪影響,過剰漁獲,混獲,問題種の増殖,逸失の問題などと管理の方向性について これまでの研究を整理して議論した。
  • 石田 武志, 田所 大樹, 高橋 洋, 吉川 廣幸, 酒井 治己
    2019 年 56 巻 1 号 p. 15-26
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    フグの雑種については,親種共通で食用とされる部位は食べても良いとされるが,親種の特定が難しくそ のほどんどは廃棄されている。このような中で,交雑種も含めた種の判別が容易になる「雑種鑑別目利技術」 が可能となれば,フグの無駄な処分が減るとともに,フグ種の誤判断の可能性も低くなると考えられる。フ グ鑑別システムの基礎として,トラフグ属の体模様を再現できるモデルをセルオートマトン(CA)モデル を用いて構築した。このモデルにより5 つのパラメータで,それぞれの種の模様の特徴が再現され,さらに 雑種の模様も再現することが可能となった。
  • 長野 晃輔, 三浦 太智, 桜井 泰憲
    2019 年 56 巻 1 号 p. 27-33
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    In octopus fisheries around the coastal water of Tsugaru Strait, Aomori, Japan, mainly fishermen use the octopus fishing basket to catch the North Pacific giant octopus, Enteroctopus dofleini. These baskets catch all size of octopus and fishes, which can’t escape from these baskets once they enter. North Pacific giant octopus is very aggressive and they kill and feed other octopuses and fishes inside the basket. In this study, we examined the effect of size of ring is installed with the basket on the escapability of the small octopus below 3 kg by captive experiments. We estimated 53.2 mm as the inner ring’s diameter through which 50% of 3 kg octopuses can escape. Subsequently, we installed 55 mm inner diameter rings to the basket to validate the effectiveness of the estimated size. Basket installed with rings had less catches of small octopus (<3 kg), but caught the almost same number large octopuses of ≥3 kg. The use of basket installed with rings in octopus fishery at Aomori is expected to contribute to the conservation of smaller E. dofleini.
  • 中村 誠, 椎木 友朗, 渡邉 敏晃, 徳永 憲洋, 高岡 佑多, 前田 俊道
    2019 年 56 巻 1 号 p. 35-45
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    Models which ensure accurate non-destructive estimates of the freshness of fish meat (K value)in real time are proposed to improve quality control and maintain the skill level of distributers of dressed puffers (Migaki). Seven kinds of dressed puffers were used to construct the models for estimating fish meat freshness. Relationships between fish coloration and K values from sample acquisition until 72 hours later under refrigeration at −2℃, +2℃, and +6℃were investigated. The statistical analysis revealed that fish coloration does reflect its K value, although the strength of the relationship differs according to fish species. Two models were designed on the basis of these results, and the usefulness of each model was evaluated. The models are as follows: (1)Model to infer the K value of fish meat based on the coloration of the fish body surface by using fuzzy inference (Model 1), and (2)Model to estimate the K value of fish meat after several hours for the same fish for which the K value was inferred with Model 1 (Model 2). For these models, a high estimation accuracy was confirmed, demonstrating their potential usefulness for quality control in the distribution of dressed puffers.
  • 伊藤 喜代志
    2019 年 56 巻 1 号 p. 47-50
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    This research aims to efficiently create detailed bathymetric charts. Our approach is to obtain fine seafloor details from coarse depth measurements only, making full use of existing data and minimizing new observation. To this end, treating gridded bathymetric data as digital images, we propose to apply super resolution, which is a technique to enhance image resolution, to bathymetry. Specifically, we employ learning-based super resolution to automatically extract characteristic features of bathymetric images. In experiments, we prepared pairs of low and high-resolution images, and let a deep neural network learn their relationship and estimate a high-resolution image from each low-resolution one. Then, we evaluated results in terms of numerical error and visual quality, and confirmed that the proposed method can recover detailed seafloor structures more plausibly than naive interpolation.
  • 橋本 博公, Haiqing Shen, 松田 秋彦, 谷口 裕樹
    2019 年 56 巻 1 号 p. 51-55
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    Most of marine accidents are caused by human errors, and hence the prevention of marine accidents is essentially difficult as long as ships are operated by human beings. In addition, further congestion of seaborne routes and harbors is apprehended, and also it will be seriously difficult to secure required number of seafarers in near future in Japan. Therefore automation of ship navigation is an urgent issue in marine transportation. As the automatic collision avoidance is a key technology for realizing autonomous ships, various algorithms and approaches related to hazard detection and maneuver for collision avoidance have been studied nowadays. In this report, we try to demonstrate the potential of automatic ship handling by AI (Artificial Intelligence)through comparisons with that by a conventional geometric model as well as a dedicated experiment using three self-propelled ship models.
  • 荒井 頼子, 出原 真理子
    2019 年 56 巻 1 号 p. 57-60
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    Fishery prediction using satellite data has been reported by many studies. However, many of these studies use catches data sets from fishing boats and research vessels. In this study, using night-time visible images from satellite data instead of catches information, we predicted potential fishing grounds for saury using random forest, support vector machine, maximum entropy of machine learning. From September to December, the fishing ground predicted by machine learning showed moving from the north to the south as in the past catches reports. The predicted fishing ground distribution pattern was consistent with past reports. Some ships like as fleet of fishing boats were also located in the predicted fishing ground outside EEZ. The fishing ground zone predicted by using random forest showed the most reasonable in the three machine learning models. We suggest that it is possible to predict the potential fishing grounds only from satellite data sets.
  • 水上 幸治
    2019 年 56 巻 1 号 p. 61-84
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    Many of the coastal embankments have been maintained for over 50 years, and inspections for deterioration diagnosis are becoming increasingly important. In addition, for the inspection of coastal embankments is conducted by visual inspection, the inspecrtion efficiency is poor, and oversights are unavoidable, and survey results show artificial variation. Therefore, improvement of the inspection method is required.   A public-private research platform was constructed to investigate the method of cost-saving or laborsaving of stock management of public facilities such as coastal embankments. We, a public-private research platform developed a degradation diagnostic system using a Unmanned Aerial Vehicle (UAV) -mounted digital camera to inspect the damage of degraded coastal embankments. The information necessary for the maintenance and management of the facility is automatically extracted using artificial intelligence (AI), using the visualization information from the aerial image by the UAV.
  • 川村 秀憲
    2019 年 56 巻 1 号 p. 65-66
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
  • 鵜飼 亮行, 中瀬 浩太
    2019 年 56 巻 1 号 p. 67-70
    発行日: 2019年
    公開日: 2020/02/03
    ジャーナル オープンアクセス
    Amitori Bay, located in northwestern Iriomote Island, is characterized by its varied physical environments such as geographical features, wave height, and current in spite of its small size. It also exhibits a diverse distribution of coral reefs in response. Physical data acquired through numerical analysis, although including errors from actual measurements, provide much information by being interpolated spatially, and are useful for the understanding of phenomena. However, the spatial distribution of corals is difficult to estimate using an ecosystem model because the coral ecology has numerous and important unknown characteristics. We proposed to use AI to estimate the horizontal distribution of coral cover based on the association between coral cover and physical data.
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