Clean and Safe Supply of Fish and Shellfish to Clear the HACCP Regulation by Use of Clean and Cold Deep Ocean Water in Rausu,Hokkaido,Japan

For the supply of fish and shellfish to consumers in fresh condition, clean handling after catch from the sea is es-sential. According to HACCP (Hazard Analysis and Critical Control Points), it is important to meet such requirement bykeeping fish and shellfish under a certain low temperature and clean conditions after catching. The deep ocean water(DOW) characterized by low temperature and cleanliness has been chosen for fish and shellfish handlings, particularly forsalmon, cod, and sea urchin in Town ’Rausu’ in Hokkaido, Japan. DOW below 2.9°C of an amount of nearly 5 000m3 isplanned to be pumped up every day from a depth of about 350 m, and temporarily stored in a large simulated tank on land.DOW is then supplied to fish boats through hydrants distributed throughout the harbor and used for keeping salmon in cleanand cold conditions. Ice made from DOW is also used for lowering temperature if necessary. DOW and ice made from DOWare also used during the transportation of fish and shellfish. The entire system is scheduled to be completed by the summer of2005.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

基于人工鱼群与蛙跳混合算法的变压器Jiles-Atherton模型参数辨识

变压器铁芯磁化特性的准确建模是研究变压器直流偏磁现象的关键,在使用Jiles-Atherton(J-A)模型对变压器的磁滞回线进行建模分析时,需要对变压器直流偏磁工况下J-A模型中的5个关键参数进行准确识别。提出了人工鱼群与蛙跳混合算法对J-A模型中的关键参数进行辨识,该算法将两种仿生算法有机融合,在鱼群算法寻找到最优区域后切换至蛙跳算法进行局部搜索,兼具了人工鱼群算法前期收敛迅速与蛙跳算法局部搜索准确的优势。分别将所提混合算法及多种现有识别算法应用于数值仿真算例与变压器直流偏磁实测曲线的参数识别,结果表明基于人工鱼群与蛙跳混合算法得到的变压器磁滞回线与实测曲线吻合良好,且具有识别精度高和计算效率高的优点,验证了该算法在变压器J-A模型参数识别中的有效性,进而可以应用于对变压器直流偏磁下运行特性的准确分析。

Intelligent Deep Learning Based Automated Fish Detection Model for UWSN

An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces.It has paved the way for new opportunities that can address questions relevant to diversity,uniqueness,and difficulty of marine life.Underwater Wireless Sensor Networks(UWSNs)are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions.In this scenario,it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies.Several models have been developed earlier for fish detection.However,they lack robustness to accommodate considerable differences in scenes owing to poor luminosity,fish orientation,structure of seabed,aquatic plantmovement in the background and distinctive shapes and texture of fishes from different genus.With this motivation,the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN,named IDLAFD-UWSN model.The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos,particularly in blurred and crowded environments.IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network(Mask RCNN)with Capsule Network as a baseline model for fish detection.Besides,in order to train Mask RCNN,background subtraction process using GaussianMixtureModel(GMM)model is applied.This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions.Finally,Wavelet Kernel Extreme Learning Machine(WKELM)model is utilized as a classifier model.The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98%and 97%on the applied blurred and crowded datasets respectively.

Machine Vision Based Fish Cutting Point Prediction for Target Weight

Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight information.This study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.

Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model

Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.The classic detection approach involves placing sensors on the skin or body of the fish,which may interfere with typical behavior and welfare.The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy.This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion(IFBC-MIWOEF)model.The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification.To accomplish this,the IFBC-MIWOEF model designs an ensemble of Deep Learning(DL)based fusion models such as VGG-19,DenseNet,and Effi-cientNet models for fish behavior classification.In addition,the hyperparam-eter tuning of the DL models is carried out using the MIWO algorithm,which is derived from the concepts of oppositional-based learning(OBL)and the IWO algorithm.Finally,the softmax(SM)layer at the end of the DL model categorizes the input into distinct fish behavior classes.The experimental validation of the IFBC-MIWOEF model is tested using fish videos,and the results are examined under distinct aspects.An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.

基于深度学习的鱼类识别相关技术研究现状及展望

为促进渔业生产智能化、现代化发展,综述了基于深度学习的鱼类识别相关技术。首先,从数据集构建、数据预处理、神经网络模型设计以及模型训练等4个方面阐述了基于深度学习的鱼类识别工作流程。然后,从图像分类、目标检测、图像分割3个角度总结了近几年鱼类识别相关技术的研究进展及应用成果。其中,图像分类主要用于识别个体鱼的色泽与种类,目标检测侧重于估计鱼群的数量和体型,而图像分割则在推断鱼类的状态和行为方面发挥着重要作用。同时,分析了不同方法所具备的优势,比较了各方法在数据集中的性能指标。最后,对深度学习在鱼类识别领域的下一步发展方向和研究重点进行了展望。综上,深度学习方法效率普遍较高、泛化能力普遍较强,深度学习技术在鱼类识别中的广泛应用能够为渔业科研人员提供有效的技术支撑。

基于深度学习的鱼病防治APP教学实践系统设计

针对移动编程技术教学方案中缺少与人工智能技术相结合的实验教学内容问题,设计了一个以Android和YOLOv5s为核心技术的鱼病防治教学实践系统。基于成果导向教育理念,将理论知识与科研项目相结合,引入CDIO(conceive(构思)-design(设计)-implement(实现)-operate(运作))教学模式,以系统功能设计、深度学习开发环境搭建、鱼病数据集构建、YOLOv5s鱼病检测算法设计、项目部署与测试、项目答辩路演等为主要教学模块进行递进式实验教学。实践表明,该系统充分调动了学生的主观能动性,培养了学生的创新精神,增强了学生的工程实践能力。

基于联合深度统计特征对齐的鱼类目标识别方法

水下鱼类目标识别技术是认识海洋、经略海洋、向海图强的重要技术之一.基于深度学习的水下目标识别技术已成为研究热点,但是针对水下鱼类数据小样本甚至零样本识别性能亟待提高.本文基于迁移学习,提出了联合深度统计特征对齐(Joint Deep Statistical Feature Alignment, JDSFA)方法,解决小样本下的鱼类目标识别问题.以ResNet-50作为骨干网络,将均方和协方差纳入权重选择算法用来构建自适应损失函数,对齐源域和目标域之间的特征分布,联合源域损失与领域间的自适应损失,设计全局损失函数,建立深度学习识别模型,实现鱼类目标识别任务.利用公开的水下鱼类数据集QUT进行实验验证,相比目前代表性的DADAN、PMTrans、DSAN方法,JDSFA方法的鱼类识别性能分别提升了3.59%、4.96%、5.91%,结果表明了本文JDSFA方法的有效性,并对鱼类目标识别具有良好的应用价值.

基于深度学习的鱼类识别系统设计

鱼类识别系统在生态环境保护和渔业资源管理中具有重要的应用价值。文章基于深度学习技术,通过百度EasyDL平台对大量的鱼类图像数据集进行训练和优化,建立一个可用于鱼类识别的深度学习模型,开发一款安卓APP,该系统通过嵌入式安卓系统摄像头拍照,调用训练好的鱼类识别模型的API接口实现鱼类的快速准确识别,并在测试集上进行准确性和性能评估。实验结果表明,该系统能够高效准确地识别鱼类。

基于深度学习的鱼类养殖监测研究进展

鱼类养殖是通过人工方式在水中养殖各种鱼类的经济活动。鱼类养殖可以在淡水、海水或者盐碱水环境中进行,通过各种监测技术和设备来培育和管理鱼的生长和繁殖。传统的鱼类养殖监测方法存在效率低和准确性差等问题。近年来,基于深度学习的视觉技术的发展为鱼类养殖监测提供了新的解决方案。该文阐述了基于深度学习的视觉技术在鱼类养殖监测中的应用,并从鱼体测量、鱼类计数、鱼类摄食、鱼类游泳行为和鱼病诊断5个方面分别对研究进展进行梳理。在此基础上总结了鱼类养殖监测在数据采集与传输、建立鱼类养殖监测数据集、超规模参数模型、终端监测设备边缘计算、数字孪生、智能监测业务化应用不足等问题和展望,旨在为深度学习在鱼类养殖监测中的推广应用提供科学参考。

基于深度学习的鱼类跟踪技术研究进展

近年来,水产养殖和渔业资源保护的智能化发展迅速,对鱼类跟踪技术的需求也随之增加。传统的鱼类跟踪方法主要依赖于目视观察和标签追踪,存在效率低、应用范围有限、准确率不高等问题,限制了其推广应用。随着深度学习技术在计算机视觉领域的快速发展,基于深度学习的鱼类跟踪技术能够提供准确、客观、可扩展和自动化的跟踪方法。首先,介绍了鱼类跟踪技术的跟踪对象和4种深度学习鱼类追踪方法,分别是语义分割、实例分割、目标检测和目标分类。其次,介绍了鱼类跟踪技术如何获取鱼类轨迹与姿态、鱼类数量以及鱼类体长等鱼类目标跟踪信息。接着,介绍了基于深度学习的鱼类跟踪技术在鱼类疾病、鱼类摄食行为以及鱼类健康状态方面的应用,并从低对比度和纹理模糊、图像颜色失真以及遮挡和变形等3个方面,探讨了目前基于深度学习的鱼类跟踪技术的主要问题和一些相应的解决方法。最后,对基于深度学习的鱼类跟踪技术的发展前景进行了总结和展望。研究认为:基于深度学习的鱼类跟踪技术具有更高的准确度和客观性,为不同场景下的实际应用提供了更多解决方案,该技术有望在水产养殖管理、鱼类科学研究以及海洋环境保护等领域发挥更重要的作用,为相关领域提供更多的数据和支持。

基于水面红外图像的深海网箱鱼群夜间智能监测方法研究

获取深海网箱养殖过程鱼群活动数据,开展鱼群监测是提升深海养殖效率、降低养殖成本的有效手段。基于水面红外相机,利用深度学习前沿技术,提出了一种鱼群智能监测方法。该方法涉及鱼群识别及计数、鱼体分割和鱼体游向判断3个功能模块。首先,通过红外相机采集鱼类的图像信息,并进行标注以构建数据集,然后采用改进的Faster RCNN模型,以Mobilenetv2+FPN网络作为特征提取器,实现鱼类的准确识别,并输出包围框表征鱼类个体位置。其次,从框图内选择亮度前20%的像素点作为分割提示点,利用Segment Anything Model对图像进行分割,生成鱼体分割图。最后,通过对鱼体分割图进行椭圆拟合处理,可以判定鱼类的游向信息。改进的Faster RCNN模型在进行100次迭代训练后,平均精确率达到84.5%,每张图片的检测时间为0.042 s。结果表明,在水面红外图像的鱼类数据集上,所提出的改进Faster RCNN模型和椭圆拟合等关键技术能够实现对鱼群的自动监测。

基于改进YOLOv7的密集鱼群计数检测

【目的】提高在水体浑浊和鱼群高密度聚集等复杂环境中的鱼群检测精度。【方法】提出一种基于双层路由注意力机制(BiFormer)和Normalized Wasserstein Distance(NWD)损失函数的改进YOLOv7的密集鱼群计数检测方法。在保留细粒度特征的基础上,提高模型对多尺度特征的学习能力,同时降低模型对模糊图像中小目标位置偏差的敏感性,加强对浑浊水域中鱼群的识别能力。为评估该模型的有效性,在红鳍东方鲀(Takifugu rubripes)数据集上与其他网络模型进行对比实验。【结果】该方法在红鳍东方鲀数据集上的准确率和召回率分别达到98.05%和97.69%,平均精度达到99.10%,较YOLOv7相比分别提升2.46%、3.73%和2.62%。与目前检测准确率较高的其他水下目标检测模型相比,平均精度平均提高4.25%。【结论】实现真实养殖环境下浑浊水域中鱼群的准确检测,有助于科学指导工业化水产养殖的生产管理,提高养殖效率,减少资源浪费。

全球尺度下的海洋鱼类图像智能分类研究进展

在全球尺度上了解鱼类物种组成、丰度及时空分布等,将有助于其生物多样保护。水下图像采集是获取鱼类物种多样性数据的主要调查手段之一,但图像信息分析工作耗时耗力。2015年以来,海洋鱼类图像数据集更新和深度学习模型算法优化等方面取得了一系列进展,但细粒度分类表现仍显不足,研究成果的生产实践应用相对薄弱。因此,该文首先分析海洋相关行业对鱼类自动化图像分类的需求,然后综合介绍鱼类图像数据集和深度学习算法应用,并分析了所面临的小样本下的细粒度分析等主要挑战及相应解决方法。最后探讨了基于深度学习的海洋鱼类图像自动化分类对相关图像信息处理研究及应用平台对未来在生态环境监测等海洋相关产业领域的重要性及其前景。该文旨在为快速了解基于深度学习的海洋鱼类图像自动化分类的研究背景、进展和未来方向的工作者提供相关信息。

水产养殖中水质与鱼类行为双向映射模型研究

在水产养殖中,水质参数与鱼类活动之间有着密不可分的相互映射关系。过去的监测更多偏向于单向映射,一般都是通过鱼类的行为表明水质的情况。针对仅仅通过鱼类行为反映水质情况会产生误判和滞后的问题,本文构建一种基于随机森林的鱼类行为与水质情况双向映射模型。双向映射模型不仅可以提供更多的信息从而提高预测的准确性,而且也可以通过相互验证提高模型的可靠性。首先,通过引入可变形卷积模块对YOLO v7进行改进,利用改进模型检测出视频中鱼类的位置再通过前后帧的坐标量化出鱼的游动参数。随后,将采集到的鱼类游动参数及对应的水质参数作为输入,使用随机森林模型进行分类、回归,分别完成鱼类游动参数和水质参数具体数值的预测以及指标异常级别的预测,从而得到双向映射关系。为了表明模型的泛化能力,分别在黎安港和新村港渔场2个数据集下进行实验。实验结果表明:提出的方法可以较好地实现鱼类行为与水质关系的双向映射,其中,分类实验平均准确率可以达到90.947%,回归实验决定系数R^(2)的平均值可以达到0.801。

预干燥对油炸外裹糊鱼块油脂渗透和质构特性的影响

本研究将外裹糊鱼块置于鼓风干燥箱中,40℃干燥3~9 h(3、4.5、6、7.5、9 h)后180℃油炸60 s,分析油炸前后外裹糊鱼块的水分状态、油炸外裹糊鱼块的油脂含量与分布、微观结构及质构特性,模拟油炸过程中油脂的吸收,探讨预干燥对油炸外裹糊鱼块油脂渗透和质构特性的影响。结果显示,油炸后外壳的深层结合水(T21)、弱结合水(T22)和自由水(T23)以及鱼块的结合水(T21)、肌纤维中(T22)和外部(T23)的水分含量均减小。随着干燥时间的延长,油炸后外壳T22峰的起始时间逐渐减小,水分的自由度降低,且T21和T22峰的信号强度减小,结合水向自由水转化;鱼块的T22峰只有干燥9h的发生明显左移;油炸外裹糊鱼块的表面油脂含量和总油脂含量呈减小趋势,表面渗透油脂含量逐渐增加;外壳结构先紧密后粗糙,鱼块表面先光滑后出现裂缝和孔洞;外壳中荧光强度逐渐减弱,苏丹红染色幅度逐渐减小,外壳与鱼块交界处的染色幅度逐渐增大;外壳的硬度和酥性先减小后增大,而鱼块的弹性和咀嚼性呈现相反趋势。这些结果表明,预干燥降低了外裹糊鱼块水分的自由度,改变了油炸外裹糊鱼块的微观结构,从而影响了油脂的渗透和质构特性。本研究可为低脂油炸外裹糊鱼制品的规模化生产提供科学指导和技术支撑。

KoiU⁃Net:基于多特征增强融合的纺锤形鱼类图像分割方法

针对纺锤形鱼类图像分割任务中存在的边缘不清、特征模糊等问题,提出一种基于多特征增强融合的纺锤形鱼类图像分割网络KoiU⁃Net。该网络在经典的U⁃Net模型基础上,设计了多尺度特征交叉感知模块和多尺度特征融合模块来增强对纺锤形鱼类特征的处理能力,以应对纺锤形鱼类图像分割中存在的边缘模糊、特征复杂的问题。同时,设计了多尺度上采样模块以提取更精细的特征信息。在纺锤形鱼类图像数据集上的实验表明,相较于原始U⁃Net等其他分割网络,KoiU⁃Net取得了平均98.63%的分割精度的显著提升。各设计模块的有效性也通过消融实验得以验证,尤其是多尺度特征交叉感知模块对提升分割性能具有关键作用。该研究为进一步实现纺锤形鱼类生长状态监测提供了有效的技术支撑,为该领域的进一步发展奠定了基础。

《中庸》引《诗经》“鸢飞戾天,鱼跃于渊”的内蕴之意

《中庸》第十二章引《诗经·大雅·旱麓》中“鸢飞戾天、鱼跃与渊”一诗句阐释论点“君子之道费而隐”,是围绕《诗经》原句本义进行论述的。在《诗经》原句歌颂君子之道沐泽大地的基础上,深化原句意味,使君子之道的实施对象具有普遍性,赋予其更深的教化意义。