Krill Swarms Research Articles

Design and Implementation of Virtualization Cloud Computing System Intelligent Terminal Application Layer

Cloud task scheduling has become a trend, and the shortcomings of traditional scheduling algorithms can be optimized through mathematical models of other cloud task scheduling scenarios. In order to improve the virtualization data processing effect of intelligent terminal application layer, this paper proposes an improved krill swarm optimization algorithm based on adaptive weight. The optimization of cluster load balancing and task average response time ratio are used to improve the convergence and accuracy of task scheduling algorithm. Moreover, this paper uses CloudSim simulation tool to conduct experiments to verify the effectiveness of the proposed model. In addition, this paper proposes an application-based virtualization method, which virtualizes the application programs inside the host machine into the virtualization software inside the virtual machine, so that the virtual machine can access it. Finally, this paper verifies the reliability of the proposed method with experiments, thus providing a theoretical reference for the subsequent design of intelligent terminal application layer virtualization cloud computing system. Compared with the traditional way of using physical hardware, using virtual machine hardware is more flexible, efficient and safe, which brings great convenience to the development and deployment of applications.

Improved krill swarm algorithm and its application in structural optimization

This paper improves and perfects the standard Krill herd algorithm (KH), which has the defects of slow convergence speed, insufficient calculation accuracy and easy to fall into local optimal solution for complex problems. An improved Krill herd algorithm (SDEKH) integrating improved differential evolution operator and S-type adaptive inertia weight is proposed. SDEKH, KH and other intelligent algorithms are compared and tested through a variety of standard test functions to verify the excellent performance of SDEKH; SDEKH is used to optimize the design of truss structure. By comparing the optimization results with other methods, it is verified that the optimization efficiency and accuracy of SDEKH are improved, which provides a more efficient and accurate method for the optimization design of engineering structures.

Evaluating the hypoxic tolerance of two maturity stages of Antarctic krill (Euphausia superba) at its range edge

AbstractThe South Georgia region of the Southern Ocean represents the northernmost range edge for Antarctic krill. Of concern is the extent to which rapid warming of surface water temperatures and reduced oxygen contents around this region might challenge the physiological tolerance of krill, particularly the later maturity stages. Hypoxia is generally considered to be less than 30 to 20% of air saturation, hereafter as threshold hypoxia, while less than 10% of air saturation would qualify as severe hypoxia. These levels are unlikely to occur in the Southern Ocean but might happen in the middle of dense krill swarms. We investigated gene expression and biochemical markers related to aerobic metabolism, antioxidant defence, and heat-shock response under 6-h threshold (4 kPa; TH) and 1-h severe (0.6 kPa; SH) hypoxia exposure, to understand how hypoxia might alter respiratory and biochemical pathways in adult and subadult krill. After 6-h TH, subadults induced expression of citrate synthase (CS), and mitochondrial superoxide dismutase (also after 1-h SH) over normoxic expression levels. The maturity stages responded differently in glutathione peroxidase (1-h SH; lower in subadults and higher in adults), and CS (6-h TH; higher in subadults and lower in adults) activities as for the oxidative damage marker to lipids (6-h TH; lower in subadults and higher in adults). Subadults had a greater capacity than adults to deal with hypoxic conditions. This may be a strategy allowing them to exist in larger swarms to reduce predation pressure before reaching reproductive condition.

A method to estimate prey densityfrom single-camera images: A case study with chinstrap penguins and Antarctic krill.

Estimating the densities of marine prey observed in animal-borne video loggers when encountered by foraging predators represents an important challenge for understanding predator-prey interactions in the marine environment. We used video images collected during the foraging trip of one chinstrap penguin (Pygoscelis antarcticus) from Cape Shirreff, Livingston Island, Antarctica to develop a novel approach for estimating the density of Antarctic krill (Euphausia superba) encountered during foraging activities. Using the open-source Video and Image Analytics for a Marine Environment (VIAME), we trained a neural network model to identify video frames containing krill. Our image classifier has an overall accuracy of 73%, with a positive predictive value of 83% for prediction of frames containing krill. We then developed a method to estimate the volume of water imaged, thus the density (N·m-3) of krill, in the 2-dimensional images. The method is based on the maximum range from the camera where krill remain visibly resolvable and assumes that mean krill length is known, and that the distribution of orientation angles of krill is uniform. From 1,932 images identified as containing krill, we manually identified a subset of 124 images from across the video record that contained resolvable and unresolvable krill necessary to estimate the resolvable range and imaged volume for the video sensor. Krill swarm density encountered by the penguins ranged from 2 to 307 krill·m-3 and mean density of krill was 48 krill·m-3 (sd = 61 krill·m-3). Mean krill biomass density was 25 g·m-3. Our frame-level image classifier model and krill density estimation method provide a new approach to efficiently process video-logger data and estimate krill density from 2D imagery, providing key information on prey aggregations that may affect predator foraging performance. The approach should be directly applicable to other marine predators feeding on aggregations of prey.

Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior.

The protection of plants in mountainous and hilly areas differs from that in plain areas due to the complex terrain, which divides the work plot into many narrow plots. When designing the path planning method for plant protection UAVs, it is important to consider the generality in different working environments. To address issues such as poor path optimization, long operation time, and excessive iterations required by traditional swarm intelligence algorithms, this paper proposes a bionic three-dimensional path planning algorithm for plant protection UAVs. This algorithm aims to plan safe and optimal flight paths between work plots obstructed by multiple obstacle areas. Inspired by krill group behavior and based on group intelligence algorithm theory, the bionic three-dimensional path planning algorithm consists of three states: "foraging behavior", "avoiding enemy behavior", and "cruising behavior". The current position information of the UAV in the working environment is used to switch between these states, and the optimal path is found after several iterations, which realizes the adaptive global and local convergence of the track planning, and improves the convergence speed and accuracy of the algorithm. The optimal flight path is obtained by smoothing using a third-order B-spline curve. Three sets of comparative simulation experiments are designed to verify the performance of this proposed algorithm. The results show that the bionic swarm intelligence algorithm based on krill swarm behavior reduces the path length by 1.1~17.5%, the operation time by 27.56~75.15%, the path energy consumption by 13.91~27.35%, and the number of iterations by 46~75% compared with the existing algorithms. The proposed algorithm can shorten the distance of the planned path more effectively, improve the real-time performance, and reduce the energy consumption.

A Study of IKH Algorithm for Civic Education Strategies of Graduate Medical Students in Information Age

Abstract In this paper, we use the krill swarm optimization algorithm to optimize the optimization-seeking ability of the projection-seeking analysis model and construct the IKH-PP model. Evaluation indexes of ideological and political education strategies are built from seven dimensions, such as moral quality and political performance, and the index values are standardized using the extreme value normalization method. Create and enhance the projection indicator function, sort based on the projection value size, and determine the advantages and disadvantages of various samples. Taking psychology as the perspective, efforts were made to explore a collaborative educational path for psychological counseling and ideological education for medical students using the psychological perspective. The results showed that 50% of the 600 medical students at University Z chose “realization of ideal pursuit,” and they were generally more positive and progressive. However, only 48 of them expressed that they were very concerned about current affairs and that their political awareness still needed to be improved. Before and after the collaborative psychological counseling and political education interventions, there were statistically significant differences between Group I and Group II in the G3/F3 dimension (P=0.047, 0.008) and the G11/F11 dimension (P=0.022, 0.034), which were statistically significant or not. There were statistically significant differences in the overall access to Civics and all dimensions of Civics among medical students of different genders (P<0.05), grades (P<0.001), and political profiles (P<0.05) students.

Integration of Marxist ideology education in the education of ideologic and politic system of colleges and universities

Abstract Based on the technology of big data, this paper utilizes the IKH algorithm to study the optimal selection of education of ideologic and politic strategies, and constructs the education of ideologic and politic strategy selection model according to the IKH-PP algorithm according to the positive and negative incentive strategies of education of ideologic and politic and the improved krill swarm algorithm. Starting from the ideological and political teaching objectives, the evaluation system is determined, and the objective function of the education of ideologic and politic strategy selection model of the IKH-PP algorithm is derived by using projection-seeking analysis. Combining the research dimensions and research hypotheses, we complete the design of the ideological and political research in institution of higher education that integrates Marxist ideology, and use statistical analysis to conduct empirical analysis of education of ideologic and politic in institution of higher education. The results depict that on the model, compared with KH-PP, the precision of education of ideologic and politic strategy selection is improved by 3.96% and 4.1%, and IKH-PP can effectively improve the precision of education of ideologic and politic strategy selection. On the analysis of teaching examples, the Sig. Values of all 11 independent variables are less than 0.01, indicating that all 11 common factors show a significant positive correlation with the ideologic and behavioral choices of making ideologic and politic of identifying with the integration of Marxism in the value conflict. This study makes the theory of education of ideologic and politic in institution of higher education richer and better and is of great significance to the development of ideological and political and Marxist ideology integration teaching.

Plasticity and seasonality of the vertical migration behaviour of Antarctic krill using acoustic data from fishing vessels.

Understanding the vertical migration behaviour of Antarctic krill is important for understanding spatial distribution, ecophysiology, trophic interactions and carbon fluxes of this Southern Ocean key species. In this study, we analysed an eight-month continuous dataset recorded with an ES80 echosounder on board a commercial krill fishing vessel in the southwest Atlantic sector of the Southern Ocean. Our analysis supports the existing hypothesis that krill swarms migrate into deeper waters during winter but also reveals a high degree of variability in vertical migration behaviour within seasons, even at small spatial scales. During summer, we found that behaviour associated with prolonged surface presence primarily occurred at low surface chlorophyll a concentrations whereas multiple ascent-descent cycles per day occurred when surface chlorophyll a concentrations were elevated. The high plasticity, with some krill swarms behaving differently in the same location at the same time, suggests that krill behaviour is not a purely environmentally driven process. Differences in life stage, physiology and type of predator are likely other important drivers. Finally, our study demonstrates new ways of using data from krill fishing vessels, and with the routine collection of additional information in potential future projects, they have great potential to significantly advance our understanding of krill ecology.

A Novel Long and Short-Term Memory Network-Based Krill Herd Algorithm for Explainable Art Sentiment Analysis in Interior Decoration Environment

Aiming at the problem that most existing models of art sentiment analysis only consider text encoding from the word level, this paper proposes a novel long and short-term memory network-based krill herd algorithm for explainable art sentiment analysis in interior decoration environment. Firstly, multi-scale convolution is used to capture local correlation of different granularity, so as to obtain more semantic information of different levels and form richer text representation. Then, a gating mechanism is introduced to control the path of sentiment information flowing to the aggregation layer. An improved krill swarm algorithm based on cosine control factor and Cauchy factor is proposed to solve the model. Finally, the full connection layer and argmax function are used to achieve sentiment classification. The experimental results show that compared with other advanced models, the proposed model can improve the accuracy of emotion classification by 2.3% and 0.8% respectively on two public data sets of IMDB and Yelp2014, and obtain the minimum root mean square error (RMSE).

Scientific echosounder data provide a predator’s view of Antarctic krill (Euphausia superba)

Raw acoustic data were collected in East Antarctica from the RSV Aurora Australis during two surveys: the Krill Availability, Community Trophodynamics and AMISOR Surveys (KACTAS) and the Krill Acoustics and Oceanography Survey (KAOS) in the East Antarctic (centre coordinate 66.5° S, 63° E). The KACTAS survey was conducted between 14th to 21st January and 2001, and the KAOS survey was conducted between 16 January and 1 February 2003. We examine the Antarctic krill (Euphausia superba) component of these surveys and provide scientific echosounder (EK500 and EK60) data collected at 38, 120 and 200 kHz, cold water (−1 °C) echosounder calibration parameters and accompanying krill length frequency distributions obtained from trawl data. We processed the acoustic data to apply calibration values and remove noise. The processed data were used to isolate echoes arising from swarms of krill and to estimate metrics for each krill swarm, including internal density and individual swarm biomass. The krill swarm data provide insights to a predators’ views of krill distribution and density.

Commercial krill fishing within a foraging supergroup of fin whales in the Southern Ocean.

Commercial krill fishing within a foraging supergroup of fin whales in the Southern Ocean.

Design and Experiment of Drifting Buoy for Antarctic Krill Swarm Detection

Design and Experiment of Drifting Buoy for Antarctic Krill Swarm Detection

Research on the Planning of Electric Vehicle Fast Charging Stations Considering User Selection Preferences

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

Spatial link between Adélie penguin foraging effort and krill swarm abundance and distribution

Understanding how predator foraging behaviour is influenced by the distribution and abundance of prey is a fundamental challenge in marine foraging ecology. This is particularly relevant in Southern Ocean ecosystems where the relationships between select predator species and Antarctic krill (Euphausia superba) can inform ecosystem conservation and precautionary fisheries management. In this study, we examine the spatial associations between krill swarm characteristics and Adélie penguin (Pygoscelis adeliae) foraging effort at Béchervaise Island, a long-term monitoring site in East Antarctica. Spatially integrating two years of regional-scale krill acoustic data with contemporaneous horizontal and vertical movement information from chick-rearing adult Adélie penguins, we assessed how penguin foraging effort changed in relation to krill swarm abundance and distribution across the survey area. Our findings show that penguin diving effort was focused in areas with a high number of krill swarms, yet they did not focus their effort in areas with high krill biomass. These results suggest the spatial organisation of Adélie penguin foraging effort can provide an indication of krill presence (and/or availability) but may not reflect krill abundance. We discuss our results in the context of penguin foraging strategies, capturing single krill within the water column rather than the engulfment feeding strategy of larger marine mammals such as whales. Our work substantially improves understanding of penguin-krill dynamics in East Antarctica and provides a greater level of nuance regarding the utility of Adélie penguins as indicator species under CCAMLR’s Ecosystem Monitoring Programme (CEMP). Understanding these predator-prey linkages will become increasingly important for managing any expanding krill fisheries in the region or changes in the prey field under future climate change scenarios. Thus, our results can be interpreted alongside other ecological indicators to support management of the East Antarctic sector of the Southern Ocean ecosystem.

Predicting foraging dive outcomes in chinstrap penguins using biologging and animal-borne cameras

Abstract Direct observation of foraging behavior is not always possible, especially for marine species that hunt underwater. However, biologging and tracking devices have provided detailed information about how various species use their habitat. From these indirect observations, researchers have inferred behaviors to address a variety of research questions, including the definition of ecological niches. In this study, we deployed video cameras with GPS and time-depth recorders on 16 chinstrap penguins (Pygoscelis antarcticus) during the brood phase of the 2018–2019 breeding season on Signy (South Orkney Islands). More than 57 h of footage covering 770 dives were scrutinized by two observers. The outcome of each dive was classified as either no krill encounter, individual krill or krill swarm encounter and the number of prey items caught per dive was estimated. Other variables derived from the logging devices or from the environment were used to train a machine-learning algorithm to predict the outcome of each dive. Our results show that despite some limitations, the data collected from the footage was reliable. We also demonstrate that it was possible to accurately predict the outcome of each dive from dive and horizontal movement variables in a manner that has not been used for penguins previously. For example, our models show that a fast dive ascent rate and a high density of dives are good indicators of krill and especially of swarm encounter. Finally, we discuss how video footage can help build accurate habitat models to provide wider knowledge about predator behavior or prey distribution.

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.

Image Segmentation under the Optimization Algorithm of Krill Swarm and Machine Learning.

This study aims to improve the efficiency and accuracy of image segmentation, and to compare and study traditional threshold-based image segmentation methods and machine learning model-based image segmentation methods. The krill herb optimization algorithm is combined with the traditional maximum between-class variance function to form a new graph segmentation algorithm. The pet dataset is used to train the algorithm model and build an image semantic segmentation system. The results show that when the traditional Ostu algorithm performs image single-threshold segmentation, the number of iterations is about 256. When double-threshold segmentation is performed, the number of iterations increases exponentially, and the execution time is about 2 s. The number of iterations of the improved Krill Herd algorithm in single-threshold segmentation is 6.95 times, respectively. The execution time for double-threshold segmentation is about 0.24 s. The number of iterations is only improved by a factor of 0.19. The average classification accuracy of the Unet network model and the SegNet network model is 86.3% and 91.9%, respectively. The average classification accuracy of the DC-Unet network model reaches 93.1%. This shows that the proposed fusion algorithm has high optimization efficiency and stronger practicability in multithreshold image segmentation. The DC-Unet network model can improve the image detail segmentation effect. The research provides a new idea for finding an efficient and accurate image segmentation method.

Research on economic dispatch of integrated energy system based on improved krill swarm algorithm

This paper constructs an economic dispatch model of an integrated energy system including grid, wind power, photovoltaic and energy storage devices based on combined cooling, heating and power system, and optimizes the dispatch model based on improved krill swarm optimization algorithm with the system integrated cost as the objective function, taking into account the electric power balance constraint, cooling and heating load balance constraint and each micro-source output constraint. The simulation results show that the improved krill swarm algorithm can reduce the daily integrated cost by $124.1 and $50.3 respectively, which can save 9.5% and 3.9% of the daily integrated cost compared with the conventional krill swarm algorithm and the artificial bee colony algorithm. The constructed integrated energy system model can effectively improve the energy utilization and economic efficiency of the system.

Self-organization and information transfer in Antarctic krill swarms.

Antarctic krill swarms are one of the largest known animal aggregations, and yet, despite being the keystone species of the Southern Ocean, little is known about how swarms are formed and maintained. Understanding the local interactions between individuals that provide the basis for these swarms is fundamental to knowing how swarms arise in nature, and what potential factors might lead to their breakdown. Here, we analysed the trajectories of captive, wild-caught krill in 3D to determine individual-level interaction rules and quantify patterns of information flow. Our results demonstrate that krill align with near neighbours and that they regulate both their direction and speed relative to the positions of groupmates. These results suggest that social factors are vital to the formation and maintenance of swarms. Furthermore, krill operate a novel form of collective organization, with measures of information flow and individual movement adjustments expressed most strongly in the vertical dimension, a finding not seen in other swarming species. This research represents a vital step in understanding the fundamentally important swarming behaviour of krill.

Antarctic krill likely avoid underwater gliders

The extent to which marine species avoid survey vessels is the subject of considerable debate, with stimuli such as noise and pressure waves receiving particular attention. By some estimates, avoidance behaviour is a major bias in the assessment of pelagic species distribution and density. Surveyors of the marine environment are increasingly using autonomous platforms; thus the question of avoidance of these novel platforms must be considered.A long-duration, underwater glider with an integrated echo-sounder was deployed to measure the density and distribution of Antarctic krill. We show that krill reacted to the glider, despite the low-noise profile of the platform. The weighted-mean depth of krill swarms was observed to deepen when a glider came within 30 m. In addition, we observed a near-range decrease in depth-integrated acoustic backscatter and we demonstrate that this effect is not explained by the instrument's acoustic footprint. The potential triggers for these behaviours are discussed.The list of sensors available for use on a glider is increasing, with some producing sound, light or other potential stimuli for behavioural response. Our paper demonstrates that caution should be used when designing mission payloads and in the interpretation of acoustic data for animal density from gliders. A key consideration for surveyors may be the factoring in of an exclusion zone from their analyses.