Search results for: drone swarm

Authors: Chen Guo, Heng Tang, Ben Niu

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Clustering splits objects into different groups based on similarity, making the objects have higher similarity in the same group and lower similarity in different groups. Thus, clustering can be treated as an optimization problem to maximize the intra-cluster similarity or inter-cluster dissimilarity. In real-world applications, the datasets often have some complex characteristics: sparse, overlap, high dimensionality, etc. When facing these datasets, simultaneously optimizing two or more objectives can obtain better clustering results than optimizing one objective. However, except for the objectives weighting methods, traditional clustering approaches have difficulty in solving multi-objective data clustering problems. Due to this, evolutionary multi-objective optimization algorithms are investigated by researchers to optimize multiple clustering objectives. In this paper, the Data Clustering algorithm based on Multi-objective Periodic Bacterial Foraging Optimization with two Learning Archives (DC-MPBFOLA) is proposed. Specifically, first, to reduce the high computing complexity of the original BFO, periodic BFO is employed as the basic algorithmic framework. Then transfer the periodic BFO into a multi-objective type. Second, two learning strategies are proposed based on the two learning archives to guide the bacterial swarm to move in a better direction. On the one hand, the global best is selected from the global learning archive according to the convergence index and diversity index. On the other hand, the personal best is selected from the personal learning archive according to the sum of weighted objectives. According to the aforementioned learning strategies, a chemotaxis operation is designed. Third, an elite learning strategy is designed to provide fresh power to the objects in two learning archives. When the objects in these two archives do not change for two consecutive times, randomly initializing one dimension of objects can prevent the proposed algorithm from falling into local optima. Fourth, to validate the performance of the proposed algorithm, DC-MPBFOLA is compared with four state-of-art evolutionary multi-objective optimization algorithms and one classical clustering algorithm on evaluation indexes of datasets. To further verify the effectiveness and feasibility of designed strategies in DC-MPBFOLA, variants of DC-MPBFOLA are also proposed. Experimental results demonstrate that DC-MPBFOLA outperforms its competitors regarding all evaluation indexes and clustering partitions. These results also indicate that the designed strategies positively influence the performance improvement of the original BFO.

Keywords: data clustering, multi-objective optimization, bacterial foraging optimization, learning archives

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Authors: Reza Karimpour

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Ports play a decisive role in the EU's external and internal trade, as about 74% of imports and exports and 37% of exchanges go through ports. Although ports, especially Deep Sea Shipping (DSS) ports, are integral nodes within multimodal logistic flows, Short Sea Shipping (SSS) and inland waterways are not so well integrated. The automated vessels and supply chain optimisations for sustainable shortsea shipping (MOSES) project aims to enhance the short sea shipping component of the European supply chain by addressing the vulnerabilities and strains related to the operation of large containerships. The MOSES concept can be shortly described as a large containership (mother-vessel) approaching a DSS port (or a large container terminal). Upon her arrival, a combined intelligent mega-system consisting of the MOSES Autonomous tugboat swarm for manoeuvring and the MOSES adapted AutoMoor system. Then, container handling processes are ready to start moving containers to their destination via hinterland connections (trucks and/or rail) or to be shipped to destinations near small ports (on the mainland or island). For the first case, containers are stored in a dedicated port area (Storage area), waiting to be moved via trucks and/or rail. For the second case, containers are stacked by existing port equipment near-dedicated berths of the DSS port. They then are loaded on the MOSES Innovative Feeder Vessel, equipped with the MOSES Robotic Container-Handling System that provides (semi-) autonomous (un) feeding of the feeder. The Robotic Container-Handling System is remotely monitored through a Shore Control Centre. When the MOSES innovative Feeder vessel approaches the small port, where her docking is achieved without tugboats, she automatically unloads the containers using the Robotic Container-Handling System on the quay or directly on trucks. As a result, ports with minimal or no available infrastructure may be effectively integrated with the container supply chain. Then, the MOSES innovative feeder vessel continues her voyage to the next small port, or she returns to the DSS port. MOSES exploitation activity mainly aims to exploit research outcomes beyond the project, facilitate utilisation of the pilot results by others, and continue the pilot service after the project ends. By the mid-lifetime of the project, the exploitation plan introduces the reader to the MOSES project and its key exploitable results. It provides a plan for delivering the MOSES innovations to the market as part of the overall exploitation plan.

Keywords: automated vessels, exploitation, shortsea shipping, supply chain

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Authors: Jessica Kate Simonds

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The last successful hijacking perpetrated by Somali pirates in 2012 represented a critical turning point for the identity and brand of Indian Ocean (IO) insecurity, coined in this paper as the era of the post-piratical. This paper explores the broadening of the PMSC business model to account and contribute to the design of a new IO security environment that prioritises foreign and insurgency drone activity and Houthi rebel operations as the main threat to merchant shipping in the post-2012 era. This study is situated within a longer history of analysing maritime insecurity and also contributes a bespoke conceptual framework that understands the sea as a space that is produced and reproduced relative to existing and emerging threats to merchant shipping based on bespoke models of information sharing and intelligence acquisition. This paper also makes a prominent empirical contribution by drawing on a post-positivist methodology, data drawn from original semi-structured interviews with senior maritime insurers and active merchant seafarers that is triangulated with industry-produced guidance such as the BMP series as primary data sources. Each set is analysed through qualitative discourse and content analysis and supported by the quantitative data sets provided by the IMB Piracy Reporting center and intelligence networks. This analysis reveals that mechanisms such as the IGP&I Maritime Security Committee and intelligence divisions of PMSC’s have driven the exchanges of knowledge between land and sea and thus the reproduction of the maritime security environment through new regulations and guidance to account dones, rebels and bombs as the key challenges in the IO, beyond piracy. A contribution of this paper is the argument that experts who may not be in the highest-profile jobs are the architects of maritime insecurity based on their detailed knowledge and connections to vessels in transit. This paper shares the original insights of those who have served in critical decision making spaces to demonstrate that the development and refinement of industry produced deterrence guidance that has been accredited to the mitigation of piracy, have shaped new editions such as BMP 5 that now serve to frame a new security environment that prioritises the mitigation of risks from drones and WBEID’s from both state and insurgency risk groups. By highlighting the experiences and perspectives of key players on both land and at sea, the key finding of this paper is outlining that as pirates experienced a financial boom by profiteering from their bespoke business model during the peak of successful hijackings, the private security market encountered a similar level of financial success and guaranteed risk environment in which to prospect business. Thus, the reproduction of the Indian Ocean as a maritime security environment reflects a new found purpose for PMSC’s as part of the broader conglomerate of maritime insurers, regulators, shipowners and managers who continue to redirect the security consciousness and IO brand of insecurity.

Keywords: maritime security, private security, risk intelligence, political geography, international relations, political economy, maritime law, security studies

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Authors: Alberto Azevedo, Ricardo Martins, André B. Fortunato, Anabela Oliveira

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Water is under severe threat today because of the rising population, increased agricultural and industrial needs, and the intensifying effects of climate change. Due to sea-level rise, erosion, and demographic pressure, the coastal regions are of significant concern to the scientific community. The Water Monitoring Sentinel Cloud platform (WORSICA) service is focused on providing new tools for monitoring water in coastal and inland areas, taking advantage of remote sensing, in situ and tidal modeling data. WORSICA is a service that can be used to determine the coastline, coastal inundation areas, and the limits of inland water bodies using remote sensing (satellite and Unmanned Aerial Vehicles - UAVs) and in situ data (from field surveys). It applies to various purposes, from determining flooded areas (from rainfall, storms, hurricanes, or tsunamis) to detecting large water leaks in major water distribution networks. This service was built on components developed in national and European projects, integrated to provide a one-stop-shop service for remote sensing information, integrating data from the Copernicus satellite and drone/unmanned aerial vehicles, validated by existing online in-situ data. Since WORSICA is operational using the European Open Science Cloud (EOSC) computational infrastructures, the service can be accessed via a web browser and is freely available to all European public research groups without additional costs. In addition, the private sector will be able to use the service, but some usage costs may be applied, depending on the type of computational resources needed by each application/user. Although the service has three main sub-services i) coastline detection; ii) inland water detection; iii) water leak detection in irrigation networks, in the present study, an application of the service to Óbidos lagoon in Portugal is shown, where the user can monitor the evolution of the lagoon inlet and estimate the topography of the intertidal areas without any additional costs. The service has several distinct methodologies implemented based on the computations of the water indexes (e.g., NDWI, MNDWI, AWEI, and AWEIsh) retrieved from the satellite image processing. In conjunction with the tidal data obtained from the FES model, the system can estimate a coastline with the corresponding level or even topography of the inter-tidal areas based on the Flood2Topo methodology. The outcomes of the WORSICA service can be helpful for several intervention areas such as i) emergency by providing fast access to inundated areas to support emergency rescue operations; ii) support of management decisions on hydraulic infrastructures operation to minimize damage downstream; iii) climate change mitigation by minimizing water losses and reduce water mains operation costs; iv) early detection of water leakages in difficult-to-access water irrigation networks, promoting their fast repair.

Keywords: remote sensing, coastline detection, water detection, satellite data, sentinel, Copernicus, EOSC

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Gaëtan Calvar, Christophe Bouvier, Alexis Blasselle

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This paper presents the Sea Striker, a compact hydrofoil designed with the goal to address some of the issues raised by the recent evolutions of naval missions, threats and operation theatres in modern warfare. Able to perform a wide range of operations, the Sea Striker is a 40-meter stealth surface combatant equipped with a gas turbine and aft and forward foils to reach high speeds. The Sea Striker's stealthiness is enabled by the combination of composite structure, exterior design, and the advanced integration of sensors. The ship is fitted with a powerful and adaptable combat system, ensuring a versatile and efficient response to modern threats. Lightly Manned with a core crew of 10, this hydrofoil is highly automated and can be remoted pilote for special force operation or transit. Such a kind of ship is not new: it has been used in the past by different navies, for example, by the US Navy with the USS Pegasus. Nevertheless, the recent evolutions in science and technologies on the one hand, and the emergence of new missions, threats and operation theatres, on the other hand, put forward its concept as an answer to nowadays operational challenges. Indeed, even if multiples opinions and analyses can be given regarding the modern warfare and naval surface operations, general observations and tendencies can be drawn such as the major increase in the sensors and weapons types and ranges and, more generally, capacities; the emergence of new versatile and evolving threats and enemies, such as asymmetric groups, swarm drones or hypersonic missile; or the growing number of operation theatres located in more coastal and shallow waters. These researches were performed with a complete study of the ship after several operational performance computations in order to justify the relevance of using ships like the Sea Striker in naval surface operations. For the selected scenarios, the conception process enabled to measure the performance, namely a “Measure of Efficiency” in the NATO framework for 2 different kinds of models: A centralized, classic model, using large and powerful ships; and A distributed model relying on several Sea Strikers. After this stage, a was performed. Lethal, agile, stealth, compact and fitted with a complete set of sensors, the Sea Striker is a new major player in modern warfare and constitutes a very attractive response between the naval unit and the combat helicopter, enabling to reach high operational performances at a reduced cost.

Keywords: surface combatant, compact, hydrofoil, stealth, velocity, lethal

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Authors: Vlad Andrei Raducanu, Mariana Lucia Angelescu, Ion Cinca, Vasile Danut Cojocaru, Doina Raducanu

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By using smart digital tools, such as generative design (GD) and digital fabrication (DF), problems of high actuality concerning resources optimization (materials, energy, time) can be solved and applications or products of free-form type can be created. In the new digital technology materials are active, designed in response to a set of performance requirements, which impose a total rethinking of old material practices. The article presents the design procedure key steps of a free-form architectural object - a column type one with connections to get an adaptive 3D surface, by using the parametric design methodology and by exploiting the properties of conventional metallic materials. In parametric design the form of the created object or space is shaped by varying the parameters values and relationships between the forms are described by mathematical equations. Digital parametric design is based on specific procedures, as shape grammars, Lindenmayer - systems, cellular automata, genetic algorithms or swarm intelligence, each of these procedures having limitations which make them applicable only in certain cases. In the paper the design process stages and the shape grammar type algorithm are presented. The generative design process relies on two basic principles: the modeling principle and the generative principle. The generative method is based on a form finding process, by creating many 3D spatial forms, using an algorithm conceived in order to apply its generating logic onto different input geometry. Once the algorithm is realized, it can be applied repeatedly to generate the geometry for a number of different input surfaces. The generated configurations are then analyzed through a technical or aesthetic selection criterion and finally the optimal solution is selected. Endless range of generative capacity of codes and algorithms used in digital design offers various conceptual possibilities and optimal solutions for both technical and environmental increasing demands of building industry and architecture. Constructions or spaces generated by parametric design can be specifically tuned, in order to meet certain technical or aesthetical requirements. The proposed approach has direct applicability in sustainable architecture, offering important potential economic advantages, a flexible design (which can be changed until the end of the design process) and unique geometric models of high performance.

Keywords: parametric design, algorithmic procedures, free-form architectural object, sustainable architecture

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Authors: David Gottlieb, Jason Yoder

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The feeling of loneliness is not uncommon in modern society, and yet, there is a fundamental lack of understanding in its origins and purpose in nature. One interpretation of loneliness is that it is a subjective experience that punishes a lack of social behavior, and thus its emergence in human evolution is seemingly tied to the survival of early human tribes. Still, a common counterintuitive response to loneliness is a state of hypervigilance, resulting in social withdrawal, which may appear maladaptive to modern society. So far, no computational model of loneliness’ effect during evolution yet exists; however, agent-based models (ABM) can be used to investigate social behavior, and applying evolution to agents’ behaviors can demonstrate selective advantages for particular behaviors. We propose an ABM where each agent contains four social behaviors, and one goal-seeking behavior, letting evolution select the best behavioral patterns for resource allocation. In our paper, we use an algorithm similar to the boid model to guide the behavior of agents, but expand the set of rules that govern their behavior. While we use cohesion, separation, and alignment for simple social movement, our expanded model adds goal-oriented behavior, which is inspired by particle swarm optimization, such that agents move relative to their personal best position. Since agents are given the ability to form connections by interacting with each other, our final behavior guides agent movement toward its social connections. Finally, we introduce a mechanism to represent a state of loneliness, which engages when an agent's perceived social involvement does not meet its expected social involvement. This enables us to investigate a minimal model of loneliness, and using evolution we attempt to elucidate its value in human survival. Agents are placed in an environment in which they must acquire resources, as their fitness is based on the total resource collected. With these rules in place, we are able to run evolution under various conditions, including resource-rich environments, and when disease is present. Our simulations indicate that there is strong selection pressure for social behavior under circumstances where there is a clear discrepancy between initial resource locations, and against social behavior when disease is present, mirroring hypervigilance. This not only provides an explanation for the emergence of loneliness, but also reflects the diversity of response to loneliness in the real world. In addition, there is evidence of a richness of social behavior when loneliness was present. By introducing just two resource locations, we observed a divergence in social motivation after agents became lonely, where one agent learned to move to the other, who was in a better resource position. The results and ongoing work from this project show that it is possible to glean insight into the evolutionary advantages of even simple mechanisms of loneliness. The model we developed has produced unexpected results and has led to more questions, such as the impact loneliness would have at a larger scale, or the effect of creating a set of rules governing interaction beyond adjacency.

Keywords: agent-based, behavior, evolution, loneliness, social

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Authors: Pablo Chico De Guzman, Cesar Sanchez

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Microservice architectures are increasingly in distributed cloud applications due to the advantages on the software composition, development speed, release cycle frequency and the business logic time to market. On the other hand, these architectures also introduce some challenges on the testing and release phases of applications. Container technology solves some of these issues by providing reproducible environments, easy of software distribution and isolation of processes. However, there are other issues that remain unsolved in current container technology when dealing with multiple machines, such as networking for multi-host communication, service discovery, load balancing or data persistency (even though some of these challenges are already solved by traditional cloud vendors in a very mature and widespread manner). Container cluster management tools, such as Kubernetes, Mesos or Docker Swarm, attempt to solve these problems by introducing a new control layer where the unit of deployment is the container (or the pod — a set of strongly related containers that must be deployed on the same machine). These tools are complex to configure and manage and they do not follow a pure immutable infrastructure approach since servers are reused between deployments. Indeed, these tools introduce dependencies at execution time for solving networking or service discovery problems. If an error on the control layer occurs, which would affect running applications, specific expertise is required to perform ad-hoc troubleshooting. As a consequence, it is not surprising that container cluster support is becoming a source of revenue for consulting services. This paper presents i2kit, a deployment tool based on the immutable infrastructure pattern, where the virtual machine is the unit of deployment. The input for i2kit is a declarative definition of a set of microservices, where each microservice is defined as a pod of containers. Microservices are built into machine images using linuxkit —- a tool for creating minimal linux distributions specialized in running containers. These machine images are then deployed to one or more virtual machines, which are exposed through a cloud vendor load balancer. Finally, the load balancer endpoint is set into other microservices using an environment variable, providing service discovery. The toolkit i2kit reuses the best ideas from container technology to solve problems like reproducible environments, process isolation, and software distribution, and at the same time relies on mature, proven cloud vendor technology for networking, load balancing and persistency. The result is a more robust system with no learning curve for troubleshooting running applications. We have implemented an open source prototype that transforms i2kit definitions into AWS cloud formation templates, where each microservice AMI (Amazon Machine Image) is created on the fly using linuxkit. Even though container cluster management tools have more flexibility for resource allocation optimization, we defend that adding a new control layer implies more important disadvantages. Resource allocation is greatly improved by using linuxkit, which introduces a very small footprint (around 35MB). Also, the system is more secure since linuxkit installs the minimum set of dependencies to run containers. The toolkit i2kit is currently under development at the IMDEA Software Institute.

Keywords: container, deployment, immutable infrastructure, microservice

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Authors: Michael Ray Brunt

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China has been the site of some of the most significant earthquakes in history; however, earthquake monitoring has long been the provenance of universities and research institutions. The China Digital Seismographic Network was initiated in 1983 and improved significantly during 1992-1993. Data from the CDSN is widely used by government and research institutions, and, generally, this data is not readily accessible to middle and high school students. An educational seismic network in China is needed to provide collaboration and research opportunities for students and engaging students around the country in scientific understanding of earthquake hazards and risks while promoting community awareness. In 2022, the Tsinghua International School (THIS) Seismology Team, made up of enthusiastic students and facilitated by two experienced teachers, was established. As a group, the team’s objective is to install seismographs in schools throughout China, thus creating an educational seismic network that shares data from the THIS Educational Seismic Network (THIS-ESN) and facilitates collaboration. The THIS-ESN initiative will enhance education and outreach in China about earthquake risks and hazards, introduce seismology to a wider audience, stimulate interest in research among students, and develop students’ programming, data collection and analysis skills. It will also encourage and inspire young minds to pursue science, technology, engineering, the arts, and math (STEAM) career fields. The THIS-ESN utilizes small, low-cost RaspberryShake seismographs as a powerful tool linked into a global network, giving schools and the public access to real-time seismic data from across China, increasing earthquake monitoring capabilities in the perspective areas and adding to the available data sets regionally and worldwide helping create a denser seismic network. The RaspberryShake seismograph is compatible with free seismic data viewing platforms such as SWARM, RaspberryShake web programs and mobile apps are designed specifically towards teaching seismology and seismic data interpretation, providing opportunities to enhance understanding. The RaspberryShake is powered by an operating system embedded in the Raspberry Pi, which makes it an easy platform to teach students basic computer communication concepts by utilizing processing tools to investigate, plot, and manipulate data. THIS Seismology Team believes strongly in creating opportunities for committed students to become part of the seismological community by engaging in analysis of real-time scientific data with tangible outcomes. Students will feel proud of the important work they are doing to understand the world around them and become advocates spreading their knowledge back into their homes and communities, helping to improve overall community resilience. We trust that, in studying the results seismograph stations yield, students will not only grasp how subjects like physics and computer science apply in real life, and by spreading information, we hope students across the country can appreciate how and why earthquakes bear on their lives, develop practical skills in STEAM, and engage in the global seismic monitoring effort. By providing such an opportunity to schools across the country, we are confident that we will be an agent of change for society.

Keywords: collaboration, outreach, education, seismology, earthquakes, public awareness, research opportunities

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