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This paper aims to develop an obstacle avoidance system for a multi-rotor micro aerial vehicle (MAV) that flies in indoor environments which usually contain transparent, texture-less or moving objects.

The system adopts a combination of a stereo camera and an ultrasonic sensor to detect obstacles and extracts three-dimensional (3D) point clouds. The obstacle map is built on a coarse global map and updated by local maps generated by the recent 3D point clouds. An efficient layered A* path planning algorithm is also proposed to address the path planning in 3D space for MAVs.

The authors conducted a lot of experiments in both static and dynamic scenes. The results show that the obstacle avoidance system works reliably even when transparent or texture-less obstacles are present. The layered A* path planning algorithm is much faster than the traditional 3D algorithm and makes the system response quickly when the obstacle map has been changed because of the moving objects.

The limited field of view of both stereo camera and ultrasonic sensor makes the system need to change heading first before moving side to side or moving backward. But this problem could be addressed when multiple systems are mounted toward different directions on the MAV.

The developed approach could be valuable to applications in indoors.

This paper presents a robust obstacle avoidance system and a fast layered path planning algorithm that are easy to be implemented for practical systems.

The purpose of this paper is to demonstrate a method for modeling of cellular structures by means of Voronoi tessellation and to conduct a validation by comparison with real metal foam structures.

Heat propagation behavior of open-pore metal foams is studied for both experimental as well as computer-modeled structures showing excellent agreement. The 3D open-pore structure of the real foam is reconstructed from 2D light microscope images in-depth.

An algorithm to create synthetic open-pore foam structures has been developed.

The algorithm for modeling synthetic open-pore cellular structures allows the random distribution of the individual pores close to reality.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

One of the techniques for improving the performance of distributed systems is data replication, wherein new replicas are created to provide more accessibility, fault tolerance and lower access cost of the data. In this paper, the authors propose a community-based solution for the management of data replication, based on the graph model of communication latency between computing and storage nodes. Communities are the clusters of nodes that the communication latency between the nodes are minimum values. The purpose of this study if to, by using this method, minimize the latency and access cost of the data.

This paper used the Louvain algorithm for finding the best communities. In the proposed algorithm, by requesting a file according to the nodes of each community, the cost of accessing the file located out of the applicant’s community was calculated and the results were accumulated. On exceeding the accumulated costs from a specified threshold, a new replica of the file was created in the applicant’s community. Besides, the number of replicas of each file should be limited to prevent the system from creating useless and redundant data.

To evaluate the method, four metrics were introduced and measured, including communication latency, response time, data access cost and data redundancy. The results indicated acceptable improvement in all of them.

So far, this is the first research that aims at managing the replicas via community detection algorithms. It opens many opportunities for further studies in this area.

This paper presents CoreLab, a sizing environment for electrical devices, based on a new software component standard, ICAR, which offers the possibility of multifaceted components. CoreLab supports the different steps of the sizing procedure of an electrical device by using an optimisation algorithm. It is open, which means that modules can be added to perform new functionalities.

The design of an electrical device has to comply with more and more constraints. In order to integrate and to manage all of these constraints during a design step, the paper proposes a sizing methodology based on an constrained optimisation by using analytical models of the device, and by encapsulating them into software components. Added to these services for the calculation of the sizing model, other services can be useful for the designer during the optimization phase, e.g. the geometry display of the device for each optimisation iteration. In this way, the approach proposes a new software component standard, Interfaces for Component Architecture (ICAR). It offers the possibility of multi‐facetted components. The paper also proposes an integrated environment to manage these software components, and their interactions: Core‐Lab. These components are then plugged to an optimisation component (algorithm), which manages the different constraints specified by the designer and finds the optimal sizing of the device.

The paper presents the ICAR standard and an environment to manage ICAR components: Core‐Lab: the creation of the components (from an analytical model or an existing computation); the projection from one component standard to another; and the composition of components to create a more complex one.

The use of software component approach is useful for the sizing of devices. The paper proposes a new standard to support the different aspects of the use of software components during the design of a device: ICAR. Complementary, an open integrated environment is proposed to use these components: CoreLab, but any environment being modified to accept ICAR standard can use ICAR component. So, components can be used in several environments, for example for calculation or optimisation. Components of different types can be gathered together to built a complete application for sizing, e.g. by connection of calculation components (for the sizing model), optimisation component and post‐processing components.

The purpose of this paper is to present the development of a system called Structured Hypermedia Algorithm Explanation (SHALEX), as a remedy for the limitations existing within the current traditional algorithm animation (AA) systems. SHALEX provides several novel features, such as use of invariants, reflection of the high‐level structure of an algorithm rather than low‐level steps, and support for programming the algorithm in any procedural or object‐oriented programming language.

By defining the structure of an algorithm as a directed graph of abstractions, algorithms may be studied top‐down, bottom‐up, or using a mix of the two. In addition, SHALEX includes a learner model to provide spatial links, and to support evaluations and adaptations.

Evaluations of traditional AA systems designed to teach algorithms in higher education or in professional training show that such systems have not achieved many expectations of their developers. One reason for this failure is the lack of stimulating learning environments which support the learning process by providing features such as multiple levels of abstraction, support for hypermedia, and learner‐adapted visualizations. SHALEX supports these environments, and in addition provides persistent storage that can be used to analyze students' performance. In particular, this storage can be used to represent a student model that supports adaptive system behavior.

SHALEX is being implemented and tested by the authors and a group of students. The tests performed so far have shown that SHALEX is a very useful tool. In the future additional quantitative evaluation is planned to compare SHALEX with other AA systems and/or the concept keyboard approach.

SHALEX has been implemented as a web‐based application using the client‐server architecture. Therefore students can use SHALEX to learn algorithms both through distance education and in the classroom setting.

This paper presents a novel algorithm explanation system for users who wish to learn algorithms.

Two-level support with Level 1 consisting of a set of beams and Level 2 consisting of a tree-like structure is an efficient support structure for extrusion-based additive manufacturing (EBAM). However, the literature for finding a slim two-level support is rare. The purpose of this paper is to design a lightweight two-level support structure for EBAM.

To efficiently solve the problem, the lightweight design problem is split into two subproblems: finding a slim Level 1 support and a slim Level 2 support. To solve these two subproblems, this paper develops three efficient metaheuristic algorithms, i.e. genetic algorithm (GA), genetic programming (GP) and particle swarm optimization (PSO). They are problem-independent and are powerful in global search. For the first subproblem, considering the path direction is a critical factor influencing the layout of Level 1 support, this paper solves it by splitting the overhang region into a set of subregions, and determining the path direction (vertical or horizontal) in each subregion using GA. For the second subproblem, a hybrid of two metaheuristic algorithms is proposed: the GP manipulates the topologies of the tree support, while the PSO optimizes the position of nodes and the diameter of tree branches. In particular, each chromosome is encoded as a single virtual tree for GP to make it easy to manipulate Crossover and Mutation. Furthermore, a local strategy of geometric search is designed to help the hybrid algorithm reach a better result.

Simulation results show that the proposed method is preferred over the existing method: it saves the materials of the two-level support up to 26.34%, the materials of the Level 1 support up to 6.62% and the materials of the Level 2 support up to 37.93%. The proposed local strategy of geometric search can further improve the hybrid algorithm, saving up to 17.88% of Level 2 support materials.

The proposed approach for sliming Level 1 support requires the overhanging region to be a rectilinear polygon and the path direction in a subregion to be vertical or horizontal. This limitation limits the further material savings of the Level 1 support. In future research, the proposed approach can be extended to handle an arbitrary overhang region, each with several choices of path directions.

The details of how to integrate the proposed algorithm into the open-source program CuraEngine 4.13.0 is presented. This is helpful for the designers and manufacturers to practice on their own 3D printers.

The path planning of the overhang is a critical factor influencing the distribution of supporting points and will thus influence the shape of the support structure. Different from existing approaches that use single path directions, the proposed method optimizes the volume of the support structure by planning hybrid paths of the overhangs.

The purpose of this paper is to propose that in order to tackle the question of bias in algorithms, a systemic, sociotechnical and holistic perspective is needed. With reference to the term “algorithmic culture,” the interconnectedness and mutual shaping of society and technology are postulated. A sociotechnical approach requires translational work between and across disciplines. This conceptual paper undertakes such translational work. It exemplifies how gender and diversity studies, by bringing in expertise on addressing bias and structural inequalities, provide a crucial source for analyzing and mitigating bias in algorithmic systems.

After introducing the sociotechnical context, an overview is provided regarding the contemporary discourse around bias in algorithms, debates around algorithmic culture, knowledge production and bias identification as well as common solutions. The key concepts of gender studies (situated knowledges and strong objectivity) and concrete examples of gender bias then serve as a backdrop for revisiting contemporary debates.

The key concepts reframe the discourse on bias and concepts such as algorithmic fairness and transparency by contextualizing and situating them. The paper includes specific suggestions for researchers and practitioners on how to account for social inequalities in the design of algorithmic systems.

A systemic, gender-informed approach for addressing the issue is provided, and a concrete, applicable methodology toward a situated understanding of algorithmic bias is laid out, providing an important contribution for an urgent multidisciplinary dialogue.

The peer-to-peer perspective on open data is an interesting topic to research, taking into account that data-driven innovations and related startups are often developed independently by civic and private stakeholders in a highly collaborative manner and are tentatively beginning to directly compete with traditional e-government solutions, providing arguably better services to citizens and businesses. In this regard, the paper aims to further debate on the potential of such independent data-driven collaboration not only to transform the traditional mechanisms of public sector innovations but also provide more democratic ways to ensure greater transparency of government and its responsibility before the society.

The research is based on a cross-country case study, resorting to the content analysis of three demonstrative cases in the development of open data-driven projects, which specifically promote peer-to-peer communication between its stakeholders. In this regard, the case study itself relies heavily on the analysis of rich empirical data that the author collected during his field studies in the Northern European region in 2015–2017, particularly in Estonia, Finland and Sweden. The practical research itself consists of three major parts, which reflect peer-to-peer perspectives of correspondingly civic, public and private stakeholders through manifested examples of related independent projects in the area.

The paper's results demonstrate that the use of peer-to-peer mechanisms in advancing related public sector reforms allows to transform the traditional understanding of e-government phenomena in a conceptually new way. E-government or its last more political interpretation – from the perspective of its peers could be regarded not necessarily as a platform to provide digital public services but as a source of raw material for various third party projects in, respectively, civic, government and business peer-to-peer dimensions of such reforms. As a result, open data provides an interesting playground to change the very nature of public sector innovations in the area.

The choice of countries for research was motivated by purposive and convenience sampling because all these countries are situated in one region, have both similarities and differences in historical, political and socioeconomic backgrounds and, therefore, provide an ideal playground to investigate open data as a context dependable phenomenon. In this regard, the unique political and socioeconomic contexts of these countries provide an interesting playground to debate on the potential of social democracy, egalitarian society and social equality, i.e. public values that are deeply embedded into the fabric of societies there, to benefit the open data movement in a fundamental manner.

This paper reports on unique practical approaches for peer-to-peer collaboration and cooperation in advancing open data-driven platforms among stakeholders. The results of the case studies in three Nordic countries, which are currently among global leaders in advancing the concept of open government, are presented in an intrinsically illustrative manner, which could help practitioners and policymakers to understand better the potential of such a peer-to-peer perspective on open data. In this regard, the models proposed, of citizen-to-citizen, business-to-business, government-to-government interactions, could be interesting to a wide audience of e-government stakeholders in many nations.

The paper also enters into philosophical debates about societal implications of digital peer-to-peer data-driven communication among people. Recent efforts to digitize almost every part of social life, starting from popularization of solutions for distant work and ending to online access to various public services, incentivize individual members of civil society to communicate in an inherently peer-to-peer way. This fact will definitely increase the demand for related digital services. Social distancing in a digital context will allow to paradoxically emancipate technically savvy and entrepreneurial people in creating new services, including using open data, which could meet the demand.

The research is intrinsically of an empirical character because recent e-government reforms in the public sector in many countries, including in the open data area, provide rich practical knowledge to test the limits of new technologies to advance society in socioeconomic and, more importantly, political development. In this regard, this paper provides the first research in analyzing open data from a unique peer-to-peer perspective with an ultimate goal of the whole investigation to draw the attention of other e-government scholars and initiate debates on the collaborative nature of the phenomena to empower civil society and ensure transparency of government.