Planning Capabilities Research Articles

An Outlook on the Future Marine Traffic Management System for Autonomous Ships

In the shipping digitalisation process, the peak will be reached with the advent of a wholly autonomous and at the same time safe and reliable ship. Full autonomy could be obtained by two linked Artificial-Intelligence systems representing the ship navigator and the ship engineer that possess sensing and analysis skills, situational awareness, planning, and control capabilities. Many efforts have been made in developing onboard systems; however, the shore facilities are not ready yet to deal with these new technologies. The paper aims to present the innovative technologies and methodologies needed to develop a futuristic Vessel Traffic System. The proposed systems will aim at faultless data acquisition and processing, provide input to decision-making systems, and suggest evasive manoeuvre; to deal with hazards and systems failure without human intervention onboard. The system is composed of three different and interacting layers. The first is an artificially intelligent tool to detect and control autonomous ships, thanks to situation recognition and obstacle avoidance strategies. The second is an orchestration and management platform designed to coordinate the sensing-actuation infrastructure and the AI algorithms results made available by multiple ships, mustering edge, and distributed computing techniques to fulfil the specific harsh requirements of the sea environment. The final part is a holistic guidance-navigation-control framework to manage autonomous ships navigation in a crowded area. Eventually, a cyber-physical scenario, using both a ship digital-twin and a real model-scale ship, is suggested to test and validate the innovative system without the availability of a full-scale scenario.

Mastering Atari, Go, chess and shogi by planning with a learned model.

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess1 and Go2, where a perfect simulator is available. However, in real-world problems, the dynamics governing the environment are often complex and unknown. Here we present the MuZero algorithm, which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. The MuZero algorithm learns an iterable model that produces predictions relevant to planning: the action-selection policy, the value function and the reward. When evaluated on 57 different Atari games3-the canonical video game environment for testing artificial intelligence techniques, in which model-based planning approaches have historically struggled4-the MuZero algorithm achieved state-of-the-art performance. When evaluated on Go, chess and shogi-canonical environments for high-performance planning-the MuZero algorithm matched, without any knowledge of the game dynamics, the superhuman performance of the AlphaZero algorithm5 that was supplied with the rules of the game.

Organizational preparation of central public administration bodies to manage the implementation of defense tasks. Part II. Organization of administrative offices of the supreme government administration bodies for the coordination of defense tasks

The second part of the report on the research on the organizational preparation of administrative offices supporting the supreme government administration bodies in the implementation of defense tasks is devoted to the characteristics and assessment of the organization of organizational units of administrative offices supporting the Council of Ministers, the prime minister, and the ministers managing government administration departments. The extensive nature of tasks in this matter and, consequently, the responsibility for their implementation resting on the Council of Ministers, the prime minister and ministers, is the basis for assessing the current state of organization of the administrative governments serving these bodies. Already in the Constitution and in ordinary acts, the role of the Council of Ministers and the prime minister were appreciated, with less attention being paid to ministers and heads of government administration departments. After 2010, the legal conditions for ensuring a uniform organizational and substantively competent structure of administrative offices in terms of defense, and more broadly also national security, were even worse. Only after 2016, and especially starting from 2019, hopeful organizational changes took place in this matter. And all this in view of the apparent lack of appropriate regulations in the field of defense law. The research leads to the conclusion that a holistic approach to national security issues, including national defense, is necessary to recognize the criteria of necessary legal and organizational changes to achieve planning, organizational, coordination, and control capabilities of the supreme government administration bodies in the field of defense.

A Novel Path Planning Algorithm for Truck Platooning Using V2V Communication.

In truck platooning, the leading vehicle is driven manually, and the following vehicles run by autonomous driving, with the short inter-vehicle distance between trucks. To successfully perform platooning in various situations, each truck must maintain dynamic stability, and furthermore, the whole system must maintain string stability. Due to the short front-view range, however, the following vehicles’ path planning capabilities become significantly impaired. In addition, in platooning with articulated cargo trucks, the off-tracking phenomenon occurring on a curved road makes it hard for the following vehicle to track the trajectory of the preceding truck. In addition, without knowledge of the global coordinate system, it is difficult to correlate the local coordinate systems that each truck relies on for sensing environment and dynamic signals. In this paper, in order to solve these problems, a path planning algorithm for platooning of articulated cargo trucks has been developed. Using the Kalman filter, V2V (Vehicle-to-Vehicle) communication, and a novel update-and-conversion method, each following vehicle can accurately compute the trajectory of the leading vehicle’s front part for using it as a target path. The path planning algorithm of this paper was validated by simulations on severe driving scenarios and by tests on an actual road. The results demonstrated that the algorithm could provide lateral string stability and robustness for truck platooning.

A conceptual framework for cloud-based advanced planning systems

ABSTRACT In the last decade, Cloud Computing (CC) and Advanced Planning Systems (APS) are two emerging topics. However, no comprehensive model that considers both topics at a conceptual level exists. Purpose of this article is to provide a conceptual framework for cloud-based APS. Within the framework, we propose a generic structure of fundamental services and components to fulfill different consumer requirements from the area of supply chain planning. In addition, we elaborate appropriate service, deployment, and pricing models and introduce the ‘Result-as-a-Service’ model. The research is generally based on the design science research approach by extending CC to the problem domain of supply chain planning. Based on the framework, several findings concerning the potential of cloud-based APS but also caveats like insufficient planning capabilities are elaborated. Furthermore, several most important research topics (e.g. process and object reference models) are formulated and form an initial agenda for research on cloud-based APS.

Remote Sensing Recognition of Construction Waste Accumulation Based on Resources satellite three Multi-view Stereoscopic Image

Due to economic growth and the development of urban modernization, the amount of construction waste is increasing year by year. The rapid identification of large-scale construction waste deposit is beneficial to improving urban planning and management capabilities. This article is based on the three-line array image stereo data of Resources satellite three to obtain DEM. At that time, Renzhuang Village in Pingdingshan was a test area. Based on the DEM elevation change information, the surface roughness coefficient of the structured surface was used as an irregular elevation texture feature, combined with the slope and aspect, to obtain the construction waste accumulation of other ground objects in the surrounding area. The overall recognition rate is 85.12%, and the Kappa coefficient is 0.70. It provides application value for the rapid identification of construction waste accumulation and its change information management and safety instability analysis.

Traffic flow prediction over muti-sensor data correlation with graph convolution network

Accurate and real-time traffic flow prediction plays an important role in improving the traffic planning capability of intelligent traffic systems. However, traffic flow prediction is a very challenging problem because the spatial–temporal correlation among roads is complex and changeable. Most of the existing methods do not reasonably analyze the dynamic spatial–temporal correlation caused by the changing relationship of traffic patterns among roads, thus cannot get satisfactory results in the medium and long-term traffic prediction. To address these issues, a novel Multisensor Data Correlation Graph Convolution Network model, named MDCGCN, is proposed in this paper. The MDCGCN model consists of three parts: recent, daily period and weekly period components, and each of which consists of two parts: 1) benchmark adaptive mechanism and 2) multisensor data correlation convolution block. The first part can eliminate the differences among the periodic data and effectively improve the quality of data input. The second part can effectively capture the dynamic temporal and spatial correlation caused by the changing relationship of traffic patterns among roads. Through substantial experiments conducted on two real data sets, results indicate that the proposed MDCGCN model can significantly improve the medium and long-term prediction accuracy for traffic networks of different sizes, and is superior to existing prediction methods.

Place recognition with deep superpixel features for brain-inspired navigation.

Navigation in primates is generally supported by cognitive maps. Such a map endows an animal with navigational planning capabilities. Numerous methods have been proposed to mimic these natural navigation capabilities in artificial systems. Based on self-navigation and learning strategies in animals, we propose in this work a place recognition strategy for brain-inspired navigation. First, a place recognition algorithm structure based on convolutional neural networks (CNNs) is introduced, which can be applied in the field of intelligent navigation. Second, sufficient images are captured at each landmark and then stored as a reference image library. Simple linear iterative clustering (SLIC) is used to segment each image into superpixels with multi-scale viewpoint-invariant landmarks. Third, highly representative appearance-independent features are extracted from these landmarks through CNNs. In addition, spatial pyramid pooling (SPP) layers are introduced to generate a fixed-length CNN representation, regardless of the image size. This representation boosts the quality of the extracted landmark features. The proposed SLIC-SPP-CNN place recognition algorithm is evaluated on one collected dataset and two public datasets with viewpoint and appearance variations.

Does Career Planning Drive Agri-entrepreneurship Intention Among University Students?

The rationale behind this study is to test the apathy of the rural youth to engage in agriculture as their profession. This research was conducted across five universities in eastern India on a sample of 345 postgraduate students to analyse their agri-entrepreneurship intent and the determinants of agri-entrepreneurship. The findings of the study show a significant positive correlation among the variables of agri-entrepreneurship intent and career planning and agri-entrepreneurship intent and entrepreneurial capabilities. Regression analysis reveals that career planning, entrepreneurship capabilities and family background (FB) in agriculture have a significant contribution as predictors of agri-entrepreneurship intent. Categorizing the respondents based on their career planning and entrepreneurship capability tendencies, the findings indicate that career planning and FB explain the growth of agri-entrepreneurship intent. This article will be of great value to educationists to carry out the pedagogical transformations and include career planning programmes and capacity-enhancing training in their curriculum for students. It will also be relevant to agri-business entities who explore growth opportunities in agri-allied sectors.

USING SCENARIO PLANNING TO ENHANCE COASTAL RESILIENCE TO CLIMATE CHANGE: COMMUNITY FUTURES IN THE ESTUARINE LANDSCAPES OF BRISBANE WATER, CENTRAL COAST, AUSTRALIA

Abstract. Local governments face daunting urban planning challenges in the face of rapid population growth, increasing demand for affordable housing and climate change and divergent community perceptions. There is currently insufficient action to meet these formidable and dynamic challenges. Greater know-how and capacity development need to be invested in local planning and political authorities to promote knowledge-based action. More than two thirds of Australians reside along the coastal cities with over 15 million addresses across 544 local government areas at risk from coastal hazards from 2020 onwards (XDI, 2019). This makes it an urgent priority for local government to fast track adaptation planning for its residents in the low-lying hazard prone areas as well as Council services and infrastructure. In 2019, UNSW students from the Master of Urban Development and Design and Master of Landscape Architecture programs addressed these challenges within a design studio environment. In the studio they developed strategies for urban resilience for a network of five communities surrounding the Brisbane Waters on the Central Coast. The studio adopted an urban scenario planning approach using open source data and on-the-ground evidence to develop diverse community resilience strategies. For each community three scenarios were developed to test alternative urban design strategies and provide insight on possible contrasting futures. This paper demonstrates the benefit of evidence-based scenario planning for local authorities. Such approaches contextualise complex abstract problems and provide new insights for local climate adaptation in accessible and transparent ways. This proactive approach emphasises the need to build the urban design and planning capabilities of local authorities to enhance coastal adaption and resilience to climate change.

An advanced method for steady-state security assessment considering dynamic thermal capacities of grid assets

The ( $n-1$ ) criterion is a commonly used criterion for secure grid operation. Violations of this criterion lead to remedial actions in the short term and the construction of additional transmission capacities in the long term to reduce congestions in the grid. An alternative strategy in reducing necessary interventions is to consider the natural dependency of the transmission capacity of an installed grid component from its environmental conditions. The application of dynamic component rating is offering additional transmission capacities for system operation. For the consideration of these capabilities in the day-ahead operation planning, forecasting the trend of the component ratings is needed. The implementation of such methods, especially for overhead lines, is tested by different system operators around the world. A further strategy in enhancing transmission capacity for the operation is to utilize the thermal inertia of a component for small and short exceedances of the ratings without violating the component’s thermal limits. Currently available static emergency ratings of components respect this capability. Component ratings as well as emergency ratings are dependent on environmental conditions. Furthermore, the available emergency operation duration is dependent on the development of the loading current in the recent past. By equipping assets with real-time monitoring systems, utilization of the component’s current transmission capacity is possible for the different operation states. This paper presents a method to consider the thermal transmission capacity of power system components, enabled by real-time monitoring systems, in the contingency analysis. The approach is an assessment at system level combined with a time-domain simulation for the evaluation of the impact of an examined contingency on a component’s relevant thermal parameters. The result of the method is compatible with the current binary statement but extended with an availability duration if a contingency occurs in the examined grid. Introducing this duration can assist the planning of curative remedial actions for the operation. Further, three specific durations were proposed for benchmarking a contingency in the different levels of analysis. These durations are the result of the time-domain simulation. The presented method is capable of analysing a network consisting of assets equipped with a monitoring system and assets without such a system. The second part of this paper presents the operation simulation of an exemplary grid for one operational year to illustrate the effect of the approach on the results of the contingency analysis in day-ahead planning.

Athlete perspectives on the enablers and barriers to nutritional adherence in high-performance sport

ObjectivesPoor adherence to nutritional guidance by athletes may compromise their health and performance. Enhancing adherence is therefore an important performance and welfare strategy. The aim of this study was to qualitatively explore the barriers and enablers of elite athletes' adherence to nutritional guidelines. DesignUnderpinned by our constructionist epistemological position and our relativist ontology, we conducted a qualitative study using focus groups. MethodsWe used the Capability, Opportunity, and Motivation Behaviour (COM-B) model and the Theoretical Domains Framework (TDF) to conduct focus group discussions with a purposive sample of 39 UK-based funded athletes (mean age = 23 ± 3.81), participating in either Olympic and Paralympic sport (n = 30) or professional sport (n = 9), who had access to a nutritionist. Data were analysed using reflexive thematic analysis. ResultsAthlete adherence to nutritional guidance was seasonal and included inadequate energy intakes and episodes of binge eating. Underpinning these behaviours, athletes' emotional barriers (motivation) are reinforced through their social interactions within the high-performance environment (opportunity) and athletes' training environment limits developmental opportunities for food planning (capability). However, a holistic-developmental approach by the sports nutritionists (opportunity) supports athlete wellbeing and nutritional adherence. ConclusionThese findings advance theoretical understanding of the barriers and enablers of nutritional adherence amongst elite-level athletes in high-performance sport and present a number of significant implications for athlete support personnel seeking to enhance performance in demanding sporting contexts. Drawing on the Behaviour Change Wheel (BCW), recommendations include the need to 1) train and educate sports nutritionists in human behaviour, 2) update regulations for sports nutrition profession practice to acknowledge the skills required to support athletes' emotional wellbeing, 3), educate coaches on the sensitivity of body weight and composition and develop guidelines for monitoring athletes' body weight and composition in sport, 4) persuade influential leaders to develop culture guidelines that shift the performance-narrative of high-performance (i.e., environmental restructuring).

The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces.
 In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

Using Group Model Building to Foster Learning for Strategic Sustainable Development

Capability for strategic planning and decision-making toward sustainability requires both individuals and organizations from different sectors and disciplines to develop necessary skills and competencies. Therefore, a growing discussion has emerged within academia and practice around how pedagogical methods and tools can be utilized for this purpose. This paper seeks to contribute to this discussion by asking ‘in which way may group model building be used to foster learning that leads to competency for strategic sustainable development?’. The potential of the group model building (GMB) process’ steps and associated modeling to foster learning for strategic sustainable development (SSD) was analyzed using four case examples. Theory on learning for sustainability and key sustainability competencies were linked to elements of a process for strategic planning toward sustainability within the framework for strategic sustainable development (FSSD) as a reference model. The results provide a discussion on how GMB can be utilized to foster sustainability competencies and learning for sustainability that can contribute to SSD, hence a basis for continued research and ideas for course and curriculum development.

The Relationship Among Demographic Factors, Transition Services, and Individual Development Account (IDA) Saving Participation Among Transition-Age Youth With Disabilities

Saving participation in adolescence is crucial for young adults to achieve financial well-being over their lifetime. Yet, very little is known about the factors that encourage saving participation among youth with disability, including youth who receive supplemental security income (SSI). This study examines relationships between personal and family factors, transition services, and participation in Wisconsin Promoting Readiness of Minors in Supplemental Security Income (PROMISE) individual development account (IDA), within a sample of youth with disabilities who receive SSI. Results of the hierarchical binary logistic and hierarchical multiple regression analyses suggests that the PROMISE interventions variable sets emerged as the most frequent predictors of IDA saving participation among youth receiving SSI. Specifically, work incentive benefits consultation services, financial capability-focused financial coaching services, and self-advocacy training were significant predictors of a youth’s IDA saving participation. This study’s findings offer important implications for transition planning and building financial capability.

Guide Circle-based Improved Ant Colony Algorithm

In order to solve the path planning problem of autonomous mobile robots in an unknown environment, the traditional ant colony algorithm and the guide circle algorithm were combined, and an improved ant colony algorithm based on the guide circle was proposed. In terms of local path planning, the guide circle algorithm is used to avoid obstacles, which effectively solved the problem of unstable path planning ability in narrow spaces. In the aspect of global path planning, the concept of safety index circle is introduced according to the length between robot and obstacle, and partial heuristic information of ant colony algorithm is improved, so as to avoid the traditional ant colony algorithm falling into local optimal. It is verified by simulation experiments that the improved algorithm has a stronger global search capability, faster convergence speed, and enhanced path planning capability.

Development of a procedure for expert estimation of capabilities in defense planning under multicriterial conditions

Improving the decision-making efficiency in defense planning based on the capabilities of military forces and means for performing purposeful tasks requires new methodological approaches and their implementation in the form of software information-analytical tools. Given the complex information environment of defense planning, it is appropriate for the variants of capabilities development options to be chosen by experts on the methodological basis of multicriterial analysis. The research result is the development of a procedure, in which it is proposed to generate criteria and evaluate alternative options by integrating ontology, the Analytical Hierarchy Process, and the method of directed graphs. The ontological representation of the data ensures the construction of the hierarchical taxonomy of a domain and the formation of the criteria vector. The Analytical Hierarchy Process is used to conduct an expert evaluation of capabilities by their pairwise comparison against determined criteria. Experts’ judgments are visualized and controlled using directed graphs. Application of the procedure will make it possible to ensure efficiency, versatility, and simplicity of technical implementation of a procedure of decision making support. The procedure was tested on the example of choosing a capability to conduct reconnaissance for the benefit of ground artillery. It was shown that the evaluation process in the expert activity is considerably simplified due to the graph visualization. The proposed procedure introduces an innovative tool to achieve strategic goals and accomplish the basic tasks of the defense reform, which is relevant for many countries. The versatility of the procedure creates the basis for its application not only in defense but also in other force departments

Data analytics and optimization for smart industry

Industrial intelligence is a core technology in the upgrading of the production processes and management modes of traditional industries. Motivated by the major development strategies and needs of industrial intellectualization in China, this study presents an innovative fusion structure that encompasses the theoretical foundation and technological innovation of data analytics and optimization, as well as their application to smart industrial engineering. First, this study describes a general methodology for the fusion of data analytics and optimization. Then, it identifies some data analytics and system optimization technologies to handle key issues in smart manufacturing. Finally, it provides a four-level framework for smart industry based on the theoretical and technological research on the fusion of data analytics and optimization. The framework uses data analytics to perceive and analyze industrial production and logistics processes. It also demonstrates the intelligent capability of planning, scheduling, operation optimization, and optimal control. Data analytics and system optimization technologies are employed in the four-level framework to overcome some critical issues commonly faced by manufacturing, resources and materials, energy, and logistics systems, such as high energy consumption, high costs, low energy efficiency, low resource utilization, and serious environmental pollution. The fusion of data analytics and optimization allows enterprises to enhance the prediction and control of unknown areas and discover hidden knowledge to improve decision-making efficiency. Therefore, industrial intelligence has great importance in China’s industrial upgrading and transformation into a true industrial power.

Product-market planning capability and profitability

We test the profit implication of product-market planning as a dynamic capability, from a contingency theory perspective. Among a sample of high-technology industrial organizations, we find that product-market planning capability is significantly and positively related to profits under marketing differentiation, but negative implications ensue for those adopting cost efficiency strategies. Pursuing hybrid strategies has no significant effect, while technological turbulence also has no moderating effect. Additional analysis establishes the temporal effects of product-market planning capability on 3-year lagged profits. These differential results are considered within a contingency framework. Implications are identified and discussed for industrial marketing management theory and practice.

3D Planning and Printing of Patient Specific Implants for Reconstruction of Bony Defects.

We are in the midst of the 3D era in most aspects of life, and especially in medicine. The surgical discipline is one of the major players in the medical field using the constantly developing 3D planning and printing capabilities. Computer-assisted design (CAD) and computer assisted manufacturing (CAM) are used to describe the 3D planning and manufacturing of the product. The planning and manufacturing of 3D surgical guides and reconstruction implants is performed almost exclusively by engineers. As technology advances and software interfaces become more user-friendly, it raises a question regarding the possibility of transferring the planning and manufacturing to the clinician. The reasons for such a shift are clear: the surgeon has the idea of what he wants to design, and he also knows what is feasible and could be used in the operating room. It allows him to be prepared for any scenario/unexpected results during the operation and allows the surgeon to be creative and express his new ideas using the CAD software. The purpose of this method is to provide clinicians with the ability to create their own surgical guides and reconstruction implants. In this manuscript, a detailed protocol will provide a simple method for segmentation using segmentation software and implant planning using a 3D design software. Following the segmentation and stl file production using segmentation software, the clinician could create a simple patient specific reconstruction plate or a more complex plate with a cradle for bone graft positioning. Surgical guides can be created for accurate resection, hole preparation for proper reconstruction plate positioning or for bone graft harvesting and re-contouring. A case of lower jaw reconstruction following plate fracture and nonunion healing of a trauma sustained injury is detailed.