Efficient Task Execution Research Articles

Butterfly: μW Level ULP Sensor Nodes with High Task Throughput.

The rapid development of Internet of Things (IoT) applications calls for light-weight IoT sensor nodes with both low-power consumption and excellent task execution efficiency. However, in the existing system framework, designers must make trade-offs between these two. In this paper, we propose an “edge-to-end integration” design paradigm, Butterfly, which assists sensor nodes to perform sensing tasks more efficiently with lower power consumption through their (high-performance) network infrastructures (i.e., a gateway). On the one hand, to optimize the power consumption, Butterfly offloads the energy-intensive computational tasks from the nodes to the gateway with only microwatt-level power budget, thereby eliminating the power-consuming Microcontroller (MCU) from the node. On the other hand, we address three issues facing the optimization of task execution efficiency. To start with, we buffer the frequently used instructions and data to minimize the volume of data transmitted on the downlink. Furthermore, based on our investigation on typical sensing data structures, we present a novel last-bit transmission and packaging mechanism to reduce the data amount on the uplink. Finally, we design a task prediction mechanism on the gateway to support efficient scheduling of concurrent tasks on multiple MCU-free Butterfly nodes. The experiment results show that Butterfly can speed up the task rate by 4.91 times and reduce the power consumption of each node by 94.3%, compared to the benchmarks. In addition, Butterfly nodes have natural security advantages (e.g., anti-capture) as they offload the control function with all application information up to the gateway.

Towards Speech Recognition and Training Utilization in the Nuclear Power Main Control Room

Speech, as the material shell of language, is the external form of language, and is the symbol system that most directly records people’s thinking activities. As a means of interaction, it has the characteristics of simplicity and speed. For a long time, due to insufficient recognition accuracy, it cannot be applied in industry. However, with the rapid development of big data and machine learning technology, the training effect of speech model has greatly increased, and the reliability of speech recognition has been greatly improved. The application of speech interaction in nuclear power plants with extremely high safety requirements brings feasibility. The purpose of this system is to establish an intelligent speech control system and its related speech model training system, and use Kaldi and Python scripts as model training scripts to train the corpus. And designed experiments to verify that after using speech recognition, it can effectively reduce the operation time by about 45%, and the overall task execution efficiency has been significantly improved. Therefore, the use of speech recognition can significantly reduce the operator’s task load and reduce the probability of human error.

Distributed Grouping Cooperative Dynamic Task Assignment Method of UAV Swarm

Aiming at the problem of UAV swarms with distributed subsets performing cooperative reconnaissance-and-attack tasks on multi-targets in complex and uncertain combat scenarios, a distributed grouping cooperative dynamic task assignment method is proposed based on extended contract network protocol. The dynamic task assignment model for the UAV swarm with the topology of distributed subsets is established considering multiple constraints such as task cooperation, performing sequence, dynamic environment, communication topology, payload model, and UAV capability. According to the characteristics of multi-participants and multi-tasks in the process of UAV swarm executing tasks, the determination mechanism on cooperators and the selection mechanism of sequential tasks are proposed, and then the contract network protocol is extended. On the basis of the above, an event-triggered task assignment strategy for dynamic tasks is designed. The simulated results show that the proposed method can achieve the cooperative dynamic assignment of the UAV swarm to perform reconnaissance-and-attack tasks to multi-targets in complex and uncertain combat scenarios, improve the adaptiveness of the swarm under the sudden circumstance, and realize the optimization for task execution efficiency of the UAV swarm.

CuNH: Efficient GPU Implementations of Post-Quantum KEM NewHope

Post-quantum cryptography was proposed in the past years due to the foreseeable emergence of quantum computers that are able to break the conventional public key cryptosystems at acceptable costs. However, post-quantum schemes are usually less efficient than conventional ones, which makes them less practical in scenarios with limited resources or high concurrency. Server-side applications always feature multiple users, therefore requiring efficient execution of batch tasks. GPU is intrinsically well-suited to batch tasks owing to its SIMD/SIMT execution fashion, so it naturally helps to achieve high performance. However, a naive GPU-based implementation cannot make the best use of hardware resources of the GPU regardless of task loads. In this article, we propose SIMD parallelization paradigms for fine-grained GPU implementations and then apply them to a post-quantum key encapsulation algorithm called NewHope, where we carefully design every module, especially NTT and inverse NTT, to fit into the SIMD parallelization paradigms. In addition, we employ multi-streaming to improve performance in user's perspective. Finally, our evaluations are made on two testbeds with GPU accelerators NVIDIA GeForce MX150 and GeForce GTX 1650, respectively. The experimental results show that the fine-grained implementations save up to 98 percent latency at low task loads, and their throughputs increase by up to 86 percent at high task loads, when compared with the naive ones in kernel's perspective, and the multi-streaming implementations greatly reduce the latency overhead percentage at high task loads by up to 86 percent, when compared with the fine-grained implementation in user's perspective. Moreover, our fine-grained implementation and multi-streaming implementation are respectively 51.5 and 45.5 percent faster than Gupta et al.'s implementations when compared with it under reasonable assumptions. Furthermore, as lattice-based post-quantum schemes have similar operations, our proposal also easily applies to other lattice-based post-quantum schemes.

Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm

When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.

Hybrid offline and online task planning for service robot using object-level semantic map and probabilistic inference

To make service robots plan a whole task execution sequence and deal with task failure intelligently in the face of object occlusion and incomplete home environment information, a hybrid offline and online task planning strategy is proposed. We establish object-level semantic map and object location probabilistic relations between dynamic and static objects. Semantic mapping helps to obtain semantic locations of static objects, and the probabilistic relationship between dynamic and static objects can obtain semantic locations of dynamic objects through probabilistic reasoning. Probabilistic planning domain definition language (PPDDL) can generate offline action sequences, while partially observable Markov decision process (POMDP) can generate online action sequences. Therefore, a hybrid task planner that can receive semantic location information is constructed to generate offline and online action sequences, and realizes the dynamic switching of the two kinds of sequences through the designed planner switching mechanism. In order to improve the robustness and intelligence of robot task planning, a task replanning mechanism considering the position relationship between robot and candidate static objects is designed. Experimental results in real environment and simulation environment show that this approach can effectively increase the intelligence of task planning and improve the robustness and efficiency of robot task execution.

Exergoeconomic analysis and parametric investigation of a gas turbine power plant

Thermoeconomic models, combining the concept of cost in economics and the concept of exergy in thermodynamics, provide the ability to optimize complex power generation systems to achieve the best balance between thermodynamic efficiency and economic cost. In this paper, a parametric analysis was carried out based on the method of calculating the unit exergy cost, as well as exergo-economic studies and cost sensitivity studies on the exergy of the cycle of a gas turbine power plant. The mathematical models of mass, energy, effort, and economy were created and presented. Thermodynamic properties and research analysis are performed using the MINI- REFerence fluid PROPerties (MINI-REFPROP) and matrix laboratory (MATLAB) SIMULINK software packages. The analysis leads to valuable benchmarks of the economic situation. The exergo-economic coefficient, the total cost of exergy loss, exergy destruction for the combustion chamber, and labor productivity are determined. When conducting parametric studies, the influence of the temperature at the inlet to the gas turbine, the temperature at the inlet to the air compressor, and the degree of pressure increase in the compressor were taken into account. The combustion chamber at the plant was found to have the highest energy destruction rate of 80%, indicating that boilers need to be given more attention in terms of design, selection, operation, and maintenance. However, in percentage terms, the combustion chamber has a high improvement potential of 94%. Sensitivity and parametric analysis show that while the exergy factor, the total cost of exergy loss, exergy destruction for the combustion chamber, and power output fall with increasing air compressor inlet temperature, it can be increased with increasing compressor pressure ratio. The total energy loss cost, combustor energy loss, and power output decrease as the gas turbine inlet temperature rises, while the cycle network and overall exergy destruction rate increase. The rates of the exergy destruction of the Venture Capital (VC), the combustion chamber of the combustion chamber, and the total exergy destruction are 25.2, 122.3, and 153.2 MW, respectively, for the proposed conditions. In addition, the results showed that it was $2,272 per hour, while the cost of work performed in energy terms is $1,769 per hour with a fuel cost of $0.003 per MJ. A valuable achievement is the availability of defined values and clear parametric influences, which can be of great help to engineers and site operators in the efficient execution of unique tasks, playing with the conflicts of energy use, exergy, and cost.

ALGORITHM AUTOMATION METHODS FOR WORKING WITH NETWORK EQUIPMENT

Relevance of the topic: methods of automating algorithms for the operation of network equipment are currently carried out using various tools and solutions for faster and more efficient execution of routine tasks, as well as for obtaining deterministic results. Production processes need algorithmization of ACS as a tool for saving time resource for management, coordinating production schedules, technical and technological limitations. You can form an alternative to quickly respond to precedents in production that have not predicted to change the course of technological processes. Most automation options today have disadvantages and advantages. The most popular way is queuing theory. Optimization of global search, additive method of criteria convolution, APS (Advanced Planning and Scheduling) systems for the most perfect planning are relevant. The specified software systematically synchronizes the planning process deployed for optimization and combines actions on the links that make up the supply chain, accentuates the framework and specifics determined by production and the industry. Most often, the network equipment is controlled by a software module that loads the machines and mechanisms needed by the enterprise for the production of products. Developers design systems to control timeslots and production lines. Known for the high efficiency of algorithms, the execution of which occurs through the automated control system, and the installation is carried out at an industrial enterprise to control loading operations. In the structure of external software, an algorithm is required. The program module contains commands for loading operations. They are distributed for technological lines, divided along the time axis. We see a rational solution to simulate a complex using simulation and analytics based on an automation product within the network equipment configured by the program. This will allow, under the conditions of the experiment, to determine how most likely traffic will be distributed in time, to prepare methods for automatic collection of information. Input traffic parameters, settings or the current load arriving at the equipment are set arbitrarily.

Attentional Skills in Soccer: Evaluating the Involvement of Attention in Executing a Goalkeeping Task in Virtual Reality

Physical abilities are essential to goalkeepers in soccer but the involved cognitive abilities for these players have only recently become the focus of extensive research. In this study, we investigated the role of different aspects of attention in a basic goalkeeping task in soccer. One hundred participants assumed the role of a goalkeeper in immersive virtual reality (VR) and carried out a task that entailed blocking balls shot towards their goal. In addition, they carried out two computerized tasks each assessing different attentional abilities: the Attention Network Test provided scores for three well-established networks of attention, namely the alerting, the orienting, and the executive control. The Whack-a-Mole task evaluated inhibitory control, by measuring performance in a classic Go/No-Go task and tapping on response inhibition. A regression analysis revealed that all three attention network scores contributed to performance in the VR goalkeeping task. Furthermore, performance in the Whack-a-Mole task correlated significantly with performance in the VR goalkeeping task. Overall, findings confirm that cognitive skills relating to attention play a critical role in the efficient execution of soccer-specific tasks. These findings have important implications for the training of cognitive skills in sports.

Private Data Aggregation Based on Fog-Assisted Authentication for Mobile Crowd Sensing

In mobile crowd sensing (MCS), the cloud as a single sensing platform undertakes a large number of communication tasks, leading to the reduction of sensing task execution efficiency and the risk of loss and leakage of users’ private data. In this paper, we propose a spatial ciphertext aggregation scheme with collaborative verification of fog nodes. Firstly, the cloud and fog collaboration architecture is constructed. Fog nodes are introduced for data validation and slices transmission, reducing computing cost on the sensing platform. Secondly, a multipath transmission method of slice data is proposed, in which the user identity and data are transmitted anonymously by the secret sharing method, and the data integrity is guaranteed by hash chain authentication. Finally, a spatial data aggregation method based on privacy protection is presented. The ciphertext aggregation calculation of the sensing platform is realized through Paillier homomorphic encryption, and the problem of insufficient data coverage in the sensing region is solved by the position-based weight interpolation method. The security analysis demonstrates that the scheme can achieve the expected security goal. The simulation results show the feasibility and effectiveness of the proposed scheme.

Ant Colony Algorithm for Time Optimal Load Balancing Scheduling Problem Strategy

In order to optimize the task execution efficiency of FPGA high-performance network clusters, this paper proposes a time-optimal load balancing scheduling problem algorithm and initialization strategy. Taking the task completion time as the optimization direction, the load balancing scheduling problem is abstracted as solving the placement matrix problem. On this basis, a node selection strategy using critical matrix is proposed, and the algorithm design is completed based on the ant colony algorithm. Aiming at the problem of low efficiency of random initialization, this paper proposes two ant colony initialization methods, which are ranked greedy initialization strategy and polling decentralized initialization strategy. Experiments show that the algorithm and initialization strategy designed in this paper can adapt to different computing scales, and the two initialization strategies can achieve a 15%∼55% improvement in the iterative convergence times and load balancing compared to random initialization.

EHEFT-R: multi-objective task scheduling scheme in cloud computing

In cloud computing, task scheduling and resource allocation are the two core issues of the IaaS layer. Efficient task scheduling algorithm can improve the matching efficiency between tasks and resources. In this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) task scheduling algorithm is proposed to optimize task execution efficiency, quality of service (QoS) and energy consumption. In EHEFT-R, ordering rules based on priority constraints are used to optimize the quality of the initial solution, and the enhanced heterogeneous earliest finish time (HEFT) algorithm is used to ensure the global performance of the solution space. Simulation experiments verify the effectiveness and superiority of EHEFT-R.

2D multi-area coverage path planning using L-SHADE in simulated ocean survey

Ocean environmental surveys typically involve multi-area coverage path planning tasks. The most important problem is improving the coverage efficiency of the task. A new path planning method based on Successful History-Based Adaptive Differential Evolution variants with Linear population size reduction(L-SHADE) is presented to solve this problem. The method comprises two parts: the part of sub area coverage path planning and the part of finding the optimized sequence of sub area start points. The key idea is establishing the relationship between the starting point of each sub area and the optimized multi-area path. We implement the method through numbering the possible starting point of sub area path and proposing a computing formula. In addition, the results of L-SHADE mutation process are optimized which make L-SHADE possible to apply in multi-area coverage path planning. This method avoids area discretization and exponential growth of computational quantities, and it is suitable for complex areas as well as multi-area. The simulation results with MATLAB showed the improvement of coverage path planning task execution efficiency. Compared with the method thinking of the sub area as the center of it, our method reduced the multi-area coverage path length by 4%–7%. From the simulations and analysis, we concluded that the method is able to improve the efficiency and stability of multi-area coverage path planning.

Knowledge-based memetic algorithm for joint task planning of multi-platform earth observation system

With the increasing complexity of earth observation tasks, the single observation mode can no longer meet the current requirements for timeliness and accuracy. The joint use of multi-platform observation resources can give full play to the advantages of each platform and improve the efficiency of task execution. The research on joint task planning of multi-platform is the key to achieve the collaborative effect of multi-platform earth observation system. In this paper, a unified observation path model is proposed. The objective functions of tracking rate, coverage rate, recognition rate and surveillance rate are proposed respectively for moving target tracking, regional target search, multi-point target recognition and continuous target surveillance. To address the problem, a problem-specific knowledge-based memetic algorithm is proposed. The mechanism of combining swarm intelligence algorithm and local search algorithm in the framework of memetic algorithm is used to enhance the ability of exploration and exploitation. Problem-specific knowledge operators are introduced into global search and local search. It can increase the learning ability of the algorithm in the iterative process, and guide the algorithm to adopt reasonable evolutionary strategy. Finally, speed up convergence while avoiding premature maturity. Simulation experiments verify the superiority of the algorithm.

Analysis of Big Data Embedded Mapping Model Oriented to Prediction of Subordinate Relationship

With the rapid development of information technology, research on big data information has gone deep into all walks of life, and massive amounts of data spew out anytime and anywhere. This paper proposes a domain-subordinate relationship acquisition that combines multiple strategies and word representations. From semi-structured text and unstructured text, the candidate entity upper-lower relationship entity pairs are extracted, and then the obtained candidate upper-lower relationship entity pairs are verified by the support vector machine method to obtain high-quality candidate entity pairs. In the training phase, an exploratory method is designed to search for the optimal solution, and a greedy mechanism is introduced to evaluate the effectiveness of the reinforcement learning agent, so that the virtual node mapping scheme can continuously move towards higher system returns. Evolution has finally achieved the optimal virtual network mapping decision, so that large-scale tasks can be deployed to task processing nodes in the appropriate underlying network to achieve efficient task execution in a big data environment. The experimental results show that the Tard method can effectively avoid model overfitting and improve the task recognition processing ability in the actual application process under the premise of meeting large-scale task requests.

A* Based Routing and Scheduling Modules for Multiple AGVs in an Industrial Scenario

A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV’s route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*—Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.

Analyzing content of tasks in Business Process Management. Blending task execution and organization perspectives

An efficient organization, management, and execution of tasks are central for the successful functioning of any organization. This topic was on the research agenda already in the early 1950s and keeps attracting the scientific community's attention today. Continuous advances and penetration of technologies in organizations are expected to increase task variety and complexity. This creates a constant demand for new methods to analyze, measure, manage, and execute tasks. In this study, we extract relevant task content aspects from textual task descriptions and build a task content model as a basis for the development of various decision-support solutions for process workers and managers. Using the Theory of Situation Awareness, we specify a method for analyzing and measuring the content of tasks and illustrate it by an industry example of ITIL IT ticket processing. We refer to the Strategic Alignment Model while discussing the implications for task management and execution research and practice.

Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms

Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.

Design and Control of Advanced Robotic Systems for Manufacturing and Assembly

Modern industrial processes must now be designed and controlled with advanced robotic systems for manufacture and assembly to increase productivity, accuracy, and flexibility. The main factors involved in creating and overseeing these robotic systems are presented in detail in this work.In the design stage, manipulators, sensors, and end-effectors that are customised for the particular manufacturing or assembly task are chosen and integrated. To ensure accurate and efficient task execution, additional control algorithms and programming techniques are also developed. This covers adaptive control techniques, path optimisation, trajectory planning, and collision avoidance.The real-time monitoring, coordinating, and synchronisation of several robots and their interactions with the environment are the main goals of the control component. The integration of human operators with robotic systems is made possible by centralised or decentralised control frameworks, which promote secure and effective production and assembly procedures.The study also discusses how to improve robotic systems' abilities to handle complicated jobs and adapt to shifting production settings by integrating cutting-edge technologies like machine learning, computer vision, and augmented reality.The difficulties and possibilities posed by the application of sophisticated robotic systems for manufacturing and assembly are also covered. These include the dependability of the system, the ethical effects of automation in the workplace, the detection and recovery of faults, and human-robot collaboration.The ultimate goal of this work is to give readers a thorough grasp of the design and control principles required for creating advanced robotic systems for manufacturing and assembly, hence promoting higher levels of productivity, quality, and adaptability in industrial settings.

Influence of Moral Values and Norms on the Accumulation of Social Capital – Activity of Ignacy Tokarczuk, a Bishop of the Diocese of Przemyśl

The aim of this analytical study carried out on the basis of existing sources was to determine the correlation between the activity of Archbishop Ignacy Tokarczuk involving the reception of moral values and norms in social life and development of social capital in the Diocese of Przemyśl. Projects initiated by Abp. Tokarczuk, in which he put special emphasis on the observance of moral law, had a positive effect on social ties, the number of connections, and the increase of social trust. Belief in the predictable behaviour of cooperating partners, as well as adherence to moral values and norms shared by many individuals promoted the development of associational activity and made teamwork more dynamic. The powers of individuals joined in cooperation allowed for the more efficient execution of tasks aimed at the common good of local communities. Cooperation brought benefits to both religious and secular communities. The movement aimed at the construction of sacral architecture developed dynamically, and associational activity increased, as did the level of religiosity. The activity of anti-communist independence organizations, demanding the democratization of social life, was also stimulated. Universal moral values and norms emphasized and promoted by Tokarczuk created axiological foundations for the establishment of a civil society.