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Unmanned Aerial Vehicles (UAVs) have been utilized to serve on-ground users with various services, e.g., computing, communication and caching, due to their mobility and flexibility. The main focus of many recent studies on UAVs is to deploy a set of homogeneous UAVs with identical capabilities controlled by one UAV owner/company to provide services. However, little attention has been paid to the issue of how to enable different UAV owners to provide services with differentiated service capabilities in a shared area. To address this issue, we propose a multi-agent imitation learning enabled UAV deployment approach to maximize both profits of UAV owners and utilities of on-ground users. Specially, a Markov game is formulated among UAV owners and we prove that a Nash equilibrium exists based on the full knowledge of the system. For online scheduling with incomplete information, we design agent policies by imitating the behaviors of corresponding experts. A novel neural network model, integrating convolutional neural networks, generative adversarial networks and a gradient-based policy, can be trained and executed in a fully decentralized manner with a guaranteed -Nash equilibrium. Performance results show that our algorithm has significant superiority on average profits, utilities and execution time compared with other representative algorithms.

Parallel sentence pairs play a very important role in many natural language processing tasks, especially cross-lingual tasks such as machine translation. So far, many Asian language pairs lack bilingual parallel sentences. As collecting bilingual parallel data is very time-consuming and difficult, it is very important for many low-resource Asian language pairs. While existing methods have shown encouraging results, they rely on bilingual data seriously or have some drawbacks in an unsupervised situation. To address these issues, we propose a new unsupervised similarity calculation and dynamic selection metric to obtain parallel sentence pairs in an unsupervised situation. First, our method maps bilingual word embedding by postdoc adversarial training, which rotates the source space to match the target without parallel data. Then, we introduce a new cross-domain similarity adaption to obtain parallel sentence pairs. Experimental results on real-world datasets show that our model can obtain better accuracy and recall on mining parallel sentence pairs. We also show that the extracted bilingual sentence corpora can significantly improve the performance of neural machine translation.

Unmanned Aerial Vehicles (UAV) have many applications in both commerce and recreation. However, irresponsibly operated UAVs will pose a threat to public safety. Therefore, developing our understanding of UAVs and their uses is of particular interest. This paper considers tracking UAVs, which provide multifaceted information around location, paths and trajectories. To facilitate research on this topic, we introduce a new benchmark, herein referred to as Anti-UAV, which provides a novel direction for UAV tracking with more than 300 video pairs containing over 580k manually annotated bounding boxes. Addressing anti-UAV research challenges could help to design anti-UAV systems, which in turn may improve surveillance. Accordingly, we have proposed a simple yet effective approach, called dual-flow semantic consistency (DFSC) is proposed for UAV tracking. Modulated by the semantic flow across video sequences, tracker learns more robust class-level semantic information and obtains more discriminative instance-level features. Experiments highlight significant performance gain with the proposed approach over state-of-the-art trackers and the challenging aspects of Anti-UAV. The Anti-UAV benchmark and the code for the proposed approach have been made publicly available at https://github.com/ucas-vg/Anti-UAV.

In this paper, we study the use of deception for strategic planning in adversarial environments. We model the interaction between the agent (player 1) and the adversary (player 2) as a two-player concurrent game in which the adversary has incomplete information about the agent's task specification in temporal logic. During the online interaction, the adversary can infer the agent's intention from observations and adapt its strategy so as to prevent the agent from satisfying the task. To plan against such an adaptive opponent, the agent must leverage its knowledge about the adversary's incomplete information to influence the behavior of the opponent, and thereby being deceptive. To synthesize a deceptive strategy, we introduce a class of hypergame models that capture the interaction between the agent and its adversary given asymmetric, incomplete information. A hypergame is a hierarchy of games, perceived differently by the agent and its adversary. We develop the solution concept of this class of hypergames and show that the subjectively rationalizable strategy for the agent is deceptive and maximizes the probability of satisfying the task in temporal logic. This deceptive strategy is obtained by modeling the opponent evolving perception of the interaction and integrating the opponent model into proactive planning. Following the deceptive strategy, the agent chooses actions to influence the game history as well as to manipulate the adversary's perception so that it takes actions that benefit the goal of the agent. We demonstrate the correctness of our deceptive planning algorithm using robot motion planning examples with temporal logic objectives and design a detection mechanism to notify the agent of potential errors in modeling of the adversary's behavior., 11 pages, 4 figures, submitted to IEEE Transactions on Automation Science and Engineering

Unmanned aerial vehicles, popularly known as UAVs, have been used in many applications in the recent past. UAVs have also been recently used to provide security as a service (SECaaS). SECaaS involves technical solutions, like antivirus and antispam software, firewalls, using secure operating systems, etc. UAVs are resource-constrained entities, and thus, they avail the computational facilities of the base station (BS) to serve the users in their range. At times, several UAVs cooperatively come together to serve a given region, and such a group of UAVs is called a swarm of UAVs. Generally, a group/swarm of UAVs connect themselves to the BS through cluster head (CH) UAVs, which are intermediary nodes. In real-world scenarios, many stakeholders come together to form a UAV swarm configuration providing services to users. Each stakeholder wants to maximize gains. This work proposes a pricing Stackelberg game among the UAVs, CHs, and the BS by formulating their behavioral utilities. Using particle swarm optimization on each entity’s utility functions, we create an optimal price strategy to maximize profit.

This article addresses decentralized robust portfolio optimization based on multiagent systems. Decentralized robust portfolio optimization is first formulated as two distributed minimax optimization problems in a Markowitz return-risk framework. Cooperative-competitive multiagent systems are developed and applied for solving the formulated problems. The multiagent systems are shown to be able to reach consensuses in the expected stock prices and convergence in investment allocations through both intergroup and intragroup interactions. Experimental results of the multiagent systems with stock data from four major markets are elaborated to substantiate the efficacy of multiagent systems for decentralized robust portfolio optimization.

Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle (UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology inference for tracking the topology changes of the UAV network, especially in non-cooperative manner. In this paper, we study the problem of inferring UAV swarm network topology based on external observations, and propose a dynamic topology inference method. First, we establish a sensing framework for acquiring the communication behavior of the target network over time. Then, we expand the multi-dimensional dynamic Hawkes process to model the communication event sequence in a dynamic wireless network. Finally, combining the sliding time window mechanism, the maximum weighted likelihood estimation is applied to inferring the network topology. Extensive simulation results demonstrate the effectiveness of the proposed method.

Future networks of unmanned aerial vehicles (UAVs) will be tasked to carry out ever-increasing complex operations that are time-critical and that require accurate localization performance (e.g., tracking the state of a malicious user). Since there is the need to preserve low UAV complexity while tackling the challenging goals of missions in effective ways, one key aspect is the UAV intelligence (UAV-I). The UAV's intelligence includes the UAV's capability to process information and to make decisions, e.g., to decide where to sense and whether to delegate some tasks to other network entities. In this paper, we provide an overview of possible solutions for the design of UAVs of low complexity, showing some of the needs of the UAVs for running efficient localization operations, performed either as a team or individually. Further, we focus on different network configurations, which possibly include assistance with edge computing. We also discuss open problems and future perspectives for these settings.

Due to the advantage of flexible and quick movement, unmanned aerial vehicles (UAVs) have been widely utilized in assisting wireless communications. A challenging problem is how to deploy UAVs to provide communication services for more user devices for a long term while minimizing the number of UAVs. In this article, we propose a UAV network system with UAV deletion due to redundancy or energy exhaustion and UAV insertion to provide long-lasting communication services. Then, a distributed algorithm based on the virtual Coulomb force and Voronoi diagram is proposed to deploy UAVs improving the communication coverage and turning redundant UAVs off. On the one hand, in order to improve the communication coverage, we propose two moving schemes to move UAVs. On the other hand, in order to save energy, we propose a sleeping scheme to turn several redundant UAVs off. Particularly, the redundancy of a UAV is evaluated according to a proposed definition of the average local coverage rate. Simulation results demonstrate that the proposed algorithm can deploy a minimal number of UAVs to provide enough communication coverage and the presented network system can offer long-lasting service.

Unmanned Aerial Vehicles (UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation (6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly, serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus, UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical challenge. In this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

In this paper, the real-time path avoidance problem of multirotor UAV in the case of sudden obstacles in two-dimensional environment is studied. The principle, model, and application of ant colony algorithm are analyzed. On this basis, the adaptive dynamic window ant colony algorithm is proposed, and the adaptive dynamic window method is designed; the heuristic function of adding obstacle detection factors and the double pheromone update strategy are made to the ant colony algorithm, and the improved ant colony algorithm is used to replan the path within the dynamic window that can be automatically adjusted to achieve the purpose of obstacle avoidance. A real-time simulation experiment of path planning was conducted by constructing an environment map in MATLAB. The simulation results show that with the continuous increase of the number of sudden obstacles, the real-time replacement path of multirotor UAV also gradually increases, and when approaching the obstacles, the replacement path is more dense, indicating that the adaptive window ant colony algorithm can be applied to dynamic path replacement, and the multirotor UAV can realize dynamic obstacle avoidance path optimization under the condition of sudden obstacles in a short time.

This article focuses on how register considerations informed and guided the design of the spoken component of the British National Corpus 2014 (Spoken BNC2014). It discusses why the compilers of the corpus sought to gather recordings from just one broad spoken register – ‘informal conversation’ – and how this and other design decisions afforded contributors to the corpus much freedom with regards to the selection of situational contexts for the recordings. This freedom resulted in a high level of diversity in the corpus for situational parameters such as recording location and activity type, each of which was captured in the corpus metadata. Focussing on these parameters, this article provides evidence for functional variation among the texts in the corpus and suggests that differences such as those observed presently could be analysable within the existing frameworks for analysis of register variation in spoken and written language, such as multidimensional analysis.

Multi-Target Tracking Guidance (MTTG) in unknown environments has great potential values in applications for Unmanned Aerial Vehicle (UAV) swarms. Although Multi-Agent Deep Reinforcement Learning (MADRL) is a promising technique for learning cooperation, most of the existing methods cannot scale well to decentralized UAV swarms due to their computational complexity or global information requirement. This paper proposes a decentralized MADRL method using the maximum reciprocal reward to learn cooperative tracking policies for UAV swarms. This method reshapes each UAV's reward with a regularization term that is defined as the dot product of the reward vector of all neighbor UAVs and the corresponding dependency vector between the UAV and the neighbors. And the dependence between UAVs can be directly captured by the Pointwise Mutual Information (PMI) neural network without complicated aggregation statistics. Then, the experience sharing Reciprocal Reward Multi-Agent Actor-Critic (MAAC-R) algorithm is proposed to learn the cooperative sharing policy for all homogeneous UAVs. Experiments demonstrate that the proposed algorithm can improve the UAVs’ cooperation more effectively than the baseline algorithms, and can stimulate a rich form of cooperative tracking behaviors of UAV swarms. Besides, the learned policy can better scale to other scenarios with more UAVs and targets.

With the growing availability and popularity of sentiment-rich resources like blogs and online reviews, new opportunities and challenges have emerged regarding the identification, extraction, and organization of sentiments from user-generated documents or sentences. Recently, many studies have exploited lexicon-based methods or supervised learning algorithms to separately conduct sentiment analysis tasks; however, the former approaches ignore contextual information of sentences and the latter ones do not take sentiment information embedded in sentiment words into consideration. To tackle these limitations, we propose a new model named Sentiment Convolutional Neural Network (SentiCNN) to analyze the sentiments of sentences with both contextual and sentiment information of sentiment words, in which, contextual information is captured from word embeddings and sentiment information is identified using existing lexicons. We incorporate a Highway Network into our model to adaptively combine sentiment and contextual information from sentences by strengthening the connection between features of both sentences and their sentiment words. Furthermore, we propose three lexicon-based attention mechanisms (LBAMs) for our SentiCNN model to find the most important indicators of sentiments and make predictions more effectively. Experiments over two well-known datasets indicate that sentiment words, the Highway Network, and LBAMs contribute to sentiment analysis.

In order to optimize robot road planning, automation will increase production efficiency and reduce production costs. An improved ant colony algorithm based on a two-dimensional flat path planning study divided the three-dimensional space into planes, rasterized each plane, and replaced the original shape by storing a pheromone at the intersection. The pheromone storage space along the route will be reduced, and a three-dimensional space route planning study will be gradually conducted. As complex 3D environments and landscape features change, obstacle avoidance strategies have increased, road heuristics have improved, and new heuristic features have emerged. The initial value of the road node pheromone increases the efficiency of early ant search because of the uneven distribution of starting point, target point location information, and forward direction. After each iteration, the stuck ants that did not reach the target point are discarded according to the high-quality ant renewal rules, the iteration threshold is set, and the pheromone fluctuation coefficient is adjusted as the algorithm tends to merge. Compared with the basic ant colony algorithm, the convergence iteration times of the improved ant colony algorithm in this paper are reduced by about 40%, and the optimal path length is shortened by about 10. The duration of the algorithm increases. In terms of algorithm performance, it takes some time to improve the ant colony algorithm. Because of the complexity of the algorithm, some search strategies are added to the algorithm. The contribution of this article is the basis for the mobile robot to walk accurately from the initial position to the working position and perform various tasks independently.

The integration of unmanned aerial vehicles (UAVs) into future-generation wireless networks is a promising strategy for complementing terrestrial communication infrastructures that will lead to capacity and coverage improvements. Unfortunately, the energy limitations of the existing off-the-shelf small-size UAVs are significant challenges, especially during long missions. In this article, first, we investigate the communication model of a heterogeneous UAV-assisted wireless network that consists of multiple energy-constrained battery-powered small-size UAVs (a.k.a. follower UAVs) and a large-size UAV (a.k.a. dispatcher UAV) with solar energy harvesting capability. Subsequently, we propose an aerial energy sharing algorithm to improve the lifetime of the system by orchestrating energy balancing among follower UAVs. In particular, the dispatcher UAV having large energy capacity owing to its battery size and solar energy harvesting capability shares its energy with follower UAVs using radio-frequency-based power transfer through beamforming. The proposed energy sharing algorithm considers the placement, available battery capacity, and mission execution time of the follower UAVs to decide the energy sharing points with optimum efficiency. Extensive simulations show that our proposed algorithm outperforms existing algorithms in terms of charging efficiency and energy balancing among UAVs.

Due to the convergence defect of the existing SDN social network node topology location methods, there is a large node topology location error problem. In order to solve the above problems, this paper proposes a topology location method of SDN social network nodes based on ant colony algorithm. Firstly, the ant colony algorithm is introduced, and then the biological principle of ant colony algorithm is analyzed to determine the basic steps of ant colony algorithm. According to the number of mobile beacon launch positions and specific launch coordinates in ROI, ant colony algorithm is introduced into the mobile beacon path acquisition program to realize SDN node topology location based on ant colony algorithm. The experimental results show that under the background of node communication radius of 2.0 and 5.0, the node topology positioning errors of this method are small, and the minimum errors are 9.10% and 5.15% respectively. It is fully proved that this method has good node topology positioning effect.

With the rapid development of information collection technology and natural language processing technology, the construction of English-Chinese bilingual parallel corpus has developed rapidly. The scale of corpus and related technology have a large research space; and how to obtain effective data and knowledge from massive resources, in order to better serve the basic and applied research, is becoming a trend. Based on the research of parallel bilingual corpora at home and abroad, this article extracts the text features of medical English and constructs English-Chinese bilingual corpora with different levels aligned. Based on the statistical analysis of the distribution characteristics of phrase structures and the acquisition of characteristic knowledge, an English-Chinese bilingual translation model is constructed based on the bilingual corpus, and then the phrase structure knowledge in English and Chinese sentences is extracted from the data-driven English-Chinese bilingual corpus through the model. The results show that the accuracy of the test set is 91.63% and the F value is 90.05% under the condition of keeping the recall rate stable. The accuracy of the text structure features has been significantly improved, and the expected effect has been achieved through the test.

Genetic Algorithm often bears with Premature Convergence for solving combinatorial optimization problems but can be improved by modification at different prospectives. In this research, an effective knowledge is applied in the procedure of Genetic Algorithm used for solving TSP. The key concept for the proposed GA is a modification in the crossover operators by applying knowledge of smallest distant cities (shortest edge) assuming that it would improve the process to find the shortest path, by optimizing the new generations. As TSP is represented as a graph, considering cities as nodes and paths as edges, in the proposed approach, crossover point selection for crossover operators is selected upon the basis of minimum edge in the given graph. To test the effect of the proposed method of knowledge application, Linear Order Crossover, Cycle Crossover Operator, and Sequential Constructive Crossover are modified and results are proved on the randomly generated data sets for TSP. This optimization not only improved the shortest path but also reduced the problem of Premature Convergence.

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning.

The performance of Vehicular Adhoc Networks (VANETs), the underlying technology for intelligent vehicles, has shown improvement with the application of swarm-based algorithms for routing. Swarm Intelligence is a self-intelligence group of similar agents functioning on distribution, flexibility, and communication among the agents. The multiple problems occurring in the modern communication systems, including VANETs, have been tried to be resolved by the application of swarm intelligence algorithms viz, Genetic Algorithms (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuck-oo Search Algorithm (CSA), Glowworm Swarm Optimization (GSO), etc., have been proposed in the literature. This paper provides a comparative analysis of operations of swarm-based algorithms. Points like their operations, basic entities, advantages, disadvantages, and applications are discussed.

Automatic Speech Recognition (ASR) is the use of computer hardware and software-based techniques to identify and process human voices. These systems used data from both male and female speakers. The majority of commercial ASR systems available on adult speech are working efficiently. Speech data collected from both male and female speakers were used in these systems. In recent decades, ASR systems for children have emerged, such as reading tutors, aids for foreign language learning, and computer games. Child ASR systems are essential but poorly understood in the field of computer speech recognition. The child data collection is a very complex task. Child corpus is not available publicly, and variability of children speakers and ASR developed for a particular age group is not suitable for other age groups. These are some of the reasons for less and ineffective child ASR systems. However, the non-availability of child corpus publicly is a primary reason for ineffective child ASR. Designing and developing child corpus is a very tedious task. Therefore, the primary focus of this state-of-the-art review is to discuss various challenges encountered while designing and developing child corpus.

This study objective was to study the work process and performance of Thai soccer player agents and develop an instrument to measure the characteristics of soccer players agents and develop a guideline to prepare soccer players into soccer agents in Thailand. Data for the work process of football agents were collected from soccer player agents and stakeholders through in-depth interviews and group discussions. Data on the performance of Thai soccer player agents were collected with the Delphi technique and qualitative method from experienced people or experts in the characteristics and marketing services of agents. The study found that regarding soccer player selection of a soccer player agent work process. The selection of soccer players differs according to each organization or company policies. In marketing a soccer player, agents consider the needs of a team or the buyer so that they can present a suitable soccer player. There are 4 capabilities for an agent. They are knowledge, skills, desired capability or behavior, and other characteristics. All these capabilities are required in a soccer player agent for them to be able to support the income and living standard of the soccer player.

Reviews and experimental verification have found that existing solution methods can be used to solve UAV path planning problems, but each approximate solution has its own advantages and disadvantages. For example, ant colony algorithm easily falls into the local optimum in the process of realizing path planning. In order to prevent too low pheromones on the longer path and too high pheromones in the shorter path, the upper and lower limits of pheromones as well as their volatile factors are set to avoid falling into the local optimum. Secondly, multi-heuristic factors are introduced, and the overall length of the path serves as an adaptive heuristic function factor that determines the probability of state transition, which affects the probability of ants choosing the corresponding path. The experimental results show that the path length planned by the improved algorithm is 93.6% of the original algorithm, and the optimal path length variance is only 14.22% of the original algorithm. The improved ant colony algorithm shortens the optimal path length and solves the UAV path planning problem in terms of local optima. At the same time, multiple enlightening factors are introduced to increase the suitability of UAV for complex environments and improve the performance of UAV.

This article addresses a fundamental question of multiagent knowledge distribution: what information should be sent to whom and when with the limited resources available to each agent? Communication requirements for multiagent systems can be rather high when an accurate picture of the environment and the state of other agents must be maintained. To reduce the impact of multiagent coordination on networked systems, for example, power and bandwidth, this article introduces two concepts for the partially observable Markov decision processes (POMDPs): 1) action-based constraints that yield constrained-action POMDPs (CA-POMDPs) and 2) soft probabilistic constraint satisfaction for the resulting infinite-horizon controllers. To enable constraint analysis over an infinite horizon, an unconstrained policy is first represented as a finite-state controller (FSC) and optimized with policy iteration. The FSC representation then allows for a combination of the Markov chain Monte Carlo and discrete optimization to improve the probabilistic constraint satisfaction of the controller while minimizing the impact on the value function. Within the CA-POMDP framework, we then propose intelligent knowledge distribution (IKD) which yields per-agent policies for distributing knowledge between agents subject to interaction constraints. Finally, the CA-POMDP and IKD concepts are validated using an asset tracking problem where multiple unmanned aerial vehicles (UAVs) with heterogeneous sensors collaborate to localize a ground asset to assist in avoiding unseen obstacles in a disaster area. The IKD model was able to maintain asset tracking through multiagent communications while only violating soft power and bandwidth constraints 3% of the time, while greedy and naive approaches violated constraints more than 60% of the time.

This article provides an overview of methodological and technical issues that arise in the collection, indexing and use of spoken learner corpora, i. e. corpora containing spoken utterances of learners of a target language. After an introductory discussion of the most important special features of this type of corpus that distinguish it from written language learner corpora and spoken corpora with L1 speakers, we will go into more detail on questions of corpus design. The main part of the paper is then an overview of the methodological and technical procedures of the individual steps of collecting, indexing, providing and using spoken learner corpora. The main aim of this overview is to highlight practices that can be considered best practices according to the current state of research. Finally, we outline the challenges that still exist for this type of corpus.

The ant colony algorithm was first introduced by M. Dorigo, V. Maniezzo and A. Colorni in 1991. This algorithm is based on the behavior of a real ant colony when searching for food. The main area of application of the algorithm is the problem of combinatorial optimization. This paper describes the implementation of the algorithm, setting the parameters and considers its effectiveness in solving the job shop problem.

In the current scenario, the demand for computerization and data security has been increasing to combat with the increasing rate of crimes in the society. In the existing solution for prison management system there exist substantive data symmetry among prisons. Thus, during access of any prisoner detail, system may provide inappropriate details. Our objective is to implement a novel approach that stores the prisoner’s credentials and efficiently verifies the prisoner’s credentials via the blockchain technology. Our proposed solution is implements in the Hyperledger framework and the experimental result shows that our proposed solution is beneficial for the existing and upcoming investigation department.

The effective integration of unmanned aerial vehicles (UAVs) in future wireless communication systems depends on the conscious use of their limited energy, which constrains their flight time. Reconfigurable intelligent surfaces (RISs) can be used in combination with UAVs with the aim to improve the communication performance without increasing complexity at the UAV side. In this paper, we propose a synergetic UAV RIS communication system, utilizing a UAV with a highly directional antenna aiming to the RIS. The proposed scenario can be applied in all air-to-ground RIS-assisted networks and numerical results illustrate that it is superior from the cases where the UAV utilizes either an omnidirectional antenna or a highly directional antenna aiming towards the ground node., 5 pages, 5 figures

We consider a ubiquitous scenario in the Internet economy when individual decision-makers (henceforth, agents) both produce and consume information as they make strategic choices in an uncertain environment. This creates a three-way tradeoff between exploration (trying out insufficiently explored alternatives to help others in the future), exploitation (making optimal decisions given the information discovered by other agents), and incentives of the agents (who are myopically interested in exploitation, while preferring the others to explore). We posit a principal who controls the flow of information from agents that came before, and strives to coordinate the agents towards a socially optimal balance between exploration and exploitation, not using any monetary transfers. The goal is to design a recommendation policy for the principal which respects agents' incentives and minimizes a suitable notion of regret. We extend prior work in this direction to allow the agents to interact with one another in a shared environment: at each time step, multiple agents arrive to play a Bayesian game, receive recommendations, choose their actions, receive their payoffs, and then leave the game forever. The agents now face two sources of uncertainty: the actions of the other agents and the parameters of the uncertain game environment. Our main contribution is to show that the principal can achieve constant regret when the utilities are deterministic (where the constant depends on the prior distribution, but not on the time horizon), and logarithmic regret when the utilities are stochastic. As a key technical tool, we introduce the concept of explorable actions, the actions which some incentive-compatible policy can recommend with non-zero probability. We show how the principal can identify (and explore) all explorable actions, and use the revealed information to perform optimally., Comment: All revisions focused on presentation; all results (except Appendix C) have been present since the initial version

In this paper we describe application of the method of physical processes simulating to build a swarm of drones. We show that this method provides the construction of a swarm of drones in the form of a crystal lattice with specified parameters. We found those additional conditions that make it possible to assemble a swarm of disparate drones. We have proposed an algorithm that allows assembling a swarm with minimal costs using a simple measuring complex. Key words UAV, self-organization, physical processes simulating, crystal lattice, swarm robotics, sensors for UAV.

This study proposes a collision-free motion planning framework for indoor autonomous flight of multirotor unmanned aerial vehicle (UAV) based on the convex model predictive control (MPC) approach u...

Promoting the efficiency of task forwarding is a key issue in search and rescue operations, aerospace applications and so on. In this paper, we introduce a reputation mechanism existing in an evolutionary game into the task forwarding process of a multiagent system to promote the cooperation rate between agents. Combining the reputation mechanism with strategy updating of a multiagent system, a model based on evolutionary game theory is built and tested, and key factors such as the reputation threshold and the percentage of agents who initially select the forwarding strategy are analyzed in this paper. A numerical simulation shows that compared with previous studies, the model in this paper can make the proportion of agents choosing the forwarding strategy in a multiagent system reach a steady state faster. Through an analysis of various parameters, we also make this new model applicable to more realistic situations, which provides a more appropriate method for task forwarding in multiagent systems.

Unmanned aerial vehicles (UAVs) can be used as aerial relays to provide quick and on-demand wireless connections for the area without network coverage. Since a UAV’s onboard energy is limited, how to prolong the duration of UAV relaying communication is challenging. In this paper, we consider applying multiple UAVs to extend the duration of communication via UAV collaboration. We first propose a UAV collaboration scheme, namely heuristic UAV substitution, which lets the UAV relays work one by one individually. Then, we propose another scheme called spectral efficient UAV substitution to improve the spectrum efficiency. Furthermore, under these two proposed schemes, we jointly design the trajectories of the UAV relays and the transmit power of the source and the UAV relays, with the goal of end-to-end throughput maximization. Although the considered optimization problem is non-convex and difficult to solve, we propose an efficient algorithm to find a suboptimal solution to it by applying the block coordinate ascent and successive convex approximation methods. Simulation results show that the proposed UAV substitution schemes can effectively extend the relaying communication duration, and the throughput performance of the proposed joint trajectory optimization and transmit power control algorithm outperforms some benchmark algorithms without considering joint design.

This paper gives you the overview of the Soft computing techniques i.e. Genetic algorithm (GA), Artificial neural networks (ANN) and some of Swarm Intelligence techniques i.e. Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO) etc. Soft Computing is basically an optimization technique which helps to find the results of problems which are very difficult to reply. It is the integration of methodologies which are planned to represent and find the solutions to real world problems that are not represented or which are very hard to represent mathematically. Swarm Intelligence (SI) can be defined as a subfield of Artificial Intelligence which is used to represent the collaborative behavior of communal swarms in nature, such as ant colonies, honey bees, bird flocks, grey wolves, fireflies and cuckoo search. The term swarm is used for the collection of animals such as fish schools, bird flocks and insect colonies which use their sorroundings and services significantly to communicate by mutual intelligence. In this paper, all these algorithms are discussed in brief

The subject of study is the process of creating an unmanned aerial vehicle of the helicopter type, which is able to perform autonomous flight and be used in the premises to test the algorithms of swarm interaction. The aim is to reduce the cost of forming and operating the UAV swarm intelligence model, ensuring compatibility with existing hardware positioning systems and simulation software platforms. Tasks: to analyze the existing prototypes of unmanned aerial vehicles of the helicopter type, used for modeling and testing of autonomous flight modes indoors; to review the systems of positioning and exchange of information between the participants of the swarm formation and to analyze the hardware systems necessary for the implementation of these systems; to develop the architecture of hardware subsystems for unmanned aerial vehicles capable of performing tasks at the level of the considered prototypes; design a case for a sound hardware platform, check its operational parameters, implement its collection and testing; install software that allows you to check the operability of the considered platform of the unmanned aerial vehicle. The methods used are: systems analysis used to compare models of unmanned aerial vehicles within the tasks, methods of analysis and synthesis of schemes for the implementation of the hardware platform, the method of graphic modeling for the design of the unmanned aerial vehicle, methods of system programming for flight program. The following results were obtained. The choice of the hardware platform of the unmanned aerial vehicle is substantiated; the hull for the helicopter was developed and implemented, two platforms of unmanned aerial vehicles were collected and tested during autonomous flight indoors. Conclusions. The study found that there are existing and widely used platforms for compatible indoor flight. These platforms are open and closed. The analysis of open hardware platforms and synthesis of architecture of own unmanned aerial vehicle is made. The designed and implemented unmanned aerial vehicle is capable of autonomous flight, has all the necessary components to perform a compatible flight program. As part of the layout of swarm interaction, it is necessary to further adjust the positioning system.

As a swarm-based optimization heuristic, the Ant System (AS) was proposed to deal with the Traveling Salesman Problem (TSP). Classifified as a constructive approach, AS was inspired by the ants’ social behavior.In Practice, its implementation reveals three basic variants that exploit two operating models: a fifirst “natural” model, where ants move and update pheromones, with consideration of distances between towns, to defifine the Ant-Quantity variant, and without considering distances for the Ant-Density variant. Or an “abnormal” second model, where ants move and delay updates until all ants have completed a full cycle, then consider the tour length which defifines the Ant-Cycle that was claimed to be the best variant. We reload the AS and reconsider these three basic ant algorithms and their respective two models. We propose to explore an “overlooked” third model, which consists of further expanding the “abnormal” ants’ attitude, forcing them to move and update pheromones, simultaneously, after every single move, thus conceiving the Ant-Step model. Keeping the option whether or not to consider distances, we propose two new AS basic algorithms: the Ant-Step-Quantity and the Ant-Step-Density. The aim of this paper is to present and assess these two new basic AS variants in respect to the three existing ones, specially the one considered the best, through an experimental study on various symmetric TSP benchmarks.

In this paper, we present the ant colony optimization algorithm (ACO for short), which was used to solve gear box problem design. Biological fundamentals of ACO algorithm, as well as method explanation are presented. The pseudo code for this algorithm was written using Matlab R2018a software suite. At the end, the results obtained by ACO algorithm are compared to the results previously obtained by other algorithms.

Spacecraft swarms can achieve mission objectives otherwise impossible by monolithic satellites. To this end, swarms need to autonomously reconfigure their relative motion while complying with space...

Offspring generation plays an important role in evolutionary multiobjective optimization. However, generating promising candidate solutions effectively in high-dimensional spaces is particularly challenging. To address this issue, we propose an adaptive offspring generation method for large-scale multiobjective optimization. First, a preselection strategy is proposed to select a balanced parent population, and then these parent solutions are used to construct direction vectors in the decision spaces for reproducing promising offspring solutions. Specifically, two kinds of direction vectors are adaptively used to generate offspring solutions. The first kind takes advantage of the dominated solutions to generate offspring solutions toward the Pareto optimal set (PS) for convergence enhancement, while the other kind uses those nondominated solutions to spread the solutions over the PS for diversity maintenance. The proposed offspring generation method can be embedded in many existing multiobjective evolutionary algorithms (EAs) for large-scale multiobjective optimization. Experiments are conducted to reveal the mechanism of our proposed adaptive reproduction strategy and validate its effectiveness. Experimental results on some large-scale multiobjective optimization problems have demonstrated the competitive performance of our proposed algorithm in comparison with five state-of-the-art large-scale EAs.

Social insects, such as ants, use various pheromones as their social signal. In addition, they use the presence of other ants for decision-making. In this study, we attempted to evaluate if individual decision-making is influenced by the complementary use of pheromones and presence of other ants. Ants were induced to form a one-way flow system. We found that when ants entered such a system at a right angle, they tended to move in the opposite direction of the one-way flow system. Interestingly, the target ants moved randomly in the experiments in which no ant and/or no pheromone trails were present. We also developed simulation algorithms and found that artificial ant foragers could reach a certain goal more often if they adopted the reverse run (similar mechanism found in ant experiments) over the forward run (moving in the same direction as their nestmates).

Ant Colony Optimization (ACO) metaheuristic is a multi-agent system in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve optimization problem. Set Covering Problems (SCP), on the other hand, deal with maximizing the coverage of every subset while the weight nodes used must be minimized. In this paper, ACO was adapted and used to solve a case of Set Covering Problem. The adapted ACO for solving the SCP was implemented as a computer program using SciLab 5.4.1. The problem of determining the optimal location of Wi-Fi Access Points using the 802.11n protocol in the UP Los Banos Math Building was solved using this metaheuristic. Results show that in order to have 100% coverage of the MB, 7 access points are required. Methodology of the study can be adapted and results of the study can be used by decision makers on related optimization problems.

The amount of data from users in Hindi language is tremendously increasing on social media, blogs, online forums due to which Sentiment analysis of Indian languages has turned out to be a predominant research area. Lexicon based analysis is one of the techniques that can be used for analyzing sentiments. While using the lexicon-based sentiment analysis, ambiguous words could be an issue. This paper proposes a graph based Lesk approach to handle the word sense disambiguation issue and tries to enhance and improve the lexicon approach. Experiments performed to evaluate the performance of the proposed algorithm shows that the performance of lexicon-based sentiment analysis is increased significantly by using the graph based Lesk approach.

This paper proposes a deep mining method of high-dimensional abnormal data in Internet of things based on improved ant colony algorithm. Preprocess the high-dimensional abnormal data of the Internet of things and extract the data correlation feature quantity; The ant colony algorithm is improved by updating the pheromone and state transition probability; With the help of the improved ant colony algorithm, the feature response signal of high-dimensional abnormal data in Internet of things is extracted, the judgment threshold of high-dimensional abnormal data in Internet of things is determined, and the objective function is constructed to optimize the mining depth, so as to realize the deep data mining. The results show that the average error of the proposed method is only 0.48%.

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Due to the special flexible structure, finger seal shows good wear and leakage characteristics, and has a great application prospect. Under the condition of high speed, the sealing and wear perform...

We consider linear-quadratic-Gaussian (LQG) network games in which agents have quadratic payoffs that depend on their individual and neighbors' actions, and an unknown payoff-relevant state. An information designer determines the fidelity of information revealed to the agents about the payoff state to maximize the social welfare. Prior results show that full information disclosure is optimal under certain assumptions on the payoffs, i.e., it is beneficial for the average individual. In this paper, we provide conditions based on the strength of the dependence of payoffs on neighbors' actions, i.e., competition, under which a rational agent is expected to benefit, i.e., receive higher payoffs, from full information disclosure. We find that all agents benefit from information disclosure for the star network structure when the game is symmetric and submodular or supermodular. We also identify that the central agent benefits more than a peripheral agent from full information disclosure unless the competition is strong and the number of peripheral agents is small enough. Despite the fact that all agents expect to benefit from information disclosure ex-ante, a central agent can be worse-off from information disclosure in many realizations of the payoff state under strong competition, indicating that a risk-averse central agent can prefer uninformative signals ex-ante., 6 pages, 4 figures

As a new type of unmanned intelligent equipment, unmanned aerial vehicles (UAVs) have attracted growing attentions in various fields, such as electronic reconnaissance in jamming environments. However, the intense confrontation and high dynamics of the hostile jamming environments require a UAV swarm to achieve flexible and autonomous deployment optimization, and enhance anti-jamming capability. In this letter, a distributed UAV swarm’s deployment approach is proposed where a congestion game is formulated to model the interaction among the UAV swarm. Particularly, each UAV independently adjusts the location, and realizes the efficient, flexible and dynamic position control, thus increasing the amount of collected data and enhancing the anti-jamming performance. Moreover, the Nash equilibrium of the formulated congestion game is analyzed, and simulation results verify the effectiveness of the proposed approach.

This paper will incorporate a survey of Unmanned Aerial Vehicle (UAV) and associated problems and present important challenges. Drones have attracted a lot of attention because of their innumerable applications as they are highly movable and provide applications such as service delivery, farming, pollution mitigation, rescue operations. Drones or Unmanned Aerial Vehicles (UAVs) which is one of the components of Unmanned Aerial System (UAS) are equipped with different IoT devices such as sensors, cameras, and actuators. They have a potential market because of their ubiquitous usability. They can be remotely controllable and regulating them in a well organized manner is a challenging task. Different communication technologies can be used such as Wi-Fi, WiMax, and Satellite Communication (SATCOM).