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1
Wang, Shun Hong, Jiu Fen Zhao, Le Fei Pan, Xin Xue Liu, and Bei Zhang. "An Evolutionary Method of Traditional Artificial Potential Field." Applied Mechanics and Materials 198-199 (September 2012): 1025–29. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1025.
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The problems of goals non-reachable with obstacles nearby (GNRON) and dead lock caused by local minimum were described when using Artificial Potential Field (APF) methods for mobile robot path planning in complex environment. Considering the geometric relationship of different obstacles, the heuristic factor of virtual attractive forces was introduced to solve the local minimum problem. Simulation results indicate that the evolutionary method of APF can realize optimal path planning in complex environment, which makes up the disadvantages of traditional APF method.
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Zhang, Eryi. "Path planning algorithm based on Improved Artificial Potential Field method." Applied and Computational Engineering 10, no. 1 (2023): 167–74. http://dx.doi.org/10.54254/2755-2721/10/20230170.
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The domain of research and development concerning mobile robot obstacle avoidance continues to remain an active area of interest. Artificial potential fields (APF) are a common and effective method for obstacle avoidance path planning, where the robot is guided to the target location by a simulated environmental potential field. Traditional artificial potential field methods tend to trap robots in local minima, impeding their ability to reach the goal. This research endeavours to introduce a new approach, the Improved Artificial Potential Field (IAPF) algorithm, which incorporates the A-star method in constructing the artificial potential field. This technique more effectively addresses the issue of path planning for mobile robots, thereby avoiding local minimum solutions. Through simulation experiments in different scenarios, the feasibility of the IAPF algorithm of this paper is verified. The results show that, compared with the traditional APF method, the IAPF algorithm can solve problem of local minimum and plan a sensible path.
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Carpenter, Chris. "Artificial Intelligence Unleashes Potential of Relative Permeability." Journal of Petroleum Technology 75, no. 07 (2023): 70–72. http://dx.doi.org/10.2118/0723-0070-jpt.
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 207855, “Unleashing the Potential of Relative Permeability Using Artificial Intelligence,” by Abdur Rahman Shah, SPE, Schlumberger; Kassem Ghorayeb, SPE, American University of Beirut; and Hussein Mustapha, Schlumberger, et al. The paper has not been peer reviewed. _ To unlock the potential of large relative permeability (Kr) databases, the work flow proposed in the complete paper integrates data analysis, machine learning (ML), and artificial intelligence (AI). The work flow allows for the automated generation of a clean database and a digital twin of Kr data, using AI to identify analog data from nearby fields by extending the rock-typing scheme across multiple fields for the same formation. Introduction Accurate Kr curves are critical because they can improve reservoir characterization, reduce uncertainty in history matching and production forecasting, and provide robust and reliable field development plans. However, preparing special core analysis (SCAL) data, particularly Kr curves, as an input for reservoir simulation traditionally has been a highly manual, time-consuming, labor-intensive process. Furthermore, no automated tools are available currently that perform a full quality review of Kr data using analytical or numerical methods. As a result, the quality and representativeness of Kr data used in reservoir simulation models is frequently inadequately checked. Furthermore, no tools exist to generate Kr curves based on analog field data in fields where Kr data is either missing or scarce. Integrated Work Flow The solution discussed in this paper aims to address challenges and automate processes using a combination of conventional algorithms and ML. It begins with a thorough quality check and cleanup of Kr laboratory data before using analog data to build Kr curves for a specific field or reservoir. The latter is especially important considering the challenges that arise when attempting to build on analog field data in the presence of large, corporate, regional, or country-scale databases. The approach classifies rocks using ML-proxy rock typing, which is subsequently used to find Kr equivalents. After that, these analogs can be used to fill up any data gaps for reservoir simulation in any discipline.
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Ryu, Jeong Yeop, Ho Yun Chung, and Kang Young Choi. "Potential role of artificial intelligence in craniofacial surgery." Archives of Craniofacial Surgery 22, no. 5 (2021): 223–31. http://dx.doi.org/10.7181/acfs.2021.00507.
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The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.
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Sabudin, Elia Nadira, Rosli Omar, Sanjoy Kumar Debnath, and Muhammad Suhaimi Sulong. "Efficient robotic path planning algorithm based on artificial potential field." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 4840. http://dx.doi.org/10.11591/ijece.v11i6.pp4840-4849.
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<span lang="EN-US">Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.</span>
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Gao, Xi Na, and Li Juan Wu. "Multi-Robot Formation Control Based on the Artificial Potential Field Method." Applied Mechanics and Materials 519-520 (February 2014): 1360–63. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1360.
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The artificial potential field method is one of multi-robot formation control methods. In this paper we make a study on multi-robot formation control based on the artificial potential field method and the leader-follower method. The robots are set leader robot and follower robots respectively. According to the known ideal distance between the leader and follower, we adjust the repulsiveness or attractiveness to maintain multi-robot formation. Multi-robots obstacle avoidance is adopted the artificial potential field method. In this paper the triangle formation is taken as an example. At last the simulation result proves the validity of this algorithm.
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Liao, Hongtao, Fu Jiang, Cheng Jin, et al. "Lithium-Ion Battery SoC Equilibrium: An Artificial Potential Field-Based Method." Energies 13, no. 21 (2020): 5691. http://dx.doi.org/10.3390/en13215691.
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Battery balance methods are the key technology to ensure the safe and efficient operation of the energy storage systems. Nevertheless, convenient balance methods experience slow convergence and difficult to adapt to quick charging applications. To solve the problem, in this paper, an artificial potential field-based lithium-ion battery balance method is proposed. Firstly, a cyber-physical model of the battery equalization system is proposed, in which the physical layer models the circuit components and the cyber layer represents the communication topology between the batteries. Then the virtual force function is established by artificial potential field to attract the voltage and state-of-charge of each cell to nominal values. With a feedback control law, the charging current of the battery is reasonably distributed to realize the rapid balance among batteries. The experimental results verify the effectiveness and superiority of the proposed method.
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Tao, Shuo. "Improved artificial potential field method for mobile robot path planning." Applied and Computational Engineering 33, no. 1 (2024): 157–66. http://dx.doi.org/10.54254/2755-2721/33/20230259.
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Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments. A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm. The simulation results demonstrate the effectiveness of the method. In comparison to APF, the improved APF could prevent the local minimum and reach the target position with fewer processing steps and better performance.
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Wang, Lihua, Zezhou Sun, Yaobing Wang, et al. "A Pre-Grasping Motion Planning Method Based on Improved Artificial Potential Field for Continuum Robots." Sensors 23, no. 22 (2023): 9105. http://dx.doi.org/10.3390/s23229105.
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Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.
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Bhardwaj, Abhaya, Shristi Kishore, and Dhananjay K. Pandey. "Artificial Intelligence in Biological Sciences." Life 12, no. 9 (2022): 1430. http://dx.doi.org/10.3390/life12091430.
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Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.
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Xing, Hanyu. "Review of unmanned aerial vehicle obstacle avoidance planning based on artificial potential field." Applied and Computational Engineering 10, no. 1 (2023): 55–63. http://dx.doi.org/10.54254/2755-2721/10/20230141.
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With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.
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Shi, Pu, and Jian Ning Hua. "Mobile Robot Dynamic Path Planning Based on Artificial Potential Field Approach." Advanced Materials Research 490-495 (March 2012): 994–98. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.994.
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Artificial potential field based mobile robot path planning approaches have been widely used. However, most methods are applied in the static environment where the target and the obstacles are stationary. In this paper, a potential field approach used in dynamic situation is proposed. Its major characteristics include a new attractive potential function as well as a repulsive potential function. The former takes the relative position and velocity between the robot and the target into consideration; the latter takes into account the relative position and velocity between the robot and the obstacles. The proposed approach guarantees the robot can track the moving target while escape from moving obstacles. Simulation experiments are carried out and the results demonstrate the effectiveness of the new potential field method.
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An, Mingyu. "Spacecraft environmental path planning method based on improved artificial potential field." Applied and Computational Engineering 10, no. 1 (2023): 129–38. http://dx.doi.org/10.54254/2755-2721/10/20230166.
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Spacecraft is the primary means of transportation for human exploration of outer space. With the advancement of science and technology, existing space exploration can no longer satisfy people's desire to explore, in the future, further and deeper space exploration is the goal of development. And the safety of spacecraft navigation in space requires the development of a path planning system. Although there have been many kinds of methods for path planning at this stage, all of them have their strengths and specialize in planning directions, but they also have defects. Based on this, this study improves the traditional artificial potential field method, by adjusting its repulsive field calculation and combining it with the RRT algorithm. The improved method can avoid the local minimum problem of the traditional artificial potential field and generate paths to meet the requirements of spacecraft. The effectiveness of the algorithm is verified by simulation experimental results.
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Bing, LI. "Path planning of mobile robot based on improved artificial potential field method." International Journal of Engineering Continuity 2, no. 2 (2023): 55–61. http://dx.doi.org/10.58291/ijec.v2i2.117.
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Artificial potential field method is widely used in robot path planning because of its simplicity, efficiency and smooth path generation. In this paper, based on the introduction of the basic principle of the artificial potential field method, the limitations of the algorithm are analysed in depth, and improvement methods are summarized for these problems. Aiming at the problem that the target near the obstacle is unreachable in the traditional artificial potential field method, an improved repulsive force potential field function is used to introduce the distance between the robot and the target point into the potential field function, so that the potential field of the target position is minimized in the global potential field, so that the robot can successfully reach the target. Using the obstacle connection method, the robot can quickly get rid of the local minimum point, go out of the local minimum area, and complete the path planning. The simulation results show that the method is effective.
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Chandra, Steven, Brian Tony, and Rahma Widyastuti. "Integrated Production Optimization of Mature Field Y Under Network Constraints." Journal of Petroleum and Geothermal Technology 4, no. 2 (2023): 8. http://dx.doi.org/10.31315/jpgt.v4i2.10986.
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The Y Field, a mature field experiencing declining reservoir pressure, the production of hydrocarbons is declining, leading to the need for production optimization. One crucial aspect of this optimization is the selection of suitable artificial lift methods. The choice of artificial lift methods in Field Y is dependent on the unique reservoir conditions of each well. The commonly utilized equipment for artificial lift methods in Field Y includes the Sucker Rod Pump (SRP) and the Electrical Submersible Pump (ESP).This bachelor thesis aims to develop an integrated production optimization strategy for maximizing well production in Structure X, a mature field. The study involves analyzing and optimizing artificial lift methods and integrating surface network simulation. The Inflow Performance Rate (IPR) curve is utilized to identify the production potential of each well in Structure X.By evaluating the pump performance and surface network in Structure X, it is possible to identify wells that utilize artificial lift or existing pumps and have the potential to be improved up to their maximum operating range, based on their gross flow rate (BFPD). Through optimization, adjusting the stroke per minute for the Sucker Rod Pump (SRP) and the operating frequency for the Electrical Submersible Pump (ESP) can lead to a significant increase in production. Specifically, with a design production rate of 2308.59 BFPD, an improvement of 82.23 BOPD can be achieved.
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Zeng, Kaiwen. "The performance of artificial intelligence in medical field." Applied and Computational Engineering 37, no. 1 (2024): 130–35. http://dx.doi.org/10.54254/2755-2721/37/20230489.
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In modern society, there is an increasing prevalence of individuals who are able to avail themselves of medical facilities for the purpose of receiving healthcare services. With the rise in population, there has been a corresponding decrease in the availability of medical resources for some disorders, leading to challenges in accurately diagnosing patients by healthcare professionals. According to specialists, artificial intelligence (AI) is being considered as a potential solution for addressing medical challenges. This paper mainly focuses on discussing the impact of artificial intelligence in the medical field. Through methods of literature review and analysis, this study explores the fundamental idea of AI and its use in the medical field. Besides, the paper also introduces the potential flaws behind AI in the medical field, and how will artificial intelligence help us further in the medical field. The study reveals that artificial intelligence is extensively employed within the medical sector. Through extensive training, AI has the potential to attain a considerable level of accuracy when it comes to diagnosing various ailments. The level of precision exhibited is akin to that observed in medical practitioners diagnoses. Nevertheless, artificial intelligence possesses certain limitations. For instance, in the context of privacy preservation, several patients exhibit a reluctance to divulge their symptoms. In the absence of adequate safeguards for information security, this situation might potentially lead to adverse consequences in the lives of these patients and their interpersonal interactions. Ultimately, AI continues to possess significant potential for advancement within the realm of medicine.
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Wang, Guangyi. "Real-time path planning based on improved artificial potential field method." Applied and Computational Engineering 10, no. 1 (2023): 139–49. http://dx.doi.org/10.54254/2755-2721/10/20230167.
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Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.
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Kownacki, Cezary. "Artificial Potential Field Based Trajectory Tracking for Quadcopter UAV Moving Targets." Sensors 24, no. 4 (2024): 1343. http://dx.doi.org/10.3390/s24041343.
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The trajectory or moving-target tracking feature is desirable, because it can be used in various applications where the usefulness of UAVs is already proven. Tracking moving targets can also be applied in scenarios of cooperation between mobile ground-based and flying robots, where mobile ground-based robots could play the role of mobile landing pads. This article presents a novel proposition of an approach to position-tracking problems utilizing artificial potential fields (APF) for quadcopter UAVs, which, in contrast to well-known APF-based path planning methods, is a dynamic problem and must be carried out online while keeping the tracking error as low as possible. Also, a new flight control is proposed, which uses roll, pitch, and yaw angle control based on the velocity vector. This method not only allows the UAV to track a point where the potential function reaches its minimum but also enables the alignment of the course and velocity to the direction and speed given by the velocity vector from the APF. Simulation results present the possibilities of applying the APF method to holonomic UAVs such as quadcopters and show that such UAVs controlled on the basis of an APF behave as non-holonomic UAVs during 90° turns. This allows them and the onboard camera to be oriented toward the tracked target. In simulations, the AR Drone 2.0 model of the Parrot quadcopter is used, which will make it possible to easily verify the method in real flights in future research.
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Apio, Anthony Lirase, Jonathan Kissi, and Emmanuel Kusi Achampong. "A systematic review of artificial intelligence-based methods in healthcare." International Journal of Public Health Science (IJPHS) 12, no. 3 (2023): 1259. http://dx.doi.org/10.11591/ijphs.v12i3.22298.
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Artificial intelligence (AI) in healthcare has enormous potential for transforming healthcare. AI is the ability of machines to learn and exhibit close to human levels of cognition in various specific ways. Leveraging AI software to support activities will improve patient satisfaction which is inextricably tied to the length of time patients spend in waiting queues. Literature searches were conducted in PubMed, Research Gate, BMC Health Services Research, JMIR Publications and Cochrane Central to find related documentation that was published between January 2011 and April 2021. The studies featured and reported on AI technologies that had been used in primary, secondary, or tertiary healthcare situations directed towards reducing waiting times. A total of 22 articles were primarily used, including 8 retrospective studies, 4 prospective studies and 3 case-control studies. AI technologies have enormous potential in the creation of a future with more reliable healthcare systems. It is however clear that more studies in the field are required to validate the existing evidence of its potential. AI in healthcare is crucial to reducing patients' time at healthcare facilities. The use of AI can also help improve patient outcomes and more research should be geared toward that.
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Yang, Wenlin, Peng Wu, Xiaoqi Zhou, et al. "Improved Artificial Potential Field and Dynamic Window Method for Amphibious Robot Fish Path Planning." Applied Sciences 11, no. 5 (2021): 2114. http://dx.doi.org/10.3390/app11052114.
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Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem of local minimum by modifying the direction and influence range of the gravitational field, increasing the virtual target and evaluation function, and the problem of unreachable targets is solved by increasing gravity. In view of the change of motion state of robot fish in amphibious environments, the improved artificial potential field method was fused with a dynamic window algorithm, and a dynamic window evaluation function of the optimal path was designed on the basis of establishing the dynamic equations of land and underwater. Then, the simulation experiment was designed under the environment of Matlab2019a. Firstly, the improved and traditional artificial potential field methods were compared. The results showed that the improved artificial potential field method could solve the above two problems well, shorten the operation time and path length, and have high efficiency. Secondly, the influence of different motion modes on path planning is verified, and the result also reflects that the amphibious robot can avoid obstacles flexibly and reach the target point accurately according to its own motion ability. This paper provides a new way of path planning for the amphibious robot.
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Sebestyén, Pál György, Lia-Anca Hangan, and Zoltán Czakó. "Anomaly Detection with Artificial Intelligence Methods : An Overview." Műszaki Tudományos Közlemények 18 (2023): 63–69. http://dx.doi.org/10.33894/mtk-2023.18.12.
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Nowadays, more and more human activities depend on computer-based automated systems. Fully automated (robotized) production lines, energy distribution infrastructures and other urban services or environmental surveillance systems are just some examples of cyber-physical systems that depend entirely on automated control systems. In these cases a significant challenge is to identify abnormal behaviors of the supervised or controlled systems, in order to avoid malfunction or sometimes catastrophic events. Our main research goal was to evaluate the potential of adapting and using AI techniques in the field of anomaly detection. We also developed a platform, called AutomaticAI, which can help specialists in different domains to identify the best approaches to solve a given anomaly detection problem. The platform can select the best AI algorithm and parameter configuration for a given set of data containing normal and abnormal data. The tool was used successfully in a variety of domains, from cyber-physical systems to the medical domain.
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Jin, Feiyi. "Path planning for unmanned automaton based on improved artificial potential field method." Applied and Computational Engineering 10, no. 1 (2023): 120–28. http://dx.doi.org/10.54254/2755-2721/10/20230163.
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The artificial potential field method (APF), a highly effective navigation technique, is currently utilized extensively in this area due to the rapid development of unmanned automatons. Traditional APF, however, have several shortcomings, including the issue of unreachable object points and the propensity to sink into local minima that prohibit the automaton from moving on. In this paper, a two-part improved APF model is created to address these issues. First, by including additional constraints, the repulsive field model at the stumbling block is enhanced to address the issue that the object point is impassable when too close a distance between the two stumbling blocks. Secondly, a new potential field is introduced to help the automaton walk out of the local minima. Analogue simulation show that the methods mentioned above can solve these problems better and make the route planning of unmanned automatons come true.
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Bini, Fabiano, Andrada Pica, Laura Azzimonti, et al. "Artificial Intelligence in Thyroid Field—A Comprehensive Review." Cancers 13, no. 19 (2021): 4740. http://dx.doi.org/10.3390/cancers13194740.
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Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.
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Melchiorre, Matteo, Laura Salamina, Leonardo Sabatino Scimmi, Stefano Mauro, and Stefano Pastorelli. "Experiments on the Artificial Potential Field with Local Attractors for Mobile Robot Navigation." Robotics 12, no. 3 (2023): 81. http://dx.doi.org/10.3390/robotics12030081.
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Obstacle avoidance is a challenging task in robot navigation, as it requires efficient and reliable methods to avoid collision and reach the desired goal. Artificial potential field methods are widely used for this purpose, as they are efficient, effective, and easy to implement. However, they are limited by the use of only one global attractor at the goal. This paper introduces and evaluates experimentally a novel technique that enhances the artificial potential field method with local attractors. Local attractors can be positioned around the obstacle so as to guide the robot detouring through preferred regions. Thus, the side the robot will pass by can be determined in advance, making the collision-free path predictable. The technique is formulated by modelling local attractors as optimal inflections, i.e., regions that do not show local minima, which coexist with the potential field generated by the obstacle and the global attractor. The method is validated using a laboratory setup that employs a camera and markers to track the poses of the robot, the obstacle, and the target. A series of experiments are conducted to examine the effect of the local attractor under different test conditions, obtained by varying the obstacle pose, the attraction intensity, and the robot velocity. The experimental results demonstrate the effectiveness of the proposed technique and highlight the aspects that require further investigation for its improvement and application.
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Bodenstedt, Sebastian, Martin Wagner, Beat Peter Müller-Stich, Jürgen Weitz, and Stefanie Speidel. "Artificial Intelligence-Assisted Surgery: Potential and Challenges." Visceral Medicine 36, no. 6 (2020): 450–55. http://dx.doi.org/10.1159/000511351.
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<b><i>Background:</i></b> Artificial intelligence (AI) has recently achieved considerable success in different domains including medical applications. Although current advances are expected to impact surgery, up until now AI has not been able to leverage its full potential due to several challenges that are specific to that field. <b><i>Summary:</i></b> This review summarizes data-driven methods and technologies needed as a prerequisite for different AI-based assistance functions in the operating room. Potential effects of AI usage in surgery will be highlighted, concluding with ongoing challenges to enabling AI for surgery. <b><i>Key Messages:</i></b> AI-assisted surgery will enable data-driven decision-making via decision support systems and cognitive robotic assistance. The use of AI for workflow analysis will help provide appropriate assistance in the right context. The requirements for such assistance must be defined by surgeons in close cooperation with computer scientists and engineers. Once the existing challenges will have been solved, AI assistance has the potential to improve patient care by supporting the surgeon without replacing him or her.
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Paliński, Andrzej. "Prognozowanie zapotrzebowania na gaz metodami sztucznej inteligencji." Nafta-Gaz 75, no. 2 (2019): 111–17. http://dx.doi.org/10.18668/ng.2019.02.07.
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The paper presents contemporary trends in artificial intelligence and machine learning methods, which include, among others, artificial neural networks, decision trees, fuzzy logic systems and others. Computational intelligence methods are part of the field of research on artificial intelligence. Selected methods of computational intelligence were used to build medium-term monthly forecasts of natural gas demand for Poland. The accuracy of forecasts obtained using the artificial neural network and the decision tree with classical linear regression was compared based on historical data from a ten-year period. The explanatory variables were: gas consumption in other EU countries, average monthly temperature, industrial production, wages in the economy and the price of natural gas. Forecasting was carried out in five stages differing in the selection of the learning and testing sample, the use of data preprocessing and the elimination of some variables. For raw data and a random training set, the highest accuracy was achieved by linear regression. For the preprocessed data and the random learning set, the decision tree was the most accurate. The forecast obtained on the basis of the first eight years and tested on the last two was most accurately created by regression, but only slightly better than with the decision tree or neural network, regardless of data normalization and elimination of collinear variables. Machine learning methods showed good accuracy of monthly gas consumption forecasts, but nevertheless slightly gave way to classical linear regression, due to too narrow set of explanatory variables. Machine learning methods will be able to show higher effectiveness as the number of data increases and the set of potential explanatory variables is expanded. In the sea of data, machine learning methods are able to create prognostic models more effectively, without the analyst’s laborious involvement in data preparation and multi-stage analysis. They will also allow for the frequent updating of the form of prognostic models even after each addition of new data into the database.
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Hovakimyan, Anna, Siranush Sargsyan, Tatev Hovakimyan, and Ani Badalyan. "Artificial Intelligence Methods in Osteoporosis Prediction Problem." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 20 (October 10, 2023): 171–77. http://dx.doi.org/10.37394/23208.2023.20.17.
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Many sectors of human activity have implemented various solutions based on artificial intelligence methods. These solutions help significantly in decision-making tasks, especially when analyzing a large amount of relevant data is required beforehand. This paper discusses developing a computer system to assist doctors in diagnosing osteoporosis based on densitometric exam results. The system was developed using machine learning and trained on patient data obtained from densitometric examinations. The STRATOS device was used to collect data at AltMed Medical Center in Armenia. The goal of the system is to provide an accurate diagnosis of osteoporosis in patients while ensuring that the diagnosis is reliable and effective. During the system’s development, we utilized three prominent machine learning models: Decision Tree, Random Forest, and SVM (Support Vector Machines). To enhance the accuracy and robustness of the system, these models were selected based on their effectiveness in solving complex classification problems. The developed system is equipped with advanced tools to detect potential diseases by exploring unidentified patterns and correlations among syndromes. The mentioned capability improves the diagnostic capabilities of the system. Achieving the medical goal requires early detection and accurate diagnosis. The AltMed Medical Center plans to utilize this system to provide medical professionals with support for informed decisions and improved patient care. The ability of the system to analyze complex medical data and reveal hidden insights makes it a valuable asset in the field.
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Xiao, Yong Qiang, and Ying Xue Cao. "Study on Thermal Environment of Sports Field in Different Materials." Applied Mechanics and Materials 361-363 (August 2013): 538–41. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.538.
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Natural lawn and artificial turf, which have a great difference on practical function and thermal environment characteristics, are widely used in sports field. In order to obtain the quantitative differences on thermal environment in summer, instrumental measurement and questionnaires are used in this paper to investigate the thermal environmental characteristics of natural lawn and artificial turf, respectively. Meanwhile human thermal sensation in the two lawns was also evaluated. The results show that the foliar surface temperature and mean air temperature in artificial turf is significantly higher than that of natural lawn in early summer. Due to thermal discomfort and the potential hurt for athletes on artificial turf field, cooling methods such as sprinkle are recommended.
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Morlando, Viviana, Jonathan Cacace, and Fabio Ruggiero. "Online Feet Potential Fields for Quadruped Robots Navigation in Harsh Terrains." Robotics 12, no. 3 (2023): 86. http://dx.doi.org/10.3390/robotics12030086.
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Quadruped robots have garnered significant attention in recent years due to their ability to navigate through challenging terrains. Among the various environments, agriculture fields are particularly difficult for legged robots, given the variability of soil types and conditions. To address this issue, this study proposes a novel navigation strategy that utilizes ground reaction forces to calculate online artificial potential fields, which are then applied to the robot’s feet to avoid low-traversability regions. The strategy also incorporates the net vector of the attractive potential field towards the goal and the repulsive field to avoid slippery regions, which dynamically adjusts the quadruped’s gait. A realistic simulation environment validates the proposed navigation framework with case studies on randomly generated terrains. A comprehensive comparison with baseline navigation methods is conducted to assess the effectiveness of the proposed approach.
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Xing, Tao, Yongchun Liu, and Lang Chen. "Survey of Robot Formation Control Methods." Academic Journal of Science and Technology 4, no. 2 (2023): 58–61. http://dx.doi.org/10.54097/ajst.v4i2.3907.
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Robot formation control technology has a certain practical significance and application value. efficient and stable formation control is the theoretical basis and technical key to perform complex tasks. Firstly, the basic idea of formation control is introduced, and then several formation control methods such as following the leader method, behavior based method, virtual structure method and artificial potential field method are introduced; This paper summarizes the research achievements of robot formation control in recent 20 years at home and abroad. Finally, the future research direction of formation control is pointed out.
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Abdelraheem, Eman, Shabnam Shaabani, and Alexander Dömling. "Artificial Macrocycles." Synlett 29, no. 09 (2018): 1136–51. http://dx.doi.org/10.1055/s-0036-1591975.
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Artificial macrocycles recently became popular as a novel research field in drug discovery. As opposed to their natural twins, artificial macrocycles promise to have better control on synthesizability and control over their physicochemical properties resulting in druglike properties. Very few synthetic methods allow for the convergent, fast but diverse access to large macrocycles chemical space. One synthetic technology to access artificial macrocycles with potential biological activity, multicomponent reactions, is reviewed here, with a focus on our own work. We believe that synthetic chemists have to acquaint themselves more with structure and activity to leverage the design aspect of their daily work.1 Introduction2 Macrocycle Properties and Receptor Binding3 Synthetic Approaches towards Artificial Macrocycles Using MCR4 Design Rules for Membrane Crossing Macrocycles5 Design Rules for Libraries of Macrocycles6 Computational Macrocyclic Methods7 Future View
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Myagkova, I. N., V. R. Shirokii, R. D. Vladimirov, O. G. Barinov, and S. A. Dolenko. "Prediction of the Dst Geomagnetic Index Using Adaptive Methods." Meteorologiya i Gidrologiya 3 (2021): 38–46. http://dx.doi.org/10.52002/0130-2906-2021-3-38-46.
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The potential is investigated of predicting the time series of the Dst geomagnetic index using various adaptive methods: artificial neural networks (classical multilayer perceptrons), decision trees (random forest), gradient boosting. The prediction is based on the parameters of the solar wind and interplanetary magnetic field measured at the Lagrange point L1 in the ACE spacecraft experiment. It is shown that the best prediction skill of the three adaptive methods is demonstrated by gradient boosting.
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Zhang, Hui, Yongfei Zhu, Xuefei Liu, and Xiangrong Xu. "Analysis of Obstacle Avoidance Strategy for Dual-Arm Robot Based on Speed Field with Improved Artificial Potential Field Algorithm." Electronics 10, no. 15 (2021): 1850. http://dx.doi.org/10.3390/electronics10151850.
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In recent years, dual-arm robots have been favored in various industries due to their excellent coordinated operability. One of the focused areas of study on dual-arm robots is obstacle avoidance, namely path planning. Among the existing path planning methods, the artificial potential field (APF) algorithm is widely applied in obstacle avoidance for its simplicity, practicability, and good real-time performance over other planning methods. However, APF is firstly proposed to solve the obstacle avoidance problem of mobile robot in plane, and thus has some limitations such as being prone to fall into local minimum, not being applicable when dynamic obstacles are encountered. Therefore, an obstacle avoidance strategy for a dual-arm robot based on speed field with improved artificial potential field algorithm is proposed. In our method, the APF algorithm is used to establish the attraction and repulsion functions of the robotic manipulator, and then the concepts of attraction and repulsion speed are introduced. The attraction and repulsion functions are converted into the attraction and repulsion speed functions, which mapped to the joint space. By using the Jacobian matrix and its inverse to establish the differential velocity function of joint motion, as well as comparing it with the set collision distance threshold between two robotic manipulators of robot, the collision avoidance can be solved. Meanwhile, after introducing a new repulsion function and adding virtual constraint points to eliminate existing limitations, APF is also improved. The correctness and effectiveness of the proposed method in the self-collision avoidance problem of a dual-arm robot are validated in MATLAB and Adams simulation environment.
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Mohsen, Farida, Balqees Al-Saadi, Nima Abdi, Sulaiman Khan, and Zubair Shah. "Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine." Journal of Personalized Medicine 13, no. 8 (2023): 1268. http://dx.doi.org/10.3390/jpm13081268.
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Precision medicine has the potential to revolutionize the way cardiovascular diseases are diagnosed, predicted, and treated by tailoring treatment strategies to the individual characteristics of each patient. Artificial intelligence (AI) has recently emerged as a promising tool for improving the accuracy and efficiency of precision cardiovascular medicine. In this scoping review, we aimed to identify and summarize the current state of the literature on the use of AI in precision cardiovascular medicine. A comprehensive search of electronic databases, including Scopes, Google Scholar, and PubMed, was conducted to identify relevant studies. After applying inclusion and exclusion criteria, a total of 28 studies were included in the review. We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. As a result, most of these studies focused on prediction (50%), followed by diagnosis (21%), phenotyping (14%), and risk stratification (14%). A variety of machine learning models were utilized in these studies, with logistic regression being the most used (36%), followed by random forest (32%), support vector machine (25%), and deep learning models such as neural networks (18%). Other models, such as hierarchical clustering (11%), Cox regression (11%), and natural language processing (4%), were also utilized. The data sources used in these studies included electronic health records (79%), imaging data (43%), and omics data (4%). We found that AI is being increasingly applied in various areas of cardiovascular medicine, including the diagnosis, prognosis of cardiovascular diseases, risk prediction and stratification, and treatment planning. The results of the review showed that AI has the potential to improve the performance of cardiovascular disease diagnosis and prognosis, as well as to identify individuals at high risk of developing cardiovascular diseases. However, further research is needed to fully evaluate the clinical utility and effectiveness of AI-based approaches in precision cardiovascular medicine. Overall, our review provided a comprehensive overview of the current state of knowledge in the field of AI-based methods for precision cardiovascular medicine and offered new insights for researchers interested in this research area.
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Zhang, Cheng, Kun Zhang, Ying Qian Zhang, and He Dan. "An Algorithm Applying to NPC Path Planning of Web Games." Applied Mechanics and Materials 556-562 (May 2014): 3420–23. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3420.
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Path planning is the core issues in the artificial intelligence field of games, and how to establish an effective method of path planning is still focused on. A new algorithm based on both the benefits of global path planner methods and local path planner methods and those of A star algorithm and improved artificial potential field method is proposed for NPC path planning in web games. Its feasibleness and effectiveness are also demonstrated by simulation results.
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Lapshin, Valeriy. "Artificial intelligence technology as a potential threat to public security protected by criminal law." Russian Journal of Deviant Behavior 2, no. 4 (2022): 374–85. http://dx.doi.org/10.35750/2713-0622-2022-4-374-385.
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Introduction. States at the present stage announce the need for active development of scientific research in the field of artificial intelligence, the creation of appropriate technologies with subsequent widespread introduction in all areas of human activity. Some first persons of leading countries declare a direct dependence of the quality of ensuring national security on the level of development and use of artificial intelligence technologies. At the same time, no one officially indicates the global risks of artificial intelligence, which has the ability to make and implement decisions beyond human control. For this reason, so far no legal mechanisms have been formed to level the threats posed by the activities to create a fully autonomous artificial intelligence.
 Objective. The present study was conducted to outline the global risks, the basis of which is formed by the uncontrolled development and spread of artificial intelligence technology, as well as the rationale for the need to criminalize certain actions in the development of artificial intelligence technologies.
 Research methodology, methods and techniques. In the process of research a variety of general scientific and private scientific methods were used, which are traditionally used in the humanities. Thus, dialectical and formal-logical methods provided a comprehensive study of artificial intelligence, which allowed to establish not only the positive results of its implementation, but also significant risks for society, which are seen in the uncontrolled spread of artificial intelligence. Among the private scientific methods of this study are system-structural, comparative-legal methods, survey, method of expert evaluations and others.
 Results. The study determined that in the medium term there will be created a full-fledged analogue of natural intelligence, with the capabilities of independent analytical thinking and learning. In a changing environment this does not exclude complete or partial destruction of the population of the planet. The introduction of real legal responsibility, primarily criminal, for the creation of «autonomous intelligence» is not planned either in international or in national law, which generates insecurity of relations in the field of public safety.
 Scientific novelty. The study substantiates the social danger of uncontrolled spread of artificial intelligence technologies in the world and proposes the establishment of criminal responsibility for the commission of these acts by analogy with the prohibition on the creation and proliferation of weapons of mass destruction.
 Practical significance. The formulated proposals may be taken into account in the preparation of bills on amendments and additions to the existing criminal law on crimes against public safety, as well as against the peace and security of mankind.
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Mogadala, Aditya, Marimuthu Kalimuthu, and Dietrich Klakow. "Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods." Journal of Artificial Intelligence Research 71 (August 30, 2021): 1183–317. http://dx.doi.org/10.1613/jair.1.11688.
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Interest in Artificial Intelligence (AI) and its applications has seen unprecedented growth in the last few years. This success can be partly attributed to the advancements made in the sub-fields of AI such as machine learning, computer vision, and natural language processing. Much of the growth in these fields has been made possible with deep learning, a sub-area of machine learning that uses artificial neural networks. This has created significant interest in the integration of vision and language. In this survey, we focus on ten prominent tasks that integrate language and vision by discussing their problem formulation, methods, existing datasets, evaluation measures, and compare the results obtained with corresponding state-of-the-art methods. Our efforts go beyond earlier surveys which are either task-specific or concentrate only on one type of visual content, i.e., image or video. Furthermore, we also provide some potential future directions in this field of research with an anticipation that this survey stimulates innovative thoughts and ideas to address the existing challenges and build new applications.
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Rice, Oliver E. K., and Anthony R. Yeates. "Global Coronal Equilibria with Solar Wind Outflow." Astrophysical Journal 923, no. 1 (2021): 57. http://dx.doi.org/10.3847/1538-4357/ac2c71.
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Abstract Given a known radial magnetic field distribution on the Sun’s photospheric surface, there exist well-established methods for computing a potential magnetic field in the corona above. Such potential fields are routinely used as input to solar wind models, and to initialize magneto-frictional or full magnetohydrodynamic simulations of the coronal and heliospheric magnetic fields. We describe an improved magnetic field model that calculates a magneto-frictional equilibrium with an imposed solar wind profile (which can be Parker’s solar wind solution, or any reasonable equivalent). These “outflow fields” appear to approximate the real coronal magnetic field more closely than a potential field, take a similar time to compute, and avoid the need to impose an artificial source surface. Thus they provide a practical alternative to the potential field model for initializing time-evolving simulations or modeling the heliospheric magnetic field. We give an open-source Python implementation in spherical coordinates and apply the model to data from solar cycle 24. The outflow tends to increase the open magnetic flux compared to the potential field model, reducing the well-known discrepancy with in situ observations.
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Meng, Xianchen, and Xi Fang. "A UGV Path Planning Algorithm Based on Improved A* with Improved Artificial Potential Field." Electronics 13, no. 5 (2024): 972. http://dx.doi.org/10.3390/electronics13050972.
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Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm’s challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm’s issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios.
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Wu, Zong Sheng, and Wei Ping Fu. "A Review of Path Planning Method for Mobile Robot." Advanced Materials Research 1030-1032 (September 2014): 1588–91. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1588.
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The ability of a mobile robot to plan its path is the key task in the field of robotics, which is to find a shortest, collision free, optimal path in the various scenes. In this paper, different existing path planning methods are presented, and classified as: geometric construction method, artificial intelligent path planning method, grid method, and artificial potential field method. This paper briefly introduces the basic ideas of the four methods and compares them. Some challenging topics are presented based on the reviewed papers.
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Surman, Magdalena, Anna Drożdż, Ewa Stępień, and Małgorzata Przybyło. "Extracellular Vesicles as Drug Delivery Systems - Methods of Production and Potential Therapeutic Applications." Current Pharmaceutical Design 25, no. 2 (2019): 132–54. http://dx.doi.org/10.2174/1381612825666190306153318.
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Drug delivery systems are created to achieve the desired therapeutic effect of a specific pharmaceutical compound. Numerous drawbacks and side effects such as unfavorable pharmacokinetics, lack of tissue selectivity, immunogenicity, increased systemic clearance and toxicity, have been observed for currently available drug delivery systems (DDSs). The use of natural and artificial extracellular vesicles (EVs) in drug delivery may help to solve the aforementioned problems faced by different DDSs. Due to their self-origin, small size, flexibility, the presence of multiple adhesive molecules on their surfaces as well as their function as biomolecules carriers, EVs are the perfect candidates for DDSs. Currently, several drug delivery systems based on EVs have been proposed. While the great potential of these particles in targeted drug delivery has been recognized in cancer, hepatitis C, neurodegenerative diseases, inflammatory states etc., this field is still in the early stage of development. Unfortunately, the use of EVs from natural sources (cell cultures, body fluids) results in numerous problems in terms of the heterogeneity of isolated vesicle population as well as the method of isolation thereof, which may influence vesicle composition and properties. Therefore, there is a significant need for the synthesis of artificial EV-based DDSs under strictly controlled laboratory conditions and from well-defined biomolecules (proteins and lipids). Vesicle-mimetic delivery systems, characterized by properties similar to natural EVs, will bring new opportunities to study the mechanisms of DDS internalization and their biological activity after delivering their cargo to a target cell.
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Sun, Wei. "Cite space-based research on the use of artificial intelligence in the field of literature and museums." Theoretical and Natural Science 34, no. 1 (2024): 172–78. http://dx.doi.org/10.54254/2753-8818/34/20241178.
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In recent years, with the rapid development and application of artificial intelligence technology, image technology based on artificial intelligence has gradually become a research hotspot in the field of cultural relic restoration. This article will explore the application of artificial intelligence based image technology in the field of cultural relic and museum restoration. Firstly, the importance of restoring cultural relics and artifacts was introduced, as well as the limitations and shortcomings of traditional restoration methods. Secondly, elaborate on the development and application of artificial intelligence technology, as well as its potential and advantages in the field of cultural relic restoration. Next, we will introduce the application of artificial intelligence based image technology in cultural relic restoration, including image enhancement, segmentation, recognition, and reconstruction. Finally, the application prospects and challenges of artificial intelligence based image technology in the field of cultural relic restoration were summarized, and future research directions and suggestions were proposed.
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Lu, Siyao, Rui Xu, Zhaoyu Li, Bang Wang, and Zhijun Zhao. "Lunar Rover Collaborated Path Planning with Artificial Potential Field-Based Heuristic on Deep Reinforcement Learning." Aerospace 11, no. 4 (2024): 253. http://dx.doi.org/10.3390/aerospace11040253.
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The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to the short day, especially near the south pole. Traditional planning methods, such as uploading instructions from the ground, can hardly handle many rovers moving on the moon simultaneously with high efficiency. Therefore, we propose a new collaborative path-planning method based on deep reinforcement learning, where the heuristics are demonstrated by both the target and the obstacles in the artificial potential field. Environments have been randomly generated where small and large obstacles and different waypoints are created to collect resources, train the deep reinforcement learning agent to propose actions, and lead the rovers to move without obstacles, finish rovers’ tasks, and reach different targets. The artificial potential field created by obstacles and other rovers in every step affects the action choice of the rover. Information from the artificial potential field would be transformed into rewards in deep reinforcement learning that helps keep distance and safety. Experiments demonstrate that our method can guide rovers moving more safely without turning into nearby large obstacles or collision with other rovers as well as consuming less energy compared with the multi-agent A-Star path-planning algorithm with improved obstacle avoidance method.
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Mikic, Darko, Hajdana Glomazic, and Andrijana Mikic. "Medical students’ perception of the role of artificial intelligence in healthcare." Medical review 76, no. 9-10 (2023): 269–74. http://dx.doi.org/10.2298/mpns2310269m.
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Introduction. Artificial intelligence is defined as a part of computer science capable of manipulating extensive data through machine learning. The aim of this study is to investigate medical students? perceptions regarding the use of artificial intelligence in the field of healthcare. Material and Methods. This research was conducted as a cross-sectional study using the Computer Assisted Web Interviewing technique for data collection by surveying students through social networks. The sample consists of 160 students who were surveyed in November 2023. The aim was to provide answers to the question of how students perceive the use of new technology - artificial intelligence in the field that represents their future profession. Results. The results have shown a well-developed awareness among students regarding the potential application of artificial intelligence in the medical field, emphasizing a positive perception of the benefits that artificial intelligence can bring. They have also recognized the importance of incorporating artificial intelligence training into medical education. Students have expressed concerns, primarily about potential misuse of artificial intelligence and ethical issues related to its use in medicine. Conclusion. Medical students are aware not only of the benefits but also the risks associated with the implementation of artificial intelligence in medicine.
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Jo, Hyun-Jae, Su-Rim Kim, Jung-Hyeon Kim, and Jong-Yong Park. "Comparison of Velocity Obstacle and Artificial Potential Field Methods for Collision Avoidance in Swarm Operation of Unmanned Surface Vehicles." Journal of Marine Science and Engineering 10, no. 12 (2022): 2036. http://dx.doi.org/10.3390/jmse10122036.
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As the research concerning unmanned surface vehicles (USVs) intensifies, research on swarm operations is also being actively conducted. A swarm operation imitates the appearance of nature, such as ants, bees, and birds, in forming swarms, moving, and attacking in the search for food. However, several problems are encountered in the USV swarm operation. One of these is the problem of collisions between USVs. A conflict between agents in a swarm can lead to operational failure and property loss. This study attempted to solve this problem. In this study, a virtual matrix approach was applied as a swarm operation. Velocity obstacle (VO) and artificial potential field (APF) methods were used and compared as algorithms for collision avoidance for USVs in a swarm when the formation is changed. For effective collision avoidance, evasive maneuvers should be performed at an appropriate time and location. Therefore, a closest point of approach (CPA)-based method, which considers both temporal and spatial factors, was used. The swarm operation was verified through a large-scale simulation in which 30 USVs changed their formation seven times in 3400 s. When comparing the averages of the distance, error to waypoint, and battery usage, no significant differences were noticed between the VO and APF methods. However, when comparing the cumulative time using the minimum distance, VO was demonstrably safer than APF, and VO completed the formation faster. In conclusion, both the APF and VO methods can evidently perform swarm operations without collisions.
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Tan, Guoge, Jiayuan Zhuang, Jin Zou, Lei Wan, and Zhiyuan Sun. "Artificial potential field-based swarm finding of the unmanned surface vehicles in the dynamic ocean environment." International Journal of Advanced Robotic Systems 17, no. 3 (2020): 172988142092530. http://dx.doi.org/10.1177/1729881420925309.
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Using multiple unmanned surface vehicle swarms to implement tasks cooperatively is the most advanced technology in recent years. However, how to find which swarm the unmanned surface vehicle belongs to is a meaningful job. So, this article proposed an artificial potential field-based swarm finding algorithm, which applies the potential field force directly to unmanned surface vehicles and leads them to their belonging swarm quickly and accurately. Meanwhile, the proposed algorithm can also maintain the formation stable while following the desired path. Based on the swarm finding algorithm, the artificial potential field-based collision avoidance method and the International Regulations for Preventing Collisions at Sea-based dynamic collision avoidance strategy are applied to the swarm control of multi-unmanned surface vehicles to enhance the performance in the dynamic ocean environment. Methods in this article are verified through numerical simulations to illustrate the feasibility and effectiveness of proposed schemes.
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Reis, Bruno, and António Quintino. "Evaluating Classical and Artificial Intelligence Methods for Credit Risk Analysis." Journal of Economic Analysis 2, no. 3 (2023): 94–112. http://dx.doi.org/10.58567/jea02030006.
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Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.
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Chu, Hongli, Yanhong Ji, Dingju Zhu, et al. "Artificial Intelligence in Tongue Image Recognition." International Journal of Software Science and Computational Intelligence 15, no. 1 (2023): 1–25. http://dx.doi.org/10.4018/ijssci.328771.
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Tongue image recognition is a traditional Chinese medicine diagnosis method, which uses the shape, color, and texture of the tongue to judge the health of the human body. With the rapid development of artificial intelligence technology, the application of artificial intelligence in the field of tongue recognition has been widely considered. Based on the intelligent analysis of tongue diagnosis in traditional Chinese medicine, this paper reviews the application progress of artificial intelligence in tongue image recognition in recent years and analyzes its potential and challenges in this field. Firstly, this paper introduces three steps of tongue image recognition, including tongue image acquisition, tongue image preprocessing, and tongue image feature analysis. The application of traditional methods and artificial intelligence methods in the whole process of tongue image recognition is reviewed, especially the tongue body segmentation, and the advantages and disadvantages of convolutional neural networks are analyzed and compared. Artificial intelligence can use technologies such as deep learning and computer vision to automatically analyze and extract features from tongue images. By constructing a tongue image recognition model, tongue shape, color, texture, and other features can be accurately recognized and quantitatively analyzed. Finally, this paper summarizes the problems existing in artificial intelligence in tongue image recognition and looks forward to the future developmental direction of this field. It can promote the modernization of TCM diagnostic methods, achieve early disease screening and prevention, personalized medicine and treatment optimization, and support medical research and knowledge accumulation. However, there is still a need for further validation and practice, with a focus on patient privacy and data security.
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Liu, Zhengqing, Xinhua Wang, and Kangyi Li. "Research on path planning of multi-rotor UAV based on improved artificial potential field method." MATEC Web of Conferences 336 (2021): 07006. http://dx.doi.org/10.1051/matecconf/202133607006.
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UAV needs sensor to fly in an environment with obstacles. However, UAV may not be able to move forward when it encounters a large obstacle, or UAV will be in a dangerous state when the sensor fails briefly which disturbed by the environment factors. In order to solve these problems, the following methods are proposed in this paper. Aiming at the first problem, this paper proposes an improved APF method for path planning, and verified by simulation experiments that this method can find the optimal path. Aiming at the second problem, this paper proposes a solution to expand the range of obstacles and dynamically change the distance in the APF repulsion function. It is verified that the UAV can fly safely within the short time of the sensor problem by simulation experiments. In conclusion, this paper has an important reference value for the application of UAV online dynamic path planning in engineering.
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Zheng, Ze. "A Review of Stock Price Prediction Based on LSTM and TCN Methods." Advances in Economics, Management and Political Sciences 46, no. 1 (2023): 48–54. http://dx.doi.org/10.54254/2754-1169/46/20230316.
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In financial analysis, stock price prediction is a difficult and important problem that has received a lot of attention from researchers and practitioners in recent years. The application of machine learning and artificial intelligence algorithms to stock price forecasting has demonstrated significant potential for increasing forecasting accuracy. Long momentary memory (LSTM) and transient convolutional networks (TCN) are two famous profound learning calculations that have been generally utilized for stock cost expectation. The most recent approaches to stock price prediction using LSTM and TCN methods are reviewed in this paper. We highlight the most recent research trends in this field and talk about these methods' benefits and drawbacks. Additionally, we discuss potential future research directions in this field. The survey is expected to give a knowledge into the present status of exploration on stock value forecast and guide specialists and experts in working on the exactness of expectation.