A Multi-Agent Motion Prediction and Tracking Method Based on Non-Cooperative Equilibrium

A Multi-Agent Motion Prediction and Tracking method based on non-cooperative equilibrium (MPT-NCE) is proposed according to the fact that some multi-agent intelligent evolution methods, like the MADDPG, lack adaptability facing unfamiliar environments, and are unable to achieve multi-agent motion prediction and tracking, although they own advantages in multi-agent intelligence. Featured by a performance discrimination module using the time difference function together with a random mutation module applying predictive learning, the MPT-NCE is capable of improving the prediction and tracking ability of the agents in the intelligent game confrontation. Two groups of multi-agent prediction and tracking experiments are conducted and the results show that compared with the MADDPG method, in the aspect of prediction ability, the MPT-NCE achieves a prediction rate at more than 90%, which is 23.52% higher and increases the whole evolution efficiency by 16.89%; in the aspect of tracking ability, the MPT-NCE promotes the convergent speed by 11.76% while facilitating the target tracking by 25.85%. The proposed MPT-NCE method shows impressive environmental adaptability and prediction and tracking ability.

Visual error correction of continuous aerobics action images based on graph difference function

In order to solve the problem of the easy appearance of blurring images (easy to appear blur) or jagged effect after correction by traditional method, and as the visual error correction effect is not good, we propose a new visual error correction method of continuous calisthenics action image and a new algorithm for visual error correction. The continuous calisthenics action image is encoded and decoded, the error between the original image and the error image is obtained and with that the difference function is processed. Then the error compensation results are obtained and the visual error correction of the continuous calisthenics action image is realised. At the same time, a new algorithm for checking and correcting visual parallax matching errors is proposed in this paper. This algorithm can not only identify all matching errors in the parallax data, but also detect and correct them according to the continuity of shape representation, and effectively calculate the various performances of such matching algorithms automatically and quantitatively. The results show that the image processed by the proposed method has no significant visual error, and the peak signal-to-noise ratio and structural similarity are very high. The experimental results of the new algorithm also prove that the algorithm is very useful for the study of visual matching. It can be seen that the proposed method is effective and can be effectively used.

An anti-tampering model of sensitive data in link network based on blockchain technology

In order to overcome the problems of traditional link network sensitive data anti tampering operation, such as long time-consuming and low data security, a tamper proof model of link network sensitive data based on blockchain technology is proposed. Calculate the evenly distributed random variables of sensitive node data and the difference of running distance to obtain the probability of meeting the sensitive data with other neighbor nodes, and determine the sensitive data in the link network; obtain the frequency domain of the sensitive data of the infected link network through the square difference function, and calculate the membership mean value of the infected data samples in the sensitive data; analyze the working principle of blockchain technology, Set the master key and public key of sensitive data, generate the encryption key of sensitive data of link network, and use blockchain technology to complete the design of tamper proof model of sensitive data in link network. The experimental results show that the shortest time-consuming of the proposed method is about 1 s, and the maximum tamper proof security factor is about 9.7.

Robust controller for cancer chemotherapy dosage using nonlinear kernel-based error function

It is well-known that chemotherapy is the most significant method on curing the most death-causing disease like cancer. These days, the use of controller-based approach for finding the optimal rate of drug injection throughout the treatment has increased a lot. Under these circumstances, this paper establishes a novel robust controller that influences the drug dosage along with parameter estimation. A new nonlinear error function-based extended Kalman filter (EKF) with improved scaling factor (NEF-EKF-ISF) is introduced in this research work. In fact, in the traditional schemes, the error is computed using the conventional difference function and it is deployed for the updating process of EKF. In our previous work, it has been converted to the nonlinear error function. Here, the updating process is based on the prior error function, though scaled to a nonlinear environment. In addition, a scaling factor is introduced here, which considers the historical error improvement, for the updating process. Finally, the performance of the proposed controller is evaluated over other traditional approaches, which implies the appropriate impact of drug dosage injection on normal, immune and tumor cells. Moreover, it is observed that the proposed NEF-EKF-ISF has the ability to evaluate the tumor cells with a better accuracy rate.

Assessment of DLG Correction Factor in Mobius3D Commissioning Affected by Couch Top

Purpose: This study assesses the dosimetric leaf gap (DLG) correction factor in Mobius3D commissioning affected by a couch top platform and calculates the optimal DLG value according to the point dose difference function. Methods: DLG optimizations were performed for 3 LINAC machines and a total of 30 patient VMAT plans (i.e., 10 plans per each LINAC). Point dose calculations were performed using an automatic dose calculation system in Mobius3D as well as Mobis3D calculation using an MVP-based QA plan with a carbon fiber couch top. Subsequently, the results were compared with measurement data. Results: The averaged point dose measured for the MVP with a couch top decreased by approximately 2% relative to that without the couch top. The average of the optimal DLG factors increased by 1.153 due to the couch top effect for a dose decrease of 2% at the measured point. Conclusions: Users should adjust the DLG correction factor using a specific phantom (including MVP) with a couch top. If the factor adjusted by using MVP automatic dose calculation system, the factor should be increased by approximately 1.153 per 2% dose difference considering user’s couch top effect.

Secure Communication of Fractional Complex Chaotic Systems Based on Fractional Difference Function Synchronization

Fractional complex chaotic systems have attracted great interest recently. However, most of scholars adopted integer real chaotic system and fractional real and integer complex chaotic systems to improve the security of communication. In this paper, the advantages of fractional complex chaotic synchronization (FCCS) in secure communication are firstly demonstrated. To begin with, we propose the definition of fractional difference function synchronization (FDFS) according to difference function synchronization (DFS) of integer complex chaotic systems. FDFS makes communication secure based on FCCS possible. Then we design corresponding controller and present a general communication scheme based on FDFS. Finally, we respectively accomplish simulations which transmit analog signal, digital signal, voice signal, and image signal. Especially for image signal, we give a novel image cryptosystem based on FDFS. The results demonstrate the superiority and good performances of FDFS in secure communication.