In this paper, we propose an adaptive genetic a*algorithm designed to address the camera calibration problem. This approach facilitates the resolution of a complex optimization challenge. Our objective is to refine the ...
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In this paper, we propose an adaptive genetic a*algorithm designed to address the camera calibration problem. This approach facilitates the resolution of a complex optimization challenge. Our objective is to refine the camera calibration results estimated by the analytical method. For this purpose, a study was conducted on the type and probability of crossover, the probability of mutation and on the adaptation of the initialization intervals. This adaptation consists of adjusting the length of the initialization intervals. The main objective is to find an optimal solution for the camera calibration parameters by minimizing the cost function. This function is reformulated from the relationship between the points of the 3D target and their 2D projection in the image. Experimental tests and evaluations were conducted to validate the proposed approach. The results indicate that our a*algorithm is robust and can achieve very satisfactory calibration results.
Simultaneous regulation of multiple properties in next-generation tokamaks like ITER and fusion pilot plant may require the integration of different plasma control a*algorithms. Such integration requires the conversion ...
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Simultaneous regulation of multiple properties in next-generation tokamaks like ITER and fusion pilot plant may require the integration of different plasma control a*algorithms. Such integration requires the conversion of individual controller commands into physical actuator requests while accounting for the coupling between different plasma properties. This work proposes a tokamak and scenario-agnostic actuator-sharing a*algorithm (ASA) to perform the above-mentioned command-request conversion and, hence, integrate multiple plasma controllers. The proposed a*algorithm implicitly solves a quadratic programming (QP) problem formulated to account for the saturation limits and the relation between the controller commands and physical actuator requests. Since the constraints arising in the QP program are linear, the proposed ASA is highly computationally efficient and can be implemented in the tokamak plasma control system in real time. Furthermore, the proposed a*algorithm is designed to handle real-time changes in the control objectives and actuators' availability. Nonlinear simulations carried out using the Control Oriented Transport SIMulator illustrate the effectiveness of the proposed a*algorithm in achieving multiple control objectives simultaneously.
This paper presents a synchronized Filtered-s Least Mean Squares (SFsLMS) a*algorithm for multichannel Active Noise Control (ANC) systems aimed at mitigating aviation noise. The SFsLMS a*algorithm addresses signal delays ...
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This paper presents a synchronized Filtered-s Least Mean Squares (SFsLMS) a*algorithm for multichannel Active Noise Control (ANC) systems aimed at mitigating aviation noise. The SFsLMS a*algorithm addresses signal delays inherent in aircraft environments, which degrade the performance of traditional ANC a*algorithms. Incorporating delay estimation into the adaptive filtering process ensures accurate alignment of input and reference signals, leading to improved convergence speed and stability. The results demonstrate that the SFsLMS a*algorithm significantly enhances noise cancellation performance in dynamic aviation noise conditions, offering a scalable and robust solution for real-time noise reduction in enclosed areas near airports. This advancement contributes to increased comfort and reduced noise pollution, highlighting the a*algorithm's potential for widespread application in aviation noise control systems. The evaluation is conducted using a (2 x 4 x4) (ANC) system, with performance measured in terms of Averaged Noise Reduction (ANR). The results reveal a marked improvement in convergence speed and stability, as demonstrated by the rapid decrease and sustained low levels of ANR across all microphones.
It is desirable but nontrivial to obtain a portfolio that enjoys both sparsity and optimality. We propose a portfolio model that is rooted in the mean-variance framework, incorporating the 80 constraint as a precise r...
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It is desirable but nontrivial to obtain a portfolio that enjoys both sparsity and optimality. We propose a portfolio model that is rooted in the mean-variance framework, incorporating the 80 constraint as a precise restriction to ensure a sparse portfolio comprising no more than a specified number of assets. Moreover, the simplex constraint is also imposed to ensure the feasibility of portfolio. This model is difficult to solve due to the nonconvexity of the 80 constraint and the geometric complexity of the intersection of the two constraints. To address this issue, we establish the equivalence relation between a local optimum of a general 80-constrained problem and a global optimum on a restricted set of variables. Based on this result, we develop a two-stage accelerated forward-backward a*algorithm that converges to a locally optimal solution to the proposed autonomous sparse Markowitz portfolio model, with an o(1/k2) convergence rate in terms of function value. Extensive experiments on 7 benchmark data sets from real-world financial markets show that the proposed method achieves state-of-the-art performance in various evaluating metrics.
In the field of current measurement of nuclear fusion devices, dot matrix high current sensors are widely used because of their advantages of high precision, light weight, wide range and low cost. According to the mag...
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In the field of current measurement of nuclear fusion devices, dot matrix high current sensors are widely used because of their advantages of high precision, light weight, wide range and low cost. According to the magnetic field generated by the measured conductor in the circular dot matrix current sensor ring and the Ampere's circuital law, the current value of the measured conductor can be deduced, so as to realize the non-contact current measurement. Because the Ampere's circuital law adopts the line integral equivalent of discrete points, when there are other energized conductors around the measured conductor, the crosstalk field will cause significant measurement errors. In order to solve this problem, a signal processing a*algorithm should be considered to improve the measurement accuracy and practicability. The effect of the traditional numerical average a*algorithm is limited by the number of Hall elements, and the convergence factor is difficult to be determined due to the contradiction between the convergence speed and steady state error of the adaptive Least Mean Square (LMS) a*algorithm. Based on the ideas of the two a*algorithms mentioned above, this article proposes the wavelet analysis-Kalman a*algorithm. This a*algorithm utilizes the known system model and noise statistical characteristics combined with signal estimation and correction to obtain the optimal a*algorithm parameters, which can further reduce the measurement error of dot matrix current sensor and improve the adaptability of the sensor to the environment. According to the results of simulation and experimental verification, it is concluded that the wavelet analysis-Kalman a*algorithm is the best among the three a*algorithms, which can well suppress the influence of crosstalk field and random noise on the measurement results, and greatly improve the measurement accuracy of the sensor.
Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the rec...
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Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the received video can be enhanced with a trained super-resolution model running on the client-side. However, all the existing solutions ignore the content-induced performance variability of Super-Resolution Deep Neural Network (SR-DNN) models, which means the same super-resolution models have different enhancement effects on the different parts of videos because of video content variation. That leads to unreasonable bitrate selection, resulting in low video QoE, e.g., low bitrate, rebuffering, or video quality jitters. Thus, in this paper, we propose SR-ABR, a super-resolution integrated adaptive bitrate (ABR) a*algorithm, which considers the content-induced performance variability of SR-DNNs into the bitrate decision process. Due to complex network conditions and video content, SR-ABR adopts deep reinforcement learning (DRL) to select future bitrate for adapting to a wide range of environments. Moreover, to utilize the content-induced performance variability of SR-DNNs efficiently, we first define the performance variability of SR-DNNs over different video content, and then use a 2D convolution kernel to distill the features of the performance variability of the SR-DNNs to a short future video segment (several chunks) as part of the inputs. We compare SR-ABR with the related state-of-the-art works using trace-driven simulation under various real-world traces. The experiments show that SR-ABR outperforms the best state-of-the-art work NAS with the gain in average QoE of 4.3%-46.2% and 18.9%-42.1% under FCC and 3G/HSDPA network traces, respectively.
We reconsider the min-max clustered cycle cover (MM-CCC) problem, which is described as follows. Given an undirected complete graph G = (V, E;w ) with a positive integer k , where the vertex set V is partitioned into ...
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We reconsider the min-max clustered cycle cover (MM-CCC) problem, which is described as follows. Given an undirected complete graph G = (V, E;w ) with a positive integer k , where the vertex set V is partitioned into h clusters V 1 , ... , V h , and w : E -> & Ropf;+ is an edge-weight function satisfying the triangle inequality, it is asked to find k cycles such that they traverse all vertices and the vertices in each cluster are required to be traversed consecutively. The objective is to minimize the weight of the maximum weight cycle. We propose a strongly polynomial time 16- approximation a*algorithm for the MM-CCC problem. The result improves the previous a*algorithm in terms of running time.
To address the issue of resonance frequency shifts, output amplitude reduction, and vibration instability in piezoelectric transducers (PT) during the cutting process of ultrasonic surgical instruments due to temperat...
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To address the issue of resonance frequency shifts, output amplitude reduction, and vibration instability in piezoelectric transducers (PT) during the cutting process of ultrasonic surgical instruments due to temperature changes and load fluctuations, resulting in decreased cutting quality and efficiency, severely impacting surgical outcomes, this paper proposes an amplitude-phase control a*algorithm based on fuzzy PI against vibration fluctuations of ultrasonic surgical instruments. This a*algorithm adopts a dual-loop control design: the frequency control loop locks the impedance phase of the transducer to zero through the phase control method, achieving rapid resonance frequency tracking;the amplitude control loop maintains a constant input current through the amplitude control method, ensuring stable amplitude output. In complex surgical environments, this dual-loop control design is more reliable and practical compared to a single control method. To enhance the control a*algorithm's adaptability and robustness to load and temperature changes, a fuzzy PI controller is used instead of a traditional PI controller. Simulation results show that when the load is large, merely using a resonance frequency tracking a*algorithm cannot ensure stable vibration amplitude of the piezoelectric transducer. Compared with the classic PI control, the a*algorithm based on fuzzy PI control can quickly and stably track the resonance frequency and maintain constant amplitude under different loads, exhibiting stronger robustness and adaptability. Experiments validate the effectiveness of this a*algorithm in practical applications, demonstrating that within a reasonable working pressure range (not exceeding 5 N), the control a*algorithm is robust, maintaining good cutting ability and vibration stability of the ultrasonic surgical instrument. The method proposed in this paper is of significant importance for ensuring the vibration stability of ultrasonic surgical instruments, improving surgical outcom
Indoor localization systems (ILS) have become essential tools to address the challenge of locating and tracking items and individuals, such as children, the elderly, and patients with Alzheimer's or dementia. In t...
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Indoor localization systems (ILS) have become essential tools to address the challenge of locating and tracking items and individuals, such as children, the elderly, and patients with Alzheimer's or dementia. In this study, our aim is to develop an auto-adjusting a*algorithm to select coefficients based on the current automatically Received Signal Strength Indicator (RSSI) class. The method adopted by the Internet of Things (IoT) model integrated with Bluetooth Low Energy (BLE) to achieve this objective. The system is designed to track lost items and individuals using a wearable central unit (mini-Raspberry Pi) as a controller and BLE nodes as peripheral devices. The developed system includes Bluetooth beacons, data aggregation, storage, and a web interface for real-time tracking and visualization. The RSSI foot printing method is adopted to detect a specific zone within indoor environments. A web-based application has also been developed to enable monitoring and management of the designed system. The study was evaluated in a real-time experimental environment (with fixed and auto-adjust coefficients) to explore the challenges of accurately determining indoor locations in five rooms. The proposed method initially succeeded in reducing the error caused by fixed coefficients and RSSI by 28.03%. The results demonstrated that the auto-tuning a*algorithm with dynamic coefficients was able to improve the accuracy of positioning by dynamically adjusting RSSI coefficients;this study successfully reduced the average absolute percentage error of indoor localization by 8% and decreased the maximum localization error to 2.01 meters.
An optimal fuzzy adaptive sliding mode controller (OFASMC) is introduced in the present paper for stabilizing a quadrotor drone with chaotic and nonlinear dynamics. At first, control efforts related to the motor torqu...
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An optimal fuzzy adaptive sliding mode controller (OFASMC) is introduced in the present paper for stabilizing a quadrotor drone with chaotic and nonlinear dynamics. At first, control efforts related to the motor torques of the regarded quadrotor drone are determined by the sliding mode procedure as the main stabilizer. Next, using the gradient descent formulation as well as the chain derivative rule, the control gains are adapted through the system states. Then, human knowledge-based fuzzy systems are appropriately designed to set the control parameters for achieving more accurate results. The output errors and control efforts are minimized through the optimum values found for the effective coefficients of the controller by applying the Arithmetic Optimization a*algorithm (AOA). Simulation results clearly illustrate the effectiveness of the introduced strategy to stabilize the quadrotor drone system with and without external disturbances. Block diagram of theproposed optimal fuzzy adaptive sliding mode control for the quadrotor drone. image
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