Method For Real-time Implementation Research Articles

Intelligent multiframe detection aided by Doppler information and a deep neural network

Multiframe detection can be effective for the detection of targets with low signal-to-noise ratios by integrating the target information in several consecutive frames. Traditional methods usually suffer from high computational complexity and insufficient use of available information. In this paper, we propose solving the multiframe detection problem through two sequential steps: Doppler-aided track formation (DTF) and data-driven track detection (DTD). By using the DTF, which considers the coupling relationship between the location measurements and Doppler measurements, the potential tracks can be extracted without introducing too many false tracks. Using DTD, which fuses multiple features along the extracted tracks through a deep neural network, the detection can be sequentially declared. Simulation results show that the detection performance of the proposed method is better than that of the traditional methods for weak targets, and that false alarms can be handled well. Both theoretical analysis and experimental studies corroborate the computational efficiency of the proposed method in real-time implementation.

Knowledge Implementation and Transfer With an Adaptive Learning Network for Real-Time Power Management of the Plug-in Hybrid Vehicle.

Essential decision-making tasks such as power management in future vehicles will benefit from the development of artificial intelligence technology for safe and energy-efficient operations. To develop the technique of using neural network and deep learning in energy management of the plug-in hybrid vehicle and evaluate its advantage, this article proposes a new adaptive learning network that incorporates a deep deterministic policy gradient (DDPG) network with an adaptive neuro-fuzzy inference system (ANFIS) network. First, the ANFIS network is built using a new global K-fold fuzzy learning (GKFL) method for real-time implementation of the offline dynamic programming result. Then, the DDPG network is developed to regulate the input of the ANFIS network with the real-world reinforcement signal. The ANFIS and DDPG networks are integrated to maximize the control utility (CU), which is a function of the vehicle's energy efficiency and the battery state-of-charge. Experimental studies are conducted to testify the performance and robustness of the DDPG-ANFIS network. It has shown that the studied vehicle with the DDPG-ANFIS network achieves 8% higher CU than using the MATLAB ANFIS toolbox on the studied vehicle. In five simulated real-world driving conditions, the DDPG-ANFIS network increased the maximum mean CU value by 138% over the ANFIS-only network and 5% over the DDPG-only network.

Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera.

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

Real Time Implementation of Terminal Security Policies Selection Based on Edge Computing

There are many kinds of terminals in the smart grid system, which have different functions and risks in the system. Different security policies are needed to protect the terminals. At the same time, the terminals will be subject to various attacks in operation. These attacks will change the security protection performance of terminals. Therefore, it is necessary to adjust the security policies in their operation. This paper proposes a real-time implementation method of terminal security policy selection under the edge computing based on the machine learning methods, which makes full use of the computing power of edge devices, adopts offline training and online judgment. The training of machine learning parameters can be completed in the edge side or in the cloud while the security strategies selection continues going on the edge computing side. By this way, the real-time training update and real-time selection of security policy in edge computing system are realized.

Real-time velocity scaling and obstacle avoidance for industrial robots using fuzzy dynamic movement primitives and virtual impedances

PurposeThis paper aims to increase the safety of the robots’ operation by developing a novel method for real-time implementation of velocity scaling and obstacle avoidance as the two widely accepted safety increasing concepts.Design/methodology/approachA fuzzy version of dynamic movement primitive (DMP) framework is proposed as a real-time trajectory generator with imbedded velocity scaling capability. Time constant of the DMP system is determined by a fuzzy system which makes decisions based on the distance from obstacle to the robot’s workspace and its velocity projection toward the workspace. Moreover, a combination of the DMP framework with a human-like steering mechanism and a novel configuration of virtual impedances is proposed for real-time obstacle avoidance.FindingsThe results confirm the effectiveness of the proposed method in real-time implementation of the velocity scaling and obstacle avoidance concepts in different cases of single and multiple stationary obstacles as well as moving obstacles.Practical implicationsAs the provided experiments indicate, the proposed method can effectively increase the real-time safety of the robots’ operations. This is achieved by developing a simple method with low computational loads.Originality/valueThis paper proposes a novel method for real-time implementation of velocity scaling and obstacle avoidance concepts. This method eliminates the need for modification of original DMP formulation. The velocity scaling concept is implemented by using a fuzzy system to adjust the DMP’s time constant. Furthermore, the novel impedance configuration makes it possible to obtain a non-oscillatory convergence to the desired path, in all degrees of freedom.

영상 화질 향상과 영상 해상도 향상의 실시간 구현을 위한 효율적인 기법

영상 화질 향상과 영상 해상도 향상의 실시간 구현을 위한 효율적인 기법

Anomaly Detection in Images With Smooth Background via Smooth-Sparse Decomposition

In various manufacturing applications such as steel, composites, and textile production, anomaly detection in noisy images is of special importance. Although there are several methods for image denoising and anomaly detection, most of these perform denoising and detection sequentially, which affects detection accuracy and efficiency. Additionally, the low computational speed of some of these methods is a limitation for real-time inspection. In this article, we develop a novel methodology for anomaly detection in noisy images with smooth backgrounds. The proposed method, named smooth-sparse decomposition, exploits regularized high-dimensional regression to decompose an image and separate anomalous regions by solving a large-scale optimization problem. To enable the proposed method for real-time implementation, a fast algorithm for solving the optimization model is proposed. Using simulations and a case study, we evaluate the performance of the proposed method and compare it with existing methods. Numerical results demonstrate the superiority of the proposed method in terms of the detection accuracy as well as computation time. This article has supplementary materials that includes all the technical details, proofs, MATLAB codes, and simulated images used in the article.

Block Region of Interest Method for Real-Time Implementation of Large and Scalable Image Reconstruction

In field programmable gate array (FPGA)-based reconstruction of high-resolution (several-gigabyte) images, architectures that store the entire source image in block memory are no longer feasible. In this letter, a block region of interest (ROI) method is proposed for large and scalable image reconstruction, using 2D $4 \times 4$ truncated sinc interpolation by storing only a slice of the source image in block memory. To ensure a high hit rate in block memory for interpolation and improve memory access efficiency, this method provides an effective criterion for partitioning, which subdivides the output image into blocks based on the ROI in the source image. Moreover, a special storage pattern is designed to simultaneously provide 16 pixels for each interpolation to enable real-time implementation by full pipelining with no memory data redundancy. The proposed method was validated by experimental results on an FPGA board along with the corresponding simulations on MATLAB. Various image sizes were tested to prove its flexibility. The time required to reconstruct an output image of $32~\hbox{k} \times 32~\hbox{k}$ at 200 MHz was 2.79 s, thereby achieving a processing speed of 385 Mpixels/s in single floating point precision.

DSP-based pulse width modulation generator for very Spare Matrix Converter

This paper present a modulation scheme for the Spare Matrix Converter (SMC) topology. The proposed method uses the space vector modulation (SVPWM) technique to control the converter’s rectifier stage and inverter stage. This method achieved the maximum modulation ratio of 0.866 with sinusoidal input/output current waveforms. In the other hand, a simple real-time implementation method avoiding additional CPLD or FPGA devices is introduced. This technique is verified through simulation results using PSIM software and experimental results a 32-bit floating-point DSP (TMS 320F28335).

능동위상배열 레이더 적용을 위한 FPGA 기반 실시간 적응 빔 형성기 설계 및 구현

위상배열 레이더 시스템에서 간섭과 재밍을 제거하기 위하여 적응빔 형성 알고리즘이 폭넓게 사용되고 있다. 최근에 와서 FPGA 기술의 발전으로 적응빔 형성 알고리즘의 실시간 처리가 가능하게 되었다. 본 논문에서는 능동위상배열 레이더를 개발하기 위해 전단신호처리기에 적용한 적응빔 형성기의 FPGA 기반 실시간 구현방법을 제안하였다. 개방형 VPX 벡플레인을 통한 통신의 상용 FPGA 보드를 활용하여 콤팩트한 적응빔 형성기를 개발하였다. 이 적응빔 형성기는 역행렬을 구하기 위해 QR 분해와 역 치환을 포함한 수많은 고속의 복소 신호처리와 벡터 및 행렬 연산으로 구성하였다. 구현 결과, FPGA를 통한 적응빔 형성 결과와 매트랩을 통한 시뮬레이션 결과가 일치함을 보였다. 또한, FPGA를 통한 적응빔 형성 알고리즘의 실시간 처리가 가능하여 능동위상배열 레이더 시스템에 적용 가능함을 확인하였다. Adaptive beamforming algorithms have been widely used to remove interference and jamming in the phased array radar system. Advances in the field programmable gate array(FPGA) technology now make possible the real time processing of adaptive beamforming (ABF) algorithm. In this paper, the FPGA based real-time implementation method of adaptive beamforming system(beamformer) in the pre-processor module for active electronically scanned array(AESA) radar is proposed. A compact FPGA-based adaptive beamformer is developed using commercial off the shelf(COTS) FPGA board with communication via OpenVPX(Virtual Path Cross-connect) backplane. This beamformer comprises a number of high speed complex processing including QR decomposition & back substitution for matrix inversion and complex vector/matrix calculations. The implemented result shows that the adaptive beamforming patterns through FPGA correspond with results of simulation through Matlab. And also confirms the possibility of application in AESA radar due to the real time processing of ABF algorithm through FPGA.

Economic Dispatch Using Improved Differential Evolution Approach: A Case Study of the Algerian Electrical Network

Differential evolution (DE) is a simple but powerful evolutionary optimization algorithm with continually outperforming many of the already existing stochastic and direct search global optimization techniques. DE algorithm is a new optimization method that can handle non-differentiable, nonlinear, and multimodal objective functions. This paper presents an efficient modified differential evolution algorithm for solving economic dispatch problem. A new mutation strategy of the conventional DE is suggested to improve the performance and avoid premature convergence. Numerical results on the IEEE 30 bus test system and the practical Algerian 59 bus system show that the proposed approach is faster and more robust compared with those reported recently in the literature. The comparison results prove the capability of the proposed method in real-time implementation for the economic dispatch problem.

Estimation of Combustion Phasing Using the Combustion Net Torque Method

AbstractA new and computationally efficient method, called the combustion net torque method, for estimating combustion phasing in an internal combustion engine using crankshaft torque data is presented. The work is motivated by the need of such an estimation method for real-time implementation of a torque based closed-loop combustion phasing control system. The ideal properties of the proposed method, based on torque calculated from cylinder pressure, are studied using simulations and experimental data from both a spark ignited and a compression ignited engine. A relation to 50% burned mass fraction, the most common measure of combustion phasing, is established and the robustness of the method is investigated. The results show that the ideal estimation accuracy of the proposed method is high, especially around the combustion phasing position where maximum engine efficiency is achieved. Also, the computational complexity of the combustion net torque method is significantly lower compared to other existing techniques for torque domain combustion phasing estimation.

Navigator Echo Motion Artifact Suppression in Synthetic Aperture Ultrasound Imaging

We develop a simple yet effective technique for motion artifact suppression in ultrasound images reconstructed from multiple acquisitions. Assuming a rigid-body motion model, a navigator echo is computed for each acquisition and then registered to estimate the motion in between acquisitions. By detecting this motion, it is possible to compensate for it in the reconstruction step to obtain images that are free of lateral motion artifacts. The theory and practical implementation details are described and the performance is analyzed using computer simulations as well as real data. The results indicate the potential of the new method for real-time implementation in lower cost ultrasound imaging systems.

A direct Newton–Raphson economic emission dispatch

In this study, a direct Newton–Raphson method based on an alternative Jacobian matrix is proposed to solve the economic emission dispatch (EED) problem with line flow constraints. The alternative Jacobian matrix is formulated by the incremental transmission loss in terms of the sensitivity factors, line flows, and line resistances. The sensitivity factors are obtained from line flow solutions based on a DC load flow model. The proposed approach is tested on the Taiwan Power Company 288-bus system. Simulation results obtained from the proposed method confirm the advantage of computation rapidity and solution accuracy over that of the AC load flow method and the conventional B-coefficients method, respectively. The comparison confirms the capability of the proposed method in real-time implementation for the EED problem.

Network-based software-in-the-loop simulation for real-time control system

This paper suggests a real-time implementation method for software-in-the-loop (SIL) simulation for control systems. The implementation is composed of a PC for the controllers, a PC for the plant, a general-purpose computer-aided control system design (CACSD)package, and an Ethernet network. The Ethernet network is investigated in terms of control issues such as sampling interval, network-induced time delay, use with multiple I/O points and data synchronization. A performance evaluation of software-in-the-loop simulation is carried out subject to a computation delay and a sampling interval. To reduce the effects of the time delay, particularly for fast plants, a time-scaling method is introduced for slow and fast motions. It is demonstrated that this real-time software-in-the-loop simulation will be very useful.

Very low bit-rate video coding using cellular neural network universal machine

A method of video coding for very low bit-rate channels, which is implemented using cellular neural network universal machine, is presented in the following paper. The presented method combines elements of a standard approach to video coding with elements of second-generation video-coding techniques. Inter-frame coding is performed using standard block-based motion estimation procedure, while intra-frame coding is based on vector quantization approach. To satisfy constraints imposed by very low bit-rate channel throughput, a number of bytes that were considered to be used for representing video sequence frames, was assumed to be less than 200. Simulations of the algorithm execution, based on actual CNN UM chip parameter values, show feasibility of using the proposed method for real-time implementation of very low bit-rate video coding.

Multiobjective power dispatch with line flow constraints using the fast Newton-Raphson method

The fast Newton-Raphson approach based on an alternative Jacobian matrix is proposed to solve the power system multiobjective power dispatch (MPD) problem with line flow constraints. Two conflicting objectives including minimization of fuel cost and environmental impact of emission are considered in this study. The Jacobian matrix is formulated by the incremental transmission loss in terms of the sensitivity factors, line flows and line resistances. The sensitivity factors are obtained from line flow solutions based on a DC load flow model. Moreover, the B-coefficients matrix and the Lagrange function can be shown as convex functions. Therefore, the existence and uniqueness of the solution for the nonlinear equation of the MPD problem can be proven. The proposed approach is tested on the IEEE 14- and 30-bus systems. Simulation results obtained from the proposed method confirm the advantage of computation rapidity and solution accuracy over that of the AC load flow method and the conventional B-coefficients method, respectively. The comparison confirms the capability of the proposed method in real-time implementation for the MPD problem.

Fast recursive algorithms for morphological operators based on the basis matrix representation

A real-time implementation method for the most general morphological system, the so-called grayscale function processing (FP) system is presented. The proposed method is an extension of our previous works (1993, 1995) using the matrix representation of the FP system with a basis matrix (BM) and a block basis matrix (BBM) composed of grayscale structuring elements (GSE). In order to further improve the computational efficiency of the basis matrix representation, we propose recursive algorithms based on the observation of the BM and BBM. The efficiency of the proposed algorithms is gained by avoiding redundant steps in computing overlapping local maximum or minimum operations. It is shown that, with the proposed scheme, both opening and closing can be determined in real time by 2N-2 additions and 2N-2 comparisons, and OC and CO by 4N-4 additions and 4N-4 comparisons, when the size of the GSE is equal to N. It is also shown that the proposed recursive opening and closing require only 3N-3 memory elements.

A unified approach to the real-time implementation of microprocessor-based PWM waveforms

A method for real-time implementation of any two-level three-phase pulse-width-modulator (PWM) invertor waveform is discussed. The hardware and software of the scheme are built around the 16 b 8086 microprocessor and its peripherals, with a few additional digital ICs. The suggested scheme realizes the PWM waveforms without harmonic distortion. The implementation procedure is a universal one and it generates in real time any two-level three-phase PWM waveform with the chosen hardware structure. There is no need for hardware alteration for realization of different types of PWM waveforms.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>