考虑阀点效应的电力系统经济分配算法

经济分配(ED)对于电力系统的节能至关重要,适当的分配方法可以为电厂节约巨额生产成本,然而阀点效应使得实际ED问题呈现出不光滑和非凸的特性,导致一些经典的优化算法和启发式算法无法在合理时间内发现最优解。提出一种新的改进教与学优化算法来求解计及阀点效应的经济分配问题,并采用一种新的修正策略取代罚函数法来处理约束条件。为了验证新算法的有效性和鲁棒性,选取典型的benchmark函数和ED实例进行仿真计算,结果表明与其他代表性算法相比,该方法求解精度高、收敛速度快,为计及阀点效应的经济分配问题求解提供了一条新途径。

Automatic segmentation of foveal avascular zone based on adaptive watershed algorithm in retinal optical coherence tomography angiography images

The size and shape of the foveal avascular zone(FAZ)have a strong positive correlation with several vision-threatening ret inovascular diseases.The identification,segmentation and analysis of FAZ are of great significance to clinical diagnosis and treatment.We presented an adaptive watershed algorithm to automatically extract F AZ from retinal optical coherence tomography angiography(OCTA)images.For the traditional watershed algorithm,"over-segmentation"is the most common problem.FAZ is often incorrectly divided into multiple regions by redundant"dams".This paper analyzed the relationship between the"dams"length and the maximum inscribed circle radius of FAZ,and proposed an adaptive watershed algorithm to solve the problem of"over-segmentation".Here,132 healthy retinal images and 50 diabetic retinopathy(DR)images were used to verify the accuracy and stability of the algorithm.Three ophthal-mologists were invited to make quan titative and qualitative evaluations on the segmentation results of this algorithm.The quantitative evaluation results show that the correlation coffi-cients between the automatic and manual segmentation results are 0.945(in healthy subjects)and 0.927(in DR patients),respectively.For qualitative evaluation,the percentages of"perfect segmentation"(score of 3)and"good segmentation"(score of 2)are 99.4%(in healthy subjects)and 98.7%(in DR patients),respectively.This work promotes the application of watershed algorithm in FAZ segmentation,making it a useful tool for analyzing and diagnosing eye diseases.

A GA approach to vehicle routing problem with time windows considering loading constraints

As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.

Design of wideband graded-index antireflection coatings at oblique light incidence

We suggest a design method of graded-refractive-index （GRIN） antireflection （AR） coating for s-polarized or p- polarized light at off-normal incidence. The spectrum characteristic of the designed antireflection coating with a quintic effective refractive-index profile for a given state of polarization has been discussed. In addition, the genetic algorithm was used to optimize the refractive index profile of the GRIN antireflection for reducing the mean reflectance of s- and p-polarizations. The average reflectance loss was reduced to only 0.04% by applying optimized GRIN AR coatings onto BK7 glass over the wavelength range from 400 to 800 nm at the incident angle of θo = 70°.

Joint power and spectrum allocation algorithm in cognitive radio networks

The spectrum sharing problem between primary and cognitive users is mainly investigated. Since the interference for primary users and the total power for cognitive users are constrained, based on the well-known water-filling theorem, a novel one-user water-filling algorithm is proposed, and then the corresponding simulation results are given to analyze the feasibility and validity. After that this algorithm is used to solve the communication utility optimization problem subject to the power constraints in cognitive radio network. First, through the gain to noise ratio for cognitive users, a subcarrier and power allocation algorithm based on the optimal frequency partition is proposed for two cognitive users. Then the spectrum sharing algorithm is extended to multiuser conditions such that the greedy and parallel algorithms are proposed for spectrum sharing. Theory and simulation analysis show that the subcarrier and power allocation algorithms can not only protect the primary users but also effectively solve the spectrum and power allocation problem for cognitive users.

Investigation of the optimum differential gear ratio for real driving cycles by experiment design and genetic algorithm

Experiment statistical method and genetic algorithms based optimization method are used to obtain the optimum differential gear ratio for heavy truck that provides best fuel consumption when changing the working condition that affects its torque and speed range. The aim of the study is to obtain the optimum differential gear ratio with fast and accurate optimization calculation without affecting drivability characteristics of the vehicle according to certain driving cycles that represent the new working conditions of the truck. The study is carried on a mining dump truck YT3621 with 9 for- ward shift manual transmission. Two loading conditions, no load and 40 t, and four on road real driving cycles have been discussed. The truck powertrain is modeled using GT-drive, and DOE -post processing tool of the GT-suite is used for DOE analysis and genetic algorithm optimization.

Parallel Test Tasks Scheduling and Resources Configuration Based on GA-ACA

A Genetic Algorithm-Ant Colony Algorithm(GA-ACA),which can be used to optimize multi-Unit Under Test(UUT)parallel test tasks sequences and resources configuration quickly and accurately,is proposed in the paper.With the establishment of the mathematic model of multi-UUT parallel test tasks and resources,the condition of multi-UUT resources mergence is analyzed to obtain minimum resource requirement under minimum test time.The definition of cost efficiency is put forward,followed by the design of gene coding and path selection project,which can satisfy multi-UUT parallel test tasks scheduling.At the threshold of the algorithm,GA is adopted to provide initial pheromone for ACA,and then dual-convergence pheromone feedback mode is applied in ACA to avoid local optimization and parameters dependence.The practical application proves that the algorithm has a remarkable effect on solving the problems of multi-UUT parallel test tasks scheduling and resources configuration.

Research of improving the dynamic scheduling algorithm in the CAN bus control networks

Currently, the article analyzes the CAN bus＇s rule of priority＇s arbitration bit by bit without destroy. It elicits the conclusion that if static priority based on the affirmatory system model is used, the lower priority＇s messages will be delayed considerably more, even some data will be lost when the bus＇s bandwidth is widely used. The scheduling cannot be modified neither during the system when static priority is used. The dynamic priority promoting method and the math model of SQSA and SQMA are presented; it analyzes the model＇s rate of taking in and sending out in large quantities, the largest delay, the problems and solutions when using SQMA. In the end, it is confirmed that the method of improving dynamic priority has good performances on the network rate of taking in and sending out in large quantities, the average delay, and the rate of network usage by emulational experiments.

Other Mountain Stones Can Attack Jade: The 5-Steps Rule

Since the 5-steps rule was proposed in 2011, it has been widely used in many areas of molecular biology, both theoretical and experimental. It can be even used to deal with the commercial problems and bank systems, as well as material science systems. Just like the machine-learning algorithms, it is the jade for nearly all the statistical systems.

Novel approach for identifying Z-axis drift of RLG based on GA-SVR model

This paper describes a novel approach for identifying the Z-axis drift of the ring laser gyroscope （RLG） based on ge-netic algorithm （GA） and support vector regression （SVR） in the single-axis rotation inertial navigation system （SRINS）. GA is used for selecting the optimal parameters of SVR. The latitude error and the temperature variation during the identification stage are adopted as inputs of GA-SVR. The navigation results show that the proposed GA-SVR model can reach an identification accuracy of 0.000 2 （？）/h for the Z-axis drift of RLG. Compared with the ra-dial basis function-neural network （RBF-NN） model, the GA-SVR model is more effective in identification of the Z-axis drift of RLG.

Low Complexity Detection Algorithms Based on ADMIN for Massive MIMO

This paper proposes the alternating direction method of multipliers-based infinity-norm(ADMIN) with threshold(ADMIN-T) and with percentage(ADMIN-P) detection algorithms,which make full use of the distribution of the signal to interference plus noise ratio(SINR) for an uplink massive MIMO system.The ADMIN-T and ADMIN-P detection algorithms are improved visions of the ADMIN detection algorithm,in which an appropriate SINR threshold in the ADMIN-T detection algorithm and a certain percentage in the ADMIN-P detection algorithm are designed to reduce the overall computational complexity.The detected symbols are divided into two parts by the SINR threshold which is based on the cumulative probability density function(CDF) of SINR and a percentage,respectively.The symbols in higher SINR part are detected by MMSE.The interference of these symbols is then cancelled by successive interference cancellation(SIC).Afterwards the remaining symbols with low SINR are iteratively detected by ADMIN.The simulation results show that the ADMIIN-T and the ADMIN-P detection algorithms provide a significant performance gain compared with some recently proposed detection algorithms.In addition,the computational complexity of ADMIN-T and ADMIN-P are significantly reduced.Furthermore,in the case of same number of transceiver antennas,the proposed algorithms have a higher performance compared with the case of asymmetric transceiver antennas.

A varying time-step explicit numerical inte-gration algorithm for solving motion equa-tion

If a traditional explicit numerical integration algorithm is used to solve motion equation in the finite element simulation of wave motion, the time-step used by numerical integration is the smallest time-step restricted by the stability criterion in computational region. However, the excessively small time-step is usually unnecessary for a large portion of computational region. In this paper, a varying time-step explicit numerical integration algorithm is introduced, and its basic idea is to use different time-step restricted by the stability criterion in different computational region. Finally, the feasibility of the algorithm and its effect on calculating precision are verified by numerical test.

Obstacle avoidance technology of bionic quadruped robot based on multi-sensor information fusion

In order to improve the ability of a bionic quadruped robot to percept the location of obstacles in a complex and dynamic environment, the information fusion between an ultrasonic sensor and a binocular sensor was studied under the condition that the robot moves in the Walk gait on a structured road. Firstly, the distance information of obstacles from these two sensors was separately processed by the Kalman filter algorithm, which largely reduced the noise interference. After that, we obtained two groups of estimated distance values from the robot to the obstacle and a variance of the estimation value. Additionally, a fusion of the estimation values and the variances was achieved based on the STF fusion algorithm. Finally, a simulation was performed to show that the curve of a real value was tracked well by that of the estimation value, which attributes to the effectiveness of the Kalman filter algorithm. In contrast to statistics before fusion, the fusion variance of the estimation value was sharply decreased. The precision of the position information is 4. 6 cm, which meets the application requirements of the robot.

A self-adaptive grey forecasting model and its application

GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.

To Construct High Level Secure Communication System： CTMI Is Not Enough

Public key cryptographic （PKC） algorithms, such as the RSA, elliptic curve digital signature algorithm （ECDSA） etc., are widely used in the secure communication sys- tems, such as OpenSSL, and a variety of in- formation security systems. If designer do not securely implement them, the secret key will be easily extracted by side-channel attacks （SCAs） or combinational SCA thus mitigat- ing the security of the entire communication system. Previous countermeasures of PKC im- plementations focused on the core part of the algorithms and ignored the modular inversion which is widely used in various PKC schemes. Many researchers believe that instead of straightforward implementation, constant time modular inversion （CTMI） is enough to resist the attack of simple power analysis combined with lattice analysis. However, we find that the CTMI security can be reduced to a hidden t-bit multiplier problem. Based on this feature, we firstly obtain Hamming weight of interme- diate data through side-channel leakage. Then, we propose a heuristic algorithm to solve the problem by revealing the secret （partial and full） base of CTMI. Comparing previous nec-essary input message for masking filtering, our procedure need not any information about the secret base of the inversion. To our knowl- edge, this is the first time for evaluating the practical security of CTM! and experimental results show the fact that CTMI is not enough for high-level secure communication systems.

Bee Colony Optimization Algorithm for Routing and Wavelength Assignment Based on Directional Guidance in Satellite Optical Networks

With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.

Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm

Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV） ＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.

A BOOSTING APPROACH FOR INTRUSION DETECTION

Intrusion detection can be essentially regarded as a classification problem,namely,dis-tinguishing normal profiles from intrusive behaviors. This paper introduces boosting classification algorithm into the area of intrusion detection to learn attack signatures. Decision tree algorithm is used as simple base learner of boosting algorithm. Furthermore,this paper employs the Principle Com-ponent Analysis (PCA) approach,an effective data reduction approach,to extract the key attribute set from the original high-dimensional network traffic data. KDD CUP 99 data set is used in these ex-periments to demonstrate that boosting algorithm can greatly improve the classification accuracy of weak learners by combining a number of simple “weak learners”. In our experiments,the error rate of training phase of boosting algorithm is reduced from 30.2% to 8% after 10 iterations. Besides,this paper also compares boosting algorithm with Support Vector Machine (SVM) algorithm and shows that the classification accuracy of boosting algorithm is little better than SVM algorithm’s. However,the generalization ability of SVM algorithm is better than boosting algorithm.

Bilevel generalized mixed equilibrium problems involving generalized mixed variational-like inequality problems in reflexive Banach spaces

A new bilevel generalized mixed equilibrium problem （BGMEP） involving generalized mixed variational-like inequality problems （GMVLIPs） is introduced and studied in the reflexive Banach spaces. First, an auxiliary generalized mixed equilibrium problem （AGMEP） is introduced to compute the approximate solutions of the BGMEP involving the GMVLIPs. By using a minimax inequality, the existence and the unique- ness of solutions of the AGMEP are proved under mild conditions without any coercive assumptions. By using an auxiliary principle technique, the new iterative algorithms are proposed and analyzed, with which the approximate solutions of the BGMEP are computed. The strong convergence of the iterative sequence generated by the algorithms is shown under mild conditions without any coercive assumptions. These new results can generalize some recent results in this field.

Utility-optimization dynamic subcarrier allocation algorithm for SC-FDMA systems

Two utility-optimization dynamic subcarrier allocation(DSA) algorithms are designed for single carrier frequency division multiple access system(SC-FDMA).The two proposed algorithms aim to support diverse transmission capacity requirements in wireless networks,which consider both the channel state information(CSI) and the capacity requirements of each user by setting appropriate utility functions.Simulation results show that with considerable lower computational complexity,the first utility-optimization algorithm can meet the system capacity requirements of each user effectively.However,the rate-sum capacity performance is poor.Furthermore,the second proposed utility-optimization algorithm can contribute a better trade-off between system rate-sum capacity requirement and the capacity requirements of each user by introducing the signal to noise ratio(SNR) information to the utility function based on the first utility-optimization algorithm,which can improve the user requirements processing capability as well as achieve a better sum-rate capacity.