An Evaluation Method of English Composition Automatic Grading Based on Genetic Optimization Algorithm and CNN Model

This paper firstly proposes an improved genetic algorithm (GA) for optimization in adaptive bus signal priority control at signalized intersections. Unlike conventional genetic algorithms with slow convergence speed, this algorithm can increase the convergence speed by utilizing the compensation rule between consecutive signal cycles to narrow new possible generated population spaces. Secondly, the paper would like to present a way to apply the algorithm to a simple adaptive bus signal priority control as well as compare how much the computation time is saved when applying the improved algorithm. Then the research thirdly investigates the efficiency of the proposed algorithm under various flow rate situations. The results show that the improved genetic algorithm can reduce the computation time considerably, by up to 48.39% for the studied case. With high saturation degrees on the cross street, the convergence rate performance of the improved genetic algorithm is significantly good. The figure can be up to 36.2% when compared with the convergence rate of the conventional GA.

The use of genetic algorithm optimisation applied to solving engineering problems has gained popularity over the last 10 years. Applications to the design of water distribution systems based on genetic algorithm optimisation first appeared in the early 1990s. This paper starts out with a brief review of the past use of genetic algorithms applied to aspects of water distribution systems. Leak detection and calibration of pipe internal roughnesses in a network are important issues for water authorities around the world. Computer simulation of water distribution systems has become a routine task of water authorities and consultants. One of the big unknowns in developing these models is the condition of the pipes, especially if they are old. It is very difficult to obtain reliable estimates of the roughness height for each pipe in the system using steady state calibration techniques. Liggett and Chen at Cornell University in 1994 developed an innovative technique called the inverse transient technique. The technique is able to determine, from unsteady pressure traces at a number of nodes in the network, the locations and magnitudes of any leaks that are occurring and the friction factor for each pipe in the network. An alternative approach to solving the minimization problem is presented in this paper. Genetic algorithm optimisation is used. A population of solutions is generated with each string representing values of the decision variables that are to be found. These include the magnitudes of leaks at nodes in the network and friction factors for each pipe. A forward transient analysis is performed for each string in the population that represents different combinations of leak magnitudes and friction factors. The sum of the absolute deviations between the measured transient pressures and the pressures predicted by the numerical model are determined and are used to determine the fitness of the string. The smaller the sum of the deviations then the larger the fitness that is assigned to the string. The genetic algorithm operators that are used include tournament selection, crossover and mutation. A new crossover operator is introduced. The genetic algorithm optimisation technique that has been developed in the research is applied to an example network. The results are encouraging and compare favorably with the inverse transient technique.

The three-dimensional spatial structure of a methylene-acetal-linked thymine dimer present in a 10 basepair (bp) sense-antisense DNA duplex was studied with a genetic algorithm designed to interpret NOE distance restraints. Trial solutions were represented by torsion angles. This means that bond angles for the dimer trial structures are kept fixed during the genetic algorithm optimization. Bond angle values were extracted from a 10 bp sense-antisense duplex model that was subjected to energy minimization by means of a modified AMBER force field. A set of 63 proton-proton distance restraints defining the methylene-acetal-linked thymine dimer was available. The genetic algorithm minimizes the difference between distances in the trial structures and distance restraints. A large conformational search space could be covered in the genetic algorithm optimization by allowing a wide range of torsion angles. The genetic algorithm optimization in all cases led to one family of structures. This family of the methylene-acetal-linked thymine dimer in the duplex differs from the family that was suggested from distance geometry calculations. It is demonstrated that the bond angle geometry around the methylene-acetal linkage plays an important role in the optimization.

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas–Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model.

For grid-scale intermittent electricity storage, liquid air energy storage (LAES) is considered to be one of the most promising technologies for storing renewable energy. In this study, a steady-state process model was developed for an LAES, by combining a Linde liquefaction process and an open Rankine power cycle. To investigate the system performance and achieve global optimization, a single-factor analysis approach and multifactor genetic algorithm (GA) optimization model were built using MATLAB software. The effects of the charging pressure, storage pressure, discharging pressure, and isentropic efficiency of the compressor/turbine on the LAES performance parameters, such as the inlet temperature of the Joule–Thomson valve, round-trip efficiency (RTE), liquefaction ratio (LR), and power consumptions in the compressor, cryo-pump, and turbine, were investigated. Subsequently, the optimal conditions were obtained using the GA optimization method to achieve the optimal performance of the LAES. The results showed that the charging pressure, discharging pressure, and isentropic efficiencies of the compressor and turbine had significant effects on the RTE; an increase in the discharging pressure resulted in an improved expansion power output; the GA optimization could achieve RTE, LR, the system energy storage and recovery exergy efficiencies of 53.33%, 86.96%, 81.00%, and 78.16%; the power consumption in the compressor of GA optimization was 10.02% maximum saving. The proposed optimization method can be used to further explore the global optimization of cryogenic energy storage systems, such as different-layout LAES systems and different cryogenic liquefaction media energy storage systems.

This paper presents an extension of fuzzy-multi-objective genetic algorithm (MOGA) optimization methodology that could effectively be used to find the overall satisfaction of objective functions (selecting the design variables) in the early stages of design process. The coupling of objective functions due to design variables in an engineering design process will result in difficulties in design optimization problems. The primary application of this methodology is the design of a liquid propellant engine with the maximum specific impulse and the minimum weight. The independent design variables in this model are combustion chamber pressure, exit pressure, oxidizer to fuel mass flow rate. To handle the mentioned problems, a fuzzy-multi-objective genetic algorithm optimization methodology is developed based on Pareto optimal set. Liquid propellant engine, F-1 is modeled to illustrate accuracy and efficiency of proposed methodology.

Urban flooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the flood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers significant benefits over traditional RTC approaches for flood risk management.

Offset, gain and time mismatch errors exist in the channels of the time-interleaved analog-to-digital converter (TIADC), which severely affect the signal-to-noise and distortion ratio (SNDR), spurious-free dynamic range (SFDR) and effective number of bits (ENOB). Here, an error detection and correction method of TIADC based on genetic algorithm (GA) optimization is presented. The algorithm based on sine fitting is used for the first estimation and rough correction of the input signal, and GA is used for the second detection and fine correction of channel mismatch errors on the signal after frequency domain filtering. Experimental results show that the SNDR of the TIADC system is enhanced to 92.24 dB, the SFDR is enhanced to 102.05 dB, the ENOB is enhanced to 15.03 bits and the spectrum spurs are reduced. This method provides a new idea for error detection and correction of high-resolution TIADC systems with more than 16 bits.

This paper uses the Genetic Algorithm(GA) optimization method to find the best S-band quadrifilar antenna for the satellite's telemetry function. Since the backradiation as the cross-polarization of the RHCP antenna on one side of the satellite will possibly degrade the desired performance of the LHCP antenna on the other side, in order to minimize the backradiation and maximize the flatness of the forward radiation from the RHCP antenna, we will get the optimal values of the physical design elements such as the number of turns, the radius of the helix and the height. Finally, we will present the excellent performance of the RHCP antenna realized with the optimized physical dimensions.

Hybrid design of PID controller for four DoF lower limb exoskeleton

Vortex beams (VBs) generated by plasmonic metasurfaces hold great potential in the field of information transmission due to their unique helical phase wavefronts and infinite eigenstates. However, achieving perfect multiplexing and superposition of VBs with different orders remains a challenging issue in nanophotonics research. In this paper, based on a single-layer metallic porous metasurface structure applicable to the infrared spectrum, VBs with orders 2, 4, 6, and 8 are realized through the arrangement of annular elliptical apertures. Moreover, perfect VBs are achieved by optimizing key structural parameters using a genetic algorithm. The optimization of key structural parameters via genetic-based optimization algorithms to attain the desired effects can significantly reduce the workload of manual parameter adjustment. In addition, leveraging the orthogonality between VBs of different orders, concentric circular multi-channel VBs array (l = 2, 6) and (l = 4, 8) are realized. High-purity multiplexing architectures (>90%) are achieved via rational optimization of critical structural parameters using a genetic optimization algorithm, which further mitigates information crosstalk in optical communication transmission. The introduction of the genetic algorithm not only reduces the workload of manual arrangement of unit arrays but also enables the generation of more perfect VBs, providing a new research direction for optical communication transmission and optical communication encryption.

The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, which guarantees the return of their capital. In this paper, considering the condition of a bank as an economic enterprise, a model is presented which not only increases profit but also reduces risk. Two objective functions have been defined that the first objective is to minimize the risk and the second objective function is to maximize the of the bank profit, which is used by robust programming and Malvi Sim model. In this paper, we have investigated the Risky and non-Risky Partfolio and the optimal portfolio of bank assets from scenario based solution of the model and by using PSO and Genetic Optimization Algorithm. At all levels of confidence and optimal values of risk based on the estimation of SPP-CVAR method by Particle Swarm Algorithm (PSA) is less than genetic algorithm, which indicates better performance of Particle Swarm Algorithm (PSA) than Genetic Algorithm (GA). Also, the optimum wealth obtained from PSA solution is higher at all levels of confidence than the corresponding value of Genetic Algorithm (GA), and this is another reason to confirm the performance of PSO algorithm compared to the Genetic Algorithm (GA). The values of the first goal function, obtained from the PSO algorithm, for all confidence levels are lower than those of the genetic algorithm. The optimum wealth obtained from PSA is higher than genetic algorithm. At 0.9 level, the value of LR of kupiec statistics for the SPP-CVAR method was less than the Chi-square statistics (Critical value) which was assumed to be acceptable.

Rotation speed and propulsive force are the two critical parameters in the work of the air-borne bolting rig. To address the problem that unreasonable rotation speed and propulsive force will induce the breakage of the drill pipe and the inability of the drill bit to cut coal adequately this paper proposes an adaptive control strategy for the air-borne bolting rig based on genetic algorithm optimization. Firstly, we obtain the corresponding coal hardness by the real-time acquisition of the working torque of the drill pipe. Then we calculate the reasonable rotation speed of the hydraulic motor and the propulsive force of the hydraulic cylinder on the coal of different hardness. Secondly, the genetic algorithm is applied to optimize the parameters of the PID (proportion integration differentiation) controller so that the system may attain the target value fast and reliably and achieve adaptive control. Finally, a simulation model of the slewing system and the propulsion system of the air-borne bolting rig are established in the AMESim hydraulic software, and the simulation tests were carried out under two distinct working conditions: single coal hardness and coal hardness of sudden change. The results indicate that the PID control strategy based on genetic algorithm optimization has a shorter response time, a smaller overshoot, and a lower steady-state error than the traditional PID control strategy.

Nowadays, the application of the Internet is more and more extensive, but also exposed many problems. Network attacks are gradually penetrating into all kinds of network terminals, and the attack means are more and more sophisticated and covert. Network paralysis, data and user information leakage and other security problems are increasingly prominent. The increasingly dangerous network security situation brings new challenges to the traditional single point and single source security defense system. At the same time, the network crime has caused more and more losses to the personal and property safety of the state, enterprises and citizens. Network security has become the most concerned problem of citizens. In order to clarify the influence of different factors on computer network security, construct effective solutions, and create a more secure environment for the development and expansion of network information technology, this paper analyzes the factors affecting computer network information security based on genetic algorithm, and proposes a BP neural network algorithm based on genetic algorithm optimization, which is used in network security optimization. The experimental results show that the accuracy of BP neural network algorithm optimized by genetic algorithm is as high as 86.5%. The combination of genetic algorithm and neural network can effectively improve the reliability of the network.KeywordsGenetic algorithmComputerNetwork securityApplication research

When a large number of tasks request different resources in a grid system, resources allocation should have been done with proper planning and scheduling to guarantee a good quality of service (QOS). There are different ways to provide these requests by choosing appropriate resources allocation to optimize the total operation of the system. In this study, first some parameters, such as priority, delay, reliability and cost are determined for each task to maximize system performance and appropriate resources distribution. Then, a hybrid optimization algorithm for choosing grid resource based on genetic algorithm and particle swarm optimization (PSO) is presented. Based on the experimental results, this method’s performance is 17.5% more than genetic and PSO algorithms in average. Key words: Grid system, resources selection, quality of service, genetic algorithm, particle swarm optimization (PSO).