36 results about "Set constraint" patented technology

The invention relates to an excavator trajectory planning method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of engineering machinery trajectory planning. The method comprises the following steps: establishing a forward kinematics model through a D-H homogeneous coordinate transformation method; based on the forward kinematics model, establishing an inverse kinematics model through a geometric method; setting six path points in the track according to the mining track; based on an inverse kinematics model, converting the space coordinates of the six path points into space angles of joints; interpolation is carried out on the joint path points through a five-time B spline curve, and a track is generated; determining an optimization objective function according to actual application requirements, and setting constraint conditions according to a specific excavator type; and finally, the optimization problem is solved through an IADEMOPSO algorithm, an optimal Pareto solution is selected by using an average optimal evaluation formula, and an optimal track is generated. The track planning of the excavator can be realized.

The invention discloses a BVT format high-speed motion instruction generation method based on time expression and a related device, a tool nose displacement instruction is firstly defined, the tool nose displacement instruction is a track point sequence sampled in an equal period, and each sampling point is expressed by sampling time and an accumulated displacement value; then segmenting a tool nose displacement instruction, dividing a continuous track into a plurality of sub-segments by analyzing the geometric curvature of a time-displacement curve, and ensuring that the curvature change of each sub-segment is gentle and each sub-segment contains a preset number of sampling points; then selecting a decatic polynomial curve as a fitting model, fitting sampling points by using a least square method under set constraints, and ensuring that the curve is smooth and third-order derivatives are continuous; introducing a Lagrange multiplier to construct a function, converting a constrained curve fitting problem into an unconstrained extreme value problem, and solving a decatic polynomial coefficient; and finally, based on the fitting polynomial curve, generating a high-speed motion instruction which covers a straight line, an arc and an NURBS interpolation instruction, and each instruction comprises parameters such as polynomial times, fitting points and the like. The invention aims to solve the problems of low interpolation precision, poor track smoothness and slow response of the traditional motion instruction format in high-speed and high-precision motion control.

The invention is suitable for the field of power distribution network post-disaster reconstruction, and discloses a power distribution network post-disaster rapid reconstruction method, system and device based on radial constraint and a medium, and the method comprises the steps: obtaining post-disaster power distribution network data, and constructing a post-disaster reconstruction model; based on the post-disaster reconstruction model, establishing an optimization objective function and various constraint conditions; establishing a mixed integer linear programming model based on the optimization objective function and a plurality of constraint conditions, judging whether the set constraint conditions are met by combining a particle swarm optimization algorithm, and solving an optimization problem of the mixed integer linear programming model to obtain a final decision result; and obtaining a switch operation sequence based on the decision result, calculating a recovery result of each power supply unit, and obtaining a reconstruction decision scheme of the post-disaster power distribution network, thereby improving reconstruction calculation efficiency and engineering applicability, and realizing rapid recovery of the key load and stable operation of the post-disaster power distribution network.

The application discloses a kind of edge cloud computing environment under Internet of Vehicles based on blockchain data sharing method and system, the method includes: collecting the user information and data demand of data sharing party and receiver;According to user information, the utility function of receiver is calculated, the utility function of sharing party and the overall utility function of proxy node;Set constraint, determine target equation, establish optimization equation under the condition of constraint;Solve the extreme value of optimization equation by Lagrange multiplier method;Design the iterative two-way auction algorithm based on game theory to transform the problem;According to the bid price of user, the direction and result of data sharing are determined.The application can improve the enthusiasm of user sharing data, ensure that user shares data safely and efficiently, protect the private information of user, and can realize the maximization of overall utility function.

This invention discloses a PIDNN-SO-based method for optimizing the layout of offshore wind farms, belonging to the field of wind farm optimization design technology. The method first inputs wind farm, turbine, and wind resource parameters, sets constraints on turbine spacing, boundaries, and wake overlap rate, and constructs a high-precision wake flow field physical model based on actuator disk theory, Navier-Stokes equations, and a weighted quadratic wake superposition model. Secondly, it builds a physical information dual neural network (PIDNN) with dual encoders, integrating physical equation loss and data loss to train the model, achieving rapid and accurate prediction of wake wind speed. Then, it uses the snake optimization algorithm (SO) to optimize the environmental evolution factor, completing an efficient search for turbine layout. Finally, it constructs a PIDNN-SO collaborative optimization framework, trains a PIDNN surrogate model through initial sampling, and outputs the optimal layout that maximizes annual power generation. This invention can effectively reduce wake loss and significantly improve the power generation efficiency of offshore wind farms.

The invention relates to the technical field of textile manufacturing, in particular to a scheduling method based on fusion of a large language model and an improved simulated annealing algorithm, and the method comprises the following steps: obtaining production data; performing problem description, setting constraint conditions, taking minimization of the maximum completion time as a target function, constructing a scheduling problem model, setting an iteration process, and repeatedly executing the iteration process; generating an initial scheme according to the production data and the problem description; in the iteration process, alternately calling the large language model and the improved simulated annealing algorithm for solving, and generating a to-be-verified scheduling scheme; the verification intelligent agent verifies the scheduling scheme to be verified, and after multiple iterations, an optimal solution is finally determined, and an optimal scheduling scheme is output. After the technical scheme is adopted, the maximum completion time of the generated scheduling scheme is relatively short, the generation speed of the scheduling scheme is relatively high, and the scheduling scheme can be ensured to be executable.

The invention discloses a power distribution network dynamic partitioning method based on a composite electrical distance. The method comprises the steps that the composite electrical distance between each pair of buses in a power distribution network is calculated; constructing an affinity matrix according to the composite electrical distance, and constructing an undirected weighted graph based on the affinity matrix; calculating a Laplacian matrix for the undirected weighted graph to obtain a preliminary partition of the power distribution network; a mixed integer linear programming method is adopted to optimize the preliminary partitioning result, it is ensured that each sub-region meets set constraints, and optimized partitions are output; topological change of a power distribution network and output fluctuation of a distributed power supply are monitored in real time, and when the change exceeds a preset threshold value, a dynamic adjustment mechanism is triggered, and a partition adjustment strategy is executed. According to the method, on the basis of comprehensively describing the electrical similarity of the bus, the engineering feasibility and the running state adaptability of the partitioning result can be realized by combining constraint optimization and a dynamic adjustment mechanism.

The application relates to the field of power systems and discloses a power grid outage scheduling system based on multi-agent deep reinforcement learning, which comprises a scheduling problem conversion module and a decision solving module; the scheduling problem conversion module is used for converting a power grid outage scheduling problem into a multi-agent Markov decision process model; and the decision solving module is used for training and solving the multi-agent Markov decision process model by using a multi-agent deep deterministic policy gradient algorithm under the set constraints of the power grid outage scheduling, so as to obtain a power grid outage scheduling strategy. The scheme is inspired by the single-agent reinforcement learning idea, converts the power grid outage scheduling problem into a multi-agent Markov decision process, regards power equipment as a single agent, trains the equipment with intelligent outage scheduling by using the multi-agent deep deterministic policy gradient algorithm, and learns the optimal outage scheduling strategy, so as to guarantee the safe operation of the power grid and the equipment.

The invention relates to the technical field of spacecraft task planning, operational research and mixed integer planning, in particular to a multi-scene multi-stage task planning method and device for manned lunar surface activities, and the method comprises the steps: constructing a plurality of to-be-planned tasks for the manned lunar surface activities into a task pool, and setting constraint parameters corresponding to the task pool; preprocessing the task pool and the constraint parameters to construct a mixed integer programming model; and inputting the mixed integer programming model into a preset mathematical solver to find a final task execution scheme. Therefore, the problems that global optimization is difficult to complete at one time in multiple scenes such as normal planning, sudden adjustment and emergency return voyage in the prior art, modeling solving needs to be conducted again every time scene switching is conducted, and efficiency is low are solved.

The invention discloses a rudder parameter optimization method and system based on a simulated annealing algorithm and an MMG model, and the method comprises the following steps: determining a to-be-optimized parameter of a rudder and a value range of the to-be-optimized parameter, and setting a constraint condition; constructing a multi-objective optimization function; setting an initial temperature, a cooling rate, an iteration number threshold and a termination condition of a simulated annealing algorithm, and generating an initial solution; based on an initial solution, the value of a multi-objective optimization function is calculated through an MMG model, and the initial solution is the current optimal solution of the rudder; finely adjusting the value of each parameter of the current optimal solution to generate a neighborhood solution; calculating a value of a multi-objective optimization function by utilizing an MMG model based on the neighborhood solution; selecting a current optimal solution of the rudder from the two solutions according to a Metropolis criterion; whether the number of iterations at the current temperature reaches an iteration number threshold value or not is judged, if not, iteration continues, and if yes, cooling is conducted according to the cooling rate; and judging whether a termination condition is met or not, if not, continuing iteration, and otherwise, outputting the current optimal solution as the optimal solution of the rudder.

The invention relates to the technical field of power system safety, and provides a power distribution system safety operation optimization method and system based on low inertia. The invention discloses a power distribution system safe operation optimization method based on low inertia. The method comprises the following steps: constructing an evaluation model based on a minimum inertia demand by taking minimization of the total operation cost of a system as an optimization target; under a set constraint condition, solving the evaluation model based on the minimum inertia demand by using a grey wolf algorithm to obtain a minimum inertia demand value at each moment; in the process of searching the minimum inertia demand value at each moment, the grey wolf algorithm adopts a convergence factor to determine a search area; the convergence factor is represented as the fractional order power of the ratio of the difference value of the maximum number of iterations and the current number of iterations to the maximum number of iterations. Response reliability of the system to sudden disturbance can be improved.

The invention provides a new energy output curve generation method and system, and relates to the technical field of new energy power generation and power system planning. The method comprises the steps of constructing a new energy output characteristic evaluation system, based on historical new energy data, adopting a Markov chain to divide M time state intervals, adopting a Monte Carlo method to divide R longitudinal output intervals, and extracting data to generate an initial curve; and after correction in a wet season and a dry season, performing cyclic screening according to a set constraint condition, and outputting a curve conforming to a multi-time-dimension feature and an extreme scene. The system corresponds to the method. By the adoption of the method, the annual scale curve can be generated only through historical data without depending on meteorological data or massive historical samples, the problems that in the prior art, a scheduling method is not matched in time scale and insufficient in historical sample size are solved, and a reliable data boundary is provided for new energy installation planning and energy storage facility design.

The present invention proposes a hybrid powertrain parameter optimization method based on time-frequency analysis, comprising: step 1, establishing a dynamic model of the hybrid powertrain; step 2, establishing a total transient torque model T of the engine. e ; Step 3, establish the transmission torque model T of the torsional vibration damper d ; Step 4, calculate the total moment of inertia J3 of the gears on the input shaft of the gearbox and the total moment of inertia J4 of the gears on the output shaft; Step 5, calculate the moment of inertia J of the vehicle body body , the output torque T of the drive motor MG2 mg2 , resistance torque T1; Step 6: Establish a system of differential equations of motion for hybrid powertrain mode; Step 7: Perform time-frequency analysis based on the system of differential equations of motion; Step 8: Select several key parameters as optimization variables; Step 9: Set constraints and objective function; Step 10: Execute a genetic algorithm to obtain the optimal solution. This invention establishes a hybrid powertrain model and combines time-frequency analysis with a multi-objective genetic algorithm to achieve optimal design of the powertrain.

The application discloses a kind of group optimization processing system and method based on big data, belong to big data technical field.The decision variable is defined, and the coding sequence structure is planned;Set constraint condition, filter individual solution;Establish objective function, calculate the objective function value of each individual solution in initialization coding sequence population;Individual solution is assigned non-dominated layer, and the crowding degree of individual solution is calculated based on non-dominated layer;Based on the non-dominant level and crowding degree of individual solution in non-dominated layer, the fitness of individual solution is obtained;Based on fitness, judge whether individual solution meets optimal solution of scheduling, if it meets optimal solution of scheduling, then output individual solution;If not, carry out crossover operation and mutation operation, generate new individual solution;Based on the individual solution output in algorithm iteration module, generate scheduling table;Further, the optimal solution is obtained, and the scheduling efficiency and quality are improved.

The invention provides a task-based employee scheduling method and a related device. The task-based employee scheduling method comprises the following steps: acquiring task information and inter-task conflict information; employee information is acquired; acquiring class system information and coverage information of a class system on a task; constructing an integer programming model by setting constraints and a target function, the constraints including a first constraint and a second constraint, the first constraint requiring employees not to execute mutually conflicting tasks, the second constraint requiring employees executing the tasks to execute a shift system covering the tasks, the target function being based on a plurality of target settings; and solving an integer programming model by using the obtained information to determine corresponding information of the employees and the tasks and corresponding information of the employees and the class system, thereby arranging the class for the employees.

The application provides a part pose determination method for ship small assembly part polishing equipment, comprising: acquiring contour information of a part through a visual sensor, grabbing the part, and moving to an initial position; inputting the contour information of the part into a trained deep learning model to predict the part position and generate a candidate pose; acquiring the candidate pose, comparing it with a set constraint condition, and selecting a candidate pose satisfying the constraint condition as a final pose; and acquiring the final pose, adjusting the pose of the part, and polishing it. The application acquires the contour information of the part through a visual sensor, determines the placement pose of the part using a deep learning algorithm after grabbing the part, and automatically polishes the free edge using a polishing tool, thereby solving the problems of low precision, low efficiency and complex constraint conditions in the prior art.

The present invention relates to a method for topological optimization design of a dual-fluid heat exchanger structure based on a multi-material model, comprising: defining a topological optimization design domain and design variables of the heat exchanger including the flow areas of the hot and cold fluids; establishing an objective function and setting constraints, with the optimization objectives of maximizing the heat transfer within the design domain and minimizing the inlet and outlet pressure drops, and constructing a topological optimization model; solving the governing equations for the flow problems of the cold and hot fluids respectively, solving the corresponding topological optimization models, and obtaining the corresponding velocity fields; solving the heat transfer problem using the heat conduction equation based on the combined velocity field obtained by superimposing the two velocity fields; performing a sensitivity analysis based on the solution to the heat transfer problem, and updating the design variables using a topological optimization algorithm based on the results of the sensitivity analysis; and iterating and updating the design variables multiple times until the objective function converges and meets the predetermined design requirements. The present invention efficiently solves the problem of heat exchanger structure optimization in different scenarios.

The invention belongs to the technical field of urban low-carbon planning, and relates to an intelligent optimization method and system for a low-carbon community evaluation index system. The method comprises the steps that a user sets constraint conditions of multiple dimensions, and query conditions are determined; the target layer with similar semantics is retrieved, the criterion layer is screened, indexes matched with the index layer are extracted in combination with a BM25 keyword retrieval mode, and a candidate index set is obtained; adjusting an expansion range according to a node centrality measurement index and an edge weight parameter of an index association network through a dynamic weight distribution strategy; reconstructing an index system topological structure by the large language model according to an evaluation criterion and an evaluation target; and carrying out visual presentation. According to the method, a cross-modal semantic fusion retrieval mechanism is adopted, and semantic matching between text description and knowledge graph entities is achieved; a hierarchical progressive index selection strategy is adopted to ensure hierarchical rationality and adaptability of an index system; dynamic reconstruction of the topological structure of the index system is realized; and the visualization of the index system is realized.

The invention provides a turbine transition section runner inverse design method based on a through-flow area, and the method comprises the following steps: S1, determining a first runner molded line, obtaining a distribution rule of the through-flow area along a flow direction, and determining constraints at two ends of a second runner molded line; s2, establishing an inverse problem model by setting constraint conditions; and S3, solving an inverse problem, solving the nonlinear equation set through a Newton iteration method, and obtaining coordinates of discrete points on the second flow channel molded line and the radius of an inscribed circle so as to obtain the second flow channel molded line. According to the inverse design method, the algorithm for inversely designing the turbine transition section flow channel based on the through-flow area is achieved, the design iteration efficiency of the turbine transition section flow channel is remarkably improved, the transition section flow channel is inversely designed through the through-flow area, and accurate control over the through-flow area can be achieved.

The application discloses a kind of detection driven adaptive man-machine cooperation disassembly line balancing method, this method includes: obtaining the disassembly task set of waste product, priority relationship, standard man-hour of each task under different operation mode, and robot resource upper limit;Build two-stage disassembly task model, determine the task before detection node as pre-disassembly mandatory task set, determine the task after detection node and the task of work time affected by part integrity state as time variable task set, the rest is detection normal task set;Establish multi-objective optimization mathematical model, and set constraint condition;For the task in time variable task set, different working condition adjustment coefficient is introduced according to different integrity state, to unify the working position work time under the constraint of each state;Improved multi-objective parrot optimization algorithm is used to solve the mathematical model, and man-machine cooperation disassembly scheme is obtained.The application realizes the adaptive balancing of man-machine cooperation disassembly line by detection driving.

The invention discloses a tunnel multi-energy multi-layer optimization scheduling method and system, and the method comprises the steps: firstly obtaining an optimization input set corresponding to a future operation cycle of a tunnel, and then constructing a multi-layer mixed integer linear programming model through employing a lexicographical order structure; and finally, based on a set constraint condition, solving the multilayer mixed integer linear programming model according to the optimized input set to obtain a plan instruction in a future operation cycle. And obtaining an optimal value for zero off-supply of the primary load through a target function of a first layer of the multi-layer mixed integer linear programming model. Under the condition that the optimal value of the first layer is locked, the optimal value with the lowest operation cost can be obtained through the target function of the second layer. And on the premise that the optimal values of the first layer and the second layer are locked, the optimal values enabling the energy storage cycle number, the unit climbing number and the switching frequency to be minimum can be obtained through the target function of the third layer. Therefore, an energy scheduling strategy with the highest economical efficiency and the longest service life of equipment can be obtained on the premise of ensuring zero off-supply of the first-level load.

This invention provides a method, apparatus, and electronic device for optimizing the refrigerant mix ratio, relating to the field of liquefied natural gas (LNG) production technology. The method includes: obtaining the set condition parameter range and feed gas change conditions of the LNG preparation process; constructing a mapping model based on the set condition parameter range using a multivariate adaptive spline regression method; the mapping model characterizes the mapping relationship between the refrigerant mix ratio, feed gas conditions, and compressor specific power consumption in LNG production; and solving for the minimum compressor specific power consumption of the mapping model under feed gas change conditions and set constraints using a nonlinear optimization method to obtain the optimized refrigerant mix ratio. This invention eliminates the need to rebuild the process simulation model or reconstruct the mathematical model when feed gas conditions change. By utilizing the multivariate adaptive regression spline method and nonlinear optimization method, it achieves automatic optimization of the refrigerant mix ratio, reducing computational load and improving computational efficiency.

The invention provides a multi-AGV flexible job shop active scheduling method based on an IWOA algorithm and a related device, and belongs to the technical field of shop active scheduling. According to the method, a multi-objective optimization model for active scheduling of a multi-AGV flexible job shop is constructed; setting constraint conditions of the multi-objective optimization model; improving the whale optimization algorithm by adopting a reverse learning mode, a nonlinear convergence factor, a segmented inertia weight and a multi-direction spiral search mechanism to obtain an improved whale optimization algorithm; based on a set constraint condition of the multi-objective optimization model, an improved whale optimization algorithm is adopted to solve decision variables in an objective function of the established multi-objective optimization model for active scheduling of the multi-AGV flexible job shop, and a Pareto frontier is obtained; and selecting a group of solutions from the Pareto frontier to obtain an active scheduling result, thereby carrying out active scheduling on the multi-AGV flexible job shop. According to the invention, the problem of low accuracy of active scheduling of the multi-AGV flexible job shop is solved.

The present disclosure provides a task-based employee scheduling method and related device. A task-based employee scheduling method includes: obtaining task information and inter-task conflict information; obtaining employee information; obtaining shift information and shift coverage information for tasks; constructing an integer programming model by setting constraints and an objective function, the constraints including a first constraint and a second constraint, the first constraint requiring that an employee cannot perform tasks that conflict with each other, the second constraint requiring that an employee performing a task performs a shift that covers the task, the objective function being set based on multiple objectives; and solving the integer programming model using the obtained information to determine corresponding information of employees and tasks and corresponding information of employees and shifts, thereby scheduling employees.

The invention discloses a high-speed rail passenger and freight train working diagram optimization method, system and device, a medium and a product, and the method comprises the steps: firstly determining an optimization target, including minimizing the total travel time of a freight train, the operation deviation of the passenger train and the cargo delivery delay time; thirdly, constructing a mathematical model, and defining decision variables, an objective function and constraint conditions to form a high-speed railway passenger and freight train working diagram optimization model; on the basis of the model, a rolling time domain decomposition strategy is adopted, and a global scheduling problem is segmented into a local optimization problem of a plurality of time windows. And for each time window, obtaining a local optimal solution by using a linear programming solver so as to ensure that the dispatching of each train accords with the set constraint. Then, global search is carried out on the local solutions through a simulated annealing algorithm, the departure time and the operation plan of the train are optimized, and local optimum is avoided. And finally, a variable neighborhood local optimization method is applied, and the quality of the whole scheme is further improved by finely adjusting the solution.

Embodiments of the present application disclose a transfer orbit design method from GTO to the Sun-Earth collinear libration point, a device and a medium; the method comprises: calculating and obtaining an invariant manifold connected with the Halo orbit around the libration point according to the spacecraft dynamics equation and the potential function in the Sun-Earth barycenter coordinate system; taking the geosynchronous transfer orbit as the earth parking orbit to construct the constraint variable, taking the Halo orbit as the target orbit to construct the control variable, and correcting the control variable by using the least square differential correction method; based on the set constraint condition and performance index, the optimal HOI point corresponding to the optimal performance transfer orbit is selected from the transfer orbit meeting the constraint condition; according to the optimal HOI point, the relationship between the control variable and the parking orbit constraint variable is analyzed, and the differential correction initial value expression for determining the control variable initial value of the multi-constraint orbit design is constructed; the transfer orbit meeting multiple constraints is obtained by using the multi-stage differential correction method according to the differential correction initial value expression.

The technique disclosed in the description of the present application is a technique for suppressing excessive constraints in trajectory setting of a moving body. This trajectory generation device pertaining to the technique disclosed in the description of the present application is provided with: a constraint condition setting unit for setting constraint conditions that a moving body passing through a trajectory should satisfy; and a trajectory generation unit for generating the trajectory through which the moving body passes so as to satisfy the constraint conditions. Furthermore, the constraint conditions include those that are set on the basis of the probability of a state of the moving body that should satisfy predetermined conditions.

The application discloses a kind of multi-dimensional vibration control method, system and device based on model predictive control, method includes: to the kinematics and dynamics analysis of multi-dimensional vibration control device, establish linear dynamics equation near equilibrium point, establish prediction model according to linear dynamics equation;According to the output of prediction time domain by the control amount of control time domain according to prediction model estimation, according to the control target setting optimization target that output of prediction time domain approaches expected trajectory, set constraint condition, determine the optimization problem of control amount;According to the pose information of carrying equipment at t time determines expected trajectory;Optimization problem is solved, determine the optimal control amount sequence, and the first value in sequence is as the actual control amount of current time, control multi-dimensional vibration control device.The application can realize early detection, predict future, early control, reduce system delay effect, optimal control under complex constraint environment and multi-dimensional high-precision damping effect.

The present application relates to the technical field of textile manufacturing, and particularly relates to a scheduling method based on a large language model and an improved simulated annealing algorithm, comprising the following steps: obtaining production data; performing problem description, setting constraint conditions, taking the minimum maximum completion time as a target function, constructing a scheduling problem model, setting an iteration process and repeatedly executing the iteration process; generating an initial scheme according to the production data and the problem description; in the iteration process, alternately calling the large language model and the improved simulated annealing algorithm for solving, and generating a to-be-verified scheduling scheme; verifying the to-be-verified scheduling scheme by an intelligent agent; after multiple iterations, finally determining an optimal solution, and outputting an optimal scheduling scheme. After the above technical scheme is adopted, the maximum completion time of the generated scheduling scheme is relatively short, the scheduling scheme generation speed is relatively fast, and the scheduling scheme can be guaranteed to be executable.

The application relates to a power system multi-resource collaborative planning method based on attribution analysis, and belongs to the technical field of power system planning. The method comprises the following steps: constructing a target function based on the load demand of a power system and the output of each unit, taking the minimum mismatch degree between supply and demand as the target, constructing a planning model based on set constraints and the target function; solving the planning model based on multiple sets of boundary condition values of the planning model to obtain corresponding planning schemes; analyzing and obtaining quantitative representations of the influence degree of each boundary condition and each constraint on the planning scheme; adjusting the boundary condition values based on the quantitative representations and solving the planning model, and selecting the planning scheme with the lowest planning cost in the solving result as the optimal planning scheme. The application determines the key factors influencing the planning scheme through attribution analysis on power planning, and determines the optimal planning scheme by adjusting the key factors, so that the scientificity and economy of the planning scheme are further improved.