Matching Network Design and Parameter Optimization Method for Low-Frequency Active Sonar System

Short-term load forecasting is generally applied in power system real-time dispatching and day-ahead generation planning, which is of great significance for power system economic dispatching and safe operation of the system. Much research on short-term load forecasting using neural networks has been carried out at home and abroad. However, how to obtain the optimal network and structure evolution accurately and quickly has evolved into a key challenge for short-term load forecasting, because the prediction effect of the neural network method is more easily affected by the existing network structure and parameters, and the personality difference of the prediction object itself makes it difficult for the parameters to be reused. Aiming at this problem, this paper proposes a TPE-based neural network structure design and parameter optimization method for short-term load forecasting. This method realizes fast optimization of parameters of different types, structures, parameters, and input and output of deep neural networks, and is widely applied in Guangxi power grid. It is verified by an actual example that the method can effectively improve prediction accuracy.

Optimal design of pumping networks in coastal aquifers using sharp interface models

In order to meet the increasing demand for beam quality of laser weapons, a design and aspheric optimization method of optical collimation system was proposed. This method was successfully applied to the lens design of a laser cauterization gun, so that the gun could obtain a higher quality collimating beam in a farther range. First, the initial structure was obtained based on the analysis of tactical and technical parameters and the calculation of the optical path, then the aspherical optimization was performed, the two simulation results were compared and analyzed based on ZEMAX. Numerical results show that the optimized structure has significant advantages. Its aspheric coefficient and curvature radius are 0.584 mm and 115.37 mm respectively, and the light energy utilization rate can reach 99.92% when the GB 1316-88 anti-reflection film is used. After tolerance analysis, the lens was processed and combined with the gun body for a board ablation experiment. The experimental results are in good agreement with the simulation results. The superiority of lens performance proves the feasibility and rationality of the proposed design and optimization method, and also provides a certain reference value for similar problems in other fields.

This paper is concerned with the problem of robust optimization method for coupling parametrization design of space observation system. The objective is to develop an robust optimization framework to design space observation system. An improved robust collaborative optimization (IRCO) method for complex system design using the hybrid optimization algorithm is developed, which associates genetic algorithm (GA) with tabu search (TS). The proposed method can avoid unsolvability or converging difficulty and handle uncertainty within and across disciplines more efficiently and reasonably. The results demonstrate the effectiveness and applicability of the proposed method.

Centralized and decentralized control of retrofit heat-exchanger networks

Water minimization in the process industry is becoming increasingly important due to increasingly stringent environmental legislation, especially for batch plants. This work proposes a dynamic programming (DP) method for the optimal design of water-using networks in batch plants. DP is a powerful framework for dealing with a large spectrum of multistage decision-making problems and has been applied in numerous chemical engineering problems. The proposed methodology is explained as follows. Firstly, based on the start time and end time of each operation, the whole process is divided into N stages. Secondly, the water requirement of water-using units in each stage is satisfied and the state of stored water and wastewater generation is determined. The backward procedure of DP is used to solve the DP problems. The target of freshwater consumption of the process and the optimal design of the water network are obtained simultaneously. In order to display the versatility of the proposed approach, four examples from literature are considered. Example 1 is a completely batch process with a fixed flowrate problem. Example 2 is a hybrid batch water system comprising various modes of operations and operating patterns. Example 3 is a fixed-mass load problem with a regeneration unit, while example 4 considers the batch water network design with multiple contaminants. The results obtained in this work were comparable with the results from literature, implying that it can be applicable to both mass transfer-based and non-mass transfer-based batch water networks.

Maximum-likelihood adjoint-state finite-element estimation of groundwater parameters under steady- and nonsteady-state conditions

This paper describes a process for establishing an optimal network design. High-level system requirements specified by a customer are used to derive performance requirements that bound the network parameters. A deterministic static model is used to derive the bandwidth requirements, e.g., identify performance requirements to address the worst-case conditions. The static model is composed of inter-related mathematical equations derived for network performance from source to destination. A probabilistic dynamic model is used to verify and fine-tune the results of the mathematical model and obtain detailed network parameters at the protocol and application layers, e.g., identify the performance requirements to address the expected-case conditions. The dynamic modeling tool used for this work is MIL3's OPNET Modeler/Radio, which achieves realistic results through simulating traffic flow to the packet level using random distributions of traffic and file sizes around a predetermined mean. The results of these models are the optimal network parameters used in the proactive network design. A proactive network design is established using static and dynamic models and simulations to predict optimal network parameters such as bandwidth requirements and traffic load. A proactive network provides its users with connectivity and sufficient bandwidth to accomplish predetermined needs; that is, the network maintains stability, a non-congestive state, and meets the end-to-end delay requirements for a particular system. Network stability is the state in which traffic intensity does not exceed network resources. A noncongestive state is a stable state. End-to-end delay is the time associated with the duration of a packet transmission from source to destination. This process was applied to a classified government network to: (1) specify the performance requirements, (2) support development of the network system-level design, and (3) assess the performance impact of changes to the network during detailed design and implementation.

Abstruct-The supply chain network design involves the choice of plants and distribution centers to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper designs supply chain network in uncertain environment, in which the demands of customers are assumed to be stochastic variables, and the operation costs are considered as fuzzy numbers. We formulate model by fuzzy programming and develop monkey-king algorithm to solve the proposed model. Moreover, a numerical example is presented to illustrate the effectiveness of model and solution algorithm.

This is the author accepted manuscript. The final version is available from ASCE via the DOI in this record

Pump location optimization is a key issue in the design of water pipe networks to reduce energy consumptions and associated costs. The pump location variables in previous papers were typically discrete, resulting in potentially sub-optimal solutions. If the discrete variables are densified, the solving time increases substantially. To overcome the shortcomings of the discrete pump location optimization method, a novel continuous location optimization method is proposed. The hydraulic calculations of pipes and operation of pumps are coupled in the model to make it practical. The objective function minimizes the installation and operating costs of pumps. A branch and a looped water pipe network are studied. The proposed continuous location optimization method is compared with the previous discrete method. The results show that the proposed method has advantages regarding cost-minimization and it reduces the time required to produce solutions. The method is generic and can be applied to other real cases to determine the optimal pump locations to achieve the goals of cost-minimization and energy saving.

This paper proposed an integrated waveform design and parameter optimization method to realize real-time detection and communication for inverted echo sounders (IES). The approach balances detection performance and communication quality in active sonar systems. An integrated detection– communication waveform (GIDC) is developed using the orthogonality and parameter diversity of generalized sinusoidal frequency modulation (GSFM). The waveform incorporates differential binary phase-shift keying (DBPSK) modulation and superimposed orthogonal GSFM signals, enabling simultaneous acoustic detection and data transmission. To enhance performance, parameter optimization constraints are formulated for the carrier GSFM based on quantitative ambiguity function (AF) analysis and bit error rate (BER) metrics, and are solved using an improved genetic algorithm (GA). The optimization effectively suppresses autocorrelation sidelobes, improves reverberation suppression, and ensures reliable error rate performance. The simulation results on Gaussian and vertical underwater acoustic channels confirm that the proposed waveform achieves robust joint detection and communication. Compared with conventional waveforms, the GIDC exhibits higher detection-delay accuracy and lower BER, demonstrating excellent integrated performance suitable for underwater applications.

The article provides a review of design methods for heterogeneous computer information systems and critical application networks, analyzes requirements, provides a description of information technologies, their components, conditions of use, results of their application. A method for the computer-aided design of heterogeneous computer systems and critical application networks is proposed when operational tasks are included in the control system. The analysis of features arising during reconfiguration is carried out. The modern basic technical and software of computer systems for critical applications are analyzed. The general requirements for computer systems of critical applications and their components are formulated. The development trends and existing needs of computer systems and networks of critical application are determined. Based on the analysis of heterogeneous computer networks and theoretical generalization of work in the field of computer-aided design of critical application systems, the authors have developed a method for the computer-aided design of heterogeneous computer systems and critical-use networks.

A method is described for simplifying the optimum design of active networks that use operational amplifiers. The optimum design is based on minimization of the sensitivity of the steady-state frequency response to changes in amplifier gain.

This paper presents a design optimization method for all-wood concrete-slab forms. This procedure was formulated to provide for a safe slab-form design with minimum cost. It considers in its design process the cost of each slab-form component (sheathing, joist, stringer, and wood shore) and all available combinations of slab-form components. The performance of the optimum design method is compared to that of the traditional design method, which uses design charts/tables. The optimum design method was found to have a potential cost savings over the traditional design method. A study was conducted to confirm these cost savings. The results of the study show that cost savings as high as 9.9% were achieved by using the design optimization method.