Operational requirements analysis method based on question answering of WEKG

The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.

Operation Control Method of Relay Protection in Flexible DC Distribution Network Compatible with Distributed Power Supply

A novel operation control method for relay protection in flexible DC distribution networks with distributed power supply is proposed to address the issue of inaccurate fault location during relay protection,leading to poor performance.The method combines a fault-tolerant fault location method based on long-term and short-term memory networks to accurately locate the fault section.Then,an operation control method for relay protection based on adaptive weight and whale optimization algorithm(WOA)is used to construct an objective function considering the shortest relay protection action time and the smallest impulse current.The adaptive weight and WOA are employed to obtain the optimal strategy for relay protection operation control,reducing the action time and impulse current.Experimental results demonstrate the effectiveness of the proposed method in accurately locating faults and improving relay protection performance.The longest operation time is reduced by 4.7023 s,and the maximum impulse current is limited to 0.3 A,effectively controlling the impact of large impulse currents and enhancing control efficiency.

Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network

As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods.

Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells.

Prediction of operational parameters effect on coal flotation using artificial neural network

Artificial neural network procedures were used to predict the combustible value （i.e. 100-Ash） and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of collector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

Effects of aggregating forests, establishing forest road networks, and mechanization on operational efficiency and costs in a mountainous region in Japan

We investigated forest road networks and forestry operations before and after mechanization on aggregated forestry operation sites. We developed equations to estimate densities of road networks with average slope angles, operational efficiency of bunching operations with road network density, and average forwarding distances with operation site areas. Subsequently, we analyzed the effects of aggregating forests, establishing forest road networks, and mechanization on operational efficiency and costs. Six ha proved to be an appropriate operation site area with minimum operation expenses. The operation site areas of the forest owners＇ cooperative in this region aggregated approximately 6 ha and the cooperative conducted forestry operations on aggregated sites. Therefore, 6 ha would be an appropriate operation site area in this region. Regarding road network density, higher-density road networks increased operational expenses due to the higher direct operational expenses of strip road establishment. Therefore, road network density should be reduced to approximately 200 m.

Optimal operation of water distribution networks under local pipe failures

The optimal operation of water distribution networks under local pipe failures, such as water main breaks, was proposed. Based on a hydraulic analysis and a simulation of water distribution networks, a macroscopic model for a network under a local pipe failure was established by the statistical regression. After the operation objectives under a local pipe failure were determined, the optimal operation model was developed and solved by the genetic algorithm. The program was developed and examined by a city distribution network. The optimal operation alternative shows that the electricity cost is saved approximately 11%, the income of the water corporation is increased approximately 5%, and the pressure in the water distribution network is distributed evenly to ensure the network safe operation. Therefore, the proposed method for optimal operation under local pipe failure is feasible and cost-effective.

Assessment for Production and Operation Ability of Medium and Small-sized Enterprises Based on Neural Network

In order to improve production and operation ability of medium and small-sized enterprises, an assessment-index system of production and operation ability was proposed, and a corresponding assessment model was established based on BP neural network. The conjunction weights of the neural network were continuously modified from output layer to input layer in the process of neural network training to reduce the errors between the anticipated and actual outputs. The results from an example show that this method is reliable and feasible. The production and operation abilitv of an enterorise with assessed result of 0.833 is fairly oowerful, and that with assessed result of 0.644 is average.

A Survey on Network Operation and Maintenance Quality Evaluation Models

The evaluation of network operation and maintenance quality is an important reference for carriers to improve their service.However,the traditional evaluation methods involve so much human participation that it cannot cope with the explosive amount of data.Therefore,both the major carriers and researchers are trying to find solutions to evaluate the quality of network operation and maintenance more objectively and accurately.In this paper,we analyze the general process of quality evaluation models for network operation and maintenance.The process has four steps:1)selection of evaluation indicators;2)data process for chosen indicators;3)determination of indicator weights;4)establishment of evaluation models.We further describe the working principle of each step,especially the methods for indicator selection and weight determination.Finally,we review the recently proposed evaluation models and the international stan dards of network operation and maintenance quality evaluation.

Porous-DeepONet:Learning the Solution Operators of Parametric Reactive Transport Equations in Porous Media

Reactive transport equations in porous media are critical in various scientific and engineering disciplines,but solving these equations can be computationally expensive when exploring different scenarios,such as varying porous structures and initial or boundary conditions.The deep operator network(DeepONet)has emerged as a popular deep learning framework for solving parametric partial differential equations.However,applying the DeepONet to porous media presents significant challenges due to its limited capability to extract representative features from intricate structures.To address this issue,we propose the Porous-DeepONet,a simple yet highly effective extension of the DeepONet framework that leverages convolutional neural networks(CNNs)to learn the solution operators of parametric reactive transport equations in porous media.By incorporating CNNs,we can effectively capture the intricate features of porous media,enabling accurate and efficient learning of the solution operators.We demonstrate the effectiveness of the Porous-DeepONet in accurately and rapidly learning the solution operators of parametric reactive transport equations with various boundary conditions,multiple phases,and multiphysical fields through five examples.This approach offers significant computational savings,potentially reducing the computation time by 50–1000 times compared with the finite-element method.Our work may provide a robust alternative for solving parametric reactive transport equations in porous media,paving the way for exploring complex phenomena in porous media.

A prognostic four-gene signature and a therapeutic strategy for hepatocellular carcinoma:Construction and analysis of a circRNA-mediated competing endogenous RNA network

Background:Hepatocellular carcinoma(HCC)has a poor long-term prognosis.The competition of circular RNAs(circRNAs)with endogenous RNA is a novel tool for predicting HCC prognosis.Based on the alterations of circRNA regulatory networks,the analysis of gene modules related to HCC is feasible.Methods:Multiple expression datasets and RNA element targeting prediction tools were used to construct a circRNA-microRNA-mRNA network in HCC.Gene function,pathway,and protein interaction analyses were performed for the differentially expressed genes(DEGs)in this regulatory network.In the proteinprotein interaction network,hub genes were identified and subjected to regression analysis,producing an optimized four-gene signature for prognostic risk stratification in HCC patients.Anti-HCC drugs were excavated by assessing the DEGs between the low-and high-risk groups.A circRNA-microRNA-hub gene subnetwork was constructed,in which three hallmark genes,KIF4A,CCNA2,and PBK,were subjected to functional enrichment analysis.Results:A four-gene signature(KIF4A,CCNA2,PBK,and ZWINT)that effectively estimated the overall survival and aided in prognostic risk assessment in the The Cancer Genome Atlas(TCGA)cohort and International Cancer Genome Consortium(ICGC)cohort was developed.CDK inhibitors,PI3K inhibitors,HDAC inhibitors,and EGFR inhibitors were predicted as four potential mechanisms of drug action(MOA)in high-risk HCC patients.Subsequent analysis has revealed that PBK,CCNA2,and KIF4A play a crucial role in regulating the tumor microenvironment by promoting immune cell invasion,regulating microsatellite instability(MSI),and exerting an impact on HCC progression.Conclusions:The present study highlights the role of the circRNA-related regulatory network,identifies a four-gene prognostic signature and biomarkers,and further identifies novel therapy for HCC.

Assessing environmental impact:Micro-energy network optimization in a Chinese industrial park

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources.This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and“cradle-to-gate”as the system boundary.Based on the baseline scenario,a natural gas generator set was added to replace central heating,and the light pipes were expanded to constitute the optimized scenario.The results showed that the key impact categories for both scenarios were global warming,fine particulate matter formation,human carcinogenic toxicity,and human non-carcinogenic toxicity.The overall environmental impact of the optimized scenario was reduced by 68%compared to the baseline scenario.A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios,followed by central heating and natural gas.Therefore,to reduce the environmental impact of network systems,it is necessary to further optimize the grid power structure.The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems.

Modified stochastic user-equilibrium assignment algorithm for urban rail transit under network operation

Based on the framework of method of successive averages(MSA), a modified stochastic user-equilibrium assignment algorithm was proposed, which can be used to calculate the passenger flow distribution of urban rail transit(URT) under network operation. In order to describe the congestion's impact to passengers' route choices, a generalized cost function with in-vehicle congestion was set up. Building on the k-th shortest path algorithm, a method for generating choice set with time constraint was embedded, considering the characteristics of network operation. A simple but efficient route choice model, which was derived from travel surveys for URT passengers in China, was introduced to perform the stochastic network loading at each iteration in the algorithm. Initial tests on the URT network in Shanghai City show that the methodology, with rational calculation time, promises to compute more precisely the passenger flow distribution of URT under network operation, compared with those practical algorithms used in today's China.

Carbon Emission Factors Prediction of Power Grid by Using Graph Attention Network

Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice,which is of immense importance in mobilizing the entire society to reduce carbon emissions.The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid.Therefore,it cannot provide carbon factor information beforehand.To address this issue,a prediction model based on the graph attention network is proposed.The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data.The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology,thereby increasing the diversity of the structure.Its input and output data are simple,without the power grid parameters.We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46%and 2.51%.

Performance of Gas-Steam Combined Cycle Cogeneration Units Influenced by Heating Network Terminal Steam Parameters

The determination of source-side extracted heating parameters is of great significance to the economic operation of cogeneration systems.This paper investigated the coupling performance of a cogeneration heating and power system multidimensionally based on the operating characteristics of the cogeneration units,the hydraulic and thermodynamic characteristics of the heating network,and the energy loads.Taking a steam network supported by a gas-steam combined cycle cogeneration system as the research case,the interaction effect among the source-side prime movers,the heating networks,and the terminal demand thermal parameters were investigated based on the designed values,the plant testing data,and the validated simulation.The operating maps of the gas-steam combined cycle cogeneration units were obtained using THERMOFLEX,and the minimum source-side steam parameters of the steam network were solved using an inverse solution procedure based on the hydro-thermodynamic coupling model.The cogeneration operating maps indicate that the available operating domain considerably narrows with the rise of the extraction steam pressure and flow rate.The heating network inverse solution demonstrates that the source-side steam pressure and temperature can be optimized from the originally designed 1.11 MPa and 238.8°C to 1.074 MPa and 191.15°C,respectively.Under the operating strategy with the minimum source-side heating parameters,the power peak regulation depth remarkably increases to 18.30%whereas the comprehensive thermal efficiency decreases.The operation under the minimum source-side heating steam parameters can be superior to the originally designed one in the economy at a higher price of the heating steam.At a fuel price of$0.38/kg and the power to fuel price of 0.18 kg/(kW·h),the critical price ratio of heating steam to fuel is 119.1 kg/t.The influence of the power-fuel price ratio on the economic deviation appears relatively weak.

Research on construction of operation architecture based on complex network

Operation architecture plays a more important role in Network Centric Warfare(NGAV),which involves dynamic operation networks with complex properties.Thus,it is essential to investigate the operation architecture under the informatization condition within NCAW and find a proper network construction method to efficiently coordinate various functional modules on a particular situation,i.e.,the aerial combat.A new method integrating the physical level and functional level of NCW is proposed to establish the operation architecture,where the concept of network operation constraints unit and net constructing mechanisms are employed to avoid conflicts among different platforms.Meanwhile,we conduct simulations to assess the effectiveness and feasibility of the constructed operation architecture and analyze the influence of the network parameters.

Study on an Audio and Video Network Monitoring System for Weather Modification Operation

An audio and video network monitoring system for weather modification operation transmitting information by 3G, ADSL and Internet has been developed and applied in weather modification operation of Tai＇an City. The all-in-one machine of 3G audio and video network highly integrates all front-end devices used for audio and video collection, communication, power supply and information storage, and has advantages of wireless video transmission, clear two-way voice intercom with the command center, waterproof and dustproof function, simple operation, good portability, and long working hours. Compression code of the system is transmitted by dynamic bandwidth, and compression rate varies from 32 kbps to 4 Mbps under different network conditions. This system has forwarding mode, that is, monitoring information from each front-end monitoring point is trans- mitted to the server of the command center by 3G/ADSL, and the server codes＇and decodes again, then beck-end users call images from the serv- er, which can address 3G network stoppage caused by many users calling front-end video at the same time. In addition, the system has been ap- plied in surface weather modification operation of Tai＇an City, and has made a great contribution to transmitting operation orders in real time, monitoring, standardizing and recording operating process, and improving operating safety.

Ripple-effect analysis for operational architecture of air defense systems with supernetwork modeling

In order to solve the problem that the ripple-effect analy- sis for the operational architecture of air defense systems （OAADS） is hardly described in quantity with previous modeling approaches, a supernetwork modeling approach for the OAADS is put for- ward by extending granular computing. Based on that operational units and links are equal to different information granularities, the supernetwork framework of the OAADS is constructed as a “four- network within two-layer” structure by forming dynamic operating coalitions, and measuring indexes of the ripple-effect analysis for the OAADS are given combining with Laplace spectral radius. In this framework, via analyzing multidimensional attributes which inherit relations between operational units in different granular scales, an extended granular computing is put forward integrating with a topological structure. Then the operation process within the supernetwork framework, including transformation relations be- tween two layers in the vertical view and mapping relations among functional networks in the horizontal view, is studied in quantity. As the application case shows, comparing with previous modeling approaches, the supernetwork model can validate and analyze the operation mechanism in the air defense architecture, and the ripple-effect analysis can be used to confirm the key operational unit with micro and macro viewpoints.

Operation Research Based Techniques in Wireless Sensors Networks

In recent years, we have seen an increasing interest in developing and designing Wireless Sensor Networks (WSNs). WSNs consist of large number of nodes, with wireless communications and computation abilities that can be used in variety of domains. It has been used in areas that have direct contact with monitoring and gathering data, to name few, health monitoring, military surveillance, geological monitoring (Earthquakes, Volcanoes, Tsunami), agriculture control and many more. However, the design and implementation of WSNs face many challenges, due to the power limitation of sensor nodes, deployment and localization, data routing and data aggregation, data security, limited bandwidth, storage capacity and network management. It is known that Operation Research (OR) has been widely used in different areas to solve optimization problems;such as improving network performance and maximizing lifetime of system. In this survey, we present the most recent OR based techniques applied to solve different WSNs problems: the node scheduling problem, energy management problems, nodes allocating issues and other WSNs related complex problems. Different Operational Research techniques are presented and discussed in details here, including graph theory based techniques, linear programing and mixed integer programming related approaches.

The Optimization on Operation Characteristicsof Urban Medium Voltage Network

Traditional small current grounding system has many advantages. Pilot operation shows that optimized one has even better operation characteristics. It has proven to be a geenrallly properand relatively perfect neutyal grounding method in urban MV network.