Generation Scheme Research Articles

A knowledge-graph based text summarization scheme for mobile edge computing

As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient and lightweight methodologies for text data processing. To swiftly extract and analysis vital information from MEC text data, an automatic generation scheme of multi-document text summarization based on knowledge graph is proposed in this paper, named KGCPN. For the text data from MEC devices and applications, the natural language processing technology is used to execute the data pre-processing steps, which transforms the MEC text data into a computationally tractable and semantically comprehensible format. Then, the knowledge graph of multi-document text is constructed by integrating the relationship paths and entity descriptions. The nodes and edges of the knowledge graph serve to symbolize the semantic relationships within the text, and the Graph Convolution Neural network (GCN) is used to understand the text and learn the semantic representation. Finally, a pointer-generator network model accepts the encoding information from GCN and automatically generate a general text summarization. The experimental results indicate that our scheme can effectively facilitate the smart pre-processing and integration of MEC data.

Tidal range electricity generation into the twenty-second century

Tidal range electricity generation schemes are designed to have a minimum operational life of at least 120 years, making it important to plan for changes such as sea-level rise (SLR). Earlier studies have shown that schemes can maintain the existing tidal range within the impoundment and protect areas from flooding. Here it is demonstrated that tidal range technology can maintain the current tidal extent despite SLR and suggests the operational strategies to achieve it. The approach is the only way to safeguard existing intertidal habitats. Mechanical and electrical plant requires a major overall, upgrade or replacement every 40 years; the levelised cost of energy is structured in 40-year periods reducing after the first period. Increasing the capacity or efficiency of the plant during the refits allows the protection of low-lying areas to be maintained and more electricity to be generated. The strategy requires energy to be used in pumping to achieve the current low tidal limits and the incoming tide to be curtailed to maintain the high tide extent, but there is very little effect on annual electricity production. Flexible operation can offer some protection from riverine flooding and existing inundation cycles can be maintained.

Secure key generation and distribution scheme based on historical fiber channel state information with LSTM.

In this paper, a scheme to realize unclonable physical-layer security key generation and distribution (PL-SKGD) based on historical fiber channel state information (HFCSI) is proposed. PL-SKGD schemes based on channel characteristics for enhancing the physical-layer security of optical networks have been proposed in recent years. However, there are potential disadvantages in these schemes, such as 1) low key generation rate (KGR): the slow frequency of the analog waveform change of the channel characteristic leading to low KGR; 2) incompatibility with existing infrastructure: active scrambling to increase the frequency of channel characteristic changes, or tracking changes of channel characteristics requires additional devices; 3)easy to be cloned: all of the optical channel state information is reflected in the signal transmitted inside the fiber, which makes it easy to reproduce by illegal eavesdropper through features analysis and other methods. In order to solve the above problems, a PL-SKGD scheme is designed which uses the chain structure composed of long short-term memory neural network (LSTM-NN) units to learn and store the unique mapping relationship between historical channel time series and provides unclonability based on the fundamental fact that the eavesdropper Eve can never obtain the full HFCSI. The simulation conducted in a quadrature phase shift keying point-to-point optical link system verified successfully that KGR = 0.82 Gbit/s error-free SKGD. The loss function of LSTM-NN drops sharply in the early stages of training and remains a small value. The security of the SKGD system is analyzed, which effectively improves the unclonability of the system. Finally, it is verified that the optimal fiber channel length for error-free SKGD of the proposed scheme is 150 km considering the error correction capability of information reconciliation and weighing key sequence error rate and valid bit generation rate.

Efficient dual-stage all-solid-state post-compression for 100 W level ultrafast lasers

Abstract We demonstrate efficient and economical all-solid-state post-compression based on dual-stage periodically placed thin fused silica plates driven by a more than 100 W ytterbium-doped yttrium aluminum garnet Innoslab amplifier seeded by a fiber frontend. Not only is a more than eight-fold pulse compression with 94% transmission achieved, but also the pulse quality and spatial mode are improved, which can be attributed to the compensation for the residual high-order dispersion and the spatial mode self-cleaning effect during the nonlinear process. It enables a high-power ultrafast laser source with 64 fs pulse duration, 96 W average power at 175 kHz repetition rates and good spatiotemporal quality. These results highlight that this all-solid-state post-compression can overcome the bandwidth limitation of Yb-based lasers with exceptional efficiency and mitigate the spatiotemporal degradation originating from the Innoslab amplifier and fiber frontend, which provides an efficient and economical complement for the Innoslab laser system and facilitates this robust and compact combination as a promising scheme for high-quality higher-power few-cycle laser generation.

A New Loss of Field Detection Scheme for Synchronous Generators by Incorporating a Dynamic Equivalent Circuit Model

A New Loss of Field Detection Scheme for Synchronous Generators by Incorporating a Dynamic Equivalent Circuit Model

Control approach for photovoltaic inverters enhancing the primary grid using the virtual synchronous generator concept

This paper presents a control scheme for virtual synchronous generators (VSGs) in PV inverters, designed to enhance grid frequency and voltage. Through the skillful management of active and reactive power, this control scheme enables PV inverters to interact seamlessly with the main grid in response to grid events, including voltage and frequency fluctuations. The VSG controller is implemented using Matlab Simulink, and its effectiveness is rigorously assessed under various scenarios in a case study involves a 50 kVA rated PV inverter, a 50 kW rated PV system, and a 220 V grid phase voltage. In conditions of low power generation from the PV system (solar radiation of 200 W/m²) and high load power (120 kW and 37.5 kVAr), the load voltage drops to 202.4 V. The VSG controller successfully raises the grid voltage by 17.6 V, stabilizing it at 220 V. Conversely, in scenarios of high PV power generation (solar radiation of 1000 W/m²) and low load power (20 kW and 7.5 kVAr), the grid voltage surges by 10 V, reaching 230 V. The proposed control strategy adeptly fine-tunes the voltage, ultimately stabilizing at 220 V. Additionally, when the frequency deviates within ±0.4 Hz from the nominal frequency of 50 Hz, the proposed control effectively restores the frequency to its nominal value.

CeGen: A Cost-Effective Probe Generation Scheme for Rule Verification in SDN

Rule verification is a prevalent way to troubleshoot network issues, which needs to generate a proper set of probes to exercise the rules. Existing probe generation schemes either determine a minimum number of probes using end-to-end probing, whose computation time is too long to handle rule dynamics, or generate and verify the rules one by one in a streaming way using per hop probing, which produces many more probes. In this letter, we propose CeGen, a cost-effective probe generation scheme that explores the possibility of generating a small number of probes leveraging per hop probing to strike a balance between the computation time and the number of probes. By modeling the network as a directed acyclic graph carrying state information, a small number of probes could be achieved by traversing the graph. We evaluate CeGen’s performance and compare with two solutions: ATPG and Monocle, on two data sets. The result shows CeGen could complete the probe generation in a short time while maintaining a small number of probes.

The impact of project network topology and resource restrictions on the performance of schedule generation schemes: a comparative study

The impact of project network topology and resource restrictions on the performance of schedule generation schemes: a comparative study

Possible Off-Grid Electricity Generation Schemes for Survival during Emergency

Possible Off-Grid Electricity Generation Schemes for Survival during Emergency

Supported nondominated points as a representation of the nondominated set: An empirical analysis

AbstractThe nondominated set of a multiple objective discrete optimization problem is known to contain unsupported nondominated points, which outnumber the supported ones and are more difficult to obtain. We treat supported nondominated points as a representation and analyse their quality using different metrics beyond their sheer numbers. Under different data generation schemes on multiobjective knapsack and assignment problems, we observe that supported nondominated points almost always provide a good representation of the entire nondominated set.

PROBLEMS OF RELIABLE HEAT SUPPLY PROVIDING IN THE CONDITIONS OF NON-GUARANTEED ELECTRICITY SUPPLY TO HEAT-GENERATING ENTERPRISES

The paper analyzes the current state of centralized heat supply systems. Considered problems that arise when the power supply is turned off for a longer period of time. For example, due to natural disasters or the destruction of power stations, substations and networks. An analysis of the consequences of long-term shutdowns of centralized heat supply sources in the winter period at extremely low temperatures, which leads to the failure of the main and auxiliary equipment, heat networks and domestic heating systems, was carried out. Considered several real options for reservation or full self-sufficiency of boiler room heat supply needs in power supply for the most common types of boiler room. Namely, with water heating boilers and direct connection to the heating network and with steam boilers and steam water boilers. It is proposed to introduce elements of own power generation on a permanent basis, as part of the technological process of the heat supply system or a separate boiler house of medium and large capacity, which, with their rational combination, can lead to a significant economic effect. It is shown that the most promising is the redundancy due to the built-in heat generation scheme, the electricity generation system, i.e. full or partial cogeneration. It was found that when gas turbines are used in the cogeneration system of heat and electricity, it is advisable to use units with a low temperature of up to 800°C and a gas pressure before the turbine of up to 0.8 MPa, which are characterized by high reliability, low cost and provide the greatest release of utilized heat. Keywords: centralized heat supply systems, reliability of power equipment, power supply of boiler houses

Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation.

Extracting beat-by-beat information from electrocardiograms (ECGs) is crucial for various downstream diagnostic tasks that rely on ECG-based measurements. However, these measurements can be expensive and time-consuming to produce, especially for long-term recordings. Traditional ECG detection and delineation methods, relying on classical signal processing algorithms such as those based on wavelet transforms, produce high-quality delineations but struggle to generalise to diverse ECG patterns. Machine learning (ML) techniques based on deep learning algorithms have emerged as promising alternatives, capable of achieving similar performance without handcrafted features or thresholds. However, supervised ML techniques require large annotated datasets for training, and existing datasets for ECG detection/delineation are limited in size and the range of pathological conditions they represent. This article addresses this challenge by introducing two key innovations. First, we develop a synthetic data generation scheme that probabilistically constructs unseen ECG traces from "pools" of fundamental segments extracted from existing databases. A set of rules guides the arrangement of these segments into coherent synthetic traces, while expert domain knowledge ensures the realism of the generated traces, increasing the input variability for training the model. Second, we propose two novel segmentation-based loss functions that encourage the accurate prediction of the number of independent ECG structures and promote tighter segmentation boundaries by focusing on a reduced number of samples. The proposed approach achieves remarkable performance, with a -score of 99.38% and delineation errors of ms and ms for ECG segment onsets and offsets across the P, QRS, and T waves. These results, aggregated from three diverse freely available databases (QT, LU, and Zhejiang), surpass current state-of-the-art detection and delineation approaches. Notably, the model demonstrated exceptional performance despite variations in lead configurations, sampling frequencies, and represented pathophysiology mechanisms, underscoring its robust generalisation capabilities. Real-world examples, featuring clinical data with various pathologies, illustrate the potential of our approach to streamline ECG analysis across different medical settings, fostered by releasing the codes as open source.

A Reliable Physical Layer Key Generation Scheme Based on RSS and LSTM Network in VANET

With the rapid development of information and communication technology, Vehicular AD hoc Networks (VANET) has attracted more and more attention. In order to provide traffic safety services, vehicles frequently share information related to road traffic, such as vehicle motion data and traffic flow data. Reliable key generation is the basis of VANET security system construction. At present, most key generation schemes rely on a trusted third-party, so there are security risks. Traditional key agreement protocols have high overhead, and is not suitable for the latency-sensitive requirements of VANET. The physical layer security technology extracts the fingerprint of the wireless channel and the identity of the device, and generates the key quickly and in real time without the third party distribution. A physical layer key generation scheme based on Received Signal Strength (RSS) is proposed to realize Vehicle-to-Vehicle (V2V) secure communications. Firstly, a network model based on Long Short-Term Memory (LSTM) network and Kalman Filtering is proposed to effectively enhance the reciprocity of physical layer information in dynamic environment. Secondly, a lossless quantization scheme is proposed, which achieves a lower bit disagreement rate and a higher bit generation rate. Thirdly, the inconsistent bits are corrected by fuzzy extractor, the confidentiality of the key exchange process is enhanced by zero knowledge proof, and the security of the key is improved by using hash function for privacy amplification. Finally, the experimental results show that the proposed scheme has great improvements in data correlation, bit disagreement rate, bit generation rate and bit distribution randomness.

New and Renewable Energy Policy Implementation for Sustainable Development

Humans are the driving force in all activities on this earth, for this reason human individuality is an important point, to accelerate himself and hone his skills properly, in order to optimize his function as an individual creature. Besides that, because humans also live in society (Zoon Politicon) they must also contribute to the order of life of society and the state as mandated by the constitution, the existence of control and exploitation of natural resources that are fundamental to the life of the nation and state is carried out by the state, including energy natural resources that can be encouraging the growth and improvement of just law, as well as the application of new and renewable energy policies for sustainable development in Indonesia. Based on constitutional support and the need for a legal umbrella to protect new and renewable energy, it is necessary to regulate it comprehensively. This study aims to determine the application of new renewable energy policies for sustainable development. This research method uses a normative juridical research method with a case approach and statutory approach (statute approach). The implementation of new renewable energy policies for sustainable development shows that it has not been optimally implemented, especially in Indonesia, while Indonesia has the potential for large sources of new and renewable energy. However, utilization of this potential is still not maximized due to technological limitations and economic considerations. The business scheme and types of new renewable energy generators which are still small scale are not yet optimal, and the interconnection system is still limited. Therefore, the need for an important urgency in regulating the application of new and renewable energy policies for sustainable development must have a legal basis in its implementation.

Investigation of Raman dissipative solitons parameters generated near 1.7 μm

This work demonstrates a study Raman dissipative solitons parameters at wavelengths of 1.6-1.7 μm as a function of pump pulse energy and external cavity quality factor. In the presented scheme of the Raman dissipative solitons generator, pulses with a repetition rate of 6.54 MHz, an energy in range of 4.3-7.2 nJ, and slope efficiency of about 83% at a wavelength of 1.66 μm were obtained. Numerical simulation of an external cavity and analysis of pulse compression in an optical fiber with anomalous dispersion are presented. It is shown that an increase in the fraction of diverted radiation from the external cavity from 50% to 80% may allow increasing the quality of generated pulses.

A Novel Dynamic S-Box Generation Scheme Based on Quantum Random Walks Controlled by a Hyper-Chaotic Map

For many years, chaotic maps have been widely used in the design of various algorithms in cryptographic systems. In this paper, a new model (compound chaotic system) of quantum random walks controlled by a hyper-chaotic map is constructed and a novel scheme for constructing a dynamic S-Box based on the new model is proposed. Through aperiodic evaluation and statistical complexity measurement, it is shown that the compound chaotic system has features such as complex structure and stronger randomness than classical chaotic systems. Based on the chaotic sequence generated by the composite system, we design a dynamic S-Box generation mechanism. The mechanism can quickly generate high-security S-Boxes. Then, an example of randomly generating S-Boxes is given alongside an analytical evaluation of S-Box standard performance criteria such as bijection, boomerang uniformity, bit independence, nonlinearity, linear approximate probability, strict avalanche effect, differential uniformity, the and generalized majority logic criterion. The evaluation results confirm that the performance of the S-Box is excellent. Thus, the proposed dynamic S-Box construction technique can be used to generate cryptographically strong substitution-boxes in practical information security systems.

A coordinated generation and voyage planning optimization scheme for all-electric ships under emission policy

The International Maritime Organization has introduced the concept of Emission Control Area to reduce sulfur emissions from coastal areas. The expensive low-sulfur fuels are required to be used in Emission Control Area, leading to increased sulfide emissions due to bypassing by ship operators. It creates new challenges for navigation and power generation scheduling of all-electric ships. This article presents a new bi-objective joint optimization scheme for ship sailing routes and energy management. It considers emission control strategies to balance the total cost of operation and sulfur dioxide emissions throughout the ship’s voyage. Unlike previous optimization problems with fixed routes and sailing time, the influence of emission policies on the operator’s navigation decisions is considered. The route options and sailing time are treated as variables embedded in the model. In addition, the optimization accuracy is improved by integrating the model into mixed integer linear programming through a modified piecewise linearization model. The results show that the total operating cost for the same route is reduced by 6.05%, and the SO2 emission is 8.36% after integrating the emission policy influencing factor in the model. The effectiveness of the proposed scheme is validated, and the model’s necessity for optimizing ship power generation and voyage within Emission Control Area is also illustrated.

Generation of high radial node vector vortex beams based on digital micromirror device

The modulation of amplitude, phase, and polarization to generate complex vector light fields with spatially variant polarization distribution has become a hot research topic in recent years. We propose a new method for generating high radial node vector vortex beams (VVB) based on a digital micromirror device (DMD). Firstly, we propose a multiplexed binary hologram with varying ellipticity, which can significantly optimize the vortex beam generated by DMD while the mode purity of the scalar vortex beam after optimization can reach 97.23%. Secondly, a novel interference experimental setup is proposed based on the Wollaston prism, which can realize arbitrary control of VVBs. Based on this, the high radial node VVBs with p=10 and high polarization order VVBs with polarization order up to 25 are generated experimentally. Finally, we reconstruct the State of Polarization (SoP) of the vector beam l=4,m=-4,p=4 based on the Stokes parameter S1,S2,S3, and the variation of vector quality factor (VQF) on the high-order Poincaré sphere (HOPS) is analyzed. Our technology provides a high-quality vector beam generation scheme with important applications in laser detection, optical communication, and optical micro-manipulation.

A novel virtual synchronous generator control scheme of DFIG-based wind turbine generators based on the rotor current-induced electromotive force

This paper focuses on the research of virtual synchronous generator (VSG) control technology for the doubly-fed induction generator (DFIG)-based wind turbines. The key for DFIG VSG operation is to construct an internal electromotive force (EMF) with the virtual rotor motion characteristic in the stator. The existing control schemes use the rotor or mutual flux-induced EMF as the internal EMF. Since the flux corresponds to a composite magnetic field generated together by the stator and rotor current. Any variations in the stator current caused by external disturbances will reflect on the rotor current to keep the flux constant. The rotor overcurrent is inevitable under a large external disturbance, threatening the safety of the rotor-side converter (RSC). Moreover, the flux is immeasurable. The accurate flux control depends on flux observers, increasing the complexity; while the control without observers cannot ensure the effectiveness, leading to control failure. To address these problems, a novel DFIG VSG control scheme with the rotor current-induced EMF as the internal EMF is proposed in this paper. The internal EMF is corresponded to the single magnetic field generated only by the rotor current. This can lead to the following benefits: the rotor current will not vary with the stator current and can be limited under large external disturbances, ensuring the safety of the RSC; the rotor current can be directly measured without the need for extra observers, reducing the difficulty of control implementations; the underlying controlled vector is unified with the traditional grid-following DFIG, favoring the retrofit of the installed DFIGs. Compared to the existing schemes, this scheme has a larger equivalent internal impedance. An additional rotor current-terminal voltage magnitude droop control is added to effectively restrain the problems brought by this larger impedance. Simulation and analysis results demonstrate that the proposed DFIG VSG control scheme effectively addresses problems in the existing schemes. Moreover, the results also exhibit that the scheme has a robust adaptability across a wide range of short-circuit ratios and possesses the grid-forming ability independent of SGs.

PPFGED: Federated learning for graphic element detection with privacy preservation in multi-source substation drawings

In digital recognition of substation drawings, a significant challenge is how to accurately detect complex graphic elements in each drawing. Existing detection schemes often operate in isolation, with data dispersed across multiple sources, which impedes the development of high-accuracy models. A straightforward solution of consolidating drawings from different substations for training does not satisfy the stringent security requirements of substations. To address these challenges, we propose a novel graphic element detection framework based on federated learning for multi-source substation drawings. Our framework involves a server provider, multiple power companies, and distributed substations, and it employs a universal model generation scheme that protects data privacy. Local substations utilize the designed universal graphic element extraction scheme to categorize drawings and extract smaller elements, achieving enhanced accuracy. Subsequently, we design a privacy-enhanced diffusion model for substations to protect elements before uploading them. Power companies then train local model and apply a designed dimension-compressed local differential privacy scheme to the model parameters to protect the training process. To deter server provider from maliciously exploiting model, a game theory-based optimization solution is designed between server provider and power companies. Finally, experiments conducted using substation drawings provided by multiple cooperating units demonstrate that our framework can accurately perform drawing classification and graphic element extraction, and while obtaining the strongest privacy guarantees and low time cost, it achieves an intelligent system for graphic element detection with an accuracy of 88.14% across different regional substations.