Dispersed Wind Power Planning Method Considering Network Loss Correction with Cold Weather

In order to play a positive role of decentralised wind power on-grid for voltage stability improvement and loss reduction of distribution network,a multi-objective two-stage decentralised wind power planning method is proposed in the paper,which takes into account the network loss correction for the extreme cold region.Firstly,an electro-thermal model is introduced to reflect the effect of temperature on conductor resistance and to correct the results of active network loss calculation;secondly,a two-stage multi-objective two-stage decentralised wind power siting and capacity allocation and reactive voltage optimisation control model is constructed to take account of the network loss correction,and the multi-objective multi-planning model is established in the first stage to consider the whole-life cycle investment cost of WTGs,the system operating cost and the voltage quality of power supply,and the multi-objective planning model is established in the second stage.planning model,and the second stage further develops the reactive voltage control strategy of WTGs on this basis,and obtains the distribution network loss reduction method based on WTG siting and capacity allocation and reactive power control strategy.Finally,the optimal configuration scheme is solved by the manta ray foraging optimisation(MRFO)algorithm,and the loss of each branch line and bus loss of the distribution network before and after the adoption of this loss reduction method is calculated by taking the IEEE33 distribution system as an example,which verifies the practicability and validity of the proposed method,and provides a reference introduction for decision-making for the distributed energy planning of the distribution network.

The Actuarial Data Intelligent Based Artificial Neural Network (ANN) Automobile Insurance Inflation Adjusted Frequency Severity Loss Reserving Model

This study proposes a novel approach for estimating automobile insurance loss reserves utilizing Artificial Neural Network (ANN) techniques integrated with actuarial data intelligence. The model aims to address the challenges of accurately predicting insurance claim frequencies, severities, and overall loss reserves while accounting for inflation adjustments. Through comprehensive data analysis and model development, this research explores the effectiveness of ANN methodologies in capturing complex nonlinear relationships within insurance data. The study leverages a data set comprising automobile insurance policyholder information, claim history, and economic indicators to train and validate the ANN-based reserving model. Key aspects of the methodology include data preprocessing techniques such as one-hot encoding and scaling, followed by the construction of frequency, severity, and overall loss reserving models using ANN architectures. Moreover, the model incorporates inflation adjustment factors to ensure the accurate estimation of future loss reserves in real terms. Results from the study demonstrate the superior predictive performance of the ANN-based reserving model compared to traditional actuarial methods, with substantial improvements in accuracy and robustness. Furthermore, the model’s ability to adapt to changing market conditions and regulatory requirements, such as IFRS17, highlights its practical relevance in the insurance industry. The findings of this research contribute to the advancement of actuarial science and provide valuable insights for insurance companies seeking more accurate and efficient loss reserving techniques. The proposed ANN-based approach offers a promising avenue for enhancing risk management practices and optimizing financial decision-making processes in the automobile insurance sector.

Age-related hearing loss accelerates the decline in fast speech comprehension and the decompensation of cortical network connections

Patients with age-related hearing loss face hearing difficulties in daily life.The causes of age-related hearing loss are complex and include changes in peripheral hearing,central processing,and cognitive-related abilities.Furthermore,the factors by which aging relates to hearing loss via changes in audito ry processing ability are still unclear.In this cross-sectional study,we evaluated 27 older adults(over 60 years old) with age-related hearing loss,21 older adults(over 60years old) with normal hearing,and 30 younger subjects(18-30 years old) with normal hearing.We used the outcome of the uppe r-threshold test,including the time-compressed thres h old and the speech recognition threshold in noisy conditions,as a behavioral indicator of auditory processing ability.We also used electroencephalogra p hy to identify presbycusis-related abnormalities in the brain while the participants were in a spontaneous resting state.The timecompressed threshold and speech recognition threshold data indicated significant diffe rences among the groups.In patients with age-related hearing loss,information masking(babble noise) had a greater effect than energy masking(speech-shaped noise) on processing difficulties.In terms of resting-state electroencephalography signals,we observed enhanced fro ntal lobe(Brodmann’s area,BA11) activation in the older adults with normal hearing compared with the younger participants with normal hearing,and greater activation in the parietal(BA7) and occipital(BA19) lobes in the individuals with age-related hearing loss compared with the younger adults.Our functional connection analysis suggested that compared with younger people,the older adults with normal hearing exhibited enhanced connections among networks,including the default mode network,sensorimotor network,cingulo-opercular network,occipital network,and frontoparietal network.These results suggest that both normal aging and the development of age-related hearing loss have a negative effect on advanced audito ry processing capabilities and that hearing loss accele rates the decline in speech comprehension,especially in speech competition situations.Older adults with normal hearing may have increased compensatory attentional resource recruitment represented by the to p-down active listening mechanism,while those with age-related hearing loss exhibit decompensation of network connections involving multisensory integration.

Position-aware packet loss optimization on service function chain placement

The advent of Network Function Virtualization(NFV)and Service Function Chains(SFCs)unleashes the power of dynamic creation of network services using Virtual Network Functions(VNFs).This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term.However,the study shows with a set of test-bed experiments that packet loss at certain positions(i.e.,different VNFs)in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets.To overcome this challenge,this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions.This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming(MILP)and proved that it is NP-hard.In this study,Palos is proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost.Extensive experiment results show that Palos can achieve up to 42.73%improvement on packet dropping cost and up to 33.03%reduction on average SFC latency when compared with two other state-of-the-art schemes.

MetaPINNs:Predicting soliton and rogue wave of nonlinear PDEs via the improved physics-informed neural networks based on meta-learned optimization

Efficiently solving partial differential equations(PDEs)is a long-standing challenge in mathematics and physics research.In recent years,the rapid development of artificial intelligence technology has brought deep learning-based methods to the forefront of research on numerical methods for partial differential equations.Among them,physics-informed neural networks(PINNs)are a new class of deep learning methods that show great potential in solving PDEs and predicting complex physical phenomena.In the field of nonlinear science,solitary waves and rogue waves have been important research topics.In this paper,we propose an improved PINN that enhances the physical constraints of the neural network model by adding gradient information constraints.In addition,we employ meta-learning optimization to speed up the training process.We apply the improved PINNs to the numerical simulation and prediction of solitary and rogue waves.We evaluate the accuracy of the prediction results by error analysis.The experimental results show that the improved PINNs can make more accurate predictions in less time than that of the original PINNs.

Unknown DDoS Attack Detection with Fuzzy C-Means Clustering and Spatial Location Constraint Prototype Loss

Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.

Minimization of Electric Power Losses on 132 kV and 220 kV Uganda Electricity Transmission Lines

The classical minimization of power losses in transmission lines is dominated by artificial intelligence techniques, which do not guarantee global optimum amidst local minima. Revolutionary and evolutionary techniques are encumbered with sophisticated transformations, which weaken the techniques. Power loss minimization is crucial to the efficient design and operation of power transmission lines. Minimization of losses is one way to meet steady grid supply, especially at peak demand. Thus, this paper has presented a gradient technique to obtain optimal variables and values from the power loss model, which efficiently minimizes power losses by modifying the traditional power loss model that combines Ohm and Corona losses. Optimality tests showed that the unmodified model does not support the minimization of power losses on transmission lines as the Hessian matrix portrayed the maximization of power losses. However, the modified model is consistent with the gradient method of optimization, which yielded optimum variables and values from the power loss model developed in this study. The unmodified (modified) models for Bujagali-Kawanda 220 kV and Masaka West-Mbarara North 132 kV transmission lines in Uganda showed maximum power losses of 0.406 (0.391) and 0.452 (0.446) kW/km/phase respectively. These results indicate that the modified model is superior to the unmodified model in minimizing power losses in the transmission lines and should be implemented for the efficient design and operation of power transmission lines within and outside Uganda for the same transmission voltages.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

Rapid assessment of flood loss based on neural network ensemble

Considering the defects of low accuracy and slow speed existing in traditional flood loss assessment, firstly, the technical route of flood loss assessment was presented based on the neural network ensemble. Secondly, through the study of certain country of Poyang Lake district, the flood loss assessment indicators of the test area were analyzed and extracted by utilizing analytic hierarchy process (AHP), and the weights of the impact factors were assigned. Subsequently, the approaches to generate individuals and conclusions of neural network ensemble model were also investigated. In the platform of C# language and neural network library under AForge.NET open source, a flood loss assessment program which could rapidly build neural network ensemble models was developed. Finally, the proposed method was tested and verified. The comparison results between the assessment results of the proposed method and the actual statistical flood loss proved the feasibility of this method, thus a new approach for flood loss assessment was provided.

Fault Tolerant Control for Networked Control Systems with Packet Loss and Time Delay

In this paper,a fault tolerant control with the consideration of actuator fault for a networked control system （NCS） with packet loss is addressed.The NCS with data packet loss can be described as a switched system model.Packet loss dependent Lyapunov function is used and a fault tolerant controller is proposed respectively for arbitrary packet loss process and Markovian packet loss process.Considering a controlled plant with external energy-bounded disturbance,a robust H ∞ fault tolerant controller is designed for the NCS.These results are also expanded to the NCS with packet loss and networked-induced delay.Numerical examples are given to illustrate the effectiveness of the proposed design method.

Robust model predictive control with randomly occurred networked packet loss in industrial cyber physical systems

For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.

Experimental Study of Current Loss of Stainless Steel Magnetically Insulated Transmission Line with Current Density at MA/cm Level

A magnetically insulated transmission line （MITL） is used to transmit high power electric pulses in large pulse power systems. However, current loss is unavoidable, especially when the current density is up to 1 MA/cm. In the paper, the current loss of an MITL made of stainless steel, which is usually used in large pulse power generators, is experimentally studied, and possible mechanisms to explain the current loss of the MITL are analyzed and discussed. From the experimental results, the relationship between loss current density and input current density follows approximately a power law. The loss is also related to the configuration of the MITL.

Energy Loss Analysis of Distributed Rooftop Photovoltaics in Industrial Parks

The analysis of the loss of distributed photovoltaic power generation systems involves the interests of energy users,energy-saving service companies,and power grid companies,so it has always been the focus of the industry and society in some manner or another.However,the related analysis for an actual case that considers different cooperative corporations’benefits is lacking in the presently available literature.This paper takes the distributed rooftop photovoltaic power generation project in an industrial park as the object,studies the analysis and calculation methods of line loss and transformer loss,analyzes the change of transformer loss under different temperatures and different load rates,and compares the data and trend of electricity consumption and power generation in industrial parks before and after the photovoltaic operation.This paper explores and practices the analysis method of the operating loss of distributed photovoltaic power generation and provides an essential reference for the benefit analysis and investment cost estimation of distributed photovoltaic power generation systems in industrial parks.The analyzed results reveal that the change loss is stable after the photovoltaic is connected,and there is no additional transformer loss.And before and after the photovoltaic system installation,there was no significant change in the total monthly data difference between the total meter and the sub-meter.

Traffic dynamics considering packet loss in finite buffer networks

In real complex systems, the limited storage capacity of physical devices often results in the loss of data. We study the effect of buffer size on packet loss threshold in scale-free networks. A new order parameter is proposed to characterize the packet loss threshold. Our results show that the packet loss threshold can be optimized with a relative small buffer size. Meanwhile, a large buffer size will increase the travel time. Furthermore, we propose a Buffered-Shortest-Path-First(BSPF) queuing strategy. Compared to the traditional First-In-First-Out(FIFO) strategy, BSPF can not only increase the packet loss threshold but can also significantly decrease the travel length and travel time in both identical and heterogeneous node capacity cases. Our study will help to improve the traffic performance in finite buffer networks.

Structural Connectivity Enhanced Anisotropic 3D Network for Brain Midline Delineation

Brain midline delineation can facilitate the clinical evaluation of brain midline shift,which has a pivotal role in the diagnosis and prognosis of various brain pathology.However,there are still challenges for brain midline delineation:1)the largely deformed midline is hard to localize if mixed with severe cerebral hemorrhage;2)the predicted midlines of recent methods are not smooth and continuous which violates the structural priority.To overcome these challenges,we propose an anisotropic three dimensional(3D)network with context-aware refinement(A3D-CAR)for brain midline modeling.The proposed network fuses 3D context from different two dimensional(2D)slices through asymmetric context fusion.To exploit the elongated structure of the midline,an anisotropic block is designed to balance the difference between the adjacent pixels in the horizontal and vertical directions.For maintaining the structural priority of a brain midline,we present a novel 3D connectivity regular loss(3D CRL)to penalize the disconnectivity between nearby coordinates.Extensive experiments on the CQ dataset and one in-house dataset show that the proposed method outperforms three state-of-the-art methods on four evaluation metrics without excessive computational burden.

MSCNN-LSTM Model for Predicting Return Loss of the UHF Antenna in HF-UHF RFID Tag Antenna

High-frequency(HF)and ultrahigh-frequency(UHF)dual-band radio frequency identification(RFID)tags with both near-field and farfield communication can meet different application scenarios.However,it is time-consuming to calculate the return loss of a UHF antenna in a dualband tag antenna using electromagnetic(EM)simulators.To overcome this,the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory(MSCNN-LSTM)for predicting the return loss of UHF antennas instead of EM simulators.In the proposed MSCNN-LSTM,the MSCNN has three branches,which include three convolution layers with different kernel sizes and numbers.Therefore,MSCNN can extract fine-grain localized information of the antenna and overall features.The LSTM can effectively learn the EM characteristics of different structures of the antenna to improve the prediction accuracy of the model.Experimental results show that the mean absolute error(0.0073),mean square error(0.00032),and root mean square error(0.01814)of theMSCNNLSTM are better than those of other prediction methods.In predicting the return loss of 100UHFantennas,compared with the simulation time of 4800 s for High Frequency Structure Simulator(HFSS),MSCNN-LSTM takes only 0.927519 s under the premise of ensuring prediction accuracy,significantly reducing the calculation time,which provides a basis for the rapid design of HF-UHF RFID tag antenna.ThenMSCNN-LSTM is used to determine the dimensions of the UHF antenna quickly.The return loss of the designed dualband RFID tag antenna is−58.76 and−22.63 dB at 13.56 and 915 MHz,respectively,achieving the desired goal.

COVID TCL:A Joint Metric Loss Function for Diagnosing COVID-19 Patient in the Early and Incubation Period

Convolution Neural Networks(CNN)can quickly diagnose COVID-19 patients by analyzing computed tomography(CT)images of the lung,thereby effectively preventing the spread of COVID-19.However,the existing CNN-based COVID-19 diagnosis models do consider the problem that the lung images of COVID-19 patients in the early stage and incubation period are extremely similar to those of the non-COVID-19 population.Which reduces the model’s classification sensitivity,resulting in a higher probability of the model misdiagnosing COVID-19 patients as non-COVID-19 people.To solve the problem,this paper first attempts to apply triplet loss and center loss to the field of COVID-19 image classification,combining softmax loss to design a jointly supervised metric loss function COVID Triplet-Center Loss(COVID-TCL).Triplet loss can increase inter-class discreteness,and center loss can improve intra-class compactness.Therefore,COVID-TCL can help the CNN-based model to extract more discriminative features and strengthen the diagnostic capacity of COVID-19 patients in the early stage and incubation period.Meanwhile,we use the extreme gradient boosting(XGBoost)as a classifier to design a COVID-19 images classification model of CNN-XGBoost architecture,to further improve the CNN-based model’s classification effect and operation efficiency.The experiment shows that the classification accuracy of the model proposed in this paper is 97.41%,and the sensitivity is 97.61%,which is higher than the other 7 reference models.The COVID-TCL can effectively improve the classification sensitivity of the CNN-based model,the CNN-XGBoost architecture can further improve the CNN-based model’s classification effect.

Water Supply Network Losses in Jordan

Water supply network losses are an international problem especially in countries suffering from water scarcity like Jordan. Jordan is one of the poorest countries in its water resources and it is estimated to be below the water poverty line. Jordan is located in the Middle East and has a surface area of approximately 90,000 km2. Its population is around 6.3 million and it is estimated that the population will be 7.8 million in 2022. The gap between water supply and demand is widening due to development and a relatively high population growth rate. In addition, global climate change is expected to intensify the water shortage problem in Jordan. Thirteen years of complete records obtained from the Ministry of Water and Irrigation were analyzed. According to these records, water losses in Jordan reach about 50%. In view of the evaluation of the data and the case study conducted in this research, it is believed that Jordan can overcome the water shortage problem by adopting a water demand management strategy. In this context, efforts should be focused on reducing water losses. If this is achieved, it will save huge quantities of water and revenue.

Combining Trend-Based Loss with Neural Network for Air Quality Forecasting in Internet of Things

Internet of Things(IoT)is a network that connects things in a special union.It embeds a physical entity through an intelligent perception system to obtain information about the component at any time.It connects various objects.IoT has the ability of information transmission,information perception,and information processing.The air quality forecasting has always been an urgent problem,which affects people’s quality of life seriously.So far,many air quality prediction algorithms have been proposed,which can be mainly classified into two categories.One is regression-based prediction,the other is deep learning-based prediction.Regression-based prediction is aimed to make use of the classical regression algorithm and the various supervised meteorological characteristics to regress themeteorological value.Deep learning methods usually use convolutional neural networks(CNN)or recurrent neural networks(RNN)to predict the meteorological value.As an excellent feature extractor,CNN has achieved good performance in many scenes.In the same way,as an efficient network for orderly data processing,RNN has also achieved good results.However,few or none of the above methods can meet the current accuracy requirements on prediction.Moreover,there is no way to pay attention to the trend monitoring of air quality data.For the sake of accurate results,this paper proposes a novel predicted-trend-based loss function(PTB),which is used to replace the loss function in RNN.At the same time,the trend of change and the predicted value are constrained to obtain more accurate prediction results of PM_(2.5).In addition,this paper extends the model scenario to the prediction of the whole existing training data features.All the data on the next day of the model is mixed labels,which effectively realizes the prediction of all features.The experiments show that the loss function proposed in this paper is effective.

Controlled Quantum Network Coding Without Loss of Information

Quantum network coding is used to solve the congestion problem in quantum communication,which will promote the transmission efficiency of quantum information and the total throughput of quantum network.We propose a novel controlled quantum network coding without information loss.The effective transmission of quantum states on the butterfly network requires the consent form a third-party controller Charlie.Firstly,two pairs of threeparticle non-maximum entangled states are pre-shared between senders and controller.By adding auxiliary particles and local operations,the senders can predict whether a certain quantum state can be successfully transmitted within the butterfly network based on the Z-{10>,|1>}basis.Secondly,when trans-mission fails upon prediction,the quantum state will not be lost,and it will sill be held by the sender.Subsequently,the controller Charlie re-prepares another three-particle non-maximum entangled state to start a new round.When the predicted transmission is successful,the quantum state can be transmitted successfully within the butterfly network.If the receiver wants to receive the effective quantum state,the quantum measurements from Charlie are needed.Thirdly,when the transmission fails,Charlie does not need to integrate the X-{1+>,1->}basis to measure its own particles,by which quantum resources are saved.Charlie not only controls the effective transmission of quantum states,but also the usage of classical and quantum channels.Finally,the implementation of the quantum circuits,as well as a flow chart and safety analysis of our scheme,is proposed.