Miracle-dz/UniFAR-FTUs huggingface dataset:
A Distributionally Robust Energy Storage Planning Model for Wind Integrated Power System Based on Scenario Probability

Global climate change places greater demands on the process of decarbonizing power systems. Battery energy storage can effectively cope with the uncertainty of renewable energy sources and reduce wind power curtailment. Because of the high cost, reasonable economic optimization is required for energy storage planning considering the uncertainty of wind. The traditional stochastic optimization is optimistic while the robust optimization is conservative. To overcome this problem, a two-stage distributionally robust energy storage planning model is proposed in this paper based on the historical information of wind power output. The economics of the energy storage investment scheme is considered in the first stage objective, and the second stage performs the system day-ahead dispatching. This paper used the norm-1 and norm-inf comprehensive constraints to establish the probability distribution uncertainty set of wind power, then tried to find the optimal solution of the model under the worst probability distribution. The column and constraint generation algorithm is used to improve the efficiency of the solution. The numerical test results demonstrate the effectiveness of the model and the method in this paper.
Cost Minimizing Reservation and Scheduling Algorithms for Public Clouds

Cloud Service Providers offer various pricing schemes to charge for their computational resources. Cloud Service Users can opt for <italic>on-demand</italic>, <italic>reserved</italic>, or <italic>spot</italic> instances for their requirements. The overall cost of an application depends on the instances chosen to execute the job. Finding the optimal reservation amount is a significant research problem, and it is more challenging when one uses spot instances with unpredictable prices. We have proposed two algorithms to determine the reservation amount, and for both algorithms, the future spot prices are unknown. But the first algorithm assumes that future demands are known. The second algorithm does not make any such assumption and yet can ensure that the cost of reservation and usage of cloud resources is within a factor <inline-formula><tex-math notation="LaTeX">$2-\frac{u_h}{e_c}$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>-</mml:mo><mml:mfrac><mml:msub><mml:mi>u</mml:mi><mml:mi>h</mml:mi></mml:msub><mml:msub><mml:mi>e</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mfrac></mml:mrow></mml:math><inline-graphic xlink:href="khatua-ieq1-3133464.gif"/></alternatives></inline-formula> of the optimal cost where <inline-formula><tex-math notation="LaTeX">$u_h$</tex-math><alternatives><mml:math><mml:msub><mml:mi>u</mml:mi><mml:mi>h</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="khatua-ieq2-3133464.gif"/></alternatives></inline-formula> is the usage cost per hour of the reserved instance and <inline-formula><tex-math notation="LaTeX">$e_c$</tex-math><alternatives><mml:math><mml:msub><mml:mi>e</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="khatua-ieq3-3133464.gif"/></alternatives></inline-formula> is the average cost per hour for unreserved instances. We have compared our findings with that of some recent works in the literature. We have also given an <italic>Integer Linear Programming</italic> (ILP) formulation of the problem. Experimental results show that our algorithm differs in cost from ILP by less than 21%.
Ultra-Low frequency dielectric loss measurement of high voltage submarine cable at low electric field

As the main factor influencing 220 kV submarine power cable, thermal aging will accelerate the insulation degradation process and finally lead to insulation failure. The ultra-low frequency dielectric loss can evaluate the degree of insulation aging, and has been widely used in 10-35kV cables, but there is no further study in 220 kV submarine cables. In this work, an ultra-low frequency voltage was applied to 220 kV submarine cable. The dielectric loss can be obtained by analyzing the phase relation between voltage and current of the tested cable with the method of this work. The experimental results show that the dielectric loss tangent value is influenced by the output of ultra-low frequency power supply. The value will increase when the output voltage increases or the frequency decreases. The dielectric loss tangent increases with the increase of aging time under the same measurement voltage and frequency. Therefore, the results in this work show that the dielectric loss at low electric field has a significant relation with the thermal aging stage of submarine cable.
Distributed VAR Optimization of an Active Distribution Network with a High Proportion of Distributed Generation Based on Network Equalization Partitioning

In a large-scale active distribution network with high distributed-energy-resource penetration, the storage and computing resources of a centralized control model face great pressure in voltage/VAR control, and traditional distributed control also faces problems such as a long computation time and insufficient guarantee of convergence due to unreasonable partition settings. Therefore, a distributed VAR optimization algorithm based on network equalization partitioning (DVO-NEP) is proposed in this paper to reduce communication pressure and achieve fast VAR optimization. First, a complex network partition evaluation index based on electrical distance and problem complexity is established by comprehensively considering the regional coupling and equilibrium. Then, virtual boundary interaction information is introduced to establish local parallel distributed optimization models. Next, the ADMM algorithm based on piecewise penalty parameters is used to achieve a fast distributed solution. Finally, numerical simulations based on the modified IEEE 33 bus test systems verify the effectiveness of the proposed method compared with traditional methods.
Multimodal Fatigue Recognition State Based on Eyelid Features

Fatigue recognition of operators in a complex human-machine interface can effectively avoid human error. Aiming at improving the operator's vigilance in the digital control room of nuclear power plants, this paper proposes a multimodal fatigue state recognition technique. It combines deep learning and eyelid feature information to recognize the fatigue state of operators. Temporal Convolutional Network (TCN) is integrated with the 3D Residual Network (ResNet3D) to retain the temporal and spatial features of the eyelid. By using a custom cosine annealing learning rate decay algorithm to avoid the training process of the model falling into the local optimum. In this study, experiments were conducted on a variety of eyelid feature information for different timesteps. The UTA-RLDD and DROZY datasets were used to train and validate the model. The experimental results show that the fusion recognition of eyelid images and eyelid aspect ratio (EAR) using ResNet3D and TCN multimodal model can achieve better recognition results. With the increase of the timestep, there is a significant increase in the effect of recognition of mental states. In addition, the study set up an eyelid fatigue dataset to better fit the working environment of the digital main control room in a nuclear power plant. When the timestep is set to 160, the recognition recall of the model for this test dataset is 88%. The multimodal model can detect the state of low vigilance and drowsiness in real-time. Compared with other methods, the recognition accuracy of the model is improved by an average of 2.25%.
Pinhole aperture design for /sup 131/I tumor imaging

A new pinhole aperture design for single photon imaging is introduced whose distinctive feature is a channeled aperture. This allows greater attenuation near the pinhole aperture which is especially important when imaging /sup 131/I with its medium and high energy emissions. Point source response functions were simulated for pinhole inserts with different pinhole diameters (0.4-2.0 mm diameter) and channel heights (0.1-3.0 mm) with acquisition parameters appropriate for /sup 131/I brain tumor imaging. A photon transport simulation code was used to comprehensively model photon transport and detection. Tradeoff curves between resolution and sensitivity suggest that varying pinhole diameter and channel height to achieve higher resolution at the expense of decreased sensitivity may be advantageous for imaging tumors with intratumoral administration of /sup 131/I radiolabeled monoclonal antibodies.
Research on Multi-Agent Reinforcement Learning Traffic Control

Optimizing the traffic control system is of great significance for improving people’s livelihood and promoting economic development. To realize an intelligent traffic control system that is different from the fixed timing scheme based on historical traffic flow and the adjustment of green signal ratio, maddpg and qmix reinforcement learning methods are tried to be applied to two different four intersection traffic network files with restricted traffic routes and the whole route. The total reward value of the algorithm is compared; the stability of the two methods applied to the traffic control system is evaluated by the growth speed of the reward items, the reduction speed of the penalty items and the convergence speed of the algorithm. Finally, through two groups of comparative experiments, the convergence speed of maddpg is slightly slower than that of qmix in the case of the whole route and the restricted route, but the total reward is significantly higher than that of qmix algorithm. At the same time, the growth rate of maddpg in reward items and the decline rate of punishment items are faster than qmix. Through the comparison of the two methods in different road environments, it is found that maddpg is more suitable for optimizing traffic control. Optimizing the traffic control system is of great significance for improving people’s livelihood and promoting economic development. To realize an intelligent traffic control system that is different from the fixed timing scheme based on historical traffic flow and the adjustment of green signal ratio, maddpg and qmix reinforcement learning methods are tried to be applied to two different four intersection traffic network files with restricted traffic routes and the whole route. The total reward value of the algorithm is compared; the stability of the two methods applied to the traffic control system is evaluated by the growth speed of the reward items, the reduction speed of the penalty items and the convergence speed of the algorithm. Finally, through two groups of comparative experiments, the convergence speed of maddpg is slightly slower than that of qmix in the case of the whole route and the restricted route, but the total reward is significantly higher than that of qmix algorithm; at the same time, the growth rate of maddpg in reward items and the decline rate of punishment items are faster than qmix. Through the comparison of the two methods in different road environments, it is found that maddpg is more suitable for optimizing traffic control.
A Universal Model Predictive Control of DFIG in the Wind Solar Hybrid System

Hybrid Systems are the future of power generation. Together, they can make up for the inefficiency of one and give more effective result, as in the case of wind and solar generation. Double Fed Induction Generators (DFIG) are mainly used for Wind Energy Conversion because of the generation during variable wind speeds. There are two Voltage Source Converters (VSC) on the machine side and grid side of the Wind Energy Conversion System (WECS). A Solar Photo Voltaic System (SPVS) with a boost converter is equipped with an incremental conductance maximum power point tracking algorithm (MPPT). The conventional vector control suffers from complex decoupling processes, computational complexity, and slow dynamic response. To address these issues, this work presents Model Predictive Power Control (MPPC) to improve performance while reducing power ripples, current distortion, computation, and system complexity. The performance analysis is conducted for both vector control and MPPC by implementing the simulation model in MATLAB/SIMUINK. The performance of the PV system under different irradiances is carried out. THD analysis is carried out for MPDPC.
Mathematical design of a highway exit curve

A highway exit curve is designed under the assumption that the tangential and normal components of the acceleration of the vehicle remain constant throughout the path. Using fundamental principles of physics and calculus, the differential equation determining the curve function is derived. The equation and initial conditions are cast into a dimensionless form first for universality of the results. It is found that the curves are effected by only one dimensionless parameter which is the ratio of the tangential acceleration to the normal acceleration. For no tangential acceleration, the equation can be solved analytically yielding a circular arc solution as expected. For nonzero tangential acceleration, the function is complicated and no closed-form solutions exist for the differential equation. The equation is solved numerically for various acceleration ratios. Discussions for applications to highway exits are given.
Modeling and Testing of Thermoelectric Generator Based Energy Harvester for Powering Wireless Sensor Node Used in Condition Monitoring System for HVDC Converter

This paper focuses on the modeling and testing of a thermoelectric generator (TEG) -based energy harvester for powering wireless sensor nodes in a high-voltage direct current (HVDC) converter. The objective is to investigate the feasibility of using an environmental power-based energy harvesting solution for powering wireless sensor nodes in harsh industrial environments. The paper firstly models the thermoelectric generator, and then the experimental validation of the TEGbased energy harvester is carried out in a real HVDC converter module. The results prove the feasibility of using a thermoelectric generator as an energy source for powering wireless sensor nodes in condition monitoring for HVDC converter.
CDP: Selective Influential Nodes Based on Cliques in the Diffusion Process in Social Networks

Influence maximization techniques emphasize selecting a set of influential nodes in order to maximize influence. Because the algorithms presented in this field ignore the topology of cliques for diffusion, there are two major challenges in influence maximization algorithms: optimal diffusion and computational overhead reduction. As a result, the CDP algorithm is presented in this article to address these issues. This algorithm first selects suitable cliques for diffusion based on their position and strategy in social networks. Furthermore, for each clique, a score is calculated based on the topological criteria of the clique, and suitable cliques are selected for diffusion by applying a threshold limit. The seed nodes are chosen from the cliques in the second step. To avoid the rich club phenomenon, only a few nodes from each clique are chosen as seed candidate nodes. Finally, the seed nodes are chosen based on the node’s topology and the strength of the node’s level one neighbor. In the experiment section, the CDP algorithm significantly outperforms the best algorithms presented in recent years in terms of influence spread rate and execution time.
Multimedia Signal Processing: A history of the Multimedia Signal Processing Technical Committee

Multimedia signal processing (MMSP) refers to processing of signals from multiple media—speech, audio, images, text, graphics, point clouds, etc.—often jointly. This article reviews the history of MMSP and, in parallel, the history of the MMSP Technical Committee (TC), with a focus on the last three decades (<xref ref-type="fig" rid="fig1">Figure 1</xref>).
Experimental study of torque and flux weakening performance of alternative Switched Flux PM machines

In this paper, the torque and flux weakening performances of alternative Switched Flux Permanent Magnet (SFPM) machines are experimentally investigated and compared. Firstly, the torque and flux weakening control performances of C-core SFPM machine with/without voltage error regulation are compared. It is shown that the flux weakening performance can be improved when the voltage error regulation method is utilised. Moreover, the performances of four different SFPM machine topologies, i.e. conventional, multi-tooth, C-core and E-core SFPM machines are measured and compared using the voltage error regulation method. Overall, it has been concluded that the performances of C-core and E-core machines have better performance in terms of torque and power than that for the multi-tooth and conventional SFPM machines.
Vulnerability Categorization for Fast Multistep Attack Modelling

For many years, attack graphs have been one of the most popular approaches to model multistep attacks. This approach allows evaluating the possibility of each host in the system being compromised and to find attack paths with the most probability and impact. This paper describes an original approach to vulnerability categorization for fast multistep attack modelling. The novelty of the approach lies in the categorization of all available CVEs into 24 categories in accordance with their access vector, initial, and obtained access rights. After that, instead of vulnerabilities, only their categories are used for constructing attack graphs of each host of the analysed system. It helps to make this process more computationally efficient for each host, while those computations can be done in parallel. Moreover, we introduce assumptions to integrate second and third versions of the CVSS vulnerabilities descriptions and allow transitions of the attacker between different access vectors. For the experimental evaluation of the approach, it was decided to generate hosts with 10 random CVEs and CPEs, from 10 hosts to 250, while 10 hosts are added on each step. After that, for each host it is analysed if it is vulnerable based on the list of CVEs and their categories. Each step of the host generation was done 5 times, and average time consumption results are taken as a result. After that, the same experiment was redone, but with 50 random CVEs and CPEs for each host. The results showed that the suggested approach is 13.4 times faster at average for 10 CVEs and CPEs, while 23.0 times faster for 50 CVEs and CPEs. Moreover, we tested the suggested approach on a fixed number of hosts equal to 100, while changing the number of random CVEs and CPEs per host from 10 to 100 with the step equal to 10. This experiment showed that the categories-based approach is 30.7 times faster at average. In addition, pros and cons of the proposed approach and future work directions are indicated.
Detection and Classification of Malware for Cyber Security using Machine Learning Algorithms

The threat of malware to information security is one that keeps growing. However, the Windows operating system faces a very high level of unintended security risk. System exploitation that is prohibited could pose a security risk. For instance, PayPal is frequently imitated because hackers can profit significantly from obtaining consumers' PayPal login information. The main drawbacks of existing system is that, it takes more time to process and they are less efficient. To overcome the above drawbacks current research arena proposes a way that businesses detect threats, adapt and implement numerous cybersecurity techniques in combination with Machine learning and IOT approaches. But still there are lot of issues occurring in the above-mentioned techniques i.e., the Signature based detection is unattainable. The conclusion stated was that no machine is able to detect the malwares of the new generation with complete preciseness. A threat's mitigation is intended in addition to its identification and prevention. This study gives an insight about the various detection and classification techniques that were proposed using Machine Learning algorithms.
Research on Modal Parameter Identification and Damage Diagnosis of Bridge Structure Based on Improved Genetic Algorithm

Large-scale bridge projects are an vital part of national infrastructure, and are directly related to the safety of people's lives and properties. The identification of modal parameters of bridge structures under working conditions is an vital part of bridge damage identification. Considering the practicability of bridge detection, bridge detection should generally be based on environmental excitation. The existing means of modal parameter identification under environmental excitation are not suitable for The identification accuracy of the modal frequency is high, while the identification of the displacement mode has a large error. The existing means of modal parameter identification under environmental excitation have relatively high identification accuracy for the modal frequency, while the identification of the displacement mode has a large error. Due to the complexity and large-scale bridge structure, the importance and necessity of bridge structure health monitoring are more and more recognized by people. The means used in this paper is to improve the genetic algorithm to simulate the phenomena of reproduction, crossover and gene mutation that occur in the spontaneous selection and spontaneous heredity process. Genetic algorithm is a global optimization adaptive probabilistic search algorithm, which has the advantages of intelligent optimization and robustness.
Design and Implementation of Patient Monitoring System Based on IoT Using Oxygen Saturation

SpO2 levels must be continuously monitored in patients with cardiac and pulmonary conditions as well as those undergoing surgery. SpO2 monitoring is frequently employed in a clinical context to assess how well lung medicines and ventilator assistance are working. A pulse oximeter is a piece of medical gear that measures how much oxygen is in a person’s bloodstream, or how much of the oxygen carrying molecules known as hemoglobin) are actually used to move oxygen about the body. If the observations are made on the pulsatile component of the signal, pulse oximetry is predicated on the idea that two wavelengths may be used to determine arterial blood oxygen levels. Here, the device continuously measures the temperature and the Spo2 level in order to create a machine learning-based algorithm for patient severity prediction and upload it to a private server after a predetermined amount of time. Therefore, the doctor or caregiver can see the patient’s status remotely.
Triple A (AAA): a Tool to Analyze Scientific Literature Metadata with Complex Network Parameters

It is essential to analyze scientific literature when conducting review studies (systematic, narrative, etc.). Review articles can improve in quality by choosing or incorporating papers with high research impact. The quality of research has been measured using a variety of indicators. These metrics primarily address certain characteristics like the citation index. It is impossible to study the caliber of research in any field on an individual basis. It has to do with connections. Therefore, it would be advantageous to create a network of research items. In this study, we introduce a novel tool for the analysis of metadata in scientific literature. We tested our technique on the literature of breast cancer. The tool extracted 49,604 papers resulting in 575,894 nodes and 1,532,328edges. We looked at the topological and structural characteristics of the constructed network, briefly. However, this tool can be utilized in any other domain of interest.