Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Full Cost Chain Model of Power Grid Development and Operation

Based on the development and operation costs of power grid companies, this paper first investigates and analyzes the node electricity price, the distribution of fixed transmission costs, the marketization status of transmission, distribution prices, and the analysis method of power grid costs, it is concluded that the existing node pricing mechanism does not take the impact of fixed transmission cost into account and fails to reflect the actual utilization of transmission line assets by users in different nodes, which leads to be unfair in the allocation of transmission cost. Secondly, based on analyzing and comparing the differences between the life cycle cost method and the whole link cost method in calculating the cost of the power grid, the calculation method of the whole cost for the development and operation in the power grid is defined and the concept of the whole cost chain is discriminated, then, the calculation model of full cost chain is established by determining the calculation and allocation method of each component of total cost chain. Finally, the example analysis shows that the full cost chain mechanism can reflect the end user's utilization of power system resources, reflect the difference in energy consumption by users at different times and different nodes, and guide users' behavior with reasonable price signals.
Square-root Extended Kalman Filter-based Detection of False Data Injection Attack in Smart Grids

This paper studies the detection of false data injection attack (FDIA) in smart grid via the Square-root Extended Kalman Filter(SREKF). Due to the deceptive characteristics of FDIA, traditional $\chi^{2}$ detector-based detection method can be bypassed. For this reason, the security of smart grids is faced with the huge challenges. In order to address this problem, a SREKF-based detection method against the FDIA is proposed in this paper. Based on the constructed nonlinear grid model, a SREKF-based state estimation algorithm is developed to enhance the accuracy of state estimation. Furthermore, a state residual-based detector is given by considering the covert characteristics of FDIA. Finally, the simulation experiments verify the effectiveness of the proposed detection method by using detailed studies on an IEEE 6-bus smart grids.
A Data-Driven Method for Electricity Theft Detection Combing ConvGRU and K-means Clustering

As the problem of electricity theft through the tampering of smart meters continues to increase, the anomalous behavior of electricity theft becomes more diversified and is difficult to be detected. Therefore, data-driven methods are needed to detect different types of electricity theft. However, most of the features used in the existing data-driven methods are designed and selected through shallow networks or based on the researchers' prior knowledge, lacking the spatial-temporal feature understanding of electricity consumption data. In this paper, a novel electricity theft detection method based on the convolutional gated recurrent unit (ConvG RU) model is proposed to address the above concerns. In particular, the con-volutional part is applied to extract the spatial features between different user data due to its global perception characteristics, and the G RU part is utilized to learn the internal temporal features of the electricity consumption data series. Besides, K-means clustering is employed as a data preprocessing method combined with the ConvGRU model to improve the detection accuracy. Numerical experiments based on the realistic dataset are conducted to show the satisfactory performance of the proposed method.
A Modified Algorithm of Visual Quality Providing in Lossy Compression of Remote Sensing Images

The development of remote sensing technology leads to a huge number of acquired images needed to be stored and/or transferred. Lossy compression is an essential mean to reduce the image size and, thus, to improve the efficiency of storing and transferring. Meanwhile, a certain quality (including visual quality) must be provided in lossy compression since this quality determines the efficiency of further image processing and/or analysis. However, the task to provide a desired quality in lossy image compression quickly and accurately is not trivial. In this paper, we propose a modified lossy compression scheme able to guarantee a desired quality with high accuracy in two steps. The quality is evaluated in terms of both a conventional metric PSNR and a human visual system (HVS)-based metric PSNR-HVS-M. The experiments are conducted for discrete cosine transform (DCT)-based AGU coder for which we reach the max variance of errors equal to 0.124 dB2, and the biases less than 0.27 dB. This result proves that the average rate-distortion curve drawn from the remote sensing category better fits remote sensing data. The combination scheme of derivative and tangent gives better parameter predictions and helps reduce the final error.
The Subarea Aggregation Model of Fast Charging Electric Vehicles

With the increasing penetration of electric vehicles in the future transportation network, it is necessary to aggregate the flexibility of scattered, small capacity and uncertain electric vehicles to make electric vehicles become the temporal and spatial flexibility resources in power grid dispatching. In this paper, a subarea aggregation model of fast charging electric vehicles to be charged as flexible loads is established. By adjusting the price of charging stations, electric vehicle drivers can be guided to change their choice of charging stations, thus improving the spatial distribution of charging loads. The aggregation model incorporates three dimensions: time coupling, space coupling, and drivers' charging decision randomness. The electric vehicles' power and energy flexibility in each area is described, where the robust method is employed to ensure the feasibility of disaggregation. Case studies validate the effectiveness of the proposed method.
IoT based Voice Assistant for Home Automation

When the Internet of Things is applied to the home, it transforms a simple home into a smart home that is safer and more automated. The goal of this paper is to create a Voice/Text controlled Home Application that allows users to operate their home appliances from afar. Users can simply send voice commands or text messages to turn appliances ON or OFF based on their needs. When the users are not physically present in the area, they can schedule the status of the equipment. They will also be given information about past schedules and will be able to turn on the appliances for a set length of time. For the functionalities of the application that is integrated with an IoT device, Node-RED Technology is employed (NodeMCU). The Dialog Flow Account is where this developed application is deployed. The NodeMCU is linked to common household appliances. The NodeMCU controls the Home Appliances based on the parameters it receives from the cloud. Because high-performance and low-cost technology are used, the application's implementation costs are very low. This programme is extremely reliable and efficient for the elderly and differently abled who are unable to reach the switch to turn on or off the device.
Research on Detection Methods of Foreign Objects and Component Defects in Transmission Lines Based on Deep Learning

The foreign objects and component defects on the transmission line will cause adverse effects on the power transmission equipment, and may even endanger the safe operation of the power grid. Therefore, it is necessary to identify the foreign objects and component defects on the transmission line. In this paper, a method based on Faster-RCNN (Regional Convolutional Neural Network) for reorganization of the transmission line images collected by aerial photography is studied, where feature maps from the images to be detected through convolutional network operations is generated. Then the Region Proposal Network (RPN) is used to generate region of interests, and then the category of the picture is obtained. Moreover, the location of foreign objects and component defects through the classification layer is also recognized. The final result shows that the average accuracy of the presented method for the identification of foreign objects and component defects in transmission lines can reach 95.24%.
Vulnerability Assessment of False Data Injection Attacks on Optimal Power Flow

In modern power systems, sensor data is transmitted over long communication lines to the control centers. There, data is analysed and utilized by various optimization or control algorithms for power system planning, operation, and scheduling. The communication lines are not secured and the data is not encrypted, which makes these networked systems vulnerable to cyber-attacks. In this paper, the false data injection (FDI) attack is launched on the communication line transmitting power sensor data to the control centers. This data carries the measurements of load demand, and branch power flows, which is further analysed and input to the optimal power flow (OPF) algorithm. The attack vectors are designed to manipulate the line power flows. We present a vulnerability assessment of FDI attacks against OPF using case studies of IEEE 2-bus and 37-bus power systems.
Walking Direction Estimation Using Smartphone Sensors: A Deep Network-Based Framework

Smartphone-based inertial and magnetic sensors can be the basis for pedestrian navigation, whenever external positioning signals are limited or unavailable. Such navigation solutions are typically accomplished by a practice known as pedestrian dead reckoning, wherein the step length and the heading angle are estimated to form the horizontal trajectory of the user. One of the main challenges in these methods is the unknown misalignment between the user’s forward axis and the device’s frame, which imposes a great difficulty in finding the user’s heading. In this article, the problem of estimating the walking direction is addressed by a learning-based approach. Specifically, a novel deep network architecture is designed for extracting the motion vector in the device coordinates using accelerometer measurements. It consists of temporal convolutions and multiscale attention layers and involves a rotation-invariant property that is analytically derived. The network model is integrated with a geometric calculation, employing gravity and geomagnetic directions, to convert the motion vector into the heading angle relative to north. Extensive experiments of natural walking activity were conducted by a single pedestrian, with smartphones placed freely in various pockets. These were used for training the proposed model, in a user-specific approach. Finally, the entire framework was evaluated on unseen data, comparing the deep network against a mechanistic baseline method. Using the proposed network, with merely 5500 parameters, the resulting heading errors had a median value of 9.8°, which was lower by 2.5° compared with the baseline method.
Development of a Brain-Computer Interface using Steady State Visual Evoked Potential

Visual Evoked Potential (VEP) is a damped oscillation-like reaction brain wave obtained from a cerebrum vision field by visual stimulation lasting almost one second. VEP is divided into two categories. One is a transient VEP (t-VEP) observed with low stimulation cycle, and the other is a steady-state VEP (ss-VEP) observed with high stimulation cycle. The former is the reaction wave for every stimulus, while the latter is the fused waveform of t-VEPs, and observed as sinusoid. Since ss-VEP is known to synchronize with the stimulation wave, from the brain wave we can decide the illumination frequency. This study aims the development of a device, which can recognize the LED at which patient gazes, when several light emitting diodes (LEDs) illuminating with different driving frequencies. By assigning the LEDs to specific functions, such as calling caregivers, opening/closing a curtain, ON/OFF of TV set, our device can work as an input part of an environment control system for quadriplegic patients. Our device records brain wave from electrodes mounted on ear pads and nose pads of an eyeglass frame, rather than from electrodes on vision cortex, while the SNR was much smaller than vision cortex lead. In order to overcome the less SNR signal processing was employed. The measured brain wave with eyeglass frame electrodes was analyzed with FFT and Autoregressive-model estimate, and we could identify the light source that the patients stared.
Resistance of Dickeya solani strain IPO 2222 to lytic bacteriophage vB_Dsol_D5 (ΦD5) results in fitness tradeoffs for the bacterium during infection

Resistance to bacteriophage infections protects bacteria in phage-full environments, allowing them to survive and multiply in the presence of their viral predators. However, such resistance may cause direct costs for strains linked with the ecological fitness expressed as reduced competitiveness for resources or reduced virulence or both. Unfortunately, limited knowledge exists about such costs paid by phage-resistant plant pathogenic bacteria in their natural environments. This study analyzed the costs of phage resistance paid by broad host phytopathogenic pectinolytic bacterium Dickeya solani both in vitro and in potato (Solanum tuberosum L.) plants. Thirteen D. solani IPO 2222 Tn5 mutants were identified that exhibited resistance to infection caused by lytic bacteriophage vB_Dsol_D5 (ΦD5). The genes disrupted in these 13 mutants encoded proteins involved in the synthesis of the bacterial envelope components (viz. LPS, EPS and capsule). The ability of ΦD5-resistant D. solani mutants to colonize and cause symptoms on potato plants as well as other phenotypes that are known to contribute to the ecological fitness of D. solani in-plant environment, including growth rate, production of effectors, swimming and swarming motility, use of various carbon and nitrogen sources and biofilm formation were assessed. Although phage resistance did not affect most of the phenotypes of ΦD5-resistant D. solani evaluated in vitro, all phage resistant mutants were significantly compromised in their ability to survive on and colonize and cause disease symptoms in potato plants. This study is, to our knowledge, one of few to show the direct link between phage resistance and the fitness of plant pathogenic bacteria and the first one to assess phage-host associations for D. solani.
Live-cell imaging in human colonic monolayers reveals ERK waves limit the stem cell compartment to maintain epithelial homeostasis

The establishment and maintenance of different cellular compartments in tissues is a universal requirement across all metazoans. Maintaining the correct ratio of cell types in time and space allows tissues to form patterned compartments and perform complex functions. Patterning is especially evident in the human colon, where tissue homeostasis is maintained by stem cells in crypt structures that balance proliferation and differentiation. Here we developed a 2D patient derived organoid (PDO) screening platform to study tissue patterning and kinase pathway dynamics in single cells across hundreds of conditions. Using this system, we discovered that waves of Erk signaling induced by apoptotic cells play a critical role in maintaining tissue patterning and homeostasis. If Erk is activated acutely across all cells instead of in wavelike patterns, then tissue patterning, and stem cell maintenance are lost. Conversely, if Erk activity is inhibited, then stem cells become unrestricted and expand dramatically. This work demonstrates that the colonic epithelium requires coordinated Erk signaling dynamics to maintain patterning and tissue homeostasis. Our work reveals how Erk can antagonize stem cells yet support cell replacement and the function of the gut.
Single-cell analysis of skeletal muscle macrophages reveals age-associated functional subpopulations

Tissue-resident macrophages represent a group of highly responsive innate immune cells that acquire diverse functions by polarizing towards distinct subgroups. The subgroups of macrophages that reside in skeletal muscle (SKM) and their changes during aging are poorly characterized. By single-cell transcriptomic analysis, we found that mouse SKM macrophages primarily comprise two large populations, “healing” LYVE1+ and “proinflammatory” LYVE1-macrophages. SKM macrophages were further classified into four functional subgroups based on the expression levels of another cell-surface marker, MHCII: LYVE1+/MHCII-lo (similar to alternatively activated M2), LYVE1-/MHCII-hi (similar to classically activated M1), and two new subgroups, LYVE1+/MHCII-hi and LYVE1-/MHCII-lo. Notably, the new subgroup LYVE1+/MHCII-hi had traits of both M2 and M1 macrophages, while the other new subgroup, LYVE1-/MHCII-lo, expressed high levels of mRNAs encoding cytotoxicity proteins. Flow cytometric analysis validated the presence of the four macrophage subgroups in SKM. In old SKM, LYVE1-macrophages were more abundant than LYVE1+ macrophages. Furthermore, complementary unsupervised classification revealed the emergence of specific macrophage subclusters expressing abundant proinflammatory markers, including S100a8 and S100a9 in aged SKM. In sum, our study has identified dynamically polarized mouse SKM macrophages and further uncovered the contribution of specific macrophage subpopulations to the proinflammatory status in old SKM.
Archimedes Optimization Algorithm with Deep Belief Network Based Mobile Network Traffic Prediction for 5G Cellular Networks

Recent developments of 5G cellular networks has resulted in a drastic increase in the amount of mobile network traffic. Proper designing of time series models for predicting the mobile network becomes essential to improve the quality of service (QoS). Modeling and prediction of 5G cellular networks act as a vital part in accomplishing favorable resource allocation by suitable allocation of bandwidth and concurrently saves maximum network usage. In this aspect, this study focuses on the design of Archimedes Optimization Algorithm with Deep Belief Network (DBN) based mobile network traffic prediction (AOADBN-MTP) model for 5G cellular networks. The proposed AOADBN-MTP technique follows a 3-stage process namely pre-processing, prediction, and hyperparameter tuning. In addition, the DBN model is applied to predict the mobile traffic flow effectively and the performance can be further improved by optimally selecting the initial hyperparameter values of the DBN model using the AOA. In order to showcase the enhanced classification performance of the AOADBN-MTP technique, a series of simulations were carried out and the results are investigated under varying runs. The simulation results reported the betterment of the AOADBN-MTP technique with the minimum average MAPE of 0.034 and running time of 0.1373s.
Power-Loss Ride-Through in a Cascaded H-Bridge Inverter Fed Vector Controlled Induction Motor Drive

A Cascaded H-Bridge (CHB) inverter fed drives are quite popular in high power industrial applications. But these drives are very sensitive to power supply disturbances like voltage sags or short-duration power interruptions on the input grid side, which causes the drive to trip on undervoltage protection, thus resulting in production losses. Therefore, it is necessary to employ techniques that improve the resilience of the drive by enabling it to smoothly ’ride-through’ such short duration disturbances. The traditional methods for ride-through in CHB-fed drives generally require extra hardware or DC link voltage sensors, which increases the cost of the drive. This paper proposes an economical and simple method of power-loss ride through for CHB multilevel inverter fed vector controlled induction motor drives. In this method, the kinetic energy stored in the rotating mass is recovered, without using the information of DC link voltages. This proposed strategy maintains all the DC link voltages constant during any momentary voltage sags or supply interruptions by controlling the voltage space phasor. The proposed method provides a reliable ride-through capability without using additional hardware or voltage sensors. The proposed scheme is experimentally validated on a 7-level CHB inverter fed vector controlled 30 kW induction motor drive.
Dense Depth-Map Estimation Based on Fusion of Event Camera and Sparse LiDAR

Depth-map estimation reflects the geometry of the visible surface in the environment directly and plays an important role in perception and decision for intelligent robots. However, sparse LiDAR only provides low-resolution depth information, which is a huge challenge for accurate sensing algorithms. To address this problem, this article proposes a novel fusion framework to generate dense depth-map based on event camera and sparse LiDAR. The approach uses the geometric information provided by the point cloud as prior knowledge and clusters point cloud data by an improved density clustering algorithm. Combined with the 3-D surface model of each cluster, the approach can provide 3-D reconstructions of the coordinate points of events and further obtain the dense-depth map by depth expansion and hole filling. Finally, we deploy our approach in MVSEC datasets and real-world applications. Experimental results show that, compared with other approaches, our approach can obtain more accurate depth information.
Privacy-preserving Voltage Consensus Control for Heterogeneous Energy Storage Systems in Islanded Microgrids Based on Virtual Node Mechanism

With the rapid development of microgrids (MGs), the level of terminal intelligence is gradually improving. Although the data processing and autonomous decision-making capabilities of the terminal have been greatly improved, the likelihood of terminal information being disclosed to its adversary also increases. To solve this problem, this paper investigates the voltage restoration for energy storage systems (ESSs) in an islanded AC microgrid as the object to study the distributed privacy preserving consensus strategy for MGs, and this method can be easily extended to other research objects. Firstly, considering the effect of droop coefficients, the model of the ESSs is considered as a linear heterogeneous multiagent system (MAS) model. Furthermore, virtual node mechanism, which avoids divulging the initial condition by inserting a virtual node associated with the real node to communicate with others instead of the real node, is adopted to make the output voltage of energy storage (ES) agents exactly converge to the reference value. It should be noted that the information flow between each pair nodes is directed and only from virtual node to the real one. After that, we carry out the consensus analysis and privacy-preservation of the controlled system. Finally, the effectiveness of the proposed control scheme is validated in the MATLAB/Simulink environment.
Capacity Optimization for Wind/Photovoltaic Prosumers and Shared Energy Storage Operator: A Cooperative Game Approach

Game theory is applied in this paper to model the capacity planning of a shared energy system in a resident community comprised of energy storage batteries and prosumers with renewable energy resources, such as wind turbines and photovoltaic panel facilities. Cooperative game model is built to realize capacity optimization of renewable energy and energy storage system by taking prosumers and the energy storage operator as participants and their annual benefits as payoffs. Simulation results illustrates the feasibility of the proposed model. Comparison among the Nash equilibriums under different kinds of alliance structures shows that complete cooperation brings out more benefits and saves total installed capacity of the system, while competition will lead to decrease of benefits.