Modeling load distribution for rural photovoltaic grid areas using image recognition

Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.

Hound:a parallel image distribution system for cluster based on Docker

Current applications,consisting of multiple replicas,are packaged into lightweight containers with their execution dependencies.Considering the dominant impact of distribution efficiency of gigantic images on container startup(e.g.,distributed deep learning application),the image“warm-up”technique which prefetches images of these replicas to destination nodes in the cluster is proposed.However,the current image“warm-up”technique solely focuses on identical image distribution,which fails to take effect when distributing different images to destination nodes.To address this problem,this paper proposes Hound,a simple but efficient cluster image distribution system based on Docker.To support diverse image distribution requests of cluster nodes,Hound additionally adopts node-level parallelism(i.e.,downloading images to destination nodes in parallel)to further improve the efficiency of image distribution.The experimental results demonstrate Hound outperforms Docker,kubernetes container runtime interface(CRI-O),and Docker-compose in terms of image distribution performance when cluster nodes request different images.Moreover,the high scalability of Hound is evaluated in the scenario of ten nodes.

Neural stem cell-derived exosomes promote mitochondrial biogenesis and restore abnormal protein distribution in a mouse model of Alzheimer's disease

Mitochondrial dysfunction is a hallmark of Alzheimer’s disease.We previously showed that neural stem cell-derived extracellular vesicles improved mitochondrial function in the cortex of AP P/PS1 mice.Because Alzheimer’s disease affects the entire brain,further research is needed to elucidate alterations in mitochondrial metabolism in the brain as a whole.Here,we investigated the expression of several important mitochondrial biogenesis-related cytokines in multiple brain regions after treatment with neural stem cell-derived exosomes and used a combination of whole brain clearing,immunostaining,and lightsheet imaging to clarify their spatial distribution.Additionally,to clarify whether the sirtuin 1(SIRT1)-related pathway plays a regulatory role in neural stem cell-de rived exosomes interfering with mitochondrial functional changes,we generated a novel nervous system-SIRT1 conditional knoc kout AP P/PS1mouse model.Our findings demonstrate that neural stem cell-de rived exosomes significantly increase SIRT1 levels,enhance the production of mitochondrial biogenesis-related fa ctors,and inhibit astrocyte activation,but do not suppress amyloid-βproduction.Thus,neural stem cell-derived exosomes may be a useful therapeutic strategy for Alzheimer’s disease that activates the SIRT1-PGC1αsignaling pathway and increases NRF1 and COXIV synthesis to improve mitochondrial biogenesis.In addition,we showed that the spatial distribution of mitochondrial biogenesis-related factors is disrupted in Alzheimer’s disease,and that neural stem cell-derived exosome treatment can reverse this effect,indicating that neural stem cell-derived exosomes promote mitochondrial biogenesis.

The Uptake and Distribution Evidence of Nano-and Microplastics in vivo after a Single High Dose of Oral Exposure

Objective Tissue uptake and distribution of nano-/microplastics was studied at a single high dose by gavage in vivo.Methods Fluorescent microspheres(100 nm,3μm,and 10μm)were given once at a dose of 200 mg/(kg∙body weight).The fluorescence intensity(FI)in observed organs was measured using the IVIS Spectrum at 0.5,1,2,and 4 h after administration.Histopathology was performed to corroborate these findings.Results In the 100 nm group,the FI of the stomach and small intestine were highest at 0.5 h,and the FI of the large intestine,excrement,lung,kidney,liver,and skeletal muscles were highest at 4 h compared with the control group(P<0.05).In the 3μm group,the FI only increased in the lung at 2 h(P<0.05).In the 10μm group,the FI increased in the large intestine and excrement at 2 h,and in the kidney at 4 h(P<0.05).The presence of nano-/microplastics in tissues was further verified by histopathology.The peak time of nanoplastic absorption in blood was confirmed.Conclusion Nanoplastics translocated rapidly to observed organs/tissues through blood circulation;however,only small amounts of MPs could penetrate the organs.

A"messenger zone hypothesis"based on the visual three-dimensional spatial distribution of motoneurons innervating deep limb muscles

Coordinated contraction of skeletal muscles relies on selective connections between the muscles and multiple classes of the spinal motoneuro ns.Howeve r,current research on the spatial location of the spinal motoneurons innervating differe nt muscles is limited.In this study,we investigated the spatial distribution and relative position of different motoneurons that control the deep muscles of the mouse hindlimbs,which were innervated by the obturator nerve,femoral nerve,inferior gluteal nerve,deep pe roneal nerve,and tibial nerve.Locations were visualized by combining a multiplex retrograde tracking technique compatible with three-dimensional imaging of solvent-cleared o rgans(3DISCO)and 3-D imaging technology based on lightsheet fluorescence microscopy(LSFM).Additionally,we propose the hypothesis that"messenger zones"exist as interlaced areas between the motoneuron pools that dominate the synergistic or antagonist muscle groups.We hypothesize that these interlaced neurons may participate in muscle coordination as messenger neurons.Analysis revealed the precise mutual positional relationships among the many motoneurons that innervate different deep muscles of the mouse.Not only do these findings update and supplement our knowledge regarding the overall spatial layout of spinal motoneurons that control mouse limb muscles,but they also provide insights into the mechanisms through which muscle activity is coordinated and the architecture of motor circuits.

Quantum Image Searching Based on Probability Distributions

A quantum image searching method is proposed based on the probability distributions of the readouts from the quantum measurements. It is achieved by using low computational resources which are only a single Hadamard gate combined with m + 1 quantum measurement operations. To validate the proposed method, a simulation experiment is used where the image with the highest similarity value of 0.93 to the particular test image is retrieved as the search result from 4 × 4 binary image database. The proposal provides a basic step for designing a search engine on quantum computing devices where the image in the database is retrieved based on its similarity to the test image.

Method of Images to Study the Charge Distribution in Cases of Potentials Deviating from Coulomb’s Law

The method of images is used to study the charge distribution for cases where Coulomb’s law deviates from the inverse square law. This method shows that in these cases some of the charge goes to the surface, while the remainder charge distributed over the volume of the conductor. In accord with the experimental work, we show that the charge distribution will depend on the photon rest mass and is very sensitive to it;a very small value of the rest of mass of the photon will create deviation from Coulomb’s law.

Multiple Images Steganography of JPEG Images Based on Optimal Payload Distribution

Multiple images steganography refers to hiding secret messages in multiple natural images to minimize the leakage of secret messages during transmission.Currently,the main multiple images steganography algorithms mainly distribute the payloads as sparsely as possible inmultiple cover images to improve the detection error rate of stego images.In order to enable the payloads to be accurately and efficiently distributed in each cover image,this paper proposes a multiple images steganography for JPEG images based on optimal payload redistribution.Firstly,the algorithm uses the principle of dynamic programming to redistribute the payloads of the cover images to reduce the time required in the process of payloads distribution.Then,by reducing the difference between the features of the cover images and the stego images to increase the detection error rate of the stego images.Secondly,this paper uses a data decomposition mechanism based on Vandermonde matrix.Even if part of the data is lost during the transmission of the secret messages,as long as the data loss rate is less than the data redundancy rate,the original secret messages can be recovered.Experimental results show that the method proposed in this paper improves the efficiency of payloads distribution compared with existing multiple images steganography.At the same time,the algorithm can achieve the optimal payload distribution of multiple images steganography to improve the anti-statistical detection performance of stego images.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

Effects of disparity distribution on visual comfort for multiple objects of stereoscopic images

With the development of stereoscopic technology, more attention is attracted on the stereoscopic three-dimensional(S3 D) content and service, and researches on images and videos have emerged in large numbers. This paper focuses mainly on visual comfort affected by characteristics of disparity for multiple objects. To find the relationship between disparity distribution and visual comfort perception, several subject evaluation experiments are done. The study contains two spatial distribution types of disparity: 1) only one of the foreground objects has zero disparity; 2) one of the foreground objects has positive disparity, while the other one has negative disparity. The experimental results and relative regression analysis provide appropriate relationship between disparity distribution and visual comfort for both conditions, which is significant to meet the applicant field in S3 D content acquisition, display adjustment and quality evaluation.

Development of a mass model in estimating weight-wise particle size distribution using digital image processing

Particle size distribution of coarse aggregates through mechanical sieving gives results in terms of cumu- lative mass percent. But digital image processing generated size distribution of particles, while being fast and accurate, is often expressed in terms of area function or number of particles. In this paper, a mass model is developed which converts the image obtained size distribution to mass-wise distribution, mak- ing it readily comparable to mechanical sieving data. The concept of weight/particle ratio is introduced for mass reconstruction from 2D images of particle aggregates. Using this mass model, the effects of several particle shape parameters （such as major axis, minor axis, and equivalent diameter） on sieve-size of the particles is studied. It is shown that the sieve-size of a particle strongly depend upon the shape param- eters, 91% of its variation being explained by major axis, minor axis, bounding box length and equivalent diameter. Furthermore, minor axis gives an overall accurate estimate of particle sieve-size, error in mean size （D-50） being just 0.4%. However, sieve-size of smaller particles （〈20 ram） strongly depends upon the length of the smaller arm of the bounding box enclosing them and sieve-sizes of larger particles （〉20 mm） are highly correlated to their equivalent diameters. Multiple linear regression analysis has been used to generate overall mass-wise particle size distribution, considering the influences of all these shape parameters on particle sieve-size. Multiple linear regression generated overall mass-wise particle size distribution shows a strong correlation with sieve generated data. The adjusted R-square value of the regression analysis is found to be 99 percent （w.r,t cumulative frequency）. The method proposed in this paper provides a time-efficient way of producing accurate （up to 99%） mass-wise PSD using digital image processing and it can be used effectively to renlace the mechanical sieving.

RAYLEIGH-DISTRIBUTION BASED MINIMUM ERROR THRESHOLDING FOR SAR IMAGES

This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other MET algorithms based on various models of histogram. The hypothesis of using Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the comparison results obtained show that the proposed new algorithm has good performance in thresholding SAR images.

The digital mapping of satellite images under no ground control and the distribution of landform, blue ice and meteorites in the Grove Mountains, Antarctica

The colorful satellite image maps with the scale of 1∶100000 were made by processing the parameters-on-satellite under the condition of no data of field surveying. The purpose is to ensure the smooth performance of the choice of expedition route, navigation and research task before the Chinese National Antarctic Research Expedition (CHINARE) first made researches on the Grove Mountains. Moreover, on the basis of the visual interpretation of the satellite image, we preliminarily analyze and discuss the relief and landform, blue ice and meteorite distribution characteristics in the Grove Mountains.

Airflow Distribution Measurements around the Human Body Using a Thermal Manikin by Particle Image Velocimetry

The human body is a heat source in a room. As the human body has a complex shape, it is difficult to accurately measure the airflow distribution around the human body using a conventional anemometer. This study measured the airflow distribution around a thermal manikin acting as a human body by visualization and particle image velocimetry (PIV). The thermal manikin was 1700 mm in height, and its surface temperature was set to 30oC. The experiments were performed in the conditions when the manikin was seated on a chair. The ambient air temperature and wind velocity were experimental variables. The airflow distribution around the manikin was reported by considering the relationships between convection and ambient wind velocity. There were no differences in the airflow distribution around the manikin due to the ambient air temperature when the wind velocity in the chamber was set as 1.0 m/s. Hence, it was assumed that the ambient wind velocity was dominant in this condition. Various airflow distributions were formed around the manikin due to the difference between the body surface temperature and the ambient air temperature in the case where the wind velocity in the chamber was set to approximately equal to 0.0 m/s.

Population Spatial Distribution Based on Luojia 1-01 Nighttime Light Image:A Case Study of Beijing

With the continuous development of urbanization in China,the country’s growing population brings great challenges to urban development.By mastering the refined population spatial distribution in administrative units,the quantity and agglomeration of population distribution can be estimated and visualized.It will provide a basis for a more rational urban planning.This paper takes Beijing as the research area and uses a new Luojia1-01 nighttime light image with high resolution,land use type data,Points of Interest(POI)data,and other data to construct the population spatial index system,establishing the index weight based on the principal component analysis.The comprehensive weight value of population distribution in the study area was then used to calculate the street population distribution of Beijing in 2018.Then the population spatial distribution was visualize using GIS technology.After accuracy assessments by comparing the result with the WorldPop data,the accuracy has reached 0.74.The proposed method was validated as a qualified method to generate population spatial maps.By contrast of local areas,Luojia 1-01 data is more suitable for population distribution estimation than the NPP/VIIRS(Net Primary Productivity/Visible infrared Imaging Radiometer)nighttime light data.More geospatial big data and mathematical models can be combined to create more accurate population maps in the future.

Beam and image experiment of beam deflection electron gun for distributed X-ray sources

Distributed X-ray sources comprise a single vacuum chamber containing multiple X-ray sources that are triggered and emit X-rays at a specific time and location. This process facilitates an application for innovative system concepts in X-ray and computer tomography. This paper proposes a novel electron beam focusing, shaping,and deflection electron gun for distributed X-ray sources.The electron gun uses a dispenser cathode as an electron emitter, a mesh grid to control emission current, and two electrostatic lenses for beam shaping, focusing, and deflection. Novel focusing and deflecting electrodes were designed to increase the number of focal spots in the distributed source. Two identical half-rectangle opening electrodes are controlled by adjusting the potential of the two electrodes to control the electron beam trajectory, and then, multifocal spots are obtained on the anode target. The electron gun can increase the spatial density of the distributed X-ray sources, thereby improving the image quality. The beam experimental results show that the focal spot sizes of the deflected(deflected amplitude 10.5 mm)and non-deflected electron beams at full width at half maximum are 0.80 mm 90.50 mm and 0.55 mm 90.40 mm, respectively(anode voltage 160 kV; beam current 30 mA). The imaging experimental results demonstrate the excellent spatial resolution and time resolution of an imaging system built with the sources, which has an excellent imaging effect on a field-programmable gate array chip and a rotating metal disk.

Effective distributed convolutional neural network architecture for remote sensing images target classification with a pre-training approach

How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

Refined Sparse Representation Based Similar Category Image Retrieval

Given one specific image,it would be quite significant if humanity could simply retrieve all those pictures that fall into a similar category of images.However,traditional methods are inclined to achieve high-quality retrieval by utilizing adequate learning instances,ignoring the extraction of the image’s essential information which leads to difficulty in the retrieval of similar category images just using one reference image.Aiming to solve this problem above,we proposed in this paper one refined sparse representation based similar category image retrieval model.On the one hand,saliency detection and multi-level decomposition could contribute to taking salient and spatial information into consideration more fully in the future.On the other hand,the cross mutual sparse coding model aims to extract the image’s essential feature to the maximumextent possible.At last,we set up a database concluding a large number of multi-source images.Adequate groups of comparative experiments show that our method could contribute to retrieving similar category images effectively.Moreover,adequate groups of ablation experiments show that nearly all procedures play their roles,respectively.

Low complexity DCT-based distributed source coding with Gray code for hyperspectral images

To compress hyperspectral images, a low complexity discrete cosine transform （DCT）-based distributed source coding （DSC） scheme with Gray code is proposed. Unlike most of the existing DSC schemes, which utilize transform in spatial domain, the proposed algorithm applies transform in spectral domain. Set-partitioning-based approach is applied to reorganize DCT coefficients into waveletlike tree structure and extract the sign, refinement, and significance bitplanes. The extracted refinement bits are Gray encoded. Because of the dependency along the line dimension of hyperspectral images, low density paritycheck-（LDPC）-based Slepian-Wolf coder is adopted to implement the DSC strategy. Experimental results on airborne visible/infrared imaging spectrometer （AVIRIS） dataset show that the proposed paradigm achieves up to 6 dB improvement over DSC-based coders which apply transform in spatial domain, with significantly reduced computational complexity and memory storage.

Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

Lung cancer is the leading cause of mortality in the world affectingboth men and women equally.When a radiologist just focuses on the patient’sbody, it increases the amount of strain on the radiologist and the likelihoodof missing pathological information such as abnormalities are increased.One of the primary objectives of this research work is to develop computerassisteddiagnosis and detection of lung cancer. It also intends to make iteasier for radiologists to identify and diagnose lung cancer accurately. Theproposed strategy which was based on a unique image feature, took intoconsideration the spatial interaction of voxels that were next to one another.Using the U-NET+Three parameter logistic distribution-based technique, wewere able to replicate the situation. The proposed technique had an averageDice co-efficient (DSC) of 97.3%, a sensitivity of 96.5% and a specificity of94.1% when tested on the Luna-16 dataset. This research investigates howdiverse lung segmentation, juxta pleural nodule inclusion, and pulmonarynodule segmentation approaches may be applied to create Computer AidedDiagnosis (CAD) systems. When we compared our approach to four otherlung segmentation methods, we discovered that ours was the most successful.We employed 40 patients from Luna-16 datasets to evaluate this. In termsof DSC performance, the findings demonstrate that the suggested techniqueoutperforms the other strategies by a significant margin.