一种脉冲电流工况下晶闸管缓冲电路的优化方法

对脉冲工况下超导磁体失超保护系统的晶闸管阀组缓冲回路参数进行设计和优化。基于晶闸管反向恢复电流的指数衰减模型建立了晶闸管关断时刻的电流数学模型。通过测试实验获得关键参数之间的关系并结合晶闸管性能及系统要求在Matlab中建立晶闸管电流反向恢复模型。考虑关断时刻电流下降率、反向恢复电压峰值等性能指标要求及回路研制费用,提出了一种脉冲工况下晶闸管缓冲回路的参数设计及优化方法。在Matlab中搭建失超保护系统模型,对比优化前后缓冲回路对系统在晶闸管关断时刻电气性能的影响,仿真结果显示,相比于原参数,最优参数下,反向恢复电压峰值降低了11%,反向恢复电压变化率峰值降低了43%。同时,回路制造成本降低为原先的1/7。

Efficient Energy and Delay Reduction Model for Wireless Sensor Networks

In every network,delay and energy are crucial for communication and network life.In wireless sensor networks,many tiny nodes create networks with high energy consumption and compute routes for better communication.Wireless Sensor Networks(WSN)is a very complex scenario to compute minimal delay with data aggregation and energy efficiency.In this research,we compute minimal delay and energy efficiency for improving the quality of service of any WSN.The proposed work is based on energy and distance parameters as taken dependent variables with data aggregation.Data aggregation performs on different models,namely Hybrid-Low Energy Adaptive Clustering Hierarchy(H-LEACH),Low Energy Adaptive Clustering Hierarchy(LEACH),and Multi-Aggregator-based Multi-Cast(MAMC).The main contribution of this research is to a reduction in delay and optimized energy solution,a novel hybrid model design in this research that ensures the quality of service in WSN.This model includes a whale optimization technique that involves heterogeneous functions and performs optimization to reach optimized results.For cluster head selection,Stable Election Protocol(SEP)protocol is used and Power-Efficient Gathering in Sensor Information Systems(PEGASIS)is used for driven-path in routing.Simulation results evaluate that H-LEACH provides minimal delay and energy consumption by sensor nodes.In the comparison of existing theories and our proposed method,HLEACH is providing energy and delay reduction and improvement in quality of service.MATLAB 2019 is used for simulation work.

ILSM:Incorporated Lightweight Security Model for Improving QOS in WSN

In the network field,Wireless Sensor Networks(WSN)contain prolonged attention due to afresh augmentations.Industries like health care,traffic,defense,and many more systems espoused the WSN.These networks contain tiny sensor nodes containing embedded processors,TinyOS,memory,and power source.Sensor nodes are responsible for forwarding the data packets.To manage all these components,there is a need to select appropriate parameters which control the quality of service of WSN.Multiple sensor nodes are involved in transmitting vital information,and there is a need for secure and efficient routing to reach the quality of service.But due to the high cost of the network,WSN components have limited resources to manage the network.There is a need to design a lightweight solution that ensures the quality of service in WSN.In this given manner,this study provides the quality of services in a wireless sensor network with a security mechanism.An incorporated hybrid lightweight security model is designed in which random waypoint mobility(RWM)model and grey wolf optimization(GWO)is used to enhance service quality and maintain security with efficient routing.MATLAB version 16 andNetwork Stimulator 2.35(NS2.35)are used in this research to evaluate the results.The overall cost factor is reduced at 60%without the optimization technique and 90.90%reduced by using the optimization technique,which is assessed by calculating the signal-to-noise ratio,overall energy nodes,and communication overhead.

Improved-Equalized Cluster Head Election Routing Protocol for Wireless Sensor Networks

Throughout the use of the small battery-operated sensor nodes encou-rage us to develop an energy-efficient routing protocol for wireless sensor networks(WSNs).The development of an energy-efficient routing protocol is a mainly adopted technique to enhance the lifetime of WSN.Many routing protocols are available,but the issue is still alive.Clustering is one of the most important techniques in the existing routing protocols.In the clustering-based model,the important thing is the selection of the cluster heads.In this paper,we have proposed a scheme that uses the bubble sort algorithm for cluster head selection by considering the remaining energy and the distance of the nodes in each cluster.Initially,the bubble sort algorithm chose the two nodes with the maximum remaining energy in the cluster and chose a cluster head with a small distance.The proposed scheme performs hierarchal routing and direct routing with some energy thresholds.The simulation will be performed in MATLAB to justify its performance and results and compared with the ECHERP model to justify its performance.Moreover,the simulations will be performed in two scenarios,gate-way-based and without gateway to achieve more energy-efficient results.

An Automated and Real-time Approach of Depression Detection from Facial Micro-expressions

Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful.Despite speech and gait,facial expressions have valuable clues to depression.This study proposes a depression detection system based on facial expression analysis.Facial features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time.Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a patient.The experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on SVM.Moreover,the results proved the superiority of the reported approach over state-of-the-art methods.

CNR:A Cluster-Based Solution for Connectivity Restoration for Mobile WSNs

Wireless Sensor Networks(WSNs)are an integral part of the Internet of Things(IoT)and are widely used in a plethora of applications.Typically,sensor networks operate in harsh environments where human intervention is often restricted,which makes battery replacement for sensor nodes impractical.Node failure due to battery drainage or harsh environmental conditions poses serious challenges to the connectivity of the network.Without a connectivity restoration mechanism,node failures ultimately lead to a network partition,which affects the basic function of the sensor network.Therefore,the research community actively concentrates on addressing and solving the challenges associated with connectivity restoration in sensor networks.Since energy is a scarce resource in sensor networks,it becomes the focus of research,and researchers strive to propose new solutions that are energy efficient.The common issue that is well studied and considered is how to increase the network’s life span by solving the node failure problem and achieving efficient energy utilization.This paper introduces a Clusterbased Node Recovery(CNR)connectivity restoration mechanism based on the concept of clustering.Clustering is a well-known mechanism in sensor networks,and it is known for its energy-efficient operation and scalability.The proposed technique utilizes a distributed cluster-based approach to identify the failed nodes,while Cluster Heads(CHs)play a significant role in the restoration of connectivity.Extensive simulations were conducted to evaluate the performance of the proposed technique and compare it with the existing techniques.The simulation results show that the proposed technique efficiently addresses node failure and restores connectivity by moving fewer nodes than other existing connectivity restoration mechanisms.The proposed mechanism also yields an improved field coverage as well as a lesser number of packets exchanged as compared to existing state-of-the-art mechanisms.

An Efficient Connectivity Restoration Technique(ECRT)for Wireless Sensor Network

Node failure in Wireless Sensor Networks(WSNs)is a fundamental problem because WSNs operate in hostile environments.The failure of nodes leads to network partitioning that may compromise the basic operation of the sensor network.To deal with such situations,a rapid recovery mechanism is required for restoring inter-node connectivity.Due to the immense importance and need for a recovery mechanism,several different approaches are proposed in the literature.However,the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration.Moreover,most of these approaches rely on the excessive mobility of nodes for restoration connectivity that affects both coverage and energy consumption.This paper proposes a novel technique called ECRT(Efficient Connectivity Restoration Technique).This technique is capable of restoring connectivity due to single and multiple node failures.ECRT achieves energy efficiency by transmitting a minimal number of control packets.It is also coverage-aware as it relocates minimal nodes while trying to restore connectivity.With the help of extensive simulations,it is proven that ECRT is effective in connectivity restoration for single and multiple node failures.Results also show that ECRT exchanges a much smaller number of packets than other techniques.Moreover,it also yields the least reduction in field coverage,proving its versatility for connectivity restoration.

A Weighted Spatially Constrained Finite Mixture Model for Image Segmentation

Spatially Constrained Mixture Model(SCMM)is an image segmentation model that works over the framework of maximum a-posteriori and Markov Random Field(MAP-MRF).It developed its own maximization step to be used within this framework.This research has proposed an improvement in the SCMM’s maximization step for segmenting simulated brain Magnetic Resonance Images(MRIs).The improved model is named as the Weighted Spatially Constrained Finite Mixture Model(WSCFMM).To compare the performance of SCMM and WSCFMM,simulated T1-Weighted normal MRIs were segmented.A region of interest(ROI)was extracted from segmented images.The similarity level between the extracted ROI and the ground truth(GT)was found by using the Jaccard and Dice similarity measuring method.According to the Jaccard similarity measuring method,WSCFMM showed an overall improvement of 4.72%,whereas the Dice similarity measuring method provided an overall improvement of 2.65%against the SCMM.Besides,WSCFMM signicantly stabilized and reduced the execution time by showing an improvement of 83.71%.The study concludes that WSCFMM is a stable model and performs better as compared to the SCMM in noisy and noise-free environments.

Growth, Yield and Quality Performance of Pearl Millet (<i>Pennisetum americanum</i>L.)

Livestock industry of Pakistan is expanding day by day. To meet its growing demand high fodder yielding and nutritious varieties of fodder crops are needed. Pearl millet (Pennisetum americanum L.) is an excellent choice for this purpose. In order to explore the possibility of the better yield potential varieties of pearl millet performed in a good manner under agro ecological conditions of Faisalabad during the year 2012. A field experiment was conducted at Agronomic Research Area, University of Agriculture, Faisalabad, Pakistan. Randomized complete block design was used with three replications;the net plot size was 1.8 m × 6 m. The experiment was comprised of nine millet varieties named Cholistani Bajra, Barani Bajra, MB-87, Sargodha Bajra 2011, 18-BY, Super Bajra-1, PARC-MS-2, 86-M-52 and FB-822. All other agronomic practices were kept normal and constant. Data on yield and yield components were recorded by standard procedure. Significant results were recorded among the varieties for forage growth and yield. The variety 86-M-52 produced maximum forage and dry matter yield because of more number of leaves (14), leaf area (3540.1 cm2) followed by Sargodha Bajra-2011. All cultivars have statistically significant differences in respect of quality characteristics. However, non-significant differences were observed among cultivars regarding ash contents. The cultivar Sargodha Bajra-2011 has the highest crude protein (10.347%) and the cultivar FB-822 has the minimum crude protein percentage (6.733%). While PARC-MS-2 has the highest crude fiber percentage (34.667%) but variety MB-87 has the minimum crude fiber (24.333%). Variety 86-M-52 proved better for getting higher forage yield followed by Sargodha Bajra-2011 than all other varieties. Sargodha Bajra-2011 is the best cultivar that performed well in respect of quality parameters under irrigated conditions of Faisalabad.

Development and challenges of green food in China

Green food in China refers to a wide array of primary and processed agricultural products that are safe,nutritious and of high quality for human consumption.Green food has been certified and produced following the principle of sustainability since the 1990 s,making historic achievements in providing quality food,protecting the environment,increasing farmer income,and nurturing agricultural brands over the past 30 years in China.Today,the green food industry enters a steady-growth stage in terms of cultivation area,product number and sales.This article summarizes the history of the development of green food in China and current achievements,analyze major challenges that may hamper further development of the industry,and propose strategies to address these challenges,i.e.,optimization of the food supply chain,deep food processing,and utilization of food wastes.