Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

A brain tumor is a mass or growth of abnormal cells in the brain.In children and adults,brain tumor is considered one of the leading causes of death.There are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)tumors.Diagnosing brain tumors as early as possible is essential,as this can improve the chances of successful treatment and survival.Considering this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain tumors.These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas.The dataset consists of 120 patients.The pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region correctly.We have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient loss.From pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%accuracy.In contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

Th-Shaped Tunable Multi-Band Antenna for Modern Wireless Applications

A compact,reconfigurable antenna supporting multiple wireless services with a minimum number of switches is found lacking in literature and the same became the focus and outcome of this work.It was achieved by designing a Th-Shaped frequency reconfigurable multi-band microstrip planar antenna,based on use of a single switch within the radiating structure of the antenna.Three frequency bands(i.e.,2007–2501 MHz,3660–3983MHz and 9341–1046 MHz)can be operated with the switch in the ON switch state.In the OFF state of the switch,the antenna operates within the 2577–3280MHz and 9379–1033MHz Bands.The proposed antenna shows an acceptable input impedance match with Voltage Standing Wave Ratio(VSWR)less than 1.2.The peak radiation efficiency of the antenna is 82%.A reasonable gain is obtained from 1.22 to 3.31 dB within the operating bands is achieved.The proposed antenna supports UniversalMobile Telecommunication System(UMTS)-1920 to 2170 MHz,Worldwide Interoperability and Microwave Access(WiMAX)/Wireless Broadband/(Long Term Evolution)LTE2500–2500 to 2690 MHz,Fifth Generation(5G)-2500/3500 MHz,Wireless Fidelity(Wi-Fi)/Bluetooth-2400 to 2480 MHz,and Satellite communication applications in X-Band-8000 to 12000 MHz.The overall planar dimension of the proposed antenna is 40×20mm2.The antennawas designed,along with the parametric study,using Electromagnetic(EM)simulation tool.The antenna prototype is fabricated for experimental validation with the simulated results.The proposed antenna is low profile,tunable,lightweight,cheap to fabricate and highly efficient and hence is deemed suitable for use in modern wireless communication electronic devices.

A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the world.Chest X-ray is considered very useful and less time-consuming for monitoring COVID disease.No doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is needed.To address this issue,we proposed to utilize deep learning approaches.In this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients efficiently.The performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray category.The proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of accuracy.Our system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 patients.Also,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

Power Scheduling with Max User Comfort in Smart Home:Performance Analysis and Tradeoffs

The smart grid has enabled users to control their home energy more effectively and efficiently.A home energy management system(HEM)is a challenging task because this requires the most effective scheduling of intelligent home appliances to save energy.Here,we presented a meta-heuristic-based HEM system that integrates the Greywolf Algorithm(GWA)and Harmony Search Algorithms(HSA).Moreover,a fusion initiated on HSA and GWA operators is used to optimize energy intake.Furthermore,many knapsacks are being utilized to ensure that peak-hour load usage for electricity customers does not surpass a certain edge.Hybridization has proven beneficial in achieving numerous objectives simultaneously,decreasing the peak-to-average ratio and power prices.Widespread MATLAB simulations are cast-off to evaluate the routine of the anticipated method,Harmony GWA(HGWA).The simulations are for a multifamily housing complex outfitted with various cool gadgets.The simulation results indicate that GWA functions better regarding cost savings than HSA.In reputes of PAR,HSA is significantly more effective than GWA.The suggested method reduces costs for single and ten-house construction by up to 2200.3 PKR,as opposed to 503.4 in GWA,398.10 in HSA and 640.3 in HGWA.The suggested approach performed better than HSA and GWA in PAR reduction.For single-family homes in HGWA,GWA and HSA,the reduction in PAR is 45.79%,21.92%and 20.54%,respectively.The hybrid approach,however,performs better than the currently used nature-inspired techniques in terms of Cost and PAR.

Automatic Detection of Outliers in Multi-Channel EMG Signals Using MFCC and SVM

The automatic detection of noisy channels in surface Electromyogram(sEMG)signals,at the time of recording,is very critical in making a noise-free EMG dataset.If an EMG signal contaminated by high-level noise is recorded,then it will be useless and can’t be used for any healthcare application.In this research work,a new machine learning-based paradigm is proposed to automate the detection of low-level and high-level noises occurring in different channels of high density and multi-channel sEMG signals.A modified version of mel fre-quency cepstral coefficients(mMFCC)is proposed for the extraction of features from sEMG channels along with other statistical parameters i-e complexity coef-ficient,hurst exponent,and root mean square.Several state-of-the-art classifiers such as Support Vector Machine(SVM),Ensemble Bagged Trees,Ensemble Sub-space Discriminant,and Logistic Regression are used to automatically identify an EMG channel either bad or good based on these extracted features.Comparison-based analyses of these classifiers have also been considered based on total classi-fication accuracy,prediction speed(observations/sec),and processing time.The proposed method is tested on 320 simulated EMG channels as well as 640 experi-mental EMG channels.SVM is used as our main classifier for the detection of noisy channels which gives a total classification accuracy of 99.4%for simulated EMG channels whereas accuracy of 98.9%is achieved for experimental EMG channels.

中国与美国和欧盟智能电网之比较研究(英文)

电力系统正面临着全世界社会和经济发展带来的众多挑战。很多国家和地区都把智能电网视为应对21世纪电力需求变化的新思路。由于不同的国家或地区有着不同的自然资源状况,处于不同的技术和社会发展阶段,因此实现智能电网的途径和方法也应该彼此不同。该文总结了中国、美国、欧盟和日本的智能电网研究和实施现状,并比较和分析了中、美、欧3者在电力需求、能源供应、电力输送的安全稳定以及电力市场等方面的异同,建议中国智能电网建设应结合自身特点走具有中国特色的坚强智能电网道路。

大规模风电接入对美国电力系统运行的影响和平抑策略(英文)

至2010年底,美国的风电装机容量仅次于中国,居世界第二。考虑政府的支持政策和可观的风资源储量,有专家预见,未来美国40%的电能可来自于风力发电。在详细分析大规模风电接入对美国电力系统运行产生的影响的基础上,文中提出了需应对的几个方面的挑战。分析结果显示,最关键的挑战来自于风电功率的波动特性,可将其转化为系统负荷减去风电出力后的净负荷波动特性。随后文章提出了已被实际应用或正在工程示范的几种电网级储能方式来平抑风电接入给电网带来的负面影响。文中也对电网级储能方式对传统发电厂利用因子的影响加以讨论。

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.

Energy Theft Identification Using Adaboost Ensembler in the Smart Grids

One of the major concerns for the utilities in the Smart Grid(SG)is electricity theft.With the implementation of smart meters,the frequency of energy usage and data collection from smart homes has increased,which makes it possible for advanced data analysis that was not previously possible.For this purpose,we have taken historical data of energy thieves and normal users.To avoid imbalance observation,biased estimates,we applied the interpolation method.Furthermore,the data unbalancing issue is resolved in this paper by Nearmiss undersampling technique and makes the data suitable for further processing.By proposing an improved version of Zeiler and Fergus Net(ZFNet)as a feature extraction approach,we had able to reduce the model’s time complexity.To minimize the overfitting issues,increase the training accuracy and reduce the training loss,we have proposed an enhanced method by merging Adaptive Boosting(AdaBoost)classifier with Coronavirus Herd Immunity Optimizer(CHIO)and Forensic based Investigation Optimizer(FBIO).In terms of low computational complexity,minimized over-fitting problems on a large quantity of data,reduced training time and training loss and increased training accuracy,our model outperforms the benchmark scheme.Our proposed algorithms Ada-CHIO andAda-FBIO,have the low MeanAverage Percentage Error(MAPE)value of error,i.e.,6.8%and 9.5%,respectively.Furthermore,due to the stability of our model our proposed algorithms Ada-CHIO and Ada-FBIO have achieved the accuracy of 93%and 90%.Statistical analysis shows that the hypothesis we proved using statistics is authentic for the proposed technique against benchmark algorithms,which also depicts the superiority of our proposed techniques.

Automated Speech Recognition System to Detect Babies’ Feelings through Feature Analysis

Diagnosing a baby’s feelings poses a challenge for both doctors and parents because babies cannot explain their feelings through expression or speech.Understanding the emotions of babies and their associated expressions during different sensations such as hunger,pain,etc.,is a complicated task.In infancy,all communication and feelings are propagated through cryspeech,which is a natural phenomenon.Several clinical methods can be used to diagnose a baby’s diseases,but nonclinical methods of diagnosing a baby’s feelings are lacking.As such,in this study,we aimed to identify babies’feelings and emotions through their cry using a nonclinical method.Changes in the cry sound can be identified using our method and used to assess the baby’s feelings.We considered the frequency of the cries from the energy of the sound.The feelings represented by the infant’s cry are judged to represent certain sensations expressed by the child using the optimal frequency of the recognition of a real-world audio sound.We used machine learning and artificial intelligence to distinguish cry tones in real time through feature analysis.The experimental group consisted of 50%each male and female babies,and we determined the relevancy of the results against different parameters.This application produced real-time results after recognizing a child’s cry sounds.The novelty of our work is that we,for the first time,successfully derived the feelings of young children through the cry-speech of the child,showing promise for end-user applications.

Impairments Approximations in Assembled mmWave and Radio Over Fiber Network

The fiber nonlinearity and phase noise(PN)are the focused impairments in the optical communication system,induced by high-capacity transmission and high laser input power.The channels include high-capacity transmissions that cannot be achieved at the end side without aliasing because of fiber nonlinearity and PN impairments.Thus,addressing of these distortions is the basic objective for the 5G mobile network.In this paper,the fiber nonlinearity and PN are investigated using the assembled methodology of millimeter-wave and radio over fiber(mmWave-RoF).The analytical model is designed in terms of outage probability for the proposed mmWave-RoF system.The performance of mmWave-RoF against fiber nonlinearity and PN is studied for input power,output power and length using peak to average power ratio(PAPR)and bit error rate(BER)measuring parameters.The simulation outcomes present that the impacts of fiber nonlinearity and PNcan be balanced for a huge capacity mmWave-RoF model applying input power carefully.

Coupon-based Demand Response for Consumers Facing Flat-rate Retail Pricing

Even though smart meters have been widely used in power systems around the world,many consumers are still finding it hard to participate in demand response(DR)due to flat-rate retail pricing policy.To address this issue,this paper proposes a coupon-based demand response(CDR)scheme to achieve equivalent dynamic retail prices to inspire consumers’inherent elasticity.First,a security-constrained unit commitment optimization model is developed in the day-ahead market to obtain coupon rewards,which are then broadcast to consumers to motivate them to reschedule their power consumption behaviors.To evaluate the adjustment value of consumers’power consumption,a collective utility function is proposed to formulate the relationship between power quantity and coupon rewards.On this basis,the security-constrained economic dispatch model is developed in the intra-day market to reschedule generating units’output power according to real-time load demands and fluctuating renewable energies.After the operation interval,a settlement method is developed to quantify consumers’electricity fees and coupon benefits on a monthly basis.The proposed CDR scheme avoids real-time iterative bidding process and effectively decreases the difficulty of massive,small consumers participating in DR.The proposed CDR is implemented in a realistic DR project in China to verify consumers’energy cost and renewables’curtailment can both be decreased.

Utilization of Energy Storage and Hydrogen in Power and Energy Systems:Viewpoints from Five Aspects

Decarbonizing power systems is crucial to mitigating climate change impacts and achieving carbon neutrality.Increasing renewable energy supply can reduce greenhouse gas emissions and accelerate the decarbonization process.However,renewable energy sources(RESs)such as wind and solar power are characterized by intermittency and often non-dispatchability,significantly challenging their high-level integration into power systems.Energy storage is acknowledged as a vital indispensable solution for mitigating the intermittency of renewables such as wind and solar power and boosting the penetrations of renewables.In the CSEE JPES Forum,five well-known experts were invited to give keynote speeches,and the participating experts and scholars had comprehensive exchanges and discussions on energy storage technologies.Specifically,the views on the design,control,performance,and applications of new energy storage technologies,such as the fuel cell vehicle,water electrolysis,and flow battery,in the coordination and operation of power and energy systems were analyzed.The experts also provided experience that could be used to develop energy storage for constructing and decarbonizing new power systems.

IEEE是职业发展的摇篮

作为2023年的IEEE主席,我的目标之一是与所有会员合作,尤其是与我们的学生会员、青年专业人士和亲和力社区的会员合作,使IEEE成为一个更成功、更具适应力的全球性技术组织,并成为全球公认的变革力量.对我来说同样重要的是,地方一级的技术专家将IEEE会员资格视为专业发展的媒介,并认识到它带来的巨大好处.这些服务包括2000多场国际会议所提供的无与伦比的线下或线上交流机会,以及获得最佳技术文献、继续教育资源和与来自世界各地的同事进行创新合作的机会.

集IEEE之力驱动变革

作为2023年就任的IEEE主席,我的目标是与全体会员通力合作,特别是我们的学生会员、年轻专家和下设委员会的委员,使IEEE成为更成功、更有韧性的全球技术组织,并成为全球公认的变革力量.

Agent-based Modeling and Simulation for the Electricity Market with Residential Demand Response

Currently,critical peak load caused by residential customers has attracted utility companies and policymakers to pay more attention to residential demand response(RDR)programs.In typical RDR programs,residential customers react to the price or incentive-based signals,but the actions can fall behind flexible market situations.For those residential customers equipped with smart meters,they may contribute more DR loads if they can participate in DR events in a proactive way.In this paper,we propose a comprehensive market framework in which residential customers can provide proactive RDR actions in a day-ahead market(DAM).We model and evaluate the interactions between generation companies(GenCos),retailers,residential customers,and the independent system operator(ISO)via an agent-based modeling and simulation(ABMS)approach.The simulation framework contains two main procedures—the bottom-up modeling procedure and the reinforcement learning(RL)procedure.The bottom-up modeling procedure models the residential load profiles separately by household types to capture the RDR potential differences in advance so that residential customers may rationally provide automatic DR actions.Retailers and GenCos optimize their bidding strategies via the RL procedure.The modified optimization approach in this procedure can prevent the training results from falling into local optimum solutions.The ISO clears the DAM to maximize social welfare via Karush-Kuhn-Tucker(KKT)conditions.Based on realistic residential data in China,the proposed models and methods are verified and compared in a large multi-scenario test case with 30,000 residential households.Results show that proactive RDR programs and interactions between market entities may yield significant benefits for both the supply and demand sides.The models and methods in this paper may be used by utility companies,electricity retailers,market operators,and policy makers to evaluate the consequences of a proactive RDR and the interactions among multi-entities.

Technical cross-fertilization between terrestrial microgrids and ship power systems

Aspects of terrestrial microgrids and ship power systems are examined.The work exposes a variety of technical synergies from these two power systems to effectively advance their technologies.Understanding their overlap allows congruent efforts to target both systems;understanding their differences hinders conflict and redundancy in early-stage design.The paper concludes by highlighting how an understanding of both systems can reduce the investment in research resources.