Terahertz (THz) massive multiple-input multiple-output (mMIMO) has recently gained significant attention because of its ability to deliver a high data rate and offer a high capacity. In this letter, a novel sub-surfac...
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Background: Cancer patients with metastasis face a much lower survival rate and a higher risk of recurrence than those without metastasis. So far, several learning methods have been proposed to predict cancer metastas...
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作者：
Bharatiraja, C.Mahesh, AgantiLehman, Bradley
Department of Electrical and Electronics Engineering Kattankulathur College of Engineering Chengalpattu Chennai603203 India Northeastern University
Department of Electrical and Computer Engineering BostonMA02115 United States
Wireless charging system (WCS) for electric vehicles (EVs) has the potential to revolutionize traditional plug-in charging methods. This technology employs different methods. Among these, resonant inductive power tran...
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Multi-input multi-output (MIMO) antennas operating in the millimeter wave (mmWave) band face challenges related to inter-element interference, limited isolation due to close spacing, and mutual coupling, all of which ...
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This study investigates the use of natural language processing language representation models as an early warning system for economic crises, and compares the performance of time series analysis and machine learning m...
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AC optimal power flow (AC OPF) is a fundamental problem in power system operations. Accurately modeling the network physics via the AC power flow equations makes AC OPF a challenging nonconvex problem. To search for g...
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Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information ...
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Changes in the Atmospheric Electric Field Signal(AEFS) are highly correlated with weather changes, especially with thunderstorm activities. However, little attention has been paid to the ambiguous weather information implicit in AEFS changes. In this paper, a Fuzzy C-Means(FCM) clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by AEFS. First, a time series dataset is created in the time domain using AEFS attributes. The AEFS-based weather is evaluated according to the time-series Membership Degree(MD) changes obtained by inputting this dataset into the FCM. Second, thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF apparatus. Thus, a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space domain. Finally, the rationality and reliability of the proposed method are verified by combining radar charts and expert experience. The results confirm that this method accurately characterizes the weather attributes and changes in the AEFS, and a negative distance-MD correlation is obtained for the first time. The detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.
On the whole, the present microgrid constitutes numerous actors in highly decentralized environments and liberalized electricity markets. The networked microgrid system must be capable of detecting electricity price c...
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Photovoltaic(PV)inverter,as a promising voltage/var control(VVC)resource,can supply flexible reactive power to reduce microgrid power loss and regulate bus ***,active power plays a significant role in microgrid voltag...
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Photovoltaic(PV)inverter,as a promising voltage/var control(VVC)resource,can supply flexible reactive power to reduce microgrid power loss and regulate bus ***,active power plays a significant role in microgrid voltage ***-based demand response(PBDR)can shift load demand via determining time-varying prices,which can be regarded as an effective means for active power ***,due to the different characteristics,PBDR and inverter-based VVC lack systematic ***,this paper proposes a PBDR-supported three-stage hierarchically coordinated voltage control method,including day-ahead PBDR price scheduling,hour-ahead reactive power dispatch of PV inverters,and realtime local droop control of PV *** their mutual influence,a stochastic optimization method is utilized to centrally or hierarchically coordinate adjacent two *** solve the bilinear constraints of droop control function,the problem is reformulated into a second-order cone programming relaxation ***,the concave constraints are convexified,forming a penalty convex-concave model for feasible solution ***,a convex-concave procedure-based solution algorithm is proposed to iteratively solve the penalty *** proposed method is tested on 33-bus and IEEE 123-bus distribution networks and compared with other *** results verify the high efficiency of the proposed method to achieve power loss reduction and voltage regulation.
Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by sol...
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Purpose: Ultrasound (US) elastography is a technique for non-invasive quantification of material properties, such as stiffness, from ultrasound images of deforming tissue. The material properties are calculated by solving the inverse problem on the measured displacement field from the ultrasound images. The limitations of traditional inverse problem techniques in US elastography are either slow and computationally intensive (iterative techniques) or sensitive to measurement noise and dependent on full displacement field data (direct techniques). Thus, we develop and validate a deep learning approach for solving the inverse problem in US elastography. This involves recovering the spatial modulus distribution of the elastic modulus from one component of the US-measured displacement field. Approach: We present a U-Net-based deep learning neural network to address the inverse problem in ultrasound elastography. This approach diverges from traditional methods by focusing on a data-driven model. The neural network is trained using data generated from a forward finite element model. This simulation incorporates variations in the displacement fields that correspond to the elastic modulus distribution, allowing the network to learn without the need for extensive real-world measurement data. The inverse problem of predicting the modulus spatial distribution from ultrasound-measured displacement fields is addressed using a trained neural network. The neural network is evaluated with mean squared error (MSE) and mean absolute percentage error (MAPE) metrics. To extend our model to practical purposes, we conduct phantom experiments and also apply our model to clinical data. Results: Our simulated results indicate that our deep learning (DL) model effectively reconstructs modulus distributions, as evidenced by low MSE and MAPE evaluation metrics. We obtain a mean MAPE of 0.32% for a hard inclusion and 0.39% for a soft inclusion. Similarly, in our phantom studies, the predicted mo
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