Miracle-dz/UniFAR-FTUs huggingface dataset:
Scenario Generation Method for Combined Heat and Power Virtual Power Plant Considering the Correlation of Electric-Heating Load

With the improvement of coupling between the power and thermal system, it is of great significance for the planning and scheduling of combined heat and power virtual power plant (CHPVPP) to accurately construct the scenario of electric-heating load (EHL). However, the uncertainty brought by the long-term continuous growth of electric and heating load will affect the rationality and effectiveness of CHPVPP planning and operation. In this context, the influence factors of EHL are analyzed first, and then the modeling method of EHL correlation based on the Copula theory is proposed to fill in the shortcomings of existing research on EHL correlation modeling. Furthermore, a scenario generation method for EHL in CHPVPP considering the Euclidean distance-based optimal Copula screening and the K-means clustering is innovatively proposed to achieve the typical scenario construction considering the temporal characteristics. The case study on the EHL of CHPVPP in a certain county of China verifies the effectiveness and practicability of the proposed method.
CNTFET-based Multiplexer Unit using INDEP Method

The integration of digital circuits and the scaling down of complementary metal oxide semiconductor (CMOS) technology are closely related. As CMOS feature size continues to shrink, it eventually reaches the nanoscale region, which has raised the effects like Short Channel Effect (SCE), leakage power, and interconnects delay. Therefore, to overcome these limits and improve the efficiency of digital circuits, researchers are looking into other nanoscale technologies that can be used in upcoming technological advancements. Due to the excellent properties, Carbon Nanotube Field Effect Transistor (CNTFET) is going to use in the replacement of CMOS technology. In the current study, a CNTFET-based multiplexer using input dependent (INDEP) leakage reduction technique is proposed for a 32nm technology node. In this work, a comparative study of conventional and proposed INDEP-based multiplexers is carried out for power consumption, delay, and power delay product (PDP). It is observed that the proposed CNTFET-based multiplexer using the INDEP method is energy efficient and leads to significant improvement in leakage power and PDP compared to the conventional architecture.
Design of Efficient Power source for Electric Vehicle using Battery and Ultracapacitor

Plug in sources has immense advantages as they use hybrid collaboration of two sources for driving an electric vehicle. Amalgamation of battery and ultracapacitor can prove a very serviceable source wherein ultracapacitor provides peak power requirements and battery provides long term energy requirements. This paper concerns coalesce of battery and ultracapacitor for operating an uneven load for improving the system response and energy/power requirements. A control strategy is developed which allocates the power chosen fed by mutually the sources and intellects the coherence of both the sources. With the implementation of this scheme a MATLAB/SIMULINK model is contrived.
Network Intrusion Detection Method Based on Conditional Generative Adversarial Network Integrating Multi-Scale CNN

Data imbalance is a key problem existing in most network traffic datasets that restricts the performance of intrusion detection. In addition, traditional intrusion detection methods are insufficient to extract features of network traffic effectively, which result in low detection accuracy. Therefore, a novel intrusion detection method based on the conditional Generative Adversarial Network (CGAN) integrating the improved Multi-scale Convolution Neural Network (MSCNN) is proposed to deal with the problem of data imbalance and improve the classification performance. In the level of data, the CGAN is applied to generate minority network traffic samples to decrease the false alarm rate of intrusion detection. In the level of the neural network, the MSCNN of novel structure adapt to the network traffic is applied to promote the detection performance by using multiple convolution kernels for feature extraction. According to the experimental result on UNSW-NB15 and CIC-IDS2017 datasets, the proposed method can detect the network traffic of minority classes effectively, and acquires higher detection accuracy and F1-score compared with other existing methods, which proves the proposed method is advanced in network intrusion detection.
Predicting Emotion From Color Present in Images and Video Excerpts by Machine Learning

This work aims at predicting emotion based on the colors present in images and video excerpts using a machine-learning approach. The purpose of this paper is threefold: (a) to develop a machine-learning algorithm that classifies emotions based on the color present in an image, (b) to select the best-performing algorithm from the first phase and apply it to film excerpt emotion analysis based on colors, (c) to design an online survey to check the accuracy of the annotations of the collected film data. In the first, three approaches to color extraction are tested, namely clustering colors into a palette of predefined colors, assigning colors to the RYB (Red, Yellow, Blue) model, and extracting a histogram of colors present in an image. This is based on image datasets containing color and emotion annotations. Classification is conducted using several algorithms, both deep learning and baseline artificial intelligence algorithms. The obtained results, under different configurations of parameters and training sets, are then presented. In the second part, the best-performing algorithm from the first phase is applied to film excerpt emotion analysis based on colors. This is followed by the third part, which is an online survey created to check the accuracy of the algorithm’s annotations to the collected film data by the questionnaire respondents. Further, a discussion of the results achieved is presented. Conclusions contain a summary of the results and further direction for improving the performance of the created algorithm.
A Fully Differential 4-Bit Analog Compute-In-Memory Architecture for Inference Application

A robust, fully differential multiplication and accumulate (MAC) scheme for analog compute-in-memory (CIM) architecture is proposed in this article. The proposed method achieves a high signal margin for 4-bit CIM architecture due to fully differential voltage changes on read bit-lines (RBL/RBLBs). The signal margin achieved for 4-bit MAC operation is 32 mV, which is 1.14×, 5.82×, and 10.24× higher than the state-of-the-art. The proposed scheme is robust against the process, voltage, and temperature (PVT) variations and achieves a variability metric (σ/µ) of 3.64 %, which is 2.36× and 2.66× lower than the reported works. The architecture has achieved an energy-efficiency of 2.53 TOPS/W at 1 V supply voltage in 65 nm CMOS technology, that is 6.2× efficient than digital baseline HW [25]. Furthermore, the inference accuracy of the architecture is 97.6% on the MNIST data set with a LeNet-5 CNN model. The figure-of-merit (FoM) of the proposed design is 355, which is 3.28×, 3.58×, and 17.75× higher than state-of-the-art.
A Switching Event Detection Method for Household Appliances Based on Improved Sliding Window

Load event detection is an important part of non-intrusive load monitoring, and for the problem that small power consumption equipment will be missed when high power consumption equipment is put in, a standard deviation(δ) difference sliding window load fluctuation detection method based on Harris Hawk Optimization (HHO) is proposed, which detects load fluctuations by finding the cumulative sum of (δ difference values, and then using the HHO algorithm to find the threshold h and noise value β of load fluctuation in the sliding window to improve the accuracy of detection, and the accuracy is also high in the range of load fluctuation from 5W to 2000W. Finally, the algorithm is verified to have high accuracy and stability by simulation and experiment.
High Precision Position Control of Magnetic Levitation Ball System Based on Generalized Disturbance Estimation

In this paper, a position control method based on generalized disturbance estimation is proposed to solve the problem of position accuracy of magnetic levitation ball system under the influence of mismatched multiple disturbances. Firstly, an Luenberger observer is designed to estimate the state variables of the system. Considering the known disturbance information, a generalized disturbance estimator is designed to estimate the disturbance using the internal model principle. Then, the disturbance estimation and its derivative are introduced into the control law design to eliminate the influence of the mismatched multiple disturbances on the position output, and the disturbance compensation gain is designed for the control law. At the same time, the reference input compensation gain is designed to solve the problem of tracking the time-varying reference position under mismatched disturbance. Then, the stability and disturbance rejection performance of the proposed method are analyzed, and it is proved that the proposed method can achieve high precision position control of the magnetic levitation ball system under mismatched multiple disturbances. In order to verify the effectiveness of the proposed method, MATLAB/Simulink is used to simulate and verify the proposed method.
Feasibility Study of a Rooftop Solar System on ULAB Permanent Campus Building

In the context of the increasing integration of renewable energy into existing grids, rooftop solar system (RSS) is an exciting notion. It is becoming more and more popular in a densely populated nation like Bangladesh since it does not require a large open space like typical PV plants. However, a comprehensive feasibility analysis of the setting is required prior to installing grid-connected RSS at a given location. In order to do this, this paper provides a comprehensive method for evaluating the feasibility of a grid connected RSS system taking performance, economic aspect, losses, and environmental aspect into account. With a test RSS of 42 kW capacity, the proposed methodology is implemented in buildings A and B of the University of Liberal Arts Bangladesh (ULAB) campus. All simulations are performed in the HelioScope software environment. First, a test RSS system with appropriate mechanical and electrical specifications is developed for the rooftop. Following that, a thorough assessment analysis is conducted on the test system. The findings indicate that the planned RSS offers a 77.2% performance ratio with a marginal levelized cost of energy (LCOE) of $0.0216 and a payback period of about 4 years. Moreover, with a minimal shading loss, the proposed RSS system minimizes CO2 emission by 24 tons per year. The installation of grid-connected RSS is therefore a very viable option for the chosen case scenario, taking into account all the findings. This research will be a useful guide for proper feasibility assessment of future installations of grid connected RSS in Bangladesh.
Reactive Power Optimal Allocation Method Based on Joint Optimization of BPA and SCCP and Economic Analysis

The renewable energy resources and loads in China are characterized by reverse division. The long-distance transmission of renewable energy power leads to the insufficient support capacity of high proportion of power electronic equipment for the grid voltage at the sending end, which further limits the transmission and consumption of renewable energy. This research adopts the technical idea of joint optimal configuration of distributed condensers and parallel capacitors to improve the voltage support capability of the sending end power grid. Aiming at improving the transmission limit of renewable energy, this research proposes a reactive power optimal configuration method based on the joint optimization of BPA-SCCP, and establishes an economic analysis model to evaluate the technical economy of reactive power configuration scheme. Finally, a large-scale renewable energy and thermal power transmission system through AC/DC lines in the north is taken as an example to verify the effectiveness of the optimal allocation method. The research results show that the reactive power optimal allocation method based on the joint optimization of BPA-SCCP can effectively solve the problem of reactive power optimal allocation in the renewable energy gathering area, and the reactive power optimal allocation scheme based on the joint optimization of distributed condensers and parallel capacitors has good economy.
An Optimization Algorithm with Hausdorff-like Derivative in GRU for SOC Estimation of Lithium-ion Battery

This paper proposes an optimization algorithm in the gate recurrent unit (GRU) for state of charge (SOC) estimation. To introduce the error into the optimization algorithm, the Hausdorff-like derivative with the cost function is first defined. The Hausdorff-like derivative as one type of fractal derivatives is applied to the back propagation in the GRU, and the Adam algorithm with Hausdorff-like derivative are proposed to optimize GRU networks. An adaptive method of the order is proposed to tune the parameters of GRU networks. The SOC estimation experiments are carried out with different working conditions, and the experimental results are given to verify the effectiveness of proposed algorithm.
Artificial Intelligence Applications for Energy Management in Microgrid

Microgrids consist of distributed energy resources such as photovoltaic (PV) systems, wind energy conversion systems, energy storage devices and backup generators. Due to the intermittent nature of renewable energy resources, storage systems and energy management systems are required to achieve sustainable and reliable power. In microgrid systems, an energy management system is required to cover load power demand at any given time and to manage power flows throughout the entire microgrid. While microgrids offer many benefits, they contain various challenges such as energy management and control due to variable factors such as wind speed and solar irradiation. To overcome these challenges, Artificial Intelligence (AI) technologies have emerged as a promising approach to realize and optimize energy management in microgrid. In this article, AI technologies used in energy management system of the microgrids is reviewed and discussed in detail. Their abilities and limitations are explained.
Assessing Student Visual Fatigue in Virtual Semiconductor Labs via Head-Mounted Displays: A Computer Vision Syndrome Questionnaire (CVS-Q)

This paper evaluates visual fatigue among students in a virtual semiconductor lab using HMDs - the HP Reverb G2 and Oculus Quest 2 - and the CVS-Q questionnaire. The study measures the frequency of visual fatigue symptoms (never, occasionally, or always) and aims to determine the effects of HMD usage on visual fatigue and eye strain, providing insight into the benefits and drawbacks of using HMDs in virtual labs. The study was conducted during the COVID-19 pandemic and aims to determine the effects of HMD usage on visual fatigue and eye strain among students in a virtual semiconductor laboratory learning environment. The results of this evaluation will provide insight into the potential benefits and drawbacks of HMD usage in virtual laboratory environments and inform the development of strategies to mitigate visual fatigue.
Towards Continuous Real-Time Plant and Insect Monitoring by Miniaturized THz Systems

In this paper, new concepts for continuous 24/7 real-time monitoring of plants and insects with miniaturized terahertz (THz) systems are described and experimentally tested. Thus, for the first time, small-scale insights into the instantaneous plant health but also in their long-term growth can be obtained. Using such compact THz systems, e.g. water uptake, insect infestation and the behavior of pollinators (i.e. honey bees) and pests can be measured dynamically and non-invasively at virtually any position in the close biotope surrounding them. In addition to general understanding, this can be used to optimize crop yield and reduce resource consumption as well as for identifying characteristics of insect-plant interactions induced by potential environmental stressors. Given such a holistic approach, the proposed concepts may provide a significant advancement in environmental monitoring technology.
Multi-view Information Fusion Network for Pneumonia Diagnosis from Full Sequence CTs

Pneumonia diagnosis based on CT scans is crucial for the effective treatment. Existing deep leanring-based methods mainly focus on the global struction of the whole lung organs, while ignore the information of local detailed lesions. This can easily lead to errors in pneumonia decisions and a decline in classification accuracy. Actully, the diagnosis process of specialists in practice involves basically two steps glancing the whole lung organs to capture global information (global view) and gazing at local regions for observing detailed lesion (local view). To mimic this behaviour, we propose a multi-view information fusion network for pneumonia diagnosis from full sequence CTs. First, we design a multi-view information fusion network to extract spatial features from lung CT slices from global and local perspectives. Then, a recursive neural network (RNN) is utilized to solve the problem of dependency between slices and the continuity of lesions. Extensive experiments on real-world datasets are conducted and the results demonstrate the effectiveness of our proposed method.
The network algorithm for polyp image segmentation with fused attention mechanism

Accurate segmentation of colorectal polyps can assist physicians in diagnosing gastrointestinal diseases and effectively reduce the risk of colorectal cancer. To solve the problem of accurate polyp segmentation, a multi-scale convolutional neural net incorporating attention mechanism for intestinal polyp segmentation is proposed based on the U-Net network model. First, a series of preprocessing techniques are used to process the original images. Secondly, with the help of images of colorectum, by incorporating channel attention mechanisms in the encoding and decoding stages of the U-Net network. The experimental results show that the model in this paper can accurately predict colorectal polyps and provide early prognostic information for patients.
4-BIT Linear Feedback Shift Register Counter In 45nm Technology Using Pass Transistor Logic

Many applications with enormous arrays of counters are discovered to be appropriate for linear-feedback registers (LFSR). Comparatively speaking, this improves efficiency compared to traditional binary-based counters. Better logic is needed to reorder the binary, which practically enables system-on-chip architectures. The proposed method provides a counter architecture supported by a 4-bit LFSR stage with single-stage benefits. Using 45nm CMOS technology, a proof of concept for a four-bit LFSR-based counter is developed. In the field of electronics, pass transistor logic (PTL) refers to a variety of logic families that are used to build integrated circuits. The effectiveness of n-channel pass transistor logic is found to be superior to CMOS logic style when compared to pass transistor logic with a wider array of distinct logic cells.
Experimental Implementation of Improved P&O MPPT Algorithm based on Fuzzy Logic for Solar Photovoltaic Applications

In this work, a literature review of the maximum power point tracking (MPPT) perturb and observe (P&O) technique for photovoltaic (PV) systems is first developed. The P & O algorithms available in the literature are distributed through three groups: a modification of the basic structure of P&O method, a combination of P&O techniques with a classical technique (FSCC, FOCV, SMC) and a combination with another smart technique (FLC, ANN, PSO). Subsequently, the experimental implementation of an improved MPPT P&O algorithm based on fuzzy logic (FL) for solar PV applications is proposed. The conventional P&O algorithm is widely used in PV generation systems due to its easy implementation and low cost. Notwithstanding, its main limitation is the trade-off between dynamic response and steady state oscillation. In order to overcome the limitations existing in the implementations of the conventional P&O algorithm, a FL based controller block is used to provide a variable step. The experimental results show that the response time of the proposed technique is better than the conventional P&O technique. In addition, it is found that the power oscillations and stability of the proposed control are almost eliminated. The improved P&O control based on FLC is accurate, simple and allows a faster optimization towards the maximum power point (MPP) relative to the suggested P&O algorithms found in the literature.
Social media fake news detection algorithm based on multiple feature groups

The spread of fake news on social media seriously jeopardizes the authenticity of online content. Such misinformation has the potential to affect public opinion, government credibility, and more. Therefore, automatic detection of fake news is an important and challenging problem that has not been well solved today. Fake news has three universally recognized characteristics: the text of an article, the user response it receives, and the source users promoting it. Existing work mainly focuses on formulating a solution for a certain feature, which cannot detect fake news more accurately. In this work, we propose a model that combines all three features to achieve more accurate and automated predictions. We propose a model called TSRI, which consists of four modules: Text, Source, Response, and Integrate. The first module is based on news text content, using the BERT+LSTM model, while considering the semantic representation of sentences and the long-distance dependencies between sentences. The second module uses GAT to model the network of replying and retweeting users based on social network features. The third module is based on the user's response to the news, using LDA to find conflicting opinions in the news tweets, and based on the identified tweets' opinions, constructs the credibility of the tweets and spreads the network, and finally obtains the module's value through iterative deduction. result. The last module combines the above three and outputs whether the news is true or false.
RC-GNN: Fast and Accurate Signoff Wire Delay Estimation with Customized Graph Neural Networks

As interconnect delay becomes more dominate in a timing path compared to the gate delay, accurate yet fast estimation of wire delay during the signoff stage is required. Prior machine learning-based wire delay estimation approaches either relied on tedious feature extraction processes or failed to capture the net topology information, incurring long turn around time. In this paper, we propose to leverage the power of graph neural networks (GNN) to estimate the interconnect delays during signoff. Different from other GNN-assisted timing analysis methods that were usually applied to a netlist, we harness the global message passing graph representation learning on RC graph directly to perform ultra-fast net delay estimation without requiring extra features. Furthermore, pre-processed graph features can be added to boost the estimation accuracy with slight run time penalty. Our proposed customized GNN models have been evaluated with the industrial design and compared against state of the art ML-based wire delay estimator. It shows that the proposed model outperforms the state-of-the-art ML-based signoff wire delay estimator by 4x in terms of run time while achieving similar accuracy levels.