Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Potential Power Savings from Household Appliance Consumption Data: A Methodological Approach and Estimated Results

The electricity grid becomes most stressed when there is high demand for electricity. Demand response programs are used as resource options for balancing supply and demand. Individual consumers can play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods. However, it can be difficult to estimate how much power can be saved in a household at a particular time. To accomplish that, appliance consumption data recorded at 1-minute intervals was analyzed from 7 houses over a period of 3 days (August 1–3, 2022), when the grid in Tokyo experienced very low reserve margins. Three power saving approaches were considered: reducing the load, leveling the load or shifting the load. The amount of power that could be saved was estimated by generating simulation data and calculating the difference before and after performing the power saving action. The amount of power that could be saved relative to the total power consumed by all the houses ranged from 7% to 15.3%. The methodology applied in this research to quantify the power saving potential can be applied to smart meter data to estimate the amount of power that can be saved in a household at a particular time, which is important information for demand response programs.
An RGB-D Semantic Map Building and Global Localization Method

Aiming at the shortcomings of indoor visual SLAM system such as lack of environment awareness, sparse map construction, low global positioning accuracy and poor robustness, this paper proposes a semantic map building and global positioning method based on visual semantic descriptors and dense point cloud map. Abstract semantic descriptors are extracted and keyframe dense point cloud map is built by real-time object detection of color images. Global positioning adopts the off-line positioning method combining coarse and fine tuning. By quickly comparing the similarity between descriptors in the map set, the most similar reference keyframe is selected from the keyframe map, and the pose is obtained as the rough estimation result. The iterative calculation is continued in the dense point cloud map to complete the off-line positioning. In this paper, an available semantic mapping and positioning system is constructed and tested on the public RGB-D sequence dataset. The result shows that the proposed method can generate high-quality indoor point cloud maps and also finish global localization with higher accuracy and better robustness than the classical algorithm.
An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests

ABSTRACT With new treatments and novel technology available, precision medicine has become a key topic in the new era of healthcare. Traditional statistical methods for precision medicine focus on subgroup discovery through identifying interactions between a few markers and treatment regimes. However, given the large scale and high dimensionality of modern datasets, it is difficult to detect the interactions between treatment and high-dimensional covariates. Recently, novel approaches have emerged that seek to directly estimate individualized treatment rules (ITR) via maximizing the expected clinical reward by using, for example, support vector machines (SVM) or decision trees. The latter enjoys great popularity in clinical practice due to its interpretability. In this article, we propose a new reward function and a novel decision tree algorithm to directly maximize rewards. We further improve a single tree decision rule by an ensemble decision tree algorithm, ITR random forests. Our final decision rule is an average over single decision trees and it is a soft probability rather than a hard choice. Depending on how strong the treatment recommendation is, physicians can make decisions based on our model along with their own judgment and experience. Performance of ITR forest and tree methods is assessed through simulations along with applications to a randomized controlled trial (RCT) of 1385 patients with diabetes and an EMR cohort of 5177 patients with diabetes. ITR forest and tree methods are implemented using statistical software R (https://github.com/kdoub5ha/ITR.Forest). Supplementary materials for this article are available online.
Investigation of the Non-Uniqueness of the ITS-90 in the Range 660 C to 962 C

Preliminary results are reported on the non-uniqueness of the ITS-90 using high temperature SPRTs (HTSPRTs) in the range 660 °C to 962 °C. To achieve temperature stability and uniformity of the test HTSPRTs, the method employs the thermal isolation of the Inconel metal comparison block inside a sealed potassium heat pipe controlled to ±0.01 K. Eleven HTSPRTs of three designs from five manufacturers were tested. In addition to measurements near the calibration temperatures 663.3 °C and 966.6 °C to test the temperature uniformity of the comparison block, measurements were made at 703.3 °C, 724.4 °C, 753.2 °C, 801.7 °C, 847.5 °C, and 913.7 °C.
Crop Disease Prediction using Machine Learning and Deep Learning: An Exploratory Study

Crop diseases are caused by pests, insects, and pathogens, and if not promptly handled, they significantly reduce the yield. Farmers are losing money because of different crop diseases. When the cultivated area is large (in acres), it becomes tiresome for the cultivators to examine the crops regularly. The farming business needs an automatic crop disease identification and analysis. It may be used to diminish the loss of money and other resources, reduce yield losses, and enhance the effectiveness of treatment leading to healthier crop output. Many industries today have benefited from the development of new technologies, particularly artificial intelligence, Machine Learning (ML) and Deep Learning (DL). This study examined the significant advancements and issues, such as reduction in harvest yield, lower quality of produce and crop damage, using ML and DL approaches for crop disease detection and prediction in the recent studies.
Trust Value-Based Energy-Efficient Routing Protocol to Improve Lifetime in Heterogeneous WBAN

Pervasive computation plays an integral part in WBANs. Along with pervasive methodologies, bio-sensors are available in a range of shapes and sizes, and depending on the state of the patient, multiple sensors can be inserted in, on, or around the human body to monitor, store, and relay vital signs for further investigation, judgments, and treatment. The tracking of patients’ vital signs, as well as the time it takes to generate results, are essential components of the WBAN’s integration into ubiquitous computing technologies. To ensure low power consumption, high precision of collected data, low latency, high efficiency, higher throughput with efficient bandwidth utilization, and synchronization with other systems and at the same time data must be stored and exchanged with care. To function successfully, a WBAN must first measure the quantity of electricity the device utilizes and then impose energy-efficient operating strategies. Current routing processes, such as the Stable Increased-Throughput Multi-hop Protocol for Link Efficiency (SIMPLE) and Mobility-supporting Adaptive Threshold-based Thermal-aware Energy-efficient Multi-hop Protocol (M-ATTEMPT), can be employed in WBANs by incorporating confidence measures into both the sensor data being monitored and the power levels needed for effective data broadcast to reach the sink. In contrast to Expected Transfers (ETX), this protocol avoids continuous communications and only forwards data of interest to the sink, resulting in minimal power usage and thereby increasing network reliability time, overall network lifetime, throughput, and end to end latency to 0.915 mw, 290 bits/s, and 250 ms, respectively.
Predictive Functional Control for Photovoltaic System Optimization

Photovoltaic systems have earned recognition as incredibly interesting renewable energy sources, due to simplicity of supply. These systems have a nonlinear characteristic which causes them a low energy conversion. However, it necessity for control systems capable of outperforming conventional controllers and overcome the existing operational instabilities. The main objective of this paper is to design a predictive functional control to the photovoltaic generators, to make it follow a desired trajectory and to generate a maximum power. A predictive functional control is defined as advanced control which exploit the future behavior of process, and its consists in using of discretized model of the photovoltaic system. The simulation results prove the highest tracking efficiency of this algorithm which are evaluated by means of efficiency, stability and robustness under different scenarios of varied metrological condition.
Decoupling of Compact MIMO Antennas Using Parasitic Element and Electromagnetic Band Gap Structure

the main concerns with wireless communication are always the limitation of services in conjunction with vast demand of huge data transmissions. MIMO system provides large amount of data transmission by deploying multiple antennas at both transmitter and receiver, however, the limitation of this technique is the occurrence of mutual coupling among elements which deteriorates the system performance and can considerably affect both power and bandwidth efficiencies. Therefore, this paper studies and simulates various techniques for reducing the mutual coupling. Two element circular patch antennas are designed on a substrate made of Rogers RT5880 with dielectric constant (permittivity) of 2.2 and loss tangent of 0.0009. The size of the MIMO antenna is 30x 15 x 1.6 mm3 with optimized space between radiating elements of 7 mm. Three techniques are used in this paper which are, defected ground structure (DGS), parasitic slab element and electromagnetic band gab (EBG) structure. All these techniques are simulated over the range between 20-80 GHz, the design provides mutual coupling reduction starts form -30 dB to -54 dB at different resonant frequencies with bandwidth reaches up to 30 GHz. The comparison between decoupling techniques shows slightly different in the performance which about -4dB for parasitic slab element and EBG. In this paper, the envelope correlation coefficient is simulated and resulting in roughly zero at resonant frequencies which indicates that the techniques of decoupling play a major role to reduce the effect of mutual coupling. All results are designed and simulated using CST MWS.
Vehicle Data Security based on Underground Garage Location -- Dead Reckoning Method

With the widespread popularity of the Internet of Things and the rapid development of vehicle communication technology, it has gradually become normal to realize vehicle evaluation, selection, and large-scale data distribution based on edge infrastructure. Collaborative data distribution through Wifi and CV modes effectively improves traffic efficiency, reduces road traffic accident rates, and improves user driving experience. Therefore, this paper will study the data security technology of Internet of Vehicles collaboration based on the dead reckoning method. To realize the security of data interaction, this paper describes the research of indoor positioning and the Internet of vehicles, respectively.
Enhanced Bio-Impedance Demodulation Method Under Non-Integer Period Sampling Conditions

In this article, an enhanced bio-impedance demodulation method is proposed to accurately extract impedance from the time-domain signals, even under non-integer period sampling (NIPS) conditions. The conventional bio-impedance demodulation approach, which is based on the direct discrete Fourier transform (DFT) operation, is often limited by spectral leakage and fence effects due to its reliance on IPS. To overcome these limitations, a maximum amplitude spectral line (MASL) is used to replace the hidden spectral values, and an optimized bio-impedance demodulation formula is established taking into account the spectral leakage error. The method is evaluated through simulations and experiments on 2 resistors and 1 capacitor (2R-1C) circuits and ex vivo biological tissues. The experimental results of the real samples (pumpkin, carrot, and beef) show that the method provides highly accurate impedance demodulation, with a maximum relative error of impedance magnitude and a maximum absolute error of impedance phase less than 0.88% and 0.63°, respectively, from 10 kHz to 1 MHz. These results demonstrate the effectiveness of the proposed approach in accurately demodulating impedance at arbitrary frequencies and weakening the constraints imposed by IPS.
Inverter-Interfaced Distributed Generator Optimal Configuration Based on Process Immune Time

In the assessment of voltage sag, due to the existence of uncertain operating interval of voltage sag tolerance characteristic curve, it is difficult to measure the economic losses caused by voltage sag when the equipment suffers from voltage sag of this interval type. In this paper, a optimal configuration model of Inverter-Interfaced Distributed Generator based on process immune time is proposed, which calculates the economic loss caused by voltage sag according to whether the key physical parameters of industrial process meet the acceptable limits. The model considers both the economy of reversible distributed generation under normal operation and its ability to suppress voltage sag of distribution network under fault operation. The objective is to minimize the investment cost, network loss, power purchase cost of upper power grid and economic loss of voltage sag. The genetic algorithm is used to solve the model. The simulation results show that the optimal configuration scheme of reversible distributed generation considering voltage sag has better overall economy and stronger ability to suppress voltage sag.
A Frequency and Pattern Reconfigurable Planar Antenna for IoT Applications

Multiple frequency bands must be integrated, and flexible radiation patterns must be used, to meet the growing demand for high-performance and efficient antennas radiators for wireless communication systems. This work indicates a dynamically tunable dual band operated frequency with pattern reconfigurable microstrip antenna. The PIN diodes designed for reconfigurability allow the antenna to adjust its radiation pattern and resonance frequency to two distinct frequency bands, such as the 3.1 GHz to 3.9 GHz and 3.3 GHz to 5 GHz bands. The results show conclusively that the device offers reconfigurable dual-band performance, flexible beamforming, and wanted antenna gain. The proposed antenna uses a microstrip transmission line to feed the antenna elements, and PIN diodes for reconfigurability. The suggested dual-band operated frequency with pattern reconfigurable microstrip antenna is a viable solution for applications needing high performance and adaptability in wireless communications, such as wireless sensor networks, wearable devices, and smart homes. The performance response of the reconfigurable antenna is analyzed using electromagnetic simulations, and the results depicts that the antenna can provide dual-band performance, flexible beamforming with a stable antenna gain.
Deriving optimal weights in deep neural networks

Training deep neural networks generally requires massive amounts of data and is very computation intensive. We show here that it may be possible to circumvent the expensive gradient descent procedure and derive the parameters of a neural network directly from properties of the training data. We show that, near convergence, the gradient descent equations for layers close to the input can be linearized and become stochastic equations with noise related to the covariance of data for each class. We derive the distribution of solutions to these equations and discover that it is related to a "supervised principal component analysis. " We implement these results on image datasets MNIST, CIFAR10 and CIFAR100 and find that, indeed, pretrained layers using our findings performs comparable or superior to neural networks of the same size and architecture trained with gradient descent. Moreover, our pretrained layers can often be calculated using a fraction of the training data, owing to the quick convergence of the covariance matrix. Thus, our findings indicate that we can cut the training time both by requiring only a fraction of the data used for gradient descent, and by eliminating layers in the costly backpropagation step of the training. Additionally, these findings partially elucidate the inner workings of deep neural networks and allow us to mathematically calculate optimal solutions for some stages of classification problems, thus significantly boosting our ability to solve such problems efficiently.
DDoS at a Glance: Attack Launch to Solutions

Distributed denial of service (DDoS) attacks form an important cyber security problem of today’s Internet. There is a large amount of academic and industrial work available related to DDoS attacks in the state of the art. In this article, we provide a brief yet comprehensive guide to DDoS attacks in the form of six important dimensions: DDoS attack launching tools, attack datasets, mitigation tools, attack incidents, and industrial and academic solutions. We also provide a summary on how attackers, solution providers, and researchers are working for this important problem and give few important pointers as part of the future directions. This short article may help a reader in grasping the overall area of DDoS research and development.
Detection of Life-threatening Malignant Cardiac Arrhythmias Using Machine Learning Methods

Malignant cardiac arrhythmias such as ventricular tachycardia (VT) and ventricular fibrillation (VF) cause Cardiac arrest (CA) or Sudden cardiac death (SCD); which accounts for 50% of all cardiac deaths. In this study, we proposed a patient-general system that analyzes and detects VT/VF arrhythmic episodes in ECG signals by classifying instances as either normal or arrhythmic. A great deal of experimental work was conducted to choose the best algorithms for preprocessing, the extraction of a comprehensive set of time-and frequency-domain features from a specific window length of the ECG signals to capture information about their amplitude, frequency, and morphology, and for feature selection, and classification. Three different window sizes were tested 10s, 20s, and 60s. On testing data with 10 s-window-size, the Random Forest and kNN ensembles obtained the best results with an accuracy (acc) of 99.11%, a sensitivity (se) of 98%, and a specificity (sp) of 99.6%. We also show through comparative results that the Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm outperforms the Principal Component Analysis (PCA) algorithm in choosing the optimal feature combinations. Our results demonstrate the potential of an automated, accurate, and robust detection system and how it could contribute significantly to the enhancement of the diagnostic process of arrhythmias and the prediction/prevention of cardiac arrest.
Extraction of modal parameters for identification of time-varying systems using data-driven stochastic subspace identification

This paper presents a method for the extraction of modal parameters for identification of time-varying systems using Data-Driven Stochastic Subspace Identification (SSI-DATA). In practical applications of SSI-DATA, both the modal parameters and computational ones are mixed together in the identified results. In order to differentiate the structural ones from computational ones, a new method based on the eigen-decomposition of the state matrix constructed in SSI-DATA is proposed. The efficiency of the proposed method is demonstrated through numerical simulation of a lumped-mass system and experimental test of a moving robot for extracting excited natural frequencies of the system.
Climatological Relationship between Warm Season Atmospheric Rivers and Heavy Rainfall over East Asia

Eddy transport of atmospheric water vapor from the tropics is important for rainfall and related natural disasters in the middle latitudes. Atmospheric rivers (ARs), intense moisture plumes that are typically associated with extratropical cyclones, often produce heavy precipitation upon encountering topography on the west coasts of mid-latitude North America and Europe. ARs also occur over the northwestern Pacific and sometimes cause floods and landslides over East Asia, but the climatological relationship between ARs and heavy rainfall in this region remains unclear. Here we evaluate the contribution of ARs to the hydrological cycle over East Asia using high-resolution daily rainfall observations and an atmospheric reanalysis during 1958 – 2007. Despite their low occurrence, ARs account for 14 – 44 % of the total rainfall and 20 – 90 % of extreme heavy-rainfall events during spring, summer, and autumn. AR-related extreme rainfall is especially pronounced over western-to-southeastern slopes of terrains over the Korean Peninsula and Japan, owing to strong orographic effects and a stable direction of low-level moisture flows. A strong relationship between warm-season AR heavy rainfall and preceding-winter El Niño is identified since the 1970s, suggesting the potential of predicting heavy-rainfall risk over Korea and Japan at seasonal leads.
Vlasov dynamics of periodically driven systems

Analytical solutions of the Vlasov equation for periodically driven systems are of importance in several areas of plasma physics and dynamical systems and are usually approximated using ponderomotive theory. In this paper, we derive the plasma distribution function predicted by ponderomotive theory using Hamiltonian averaging theory and compare it with solutions obtained by the method of characteristics. Our results show that though ponderomotive theory is relatively much easier to use, its predictions are very restrictive and are likely to be very different from the actual distribution function of the system. We also analyse all possible initial conditions which lead to periodic solutions of the Vlasov equation for periodically driven systems and conjecture that the irreducible polynomial corresponding to the initial condition must only have squares of the spatial and momentum coordinate. The resulting distribution function for other initial conditions is aperiodic and can lead to complex relaxation processes within the plasma.Analytical solutions of the Vlasov equation for periodically driven systems are of importance in several areas of plasma physics and dynamical systems and are usually approximated using ponderomotive theory. In this paper, we derive the plasma distribution function predicted by ponderomotive theory using Hamiltonian averaging theory and compare it with solutions obtained by the method of characteristics. Our results show that though ponderomotive theory is relatively much easier to use, its predictions are very restrictive and are likely to be very different from the actual distribution function of the system. We also analyse all possible initial conditions which lead to periodic solutions of the Vlasov equation for periodically driven systems and conjecture that the irreducible polynomial corresponding to the initial condition must only have squares of the spatial and momentum coordinate. The resulting distribution function for other initial conditions is aperiodic and can lead to complex relaxation proc.