Hasil untuk "Electric apparatus and materials. Electric circuits. Electric networks"

Hyeongmin Park, Jinsung Kim, Minwoo Kim et al.

ABSTRACT Despite significant advances being made in pressure sensor technologies, driven by increasing demand for wearable devices, future Internet of Things (IoT) applications, and electronic skin (e‐skin), critical challenges persist in achieving high sensitivity, high pressure resolution, rapid response, and a wide linear range. Here, we report a cost‐effective and easy‐to‐fabricate pressure sensor that simultaneously achieves high sensitivity and an extensive linear operating range by emulating the multi‐modulus structure of human skin. Typically, these two properties are inversely related, rendering their simultaneous optimization highly challenging. Our sensor design employs a porous structure, composed of two layers of distinct moduli; this is achieved by precisely adjusting the base to crosslinker ratio of polydimethylsiloxane mixed with multi‐walled carbon nanotubes (MWCNTs). The synergistic effect of the MWCNTs and porous structure results in a high sensitivity (2.24 kPa−1), while the dual‐modulus configuration extends the linear response (up to 45 kPa). Moreover, the sensor demonstrates excellent reproducibility and can maintain a stable response even after 6000 cycles of mechanical deformation at 15 kPa. These findings underscore the sensor's efficacy in diverse pressure detection scenarios and its potential for applications in human–machine interface systems and soft robotics.

Deblina Bhowmick, Dipu Sarkar, Nilangshu Acharya

Sara Lafuerza, Javier Blasco, Marco Evangelisti et al.

Abstract High‐quality purely c‐axis oriented epitaxial thin films of the Aurivillius phase Sr2Bi4Ti5O18 with n = 5 (Sr,Bi)TiO3 perovskite‐like layers, are grown on SrTiO3 substrates by pulsed laser deposition. The highest crystalline quality is obtained with a 20 wt.% Bi‐excess target and average stacking order values in the proximity of the ideal value n = 5 are attained for an optimum deposition temperature of 650 °C. Scanning transmission electron microscopy reveals regions with n ranging from 4 to 6 around an average thickness of n = 5, in agreement with the X‐ray diffraction analysis. Interdigital electrodes are used to probe the in‐plane polarization and survey the electrocaloric properties. A maximum adiabatic temperature change of ΔT ∼ 0.95 °C for an electric field of 150 kV cm−1 is observed at ≈135 °C. Larger values are expected at higher temperatures around the ferroelectric Curie temperature, TC. Since TC of Sr2Bi4Ti5O18 can be tuned by codoping, the findings pave the way toward a large electrocaloric effect at ambient temperature.

Chengfei Qi, Yan Liu, Yaoyu Wang et al.

During the actual operation of smart energy meters used in agriculture, there may be situations where current overload (greater than Imax) occurs. Some smart energy meters used in agriculture may experience power reduction or even reverse operation during overload operation. When the current returns to the measurement range, the energy meter is still in an abnormal state. This article starts from the case of on-site operation failure of intelligent energy meters for agriculture, simulates the overflow effect in ADC filters and metering chips, explains the principles of the above two phenomena, and provides solutions. Meanwhile, the correctness of the solution method was verified through experimental data.Corresponding guidance has been provided to provincial power companies regarding the performance requirements of energy meters after overload.

Dasari Ayodhya

Now a days, the developemnt of eco-friendly, economical, and efficient photocatalysts for escalating global energy crisis and environmental degradation underscore the urgent need for sustainable hydrogen production via water splitting. For this, traditional photocatalysts are constrained by their limited light absorption, rapid electron-hole recombination, and stability concerns. The non-metal semiconductor polymeric photocatalyst known as g-C3N4 possesses an acceptable energy band gap (2.7 eV), excellent structure, low toxicity, chemical stability, high thermal resistance, cost-effectiveness, easy synthesis, and the ability to absorb light. However, despite all these excellent properties, g-C3N4 has some alarming drawbacks that limit its performance for photocatalytic H2 generation via photocatalytic water splitting, these drawbacks include massive recombination of charge carriers, limited visible light absorption, and low surface area. A lot of attempts and/or efforts has been made to address these drawbacks for better performance of g-C3N4–based composites. However, despite all these attempts, there is still little review papers that discuss more on the recent synthesis method and strategies to enhance the performance of g-C3N4–based photocatalyst for hydrogen production. In this review, we explore the photocatalytic water splitting for hydrogen production using g-C3N4 based mixed metal-semiconductor heterojunction nanocomposites under light irradiation. The study also goes further to discuss more on the structure and properties of g-C3N4, recent synthesis methods, coupled with metal, semiconductors to form heterojunctions, and elemental doping, as well as photocatalytic performance of H2 generation of g-C3N4 based heterojunction nanocomposites. Moreover, we outlook the challenges and future directions of g-C3N4-based photocatalysts, which also provides a reference for the design of other g-C3N4 based catalysts.

Hongkun Chen, Peng Gao, Kaicheng Liu et al.

Hemlata Sandip Ohal, Shamla Mantri

This paper presents a comprehensive investigation into Electroencephalogram (EEG) signal processing and analysis techniques aimed at enhancing early diagnosis methods for Alzheimer's Disease (AD). Leveraging a dataset that has EEG data of individuals diagnosed with Mild Cognitive Impairment (MCI), AD, Healthy Controls, and the study explores Preprocessing Methods and Feature Extraction Techniques, with machine learning model notably Support Vector Machines (SVM).In the preprocessing phase, a combination of high pass, lowpass, Savitzky–Golay, and median filters are applied, informed by a comprehensive review of filter comparison literature. Feature extraction encompasses three primary categories: ‘Statistical, ‘Frequency Domain’ and ‘Time Domain’. The scope of this work is to explore features in all these three domains and build SVM based model for efficient classification. In our investigation, we achieved a categorization accuracy of 92 % through the utilization of statistical features. Employing time domain features resulted in an accuracy of 87 %, while frequency domain features also yielded an 87 % accuracy rate in our study. The primary objective of this study is that it aims to enhance early AD diagnosis through advanced EEG signal processing and machine learning techniques, focusing on preprocessing methods, feature extraction, and classification accuracy.

William Kwak, Junyi Yin, Shaolei Wang et al.

Abstract Triboelectric nanogenerators (TENGs) have recently gained attention as a compelling platform technology for building wearable bioelectronics. Aside from being self‐powered, TENGs are lightweight, low in cost, rich in material choice, comfortable to wear, and increasingly versatile with advances in sensitivity and efficiency. Due to these features, TENGs have become appealing in biomedical sensing applications, especially for human respiration monitoring. A wealth of information can be collected by breath‐induced electrical signals, which are crucial in the analysis of a patient's respiratory condition and the early detection of harmful respiratory‐linked diseases. TENGs have thus been used to continuously collect important respiratory data, from the breathing patterns, flow rate, and intensity of an individual's respiratory cycle to the chemicals that may be present in their breath. This review paper provides an overview of recent developments in TENG‐based wearable respiratory monitoring as well as future opportunities and challenges for respiratory healthcare.

M. Naveen Babu, P.K. Dhal

This study focuses on mitigating power losses within unbalanced radial distribution networks by employing transformer modeling and network reconfiguration. The process commences with load flow analysis, utilizing a simplified three-phase load flow method tailored for unbalanced radial distribution networks (URDNs) featuring voltage-dependent loads. Using vector data and fundamental electric circuit analysis, the algorithm efficiently resolves voltage magnitude equations, conserving memory resources, and accurately identifies buses and branches downstream from a designated bus. This method circumvents the repetitive identification issues inherent in conventional forward-backward sweep approaches. The proposed methodology demonstrates robust convergence when applied to URDNs with realistic resistance/reactance ratios and has been rigorously tested on 19-bus and 25-bus unbalanced radial distribution networks. Evaluation criteria encompass CPU execution time and iteration benchmarks. Leveraging empirical formulas, this study achieves optimal designs characterized by improved voltage profiles and reduced power losses. An asymmetric power flow program is employed to compare bus voltages and system power losses, facilitating informed switch operation decisions and allowing for the elimination of feeder sectionalizing switch actions. This approach streamlines CPU processing time by eliminating switching procedures and has been successfully validated using 19-bus and 25-bus URDN samples. This work distinguishes itself through its efficiency, necessitating fewer switching operations when compared to existing methodologies.

Lv Songyang

Based on the increasing learning needs of students that cannot be met by traditional education in schools, the use of online learning platforms has become a way for many college students to learn Chinese, thereby improving their academic performance and literary literacy. However, it is difficult to guarantee the service quality of online platforms for Chinese learning at present, so this paper proposes an association rule mining algorithm to strengthen Chinese online learning. We using user service quality as a constraint to improve spectrum utilization and energy efficiency, and defining the user state space, borrowing the obtained resource allocation optimization function to reward a small portion of users' communication costs, ultimately obtaining user state space information and one-dimensional state space data. This algorithm performs grouping operations on the data estimated by a large amount of computation, and divides the data into balanced categories to reduce the amount of input data in the network. The performance test results show that this paper has made a great breakthrough in the personalization of Chinese learning, and has outstanding performance in the processing and classification of Big data. There are also some solutions to the problem of too much existing data. In a period of use experience analysis report, we found that the online platform for Chinese learning can give consideration to students' personalized needs and experience.

Liping Zhong, Quan Pan

The increasing demand for bandwidth in data centers is driving the advancement of wireline receivers to support higher data rates, even up to 224 Gb/s. A single-ended scheme, which utilizes two single-ended signals on a pair of differential channels, offers a promising solution for achieving this goal. This approach effectively doubles the data throughput of the links and reduces the bandwidth requirements for both active and passive components. However, this scheme suffers from severe crosstalk, especially far-end crosstalk (FEXT). At higher data rates, single-ended crosstalk cancellation interfaces encounter several issues. First, FEXT noise becomes more pronounced at higher frequencies. Additionally, the increased bandwidth demands lead to higher power consumption. Finally, as frequency increases, the channel exhibits severe insertion loss, intensifying the equalization burden on receivers. This article introduces several techniques that enable single-ended crosstalk cancellation receivers to achieve data rates of up to 56 and 112 Gb/s per lane using four-level pulse amplitude modulation (PAM-4) in 28-nm CMOS technology. These 56 and 112 Gb/s receivers achieve a bit error rate of &#x003C;<inline-formula> <tex-math notation="LaTeX">$10{^{-}10 }$ </tex-math></inline-formula> and &#x003C;<inline-formula> <tex-math notation="LaTeX">$10{^{-}12 }$ </tex-math></inline-formula> with a single-ended channel loss of 24 and 25 dB, respectively.

Jinseok Oh, Minkyong Kang, Jae Park et al.

Abstract Triboelectric nanogenerators (TENGs) are promising candidates replacing conventional batteries in wearable devices, owing to their self‐powering properties. Although TENGs can convert mechanical energy into electrical energy, excessive mechanical stresses can degrade their performance. Moreover, conformal adhesion to skin is required to improve its performance in wearable devices. Here, a stretchable, adhesive, and self‐healing single‐electrode TENG with a multifunctional hydrogel‐elastomer double layer is developed. Carbon nanotubes (CNTs)‐doped hydrogel is used as the electrode. Subsequently, the electrode is covered with an Ecoflex elastomer that acted as a triboelectrification layer. The soft tissue‐like properties of the hydrogel allow conformal adhesion to nonplanar skin, whereas the dynamic polymer network of the hydrogel endows toughness and ability to self‐heal (within 5 min) against external damage. The TENG demonstrates an output voltage and current peak at 180 V and 0.8 µA. Moreover, it can generate a maximum power peak at 37.8 mW m−2, which is sufficient to power small electronics like stopwatches and light‐emitting diodes, with a power management circuit. The proposed TENG devices can function as telecommunication touch panels via Morse code. Thus, this study presents a promising approach for advancing the flexible power supplies and self‐powered sensors that can be applied to wearable devices.

Shuguang Li, Pu Yuan, Huaibin Zhen et al.

In the eastern edge of the Ordos Basin in China, coalbed methane geological resources are very abundant, and efficient extraction of coalbed methane has become an important demand for energy development strategies and coal safety production. Therefore, this article uses IoT detection technology to optimize the coal seam pressure fracturing fluid system in this area, in order to improve its efficiency and reliability. Through on-site experiments, the relationship between different concentrations of fracturing fluid systems and coal seam permeability was studied. Divide the architecture of the Internet of Things to achieve more efficient information transmission and application. Based on the actual situation of the coal industry, we have constructed a system architecture for the Internet of Things in coal mines. Based on in-depth research on the occurrence characteristics of coalbed methane in the eastern edge of the Ordos Basin and the characteristics of underground fracturing fluid technology in coal mines, we have obtained the viscosity, rheological properties, viscoelasticity of underground fracturing fluid in coal mines, as well as specific requirements for the action of coalbed methane. On this basis, an interaction model between coal seam fracturing proppant and coal reservoir was established, and its mechanical response process was analyzed using numerical simulation methods. Selecting the optimal fracturing fluid ratio can significantly improve the surface wettability and strength of coal samples, while reducing the content of clay minerals and increasing the porosity of coal samples.

Salim CHENNAI

This paper proposes a simple control scheme for UPQC (Unified Power Quality Conditioner) system based on five-level NPC (Neutral Point Clamped) inverter capable to compensate all disturbances under all voltage conditions. The proposed UPQC is designed by the integration of shunt and series APFs (Active Power Filters) sharing a common dc bus capacitor. The dc voltage is maintained constant using proportional integral voltage controller. To get the reference signals for shunt and series APFs, synchronous current detection method (SCD) and instantaneous reactive power (PQ) strategies are adopted. These reference signals are derived from the control algorithm and injected in LS-SPWM (Level Shifted-Sin Pulse Width Modulation) controllers to generate the switching signals. The performance of proposed UPQC system is evaluated using MATLAB-Simulink software and SimPowerSystem Toolbox for all voltage disturbances compensation. The simulation results demonstrate the effectiveness of proposed UPQC system to improve the power quality at the common connection point of the non-linear load in steady and transient conditions operation.

Setareh Kazemzadeh, Lloyd Dodsworth, Inês Figueiredo Pereira et al.

Abstract Recent developments in organic neuromorphic devices and biohybrid interfaces are promising examples that show potential to improve implantable devices toward organic adaptive brain‐machine interfaces. However, fully integrated neuromorphic arrays still require relatively complex circuitry that includes multiple access devices to ensure synaptic weight stability and prevent sneak paths. Here, it is shown that polydopamine (PDA), the byproduct of dopamine autoxidation, promotes proton conductivity and can serve as a solid‐state electrolyte. Slow kinetics and high energy barriers of the PDA solid electrolyte prevent loss of conductance state for the device with a three‐terminal configuration without an access device, while partial dedoping of the conductive polymer channel by PDA simultaneously increases its stability in ambient environments. Fabricating the neuromorphic device on a flexible poly(styrene‐block‐isobutylene‐block‐styrene) substrate and the inherent biocompatibility of PDA demonstrates its potential toward more sophisticated implantable neuromorphic circuits for advanced neuroprosthetics.

Mustafa Wassef Hasan

In particular, predicting the temperature and humidity information plays a crucial role in plantation, estimating rainfalls and climate change, and predicting air quality via specified geographical regions. The temperature and humidity forecasting information is occasionally presented with low accuracy due to uncertain techniques and vast methods that employ different sensors and models. For this reason, this work proposes an Internet of Things (IoT) temperature and humidity forecasting model based on an improved whale optimization algorithm with long short-term memory (IWOA-LSTM) technique. To increase the convergence speed processing time and overcome the local optimization problem, the IWOA is introduced. The number of hidden layers, learning rate momentum, and weight decay of the LSTM optimized using the IWOA. The actual temperature and humidity data are collected using DHT11 and ESP8266 NodeMCU practical model and processed using the ThingSpeak platform. The processing data stage depends on filling the missing data gaps using the rolling average technique (RAT). The performance evaluation of the proposed IWOA-LSTM forecasting model is assessed using some statistical functions, namely known as mean square error, mean absolute error, root mean square error, and mean absolute percentage error. The IWOA-LSTM techniques were also assessed using throughput, latency, and power consumption. The developed IWOA-LSTM model shows high accuracy, leading to better forecasting information than other forecasting models.

P. Deepa, Rashmita Khilar

As the population ages with advances in technology, health monitoring through early detection is increasing. There are several approaches to the analysis, monitoring and management of human activity in health care. Gaining proficiency and knowledge from complicated, multidimensional, and dissimilar biomedical data remains a key challenge for healthcare innovation. Different kinds of data have been appearing in modern biomedical research. Recent advances in deep learning technology provide a powerful new paradigm for deriving end to end learning models out of complicated data. Speech technology is anticipated to lead to changes in health care, transforming traditional treatment modalities. The present language interface is being transformed into a next generation medical companion who interacts among people and monitors their health, according to researchers. New genuine mechanisms are required to reinforce the medical prognosis of the diseases and lessen the hour spent in the diagnostic process. This allows speech to be used as a future digital biomarker for disease detection, with benefits such as improved access to diagnosis, symptom monitoring, cost savings and improved diagnostic accuracy. The motive of this paper is to conduct an extensive survey related to speech technology in healthcare. Because of its societal implications, research in this subject has a broad reach.

Liwei Liu, Chunsen Liu, Xiaohe Huang et al.

Abstract Field‐effect transistors (FETs) are the main building block of microelectronic devices. For most of the FETs, the conduction channel relies on either n‐type or p‐type semiconductor materials. Recent advances in 2D materials offer an opportunity to form van der Waals heterojunction‐based FETs with novel electrical performance. Here, a MoS2/WSe2 heterojunction–channel FET using h‐BN as insulating layer and Cr/Au metal as the gate, is demonstrated. Two asymmetric Schottky contacts are formed at both sides of MoS2, WSe2 due to electron tunneling effects, work function differences, and Fermi level pinning. Benefiting from that, the authors observe a forward current regulating diode behavior, where the drain current remains unchanged regardless of the drain voltage (VDS) fluctuations and can be modulated by the gate voltage, atmosphere temperature, vacuum pressure, and h‐BN layer thickness. In addition, under higher VDS, heterojunction–channel breakdown induced by the Fowler–Nordheim (F–N) tunneling at the WSe2/h‐BN/metal region, is observed. Furthermore, the transistor demonstrates a reverse rectification behavior with onset voltage linearly depending on the temperature (3 mV T−1). This work paves the way for the potential application of heterojunction–channel FETs for high‐performance current regulator and current rectifier.

Daria Bondarenko, Igor Voronin, Pavel Voronin et al.

Igor Voronin, Pavel Voronin, Oleg Osipov