Power Consumption Characteristics Research Articles

Power Compensation Strategy and Experiment of Large Seedling Tree Planter Based on Energy Storage Flywheel

The intermittent hole-digging tree-planting machine shows a periodic short-time peak load law in planting operation, and the operation process is “idling” for small loads most of the time, leading to large torque fluctuations in the transmission system, unscientific power matching, and high energy consumption. To solve the above problems, this article proposes to use a series of energy-saving flywheels in the transmission system of the tree planting machine. On the premise of obtaining holes that meet the target young tree planting requirements, the optimal power compensation strategy for the flywheel system of the tree planting machine is studied to reduce torque fluctuations in the power transmission system, use smaller power drive units, and save energy. Firstly, the nonlinear multi-body dynamics simulation model of soil cutting by the hole-digging component is established. The boundary and contact conditions are set to simulate the power consumption of the hole-digging component at three rotating speeds. Based on the simulation results, the flywheel power compensation strategy is discussed, and the torque fluctuation of the flywheel balance system is analyzed. The results showed that the higher the speed, the greater the power consumption. The power value suddenly increased from 17.82 kW (1.28 s) to 27.93 kW (1.43 s) when the speed was 220 r/min. Then, the power value rapidly decreased, and the power consumption presented a short-term peak feature. The transmission system’s maximum input power is determined as 17.82 kW according to the various simulated power consumption characteristics. The part exceeding the power consumption is compensated by the energy storage flywheel. The total compensation energy was 2382.5 J. After the flywheel system was involved, the maximum output power of the tractor power output shaft decreased by 36.2%, and the peak torque decreased from 445.7 N·m to 285.1 N·m. The power consumption obtained from the field test and simulation was similar, but the energy required to overcome peak load was jointly provided by the flywheel and the engine. The actual input power of the power output shaft during the energy release period of the flywheel system was 18.51 kW when the rotating speed of the hole-digging component was 220 r/min, and the relative error with the simulation value was 2.43%. The measured actual speed reduction of the flywheel system was 8.9%. After installing an energy storage flywheel in the transmission system of the tree planting machine, the output power of the power unit can be stabilized. Tree planting machines can be equipped with smaller power units, which can reduce energy consumption and exhaust emissions.

Performance optimization of the nano-scale carry-skip adder based on quantum dots and its application in the upcoming Internet of Things

Internet of Things (IoT) is a network of linked computers, electronic and mechanical equipment, objects, living things, and people that can exchange data without human-to-human or human-to-computer interaction. Real objects with sensors, computing power, software, and other technologies are called IoT when they connect to and exchange data with other systems and devices over the Internet or other communication networks. On the other hand, Quantum-dot Cellular Automata (QCA) is a highly appealing alternative to transistor-based technologies for future circuit design, thanks to its excellent performance and low power consumption characteristics. Therefore, researchers are trying to implement IoT-related circuits in this technology and take advantage of it. In IoT nodes, the circuits were previously designed based on transistors and used extensively are adder circuits and their various forms, such as carry-look-ahead adder, ripple carry adder, and Carry-Skip Adder (CSA). The proposed CSA is part of the bypass adder family, and its major goal in IoT devices is to reduce latency and power consumption. Designing CSA is a fascinating area of research in the QCA and IoT domains. In this research, we will offer nano-scale CSA designs based on logic gate structures. This CSA aids in improving system performance in terms of average power dissipation, area, and delay. This design has performed admirably in terms of physical qualities, particularly power usage. As a result, an ultra-low-power CSA is proposed, which employs a reduced number of cells and a smaller area while achieving the desired highly polarized output. All the circuits provided in the simulator software have been tested, and their correctness has been confirmed. 36 quantum cells were employed, and the adder's area with propagating signal is around 0.04 µm2. The latency is about 2 clock phases. Additionally, the CSA for IoT devices is roughly 0.4 µm2, which results in a delay of 17 clock phases and the need for 374 quantum cells. The suggested CSA reduces cell consumption by 64 %, improves latency by 15 %, and increases area by 85 %. Compared to a carry-look-ahead adder, the CSA is the only way to reduce the latency of a ripple carry adder with low effort.

Memristor bridge synapse for better artificial neuron perceptron

In artificial neural networks, the fourth passive element memristor can be utilized as an electronic synapse that serves as the interface between neurons. The artificial neuron composed of the memristor bridge synapse not only has the characteristics of low power consumption and high integration but also has a more simplified circuit and weight change conditions. Particularly, it has the ability of bionic intelligent information processing. This paper established two novel synaptic structures on the basis of memristor bridges (type 1 and type 2) and then synthetically analyzed how to realize the artificial neuron perceptron. Herein, the artificial synapses (type 1 and type 2) have the following characteristics: continuous changes in synaptic weights (positive, negative, and zero) and memory properties. Among them, the type 2 memristor bridge has the advantage of a wider range of weight updates for the synaptic circuit, which can realize the function of the artificial neuron perceptron with less error. This work lays the foundation for the future exploitation of artificial intelligence.

Day-ahead aggregated load forecasting based on household smart meter data

Smart meters provide much energy consumption information at the residential level, making it possible to improve short-time load forecasting accuracy by identifying more specific load patterns for each consumer type. This paper proposes a day-ahead load forecasting approach that uses the smart meter data aggregated by residential customers’ power consumption characteristics. First, the long-term trend information and daily fluctuation information are extracted from the residential load time series. According to the load characteristics reflected by the daily load fluctuation information, the residential consumers are clustered into several groups using the K-means algorithm. The non-linear autoregressive neural network is used to forecast each cluster of consumers to capture their specific load patterns. Finally, the aggregated load at the system level is obtained by combining each cluster’s forecasting results. The proposed method’s forecasting performance is evaluated on the data from Irish household customers’ actual smart meters. The outcomes show that the suggested approach can improve forecasting performance under a specific clustering scale.

Analysis of UWB Indoor Positioning Accuracy Based on TW-TOF

UWB has the characteristics of low power consumption, low system complexity, strong anti-interference, high penetration, and high positioning accuracy. In this paper, the principle of ultra-wide-band ranging and positioning is introduced in detail, followed by the TOA positioning method based on two-way time-of-flight ranging, and the positioning results are solved by using the least-squares and Gauss-Newton iterative algorithms, and the experimental positioning The experimental positioning errors were analyzed. The experimental results show that UWB has good indoor ranging and positioning accuracy.

Development of a Graphene-Based Wireless Displacement Transducer

Graphene-based sensors have drawn significant attention in the field of monitoring due to their excellent mechanical and electrical properties. In this study, an innovative graphene wireless displacement transducer was developed. This displacement transducer consists of two components: a processing unit characterized by the features of low cost, low latency, and low power consumption, and a sensing unit composed of a graphene-based displacement sensor with the advantages of simplification, a large measurement range, and high durability. The structural design of the graphene-based displacement sensor was realized using 3D printing technology. A series of calibration experiments were carried out to evaluate the measurement performance of the displacement transducer. Measurement results of calibration experiments indicate that the measurement sensitivity and static error were 29.6 kΩ/mm and 7.62%, respectively. The maximum measurement range was 0-90 mm. The obtained maximum resistance difference of the graphene sensor is less than 4.5% under the cyclic test, indicating the sensor has excellent stability. The displacement transducer is expected to apply for structural health monitoring of infrastructures such as crack monitoring, relative movement measurement, etc.

Security Authentication Protocol for Massive Machine Type Communication in 5G Networks

As one of the three major applications of 5G, massive machine type communication (mMTC) is mainly oriented to network access scenarios for massive devices. mMTC focuses on solving the problem that traditional mobile communication cannot well support the Internet of Things and vertical industry applications. According to the current 3GPP standard, these massive devices still use the traditional authentication process to realize mutual authentication with 5G core network, which brings a lot of communication and computing overhead. In addition, privacy protection will also be threatened in the authentication process. In order to alleviate the signaling congestion during authentication and solve the insecurity in authentication, this paper proposes a group authentication scheme for mMTC. Due to the characteristics of low power consumption and massive connection, the scheme mainly adopts lightweight encryption operation to avoid the computational burden of equipment and server. We verify the security of our scheme by using BAN logic to formally analyze the scheme. Then, through informal analysis, our proposed scheme can not only avoid signaling blocking and provide mutual authentication but also resist various possible attacks. Through performance evaluation, it is proved that our scheme has better efficiency.

P‐2.20: Hand Interaction Acquisition Technology Based on Hall Sensor

The design adopts the assistance of Hall sensor and permanent magnet to realize the capture of finger movement, which has the advantages of low cost and accurate recognition. STM32 is used as the main control module and MDK5 development platform in the design. The platform has the advantages of high integration and convenient development, and also has the characteristics of digital signal processing, high performance, real-time function, low voltage and low power consumption. A permanent magnet is designed at the center of the palm of the glove to form a constant magnetic field near the palm. The Hall sensor is a magnetic field sensor made according to the Hall effect, which can convert the magnetic field strength into a voltage signal. Through the AD conversion module built in STM32 chip, the output voltage of Hall sensor can be collected and converted into finger position and posture information through algorithm.

1-b Delta-Sigma ADC-Based Power Side-Channel Attack Detection Sensor

Countermeasures against power side-channel attack (SCA) range from algorithmic modifications to modifying the power consumption characteristics of the device by the inclusion of noise sources or suppressing the current signature. However, these techniques are, in most cases, passive protection techniques and come with the unacceptable area and power overheads for resource-constrained devices. In this letter, we propose a power SCA detection sensor based on a 1-b delta-sigma analog-to-digital converter, which allows for detection of an ongoing attack within as fast as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$6.4\,\mu \text{s}$</tex-math></inline-formula> , with immunity to power supply noise, at an area overhead of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leq 8\%$</tex-math></inline-formula> for advanced encryption standard (AES)-256, while reducing the power overhead by 66% compared to the current state-of-the-art.

Radiation Dose Detection on FY-4B Satellite

The radiation damage effect is relatively serious during spacecraft operation in orbit. To know the real cumulative dose in orbit accurately, a radiation dose detector is carried on the FY-4B satellite. It comprises one electric control unit and three dose detectors, with a total of four payloads. Each dose detector includes five dose-monitoring points corresponding to different shielding thicknesses, which are mainly used for the measurement of the dose depth. Three dose detectors with the same function are installed in the X, Y and Z positions and can detect the total dose in different directions during the operation of the satellite. Each detector has the characteristics of small size, high integration and low power consumption. At present, the FY-4B satellite is in orbit. The detectors have been working normally since they were started up, and good preliminary detection results have been obtained. From the power-up of the detectors to April 2022, the maximum dose has reached 2.7 × 103 rad(Si). We conducted a preliminary analysis of the relationship between total dose and shielding thickness. In addition, the relationship between daily average dose and shielding thickness was further analyzed. These studies have application value for radiation protection design.

Cooling Capacity Characteristics of Low Power Consumption （2 kW Class） 4 K-GM Cryocoolers

From the COP （Coefficient of Performance） equation, there are two ways to improve the cooling efficiency of cryocoolers: （1） increasing the cooling capacity, and （2） decreasing the electrical input power. In recent years, we have focused on the possibility of method （2） for a 4 K-GM （Gifford-McMahon） cryocooler. In this paper, a 7 kW class compressor, which is commonly connected to a 4 K-GM cold head, was replaced with a low power consumption （2 kW class） compressor. The experiments achieved a measured cooling capacity of 0.95 W at 4.2 K with a cold head reciprocation speed of 36 rpm and a compressor electrical input of 1.9 kW. The calculated COP and the relative Carnot efficiency were 5.0×10-4 and 3.5 %, respectively. A low reciprocating speed was selected to secure sufficient expansion work in the second expansion space. This is an effective method to improve the cooling capacity at 4.2 K by a low power consumption compressor.

Improved switching stability in SiNx-based RRAM by introducing nitride insertion layer with high conductivity

In this Letter, a Pt/SiNx/TiN/Ta resistive random access memory (RRAM) is proposed, which has low switching voltage, uniform resistance distribution, excellent cycle-to-cycle stability, and excellent nonvolatile performance. As an insertion layer, TiN prevents excessive absorption of nitrogen ions by a Ta electrode and avoids the formation of the unstable metal–semiconductor interface, which significantly reduces cycle-to-cycle variability of SiNx-based RRAM. Due to high conductivity, the TiN layer has a small voltage divider effect when voltage was applied, which helps to achieve low power consumption characteristics. This paper provides a direction for improving performance of nitride-based RRAM, which is useful for further development of highly reliable RRAM.

Customer characteristics analysis method based on the selection of electricity consumption characteristics and behavioral portraits of different groups of people

Because the traditional methods do not select the best feature collection in feature analysis, the accuracy and effectiveness of user feature clustering are not high, and the accuracy of user feature classification is not high. Therefore, this paper proposes a customer feature analysis method based on power consumption feature selection and behavior portrait of different people. The optimal feature set is obtained according to the maximum correlation and minimum redundancy criterion, and the user portrait task is described. The spatial feature domain classification method is used to classify the user portrait information, and the user label database is constructed according to the classification results. The AP clustering algorithm is used to cluster the power user portrait information and complete the customer feature analysis. Experimental results show that this method effectively improves the accuracy and effectiveness of user feature clustering, and the accuracy of user feature classification is high, indicating that the application effect is good.

Bionic Design of Liquid Fertilizer Deep Application Spray Needle, Based on Badger Claw-Toe, for Improving the Operating Performance of Liquid Fertilizer Deep Application in Northeast China

Deep application of liquid fertilizer is a technique that applies liquid fertilizer deep near the root system of crops, which has many advantages such as high fertilizer utilization rate and low environmental pollution. However, high power and high specific energy consumption caused by soil-engaging components in liquid fertilizer deep application make it difficult to popularize in northeast China. The claw-toe structure of burrowing animals has the characteristics of low resistance and low friction, which has been the focus of many scholars’ research on soil-engaging components. The claw-toe structure of the badger, a widely distributed burrowing animal in northeast China, has good characteristics of low power and low specific energy consumption. Therefore, in this research, a bionic liquid fertilizer deep application spray needle was designed, based on the claw-toe structure of the badger, to improve the operating performance of liquid fertilizer deep application. In this research, the discrete element method (DEM) was used for a computer simulation test, and the indoor soil bin verification test was carried out. The results showed that the operating angle, operating speed and fertilization depth of bionic liquid fertilizer deep application spray needle had significant effects on the power and specific energy consumption, and the optimal operating performance combination of bionic liquid fertilizer deep application spray needle was obtained as follows: The fertilization depth is 60 mm; the operating speed is 6 km h−1; the operating angle is 24.8°; the power consumption is 0.066 kW; and the specific energy consumption is 4.257 kJ m−3 under this operating condition. Through the comparison of operating performance, the operating performance of the bionic liquid fertilizer deep application spray needle is significantly better than that of other types of liquid fertilizer deep application furrow opener, with the power reduced by 9.52~40.5% and the specific energy consumption reduced by 93.9~208.6%. This research clarified the internal mechanism affecting the operating performance. Finally, based on the above findings, this research suggests that more attention should be paid to finding suitable bionic prototype and design scheme in the future design and research of soil-engaging components of liquid fertilizer deep application.

Flow Characteristics Analysis of A New Combination Agitator Based on CFD

The computational fluid dynamics (CFD) method was used to simulate the velocity distribution, turbulent kinetic energy distribution and power consumption characteristics of two new types of large-blade combined agitators and traditional single-blade six-blade disc turbines designed for EVOH polymerization kettle in a factory. The results show that the new type of large-blade combination agitator has a better bottom turbulent kinetic energy effect, which can effectively avoid the stirring dead zone caused by single-layer stirring. At the same speed, compared with six-straight blade disc turbines, the torque and unit energy consumption of the two types of large-blade combination agitators are higher, but the effective stirring ratio is also increased by 31% and 30.8%, which can enhance the stirring effect.

F-LSTM: FPGA-Based Heterogeneous Computing Framework for Deploying LSTM-Based Algorithms

Long Short-Term Memory (LSTM) networks have been widely used to solve sequence modeling problems. For researchers, using LSTM networks as the core and combining it with pre-processing and post-processing to build complete algorithms is a general solution for solving sequence problems. As an ideal hardware platform for LSTM network inference, Field Programmable Gate Array (FPGA) with low power consumption and low latency characteristics can accelerate the execution of algorithms. However, implementing LSTM networks on FPGA requires specialized hardware and software knowledge and optimization skills, which is a challenge for researchers. To reduce the difficulty of deploying LSTM networks on FPGAs, we propose F-LSTM, an FPGA-based framework for heterogeneous computing. With the help of F-LSTM, researchers can quickly deploy LSTM-based algorithms to heterogeneous computing platforms. FPGA in the platform will automatically take up the computation of the LSTM network in the algorithm. At the same time, the CPU will perform the pre-processing and post-processing in the algorithm. To better design the algorithm, compress the model, and deploy the algorithm, we also propose a framework based on F-LSTM. The framework also integrates Pytorch to increase usability. Experimental results on sentiment analysis tasks show that deploying algorithms to the F-LSTM hardware platform can achieve a 1.8× performance improvement and a 5.4× energy efficiency improvement compared to GPU. Experimental results also validate the need to build heterogeneous computing systems. In conclusion, our work reduces the difficulty of deploying LSTM on FPGAs while guaranteeing algorithm performance compared to traditional work.

Power Consumption Characteristics Research on Mobile System of Electrically Driven Large-Load-Ratio Six-Legged Robot

The electrically driven large-load-ratio six-legged robot with engineering capability can be widely used in outdoor and planetary exploration. However, due to the particularity of its parallel structure, the effective utilization rate of energy is not high, which has become an important obstacle to its practical application. To research the power consumption characteristics of robot mobile system is beneficial to speed up it toward practicability. Based on the configuration and walking modes of robot, the mathematical model of the power consumption of mobile system is set up. In view of the tripod gait is often selected for the six-legged robots, the simplified power consumption model of mobile system under the tripod gait is established by means of reducing the dimension of the robot's statically indeterminate problem and constructing the equal force distribution. Then, the power consumption of robot mobile system is solved under different working conditions. The variable tendencies of the power consumption of robot mobile system are respectively obtained with changes in the rotational angles of hip joint and knee joint, body height, and span. The articulated rotational zones and the ranges of body height and span are determined under the lowest power consumption. According to the walking experiments of prototype, the variable tendencies of the average power consumption of robot mobile system are respectively acquired with changes in duty ratio, body height, and span. Then, the feasibility and correctness of theory analysis are verified in the power consumption of robot mobile system. The proposed analysis method in this paper can provide a reference on the lower power research of the large-load-ratio multi-legged robots.

Design of portable high-speed oscilloscope analyzer for multifunctional integrated signal testing

In response to the multifunctional test and miniaturized requirements for broadband signal analysis, this paper designs a new portable high-speed oscilloscope analyzer (HOA) based on USB3.0 interface. It not only has high-speed and high-resolution signal acquisition and analysis functions, but also can generate broadband arbitrary waveforms for the testing occasions associated with excitation signals. First, a modularized scheme is proposed for the HOA design, and then the specific implementation key technologies are discussed in detail. The design is to achieve a time base of 400 ps/div ∼ 50 s/div, input impedance of 50 Ω or 1MΩ, vertical sensitivity of 1 mV/div ∼ 5 V/div(@1MΩ) or 1 mV/div ∼ 1 V/div(@50 Ω), input coupling mode of DC or AC. The test results show that the designed HOA has very high signal acquisition performance, such as 2.2 GHz analog bandwidth, 5GSPS sampling rate, 12-bit resolution, 1Gpts storage depth, maximum 2.062million wfms/s three-dimension waveform mapping and fast data recording. And it also integrates an arbitrary waveform generation with 110 MHz bandwidth, 500MSPS sampling rate and 16-bit resolution. In addition, the analyzer in portability also has the characteristics of small size, light weight and low power consumption.

Residential load forecasting based on electricity consumption pattern clustering

In order to reduce the peak load on the power grid, various types of demand response (DR) programs have been developed rapidly, and an increasing number of residents have participated in the DR. The change in residential electricity consumption behavior increases the randomness of electricity load power, which makes residential load forecasting relatively difficult. Aiming at increasing the accuracy of residential load forecasting, an innovative electricity consumption pattern clustering is implemented in this paper. Six categories of residential load are clustered considering the power consumption characteristics of high-energy-consuming equipment, using the entropy method and criteria importance though intercrieria correlation (CRITIC) method. Next, based on the clustering results, the residential load is predicted by the fully-connected deep neural network (FDNN). Compared with the prediction result without clustering, the method proposed in this paper improves the accuracy of the prediction by 5.21%, which is demonstrated in the simulation.

Photonic machine learning with on-chip diffractive optics

Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm2 and 0.3 mm2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance.