Trip Point Research Articles

Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles

To enhance urban freight efficiency and green development, China has implemented the Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic control policies and increasing the number of new energy vehicles (NEV). However, range anxiety is a significant challenge for freight drivers performing delivery tasks with electric vehicles (a major component of NEV). We constructed a prediction model for the state of charge (SOC), or battery remaining energy percentage when UGFD vehicles reach the next trip point, aiming to alleviate this issue. The model consists of three modules: (1) a vehicle SOC context prediction module, (2) a vehicle energy consumption prediction module, and (3) a multi-perspective SOC prediction value fusion module. Specifically, in the SOC context prediction module, historical SOC sequences, vehicle status (loading/unloading, charging), and time intervals between SOC points are used to accurately describe context change trends, and directly predict the vehicle SOC at the next trip point. The energy consumption prediction module combines community-level and grid-level geographical location information for the vehicle stops using weather, vehicle parameters, etc., to model the spatial dynamic correlation of energy consumption. The vehicle SOC at the next trip point is the difference between the current vehicle SOC and the predicted energy consumption. The multi-perspective SOC prediction value fusion module is a combination of the predicted values from the context and energy consumption perspectives, resulting in the final vehicle SOC prediction value. Taking Suzhou, China as an example, the results show that the mean absolute error, root mean square error, and symmetric mean absolute percentage error for the constructed model are 23.67%, 10.39%, and 20.03% less, respectively, than for the baseline models focusing on SOC short-term time series prediction. The research results can provide scientific evidence for formulating refined energy management, charging station layout, and freight delivery optimization approaches.

Noninverting Schmitt trigger circuit with electronically tunable hysteresis

This paper reports a tunable hysteresis CMOS Schmitt trigger design techniques and an investigation of new buffer-based designs. The sizing of the two feedback inverters controls the two trip points of the structure independently. By the addition of voltage tune controlled sourcing transistor, the hysteresis window can be easily changed. Moreover, the new designs are immune to the kick-back noise coming from the succeeding inverter blocks. In this work, the VT-ST proves itself more reliable regarding dynamic and leakage power consumption and propagation delay. In this work, aspect ratio of the transistors is kept as small as possible, and the hysteresis loop is varied with a tunable voltage transistor. 5000 Monte Carlo simulations reveal that the VT-ST is more reliable as ΔVLHandΔVHL are almost equal, and a small variation is observed. For the comprehensive analysis, a figure of merit for all the circuits is derived and it is 3.16× higher for the proposed VT-ST than the conventional circuit. All simulation work is handled by the cadence virtuoso tool using UMC 40 nm technology.

Gas Turbine Bearing Temperature Monitoring via Regression Modelling

This paper focuses on using Regression technique (MLR) towards finding solution to incidence of high compressor bearing temperature on one of the units at Geregu power plant in Ajaokuta, Nigeria. Monitoring of parameters related to the bearing temperature was carried out to find out causes for the high bearing temperature fault and came up with successful diagnosis by interrelating the gas turbine current lube oil test results of parameters like the kinematic viscosities, % concentration of additives and flash point with reference and standard VG46 lube oil data published in literature. Using statistical tools like the Pearson correlation and co-variant metrics for the five-years, the oil viscosities at 100oC and 40oC were selected as the input of the MLR model based on their Pearson coefficients of (-98.08%) and (-99.68%) respectively relative to the compressor bearing temperature. The MLR model for the bearing temperature prediction gave a root mean square error of 0.121 and coefficient of determination (R2) of 99.71%. The model predicts that by the 2nd quarter of 2025, the bearing temperature would have reached the alarm point (900C) from the current value of 850C and that by the 1st quarter of 2027, the bearing temperature would have reached the trip point (1200C). Conclusion reached is that a well formulated data driven model can reliably forecast bearing temperature and together with sensors aid in gas turbine condition monitoring. Likewise, it is concluded that shearing due to the consistent high temperature operation of the gas turbine lube oil is responsible for the depletion of the Zinc (-23.9%) and Magnesium(-26%) additives leading to the decay in the viscosity and consequent bearing temperature increment. Recommendation made is to either replenish oil with anti-wear additives or completely replace the oil to minimize the bearing wear rate and thus the bearing temperature.

An efficient common source sense amplifier for single ended SRAM

Sense amplifiers (SA) play a vital role in supporting the read performance of static random-access memory (SRAM). Single ended SRAM has attracted importance due to low leakage current and absence of time margin compared to differential SA. This paper proposes a common source sense amplifier (CSSA) for low power single ended SRAM for read operation. The sense amplifier performs dual task by charging the bit line during pre-charge phase and amplifying the bit line during evaluation phase. The proposed CSSA shows good improvement in sensing time and power at higher number of cells per bit line (CpBL). The proposed CSSA exhibits 53%, 48%, 24%, 23%, and 41% lower sensing time for 256 CpBL and 52%, 51%, 50%, 37%, and 47% lesser power consumption than the conventional domino sensing scheme (DSS), AC coupled sense amplifier (ACSA), non-strobed regenerative sense amplifier (NSRSA), switching PMOS sense amplifier (SPSA) and trip point bit line pre-charge sensing scheme (TBPSS). The proposed CSSA occupies 18%, 25%, 53%, 61%, and 37% lesser area compared to DSS, ACSA, SPSA, NSRSA, and TBPSS. The proposed CSSA has 88%, 88%, 85%, 91%, and 87% lesser APDP (area power delay product) compared to DSS, ACSA, SPSA, NSRSA, and TBPSS. The proposed CSSA sensing scheme is implemented and simulated in Cadence Virtuoso tool with 45 nm technology. The simulation results of CSSA prove that the proposed CSSA sense amplifier is suitable for high speed and low power SRAM architecture.

Early‐warning signal recognition methods in flawed sandstone subjected to uniaxial compression

AbstractTo investigate the precursor information related to rock fracturing, a series of indoor uniaxial compression tests are conducted on flawed sandstone. Four methods of identifying potential precursors to rock catastrophic failure, namely, cumulative acoustic emission (AE) activity, AE b‐value, inter‐event time function, and critical slowing down, are discussed. The results show that the four precursory identification methods can reasonably identify the early‐warning signal, subcritical failure, and final catastrophic failure of flawed sandstone involving the whole loading process. The minimum b‐value is correspondingly obtained when the specimen approaches failure. The inter‐event rate of AE multi‐parameters shows a significant increase, following a power law distribution. By comparison with the other three precursor identification methods, the time intervals of AE parameters‐based variance between the precursory signal and its corresponding tripping points become increasingly shortened. Additionally, the precursory characteristics of variance of AE multi‐parameters are more accurate and reliable than that of autocorrelation coefficient.

Implementing the Concept of Alarm Flexibility During Production

An interruption at a workstation is a major cause of production downtime, which has negative effects on performance, cost, consumer-centric value, and efficiency. Blockage and starvation brought on by broken workstations have a domino effect on the entire production line. To allocate resources effectively and minimize downtime, it is crucial to thoroughly analyze each disruption's effects. Quantitative analysis of disruption at a single workstation and its effects on neighbouring workstations are performed in the work presented. Faulty workstations have an additional threshold value added to the Alarm Trip Point (ATP), depending on their current state. This one significantly improves upon downtime mitigation and batch rejection reduction compared to previous approaches. A more general model based on multiple workstations has been developed by expanding the analysis for a single workstation. The proposed work is demonstrated in an experimental case study, an Asphalt-Processing Plant. It is applied in addition to developing a theoretical model to reduce downtime, multiple starts and stops, and numerous rejections. The proposed method reduces start-stop events by 60%, reducing rejections and downtime while increasing consumer-centric value and efficiency.

Transient analysis of MTR research reactor during fast and slow loss of flow accident

Abstract The main objective for reactor safety is to keep the fuel in a safe condition with adequate safety margins during all operational modes (normal-abnormal and accidental states). To achieve this purpose an accident analysis of different design base accidents (DBAs), one of them is the loss of flow accident (LOFA), is required for assessing reactor safety. In this research, the safety aspects of 22 MW MTR research reactor under steady state and during loss of flow accident is studied. The flow transients considered include fast loss of flow accident (FLOFA) and slow loss of flow accident (SLOFA) modeled with exponential flow decay and time constants of 1 and 25 s, respectively. The analysis is done using PARET, a neutronics-hydrodynamics-heat transfer code. The transients were initiated from a full power with a flow trip point at 85% nominal. The calculated parameters are the temperatures of different components (fuel, clad and coolant) as a function of time for the hot channel. The results indicate that in both accidents the calculated maximum cladding surface temperature for the hottest channel of the reactor core does not exceed the allowable safety limit and the fuel integrity is maintained.

Interval-thresholding effect of cooling and recreational services of urban parks in metropolises

Highly urbanized and scarcity of land resource have posed urgent need of threshold analyses of ecological services to achieve optimal ecosystem services efficiency. Whereas previous studies have highlighted the threshold value of efficiency (TVoE) of cooling service, the interval non-linear characteristic of TVoEs of recreational service and co-benefit in urban parks remain unclear, especially identifying the socioeconomic impact on the TVoEs. Taking Beijing as our study area, we identified the dominant impact factors on cooling, recreational services and co-benefit, and assessed the interval variation characteristics of TVoEs of these three services in diversely developed regions. The result indicated that park size and socioeconomic development were dominant factors. And interval-thresholding, which can determine the change interval and trip point, is innovatively used to analyze the relationship between park size and services. This relationship showed different characteristics in two intervals and the TVoE ranges of the parks’ co-benefit increased with the improvement of local socioeconomic level. These results can provide quantitative inputs for green space smart planning relating to climate change and public health considerations.

Printed dual-gate organic thin film transistors and PMOS inverters on flexible substrates: role of top gate electrode

We report printed single and dual-gate organic thin film transistors (OTFTs) and p-channel metal-oxide-semiconductor (PMOS) inverters fabricated on 125 µm thick flexible polyethylene naphthalate substrate. All the electrodes (gate, source, and drain) are inkjet-printed, while the parylene dielectric is formed by chemical vapor deposition. A dispenser system is used to print the active channel material using a blend of 2,7-dihexyl-dithieno[2,3-d;2′,3′-d′]benzo [1,2-b;4,5-b′]dithiophene and polystyrene in tetralin solvent, which gives highest mobility of 0.43 cm2 V−1s−1. Dual-gate OTFTs are characterized by keeping the other gate electrode either in grounded or floating state. Floating gate electrode devices shows higher apparent mobility and current ratio due to additional capacitance of the parylene dielectric. PMOS inverter circuits are characterized in terms of gain, trip point and noise margin values calculated from the voltage transfer characteristics (VTC). Applied top gate voltage on the load OTFT control the conductivity or threshold voltage (V Th) of the bottom TFT and shift the trip point towards the middle of the VTC curve, and hence increase the noise margin.

A New Protection-Based Approach for Link Failure Management of Software-Defined Networks

Restoration-based failure management techniques select new paths for software-defined networks (SDN) after diagnosing broken links, which are time-consuming and require steady network inspection. In this paper, a new protection-based link failure management approach is presented for SDN, which attempts to identify links with high probability of failure before they fail definitively. The proposed approach calculates failure index of links by examining three parameters, latency, throughput, and packet loss ratio. In addition, probability density functions (PDFs) of these univariate parameters are used to distinguish between failure-free and broken statuses of a link. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> -sample delay timer technique is applied to adjust the proper trip point and improve detection accuracy of the proposed approach. The results of simulations conducted in Mininet tool have validated effectiveness of the proposed approach in terms of success ratio in finding backup paths, average allocated backup rules, recovery latency, and end-to-end delay. Likewise, influence of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{m}}$</tex-math></inline-formula> and Mean Time between Failures (MTBF) on performance of the proposed approach is investigated and the optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\boldsymbol{m}}$</tex-math></inline-formula> is determined for various circumstances. Eventually, time and message complexity analysis indicates that overhead of the proposed approach is negligible.

Dynamic trip point categorisation using manufacturing process for polycrystalline diamond compact bits as case study

Disruptions in the manufacturing process cast substantial impacts on the consumer's centric values, cost, and overall efficiency. The ripple effect of these disruptions is observed in the whole assembly line. The proper analysis and mitigation of disruptions is the key to the proficiency of the plant and resource utilisation. The research focuses on the quantitative analysis of the impact of disruptions of the manufacturing process, which is performed by analysing the disruptive workstation dynamically. The disruptive workstations have a significant impact on succeeding and preceding workstations, which have been analysed dynamically, and accordingly, the pre-set alarm trip points are elevated. The results show that lowering the number of starts and stops decreases the number of rejections of valuable process batches that finally improve efficiency. Finally, the developed model is implemented on a polycrystalline diamond compact drill bit manufacturing plant. The results are compared with the research work of other researchers, showing a 60% reduction in the number of start/stop events, 60% reduction in downtime, while a 75% reduction in the number of rejections is noted.

Model-Based Dynamic Categorization of Alarm Trip Points for Manufacturing Process Disruption Minimization

ABSTRACT Faults in manufacturing processes can cause disruptions and downtime, which adversely affects process efficiency and increases production costs. Process downtime can be reduced by monitoring manufacturing processes for faults, categorizing those faults, and prioritizing action. A new approach is proposed for minimizing function downtime, which involves monitoring system faults and generating a system response depending upon the status of the individual workstations in the manufacturing process line. The proposed model dynamically categorizes the faults considering the fault repair time, the weightage assigned to each workstation, and the process status, i.e. idle or running. The proposed model generates the threshold values for setting up trip points and categorizes critical faults into low, medium, and high values. The proposed model is tested using software simulations and an experimental test rig, and the results demonstrate that it can significantly reduce downtime in manufacturing processes. The model has been successfully implemented on a test rig and Asphalt manufacturing plant. In the test rig, 12% improvement is seen while on the Asphalt plant, 60% reduction is noticed in terms of start/ stops incidents, 60 minutes in downtime, and 800 kg of a valuable processed batch is saved in one week.

How does Dockless bike sharing serve users in Nanjing, China? User surveys vs. trip records

Dockless bike sharing (DBS), as an emerging bike sharing scheme, effectively promotes active travel and improves the mobility of users. Travel behavior analysis of DBS users is a key basis of service improvement, and the data sources include user surveys and trip records. User surveys are widely applied but have the drawback of perceptual errors. Thus, there is a compelling need for exploring travel behavior using automatically-collected trip records. This study examines DBS users' travel frequency and purpose with five semi-annual user surveys and trip record data in Nanjing, China. User surveys reflect user perception of travel behavior, while trip records can be used to calculate actual travel frequency and infer travel purpose. The results show that perceptual travel frequency based on user surveys has a central tendency, and actual travel frequency based on trip records follows a heavy-tailed distribution. Older people tend to underestimate their travel frequency, but other age groups tend to overestimate their frequency. In addition, a dockless bike sharing's travel purpose inferring (DBS-TPI) model is proposed to infer the travel purpose based on trip record data and points of interest data. The DBS-TPI model is reliable and transferable under different scenarios, which could complement user surveys. This study provides insights into improving data collection for better understanding of travel behaviors among DBS users.

A Comprehensive Study of Nanosheet and Forksheet SRAM for Beyond N5 Node

SRAM bitcell area reduction, lower SRAM parasitic resistance, and higher drive strength are necessary to continue with technology scaling. Nanosheet (NSH) technology improves SRAM cell write-ability by having 50 mV more write trip point (WTP) than FinFET (FF) SRAM due to reduced bit line (BL) resistance (due to wider metal CD) and more drive current strength (more than 15%) than FF for the same leakage. However, due to higher bitcell area (20% larger), BL, and word line (WL) parasitic capacitance than FF, NSH SRAM would not compete with FF SRAM in terms of the read delay (26% more delay) and energy at the 3-nm technology node. PFET to NFET (PN) spacing, composed of gate cut, gate extension, and Fin pitch in an SRAM bitcell, is critical for SRAM cell height because it can take ~46% of the 111 SRAM total cell height. The forksheet (FSH), achieving extremely scaled PN space in SRAM bitcell due to device structure with limited additional processing complexity, reduces the SRAM bitcell area. As a result, BL and WL parasitics reduce and improve the SRAM read delay and stability. FSH SRAM saves 6% area benefit and achieves 24% lesser read delay than FF high density (HD) SRAM.

2D MXene-Molecular Hybrid Additive for High-Performance Ambipolar Polymer Field-Effect Transistors and Logic Gates.

MXenes are highly conductive layered materials that are attracting a great interest for high-performance opto-electronics, photonics, and energy applications.. Their non-covalent functionalization with ad hoc molecules enables the production of stable inks of 2D flakes to be processed in thin-films. Here, the formation of stable dispersions via the intercalation of Ti3 C2 Tx with didecyldimethyl ammonium bromide (DDAB) yielding Ti3 C2 Tx -DDAB, is demonstrated. Such functionalization modulates the properties of Ti3 C2 Tx , as evidenced by a 0.47eV decrease of the work function. It is also shown that DDAB is a powerful n-dopant capable of enhancing electron mobility in conjugated polymers and 2D materials. When Ti3 C2 Tx -DDAB is blended with poly(diketopyrrolopyrrole-co-selenophene) [(PDPP-Se)], a simultaneous increase by 170% and 152% of the hole and electron field-effect mobilities, respectively, is observed, compared to the neat conjugated polymer, with values reaching 2.0 cm2 V-1 s-1 . By exploiting the balanced ambipolar transport of the Ti3 C2 Tx -DDAB/PDPP-Se hybrid, complementary metal-oxide-semiconductor (CMOS) logic gates are fabricated that display well-centered trip points and good noise margin (64.6% for inverter). The results demonstrate that intercalant engineering represents an efficient strategy to tune the electronic properties of Ti3 C2 Tx yielding functionalized MXenes for polymer transistors with unprecedented performances and functions.

A method to enhance SNR based on CEEMDAN and the interval thresholding in Φ_OTDR systems

The present paper proposes a novel denoising method to suppress multi-source noise and enhance the signal-to-noise ratio (SNR) of the phase-sensitive optical-time-domain reflectometer (Ф-OTDR). The method is based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the interval thresholding. Firstly, the CEEMDAN is applied to the vibration signals to obtain a series of functions called the Intrinsic Mode Functions (IMFs). Secondly, the trip point is determined according to the fact that the product of energy density of the IMF and its corresponding averaged period is a constant. Then, the IMFs of noise dominant mode are treated with interval thresholding, and finally the IMFs and residual are reconstructed to obtain denoised signals. The results show that the SNR of disturbance location increases to 51.21 dB and 52.11 dB respectively, which represents a great improvement when compared with the other two methods.

Optimization of engine performance parameters and exhaust emissions in compression ignition engine fueled with biodiesel-alcohol blends using taguchi method, multiple regression and artificial neural network

Abstract Humanity is on the verge of setting new benchmarks in warming up the planet 4-5°C by triggering a series of cascading tripping points. Rising content in the atmosphere is causing an unprecedented spike in global temperatures leading to drastic climatic changes like massive heat waves, droughts, melting of polar ice caps and a rise in the sea level. Vehicles alone are responsible for at least 12% of the total Carbon dioxide (CO2) emissions in the world. Carbon Monoxide (CO), a toxic air pollutant, is produced by the incomplete combustion of carbon-containing fuels like gasoline in automobiles and its emissions are responsible for a wide range of health problems like headache, nausea, vomiting, and dizziness. Nitrogen Oxides (NOx) emissions produced by motor vehicles are instrumental in causing smog, acid rain which can lead to adverse health effects like respiratory disorders, heart problems etc. Alternative fuels will act as a life jacket to save humanity from drowning in this ocean of its design. Biodiesel is a carbon neutral fuel that can be blended with diesel and used in driving compression ignition engines. In the study engine performance in terms of brake thermal efficiency (BTE), brake specific fuel consumption (BPSC) and CO, HC, CO2, and NOx emissions are optimized for Kusum Oil Ethyl Ester (KOEE) with butanol blend using Taguchi's method, to obtain an optimized dataset of the input parameters viz. engine rpm, fuel properties and lower heating value of fuel which are then fed into the two prediction techniques viz. Artificial Neural Network (ANN) and multiple regression. These techniques are also ranked in terms of precision, based on generated errors.

Denoising algorithm of [formula omitted]_OTDR signal based on clear iterative EMD interval-thresholding

Aiming at the problem of low SNR of the phase-sensitive optical-time-domain reflectometer (Φ-OTDR), a novel signal denoising method based on clear iterative EMD interval-thresholding (EMD-CIIT) is proposed. Firstly, the Pearson correlation coefficient (PCC) between intrinsic mode functions (IMFs) and measured signal are calculated so that can determine the trip point between noise dominant mode and signal dominant mode in IMFs. Then, the IMFs with PCC value greater than 0.1 are denoised based on the EMD-CIIT algorithm. Finally, the IMFs and residual are reconstructed to obtain denoised signals. As seen from the knocking and crushing test results, it proves the Signal Noise Ratio (SNR) of signal for the proposed method has been obviously enhanced compared with the wavelet threshold, Wiener filter and spectral subtraction denoising method, which can be raised up to 30.9 dB and 32.82 dB respectively.

Analysis of SRAM Enhancements Through Sense Amplifier Capacitive Offset Correction and Replica Self-Timing

An analysis of timing and input-referred offset of sense amplifiers (SA) is presented, and a new SA architecture with capacitive offset correction is proposed. Offset sources are first analyzed in a cross-coupled latch-based SA design, and the analysis is then extended to the proposed SA. The results show the proposed offset correction technique can reduce the total sensing time by up to 40%, while eliminating dynamic offset due to the difference in resolving inverters trip points. A self-timed SRAM with a new replica timing structure is designed to generate optimal SA enable timing with respect to the total input-referred offset. Calculations for timing delay and offset are compared with simulation results in both a conventional and a proposed SA design. The presented simulation results are based on a 10 Kb CMOS SRAM array in 130 nm BiCMOS SiGe technology operating at a 500 MHz clock and 1.5 V supply.

Snap-fit Assembly Process with Industrial Robot Including Force Feedback

SummaryThis paper investigates a battery snap-in operation performed by an industrial robot. The snap-fit phenomenon is experienced during the insertion of batteries into the battery holder. In order to understand the nature of the snap-fit phenomenon, the large displacement but small deformation theory is used to model the insertion operation. The stability of equilibrium points is analysed in order to determine the so-called trip point. The purpose of the investigation is to augment the displacement control of the robot with force feedback. A microcontroller-based measurement system is implemented to provide the force feedback. The proposed method is valid for a class of snap-fit problems not only for the investigated specific one.