Dynamic Weight Distribution Research Articles

Text Triplet Extraction Algorithm with Fused Graph Neural Networks and Improved Biaffine Attention Mechanism

In the realm of aspect-based sentiment analysis (ABSA), a paramount task is the extraction of triplets, which define aspect terms, opinion terms, and their respective sentiment orientations within text. This study introduces a novel extraction model, BiLSTM-BGAT-GCN, which seamlessly integrates graph neural networks with an enhanced biaffine attention mechanism. This model amalgamates the sophisticated capabilities of both graph attention and convolutional networks to process graph-structured data, substantially enhancing the interpretation and extraction of textual features. By optimizing the biaffine attention mechanism, the model adeptly uncovers the subtle interplay between aspect terms and emotional expressions, offering enhanced flexibility and superior contextual analysis through dynamic weight distribution. A series of comparative experiments confirm the model’s significant performance improvements across various metrics, underscoring its efficacy and refined effectiveness in ABSA tasks.

Time course and localization of nerve growth factor expression and sensory nerve growth during progression of knee osteoarthritis in rats

Nerve growth factor (NGF) and sensory nerves are key factors in established osteoarthritis (OA) knee pain. We investigated the time course of NGF expression and sensory nerve growth across early and late stages of OA progression in rat knees. Knee OA was induced by medial meniscectomy in rats. OA histopathology, NGF expression, and calcitonin gene-related peptide immunoreactive (CGRP-IR) nerves were quantified pre-surgery and post-surgery at weeks 1, 2, 4 and 6. Pain-related behavior was evaluated using dynamic weight distribution and mechanical sensitivity of the hind paw. NGF expression in chondrocytes increased from week 1 and remained elevated until the advanced stage. In synovium, NGF expression increased only in early stages, whereas in osteochondral channels and bone marrow, NGF expression increased in the later stages of OA progression. CGRP-IR nerve density in suprapatellar pouch peaked at week 4 and decreased at week 6, whereas in osteochondral channels and bone marrow, CGRP-IR innervation increased through week 6. Percent ipsilateral weight-bearing decreased throughout the OA time course, whereas reduced paw withdrawal thresholds were observed only in later stages. During progression of knee OA, time-dependent alterations of NGF expression and CGRP-IR sensory innervation are knee tissue specific. NGF expression increased in early stages and decreased in advanced stage in the synovium but continued to increase in osteochondral channels and bone marrow. Increases in CGRP- IR sensory innervation followed increases in NGF expression, implicating that NGF is a key driver of articular nerve growth associated with OA pain.

Design and analysis of brake disc assembly for an FSAE vehicle

When it comes to driver’s safety, the quality of car’s braking system plays a crucial part. The objective of this paper is to design and fabricate a reliable brake disc assembly which is both structurally and thermally stable along with selecting suitable brake calipers for an FSAE vehicle. To enhance the vehicle performance, it is necessary to optimize and design a light weight brake disc assembly yet having high braking power, strength and rigidity. In order to achieve desirable results, parameters of assembly like effective diameter of brake disc, thickness of brake disc, heat flux, maximum temperature, fluid parameters, caliper specifications, design and arrangement of floaters, etc. are determined and calculated to have an optimized geometry. These values are determined on the basis of vehicle parameters such as overall weight, velocity of vehicle, braking time, kinetic energy of vehicle, static and dynamic weight distribution, height of center of gravity of vehicle, wheelbase, wheel diameter and coefficient of friction of tyres. Multiple simulations and topology optimizations are performed to finalize the material and corresponding manufacturing technique, so as to make assembly efficient and cost effective without compromising on strength.

Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information

Fault diagnosis driven by the single signal has been widely used in motor fault diagnosis, but it can not meet the diagnostic requirements of complex motor system. In this study, a motor fault diagnosis method using attention mechanism and improved AdaBoost driven by multi-sensor information is proposed. Firstly, the corresponding frequency domain feature information is obtained by Hilbert transform and Fourier transform in different signals. The improved AdaBoost multi-classification classifier is then used to train signals from different sources and obtain sub classifier results. Finally, a dynamic weight distribution matrix is used to obtain the final diagnosis results with sub classifiers. The proposed method is verified by current, magnetic and vibration signals. The results show that the proposed method dynamically evaluates sensitivity of different detection signals to different faults. Compared with the conventional method, the proposed method can enhance the robustness, generalization ability and accuracy of fault diagnosis.

Transference of reference intervals for variables of instrumented gait analysis in walking dogs.

To use the small data approach of the Clinical and Laboratory Standards Institute (CLSI) to evaluate the transferability of reference intervals (RIs) for kinetic variables obtained with instrumented gait analysis (IGA) in dogs from an RI-originator laboratory to another laboratory that used the same data acquisition and analytic techniques for IGA in walking dogs. 27 adult client-owned dogs without evidence of lameness. Dogs were individually walked at their preferred velocity on a pressure-sensing walkway for IGA at the Colorado State University Animal Gait Laboratory (CSU-AGL), and 6 valid trials were analyzed for each dog. The small data approach of the CLSI was then used to evaluate transferability of RIs previously established at the Purdue University Animal Gait Laboratory (PU-AGL). A linear model was used to establish weight-dependent RIs for peak vertical force (PVF). Results indicated that RIs of dynamic weight distribution (DWD), DWD symmetry index, DWD coefficient of variation, PVF symmetry index, and PVF coefficient of variation were transferable from PU-AGL to CSU-AGL, whereas the weight-dependent RIs for PVF were not. Regression slopes for PVF versus body weight were greater for all limbs in dogs tested at the CSU-AGL, compared with historic results for dogs tested at the PU-AGL. Use of the small data approach method of the CLSI to validate transference of RIs for IGA kinetic variables in walking dogs was simple and efficient to perform and may help facilitate clinical and research collaborations on gait analysis.

Short-term load forecasting algorithm based on LSTM-DBN considering the flexibility of electric vehicle

Due to the rapid development of electric vehicles, the randomness of charging and discharging modes brings huge challenges to load forecasting. Considering the large-scale gridding of electric vehicles, a load combination forecasting algorithm is proposed in this paper which considers the flexibility of electric vehicles. Firstly, the user behaviors of electric vehicle are modeled based on Monte Carlo method. Secondly, different from traditional single forecasting models, the combination of deep belief network and long short-term memory net-work is used in order to improve the forecasting accuracy and calculation speed for the complex case. Thirdly, a dynamic weight distribution method is proposed to combine the two forecasting results. Finally, Numerical examples have been performed with the results proving that the proposed method outperforms the state-of-art significantly in load forecasting, and the combined model has a higher forecasting accuracy than traditional methods.

A genuine smile is indeed in the eyes – The computer aided non-invasive analysis of the exact weight distribution of human smiles across the face

Understanding the detailed differences between posed and spontaneous smiles is an important topic with a range of applications such as in human-computer interaction, automatic facial emotion analysis and in awareness systems. During the past decade or so, there have been very promising solutions for accurate automatic recognition and detailed facial emotion analysis. To this end, many methods and techniques have been proposed for distinguishing between spontaneous and posed smiles. Our aim here is to go beyond the present state of the art in this field. Hence, in this work, we are concerned with understanding the exact distribution of a smile – both spontaneous and posed – across the face. To do this, we utilise a lightweight computational framework which we have developed to analyse the dynamics of human facial expressions. We utilise this framework to undertake a detailed study of the smile expression. Based on computing the optical flow across the face – especially across key parts of the face such as the mouth, the cheeks and around the eyes – we are able to accurately map the dynamic weight distribution of the smile expression. To validate our computational model, we utilise two publicly available datasets, namely the CK + dataset in which the subjects express posed smiles and the MUG dataset in which the subjects express genuine smiles. Our results not only confirm what already exists in the literature – i.e. that the spontaneous genuine smile is truly in the eyes – but it also gives further insight into the exact distribution of the smile across the face.

Adjustment of the dynamic weight distribution as a sensitive parameter for diagnosis of postural alteration in a rodent model of vestibular deficit.

Vestibular disorders, by inducing significant posturo-locomotor and cognitive disorders, can significantly impair the most basic tasks of everyday life. Their precise diagnosis is essential to implement appropriate therapeutic countermeasures. Monitoring their evolution is also very important to validate or, on the contrary, to adapt the undertaken therapeutic actions. To date, the diagnosis methods of posturo-locomotor impairments are restricted to examinations that most often lack sensitivity and precision. In the present work we studied the alterations of the dynamic weight distribution in a rodent model of sudden and complete unilateral vestibular loss. We used a system of force sensors connected to a data analysis system to quantify in real time and in an automated way the weight bearing of the animal on the ground. We show here that sudden, unilateral, complete and permanent loss of the vestibular inputs causes a severe alteration of the dynamic ground weight distribution of vestibulo lesioned rodents. Characteristics of alterations in the dynamic weight distribution vary over time and follow the sequence of appearance and disappearance of the various symptoms that compose the vestibular syndrome. This study reveals for the first time that dynamic weight bearing is a very sensitive parameter for evaluating posturo-locomotor function impairment. Associated with more classical vestibular examinations, this paradigm can considerably enrich the methods for assessing and monitoring vestibular disorders. Systematic application of this type of evaluation to the dizzy or unstable patient could improve the detection of vestibular deficits and allow predicting better their impact on posture and walk. Thus it could also allow a better follow-up of the therapeutic approaches for rehabilitating gait and balance.

Mitigating load carriage burden – The Dynamic Weight Distribution (DWD) hip belt

Mitigating load carriage burden – The Dynamic Weight Distribution (DWD) hip belt

Development of a regenerative braking control strategy for hybridized solar vehicle

This paper focuses on the development of a braking control strategy that allows the best tradeoff between mechanical and regenerative braking on a hybridized vehicle. The research work is part of a project for the development of an automotive hybridization kit aimed at converting conventional cars into Through The Road hybrid solar vehicles. The main aspect of the project is the integration of state-of-the-art components (i.e. in-wheel motors, photovoltaic panels, batteries) with the development of an optimal controller for power management. A mild parallel hybrid structure is obtained by substituting/integrating the rear wheels with in-wheel motors and adding photovoltaic panels and a lithium-ion battery. A hybridizing equipment prototype, patented by the University of Salerno, is installed on a FIAT Grande Punto. A model useful for real-time braking control has been developed, starting from vehicle longitudinal model and considering dynamic weight distribution in front and rear axles and related wheel slipping effects. Different braking strategies have then been investigated, in order to maximize the benefits of regenerative braking.

Tide forecasting method based on dynamic weight distribution for operational evaluation

Through analysis of operational evaluation factors for tide forecasting, the relationship between the evaluation factors and the weights of forecasters was examined. A tide forecasting method based on dynamic weight distribution for operational evaluation was developed, and multiple-forecaster synchronous forecasting was realized while avoiding the instability cased by only one forecaster. Weights were distributed to the forecasters according to each one's forecast precision. An evaluation criterion for the professional level of the forecasters was also built. The eligibility rates of forecast results demonstrate the skill of the forecasters and the stability of their forecasts. With the developed tide forecasting method, the precision and reasonableness of tide forecasting are improved. The application of the present method to tide forecasting at the Huangpu Park tidal station demonstrates the validity of the method.

English

During the night driving, the reaction time of a human driver go into a compromise due to inadequate peripheral vision or lack of color and depth perception. The conventional headlamps of any automotive vehicle are insufficient to provide an effective illumination in the road while the vehicle is negotiating a curve. The Adaptive Front Light System assists to improve the visibility of the road during the night time driving, which in turn improves safety by reducing the probability of accidents. The Adaptive Front Light System controls the aiming direction and pattern distribution of the low beam according to the amount of steering wheel angle, the vehicle speed and the direction of cornering. This feature is known as low beam “Swiveling”. Additionally, the Adaptive Front Light System is featured with a functionality called low beam “Leveling” which makes the head lamp capable of positioning the low beam exactly in to the intended position irrespective of the weight distribution between the front and rear side of the vehicle. Static weight distribution might happen if the vehicle is loaded with additional passengers or extra luggage in the boot space. Dynamic weight distribution might happen during the acceleration or braking, which introduces a pitching motion to the vehicle over the lateral horizontal axis. Furthermore, the Adaptive Front Light System features the High beam length adjustment in country roads according to the speed of the vehicle which in turn improves the visibility of the road while the vehicle is cruising with a high speed.

A Combination Model for Short Term Wind Power Forecasting

For analyzing the accuracy of wind power prediction, an analyzing model combined with multi-leaner and dynamic weight distribution is proposed. With this method, Numerical Weather Prediction (NWP), Wind power data (historical) and weather data (historical) are structured into several sample sets, each set has a different weight value, which determined by the training errors, these sample set is trained by different learner algorithm with a weight too. Finally, using these models to predict the outputs. The experiments indicate the effectiveness of the method this paper proposed. Compared with Single model of Support Vector Machine and Artificial Neural Network, the combination method has better performance in both calculation accuracy and generalization.

Network cognitive model based on fuzzy comprehensive evaluation

In view of the limitation of the traditional Transmission Control Protocol(TCP) used in the heterogeneous network,a network cognitive model based on fuzzy comprehensive evaluation was proposed.This model,by building the membership functions and different dynamic weight distribution under the different network environment,distinguished the wireless error loss from congestion loss according to fuzzy comprehensive evaluation.The simulation results show: compared with the traditional TCP,under different network conditions,the model can more accurately distinguish the wireless error loss from congestion loss,and improve the TCP throughput and network performance.

Studies on Measurement of Transmitting Torque and Dynamic Weight Distribution of a Tractor under Several Transitional Conditions

Studies on Measurement of Transmitting Torque and Dynamic Weight Distribution of a Tractor under Several Transitional Conditions