Optimization Control of Multi-Mode Coupling All-Wheel Drive System for Hybrid Vehicle

The all-wheel drive(AWD)hybrid system is a research focus on high-performance new energy vehicles that can meet the demands of dynamic performance and passing ability.Simultaneous optimization of the power and economy of hybrid vehicles becomes an issue.A unique multi-mode coupling(MMC)AWD hybrid system is presented to realize the distributed and centralized driving of the front and rear axles to achieve vectored distribution and full utilization of the system power between the axles of vehicles.Based on the parameters of the benchmarking model of a hybrid vehicle,the best model-predictive control-based energy management strategy is proposed.First,the drive system model was built after the analysis of the MMC-AWD’s drive modes.Next,three fundamental strategies were established to address power distribution adjustment and battery SOC maintenance when the SOC changed,which was followed by the design of a road driving force observer.Then,the energy consumption rate in the average time domain was processed before designing the minimum fuel consumption controller based on the equivalent fuel consumption coefficient.Finally,the advantage of the MMC-AWD was confirmed by comparison with the dynamic performance and economy of the BYD Song PLUS DMI-AWD.The findings indicate that,in comparison to the comparative hybrid system at road adhesion coefficients of 0.8 and 0.6,the MMC-AWD’s capacity to accelerate increases by 5.26%and 7.92%,respectively.When the road adhesion coefficient is 0.8,0.6,and 0.4,the maximum climbing ability increases by 14.22%,12.88%,and 4.55%,respectively.As a result,the dynamic performance is greatly enhanced,and the fuel savings rate per 100 km of mileage reaches 12.06%,which is also very economical.The proposed control strategies for the new hybrid AWD vehicle can optimize the power and economy simultaneously.

Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

The CCD instrument background of the SMILE SXI

The ESA and CAS SMILE mission orbit is highly elliptical and will pass through multiple radiation environments.The Soft X-ray Imager(SXI)instrument aboard has a radiation shutter door designed to close when the surrounding radiation flux is high.The shutter door will close when passing below an altitude threshold to protect against trapped particles in the Earth’s Van Allen Belts.Therefore,two radiation environments can be approximated based on the shutter door position:open and closed.The instrument background for the CCDs(Charge-Coupled Devices)that form the focal plane array of the SXI were evaluated for the two environments.Due to the correlation of the space environment with the solar cycle,the solar minima and maxima,the background was also evaluated at these two extremes.The results demonstrated that the highest instrument background will occur during solar minima due to the main contributing source being Galactic Cosmic Rays(GCRs).It was also found that the open background was highest for solar minima and that the closed background was highest during solar maxima.This is due to the radiation shutter door acting as a scattering centre and the changes in the energy flux distribution of the GCRs between the two solar extremes.

A Multi-mode Electronic Load Sensing Control Scheme with Power Limitation and Pressure Cut-off for Mobile Machinery

In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.

Glaucoma surgery experiments using digital microscope-integrated optical coherence tomography and OCT-compatible instruments

There is a certain failure rate in traditional glaucoma surgery because of the lack of depth information in microscope images.In this work,we present a digital microscope-integrated optical coherence tomography(MIOCT)system and several custom-made OCT-compatible instruments for glaucoma surgery.Sixteen ophthalmologists were asked to perform trabeculectomy and canaloplasty on live porcine eyes using the system and instruments.After surgery,a subjective feedback survey about the user experience was taken.The experiment results showed that our system can help surgeons easily locate important tissue structures during surgery.The custom-made instruments also solved the shadowing problem in OCT imaging.Surgeons preferred to use the system in their future practice.

Multi-mode Multi-frequency GNSS-IR Combination System for Sea Level Retrieval

With the development of Global Navigation Satellite Systems(GNSS),geodetic GNSS receivers have been utilized to monitor sea levels using GNSS-Interferometry Reflectometry(GNSS-IR)technology.The multi-mode,multi-frequency signals of GPS,GLONASS,Galileo,and Beidou can be used for GNSS-IR sea level retrieval,but combining these retrievals remains problematic.To address this issue,a GNSS-IR sea level retrieval combination system has been developed,which begins by analyzing error sources in GNSS-IR sea level retrieval and establishing and solving the GNSS-IR retrieval equation.This paper focuses on two key points:time window selection and equation stability.The stability of the retrieval combination equations is determined by the condition number of the coefficient matrix within the time window.The impact of ill-conditioned coefficient matrices on the retrieval results is demonstrated using an extreme case of SNR data with only ascending or descending trajectories.After determining the time window and removing ill-conditioned equations,the multi-mode,multi-frequency GNSS-IR retrieval is performed.Results from three International GNSS Service(IGS)stations show that the combination method produces high-precision,high-resolution,and high-reliability sea level retrieval combination sequences.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

A Comprehensive Survey on Deep Learning Multi-Modal Fusion:Methods,Technologies and Applications

Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Exploring the Mysteries of the Universe, and Innovation Leading the Future——Astronomical Techniques and Instruments

Dear Readers:Astronomical Techniques and Instruments is a professional English journal of open access devoted to recent develop-ments,discoveries,and theories in astronomical techniques,methods,and instruments.It will serve as a platform for dis-course,learning,and information sharing for astronomic and astrophysics professionals worldwide.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Reform of Teaching Practice of Modern Instrumental Analysis in Biotechnology Programs

Through the online and offline practical reform of the Modern Instrumental Analysis course of biotechnology majors from teaching content,teaching method,teaching demonstration to teaching effect,the traditional single offline lecture is transformed into diversified and interactive modern teaching.The practical reform enriches and optimizes the course content,perfects and improves the course assessment system,and improves the teaching quality.It achieves the student-centered and application-oriented teaching goal,and also provides reference for further cultivating high-quality applied talents.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

Surgical Management of Lumbar Spinal Canal Stenosis with Instrumentation at the Yaounde Central Hospital: Comparison of Unilateral versus Bilateral Pedicle Screw Fixation Combined with Transforaminal Lumbar Interbody Fusion

Introduction: The choice of adopting unilateral pedicle screw fixation or using bilateral pedicle screw fixation in lumbar spinal stenosis remains controversial. In our context, very few studies have been performed comparing the clinical effectiveness of unilateral versus bilateral fixation in the surgical management of lumbar spinal canal stenosis. Objective: Evaluate the impact on quality of life and clinical efficacy of unilateral spondylodesis compared to bilateral spondylodesis in the surgical management of lumbar spinal canal stenosis at the Yaounde Central Hospital. Methods: This was a retrospective descriptive cross-sectional study for a period of 4 years, from June 2015 to June 2019. It involved all patients operated for lumbar canal stenosis and who underwent spondylodesis or spinal fusion at the neurosurgery department of the Yaounde Central Hospital. Results: A total of 68 participants were recruited during our study period. 32 (47%) of the study population were in the 50 - 60 age group, with a mean age of 56.98 years ranging from 41 to 75 years. Females, housewives and farmers were the most affected. In our study, 72% of patients had unilateral spondylodesis and 28% had bilateral fusion. Preoperatively, 71% of patients had insurmountable pain, refractory to medical treatment. At 3 months postoperatively, 73.7% of patients with bilateral setup had moderate pain compared to 69% of those with unilateral setup. At 6 months postoperatively, 79% of patients with bilateral fusion had mild pain compared to 82% of patients with unilateral setup. At 1 year postoperatively, all patients had mild pain. Preoperatively, 66.2% of patients were unable to walk and 19.1% of patients were bedridden according to the Oswestry score. At 3 months postoperatively, 10.2% of patients with unilateral setup were unable to walk compared to 10.5% of patients with bilateral fixation, while 67.3% of patients with unilateral fixation had moderate disability compared to 52.6% of patients with bilateral fixation. At 6 months postoperatively, 51% of patients with unilateral setup had moderate disability compared to 47.4% of patients with bilateral fixation, while 42.9% of patients with unilateral fixation had mild disability compared to 42.1% of patients with bilateral fixation. At 1 year postoperatively, 81.6% of patients who underwent unilateral fixation had only mild disability compared to 73.7% of patients with bilateral fixation. Conclusion: The assessment of quality of life according to the set-up used shows similar results at 3 months, 6 months and 1 year, with no statistically significant differences. Single-sided pedicle screw fixation combined with transforaminal lumbar interbody fusion or mounting has the advantage of being faster, with less bleeding and is less expensive compared to bilateral fixation.

Development and validation of an instrument to assess knowledge,attitudes,and practices on digital health among nursing officers

Objective:Validation is an important aspect of an instrument,and it ensures the confidence of researchers to employ the instrument in their studies.This study was conducted to develop and validate an instrument to assess knowledge,attitudes,and practices(KAP) on digital health among nurses since digital health capacity is a major concern in health care that needs to be assessed.Methods:We conducted a methodological study to assess the content validity and reliability of the instrument.First,items were generated through a comprehensive literature review and by obtaining an expert opinion.Second,content and face validity were assessed by a panel of 7 experts.Both the item-level content validity index(I-CVI) and the scale-level content validity index(S-CVI) were established.Moreover,test–retest reliability and internal consistency of the instrument were assessed.Data were analyzed using SPSS version 25.Results:The initial pool consisted of 60 items and after obtaining content,face,and construct validity,51 items remained.Items with an I-CVI <0.78 were considered relevant.The S-CVI for relevancy,clarity,ambiguity,and simplicity were 0.93,0.91,0.94,and 0.92,respectively.Five subcomponents were constructed in each knowledge and attitudes domain,and the test–retest reliability test revealed that the instrument has good reliability,showing correlation coefficient values for the KAP domains and the total questionnaire of 0.76,0.98,0.99,and 0.99,respectively.The independent Cronbach's α for all items was 0.76,indicating good internal consistency.Conclusions:The present study established the acceptable validity and ensured the good reliability and internal consistency of the instrument,which can serve as an assessment tool of KAP on digital health among healthcare professionals.

Analysis Of Urban Planning Instruments in Comala,Colima,Mexico(1997-2009)from a Gender Perspective-A Secondary Publication

This work is carried out based on the analysis of urban planning instruments,taking the gender perspective as a foundation.It arises from the inclusion of women in the participation of urban development,through an analysis of the gender gaps that have marked the history of women,the inequalities serve as a basis for carrying out this study.It highlights the challenges we face today as a society in the process of building citizen participation,where we must all be recognized and have equal opportunities within the territory in which we live.This article analyzes the extent to which the Municipal Urban Development Programs of the Mexican municipality of Comala,Colima,Mexico,consider the recommendations on gender and urbanism,established since the 1990s by international entities and applied transversally to urban planning policies.Considerable differences are found between women and men in terms of empowerment and participation in urban territorial planning instruments,mainly in the oldest instrument(1997).Significant progress is observed in the most recent document and is currently in force in the municipality of Comala(2009).

Instrument Automation Control System Faults and Maintenance

This article explores the topic of fault diagnosis and maintenance strategies for instrument automation control systems,analyzing them through specific cases.The aim of this research is to improve the stability and reliability of the system by conducting a thorough investigation of faults and maintenance in instrument automation control systems.By doing so,this research hopes to provide a strong guarantee for the smooth progress of industrial production.