Search Results

Abstract This study underscores the benefits of refining the intralogistics process for small- to medium-sized manufacturing businesses (SMEs) in the engineer-to-order (ETO) sector, which relies heavily on manual tasks. Based on industrial visits and primary data from six SMEs, a new intralogistics concept and process was formulated. This approach enhances the value-added time of manufacturing workers while also facilitating complete digital integration as well as improving transparency and traceability. A practical application of this method in a company lead to cutting its lead time by roughly 11.3%. Additionally, improved oversight pinpointed excess inventory, resulting in advantages such as reduced capital needs and storage requirements. Anticipated future enhancements include better efficiency from more experienced warehouse staff and streamlined picking methods. Further, digital advancements hold promise for cost reductions in administrative and supportive roles.

Abstract The swift progress of electric vehicles (EVs) and hybrid electric vehicles (HEVs) has driven advancements in battery management systems (BMS). However, optimizing the algorithms that drive these systems remains a challenge. Recent breakthroughs in data science, particularly in deep learning networks, have introduced the long–short-term memory (LSTM) network as a solution for sequence problems. While graphics processing units (GPUs) and application-specific integrated circuits (ASICs) have been used to improve performance in AI-based applications, field-programmable gate arrays (FPGAs) have gained popularity due to their low power consumption and high-speed acceleration, making them ideal for artificial intelligence (AI) implementation. One of the critical components of EVs and HEVs is the BMS, which performs operations to optimize the use of energy stored in lithium-ion batteries (LiBs).

Abstract Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing.

Abstract The closet in-path vehicle (CIPV) is recognized relying on the detection results for road lane lines in most current ACC system, which may not work well in the poor conditions, for example, unclear road lane lines, low light level, bad weather, and so on. To solve this problem, the article proposes a sensor fusion-based CIPV recognition algorithm independent of road lane lines. First, a robust Kalman filter based on the global coordinate system is designed to fuse the millimeter-wave radar and camera targets. The fusion algorithm can dynamically adjust the covariance matrix of sensor observations to avoid the influence of anomalous observations on the fusion results. Stable detection of targets by the fusion algorithm is the basis of the CIPV recognition algorithm.

Abstract Speckle noise degrades the visual appearance and the quality of a synthetic aperture radar (SAR) image. The reduction of speckle noise is the first step in any remote-sensing device. To improve the noisy SAR images, a variety of adaptive and nonadaptive noise reduction filters were used. In order to eliminate speckle noise present in SAR images, an adaptive cuckoo search optimization-based speckle reduction bilateral filter has been designed in this article. To test the ability to eliminate multiplicative noise, the suggested filter’s effectiveness was compared to that of several de-speckling approaches. It has been measured with different assessment metrics such as PSNR, EPI, SSIM, and ENL. When compared to conventional de-noising filters, the proposed filter shows promising results for lowering speckle noise and retaining edge properties.

Abstract Electric vehicles (EVs) suffer from long charging time and inconvenient charging due to limited charging stations, which are the main causes of drivers’ range anxiety. Real-time and accurate driving range prediction can help drivers plan journeys, alleviate range anxiety, and promote EV development. However, predicting the EV driving range is challenging due to different weather, road conditions, driver habits, and limited available data. To address this issue, this article proposes a novel digital twin-based driving range prediction method. First, a one-year real-world EV dataset in Beijing is utilized. Detailed feature selection is conducted for the dataset, and six key features are extracted: battery SOC, consumed battery SOC, battery total voltage, battery maximum cell voltage, battery minimum cell voltage, and mileage already driven. Then, a random forest method is used to train the EV driving range prediction model using the features described earlier.

Abstract Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data.

Abstract Certain advanced driver assistance systems (ADAS) have the potential to boost energy efficiency in real-world scenarios. This article details a radar-based driver assistance scheme designed to minimize fuel consumption for a commercial vehicle by predictively optimizing braking and driving torque inputs while accommodating the driver’s demand. The workings of the proposed scheme are then assessed with a novel integration of the driver assistance functionality in randomized traffic microsimulation. Although standardized test procedures are intended to mimic urban and highway speed profiles for the purposes of evaluating fuel economy and emissions, they do not explicitly consider the interactions present in real-world driving between the ego vehicle equipped with ADAS and other vehicles in traffic. This article presents one approach to address the drawback of standardized test procedures for evaluating the fuel economy benefits of ADAS technologies.

Abstract While a majority of transportation and mobility solutions rely on in-vehicle sensors and the availability of the global positioning system (GPS) for absolute localization, alternate paradigms leveraging smart infrastructure have started becoming a viable solution for localization without needing GPS. However, the majority of approaches involving smart infrastructure require a means for wireless communication. In this article, we describe a novel method that can accurately localize the vehicle without using GPS and wireless communication by leveraging embedded digital and analog information on the roadside signage. The embedded information consists of a digital signature which can be used to cross-reference the ground truth (GT) location of the signage, as well as geometric information of the signage. This information is directly leveraged by on-vehicle sensors to generate absolute localization information.

Abstract Safe navigation of an autonomous vehicle (AV) requires a fast and correct perception of its driving environment. Meaning, the AV needs to persistently detect and track moving objects around it with high accuracy for safe navigation. These tasks of detection and tracking are performed by the AV perception system that utilizes data from sensors such as LIDARs, radars, and cameras. The majority of AVs are typically fitted with multiple sensors to create redundancy and avoid dependence on a single sensor. This strategy has been shown to yield accurate perception results when the sensors work well and are calibrated correctly. However, over time, the cumulative use of the AV or poor placement of sensors may lead to faults that need correcting.

Abstract Effective circumstance perception technology is the prerequisite for the successful application of autonomous driving, especially the detection technology of traffic objects that affects other tasks such as driving decisions and motion execution in autonomous vehicles. However, recent studies show that a single sensor cannot perceive the surrounding environment stably and effectively in complex circumstances. In the article, we propose a multi-scale feature fusion framework that exploits a dual backbone network to extract camera and radar feature maps and performs feature fusion on three different feature scales using a new fusion module. In addition, we introduce a new generation mechanism of radar projection images and relabel the nuScenes dataset since there is no other suitable autonomous driving dataset for model training and testing.

Abstract The Aft Collision Assist (ACA) is an Advanced Driver Assistance System (ADAS) that is added to a vehicle and integrates with the native systems of that vehicle. The ACA is used to monitor and reengage a distracted driver of an approaching vehicle that the ACA system calculates will imminently rear-end the host vehicle. This work provides a brief overview of existing ADAS that perform similar functions, the regulatory statutes and requirements that impact the ACA functionality, and Model-Based System Engineering (MBSE) model diagrams of the ACA. The MBSE model diagrams presented are State Machine, Conceptual Data Model, Use Case, System Requirements, and Regulatory Requirements for the entire ACA system. The MBSE models and regulatory constraints presented within are used to refine and specify the ACA method of attracting a distracted driver’s attention.

Abstract The engineering of vehicular systems is becoming increasingly difficult, mainly due to the ongoing integration of cyber-physical systems (CPS) aiming to automate difficult tasks or provide additional features to drivers. This automation potential leads to increasing complexity when engineering the vehicle itself or its subcomponents. In particular the development of a future-oriented kind of mobility, namely, connected autonomous vehicles (CAVs), is accompanied by new challenges, leading back to the different domains to be considered. To cope with this complexity and enable the mutual engineering of vehicular embedded systems, the Software Platform Embedded Systems (SPES) framework provides viewpoints and hierarchy layers in the shape of a matrix. However, to address all domains considered during the development of CAVs, the SPES methodology lacks specifications of how to model such vehicles across multiple domains, which impede its utilization in actual industrial projects.

Abstract Additive manufacturing (AM) technologies can produce lighter parts; reduce manual assembly processes; reduce the number of production steps; shorten the production cycle; significantly reduce material consumption; enable the production of prostheses, implants, and artificial organs; and produce end-user products since it is used in many sectors for many reasons; it has also started to be used widely, especially in the field of aerospace. In this study, polylactic acid (PLA) was preferred for the antenna substrate because it is environmentally friendly, easy to recycle, provides convenience in production design with a three-dimensional (3D) printer, and is less expensive compared to other available materials. Copper (Cu) tape and graphene filament were employed for the antenna patch component due to their benefits.

Abstract Demand-Driven Material Requirements Planning (DDMRP) is regarded as a potential method of material management to provide planning and execution performance improvements in variable environments. However, Industry 4.0 refers to the fourth industrial revolution that allows creating a smart manufacturing system by using the new technologies of communication, automation, and digitalization. DDMRP and Industry 4.0 are crucial as new technologies are introduced to companies to improve their performance. Nevertheless, there is an absence of reviews showing the relationships between DDMRP and Industry 4.0. A literature review is used to identify the key constructs of DDMRP and Industry 4.0, and the relationships postulated between them are presented. The main objective of this study is to investigate the relationship between DDMRP and Industry 4.0. The result of this article was a model for integrating the DDMPRP and Industry 4.0 proposed upon a robust theoretical method.

Abstract Vehicle aerodynamics has been the subject of extensive research, with a heavy emphasis on the vehicle. Heavy vehicles, such as trucks and buses, have undergone aerodynamic studies in recent years to reduce drag and improve fuel economy [1]. In this study, the distribution of air conditioning in the cabin of a passenger bus was investigated by discussing the factors that influence in attaining the desired thermal comfort values such as temperature distribution, relative humidity ratios, and air velocities inside the bus. The research was conducted on three different cases. In this study, different types of air-conditioning (AC) outlets—linear grills, slots diffusers, and gaspers—were used, and the effect of each outlet on temperature distribution, air velocities, and relative humidity ratios within the bus was investigated. In all three cases, the inlet air velocity was set to 0.8 m/s, and the return air was combined in the middle of the bus.

Abstract This article concerns an improved vector control model. This model is developed in a phase which comes just before the phase of its integration on electronic boards such as those with FPGA or DSP. The innovative character of this model is based on the replacement of the average model of the Direct Current (DC) to Alternating Current (AC) converter powering a synchronous motor with permanent magnets by a precise model considering the transient model of the power transistors, electromagnetic switches, and diodes. The overall model generates the six DC-AC converter control signals to regulate the speed of the permanent magnet synchronous motor (PMSM) using the technique of back electromotive forces compensation to reduce the power chain energy consumption for variable rectilinear speed operation. This model makes it possible to consider the role of diodes.

Abstract This article presents the application of the Enhanced Sequence Diagram (ESD) for the analysis of the functionality of a system with shape-changing aspects in the context of its multiple operational modes, considering an active real spoiler as a case study. The article provides new insights on the ESD support for model-based capture and articulation of functional requirements across multiple operation modes of the same system, with appropriate detail on attributes and metrics, and the alignment of these attributes and metrics in line with the concept of time through scope lines. The article also provides a comprehensive argument and discussion, exemplified based on the case study, for the support that the ESD provides for early systems functional and architecture analysis, within the context of a broader model-based Failure Mode Analysis methodology.

Abstract The millimeter-wave radar has good weather robustness, but currently lacks performance in object classification. With the advent of imaging radar, three-dimensional (3D) point clouds of objects can be obtained. Based on 3D radar point clouds, an support vector machine (SVM algorithm using 3D features is proposed to solve poor radar classification performance. First, a new 29-feature vector is proposed from many perspectives, such as shape features, statistical features, and other features. Then the SVM classifier with four different kernel functions and other machine learning methods are used to achieve multi-objective classification. Finally, experiments are carried out on three types of datasets collected by ourselves, and the results show that the algorithm achieves a 95.1% classification accuracy, which is 15.7% higher than the traditional 2D radar point cloud.

Abstract To design better power converters with enhancement-mode Gallium Nitride high-electron-mobility transistor (eGaN HEMT) for emerging applications such as Electric Vehicles (EV), it is essential to model their switching transients and loss accurately. Analytical modeling has proved to be an effective approach to study the transistor’s dynamic behaviors and analyze the switching energy loss during the turn-on and turn-off transients. Furthermore, it helps to understand the essential factors that influence the switching transients and loss calculation. The accuracy of the analytical model mainly depends on the equivalent circuits and the parasitic parameters inside the transistor packaging and external circuits under different switching stages. It is always challenging to extract the parasitic parameters accurately due to its natural character of nonlinearity and complex correlation during the switching transients.