PENGARUH METODE DEMONSTRASI TERHADAP HASIL BELAJAR PEMELIHARAAN SASIS DAN PEMINDAH TENAGA KENDARAAN RINGAN KELAS XI TKR SMK NEGERI 1 MOTOLING

THE EFFECT OF DEMONSTRATION METHODS ON LEARNING RESULTS OF SASIS AND MANAGEMENT MAINTENANCE LIGHTWEIGHT VEHICLE CLASS XI TKR SMK NEGERI 1 MOTOLING By: FRANDO RUNTUNUWU NIM 14 212 036 Supervisor : Dr. H. M. Sumual, ST, M.Eng Ir. D. J. I. Manongko, M.Eng ABSTRACT This study aims to determine and analyze the effect of demonstration methods on learning outcomes applying manual transmission maintenance at SMK Negeri 1 Motoling. This research method uses a quantitative approach, using the experimental method. Namely a method that is directed at solving problems by describing or describing what the research results are. The results showed that the effect of the demonstration method can significantly improve student learning outcomes in the Subject of Light Vehicle Chassis Maintenance at SMK Negeri 1 Motoling. Effect of learning Demonstration method through statistical tests using t-test, it turns out that the value of t is greater than t table or t = 3.071> t table = 1.684 at α = 0.05 dk = n - 2.Thus this study accepts the alternative hypothesis (Ha) and rejecting the normal hypothesis (H0) means that there is an effect of Demonstration Method Learning on student learning outcomes in the subject of Light Vehicle Chassis Maintenance at SMK Negeri 1 Motoling. . Keywords: Demonstration Method, Learning Outcomes and SPT TKR.

Impact of Hydraulic System Stiffness on Its Energy Losses and Its Efficiency in Positioning Mechanical Systems

Hydraulic steering systems for mechanical devices, for example, manipulators or vehicle steering systems, should be able to achieve high positioning precision with high energy efficiency. However, this condition is very often not met in practical applications. This is usually due to the stiffness of the hydraulic system being too low. As a result, additional corrections are required to achieve the required positioning precision. Unfortunately, this means additional energy losses in the hydraulic control system. In this study, this problem is presented using the example of a hydraulic steering system for an articulated frame steer vehicle. This hydraulic steering system should provide the required directional stability for road traffic safety reasons. So far, this issue, connected mainly with the harmful phenomenon of so-called vehicle snaking behaviour, has not been solved sufficiently practically. To meet the needs of industrial practice, taking into account the current global state of knowledge and technology, Wrocław University of Science and Technology is performing comprehensive experimental and computational studies on the snaking behaviour of an articulated frame steer wheeled commercial vehicle. The results of these tests and analyses showed that the main cause of problems that lead to the snaking behaviour of this vehicle class is the effective torsional stiffness of the hydraulic steering system. For this reason, a novel mathematical model of the effective torsional stiffness was developed and validated. This model comprehensively took into account all important mechanical and hydraulic factors that affect the stiffness of a hydraulic system, resulting in the examined snaking behaviour. Because of this, it is possible at the design stage to select the optimal parameters of the hydraulic steering system to minimise any adverse influence on the snaking behaviour of articulated frame steer wheeled vehicles. This leads to minimising the number of required corrections and minimising energy losses in this hydraulic steering system. The innovative model presented in the article can be used to optimise positioning accuracy, for example, in manipulators and any mechanical system with hydraulic steering of any system of any mechanical parts.

3 kW Thermoelectric Generator for Natural Gas-Powered Heavy-Duty Vehicles—Holistic Development, Optimization and Validation

Emissions from heavy-duty vehicles need to be reduced to decrease their impact on the climate and to meet future regulatory requirements. The use of a cost-optimized thermoelectric generator based on total cost of ownership is proposed for this vehicle class with natural gas engines. A holistic model environment is presented that includes all vehicle interactions. Simultaneous optimization of the heat exchanger and thermoelectric modules is required to enable high system efficiency. A generator design combining high electrical power (peak power of about 3000 W) with low negative effects was selected as a result. Numerical CFD and segmented high-temperature thermoelectric modules are used. For the first time, the possibility of an economical use of the system in the amortization period of significantly less than 2 years is available, with a fuel reduction in a conventional vehicle topology of already up to 2.8%. A significant improvement in technology maturity was achieved, and the power density of the system was significantly improved to 298 W/kg and 568 W/dm3 compared to the state of the art. A functional model successfully validated the simulation results with an average deviation of less than 6%. An electrical output power of up to 2700 W was measured.

Should India move towards vehicle electrification? Assessing life-cycle greenhouse gas and criteria air pollutant emissions of alternative and conventional fuel vehicles in India.

India is the third largest contributor of greenhouse gases and its transportation emissions account for nearly one-fifth of all greenhouse gas (GHG) emissions. Furthermore, the transportation sector accounts a significant part of other air pollutant emissions that have damaging consequences to human health. Up until now, it was unclear what the greenhouse gas and air pollutant emissions consequences of electrifying vehicles in India would be, as replacing traditional vehicles with electrified ones reduces tailpipe emissions, but it will increase the emissions from the power sector when vehicles are charging. We mitigate that gap in the literature by performing a state specific life-cycle assessment of GHGs and criteria air pollutant emissions for representative passenger vehicles (four-wheelers, three-wheelers, two-wheelers and buses) driven in Indian states/union territories. We consider several vehicle technologies (internal combustion engine (ICE) vehicles, battery electric vehicles (BEVs), hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs)). We find that in most states, four-wheeler BEVs have higher greenhouse gases and criteria air pollutant emissions than other conventional or alternative vehicles and thus electrification of that vehicle class would not lead to emissions reductions. In contrast, in most states, electrified buses and three-wheelers are the best strategy to reduce greenhouse gases, but these are also the worst solution in terms of criteria air pollutant emissions. Electrified two-wheelers have lower criteria air pollutant emissions than gasoline only in five states. The striking conclusion is that unless the Indian grid becomes less polluting, the case for widespread electrification of vehicles for sustainability purposes is simply not there. Moving towards a sustainable, low carbon and low pollution electricity grid is a requirement to make a widespread transportation electrification case for India.

Efficient Video-Based Vehicle Queue Length Estimation Using Computer Vision and Deep Learning for an Urban Traffic Scenario

In the traffic engineering realm, queue length estimation is considered one of the most critical challenges in the Intelligent Transportation System (ITS). Queue lengths are important for determining traffic capacity and quality, such that the risk for blockage in any traffic lane could be minimized. The Vision-based sensors show huge potentials compared to fixed or moving sensors as they offer flexibility for data acquisition due to large-scale deployment at a huge pace. Compared to others, these sensors offer low installation/maintenance costs and also help with other traffic surveillance related tasks. In this research, a CNN-based approach for estimation of vehicle queue length in an urban traffic scenario using low-resolution traffic videos is proposed. The system calculates queue length without the knowledge of any camera parameter or onsite calibration information. The estimation in terms of the number of cars is considered a priority as compared to queue length in the number of meters since the vehicular delay is the number of waiting cars times the wait time. Therefore, this research estimates queue length based on total vehicle count. However, length in meters is also provided by approximating average vehicle size as 5 m. The CNN-based approach helps with accurate tracking of vehicles’ positions and computing queue lengths without the need for installation of any roadside or in-vehicle sensors. Using a pre-trained 80-classes YOLOv4 model, an overall accuracy of 73% and 88% was achieved for vehicle-based and pixel-based queue length estimation. After further fine-tuning of model on the low-resolution traffic images and narrowing down the output classes to vehicle class only, an average accuracy of 83% and 93%, respectively, was achieved which shows the efficiency and robustness of the proposed approach.

Analysis and Determination of Axle Load Spectra and Traffic Input for Pavement Design

This study examines the vehicle class distribution, hourly distribution factors, weekly distribution factors, monthly distribution factors, axle load spectra for each vehicle class, and each axle of each vehicle class for the WIM station installed on the N-55 highway to aid analysis and design of new Mechanistic-Empirical Pavement Design Guide. The maximum, minimum and permissible load limit for the different vehicle class, average gross vehicle weight (GWV) and permissible load limits also being incorporated. The directional distribution for north bound and south bound traffic were observed to be almost 50% for both directions, except for 5 axle trucks which was 74% for north bound and 26% for south bound. The truck class most prevalent on the highway were identified to be 3-axle tandem truck (47.50%) and also it was observed that 94.1% of this vehicle class carried load above permissible limits. Keywords: Traffic characteristics, Load distribution factor, Axle Load Spectra.

Driving Behavior at Signalized Intersections Operating under Disordered Traffic Conditions

A higher degree of heterogeneity in vehicle class and drivers, coupled with non-lane-based driving habits, creates several challenges in traffic flow analysis. This study investigates vehicles’ microscopic driving behavior at signalized intersections operating under weak lane discipline with mixed traffic (disordered) conditions. For this purpose, a comprehensive vehicular trajectory data set is developed from field-recorded video footage using a semi-automated tool for data extraction. Microscopic parameters such as relative velocity, spacing between vehicles, following time, lane preference, longitudinal and lateral speed profile, hysteresis evidence, and lateral movement of different vehicle classes during different traffic phases are presented in the study. The data is then segregated into three flow conditions: stopped flow, saturated flow, and unaffected flow. It is found that smaller vehicles prefer near-side lanes over far-side lanes. Motorized three-wheeler (3W) and motorized two-wheeler (2W) vehicle classes exhibit the greatest lateral velocity, lateral movement, and aggressiveness. This results in several interactions between vehicles as a function of different leader–follower vehicle pairs. Signalized intersections with more heterogeneity in traffic composition, especially higher composition of 2W and 3W vehicle classes, exhibit higher levels of aggressive driving behavior that might lower safety standards. As a practical application, ranges of various driving behavior parameter values for different leader–follower combinations and traffic conditions are quantified in the study. The observations and results are expected to help better understand prevailing driving behavior in disordered traffic and contribute toward robust calibration of microscopic traffic flow models for better replicating disordered traffic conditions at signalized intersections.

Scalable Machine Learning Approach to Classifying Transportation Noise at Two Urban Sites in Greater Boston, Massachusetts

The goal of this study was to characterize transportation noise by vehicle class in two urban communities, to inform studies of transport noise and ultra-fine particulates. Data were collected from April to September 2016 (150 days) of continuous recording in each urban community using high-resolution microphones. Training data was created for airplanes, trucks/buses, and train events by manual listening and extraction of audio files. Digital signal processing using STFT and Hanning windowing was performed in MATLAB, creating audio spectrograms with varying frequency: log vs linear frequency scales, and 4K vs 20K max frequency. For each of the four spectrogram sets, a neural net model using PyTorch was trained via a compute cluster. Initial results for a multi-class model provide an accuracy of 85%. Comparison between a selection of frequency scales and expanding to longer time periods is ongoing. Validation with airport transport logs and local bus and train schedules will be presented.

Crowd-Sensing Road Surface Quality Using Connected Vehicle Data

This work presents an approach for collecting road surface data using connected vehicles. Road surface readings from multiple production vehicles were collected and aggregated to estimate road roughness measured by the International Roughness Index (IRI). The analysis compared multiple instances of connected vehicle data with high speed pavement profile vehicle (Class 1 profiler) data. A separate analysis compared multiple instances of connected vehicle data to an advanced walking profiler. Results demonstrate the feasibility of harvesting road surface data from the existing connected vehicles to support continuous road surface monitoring applications. Benefits include more timely acquisition of pavement data, broader coverage of the road network, and potential for aiding existing survey fleet in targeting early signs of pavement degradation. Collected roughness measurements were found to be closely aligned with reference devices that were employed as part of this study. A regional experiment in the Detroit Metropolitan area that covered 64 mi of roadways found that the connected vehicle data was highly correlated with Class 1 profiler data where 83% of traveled miles had a 0.8 or higher correlation. Moreover, 85% of the measurements had small absolute errors less than 50 in./mi and half of the measurements had absolute errors less than 20 in./mi. A test track experiment at Virginia Tech Transportation Institute Smart Road facility compared the connected vehicle data to the advanced walking profiler and showed that the correlations for repeatability and reproducibility are 0.90 and 0.91, respectively, which are very close to the standard requirement for certified profilers.

Thermal Feature Detection of Vehicle Categories in the Urban Area

The main goal of this paper is to present new possibilities for the detection and recognition of different categories of electric and conventional (equipped with combustion engines) vehicles using a thermal video camera. The paper presents a draft of a possible detection and classification system of vehicle propulsion systems working with thermal analyses. The differences in thermal features of different vehicle categories were found out and statistically proved. The thermal images were obtained using an infrared thermography camera. They were utilized to design a database of vehicle class images of passenger vehicles (PVs), vans, and buses. The results confirmed the hypothesis that infrared thermography might be used for categorizing the vehicle type according to the thermal features of vehicle exteriors and machine learning methods for vehicle type recognition.