Data Bus Research Articles

Efficiency Assessment of Fully Mechanized Harvesting System through the Use of Fleet Management System

Nowadays the spread of precision forestry has led to the possibility of collecting data related to forest machines for an extended period and with enough precision to support decisions in the optimization of harvesting strategies in terms of technological and environmental efficiency. This study aims to evaluate the effective benefit of automatic data collection through the fleet management system (FMS) of two forest harvesters and two forwarders in pine forests in Poland. The study also aims to determine how the use of FMS can help forest companies to manage their fleet and take advantage of long-term monitoring. Focusing on performance indicators of fuel consumption and CO2 emissions, as well as on the engine parameters from the Can Bus data, the exploration of data was performed following a Big Data approach, from the creation of an aggregate dataset, pre-elaboration (data cleaning, exploration, selection, etc.) using GIS and R software. The investigation has considered the machine productivity, in the case of the harvesters, and the specific fuel consumption of each machine studied, as well as the time used by each of them during the different working cycle activities and the total amount of timber processed. The main results indicate an average emission of 2.1 kg of CO2 eq/m3 for the harvesters and 2.56 kg of CO2 eq/m3 for the forwarders, which equates in total to 0.24% of the carbon stored in one cubic meter of wood.

School accessibility evaluation under mixed-load school bus routing problem strategies

Mixed-load school bus routing problem (SBRP) strategy helps to improve the efficiency of school bus systems. School accessibility is a student-oriented measurement indicator that visually demonstrates the influence of mixed-load SBRP strategies on student commuting efficiency. In order to improve the convenience of students commuting to school while greatly reducing the operational cost of school buses, this paper proposes two mixed-load SBRP strategies (strategy 1: mixed-load mode and strategy 2: single-load mode). The bi-level programming model is constructed to model the two mixed-load SBRP strategies. In the bi-level programming model, the upper-level is to maximize school accessibility, and the lower-level optimizes school bus routes with the goal of minimizing the total number of school buses. The entropy-based weight method is introduced for comprehensive evaluation of strategies. Case studies are presented based on school bus data in Dalian, China to measure the performance of the two strategies. In computational experiments, the effect of weighted commuting time on school accessibility is also investigated. The results indicate that the SBRP strategy of mixed load mode (strategy 1) is the most effective in terms of comprehensive assessment of operating costs and school accessibility. In addition, it is found that the weighted commuting time has expanded the horizontal differentiation of school accessibility.

Learning industrial vehicles’ duty patterns: A real case

Industrial vehicles’ activities are rather heterogeneous and depend on vehicle’s type, model, and context of usage. Vehicles’ workload is usually summarized by means of standard labels, called duties. Fleet managers exploit duties to optimize productivity, minimize costs, show compliance with industrial regulations, and prevent vehicle misuses and failures. Duty levels are commonly manually enforced by manufacturers separately for each vehicle. However, since vehicles’ usage levels may fluctuate from light to heavy with variable periodicity, duty level assignment requires periodic customization. This paper addresses the problem of automatically defining the current duties of industrial vehicles and their corresponding levels by means of machine learning techniques applied to CAN bus data. In the context of industrial vehicle management, a key challenge is the lack of annotated data due to the following reasons: (1) The high variability in vehicles’ models and types makes the annotation process extremely labor-intensive. (2) Duty levels depend on the current usage context thus requiring periodic refinements. To tackle these issues, we propose a new two-stage method based on segment-wise clustering of per-vehicle CAN bus signals followed by a point-wise duty level assignment applied to the samples of each segment. Segments of per-vehicle CAN bus signals are first grouped together to discover similar vehicle usage profiles. Then, separately for each group we automatically derive the point-wise duty levels characterizing each usage profile. The proposed methodology is unsupervised, i.e., it does not require the availability of any annotated data, and is portable to arbitrary vehicles and context of usage without the need of human intervention. The effectiveness of the proposed methodology is validated on CAN bus data acquired from real vehicles of different types and models. The empirical evidence confirms its applicability to real industrial scenarios.

Avionics Architecture Design for Military Unmanned Aerial Vehicles

This paper describes the design of the avionics architecture for military unmanned aerial vehicles considering the airworthiness requirements for the first time. This design considers the redundancy in the system data bus and the power system and the data link system to meet the system safety requirements of the airworthiness requirements of military UAVs. This avionics architecture design has been verified through the system integration test and the flight test after manufacturing the UAV.

The Impact of Mobility on Location Privacy: A Perspective on Smart Mobility

People use smart transportation systems to move around in smart cities, producing a massive amount of valuable mobility data. Although this characteristic enables the development of many intelligent applications, it can expose users to privacy threats. Location privacy is an issue addressed in many mobility contexts, in which there is a privacy concern. Currently, there are some proposals to tackle this problem, and some questions naturally arise: Are these proposals suitable for a dynamic environment, such as smart mobility? What are the impacts of mobility on privacy? In this article, we answer these questions to explore location privacy in smart mobility considering open and online data, one of the fundamental pillars of smart city platforms. We have evidenced the hypothesis that mobility can impact privacy approaches of anonymization (mix zones) and obfuscation (GEO-I) in the context of smart mobility. For this, we performed experiments to characterize and find similarities in the statistical distributions extracted from two stay points metrics, which operate as substrates to build location privacy protection mechanisms. We use an accuracy metric to quantify the datasets’ distributions that matched each other. We conducted a comprehensive evaluation of seven real datasets of mono or multimodal mobility. The results showed that the stay point count metric reached 100% and 83.3% accuracy for coarse-grained (person and vehicle) and fine-grained (bus, taxi, and person) data. Additionally, we show a similarity between distributions for the same vehicle type for mono and multimodal datasets. Results suggest that privacy has a high dependence on mobility in different granularity levels.

Driver identification through vehicular CAN bus data: An ensemble deep learning approach

AbstractDriver identification using in‐vehicle data is receiving considerable attention in the field of intelligent transportation owing to the advances in deep learning (DL). In order to improve accuracy and robustness of identification, this paper proposes an ensemble deep learning framework that integrates a modified one‐dimensional convolutional neural network (M 1‐D CNN) and bidirectional long short‐term memory (BLSTM) to improve the performance and robustness of driver identification using information extracted from vehicular CAN‐bus signals. The M 1‐D CNN architecture is developed by adopting inception blocks, residual connection, and global average pooling to obtain optimal deep‐feature representations of local time series. The BLSTM is used to learn the bidirectional long‐term temporal dependencies. Results of extensive experiments using real driving data show that the proposed ensemble DL model can improve the accuracy and robustness of driver identification. Furthermore, four data augmentation methods, namely up‐sampling, adding noise, data reversal, and random drifting, are used to expand the original training data to improve the performance of the ensemble method. Especially, few‐shot learning is performed using the four data augmentation methods, and it shows excellent potential for driver identification with limited data.

ROPAD: Enhancing the Digital Ring Oscillator Probing Attempt Detector for Protecting Irregular Data Buses

Microprobing is applied to intercept data from on-chip signals, such as data passing through a data bus. Hence, it allows for extracting a full dump of this data, e.g., the firmware of a microcontroller, cryptographic key material, or any other type of passing data on the physical metal lines and/or the physical cells of the data bus connected to the metal lines. It is categorized as an invasive and physical attack vector against which software measures are insufficient for protection. As a countermeasure detecting microprobing attacks and enabling appropriate protection mechanisms, we propose a new probing detector for an industrial sub-40-nm advanced process node. It is based on ring oscillators (ROs), which are formed from the data bus lines. The oscillation frequency, caused by the capacity of bus lines, is measured and compared to detect any attached microprobes. The concept is optimized for detection of placed microprobes on both regular and irregular data buses or on any other pair of lines. For this purpose, a statistics-driven decision is made to distinguish probed from not probed lines. To improve the concept for high capacitance irregular lines, a hybrid design and test time calibration is proposed and analyzed, which shows the applicability of the concept under irregular bus lines, local variations, and jittery conditions. The results show that the approach results in low false positive (FP) and false negative (FN) rate at lower overhead comparing with alternative approaches.

Environmental screening model of driving behavior for an electric bus entering and leaving stops

Framework of the environmental screening model of the driving behavior. Energy consumption determines the environmental benefits and driving range of electric vehicles. The driving behavior significantly affects vehicle energy consumption, but the implicit relationship between them is unclear. Natural driving data of Electric buses (E-Bus) on the BRT line were collected to evaluate the implicit relationship between driving behavior and energy consumption while entering and leaving bus stops. Statistical methods were used to analyze the driving characteristics and their influence on energy consumption. An environmental screening model of the driving behavior was established based on the extreme gradient boosting (XGBoost) algorithm to analyze the impact of the driving parameters on energy consumption and extract their implicit relationship. The results were compared with predictions obtained from random forest (RF) and support vector machine (SVM) models. The results showed that the prediction accuracy of the XGBoost algorithm was higher than that of the RF and SVM models for two-class (95.2%) and three-class (84.9%) classifications. Although the prediction accuracy was lower for the three-class model, its classification was more specific and accurately predicted the energy consumption level.

Design and Development of a Bus Stop Inventory to Support an Intelligent Transportation System: The MARTA Experience

Bus stops are a critical element in any transit system. The advent of Intelligent Transportation System (ITS) technologies in the area of transit has developed a need for transit agencies to keep an up-to-date inventory of bus stop data. Unfortunately, most transit agencies do not have a bus stop inventory that can adequately support Advanced Public Transportation System (APTS) applications, and many other transit agencies do not have an inventory at all. The design, development, and maintenance of a bus stop inventory that will support an array of available APTS applications is described in this paper. Procedures used in the development of the Metropolitan Atlanta Rapid Transit Authority bus stop inventory are highlighted. The lessons learned from MARTA’s experience should be valuable to other transit agencies that are planning to or are in the process of implementing an APTS.

Duty Cycle Detection Method for High Speed Input-Output Systems

A clock coupled duty cycle detection method for high speed input-output is presented in this paper. In High speed systems duty cycle (DC) of output signal needs to be calibrated at 50% for having acceptable performance in the system. The proposed method introduces a synchronous signal in the output of system with 50% duty cycle with maximum 1 % error over process, voltage, and temperature (PVT). Proposed method also compensate input referred offset of DC detector which helps to improve overall system performance. The duty cycle detection method was implemented in 16nm technology with a power supply of 1.2V. With this type of designed architecture, the circuit can provide up to 5Gbps frequency data signal. Experimental results show that proposed architecture is reliable, and it can work operative in high frequency intervals. The presented circuit can be implemented in special serial links of several standards such as Peripheral Component Interconnect (PCI), Universal Serial Bus (USB) and Double Data Rate (DDR).

Majority Approximators for Low-Latency Data Bus Inversion

Data bus inversion (DBI) is an encoding technique that saves power in data movement in which the majority function plays an essential role. For a latency optimization, the majority function can be replaced by a majority approximator that allows for a small error in majority voting to obtain a faster encoder that still saves power. In this work, we propose two systematic approaches for finding high-performance majority approximators. First, we perform an exhaustive search of all possible Boolean functions to find an optimal approximator based on a certain circuit structure comprised of fifteen logic gates. The approximator found by the systematic search can be implemented using compound gates, resulting in a latency-efficient design with only two gate levels. Compared with prior works using a heuristic idea, the proposed circuit runs at the same speed but achieves greater switching activity savings. Second, we propose another majority approximator using the average of three randomly permuted copies of the approximator found in the first approach. We show that the second proposed approximator achieves even higher savings in switching activity as its function is closer to a true majority voter. We report various performance metrics of the newly found majority approximators based on syntheses using a 65 nm process.

Optimal electric bus scheduling by considering photovoltaic charging infrastructure

Battery electric buses (BEBs) are being introduced to substitute for diesel buses worldwide owing to their zero emissions. Investigating BEB scheduling problems is crucial for minimising charging costs and satisfying passenger demands. In addition, photovoltaic and energy-storage system (PESS) adoption in public transport could offer a promising alternative towards reducing charging costs and carbon dioxide emissions. This paper presents a novel BEB scheduling problem in which a PESS is deployed at bus-charging stations. The proposed problem is formulated as mixed-integer linear programming in which BEB scheduling and photovoltaic (PV) energy scheduling are optimised. To improve solution efficiency, a two-step solution approach is proposed: a BEB scheduling problem without considering the PESS is solved in the first step, and then a PV energy scheduling problem is solved in the second step. A case study is performed using the operational data of buses 404 and 109 in Beijing, China. The results show that the sum of charging and carbon dioxide emission costs will be reduced considerably if the PESS is introduced in a transit system. This study provides new insights for engineering a sustainable future in green transportation.

Soil, Driving Speed and Driving Intensity Affect Fuel Consumption of Forwarders

Fuel consumption is one of the key parameters in mechanised forest operations, particularly on lower bearing capacity soils, as wheel chains or bogie tracks can have a strong effect on it. This study aims to analyse the fuel consumption of several individual wheeled cut-to-length forwarder set-ups with different types of bogie tracks on peatland using automatic recording of data bus information. Two types of forwarders, 8-wheeled and 10-wheeled, and three types of tracks were tested on peatland in Eastern Finland. A mixed-model approach is the basis to study the fuel consumption as a function of the soil bearing capacity, the number of passes of the machine on the same soil, the section (curve or straight) and other variables related to the machine performance and set-up, for a total of N=27,928 fuel observations on three machines in 33 plots (trail segments). The model results in an R2=0.78; the number of passes increases the fuel consumption significantly, while the soil bearing capacity did not affect the fuel consumption. There are, however, important differences between the machines performance, which are addressed in the model. By contributing to the knowledge on the connection between operational conditions and fuel consumption, the study can contribute to the aim towards a sustainable forest operation through minimizing negative environmental impacts and providing the necessary tools for further research efforts.

Open Government Data and Public Transportation

Governments are increasingly making public transportation data available to the public on the Internet. The data can be used to explore and characterize current and historical service levels or to forecast operations in the immediate future. This paper considers, as an example, real-time bus location data provided by the San Francisco Municipal Transit Agency. General techniques for making use of such data to benefit both providers and users of public transportation are described. There is a brief discussion of why the advantages of making data available often outweigh the disadvantages.

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

Implementation of the extended diagnostics domestic system for wheeled vehicles using the UDS protocol in the CAN data bus

Introduction (problem statement and relevance). This work is devoted to the peculiarities of developing and building a general diagnostic structure for the domestic vehicles, the requirements for it and the description of the necessary diagnostic services in the process of its implementing. The purpose of the work was to develop domestic software diagnostic system for the wheeled vehicles and their environment to meet the international standards of the Unified Diagnostic Services protocol according to ISO 14229 and could implement various diagnostic services.Methodology and research methods. The basic requirements and specifications of the CAN bus (ISO 15765) and the UDS protocol (ISO 14229) were described. The developed system was implemented in the Python programming environment, with the algorithm, logic of the application and the directly received data were considered and sent to the CAN data bus during the reading procedure at the address.Scientifi c novelty and results. To diagnose vehicle malfunctions the principles of testing and validating software as part of the electronic control units equipment structure of a wheeled vehicle and necessary to complete the development and implementation of the diagnostic system software in real operating conditions were suggested. The principles of carrying out software tests, as well as the documentation data and analysis of the results obtained were described, together with the formulated conclusions on the work.Practical significance. The paper presents the diagnostic software development results and their implementation in production to meet the requirements of ISO 14229 and ISO 15765. The software is designed to improve the efficiency of the production process to detect and localize vehicle malfunctions during their maintenance work in a service center.

Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques.

The human-computer interaction has become inevitable in digital world. HCI helps humans to incorporate technology to resolve even their day-to-day problems. The main objective of the paper is to utilize HCI in Intelligent Transportation Systems. In India, the most common and convenient mode of transportation is the buses. Every state government provides the bus transportation facility to all routes at an affordable cost. The main difficulty faced by the passengers (humans) is lack of information about bus numbers available for the particular route and Estimated Time of Arrival (ETA) of the buses. There may be different reasons for the bus delay. These include heavy traffic, breakdowns, and bad weather conditions. The passengers waiting in the bus stops are neither aware of the delay nor the bus arrival time. These issues can be resolved by providing an HCI-based web/mobile application for the passengers to track their bus locations in real time. They can also check the Estimated Time of Arrival (ETA) of a particular bus, calculated using machine learning techniques by considering the impacts of environmental dynamics, and other factors like traffic density and weather conditions and track their bus locations in real time. This can be achieved by developing a real-time bus management system for the benefit of passengers, bus drivers, and bus managers. This system can effectively address the problems related to bus timing transparency and arrival time forecasting. The buses are equipped with real-time vehicle tracking module containing Raspberry Pi, GPS, and GSM. The traffic density in the current location of the bus and weather data are some of the factors used for the ETA prediction using the Support Vector Regression algorithm. The model showed RMSE of 27 seconds when tested. The model is performing well when compared with other models.

DEVELOPMENT OF LED SCREEN TO RECEIVE REAL-TIME TRANSMISSION OF EVACUATION INFORMATION

In this paper, an LED screen with ultra-low power consumption is developed. The work proposes methods for modeling mechanical and electronic systems. The combination of these scientific works gives us an opportunity to reduce power consumption. Spatial-mechanical sweep is developed, which allows to refuse decoding circuits, which in turn consume 35-40% of the total power consumption. We studied the setup of SDRAM controller for reading video data stored in memory or entering data into memory, and developed a code for reading data from the controller memory and outputting it to the data bus on demand of the output device. The results of this study can be widely used by the system for the integrated operation of the evacuation information system in training mode in order to train and mobilize personnel responsible for evacuation, as well as to keep all information and communication resources in readiness.

Cryptography-Based Medical Signal Securing Using Improved Variation Mode Decomposition with Machine Learning Techniques.

There is no question about the value that digital signal processing brings to the area of biomedical research. DSP processors are used to sample and process the analog inputs that are received from a human organ. These inputs come from the organ itself. DSP processors, because of their multidimensional data processing nature, are the electrical components that take up the greatest space and use the most power. In this age of digital technology and electronic gizmos, portable biomedical devices represent an essential step forward in technological advancement. Electrocardiogram (ECG) units are among the most common types of biomedical equipment, and their functions are absolutely necessary to the process of saving human life. In the latter part of the 1990s, portable electrocardiogram (ECG) devices began to appear on the market, and research into their signal processing and electronics design capabilities continues today. System-on-chip (SoC) design refers to the process through which the separate computing components of a DSP unit are combined onto a single chip in order to achieve greater power and space efficiency. In the design of biomedical DSP devices, this body of research presents a number of different solutions for reducing power consumption and space requirements. Using serial or parallel data buses, which are often the region that consumes the most power, it is possible to send data between the system-on-chip (SoC) and other components. To cut down on the number of needless switching operations that take place during data transmission, a hybrid solution that makes use of the shift invert bus encoding scheme has been developed. Using a phase-encoded shift invert bus encoding approach, which embeds the two-bit indication lines into a single-bit encoded line, is one way to solve the issue of having two distinct indicator bits. This method reduces the problem. The PESHINV approach is compared to the SHINV method that already exists, and the comparison reveals that the suggested PESHINV method reduces the total power consumption of the encoding circuit by around 30 percent. The computing unit of the DSP processor is the target of further optimization efforts. Virtually, all signal processing methods need memory and multiplier circuits to function properly.

Hardware Implementation of Code Converters Designed to Reduce the Length of Binary Encoded Words

The problem of synthesis of combinational circuits of code converters designed to reduce the length of words from a given set of encoded binary words is considered. The encoding assumes that different binary words will be encoded by different binary codes of shorter length. Code converters of this type are designed to reduce the length of binary words transmitted in digital systems over data buses when the bit depth of the transmitted words exceeds the bit depth of the data bus. For example, 18-bit or 17-bit words need to be transmitted over a 16-bit data bus. Each such word can be transmitted in two cycles of operation of a digital system, however, this approach reduces the overall performance of the system. One of the approaches to solve such problems is the development of combinational circuits that convert long binary encoded words into shorter ones. The proposed methods for solving the problem of synthesizing circuits of code converters are based on the compilation and logical minimization of such forms of systems of incompletely specified Boolean functions as disjunc­tive normal forms (DNF) and binary decision diagrams (BDD). Using BDD to minimize representations of k-valued functions that depend on Boolean variables is also proposed. Technologically independent logical minimization of functional descriptions of the designed code converters is proposed to be performed by programs for minimizing systems of Boolean functions in the DNF class and programs for joint minimization of BDD representations of systems of completely specified Boolean functions. Minimization of functional descriptions is aimed at reducing the hardware complexity of combinational circuits in the basis of library elements or programmable FPGA elements implementing code converters of the class in question.