The investigation on evolution of coal char structure during pressurized pyrolysis can reveal the combustion reactivity of coal char in thermal utilization at elevated pressure. In this study, Zhundong subbituminous coal was demineralized and a pressurized drop tube reactor (PDTR) was used to prepare coal char under different temperature and pressure conditions. The physicochemical structures of raw

A novel tungsten doping magnetic iron oxide catalyst was fabricated for selective catalytic reduction of NOx with NH3 (NH3-SCR), which was supported by carbon-included magnetic materials of Fe2O3-H synthesized by the solution combustion followed by the oxidization of hydrogen peroxide. As a consequence, the thermal stability of γ-Fe2O3 crystallites located in the magnetic support is significantly enhanced

Reactivity controlled compression ignition concept has been highlighted among the low temperature combustion strategies. However, this combustion strategy presents some problems related to high levels of hydrocarbon and carbon monoxide emissions at low load and high-pressure rise rate at high load. Therefore, to diminish these limitations, the dual-mode dual-fuel concept has been presented as an excellent

High nitrogen oxide levels of the conventional diesel engine combustion often requires the introduction of exhaust gas recirculation at high engine loads. This can adversely affect the smoke emissions and fuel conversion efficiency associated with a reduction of the in-cylinder air-fuel ratio (lambda). In addition, low exhaust gas temperatures at low engine loads reduce the effectiveness of aftertreatment

Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This article contributes to this fast evolving body of knowledge by focusing on the evaluation of NOx emission prediction capability of a probability density function–based stochastic reactor engine models for a Diesel engine. The research

Following the resonance theory by Bradley and co-workers, engine knock is a consequence of an auto-ignition in the developing detonation regime. Their detonation diagram was developed using direct numerical simulations and was applied in the literature to engine knock assessment using large eddy simulations. In this work, it is analyzed if the detonation diagram can be applied for post-processing and

A calibrated multiple high-speed camera arrangement recording the flame emission from three different directions has been demonstrated on an engine. From the multiple views, the flame position inside the engine cylinder can be spatially mapped, allowing quantitative studies of the dynamics of ignition, flame development and propagation. Through space carving, the three-dimensional flame contour can

A zonal hybridization of the RNG k-ε URANS model is proposed for the simulation of turbulent flows in internal combustion engines. The hybrid formulation is able to act as URANS, DES or LES in different zones of the computational domain, which are explicitly set by the user. The resulting model has been implemented in a commercial computational fluid dynamics code and the LES branch of the modified

Cycle-to-cycle variations are important to consider in the development of spark-ignition engines to further increase fuel conversion efficiency. Direct numerical simulation and large eddy simulation can predict the stochastics of flows and therefore cycle-to-cycle variations. However, the computational costs are too high for engineering purposes if detailed chemistry is applied. Detailed chemistry

This work summarizes the numerical analysis of the effect of early fuel injection on the charge motion in a direct injection spark ignition engine concerning cyclic fluctuations of the flow field. The combination of the scale-resolving turbulence model “Scale Adaptive Simulation” and post-processing routines for vortex trajectory visualization allows for a detailed insight into the temporal resolved

Although there are already several works where the influence of injection parameters on exhaust emissions, and specifically on particulate matter emissions, in diesel engines has been evaluated, the diversity in the results that can be found in the literature indicates the need to carry out new experiments that can provide more information about the influence of these parameters on modern diesel engines

Pre-mixed diesel combustion has the potential of offering high thermal efficiency with low emissions; however, this may result in loud combustion noise because of the high maximum rate of pressure rise. Combustion noise and thermal efficiency work in a trade-off relation, and it has not been possible to achieve high thermal efficiency with low combustion noise, so far. Our laboratory has worked on

In current lubrication analysis of piston skirt, the flooded status is generally considered in the piston skirt–cylinder liner frictional pair in all strokes of an engine operating cycle. However, the quantity of lubricating oil at the entrance of piston skirt cannot always ensure the sufficient lubrication status of piston skirt–cylinder liner frictional pair when the piston moves from the bottom

We propose in this paper a new method for targets recognition from radar images. To characterize the radar images, we adopt a statistical multivariate modeling using copula in the complex wavelet domain. For the recognition step, we investigate the weighted sparse representation-based classification (WSRC) method. To build the dictionary, the estimated copula parameters are stacked together in a matrix

In this work, a small hybrid photovoltaic-wind water pumping system is proposed, modeled and analyzed through computational simulations. In the proposed system, the energy sources are connected via a multi-input DC-DC converter (MIC), whose output is directly connected to a DC motor-pump without using a battery bank. The MIC acts on the sources drained currents simultaneously or individually to operate

We propose a machine learning approach based on relevance feedback to visually retrieve e-commerce items, represented by images, from huge catalogs. A threefold strategy is presented to deal with the exploration–exploitation dilemma: retrieve user-relevant items; retrieve items that do not comply with user learned preferences, to avoid convergence to local minima; and retrieve items from uncertainty

Fast Internet Protocol Version 4 (IPv4) lookup is one of the key challenges that have always been faced with growing internet speed. Next generation networks promise hundreds of gigabit communication bandwidths. To support such high data rates, core networking devices require very fast IPv4 lookup for incoming packets to sustain their functionality. Researchers from academia and industry have contributed

The Codesign is a powerful methodology adopted in designing modern embedded systems. The objective of this methodology is to optimize some non-functional factors of the system while meeting its functional specifications. Partitioning the system into a hardware set and a software set (Hardware/Software Partitioning (HSP)) is a key step in this process of Codesign. In this paper, we propose two heuristic

Video streaming today shows a widespread success, representing already the greatest portion of the Internet's IP traffic. The most common approach in today's networks is to adopt HTTP-based solutions, such as the Dynamic Adaptive Streaming over HTTP (DASH) protocol. This choice allows using general purpose web clients (web browsers) to access video streaming, while implementing a client-side dynamic

Although lung cancer is one of the leading causes of cancer deaths worldwide, the chances of survival are higher in the early stages. One of the best tools for diagnosis is computerized tomography. The main problem with this method is that it depends directly on the specialist who is analyzing the image, since the process involved is tiring, and can lead to error. Computer-aided detection systems have

In recent years, the use of touchscreen mobile devices in a variety of places and contexts has been raised. Specific interaction context induced impairment and disabilities that can limit the usage of these devices. For instance, the use of a mobile device while walking in sunlight limits the visual attention and results in performance reduction. In this paper, we present a study that investigates

This paper introduces a blockchain-based secure healthcare scheme in which health data (HD) are collected from users via an unmanned aerial vehicle (UAV) and stored on the nearest server. In the proposed scheme, a UAV first establishes a relation with body sensor hives (BSHs) via a token and then shares a shared key with BSHs to enable low-power secure communication. After retrieving the HD, the UAV

The cornea is an essential structure for the proper functioning of human vision. It can suffer injuries like tumors, areas of epithelial removal, infections, and post-surgical injuries that need prompt and effective treatment. The affected region’s area evolution monitoring enables the physician to evaluate the treatment effectiveness. This paper presents a semi-automatic method that can assist the

Cancer detection can be formulated as a binary classification in a machine learning paradigm. Loss functions are a critical part of almost every machine learning algorithm. While each loss function comes up with its own advantages and disadvantages, in this paper, inspired by ensemble methods, we propose a novel objective function that is a linear combination of single losses. We then integrate the

Electrocardiogram (ECG) analysis is one of the most important techniques to classify myocardial infarction. It is possible to diagnose that the patient may have a heart attack with ST segment elevation or depression in the ECG recordings taken before patient has a myocardial infarction. We propose a method to classify ST segment using time-frequency distribution based features from multi-lead ECG signals

In this work, we propose to automate the pre-cancerous tissue abnormality analysis by performing the classification of image patches using a novel two-stage convolutional neural network (CNN) based framework. Rather than training a model with features that may correlate among various classes, we propose to train a model using the features which vary across the different classes. Our framework processes

Improper operation of a multi-chiller plant would significantly increase associated electric consumption. This paper introduces a new model to determine the optimal loading point of the chillers. To do so, optimal dispatching of multi-chiller plant is expressed as an optimization problem and associated constraints are all together accommodated in the process of optimization. The problem is tackled

Despite the development of new technologies in order to prevent the stealing of cars, the number of car thefts is sharply increasing. With the advent of electronics, new ways to steal cars were found. In order to avoid auto-theft attacks, in this paper we propose a machine learning based method to silently and continuously profile the driver by analyzing built-in vehicle sensors. We consider a dataset

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A Mixed Path Size Logit-Based Taxi Customer-Search Model Considering Spatio-Temporal Factors in Route Choice

This paper introduces a model to analyze route choice behavior of taxi drivers for finding next passenger in urban road network. Considering the situation of path overlapping between selected routes in the process of customer-searching, a mixed path size logit (MPSL) model is proposed to analyze route choice behaviors through considering spatio-temporal features of route including customer generation

For the $N$ -impulse transfer between two earth orbits, this paper introduces $N - 1$ intermediate orbits to describe the orbit transfer scheme. The eccentricity vector of the intermediate orbits and the true anomalies corresponding to the impulsive points are chosen as the optimization variables. Based on the patched conic theory, candidate solution can be analytically derived, constraints are removed

Battery swapping stations (BSSs) present an alternative way of charging electric vehicles (EVs) that can lead toward a sustainable EV ecosystem. Although research focusing on the BSS strategies has been ongoing, the results are fragmented. Currently, an integrated way of considering stochastic EV station visits through planning and operations has not been fully investigated. To create comprehensive

Global localization in 3D point clouds is a challenging task for mobile vehicles in outdoor scenarios, which requires the vehicle to localize itself correctly in a given map without prior knowledge of its pose. This is a critical component of autonomous vehicles or robots on the road for handling localization failures. In this paper, based on reduced dimension scan representations learned from neural

Automated vehicles can change the society by improved safety, mobility, and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the highly automated (robotic) vehicles mainly considers safety and efficiency but failed

Background subtraction is an example of a moving object detection technique that uses machine vision systems. Conventional moving object detection methods need complicated thresholds for background modeling to address changes in illumination. This paper proposes a novel background modeling approach without thresholding based on a bit-planes method, which fully utilizes color characteristics through

The performance of offline learned pedestrian detectors significantly drops when they are applied to video scenes of various camera views, occlusions, and background structures. Learning a detector for each video scene can avoid the performance drop but it requires repetitive human effort on data annotation. In this paper, a self-learning approach is proposed, toward specifying a pedestrian detector

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community, the recognition and detection of traffic signs are a well-researched problem. A vast majority of existing approaches perform well on traffic signs

Pedestrian detection in the embedded system, such as video surveillance equipment, usually involves low-resolution pedestrian samples and requires a low computational cost. Many pedestrian detectors rely on a large feature pool and suffer in their efficiency and performance for real-time monitoring. In this paper, a set of light-weight features is proposed to enhance the pedestrian detection performance

With the development of modern wireless communication technology, especially the vehicle infrastructure integration (VII) technology, vehicles’ information such as identification, location, and speed can be readily obtained at upstream cross-section. This information can be used to support traffic signal timing optimization in real time. A dynamic predictive traffic signal control framework for isolated

Traffic sign detection is a central part of autonomous vehicle technology. Recent advances in deep learning algorithms have motivated researchers to use neural networks to perform this task. In this paper, we look at traffic sign detection as an image segmentation problem and propose a deep convolutional neural network-based approach to solve it. To this end, we propose a new network, the SegU-Net

Accurate short-term passenger demand prediction contributes to the coordination of traffic supply and demand. This paper proposes an end-to-end multi-task learning temporal convolutional neural network (MTL-TCNN) to predict the short-term passenger demand in a multi-zone level. Along with a feature selector named spatiotemporal dynamic time warping (ST-DTW) algorithm, this proposed MTL-TCNN is quite

How well stations are connected with each other is a critical performance assessment of railway systems. Network analysis is a highly intuitive and interpretable approach to characterizing the physical connectivity of railway systems. However, the physical connectivity is often limited in depicting transportation network dynamics due to the lack of traffic flow information. This paper first comprehensively

Protecting critical infrastructures has been deemed a natural security imperative by the U.S. Government for the past 20 years. The resilience of transportation infrastructures is of particular importance, as they serve a crucial rule in economic development and citizen mobility throughout the world. This paper presents a defender–attacker–defender model to analyze the potential impacts of intelligent

In this paper, we propose a novel semantic segmentation-based stereo reconstruction method that can keep up with the accuracy of the state-of-the art approaches while running in real time. The solution follows the classic stereo pipeline, each step in the stereo workflow being enhanced by additional information from semantic segmentation. Therefore, we introduce several improvements to computation

Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with

This paper presents a newly developed microsimulation model for motorway roadwork sections to evaluate the efficiency of different temporary traffic management schemes (TTMSs) such as the use of narrow lanes, offside and inside lane closures. The effect on traffic performance of various parameters (e.g. flow rates, percentage of heavy goods vehicles (HGVs), roadwork zone lengths and speed limits) has

This paper presents the problem of block simplex constrained signal recovery, which has been demonstrated to be a suitable formulation for estimation problems in networks such as route flow estimation in traffic. There are several natural approaches to this problem: compressed sensing, Bayesian inference, and convex optimization. This paper presents new methods within each framework and assesses their

This paper proposes a robust obstacle detection and recognition method for driver assistance systems. Unlike existing methods, our method aims to detect and recognize obstacles on the road rather than all the obstacles in the view. The proposed method involves two stages aiming at an increased quality of the results. The first stage is to locate the positions of obstacles on the road. In order to accurately

Object detection is a critical problem for advanced driving assistance systems (ADAS). Recently, convolutional neural networks (CNN) achieved large successes on object detection, with performance improvement over traditional approaches, which use hand-engineered features. However, due to the challenging driving environment (e.g., large object scale variation, object occlusion, and bad light conditions)

Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicenter of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent

In this paper, we investigate a hybrid dynamical system which incorporates flow swap process, green-time proportion swap process, and flow divergence for a general network with multiple Origin-Destination (OD) pairs and multiple routes, where flow swap process is specified in which traffic swaps from more costly to less costly input links, green-time proportion swap process is specified in which green

Visual tracking is a vital component of autonomous driving perception system. Siamese networks have achieved great success in both accuracy and speed for visual tracking tasks. These Siamese trackers share a similar framework in which each tracker consists of two network branches for exploring semantic information. However, the performance of Siamese trackers is limited by an insufficient semantic

Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks

The advent of vehicles’ connectivity and automation opens up unprecedented opportunities to make road transport more efficient and sustainable going far beyond what can be achieved by the vehicle’s intelligence alone, even in the presence of imperfect information of the surrounding traffic. Our community, which has been trying for decades to manage one of the most complex human dynamics based on limited

Calibration and validation have long been a significant topic in traffic model development. In fact, when moving to dynamic traffic assignment (DTA) models, the need to dynamically update the demand and supply components creates a considerable burden on the existing calibration algorithms, often rendering them impractical. These calibration approaches are mostly restricted either due to non-linearity

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with

Current Urban Traffic Control (UTC) systems rely heavily on inductive loop detectors. The emergence of Connected Vehicles (CV) opens new possibilities for improving signal control, while reducing the need for loop detectors. However, for low penetration rates, the CV measurements are sporadic and thus difficult to exploit by existing UTC systems. In this paper, a methodology that enables cycle-to-cycle