Abstract: With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.
Abstract: Rigorous international maritime regulations are in place to limit boat and ship hydrocarbon emissions. The global sustainability goals are reducing the fuel consumption and minimizing the emissions from the ships and boats. These maritime sustainability goals have attracted a lot of research interest. Energy harvesting and storage system is designed in this study based on hybrid renewable and conventional energy systems. This energy harvesting and storage system is designed for marine applications, such as, boats and small ships. These systems can be utilized for mobile use or off-grid remote electrification. This study analyzed the use of micro power generation for boats and small ships. The energy harvesting and storage system has two distinct systems i.e. dockside shore-based system and on-board system. The shore-based system consists of a small wind turbine, photovoltaic (PV) panels, small gas turbine, hydrogen generator and high-pressure hydrogen storage tank. This dockside system is to provide easy access to the boats and small ships for supply of hydrogen. The on-board system consists of hydrogen storage tanks and fuel cells. The wind turbine and PV panels generate electricity to operate electrolyzer. A small gas turbine is used as a supplementary power system to contribute in case the hybrid renewable energy system does not provide the required energy. The electrolyzer performs the electrolysis on distilled water to produce hydrogen. The hydrogen is stored in high-pressure tanks. The hydrogen from the high-pressure tank is filled in the low-pressure tanks on-board seagoing vessels to operate the fuel cell. The boats and small ships use the hydrogen fuel cell to provide power to electric propulsion motors and for on-board auxiliary use. For shore-based system, a small wind turbine with the total length of 4.5 m and the disk diameter of 1.8 m is used. The small wind turbine dimensions make it big enough to be used to charge batteries yet small enough to be installed on the rooftops of dockside facility. The small dimensions also make the wind turbine easily transportable. In this paper, PV, sizing and solar flux are studied parametrically. System performance is evaluated under different operating and environmental conditions. The parametric study is conducted to evaluate the energy output and storage capacity of energy storage system. Results are generated for a wide range of conditions to analyze the usability of hybrid energy harvesting and storage system. This energy harvesting method significantly improves the usability and output of the renewable energy sources. It also shows that small hybrid energy systems have promising practical applications.
Abstract: In recent years, object detection has gained much
attention and very encouraging research area in the field of computer
vision. The robust object boundaries detection in an image is
demanded in numerous applications of human computer interaction
and automated surveillance systems. Many methods and approaches
have been developed for automatic object detection in various fields,
such as automotive, quality control management and environmental
services. Inappropriately, to the best of our knowledge, object
detection under illumination with shadow consideration has not
been well solved yet. Furthermore, this problem is also one of
the major hurdles to keeping an object detection method from the
practical applications. This paper presents an approach to automatic
object detection in images under non-standardized environmental
conditions. A key challenge is how to detect the object, particularly
under uneven illumination conditions. Image capturing conditions
the algorithms need to consider a variety of possible environmental
factors as the colour information, lightening and shadows varies
from image to image. Existing methods mostly failed to produce the
appropriate result due to variation in colour information, lightening
effects, threshold specifications, histogram dependencies and colour
ranges. To overcome these limitations we propose an object detection
algorithm, with pre-processing methods, to reduce the interference
caused by shadow and illumination effects without fixed parameters.
We use the Y CrCb colour model without any specific colour
ranges and predefined threshold values. The segmented object regions
are further classified using morphological operations (Erosion and
Dilation) and contours. Proposed approach applied on a large image
data set acquired under various environmental conditions for wood
stack detection. Experiments show the promising result of the
proposed approach in comparison with existing methods.
Abstract: The investigation of the vibrational character of magnetic cylindrical shells placed in an axial magnetic field has important practical applications. In this work, we study the vibrational behaviour of such a cylindrical shell by making use of the so-called exact space treatment, which does not assume any hypothesis. We discuss the effects of several practically important boundary conditions on the vibrations of the described setup. We find that, for some cases of boundary conditions, e.g. clamped, simply supported or peripherally earthed, as well as for some values of the wave numbers, the vibrational frequencies of the shell are approximately zero. The theoretical and numerical exploration of this fact confirms that the vibrations are absent or attenuate very rapidly. For all the considered cases, the imaginary part of the frequencies is negative, which implies stability for the vibrational process.
Abstract: Carbon nanofiber-epoxy composite electrode has been investigated through voltammetric and amperometric techniques in order to detect parabens from aqueous solutions. The occurrence into environment as emerging pollutants of these preservative compounds has been extensively studied in the last decades, and consequently, a rapid and reliable method for their quantitative quantification is required. In this study, methylparaben (MP) and propylparaben (PP) were chosen as representatives for paraben class. The individual electrochemical detection of each paraben has been successfully performed. Their electrochemical oxidation occurred at the same potential value. Their simultaneous quantification should be assessed electrochemically only as general index of paraben class as a cumulative signal corresponding to both MP and PP from solution. The influence of pH on the electrochemical signal was studied. pH ranged between 1.3 and 9.0 allowed shifting the detection potential value to smaller value, which is very desired for the electroanalysis. Also, the signal is better-defined and higher sensitivity is achieved. Differential-pulsed voltammetry and square-wave voltammetry were exploited under the optimum pH conditions to improve the electroanalytical performance for the paraben detection. Also, the operation conditions were selected, i.e., the step potential, modulation amplitude and the frequency. Chronomaprometry application as the easiest electrochemical detection method led to worse sensitivity, probably due to a possible fouling effect of the electrode surface. The best electroanalytical performance was achieved by pulsed voltammetric technique but the selection of the electrochemical technique is related to the concrete practical application. A good reproducibility of the voltammetric-based method using carbon nanofiber-epoxy composite electrode was determined and no interference effect was found for the cation and anion species that are common in the water matrix. Besides these characteristics, the long life-time of the electrode give to carbon nanofiber-epoxy composite electrode a great potential for practical applications.
Abstract: Collaborative filtering (CF) algorithm has been popularly used for recommender systems in both academic and practical applications. It basically generates recommendation results using users’ numeric ratings. However, the additional use of the information other than user ratings may lead to better accuracy of CF. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's review can be regarded as the new informative source for identifying user's preference with accuracy. Under this background, this study presents a hybrid recommender system that fuses CF and user's review mining. Our system adopts conventional memory-based CF, but it is designed to use both user’s numeric ratings and his/her text reviews on the items when calculating similarities between users.
Abstract: A jet pump is a type of pump that accelerates the flow of a secondary fluid (driven fluid) by introducing a motive fluid with high velocity into a converging-diverging nozzle. Jet pumps are also known as adductors or ejectors depending on the motivator phase. The ejector's motivator is of a gaseous nature, usually steam or air, while the educator's motivator is a liquid, usually water. Jet pumps are devices that use air bubbles and are widely used in wastewater treatment processes. In this work, we will discuss about the characteristics of the jet pump and the computational simulation of this device. To find the optimal angle and depth for the air pipe, so as to achieve the maximal air volumetric flow rate, an experimental apparatus was constructed to ascertain the best geometrical configuration for this new type of jet pump. By using 3D printing technology, a series of jet pumps was printed and tested whilst aspiring to maximize air flow rate dependent on angle and depth of the air pipe insertion. The experimental results show a major difference of up to 300% in performance between the different pumps (ratio of air flow rate to supplied power) where the optimal geometric model has an insertion angle of 600 and air pipe insertion depth ending at the center of the mixing chamber. The differences between the pumps were further explained by using CFD for better understanding the reasons that affect the airflow rate. The validity of the computational simulation and the corresponding assumptions have been proved experimentally. The present research showed high degree of congruence with the results of the laboratory tests. This study demonstrates the potential of using of the jet pump in many practical applications.
Abstract: Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.
Abstract: In wastewater treatment processes, aeration introduces
air into a liquid. In these systems, air is introduced by different
devices submerged in the wastewater. Smaller bubbles result in more
bubble surface area per unit of volume and higher oxygen transfer
efficiency. Jet pumps are devices that use air bubbles and are widely
used in wastewater treatment processes. The principle of jet pumps is
their ability to transfer energy of one fluid, called primary or motive,
into a secondary fluid or gas. These pumps have no moving parts and
are able to work in remote areas under extreme conditions. The
objective of this work is to study experimentally the characteristics of
the jet pump and the size of air bubbles in the laboratory water tank.
The effect of flow rate ratio on pump performance is investigated in
order to have a better understanding about pump behavior under
various conditions, in order to determine the efficiency of receiving
air bubbles different sizes. The experiments show that we should take
care when increasing the flow rate ratio while seeking to decrease
bubble size in the outlet flow. This study will help improve and
extend the use of the jet pump in many practical applications.
Abstract: The exact theoretical expression describing the
probability distribution of nonlinear sea-surface elevations derived
from the second-order narrowband model has a cumbersome form
that requires numerical computations, not well-disposed to theoretical
or practical applications. Here, the same narrowband model is reexamined
to develop a simpler closed-form approximation suitable
for theoretical and practical applications. The salient features of the
approximate form are explored, and its relative validity is verified
with comparisons to other readily available approximations, and
oceanic data.
Abstract: This paper studies a random fuzzy queueing system
that the interarrival times of customers arriving at the server and
the service times are independent and identically distributed random
fuzzy variables. We match the random fuzzy queueing system with
the random fuzzy alternating renewal process and we do not use from
α-pessimistic and α-optimistic values to estimate the average chance
of the event ”random fuzzy queueing system is busy at time t”, we
employ the fuzzy simulation method in practical applications. Some
theorem is proved and finally we solve a numerical example with
fuzzy simulation method.
Abstract: Recently, the Field Programmable Gate Array (FPGA) technology offers the potential of designing high performance systems at low cost. The discrete wavelet transform has gained the reputation of being a very effective signal analysis tool for many practical applications. However, due to its computation-intensive nature, current implementation of the transform falls short of meeting real-time processing requirements of most application. The objectives of this paper are implement the Haar and Daubechies wavelets using FPGA technology. In addition, the Bit Error Rate (BER) between the input audio signal and the reconstructed output signal for each wavelet is calculated. From the BER, it is seen that the implementations execute the operation of the wavelet transform correctly and satisfying the perfect reconstruction conditions. The design procedure has been explained and designed using the stat-ofart Electronic Design Automation (EDA) tools for system design on FPGA. Simulation, synthesis and implementation on the FPGA target technology has been carried out.
Abstract: Iris pattern is an important biological feature of human body; it becomes very hot topic in both research and practical applications. In this paper, an algorithm is proposed for iris recognition and a simple, efficient and fast method is introduced to extract a set of discriminatory features using first order gradient operator applied on grayscale images. The gradient based features are robust, up to certain extents, against the variations may occur in contrast or brightness of iris image samples; the variations are mostly occur due lightening differences and camera changes. At first, the iris region is located, after that it is remapped to a rectangular area of size 360x60 pixels. Also, a new method is proposed for detecting eyelash and eyelid points; it depends on making image statistical analysis, to mark the eyelash and eyelid as a noise points. In order to cover the features localization (variation), the rectangular iris image is partitioned into N overlapped sub-images (blocks); then from each block a set of different average directional gradient densities values is calculated to be used as texture features vector. The applied gradient operators are taken along the horizontal, vertical and diagonal directions. The low order norms of gradient components were used to establish the feature vector. Euclidean distance based classifier was used as a matching metric for determining the degree of similarity between the features vector extracted from the tested iris image and template features vectors stored in the database. Experimental tests were performed using 2639 iris images from CASIA V4-Interival database, the attained recognition accuracy has reached up to 99.92%.
Abstract: This paper discusses a genetic algorithm (GA) based optimal load shedding that can apply for electrical distribution networks with and without dispersed generators (DG). Also, the proposed method has the ability for considering constant and variable capacity deficiency caused by unscheduled outages in the bulked generation and transmission system of bulked power supply. The genetic algorithm (GA) is employed to search for the optimal load shedding strategy in distribution networks considering DGs in two cases of constant and variable modelling of bulked power supply of distribution networks. Electrical power distribution systems have a radial network and unidirectional power flows. With the advent of dispersed generations, the electrical distribution system has a locally looped network and bidirectional power flows. Therefore, installed DG in the electrical distribution systems can cause operational problems and impact on existing operational schemes. Introduction of DGs in electrical distribution systems has introduced many new issues in operational and planning level. Load shedding as one of operational issue has no exempt. The objective is to minimize the sum of curtailed load and also system losses within the frame-work of system operational and security constraints. The proposed method is tested on a radial distribution system with 33 load points for more practical applications.
Abstract: This paper investigates the optimization problem of
multi-product aggregate production planning (APP) with fuzzy data.
From a comprehensive viewpoint of conserving the fuzziness of input
information, this paper proposes a method that can completely
describe the membership function of the performance measure. The
idea is based on the well-known Zadeh-s extension principle which
plays an important role in fuzzy theory. In the proposed solution
procedure, a pair of mathematical programs parameterized by
possibility level a is formulated to calculate the bounds of the
optimal performance measure at a . Then the membership function of
the optimal performance measure is constructed by enumerating
different values of a . Solutions obtained from the proposed method
contain more information, and can offer more chance to achieve the
feasible disaggregate plan. This is helpful to the decision-maker in
practical applications.
Abstract: Most Decision Support Systems (DSS) for waste
management (WM) constructed are not widely marketed and lack
practical applications. This is due to the number of variables and
complexity of the mathematical models which include the
assumptions and constraints required in decision making. The
approach made by many researchers in DSS modelling is to isolate a
few key factors that have a significant influence to the DSS. This
segmented approach does not provide a thorough understanding of
the complex relationships of the many elements involved. The
various elements in constructing the DSS must be integrated and
optimized in order to produce a viable model that is marketable and
has practical application. The DSS model used in assisting decision
makers should be integrated with GIS, able to give robust prediction
despite the inherent uncertainties of waste generation and the plethora
of waste characteristics, and gives optimal allocation of waste stream
for recycling, incineration, landfill and composting.
Abstract: Multilevel inverters supplied from equal and constant
dc sources almost don-t exist in practical applications. The variation
of the dc sources affects the values of the switching angles required
for each specific harmonic profile, as well as increases the difficulty
of the harmonic elimination-s equations. This paper presents an
extremely fast optimal solution of harmonic elimination of multilevel
inverters with non-equal dc sources using Tanaka's fuzzy linear
regression formulation. A set of mathematical equations describing
the general output waveform of the multilevel inverter with nonequal
dc sources is formulated. Fuzzy linear regression is then
employed to compute the optimal solution set of switching angles.
Abstract: Combinatorial optimization problems arise in many scientific and practical applications. Therefore many researchers try to find or improve different methods to solve these problems with high quality results and in less time. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used to solve optimization problems. Both GA and SA search a solution space throughout a sequence of iterative states. However, there are also significant differences between them. The GA mechanism is parallel on a set of solutions and exchanges information using the crossover operation. SA works on a single solution at a time. In this work SA and GA are combined using new technique in order to overcome the disadvantages' of both algorithms.
Abstract: A prototype for audio and video capture and compression in real time on a Linux platform has been developed. It is able to visualize both the captured and the compressed video at the same time, as well as the captured and compressed audio with the goal of comparing their quality. As it is based on free code, the final goal is to run it in an embedded system running Linux. Therefore, we would implement a node to capture and compress such multimedia information. Thus, it would be possible to consider the project within a larger one aimed at live broadcast of audio and video using a streaming server which would communicate with our node. Then, we would have a very powerful and flexible system with several practical applications.