Abstract: The quality of press-fit assembly is closely related to
reliability and safety of product. The paper proposed a keypoint
detection method based on convolutional neural network to improve
the accuracy of keypoint detection in press-fit curve. It would
provide an auxiliary basis for judging quality of press-fit assembly.
The press-fit curve is a curve of press-fit force and displacement.
Both force data and distance data are time-series data. Therefore,
one-dimensional convolutional neural network is used to process
the press-fit curve. After the obtained press-fit data is filtered, the
multi-layer one-dimensional convolutional neural network is used to
perform the automatic learning of press-fit curve features, and then
sent to the multi-layer perceptron to finally output keypoint of the
curve. We used the data of press-fit assembly equipment in the actual
production process to train CNN model, and we used different data
from the same equipment to evaluate the performance of detection.
Compared with the existing research result, the performance of
detection was significantly improved. This method can provide a
reliable basis for the judgment of press-fit quality.
Abstract: In the current research structural health
monitoring is considered for addressing the critical issue of
post-earthquake damage detection. A non-standard approach
for damage detection via acoustic emission is presented -
acoustic emissions are monitored in the low frequency range
(up to 120 Hz). Such emissions are termed high-frequency
transients. Further a damage indicator defined as the
Time-Ratio Damage Indicator is introduced. The indicator
relies on time-instance measurements of damage initiation and
deformation peaks. Based on the time-instance measurements
a procedure for estimation of the maximum drift ratio is
proposed. Monitoring data is used from a shaking-table test
of a full-scale reinforced concrete bridge pier. Damage of the
experimental column is successfully detected and the proposed
damage indicator is calculated.
Abstract: In this paper, an interactive in-car interface called HoloDash is presented. It is intended to provide information and infotainment in both autonomous vehicles and ‘connected cars’, vehicles equipped with Internet access via cellular services. The research focuses on the development of interactive avatars for this system and its gesture-based control system. This is a case study for the development of a possible human-centred means of presenting a connected or autonomous vehicle’s On-Board Diagnostics through a projected ‘holographic’ infotainment system. This system is termed a Holographic Human Vehicle Interface (HHIV), as it utilises a dashboard projection unit and gesture detection. The research also examines the suitability for gestures in an automotive environment, given that it might be used in both driver-controlled and driverless vehicles. Using Human Centred Design methods, questions were posed to test subjects and preferences discovered in terms of the gesture interface and the user experience for passengers within the vehicle. These affirm the benefits of this mode of visual communication for both connected and driverless cars.
Abstract: Digital multimedia contents such as image, video, and audio can be tampered easily due to the availability of powerful editing softwares. Multimedia forensics is devoted to analyze these contents by using various digital forensic techniques in order to validate their authenticity. Digital image forensics is dedicated to investigate the reliability of digital images by analyzing the integrity of data and by reconstructing the historical information of an image related to its acquisition phase. In this paper, a survey is carried out on the forgery detection by considering the most recent and promising digital image forensic techniques.
Abstract: Cartesian Genetic Programming (CGP) is explored to
design an optimal circuit capable of early stage breast cancer
detection. CGP is used to evolve simple multiplexer circuits for
detection of malignancy in the Fine Needle Aspiration (FNA) samples
of breast. The data set used is extracted from Wisconsins Breast
Cancer Database (WBCD). A range of experiments were performed,
each with different set of network parameters. The best evolved
network detected malignancy with an accuracy of 99.14%, which is
higher than that produced with most of the contemporary non-linear
techniques that are computational expensive than the proposed
system. The evolved network comprises of simple multiplexers
and can be implemented easily in hardware without any further
complications or inaccuracy, being the digital circuit.
Abstract: One of the major shortcomings of widely used
scientometric indicators is that different disciplines cannot be
compared with each other. The issue of cross-disciplinary
normalization has been long discussed, but even the classification
of publications into scientific domains poses problems. Structural
properties of citation networks offer new possibilities, however, the
large size and constant growth of these networks asks for precaution.
Here we present a new tool that in order to perform cross-field
normalization of scientometric indicators of individual publications
relays on the structural properties of citation networks. Due to the
large size of the networks, a systematic procedure for identifying
scientific domains based on a local community detection algorithm
is proposed. The algorithm is tested with different benchmark
and real-world networks. Then, by the use of this algorithm, the
mechanism of the scientometric indicator normalization process is
shown for a few indicators like the citation number, P-index and
a local version of the PageRank indicator. The fat-tail trend of the
article indicator distribution enables us to successfully perform the
indicator normalization process.
Abstract: The massive development of online social networks
allows users to post and share their opinions on various topics.
With this huge volume of opinion, it is interesting to extract and
interpret these information for different domains, e.g., product and
service benchmarking, politic, system of recommendation. This is
why opinion detection is one of the most important research tasks.
It consists on differentiating between opinion data and factual data.
The difficulty of this task is to determine an approach which returns
opinionated document. Generally, there are two approaches used
for opinion detection i.e. Lexical based approaches and Machine
Learning based approaches. In Lexical based approaches, a dictionary
of sentimental words is used, words are associated with weights. The
opinion score of document is derived by the occurrence of words from
this dictionary. In Machine learning approaches, usually a classifier
is trained using a set of annotated document containing sentiment,
and features such as n-grams of words, part-of-speech tags, and
logical forms. Majority of these works are based on documents text
to determine opinion score but dont take into account if these texts
are really correct. Thus, it is interesting to exploit other information
to improve opinion detection. In our work, we will develop a new
way to consider the opinion score. We introduce the notion of
trust score. We determine opinionated documents but also if these
opinions are really trustable information in relation with topics. For
that we use lexical SentiWordNet to calculate opinion and trust
scores, we compute different features about users like (numbers of
their comments, numbers of their useful comments, Average useful
review). After that, we combine opinion score and trust score to
obtain a final score. We applied our method to detect trust opinions in
TRIPADVISOR collection. Our experimental results report that the
combination between opinion score and trust score improves opinion
detection.
Abstract: Both Lidars and Radars are sensors for obstacle
detection. While Lidars are very accurate on obstacles positions
and less accurate on their velocities, Radars are more precise on
obstacles velocities and less precise on their positions. Sensor
fusion between Lidar and Radar aims at improving obstacle
detection using advantages of the two sensors. The present
paper proposes a real-time Lidar/Radar data fusion algorithm
for obstacle detection and tracking based on the global nearest
neighbour standard filter (GNN). This algorithm is implemented
and embedded in an automative vehicle as a component generated
by a real-time multisensor software. The benefits of data fusion
comparing with the use of a single sensor are illustrated through
several tracking scenarios (on a highway and on a bend) and
using real-time kinematic sensors mounted on the ego and tracked
vehicles as a ground truth.
Abstract: Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.
Abstract: With the Internet becoming the dominant channel for business and life, many IPs are increasingly masked using web proxies for illegal purposes such as propagating malware, impersonate phishing pages to steal sensitive data or redirect victims to other malicious targets. Moreover, as Internet traffic continues to grow in size and complexity, it has become an increasingly challenging task to detect the proxy service due to their dynamic update and high anonymity. In this paper, we present an approach based on behavioral graph analysis to study the behavior similarity of web proxy users. Specifically, we use bipartite graphs to model host communications from network traffic and build one-mode projections of bipartite graphs for discovering social-behavior similarity of web proxy users. Based on the similarity matrices of end-users from the derived one-mode projection graphs, we apply a simple yet effective spectral clustering algorithm to discover the inherent web proxy users behavior clusters. The web proxy URL may vary from time to time. Still, the inherent interest would not. So, based on the intuition, by dint of our private tools implemented by WebDriver, we examine whether the top URLs visited by the web proxy users are web proxies. Our experiment results based on real datasets show that the behavior clusters not only reduce the number of URLs analysis but also provide an effective way to detect the web proxies, especially for the unknown web proxies.
Abstract: Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.
Abstract: Software-defined networking (SDN) provides a solution
for scalable network framework with decoupled control and data
plane. However, this architecture also induces a particular distributed
denial-of-service (DDoS) attack that can affect or even overwhelm
the SDN network. DDoS attack detection problem has to date been
mostly researched as entropy comparison problem. However, this
problem lacks the utilization of SDN, and the results are not accurate.
In this paper, we propose a DDoS attack detection method, which
interprets DDoS detection as a signature matching problem and is
formulated as Earth Mover’s Distance (EMD) model. Considering
the feasibility and accuracy, we further propose to define the cost
function of EMD to be a generalized Kullback-Leibler divergence.
Simulation results show that our proposed method can detect DDoS
attacks by comparing EMD values with the ones computed in the case
without attacks. Moreover, our method can significantly increase the
true positive rate of detection.
Abstract: The north-eastern, Himalayan, and Eastern Ghats Belt
of India comprise of earthquake-prone, remote, and hilly terrains.
Earthquakes have caused enormous damages in these regions in the
past. A wireless sensor network based earthquake early warning
system (EEWS) is being developed to mitigate the damages caused
by earthquakes. It consists of sensor nodes, distributed over the
region, that perform majority voting of the output of the seismic
sensors in the vicinity, and relay a message to a base station to alert
the residents when an earthquake is detected. At the heart of the
EEWS is a low-power two-stage seismic sensor that continuously
tracks seismic events from incoming three-axis accelerometer signal
at the first-stage, and, in the presence of a seismic event, triggers
the second-stage P-wave detector that detects the onset of P-wave
in an earthquake event. The parameters of the P-wave detector have
been optimized for minimizing detection time and maximizing the
accuracy of detection.Working of the sensor scheme has been verified
with seven earthquakes data retrieved from IRIS. In all test cases, the
scheme detected the onset of P-wave accurately. Also, it has been
established that the P-wave onset detection time reduces linearly with
the sampling rate. It has been verified with test data; the detection
time for data sampled at 10Hz was around 2 seconds which reduced
to 0.3 second for the data sampled at 100Hz.
Abstract: The main objective of this work is to enhance the Pulsed Eddy Current (PEC) response from the aluminum structure using signal processing. Cracks and metal loss in different structures cause changes in PEC response measurements. In this paper, time-frequency analysis is used to represent PEC response, which generates a large quantity of data and reduce the noise due to measurement. Power Spectral Density (PSD) after Wavelet Decomposition (PSD-WD) is proposed for defect detection. The experimental results demonstrate that the cracks in the surface can be extracted satisfactorily by the proposed methods. The validity of the proposed method is discussed.
Abstract: In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments.
Abstract: 5-FU is a chemotherapeutic agent that has been used in colorectal cancer (CRC) treatment. However, it is usually associated with the acquired resistance, which decreases the therapeutic effects of 5-FU. miR-200c is involved in chemotherapeutic drug resistance, but its mechanism is not fully understood. In this study, the effect of inhibition of miR-200c in sensitivity of HCT-116 CRC cells to 5-FU was evaluated. HCT-116 cells were transfected with LNA-anti- miR-200c for 48 h. mRNA expression of miR-200c was evaluated using quantitative real- time PCR. The protein expression of phosphatase and tensin homolog (PTEN) and E-cadherin were analyzed by western blotting. Annexin V and propidium iodide staining assay were applied for apoptosis detection. The caspase-3 activation was evaluated by an enzymatic assay. The results showed LNA-anti-miR-200c inhibited the expression of PTEN and E-cadherin protein, apoptosis and activation of caspase 3 compared with control cells. In conclusion, these results suggest that miR-200c as a prognostic marker can overcome to 5-FU chemoresistance in CRC.
Abstract: Production of specific antibody responses against hTSH is a cumbersome process due to the high identity between the hTSH and the other members of the glycoprotein hormone family (FSH, LH and HCG) and the high identity between the human hTSH and host animals for antibody production. Therefore, two polyclonal antibodies were purified against two recombinant proteins. Four possible ELISA tests were designed based on these antibodies. These ELISA tests were checked against hTSH and other glycoprotein hormones, and their sensitivity and specificity were assessed. Bioinformatics tools were used to analyze the immunological properties. After the immunogen region selection from hTSH protein, c terminal of B hTSH was selected and applied. Two recombinant genes, with these cut pieces (first: two repeats of C terminal of B hTSH, second: tetanous toxin+B hTSH C terminal), were designed and sub-cloned into the pET32a expression vector. Standard methods were used for protein expression, purification, and verification. Thereafter, immunizations of the white New Zealand rabbits were performed and the serums of them were used for antibody titration, purification and characterization. Then, four ELISA tests based on two antibodies were employed to assess the hTSH and other glycoprotein hormones. The results of these assessments were compared with standard amounts. The obtained results indicated that the desired antigens were successfully designed, sub-cloned, expressed, confirmed and used for in vivo immunization. The raised antibodies were capable of specific and sensitive hTSH detection, while the cross reactivity with the other members of the glycoprotein hormone family was minimum. Among the four designed tests, the test in which the antibody against first protein was used as capture antibody, and the antibody against second protein was used as detector antibody did not show any hook effect up to 50 miu/l. Both proteins have the ability to induce highly sensitive and specific antibody responses against the hTSH. One of the antibody combinations of these antibodies has the highest sensitivity and specificity in hTSH detection.
Abstract: Vehicular Ad hoc Network is basically the solution of several problems associated while vehicles are plying on the road. In this paper, we have focused on the detection of imposter node while it has stolen the ID's of the authenticated vehicle in the network. The purpose is to harm the network through imposter messages. Here, we have proposed a protocol namely Imposter Detection based on Location (IDBL), which will store the location coordinate of the each vehicle as the key of the authenticity of the message so that imposter node can be detected. The imposter nodes send messages from a stolen ID and show that it is from an authentic node ID. So, to detect this anomaly, the first location is checked and observed different from original vehicle location. This node is known as imposter node. We have implemented the algorithm through JAVA and tested various types of node distribution and observed the detection probability of imposter node.
Abstract: This study mainly concerns a safety device designed for child care. When children are out of sight or the caregivers cannot always pay attention to the situation, through the functions of this device, caregivers can immediately be informed to make sure that the children do not get lost or hurt, and thus, ensure their safety. Starting from this concept, a device is produced based on the relatively low-cost Bluetooth piconet system and a three-axis gyroscope sensor. This device can transmit data to a mobile phone app through Bluetooth, in order that the user can learn the situation at any time. By simply clipping the device in a pocket or on the waist, after switching on/starting the device, it will send data to the phone to detect the child’s fall and distance. Once the child is beyond the angle or distance set by the app, it will issue a warning to inform the phone owner.
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.