The ongoing trend towards shorter product life cycles, smaller batch sizes and the desire for individual products challenges established manufacturing processes such as roll forming. To meet recent market requirements...
ISBN:
(纸本)9780735418479
The ongoing trend towards shorter product life cycles, smaller batch sizes and the desire for individual products challenges established manufacturing processes such as roll forming. To meet recent market requirements, process setup and troubleshooting must be accelerated. So far, these activities have been strongly based on the experience of employees. At the same time, both developing and developed countries tend to have a shortage of skilled workers for different reasons. While demographic change is responsible for this development in developed countries, the lack of vocational training is the reason for this challenge in developing countries. To solve this problem, new paths have to be taken to support machine operators to accelerate and safeguard their actions. The integration of sensor systems into manufacturing processes combined with an automatic evaluation of sensor data might help to create operator assistance systems serving this purpose. This paper draws a perspective for sensor integration and automated signalprocessing in roll forming. For this reason, load sensors are implemented into an industrial roll forming process and disturbances frequently observed in manufacturing processes are intendedly set into the process. Simultaneously, a framework for an automated signal evaluation within an operator assistance system evaluating and analyzing the current process states based on sensor signals and providing recommendations for process improvements is introduced. The results demonstrate that force measurements can contribute to error diagnosis.
Surfing internet becomes common now-a-days that gave a chance for intruders to steal information. Therefore security is very important to detect any unwanted activities by using intrusion detection system. Intrusion d...
Surfing internet becomes common now-a-days that gave a chance for intruders to steal information. Therefore security is very important to detect any unwanted activities by using intrusion detection system. Intrusion detection system is one of the vast new technologies in this decade which makes the system to learn by itself and predict the values using machinelearning techniques. To analyze intrusion detection system for detecting network attacks using various machinelearning techniques has been proposed in this paper. machinelearning algorithms such as J48, Naive Bayes, Random Forest and REP tree are compared using Kddcup99 dataset. When comparing these machinelearning algorithms in which random forest gives high detection rate.
Extreme learningmachine (ELM) is most popular emerging learning algorithm that modify classical ‘Generalized’ single hidden layer feed forward network. Though some traditional gradient based learning algorithm like...
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The proceedings contain 46 papers. The topics discussed include: design of an autonomous underwater maintenance dredger;biodosimetric studies for ballast water treatment;development of an automatic analysis methodolog...
The proceedings contain 46 papers. The topics discussed include: design of an autonomous underwater maintenance dredger;biodosimetric studies for ballast water treatment;development of an automatic analysis methodology by integration of digital hull design, model, processing, and evaluation;simplifying interactions between autonomous and conventional ships with e-navigation;towards automated identification of ice features for surface vessels using deep learning;an experimental result on information exchange using USV communication relay system;and inference model of collision risk index based on artificial neural network using ship near-collision data.
Watermarking algorithms based on the geometric invariance of image histogram are effective and can resist various common attacks. However, all existing histogram-based image watermarking algorithms are constructed fro...
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ISBN:
(数字)9781510634107
ISBN:
(纸本)9781510634107
Watermarking algorithms based on the geometric invariance of image histogram are effective and can resist various common attacks. However, all existing histogram-based image watermarking algorithms are constructed from all the pixels of the entire image;thus the embedded watermark energy is randomly distributed throughout the image, causing visual quality degradation in the smooth areas. In this paper, an improved algorithm using human visual perception characteristics is proposed. Firstly, we calculate the Jnd threshold mapping of the carrier image and select a portion of the pixels with the largest threshold as samples of the statistical histogram. Secondly, we calculate the mean of the selected pixel set, determine the embedding region and divide it into several groups. Finally, by adjusting the number of pixels in three bins per group, 2 bits of the watermark are embedded. According to the geometric invariance of the histogram and the different sensitivity of human eyes to the smooth and textured areas, we embed the watermark in the positions which are not easily perceived by human eyes. Experiments show that the proposed algorithm significantly improves the visual quality of the smooth areas in the watermarked image, but it has weaker robustness to signalprocessing attacks.
The number of new mobile and wearable technologies with built-in sensors for quantifying every aspect of our lives is increasing. Consequently, new data sources and opportunities arise for the development of machine l...
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ISBN:
(纸本)9781450361262
The number of new mobile and wearable technologies with built-in sensors for quantifying every aspect of our lives is increasing. Consequently, new data sources and opportunities arise for the development of machinelearning (ML) models and their applications. In this paper, we report on a four weeks field study with 16 older adults, aged between 66 and 81 years (50% female), who were asked to provide stress-related experience samples in different modalities, including paper-based diaries and data collected with the help of a wearable (i.e., a Microsoft Band 2). We provide insights into participants' stress annotation behavior, report on a detailed analysis of the recorded data and the resulting implications regarding the annotation of stressful situations by older adults, discuss how mobile annotation technology can benefit from the synergies with traditional methods and argue why we believe that appropriate annotation techniques are the basis to benefit individually from future powerful machinelearning models.
In this paper, we have developed a QoS-enhanced smart scheduler for multi-core processor in intelligent IP routers using machine (deep) learning. The proposed packet scheduler is stochastic in nature, it can process r...
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The proceedings contain 24 papers. The special focus in this conference is on Artificial Intelligence. The topics include: Text Mining-Based Human Computer Interaction Approach for On-line Purchasing;feature Based Opi...
ISBN:
(纸本)9789811391286
The proceedings contain 24 papers. The special focus in this conference is on Artificial Intelligence. The topics include: Text Mining-Based Human Computer Interaction Approach for On-line Purchasing;feature Based Opinion Mining for Hotel Profiling;a Hybrid Agent System to Detect Stress Using Emotions and Social Media Data to Provide Coping Methodologies;thinking Like Humans: A New Approach to machine Translation;rice Express: A Communication Platform for Rice Production Industry;fuzzy Logic Based Backtesting System;diagnosis of Coronary Artery Diseases and Carotid Atherosclerosis Using Intravascular Ultrasound Images;performance Analysis: Preprocessing of Respiratory Lung Sounds;a Classification Based Approach to Predict the Gender Using Craniofacial Measurements;an Optimized Predictive Coding Algorithm for Medical Image Compression;Palm Vein Recognition Based on Competitive Code, LBP and DCA Fusion Strategy;six-State Continuous processing Model for a New Theory of Computing;Locating the Position of a Cell Phone User Using GSM signals;modeling of Hidden Layer Architecture in Multilayer Artificial Neural Networks;a Novel Hybrid Back Propagation Neural Network Approach for Time Series Forecasting Under the Volatility;flood Forecasting Using Artificial Neural Network for Kalu Ganga;role of Deep Neural Network in Speech Enhancement: A Review;intelligent Time of Use Deciding System for a Melody to Provide a Better Listening Experience;preface;a Preliminary Study on Kinematic Analysis of Human Hand;invoke Artificial Intelligence andmachinelearning for Strategic-Level Games and Interactive Simulations;an Ontological Approach for Knowledge Representation of Dental Extraction Forceps.
Chatbot as a conversational system that can interact with human naturally is a Natural Language processing task that require modeling semantics of complicated relationships of the language for communication. Various a...
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ISBN:
(纸本)9781450372619
Chatbot as a conversational system that can interact with human naturally is a Natural Language processing task that require modeling semantics of complicated relationships of the language for communication. Various attempts have been made to reduce the complexities (language understanding, feature extraction, domain recognition, intent detection, semantic slot filling and language generation) of training text-based Chatbot. While Traditional machinelearning models are usually unable to be truly generic, recent advances in deep learning allow end-to-end models to be trained with large dataset. We train an open domain Chatbot end-to-end by directly mapping input tags as sequences to generate optimized output sequence tags. Our model is trained on Google Tensorflow framework running GPUs with deep neural architecture of sequence-to-sequence and attention mechanism. The interaction of the chatbot with human performed common sense reasoning. It also achieved a competitive result when quantified based on Human Performance Evaluation. However, we suggest networked reinforcement learning to incrementally update the consistency drawbacks towards achieving a generic Chatbot.
The emotion is recognized from facial expression by using static images. It is one of the categories in signalprocessing which is applied in various fields, similarly for human and computer interaction. Some sources ...
The emotion is recognized from facial expression by using static images. It is one of the categories in signalprocessing which is applied in various fields, similarly for human and computer interaction. Some sources are proposed to automatic emotion recognition, which uses machinelearning approach. Many real-time problems have been solved by Deep learning technique. In this work we have defined Convolutional Neural Network (CNN) is used to identify 6 elementary emotions this technique has been implemented in MATLAB.
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