TOOL CONDITION MONITORING METHOD IN MILLING PROCESS USING WAVELET TRANSFORM AND LONG SHORT-TERM MEMORY
Fatemeh Aghazadeh1, Antoine Tahan2 and Marc Thomas3
1,2,3 Departement de Génie mécanique, École de Technologie Supérieure, 1100, rue NotreDame Ouest, Montréal (Québec), Canada.
Industrial automation is a promising move to fulfill today’s competitive manufacturing industry demands by lowering operation costs, increasing productivity and quality. Monitoring the production process is one of the important steps toward total autonomy of manufacturing plants, which reduces routine checks, enables proactive maintenance and reduces repair costs. This research investigates tool wear as one of the most common faults in milling process during cutting of the D2 high speed steel as a hard to cut material using Carbide Walter End Mill Protostar tool. Vibration signal is chosen to represent the system status due to its applicability in industry. Signals are transformed into time-frequency domain using Wavelet Transform method to reveal both time domain and frequency domain features of the signal simultaneously. In order to model the complex and non-linear relations between tool wear and vibration signals under varying cutting parameters, a deep learning based algorithm, Long Short-Term Memory (LSTM) Artificial neural networks (ANNs) is employed. Deep learning algorithms are getting lots of attention recently within the diagnosis and prognosis community because of their exceptional performance in exploiting information in big data to solve complex problems. LSTM network is a type of recurrent ANNs that have some internal cells that act as long-term or short-term memory units, which is most suitable for sequential data and time series like vibration signals in our analysis. After designing the system, performance of the monitoring method is validated using experimentally acquired data with K2X10 Huron high speed CNC machine in LIPPS and Dynamo labs of ETS.
Deep Learning, Tool Wear, Wavelet Transform, Condition Monitoring, Time-Frequency Transformation, Machining Process
ARTIFICIAL INTELLIGENCE-BASED PROCESS FOR METAL SCRAP SORTING
Maximilian Auer, Kai Oßwald, Raphael Volz, Jörg Woidasky, Pforzheim University, School of Engineering, 75175 Pforzheim, Germany
Machine learning offers remarkable benefits for improving workplaces and working conditions amongst others in the recycling industry. Here e. g. hand-sorting of medium value scrap is labor intensive and requires experienced and skilled workers. On the one hand they have to be highly concentrated for making proper readings and analyses of the material, but on the other hand this work is monotonous Therefore, a machine learning approach is proposed for a quick and reliable automated identification of alloys in the recycling industry, while the mere scrap handling is regarded to be left in the hands of the workers. To this end, a set of twelve tool and high-speed steels from the field were selected to be identified by their spectrum induced by electric arcs. For data acquisition the optical emission spectrometer Thorlabs CCS 100 was used. Spectra have been post-processed to be fed into the supervised machine learning algorithm. The development of the machine learning software is conducted according to the steps of the VDI 2221 standard method. For programming Python 3 as well as the python-library sklearn were used. By systematic parameter variation, the appropriate machine learning algorithm was selected and validated. Subsequent validation steps showed that the automated identification process using a machine learning approach and the optical emission spectrometry is applicable, reaching a maximum F1 score of 96.9 %. This performance is as good as the performance of a highly trained worker using visual grinding spark identification. The tests were based on a self-generated set of 600 spectra per single alloy (7,200 spectra in total) which were produced using an industry workshop device.
Supervised Learning, Spectroscopy, Metal scrap recycling
ARTIFICIAL INTELLIGENCE SURPASSING HUMAN INTELLIGENCE: FACTUAL OR HOAX
M Tech –CSE (With Specialization In Big Data Analytics) Jamia Hamdard University
AI is a trending topic in the field of Computer Science which aims to make computers "smart". There are various diverse technical and specialized research associated with artificial intelligence. Every day we can hear new stories on how AI is making progress in yet another industry. Artificial Intelligence has already taken over a lot of Human jobs and performing more efficiently and effectively, than humans. However a lot of time this question has arisen - Will Artificial Intelligence Surpass Human Intelligence? Is computers’ ever accelerating abilities to outpace human skills is a matter of concern? The different views and myths on the subject have made it even a more - topic of discussion.
This research studies, analyzes, collects and summarizes existing re-search and future aspects on the topic. Later we discuss the possibilities if AI can eventually replace human jobs in the market. Also, we analyze different AI types and discuss whether machines will ultimately surpass human beings or not.
COMMUNICATION BETWEEN DRIVERLESS CARS FOR EFFECTIVE PERFORMANCE: VEHICLE TO VEHICLE COMMUNICATION
Alain Nkongnenwi1, FelicitasMokom2, Harry Ngatchu3, GilemondNchiwo4
1,2,4Catholic University Institute of Buea, Cameroon, 3University of Buea, Cameroon
Designing an autonomous vehicle involves designing a system that allows the vehicle to move to a predefined destination point avoiding obstacles during the motion, using information from sensors installed on the vehicle. For effective performance automakers will need these vehicles to communicate amongst themselves, sharing information and resources in a secure manner.
The current state of vehicle to vehicle (V2V) communication is dependent on individual auto companies who often rely on cloud service providers. This paper reviews some articles to investigate effective means of communication between fully automated cars and proposes a model that could be used by the entire auto industry. The model would facilitate the intercommunication of different fully automated cars which individually may have limitations.
Autonomous cars, network of self-driving car, communication, performance of driverless cars, Security of autonomous cars.
Condition Based Maintenance of Turbine and
Compressor of a CODLAG Naval Propulsion System using Deep Neural Network
, Rituparna Datta2
, Aviv Segev2
, and Alec Yasinsac2
1University Institute of Technology, Burdwan University,
West Bengal, India,2Department of Computer Science, University of South Alabama,
150 Jaguar Drive, Mobile, AL 36688, USA
System and sub-system maintenance is a significant task for every dynamic system. A plethora
of approaches, both quantitative and qualitative, have been proposed to ensure the system safety and to minimize the system downtime. The rapid progress of computing technologies and different machine learning
approaches makes it possible to integrate complex machine learning techniques with maintenance strategies to
predict system maintenance in advance. The present work analyzes different methods of integrating an Artificial Neural Network (ANN) and ANN with Principle Component Analysis (PCA) to model and predict compressor decay state coefficient and turbine decay state coefficient of a Gas Turbine (GT) mounted on a frigate
characterized by a Combined Diesel-Electric and Gas (CODLAG) propulsion plant used in naval vessels. The
input parameters are GT parameters and the outputs are GT compressor and turbine decay state coefficients.
Due to the presence of a large number of inputs, more hidden layers are required, and as a result a deep neural
network is found appropriate. The simulation results confirm that most of the proposed models accomplish
the prediction of the decay state coefficients of the gas turbine of the naval propulsion. The results show that
a consistently declining hidden layers size which is proportional to the input and to the output outperforms
the other neural network architectures. In addition, the results of ANN outperforms hybrid PCA-ANN in most
cases. The ANN architecture design might be relevant to other predictive maintenance systems
Supervised Learning, Spectroscopy, Metal scrap recycling
Condition based maintenance, Neural Network, Deep neural network, Principle Component Analysis(PCA), Naval propulsion
Automated Advanced Remote-Control Car System
BRAC University, Department of Computer Science and Engineering, Mohakhali, Dhaka 1212, Bangladesh
Our work is based on Arduino, motor driver, Bluetooth module, and Firebase and sonar sensor. Arduino is an open source platform which is easy-to-use between hardware and software. Arduino uses ATmega328 microcontroller. We present our automated advanced remote control car that is controlled by an Android application through a server and also have camera for live broadcasting. Firebase receive data from web application, stores those data and updates the data into our android application. Our robotic car is controlled using those data and also can avoid any obstacle using those data. All in all, we propose an IOT based robotic car that is controlled over the internet using Firebase Realtime database. It can be made using in a bigger scale for real time vehicles in next generation automated vehicle.
Automation, IOT RC car, Bluetooth, Arduino, Google Firebase, Realtime database.
An Advising System For Parking Using Canny And K-NN Techniques
Chyi-Ren Dow, Wei-Kang Wang, Huu-Huy Ngo, and Shiow-Fen Hwang
Department of Information Engineering and Computer Science Feng Chia University, Taichung, Taiwan
This study proposes a system which provides the parking characteristics and an application service platform. This system can be used to assist in selecting the parking space for drivers. The system can identify the contours of vehicles, such as cars and motorcyclesby using the Canny algorithm. The data can beused to create the dataset and calculate the Parking density. Next, we use the k-nearest neighbor (K-NN) algorithm to produce the parking pattern. The model makes predictions for different conditions at different time.
Parking Space,Big Data of Traffic, k-Nearest Neighbor, Canny Edge Detection.
Probability-Directed Problem Optimization Technique For Solving Systems Of Linear And Non-Linear Equations
Muhammed J. Al-Muhammed
Faculty of Information Technology,American University of Madaba,Madaba, Jordan
Although many methods have been proposed for solving linear or nonlinear systems of equations, there is always a pressing need for more effective and efficient methods. Good methods should produce solutions with high precision andspeed. This paper proposed an innovative method for solving systems of linear and nonlinear equations. This method transforms the problem into an optimization problem and uses a probability guided search technique for solving this optimization problem, which is the solution for the system of equations. The transformation results in an aggregate violation function and a criterion function. The aggregation violation function is composed of the constraints that represent the equations and whose satisfaction is a solution for the system of equations.The criterion function intelligently guides the search for the solution to the aggregate violation function by determining when the constraints must be checked; thereby avoiding unnecessary, time-intensivechecks forthe constraints. Experiments conducted with our prototype implementation showed that our method is effective in finding solutions with high precision and efficient in terms of CPU time.
Solutions for systems of linear and non-linear equations,random-guided search, optimization problem, global minimum.
A Novel Method To Prevent Phishing
Yunjia Wang and Ishbel Duncan
School of Computer Science, University of St Andrews, UK
Phishing is one of the most common attacks in the world, especially with the increasing usage of mobile platforms and e-commerce. Although many users are sensible about phishing attacks from suspicious paths in the URL address, phishing still accounts for a large proportion of all of malicious attacks as it is easy to deploy. Most browser vendors mainly adopt two approaches against phishing; the blacklist and the heuristic-based. However, both have related limitations.In this paper, a novel method was presented and developed to protect against phishing attacks. An easy to implement prototype demonstrated high accuracy detection in the experimental trials. .
Phishing, OCR, Phishing Prevention.
Panel Analysis Of Physiological Signals: Study Of Obstructive Sleep Apnea Syndrome
Samir Ghouali1,3 ,Fayçal Amine Haddam2 and Mohammed Feham3
1 Faculty of Sciences and Technology, Mustapha Stambouli University, 29000 Mascara
2 Faculty of Engineering Science of Tlemcen, Algeria & ZTE (Zhongxing Telecommunication Equipment) Company
3 Faculty of Engineering Science of Tlemcen, STIC Laboratory, Tlemcen, Algeria
This paper provided an overview of the methods used for the main unit root tests, panel data cointegration, estimation models and the use of Granger causality in panels. This research has developed considerably since the pioneering work of Levin and Lin and is now being applied in many empirical ways. The theoretical framework, which is the basis of any empirical study, provides a content of legitimacy to our problem, as it serves to clarify concepts and makes it possible to define each notion. In this article, we have contributed with the study of sleep Apnea. Non-stationary panel data estimators can still solve a number of problems, including estimation and inference. To estimate co-integrated variable systems, as well as to perform tests on co-integration vectors, it is necessary to use efficient estimation methods. The FM-OLS and DOLS models are used to quantify our results. The results found show the long-term interaction between physiological signals, and can help the physician to understand the risks associated with these interactions.
FM-OLS, DOLS, Panel Granger Causality, Sleep Apnea, MATLAB .
A Dendritic Cell Algorithm Based Approach for Malicious TCP Port Scanning Detection
Nuha Almasalmeh1,Zouheir Trabelsi1and Firas Saidi2
1College of Information Technology,United Arab Emirates University AlAin, UAE
2National School of Computer Sciences,University of Manouba,Tunisia
The proliferation of cyber-attacks
brings up an urgent need to develop sophisticated
detection tools. Some of these tools are based on
algorithms inspired from the Human Immune System
(HIS). The Dendritic Cell Algorithm (DCA) is one of
such HIS inspired methods, which is based on the
Danger model. In this paper, we applied and enhanced
the DCA algorithm to cover the malicious TCP port
scanning detection. Experimentations and evaluation
results of different use cases show the efficiency of the
two versions of DCA algorithm in abnormal Port
Artificial immune systems, dendritic cell
algorithm, denial of service, intrusion detection, port scanning,
Mitigating The Threat Of Lsb Steganography Within Data Loss Prevention Systems
Yunjia Wang and Ishbel Duncan
School of Computer Science, University of St Andrews, Scotland
Data Loss Prevention systems need to consider the passing of information out of an organisation through
emails or common shared data repositories via images or diagrams. Steganography has historically been
used to hide information, images or tex, within cover images and as email and shared data spaces allow
high MB or GB file transfers it is important to detect or destroy hidden information. This paper discusses
an empirical trial to validate protection mechanisms against data loss through steganographic images.
Security; LSB Steganography; Data Loss Prevention
FORCE PARAMETERIZATION OF LITERALS
Bhashyam Ramesh1, Mohankumar KJ2, J Venkataramana3,
Shrikant Salunke4, Ganit Kumar5, Syed Nawaz6
1Teradata India Pvt Ltd, India
Query plan cache (QPC) in any database avoids repeated query optimization for the same query. QPC has strong significance in relational database management systems (RDBMSs). In Teradata QPC saves the plan of the query when the query is seen for the first time and optimized. The saved optimized plan is used if the query repeats and repeated optimization is avoided. In most RDBMSs, the query must exactly match with the saved query in QPC in order to reuse the query plan. In most of the cases user given queries are exactly same and may vary only in the literal values present in the predicates. Since the queries vary in the literal values used in the predicates, queries are treated as distinct queries and query optimization is done to generate the plan. We propose an approach called as Force Parameterization of Literals (FPL). In this approach, we parameterize the literals present in the predicates and generate a query template. Use this query template to generate a generic plan and reuse the plan for all the queries that matches with the query template after parameterizing the literals present in the predicates. One of the key challenge is making sure the generic plan generated is optimal for all the subsequent literal values come. We experimentally validate that our approach is efficient in space and time and adaptive, requiring no repeated query optimization for queries that vary only in the literal values used in the predicates.
Query Plan Cache, Query Optimization, Query Plan Generation, Database, RDBMS
EXPONENTIAL MODEL: A BAYESIAN STUDY WITH STAN
Mohammed H AbuJarad, Athar Ali Khan
Department of Statistics and Operations Research, AMU, Aligarh-202002
The exponential distribution possesses an essential position in lifetime distribution study. In this paper, an endeavor has been made to ﬁt the Bayesian inference procedures for exponential distribution, exponentiated exponential and the two-parameter extension of exponential distribution. keeping in mind the end goal to actualize Bayesian techniques to examine and applied to a real survival censored data, visualization of lung cancer survival data and demonstrate through utilizing Stan. Stan is a high level language written in a C++ library for Bayesian modeling. This model applies to survival censoring data with the goal that every one of the ideas and calculations will be around similar data. Stan code has been created and enhanced to actualize a censored system all through utilizing Stan technique. Moreover, parallel simulation tools are also implemented and additionally actualized with a broad utilization of rstan.
exponential, exponentiated exponential, exponentiated extension, Posterior, Simulation, RStan, Bayesian Inference, R, HMC.
EDGEBASE: A COOPERATIVE QUERY ANSWERING DATABASE SYSTEM WITH A NATURAL LANGUAGE INTERFACE
Edmund Sowah and Jianqiu Xu
Department of Computer and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Traditional Database Management Systems (DBMS) require users to meticulously construct and submit queries to generate answers. The lack of query syntax flexibility in traditional database systems make results in simple and direct answers – queries retrieve precisely matched elements stated in the given Boolean query. In this paper, we propose a Cooperative Query Answering Database System (CDBS) that provide answers to user queries in the same manner as humans do, and not as machines. The method of “Cooperative Query Answering (CQA)”, emanated from the perception that to provide adequate and “complete” answers to queries, recognition of users’ intentions is vital. Most database systems require users to submit their queries using SQL syntax. In addition to presenting answers to queries in human-like manner, we present a cooperative approach to query submission. By this, we present an architecture that combines the rich features of html, Natural Language (NL) with Query-ByForm (QBF) method and MySQL to enable our proposed system accept user queries in plain English language. To authenticate our approach and proposed system, a set of thorough experiments were conducted on two database systems using mysqlslap benchmark and a comparative study with other methods is done.
Cooperative Query Answering, Natural Language, Query Language, Query-By-Form, Query syntax
DEVELOPMENT OF A KNOWLEDGE- BASED SYSTEM FOR UNDERTAKING THE RISK ANALYSIS OF PROPOSED BUILDING PROJECTS FOR A SELECTED CLIENT
Department of Quantity Surveying, Faculty of Environmental Design, Abubakar Tafawa Balewa University, P.M,B. 0248, Bauchi, Bauchi State, Nigeria
A Knowledge-Based System for the risk analysis of proposed building projects was developed for a selected client. The Fuzzy Decision Variables (FDVs) that cause differences between initial and final contract sums of building projects were identified, the likelihood of the occurrence of the risks were determined and a Knowledge-Based System that would rank the risks was constructed using JAVA programming language and Graphic User Interface. The Knowledge-Based System is composed a Knowledge Base for storing data, an Inference Engine for controlling and directing the use of knowledge for problem-solution, and a User Interface that assists the user retrieve, use and alter data in the Knowledge Base. The developed Knowledge-Based System was compiled, implemented and validated with data of previously completed projects. The client could utilize the Knowledge-Based System to undertake proposed building projects.
RISK ANALYZER, Risk analysis, Knowledge-Based Systems, JAVA, Graphic User Interface
INTELLIGENCE ANALYSIS WITH MATRIX CREATION MODEL FOR ALL MODELS AND COMBINING MATRIX INTELLIGENCE ANALYSIS WITH NETWORK ANALYSIS
Mohammad Hassan Anjom SHoa
Department of Mathematics, Vali-e-Ars University, Rafsanjan, Iran
In this paper, we try to express the strengths and weaknesses of each of intelligence techniques to form a suitable combination of these techniques for a moment of maximum intelligence, as well as more and more accurate mathematical structures. A new technique is a combination of applied methods. In this new technique for all previous techniques such as a coherent method, actors, etc., a matrix has been proposed to establish a matrix of elements and parameters which communicate with the help of network analysis techniques between their parameters. While using the weak assumptions that are used in the competing assumptions analysis technique, the other matrices can be used, and with the help of the network, parameters that have the greatest relevance to other parameters are selected as a job preference for analysis.
competitor analysis technique, coherent effect analysis model, model of analysis of actors, intelligence network analysis technique, parameter matrix, and component.
SUPPORT VECTOR MACHINE METHOD APPLICATION IN THE DATA MINING PROCESS FOR OIL WELL CLASSIFICATION PROBLEM DECISION
Mihailov Ilya Sergeevich, Zayar Aung
Applies Mathematics and Informatics National Research University Moscow Power Engineering Institute (MPEI) Moscow, Russian
SVM method for classification problems solving is considered in the article. The main aspects of this method application for classification problems solving are formulated. Its advantages and disadvantages are shown. The oil wells classification problem description and approaches to solving this problem using the developed SVM method modification are also given. The SVM method modification for solving the considered problem is substantiated.
Artificial Intelligence, Support Vector Machine, Data Mining, oil well.
Data Mining for Money Laundering Transactions
Royal Roads University,Canada
With the increase in money laundering activities across various sectors in some of the world’s
leading democracies, the ability to detect such transactions is gaining grounds with more
urgency. Regulators and practitioners have been calling for an approach that can mine the large
volume of unstructured data form suspicious money laundering transactions to inform policy
making. Base on the current trend, it is expected that money laundering activities will continue to
increase. This paper presented an overview of data mining technology for detecting suspicious
transactions. After chronicling the data mining process, the paper delves into an analysis of the
statistical approaches that can be employed to differentiate between legitimate and suspicious
money laundering transactions. The different stages of the data mining process are carefully
explained in relation to their application to AML compliance. A discussion on how the data can
be mined to facilitate statistical analysis and inform regulatory policies is then carried out.
Data mining, Statistical methods, Algorithm, Machine learning, Data Scientist
Obstacles detection on road
Mosbah Ramzi, Guezouli Larbi,University of Batna,Algeria
According to the World Health Organization (WHO) , more than 1.25 million people have died
in a car accident caused by the driver’s lack of attention, sleep or fatigue. Almost half of those who
die on the world’s roads are ”vulnerable road users”: pedestrians, cyclists and motorcyclists.
In this work we present an approach where we detect roadsides, then we seek objects located on the
road area to prevent driver.
Parallel to this, we provide a system for detecting driver’s drowsiness.
In some critical cases, we built an arduino microcontroller to take control of the car when driver
sleeps or an obstacle appeared in a way and a collision is imminent. We choose the 4th level
of autonomous driving for our system. Levels  are defined by experts where they categorize
the evolution of autonomous driving in 5 categories. Each level describes how the car and driver
We compared the detection accuracy among object classes and analyzed the recognition results with
another detectors on KITTI dataset.
Object detection, help driving, road-edges detection, arduino.
MACHINE LEARNING AND WEARABLE DEVICES FOR PHONOCARDIOGRAM-BASED DIAGNOSIS
Shaima Abdelmageed and Mohammed Elmusrati, University of Vasa, Finland
The heart sound signal, Phonocardiogram (PCG) is difficult to interpret even for experienced cardiologists. Interpretation are very subjective depending on the hearing ability of the physician. mHealth has been the adopted approach towards simplifying that and getting quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this paper is to diagnose the heart condition based on Phonocardiogram analysis using Machine Learning techniques assuming limited processing power to be encapsulated later in a wearable device. The cardiovascular system is modelled in a transfer function to provide PCG signal recording as it would be recorded at the wrist. The signal is, then, decomposed using filter bank and the analysed using discriminant function. The results showed that PCG with a 19 dB Signal-to-Noise-Ratio can lead to 97.33% successful diagnosis. The same decomposed signal is then analysed using pattern recognition neural network, and the classification was 100% successful with 83.3% trust level.
Analysis, Classification, data quality, diagnosis, filter banks, mHealth, PCG, SNR, transfer function, Wavelet Transform, wearable
CONSTRUCTING A SEMANTIC GRAPH WITH DEPRESSION SYMPTOMS EXTRACTED FROM TWITTER
Long Ma, Troy University, USA
Depression diagnosis is a critical challenge in mental precision medicine since there is currently no a gold standard using depression symptoms. Usually, a doctor makes depression diagnosis based on patients’ answers to interview questions. The depression diagnosis depends on a person’s behavior symptoms. Due to privacy of clinical data in a hospital, it is very hard to get patients’ medical data. Thus, we directly use public social media data containing much information from patients, doctors and other people on Twitter. The research goal is to extract depression symptoms from the massive social data from Twitter via text mining and then make a semantic graph to representing relations among the depression symptoms. Different from commonly used statistical methods, we propose a hybrid method that integrates the statistical analysis and natural language processing techniques to make the semantic graph with the discovered depression symptoms from tweets. In the future, the depression symptom semantic graph will be used to build an intelligent depression diagnosis software system for medical doctors and a convenient depression self-screening software system for ordinary people.
Depression, Social Media, Text Mining, Twitter, Social Networks, Natural Language Processing, Word2Vec
Detection of Malicious nodes using collaborative neighbour monitoring in DSA networks
Takyi Augustine,David Johnson
University of Cape Town, South Africa
This work addresses position falsiﬁcation attack in dynamic spectrum access networks. The work models possible threats of malicious nodes and presents a novel detection algorithm. Our algorithm detection strategy uses collaborative neighbor monitoring by the secondary nodes within the deployment area to detect malicious nodes. The simulation results obtained show that our algorithm works well in detecting position falsiﬁcation attacks in the dynamic spectrum access networks, provided the distance between the actual malicious node position and the falsiﬁed position is at least 0.035 km. Even with high ﬂuctuation with RSSI values, we obtained right samples that were closer to the means using Kullback Leibler (KL) divergence.
Spectrum sensing, neighbor monitoring, malicious node, secondary node, position falsiﬁcation, attack.
RESEARCH ON GAIT PREDICTION BASED ON LSTM
Bo Fan Liang1 and Q. Chen2
1 1,2Department of Automation, Beijing Information Science and Technology University, Beijing, China
With the increase of the proportion of the aging population, the protection and assistance of the older persons has become an important issue in the society. Among them, the safety problems of the elderly due to falls accounts for a large proportion, so it is very important to predict the fall. The fall is mainly characterized by abnormal gait, for gait mode, the gait has a strong periodicity, each step is completed in one cycle, and each cycle is repeatable. In this paper, a gait prediction method is proposed. Firstly, the lumbar posture of the human body is measured by the acceleration gyroscope as the gait feature, and then the gait is predicted by the LSTM network. The experimental results show that the RMSE between the gait trend predicted by the method and the actual gait trend can be reached a level of 0.06 ± 0.01.
ELDLY FALL, ACCELERASTION GYRO, LUMBAR POSTURE, GAIT PREDICTION, LSTM .
Performance Analysis of Witricity in Typical Real World Model Situations
Hafiz Usman Tahseen1, Lixia Yang2
1School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, China,2 Jiangsu University, Zhenjiang, China
Witricity is transfer of electric power wirelessly at resonant. At resonant circuits behave as pure resonant circuit. This happens when two eleromagnetic coupled coils are present in very near field and at resonant frequency. Both circuits transfer maximum energy with the minimum energy reflection. The transformers with the same technique are usually used in switching circuits, radio etc. At resonant, oscillating current becomes a source of magnetic field, so during consecutive many cycles energy loss is very low, hence any circuit lying in reactive near field causes mutual induction and there is a definite wireless energy transmission. In this research, authors design two multi track coil Resonators and observe energy transmission at near field from first Resonator to the 2nd in simulation and hardware both. The authors further analyse the said Witricity technique with misalignment at three different positions; lateral, angular, and axial rotational. They observe energy transmission at each position with all three misalignments at fixed resonant frequency to demonstrate the feasibility of the system through practical measurements in order to make meaningful comparison. They develop a design procedure for charging low power electronic devices wirelessly through inductive linkage within a room through single source.
Lateral misalignment, Angular misalignment, Axial rotational misalignment.