Kheng Cher Yeo

Adaptive feedforward control to compensate cogging torque and current measurement errors for PMSMs

Torque ripple minimisation is achieved by an adaptive feedforward control method to reduce cogging torque and current measurement errors. Experiments have been conducted to show that torque ripples are highly sensitive to changes in current offset and cogging torque. An easy way to adapt to cogging torque changes (due to temperature) is proposed and implemented after an initial estimation of the cogging torque is done. This initial estimation is necessary for manufacturing inaccuracies and ongoing adaptation is to cope with operating conditions changes. Higher harmonics of the cogging torque can also be compensated through this way. Current offset which contributes to a large proportion of the torque ripple is also compensated through an adaptive method. The advantages of the proposed scheme combine the benefits of preprogrammed waveform methods which are easy to implement and a simple adaptive system to effectively reduce RMS torque ripple factor from 11.30% to 1.7%.

Indirect adaptive feedforward control in compensating cogging torque and current measurement inaccuracies for Permanent Magnet motors

Theoretically, if the inverse transfer function of the plant is known, feedforward control can achieve zero error between the reference and the output. The main aim of this paper is to design a simple feedforward model and to compensate for the errors brought about by a change in the cogging torque and current measurement inaccuracies. The reference position, speed and acceleration have been designed so that they can be used as inputs to the feedforward model. The feedforward model is a stable form of the inverse transfer function of the motor dynamics of a PM motor. MRAS technique has been used to compensate for changes to the cogging torque and current measurement inaccuracies. A successful indirect adaptive feedforward control has been designed for three different shapes of the back EMF (sinusoidal, trapezoidal and a mix of the first two shape using real motor back EMF) and the control scheme is validated through hardware experimentation.

A critical review of Bitcoins usage by cybercriminals

Bitcoin is a new form of global digital currency based on peer-to-peer network, enabling a new payment system, and also a completely decentralised cryptocurrency. The P2P network consists of a digital file listing transactions like a ledger, a copy of which is also maintained on every computer on the network, and the transactions are also broadcasted in the public ledger of the Bitcoin network. Anonymity is the core feature that makes Bitcoin popular among people around the world. This feature is attained through the Bitcoin addresses in the public ledger, which represents the users in the Bitcoin network. Because of the illicit use of Bitcoins, the level of anonymity has reduced even though the users are still using the anonymizers like TOR to keep the anonymity stronger to connect to the Bitcoin network. In this paper, we analyse the complete process of transaction of Bitcoins and the anonymity thatlies in that process. The study also focuses on finding the forensic artefacts and the investigative way of approach towards the Bitcoin using forensic tools. The forensic tools are used to analyse the web browser activities, local drive, hard disk image, cookies, downloads and session data related to Bitcoins. The attacker can relate the transaction of the users and can control the Bitcoin blocks by even delaying the transactions. This research will focus on the methods in which the memory and even mobile devices involved in the transaction could be captured and analysed.

An improved face recognition method using Local Binary Pattern method

Security system based on biometrics is becoming more popular everyday as a part of safety and security measurement against all kind of crimes. Among several kinds of biometric security systems, face recognition is one of the most popular one. It is one of the most accurate, mostly used recognition methods in modern world. In this paper, two most popular face recognition methods have been discussed and compared using average image on Yale database. To reduce calculation complexity, all training and test images are converted into gray scale images. The whole face recognition process can be divided into two parts face detection and face identification. For face detection part, Viola Jones face detection method has been used out of several face detection methods. After face detection, face is cropped from the actual image to remove the background and the resolution is set as 150×150 pixels. Eigenfaces and fisherfaces methods have been used for face identification part. Average images of subjects have been used as training set to improve the accuracy of identification. Both methods are investigated using MATLAB to find the better performance under average image condition. Accuracy and time consumption has been calculated using MATLAB code on Yale image database. In future, this paper will be helpful for further research on comparison of different face recognition methods using average images on different database.

A theoretical review of social media usage by cyber-criminals

Social media plays an integral part in individuals everyday lives as well as for companies. Social media brings numerous benefits in peoples lives such as to keep in touch with close ones and specially with relatives who are overseas, to make new friends, buy products, share information and much more. Unfortunately, several threats also accompany the countless advantages of social media. The rapid growth of the online social networking sites provides more scope for criminals and cyber-criminals to carry out their illegal activities. Hackers have found different ways of exploiting these platform for their malicious gains. This research englobes some of the common threats on social media such as spam, malware, Trojan horse, cross-site scripting, industry espionage, cyber-bullying, cyber-stalking, social engineering attacks. The main purpose of the study to elaborates on phishing, malware and click-jacking attacks. The main purpose of the research, there is no particular research available on the forensic investigation for Facebook. There is no particular forensic investigation methodology and forensic tools available which can follow on the Facebook. There are several tools available to extract digital data but its not properly tested for Facebook. Forensics investigation tool is used to extract evidence to determine what, when, where, who is responsible. This information is required to ensure that the sufficient evidence to take legal action against criminals.

Heuristic systematic model based guidelines for phishing victims

The purpose of this research is to identify factors of phishing victim based on the Heuristic Systematic Model and propose phishing awareness guidelines. In this research, the explanatory sequential mixed method is chosen. Therefore, survey and interview method been applied for data collection purpose. In summary, this research concluded that the major factors that influence user becomes a phishing victim based on the Heuristic Systematic Model are argument quality, source credibility, genre conformity, need for cognition, time pressure, pre-texting, less damage, knowledge and trust. On the other hand, the phishing awareness guidelines are proposed based on previous studies and also feedback from interviewees. Moreover, the proposed phishing awareness guidelines are expected to educate Internet users to identify phishing tricks and prevent phishing attacks in the future.

Cost effective analysis of web based transmission

This paper examines the techniques for passing information from one web site to another, the methodologies that have been developed and the advances in technology that has occurred in relation to these techniques. This paper then utilises one of these technologies to develop an environment where the major techniques (Polling and Push) can be tested. This papers research finds that Polling uses substantially more resources than Push, particularly in regard to server usage. In situations where this is an issue the use of Push can reduce server cost by at least a factor of 5 and possibly more in a real world application. The reduction in network cost is more difficult to determine using the tools in this paper but here the indications are that the reduction in cost may be by a factor of 3. Finally this paper presents the view that the techniques are complementary and both should be used within most real world application.

Position based iterative learning control to minimise torque ripple for PMSMs

Torque ripple minimisation is achieved by an adaptive feedforward control for mass produced permanent magnet synchronous motors. Torque ripple, a major problem for such motors can be reduced using appropriate estimation scheme from speed information. The proposed method employs online zero phase filtering which can effectively minimise all torque harmonics at low speed operation. Experimental results validate the proposed scheme reducing the torque ripple factor from 6.87% to 1.28% using the proposed method. The adaptation scheme make use of PD type iterative learning control scheme which has the advantage of fast convergence compared to the more commonly used P type iterative learning control. The PD-ILC scheme is also quite robust to an initial error in the estimation of the parameter J.