Paper making from bagasse

Though the revolution of technology has reduced paper production everywhere around the world, the amount of paper consumed each day is still tremendously large. The paper industry remains a growth industry globally, with volumes forecast to increase 50% by 2035 with most of the growth occurring in the packaging and tissues sector. The main material used in paper production is wood fibre. In order to satisfy the large demand for paper, thousands of trees must be cut down every year. Deforestation caused by worldwide consumption of wood-based paper has been a huge environmental issue. As the awareness of sustainability continues to rise, the demand for sustainable paper increases. The investigation of looking for alternatives has been a critical issue. The new alternative on the market is sugarcane fibre paper [1]

Development of sustainable material for hybrid wall system to improve indoor thermal performance

Thermal performance of building envelope has been of great importance in determining the indoor thermal environment mainly due to the impact of existing global warming issues. Due to the hot and humid climate of Malaysia, and poor thermal design of building envelope, mechanical cooling of buildings is becoming almost a necessity. This necessity in the case of low-income home owners is an added burden. Thus there is a need to provide wall system with better thermal performance than conventional wall systems. Due to the emphasis on developing sustainable built environments, researchers are striving for waste incorporation in building wall material. However, the waste incorporated within the building wall system, especially in bricks still lacks practical applicability when it comes to the overall performance of the system in terms of mechanical, thermal and physical properties. The focus of the research is to tackle the twin issues of sustainability and thermal performance of building wall systems for affordable homes using a Design Science methodology. A cost-effective sustainable alternative building wall system with better thermal performance than conventional material is proposed by utilizing locally available waste materials such as waste glass and oil palm industry byproducts. The enhancement of thermal performance of wall materials was done by the introduction of cellular porous palm oil fibers to lower the heat transfer. Fiber reinforced mortar (FRM) and thermally enhanced sustainable hybrid (TESH) bricks were developed by optimizing the mix design using Glass Powder, Palm Oil Fly Ash and Oil Palm Fibers based on Taguchi’s Process Parameter approach. Both the FRM and TESH bricks, which constitute the thermally enhanced sustainable hybrid (TESH) wall system, were analyzed for physical, mechanical and thermal performance and they comply with the various codes of practice for building materials. ANSYS WORKBENCH software was used to determine the thermal performance of the newly developed TESH. The temperature distribution and rate of heat transfer through the wall system was found to be significantly lower than conventional wall systems. Also, comparative energy analysis established that the energy consumption is 10.6 % lower for TESH. Due to the lower electricity consumption, the total energy costing for the building was also reduced by 10.2 %. Thus, TESH proves to be more sustainable and cost effective within the operational phase of the building. TESH is a sustainable alternative for low-cost housing units due to its proven low embodied energy as it comprises mainly of locally available waste materials for its production

Incremental learning for large-scale stream data and its application to cybersecurity

As many human currently depend on technologies to assist with daily tasks, there are more and more applications which have been developed to be fit in one small gadget such as smart phone and tablet. Thus, by carrying this small gadget alone, most of our tasks are able to be settled efficiently and fast. Until the end of 20th century, mobile phones are only used to call and to send short message service (sms). However, in early 21st century, a rapid revolution of communi�cation technology from mobile phone into smart phone has been seen in which the smart phone is equipped by 4G Internet line along with the telephone service provider line. Thus, the users are able to make a phone call, send messages using variety of application such as Whatsapp and Line, send email, serving websites, accessing maps and handling some daily tasks via online using online banking, online shopping and online meetings via video conferences. In previous years, if there are cases of missing children or missing cars, the victims would rely on the police investigation. But now, as easy as uploading a notification about the loss on Facebook and spread the news among Facebook users, there are more people are able to help in the search. Despite the advantages that can be obtained using these technologies, there are a group of irresponsible people who take advan�tage of current technologies for their own self-interest. Among the applications that are usually being used by almost Internet users and also are often misused by cyber criminals are email and websites. Therefore, we take this initiative to make enhancement in cyber security application to avoid the Internet users from being trapped and deceived by the trick of cyber criminals by developing detec�tion system of malicious spam email and Distributed Denial of Services (DDoS) 377$3(53867$.1781.8781$0,1$+ iii backscatter. Imagine that a notice with a logo of Mobile Phone company is received by an email informing that the customer had recently run up a large mobile phone bill. A link regarding the bill is attached for him/her to find out the details. Since, the customer thinks that the billing might be wrong, thus the link is clicked. However, the link is directed to a webpage which displays a status that currently the webpage is under construction. Then the customer closes the page and thinking of to visit the website again at other time. Unfortunately, after a single click actually a malicious file is downloaded and installed without the customer aware of it. That malicious file most probably is a Trojan that capable to steal confidential information from victim’s computer. On the next day, when the same person is using the same computer to log in the online banking, all of a sudden find out that his/her money is lost totally. This is one of a worst case scenario of malicious spam email which is usually handled by cybersecurity field. Another different case of cybersecurity is the Distributed Denial of Services (DDoS) attack. Let say, Company X is selling flowers via online in which the market is from the local and international customer. The online business of Company X is running normally as usual, until a day before mother’s day, the webpage of Company X is totally down and the prospective customers could not open the webpage to make order to be sent specially for their beloved mother. Thus, the customers would search another company that sells the same item. The Company X server is down, most probably because of the DDoS attack where a junk traffic is sent to that company server which makes that server could not serve the request by the legitimate customers. This attack effect not only the profit of the company, but also reputation damage, regular customer turnover and productivity decline. Unfortunately, it is difficult for a normal user like us to detect malicious spam 377$ 3(53867$.1781.8781$0,1$+ email or DDoS attack with naked eyes. It is because recently the spammers and attacker had improved their strategy so that the malicious email and the DDoS packets are hardly able to be differentiated with the normal email and data packets. Once the Social Engineering is used by the spammers to create relevant email content in the malicious spam email and when a new campaign of DDoS attack is launched by the attacker, no normal users are capable to distinguish the benign and malicious email or data packets. This is where my Ph.D project comes in handy. My Ph.d is focusing on constructing a detection system of malicious spam email and DDoS attack using a large number of dataset which are obtained by a server that collect double-bounce email and darknet for malicious spam email detection system and DDoS backscatter detection system, respectively. As many up-to-date data are used during the learning, the detection system would become more robust to the latest strategy of the cybercriminal. Therefore, the scenario mentioned above can be avoided by assisting the user with important information at the user-end such as malicious spam email filter or at the server firewall. First of all, the method to learn large-scale stream data must be solved before implementing it in the detection system. Therefore, in Chapter 2, the general learning strategy of large-scale data is introduced to be used in the cybersecurity applications which are discussed in Chapter 3 and Chapter 4, respectively. One of a critical criterion of the detection system is capable to learn fast because after the learning, the updated information needs to be passed to user to avoid the user from being deceived by the cybercriminal. To process large-scale data sequences, it is important to choose a suitable learning algorithm that is capable to learn in real time. Incremental learning has an ability to process large data in chunk and update the parameters after learning each chunk. Such type of learning keep and update only the minimum information on a classifier model. 377$3(53867$.1781.8781$0,1$+ Therefore, it requires relatively small memory and short learning time. On the other hand, batch learning is not suitable because it needs to store all training data, which consume a large memory capacity. Due to the limited memory, it is certainly impossible to process online large-scale data sequences using the batch learning. Therefore, the learning of large-scale stream data should be conducted incrementally. This dissertation contains of five chapters. In Chapter 1, the concept of in�cremental learning is briefly described and basic theories on Resource Allocating Network (RAN) and conventional data selection method are discussed in this chapter. Besides that, the overview of this dissertation is also elaborated in this chapter. In Chapter 2, we propose a new algorithm based on incremental Radial Basis Function Network (RBFN) to accelerate the learning in stream data. The data sequences are represented as a large chunk size of data given continuously within a short time. In order to learn such data, the learning should be carried out incrementally. Since it is certainly impossible to learn all data in a short pe�riod, selecting essential data from a given chunk can shorten the learning time. In our method, we select data that are located in untrained or “not well-learned” region and discard data at trained or “well-learned” region. These regions are represented by margin flag. Each region is consisted of similar data which are near to each other. To search the similar data, the well-known LSH method pro�posed by Andoni et al. is used. The LSH method indeed has proven be able to quickly find similar objects in a large database. Moreover, we utilize the LSH ʼs properties; hash value and Hash Table to further reduced the processing time. A flag as a criterion to decide whether to choose or not the training data is added in the Hash Table and is updated in each chunk sequence. Whereas, the hash value of RBF bases that is identical with the hash value of the training data is used to select the RBF bases that is near to the training data. The performance results of 377$ 3(53867$.1781.8781$0,1$+ vi the numerical simulation on nine UC Irvine (UCI) Machine Learning Repository datasets indicate that the proposed method can reduce the learning time, while keeping the similar accuracy rate to the conventional method. These results indi�cate that the proposed method can improve the RAN learning algorithm towards the large-scale stream data processing. In Chapter 3, we propose a new online system to detect malicious spam emails and to adapt to the changes of malicious URLs in the body of spam emails by updating the system daily. For this purpose, we develop an autonomous system that learns from double-bounce emails collected at a mail server. To adapt to new malicious campaigns, only new types of spam emails are learned by introducing an active learning scheme into a classifier model. Here, we adopt Resource Allocating Network with Locality Sensitive Hashing (RAN-LSH) as a classifier model with data selection. In this data selection, the same or similar spam emails that have already been learned are quickly searched for a hash table using Locally Sensitive Hashing, and such spam emails are discarded without learning. On the other hand, malicious spam emails are sometimes drastically changed along with a new arrival of malicious campaign. In this case, it is not appropriate to classify such spam emails into malicious or benign by a classifier. It should be analyzed by using a more reliable method such as a malware analyzer. In order to find new types of spam emails, an outlier detection mechanism is implemented in RAN-LSH. To analyze email contents, we adopt the Bag-of-Words (BoW) approach and generate feature vectors whose attributes are transformed based on the normalized term frequency-inverse document frequency. To evaluate the developed system, we use a dataset of double-bounce spam emails which are collected from March 1, 2013 to May 10, 2013. In the experiment, we study the effect of introducing the outlier detection in RAN-LSH. As a result, by introducing the outlier detection, we confirm that the detection accuracy is enhanced on 377$3(53867$.$$1781.8781$0,1$+ average over the testing period. In Chapter 4, we propose a fast Distributed Denial of Service (DDoS) backscat�ter detection system to detect DDoS backscatter from a combination of protocols and ports other than the following two labeled packets: Transmission Control Protocol (TCP) Port 80 (80/TCP) and User datagram Protocol (UDP) Port 53 (53/UDP). Usually, it is hard to detect DDoS backscatter from the unlabeled packets, where an expert is needed to analyze every packet manually. Since it is a costly approach, we propose a detection system using Resource Allocating Network (RAN) with data selection to select essential data. Using this method, the learning time is shorten, and thus, the DDoS backscatter can be detected fast. This detection system consists of two modules which are pre-processing and classifier. With the former module, the packets information are transformed into 17 feature-vectors. With the latter module, the RAN-LSH classifier is used, where only data located at untrained region are selected. The performance of the proposed detection system is evaluated using 9,968 training data from 80/TCP and 53/UDP, whereas 5,933 test data are from unlabeled packets which are col�lected from January 1st, 2013 until January 20th, 2014 at National Institute of Information and Communications Technology (NICT), Japan. The results indi�cate that detection system can detect the DDoS backscatter from both labeled and unlabeled packets with high recall and precision rate within a short time. Finally, in Chapter 5, we discussed the conclusions and the future work of our study: RAN-LSH classifier, malicious spam email detection system and DDoS backscatter detection system

Power quality issues of 3MW direct-driven PMSG wind turbine

This paper presents power quality issues of a grid connected wind generation system with a MW-class direct-driven permanent magnet synchronous generator (PMSG). A variable speed wind turbine model was simulated and developed with the simulation tool of PSCAD/EMTDC. The model includes a wind turbine with one mass-model drive train model, a PMSG model and a full-scale voltage source back to back PWM converter. The converter controller model is employed in the dq-synchronous rotating reference frame and applied to both generator and grid sides. To achieve maximum power point tracking, a tip speed ratio method is applied in machine side, whereas DC voltage control is applied in grid side to achieve constant DC voltage. Due to wind fluctuation and power oscillation as a result of wind shear and tower shadow effects (3p), there will be a fluctuation in the output power and voltage. The concerned power quality issues in this work are Harmonics, power fluctuation and flicker emission. The measurements will be carried out under different wind speed and circumstances

Study of resonances in 1 x 25 kV AC traction systems with external balancing equipment

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.AC traction systems are 1 × 25 or 2 × 25 kV/50 Hz single-phase, nonlinear, time-varying loads that can cause power-quality problems, such as unbalanced or distorted voltages. To reduce unbalance, external balancing equipment is usually connected to these systems, forming the Steinmetz circuit. Parallel resonances can occur in these types of circuits, exciting the harmonic emissions (below 2 kHz) of railway-adjustable speed drives. This paper studies these resonances at pantograph terminals and provides analytical expressions to determine their harmonic frequencies. The expressions are validated from several traction systems in the literature and PSCAD simulations.Postprint (author's final draft

A Decision Tree and S-Transform Based Approach for Power Quality Disturbances Classification

In this paper, it is presented an automated classification based on S-transform as feature extraction tool and Decision Tree as algorithm classifier. The signals generated according to mathematical models, including complex disturbances, have been used to design and test this approach, where noise is added to the signals from 40dB to 20dB. Finally, several disturbances, simple and complex, have been considered to test the implemented system. Evaluation results verifying the accuracy of the proposed method are presented.IEE

Modeling and enhanced control of hybrid full bridge–half bridge MMCs for HVDC grid studies

Modular multilevel converters (MMCs) are expected to play an important role in future high voltage direct current (HVDC) grids. Moreover, advanced MMC topologies may include various submodule (SM) types. In this sense, the modeling of MMCs is paramount for HVDC grid studies. Detailed models of MMCs are cumbersome for electromagnetic transient (EMT) programs due to the high number of components and large simulation times. For this reason, simplified models that reduce the computation times while reproducing the dynamics of the MMCs are needed. However, up to now, the models already developed do not consider hybrid MMCs, which consist of different types of SMs. In this paper, a procedure to simulate MMCs having different SM topologies is proposed. First, the structure of hybrid MMCs and the modeling method is presented. Next, an enhanced procedure to compute the number of SMs to be inserted that takes into account the different behavior of full-bridge SMs (FB-SMs) and half-bridge submodules (HB-SMs) is proposed in order to improve the steady-state and dynamic response of hybrid MMCs. Finally, the MMC model and its control are validated by means of detailed PSCAD simulations for both steady-state and transients conditions (AC and DC faults)