Search results for: Associativity Based Routing (ABR)

Authors: Mansour Tarafdar, Mohammad Saniee Abade

Abstract:

This paper investigates the problem of sampling from transactional data streams. We introduce CFISDS as a content based sampling algorithm that works on a landmark window model of data streams and preserve more informed sample in sample space. This algorithm that work based on closed frequent itemset mining tasks, first initiate a concept lattice using initial data, then update lattice structure using an incremental mechanism.Incremental mechanism insert, update and delete nodes in/from concept lattice in batch manner. Presented algorithm extracts the final samples on demand of user. Experimental results show the accuracy of CFISDS on synthetic and real datasets, despite on CFISDS algorithm is not faster than exist sampling algorithms such as Z and DSS.

Keywords: Sampling, data streams, closed frequent item set mining.

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Authors: Elis N. Herliyana , Kunio Tsunoda, Yusuf S. Hadi, Arinana, Dewi A. Natalia

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This study aims to determine the level of resistance of Hevea brasiliensis and Paraserianthes falcataria (synonym: Falcataria molucana) against wood rot fungi Pleurotus ostreatus based on Indonesian standard SNI 01.7207-2006 and Japanese standard JIS K 1571-2004. The variables measured were visual appearance and weight loss percentage of wood based on longitudinal and cross section fiber directions of rubber wood and sengon wood. Measurement of oven dry weight loss of wood samples performed after 12 weeks incubation. Replication performed was 10 times at each treatment combination. The results based on SNI 01.7207-2006, weight loss value of H. brasiliensis and P. falcataria wood with fiber direction longitudinal were 23,12 and 22,25% respectively and cross section were 20,77 and 18,76% respectively, and all were classified to resistance class IV (no resistance). The results based on JIS K 1571-2004, weight loss value of both woods with fiber direction cross section were 10,95 and 14,20% respectively.

Keywords: H. brasiliensis wood, Natural durability, P. falcataria wood, P. ostreatus.

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Authors: Uttam Vijay, Nitin Gupta

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Due to heavy energy constraints in WSNs clustering is an efficient way to manage the energy in sensors. There are many methods already proposed in the area of clustering and research is still going on to make clustering more energy efficient. In our paper we are proposing a minimum spanning tree based clustering using divide and conquer approach. The MST based clustering was first proposed in 1970’s for large databases. Here we are taking divide and conquer approach and implementing it for wireless sensor networks with the constraints attached to the sensor networks. This Divide and conquer approach is implemented in a way that we don’t have to construct the whole MST before clustering but we just find the edge which will be the part of the MST to a corresponding graph and divide the graph in clusters there itself if that edge from the graph can be removed judging on certain constraints and hence saving lot of computation.

Keywords: Algorithm, Clustering, Edge-Weighted Graph, Weighted-LEACH.

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Authors: Maythapolnun Athimethphat, Boontarika Lerteerawong

Abstract:

There are several approaches for handling multiclass classification. Aside from one-against-one (OAO) and one-against-all (OAA), hierarchical classification technique is also commonly used. A binary classification tree is a hierarchical classification structure that breaks down a k-class problem into binary sub-problems, each solved by a binary classifier. In each node, a set of classes is divided into two subsets. A good class partition should be able to group similar classes together. Many algorithms measure similarity in term of distance between class centroids. Classes are grouped together by a clustering algorithm when distances between their centroids are small. In this paper, we present a binary classification tree with tuned observation-based clustering (BCT-TOB) that finds a class partition by performing clustering on observations instead of class centroids. A merging step is introduced to merge any insignificant class split. The experiment shows that performance of BCT-TOB is comparable to other algorithms.

Keywords: multiclass classification, hierarchical classification, binary classification tree, clustering, observation-based clustering

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Authors: Mei-Fang Wu, Shu-Li Wang, Tsung-Yueh Lu, Feng-Tsung Cheng

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Strengthening core competitiveness is the main goal of enterprises in a fierce competitive environment. Accurate cost information is a great help for managers in dealing with operation strategies. This paper establishes a profitability management mechanism that applies the Activity-Based Benefit approach (ABBA) to solve the profitability for each customer from the market. ABBA provides financial and non-financial information for the operation, but also indicates what resources have expired in the operational process. The customer profit management model shows the level of profitability of each customer for the company. The empirical data were gathered from a case company operating in the leather industry in Taiwan. The research findings indicate that 30% of customers create little profit for the company as a result of asking for over 5% of sales discounts. Those customers ask for sales discount because of color differences of leather products. This paper provides a customer’s profitability evaluation mechanism to help enterprises to greatly improve operating effectiveness and promote operational activity efficiency and overall operation profitability.

Keywords: Activity-based benefit approach, customer profit analysis, leather industry, profitability management mechanism.

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Authors: Andhe Dharani, P. S. Satyanarayana, Andhe Pallavi

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Considering payload, reliability, security and operational lifetime as major constraints in transmission of images we put forward in this paper a steganographic technique implemented at the physical layer. We suggest transmission of Halftoned images (payload constraint) in wireless sensor networks to reduce the amount of transmitted data. For low power and interference limited applications Turbo codes provide suitable reliability. Ensuring security is one of the highest priorities in many sensor networks. The Turbo Code structure apart from providing forward error correction can be utilized to provide for encryption. We first consider the Halftoned image and then the method of embedding a block of data (called secret) in this Halftoned image during the turbo encoding process is presented. The small modifications required at the turbo decoder end to extract the embedded data are presented next. The implementation complexity and the degradation of the BER (bit error rate) in the Turbo based stego system are analyzed. Using some of the entropy based crypt analytic techniques we show that the strength of our Turbo based stego system approaches that found in the OTPs (one time pad).

Keywords: Halftoning, Turbo codes, security, operationallifetime, Turbo based stego system.

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Authors: Zuraidasahana Zulkarnain, Mohd Shafry Mohd Rahim, Nor Anita Fairos Ismail, Mohd Azhar M. Arsad

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Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification.

Keywords: biometrics, euclidean classifier, feature extraction, offline signature verification, VOTING-based classifier

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Authors: Behrooz Movahedi

Abstract:

Fe-based amorphous feedstock powders are used as the matrix into which various ratios of hard B4C nanoparticles (0, 5, 10, 15, 20 vol.%) as reinforcing agents were prepared using a planetary high-energy mechanical milling. The ball-milled nanocomposite feedstock powders were also sprayed by means of high-velocity oxygen fuel (HVOF) technique. The characteristics of the powder particles and the prepared coating depending on their microstructures and nanohardness were examined in detail using nanoindentation tester. The results showed that the formation of the Fe-based amorphous phase was noticed over the course of high-energy ball milling. It is interesting to note that the nanocomposite coating is divided into two regions, namely, a full amorphous phase region and homogeneous dispersion of B4C nanoparticles with a scale of 10–50 nm in a residual amorphous matrix. As the B4C content increases, the nanohardness of the composite coatings increases, but the fracture toughness begins to decrease at the B4C content higher than 20 vol.%. The optimal mechanical properties are obtained with 15 vol.% B4C due to the suitable content and uniform distribution of nanoparticles. Consequently, the changes in mechanical properties of the coatings were attributed to the changes in the brittle to ductile transition by adding B4C nanoparticles.

Keywords: Fe-based amorphous, B4C nanoparticles, nanocomposite coating, HVOF.

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Authors: Pejman Hosseinioun, Rose Shayeghi, Ghasem Ghorbani Rostam

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Nowadays, obtaining traditional statistics and reports is not adequate for the needs of organizational managers. The managers need to analyze and to transform the raw data into knowledge in the world filled with information. Therefore in this regard various processes have been developed. In the meantime the artificial intelligence-based processes are used and the new topics such as business intelligence and knowledge discovery have emerged. In the current paper it is sought to study the business intelligence and its applications in the organizations.

Keywords: Business intelligence, business intelligence infrastructures, business processes.

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Authors: Melike Şah, Konstantin Y.Degtiarev

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This paper proposes a novel improvement of forecasting approach based on using time-invariant fuzzy time series. In contrast to traditional forecasting methods, fuzzy time series can be also applied to problems, in which historical data are linguistic values. It is shown that proposed time-invariant method improves the performance of forecasting process. Further, the effect of using different number of fuzzy sets is tested as well. As with the most of cited papers, historical enrollment of the University of Alabama is used in this study to illustrate the forecasting process. Subsequently, the performance of the proposed method is compared with existing fuzzy time series time-invariant models based on forecasting accuracy. It reveals a certain performance superiority of the proposed method over methods described in the literature.

Keywords: Forecasting, fuzzy time series, linguistic values, student enrollment, time-invariant model.

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: Convolutional Neural Network, Deep Learning, Deep Learning Based FER, Facial Emotion Recognition.

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Authors: Ehsan Shahrabian, Ali Soleymani

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Several yield-based stress indices have been developed that may be more applicable to work on drought tolerance. In this study, we investigate possibility of using stress susceptibility index (SSI), tolerance index (TOL), yield stability index (YSI), yield index (YI), stress tolerance index (STI), geometric mean productivity (GMP), harmonic mean (HARM), mean productivity (MP) to identify genotypic performance of some maize cultivars under normal and stressed condition. The results indicate that it was possible to identify superior genotypes for drought tolerance based on their stress indices and generally SSI indices which showed the lowest negative correlation with dry matter yield can be used as the best index for maize breeding programs to introduce drought tolerant hybrids. It was found that SC 647 showed the best behavior under drought stress condition based on TOL and SSI. A higher STI, GMP, and HARM values were attained for ko_6. It can be suggested that ko₆ should be cultivated in moderate stressful environment of Iran.

Keywords: Index, productivity, stress, susceptibility tolerance, yield.

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Authors: Husam Kaid, Abdulrahman Al-Ahmari, Zhiwu Li

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This paper develops a robust deadlock control technique for shared and unreliable resources in automated manufacturing systems (AMSs) based on structural analysis and colored Petri nets, which consists of three steps. The first step involves using strict minimal siphon control to create a live (deadlock-free) system that does not consider resource failure. The second step uses an approach based on colored Petri net, in which all monitors designed in the first step are merged into a single monitor. The third step addresses the deadlock control problems caused by resource failures. For all resource failures in the Petri net model a common recovery subnet based on colored petri net is proposed. The common recovery subnet is added to the obtained system at the second step to make the system reliable. The proposed approach is evaluated using an AMS from the literature. The results show that the proposed approach can be applied to an unreliable complex Petri net model, has a simpler structure and less computational complexity, and can obtain one common recovery subnet to model all resource failures.

Keywords: Automated manufacturing system, colored Petri net, deadlock, siphon.

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Authors: Yingying Ding

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This study presents a methodology of specialized translation with the objective of helping teachers to improve the strategies in teaching translation. In order to allow students to acquire skills to translate specialized texts, they need to become familiar with the semantic and syntactic features of source texts and target texts. The aim of our study is to use a corpus-based approach in the teaching of specialized translation between Chinese and Italian. This study proposes to construct a specialized Chinese - Italian comparable corpus that consists of 50 economic contracts from the domain of food. With the help of AntConc, we propose to compile a comparable corpus in for translation teaching purposes. This paper attempts to provide insight into how teachers could benefit from comparable corpus in the teaching of specialized translation from Italian into Chinese and through some examples of passive sentences how students could learn to apply different strategies for translating appropriately the voice.

Keywords: Corpus-based approach, translation teaching, specialized translation.

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

Abstract:

Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: Communication signal, feature extraction, holder coefficient, improved cloud model.

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Authors: M. R. Vaezi, R. Ghasemi, A. Akramizadeh

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Frequency stability of microgrids under islanded operation attracts particular attention recently. A new cooperative frequency control strategy based on centralized multi-agent system (CMAS) is proposed in this study. Based on this strategy, agents sent data and furthermore each component has its own to center operating decisions (MGCC).After deciding on the information, they are returned. Frequency control strategies include primary and secondary frequency control and disposal of multi-stage load in which this study will also provide a method and algorithm for load shedding. This could also be a big problem for the performance of micro-grid in times of disaster. The simulation results show the promising performance of the proposed structure of the controller based on multi agent systems.

Keywords: Frequency Control, Islanded Micro-grid, Load shedding, Multi-agent System.

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Authors: Yi Chao Ma, Cheng Siong Chin, Wai Lok Woo

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Since the last decade, there has been a rapid growth in digital multimedia, such as high-resolution media files and threedimentional movies. Hence, there is a need for large digital storage such as Hard Disk Drive (HDD). As such, users expect to have a quieter HDD in their laptop. In this paper, a jury test has been conducted on a group of 34 people where 17 of them are students who are the potential consumer, and the remaining are engineers who know the HDD. A total 13 HDD sound samples have been selected from over hundred HDD noise recordings. These samples are selected based on an agreed subjective feeling. The samples are played to the participants using head acoustic playback system, which enabled them to experience as similar as possible the same environment as have been recorded. Analysis has been conducted and the obtained results have indicated different group has different perception over the noises. Two neural network-based acoustic annoyance models are established based on back propagation neural network. Four psychoacoustic metrics, loudness, sharpness, roughness and fluctuation strength, are used as the input of the model, and the subjective evaluation results are taken as the output. The developed models are reasonably accurate in simulating both training and test samples.

Keywords: Hard disk drive noise, jury test, neural network model, psychoacoustic annoyance.

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Authors: Hardik Vaghela, Mamta Khosla, Balwindar Raj

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In this paper, we present and investigate a double gate PN diode based tunnel field effect transistor (DGPNTFET). The importance of proposed structure is that the formation of different drain doping is not required and ambipolar effect in OFF state is completely removed for this structure. Validation of this structure to behave like a Tunnel Field Effect Transistor (TFET) is carried out through energy band diagrams and transfer characteristics. Simulated result shows point subthreshold slope (SS) of 19.14 mV/decade and ON to OFF current ratio (I_ON / I_OFF) of 2.66 × 10¹⁴ (I_ON at V_GS=1.5V, V_DS=1V and I_OFF at V_GS=0V, V_DS=1V) for gate length of 20nm and HfO₂ as gate oxide at room temperature. Which indicate that the DGPNTFET is a promising candidate for nano-scale, ambipolar free switch.

Keywords: Ambipolar effect, double gate PN diode based tunnel field effect transistor, high-κ dielectric material, subthreshold slope, tunnel field effect transistor.

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Authors: L. M. Stefan, A. M. Pana, M. Silion, M. Balan, G. Bandur, L. M. Rusnac

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The field of polymeric biomaterials is very important from the socio-economical viewpoint. Synthetic carbohydrate polymers are being increasingly investigated as biodegradable, biocompatible and biorenewable materials. The aim of this study was to synthesize and characterize some derivatives based on D-mannose. D-mannose was chemically modified to obtain 1-O-allyl-2,3:5,6-di- O-isopropylidene-D-mannofuranose and 1-O-(2-,3--epoxy-propyl)- 2,3:5,6-di-O-isopropylidene-D-mannofuranose. The chemical structure of the resulting compounds was characterized by FT-IR and NMR spectroscopy, and by HPLC-MS.

Keywords: D-mannose, biopolymers , spectroscopy, synthesis.

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Authors: P. M. Arun Kumar, S. Chandramathi

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The proliferation of multimedia technology and services in today’s world provide ample research scope in the frontiers of visual signal processing. Wide spread usage of video based applications in heterogeneous environment needs viable methods of Video Quality Assessment (VQA). The evaluation of video quality not only depends on high QoS requirements but also emphasis the need of novel term ‘QoE’ (Quality of Experience) that perceive video quality as user centric. This paper discusses two vital video quality assessment methods namely, subjective and objective assessment methods. The evolution of various video quality metrics, their classification models and applications are reviewed in this work. The Mean Opinion Score (MOS) based subjective measurements and algorithm based objective metrics are discussed and their challenges are outlined. Further, this paper explores the recent progress of VQA in emerging technologies such as mobile video and 3D video.

Keywords: 3D-Video, no reference metric, quality of experience, video quality assessment, video quality metrics.

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Authors: Chyn Liaw, Chun-Wei Tung, Shinn-Jang Ho, Shinn-Ying Ho

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The γ-turns play important roles in protein folding and molecular recognition. The prediction and analysis of γ-turn types are important for both protein structure predictions and better understanding the characteristics of different γ-turn types. This study proposed a physicochemical property-based decision tree (PPDT) method to interpretably predict γ-turn types. In addition to the good prediction performance of PPDT, three simple and human interpretable IF-THEN rules are extracted from the decision tree constructed by PPDT. The identified informative physicochemical properties and concise rules provide a simple way for discriminating and understanding γ-turn types.

Keywords: Classification and regression tree (CART), γ-turn, Physicochemical properties, Protein secondary structure.

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Authors: Mandep Kaur, Surbhi Gupta, Parvinder S. Sandhu, Jagdeep Kaur

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Steganography meaning covered writing. Steganography includes the concealment of information within computer files [1]. In other words, it is the Secret communication by hiding the existence of message. In this paper, we will refer to cover image, to indicate the images that do not yet contain a secret message, while we will refer to stego images, to indicate an image with an embedded secret message. Moreover, we will refer to the secret message as stego-message or hidden message. In this paper, we proposed a technique called RGB intensity based steganography model as RGB model is the technique used in this field to hide the data. The methods used here are based on the manipulation of the least significant bits of pixel values [3][4] or the rearrangement of colors to create least significant bit or parity bit patterns, which correspond to the message being hidden. The proposed technique attempts to overcome the problem of the sequential fashion and the use of stego-key to select the pixels.

Keywords: Steganography, Stego Image, RGB Image, Cryptography, LSB.

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Authors: Byung Ki Kim, Ho Min Jung, Jae-Bong Yoo, Wan Yeon Lee, Chan Young Park, Young Woong Ko

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In this paper, we present a novel approach to location system under indoor environment. The key idea of our work is accurate distance estimation with cricket-based location system using A* algorithm. We also use magnetic sensor for detecting obstacles in indoor environment. Finally, we suggest how this system can be used in various applications such as asset tracking and monitoring.

Keywords: Cricket, Indoor Location Tracking, Mobile Robot, Localization.

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Authors: Indradip Banerjee, Souvik Bhattacharyya, Gautam Sanyal

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‘Steganalysis’ is one of the challenging and attractive interests for the researchers with the development of information hiding techniques. It is the procedure to detect the hidden information from the stego created by known steganographic algorithm. In this paper, a novel feature based image steganalysis technique is proposed. Various statistical moments have been used along with some similarity metric. The proposed steganalysis technique has been designed based on transformation in four wavelet domains, which include Haar, Daubechies, Symlets and Biorthogonal. Each domain is being subjected to various classifiers, namely K-nearest-neighbor, K* Classifier, Locally weighted learning, Naive Bayes classifier, Neural networks, Decision trees and Support vector machines. The experiments are performed on a large set of pictures which are available freely in image database. The system also predicts the different message length definitions.

Keywords: Steganalysis, Moments, Wavelet Domain, KNN, K*, LWL, Naive Bayes Classifier, Neural networks, Decision trees, SVM.

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Authors: Yulia Stukalina

Abstract:

This paper explores the main issues related to implementing a cross-disciplinary research project (LEARNIT) based on collaboration between universities from three European countries. The paper discusses the importance of using the holistic approach to managing scientific projects with due account for the complicated nature of the educational environment of a modern university. To illustrate this approach, the author describes some actions to be taken for supporting different focus areas of LEARNIT project, in the process using integrated tangible, non-tangible, and semi-tangible resources of the partner university. The methodology of the paper is based on the academic literature and research papers analysis within management discipline. The analysis reported in the paper is also based on the author’s professional experience in the area of managing international research projects in a university.

Keywords: LEARNIT, focus area, project management, resources.

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Authors: P. Ashok, G. M. Kadhar Nawaz

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Rough set theory is used to handle uncertainty and incomplete information by applying two accurate sets, Lower approximation and Upper approximation. In this paper, the rough clustering algorithms are improved by adopting the Similarity, Dissimilarity–Similarity and Entropy based initial centroids selection method on three different clustering algorithms namely Entropy based Rough K-Means (ERKM), Similarity based Rough K-Means (SRKM) and Dissimilarity-Similarity based Rough K-Means (DSRKM) were developed and executed by yeast dataset. The rough clustering algorithms are validated by cluster validity indexes namely Rand and Adjusted Rand indexes. An experimental result shows that the ERKM clustering algorithm perform effectively and delivers better results than other clustering methods. Outlier detection is an important task in data mining and very much different from the rest of the objects in the clusters. Entropy based Rough Outlier Factor (EROF) method is seemly to detect outlier effectively for yeast dataset. In rough K-Means method, by tuning the epsilon (ᶓ) value from 0.8 to 1.08 can detect outliers on boundary region and the RKM algorithm delivers better results, when choosing the value of epsilon (ᶓ) in the specified range. An experimental result shows that the EROF method on clustering algorithm performed very well and suitable for detecting outlier effectively for all datasets. Further, experimental readings show that the ERKM clustering method outperformed the other methods.

Keywords: Clustering, Entropy, Outlier, Rough K-Means, validity index.

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Authors: Hossein Ali Akbarpour, Fatemeh Dadkhah

Abstract:

This paper considers a multi criteria cell formation problem in Cellular Manufacturing System (CMS). Minimizing the number of voids and exceptional elements in cells simultaneously are two proposed objective functions. This problem is an Np-hard problem according to the literature, and therefore, we can-t find the optimal solution by an exact method. In this paper we developed two ant algorithms, Ant Colony Optimization (ACO) and Max-Min Ant System (MMAS), based on Data Envelopment Analysis (DEA). Both of them try to find the efficient solutions based on efficiency concept in DEA. Each artificial ant is considered as a Decision Making Unit (DMU). For each DMU we considered two inputs, the values of objective functions, and one output, the value of one for all of them. In order to evaluate performance of proposed methods we provided an experimental design with some empirical problem in three different sizes, small, medium and large. We defined three different criteria that show which algorithm has the best performance.

Keywords: Ant algorithm, Cellular manufacturing system, Data envelopment analysis, Efficiency

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Authors: Gideon Avigad, Amiram Moshaiov, Neima Brauner

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An IEC technique is described for a multi-objective search of conceptual solutions. The survivability of solutions is influenced by both model-based fitness and subjective human preferences. The concepts- preferences are articulated via a hierarchy of sub-concepts. The suggested method produces an objectivesubjective front. Academic example is employed to demonstrate the proposed approach.

Keywords: Conceptual solution, engineering design, hierarchical planning, multi-objective search, problem reduction.

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Authors: T. K N. Hoang, P. A. Tuan, R. Finsy, L. Deriemaeker

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The pigments covered by film-forming polymers have opened a prospect to improve the quality of water-based printing inks. In this study such pigments were prepared by the initiated polymerization of styrene and methacrylate derivative monomers in the aqueous pigment dispersions. The formation of polymer films covering pigment cores depends on the polymerization time and the ratio of pigment to monomers. At the time of 4 hours and the ratio of 1/10 almost pigment particles are coated by the polymer. The formed polymer covers of pigments have the average thickness of 5.95 nm. The size increasing percentage of the coated particles after a week is 4.5 %, about fourteen-fold lower than of the original ones. The obtained results indicate that the coated pigments are improved dispersion stability in water medium along with a guarantee for the optical colour.

Keywords: Aqueous pigment dispersion stability, colored resin particles, emulsion polymerization, water based ink.

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Authors: Santirat Nansaarng, Sittichai Kaewkuekool, Supreeya Siripattanakunkajorn

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The purpose of this research was to study the inspector performance by using computer based training (CBT). Visual inspection task was printed circuit board (PCB) simulated on several types of defects. Subjects were 16 undergraduate randomly selected from King Mongkut-s University of Technology Thonburi and test for 20/20. Then, they were equally divided on performance into two groups (control and treatment groups) and were provided information before running the experiment. Only treatment group was provided feedback information after first experiment. Results revealed that treatment group was showed significantly difference at the level of 0.01. The treatment group showed high percentage on defects detected. Moreover, the attitude of inspectors on using the CBT to inspection was showed on good. These results have been showed that CBT could be used for training to improve inspector performance.

Keywords: Training, Feedback, Computer based Training (CBT)

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