Ebook: New Trends on System Sciences and Engineering

System Science and Engineering has emerged as a research field that covers a wide spectrum of modern technology. A system can be considered as a collection of entities and their interrelationships gathered together to form a whole greater than the sum of its parts. It also involves people, organizations, cultures, activities and interrelationships among them. While systems composed of autonomous subsystems are not new, greater data density, connectivity and ubiquitous computational resources have increased their interdependence and interaction complexity. This has in turn made the already difficult job of planning, developing and deploying complex systems even more difficult. Technology advancement not only provides opportunities for improving system capabilities but also introduces development risks that must be weighed and managed.

Conventional System Science technologies in spite of their high cost were not providing sufficient or reliable systems for our evolving demanding needs. Research has participated in providing a modern and superior outlook on such technologies and highlighted better awareness in providing a new direction and having better techniques that are more intelligent, robust and adaptable to evolving needs.

Thus, it is our goal to bring together scholars from all areas to have a forum to discuss, demonstrate and exchange research ideas.

This book volume aims to capture the essence of a new state of the art in system sciences and their supporting technology, and to identify the challenges that such a technology will have to master. It contains extensively reviewed papers presented at the International Conference on System Science and Engineering (ICSSE2015) held in Morioka, Japan with the collaboration of Taiwan Association of System Science and Engineering (TASSE) on July 06–08 2015 (http://www.somet.soft.iwate-pu.ac.jp/ICSSE_2015/index.htm).

The conference brought together researchers and practitioners to share their original research results and practical development experience in system science and related new technologies. This book volume participates in the conference providing an outcome for exchanging ideas and experiences in the field of system science and technology; opening up new avenues for science development, methodologies, tools, and techniques.

The book is a collection of carefully selected refereed papers by the reviewing committee (Program Committee members) and covering:

• Systems Modeling, Tools and Simulation

• Medical and Health Systems

• Cloud Robotics and Cloud Computing Systems

• Traffic Science and Engineering

• Knowledge-Based Systems and applications

• Biological systems and Evolution

• Communication Systems

• Systems Engineering Management Process

• Decision and Control Systems, Decision Support System Modeling and Techniques, and Intelligent Systems

• Systems Safety and Security

• Optimization and Systems Complexity

• Systems Risk Management

• System Engineering Language, and Information and System Engineering

• Smart Grid, Human Systems, and Industrial Organization and Management

• E-Commerce Systems, and Systems Science and Cybernetics

• Integrated Automation Technology Applications

• Novel Applications of Systems Engineering and Systems Architecting

• Systems Engineering Technical and Support Process

• Energy Saving

• Systems Application in Business and Industry

All articles in this book have been carefully reviewed, on the basis of technical soundness, relevance, originality, significance, and clarity, by up to three reviewers. They were then revised on the basis of the review reports before being selected by the editors.

This book is the result of a collective effort from many partners and colleagues. In special we would like acknowledge our gratitude to Taiwan Association of System Science and Engineering, (TASSE), Iwate Prefectural University, The Telecommunication Advancement Foundation, and Foundation Tateisi Science and Technology Japan for their invaluable support for this conference and its proceedings.

We also appreciate the quality of support provided by the outstanding prestigious contributed plenary speakers:

• Prof. Dr. Philip Chen, IEEE Fellow, University of Macau, Macau

• Prof. Dr. Imre Rudas, IEEE Fellow, Óbuda University, Hungary

• Prof. Dr. William A. Gruver, IEEE Fellow, Simon Fraser University, Canada

• Prof. Dr. Love Ekenberg, IIASA, Austria

Their support in providing their knowledge and expertise to the conference's participants reflected by their plenary talks, round session's discussions and contribution in conference technical proceedings all could provide excellent gaining capabilities that collectively enhanced the quality of the outcome in this book.

Most especially, we thank the reviewing committee and all those who participated in the rigorous reviewing process and the lively discussion and evaluation meetings which led to the selected papers appeared in this book.

Last but not least, we would also like to thank the Microsoft Conference Management Tool team for their expert guidance on the use of the Microsoft CMT System as a conference-support tool during all the phases of ICSSE2015.

To asses the quality of the services provided by a digital library, traditional measures, such as the size of its collection, have usually been utilized. However, service quality also has to be evaluated by considering users' expectations. In addition, as a digital library plays an important role in the educational progress of a society, it is very important not only to measure the quality of its services but also to improve them. In this contribution, we present a web information system which supports the staff of a digital library to carry out decisions with the aim of improving the services offered by it. To do so, this system provides some advice taking into account both objective criteria, related to quantitative data, and subjective criteria, related to users' judgments.

In Social Networks there is a trend for people of similar interests to associate with each other. Such a tendency is called homophily, and in this paper we study the effect it has on calculating user sentiment profiles. We collect Twitter data such as tweets and follower relationships, and we use that to calculate initial user profiles (topic sentiments) and user connectivity. The novel approach in this paper is in the way user sentiment is calculated by using the impact of the related users. This impact related users have on one another depends on the connectivity factor between two people. We show that applying such an approach, which utilizes user connectivity when calculating user profiles, leads to better accuracy and we also show that there are indications it might help in tackling the cold start problem.

The invisible hand behind of economics cannot be seen and approved from the general understanding. When facing the global financial crisis, the economic behavior following subjective momentum has to be changed to avoid crash. The power of strategic game might potentially turn around the world heading to destruction. Therefore, we propose a model of strategy identification to analyze how the policy of Quantitative Easing helped USA and the emerging nations to get out the global financial crisis. The proposed model is constructed with formation of strategic game, definition of strategic moves, and identification of game moves. The application results show Quantitative Easing successfully turned around the global financial crisis.

This study proposed Particle Swarm Optimization-based (PSO-based) estimation method to overcome the error of time difference of arrival (TDOA) in a reverberant environment. It uses numerical TDOA estimations to be the initial value and the information of TDOA of first reflective paths is considered. Moreover, the objective function utilizes the property of alignment to search for the TDOA of direct paths. And then the results of PSO, we will obtain three estimations of TDOA. It can simply enhance the signal quality.

To reduce the accident, technology of computer vision and the iPhone are used to develop of forward collision warning system. Generally, hardware device was used for the system, such as infrared. Technology of computer vision was used for lane, vehicle and distance. Lane data was detected from features about line. Lane data was used for average method, make lane detection robust. Based on the shadow of vehicle, the vehicle position was detected. Warning machine is judges dangerous for vehicle. The warning mechanism depends on the condition that is sorted. Each country has prescribed a length of dashed line in highway. Based on length of dashed line, 4 meters of the area in front of the vehicle is calculated based on Pinhole camera model. This system can be applied to iPhone. The iPhone has a best camera capabilities, auto-exposure, and auto-correction. Experimental results show that rate of lane detection does not affect the rate of error alert in warning mechanism. Forward collision warning system is robust.

Recently, some of applications (APP) have been proposed about multi-cloud manage systems for iPhone. In this study, the personal style of multi-cloud manage systems is proposed. Instead of the other multi-cloud manage systems APPs on market which have only the limited-function cloud-drive that offered by companies. In this system, the users of iOS mobile devices not only storage the cloud-drive on the net that offered by companies, but also manage the own cloud-drive created on FTP server. In order to ensure the integrity of the file after be transferred, iOS system use the MD5 algorithm to synchronize files. Comparing the file stored between iOS device and FTP server, while avoiding the occurrence of missing packets.Though this proposed system, we can manage more varied cloud drive on iOS device. With the private cloud-drive, users will not be limited the size of file, storage-size and type, etc.

The mobile wireless sensor nodes are expected to be used in a high dangerous region and to replace people for discovering and collecting the data information. Suitable deployment is one of the most important issues for the sensor nodes to obtain a reasonable coverage ratio and to reduce the energy consumption in a large scale sensing area. Because the moving distance of sensor nodes will affect the deployment time and energy consumption, it is important and necessary to develop an efficient self-deployment strategy. In this paper, we propose a new approach for the utilization of repulsion and center attraction virtual forces by adopting the concepts of virtual local center. The main goal of this strategy is to keep suitable distances among the sensor nodes. It leads to a lower deployment time and a higher sensing region for the sensor nodes. Simulation results show that the proposed strategy provides an appropriate coverage ratio, deployment time, and moving distance for the sensor nodes in mobile wireless sensor networks.

Frequent pattern mining research has raised an interest in studying the dynamic behavior of patterns whose frequency significantly alters over time periods. This paper presents an investigation into using a sliding window approach in exploring the dynamic behavior of frequent patterns in consecutive time periods where they occur in a data set. This approach gives a promising alternative to the existing techniques being used for decision makers to know the exact period in which a pattern's frequency significantly alters and therefore responds appropriately. The results will help in detecting and reporting significant changes in the frequency of patterns sequentially from one time period to another.

In this paper, the feature selection algorithm with multi-granulation is proposed for symbolic interval-value data. A subset of a data set can be considered as a small granularity. Given a large-scale data set, the algorithm first selects different small granularities and then estimate on each small granularity the reduct of the original data set. Furthemore, it will introduce IT2 into hybrid fuzzy-rough QuickReduct algorithm to deal with interval-value data. Consequently, the algorithm will includes lower and upper dependency functions, lower and upper uncertainty degrees simultaneous. The “weighted” concept is not only enhance prior knowledge (important attribute), and also reduce unknown information effect. Fusing all of the estimates on small granularities together, the algorithm can get an approximate reduct.

In this paper, a concise method is proposed to locate the nipple position on a mammogram image. Mammogram registration is an important preprocessing technique, which can help in finding asymmetrical regions in left or the corresponding right breast. In particular, correct nipple position is the crucial key point of mammogram registration since it is still the most consistent and stable landmark on a mammogram. This work first presents an algorithm of maximum height of the breast border (MHBB) and then proposes two novel approaches, local spatial-maximum mean intensity (MMI) and local maximum zero-crossing (MZC) based on the second-order derivative, finally a combined process depending on the MMI and MZC is obtained. The 213 mammogram images from MIAS and 200 ones from DDSM database are tested for estimating the proposed method. Consequently, the mean Euclidean distance (MED) between the nipple position detected and the ground truth identified by the radiologist is 0.63 cm, within 1 cm of the gold standard. The experimental results hence indicate that our combined process can detect the nipple positions more accurately, as compared to other previous methods. Moreover, the proposed LVNM (Locate visible-nipple mammograms) algorithm designed with the generalization ability for automatic nipple clustering in the MIAS database has also yielded an identification rate of 99.53%.

Using Depth Image Based Rendering (DIBR) is the most popular method to generate virtual views for multi-view stereoscopic images or videos. However, when the depth values change rapidly, there are some hole occurrences in the region of the edges of the objects. This paper proposes a preprocessing method for the depth map by an edge-based asymmetric Gaussian blurring matrix before doing DIBR. The proposed depth preprocessing is based on edge direction of the edge of the objects; therefore, the proposed algorithm not only reduces the hole occurrences to enhance 3D virtual view quality but also preserves the shape of the objects in the foreground to keep the 3D view experience. The simulation results show that the boundaries of the objects are shape by using the proposed edge based asymmetric Gaussian blurring matrix than the other comparison methods. In addition, the proposed algorithm can keep the average depth value of the depth map to maintain more stereo vision for 3D view experience.

Face detection is an important technique and has been employed in many applications. Many literatures have been proposed to extract the features of face. However, in many cases, the human face may be obscured by other people or objects which cause the face cannot be detected. This paper focuses on how to do partial face detection and discusses what the significant feature parts on face are. We employed SURF algorithm to compute the reliable feature parts on face so that the partial face can be detected. In order to evaluate the degree of importance for each feature part, this paper introduces CV value to appraise each feature part of face. Finally, the experimental result demonstrates the effectiveness of the proposed algorithm.

The paper proposes a foreground extraction method for X-ray imaging auxiliary system. In the radiographic projection process, patient will put their palm on the receptor board. When the depth histogram of palm image and receptor board image are very similar, traditional method is hard to obtain satisfied segmentation results. Therefore, the paper utilizes the improved maximum filter to distinguish the palm image and receptor board image from the original image. In the experimental results, we compare Otsu method with our proposed method. It is evident that our proposed method can obtain a more complete palm image than the Otsu method.

Virtual Histology Intravascular Ultrasound (VH-IVUS) is a clinically available for visualizing color coded of coronary artery plaque. However, current VH-IVUS image processing techniques have not considered the combinations of features to identify vulnerable plaque. This paper presents a new method for classification of TCFA (thin-cap fibroatheromas) and Non-TCFA plaque based on combined features using the VH-IVUS images using support vector machine (SVM). The proposed method is applied to 546 in-vivo VH-IVUS images. Results proved the dominance of our proposed method with accuracy rates of 98.15% for TCFA.

Tandem repeat structures are widely distributed among all classes of proteins. Various basic structural units of repetitive nature possess functional diversity and reflect important influences on biological responses for different organisms. One of the most common types of protein repeat structure is the α-solenoid tandem repeat class which possesses low sequence similarity between any two repeat units within a structure. Therefore, a successful segmentation system for identifying each repeat unit cannot be achieved mainly based on sequence comparison approaches. For a comprehensive analysis on fundamental repeat unit segmentation, subclass identification, and functional annotation on such repeat structures, we have developed an automatic segmentation system according to geometrical coordinates and physical characteristics. Dihedral angles of Psi and Alpha were applied to define the range of candidate α helix elements, and the included angle between the vectors formulated by previously defined neighboring α helix elements was analyzed for constructing a fundamental repeat unit. To evaluate the performance of our developed prediction system, we employed 923 protein structures collected in the RepeatsDB database and clustered as α-solenoid repeat class. The testing result has shown that our proposed system could achieve a recall rate of 92.39% and a precision rate of 93.52%, respectively.

Ankyrin Repeat Domain (ARD) is an alpha-solenoid repeat structure formed by cascading a series of ankyrin repeat units. These fundamental repeat units within a structure possess low sequence similarity but high structural conservation. An ARD serves as a protein–protein interaction platform in nature, and it is discovered as an important factor influencing hypoxia response through hydroxylation interaction with Factor Inhibiting HIF (FIH) enzymes which can repress HIF under normoxia environment. In this study, we designed a sequence based method incorporated with secondary structural features to predict boundaries of all internal repeats within an ARD protein, and the binding positions for hydroxylation were also identified through pattern matching approaches. Performance of the proposed prediction system achieved a sensitivity of 73.1%, a specificity of 99.3%, and an accuracy of 94.1% for ARD recognition. In addition, a comprehensive web database system was constructed with a total of 15,322 identified ARD candidates from all 63 model species genomes collected in Ensembl (release version of 73). We believe that the proposed prediction system and developed database can facilitate biologists in further exploration on ARD related researches regarding protein-protein interaction mechanisms.

The theme of this paper is to analyze the effect of the eye-mask soothing aromatherapy treatment by using the grey model theory. The eye mask agent is made of stress relief floral water, and we lay it over two eyes to relax stress of subjects. Although traditional alterative medicine describes that the floral water can be transported through the blood circulation from the skin to the various organs, and finally reaches the central nervous system, it is lacked scientific verification. Thus, this paper measures the heart rate variability of subjects and finds the relation between the stress relief and eye-mask aromatherapy. The grey GM(0,N) model theory is applied to find the activity relationship of the pressure index to influence factors: heart rate, standard deviation of all normal to normal intervals, total power, very low frequency power, low-frequency power, and high-frequency power. Based on the grey analysis, the action of the eye-mask aromatherapy is constructed with more scientific study than previous works. The results can be a reference to aromatherapy alterative medicine.

In this paper, we propose an energy-based ant colony optimization algorithm for path planning. Because the shortest path does not guarantee the optimal energy-conserving way, this paper utilizes the ant colony optimization algorithm to acquire the optimal energy-conserving path. For battery-powered electric vehicles, the energy consumption depends on the road condition. Therefore, according to the road condition, the update law with energy pheromone is obtained. Finally, computer simulations and real road experiments of the battery-powered electric vehicle were conducted to verify the efficiency of the proposed method.

Q-Learning is one of the well-known model-free RL methods. When it discovers a good reward, it needs much iteration to diffuse the information. In this paper, we used an adaptive model learning method based on tree structure to imitate experience. Predictions let agent have extra experience to learn policy indirectly. Agent can use the tree-model to imitate the transition between two states. However, when model is not accurate, it may get through absorbing states. Getting through absorbing states influence the learning efficiency. In order to solve this problem, we propose two methods to enhance the tree-model. For demonstrating the proposed method, we introduce two simulations to verify the proposed methods. The simulation results demonstrate that the training rate of our method can improve obviously.