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Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.
Rusul Abduljabbar; Hussein Dia; Pei-Wei Tsai; Sohani Liyanage. Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction. Future Transportation 2021, 1, 21 -37.
AMA StyleRusul Abduljabbar, Hussein Dia, Pei-Wei Tsai, Sohani Liyanage. Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction. Future Transportation. 2021; 1 (1):21-37.
Chicago/Turabian StyleRusul Abduljabbar; Hussein Dia; Pei-Wei Tsai; Sohani Liyanage. 2021. "Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction." Future Transportation 1, no. 1: 21-37.
Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient.
Xingsi Xue; Chaofan Yang; Chao Jiang; Pei-Wei Tsai; Guojun Mao; Hai Zhu. Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences. Complexity 2021, 2021, 1 -12.
AMA StyleXingsi Xue, Chaofan Yang, Chao Jiang, Pei-Wei Tsai, Guojun Mao, Hai Zhu. Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences. Complexity. 2021; 2021 ():1-12.
Chicago/Turabian StyleXingsi Xue; Chaofan Yang; Chao Jiang; Pei-Wei Tsai; Guojun Mao; Hai Zhu. 2021. "Optimizing Ontology Alignment through Linkage Learning on Entity Correspondences." Complexity 2021, no. : 1-12.
Literature Based Discovery (LBD) aims at building bridges between existing literatures and discovering new knowledge from them. Biomedical ontology is such a literature that provides an explicit specification on biomedical knowledge, i.e., the formal specification of the biomedical concepts and data, and the relationships between them. However, since biomedical ontologies are developed and maintained by different communities, the same biomedical information or knowledge could be defined with different terminologies or in different context, which makes the integration of them becomes a challenging problem. Biomedical ontology matching can determine the semantically identical biomedical concepts in different biomedical ontologies, which is regarded as an effective methodology to bridge the semantic gap between two biomedical ontologies. Currently, Evolutionary Algorithm (EA) is emerging as a good methodology for optimizing the ontology alignment. However, EA requires huge memory consumption and long runtime, which make EA-based matcher unable to efficiently match biomedical ontologies. To overcome these problems, in this paper, we define a discrete optimal model for biomedical ontology matching problem, and utilize a compact version of Evolutionary Algorithm (CEA) to solve it. In particular, CEA makes use of a Probability Vector (PV) to represent the population to save the memory consumption, and introduces a local search strategy to improve the algorithm’s search performance. The experiment exploits Anatomy track, Large Biomed track and Disease and Phenotype track provided by the Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance. The experimental results show that CEA-based approach can effectively reduce the runtime and memory consumption of EA-based matcher, and determine high-quality biomedical ontology alignments.
Xingsi Xue; Pei-Wei Tsai. Matching Biomedical Ontologies with Compact Evolutionary Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 3 -10.
AMA StyleXingsi Xue, Pei-Wei Tsai. Matching Biomedical Ontologies with Compact Evolutionary Algorithm. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():3-10.
Chicago/Turabian StyleXingsi Xue; Pei-Wei Tsai. 2020. "Matching Biomedical Ontologies with Compact Evolutionary Algorithm." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 3-10.
Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.
George Baryannis; Ilias Tachmazidis; Sotiris Batsakis; Grigoris Antoniou; Mario Alviano; Timos Sellis; Pei-Wei Tsai. A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming. Theory and Practice of Logic Programming 2018, 18, 355 -371.
AMA StyleGeorge Baryannis, Ilias Tachmazidis, Sotiris Batsakis, Grigoris Antoniou, Mario Alviano, Timos Sellis, Pei-Wei Tsai. A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming. Theory and Practice of Logic Programming. 2018; 18 (3-4):355-371.
Chicago/Turabian StyleGeorge Baryannis; Ilias Tachmazidis; Sotiris Batsakis; Grigoris Antoniou; Mario Alviano; Timos Sellis; Pei-Wei Tsai. 2018. "A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming." Theory and Practice of Logic Programming 18, no. 3-4: 355-371.
Forecasting is an important technique in many industries and business fields for reading the terrain. The category of technology industry stock, which includes 7 independent stocks, in Taiwan Stock Exchange (TWSE) is selected to be the study subject in this paper. The goal is to forecast the return index of the individual stocks base on the information observed from the trading historical da-ta of the subjects. By including the trading volume, the number of trading rec-ords, the opening price, and the closing price in the inputs to the representative models in time-series and computational intelligence: EGARCH(1,1) and the In-teractive Artificial Bee Colony (IABC), respectively, the forecasting accuracy are compared by the Mean Absolute Percentage Error (MAPE) value. The experi-mental results indicate that the IABC forecasting model with the selected input variables presents superior results than the EGARCH(1,1).
Tien-Wen Sung; Cian-Lin Tu; Pei-Wei Tsai; Jui-Fang Chang. Short-Term Forecasting on Technology Industry Stocks Return Indices by Swarm Intelligence and Time-Series Models. Blockchain Technology and Innovations in Business Processes 2017, 81, 272 -279.
AMA StyleTien-Wen Sung, Cian-Lin Tu, Pei-Wei Tsai, Jui-Fang Chang. Short-Term Forecasting on Technology Industry Stocks Return Indices by Swarm Intelligence and Time-Series Models. Blockchain Technology and Innovations in Business Processes. 2017; 81 ():272-279.
Chicago/Turabian StyleTien-Wen Sung; Cian-Lin Tu; Pei-Wei Tsai; Jui-Fang Chang. 2017. "Short-Term Forecasting on Technology Industry Stocks Return Indices by Swarm Intelligence and Time-Series Models." Blockchain Technology and Innovations in Business Processes 81, no. : 272-279.
In this study, we focus on analysing the relationship between the foreign exchange rate and the international tourism flow. Three foreign exchange rate forecasting models including GARCH(1,1), EGARCH(1,1), and the IABC forecasting model based on the computational intelligence are employed to produce the forecasting results. The Mean Absolute Percentage Error (MAPE) is selected to be the evaluation criterion for comparing the forecasting results of these models. The experiments contain the USD/NTD foreign exchange rate and the inbound international tourism flows in years of 2009 to 2010. The experimental results reveal that adding the international tourism flow as the new reference in the forecasting process has the positive contribution to the foreign exchange rate forecasting results.
Pei-Wei Tsai; Zhi-Sheng Chen; Xingsi Xue; Jui-Fang Chang. Studying the Influence of Tourism Flow on Foreign Exchange Rate by IABC and Time-Series Models. Blockchain Technology and Innovations in Business Processes 2017, 81, 225 -232.
AMA StylePei-Wei Tsai, Zhi-Sheng Chen, Xingsi Xue, Jui-Fang Chang. Studying the Influence of Tourism Flow on Foreign Exchange Rate by IABC and Time-Series Models. Blockchain Technology and Innovations in Business Processes. 2017; 81 ():225-232.
Chicago/Turabian StylePei-Wei Tsai; Zhi-Sheng Chen; Xingsi Xue; Jui-Fang Chang. 2017. "Studying the Influence of Tourism Flow on Foreign Exchange Rate by IABC and Time-Series Models." Blockchain Technology and Innovations in Business Processes 81, no. : 225-232.
Monkey King evolutionary algorithm (MKEA) is a new type and innovation of gene method that can be more effective evolution of the algorithm to reach goal or objective function. In this study and research is applied the monkey king evolutionary algorithm is used to apply the evolutionary particle to find the optimal power flow of system and calculate the complex power of each line, bus and to minimize power generation cost of the power plant. In order to study the practicability of the algorithm, it is applied to the standard IEEE 5bus load flow test system, and its convergence characteristic curve is observed and compared with the genetic algorithm (GA). The experimental results show that the MKEA can effectively solve the power system optimal power flow problem and this method is find the global solution not local solution that be confirmed in minimum fuel cost of generator of power plants. The minimum fuel cost obtained by MKE and GA is 5369.55 and 5422.0 US Dollars, respectively, when the number of population particles is 100 and the number of iterations is 300 that compared with GA which is 7.6% lower than GA. The results show that MKE has the obvious superiority to find the global solution.
Jing Tang; Jeng-Shyang Pan; Yen-Ming Tseng; Pei-Wei Tsai; Zhenyu Meng. Optimal Economic Dispatch of Fuel Cost Based on Intelligent Monkey King Evolutionary Algorithm. Blockchain Technology and Innovations in Business Processes 2017, 82, 236 -243.
AMA StyleJing Tang, Jeng-Shyang Pan, Yen-Ming Tseng, Pei-Wei Tsai, Zhenyu Meng. Optimal Economic Dispatch of Fuel Cost Based on Intelligent Monkey King Evolutionary Algorithm. Blockchain Technology and Innovations in Business Processes. 2017; 82 ():236-243.
Chicago/Turabian StyleJing Tang; Jeng-Shyang Pan; Yen-Ming Tseng; Pei-Wei Tsai; Zhenyu Meng. 2017. "Optimal Economic Dispatch of Fuel Cost Based on Intelligent Monkey King Evolutionary Algorithm." Blockchain Technology and Innovations in Business Processes 82, no. : 236-243.
A wireless sensor network is a sensing system composed of a few or thousands of sensor nodes. These nodes, however, are powered by internal batteries, which cannot be recharged or replaced, and have a limited lifespan. Traditional two-tier networks with one sink node are thus vulnerable to communication gaps caused by nodes dying when their battery power is depleted. In such cases, some nodes are disconnected with the sink node because intermediary nodes on the transmission path are dead. Energy load balancing is a technique for extending the lifespan of node batteries, thus preventing communication gaps and extending the network lifespan. However, while energy conservation is important, strategies that make the best use of available energy are also important. To decrease transmission energy cost and prolong network lifespan, a three-tier wireless sensor network is proposed, in which the first level is the sink node and the third-level nodes communicate with the sink node via the service sites on the second level. Moreover, this study aims to minimize the number of service sites to decrease the construction cost. Statistical evaluation criteria are used as benchmarks to compare traditional methods and the proposed method in the simulations.1. IntroductionWireless sensor networks (WSNs) are spatially distributed autonomous sensors used to monitor physical or environmental conditions, such as pressure, sound, and temperature. WSNs are composed of common sensor nodes and sink nodes [1, 2]; the common sensor nodes cooperatively pass their data through the network to a sink node. The development of wireless sensor networks was originally motivated by military applications such as remote sensing or data collection in dangerous or remote environments [3]. Today, these networks are used in many industrial and consumer applications and have become part of daily life. WSNs are built of a few to several hundreds or even thousands of nodes, where each node can connect with one or more sensors. Each sensor node is equipped with several parts, namely, a transceiver, a sensing device, and an energy source. These sensor nodes differ in size and cost, which results in corresponding constraints on resources such as energy, memory, and computational speed [4–7]. Their energy source is usually a battery, which is undesirable and infeasible to replace or recharge [8–10]. Therefore, network lifespan becomes a vital concern in the construction of a WSN [11]. However, unbalanced energy consumption between inner nodes (the nodes close to the sink node) and outer nodes (the node far away from sink node) always occurs and is uncontrolled in two-tier network structures. Sink nodes, the only nodes that control and operate as processing centers, collect all the valuable packages from the sensor nodes via a predefined routing path. The inner nodes not only transfer their own sensed data, but also pass on data from outer nodes. Thus, inner nodes have greater energy consumption than that of outer nodes. The more energy one node uses, the earlier it depletes its battery. The worst case scenario resulting from this is if the depleted node is the only communication line between outer nodes and the sink node. In this network structure, if even a few inner nodes die, many outer nodes will be affected. In this situation, several service sites which have part of the functions of a sink node become necessary, and the sensor nodes then send their data to the nearest service site instead of the sink node. This also decreases the workload on inner nodes and extends the lifespan of the overall network. This paper focuses on developing a method to determine the optimal number of service sites for a given network. The cost of deployment and construction of a service site is much greater than that of a common sensor; thus, there should be a minimum necessary number of service sites in the network to satisfy full coverage demand.Given nodes with specified distances, centers must be constructed for groups of nodes in such a way as to minimize the maximum distance between nodes and their centers. This is the -center problem. The goal of this paper is to minimize the number of service sites in a wireless sensor network, thus reducing the construction cost of a three-tier network caused by service sites. More importantly, this three-tier network must satisfy the full coverage requirement. The number of service sites is considered in the -center problem. However, is not yet known. One of the most popular methods for resolving the -center problem is the farthest first method [12]; although this method satisfies a 2-approximation solution, it is not perfect. This paper proposes a new scheme, HHSG, to solve the service site problem. The name of “HHSG” was given by an integrated abbreviation of “Huffman coding,” “Hilbert curve,” “Sudoku puzzle,” and “genetic algorithm” because the concepts of these four classical terms were utilized in our proposed scheme. Furthermore, several other methods are simulated and applied to wireless sensor networks.The remainder of this paper is structured as follows: Section 2 reviews background work on Hilbert curves, the -center problem, and wireless sensor networks and will also describe related work on basic genetic algorithms and Sudoku and Huffman codes. Section 3 describes the HHSG process in detail. Experimental results and some analysis with other methods are given in Section 4. Conclusion is offered in Section 5.2. Related WorksWireless sensor networks have been widely used in vast variety of different fields. Driven by microelectromechanical systems technology advances in low-cost networking, there have been rapid development and use of wireless sensor networks in recent years [13, 14]. These sensor networks carry the promise of significantly improving and expanding the quality of care across a wide range of applications, which include air pollution monitoring, medicine and public health, and natural disaster prevention. Although a general two-tier network is considered to be a flat network and has a very simple structure, it has an inherent disadvantage in terms of balancing the workload of its sensor nodes. When inner nodes deplete their batteries, they die and disconnect from their outer nodes, interrupting the routing path from the outer nodes to a sink node. As a result, many nodes that still have sufficient energy to function will be removed from the network, and their information will no longer be forwarded to a sink node. Alternatively, a hierarchical network is a network in which all sensor nodes are clustered through some specific technique according to given protocols [15, 16]. Hierarchical networks facilitate equalized power consumption.2.1. Genetic AlgorithmsGenetic algorithms are a family of computational models inspired by natural evolution [17–20]. In a genetic algorithm, a population of candidate solutions to a problem is evolved toward better solutions. Each candidate solution, which is expressed in binary string of 0 or 1, is a chromosome with a set of attributes which can be mutated and modified. The basic genetic algorithm usually starts by generating several random chromosome solutions, then evaluating each chromosome, and storing the ones with better fitness values as the algorithm approaches an optimal solution by randomly mutating and altering the predefined number of genes to generate a new solution. This new solution will be used in the next iteration. Commonly, the algorithm stops when it reaches a predefined number of iterations or time limit or when there is one solution that is satisfied. Genetic algorithm is widely used in many applications and is also combined with other methods to generate new optimal solutions [21, 22].2.2. Space-Filling CurveA space-filling curve is a single one-dimensional curve that tours around an entire 2 or more dimensional space and recursively fills up all points when the number of iterations approaches infinity [23, 24]. Because Giuseppe Peano (1858–1932) was the first to discover one of the filling curve constructions, space-filling curves in 2-dimensional planes are sometimes called Peano curves. Some of the most celebrated are the Hilbert curve and the Sierpiński curve [23]. Space-filling curves are used in many fields. In 2014, Yan and Mostofi [24] scheduled a data collection path for mobile robots using space-filling curves; his goal was to minimize the total energy consumption, including the communication cost between the robot and sensors and the motion cost of the robot. In this study [25], the problem of how mobile sinks should move is addressed. A good strategy for a moving trajectory for mobile sinks can reduce data loss and delivery delay, increase network lifetime, and enable better handling of sparse networks. A dynamic Hilbert curve is used to design a trajectory for a mobile sink while achieving efficient network coverage. The dynamic curve order varies with node densities in a network. Simulation results show the effectiveness of network coverage and scalability.For Hilbert curves, if there is a point within the unit square, with coordinates (, ), is the distance along the curve from the start till it reaches that point. Points from the curve that have nearby values will also have nearby coordinate (, ) values. The basic level one (also called first order) Hilbert trajectory is a 2 × 2 grid. The method of recursively constructing a Hilbert filling curve is described as follows: dividing the network field into 4 small grid cells, the one-level Hilbert curve will be the line passing through the centers of those four-grid cells in a specific order of points. To derive a two-level curve, it simply replaces each small grid cell with a one-level curve which may be appropriately rotated and reflected. And -level curve is derived from an -level curve. Intuitively, the higher the level of the curve is, the more accurate its localization precision will be. Ho
Lingping Kong; Jeng-Shyang Pan; Tien-Wen Sung; Pei-Wei Tsai; Václav Snášel. An Energy Balancing Strategy Based on Hilbert Curve and Genetic Algorithm for Wireless Sensor Networks. Wireless Communications and Mobile Computing 2017, 2017, 1 -13.
AMA StyleLingping Kong, Jeng-Shyang Pan, Tien-Wen Sung, Pei-Wei Tsai, Václav Snášel. An Energy Balancing Strategy Based on Hilbert Curve and Genetic Algorithm for Wireless Sensor Networks. Wireless Communications and Mobile Computing. 2017; 2017 ():1-13.
Chicago/Turabian StyleLingping Kong; Jeng-Shyang Pan; Tien-Wen Sung; Pei-Wei Tsai; Václav Snášel. 2017. "An Energy Balancing Strategy Based on Hilbert Curve and Genetic Algorithm for Wireless Sensor Networks." Wireless Communications and Mobile Computing 2017, no. : 1-13.
The popularity of Internet and growing B2C electronic commerce nowadays make product or service information easy to be acquired. However, making an optimal choice from the various alternative products becomes a laborious process. In this paper, an ontology-based Decision Support System (DSS) with Analytic Hierarchy Process (AHP) was proposed for the specific application of tour package selection. The system is composed of two subsystems, the product gatherer and the decision maker, which are used to find out right products and make an expected choice respectively. In the product gatherer subsystem, an ontology-based web service architecture with Web Ontology Language (OWL) was established for the semantic content processing of product information. The Simple Object Access Protocol (SOAP) is utilized to establish the communication interface and gather XML-based contents through Remote Procedure Calls (RPC) between the system and the database servers of travel agencies. In the decision maker subsystem, the Analytic Hierarchy Process is utilized to make an optimal decision for satisfying the requirement given by the consumer. The system aims to help consumers to avoid falling into decision-making hesitation and get an expected choice from various and similar products.
Tien-Wen Sung; Chia-Jung Lee; Pei-Wei Tsai; Sheng-Hui Meng; Fu-Tian Lin. Ontology-based Decision Support System with Analytic Hierarchy Process for Tour Package Selection. DEStech Transactions on Engineering and Technology Research 2017, 1 .
AMA StyleTien-Wen Sung, Chia-Jung Lee, Pei-Wei Tsai, Sheng-Hui Meng, Fu-Tian Lin. Ontology-based Decision Support System with Analytic Hierarchy Process for Tour Package Selection. DEStech Transactions on Engineering and Technology Research. 2017; (apetc):1.
Chicago/Turabian StyleTien-Wen Sung; Chia-Jung Lee; Pei-Wei Tsai; Sheng-Hui Meng; Fu-Tian Lin. 2017. "Ontology-based Decision Support System with Analytic Hierarchy Process for Tour Package Selection." DEStech Transactions on Engineering and Technology Research , no. apetc: 1.
Energy saving and effective utilization are an essential issue for wireless sensor network. Most previous cluster based routing protocols only care the relationship of cluster heads and sensor nodes but ignore the huge difference costs between them. In this paper, we present a routing protocol based on genetic algorithm for a middle layer oriented network in which the network consists of several stations that are responsible for receiving data and forwarding the data to the sink. The amount of stations should be not too many and not too few. Both cases will cause either too much construction cost or extra transmission energy consumption. We implement five methods to compare the performance and test the stability of our presented methods. Experimental results demonstrate that our proposed scheme reduces the amount of stations by 36.8 and 20% compared with FF and HL in 100-node network. Furthermore, three methods are introduced to improve our proposed scheme for effective cope with the expansion of network scale problem.
Lingping Kong; Jeng-Shyang Pan; Václav Snášel; Pei-Wei Tsai; Tien-Wen Sung. An energy-aware routing protocol for wireless sensor network based on genetic algorithm. Telecommunication Systems 2017, 67, 451 -463.
AMA StyleLingping Kong, Jeng-Shyang Pan, Václav Snášel, Pei-Wei Tsai, Tien-Wen Sung. An energy-aware routing protocol for wireless sensor network based on genetic algorithm. Telecommunication Systems. 2017; 67 (3):451-463.
Chicago/Turabian StyleLingping Kong; Jeng-Shyang Pan; Václav Snášel; Pei-Wei Tsai; Tien-Wen Sung. 2017. "An energy-aware routing protocol for wireless sensor network based on genetic algorithm." Telecommunication Systems 67, no. 3: 451-463.
This paper proposes a distance coefficient-based scheme to solve the problem of selecting k control centers from the sensors in a wireless sensor network as well as dividing the sensors into k groups for the minimization of the distance between each control center and its farthest sensor. The proposed scheme can avoid the drawback of applying the farthest-first or the nearest-first method in the control centers selection.
Tien-Wen Sung; Lingping Kong; Pei-Wei Tsai; Jeng-Shyang Pan. A distance coefficient-based algorithm for k-center selection in wireless sensor networks. 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) 2017, 293 -294.
AMA StyleTien-Wen Sung, Lingping Kong, Pei-Wei Tsai, Jeng-Shyang Pan. A distance coefficient-based algorithm for k-center selection in wireless sensor networks. 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). 2017; ():293-294.
Chicago/Turabian StyleTien-Wen Sung; Lingping Kong; Pei-Wei Tsai; Jeng-Shyang Pan. 2017. "A distance coefficient-based algorithm for k-center selection in wireless sensor networks." 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) , no. : 293-294.
Taiwanese economy is extremely export-oriented. However, Taiwan also starts to actively look for opportunities for participation, because of the slowdown of multinational liberalizations, and as a result of the advance of the regional economic integration trend. As an example, Taiwan vigorously participates in FTAs with countries in Asia and Central America. International economic conditions are significantly important to the international trade and the volume of export/import volume, which is affected by the foreign exchange rate. If contemporary researchers could take full advantage of the exchange rate forecasting, Taiwan could maximize its trade surplus, thus boosting the economic growth. Conventional foreign exchange rate forecasting is usually provided by analyzing many financial indices or with the time-series method. Our goal is to produce the foreign exchange rate forecasting result by the robotic way with an evolutionary computing method called Interactive Artificial Bee Colony algorithm. Based on the event study methodology, the selected agreements include four FTA that are ECFA, BIA, ASTEP and ANZTEC, and the observation period setting is 70 days of pre-event period and 70 days of post-event period. This paper uses time series model (GARCH, EGARCH) and Interactive artificial bee colony (IABC) to establish the exchange rate predicting models. In addition, we adopt Mean Absolutely Percentage Error (MAPE) to compare the accuracy of exchange rate prediction. There are many exchange rate predicting models and the most frequently one to conduct maybe the time series model. This research reveals that even the IABC is relatively new it is the model has the best predictive ability among all the models.
Pei-Wei Tsai; Jing Zhang; Yao He; Jui-Fang Chang; Li-Hui Yang; Wein-Duo Yang. IABC robotic evolutionary model for the foreign exchange rate prediction in Central America trading agreement events. 2016 IEEE Symposium Series on Computational Intelligence (SSCI) 2017, 1 -6.
AMA StylePei-Wei Tsai, Jing Zhang, Yao He, Jui-Fang Chang, Li-Hui Yang, Wein-Duo Yang. IABC robotic evolutionary model for the foreign exchange rate prediction in Central America trading agreement events. 2016 IEEE Symposium Series on Computational Intelligence (SSCI). 2017; ():1-6.
Chicago/Turabian StylePei-Wei Tsai; Jing Zhang; Yao He; Jui-Fang Chang; Li-Hui Yang; Wein-Duo Yang. 2017. "IABC robotic evolutionary model for the foreign exchange rate prediction in Central America trading agreement events." 2016 IEEE Symposium Series on Computational Intelligence (SSCI) , no. : 1-6.
Jeng-Shyang Pan; Pei-Wei Tsai; Hsiang-Cheh Huang. Advances in Intelligent Information Hiding and Multimedia Signal Processing. Blockchain Technology and Innovations in Business Processes 2017, 63, 1 .
AMA StyleJeng-Shyang Pan, Pei-Wei Tsai, Hsiang-Cheh Huang. Advances in Intelligent Information Hiding and Multimedia Signal Processing. Blockchain Technology and Innovations in Business Processes. 2017; 63 ():1.
Chicago/Turabian StyleJeng-Shyang Pan; Pei-Wei Tsai; Hsiang-Cheh Huang. 2017. "Advances in Intelligent Information Hiding and Multimedia Signal Processing." Blockchain Technology and Innovations in Business Processes 63, no. : 1.
Unwanted convergence to a local optimum, rather than global optimum, is possible to take place in practical multimodal optimization problems. Communication between artificial agents in the stochastic algorithms is one of the solutions to this issue. This paper proposes a novel parallel optimization algorithm, namely FDA, based on the communication of the pollen in Flower pollination algorithm (FPA) with the agents in Differential evolution algorithm (DEA) to solve the optimization problems. A communication strategy for Pollens and Agents is to take advantages of the strength points of each algorithm to explore and exploit the diversity solutions in avoiding of dropping to a local optimum. A set of benchmark functions is used to test the quality performance of the proposed algorithm. Simulation results show that the proposed algorithm in-creases the accuracy more than the existing algorithms.
Pei-Wei Tsai; Trong-The Nguyen; Jeng-Shyang Pan; Thi-Kien Dao; Wei-Min Zheng. A Parallel Optimization Algorithm Based on Communication Strategy of Pollens and Agents. Blockchain Technology and Innovations in Business Processes 2016, 64, 315 -324.
AMA StylePei-Wei Tsai, Trong-The Nguyen, Jeng-Shyang Pan, Thi-Kien Dao, Wei-Min Zheng. A Parallel Optimization Algorithm Based on Communication Strategy of Pollens and Agents. Blockchain Technology and Innovations in Business Processes. 2016; 64 ():315-324.
Chicago/Turabian StylePei-Wei Tsai; Trong-The Nguyen; Jeng-Shyang Pan; Thi-Kien Dao; Wei-Min Zheng. 2016. "A Parallel Optimization Algorithm Based on Communication Strategy of Pollens and Agents." Blockchain Technology and Innovations in Business Processes 64, no. : 315-324.
The most recent return in the investment is one of the most popular element that investors take into consideration because of its fast and direct return characteristic. With the ongoing discussion about stock portfolio design, we construct the investment model by two phases and draw an analogy in this research. The first step is using ISCI in selecting the potential stocks; and the second step is designing the stock portfolio by the GARCH model and the IABC algorithm aiming for gaining the highest return in the investment. The analysis data used in our experiments include the stock price of daily return in continuous five years in 2011 to 2015. The experimental results indicate that using IABC in constructing the stock portfolio is a stable investment strategy and the gained maximum return is also higher than the portfolio constructed by the GARCH in the Taiwan stock market
Pei-Wei Tsai; Ko-Fang Liu; Xingsi Xue; Jui-Fang Chang; Cian-Lin Tu; Vaci Istanda; Chih-Feng Wu. A Comparative Analysis of Stock Portfolio Construction by IABC and GARCH with the ISCI in Taiwan Stock Market. Blockchain Technology and Innovations in Business Processes 2016, 325 -332.
AMA StylePei-Wei Tsai, Ko-Fang Liu, Xingsi Xue, Jui-Fang Chang, Cian-Lin Tu, Vaci Istanda, Chih-Feng Wu. A Comparative Analysis of Stock Portfolio Construction by IABC and GARCH with the ISCI in Taiwan Stock Market. Blockchain Technology and Innovations in Business Processes. 2016; ():325-332.
Chicago/Turabian StylePei-Wei Tsai; Ko-Fang Liu; Xingsi Xue; Jui-Fang Chang; Cian-Lin Tu; Vaci Istanda; Chih-Feng Wu. 2016. "A Comparative Analysis of Stock Portfolio Construction by IABC and GARCH with the ISCI in Taiwan Stock Market." Blockchain Technology and Innovations in Business Processes , no. : 325-332.
This paper presents an adaptive approach based on chicken swarm optimization algorithm (ACSO) for community detection problem in complex social networks. The proposed approach is able to define dynamically the number of communities for complex social network. The basic chicken swarm algorithm by its nature is continuous which can’t fit for community detection domain so it needs to be redesigned as a discrete chicken swarm for a better exploration of the search space. Locus-based adjacency scheme is used for encoding and decoding tasks while NMI and Modularity are used as an objective function. The proposed approach is executed over four popular cited benchmarks data sets with different size of small, medium and large scale data sets such as Zachary karate club, Bottlenose dolphin, American college football and Facebook. Experimental results are measured with quality measures such as NMI, Modularity and Ground truth. ACSO’s results are compared with eight well-known community detection algorithms such as A discrete BAT, Artificial fish swarm, Infomap, Fast Greedy, label propagation, Walktrap, Multilevel and A discrete Krill herd Algorithm. ACSO has achieved high accuracy and quality results for community detection and community structure for complex social networks.
Khaled Ahmed; Aboul Ella Hassanien; Ehab Ezzat; Pei-Wei Tsai. An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm. Advances in Intelligent Systems and Computing 2016, 281 -288.
AMA StyleKhaled Ahmed, Aboul Ella Hassanien, Ehab Ezzat, Pei-Wei Tsai. An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm. Advances in Intelligent Systems and Computing. 2016; ():281-288.
Chicago/Turabian StyleKhaled Ahmed; Aboul Ella Hassanien; Ehab Ezzat; Pei-Wei Tsai. 2016. "An Adaptive Approach for Community Detection Based on Chicken Swarm Optimization Algorithm." Advances in Intelligent Systems and Computing , no. : 281-288.
Today organizations are deeply involved in the Big Data era as the amount of data has been exploding with un-predictable rate and coming from various sources. To process and analyze this massive data, privacy is a major concern together with utility of data. Thus, privacy preservation techniques which target at the balance between utility and privacy begin to be one of the recent trends for big data researchers. In this paper, we discuss a technique for big data privacy preservation by means of clustering method. Here, hierarchical particle swarm optimization (HPSO) is used for clustering similar data. To attain scalability for big data, our method is constructed on the novel cloud infrastructure, MapReduce Hadoop. The method is tested by using a novel UCI dataset and the results are compared with an existing approach.
Ei Nyein Chan Wai; Pei-Wei Tsai; Jeng-Shyang Pan. Hierarchical PSO Clustering on MapReduce for Scalable Privacy Preservation in Big Data. Advances in Intelligent Systems and Computing 2016, 536, 36 -44.
AMA StyleEi Nyein Chan Wai, Pei-Wei Tsai, Jeng-Shyang Pan. Hierarchical PSO Clustering on MapReduce for Scalable Privacy Preservation in Big Data. Advances in Intelligent Systems and Computing. 2016; 536 ():36-44.
Chicago/Turabian StyleEi Nyein Chan Wai; Pei-Wei Tsai; Jeng-Shyang Pan. 2016. "Hierarchical PSO Clustering on MapReduce for Scalable Privacy Preservation in Big Data." Advances in Intelligent Systems and Computing 536, no. : 36-44.
This paper proposes an approach for liver segmentation, depending on Antlion optimization algorithm. It is used as a clustering technique to accomplish the segmentation process in MRI images. Antlion optimization algorithm is combined with a statistical image of liver to segment the whole liver. The segmented region of liver is improved using some morphological operations. Then, mean shift clustering technique divides the segmented liver into a number of regions of interest (ROIs). Starting with Antlion algorithm, it calculates the values of different clusters in the image. A statistical image of liver is used to get the potential region that liver might exist in. Some pixels representing the required clusters are picked up to get the initial segmented liver. Then the segmented liver is enhanced using morphological operations. Finally, mean shift clustering technique divides the liver into different regions of interest. A set of 70 MRI images, was used to segment the liver and test the proposed approach. Structural Similarity index (SSIM) validates the success of the approach. The experimental results showed that the overall accuracy of the proposed approach, results in 94.49 % accuracy.
Abdalla Mostafa; Mohamed Houseni; Naglaa Allam; Aboul Ella Hassanien; Hesham Hefny; Pei-Wei Tsai. Antlion Optimization Based Segmentation for MRI Liver Images. Advances in Intelligent Systems and Computing 2016, 265 -272.
AMA StyleAbdalla Mostafa, Mohamed Houseni, Naglaa Allam, Aboul Ella Hassanien, Hesham Hefny, Pei-Wei Tsai. Antlion Optimization Based Segmentation for MRI Liver Images. Advances in Intelligent Systems and Computing. 2016; ():265-272.
Chicago/Turabian StyleAbdalla Mostafa; Mohamed Houseni; Naglaa Allam; Aboul Ella Hassanien; Hesham Hefny; Pei-Wei Tsai. 2016. "Antlion Optimization Based Segmentation for MRI Liver Images." Advances in Intelligent Systems and Computing , no. : 265-272.
On the basis of our former work based on Compact Evolutionary Algorithm (CEA), in this paper, we introduce parallel technology into Compact Evolutionary Algorithm (CEA), and design an Parallel Compact Evolutionary Algorithm (PCEA) based ontology matching technology to further improve the efficiency of solving the ontology meta-matching problem. Comparing with CEA based approach, our approach is able to further reduce the time and memory consumption while at the same time ensures the correctness and completeness of the alignments. The Experiment is carried out on the OAEI 2015 benchmark, and the results show that our approach is able to reduce the executing time and main memory consumption of the tuning process while at the same time ensures the quality of the alignment.
Xingsi Xue; Pei-Wei Tsai; Li-Li Zhang. Using Parallel Compact Evolutionary Algorithm for Optimizing Ontology Alignment. Advances in Intelligent Systems and Computing 2016, 157 -165.
AMA StyleXingsi Xue, Pei-Wei Tsai, Li-Li Zhang. Using Parallel Compact Evolutionary Algorithm for Optimizing Ontology Alignment. Advances in Intelligent Systems and Computing. 2016; ():157-165.
Chicago/Turabian StyleXingsi Xue; Pei-Wei Tsai; Li-Li Zhang. 2016. "Using Parallel Compact Evolutionary Algorithm for Optimizing Ontology Alignment." Advances in Intelligent Systems and Computing , no. : 157-165.
This work focuses on the NTD/USD exchange rate and the Monitoring Indicator in years of 2006 to 2010 to forecast the foreign exchange rate via Time-series models including GARCH (1,1) and EGARCH (1,1), and a computational intelligence model called IABC. In order to compare the rate forecasting ability of these models, the MAPE is consecutively applied as the evaluating criterion after the forecasting process. The experimental results indicate that it is effective to enhance the ability of foreign exchange rate forecasting by adding the Monitoring Indicator as a new reference variable in the IABC model. Based on the experimental results, we find that IABC is the most effective one to forecast the foreign exchange rate. Nevertheless, when IABC is suffered from the local optimum in the solution space, the forecasting ability would present a significant drop.
Pei-Wei Tsai; Wen-Ling Wang; Jui-Fang Chang; Zhi-Sheng Chen; Yong-Hui Zhang. Utilizing IABC and Time Series Model in Investigating the Influence of Adding Monitoring Indicator for Foreign Exchange Rate Forecasting. Advances in Intelligent Systems and Computing 2016, 536, 183 -191.
AMA StylePei-Wei Tsai, Wen-Ling Wang, Jui-Fang Chang, Zhi-Sheng Chen, Yong-Hui Zhang. Utilizing IABC and Time Series Model in Investigating the Influence of Adding Monitoring Indicator for Foreign Exchange Rate Forecasting. Advances in Intelligent Systems and Computing. 2016; 536 ():183-191.
Chicago/Turabian StylePei-Wei Tsai; Wen-Ling Wang; Jui-Fang Chang; Zhi-Sheng Chen; Yong-Hui Zhang. 2016. "Utilizing IABC and Time Series Model in Investigating the Influence of Adding Monitoring Indicator for Foreign Exchange Rate Forecasting." Advances in Intelligent Systems and Computing 536, no. : 183-191.