Browsing by Author "Lin, Chun-Cheng"

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3GPP NR Sidelink Transmissions Toward 5G V2X
(2020-01-01)
Lien, Shao-Yu
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Deng, Der-Jiunn
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Lin, Chun-Cheng
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Tsai, Hua-Lung
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Chen, Tao
Featuring direct communications between two user equipments (UEs) without signal relay through a base station, 3GPP sidelink transmissions have manifested their crucial roles in the Long-Term Evolution (LTE) Advanced (LTE-A) for public safety and vehicle-to-everything (V2X) services. With this successful development in LTE-A, the evolution of sidelink transmissions continues in 3GPP New Radio (NR), which renders sidelink an inevitable component as well as downlink and uplink. Targeting at offering low latency, high reliability and high throughout V2X services for advanced driving use cases, a number of new sidelink functions not provided in the LTE-A are supported in NR, including the feedback channel, grant-free access, enhanced channel sensing procedure, and new control channel design. To fully comprehend these new functions, this paper therefore provides essential knowledge of 3GPP NR sidelink transmissions, including the physical layer structure, resource allocation mechanisms, resource sensing and selection procedures, synchronization, and quality-of-service (QoS) management. Furthermore, this paper also provides performance evaluation to assess the gains brought from the new control channel design. As NR sidelink transmissions have been regarded as a foundation to provide advanced services other than V2X in future releases (e.g., advanced relay), potential enhancements are also discussed to serve the urgent demand in corresponding normative works.
A Bat-inspired Algorithm for Router Node Placement with Weighted Clients in Wireless Mesh Networks
(2014)
Lin, Chun-Cheng
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Li, Yan-Sing
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Deng, Der-Jiunn
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper considers the problem of the dynamic router node placement (dynRNP) with weighted clients in wireless mesh networks (WMNs), in which both mesh clients and mesh router have mobility, and mesh clients can switch on or off their network access at different times. The main objective of the conventional dynRNP problem is to maximize both network connectivity and client coverage, i.e., the size of the greatest subgraph component of the WMN topology and the number of the clients within radio coverage of mesh routers, respectively. Furthermore, this paper considers a more complicated dynRNP problem in WMNs, in which each client is associated with a weighted value (i.e., the mesh clients with higher weights values are served with larger priority), so that the two objective measures are redefined with consideration of clients\' weights. We resolve the weighted problem by a bat-inspired algorithm (BA) with dynamic local search selection scheme, which simulates the echolocation of bats to find the optimal solution. Finally, the performance of the proposed BA is compared with the original BA and PSO, and the effects of weights are analyzed.
A Cultural Algorithm for Spatial Forest Harvest Scheduling
(2014-01-01)
Liu, Wan-Yu
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Lin, Chun-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
This paper proposes a cultural algorithm for the spatial forest harvest scheduling for maximizing the total harvested timber volume, under the constraints of minimum harvest age, minimum adjacency green-up age, and approximately even volume flow for each period of the schedule. In order to increase the solution-search ability, the cultural algorithm extracts problem-specific information during the evolutionary solution search to update the belief space of a generation, which has cultural influences and guidance on the next generation. The key design of our cultural algorithm is to propose the cultural and evolutionary operators specifically for the problem. Experimental analysis shows that our cultural algorithm performs better than the previous approaches.
A Disk-Based Mining Algorithm for Frequent Pattern Discovery from Big Data in Distributed Computing Environments
(2016-11)
Lin, Kawuu W.
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Chung, Sheng-Hao
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Hsiao, Chun-Yuan
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Lin, Chun-Cheng
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Chen, Pei-Ling
In distributed computing environments, frequent pattern mining by a multi-computing node can greatly improve mining efficiency. However, the drawback of memory limitations may cause interruption in the kernel and computing nodes when recursively building a frequent pattern (FP) tree or an FP-growth algorithm. In this paper, we propose disk-based FP-tree generation and node-based clustering mechanisms to solve the insufficient memory problem. Results from empirical evaluations show that the proposed method delivers excellent scalability.
A Disk-Based Mining Algorithm for Frequent Pattern Discovery from Big Data in Distributed Computing Environments
(2016-11-01)
Lin, Kawuu W.
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Chung, Sheng-Hao
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Hsiao, Chun-Yuan
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Lin, Chun-Cheng
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Chen, Pei-Ling
In distributed computing environments, frequent pattern mining by a multi-computing node can greatly improve mining efficiency. However, the drawback of memory limitations may cause interruption in the kernel and computing nodes when recursively building a frequent pattern (FP) tree or an FP-growth algorithm. In this paper, we propose disk-based FP-tree generation and node-based clustering mechanisms to solve the insufficient memory problem. Results from empirical evaluations show that the proposed method delivers excellent scalability.
A Distributed Algorithm for Fast Mining Frequent Patterns in Limited and Varying Network Bandwidth Environments
(2019-05-01)
Lin, Chun-Cheng
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Li, Wei-Ching
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Chen, Ju-Chin
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Chung, Wen-Yu
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Chung, Sheng-Hao
Data mining is a set of methods used to mine hidden information from data. It mainly includes frequent pattern mining, sequential pattern mining, classification, and clustering. Frequent pattern mining is used to discover the correlation among various sets of items within large databases. The rapid upward trend in data size slows the mining of frequent patterns. Numerous studies have attempted to develop algorithms that operate in distributed computing environments to accelerate the mining process. FLR-mining (Fast, Load balancing and Resource efficient mining algorithm) is one of the fastest methods of mining with efficient consideration of load balancing and resources. FLR-mining can automatically determine the appropriate number of computing nodes. However, FLR-mining and existing methods assume that the network bandwidth is constant. In practical distributed and many-task computing systems, this assumption fails because there are packet collisions caused by many mining tasks that run in a simultaneous manner. Therefore, a method that can consider the varying network bandwidth is necessary. In this study, we propose a method that can rapidly mine frequent patterns under the varying network bandwidth. The proposed method can also determine the appropriate number of computing nodes to efficiently utilize computing resources and achieve load balancing. Through empirical evaluation, the proposed method is shown to deliver excellent performance in terms of execution efficiency and load balancing.
A fast and distributed algorithm for mining frequent patterns in congested networks
(2016-03)
Lin, Kawuu W.
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Chung, Sheng-Hao
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Lin, Chun-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
With advances in technology, frequent pattern mining has been used widely in our daily lives. By using this technology, one can obtain interesting or useful information that would help one make decisions and apply judgment. For example, marketplace managers mine transaction data to obtain information that can help improve services, understand customer buying habits, determine a suitable scheme for placement of goods to increase profits, or for medical and biotechnology applications. However, the rate at which data is generated is very rapid, leading to problems caused by Big Data. Therefore, many researchers have studied distributed, parallel and cloud computing technology to select the best among them. However, data mining uses multiple computing nodes, which requires the transmission of a considerable amount of data in a network environment. The available network bandwidth is limited when many different tasks are being transmitted at the same time and many servers are working in the same network segment. This results in poor transmission, causing severe transfer delay, either internal or external to the network. Thus, we propose the fast and distributed mining algorithm for discovering frequent patterns in congested networks (FDMCN) algorithm, which is based on CARM. The main purpose is to reduce FP-tree transmission such that only a portion of the information is required for mining using computing nodes. The results of empirical evaluation under various simulation conditions show that the proposed method FDMCN delivers excellent performance in terms of execution efficiency and scalability when compared with the PSWS algorithm.
A fast and low idle time method for mining frequent patterns in distributed and many-task computing environments
(2018-12-01)
Lin, Chun-Cheng
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Chung, Sheng-Hao
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Chen, Ju-Chin
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Yu, Yuan-Tse
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Lin, Kawuu W.
Association rules mining has attracted much attention among data mining topics because it has been successfully applied in various fields to find the association between purchased items by identifying frequent patterns (FPs). Currently, databases are huge, ranging in size from terabytes to petabytes. Although past studies can effectively discover FPs to deduce association rules, the execution efficiency is still a critical problem, particularly for big data. Progressive size working set (PSWS) and parallel FP-growth (PFP) are state-of-the-art methods that have been applied successfully to parallel and distributed computing technology to improve mining processing time in many-task computing, thereby bridging the gap between high-throughput and high-performance computing. However, such methods cannot mine before obtaining a complete FP-tree or the corresponding subdatabase, causing a high idle time for computing nodes. We propose a method that can begin mining when a small part of an FP-tree is received. The idle time of computing nodes can be reduced, and thus, the time required for mining can be reduced effectively. Through an empirical evaluation, the proposed method is shown to be faster than PSWS and PFP.
A fast method for frequent pattern discovery with secondary memory
(2017-01-01)
Lin, Kawuu W.
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Chung, Sheng-Hao
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Chen, Ju-Chin
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Huang, Sheng-Shiung
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Lin, Chun-Cheng
Data mining technology has been widely studied and applied in recent years. Frequent pattern mining is one important technical field of such research. The frequent pattern mining technique is popular not only in academia but also in the business community. With advances in technology, databases have become so large that data mining is impossible because of memory restrictions. In this study, we propose a novel algorithm for Fast mining with Secondary Memory, abbreviated as FSM-Mining, to help improve this situation. FSM-Mining saves a part of the information that is not stored in the memory, and through the use of mixed hard disk and memory mining we are able to complete data mining with limited memory. The results of empirical evaluation under various simulation conditions show that FSM-Mining delivers excellent performance in terms of execution efficiency and scalability.
A Framework of a Recommendation System Utilizing Expert Groups on a Social Network
(2011)
Lin, Tzong-Shyan
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Lin, Chun-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
A social network is used as a mechanism to link people together to solicit and relay recommendations from one another. However, in a large social network where most people would have hundreds of acquaintances and millions of people within the social network, relying solely on the recommendations obtained through a search that involves a significant number of people within a network, which may not be the most practical and economical option. A solution to this is to limit the number of people between two people within a social network, between the person soliciting a recommendation and a person potentially providing a recommendation. To compensate for the potential loss of recommendations as a result of the limit, a mechanism to compliment the recommendation system, known as expert groups. is created. Expert groups are a collection of people with a common expertise in a common knowledge area and a certain degree of like-mindedness. People within these expert groups can provide recommendations on issues within the common knowledge area. The proposed framework uses software agents to model the behavior of people when soliciting recommendations and providing recommendations.
A Genetic Algorithm Approach for Detecting Hierarchical and Overlapping Community Structure in Dynamic Social Networks
(2013)
Lin, Chun-Cheng
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Liu, Wan-Yu
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Deng, Der-Jiunn
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工業工程與管理學系
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Department of Industrial Engineering and Management
Social networks are merely a reflection of certain realities among people that have been identified. But in order for people or even computer systems (such as expert systems) to make sense of the social network, it needs to be analyzed with various methods so that the characteristics of the social network can be understood in a meaningful context. This is challenging not only due to the number of people that can be on social networks, but the changes in relationships between people on the social network over time. In this paper, we develop a method to help make sense of dynamic social networks. This is achieved by establishing a hierarchical community structure where each level represents a community partition at a specific granularity level. By organizing each level of the hierarchical community structure by granularity level, a person can essentially "zoom in" to view more detailed (smaller) communities and "zoom out" to view less detailed (larger) communities. Communities consisting of one or more subsets of people having relatively extensive links with other communities are identified and represented as overlapping community structures. Mechanisms are also in place to enable modifications to the social network to be dynamically updated on the hierarchical and overlapping community structure without recreating it in real time for every modification. The experimental results show that the genetic algorithm approach can effectively detect hierarchical and overlapping community structures.
A Hybrid Memetic Algorithm for Simultaneously Selecting Features and Instances in Big Industrial IoT Data for Predictive Maintenance
(2019-01-01)
Liang, Yu-Lin
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Kuo, Chih-Chi
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Lin, Chun-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
In Industry 4.0, various types of IoT sensors which are installed on machines to collect data for predictive maintenance. As the collected data increases, there are more missing values and noisy data. Related studies have already proposed various methods to solve the problems in big data. Among them, most studies focused on either feature selection or instance selection for data preprocessing before training forecast models. Metaheuristic algorithm is one of the mainstream methods in data preprocessing. However, most of these studies rarely considered feature and instance selection simultaneously. In addition, they seldom focused on noisy data. Therefore, this work combines the UCI datasets with noisy data to simulate the real situation. Memetic algorithm (MA) has excellent performance in machine learning of data selection, and variable neighborhood search (VNS) was also proved to be widely applied to the systematic change of local search algorithms. This work proposes a hybrid MA and VNS to find a new subset that maximizes the accuracy of the classifier while preserving the minimum amount of data by feature and instance selection simultaneously. Experimental results show that the proposed method can efficiently reduce the amount of data and the ratio of noisy data. By comparison with other metaheuristic algorithms, the proposed method has good performance by an excellent balance between exploration and exploitation.
A MapReduce-Based Ensemble Learning Method with Multiple Classifier Types and Diversity for Condition-Based Maintenance with Concept Drifts
(2017-11-01)
Lin, Chun-Cheng
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Shu, Lei
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Deng, Der-Jiunn
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Yeh, Tzu-Lei
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Chen, Yu-Hsiang
Condition-based maintenance in Industry 4.0 collects a huge amount of production datastreams continuously from the Internet of Things attached to machines to forecast the time when to maintain machines or replace components. However, as conditions of machines change dynamically with time owing to machine aging, malfunction, or replacement, the concept of capturing the forecasting pattern from the datastream could drift unpredictably, so it is hard to find a robust forecasting method with high precision. Therefore, this work proposes an ensemble learning method with multiple classifier types and diversity for condition-based maintenance in manufacturing industries, to address the bias problem when using only one base classifier type. Aside from manipulating data diversity, this method includes multiple classifier types, dynamic weight adjusting, and data-based adaption to concept drifts for offline learning models, to promote precision of the forecasting model and precisely detect and adapt to concept drifts. With these features, the proposed method requires powerful computing resources to efficiently respond to practical condition-based maintenance applications. Therefore, the implementation of this method based on the MapReduce framework is proposed to increase computational efficiency. Simulation results show that this method can detect and adapt to all concept drifts with a high precision rate.
A multiple warning and smart monitoring system using wearable devices for home care
(2020-04-01)
Hung, Lun-Ping
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Lin, Chun-Cheng
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工業工程與管理學系
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Department of Industrial Engineering and Management
In line with the advanced medical technologies and popularity of media (especially, social media), an emerging trend regarding remote home care services has received increasing attention. This paper proposed a smart system for assistance in the home self-care process, which concentrates on a management process for real-time analysis on patient information (e.g., psychological status). The proposed system provides the EKG (electrocardiogram) home monitoring function for the care of patient cases after cardiovascular surgery. To provide supports for self-care at home, a private cloud was employed to design intelligent modules that implemented the smart system. In addition, professional medical advices and response measures were proposed so that individuals could safely and effectively engage self-care at home under the support. We conducted a user study, and the experiment results showed that the proposed approach can reduce partial costs on human resources for home care and enhance the quality of medical service, thereby achieving the goal of seamless integration of external healthcare systems of hospital.
A new force-directed graph drawing method based on edge-edge repulsion
(2012-02-01)
Lin, Chun-Cheng
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Yen, Hsu-Chun
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工業工程與管理學系
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Department of Industrial Engineering and Management
The conventional force-directed methods for drawing undirected graphs are based on either vertex-vertex repulsion or vertex-edge repulsion. In this paper, we propose a new force-directed method based on edge-edge repulsion to draw graphs, In our framework, edges are modelled as charged springs, and a final drawing can be generated by adjusting positions of vertices according to spring forces and the repulsive forces, derived from potential fields, among edges. Different from the previous methods, our new framework has the advantage of overcoming the problem of zero angular resolution, guaranteeing the absence of any overlapping of edges incident to the common vertex. Given graph layouts probably generated by previous algorithms as the inputs to our algorithm, experimental results reveal that our approach produces promising drawings not only preserving the original properties of a high degree of symmetry and uniform edge length, but also preventing zero angular resolution and usually having larger average angular resolution. However, it should be noted that exhibiting a higher degree of symmetry and larger average angular resolution does not come without a price, as the new approach might result in the increase in undesirable overlapping of vertices as some of our experimental results indicate. To ease the problem of node overlapping, we also consider a hybrid approach which takes into account both edge-edge and vertex-vertex repulsive forces in drawing a graph. (C) 2011 Elsevier Ltd. All rights reserved.
A novel centrality-based method for visual analytics of small-world networks
(2019-10-01)
Lin, Chun-Cheng
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Huang, Weidong
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Liu, Wan-Yu
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Wu, Sheng-Feng
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工業工程與管理學系
Nowadays, the network data that we need to deal with and make sense of are becoming increasingly large and complex. Small-world networks are a type of complex networks whose underling graphs have small diameter, shorter average path length between nodes, and a high degree of clustering structures and can be found in a wide range of scientific fields, including social networks, sociology, computer science, business intelligence, and biology. However, conventional visualization algorithms for small-work networks lead to a uniform clump of nodes or are restricted to a tree structure, making the network structure difficult to identify and analyze. This work provides a new visual analytical method to improve the situation. Different from previous methods based on spanning trees, this method first generates a weighted planar sub-network based on the measurement of network centrality metrics. A force-directed algorithm based on node-edge repulsion is then applied to visualize this sub-network into a proper layout for better understanding of the data. Finally, the remaining links are placed back to maintain the original network's integrity. The experimental results show that compared to previous methods, the proposed method can be more effective in differentiating clusters and revealing relationship patterns among individual nodes and clusters in the network. Furthermore, the proposed method is applied to a data of the semiconductor wafer manufacturing industry as a case study. The work shows that this new approach allows users to gain useful insights into the data.
A Novel Joint Problem of Routing, Scheduling, and Variable-Width Channel Allocation in WMNs
(2014)
Lin, Chun-Cheng
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Liu, Wan-Yu
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Chou, Chun-Hung
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Deng, Der-Jiunn
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工業工程與管理學系
This paper investigates a novel joint problem of routing, scheduling, and channel allocation for single-radio multichannel wireless mesh networks in which multiple channel widths can be adjusted dynamically through a new software technology so that more concurrent transmissions and suppressed overlapping channel interference can be achieved. Although the previous works have studied this joint problem, their linear programming models for the problem were not incorporated with some delicate constraints. As a result, this paper first constructs a linear programming model with more practical concerns and then proposes a simulated annealing approach with a novel encoding mechanism, in which the configurations of multiple time slots are devised to characterize the dynamic transmission process. Experimental results show that our approach can find the same or similar solutions as the optimal solutions for smaller-scale problems and can efficiently find good-quality solutions for a variety of larger-scale problems.
A straightforward alpha-selective aromatic glycosylation and its application for stereospecific synthesis of 4-methylumbelliferyl alpha-T-antigen
(2009-03-10)
Chang, Shih-Sheng
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Lin, Chun-Cheng
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Li, Yaw-Kuen
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Mong, Kwok-Kong Tony
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應用化學系
A practical and efficient alpha-selective aromatic glycosylation with simple per-O-acetyl glycopyranosyl trichloroacetimidates is reported. The method is particularly effective for L-fucosyl and 2-azido-2-deoxy-D-galatosaminyl imidates, with which exclusive alpha-selectivity was achieved. The synthetic utility of this method was demonstrated in the stereoselective synthesis of 4-methylumbelliferyl alpha-T-antigen. (C) 2008 Elsevier Ltd. All rights reserved.
A Triangular NodeTrix Visualization Interface for Overlapping Social Community Structures of Cyber-Physical-Social Systems in Smart Factories
(2020-01-01)
Lin, Chun-Cheng
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Deng, Der-Jiunn
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Jhong, Shun-Yu
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工業工程與管理學系
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Department of Industrial Engineering and Management
This work proposes a cyber-physical-social system (CPSS) framework for smart factories which associates the social-network-based physical factory circumference with the cyber control through a visualization interface based on NodeTrix. The NodeTrix representation consisting of adjacency matrices and node-link diagrams has been popularly used in social network analysis. Different from previous works on NodeTrix based on square-shaped matrices, the proposed NodeTrix applies triangle-shaped matrices for visualizing overlapping communities to save the displaying space, and a simulated annealing algorithm is used to reduce the crossings among links and communities. In the CPSS for smart factories, working-in-process (WIP) products are categorized according to their attributes, and are placed at different storage locations for later different production processes. By the NodeTrix-based interface for CPSS, when users with different domains and experiences work together to make instant decisions on production and logistics, the physical production environment will be adjusted accordingly and immediately through technological support of Industry 4.0. Through the interface, the information on locations, parameters, and interoperability of WIP products can be visualized and adjusted in time. By comparison with other works, the proposed representation looks promising in displaying social networks for the CPSS with fewer crossings within a smaller screen space.
A Zone-Based Approach for Placing Annotation Labels on Metro Maps
(2011)
Wu, Hsiang-Yun
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Takahashi, Shigeo
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Lin, Chun-Cheng
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Yen, Hsu-Chun
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工業工程與管理學系
Hand-drawn metro map illustrations often employ both internal and external labels in a way that they can assign enough information such as textual and image annotations to each landmark. Nonetheless, automatically tailoring the aesthetic layout of both textual and image labels together is still a challenging task, due to the complicated shape of the labeling space available around the metro network. In this paper, we present a zone-based approach for placing such annotation labels so that we can fully enhance the aesthetic criteria of the label arrangement. Our algorithm begins by decomposing the map domain into three different zones where we can limit the position of each label according to its type. The optimal positions of labels of each type are evaluated by referring to the zone segmentation over the map. Finally, a new genetic-based approach is introduced to compute the optimal layout of such annotation labels, where the order in which the labels are embedded into the map is improved through the evolutional computation algorithm. We also equipped a semantic zoom functionality, so that we can freely change the position and scale of the metro map.