[[alternative]]The Research of Adaptive Digital Training Attention Platform Based on Eeg Features

系統編號: PF10406-3153計畫編號: MOST104-2511-S343-002執行機構: 南華大學資訊管理學系研究期間: 10408~10507[[abstract]]科技的發達使得現代人同時要處理太多的訊息，在長期一心多用的情況下，整個社會走入分心的黑暗時期。誠然，相關研究已提出學生專注力不足已成為學習的一大警訊，專注力是學習的第一步，在學習過程學生必須主動去注意某事才有學習發生，一旦專注力有了缺陷，其他較複雜的認知功能將會受到影響，故如何發展專注力訓練以提昇未來學習力將是未來教育重點，亦是本研究的主要目標。本計畫「以腦波特徵為基自調適數位專注力訓練平台」乃是在數位化訓練過程中透過個人腦波活動偵測推論專注力狀況，即時調整訓練策略以輔導學生進行適性化專注力訓練，最後，我們將研究學生是否能將此訓練成果順利遷移到學習環境中。故本計畫的工作項目包括：專注力訓練領域專家知識擷取、訓練個案蒐集與數位化設計、專注力腦波特徵分類與推論、自調適數位專注力訓練平台建置、課室專注力與學習成效之評估。[[abstract]]Developed technology makes modern to deal with too much information at the same time. In the case of long-term multitasking, the whole society fell into the dark age of distraction. Admittedly, related research has proposed that students’ Attention Deficit has become a major warning to learn. Attention is the first step in learning. Students must take the initiative to pay attention to something, then they bechance upon learning in the learning process. Once attention has flaws, other more complex cognitive function will be affected. Therefore how to develop attention training to enhance the learning ability will be both the future focus of education and the major objective of this study. In this plan, the “adaptive digital training attention platform based on EEG features” will infer the situation of attention through detection of personal brainwave activity in the digital training process, and then to adjust training strategies real time when student is ongoing attention training adaptively, finally, we will examine whether students can successfully migrate the results of the training to the learning environment. The content of work in this plan includes: retrieving expert knowledge in the field of attention training, collecting training cases and digital design, classifying and inferring attention-EEG feature, constructing adaptive digital training attention platform based on EEG features and evaluating classroom attention and learning effectiveness

Extended artificial chromosomes genetic algorithm for permutation flowshop scheduling problems

[[abstract]]In our previous researches, we proposed the artificial chromosomes with genetic algorithm (ACGA) which combines the concept of the Estimation of Distribution Algorithms (EDAs) with genetic algorithms (GAs). The probabilistic model used in the ACGA is the univariate probabilistic model. We showed that ACGA is effective in solving the scheduling problems. In this paper, a new probabilistic model is proposed to capture the variable linkages together with the univariate probabilistic model where most EDAs could use only one statistic information. This proposed algorithm is named extended artificial chromosomes with genetic algorithm (eACGA). We investigate the usefulness of the probabilistic models and to compare eACGA with several famous permutation-oriented EDAs on the benchmark instances of the permutation flowshop scheduling problems (PFSPs). eACGA yields better solution quality for makespan criterion when we use the average error ratio metric as their performance measures. In addition, eACGA is further integrated with well-known heuristic algorithms, such as NEH and variable neighborhood search (VNS) and it is denoted as eACGAhybrid to solve the considered problems. No matter the solution quality and the computation efficiency, the experimental results indicate that eACGAhybrid outperforms other known algorithms in literature. As a result, the proposed algorithms are very competitive in solving the PFSPs

Artificial chromosomes with genetic algorithm 2 (ACGA2) for single machine scheduling problems with sequence-dependent setup times

[[abstract]]Artificial chromosomes with genetic algorithm (ACGA) is one of the latest versions of the estimation of distribution algorithms (EDAs). This algorithm has already been applied successfully to solve different kinds of scheduling problems. However, due to the fact that its probabilistic model does not consider variable interactions, ACGA may not perform well in some scheduling problems, particularly if sequence-dependent setup times are considered. This is due to the fact that the previous job will influence the processing time of the next job. Simply capturing ordinal information from the parental distribution is not sufficient for a probabilistic model. As a result, this paper proposes a bi-variate probabilistic model to add into the ACGA. This new algorithm is called the ACGA2 and is used to solve single machine scheduling problems with sequence-dependent setup times in a common due-date environment. A theoretical analysis is given in this paper. Some heuristics and local search algorithm variable neighborhood search (VNS) are also employed in the ACGA2. The results indicate that the average error ratio of this ACGA2 is half the error ratio of the ACGA. In addition, when ACGA2 is applied in combination with other heuristic methods and VNS, the hybrid algorithm achieves optimal solution quality in comparison with other algorithms in the literature. Thus, the proposed algorithms are effective for solving the scheduling problems

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

[[abstract]]An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs). This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named "EA/G-GA" is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs

Addressing the advantages of using ensemble probabilistic models in Estimation of Distribution Algorithms for scheduling problems

[[abstract]]Estimation of Distribution Algorithms (EDAs) have recently been recognized as a prominent alternative to traditional evolutionary algorithms due to their increasing popularity. The core of EDAs is a probabilistic model which directly impacts performance of the algorithm. Previous EDAs have used a univariate, bi-variate, or multi-variable probabilistic model each time. However, application of only one probabilistic model may not represent the parental distribution well. This paper advocates the importance of using ensemble probabilistic models in EDAs. We combine the univariate probabilistic model with the bi-variate probabilistic model which learns different population characteristics. To explain how to employ the two probabilistic models, we proposed the Ensemble Self-Guided Genetic Algorithm (eSGGA). The extensive computation results on two NP-hard scheduling problems indicate the advantages of adopting two probabilistic models. Most important of all, eSGGA can avoid the computation effort overhead when compared with other EDAs employing two models. As a result, this paper might point out a next generation approach for EDAs

Multiple parents crossover operators: A new approach removes the overlapping solutions for sequencing problems

[[abstract]]Maintaining population diversity throughout generations of Genetic Algorithms (GAs) is key to avoid premature convergence. Redundant solutions is one cause for the decreasing population diversity. To prevent the negative effect of redundant solutions, we propose a framework that is based on the multi-parents crossover (MPX) operator embedded in GAs. Because MPX generates diversified chromosomes with good solution quality, when a pair of redundant solutions is found, we would generate a new offspring by using the MPX to replace the redundant chromosome. Three schemes of MPX will be examined and will be compared against some algorithms in literature when we solve the permutation flowshop scheduling problems, which is a strong NP-Hard sequencing problem. The results indicate that our approach significantly improves the solution quality. This study is useful for researchers who are trying to avoid premature convergence of evolutionary algorithms by solving the sequencing problems

系統化規劃遠距教學－以南華大學為例

[[abstract]]資訊科技的突飛猛進，使得遠距教學更為多元化與時效性，終身學習不但從一個概念發展為意識型態，甚至形成具體的行動。然而，若想要落實遠距教學的功能，其策劃與執行都必須要有一套系統化的規劃，利用資訊科技所建置的教學環境才能夠符合長遠的遠距教學計畫需要。本研究的主要目的就在於希望透過系統化的模式達成遠距教學的規劃。 本研究首先採用「需求評估」的觀點，藉由學生完成的需求問卷，調查遠距教學在教學環境、課程內容與互動模式方面的需求。其次，將這些需求與資訊技術配合，規劃出遠距教學環境。最後，利用「需求－能力評估矩陣」進行學校組織的需求與能力分析，瞭解此規劃方案在矩陣中的位置，並針對組織的需求與能力，提出具體建議。本研究結果可供國內各教學單位採用系統化方式進行遠距教學規劃時的重要參考，使資訊科技能夠真正在遠距教學方面充分發揮效用。[[abstract]]Due to the rapid enhancement of information technology, the long-distance lecture becomes more multi-dimension and time-effective. This kind of technology development not only makes life long education, initially a simple idea, become an ideology. Even more, it has become a consoled movement. However, if we want to realize and fulfil the function of distance education, we have to formulate a systematic plan. So that, we can fully utilize the education environment structured by the information technology and carry out the long-term goal of long distance education. Thus, this study tries to develop a systematic mode to carry out the distance education planning. This study will begin by using the viewpoint of Need-Capability Assessment. We will ask students what they need in the field of distance education regarding the lecture environment, course content and interaction mode. Furthermore, we will combine those needs with information technology and formulate a moderate distance education environment. And finally, we will use the ＂Need-Capability Assessment Matrix＂ to analyze the needs and capabilities of campus organizations. We try to find out the position of our planning project in the matrix and submit a consoled suggestion for the organization's needs and capabilities in this subject. We hope this will offer a further reference for installing the distance education environment. The result of this study will help our domestic education organizations develop a systematic approach to formulate the distance education planning and essentially make the information technology a real tool in the field of distance education

[[alternative]]Problem-Based Learning in a Course of Intelligent Interaction and Innovative Applications with Robots: An Action Research Study

計畫編號: PBM107120執行機構: 南華大學資訊工程學系主管機關: 教育部研究期間: 10708~10807[[abstract]]機器人的服務設計與生活領域的應用是否能夠符合大眾需求，有賴於培養機器人應用發展的人才，本計畫提出「機器人智能互動與創新應用」的課程，讓學生體驗機器人產業的服務設計與開發，有助於培養資管人才未來專長與興趣的發展，這是一門資管領域新課程而且是與產業脈動緊密結合的實務課程。本課程不同於傳統教學的上課講述方式而採用問題導向教學，課程中規劃六大問題主軸：「誰來挑戰」、「五百障礙」、「機器人導覽員」、「機器人小助教」、「機器人點餐員」、「機器人操作員」，透過這些主軸來組織教學內容與設計教材並引導學生解決問題，為了讓問題能與產業實務更貼近，本計畫與業界專家或是企業合作，專家提出他們自己產業對機器人服務的真正需求，教師擔任需求協調者與解決問題進度控制者，學生組成學習團隊進行問題解決，最後由教師與業界專家擔任評分與提出改善建議。在課程實踐計畫中，對於每個問題的解決過程都是透過行動研究，構成自我反思循環，促使研究者針對問題導向教學的狀況來進行自我的教學反省與檢討，依據學生的學習情形做適當的教學轉變進而改進自我的教學和促進自我的專業成長。[[abstract]]Whether the service design and the living application of the robot can meet the needs of the people depends on the talent education of the robotic application and development. This plan proposes a course of ‘Intelligent Interaction and Innovative Applications of Robots’ to let students experience the service design and development of robot industry. This course helps to develop talents ' future expertise and interest in. This is a new course in the field of information management and a practical course closely combined with industry pulsation. Since no textbooks and learning materials have been found at home and abroad, it is necessary to design and develop new textbooks before teaching. This course is different from the method of traditional teaching lectures and adopts with problem-based teaching. Six major issues in the curriculum: 'Who to challenge', '500 m obstacle', 'Robot Navigators', 'Robot Little TAs', 'Robot Orders', 'Robot Operators'. Through these issues, we organize teaching content, design textbooks and guide students to solve problems. In order to bring the problem closer to the industry practice, this plan works with industry experts or companies. The experts prompt the real demand for robotic services in their own industries. The teacher is as a demand coordinator and a problem-solving progress controller. Students form a learning team to solve problems. Finally, teachers and industry experts score and suggest teams’ solution for improvement. In the curriculum practice plan, the process of solving each problem is through action research to form the cycle of self-reflection. The action research can encourage the researcher to conduct self-examination and review on the situation of problem-oriented teaching. Teacher can make appropriate teaching changes according to students ' learning situation, improve self teaching and promote self professional growth