國立臺南大學專任教師基本資料
姓名梁家銘
系所電機工程學系
校內分機7821
EMAILjmliang@mail.nutn.edu.tw
辦公室ZC103-1
網址https://people.cs.nctu.edu.tw/~jmliang/
專長/研究領域新一代行動通訊網路、物聯網、智慧家庭與監控、人工智慧應用
學位畢業學校國別主修學門修業期間
博士國立交通大學台灣資訊工程與科學 
著作名稱:Dynamic Set Planning for Coordinated Multi-Point in B4G/5G Networks
年度:2021
類別: 期刊論文 Sensors
摘要:Coordinated Multi-Point (CoMP) is an important technique in B4G/5G networks. With CoMP, multiple base stations can be clustered to compose a cooperating set to improve system throughput, especially for the users in cell edges. Existed studies have discussed how to mitigate overloading scenarios and enhance system throughput with CoMP statically. However, static cooperation fixes the set size and neglects the fast-changing of B4G/5G networks. Thus, this paper provides a full study of off-peak hours and overloading scenarios. During off-peak hours, we propose to reduce BSs’ transmission power and use the free radio resource to save energy while guaranteeing users’ QoS. In addition, if large-scale activities happen with crowds gathering or in peak hours, we dynamically compose the cooperating set based on instant traffic requests to adjust base stations’ BSs’ transmission power; thus, the system will efficiently offload the traffic to the member cells which have available radio resources in the cooperating set. Experimental results show that the proposed schemes enhance system throughput, radio resource utilization, and energy efficiency, compared to other existing schemes.
關鍵字:coordinated multi-point (CoMP); cooperating set; dynamic cell selection (DCS); energy efficiency; beyond fourth-generation/fifth-generation (B4G/5G); resource allocation; soft frequency reuse (SFR)
著作名稱:Applying Machine Learning Technologies Based on Historical Activity Features for Multi-Resident Activity Recognition
年度:2021
類別: 期刊論文 Sensors
摘要:Due to the aging population, home care for the elderly has become very important. Currently, there are many studies focusing on the deployment of various sensors in the house to recognize the home activities of the elderly, especially for the elderly living alone. Through these, we can detect the home situation of the single person and ensure his/her living safety. However, the living environment of the elderly includes, not only the person living alone, but also multiple people living together. By applying the traditional methods for a multi-resident environment, the “individual” activities of each person could not be accurately identified. This resulted in an inability to distinguish which person was involved in what activities, and thus, failed to provide personal care. Therefore, this research tries to investigate how to recognize home activities in multi-resident living environments, in order to accurately distinguish the association between residents and home activities. Specifically, we propose to use the special characteristics of historical activity of residents in a multi-person environment, including activity interaction, activity frequency, activity period length, and residential behaviors, and then apply a suite of machine learning methods to train and test. Five traditional models of supervised learning and two deep learning methods are explored to tackle this problem. Through the experiments with real datasets, the proposed methods were found to achieve higher precision, recall and accuracy with less training time. The best accuracy can reach up to 91% and 95%, by J48DT, and LSTM, respectively, in different living environments.
關鍵字:ambient assisted living; multi-person activity recognition; machine learning; deep learning