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
著作名稱:Applying Image Recognition and Tracking Methods for Fish Physiology Detection Based on a Visual Sensor
年度:2022
類別: 期刊論文 Sensors
摘要:The proportion of pet keeping has increased significantly. According to the survey results of Business Next, the proportion of Taiwan families keeping pets was 70% in 2020. Among them, the total number of fish pets was close to 33% of the overall pet proportion. Therefore, aquarium pets have become indispensable companions for families. At present, many studies have discussed intelligent aquarium systems. Through image recognition based on visual sensors, we may be able to detect and interpret the physiological status of the fish according to their physiological appearance. In this way, it can help to notify the owner as soon as possible to treat the fish or isolate them individually, so as to avoid the spread of infection. However, most aquarium pets are kept in groups. Traditional image recognition technologies often fail to recognize each fish’s physiological states precisely because of fish swimming behaviors, such as grouping swimming, shading with each other, flipping over, and so on. In view of this, this paper tries to address such problems and then proposes a practical scheme, which includes three phases. Specifically, the first phase tries to enhance the image recognition model for small features based on the prioritizing rules, thus improving the instant recognition capability. Then, the second phase exploits a designed fish-ID tracking mechanism and analyzes the physiological state of the same fish-ID through coherent frames, which can avoid temporal misidentification. Finally, the third phase leverages a fish-ID correction mechanism, which can detect and correct their IDs periodically and dynamically to avoid tracking confusion, and thus potentially improve the recognition accuracy. According to the experiment results, it was verified that our scheme has better recognition performance. The best accuracy and correctness ratio can reach up to 94.9% and 92.67%, which are improved at least 8.41% and 26.95%, respectively, as compared with the existing schemes.
關鍵字:image recognition; object tracking; correction mechanism; Internet of Things
著作名稱:Smart Infusion System based on Physiological Sensing and Machine Learning Technology
年度:2022
類別: 期刊論文 International Journal of Science and Engineering
摘要:為因應逐漸升高的護病比與提升病患的照護品質,本研究將針對靜脈注射這項常見的醫療處置,配合物聯網與機器學習,設計一套具有以下三項功能的系統:1.能夠實時呈現患者生理數據的網頁, 2.量測與控制輸液速度的裝置, 3.以機器學習發出輸液速度建議及發出患者身體異常的提醒。在前端硬體裝置的部分,本研究採用Arduino作為前端感測器的物聯網通訊平台,透過無線網路傳送前端裝置測得的數據與接收後端發送的指令。在後端的部分採用 Node.js 架設物聯網與網頁伺服器,達成即時的數據顯示與遠端控制。為了進一步加速患者生理情況之判讀,此系統亦加入機器學習將讀取到的資料進行分析,以幫助護理人員提供患者更快速且精確的醫療處置,透過科技輔助達成精準地輸液照護。
關鍵字:生理感測、機器學習、無線網路、輸液控制、物聯網
著作名稱:The Eye of Healthcare: Visualization of Personal Physiological Condition
年度:2022
類別: 期刊論文 International Journal of Science and Engineering
摘要:近年來,心臟疾病的患者年齡逐年下降,而死亡率最高的心肌梗塞的前兆多為心肌缺血。傳統上的心臟疾病檢測方法無法提供醫療照護者一套即時且有效的監測方式,造成無法適時進行醫療措施。本論文提出一種可即時 AR 視覺化方式監測場域中人員生理狀況,在穿戴式裝置上直接進行特徵的計算與心電訊號判讀,考慮到 RGB 攝影機有隱私權的爭議,結合 RPLiDAR 與慣性穿戴裝置之身分辨識系統,可以對場域內人員進行身份辨識與追蹤,接著透過 AR 視覺化技術於監控畫面上即時顯示每位受照護人員的生理狀況,每位人員的生理感測資訊即時地被紀錄與執行特徵分析。透過機器學習模型判斷心肌缺血與基於感測融合之身份辨識技術,當受照護者的生理狀況異常的時,會即時呈現於監控視窗,協助醫療照護者預防緊急狀況。根據實驗結果,本系統所開發的演算法較其他演算法準確且具效率。
關鍵字:心肌缺血、光學雷達、慣性感測器、物聯網、資料融合
著作名稱:Design and Analysis for Wireless Tire Pressure Sensing System
年度:2023
類別: 會議論文
摘要:With the expansion of Internet of Things (IoT) technologies, intelligent vehicles are now in increasing public demand, mostly equipped with sensors for measuring various situations, such as distance proximity, driving speed, fuel consumption, and tire anomaly. Nowadays, tire pressure and temperature play an important role in reducing fuel consumption, increasing mileage, improving driving safety, and reducing potential traffic accidents. In this paper, we investigate how to collect the tire data based on the designed sensors and then develop an energy-efficient system, called Wireless Tire Pressure Sensing System (WTPSS), which leverages the energy-saving technique by applying wakeup-sleep operation, while ensuring the data retrieval delay when monitoring. In addition, the worst receiving delay is also discussed and analyzed theoretically. In the simulation, our system has verified that it can have better performance in terms of energy saving.
關鍵字:Tire pressure sensing system, Worst-delay, Sensor data transmission, Wireless transmission
著作名稱:Tire Anomaly Detection System Based on Machine Learning and IoT Sensing Technology
年度:2022
類別: 會議論文
摘要:傳統市售的胎壓偵測器僅能透過測量胎壓、胎溫來判斷輪胎狀態,無法即時檢測輪胎的異常狀況,如:輪框變形、胎面異常剝離、輪胎附著異物、異常抖動及打滑。一旦駕駛疏於注意,將造成嚴重的車禍。因此,我們研究設計一套智慧輪胎異常偵測系統,透過量測輪胎運行間的慣性感測資訊,包含:多軸加速度、角速度、磁力計,再結合機器學習之辨識技術,以動態檢測出輪胎異常狀況,並實作設計下述物聯網平台元件:1. 偵測輪胎慣性感測數值之硬體裝置, 2. 具即時監控、異常提醒、歷史紀錄與統計等功能之手機App, 3. 基於機器學習技術辨識各式輪胎異常狀況, 4. 建立跨平台網頁介面供停車場管理車輛、資料蒐集與大數據分析。經實驗測試結果,本系統的AI判讀準確率高達95%。
關鍵字:物聯網、機器學習、智慧終端裝置、輪胎異常、無線網路