Browsing by Author "Lin, Jiun-Yi"

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Decision Support System for Real-Time Trading based on On-Line Learning and Parallel Computing Techniques
(2016-01-01)
Chen, Chiao-Ting
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Huang, Shih-Jung
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Chang, Yang
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Hsiao, Chih-Yen
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Lin, Jiun-Yi
A novel intraday algorithmic trading strategy is developed based on various machine learning techniques and paralleled computing architectures in this paper. The proposed binary classification framework can predict the price trends of Taiwan stock index futures after thirty minutes. Traditional learning-based approaches collect all samples during the training period as the learning materials. The major contribution of this paper is to collect a subset of similar historical financial data to train the real-time trading model. This goal can be achieved by an on-line learning technique which is required to calculate an accurate model with training time limitation. In addition, the proposed joint-AdaBoost algorithm is to improve the system performance based on the concept of paired feature learning and planar weak classifier design. The core execution components in this algorithm can be further accelerated with the aid of Open Computing Language (OpenCL) parallel computing platform. The experimental results show that the proposed learning algorithm can improve the prediction accuracy of final classifier from 53.8% to 61.68%. Compared to the pure CPU implementation, the OpenCL version which uses CPU and GPGPU simultaneously can reduce the calculation time around 83.02 times. The efficiency improvement can decrease the delay of investment opportunity which is a critical issue in real-time financial decision support system application. To sum up, this paper proposed a novel learning framework based on joint-AdaBoost algorithm with similar learning samples and OpenCL parallel computation. The extended financial decision support system is also proven to work effectively and efficiently in our simulation experiments to trade the Taiwan stock index futures.
Implementing AI as Cyber IoT Devices: The House Valuation Example
(2020-04-01)
Lin, Yun-Wei
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Lin, Yi-Bing
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Liu, Chun-You
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Lin, Jiun-Yi
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Shih, Yu-Lin
Internet of Things (IoT) has been widely utilized with artificial intelligence (AI). However, it requires substantial effort to integrate AI and big data with IoT. To mitigate this problem, AItalk was proposed. By treating AI as a cyber IoT device, we do not need to write code of the AI mechanism in the network applications, as traditional AI-based IoT applications did. This article describes how the AI tools such as scikit-learn and TensorLayer are accommodated as cyber IoT devices in AItalk, and extends AItalk for non-IoT applications. We use house valuation as an example to show how AItalk can flexibly include the factors that have significant impact on the house price. We show that by adding extra features other than housing profile features, the accuracy for the prediction (valuation) can be improved by 38 & x0025;. We also investigated the communication overhead of the distributed AItalk structure, which is 3.7 & x0025; for the computation of one house price valuation.
Mixed-Vth (MVT) CMOS circuit design for low power cell libraries
(2007)
Lin, Jiun-Yi
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Wang, Li-Rong
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Hu, Chia-Lmi
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Jou, Shyh-Jye
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電子工程學系及電子研究所
Mixed-Vth (MVT) technique has been proposed to resize the MOS size and then reduce dynamic power in logic gates by applying a low threshold voltage to transistors in some critical paths, while a standard threshold voltage is used in non-critical paths. This paper presents 130nm and 90nm low power cell libraries using MVT technique. The dynamic power consumption of the cells has been reduced around 5% to 30% and with the same timing specifications.
SensorTalk: An IoT Device Failure Detection and Calibration Mechanism for Smart Farming
(2019-11-01)
Lin, Yi-Bing
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Lin, Yun-Wei
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Lin, Jiun-Yi
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Hung, Hui-Nien
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交大名義發表
In an Internet of Things (IoT) system, it is essential that the data measured from the sensors are accurate so that the produced results are meaningful. For example, in AgriTalk, a smart farm platform for soil cultivation with a large number of sensors, the produced sensor data are used in several Artificial Intelligence (AI) models to provide precise farming for soil microbiome and fertility, disease regulation, irrigation regulation, and pest regulation. It is important that the sensor data are correctly used in AI modeling. Unfortunately, no sensor is perfect. Even for the sensors manufactured from the same factory, they may yield different readings. This paper proposes a solution called SensorTalk to automatically detect potential sensor failures and calibrate the aging sensors semi-automatically. Numerical examples are given to show the calibration tables for temperature and humidity sensors. When the sensors control the actuators, the SensorTalk solution can also detect whether a failure occurs within a detection delay. Both analytic and simulation models are proposed to appropriately select the detection delay so that, when a potential failure occurs, it is detected reasonably early without incurring too many false alarms. Specifically, our selection can limit the false detection probability to be less than 0.7%.