Industrial Technology Research Institute

En-Tzu Wang

Tsung-Wen Tso

Tzi-Cker Chiueh

Lun-Chia Kuo

Guan-Rong Lin

Yao-Chung Fan

Arbee L P Chen

Tai-Ting Wu

Shu-Chun Yeh

Cheng-Liang Lin

Sian-Hong Huang

Chin-Yang Lin

Jyun-Tang Huang

Je-Jone Ko

Ming-Yao Tsai

Chi-Yuan Yeh

Chi-Chun Kao

Chao-Chih Wang

I-Hong Kuo

Jie-Sin Li

A project generating method comprises extracting a keyword from a plurality of text files with a specified category, determining whether the keyword is a theme, extracting a geographical name from the text files corresponding to the theme, determining whether to keep the theme according to an internet volume of the theme, filtering a plurality of review files from a review website according to the geographical name, calculating a first ratio therein to determining whether to keep the theme, and generating a project including the geographical name and the theme serving as a recommendation row.

Project generating system and method thereof

An investment strategy rule generation method including the following steps is provided. Firstly, an investment strategy rule generator generates an investment strategy model according to an investment history trend. Then, a total investment return of each of N candidate investment rules is obtained by the investment strategy rule calculator, wherein each of the N candidate investment rules includes a candidate market direction rule. The obtaining step includes: performing an investment simulation in each of multiple time points in a time window of a time interval. Then, the total investment return under the operation of the investment simulations over the time interval is calculated by the investment strategy rule calculator. Then, the candidate investment rule corresponding to the best of the total investment returns is used as an investment strategy rule of the investment strategy model by the investment strategy rule calculator.

Investment strategy rule generation method and an investment strategy rule device using the same

A decision factors analyzing device and a decision factors analyzing device for analyzing a plurality of decision factors which cause a product of a product type to be purchased are provided. The method includes identifying a plurality of product sequences corresponding to the product type from a plurality of browse history data and a plurality of purchase history data corresponding to a plurality of consumers of a consumer database, wherein each of the product sequences includes a unpurchased product and a purchased product; obtaining a feature sequence according to the produce sequences and a plurality of product information; training a regression model corresponding to the product type according to K decision factors of the feature sequence to obtain an optimized regression model, and obtaining K decision values respectively corresponding to the K decision factors according to the optimized regression model to generate a decision factor sequence corresponding to the product type.

Decision factors analyzing device and decision factors analyzing method

A method for finding crowd movements is provided. The method includes: collecting location data associated with multiple user devices, mining frequent patterns in the location data to generate multiple representative sequences, and grouping the representative sequences into clusters to find crowd movements according to sequence distances between the representative sequences. Each representative sequence includes at least one line segment between a starting location point and an end location point.

System and computer readable medium for finding crowd movements

According to one exemplary embodiment, a method for analyzing root causes applies an application-level dependency discovery and anomaly detection to find application-level dependencies in one or more virtual machines (VMs), and generate an application-level topology with anomaly, and then transfers the application-level topology with anomaly to a VM-level dependency, and transfers the VM-level dependency to a physical machine level (PM-level) dependency via a physical and virtual resource mapping, and eventually generates a group of event sets. A prioritized event list is generated by prioritizing the group of event sets.

Method and system for analyzing root causes of relating performance issues among virtual machines to physical machines

A wireless sensor network and data sensing method thereof are provided. A prediction model is established according to sensed data. When statistical value of the sensed data is within a user allowable range, the average value of the sensed data is returned to a user based on the prediction model. Alternatively, when the statistical value of the sensed data is beyond the user allowable range, actually sensed data is returned to the user based on the prediction model. In addition, the prediction model may further be dynamically updated according to subsequently received sensed data.

Wireless sensor network and data sensing method thereof

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Kaohsiung, Taiwan

En-Tzu Wang