Concession Period Decision Models for Public Infrastructure Projects Based on Option Games

Concession period is an important decision-making variable for the investment and construction of public infrastructure projects. However, we currently have few scientific methods to exactly determine the concession period. This paper managed to seek out concession period decision models for public infrastructure with option game theory, studied the influence of minimum government income guarantee and government investment on concession period, and demonstrated those models in the formulas mentioned in the paper. The research results showed that the increase of minimum government income guarantee value would shorten the concession period, while the increase of income volatility, that is, the uncertainty, would lengthen the concession period. In terms of government investment, optimal concession period would lengthen to some extent with the increase of government investment ratio and the income and the decrease of its guarantee value. Yet, optimal concession period would shorten in case of extreme highness of the government investment ratio due to its high guarantee value. And the government would accordingly shorten the concession period in case of the unchanged government investment ratio with the increased income volatility and risks. Still, the paper put forward the argument that the government would apply various guarantee methods and implement flexible concession period in accordance with the specific circumstances of public infrastructure projects

Progressive sequence matching for ADL plan recommendation

Activities of Daily Living (ADLs) are indicatives of a person's lifestyle. In particular, daily ADL routines closely relate to a person's well-being. With the objective of promoting active lifestyles, this paper presents an agent system that provides recommendations of suitable ADL plans (i.e., selected ADL sequences) to individual users based on the more active lifestyles of the others. Specifically, we develop a set of quantitative measures, named wellness scores, spanning the evaluation across the physical, cognitive, emotion, and social aspects based on his or her ADL routines. Then we propose an ADL sequence learning model, named Recommendation ADL ART, or RADLART, which proactively recommends healthier choices of activities based on the learnt associations among the user profiles, ADL sequence, and wellness scores. For empirical evaluation, extensive simulations have been conducted to assess the improvement in wellness scores for synthetic users with different acceptance rates of the provided recommendations. Experiments on real users further show that recommendations given by RADLART are generally more acceptable by the users because it takes into considerations of both the user profiles and the performed activities.NRF (Natl Research Foundation, S’pore)Accepted versio

Robust human activity recognition using lesser number of wearable sensors

In recent years, research on the recognition of human physical activities solely using wearable sensors has received more and more attention. Compared to other types of sensory devices such as surveillance cameras, wearable sensors are preferred in most activity recognition applications mainly due to their non-intrusiveness and pervasiveness. However, many existing activity recognition applications or experiments using wearable sensors were conducted in the confined laboratory settings using specifically developed gadgets. These gadgets may be useful for a small group of people in certain specific scenarios, but probably will not gain their popularity because they introduce additional costs and they are unusual in everyday life. Alternatively, commercial devices such as smart phones and smart watches can be better utilized for robust activity recognitions. However, only few prior studies focused on activity recognitions using multiple commercial devices. In this paper, we present our feature extraction strategy and compare the performance of our feature set against other feature sets using the same classifiers. We conduct various experiments on a subset of a public dataset named PAMAP2. Specifically, we only select two sensors out of the thirteen used in PAMAP2. Experimental results show that our feature extraction strategy performs better than the others. This paper provides the necessary foundation towards robust activity recognition using only the commercial wearable devices.NRF (Natl Research Foundation, S’pore)Accepted versio

A novel density peak clustering algorithm based on squared residual error

The density peak clustering (DPC) algorithm is designed to quickly identify intricate-shaped clusters with high dimensionality by finding high-density peaks in a non-iterative manner and using only one threshold parameter. However, DPC has certain limitations in processing low-density data points because it only takes the global data density distribution into account. As such, DPC may confine in forming low-density data clusters, or in other words, DPC may fail in detecting anomalies and borderline points. In this paper, we analyze the limitations of DPC and propose a novel density peak clustering algorithm to better handle low-density clustering tasks. Specifically, our algorithm provides a better decision graph comparing to DPC for the determination of cluster centroids. Experimental results show that our algorithm outperforms DPC and other clustering algorithms on the benchmarking datasets.NRF (Natl Research Foundation, S’pore)Accepted versio

A systematic density-based clustering method using anchor points

National Research Foundation (NRF) Singapore under its AI Singapore Programme; Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Agein