Purpose
The synthetic application and interaction of/between the internet, Internet of Things, cloud computing, big data, Industry 4.0 and other new patterns and new technologies shall breed future Web-based industrial operation system and social operation management patterns, manifesting as a crowd cyber eco-system composed of multiple interconnected intelligent agents (enterprises, individuals and governmental agencies) and its dynamic behaviors. This paper aims to explore the basic principles and laws of such a system and its behavior.
Design/methodology/approach
The authors propose the concepts of crowd science and engineering (CSE) and expound its main content, thus forming a research framework of theories and methodologies of crowd science.
Findings
CSE is expected to substantially promote the formation and development of crowd science and thus lay a foundation for the advancement of Web-based industrial operation system and social operation management patterns.
Originality/value
This paper is the first one to propose the concepts of CSE, which lights the beacon for the future research in this area.
Decision tree algorithm has been widely used to classify numeric and categorical attributes. Lots of approaches were suggested in order to induce decision trees. ID3 (Quinlan, 1986), as a heuristic algorithm, is very classic and popular in the induction of decision trees. The key of ID3 is to choose information gain as the standard for testing attributes. In this paper, we propose a novel measure based on rough set theory to select attributes that will best split current samples into individual classes. In the view of rough set theory, we analyze the shortcomings of ID3 algorithm and rationality of the new approach, and then propose a fixed algorithm based on original idea. The results of example and experiments show that our approach is better in selecting nodes for inducing decision trees than ID3.
Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.
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