作为在杭州，南京和南昌等地成功举办的IHMSC 2009到IHMSC 2019 的延续，第十二届智能人机系统与控制论国际会议（IHMSC 2020）将在中国杭州的浙江大学举行，2020年8月22日至23日之间，本次会议的目的是提供一个论坛，供涉及人机系统和控制论各个方面的研究人员和从业人员交流研究结果，思路和应用经验。
[学术文献] Internet of things-based fog and cloud computing technology for smart traffic monitoring 进入全文
Internet of Things
Internet of Things (IoT) is changing the world by connecting billions of physical and virtual objects with distinctive identities to the Internet. This fusion results in generating huge volumes of data that might not be manageable using today's storage and data analytics technologies. Although cloud computing offers services to tackle this issue at infrastructural level, its efficiency for time sensitive applications (e.g. oil, gas, and traffic monitoring) is still questionable. Arguably, transferring massive amount of data to the cloud for storage and processing may lead to cloud overloading and saturation of network bandwidth. In this study, an integrated fog and cloud computing framework is introduced to overcome the limitations of real-time analytics, latency and network congestion of basic cloud services for traffic monitoring. The proposed approach is implemented to prototype a smart traffic monitoring system (STMS). The proposed monitoring system is designed for congestion monitoring and traffic light management. It can also be tuned to detect traffic incidents that requires immediate assistance during congestion. In this framework, a tiny computer-on-module serves as a fog node to collect real-time data from geographically distributed sensors and to transfer it to the cloud for storage and processing. The results show the efficiency of the fog network in improving the performance of the cloud platform in terms of reducing the response time and increasing the bandwidth. Furthermore, the proposed integrated fog and cloud framework is interfaced with Tweeter to send alerts about traffic congestion to be subscribed users in the form of Tweet messages .
[学术文献] Heuristic swarm intelligent optimization algorithm for path planning of agricultural product logistics distribution 进入全文
Journal of Intelligent & Fuzzy Systems
In the current agricultural product consumption market, the consumer’s demand for agricultural products tends to be diversified and individualized, and they need stricter links of cold chain logistics distribution of agricultural products. The cold chain logistics of fresh agricultural products is also the business of high energy consumption and high carbon emission in the logistics industry. The contradictory relationship between it and the “low-carbon economy” advocated today leads to the necessity to consider the relationship between economic benefit and environmental impact in the process of rapid development. Based on this, this paper, from the perspective of low-carbon economy, based on the analysis of the necessity of optimizing the distribution path of cold chain logistics of agricultural products, constructs a model for this and puts forward a swarm intelligence optimization algorithm-particle swarm optimization (PSO) algorithm. The algorithm is improved from inertia weight, convergence factor, learning factor, and population size and so on. The optimized PSO algorithm is compared with the traditional PSO algorithm. Finally, the simulation results show that the improved algorithm can be used to optimize the distribution path of cold chain logistics of agricultural products effectively, and the specific optimization countermeasures are put forward according to the actual situation.