Future Prediction Methods Research Articles

Impact of COVID-19 on city-scale transportation and safety: An early experience from Detroit.

The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City---Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus and the rising number of COVID-19 confirmed cases and deaths. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goals of this work are: i) figuring out the impacts of COVID-19 on the transportation network usage (traffic volume) and safety (crashes) for the City of Detroit, ii) determining whether each type of data (e.g. traffic volume data) could be a useful factor in the confirmed-cases prediction, and iii) providing an early future prediction method for COVID-19 rates, which can be a vital contributor to life-saving advanced preventative and preparatory responses.In addressing these problems, the prediction results of six feature groups are presented and analyzed to quantify the prediction effectiveness of each type of data. Then, a deep learning model was developed using long short-term memory networks to predict the number of confirmed cases within the next week. The model demonstrated a promising prediction result with a coefficient of determination () of up to approximately 0.91. Furthermore, six essential observations with supporting evidence are presented, which will be helpful for decision-makers to take specific measures that aid in preventing the spread of COVID-19 and protecting public health and safety. The proposed approaches could be applied, customized, adjusted, and replicated for analysis of the impact of COVID-19 on a transportation network and prediction of the anticipated COVID-19 cases using a similar data set obtained for other large cities in the USA or from around the world.

Optimal scheduling of multiple multi-energy supply microgrids considering future prediction impacts based on model predictive control

Renewable energy based multi-energy supply microgrids not only can cover different types of demands (such as, electricity/heat/gas), but also can interconnect with different utility grid networks (electricity/heat/gas). When there are large numbers of grid-connected microgrids, how to operate these multiple microgrids in real-time is a problem. In this paper, day-ahead stochastic optimization scheduling and real-time sliding window model predictive control are used to control the operation of microgrids. In order to consider the influence of future prediction on the current optimal decision results, different prediction methods are adopted to predict the load demands and renewable energy output. For example, online learning Markov chain prediction, and support vector machine are used to predict the future values. As for comparison, robust prediction and bilevel optimization are adopted to describe the future prediction uncertainty. The real-time operation of microgrids aims to follow the day-ahead exchanged energy with utility grids, which can minimize the impact of the microgrid on the utility grids. The supply network is an IEEE30 + gas20+heat14 network. The results show that: 1) when the sliding window number is smaller, the total operation cost is larger, but the calculation time is smaller, the trade-off between sliding window numbers and calculation time should be considered; 2) the accuracy of the prediction impacts the 2-norm error of the operation cost, when we decrease by “1” unit of 2-norm prediction error of the whole system, the 2-norm operation cost will decrease by “0.15” unit; 3) from the view of the post-event analysis (total operation cost), for the Markov chain prediction method, the relative error is about 0.32%, is better than the support vector machine method; 4) in the robust cases, the larger the conservative value, the higher the stored hydrogen energy. At last, the results of real-time sliding window model predictive control problem are influenced by the future prediction methods and the window numbers.

미래 예측 방식, 정서 사건에 따른 메시지 프레이밍, 그리고 실행 의도 : 시간 지각의 매개 효과를 중심으로

미래 예측 방식, 정서 사건에 따른 메시지 프레이밍, 그리고 실행 의도 : 시간 지각의 매개 효과를 중심으로

A Study on the Development of App Ecosystem based Smart Home

Smart Home has achieved remarkable developments over the past few decades. In the ICT(Information and Communications Technology) field, 'app ecosystem'-a collection of multiple devices such as mobile phones and tablets, software (operating system and development tools), companies (manufacturers, carriers, app-stores, etc.) and the process through which data is transferred/shared by a user from one device to another device or by the device itself-has come into wide use since the advent of the smart phone. Due to the synergy effect of the 'app ecosystem', it has been applied to various fields such as televisions and automobile industries. As a result, both the Smart TV and connected vehicle have developed their own ecosystem. Although much research has been conducted on these two ecosystems, there is a lack of research regarding 'App Ecosystem based Smart Home' (AESH). This research focuses on the building scenarios based on 'Tracking, Analyzing, Imaging, Deciding, and Acting (T.A.I.D.A), a future prediction method process. Rather than taking an approach from the perspective of providing and applying advanced technology for research on building future scenarios, this paper focuses on research from the perspective of architectural planning. As a result, two future scenarios of AESH are suggested.

Prediction of subcellular locations of proteins: Where to proceed?

Since the proposal of the signal hypothesis on protein subcellular sorting, a number of computational analyses have been performed in this field. A typical example is the development of prediction algorithms for the subcellular localization sites of input protein sequences. In this review, we mainly focus on the biological grounds of the prediction methods rather than the algorithmic issues because we believe the former will be more fruitful for future development. Recent advances on the study of protein sorting signals will hopefully be incorporated into future prediction methods. Unfortunately, many of the state-of-the-art methods are published without sufficient objective tests. In fact, a simple test employed in this article shows that the performance of specifically developed predictors is not significantly better than that of a homology search. We suspect that this is a general problem associated with the interpretation of genome sequences, which have evolved through gene duplication and speciation.