Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research.

In this letter, we introduce a deep reinforcement learning (RL) based multi-robot formation controller for the task of autonomous aerial human motion capture (MoCap). We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose and shape of a single moving person using multiple micro aerial vehicles. State-of-the-art solutions to this problem are based on classical control methods, which depend on hand-crafted system and observation models. Such models are difficult to derive and generalize across different systems. Moreover, the non-linearity and non-convexities of these models lead to sub-optimal controls. In our work, we formulate this problem as a sequential decision making task to achieve the vision-based motion capture objectives, and solve it using a deep neural network-based RL method. We leverage proximal policy optimization (PPO) to train a stochastic decentralized control policy for formation control. The neural network is trained in a parallelized setup in synthetic environments. We performed extensive simulation experiments to validate our approach. Finally, real-robot experiments demonstrate that our policies generalize to real world conditions. Video Link: https://bit.ly/38SJfjo Supplementary: https://bit.ly/3evfo1O

In fog-assisted IoT systems, it is a common practice to offload tasks from IoT devices to their nearby fog nodes to reduce task processing latencies and energy consumptions. However, the design of online energy-efficient scheme is still an open problem because of various uncertainties in system dynamics such as processing capacities and transmission rates. Moreover, the decision-making process is constrained by resource limits on fog nodes and IoT devices, making the design even more complicated. In this paper, we formulate such a task offloading problem with unknown system dynamics as a combinatorial multi-armed bandit (CMAB) problem with long-term constraints on time-averaged energy consumptions. Through an effective integration of online learning and online control, we propose a \textit{Learning-Aided Green Offloading} (LAGO) scheme. In LAGO, we employ bandit learning methods to handle the exploitation-exploration tradeoff and utilize virtual queue techniques to deal with the long-term constraints. Our theoretical analysis shows that LAGO can reduce the average task latency with a tunable sublinear regret bound over a finite time horizon and satisfy the long-term time-averaged energy constraints. We conduct extensive simulations to verify such theoretical results.

"Artificial Intelligence in Oil and Gas Market is growing at a High CAGR during the forecast period 2020-2026.

Australia is deploying a fleet of uncrewed robot boats to patrol its waters and monitor weather and wildlife. They will also flag boats potentially transporting asylum seekers, a plan that has concerned human rights groups. The 5-metre-long vessels, known as Bluebottles after an Australian jellyfish, look like miniature sailing yachts. They use a combination of wind, wave and solar power to maintain a steady 5-knot speed in all conditions. Sydney-based Ocius Technology delivered the prototype in 2017 and Australia's Ministry of Defence has now awarded an AU$5.5 million (£3m) …

Motorized window treatments that can open and close on command, on a schedule, or even based on room occupancy are the ultimate finishing touch for any smart home. Like smart lighting, smart window treatments offer a host of benefits in terms of convenience, security, and energy conservation. There's a safety angle, too: There are no pull cords that pose a strangulation risk to children and pets. But the wow factor they deliver also renders them a luxury item--even deploying them one room at a time can cost thousands of dollars if each room has a lot of windows. Shades are a soft window covering, typically made of fabric.

Real-time motion prediction of a vessel or a floating platform can help to improve the performance of motion compensation systems. It can also provide useful early-warning information for offshore operations that are critical with regard to motion. In this study, a long short-term memory (LSTM) -based machine learning model was developed to predict heave and surge motions of a semi-submersible. The training and test data came from a model test carried out in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The motion and measured waves were fed into LSTM cells and then went through serval fully connected (FC) layers to obtain the prediction. With the help of measured waves, the prediction extended 46.5 s into future with an average accuracy close to 90%. Using a noise-extended dataset, the trained model effectively worked with a noise level up to 0.8. As a further step, the model could predict motions only based on the motion itself. Based on sensitive studies on the architectures of the model, guidelines for the construction of the machine learning model are proposed. The proposed LSTM model shows a strong ability to predict vessel wave-excited motions.

In many applications, one may benefit from the collaborative collection of data for sensing a physical phenomenon, which is known as social sensing. We show how to make social sensing (1) predictable, in the sense of guaranteeing that the number of queries per participant will be independent of the initial state, in expectation, even when the population of participants varies over time, and (2) fair, in the sense of guaranteeing that the number of queries per participant will be equalised among the participants, in expectation, even when the population of participants varies over time. In a use case, we consider a large, high-density network of participating parked vehicles. When awoken by an administrative centre, this network proceeds to search for moving, missing entities of interest using RFID-based techniques. We regulate the number and geographical distribution of the parked vehicles that are "Switched On" and thus actively searching for the moving entity of interest. In doing so, we seek to conserve vehicular energy consumption while, at the same time, maintaining good geographical coverage of the city such that the moving entity of interest is likely to be located within an acceptable time frame. Which vehicle participants are "Switched On" at any point in time is determined periodically through the use of stochastic techniques. This is illustrated on the example of a missing Alzheimer's patient in Melbourne, Australia.

With digital transformation in full swing, the number of connected devices is increasing at a fast pace. IDC Data predicts 152,200 connected IoT devices every minute by the year 2025. While this translates to more data and, subsequently, more avenues to improve efficiency, a robust network is necessary for this data exchange. The fifth-generation wireless technology has features that will not only support high-speed mobile communication but also make IoT data transfer more efficient. Let's look at these features in contrast with the existing 4G network: All these features make the 5G network adaptable to the external environment, unlike its predecessors, which has limited network flexibilities.

Electric cars are all the rage these days and several governments have plans to slowly phase out internal combustion engines in favor of them over the next decade. However, before electric cars fill our roads, the battery sector will have to find ways to drastically improve the current recharging rates. A wide ranging team of researchers have taken advantage of another technology that has captured the public eye, artificial intelligence ("AI") for clues on how to improve recharging rates. The team of researchers from the Massachusetts Institute of Technology, the Stanford University and the Toyota Research Institute wanted to see whether artificial intelligence could speed up the testing process required for novel charging techniques. They wrote a program that predicted how batteries would respond to different charging approaches, and it was able to cut the testing process from almost two years to 16 days.