L 2 C: Combining Lossy and Lossless Compression on Memory and I/O

In this article, we introduce L 2 C, a hybrid lossy/lossless compression scheme applicable both to the memory subsystem and I/O traffic of a processor chip. L 2 C employs general-purpose lossless compression and combines it with state-of-the-art lossy compression to achieve compression ratios up to 16:1 and to improve the utilization of chip’s bandwidth resources. Compressing memory traffic yields lower memory access time, improving system performance, and energy efficiency. Compressing I/O traffic offers several benefits for resource-constrained systems, including more efficient storage and networking. We evaluate L 2 C as a memory compressor in simulation with a set of approximation-tolerant applications. L 2 C improves baseline execution time by an average of 50% and total system energy consumption by 16%. Compared to the lossy and lossless current state-of-the-art memory compression approaches, L 2 C improves execution time by 9% and 26%, respectively, and reduces system energy costs by 3% and 5%, respectively. I/O compression efficacy is evaluated using a set of real-life datasets. L 2 C achieves compression ratios of up to 10.4:1 for a single dataset and on average about 4:1, while introducing no more than 0.4% error.

Energy Storage System and Its Power Electronic Interface

This chapter examines the modeling and simulation of energy storage (battery, flywheel, etc.) systems interfaced to the power grid by using power electronic device, like chopper module, Rectifier module, and filter circuits, which are essential to the load balance between supply and demand, and to eliminate harmonics and to ensure efficient, cost effective, and reliable operations. Energy storage system in power grid is the same as memory in computer system. Energy efficiency is a key performance indicator for energy storage system. The energy storage system is the most promising component to enhance the system reliability and flexibility.

The Relation between Burning Time and Burning Energy of HTPB - Based Composite Propellant

This paper represented a result of several visions of chemical phenomenon and several extractions and extrapolations of experimental works which included a relationship between energy related to a chemical process and the relevant time which is required to achieve this process, but it must be taken into account that those mentioned experimental works hadn’t aimed substantially to study and state this relationship neither implicitly nor explicitly, but the results of those works have been exploited for another field after being compared with the relevant thermodynamic calculations. The selected case study for this paper was the relation between the burning time of Hydroxyl terminated poly butadiene propellant ( HTPB) and the caloric value of this material. The results reflected some relationship between the burning time and the change of the system energy during the burning process.

Deep Reinforcement Learning Based Resource Allocation with Radio Remote Head Grouping and Vehicle Clustering in 5G Vehicular Networks

With increasing data traffic requirements in vehicular networks, vehicle-to-everything (V2X) communication has become imperative in improving road safety to guarantee reliable and low latency services. However, V2X communication is highly affected by interference when changing channel states in a high mobility environment in vehicular networks. For optimal interference management in high mobility environments, it is necessary to apply deep reinforcement learning (DRL) to allocate communication resources. In addition, to improve system capacity and reduce system energy consumption from the traffic overheads of periodic messages, a vehicle clustering technique is required. In this paper, a DRL based resource allocation method is proposed with remote radio head grouping and vehicle clustering to maximize system energy efficiency while considering quality of service and reliability. The proposed algorithm is compared with three existing algorithms in terms of performance through simulations, in each case outperforming the existing algorithms in terms of average signal to interference noise ratio, achievable data rate, and system energy efficiency.

Indonesian hydro energy potential map with run-off river system

Indonesia aims to decarbonize the energy sector by accelerating the use of new and renewable energy, expected to reach 31% of total energy supply in 2050. One of important tools to achieve the target is renewable energy potential maps including hydro energy potential maps. Yet, existing hydro energy potential maps have several weaknesses such as sites coordinate not on the river network. This study aims to update and to improve the run-off river system energy hydro maps by using a novel method considering multiple factors that are head values, discharge river, gravity, and the efficiency of the hydro system. In calculating the head value, we use DEM data from SRTM 1 arc second to estimate difference between upstream and downstream elevations. We also did Q90 modeling using WFLOW software as generate the discharge value. In the end, we verified the maps by using field measurement data in 776 sites from previous study. As a result, we estimate the total potential of hydro energy with the run-off river system in Indonesia reaches 94,627 MW distributed in 52,566 sites.

Cooperative Sensing and Allocation Algorithm of Cognitive Radio Spectrum Based on Artificial Intelligence

In recent years, with the rapid development of artificial intelligence technology, people’s demand for wireless spectrum resources is increasing, which poses a huge challenge to the originally tight and limited wireless spectrum resources. On the other hand, the traditional fixed spectrum cooperative sensing and allocation algorithms result in extremely low spectrum utilization for a considerable part of the licensed spectrum. The purpose of this paper is to study the cooperative sensing and allocation algorithm of cognitive RS (radio spectrum) based on artificial intelligence. This dissertation focuses on cooperative perception and cognitive radio systems, respectively, from the aspects of cooperative perception of user fairness, maximization of system energy efficiency, and user detection when user access is busy. Firstly, a joint optimization model of fairness cooperative spectrum sensing and allocation is established to compensate the sensing overhead of cooperative users to ensure its fairness; then, define and analyze the energy efficiency of the cognitive system, and establish a joint optimization model of cooperative spectrum sensing and allocation based on artificial intelligence to maximize energy efficiency, and optimize wireless sensing and allocation parameters while ensuring maximum system energy efficiency. The experimental results show that when = 0.7, the algorithm proposed in this study has reached 100% of the RS perception performance, while the traditional algorithm only has 93%. The algorithm proposed in this paper has greater advantages in perception and distribution performance.