The early prediction of battery life (EPBL) is vital for enhancing the
efficiency and extending the lifespan of lithium batteries. Traditional models
with fixed architectures often encounter underfitting or overfitting issues due
to the diverse data distributions in different EPBL tasks. An interpretable
deep learning model of flexible parallel neural network (FPNN) is proposed,
which includes an InceptionBlock, a 3D convolutional neural network (CNN), a 2D
CNN, and a dual-stream network. The proposed model effectively extracts
electrochemical features from video-like formatted data using the 3D CNN and
achieves advanced multi-scale feature abstraction through the InceptionBlock.
The FPNN can adaptively adjust the number of InceptionBlocks to flexibly handle
tasks of varying complexity in EPBL. The test on the MIT dataset shows that the
FPNN model achieves outstanding predictive accuracy in EPBL tasks, with MAPEs
of 2.47%, 1.29%, 1.08%, and 0.88% when the input cyclic data volumes are 10,
20, 30, and 40, respectively. The interpretability of the FPNN is mainly
reflected in its flexible unit structure and parameter selection: its diverse
branching structure enables the model to capture features at different scales,
thus allowing the machine to learn informative features. The approach presented
herein provides an accurate, adaptable, and comprehensible solution for early
life prediction of lithium batteries, opening new possibilities in the field of
battery health monitoring.

为了提高锂电池的效率和延长使用寿命，本研究提出了一种可解释的深度学习模型 FPNN，它可以自适应地调整不同复杂度的任务，从而提高锂电池寿命的预测准确性。

柔性并行神经网络架构模型用于锂电池寿命的早期预测

Flexible Parallel Neural Network Architecture Model for Early Prediction  of Lithium Battery Life

Smartphones and smartphone apps have undergone an explosive growth in the
past decade. However, smartphone battery technology hasn't been able to keep
pace with the rapid growth of the capacity and the functionality of smartphones
and apps. As a result, battery has always been a bottleneck of a user's daily
experience of smartphones. An accurate estimation of the remaining battery life
could tremendously help the user to schedule their activities and use their
smartphones more efficiently. Existing studies on battery life prediction have
been primitive due to the lack of real-world smartphone usage data at scale.
This paper presents a novel method that uses the state-of-the-art machine
learning models for battery life prediction, based on comprehensive and
real-time usage traces collected from smartphones. The proposed method is the
first that identifies and addresses the severe data missing problem in this
context, using a principled statistical metric called the concordance index.
The method is evaluated using a dataset collected from 51 users for 21 months,
which covers comprehensive and fine-grained smartphone usage traces including
system status, sensor indicators, system events, and app status. We find that
the remaining battery life of a smartphone can be accurately predicted based on
how the user uses the device at the real-time, in the current session, and in
history. The machine learning models successfully identify predictive features
for battery life and their applicable scenarios.

通过使用机器学习模型和从智能手机收集的数据，本文提出了一种新的方法来准确预测智能手机的剩余电池寿命，并成功地识别了电池寿命的预测特征及其适用场景。

基于全面实时使用数据预测智能手机电池寿命

Predicting Smartphone Battery Life based on Comprehensive and Real-time  Usage Data

Mobile-edge computation offloading (MECO) offloads intensive mobile
computation to clouds located at the edges of cellular networks. Thereby, MECO
is envisioned as a promising technique for prolonging the battery lives and
enhancing the computation capacities of mobiles. In this paper, we study
resource allocation for a multiuser MECO system based on time-division multiple
access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First,
for the TDMA MECO system with infinite or finite computation capacity, the
optimal resource allocation is formulated as a convex optimization problem for
minimizing the weighted sum mobile energy consumption under the constraint on
computation latency. The optimal policy is proved to have a threshold-based
structure with respect to a derived offloading priority function, which yields
priorities for users according to their channel gains and local computing
energy consumption. As a result, users with priorities above and below a given
threshold perform complete and minimum offloading, respectively. Moreover, for
the cloud with finite capacity, a sub-optimal resource-allocation algorithm is
proposed to reduce the computation complexity for computing the threshold.
Next, we consider the OFDMA MECO system, for which the optimal resource
allocation is formulated as a non-convex mixed-integer problem. To solve this
challenging problem and characterize its policy structure, a sub-optimal
low-complexity algorithm is proposed by transforming the OFDMA problem to its
TDMA counterpart. The corresponding resource allocation is derived by defining
an average offloading priority function and shown to have close-to-optimal
performance by simulation.

研究了基于时分多址和正交频分多址的多用户 MECO 系统的资源分配，其中优化的资源分配被证明具有基于阈值的结构，而云计算的有限容量则通过提出子优化算法减少了计算复杂度。