Traditional exoskeletons have made considerable contributions to people in terms of providing wearable assistance and
rehabilitation. However, exoskeletons still have some disadvantages, such as being heavy, bulky, stiff, noisy, and having a fixed
center of rotation that can be a burden on elders and patients with weakened muscles. Conversely, artificial muscles based on soft,
smart materials possess the attributes of being lightweight, compact, highly flexible, and have mute actuation, for which they are
considered to be the most similar to natural muscles. Among these materials, dielectric elastomer (DE) and polyvinyl chloride
(PVC) gel exhibit considerable actuation strain, high actuation stress, high response speed, and long life span, which give them
great potential for application in wearable assistance and rehabilitation. Unfortunately, there is very little research on the appli-
cation of these two materials in these fields. In this review, we first introduce the working principles of the DE and PVC gel
separately. Next, we summarize the DE materials and the preparation of PVC gel. Then, we review the electrodes and self-sensing
systems of the two materials. Lastly, we present the initial applications of these two materials for wearable assistance and
rehabilitation.

The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate
translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate trans-
lational base frame parameters, internal force, modeling uncertainties, joint friction, and external disturbances. A radial basis
function neural network is adopted for all kinds of dynamical estimation, including undesired internal force. To validate the
effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to
asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this ap-
proach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Spe-
cialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the
face of inaccurate base frame parameters and the aforementioned uncertainties.

Cabled seafloor observatories play an important role in ocean exploration for its long-term, real-time, and in-situ ob-
servation characteristics. In establishing a permanent, reliable, and robust seafloor observatory, a highly reliable cable switching
and fault isolation method is essential. After reviewing the advantages and disadvantages of existing switching methods, we
propose a novel active switching method for network configuration. Without additional communication path requirements, the
switching method provides a way to communicate with a shore station through an existing power transmission path. A coded
voltage signal with a distinct sequence is employed as the communication medium to transmit commands. The analysis of the
maximum bit frequency of the voltage signals guarantees the accuracy of command recognition. A prototype based on the
switching method is built and tested in a laboratory environment, which validated the functionality and reliability of the method.

To reduce the computation complexity of the optimization algorithm used in energy management of a multi-microgrid
system, an energy optimization management method based on model predictive control is presented. The idea of decomposition
and coordination is adopted to achieve the balance between power supply and user demand, and the power supply cost is mini-
mized by coordinating surplus energy in the multi-microgrid system. The energy management model and energy optimization
problem are established according to the power flow characteristics of microgrids. A dual decomposition approach is imposed to
decompose the optimization problem into two parts, and a distributed predictive control algorithm based on global optimization is
introduced to achieve the optimal solution by iteration and coordination. The proposed method has been verified by simulation,
and simulation results show that the proposed method provides the demanded energy to consumers in real time, and improves
renewable energy efficiency. In addition, the proposed algorithm has been compared with the particle swarm optimization (PSO)
algorithm. The results show that compared with PSO, the proposed method has better performance, faster convergence, and
significantly higher efficiency.

We present a method of discriminant diffusion maps analysis (DDMA) for evaluating tool wear during milling pro-
cesses. As a dimensionality reduction technique, the DDMA method is used to fuse and reduce the original features extracted from
both the time and frequency domains, by preserving the diffusion distances within the intrinsic feature space and coupling the
features to a discriminant kernel to refine the information from the high-dimensional feature space. The proposed DDMA method
consists of three main steps: (1) signal processing and feature extraction; (2) intrinsic dimensionality estimation; (3) feature fusion
implementation through feature space mapping with diffusion distance preservation. DDMA has been applied to current signals
measured from the spindle in a machine center during a milling experiment to evaluate the tool wear status. Compared with the
popular principle component analysis method, DDMA can better preserve the useful intrinsic information related to tool wear
status. Thus, two important aspects are highlighted in this study: the benefits of the significantly lower dimension of the intrinsic
features that are sensitive to tool wear, and the convenient availability of current signals in most industrial machine centers.

Cloud-based storage is a service model for businesses and individual users that involves paid or free
storage resources. This service model enables on-demand storage capacity and management to users anywhere
via the Internet. Because most cloud storage is provided by third-party service providers, the trust required for
the cloud storage providers and the shared multi-tenant environment present special challenges for data protection
and access control. Attribute-based encryption (ABE) not only protects data secrecy, but also has ciphertexts or
decryption keys associated with fine-grained access policies that are automatically enforced during the decryption
process. This enforcement puts data access under control at each data item level. However, ABE schemes have
practical limitations on dynamic user revocation. In this paper, we propose two generic user revocation systems for
ABE with user privacy protection, user revocation via ciphertext re-encryption (UR-CRE) and user revocation via
cloud storage providers (UR-CSP), which work with any type of ABE scheme to dynamically revoke users.

Spectral clustering is one of the most popular and important clustering methods in pattern recognition,
machine learning, and data mining. However, its high computational complexity limits it in applications involving
truly large-scale datasets. For a clustering problem with n samples, it needs to compute the eigenvectors of the graph
Laplacian with O(n
3
) time complexity. To address this problem, we propose a novel method called anchor-based
spectral clustering (ASC) by employing anchor points of data. Specifically, m (m  n) anchor points are selected
from the dataset, which can basically maintain the intrinsic (manifold) structure of the original data. Then a
mapping matrix between the original data and the anchors is constructed. More importantly, it is proved that
this data-anchor mapping matrix essentially preserves the clustering structure of the data. Based on this mapping
matrix, it is easy to approximate the spectral embedding of the original data. The proposed method scales linearly
relative to the size of the data but with low degradation of the clustering performance. The proposed method, ASC,
is compared to the classical spectral clustering and two state-of-the-art accelerating methods, i.e., power iteration
clustering and landmark-based spectral clustering, on 10 real-world applications under three evaluation metrics.
Experimental results show that ASC is consistently faster than the classical spectral clustering with comparable
clustering performance, and at least comparable with or better than the state-of-the-art methods on both effectiveness
and efficiency.

We propose a new framework for image-based three-dimensional (3D) model retrieval. We first model the query image
as a Euclidean point. Then we model all projected views of a 3D model as a symmetric positive definite (SPD) matrix, which is a
point on a Riemannian manifold. Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric
learning. To solve this heterogeneous matching problem, we map the Euclidean space and SPD Riemannian manifold to the same
high-dimensional Hilbert space, thus shrinking the great gap between them. Finally, we design an optimization algorithm to learn
a metric in this Hilbert space using a kernel trick. Any new image descriptors, such as the features from deep learning, can be easily
embedded in our framework. Experimental results show the advantages of our approach over the state-of-the-art methods for
image-based 3D model retrieval.

Inferring query intent is significant in information retrieval tasks. Query subtopic mining aims to find possible sub-
topics for a given query to represent potential intents. Subtopic mining is challenging due to the nature of short queries. Learning
distributed representations or sequences of words has been developed recently and quickly, making great impacts on many fields.
It is still not clear whether distributed representations are effective in alleviating the challenges of query subtopic mining. In this
paper, we exploit and compare the main semantic composition of distributed representations for query subtopic mining. Specif-
ically, we focus on two types of distributed representations: paragraph vector which represents word sequences with an arbitrary
length directly, and word vector composition. We thoroughly investigate the impacts of semantic composition strategies and the
types of data for learning distributed representations. Experiments were conducted on a public dataset offered by the National
Institute of Informatics Testbeds and Community for Information Access Research. The empirical results show that distributed
semantic representations can achieve outstanding performance for query subtopic mining, compared with traditional semantic
representations. More insights are reported as well.

Since only one inverter voltage vector is applied during each duty cycle, traditional model predictive direct power
control (MPDPC) for grid-connected inverters (GCIs) results in serious harmonics in current and power. Moreover, a high sam-
pling frequency is needed to ensure satisfactory steady-state performance, which is contradictory to its long execution time due to
the iterative prediction calculations. To solve these problems, a novel dead-beat MPDPC strategy is proposed, using two active
inverter voltage vectors and one zero inverter voltage vector during each duty cycle. Adoption of three inverter vectors ensures a
constant switching frequency. Thus, smooth steady-state performance of both current and power can be obtained. Unlike the
traditional three-vector based MPDPC strategy, the proposed three vectors are selected based on the power errors rather than the
sector where the grid voltage vector is located, which ensures that the duration times of the selected vectors are positive all the time.
Iterative calculations of the cost function in traditional predictive control are also removed, which makes the proposed strategy
easy to implement on digital signal processors (DSPs) for industrial applications. Results of experiments based on a 1 kW inverter
setup validate the feasibility of the proposed three-vector based dead-beat MPDPC strategy.

We investigate a collaborative-relay beamforming design for simultaneous wireless information and power
transfer. A non-robust beamforming design that assumes availability of perfect channel state information (CSI) in the
relay nodes is addressed. In practical scenarios, CSI errors are usually inevitable; therefore, a robust collaborative-
relay beamforming design is proposed. By applying the bisection method and the semidefinite relaxation (SDR)
technique, the non-convex optimization problems of both non-robust and robust beamforming designs can be solved.
Moreover, the solution returned by the SDR technique may not always be rank-one; thus, an iterative sub-gradient
method is presented to acquire the rank-one solution. Simulation results show that under an imperfect CSI case, the
proposed robust beamforming design can obtain a better performance than the non-robust one.

Inspired by the geometric method proposed by Jean-Pierre MAREC, we first consider the Hohmann
transfer problem between two coplanar circular orbits as a static nonlinear programming problem with an inequality
constraint. By the Kuhn-Tucker theorem and a second-order sufficient condition for minima, we analytically prove
the global minimum of the Hohmann transfer. Two sets of feasible solutions are found: one corresponding to the
Hohmann transfer is the global minimum and the other is a local minimum. We next formulate the Hohmann
transfer problem as boundary value problems, which are solved by the calculus of variations. The two sets of feasible
solutions are also found by numerical examples. Via static and dynamic constrained optimizations, the solution
to the Hohmann transfer problem is re-discovered, and its global minimum is analytically verified using nonlinear
programming.