As the number and complexity of constraints in constrained multi-objective optimization problems (CMOPs) increase, the performance of existing constrained multi-objective evolutionary algorithms (CMOEAs) declines significantly. A novel idea is to sequentially address each constraint based on priority, effectively reducing the complexity of CMOPs. However, in these algorithms, the constraint-handling priority is determined statically in the initial stage. This may lead to inappropriate determination of constraint-handling priority since accurately estimating the constraint landscape in the initial stage is quite challenging. Moreover, these algorithms tackle constraints separately, neglecting the potential for inter-constraint cooperation and thus compromising their efficiency in constraint handling. Thus, we propose a constrained multi-objective evolutionary algorithm based on dynamic priority and cooperative offspring generation called DPCMOEA. Firstly, the constraint-handling priority is determined dynamically by the estimated inconsistency degree (EID) between the Pareto fronts of the candidate constraints and the current population. Secondly, computational resources are automatically allocated to each constraint according to EID based constraint relationship analysis. Finally, a new offspring generation strategy based on constraint cooperation is designed to enhance the quality of new solutions. Experimental results on six CMOP test suites demonstrate that DPCMOEA outperforms six state-of-the-art algorithms.

We present a continuous attitude-tracking control strategy for energy-efficient real-time torque selection in autonomous aerial systems (AAS). The conventional unit-quaternion-based controllers may induce unwanted extra rotations due to the double-cover property of quaternions, which makes them inefficient in terms of time and energy. This paper presents an energy-efficient framework for attitude-tracking control of AAS in the presence of external disturbances and parameter uncertainties. We introduce a scalar error function to construct an associated error vector that regulates attitude convergence. The error vector serves as the basis for controller design when both the unit-quaternion equilibria are stable. The proposed energy-efficient framework-based controller inherently avoids unwanted rotations during attitude maneuvers and ensures almost global orientation tracking. Compared to conventional unit-quaternion-based controllers, the proposed energy-efficient framework is 71% more efficient in terms of energy. The simulation results show satisfactory performance of the proposed energy-efficient framework.

Cancer is inherently a complex disease and cancer somatic driver mutations within the cancer pathway often show mutually exclusive patterns in a group of patients, providing a novel signal to distinguish driver mutations from mass passenger mutations. However, in biological pathways, most mutated genes are low exclusivity and mutual exclusivity-based driver genes identification methods do not identify low exclusivity driver genes well. Furthermore, mutual exclusivity-based methods are largely influenced by the integrity of the biological network, resulting in their ineffectiveness in identifying driver genes. Therefore, in this paper, we propose a driver genes identification method with improved cross-entropy measure (ICE), which measures mutation frequencies of mutated genes in biological pathways while measuring mutual exclusivity through improved cross-entropy, thereby achieving complementary mutual exclusivity and mutation rates. Experimental results show that our method outperforms eight state-of-the-art methods in identifying driver genes, especially in identifying low-exclusivity genes. In addition, our method is more effective when protein networks and biological pathways are error-prone or incomplete.

Tooth arrangement is an essential step in the digital orthodontic planning process. Existing learning-based methods use hidden teeth features to directly regress teeth motions, which couples target pose perception and motion regression. It could lead to poor perceptions of three-dimensional transformation. They also ignore the possible overlaps or gaps between teeth of predicted dentition, which is generally unacceptable. Therefore, we propose DTAN, a differentiable collision-supervised tooth arrangement network, decoupling predicting tasks and feature modeling. DTAN decouples the tooth arrangement task by first predicting the hidden features of the final teeth poses and then using them to assist in regressing the motions between the beginning and target teeth. To learn the hidden features better, DTAN also decouples the teeth-hidden features into geometric and positional features, which are further supervised by feature consistency constraints. Furthermore, we propose a novel differentiable collision loss function for point cloud data to constrain the related gestures between teeth, which can be easily extended to other 3D point cloud tasks. We propose an arch-width guided tooth arrangement network, named C-DTAN, to make the results controllable. We construct three different tooth arrangement datasets and achieve drastically improved performance on accuracy and speed compared with existing methods.

Cancer is a disease with a complex genome of altered functions.However, most existing driver gene identification approaches rarely consider driver genes may have the same functional properties.To overcome this issue, we propose the gamma distribution test for the driver gene identification based on similarity networks, termed GASN, which identifies driver genes by combining machine learning and distributional statistics methods.Similarity networks are able to learn gene similarities and key features that represent the functional impact of genes.In addition, we classify genes into different cellular compartments and use the gamma distribution test within cellular compartments to identify significant driver genes.The experimental results show that our method outperforms the other 17 comparative methods.

Aggressive scaling of CMOS technologies requires to pay attention to the reliability issues of circuits. This paper presents two highly reliable RHBD 10T and 12T SRAM cells, which can protect against single-node upsets (SNUs) and double-node upsets (DNUs). The 10T cell mainly consists of two cross-coupled input-split inverters and the cell can robustly keep stored values through a feedback mechanism among its internal nodes. It also has a low cost in terms of area and power consumption, since it uses only a few transistors. Based on the 10T cell, a 12T cell is proposed that uses four parallel access transistors. The 12T cell has a reduced read/write access time with the same soft error tolerance when compared to the 10T cell. Simulation results demonstrate that the proposed cells can recover from SNUs and a part of DNUs. Moreover, compared with the state-of-the-art hardened SRAM cells, the proposed 10T cell can save 28.59% write access time, 55.83% read access time, and 4.46% power dissipation at the cost of 4.04% silicon area on average.

As network supporting devices and sensors in the Internet of Things are leaping forward, countless real‐world data will be generated for human intelligent applications. Speech sensor networks, an important part of the Internet of Things, have numerous application needs. Indeed, the sensor data can further help intelligent applications to provide higher quality services, whereas this data may involve considerable noise data. Accordingly, speech signal processing method should be urgently implemented to acquire low‐noise and effective speech data. Blind source separation and enhancement technique refer to one of the representative methods. However, in the unsupervised complex environment, in the only presence of a single‐channel signal, many technical challenges are imposed on achieving single‐channel and multiperson mixed speech separation. For this reason, this study develops an unsupervised speech separation method CNMF+JADE, i.e., a hybrid method combined with Convolutional Non‐Negative Matrix Factorization and Joint Approximative Diagonalization of Eigenmatrix. Moreover, an adaptive wavelet transform‐based speech enhancement technique is proposed, capable of adaptively and effectively enhancing the separated speech signal. The proposed method is aimed at yielding a general and efficient speech processing algorithm for the data acquired by speech sensors. As revealed from the experimental results, in the TIMIT speech sources, the proposed method can effectively extract the target speaker from the mixed speech with a tiny training sample. The algorithm is highly general and robust, capable of technically supporting the processing of speech signal acquired by most speech sensors.

Cuckoo search algorithm (CS), as a new heuristic algorithm, has been paid increasing attention and studied by many scholars because of its efficient performance. However, premature convergence is a defect of CS. Recently, some heuristic algorithms have been successfully applied to some high performance computing frameworks, effectively overcome the premature convergence problem of the algorithm, which provides us with a new idea to enhance CS. Therefore, a novel CS variant, called Spark-based gaussian bare-bones cuckoo search with dynamic parameter selection (SparkGDCS), which combines a novel CS variant with the efficient Spark framework, is proposed in this paper. In SparkGDCS, GDCS is a new variant which combines Gaussian bare-bones strategy and dynamic parameter selection, whose purpose is to enhance the search ability of CS. Finally, by testing the benchmark functions proposed by the 2010 and 2013 IEEE Congress on Evolutionary Computation special session (CEC 2010 and CEC 2013), comprehensive experiments prove the effectiveness of SparkGDCS.