Although power conversion efficiency (PCE) of state‐of‐the‐art perovskite solar cells has already exceeded 20%, photo‐ and/or moisture instability of organolead halide perovskite have prevented ...further commercialization. In particular, the underlying weak interaction of organic cations with surrounding iodides due to eight equivalent orientations of the organic cation along the body diagonals in unit cell and chemically non‐inertness of organic cation result in photo‐ and moisture instability of organometal halide perovskite. Here, a perovskite light absorber incorporating organic–inorganic hybrid cation in the A‐site of 3D APbI3 structure with enhanced photo‐ and moisture stability is reported. A partial substitution of Cs+ for HC(NH2)2+ in HC(NH2)2PbI3 perovskite is found to substantially improve photo‐ and moisture stability along with photovoltaic performance. When 10% of HC(NH2)2+ is replaced by Cs+, photo‐ and moisture stability of perovskite film are significantly improved, which is attributed to the enhanced interaction between HC(NH2)2+ and iodide due to contraction of cubo‐octahedral volume. Moreover, trap density is reduced by one order of magnitude upon incorporation of Cs+, which is responsible for the increased open‐circuit voltage and fill factor, eventually leading to enhancement of average PCE from 14.9% to 16.5%.
FA0.9Cs0.1PbI3 with improved moisture‐ and photostability is developed. Incorporation of 10% of Cs cation in the FA cation sites improves photovoltaic performance as well as photo‐ and moisture stability. Property–structure correlation plays important role in improving the stability of perovskite solar cells.
This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for ...performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics.
Due to the immutability of blockchain, the integration with big-data systems creates limitations on redundancy, scalability, cost, and latency. Additionally, large amounts of invaluable data result ...in the waste of energy and storage resources. As a result, the demand for data deletion possibilities in blockchain has risen over the last decade. Although several prior studies have introduced methods to address data modification features in blockchain, most of the proposed systems need shorter deletion delays and security requirements. This study proposes a novel blockchain architecture called Unlichain that provides data-modification features within public blockchain architecture. To achieve this goal, Unlichain employed a new indexing technique that defines the deletion time for predefined lifetime data. The indexing technique also enables the deletion possibility for unknown lifetime data. Unlichain employs a new metadata verification consensus among full and meta nodes to avoid delays and extra storage usage. Moreover, Unlichain motivates network nodes to include more transactions in a new block, which motivates nodes to scan for expired data during block mining. The evaluations proved that Unlichain architecture successfully enables instant data deletion while the existing solutions suffer from block dependency issues. Additionally, storage usage is reduced by up to 10%.
•The algorithm uses the curve matching between input EMG and reference EMG signals.•The proposed method reflects better timing characteristics than the time features.•The method provides real-time ...recognition of the gait subphase using EMG signals.
This study presents a gait subphase recognition method using an electromyogram (EMG) with a signal graph matching (ESGM) algorithm. Existing pattern recognition and machine learning using EMG signals has several innate problems in gait subphase detection. With respect to time domain features, their feature values may be analogous because two different gait steps may have similar muscle activation. In addition, the current gait subphase might not be recognized until the next gait subphase passes because the window size needed for feature extraction is larger than the period of the gait subphase. The ESGM algorithm is a new approach that compares reference EMG signals and input EMG signals according to time variance to solve these problems and considers variations of physiological muscle activity. We also determined all the elements of the ESGM algorithm using kinematic gait analysis and optimized the algorithm using experiments. Therefore, the ESGM algorithm reflects better timing characteristics of EMG signals than the time domain feature extraction algorithm. In addition, it can provide real-time and user-adaptive recognition of the gait subphase by using only EMG signals. Experimental results show that the average accuracy of the proposed method is 13% better than existing methods and the average detection latency of the proposed method was 5.5 times lower than existing methods.
This study aims to propose a novel approach for gender recognition using best feature subset based on recursive feature elimination (RFE) in normal walking. This study has focused on the analysis of ...gait characteristics by distinguishing the gait phases as initial contact (IC), Mid-stance (MS), Pre-swing, and swing (SW), and collected the large number of gait to improve the reliability of quantitative assessment of natural variability associated with muscle activity during free walking. The gait system was designed using pressure and a tri-axis accelerometer sensor, and a 9-channel electromyography sensor for measuring the data. Gender recognition method was proposed using support vector machine (SVM) and random forest (RF) based on RFE to determine best feature subset. Statistical results show that effects of gender-based differences on gait characteristic including temporal, kinematics, and muscle activity were investigated. The temporal parameters of stride time and gait cycle (%) in the gait phases of IC, MS, and SW were significantly different between females and males (p<0.01). The females exhibited both a lower angle and a root mean square acceleration of the knee joint as compared to the males, and there was a clear gender-based difference with respect to knee angle movement. In addition, most muscle activation measurements in the females were larger than those of the males with respect to the gait phases. Gender classification result shows that SVM-RFE was 99.11% (SVM classifier) and RF-RFE was 98.89% (SVM and RF classifier), having powerful performance.
•The paper investigates the statistical effect of gender-based differences on gait.•The paper has focused to analysis gait characteristics in gait sub-phases.•A novel approach for gender classification is proposed using RFE.•The paper has the powerful performance for gender classification using SVMRFE.
•This paper proposes a new DOSbFC algorithm to remove baseline drift of EOG Signals.•This paper presents a new electrode positioning scheme based on eyeglasses.•This paper provides a long-term eye ...movement detection function with high accuracy.
This paper presents a new method to remove baseline drift and noise by using a differential electrooculography (EOG) signal based on a fixation curve (DOSbFC) and a new electrode positioning scheme based on eyeglasses for user convenience. In addition, a desktop application and mobile applications to control the human–computer interface were implemented. Finally, we created experimental EOG eyeglasses and a new detection protocol using the proposed method for long-term step-by-step detection of eye movements and user comfort. The proposed DOSbFC calculates the difference values of accumulated EOG signals between the initial eye movement and fixation time. It allows long-term detection of eye movements with high accuracy and only requires a single calibration. The vertical and ground electrodes of the standard electrode positioning scheme caused discomfort of subjects; the proposed electrode positioning scheme solves these problems and enables the use of existing eyeglasses without design modification. The experimental results demonstrated that the average accuracy of the long-term eye movement detection was 94%, whereas those of the band pass filter and wavelet transform were 61% and 64%, respectively. This was because baseline drift and noise were removed by averaging the signal variations. Further experimental results demonstrated that the average information transfer rate of the proposed method was 6.0, whereas those of the band pass filter and wavelet transform were 1.1 and 0.9, respectively.
Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a ...proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model.
This letter presents lower limb human motion detection using a surface electromyogram (sEMG) with a top and slope (TAS) feature extraction algorithm. Lower limb human motion detection using sEMG ...signal is generally divided into gait subphase detection, locomotion mode recognition, and mode change detection. Existing feature extraction algorithms using sEMG signal have several innate problems in recognizing lower limb human motion detection. With respect to time-domain features, their values may be analogous because two different gait subphases and locomotion mode may have similar muscle activity pattern. Therefore, it is not easy to select the proper feature set of sEMG signals. The TAS feature extraction algorithm reflects better timing characteristics of sEMG signals than the existing time-domain feature extraction algorithm. Therefore, it can provide high accuracy in lower limb human motion detection. Experimental results show that the average detection accuracy values of the proposed method in terms of the gait subphase detection, locomotion mode recognition, and mode change detection are increased by 8%, 5%, and 4% better than those of feature values of the Willison amplitude, respectively.
In cloud storage scenarios, each chunk with multiple symbols of a coding strip resides in a different node and, hence, on a different disk. Existing STAIR and sector-disk (SD) scheduling techniques ...are not scalable because the performance of existing STAIR and SD encoding and decoding schedulers decreases sharply as the number of parity disks increases. This paper proposes a new, scalable, multisector (MS) scheduler that provides three-level fault tolerance for multiple sectors, disks, and nodes. In addition, the MS scheduler is extendable to multilevel failure recovery. MS schedulers are aware of the inner structure of each node, such as the number of disks and sectors for each node. Compared with SD and STAIR schedulers, an MS scheduler improves the average encoding and decoding performance by 198.34%, on average (ranging from 170.46% to 228.54%). However, STAIR and SD schedulers are bound to a specific number of parity disks and sectors. This is because the MS scheduler uses streaming SIMD extensions (SSE) for the dense matrix-based parity computation software implementation. In contrast, an MS scheduler can perform parity calculations for a large number of parity disks and sectors using advanced vector extensions (AVX)-2, OpenMP thread parallelism, and sparse parity computation software implementation. As a result, fewer XOR operations are performed to calculate parity chunks owing to the diagonal structure of the MS scheduler. The sparse AVX-2/-512 diagonal erasure coding implementation of the MS scheduler enables the proposed scheduler to be compatible with recovery from multilevel failures by skipping a large number of XOR and multiplication operations. This proves that the MS scheduler is suitable for use in a scalable cloud storage environment.
•A scalable, multilevel erasure coding schema for a cloud environment is proposed.•A cloud architecture for distribution of the parity computations is proposed.•A sparse AVX-2/-512 diagonal erasure coding technique is proposed.•Extensive experiments are conducted to confirm the advantages of our approach.
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