Cognitive Computation and Systems最新文献

Due to the improvement in power demand usage, the system causes more stress. Suitable placement of FACTS is introduced to improve power flows, stability, and power system security. An advanced optimal method has been introduced to resolve optimal power flow under various conditions. Five varied FACTS devices were positioned inside the power system to improve system safety at the least cost. Selecting the suitable location and size of FACTS device enabling high security and less cost was proposed using RSA. The objective functions were mitigated based on the RSA's inspiration to improve system security. The placement of the FACTS compensator was based on objective functions like LOSI, voltage deviation, real power loss, investment cost, sensitivity index, fuel costs, and constraints. The proposed model was validated under three conditions, namely generator outage, line outage, and both outage in IEEE 118 and IEEE 30 bus-systems. In the IEEE 30 bus system, TCSC provides better security of 1.4 severity at normal conditions and 1.3 severity in contingency conditions. In the IEEE 118 bus system, UPFC has less severity of 2.4 at normal conditions, and STATCOM has the least severity of 3 at contingency conditions. The proposed model provides enhanced security in all circumstances and reduces overall costs.

Detecting the human operator's cognitive state is paramount in settings wherein maintaining optimal workload is necessary for task performance. Blink rate is an established metric of cognitive load, with a higher blink frequency being observed under conditions of greater workload. Measuring blink rate requires the use of eye-trackers which limits the adoption of this metric in the real-world. The authors aim to investigate the effectiveness of using a generic camera-based system as a way to assess the user's cognitive load during a computer task. Participants completed a mental task while sitting in front of a computer. Blink rate was recorded via both the generic camera-based system and a scientific-grade eye-tracker for validation purposes. Cognitive load was also assessed through the performance in a single stimulus detection task. The blink rate recorded via the generic camera-based approach did not differ from the one obtained through the eye-tracker. No meaningful changes in blink rate were however observed with increasing cognitive load. Results show the generic-camera based system may represent a more affordable, ubiquitous means for assessing cognitive workload during computer task. Future work should further investigate ways to increase its accuracy during the completion of more realistic tasks.

As a fundamental function, lane following plays an important role for driverless vehicles. Unfortunately, lane followers generally confront great difficulty in lane line missed situations caused by vague line, shadows etc. However, for most lane line missed situation, clues of the line may be hidden in prior view of it. Consequently, a lane follower called UNL Lane Follower, which contains two deep learning network modules is proposed. The first module is a lane line detection model called UNET_CLB. Here, the sequence of image frames is utilised rather than only the current frame to deal with the missing lane lines. The second module is a lane-following model called LSTM_DTS, which combines a deep learning attention mechanism (temporal attention network and spatial attention network) with a recurrent neural network. As a result, the proposed UNL Lane Follower produces smoother driving behaviour, especially when a lane line is temporally missed. For better explain ability, the role of each part of the network structure is analysed and explained intuitively. As a modularised network, the UNET_CLB is firstly trained and tested on the TuSimple dataset and CULane dataset. The LSTM_DTS lane follow is then trained and tested on our actual lane following dataset. Finally, the UNL Lane Follower is trained and tested as a whole in a simulation running on Webots, after importing the weight of the two modules trained separately. All testing results showed that the UNL Lane Follower can provide better robustness and accuracy for lane line following mission in the line missed situations.

The authors present the investigation of a new hybrid predictive model of Duplex Attention-based Coupled LSTM Markov Averaged Neural Network, known as DACLMANN. The financial field, particularly the stock market, heavily relies on accurate predictive models. DACLMANN comprises four essential components: two LSTM blocks, an Averagiser and a Markov Neural Network block. The first LSTM block is composed of two hidden layers, each containing 50 neurons and a dense layer with 25 neurons. The second LSTM block consists of two hidden layers, each with 100 neurons, and a dense layer with 50 neurons. The Averagiser plays a crucial role by averaging the closing prices and predicted values from the first LSTM block, resulting in a 90% gain. These averaged values are then fed into the second LSTM block for further prediction. Finally, the predictions undergo evaluation using the Markov model, yielding the final prediction. To assess the performance of DACLMANN, it was tested on 22 years of stock prices for the AMZN index. The evaluation metrics used by the authors include an R2 of 0.76, mean absolute error of 6.81216, root mean square error of 8.6040, Precision of 1, Accuracy of 1, Recall of 1 and F1 of 1. Additionally, DACLMANN achieved a Mean Absolute Percentage Error of less than 0.043% and an RMSPE of less than 2.1%. These results not only demonstrate the effectiveness of the proposed model but also authenticate the prediction outcomes. DACLMANN offers several advantages over traditional predictive models in the stock market. By combining the strengths of Duplex Attention-based Coupled LSTM, Averagiser, and Markov Neural Network, DACLMANN leverages the power of deep learning, attention mechanisms, and sequential modelling. This hybrid approach enables DACLMANN to capture intricate patterns and dependencies present in stock market data, leading to more accurate and reliable predictions. The robust evaluation metrics further validate the superiority of DACLMANN in predicting stock prices.

Self-reflection practice in coaching can help with time management by promoting self-awareness. Through this process, a coach can identify habits, tendencies and behaviours that may be causing distraction or make them less productive. This insight can be used to make changes in behaviour and establish new habits that promote effective use of time. This can also help the coach to prioritise goals and create a clear roadmap. An AI powered system has been proposed that maps the conversion onto topics and relations that could help the coach with note-taking and progress identification throughout the session. This system enables the coach to actively self-reflect on time management and make sure the conversation follows the target framework. This will help the coach to better understand the goal setting, breakthrough moment, and client accountability. The proposed end-to-end system is capable of identifying coaching segments (Goal, Option, Reality, and Way forward) across a session with 85% accuracy. Experimental evaluation has also been conducted on the coaching dataset which includes over 1k one-to-one English coaching sessions. In regards to the novelty, there are no datasets of such nor study of this kind to enable self-reflection actively and evaluate in-session performance of the coach.

Machine vision in low-light conditions is a critical requirement for object detection in road transportation, particularly for assisted and autonomous driving scenarios. Existing vision-based techniques are limited to daylight traffic scenarios due to their reliance on adequate lighting and high frame rates. This paper presents a novel approach to tackle this problem by investigating Vehicle Detection and Localisation (VDL) in extremely low-light conditions by using a new machine learning model. Specifically, the proposed model employs two customised generative adversarial networks, based on Pix2PixGAN and CycleGAN, to enhance dark images for input into a YOLOv4-based VDL algorithm. The model's performance is thoroughly analysed and compared against the prominent models. Our findings validate that the proposed model detects and localises vehicles accurately in extremely dark images, with an additional run-time of approximately 11 ms and an accuracy improvement of 10%–50% compared to the other models. Moreover, our model demonstrates a 4%–8% increase in Intersection over Union (IoU) at a mean frame rate of 9 fps, which underscores its potential for broader applications in ubiquitous road-object detection. The results demonstrate the significance of the proposed model as an early step to overcoming the challenges of low-light vision in road-object detection and autonomous driving, paving the way for safer and more efficient transportation systems.

Wireless sensor network have also played a vital role in the observation and management of agricultural land in terms of climate, water usage, crops, etc. Due to the open communication system and low battery power of sensors, the agricultural sector still faces issues with energy consumption, information forwarding, and privacy. Thus, an energy-efficient routing during transmission in WSN-based smart agriculture is suggested in this study applying a feed-forward neural network to detect outliers. Outlier identification, CH-selection, and Relay Node (RN) selection are the three phases of this suggested method. Outlier detection is performed in the deployed nodes for categorises attack nodes from the normal nodes. CH-selection is performed using a chaotic moth-flame optimization technique according to distance, node degree, centrality factor and residual energy level, these parameters determine which node will become a Cluster Head. Then reliable routing protocol is designed using NB-based probability method for RN selection. MATLAB software is used to test the proposed Outlier Detection based Energy Efficient and Reliable Routing Protocol and verify its performance. The effectiveness of the proposed-model is tested with some prior wireless sensor network routing protocols environment-fusion multipath routing protocol, dynamic Multi-hop Energy Efficient Routing Protocol, SEMantic CLustering, and Reliable and energy efficient routing protocol. Outlier Detection based Energy Efficient and Reliable Routing Protocol algorithm attained a 0.91 (%)Packet Delivery ratio, 0.08% of packet loss, 0.91% of Average residual energy, 2.8 (Mbps) throughput, and 26 (sec) Delay.

Surveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance-system utilising hybrid ResNet101-ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre-processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre-processed frame is fetched into hybrid ResNet101-ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal-events detected the context aware services generate alert to the user for preventing abnormal-activities. Accuracy, precision, recall, and error values reached for the proposed-model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.

The accuracy of regional convolutional neural network (R-CNN) algorithms on image recognition detection remains to be improved. The authors optimised the Mask R-CNN algorithm and tested it through experiments on the automatic recognition of building targets in urban remote sensing images. It was found that the improved Mask R-CNN algorithm recognised more complete building targets and clearer edges than the original algorithm with a precision of 95.75%, a recall rate of 96.28% and a mean average precision (mAP) of 0.9403, and it also reduced the detection time per image to 0.264 s, all of which were better than other R-CNN algorithms. The ablation experiments showed that compared with the original Mask R-CNN algorithm, the improvement in the mAP of the Mask R-CNN algorithms with an improved feature pyramid network and an improved non-maximum suppression (NMS) algorithm was 0.0206 and 0.0119, respectively, while the improvement in the mAP of the improved Mask R-CNN algorithm was 0.0376. The two improvement methods adopted for the Mask R-CNN algorithm were proved to be feasible and can effectively improve the automatic recognition and detection accuracy and efficiency of building targets in urban remote sensing images.