Comparison between P&O and SSO techniques based MPPT algorithm for photovoltaic systems

Solar photovoltaic (SPV) systems are a renewable source of energy that are environmentally friendly and recyclable nature. When the solar panel is connected directly to the load, the power delivered to the load is not the optimal power. It is therefore important to obtain maximum power from SPV systems for enhancing efficiency. Various maximum power point tracking (MPPT) techniques of SPV systems were proposed. Traditional MPPT techniques are commonly limited to uniform weather conditions. This paper presents a study of MPPT for photovoltaic (PV) systems. The study includes a discussion of different MPPT techniques and performs comparison for the performance of the two MPPT techniques, the P&O algorithm, and salp swarm optimization (SSO) algorithm. MATLAB simulations are performed under step changes in irradiation. The results of SSO show that the search time of maximum power point (MPP) is significantly decreased and the MPP is obtained in the shortest time with high accuracy and minimum oscillations in the generated power when compared with P&O.

Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues

The past decade has been characterized by the growing volumes of data due to the widespread use of the Internet of Things (IoT) applications, which introduced many challenges for efficient data storage and management. Thus, the efficient indexing and searching of large data collections is a very topical and urgent issue. Such solutions can provide users with valuable information about IoT data. However, efficient retrieval and management of such information in terms of index size and search time require optimization of indexing schemes which is rather difficult to implement. The purpose of this paper is to examine and review existing indexing techniques for large-scale data. A taxonomy of indexing techniques is proposed to enable researchers to understand and select the techniques that will serve as a basis for designing a new indexing scheme. The real-world applications of the existing indexing techniques in different areas, such as health, business, scientific experiments, and social networks, are presented. Open problems and research challenges, e.g., privacy and large-scale data mining, are also discussed.

Alternation emerges as a multi-modal strategy for turbulent odor navigation

Foraging mammals exhibit a familiar yet poorly characterized phenomenon, "alternation", a momentary pause to sniff in the air often preceded by the animal rearing on its hind legs or raising its head. Intriguingly, rodents executing an olfactory search task spontaneously exhibit alternation in the presence of airflow, suggesting that alternation may serve an important role during turbulent plume-tracking. To test this hypothesis, we combine fully-resolved numerical simulations of turbulent odor transport and Bellman optimization methods for decision-making under partial observability. We show that an agent trained to minimize search time in a realistic odor plume exhibits extensive alternation together with the characteristic cast-and-surge behavior commonly observed in flying insects. Alternation is tightly linked with casting and occurs more frequently when the agent is far downwind of the source, where the likelihood of detecting airborne cues is higher relative to cues close to the ground. Casting and alternation emerge as complementary tools for effective exploration when cues are sparse. We develop a model based on marginal value theory to capture the interplay between casting, surging and alternation. More generally, we show how multiple sensorimotor modalities can be fruitfully integrated during complex goal-directed behavior.

Time Classification Algorithm Based on Windowed-Color Histogram Matching

A web-based search system recommends and gives results such as customized image or video contents using information such as user interests, search time, and place. Time information extracted from images can be used as a important metadata in the web search system. We present an efficient algorithm to classify time period into day, dawn, and night when the input is a single image with a sky region. We employ the Mask R-CNN to extract a sky region. Based on the extracted sky region, reference color histograms are generated, which can be considered as the ground-truth. To compare the histograms effectively, we design the windowed-color histograms (for RGB bands) to compare each time period from the sky region of the reference data with one of the input images. Also, we use a weighting approach to reflect a more separable feature on the windowed-color histogram. With the proposed windowed-color histogram, we verify about 91% of the recognition accuracy in the test data. Compared with the existing deep neural network models, we verify that the proposed algorithm achieves better performance in the test dataset.

Efficient collective search by BRT algorithm using fast rising threshold agent

The BRT algorithm is a method for the best-of-n problem that allows a group of distributed robots to find out the most appropriate collective option among many alternatives. Computer experiments show that the time required for finding out the best option is proportional to the number of options. In this paper, we aim to shorten this search time by introducing a few agents whose threshold increases faster than the normal one to achieve higher scalability of the BRT algorithm. The results show that the search time is reduced, and the variance is improved, especially under challenging problems where robots are required to make decisions out of a large number of options.

Comparison of Keyword Search Techniques with Respect to Electronic Health Records

As the world progresses towards automation, manual search for data from large databases also needs to keep pace. When the database includes health data, even minute aspects need careful scrutiny. Keyword search techniques are helpful in extracting data from large databases. There are two keyword search techniques: Exact and Approximate. When the user wants to search through EHR, a short search time is expected. To this end, this work investigates Metaphone (Exact search) and Similar_Text (approximate search) Techniques. We have applied keyword search to the data, which includes the symptoms and names of medicines. Our results indicate that the search time for Similar_text is better than for Metaphone.

Fast-RRT: A RRT-Based Optimal Path Finding Method

As a sampling-based pathfinding algorithm, Rapidly Exploring Random Trees (RRT) has been widely used in motion planning problems due to the ability to find a feasible path quickly. However, the RRT algorithm still has several shortcomings, such as the large variance in the search time, poor performance in narrow channel scenarios, and being far from the optimal path. In this paper, we propose a new RRT-based path find algorithm, Fast-RRT, to find a near-optimal path quickly. The Fast-RRT algorithm consists of two modules, including Improved RRT and Fast-Optimal. The former is aims to quickly and stably find an initial path, and the latter is to merge multiple initial paths to obtain a near-optimal path. Compared with the RRT algorithm, Fast-RRT shows the following improvements: (1) A Fast-Sampling strategy that only samples in the unreached space of the random tree was introduced to improve the search speed and algorithm stability; (2) A Random Steering strategy expansion strategy was proposed to solve the problem of poor performance in narrow channel scenarios; (3) By fusion and adjustment of paths, a near-optimal path can be faster found by Fast-RRT, 20 times faster than the RRT* algorithm. Owing to these merits, our proposed Fast-RRT outperforms RRT and RRT* in both speed and stability during experiments.

Survival, predation, and behaviour of the Mahoenui giant wētā ('Deinacrida mahoenui': Anostostomatidae: Orthoptera)

<p>Introduced mammalian pests, such as rats (Rattus spp.), house mice (Mus musculus), brushtail possums (Trichosurus vulpecula), and European hedgehogs (Erinaceus europaeus), have been implicated in the suppression or extinction of many endemic invertebrate species in New Zealand, including the large-bodied giant wētā (Anostostomatidae: Deinacrida). The Mahoenui giant wētā (MGW; D. mahoenui) is the only lowland giant wētā species still naturally present on the mainland of New Zealand, where the last remaining individuals of the original population are currently restricted to an 187ha mainland reserve (Mahoenui Giant Wētā Scientific Reserve; MGWSR) in Mahoenui, western King Country. Having sought refuge in the introduced woody shrub, gorse (Ulex europaeus), these wētā have survived in the presence of introduced mammalian predators for almost six decades. However, due to natural succession, the reserve is gradually reverting to native bush and wētā monitoring data shows potential signs of population decline. Concerns for the species survival have been raised as it is unknown how wētā will cope in an altered habitat alongside mammalian predators. In chapter 2, we used 14-years’ of site-occupancy monitoring data to explore changes to the reserves’ gorse mosaic and MGW population. We additionally assessed the effect of abiotic covariates on MGW occupancy and detection probabilities in 2005 and 2018. Furthermore, we assessed mammalian pest population dynamics within the reserve over the past seven years. Significant changes to the reserve’s gorse mosaic were identified, whereby unbrowsed, tall bushes, which may provide less protection to wētā, are now dominant in 2018. Population trajectory analysis revealed the MGW population has decline since 2012. This result was consistent with naïve occupancy estimates and the increase in search time (0.3hrs/year) required to find wētā, suggesting the population is in a state of decline. Plot location was identified as an important covariate for predicting MGW occupancy in 2018, whereby plots in edge habitat, potentially being preferred or safer, had a higher occupancy probability. Mammalian pests (rats, house mice, brushtail possums, and European hedgehogs) appear to be present within the reserve year-round, populations peaking in summer and autumn. In chapter 3, we used radiotelemetry to explore MGW survival rates, movement patterns, and diurnal refuge use in gorse and native vegetation during summer (n=14), autumn (n=31), and spring (n=10). Survival rates, in relation to predation, revealed MGW inhabiting native vegetation were nine times more likely to be predated than those inhabiting gorse. This result suggests native species such as mahoe (Melicytus ramiflorus), and tree ferns (Dicksonia fibrosa and Cyathea spp.) do not provide good protection to MGW from mammalian predators. Assessment of movement behaviour revealed MGW move less in autumn (~3m/48hrs) compared to summer (~10m/48hrs) and spring (~8m/48hrs), and most commonly follow a movement pattern consistent with random-walk. Movement behaviour was also found to be temperature dependant, with both male and female MGW moving significantly further in warmer weather (>13.5°C). Radiotracked MGW were found to take refuge above 2.5m in the canopy of native vegetation, whereas in gorse habitat, wētā were most commonly found taking refuge between 0.62 – 2.38m in the denser foliage of unbrowsed gorse bushes. Furthermore, no radiotracked wētā were observed with another individual in autumn, compared to eight and 26 observations in summer and spring. In chapter 4, we attempted to identify potential mammalian predators of the MGW by analysing the stomach contents of ship rats (R. rattus; n=10), house mice (n=10), brushtail possums (n=5), and feral cats (Felis catus; n=2). Ship rats were identified as likely predators of MGW within the MGWSR. However, due to the limited number of stomachs and species analysed, further analysis is recommended. Collectively, these results provide an overview of the MGW reserve and population status, in addition to important ecological information that can be used to inform future management, monitoring, and translocation.</p>

Survival, predation, and behaviour of the Mahoenui giant wētā ('Deinacrida mahoenui': Anostostomatidae: Orthoptera)

<p>Introduced mammalian pests, such as rats (Rattus spp.), house mice (Mus musculus), brushtail possums (Trichosurus vulpecula), and European hedgehogs (Erinaceus europaeus), have been implicated in the suppression or extinction of many endemic invertebrate species in New Zealand, including the large-bodied giant wētā (Anostostomatidae: Deinacrida). The Mahoenui giant wētā (MGW; D. mahoenui) is the only lowland giant wētā species still naturally present on the mainland of New Zealand, where the last remaining individuals of the original population are currently restricted to an 187ha mainland reserve (Mahoenui Giant Wētā Scientific Reserve; MGWSR) in Mahoenui, western King Country. Having sought refuge in the introduced woody shrub, gorse (Ulex europaeus), these wētā have survived in the presence of introduced mammalian predators for almost six decades. However, due to natural succession, the reserve is gradually reverting to native bush and wētā monitoring data shows potential signs of population decline. Concerns for the species survival have been raised as it is unknown how wētā will cope in an altered habitat alongside mammalian predators. In chapter 2, we used 14-years’ of site-occupancy monitoring data to explore changes to the reserves’ gorse mosaic and MGW population. We additionally assessed the effect of abiotic covariates on MGW occupancy and detection probabilities in 2005 and 2018. Furthermore, we assessed mammalian pest population dynamics within the reserve over the past seven years. Significant changes to the reserve’s gorse mosaic were identified, whereby unbrowsed, tall bushes, which may provide less protection to wētā, are now dominant in 2018. Population trajectory analysis revealed the MGW population has decline since 2012. This result was consistent with naïve occupancy estimates and the increase in search time (0.3hrs/year) required to find wētā, suggesting the population is in a state of decline. Plot location was identified as an important covariate for predicting MGW occupancy in 2018, whereby plots in edge habitat, potentially being preferred or safer, had a higher occupancy probability. Mammalian pests (rats, house mice, brushtail possums, and European hedgehogs) appear to be present within the reserve year-round, populations peaking in summer and autumn. In chapter 3, we used radiotelemetry to explore MGW survival rates, movement patterns, and diurnal refuge use in gorse and native vegetation during summer (n=14), autumn (n=31), and spring (n=10). Survival rates, in relation to predation, revealed MGW inhabiting native vegetation were nine times more likely to be predated than those inhabiting gorse. This result suggests native species such as mahoe (Melicytus ramiflorus), and tree ferns (Dicksonia fibrosa and Cyathea spp.) do not provide good protection to MGW from mammalian predators. Assessment of movement behaviour revealed MGW move less in autumn (~3m/48hrs) compared to summer (~10m/48hrs) and spring (~8m/48hrs), and most commonly follow a movement pattern consistent with random-walk. Movement behaviour was also found to be temperature dependant, with both male and female MGW moving significantly further in warmer weather (>13.5°C). Radiotracked MGW were found to take refuge above 2.5m in the canopy of native vegetation, whereas in gorse habitat, wētā were most commonly found taking refuge between 0.62 – 2.38m in the denser foliage of unbrowsed gorse bushes. Furthermore, no radiotracked wētā were observed with another individual in autumn, compared to eight and 26 observations in summer and spring. In chapter 4, we attempted to identify potential mammalian predators of the MGW by analysing the stomach contents of ship rats (R. rattus; n=10), house mice (n=10), brushtail possums (n=5), and feral cats (Felis catus; n=2). Ship rats were identified as likely predators of MGW within the MGWSR. However, due to the limited number of stomachs and species analysed, further analysis is recommended. Collectively, these results provide an overview of the MGW reserve and population status, in addition to important ecological information that can be used to inform future management, monitoring, and translocation.</p>